The Journal of Research ANGRAU
Contents of 41(1) 2013 - acharya ng ranga agricultural university
Contents of 41(1) 2013 - acharya ng ranga agricultural university
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1
<strong>The</strong> <strong>Journal</strong> <strong>of</strong> <strong>Research</strong> <strong>ANGRAU</strong><br />
(Published quarterly in March, June, September and December)<br />
ADVISORY BOARD<br />
Dr. P. Gidda Reddy<br />
Director <strong>of</strong> Extension,<br />
Rajendranagar, Hyderabad.<br />
Dr. R. Sudhakar Rao<br />
Director <strong>of</strong> <strong>Research</strong>,<br />
Rajendranagar, Hyderabad.<br />
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Pr<strong>of</strong>. & University Head,<br />
Dept. <strong>of</strong> Soil Science & Agril. Chemistry<br />
& Dean <strong>of</strong> Agriculture i/c<br />
Rajendranagar, Hyderabad.<br />
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<strong>ANGRAU</strong>, Rajendranagar, Hyderabad.<br />
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Rajendranagar, Hyderabad.<br />
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Principal Scientist(Breeding),<br />
Maize <strong>Research</strong> Station,<br />
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Dr. R. Sudhakar<br />
Senior Scientist (Plant Pathology),<br />
Seed <strong>Research</strong> & Technology Centre,<br />
<strong>ANGRAU</strong>, Rajendranagar, Hyderabad.<br />
Dr. M. V. Ramana<br />
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AICRP on Integrated Farming Systems,<br />
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College <strong>of</strong> Agriculture, Rajendranagar,<br />
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EDITOR<br />
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<strong>ANGRAU</strong>, Rajendranagar, Hyderabad.<br />
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Associate Pr<strong>of</strong>essor<br />
Dept. <strong>of</strong> Agril. Engineering & Technology<br />
College <strong>of</strong> Agriculture, Rajendranagar,<br />
Hyderabad.<br />
Dr. T. Ramesh<br />
Associate Pr<strong>of</strong>essor<br />
Dept. <strong>of</strong> Plant Physiology<br />
College <strong>of</strong> Agriculture, Rajendranagar,<br />
Hyderabad.<br />
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Pr<strong>of</strong>essor and Head, Dept. <strong>of</strong> Extension Education,<br />
<strong>ANGRAU</strong>, Rajendranagar, Hyderabad.<br />
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Consumer Sciences,<br />
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with effect from April, 2012:<br />
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2
CONTENTS<br />
PART I : PLANT SCIENCE<br />
Effect <strong>of</strong> foliar application <strong>of</strong> NPK nutrients on growth and yield <strong>of</strong> chilli 1<br />
(Capsicum annuum L.)<br />
A.KIRAN KUMAR<br />
Nutrient uptake by rice crop under long term integrated nutrient management in 5<br />
rice – rice cropping system in Alfisols<br />
V. MAHESWARA PRASAD and P. PRABHU PRASADINI<br />
Changes in maturity indices during vermicompsoting Vs conventional Composting 14<br />
<strong>of</strong> agricultural wastes<br />
CH. S. RAMA LAKSHMI, P.C. RAO, G.PADMAJA, T.SREELATHA,<br />
M.MADHAVI, P.V.RAO and A.SIREESHA<br />
Influence <strong>of</strong> integrated nutrient management on physical properties <strong>of</strong> Alfisols 20<br />
under rice –rice cropping system in southern telangana zone<br />
V. MAHESWARA PRASAD and P. PRABHU PRASADINI<br />
Genetic variability, heritability and character association studies in 30<br />
sweet Sorghum [Sorghum bicolor (L.) Moench]<br />
VEMANNA IRADDI, T. DAYAKAR REDDY, A. V. UMAKANTH, CH. RANI,<br />
D. VISHNU VARDHAN REDDY and M. H. V. BHAVE<br />
PART II : VETERINARY SCIENCE<br />
Estrus synchronization response and fertility rate following treatment 39<br />
with Pgf 2α And Gnrh in acyclic lactating ongole cows<br />
K.VENKATA RAMANA, K.SADASIVA RAO, K.SUPRIYA and N.RAJANNA<br />
A study on migration pattern <strong>of</strong> sheep flocks in telangana region <strong>of</strong> Andhra Pradesh 42<br />
N. RAJANNA, M. MAHENDAR and K. VENKATA RAMANA<br />
Utilization <strong>of</strong> poultry waste an un-conventional protein source in small ruminant rations 47<br />
J. NARASIMHA, V.CHINNI PREETHAM and S.T.VIROJI RAO<br />
Postpartum ovarian follicular dynamics and estrus activity in lactating ongole cows 51<br />
K. VENKATA RAMANA, K. SADASIVA RAO, K. SUPRIYA and N. RAJANNA<br />
PART III : RESEARCH NOTES<br />
Development and evaluation <strong>of</strong> fiber enriched khakra 56<br />
M. KIRTHY REDDY, UMADEVI, P.S.S SAILAJA and KUNA APARNA<br />
Influence <strong>of</strong> nitrogen and weed management on growth and yield <strong>of</strong> 61<br />
aerobic rice (Oryza sativa L.)<br />
K. SANDHYARANI, M. MALLA REDDY, R. UMA REDDY and P.V. RAO<br />
Effect <strong>of</strong> seed priming on biochemical changes during seed storage 66<br />
<strong>of</strong> Maize (Zea mays L.) hybrids<br />
M. RAM KUMAR, P. S. RAO, V. PADMA and K. V. RADHA KRISHNA<br />
3
An economic analysis <strong>of</strong> Blackgram in Gulbarga district <strong>of</strong> Karnataka 70<br />
DEEPAK HEGDE, D. V. SUBBA RAO, N. VASUDEV and K. SUPRIYA<br />
Gene action and combining ability studies in Chickpea (Cicer arietinum L.) 74<br />
B. REDDY YAMINI, V. JAYALAKSHMI, B. NARENDRA and P. UMAMAHESHWARI<br />
Genetic divergence in Brinjal (Solanum melongena L.) 79<br />
BALAJI LOKESH, P.SURYANARAYANA REDDY, R.V.S.K.REDDY and N.SIVARAJ<br />
Relationship between pr<strong>of</strong>ile <strong>of</strong> beneficiary farmers and the socio-economic impact 83<br />
<strong>of</strong> irrigated agriculture modernization and water bodies restoration and management<br />
(IAMWARM) project in Pudukkottai district<br />
G. ABIRAMI, B.VIJAYABHINANDANA and T. GOPI KRISHNA<br />
Construction <strong>of</strong> selection indices for F 2<br />
population derived from crosses between 88<br />
grain Sorghum × sweet Sorghum [Sorghum bicolor (L.) Moench]<br />
VEMANNA IRADDI, T. DAYAKAR REDDY, A. V. UMAKANTH, CH. RANI,<br />
D. VISHNU VARDHAN REDDY and M. H. V. BHAVE<br />
Evaluation <strong>of</strong> performance <strong>of</strong> Dendrobium orchid hybrids 93<br />
B. GOPALA RAO, P.T.SRINIVAS and M.H.NAIK<br />
Pr<strong>of</strong>ile characteristics <strong>of</strong> Sugarcane farmers in Chittoor district <strong>of</strong> Andhra Pradesh 96<br />
S. RAMALAKSHMI DEVI, P. V. SATYA GOPAL, V.SAILAJA and S.V. PRASAD<br />
A study on diffusion status <strong>of</strong> System <strong>of</strong> Rice intensification (SRI) in Andhra Pradesh<br />
K. NIRMALA and R. VASANTHA<br />
```101<br />
Correlation and Path Coefficient analysis for yield and physiological attributes 105<br />
In Rice (Oryza sativa L.) hybrids under saline soil conditions<br />
M.SUDHARANI, P.RAGHAVA REDDY, G.HARIPRASAD REDDY and CH.SURENDRA RAJU<br />
Genetic divergence studies for yield and physiological Attributes in 109<br />
Groundnut (Arachis hypogaea L.)<br />
D. NIRMALA, V. JAYALAKSHMI, B. NARENDRA and P. UMAMAHESHWARI<br />
Influence <strong>of</strong> methods <strong>of</strong> irrigation on plant growth, yield, flower quality and 114<br />
vase life in Dendrobium orchid hybrid Sonia-17 under shade net<br />
B. GOPALA RAO, P.T. SRINIVAS and M.H.NAIK<br />
Study on pesticide residues <strong>of</strong> selected vegetables grown in north coastal 116<br />
zone <strong>of</strong> Andhra Pradesh<br />
Y. PUNYAVATHI and V. VIJAYALAKSHMI<br />
A study on the knowledge level <strong>of</strong> farmers on recommended Tea cultivation 121<br />
practices in Nepal<br />
KESHAV KATTEL, R. VASANTHA and M. JAGAN MOHAN REDDY<br />
An economic analysis <strong>of</strong> value addition to Cotton 124<br />
E. RADHIKA, R. VIJAYA KUMARI and D.V. SUBBA RAO<br />
Development <strong>of</strong> phytosterol enriched flavoured milk 127<br />
M. PENCHALA RAJU , ANURAG CHATHURVEDI and KUNA APARNA<br />
Correlation and path analysis for yield and its components in Rice (Oryza sativa L.) 132<br />
C.MANIKYA MINNIE, T.DAYAKAR REDDY and CH.SURENDER RAJU<br />
4
J.Res. <strong>ANGRAU</strong> 41(1) 1-4, 2013<br />
EFFECT OF FOLIAR APPLICATION OF NPK NUTRIENTS ON GROWTH AND<br />
YIELD OF CHILLI (Capsicum annuum L.)<br />
A.KIRAN KUMAR<br />
Fruit <strong>Research</strong> Station, Dr.Y.S.R.Horticultural University, Sangareddy, Medak-502001<br />
Date <strong>of</strong> Receipt : 03.07.2012 Date <strong>of</strong> Acceptance : 02.02.2013<br />
ABSTRACT<br />
A field trial was conducted on foliar application <strong>of</strong> NPK in randomized block design on chilli variety “Prakash<br />
(LCA 206)” at Jannareddy Venkatreddy Horticultural <strong>Research</strong> Station, Malyal, Warangal district <strong>of</strong> Andhra Pradesh<br />
(19.57 0 N and 78.66 0 E) with eleven treatment combinations from foliar sprays <strong>of</strong> 19:19:19 NPK @ 2.5 g l -1 , 5 g l -1 , 7.5<br />
g l -1 and 10 g l -1 ) and KNO 3<br />
@ 5 g l -1 and control. <strong>The</strong> results revealed that four foliar sprays <strong>of</strong> 19:19:19 NPK @ 7.5 g<br />
l -1 + KNO 3<br />
@ 5 g l -1 scheduled at monthly intervals starting one month after transplanting significantly enhanced the<br />
fresh (9820 kg ha -1 ) and dry (3320 kg ha -1 ) pod yield and resulted in significantly longer fruits (7.6 cm).<strong>The</strong>re was no<br />
significant difference in plant height, plant spread (E-W and N-S), fruit girth and dry pod recovery percentage.<br />
Chilli (Capsicum annuum L.) belongs to the<br />
family, Solanaceae and originated from South<br />
America (Wikipedia, 2006). Chilli is rich in vitamin C<br />
and pro-vitamin A, particularly the red chilli (Sparkyby,<br />
2006). India is the largest producer, consumer and<br />
exporter <strong>of</strong> chilli and contribute to 25% <strong>of</strong> total world’s<br />
production. In India, chilli is grown in almost all the<br />
states across the length and breadth <strong>of</strong> the country.<br />
Andhra Pradesh is the largest producer <strong>of</strong> chilli in<br />
India, contributes about 30% to the total area under<br />
chilli, followed by Karnataka (20%), Maharashtra<br />
(15%), Orissa (9%), Tamil Nadu (8%) and other states<br />
contributing 18 % to the total area under chilli<br />
(Agrocrops, 2012).<br />
Recently, foliar feeding has been widely used<br />
and accepted as an essential part <strong>of</strong> crop production,<br />
especially on horticultural crops (Pace Gary, 1982).<br />
<strong>The</strong> purpose <strong>of</strong> foliar feeding is not to replace soil<br />
fertilization. Supplying a plant’s major nutrient needs<br />
(nitrogen, phosphorus and potassium) is most<br />
effective and economical via soil application. Foliar<br />
feeding can be an effective management tool to<br />
favorably influence pre-reproductive growth stages<br />
by compensating for environmentally induced<br />
stresses <strong>of</strong> adverse growing conditions and/or poor<br />
nutrient availability. Early foliar applications can make<br />
an already good crop better, either by stimulating<br />
more vigorous growth or maximizing the yield<br />
potential growth stage period. <strong>The</strong> advantages <strong>of</strong> foliar<br />
feeding in accomplishing the desired crop responses<br />
are two-fold. In order to enhance the effectiveness<br />
<strong>of</strong> any foliar application certain base solutions should<br />
be applied. Nitrogen should be present in any base<br />
solution. N-P, N-S or N-P-S base solutions are<br />
influential during early stages <strong>of</strong> growth utilizing 1:2<br />
or 1:3 N-P 2<br />
O 5<br />
ratios. N-P-K-S base solutions are<br />
suggested to influence the flowering /fruiting stages,<br />
utilizing 2:1:1 N-P 2<br />
O 5<br />
-K 2<br />
O ratio. <strong>The</strong> main objective<br />
<strong>of</strong> the present investigation was to study the effect<br />
<strong>of</strong> foliar applied nutrients i.e., NPK and ultimately to<br />
study the effect on growth and yield.<br />
MATERIALS AND METHODS<br />
<strong>The</strong> experiment was laid out in randomized<br />
block design with three replications on chilli variety<br />
“Prakash (LCA - 206)” at Jannareddy Venkatreddy<br />
Horticultural <strong>Research</strong> Station, Malyal, Warangal<br />
district <strong>of</strong> Andhra Pradesh (19.57 0 N and 78.66 0 E) with<br />
eleven treatment combinations <strong>of</strong> foliar sprays (19:<br />
19:19 NPK@2.5 g l -1 , 5 g l -1 , 7.5 g l -1 and 10 g l -1 ) and<br />
KNO 3<br />
@ 5g l -1 and control for three years. FYM was<br />
applied @ 25 t/ ha -1 , the NPK fertilizers were uniformly<br />
applied @ 220-60-80 kg ha -1 in all the experimental<br />
plots. One month old seedlings were transplanted at<br />
60 x 60 cm spacing. Four foliar sprays <strong>of</strong> 19:19:19<br />
NPK @ 2.5g l -1 , 5g l -1 , 7.5 g l -1 and 10 g l -1 (T 1<br />
to T 4<br />
),<br />
19:19:19 NPK@2.5g l -1 , 5g l -1 , 7.5 g l -1 and 10 g l -1 in<br />
email: adapakirankumar@gmail.com<br />
5
KIRAN KUMAR<br />
Table 1. Pooled analysis data for growth components <strong>of</strong> chilli as influenced by foliar spray<br />
Plant<br />
Plant<br />
Plant<br />
Treatments<br />
Height<br />
(cm)<br />
Spread<br />
E-W(cm)<br />
Spread<br />
N-S(cm)<br />
T 1-19:19:19 NPK @ 2.5 g l -1 90.3 57.7 55.1<br />
T 2 -19:19:19 NPK @ 5.0 g l -1 93.8 56.4 54.6<br />
T 3-19:19:19 NPK @ 7.5 g l -1 96.8 56.0 54.9<br />
T 4 -19:19:19 NPK @ 10.0 g l -1 96.2 56.7 54.4<br />
T 5 -19:19:19 NPK @ 2.5 g l -1 + KNO 3 @5 g l -1 92.7 54.5 53.5<br />
T 6 -19:19:19 NPK @ 5g/l + KNO 3 @ 5 g l -1 89.9 54.2 55.0<br />
T7-19:19:19 NPK @ 7.5 g/l + KNO3 @ 5 g l -1 98.4 57.4 55.2<br />
T 8 -19:19:19NPK @ 10 g/l + KNO 3 @ 5 g l -1 91.1 56.7 55.9<br />
T 9 -KNO 3 @ 5 g l -1 from one month after transplanting 94.4 57.5 56.3<br />
T 10 -KNO 3 @ 5 g l -1 at the time <strong>of</strong> flowering 91.6 54.4 52.7<br />
T 11-Control 89.9 53.9 52.8<br />
CD @ 5% NS NS NS<br />
SEm ±<br />
2.72 1.39 1.25<br />
application <strong>of</strong> 19:19:19 NPK and KNO 3.<br />
combination with KNO 3<br />
@ 5g l -1 (T 5<br />
to T 8<br />
) and KNO 3<br />
@ 5g l -1 (T 9<br />
) were scheduled at monthly intervals<br />
starting one month after transplanting. At the time <strong>of</strong><br />
flower initiation two sprays <strong>of</strong> KNO 3<br />
@ 5g l -1 (T 10<br />
)<br />
was scheduled at monthly intervals and unsprayed<br />
control (T 11<br />
). Intercultural operations and Plant<br />
protection measures were uniform in all the<br />
experimental plots. For recording observations on<br />
plant height, plant spread, fresh weight, dry weight,<br />
fruit length and fruit girth, five plants in each bed<br />
were selected at random and labelled. <strong>The</strong> data thus<br />
recorded for three years was pooled and subjected<br />
to statistical analysis (Panse and Sukhatme, 1985).<br />
RESULTS AND DISCUSSION<br />
<strong>The</strong>re was no significant difference in the<br />
plant height and plant spread (Table 1), fruit girth<br />
and percentage <strong>of</strong> dry pod recovery (Table 2). Fresh<br />
pod yield (9820 kg ha -1 ) and dry pod yield (3320 kg<br />
ha -1 ) were significantly maximum with four foliar<br />
6
EFFECT OF FOLIAR APPLICATION OF NPK NUTRIENTS ON GROWTH AND YIELD OF CHILLI<br />
Table 2. Pooled analysis data for yield components <strong>of</strong> chilli as influenced by foliar spray<br />
Treatments<br />
Fruit<br />
Length<br />
(cm)<br />
Fruit<br />
Girth<br />
(cm)<br />
Fresh pod<br />
yield (kg<br />
ha -1 )<br />
Dry pod<br />
yield (kg<br />
ha -1 )<br />
Recovery<br />
(%)<br />
T 1 -19:19:19 NPK @ 2.5 g l -1 7.3 0.9 8380 2650 31.6<br />
T 2 -19:19:19 NPK @ 5.0 g l -1 7.4 1.0 8380 2800 32.8<br />
T 3 -19:19:19 NPK @ 7.5 g l -1 7.6 1.0 8870 2910 33.4<br />
T 4 -19:19:19 NPK @ 10.0 g l -1 7.4 1.0 8330 2850 33.5<br />
T 5 -19:19:19 NPK @ 2.5 g l -1 +<br />
KNO 3 @5 g l -1 7.2 1.0 7970 2670 34.2<br />
T 6 -19:19:19 NPK @ 5g/l + KNO 3<br />
@ 5 g l -1 7.5 1.0 8590 2730 31.8<br />
T 7 -19:19:19 NPK @ 7.5 g/l +<br />
KNO 3 @ 5 g l -1 7.6 1.0 9820 3320 33.8<br />
T 8 -19:19:19NPK @ 10 g/l +<br />
KNO 3 @ 5 g l -1 7.5 1.0 8490 2720 32.0<br />
T 9 -KNO 3 @ 5 g l -1 from one<br />
month after transplanting<br />
T 10 -KNO 3 @ 5 g l -1 at the time <strong>of</strong><br />
flowering<br />
7.5 0.9 8070 2730 33.8<br />
7.5 1.0 8310 2690 32.3<br />
T 11 -Control 7.1 0.9 6450 2240 34.7<br />
CD @ 5% 0.3 NS 1444 480 NS<br />
SEm ± 0.1 0.02 489 160 0.82<br />
application <strong>of</strong> 19:19:19 NPK and KNO 3.<br />
sprays <strong>of</strong> 19:19:19 NPK @ 7.5g l -1 + KNO 3<br />
@ 5g l -1<br />
scheduled at monthly intervals starting from one<br />
month after transplanting (Table 2). <strong>The</strong>se results<br />
were supported by Lovatt (2005), who reported that<br />
foliar spray <strong>of</strong> 1% either 19:19:19 or KNO 3<br />
at 45, 60<br />
and 75 days after planting increased the crop yield<br />
by about 10% over unsprayed control. <strong>The</strong>se results<br />
are in line with those <strong>of</strong> Patil and Biradar (2001), who<br />
applied NPK 19:19:19 as foliar application and found<br />
significant effect on fruit weight <strong>of</strong> chilli. Singh et al.<br />
(2002) reported that gross and marketable yield <strong>of</strong><br />
Onion was highest with basal application <strong>of</strong> NPK and<br />
foliar application <strong>of</strong> 1% KNO 3<br />
at 30, 45 and 60 days<br />
after transplanting.<br />
Foliar application <strong>of</strong> 19:19:19 NPK@7.5 g l -1<br />
+ KNO 3<br />
@ 5g l -1 resulted significantly longer fruits<br />
(7.6 cm) (Table 2). Similarly, Baloch (2008) reported<br />
significant increase in fruit length by foliar application<br />
7
KIRAN KUMAR<br />
<strong>of</strong> Nitrophen 4%, Nitrogen compound 12%, Iron 2%,<br />
Magnesium 2%, Manganese 2%, Boron 2%, Copper<br />
4%, Molybdenum 2%, Potash 8%, P 2<br />
O 5<br />
12% and<br />
Calcium 8%. Similar studies have also been<br />
conducted by Jiskani (2005), who found that foliar<br />
application <strong>of</strong> zinc 3.0 ppm, copper 1.0 ppm and boron<br />
0.5 ppm produced the highest number <strong>of</strong> fruits per<br />
plant and increasing frequency <strong>of</strong> NPK (19:19:19)<br />
spraying from three to four times did not increase<br />
the number <strong>of</strong> chilli fruits per plant. Increased yields<br />
due to foliar spray could be attributed to the reason<br />
that foliar feeding is <strong>of</strong>ten effective when roots are<br />
unable to absorb sufficient nutrients from the soil and<br />
such a condition could arise from an infertile soil, a<br />
high degree <strong>of</strong> soil fixation, losses from leaching,<br />
soil temperatures, lack <strong>of</strong> soil moisture, or restricted,<br />
injured or diseased root system. Further, Silberbush<br />
(2002) also reported that foliar fertilization is a widely<br />
used practice to correct nutritional deficiencies in<br />
plants caused by improper supply <strong>of</strong> nutrients to roots.<br />
Improvement in yield <strong>of</strong> chilli was evident<br />
with increase in NPK 19:19:19 concentration.<br />
However, application beyond 7.5 g l -1 water was not<br />
effective and thus 7.5g l -1 water along with KNO 3<br />
@<br />
5g l -1 was considered to be an optimum concentration<br />
for commercial production <strong>of</strong> Chilli.<br />
REFERENCES<br />
Agrocrops. 2012. Crop report 2011/12 Oilseeds &<br />
spices pp.6-7<br />
Baloch, Q. B, Chachar ,Q.I and Tareen, M.N. 2008.<br />
Effect <strong>of</strong> foliar application <strong>of</strong> macro and micro<br />
nutrients on production <strong>of</strong> green chillies<br />
(Capsicum annuum L.) <strong>Journal</strong> <strong>of</strong> Agricultural<br />
Technology, 4 (2):177-184<br />
Jiskani, M.M. 2005. Foliar fertilizers—fast acting<br />
agents. Daily DAWN, the Internet Edition,<br />
Monday December 5, 2005.<br />
Lovatt, C.J. 2005. Formulation <strong>of</strong> foliar phosphorus<br />
fertilize for chilli. www.freepatentsonline.com<br />
Gary, P.M. 1982. Foliar fertilization: some<br />
physiological perspectives. Paper presented<br />
to American Chemical Society, 13 th<br />
September, 1982.<br />
Panes, V.G and Sukhatme, G.V. 1985. Statistical<br />
methods for agricultural workers, Indian<br />
Council <strong>of</strong> Agrilcultural <strong>Research</strong>, New Delhi.<br />
Patil, R and Biradar, R. 2001. Effect <strong>of</strong> foliar<br />
application <strong>of</strong> essential nutrients on chillies.<br />
Agricultura Tecnica Santiago 51(3): 256-259.<br />
Silberbush, L.F. 2002. Response <strong>of</strong> maize to foliar<br />
vs. soil application <strong>of</strong> nitrogen-phosphoruspotassium<br />
fertilizers. <strong>Journal</strong> <strong>of</strong> Plant<br />
Nutrition. 25 (11): 2333-2342<br />
Singh, D.K, Pandey, A.K, Pandey, U.B and Bhonde,<br />
S.R. 2002. Effect <strong>of</strong> farm yard manure<br />
combined with foliar application <strong>of</strong> NPK mixture<br />
and micronutrients on growth, yield and quality<br />
<strong>of</strong> onion. News letter-National Horticultural<br />
<strong>Research</strong> and Development Foundation. 21-<br />
22(1): 1-7<br />
Sparkyby, F. 2006. Sparky Boy Enterprises. Planet<br />
Natural.1-6.<br />
Wikipedia.2006. Chillies: history, cultivation and<br />
processing pp.1-6.<br />
8
J.Res. <strong>ANGRAU</strong> 41(1) 5-13, 2013<br />
NUTRIENT UPTAKE BY RICE CROP UNDER LONG TERM INTEGRATED<br />
NUTRIENT MANAGEMENT IN RICE – RICE CROPPING SYSTEM IN ALFISOLS<br />
V. MAHESWARA PRASAD and P. PRABHU PRASADINI<br />
DAATT Centre, Krishna District, Machilipatnam – 521 002<br />
Date <strong>of</strong> Receipt : 07.12.2012 Date <strong>of</strong> Acceptance : 31.01.2013<br />
ABSTRACT<br />
Nutrient uptake by rice crop in different integrated nutrient management treatments at different stages <strong>of</strong><br />
crop growth in rice-rice cropping system was studied in alfisols <strong>of</strong> Southern Telangana Zone <strong>of</strong> Andhra Pradesh for<br />
two consecutive years during 2005-06 and 06-07. <strong>The</strong> crop fertilized with increased level <strong>of</strong> nutrients accumulated<br />
more phytomass at every stage <strong>of</strong> sampling in kharif or rabi seasons during the two years. <strong>The</strong> uptake <strong>of</strong> NPK, Zn, Cu,<br />
Fe and Mn was also maximum in response to the application <strong>of</strong> recommended dose <strong>of</strong> fertilizers. <strong>The</strong>re was no<br />
definite increase in the phytomass or uptake <strong>of</strong> major or minor nutrients consistently throughout the crop growth<br />
period during the two years by the integrated nutrient supply system compared to the application <strong>of</strong> recommended<br />
dose <strong>of</strong> fertilizers.<br />
India is one <strong>of</strong> the main countries producing<br />
Rice (Oryza sativa L.) in the world. Rice –Rice is the<br />
most predominant cropping system in southern<br />
telangana zone <strong>of</strong> Andhra Pradesh state.<br />
Deterioration <strong>of</strong> soil fertility and declining productivity<br />
due to indiscriminate application <strong>of</strong> nutrients through<br />
the fertilizers with the threat <strong>of</strong> the declining<br />
productivity has become major problem. Continuous<br />
cropping and long term fertilization are liable to<br />
change the soil properties and crop production,<br />
depending upon the type <strong>of</strong> management practices<br />
(Santhy et al, 1998). <strong>The</strong> micronutrient deficiencies<br />
are being recognized in soils intensively cultivated<br />
with cereals in several parts <strong>of</strong> the country. This is<br />
aggravated by the continuous application <strong>of</strong> high<br />
analysis fertilizers without replenishing the depleted<br />
micronutrients. <strong>The</strong>refore, the incorporation <strong>of</strong> organic<br />
material is emphasized to supply the micronutrients<br />
and thereby maintain the nutrient balance.<br />
Hence, an investigation was made to assess<br />
the soil nutrient supplying capacity under different<br />
INM practices in a long-term fertilization experiment<br />
with continuous rice - rice cropping system.<br />
MATERIALS AND METHODS<br />
<strong>The</strong> present studies were conducted during<br />
two consecutive years 2005-06 and 2006-07 at<br />
Agricultural College Farm, Rajendranagar,<br />
Hyderabad. <strong>The</strong> experiment was conducted on a<br />
sandy clay loam soil on which only rice is grown<br />
continuously in both kharif and rabi seasons since<br />
1988. <strong>The</strong> experiment was laid out in randomized<br />
block design with 12 treatments (Table 1) in three<br />
replications. Rice variety RNR 23064 was planted<br />
adopting a spacing <strong>of</strong> 20 cm x 10 cm in 59.8 m 2<br />
sized plot.<br />
Sampling <strong>of</strong> plants was done by uprooting<br />
five hills per treatment at tillering, panicle initiation<br />
and harvesting stage <strong>of</strong> the crop in each season<br />
during the two years <strong>of</strong> the investigation for the<br />
nutrient removal studies. <strong>The</strong> samples were ground<br />
using Willey mill and extracted with triacid. <strong>The</strong><br />
resultant extract was subjected to analysis <strong>of</strong> N.P<br />
and K as per the procedures outlined in Tandon<br />
(1995).<br />
Finally, the uptake <strong>of</strong> macro nutrients was<br />
calculated by using the following formula; Nutrient<br />
uptake (kg ha -1 ) = nutrient content (%) x dry matter<br />
(kg ha -1 ) divided by 100. Whereas the uptake <strong>of</strong> micro<br />
nutrients was calculated by using the formula;<br />
Nutrient uptake (g ha -1 ) = nutrient content (%) x dry<br />
matter (g ha -1 ) divided by 100.<br />
RESULTS AND DISCUSSION<br />
Major Nutrients<br />
a. Nitrogen<br />
<strong>The</strong> crop grown without the external input <strong>of</strong><br />
manures and fertilizers removed lesser quantities <strong>of</strong><br />
26, 35, and 63 kg N ha -1 in kharif 2005 at tillering,<br />
panicle initiation and harvesting stage <strong>of</strong> the crops<br />
email: vemulamadamp@gmail.com<br />
9
PRASAD and PRASADINI<br />
respectively. In the subsequent rabi, the crop<br />
removed 38, 33 and 65 kg N ha -1 at the respective<br />
stages (Table 2). In kharif 2006, rice removed 24 kg<br />
N ha -1 at tillering, 40 kg at panicle initiation and 40 kg<br />
N ha -1 at harvesting stage <strong>of</strong> the crop in T 2<br />
. In the<br />
rabi season, it removed 29, 31 and 27 kg ha -1 . <strong>The</strong><br />
application <strong>of</strong> 50 % recommended dose <strong>of</strong> fertilizers<br />
continuously in the kharif and rabi season,<br />
significantly enhanced the quantity <strong>of</strong> N removed by<br />
the crop at every stage <strong>of</strong> its growth. This trend<br />
improved with increase in the quantity <strong>of</strong> nutrients<br />
applied upto the optimum schedule. <strong>The</strong> integrated<br />
supply <strong>of</strong> nutrients by substituting 25% N fertilizer<br />
with glyricidia and application <strong>of</strong> 75 % recommended<br />
dose <strong>of</strong> fertilizers in kharif significantly increased the<br />
uptake <strong>of</strong> nitrogen at tillering both in kharif and rabi<br />
during two years.<br />
b. Phosphorus<br />
<strong>The</strong> uptake <strong>of</strong> this nutrient increased from<br />
1.0 kg at tillering to 4.4 kg at panicle initiation and<br />
further to 8.0 kg ha -1 at harvesting stage <strong>of</strong> the crop<br />
during kharif 2005 (Table 3). <strong>The</strong> uptake <strong>of</strong> the nutrient<br />
was increased by the application <strong>of</strong> fertilizers in<br />
different proportions during kharif and rabi season.<br />
<strong>The</strong> adoption <strong>of</strong> integrated nutrient management<br />
options by substituting 50 % N fertilizer with FYM,<br />
paddy straw or glyricidia appreciably increased the<br />
P uptake compared to the effect <strong>of</strong> chemical fertilizers<br />
applied at recommended dose. <strong>The</strong> substitution <strong>of</strong><br />
25 % N fertilizer with any one <strong>of</strong> these three organic<br />
sources in the kharif season and application <strong>of</strong> 75 %<br />
recommended dose <strong>of</strong> fertilizers in the rabi season<br />
also showed similar results.<br />
c. Potassium<br />
Rice removed more Potassium in response<br />
to the application <strong>of</strong> different levels <strong>of</strong> fertilizers in<br />
kharif and rabi season and their integration with the<br />
organic source <strong>of</strong> nutrients (Table 4). <strong>The</strong> unfertilized<br />
crop removed 32, 48 and 66 kg ha -1 in kharif and 38,<br />
62 and 71 kg K ha -1 in rabi at tillering, panicle initiation<br />
and harvesting stage <strong>of</strong> the crop during 2005-06. <strong>The</strong><br />
application <strong>of</strong> 50 % recommended dose <strong>of</strong> fertilizers<br />
in the kharif season increased the uptake significantly<br />
to 38, 77 and 71 kg K ha -1 at the respective stages<br />
<strong>of</strong> crop growth. <strong>The</strong> application <strong>of</strong> 50 %<br />
recommended dose <strong>of</strong> fertilizers in the rabi season<br />
also increased the uptake significantly to 49, 70, 114<br />
kg K ha -1 . <strong>The</strong>se trends repeated during the second<br />
year also. A large quantity <strong>of</strong> 60, 88 and 84 kg K ha -<br />
1<br />
in kharif and 62, 84 and 158 kg K ha -1 was removed<br />
in rabi during the first year (2005-06) by the<br />
application <strong>of</strong> recommended dose <strong>of</strong> fertilizers both<br />
in the kharif and rabi season. <strong>The</strong> uptake at tillering<br />
was 61 kg ha -1 in kharif 2006 and 63 kg ha -1 in rabi.<br />
<strong>The</strong> uptake was 61, 82 and 63 kg ha -1 at tillering,<br />
panicle initiation and harvesting in kharif while the<br />
crop removed 63, 82 and 100 kg ha -1 K during<br />
corresponding stages in the rabi seasons during 2006-<br />
07 in response to this optimum fertilizer schedule to<br />
rice-rice crop sequence. <strong>The</strong> integrated supply <strong>of</strong><br />
nutrients by substituting 50 % N fertilizer either with<br />
FYM, paddy straw or glyricidia in the kharif season<br />
and application <strong>of</strong> recommended dose <strong>of</strong> fertilizers<br />
in the rabi season did not improve the uptake <strong>of</strong> K by<br />
rice compared to the crop supplied with nutrients<br />
continuously through the recommended dose <strong>of</strong><br />
fertilizers.<br />
<strong>The</strong> role <strong>of</strong> any <strong>of</strong> these organics to substitute<br />
25 % N fertilizer in the kharif season and application<br />
<strong>of</strong> 75 % recommended dose <strong>of</strong> fertilizers in the rabi<br />
season was also not distinct to significantly increase<br />
the uptake <strong>of</strong> K compared to rice grown with fertilizers<br />
alone.<br />
B. Micronutrients<br />
a. Zinc<br />
<strong>The</strong> application <strong>of</strong> fertilizers with organic<br />
source <strong>of</strong> nutrients activated the crop to remove large<br />
quantity <strong>of</strong> Zn from the soil. <strong>The</strong> results showed that<br />
a low quantity <strong>of</strong> 29.2 g Zn was removed at tillering,<br />
56.5 g at panicle initiation and 88.3 g ha -1 at<br />
harvesting stage <strong>of</strong> rice in the kharif season while<br />
the uptake was 44.6, 53.7 and 77.93 g ha -1 from the<br />
unfertilized soil during the first year (Table 5). <strong>The</strong><br />
application <strong>of</strong> 50 % recommended dose <strong>of</strong> fertilisers<br />
continuously in kharif and rabi season to the<br />
sequence crops significantly increased the uptake<br />
<strong>of</strong> this nutrient throughout the crop growth period.<br />
<strong>The</strong> crop was estimated to remove 43.6, 95.1 and<br />
135.6 g Zn ha -1 in kharif and 48.6, 85.5 and 160.4 g<br />
ha -1 in rabi at tillering, panicle initiation and harvesting<br />
during the first year. It removed 28.3, 63.5 and 174.9<br />
g Zn ha -1 in kharif and 40.6, 75.74 and 146.1 g ha -1 in<br />
the rabi season during the three corresponding growth<br />
10
NUTRIENT UPTAKE BY RICE CROP UNDER LONG TERM INM<br />
stages <strong>of</strong> crop in the second year. <strong>The</strong> uptake <strong>of</strong> this<br />
micronutrient in general tended to increase with the<br />
fertilizer schedule upto the optimum. Rice fertilized<br />
with the recommended level <strong>of</strong> nutrients through the<br />
fertilisers removed a large quantity <strong>of</strong> 50.1, 112.1<br />
and 210.7 g Zn ha -1 in kharif and 57.2, 104.3 and<br />
255.4 g Zn ha -1 in rabi at tillering, panicle initiation<br />
and harvesting stages respectively. During the second<br />
year rice removed 36.1, 92.2 and 250 g Zn ha -1 in<br />
kharif season and 46.3, 101.6 and 174.4 g ha -1 in the<br />
rabi season. <strong>The</strong> substitution <strong>of</strong> 50 % N fertilizer in<br />
kharif and application <strong>of</strong> recommended dose <strong>of</strong><br />
fertilizers in rabi significantly increased the uptake<br />
<strong>of</strong> the micronutrient throughout the crop growing<br />
period compared to the supply <strong>of</strong> nutrients<br />
continuously through the chemical fertilizers only<br />
during the second year.<br />
<strong>The</strong> substitution <strong>of</strong> 50% N fertilizer with<br />
glyricidia in kharif and application <strong>of</strong> recommended<br />
dose <strong>of</strong> fertilizers in the rabi season was the best<br />
nutrient management strategy. It enabled the crop to<br />
remove significantly more quantity <strong>of</strong> Zn consistently<br />
at every stage <strong>of</strong> crop growth during kharif and rabi<br />
during the two year crop sequence. <strong>The</strong> substitution<br />
<strong>of</strong> 25 % N fertilizer with glyricidia in kharif and<br />
application <strong>of</strong> 75 % recommended dose <strong>of</strong> fertilizers<br />
in rabi was also superior to the complete dependence<br />
on chemical fertilizers. However, the uptake <strong>of</strong> Zn<br />
by this treatment was not significantly different at<br />
panicle initiation in the first year and at harvest in<br />
the second year.<br />
b. Copper<br />
Rice removed 4.90, 10.29 and 19.22 g ha -1<br />
Cu in kharif and 7.57, 9.32 and 14.94 g ha -1 in rabi at<br />
tillering, panicle initiation and harvesting stage from<br />
the soil mineralized nutrients without the external<br />
supply during 2005-06 (Table 6). <strong>The</strong> uptake was<br />
4.14, 8.93 and 23.61 g Cu ha -1 in the kharif season<br />
while it was 5.08, 8.06 and 10.71 g ha -1 in the rabi<br />
season during the second cycle <strong>of</strong> the crop sequence.<br />
<strong>The</strong> fertilizer application triggered the uptake <strong>of</strong> this<br />
micronutrient. <strong>The</strong> crop fertilized with 50 %<br />
recommended dose <strong>of</strong> fertilizers invariable removed<br />
significantly larger quantity <strong>of</strong> Cu ha -1 throughout the<br />
growth period. <strong>The</strong> dressing <strong>of</strong> soil with optimum<br />
fertilizer schedule for rice in kharif and rabi season<br />
maximized the uptake <strong>of</strong> Cu compared to relatively<br />
high level <strong>of</strong> farmers schedule. Rice removed 8.86,<br />
19.92 and 44.75 g Cu ha -1 in kharif while it removed<br />
9.30, 18.30 and 54.37 g Cu ha -1 in rabi during 2005-<br />
06. Similarly, maximum uptake <strong>of</strong> 6.00, 15.26 and<br />
52.82 g Cu ha -1 was recorded at tillering, panicle<br />
initiation and harvesting stage in kharif while it was<br />
7.56, 17.28 and 38.47 g ha -1 in rabi during 2006-07.<br />
<strong>The</strong> substitution <strong>of</strong> 50 % N fertilizer in kharif with<br />
FYM, paddy straw or glyricidia and the application <strong>of</strong><br />
recommended level <strong>of</strong> fertilizers in rabi were not<br />
significantly superior to enhance the uptake <strong>of</strong> this<br />
micronutrient significantly compared to the<br />
supplement <strong>of</strong> nutrients entirely through chemical<br />
fertilizers. <strong>The</strong> substitution <strong>of</strong> 25 % N fertilizer with<br />
any <strong>of</strong> the three organic sources in kharif and<br />
application <strong>of</strong> 75 % recommended dose <strong>of</strong> fertilizers<br />
in the rabi season were also not distinguished as<br />
superior nutrient management practices to fertilizer<br />
treatments.<br />
c. Iron<br />
<strong>The</strong> uptake <strong>of</strong> nutrients was low by growing<br />
rice in sequence on the native soil fertility without<br />
external input <strong>of</strong> organic or inorganic source <strong>of</strong><br />
nutrients in the kharif season. <strong>The</strong> crop removed<br />
156.9, 308.9 and 399.2 g Fe ha -1 at tillering, panicle<br />
initiation and harvesting during kharif 2005 (Table 7).<br />
In the subsequent rabi, rice removed 251.3, 302.9<br />
and 318.9 g ha -1 Fe. <strong>The</strong> application <strong>of</strong> fertilizers<br />
increased the uptake <strong>of</strong> this nutrient. <strong>The</strong> uptake was<br />
significantly more even by the application <strong>of</strong> 50 %<br />
recommended dose <strong>of</strong> fertilizers continuously in the<br />
kharif and rabi season. <strong>The</strong> crop fertilized with 50 %<br />
recommended dose <strong>of</strong> nutrients removed 152.2,<br />
314.2 and 984.3 g Fe ha -1 in kharif and 223.4, 461.1<br />
and 769.5 g Fe ha -1 in the rabi season. <strong>The</strong> uptake <strong>of</strong><br />
this micronutrient further increased with the level<br />
fertilizer application to a maximum upto the<br />
recommended dose.<br />
<strong>The</strong> substitution <strong>of</strong> 50 or 25 % N fertilizer<br />
with FYM in the kharif season was beneficial. It<br />
increased the uptake <strong>of</strong> Fe significantly more at<br />
tillering stage <strong>of</strong> the crop both in kharif and rabi<br />
11
PRASAD and PRASADINI<br />
seasons during the two years over the crop grown<br />
entirely with chemical fertilizers. But, the substitution<br />
<strong>of</strong> 50 % N fertilizer with glyricidia was more beneficial.<br />
It increased the uptake <strong>of</strong> Fe by rice in significantly<br />
larger quantities upto panicle initiation both in kharif<br />
and rabi season during the two years. Such a<br />
beneficial effect <strong>of</strong> integrated nutrient management<br />
by substituting 25 % N fertilizer with glyricidia was<br />
on the other hand persistent only at tillering stage.<br />
d. Manganese<br />
Rice grown with different levels <strong>of</strong> fertilizers<br />
and integrated with organic source <strong>of</strong> nutrients<br />
removed larger quantities <strong>of</strong> this micronutrient than<br />
from the unfertilized soil. <strong>The</strong> crop removed 187.4 g<br />
ha -1 Mn at tillering, 384.9 g at panicle initiation and<br />
137.9 g ha -1 at harvesting stage during kharif 2005-<br />
06 (Table 8). <strong>The</strong> crop grown in the subsequent<br />
season removed 269.8, 328.5 and 324.69 g ha -1 Mn.<br />
During the second year rice removed 154.9, 351.9<br />
and 474.9 kg Mn ha -1 in kharif. In the rabi season it<br />
removed 192.1, 313.6 and 215.7 g Mn ha -1 at tillering,<br />
panicle initiation and harvesting stage. <strong>The</strong><br />
continuous application <strong>of</strong> 50 % recommended dose<br />
<strong>of</strong> fertilizers increased the uptake <strong>of</strong> this micronutrient<br />
during the corresponding crop growth stages in kharif<br />
and rabi in both the years. <strong>The</strong> crop fertilized with<br />
this treatment dose removed 276.0, 629.9 and 574.9<br />
g Mn ha -1 at tillering, panicle initiation and harvesting<br />
stage in the kharif season, while it removed 287.4,<br />
525.1 and 676.8 g ha -1 in rabi 2005-06. <strong>The</strong> uptake<br />
was 183.1, 374.7 and 784.5 g Mn ha -1 in kharif, while<br />
it was 232.3, 483.3 and 643.7 g ha -1 in the rabi season<br />
during the three crop growth stages in the second<br />
year crop cycle. <strong>The</strong> contribution <strong>of</strong> FYM or glyricidia<br />
to substitute 50 % N fertilizer was superior to<br />
chemical fertilizers alone in enhancing the uptake <strong>of</strong><br />
this micronutrient only at tillering stage during 2005-<br />
06.<br />
<strong>The</strong> available Zn and Cu in the fertilized soil<br />
was on par with the unfertilized soil at different stages<br />
<strong>of</strong> crop growth during the two year rice-rice cropping<br />
sequence. <strong>The</strong> co-application <strong>of</strong> 50 per cent<br />
recommended dose <strong>of</strong> NPK through fertilizers and<br />
FYM or glyricidia equivalent 50 per cent N fertilizer<br />
in the kharif season followed by the recommended<br />
dose <strong>of</strong> fertilizers in the rabi season significantly<br />
enhanced the availability <strong>of</strong> Fe both in the surface 0-<br />
15 and lower soil depth <strong>of</strong> 15-30 cm. <strong>The</strong> substitution<br />
<strong>of</strong> 25 per cent N fertilizer with these organic materials<br />
in the kharif season and application <strong>of</strong> 75 per cent<br />
recommended dose <strong>of</strong> fertilizers in rabi season also<br />
enriched the soil with more quantity <strong>of</strong> available Zn.<br />
But, this improvement was relatively less consistent<br />
at different stages <strong>of</strong> crop growth than their<br />
substitution for 50 % N fertilizer. This trend was also<br />
similar for the soil available Cu content. Additionally,<br />
its improvement was also recorded by substituting<br />
50 or 25 % N fertilizer with paddy straw only in the<br />
first year. <strong>The</strong> benefit <strong>of</strong> increased availability <strong>of</strong> Fe<br />
in the soil was recorded at the tillering stage during<br />
2005-06 by the substitution <strong>of</strong> 50 per cent N fertilizer<br />
with FYM in kharif season and application <strong>of</strong><br />
recommended dose <strong>of</strong> fertilizers in the rabi season<br />
only in the surface layer. On the other hand, the<br />
availability <strong>of</strong> Mn was not influenced by the<br />
application <strong>of</strong> fertilizers or the conjunctive use <strong>of</strong><br />
nutrients through organic and inorganic sources.<br />
Rajeev Kumar et al., (1993), Singh et al., (1999)<br />
reported that the incorporation <strong>of</strong> organic sources in<br />
the soil along with the fertilizers increased the<br />
available micronutrients. <strong>The</strong> magnificent responses<br />
<strong>of</strong> integrated nutrient management treatments in<br />
leaving behind larger quantities <strong>of</strong> copper sustained<br />
the crop requirement in sufficient quantity. <strong>The</strong><br />
availability <strong>of</strong> Zn increased by the co application <strong>of</strong><br />
FYM by substituting 25 or 50% N fertilizer.<br />
Nonetheless, the site <strong>of</strong> present experiment<br />
continuously cropped with rice-rice cropping<br />
sequence for the past 17 years contained much higher<br />
quantities <strong>of</strong> Zn, Cu, Fe, Mn than the critical limit <strong>of</strong><br />
0.6 mg kg -1 Zn, 0.2 mg kg -1 Cu, 4 mg kg -1 Fe, and 3<br />
mg kg -1 Mn even in the unfertilized control. Not<br />
complacent with the data so achieved, it would be a<br />
wise step to substitute 50 per cent N fertilizer with<br />
FYM or glyricidia to averse the likely depletion <strong>of</strong><br />
these micro but essential elements for crop growth<br />
in the years to come<br />
12
NUTRIENT UPTAKE BY RICE CROP UNDER LONG TERM INM<br />
Table 1. Details <strong>of</strong> the treatments<br />
Sl. No Kharif Rabi<br />
T 1 No fertilizers, No organic manures No fertilizers, No organic manures<br />
T 2 50 Rec. NPK dose through fertilizers 50 Rec. NPK dose through fertilizers<br />
T 3 50 % Rec. NPK dose through fertilizers 100 % Rec. NPK dose through OM<br />
T 4 75 % Rec. NPK dose through fertilizers 75 % Rec. NPK dose through fertilizers<br />
T 5<br />
T 6<br />
T 7<br />
T 8<br />
100 % Rec. NPK dose through fertilizers 100 % Rec. NPK dose through fertilizers<br />
120:60:60 kg ha -1 120:60:60 kg ha -1<br />
50 % Rec. NPK dose through fertilizers + 50 % N<br />
through FYM<br />
75 % Rec. NPK dose through fertilizers + 25 % N<br />
through FYM<br />
50 % Rec. NPK dose through fertilizers + 50 % N<br />
through paddy straw<br />
T 9 75 % Rec. NPK dose through fertilizers + 25 %<br />
N through paddy straw<br />
T 10<br />
50 % Rec. NPK dose through fertilizers + 50 % N<br />
through glyricidia<br />
T 11 75 % Rec. NPK dose through fertilizers + 25 % N<br />
through glyricidia<br />
T 12 Conventional farmers practice 80:50:20 kg ha -1<br />
NPK<br />
100 % Rec. NPK dose through fertilizers<br />
75 % Rec. NPK dose through fertilizers<br />
100 % Rec. NPK dose through fertilizers<br />
75 % Rec. NPK dose through fertilizers<br />
100 % Rec. NPK dose through fertilizers<br />
75 % Rec. NPK dose through fertilizers<br />
Conventional farmers practice 80:50:20 kg<br />
ha -1 NPK<br />
Table 2. Influence <strong>of</strong> integrated nutrient management practices on N uptake (kg ha -1 ) in rice-rice cropping<br />
system<br />
Treatments<br />
Tillering<br />
2005-06 2006-07<br />
Panicle<br />
initiation<br />
Harvesting Tillering Panicle<br />
initiation<br />
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif<br />
Harvesting<br />
T 1 26 38 35 33 63 65 24 29 40 31 40 27<br />
T 2 47 45 77 73 100 114 31 39 51 63 60 59<br />
T 3 32 46 70 80 111 134 29 41 58 81 77 86<br />
T 4 49 49 82 84 105 173 35 43 80 78 75 68<br />
T 5 55 48 119 110 112 126 42 48 106 96 79 92<br />
T 6 39 51 94 104 115 128 39 42 89 95 72 74<br />
T 7 53 59 106 107 112 143 41 48 97 94 69 75<br />
T 8 30 45 88 81 108 179 37 42 85 82 61 75<br />
T 9 48 46 97 83 105 150 30 41 68 93 69 76<br />
T 10 59 66 121 119 121 181 45 54 109 118 91 80<br />
T 11 62 70 125 121 119 135 50 53 115 117 92 80<br />
T 12 42 52 85 79 103 123 35 43 69 82 68 72<br />
SE + 2.25 1.6 9.7 7.7 5.8 10.6 2.4 4.2 4.7 8.7 7.9 9.0<br />
CD at 5 % 4.6 3.4 20.2 16.0 12.0 22 5.0 8.6 9.8 20.2 16.4 18.6<br />
Rabi<br />
13
PRASAD and PRASADINI<br />
Table 3. Influence <strong>of</strong> integrated nutrient management practices on P uptake (kg ha -1 ) in rice-rice cropping<br />
system<br />
Treat<br />
ments<br />
Tillering<br />
2005-06 2006-07<br />
Panicle<br />
initiation<br />
Harvesting Tillering Panicle<br />
initiation<br />
Harvesting<br />
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi<br />
T 1 1.0 1.6 4.4 4.1 8.0 3.5 1.0 1.2 5.0 3.8 8.8 4.7<br />
T 2 1.6 1.7 6.9 6.7 10.0 10.6 1.1 1.4 5.1 5.7 12.9 10.5<br />
T 3 1.3 1.8 6.3 7.5 11.9 19.0 1.2 1.7 5.2 7.7 9.6 16.3<br />
T 4 1.6 1.8 6.7 7.2 10.7 17.4 1.2 1.5 6.8 6.7 12.1 12.4<br />
T 5 3.6 1.9 8.0 7.6 13.3 25.5 1.4 1.6 7.3 7.3 13.1 13.3<br />
T 6 2.9 2.0 7.1 7.9 13.7 28.7 1.3 1.7 7.0 7.4 11.9 15.0<br />
T 7 3.5 1.9 7.8 7.9 13.6 22.6 1.3 1.6 7.3 7.0 12.0 14.5<br />
T 8 3.4 1.8 6.3 7.3 11.6 28.1 1.1 1.7 6.1 7.4 11.1 13.6<br />
T 9 3.3 2.1 7.1 7.0 11.3 23.0 1.1 1.6 6.2 7.0 13.2 14.6<br />
T 10 3.7 2.2 8.1 8.3 14.9 27.4 1.4 1.7 7.5 8.2 16.7 17.5<br />
T 11 3.8 2.3 8.2 8.0 14.9 20.4 1.6 1.7 7.8 7.7 11.9 15.5<br />
T 12 3.5 1.8 7.0 6.5 10.6 15.9 1.1 1.4 5.5 6.5 11.3 16.5<br />
SE + 0.14 0.04 0.54 0.91 1.06 2.55 0.02 0.04 0.54 0.63 0.57 1.54<br />
CD at<br />
5 %<br />
0.30 0.08 1.12 1.88 2.20 5.30 0.05 0.09 1.12 1.30 1.18 3.20<br />
Table 4. Influence <strong>of</strong> integrated nutrient management practices on K uptake (kg ha -1 ) in rice-rice cropping<br />
system<br />
Treatment<br />
Tillering<br />
2005-06 2006-07<br />
Panicle<br />
initiation<br />
Harvesting Tillering Panicle<br />
initiation<br />
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif<br />
Harvesting<br />
T 1 32 38 48 62 66 71 34 39 48 42 49 65<br />
T 2 38 49 77 70 71 114 41 50 57 62 54 99<br />
T 3 34 51 70 82 84 114 38 52 59 86 62 90<br />
T 4 50 60 77 80 77 114 51 62 76 75 80 90<br />
T 5 60 62 88 84 84 158 61 63 82 82 63 100<br />
T 6 59 62 80 88 89 177 60 64 78 85 71 160<br />
T 7 49 60 86 86 87 149 51 62 80 80 80 157<br />
T 8 58 60 60 80 82 179 60 62 65 82 82 182<br />
T 9 60 63 78 76 77 149 62 65 70 80 91 160<br />
T 10 61 65 90 90 100 181 65 65 84 90 90 185<br />
T 11 60 63 92 88 94 152 61 65 86 88 90 180<br />
T 12 49 50 76 72 74 114 50 52 62 72 62 115<br />
SE + 2.1 4.4 1.1 3.3 0.5 10.6 2.3 3.1 1.6 8.8 1.4 1.1<br />
CD at 5 % 4.3 9.2 14.7 6.9 4.2 22.0 4.7 6.5 3.4 18.3 3.0 22.4<br />
Rabi<br />
14
NUTRIENT UPTAKE BY RICE CROP UNDER LONG TERM INM<br />
Table 5. Influence <strong>of</strong> integrated nutrient management practices on Zn uptake (g ha -1 ) in Rice-Rice<br />
cropping system<br />
Treatment<br />
Tillering<br />
2005-06 2006-07<br />
Panicle<br />
initiation<br />
Harvesting Tillering Panicle<br />
initiation<br />
Harvesting<br />
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi<br />
T 1 29.2 44.6 56.5 53.7 88.3 77.93 24.7 34.1 58.7 56.3 117.0 55.1<br />
T 2 43.6 48.6 95.1 85.5 135.6 160.4 28.3 40.6 63.5 75.74 174.9 146.1<br />
T 3 35.1 54.7 86.3 99.3 151.2 221.5 29.3 47.0 64.2 103.48 214.1 169.0<br />
T 4 45.60 53.9 96.7 96.9 171.2 220.5 33.2 42.6 84.3 90.60 220.8 145.0<br />
T 5 50.1 57.2 112.1 104.3 210.7 255.4 36.1 46.3 92.2 101.6 250.0 174.4<br />
T 6 46.3 67.8 114.3 124.2 174.0 307.9 41.0 56.3 106.8 117.0 293.8 197.9<br />
T 7 53.5 67.5 129.7 118.6 217.5 292.7 40.9 48.5 107.4 105.3 271.6 119.8<br />
T 8 35.7 55.7 80.7 110.3 163.0 251.3 32.7 51.0 68.3 112.5 195.4 179.5<br />
T 9 46.8 51.2 106.1 104.0 184.3 241.4 34.0 47.0 69.2 101.4 201.9 183.3<br />
T 10 56.57 65.0 128.2 127.8 242.6 312.8 44.0 55.3 109.9 123.8 294.6 206.5<br />
T 11 57.97 69.1 124.5 121.1 258.1 307.9 47.2 53.6 112.9 117.2 286.7 193.8<br />
T 12 49.3 46.3 90.8 87.1 178.7 199.0 34.2 41.8 72.1 91.8 195.6 151.6<br />
SE + 2.3 1.8 7.4 10.8 12.8 27.0 1.5 2.8 2.3 7.0 19.6 9.8<br />
CD at 5 % 4.8 3.8 15.4 22.4 26.5 56.4 3.2 5.8 4.7 14.5 40.6 20.4<br />
Table 6. Influence <strong>of</strong> integrated nutrient management practices on Cu uptake (g ha -1 ) in Rice-Rice<br />
cropping system<br />
Treat<br />
ments<br />
Tillering<br />
2005-06 2006-07<br />
Panicle<br />
initiation<br />
Harvesting Tillering Panicle<br />
initiation<br />
Harvesting<br />
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi<br />
T 1 4.90 7.57 10.29 9.32 19.22 14.94 4.14 5.08 8.93 8.06 23.61 10.71<br />
T 2 7.49 8.23 17.07 15.31 29.10 33.43 4.90 6.26 10.37 12.37 37.23 31.35<br />
T 3 5.80 8.82 15.82 17.52 33.12 50.12 4.96 7.42 10.64 16.88 46.14 38.05<br />
T 4 7.82 9.29 17.48 16.91 76.25 46.30 5.50 6.93 14.14 15.36 47.75 31.79<br />
T 5 8.86 9.30 19.92 18.30 44.75 54.37 6.00 7.56 15.26 17.28 52.82 38.47<br />
T 6 7.44 11.02 19.56 19.97 38.75 59.38 6.28 8.89 16.57 18.52 55.07 39.97<br />
T 7 8.66 9.80 20.52 19.58 41.24 56.68 6.24 8.66 16.67 16.51 53.10 38.68<br />
T 8 5.60 8.98 13.73 17.47 32.34 48.38 4.90 8.10 11.53 17.25 39.14 36.55<br />
T 9 3.28 8.38 17.41 16.37 34.14 45.06 5.41 7.45 11.26 15.60 42.01 36.38<br />
T 10 9.16 10.86 19.44 20.61 50.01 58.68 6.65 8.95 17.11 19.15 58.08 41.07<br />
T 11 9.27 11.30 19.36 19.18 46.47 59.74 7.40 8.37 17.06 17.22 57.74 38.86<br />
T 12 7.85 8.67 16.10 15.42 37.20 49.27 5.15 6.85 11.97 14.66 41.77 32.53<br />
SE + 0.54 0.46 1.37 1.50 3.30 4.18 0.32 0.55 1.16 0.89 4.10 3.61<br />
CD at<br />
5 %<br />
1.12 0.96 2.84 3.12 6.84 8.68 0.66 1.14 2.42 1.86 8.50 7.50<br />
15
PRASAD and PRASADINI<br />
Table 7. Influence <strong>of</strong> integrated nutrient management practices on uptake <strong>of</strong> Fe (g ha -1 ) in rice-rice<br />
cropping system<br />
Treatments<br />
Tillering<br />
2005-06 2006-07<br />
Panicle<br />
initiation<br />
Harvesting Tillering Panicle<br />
initiation<br />
Harvesting<br />
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi<br />
T 1 156.9 251.3 308.9 302.9 399.2 318.1 136.0 185.1 293.2 292.1 543.1 262.4<br />
T 2 239.4 277.8 578.2 510.6 658.2 671.0 152.2 223.4 314.2 461.1 984.3 769.5<br />
T 3 193.5 308.7 530.9 603.7 765.2 972.9 159.5 260.8 314.2 630.5 1240.3 893.3<br />
T 4 251.5 309.9 606.2 606.7 933.8 975.2 178.6 241.8 440.6 566.9 1274.9 746.7<br />
T 5 280.0 322.2 690.3 631.6 1152.6 1168.1 202.2 262.9 488.6 599.4 1479.5 977.4<br />
T 6 314.4 447.5 738.7 751.0 1055.9 1478.8 260.3 319.9 608.4 728.3 1665.0 1110.6<br />
T 7 349.9 393.8 781.5 711.3 1105.3 1297.5 251.1 290.5 593.0 623.5 1577.9 1073.2<br />
T 8 231.9 365.5 489.5 666.3 719.0 1193.9 208.3 287.0 412.0 667.9 1125.0 991.3<br />
T 9 287.3 330.3 597.8 566.2 707.4 1068.9 209.8 262.8 400.0 590.2 1107.6 908.2<br />
T 10 362.4 448.5 773.2 754.8 1099.0 1391.7 275.7 315.2 630.6 770.6 1721.6 1057.2<br />
T 11 355.7 457.1 756.0 704.9 1183.0 1382.3 306.5 303.6 618.2 656.8 1598.7 886.0<br />
T 12 274.8 320.8 569.5 546.7 798.0 870.5 199.4 241.1 390.8 534.0 1109.2 694.4<br />
SEM + 1.1 8.1 35.0 39.1 46.3 68.7 6.2 12.3 11.0 41.1 120.5 7.7<br />
CD at<br />
5 %<br />
22.6 18.8 72.6 81.2 96.0 142.6 12.8 25.4 22.8 85.2 250.0 160.8<br />
Table 8. Influence <strong>of</strong> integrated nutrient management practices on uptake <strong>of</strong> Mn (g ha -1 ) in rice-rice<br />
cropping system<br />
Treat<br />
ments<br />
Tillering<br />
2005-06 2006-07<br />
Panicle<br />
initiation<br />
Harvesting Tillering Panicle<br />
initiation<br />
Harvesting<br />
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi<br />
T 1 187.4 269.8 384.9 328.5 137.0 324.6 154.9 192.1 351.9 313.6 474.7 215.7<br />
T 2 276.0 287.4 629.9 525.1 574.9 676.8 183.1 232.3 374.7 483.3 784.5 643.7<br />
T 3 222.8 312.9 618.8 616.2 767.5 940.5 191.7 270.3 381.9 660.8 993.9 746.9<br />
T 4 286.9 322.2 695.5 618.7 935.2 964.8 211.2 251.0 536.4 626.6 1099.3 647.2<br />
T 5 300.2 346.0 811.4 682.7 1222.4 1170.1 243.3 273.7 584.4 673.3 1343.0 845.1<br />
T 6 332.6 438.5 802.5 751.2 1009.2 1432.9 267.7 338.8 608.4 807.4 1516.4 965.6<br />
T 7 371.5 400.0 812.0 711.3 961.5 1270.3 261.7 290.3 576.1 713.8 1349.3 914.44<br />
T 8 225.7 373.9 519.5 670.3 729.0 1146.9 204.6 311.1 412.0 730.2 1011.6 867.4<br />
T 9 276.3 334.3 653.2 614.6 771.12 1060.3 208.7 273.5 399.6 656.2 1017.3 862.6<br />
T 10 354.5 436.7 859.7 780.8 1215 1423.6 274.1 334.7 630.6 843.6 1576.6 988.1<br />
T 11 355.7 449.6 802.1 736.2 1285.8 1347.4 293.8 322.8 613.9 756.9 1454.1 883.3<br />
T 12 266.9 318.8 628.4 572.11 791.1 894.2 202.1 249.4 421.8 545.0 928.4 807.1<br />
SEM + 13.6 7.7 61.0 45.8 90.3 106.5 12.7 29.8 35.7 58.0 66.2 112.8<br />
CD at 5 % 28.3 16.0 126.6 95.0 166.5 220.8 26.4 61.8 74.0 120.4 116.5 234.0<br />
16
NUTRIENT UPTAKE BY RICE CROP UNDER LONG TERM INM<br />
REFERENCES<br />
Rajeev Kumar, Singh, K.P and Sarkar, A K 1993.<br />
Cumulative effects <strong>of</strong> cropping and fertilizer<br />
use on the status <strong>of</strong> micronutrients in soil and<br />
crop. Fertilizer News. 38(11): 13-17.<br />
Santhy, P., Jayashree Sankar, S., Muthuvel, P and<br />
Selvi, D. 1998. Long term fertilizer experiments<br />
– status <strong>of</strong> N, P and K fractions in soil, <strong>Journal</strong><br />
<strong>of</strong> Indian Society Soil Science 46(3): 395-398.<br />
Singh, N.P., Sachan, R.S., Pandey, P.Cand Bisht,<br />
P.S. 1999. Effect <strong>of</strong> decade long fertilizer and<br />
manure application on soil fertility and<br />
productivity <strong>of</strong> rice – wheat system in a<br />
Mollisol. <strong>Journal</strong> <strong>of</strong> Indian Society <strong>of</strong> soil<br />
Science 48(1):72-79.<br />
Tandon, H.L.S. 1995, Methods <strong>of</strong> analysis <strong>of</strong> soils,<br />
plants, water and fertilizers FDCO, New Delhi,<br />
pp.143.<br />
17
J.Res. <strong>ANGRAU</strong> 41(1) 14-19, 2013<br />
CHANGES IN MATURITY INDICES DURING VERMICOMPSOTING VS<br />
CONVENTIONAL COMPOSTING OF AGRICULTURAL WASTES<br />
CH. S. RAMA LAKSHMI, P.C. RAO, G.PADMAJA, T.SREELATHA,<br />
M.MADHAVI, P.V.RAO and A. SIREESHA<br />
Regional Agricultural <strong>Research</strong> Station, ANGR Agricultural University, Anakapalle - 531001<br />
Date <strong>of</strong> Receipt : 30.07.2011 Date <strong>of</strong> Acceptance : 12.12.2012<br />
ABSTRACT<br />
<strong>The</strong> present investigation was carried out at Regional Agricultural <strong>Research</strong> Station, Anakapalle during<br />
2009 to monitor the changes in maturity indices i.e total organic carbon, total nitrogen, C/N ratio, humic substances<br />
and humification index during conventional method <strong>of</strong> composting and vermicomposting <strong>of</strong> different organic residues<br />
i.e sugarcane trash, weeds, vegetable market waste and paddy straw. <strong>The</strong> results revealed that the total organic<br />
carbon decreased with the passage <strong>of</strong> time during vermicomposting and conventional composting in all the organic<br />
residues. However the percent decrease was more in vermicomposting than conventional composting in a particular<br />
period <strong>of</strong> time. <strong>The</strong> total nitrogen content <strong>of</strong> different vermicomposts and conventional composts increased during<br />
composting process, high increase was observed in vermicomposting than conventional composting. Total Nitrogen<br />
content in both the composts was higher in vegetable market waste and lower in paddy straw. C/N ratio decreased<br />
with the passage <strong>of</strong> time during vermicomposting and conventional composting in all the organic residues, however<br />
paddy straw recorded the highest C/N ratio while vegetable market waste exhibited lowest C/N ratio. <strong>The</strong> humic and<br />
fulvic production increased with incubation in both the composting methods and in all the treatments, yield <strong>of</strong> humic<br />
acid was maximum from the vegetable market waste vermicompost followed by weed vermicompost. Minimum per<br />
cent <strong>of</strong> humic substances were recorded with cane trash and rice straw. A well known index for humification is the<br />
HA/FA ratio, in both the composts paddy straw compost recorded low ratio and high ratio was recorded in vegetable<br />
market waste compost. Thus, Vermicomposting <strong>of</strong>fers a promising solution for the recycling <strong>of</strong> organic wastes into<br />
valuable organic manure with in a short period <strong>of</strong> time over conventional composting.<br />
<strong>The</strong> recycling <strong>of</strong> crop residues and organic<br />
wastes through composting is the key technology<br />
for production <strong>of</strong> organic manures. Vermicomposting<br />
<strong>of</strong>fers a promising for the recycling <strong>of</strong> organic wastes.<br />
Present study is the comparative assessment <strong>of</strong><br />
vermicomposting and composting for their maturity<br />
indices <strong>The</strong> study was carried out at Regional<br />
Agricultural <strong>Research</strong> Station, Anakapalle,<br />
Visakhapatnam district during 2009.<br />
<strong>The</strong> basic raw materials used for composting<br />
and vermicomposting were<br />
1) Sugarcane trash, 2) Weeds (Cyperus rot<br />
undus,Cynodon dactylon,Cleome viscosa, Com<br />
malina bengalensis and Trianthema portul acastrum)<br />
3) Vegetable market waste and 4) Paddy straw. In<br />
case <strong>of</strong> Earthworm species Eisenia foetida was<br />
used for vermicomposting @ 1 kg per ton <strong>of</strong> organic<br />
residue and 1 % N as urea and 2 % SSP were used<br />
as chemical additives for conventional composting.<br />
Both methods <strong>of</strong> composting were carried out in<br />
cement pits with 6 x 2 x 0.6 m size. <strong>The</strong> compost<br />
samples at 15, 30, 45 and 60 days interval for<br />
vermicomposts and 15, 30, 45, 60 and 110 days<br />
interval for composting composts were collected from<br />
each treatment for laboratory analysis.<br />
<strong>The</strong> organic residues used for vermi<br />
composting and conventional composting were<br />
analysed for their maturity indices by using standard<br />
procedures as pH and was Electrical Conductivity<br />
(dSm -1 ) determined in 1 : 50 organic material (dried<br />
and powdered) and water suspension by using<br />
combined glass electrode pH meter and EC meter<br />
(Jackson, 1973). Organic carbon content was<br />
determined by using dry combustion method<br />
(Jackson, 1973). <strong>The</strong> total nitrogen content (%) in<br />
the dried compost sample was determined by<br />
microkjeldahl distillation method after destroying the<br />
organic matter using H 2<br />
SO 4<br />
and H 2<br />
O 2<br />
(Piper, 1966).<br />
C/N ratio was calculated from the above parameters.<br />
Extraction, fractionation and purification <strong>of</strong> compost<br />
samples. <strong>The</strong> humic substances were isolated,<br />
extracted and purified by following Tyurin’s method<br />
email: sitaramalakshmi20@yahoo.com<br />
18
LAKSHMI et al<br />
Fig 1. Changes in C/N ratio during<br />
vermicomposting<br />
Fig 2. Changes in C/N ratio during<br />
conventional composting<br />
Fig 3.<br />
Changes in humic acid content<br />
(%) during vermicomposting<br />
Fig. 4 Changes in humic acid content<br />
(%) during conventional composting<br />
18 19A
CHANGES IN MATURITY INDICES DURING VERMICOMPSOTING VS CONVENTIONAL<br />
as described by Kononova (1966). Humification index<br />
was computed from ratio <strong>of</strong> humic acid to fulvic acid.<br />
RESULTS AND DISCUSSION<br />
<strong>The</strong> organic residues used in the study were<br />
neutral in reaction with non saline electrical<br />
conductivity. Total organic carbon and C/N ratio varied<br />
from 35.22 (vegetable market waste) to 37.05 %<br />
(paddy straw) and 22.29 : 1 (vegetable market waste)<br />
to 68.61 : 1 (paddy straw), respectively. Regarding<br />
macro nutrient status <strong>of</strong> the organic residues, 0.54<br />
(paddy straw) to 1.58 (vegetable market waste) % N,<br />
0.10 (paddy straw) to 0.81 (vegetable market<br />
waste) % P and 0.90 (weeds) to 1.10 (cane trash) %<br />
K was recorded.<br />
Changes in total organic carbon (TOC)<br />
during vermicomposting and composting<br />
<strong>The</strong> total organic carbon content (%) <strong>of</strong> all<br />
the organic residues showed decreasing trend during<br />
both the methods <strong>of</strong> composting i.e vermicomposting<br />
and conventional composting (Table 1 and 2). <strong>The</strong><br />
total organic carbon content during vermicomposting<br />
varied from 32.28 (vegetable market waste) to 36.89<br />
(paddy straw) on 15 th day, while on 60 th day, it was<br />
varied from 23.88 (weeds) to 24.62 % (cane trash).<br />
In case <strong>of</strong> conventional composting, the total organic<br />
carbon content ranged between 32.45 (vegetable<br />
market waste) to 36.50 (paddy straw) on 15 th day,<br />
while on 110 th day, they varied from 23.05 (vegetable<br />
market waste) to 24.22 (cane trash). Total organic<br />
carbon decreased with the passage <strong>of</strong> time in all the<br />
organic residues and in both the composting methods.<br />
Total organic carbon content decreased with the<br />
decomposition during vermicomposting and<br />
composting in all the organic residues, might be due<br />
to total organic carbon is lost as carbon dioxide<br />
through microbial respiration and mineralization <strong>of</strong><br />
organic matter causing increase in total nitrogen, part<br />
<strong>of</strong> the carbon in the decomposing residues released<br />
as CO 2<br />
and part was assimilated by the microbial<br />
biomass, microorganisms used the carbon as a<br />
source <strong>of</strong> energy and decomposing the organic matter<br />
(Swathi Pattnaik and Vikram Reddy, 2010). <strong>The</strong><br />
reduction was higher in vermicomposting as compared<br />
to composting at a particular period <strong>of</strong> time, which<br />
may be due to the fact that earthworms have higher<br />
assimilating capacity and the earthworms affect the<br />
loss <strong>of</strong> carbon in the form <strong>of</strong> carbon dioxide through<br />
mineralization <strong>of</strong> organic carbon (Swathi Pattnaik and<br />
Vikram Reddy, 2010).<br />
Changes in total nitrogen during<br />
vermicomposting and conventional composting<br />
<strong>The</strong> changes in total nitrogen content during<br />
vermicomposting from 15 to 60 days were 0.62 to<br />
1.14 % (cane trash), 1.30 to 1.88 % (weeds), 1.58 to<br />
2.11 % (vegetable market waste) and 0.51 to 1.12 %<br />
(paddy straw). In case <strong>of</strong> conventional composting<br />
from 15 to 110 days, it was varied from 0.63 to 0.98<br />
% (cane trash), 1.34 to 1.68 % (weeds), 1.61 to 1.81<br />
% (vegetable market waste) and 0.53 to 0.96 %<br />
(paddy straw). <strong>The</strong> total nitrogen content increased<br />
during composting process, however more increase<br />
was observed in vermicomposting than normal<br />
composting. Irrespective <strong>of</strong> the composting methods,<br />
significantly higher and lower nitrogen content was<br />
recorded in vegetable market waste and paddy straw,<br />
respectively.<br />
<strong>The</strong> increase in nitrogen content during<br />
vermicomposting was due to decomposition <strong>of</strong> organic<br />
matter containing proteins and conversion <strong>of</strong><br />
ammonical nitrogen to nitrate nitrogen. As the organic<br />
matter passes through the gut <strong>of</strong> the earthworms,<br />
the material gets digested by enzyme activity which<br />
results in breakdown <strong>of</strong> proteins and nitrogen<br />
containing compounds. Decrease in pH is another<br />
important factor in retention <strong>of</strong> nitrogen as it is lost<br />
as ammonia at high pH values. <strong>The</strong> increase in total<br />
nitrogen content during conventional composting may<br />
be due to direct manifestation <strong>of</strong> mass loss due to<br />
mineralization <strong>of</strong> organic fraction (Krishna Murthy et<br />
al., 2010). Lower nitrogen values during conventional<br />
composting than vermicomposting might be due to<br />
loss <strong>of</strong> nitrogen in the form <strong>of</strong> ammonia volatilization<br />
during thermophilic phase. Higher nitrogen values in<br />
vermicomposting might be due to high degree <strong>of</strong><br />
decomposition and release <strong>of</strong> nitrogenous products<br />
through excreta, urine and mucoproteins (Kitturmath<br />
et al. 2007).<br />
20
LAKSHMI et al<br />
Changes in C/N ratio during<br />
vermicomposting and conventional composting<br />
<strong>The</strong> data presented in Table 1 and 2 revealed<br />
that the C/N ratio <strong>of</strong> cane trash, weeds, vegetable<br />
market waste and paddy straw at 15 days <strong>of</strong><br />
vermicomposting was 57.74:1, 25.69:1, 20.43:1 and<br />
72.33:1, respectively. At the end <strong>of</strong> vermicomposting<br />
i.e at 60 days the C/N ratio was further reduced to<br />
21.60:1,12.70:1,11.34:1 and 21.57:1 in cane trash,<br />
weeds, vegetable market waste and paddy straw,<br />
respectively. Where as in conventional composting<br />
the C/N ratio at 15 days was 57.02:1, 24.87:1, 20.16:1<br />
and 68.87:1, respectively and 60 days after<br />
composting the reduction was 34.63:1, 16.56:1,<br />
14.19:1 and 36.08:1 and at the end <strong>of</strong> composting<br />
i.e. at 110 days it was further reduced to 24.71:1,<br />
13.76:1, 12.73:1 and 24.89:1 in cane trash, weeds,<br />
vegetable market waste and paddy straw,<br />
respectively. In both the composting methods paddy<br />
straw recorded the highest C/N ratio while vegetable<br />
market waste exhibited lowest C/N ratio, however<br />
the percent decrease was more in vermicomposting<br />
than conventional composting in a particular period<br />
<strong>of</strong> time (Fig.1 & 2).<br />
<strong>The</strong> decrease in C/N ratio during<br />
vermicomposting was due to respiratory activity <strong>of</strong><br />
earthworms and microorganisms and increase in<br />
nitrogen by mineralization <strong>of</strong> organic matter and<br />
excretion <strong>of</strong> nitrogenous wastes. Similar results were<br />
reported by Alok Bhardwaj (2010). <strong>The</strong> reduction in<br />
carbon and lowering <strong>of</strong> C/N ratio in the<br />
vermicomposting and conventional composting could<br />
be achieved either by the respiratory activity <strong>of</strong><br />
earthworms and microorganisms or by increase in<br />
nitrogen by microbial mineralization <strong>of</strong> organic matter<br />
in combination with addition <strong>of</strong> the worm’s nitrogenous<br />
wastes through their excretion. <strong>The</strong> rate <strong>of</strong> reduction<br />
<strong>of</strong> C/N ratio was high during vermicomposting than<br />
conventional composting. <strong>The</strong> duration <strong>of</strong><br />
vermicomposting varied from 55 to 60 days for<br />
various organic residues under study, while it took<br />
almost 110 days for conventional composting (Auldry<br />
et al., 2009). During vermicomposting given optimum<br />
conditions <strong>of</strong> temperature and moisture, earthworms<br />
feed on organic component <strong>of</strong> organic residues which<br />
is ground into smaller particles in their gizzard. Later<br />
on the enzyme activity in the intestine brings about<br />
rapid conversion <strong>of</strong> cellulose and protenaceous<br />
materials. This may account for reduced time in<br />
vermicomposting than conventional composting.<br />
Changes in Humic Acid content (%)<br />
during vermicomposting and composting<br />
<strong>The</strong> humic acid production increased with progress<br />
<strong>of</strong> decomposition in both the composting methods.<br />
<strong>The</strong> increase in humic acid content in<br />
vermicomposting from 15 to 60 days varied from 7.50<br />
to 9.85 % in cane trash, 8.12 to 10.40 % in weeds,<br />
9.00 to 10.85 % in vegetable market waste and 7.12<br />
to 9.10 % in paddy straw. At the end <strong>of</strong><br />
vermicomposting significant increase in humic acid<br />
content (10.40 %) was recorded in vegetable market<br />
waste than cane trash and paddy straw, however<br />
vermicomposting <strong>of</strong> weeds recorded on par result<br />
(10.40 %) with vegetable market waste, while<br />
significantly lowest humic acid (9.10 %) was recorded<br />
in paddy straw. At the end <strong>of</strong> conventional composting<br />
i.e 110 days high humic acid content <strong>of</strong> 10.22 %<br />
was observed in vegetable market waste, while<br />
minimum content <strong>of</strong> 9.75 % was recorded in cane<br />
trash. Vegetable market waste recorded 20 & 25 %<br />
increase <strong>of</strong> humic acid content from initial to maturity<br />
in vermicomposting and conventional composting,<br />
respectively.<br />
<strong>The</strong> humic acid production increased with<br />
incubation in both the composting methods and in all<br />
the treatments, Xiaowei et al. (2010) observed that<br />
increasing levels <strong>of</strong> humic acid represent high degree<br />
<strong>of</strong> humification. Humification was found to be<br />
dependent on biochemical characteristics and<br />
composition <strong>of</strong> raw material. <strong>The</strong> high humic acid<br />
content during vermicomposting implies good quality<br />
and maturity <strong>of</strong> compost. Vegetable market waste<br />
recorded highest humic acid content (10.85 %)<br />
followed by weeds (10.40%), cane trash (9.85 %,)<br />
and rice straw (9.10 %) during vermicomposting. This<br />
was probably due to variation in the amount,<br />
composition and differential degradation <strong>of</strong> lignins<br />
(Tejada, 2009) (Fig.3 & 4)<br />
Changes in Fulvic Acid content (%)<br />
during vermicomposting and composting<br />
21
CHANGES IN MATURITY INDICES DURING VERMICOMPOSTING VS CONVENTIONAL<br />
Table 1. Changes in contents <strong>of</strong> total organic carbon (TOC), total nitrogen (TN) and C/N ratio (vermicomposting) at different time intervals<br />
S.E.m+<br />
CD at (5%)<br />
Table 2. Changes in contents <strong>of</strong> total organic carbon (TOC), total nitrogen (TN) and C/N ratio (conventional) composting at different time intervals<br />
CD at<br />
(5%)<br />
22
LAKSHMI et al<br />
Table 3. Changes in humic acid (HA), fulvic acid (FA) and humic acid/fulvic acid ratio during vermicomposting at different time intervals<br />
Table 4. Changes in humic acid (HA), fulvic acid (FA) and humic acid/fulvic acid ratio during conventional composting at different time intervals<br />
23
CHANGES IN MATURITY INDICES DURING VERMICOMPOSTING VS CONVENTIONAL<br />
During vermicomposting the change in fulvic<br />
acid content from 15 to 60 days varied from 2.58 to<br />
2.42 % in cane trash, 2.56 to 2.41 % in weeds, 2.61<br />
to 2.50 % in vegetable market waste and 2.11 to<br />
2.08 % in paddy straw. In conventional composting<br />
the changes in fulvic acid content from 15 to 110<br />
days were 2.18 to 2.16 % in cane trash, 2.28 to 2.22<br />
% in weeds, 2.29 to 2.26 % in vegetable market waste<br />
and 2.17 to 2.15 % in paddy straw. Higher fulvic acid<br />
content was recorded in vermicomposting than<br />
conventional composting. With the progress <strong>of</strong><br />
decomposition fulvic acid did not followed any<br />
particular trend in both the methods <strong>of</strong> composting.<br />
Krishna Murthy et al. (2010) were <strong>of</strong> the opinion that<br />
low fulvic acid and high humic acid percentage were<br />
the indications that the compost has reached an<br />
advanced stage <strong>of</strong> maturity and also stated that the<br />
compost quality increased with increasing humic acid<br />
percentage.<br />
Changes in humic acid/fulvic acid ratio (HA/<br />
FA ratio) during vermicomposting and composting<br />
<strong>The</strong> humification index (HI), which is the ratio between<br />
the humic acid and fulvic acid, is believed to be a<br />
good maturity and stability index. <strong>The</strong> changes in<br />
humic acid/fulvic acid ratio during vermicomposting<br />
and conventional composting were presented in Table<br />
3 and 4. During vermicomposting the HA/FA ratio<br />
varied from 2.91 (cane trash) to 3.45 (vegetable<br />
market waste) at 15 days and 3.72 (paddy straw) to<br />
4.34 (vegetable market waste) at 60 days, where as<br />
in conventional composting it was ranged between<br />
3.46 to 3.56 at 15 days and 4.23 to 4.55 at 110 days.<br />
In both the composting methods minimum HA/FA<br />
ratio was recorded in paddy straw and maximum ratio<br />
was recorded in vegetable market waste. <strong>The</strong><br />
increase in humic acid to fulvic acid ratio reflects<br />
the formation <strong>of</strong> complex molecules (humic acids)<br />
from more simple molecules (fulvic acids). Similar<br />
increase in humic acid/fulvic acid ratio during<br />
incubation <strong>of</strong> organic residues was observed by<br />
Xiaowei et al., (2010).<br />
REFERENCES<br />
Alok Bhardwaj. 2010. Management <strong>of</strong> kitchen waste<br />
material through vermicomposting. Asian<br />
<strong>Journal</strong> <strong>of</strong> Experimental Biological Sciences.<br />
1 (1): 175-177.<br />
Auldry Chaddy Petrus., Osumanu Haruna Ahmed and<br />
Ab Majid Nik Muhamad. 2009. Chemical<br />
characteristics <strong>of</strong> compost and humic acid<br />
from sago waste. American <strong>Journal</strong> <strong>of</strong> Applied<br />
Science. 6(11): 1880-1884.<br />
Jackson ML. 1973. Soil Chemical Analysis. Prentice<br />
Hall <strong>of</strong> India Pvt. Ltd., New Delhi PP:1-485.<br />
Kitturmath, M.S., Giraddi, R.S and Basavaraj, B.<br />
2007. Nutrient changes during earthworm-<br />
Eudrilus eugeniae mediated vermicomposting<br />
<strong>of</strong> Agro-industrial wastes. Karnataka <strong>Journal</strong><br />
<strong>of</strong> Agricultural Sciences. 20(3): 653-654.<br />
Kononova, M.M. 1966. Soil organic matter, its nature,<br />
origin and role in soil fertility. 2 nd Edition,<br />
Pergamon Press. Oxford. PP:400-410.<br />
Krishna Murthy, R., Sreenivasan, N and Prakash,<br />
S.S. 2010. Chemical and biochemical<br />
properties <strong>of</strong> Parthenium and Chormolaena<br />
compost. International <strong>Journal</strong> <strong>of</strong> Science and<br />
Nature. 1(2): 166-171.<br />
Manuel Tejada, Ana Maria Garcia-Martinez and Juan<br />
Parrado. 2009. Relationships between<br />
biological and chemical parameters on the<br />
composting <strong>of</strong> a municipal solid waste.<br />
Bioresource Technology. 100: 4062-4065.<br />
Piper, C.S. 1966. Soil and Plant Analysis. Hans<br />
Publishers, Bombay<br />
Swati Pattnaik and Vikram Reddy, M. 2010. Nutrient<br />
status <strong>of</strong> vermicompost <strong>of</strong> urban green waste<br />
processed by three earthworm species-<br />
Eisenia foetida, Eudrilus eugeniae and<br />
Perionyx excavatus. Applied and<br />
Environmental Soil Science.<br />
Xiaowei, Li., Meiyan Xing., Jian Yang and Zhidong<br />
Huang. 2010. Compositional and functional<br />
features <strong>of</strong> humic acid like fractions from<br />
vermicomposting <strong>of</strong> sewage sludge and<br />
cowdung. <strong>Journal</strong> <strong>of</strong> Hazardous material.<br />
185(2,3): 740-748.<br />
24
J.Res. <strong>ANGRAU</strong> 41(1) 20-29, 2013<br />
INFLUENCE OF INTEGRATED NUTRIENT MANAGEMENT ON PHYSICAL<br />
PROPERTIES OF ALFISOLS UNDER RICE –RICE CROPPING SYSTEM IN<br />
SOUTHERN TELANGANA ZONE<br />
V. MAHESWARA PRASAD and P. PRABHU PRASADINI<br />
DAATT Centre, Krishna District, Machilipatnam - 521002<br />
Date <strong>of</strong> Receipt :07.12.2012 Date <strong>of</strong> Acceptance : 31.01.2013<br />
ABSTRACT<br />
Studies were conducted to understand the influence <strong>of</strong> integrated nutrient management on physical properties<br />
<strong>of</strong> Alfisols under rice-rice cropping system during 2005-06 and 2006-07 at Agricultural College Farm, Rajendranagar,<br />
Hyderabad. <strong>The</strong> bulk density, porosity and water holding capacity did not change significantly by the application <strong>of</strong><br />
different levels <strong>of</strong> fertilizers in both kharif and rabi season compared to the unfertilized soil. <strong>The</strong> application <strong>of</strong> 50%<br />
recommended dose <strong>of</strong> 120:60:60 kg ha -1 NPK integrated with 50% N fertilizer equivalent through FYM, paddy straw<br />
or glyricidia in kharif season followed by the application <strong>of</strong> recommended dose <strong>of</strong> 120:60:60 kg NPK ha -1 through<br />
fertilizers in rabi season significantly reduced the bulk density and porosity in the upper 0-15 cm and increased the<br />
water holding capacity upto 30 cm depth. Similar change in the physical properties was observed due to application<br />
<strong>of</strong> 75% recommended dose <strong>of</strong> fertilizers integrated with 25% N fertilizer equivalent through any one <strong>of</strong> the three<br />
organic sources in kharif season and application <strong>of</strong> 75% recommended dose <strong>of</strong> fertilizers in rabi. <strong>The</strong> improvement<br />
in the rate <strong>of</strong> infiltration <strong>of</strong> water and hydraulic conductivity was recorded only at the transplanting stage and was not<br />
consistent during the two seasons.<br />
<strong>The</strong> soil physical properties play an<br />
important role in determining its suitability for crop<br />
production. Soil should be physically fertile to provide<br />
a good crop growth medium. This is greatly influenced<br />
by several management practices. Soil physical<br />
properties such as water holding capacity, bulk<br />
density, total porosity, air-filled porosity, hydraulic<br />
conductivity, and soil-depth greatly influence root<br />
development which in turn influence plant growth and<br />
performance. Rice –rice is the most predominant<br />
cropping system in the Andhra Pradesh, particularly<br />
in southern telangana zone. <strong>The</strong> major problem is<br />
the deterioration <strong>of</strong> soil physical properties due to<br />
continuous puddling and impaired soil fertility due to<br />
indiscriminate application <strong>of</strong> nutrients through the<br />
fertilizers with the threat <strong>of</strong> the declining productivity.<br />
<strong>The</strong> use <strong>of</strong> locally available organic sources<br />
has higher potential to improve the soil physical<br />
properties that is in terms <strong>of</strong> water holding capacity,<br />
soil porosity and bulk density, soil fertility and there<br />
by soil quality as a whole sustain the level <strong>of</strong> crop<br />
productivity in the Rice –rice cropping system.<br />
Hence, an investigation was made to understand the<br />
influence <strong>of</strong> integrated nutrient management (INM)<br />
on physical properties <strong>of</strong> Alfisols under rice-rice<br />
cropping system.<br />
MATERIALS AND METHODS<br />
<strong>The</strong> Present studies were conducted in two<br />
consecutive years 2005-06 and 2006-07 at<br />
Agricultural College Farm, Rajendranagar,<br />
Hyderabad. <strong>The</strong> experiment was conducted on a<br />
sandy clay loam soil on which only rice was grown<br />
continuously in both Kharif and Rabi seasons since<br />
1988. <strong>The</strong> experiments were laid out in randomized<br />
block design with 12 treatments in three replications.<br />
Rice variety; RNR 23064 was planted adopting a<br />
spacing <strong>of</strong> 20 cm x 10 cm in 59.8 m 2 sized plot. <strong>The</strong><br />
treatments comprised <strong>of</strong> control treatment with out<br />
fertilizers and organic manures (T 1<br />
), 50 %<br />
Recommended NPK dose through fertilizers (T 2<br />
), 50<br />
% Recommended NPK dose through fertilizers (T 3<br />
),<br />
75 % Recommended NPK dose through fertilizers<br />
(T 4<br />
), 100 % Recommended NPK dose through<br />
fertilizers 120:60:60 kg ha -1 (T 5<br />
), 50 % Recommended<br />
NPK dose through fertilizers + 50 % N through FYM<br />
(T 6<br />
), 75 % Recommended NPK dose through<br />
fertilizers + 25 % N through FYM (T 7<br />
), 50 %<br />
Recommended NPK dose through fertilizers + 50 %<br />
N through paddy straw (T 8<br />
), 75 % Recommended<br />
NPK dose through fertilizers + 25 % N through paddy<br />
straw (T 9<br />
), 50 % Recommended NPK dose through<br />
fertilizers + 50 % N through glyricidia (T 10<br />
), 75 %<br />
Recommended NPK dose through fertilizers + 25 %<br />
email: vemulamadamp@gmail.com<br />
25
INFLUENCE OF INM ON PHYSICAL PROPERTIES<br />
N through glyricidia (T 11<br />
) and Conventional farmers<br />
practice 80:50:20 kg ha -1 NPK (T 12<br />
) during kharif. While<br />
the treatments during rabi were; control treatment with<br />
out fertilizers and organic manures (T 1<br />
), 50 %<br />
recommended NPK dose through fertilizers (T 2<br />
), 100<br />
% recommended NPK dose through fertilizers (T 3<br />
),<br />
75 % recommended NPK dose through organic<br />
manures (T 4<br />
), 100 % Recommended NPK dose<br />
through fertilizers 120:60:60 kg ha -1 (T 5<br />
), 100 %<br />
recommended NPK dose through fertilizers (T 6<br />
), 75<br />
% recommended NPK dose through fertilizers (T 7<br />
),<br />
100 % Recommended NPK dose through fertilizers<br />
(T 8<br />
), 75 % Recommended NPK dose through<br />
fertilizers (T 9<br />
), 100 % Recommended NPK dose<br />
through fertilizers (T 10<br />
), 75 % Recommended NPK<br />
dose through fertilizers (T 11<br />
) and Conventional<br />
(farmers) practice 80:50:20 kg ha -1 NPK (T 12<br />
).<br />
Sample Collection<br />
<strong>The</strong> soil samples were collected with soil<br />
auger at random from each treatment plot at 0-15<br />
and 15-30 cm depth before transplanting, panicle<br />
initiation and harvesting stages <strong>of</strong> the crop in each<br />
season. <strong>The</strong> soil samples were dried under shade,<br />
powdered using wooden mortar and pestle and then<br />
passed through a 2 mm sieve before taking up<br />
analysis. Soil samples were collected with core<br />
sampler <strong>of</strong> size 5.25 x 6 cm to determine soil bulk<br />
density by using the method suggested by Black<br />
(1965). Porosity was calculated by using the formula:<br />
Porosity = [1-BD/PD] x 100; Where, BD = Bulk<br />
density <strong>of</strong> soil (Mg m -3 ); PD = Particle density (Mg<br />
m -3 ) <strong>of</strong> soil.<br />
Undisturbed soil samples collected in<br />
cylindrical cores at different stages from 0-15 and<br />
15-30 cm depths were used for the determination <strong>of</strong><br />
hydraulic conductivity using constant pressure head<br />
method as per the procedure outlined by Jalota et al.<br />
(1998). Infiltration rate was determined in situ, at the<br />
time <strong>of</strong> sowing, 60 DAS and harvest <strong>of</strong> the crop with<br />
double ring infiltrometer as suggested by Bertrand<br />
(1965) and described by Jalota et al. (1998) and the<br />
infiltration rate was reported as cm hr -1 . <strong>The</strong> Water<br />
Holding Capacity <strong>of</strong> soils was estimated by Keens<br />
Cup method (Black, 1965). Grain yield was recorded<br />
at the end <strong>of</strong> each season for two years.<br />
RESULTS AND DISCUSSION<br />
Bulk density<br />
<strong>The</strong> data on bulk density <strong>of</strong> soil in the surface<br />
layer upto 15 cm depth in response to different<br />
nutrient management treatments is furnished in Table<br />
1. <strong>The</strong> trend exhibited a progressive increase in the<br />
bulk density with advance in age <strong>of</strong> the crop from<br />
transplanting to panicle initiation and at harvest in all<br />
the treatments during the 2005-06 as well as 2006-<br />
07. <strong>The</strong> soil cultivated with rice without the application<br />
<strong>of</strong> fertilizer or manures had low bulk density <strong>of</strong> 1.53<br />
g cm -3 at the time <strong>of</strong> transplanting both during kharif<br />
and rabi seasons in 2005-06. No significant difference<br />
was recorded by the application <strong>of</strong> different<br />
proportions <strong>of</strong> fertilizers in kharif and rabi seasons<br />
compared to un fertilized soil. But the integrated<br />
supply <strong>of</strong> nutrients by substituting 50 or 25 % N<br />
fertilizer with FYM in the rainy season dropped down<br />
the bulk density significantly. <strong>The</strong> substitution <strong>of</strong> 50<br />
or 25 % N through paddy straw or glyricidia in kharif<br />
season and application <strong>of</strong> recommended or 75 %<br />
recommended N P K dose in rabi after the respective<br />
kharif season treatments significantly reduced the<br />
bulk density <strong>of</strong> the soil at transplanting compared to<br />
control both during 2005-06 and 2006-07. <strong>The</strong><br />
reduction in the bulk density <strong>of</strong> the soil was also<br />
recorded at the panicle initiation and at harvesting <strong>of</strong><br />
the crop due to the integrated nutrient management<br />
in kharif season and fertilizer application in rabi<br />
season compared to the fertilized or unfertilized soil.<br />
<strong>The</strong> layer at 15 - 30 cm invariably recorded higher<br />
bulk density than the top layer irrespective <strong>of</strong> the<br />
treatment (Table 2). Unlike in the top layer, the<br />
substitution <strong>of</strong> 50 or 25 % N through organic sources<br />
had no significant influence on this variable at any<br />
stage <strong>of</strong> the crop growth either in kharif or rabi season<br />
during 2005-06 or in 2006-07.<br />
<strong>The</strong> bulk density <strong>of</strong> the soil was relatively<br />
low at transplanting, but it increased at panicle<br />
initiation and harvesting stage <strong>of</strong> the crop consequent<br />
to the settlement <strong>of</strong> the soil and trafficking by human<br />
labour for cultural operations both during kharif and<br />
rabi in the two years. As a result, the porosity and<br />
water holding capacity reduced from transplanting to<br />
harvesting stage <strong>of</strong> the crop. This inverse relationship<br />
26
PRASAD and PRASADINI<br />
<strong>of</strong> bulk density with these two parameters is an <strong>of</strong>t -<br />
cited phenomenon (Tripathi et al., 2003). Chawla<br />
and Chhabra (1991) also reported that the continuous<br />
application <strong>of</strong> N fertilizer had no significant influence<br />
on this soil physical parameter. However, the present<br />
study confirmed that the co application <strong>of</strong> organic<br />
source <strong>of</strong> nutrients by partly replacing the chemical<br />
fertilizers had a subtle advantage to improve the soil<br />
physical properties. <strong>The</strong> substitution <strong>of</strong> 50 % N<br />
fertilizer through FYM, paddy straw or glyricidia in<br />
kharif and the application <strong>of</strong> recommended dose <strong>of</strong><br />
fertilizers in rabi or the substitution <strong>of</strong> 25 % N fertilizer<br />
with any one <strong>of</strong> the three organic sources in kharif<br />
and the application <strong>of</strong> 75 % recommended dose <strong>of</strong><br />
fertilizers in the rabi season significantly reduced the<br />
bulk density at transplanting, panicle initiation and<br />
harvesting stage <strong>of</strong> rice continuously during the four<br />
seasons in the biologically active rooting depth <strong>of</strong> 0-<br />
15 cm. This reduction in bulk density could probably<br />
be assigned to the reason that the addition <strong>of</strong> organic<br />
matter increased the volume <strong>of</strong> the soil per unit<br />
weight. Such benefit <strong>of</strong> reduction in bulk density <strong>of</strong><br />
the soil through the incorporation <strong>of</strong> organic matter<br />
has been well documented by Vasanthi and<br />
Kumarswamy (1999).<br />
Porosity<br />
<strong>The</strong> soil was more porous at 0-15 cm soil<br />
depth in all the treatments at transplanting than at<br />
panicle initiation stage both in kharif and rabi seasons<br />
(Table 3). It reduced further at harvest. <strong>The</strong> porosity<br />
was 42.64 and 41.89 % at transplanting in kharif 2005<br />
and 2006, respectively by growing rice without the<br />
application <strong>of</strong> manures or fertilizers. <strong>The</strong><br />
corresponding values were 42.26 and 41.89 % in rabi<br />
season <strong>of</strong> 2005 and 2006 respectively. <strong>The</strong><br />
continuous application <strong>of</strong> recommended dose <strong>of</strong><br />
fertilizers to rice-rice cropping system had no<br />
significant influence and substitution <strong>of</strong> 50 % N<br />
through the organics i.e. FYM, paddy straw and<br />
glyricidia in kharif season and the application <strong>of</strong><br />
recommended dose <strong>of</strong> NPK through fertilizers in rabi<br />
season significantly increased the porosity <strong>of</strong> the soil<br />
during both the seasons. <strong>The</strong> application <strong>of</strong> 25 % N<br />
through the organics in the kharif season and 75 %<br />
recommended NPK through fertilizers in the rabi<br />
season also made the soil more porous than the<br />
unfertilised soil.<br />
<strong>The</strong> porosity was significantly more than in<br />
unfertilized soils due to the carry over effect <strong>of</strong><br />
integrated nutrient management treatments. <strong>The</strong><br />
trends were persistent at transplanting, panicle<br />
initiation and harvest. <strong>The</strong> porosity was relatively low<br />
in the lower layer <strong>of</strong> 15-30 cm soil depth (Table 4).<br />
<strong>The</strong> porosity increased significantly at transplanting<br />
by the substitution <strong>of</strong> 50 % recommended level <strong>of</strong> N<br />
with FYM compared to continuous fertilizer<br />
application at recommended level in the kharif and<br />
rabi season during both the years. This improvement<br />
also persisted at panicle initiation and harvesting<br />
stage in kharif and rabi seasons during the second<br />
year. <strong>The</strong> trends with the other sources <strong>of</strong> organic<br />
nutrients substituted with 25 or 50 % <strong>of</strong> the<br />
recommended level <strong>of</strong> N were highly irregular.<br />
<strong>The</strong> porosity <strong>of</strong> the fertilized soil was similar<br />
to the unfertilized soil. But, a magnificent<br />
improvement in the volume fraction <strong>of</strong> pores was<br />
evident due to the integrated nutrient management<br />
<strong>of</strong> rice in kharif followed by fertilizer application in<br />
rabi season during the two year rice-rice cropping<br />
sequence. This trend was obviously due to an<br />
increase in the volume <strong>of</strong> pore space because <strong>of</strong> the<br />
addition <strong>of</strong> organic matter to the total volume <strong>of</strong> the<br />
soil. But, Katele et al., (1992) observed that the<br />
addition <strong>of</strong> FYM in an alfisol did not bring a significant<br />
change in this parameter. Bhagat et al. (2003) and<br />
Tripathi et al (2003) also observed that the<br />
incorporation <strong>of</strong> Lantana camera into the soil reduced<br />
the bulk density and increased the porosity which in<br />
turn improved the retention <strong>of</strong> water.<br />
Infiltration<br />
<strong>The</strong>re was a distinct response <strong>of</strong> significant<br />
improvement in infiltration <strong>of</strong> water at transplanting<br />
due to substitution <strong>of</strong> 25 or 50 % recommended level<br />
<strong>of</strong> N with FYM compared to the practice <strong>of</strong><br />
recommended level <strong>of</strong> fertilizer application both in<br />
kharif and rabi season during the two years (Table<br />
5). <strong>The</strong> substitution <strong>of</strong> 25 % recommended level <strong>of</strong> N<br />
with glyricidia also established similar trend. <strong>The</strong><br />
response due to the integration <strong>of</strong> organic nutrients<br />
through paddy straw was irregular. <strong>The</strong> differences<br />
in infiltration due to fertilizer application and the<br />
integration <strong>of</strong> organic nutrients were not apparent<br />
during the panicle initiation and harvesting stage <strong>of</strong><br />
the crop in either <strong>of</strong> the two years. Among the organic<br />
27
INFLUENCE OF INM ON PHYSICAL PROPERTIES<br />
sources FYM, had a consistent response to improve<br />
the infiltration <strong>of</strong> water significantly atleast during the<br />
part <strong>of</strong> rice growing period. Kumar et al., (1992) also<br />
recorded significant improvement in infiltration rate<br />
<strong>of</strong> water due to integrated nutrient management <strong>of</strong><br />
nutrients in rice-wheat cropping system.<br />
Hydraulic conductivity<br />
<strong>The</strong> hydraulic conductivity <strong>of</strong> the soil supplied<br />
with different levels <strong>of</strong> fertilizers ranged from 0.25 to<br />
0.27 cm h -1 at transplanting in kharif season during<br />
2005-06 and from 0.23 to 0.25 cm h -1 in the<br />
subsequent rabi (Table 6). A significant improvement<br />
in this physical property <strong>of</strong> the soil was recorded both<br />
in kharif and rabi due to the substitution <strong>of</strong> FYM @<br />
25 % N fertilizer equivalent in kharif. But the<br />
substitution <strong>of</strong> 25 or 50 % recommended level <strong>of</strong> N<br />
through glyricidia significantly improved the hydraulic<br />
conductivity both in kharif and rabi seasons during<br />
the second year. <strong>The</strong> hydraulic conductivity was<br />
significantly low in the kharif season in the fertilizer<br />
treatments than in integrated nutrient management<br />
treatments by substituting 25 or 50 % recommended<br />
level <strong>of</strong> NPK through FYM or glyricidia only in the<br />
second year. Such improvement was not recorded<br />
in the rabi season during both the years. No significant<br />
variation in the hydraulic conductivity in the surface<br />
soil upto 15 cm depth was recorded either in kharif or<br />
rabi season at harvest stage <strong>of</strong> the crop during 2005-<br />
06 or 2006-07.<br />
<strong>The</strong> hydraulic conductivity was invariably low<br />
in the lower (15 – 30 cm) depth than upper layer <strong>of</strong><br />
the soil in all the treatments at different stages <strong>of</strong><br />
crop growth (Table 7). It ranged from 0.20 to 0.22 cm<br />
h -1 at the time <strong>of</strong> transplanting in kharif 2005 and from<br />
0.19 to 0.21 cm h -1 during 2006 due to different levels<br />
<strong>of</strong> fertilizer application. During the subsequent growth<br />
phases <strong>of</strong> panicle initiation and harvesting, hydraulic<br />
conductivity was similar in unfertilized, fertilized and<br />
integrated nutrient management treatments during<br />
both the years.<br />
Water holding capacity<br />
<strong>The</strong> maximum water holding capacity <strong>of</strong> the<br />
soil was reduced consistently with advance in age <strong>of</strong><br />
the crop from transplanting to panicle initiation and<br />
at harvest in all the treatments (Table 8). <strong>The</strong> soil<br />
supplied with different levels <strong>of</strong> nutrients through the<br />
fertilizers held 42.04 - 42.14 % water at transplanting<br />
in kharif and 41.26 – 41.41 % in rabi during 2005-06.<br />
<strong>The</strong> water retention <strong>of</strong> the soil improved significantly<br />
by substituting 25 or 50 % recommended level <strong>of</strong> N<br />
with FYM. This effect was long lasting until harvest<br />
both during kharif and rabi in the first year. Paddy<br />
straw and glyricidia were also effective sources to<br />
retain more moisture at different stages <strong>of</strong> crop growth<br />
during the two years. <strong>The</strong> water holding capacity was<br />
relatively low at 15-30 than 0-15 cm soil depth<br />
irrespective <strong>of</strong> the treatment during both the years<br />
(Table 9). Unlike in the top layer, the substitution <strong>of</strong><br />
organics at 25 or 50 % fertilizer N equivalent was<br />
distinct with significantly higher moisture content than<br />
in the fertilized or unfertilized plots from transplanting<br />
to panicle initiation stage in the kharif season.<br />
<strong>The</strong> water holding capacity was also best<br />
improved by the substitution <strong>of</strong> 50 or 25 per cent N<br />
fertilizer with FYM in the Kharif season. <strong>The</strong><br />
improvement in this soil physical property was long<br />
sustained until harvest due to the cumulative<br />
influence <strong>of</strong> reduced bulk density, increased porosity<br />
vis –a- vis an increase in the infiltration and hydraulic<br />
conductivity <strong>of</strong> the soil during the early periods <strong>of</strong><br />
crop growth. <strong>The</strong> substitution <strong>of</strong> 50 or 25 per cent N<br />
fertilizer with rice straw or glyricidia also in general<br />
had a long lasting effect in better water retention for<br />
good crop management. <strong>The</strong> lower 15-30 cm soil layer<br />
was not influenced by organic matter additions. An<br />
improvement in water holding capacity <strong>of</strong> the soil by<br />
the combined application <strong>of</strong> organic and inorganic<br />
source <strong>of</strong> nutrients was also recorded by Vennila and<br />
Muthuvel (1998).<br />
Grain yield<br />
<strong>The</strong> unfertilized crop produced low grain yield<br />
<strong>of</strong> 2475 and 2199 kg ha -1 in kharif and 2025 and 1545<br />
kg ha -1 in rabi during 2005-06 and 2006-07,<br />
respectively (Table 10). <strong>The</strong> fertilizer application<br />
benefited the crop to produce more yield. <strong>The</strong><br />
production increased to 3739 and 3920 kg ha -1 in two<br />
seasons during 2005-06, while it increased to 2911<br />
and 2801 kg ha -1 in kharif and rabi in 2006-07. <strong>The</strong><br />
strategy to apply 50 % recommended dose <strong>of</strong><br />
fertilizers in kharif and recommended dose <strong>of</strong><br />
fertilizers in rabi maintained the proportion <strong>of</strong><br />
production in consonance with the increase in level<br />
<strong>of</strong> nutrients added. <strong>The</strong> production raised enormously<br />
28
PRASAD and PRASADINI<br />
to as high as 5405 kg ha -1 in rabi 2005-06 and 4105<br />
kg ha -1 during 2006-07 due to the high dose <strong>of</strong><br />
recommended level <strong>of</strong> fertilizers. <strong>The</strong> application <strong>of</strong><br />
recommended dose <strong>of</strong> fertilizers continuously in kharif<br />
and rabi seasons invariably produced significantly<br />
more grain yield. High produce <strong>of</strong> 4676 and 5890 kg<br />
ha -1 was realized by this treatment in the kharif and<br />
rabi seasons during 2005-06. Similarly, maximum<br />
grain yield <strong>of</strong> 3983 kg ha -1 in kharif and 3801 kg ha -1<br />
in rabi was realized from the optimum fertilizer<br />
schedule in the second year.<br />
<strong>The</strong> substitution <strong>of</strong> 50 % N fertilizer with<br />
paddy straw in kharif season reduced the grain yield<br />
compared to the production realized by application<br />
<strong>of</strong> recommended dose <strong>of</strong> fertilizers during both the<br />
years. <strong>The</strong> substitution <strong>of</strong> 25 % N fertilizer with paddy<br />
straw in kharif season reduced the grain yield<br />
significantly only during the second year. <strong>The</strong><br />
substitution <strong>of</strong> 50 % N fertilizer with glyricidia in kharif<br />
season and application <strong>of</strong> recommended dose <strong>of</strong><br />
fertilizers in rabi season were the best integrated<br />
nutrient management strategies. <strong>The</strong> substitution <strong>of</strong><br />
25 % N fertilizer with glyricidia in kharif season and<br />
application <strong>of</strong> 75 % recommended dose <strong>of</strong> fertilizers<br />
in rabi season was also highly productive.<br />
<strong>The</strong> results <strong>of</strong> the present investigation<br />
showed that the physical properties <strong>of</strong> the soil were<br />
influenced by different nutrient management practices.<br />
<strong>The</strong>se effects were highly distinguished in the upper<br />
0-15 cm depth and were less distinct in the lower 15-<br />
30 cm depth. This layer wise differentiation was<br />
probably due to the more weathered and<br />
microbiologically intensive portion within which the<br />
organic and inorganic sources <strong>of</strong> nutrients were<br />
incorporated for their reactions than in the lower layer.<br />
Table 1. Influence <strong>of</strong> integrated nutrient management treatments on bulk density (g cm -3 ) <strong>of</strong> soil in<br />
rice-rice cropping system at 0-15 cm depth<br />
Treat<br />
2005-06 2006-07<br />
ment Transplanting Panicle Harvesting Transplanting Panicle<br />
initiation<br />
initiation<br />
Harvesting<br />
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi<br />
T 1 1.53 1.53 1.56 1.56 1.59 1.60 1.54 1.54 1.57 1.57 1.60 1.61<br />
T 2 1.54 1.54 1.57 1.56 1.60 1.60 1.55 1.55 1.58 1.58 1.61 1.62<br />
T 3 1.53 1.54 1.57 1.56 1.60 1.61 1.54 1.55 1.58 1.58 1.61 1.61<br />
T 4 1.53 1.54 1.57 1.56 1.60 1.61 1.54 1.55 1.58 1.58 1.61 1.61<br />
T 5 1.53 1.54 1.57 1.56 1.60 1.61 1.54 1.55 1.58 1.58 1.61 1.62<br />
T 6 1.43 1.45 1.45 1.47 1.61 1.49 1.44 1.43 1.46 1.45 1.48 1.47<br />
T 7 1.45 1.47 1.47 1.49 1.49 1.51 1.46 1.45 1.48 1.47 1.50 1.53<br />
T 8 1.45 1.47 1.47 1.49 1.49 1.51 1.46 1.45 1.48 1.47 1.50 1.52<br />
T 9 1.47 1.49 1.49 1.51 1.51 1.53 1.48 1.45 1.50 1.50 1.52 1.53<br />
T 10 1.46 1.48 1.49 1.50 1.51 1.52 1.47 1.45 1.50 1.47 1.52 1.54<br />
T 11 1.47 1.49 1.48 1.51 1.50 1.53 1.48 1.47 1.49 1.49 1.51 1.53<br />
T 12 1.53 1.53 1.50 1.56 1.59 1.59 1.54 1.54 1.57 1.57 1.60 1.61<br />
SEm + 0.03 0.03 0.03 0.02 0.03 0.03 0.02 0.02 0.03 0.02 0.30 0.30<br />
CD at<br />
5 %<br />
0.06 0.05 0.07 0.04 0.05 0.07 0.04 0.05 0.06 0.05 0.07 0.07<br />
29
INFLUENCE OF INM ON PHYSICAL PROPERTIES<br />
Table 2 . Influence <strong>of</strong> integrated nutrient management treatments on bulk density (g cm -3 ) <strong>of</strong> soil in<br />
rice-rice cropping system at 15-30 cm depth<br />
Treatment<br />
Transplanting<br />
2005-06 2006-07<br />
Panicle<br />
initiation<br />
Harvesting Transplanting<br />
Panicle<br />
initiation<br />
Harvesting<br />
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi<br />
T 1 1.56 1.57 1.59 1.59 1.64 1.63 1.57 1.58 1.60 1.61 1.64 1.65<br />
T 2 1.57 1.58 1.60 1.57 1.65 1.61 1.58 1.59 1.61 1.62 1.65 1.66<br />
T 3 1.56 1.58 1.59 1.57 1.64 1.61 1.57 1.58 1.60 1.61 1.64 1.65<br />
T 4 1.56 1.58 1.59 1.57 1.64 1.61 1.57 1.58 1.60 1.61 1.64 1.66<br />
T 5 1.56 1.58 1.59 1.57 1.64 1.61 1.57 1.58 1.60 1.61 1.64 1.65<br />
T 6 1.49 1.48 1.51 1.49 1.55 1.52 1.48 1.47 1.50 1.49 1.55 1.54<br />
T 7 1.51 1.50 1.53 1.51 1.57 1.54 1.50 1.49 1.52 1.51 1.57 1.56<br />
T 8 1.51 1.50 1.53 1.51 1.57 1.54 1.50 1.49 1.52 1.51 1.57 1.56<br />
T 9 1.53 1.52 1.55 1.53 1.57 1.56 1.52 1.51 1.52 1.51 1.57 1.55<br />
T 10 1.52 1.51 1.54 1.52 1.58 1.55 1.51 1.50 1.53 1.52 1.58 1.57<br />
T 11 1.53 1.52 1.55 1.53 1.59 1.56 1.51 1.50 1.53 1.52 1.58 1.58<br />
T 12 1.56 1.57 1.59 1.60 1.64 1.64 1.57 1.58 1.60 1.60 1.65 1.66<br />
SEm + 0.04 0.05 0.04 0.06 0.50 0.07 0.08 0.10 0.08 0.06 0.09 0.07<br />
CD at<br />
5 %<br />
NS NS NS NS NS NS NS NS NS NS NS NS<br />
Table 3 . Influence <strong>of</strong> integrated nutrient management treatments on porosity (%) <strong>of</strong> soil in ricerice<br />
cropping system at 0-15 cm depth<br />
Treatment<br />
Transplanting<br />
2005-06 2006-07<br />
Panicle<br />
initiation<br />
Harvesting Transplanting<br />
Panicle<br />
initiation<br />
Harvesting<br />
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi<br />
T 1 42.64 42.26 41.51 41.13 40.00 39.62 41.89 41.89 40.75 40.75 39.62 39.24<br />
T 2 42.26 41.89 41.31 41.13 39.62 39.62 41.51 41.51 40.38 40.28 39.24 38.87<br />
T 3 42.64 41.89 42.26 41.13 40.75 39.24 41.51 41.89 40.38 40.28 39.24 39.24<br />
T 4 42.64 41.89 42.26 41.13 40.75 39.24 41.51 41.89 40.38 40.28 39.24 39.24<br />
T 5 42.64 41.89 42.26 41.13 40.75 39.24 41.51 41.51 40.38 40.28 39.24 39.24<br />
T 6 44.98 45.28 44.15 44.53 43.02 43.77 45.66 46.04 44.91 40.28 44.15 44.13<br />
T 7 44.15 44.53 43.40 43.77 42.26 43.02 44.91 45.28 44.15 44.53 43.40 42.26<br />
T 8 44.15 44.53 43.40 43.77 42.26 43.02 44.91 45.28 44.15 44.53 43.40 42.64<br />
T 9 43.40 43.77 42.64 43.02 41.51 42.26 44.15 44.15 43.40 43.40 42.64 42.26<br />
T 10 43.77 44.15 43.02 43.40 41.89 42.64 44.53 45.28 43.40 44.53 42.64 41.89<br />
T 11 43.40 43.77 42.64 43.02 41.51 42.66 44.15 44.53 43.77 44.53 43.02 42.26<br />
T 12 42.64 41.89 41.51 41.13 40.00 39.62 41.57 41.89 40.38 40.28 39.24 38.17<br />
SEm+ 0.17 0.79 0.12 0.76 0.26 1.04 1.10 1.36 1.30 1.32 1.37 1.14<br />
CD at 5 % 0.31 1.65 0.25 1.58 0.54 2.16 2.31 2.83 2.69 2.73 2.85 2.37<br />
30
PRASAD and PRASADINI<br />
Table 4 . Influence <strong>of</strong> integrated nutrient management treatments on porosity (%) <strong>of</strong> soil in rice-rice<br />
cropping system at 15-30 cm<br />
Treatment<br />
Transplanting<br />
2005-06 2006-07<br />
Panicle<br />
initiation<br />
Harvesting Transplanting<br />
Panicle<br />
initiation<br />
Harvesting<br />
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi<br />
T 1 41.13 40.75 40.00 10.00 38.11 38.49 40.75 40.38 39.62 39.27 38.11 38.00<br />
T 2 40.75 40.38 39.62 40.75 37.74 39.24 40.78 40.00 39.24 38.87 37.74 37.74<br />
T 3 41.33 40.38 40.00 40.75 38.11 39.24 40.38 40.38 39.62 39.24 38.11 37.26<br />
T 4 41.33 40.38 40.00 40.75 38.11 39.27 40.38 40.38 39.62 39.24 38.00 37.74<br />
T 5 41.33 40.38 40.00 40.75 38.11 39.27 40.38 40.38 39.62 39.24 38.11 37.74<br />
T 6 43.77 44.18 40.00 43.17 41.51 42.64 44.40 44.53 43.40 43.77 41.51 41.89<br />
T 7 43.02 43.40 43.02 43.02 40.75 44.89 43.40 43.77 42.64 43.02 40.75 41.13<br />
T 8 43.02 43.80 42.26 43.02 40.75 41.89 43.40 43.77 42.64 43.02 40.75 41.13<br />
T 9 42.26 42.64 42.26 42.26 40.75 41.13 42.64 43.02 42.64 43.02 40.75 41.51<br />
T 10 42.64 43.02 41.51 42.64 40.38 41.51 43.02 43.40 42.26 42.64 40.38 40.75<br />
T 11 42.26 43.64 41.51 42.26 40.00 41.13 43.02 43.40 42.26 42.64 40.38 40.75<br />
T 12 41.13 40.38 40.00 40.75 38.11 39.24 40.38 40.300 39.62 39.24 38.11 37.28<br />
SEm + 0.92 1.30 1.43 1.35 1.52 2.00 1.33 1.01 1.52 1.45 1.56 1.46<br />
CD at 5 % 1.87 2.68 2.95 NS 3.16 4.15 2.74 2.11 3.15 3.00 3.22 3.02<br />
Table 5 . Influence <strong>of</strong> integrated nutrient management treatments on infiltration (mm h -1 ) <strong>of</strong> soil in<br />
rice-rice cropping system<br />
Treatment<br />
Transplanting<br />
2005-06 2006-07<br />
Panicle<br />
initiation<br />
Harvesting Transplanting Panicle initiation Harvesting<br />
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi<br />
T 1 6.56 6.45 6.36 6.25 6.96 6.88 6.35 6.25 6.15 6.00 6.75 6.65<br />
T 2 6.58 6.47 6.37 6.26 6.98 6.85 6.37 6.27 6.17 6.06 6.77 6.67<br />
T 3 6.59 6.46 6.33 6.27 7.00 6.87 6.35 6.26 6.15 6.06 6.75 6.66<br />
T 4 6.58 6.48 6.36 6.28 6.97 6.88 6.37 6.27 6.10 6.07 6.77 6.67<br />
T 5 6.56 6.46 6.35 6.26 6.95 6.86 6.36 6.26 6.15 6.06 6.76 6.66<br />
T 6 6.75 6.66 6.55 6.46 7.01 6.85 6.56 6.46 6.47 6.26 6.97 6.86<br />
T 7 6.95 6.80 6.75 6.60 7.35 6.80 6.72 6.62 6.52 6.44 7.12 7.02<br />
T 8 6.61 6.50 6.40 6.30 7.01 6.70 6.40 6.30 6.25 6.10 6.80 6.90<br />
T 9 6.70 6.60 6.50 6.45 7.10 6.75 6.55 6.45 6.35 6.25 6.95 6.85<br />
T 10 6.62 6.52 6.42 6.35 7.00 6.75 6.42 6.32 6.22 6.14 6.82 6.75<br />
T 11 6.72 6.65 6.52 6.30 7.12 6.70 6.55 6.45 6.35 6.26 6.95 6.85<br />
T 12 6.55 6.40 6.35 6.20 6.85 6.60 6.30 6.20 6.10 6.00 6.80 6.60<br />
SEm + 0.07 0.09 0.08 0.07 0.17 0.16 0.09 0.09 0.21 0.15 0.31 0.24<br />
CD at 5 % 0.14 0.18 0.17 0.14 NS NS 0.19 0.17 NS NS NS NS<br />
31
INFLUENCE <strong>of</strong> INM on PHYSICAL PROPERTIES<br />
Table 6 . Influence <strong>of</strong> integrated nutrient management treatments on hydraulic conductivity (cm h -1 ) <strong>of</strong><br />
soil in rice-rice cropping system at 0-15 cm<br />
Treatment<br />
2005-06 2006-07<br />
Trans Panicle Harvesting Trans Panicle Harvesting<br />
planting initiation<br />
planting initiation<br />
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi<br />
T 1 0.26 0.25 0.24 0.22 0.30 0.29 0.24 0.24 0.22 0.22 0.28 0.28<br />
T 2 0.25 0.24 0.23 0.21 0.29 0.28 0.23 0.23 0.21 0.21 0.27 0.27<br />
T 3 0.25 0.24 0.23 0.21 0.29 0.28 0.23 0.22 0.21 0.20 0.27 0.26<br />
T 4 0.26 0.25 0.27 0.22 0.30 0.29 0.24 0.24 0.22 0.22 0.28 0.28<br />
T 5 0.27 0.26 0.25 0.23 0.31 0.30 0.25 0.25 0.23 0.23 0.29 0.29<br />
T 6 0.29 0.28 0.27 0.25 0.33 0.32 0.27 0.26 0.25 0.24 0.31 0.30<br />
T 7 0.31 0.30 0.29 0.27 0.34 0.34 0.29 0.29 0.28 0.27 0.33 0.33<br />
T 8 0.28 0.27 0.26 0.27 0.32 0.31 0.26 0.26 0.24 0.24 0.30 0.30<br />
T 9 0.29 0.28 0.27 0.25 0.33 0.32 0.27 0.27 0.25 0.25 0.31 0.31<br />
T 10 0.29 0.28 0.27 0.25 0.33 0.32 0.28 0.27 0.25 0.25 0.32 0.31<br />
T 11 0.30 0.29 0.28 0.26 0.33 0.33 0.28 0.28 0.26 0.26 0.32 0.32<br />
T 12 0.26 0.25 0.24 0.22 0.30 0.29 0.24 0.24 0.22 0.22 0.28 0.28<br />
SEm + 0.01 0.01 0.22 0.18 0.19 0.16 0.02 0.08 0.09 0.21 0.15 0.17<br />
CD at 5 % 0.02 0.03 NS NS NS NS 0.021 0.015 0.16 NS NS NS<br />
Table 7 . Influence <strong>of</strong> integrated nutrient management treatments on hydraulic conductivity (cm h -1 ) <strong>of</strong><br />
soil in rice-rice cropping system at 15-30 cm<br />
T re a tm e n t<br />
2 0 0 5 -0 6 2 0 0 6 -0 7<br />
T ra n s - P a n ic le H a rv e s tin g T ra n s - P a n ic le<br />
H a rv e s tin g<br />
p la n tin g in itia tio n<br />
p la n tin g in itia tio n<br />
K h a rif R a b i K h a rif R a b i K h a rif R a b i K h arif R a b i K h a rif R a b i K h a rif R a b i<br />
T 1 0 .2 1 0 .2 0 0 .1 9 0 .1 8 0 .2 5 0 .2 4 0 .2 0 0 .1 9 0 .1 8 0 .1 7 0.2 4 0.2 3<br />
T 2 0 .2 0 0 .1 9 0 .1 8 0 .1 7 0 .2 4 0 .2 3 0 .1 9 0 .1 8 0 .1 8 0 .1 6 0.2 3 0.2 2<br />
T 3 0 .2 0 0 .1 9 0 .1 8 0 .1 7 0 .2 4 0 .2 3 0 .1 9 0 .1 8 0 .1 7 0 .1 6 0.2 3 0.2 2<br />
T 4 0 .2 1 0 .2 0 0 .1 9 0 .1 8 0 .2 5 0 .2 4 0 .2 1 0 .1 9 0 .1 9 0 .1 7 0.2 4 0.2 3<br />
T 5 0 .2 2 0 .2 1 0 .2 0 0 .1 9 0 .2 6 0 .2 5 0 .2 1 0 .2 0 0 .1 9 0 .1 8 0.2 5 0.2 4<br />
T 6 0 .2 4 0 .2 3 0 .2 2 0 .2 1 0 .2 8 0 .2 8 0 .2 3 0 .2 1 0 .2 1 0 .1 9 0.2 8 0.2 5<br />
T 7 0 .2 6 0 .2 5 0 .2 4 0 .2 3 0 .3 0 0 .2 9 0 .2 5 0 .2 4 0 .2 3 0 .2 2 0.2 8 0.2 8<br />
T 8 0 .2 3 0 .2 2 0 .2 1 0 .2 0 0 .2 7 0 .2 6 0 .2 2 0 .2 1 0 .2 0 0 .2 0 0.2 6 0.2 5<br />
T 9 0 .2 4 0 .2 3 0 .2 2 0 .2 1 0 .2 8 0 .2 7 0 .2 3 0 .2 2 0 .2 1 0 .2 0 0.2 7 0.2 6<br />
T 1 0 0 .2 4 0 .2 3 0 .2 2 0 .2 1 0 .2 8 0 .2 7 0 .2 3 0 .2 2 0 .2 1 0 .2 0 0.2 7 0.2 6<br />
T 1 1 0 .2 5 0 .2 4 0 .2 3 0 .2 2 0 .2 9 0 .2 8 0 .2 4 0 .2 3 0 .2 2 0 .2 1 0.2 8 0.2 7<br />
T 1 2 0 .2 1 0 .2 0 0 .1 9 0 .1 8 0 .2 5 0 .2 4 0 .2 0 0 .1 9 0 .1 8 0 .1 7 0.2 4 0.2 4<br />
S E + 0 .0 1 0 .0 1 0 .0 2 0 .0 3 0 .0 5 0 .0 6 0 .0 2 0 .0 1 0 .0 4 0 .0 3 0.0 4 0.0 3<br />
C D a t 5 % 0 .0 3 0 .0 3 N S N S N S N S 0 .0 4 0 .0 2 N S N S N S N S<br />
32
PRASAD and PRASADINI<br />
Table 8. Influence <strong>of</strong> integrated nutrient management treatments on water holding capacity (%) <strong>of</strong> soil<br />
in rice-rice cropping system at 0-15 cm<br />
Treatment<br />
Transplanting<br />
2005-06 2006-07<br />
Panicle<br />
initiation<br />
Harvesting Transplanting<br />
Panicle<br />
initiation<br />
Harvesting<br />
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi<br />
T 1 42.04 41.74 41.00 40.51 39.02 38.25 41.63 41.21 40.20 40.05 38.11 39.00<br />
T 2 41.76 41.38 40.65 40.31 38.53 38.52 41.26 41.00 39.75 39.52 37.42 38.44<br />
T 3 42.14 41.33 41.65 40.31 39.26 38.14 41.59 41.34 39.331 39.50 38.16 38.52<br />
T 4 42.14 41.41 41.66 40.25 39.24 38.29 41.54 41.30 39.44 39.50 38.25 38.52<br />
T 5 42.10 41.26 41.55 40.28 39.28 38.42 41.63 41.05 39.39 39.50 38.14 38.38<br />
T 6 44.48 44.72 43.74 43.83 42.00 42.65 43.81 45.28 42.67 39.50 41.00 38.21<br />
T 7 43.65 44.03 42.78 43.06 41.19 42.01 43.16 45.31 41.86 44.00 40.09 43.05<br />
T 8 43.25 44.00 42.63 42.29 41.02 42.02 42.72 43.46 41.56 44.00 40.00 43.10<br />
T 9 42.95 43.20 41.98 42.81 40.14 41.16 42.44 43.21 40.00 42.95 39.24 41.00<br />
T 10 43.17 43.84 42.21 42.91 40.25 41.54 42.64 44.33 41.34 44.05 39.21 43.11<br />
T 11 42.90 43.19 41.88 42.35 40.16 41.36 42.69 43.47 40.92 44.00 39.18 42.93<br />
T 12 42.00 41.35 40.72 40.74 39.06 38.54 41.37 41.08 39.88 39.76 38.08 38.02<br />
SEm + 0.53 0.90 0.47 0.59 0.72 0.77 0.57 1.04 0.65 0.89 1.01 1.05<br />
CD at 5 % 1.10 1.87 0.98 1.22 1.50 1.86 1.18 2.15 1.36 1.85 2.11 2.18<br />
Table 9. Influence <strong>of</strong> integrated nutrient management treatments on water holding capacity (%) <strong>of</strong> soil<br />
in rice-rice cropping system at 15-30 cm<br />
Treatment<br />
Transplanting<br />
2005-06 2006-07<br />
Panicle<br />
initiation<br />
Harvesting Transplanting<br />
33<br />
Panicle<br />
initiation<br />
Harvesting<br />
Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi Kharif Rabi<br />
T 1 40.73 40.18 39.29 39.19 38.26 37.33 40.12 39.74 38.84 38.15 37.19 36.09<br />
T 2 40.26 39.83 39.08 40.00 38.16 38.14 39.83 39.19 38.49 37.92 36.67 36.57<br />
T 3 40.79 39.80 39.17 40.02 38.21 38.21 39.80 39.45 38.46 37.92 37.15 36.00<br />
T 4 40.72 39.81 39.23 40.10 38.34 38.19 39.85 39.48 38.51 37.81 37.18 36.54<br />
T 5 40.10 39.85 39.20 40.15 38.19 38.16 39.81 39.45 39.39 37.84 37.20 36.50<br />
T 6 42.68 43.64 39.18 42.05 38.05 41.46 43.72 43.42 42.81 42.95 40.44 40.30<br />
T 7 42.34 42.86 42.29 42.18 41.05 40.75 42.61 42.67 41.72 42.06 39.86 41.00<br />
T 8 42.48 42.85 41.51 42.14 40.44 40.71 42.64 42.47 41.46 42.06 39.79 40.29<br />
T 9 41.65 42.05 41.64 41.19 40.26 40.11 41.96 42.21 41.40 42.00 39.81 40.25<br />
T 10 42.06 42.56 40.25 41.21 39.08 40.28 42.21 41.20 41.51 41.46 39.25 40.25<br />
T 11 41.84 43.10 40.36 41.20 39.17 40.19 42.36 41.20 41.59 41.46 39.18 40.30<br />
T 12 40.69 39.43 39.24 40.00 38.30 38.22 39.55 39.19 38.73 38.37 37.10 37.00<br />
SEm + 0.45 0.90 1.02 1.86 1.35 1.20 0.96 1.04 0.91 1.53 1.35 1.42<br />
CD at 5 % 0.94 1.86 2.12 NS NS NS 2.00 2.16 1.87 3.18 NS NS
INFLUENCE OF INM ON PHYSICAL PROPERTIES<br />
Table 10. Influence <strong>of</strong> integrated nutrient management treatments on yield in Rice - Rice cropping<br />
system<br />
Grain yield (kg ha -1 )<br />
Treatment 2005-06 2006-07<br />
Kharif Rabi Kharif Rabi<br />
T 1 2475 2025 2199 1545<br />
T 2 3739 3920 2911 2801<br />
T 3 3676 5405 3111 4150<br />
T 4 3891 5200 3601 3397<br />
T 5 4676 5890 3983 3801<br />
T 6 4140 5795 3782 3889<br />
T 7 4649 5665 3856 3582<br />
T 8 3856 5375 2444 3815<br />
T 9 4411 5230 2980 3546<br />
T 10 4973 6070 4052 4480<br />
T 11 4977 6915 4251 4244<br />
T 12 4371 4745 3244 3333<br />
SEm + 236 91 149 375<br />
CD at 5 % 492 191 311 783<br />
REFERENCES<br />
Bertrand, A.R. 1965. Rate <strong>of</strong> water intake in the field<br />
in: Measures <strong>of</strong> soil analysis by Black, C.A.<br />
part I Agronomy 9 : 374-390.<br />
Bhagat, R.M., Bhardwaj, A.K and Pradeep, K.,<br />
Sharma. 2003. Long term Effect <strong>of</strong> Residue<br />
Management on soil physical properties, water<br />
use and yield <strong>of</strong> rice in north – western India,<br />
journal <strong>of</strong> the Indian Society <strong>of</strong> Soil Science,<br />
51 : 111-117.<br />
Black, C.A. 1965. Methods <strong>of</strong> soil analysis Part,<br />
American Society <strong>of</strong> Agronomy, Wisconsin,<br />
USA. Density, water contents and microbial<br />
population <strong>of</strong> soil. <strong>Journal</strong> <strong>of</strong> Indian Society <strong>of</strong><br />
soil science 40 :553-555.<br />
Chawla, K.L and Chabra, R. 1991. Physical<br />
properties <strong>of</strong> gypsum amended sodic soils as<br />
affected by long term use <strong>of</strong> fertilizers. <strong>Journal</strong><br />
<strong>of</strong> Indian Society <strong>of</strong> Soil Science 39 :40-46<br />
Jalota, S.K., Ramesh Khera and Ghuman, B.S. 1998.<br />
Measures in soil physics Narosa publishing<br />
house, New Delhi<br />
Katele, P., Leinweber, P and Menning, P. 1992. On<br />
the influence <strong>of</strong> soil organic mater on physical<br />
properties <strong>of</strong> soil. Agrobiological <strong>Research</strong><br />
45 : 18-27<br />
Kumar, K., Meelu, O.P., Singh, Y and Singh, B. 1992.<br />
Effect <strong>of</strong> continuous application <strong>of</strong> organic<br />
manures on the physical properties <strong>of</strong> soils in<br />
rice-wheat cropping system. International Rice<br />
<strong>Research</strong> news Letter. 17 : 4-16.<br />
Tripathi, R.P., Gaur, M.K and Rawat, M.S. 2003.<br />
Puddling Effects on soil physical properties<br />
and rice performance under shallow water table<br />
conditions <strong>of</strong> tarai. <strong>Journal</strong> <strong>of</strong> the Indian Society<br />
<strong>of</strong> Soil Science, 51: 118-124<br />
Vasanthi, D and Kumarswamy, K. 1999. Efficacy <strong>of</strong><br />
vermicompost to improve soil fertility and rice<br />
yield. <strong>Journal</strong> <strong>of</strong> the Indian society <strong>of</strong> soil<br />
science 47 : 268-272<br />
Vennila, R.K and Muthuvel, P. 1998. Effect <strong>of</strong> long<br />
term fertilization on physical properties <strong>of</strong> soils.<br />
Madras Agricultural <strong>Journal</strong> 85 : 290-292.<br />
34
J.Res. <strong>ANGRAU</strong> 41(1) 30-38, 2013<br />
GENETIC VARIABILITY, HERITABILITY AND CHARACTER ASSOCIATION<br />
STUDIES IN SWEET SORGHUM [Sorghum bicolor (L.) Moench]<br />
VEMANNA IRADDI, T. DAYAKAR REDDY, A. V. UMAKANTH, CH. RANI,<br />
D. VISHNU VARDHAN REDDY and M. H. V. BHAVE<br />
Department <strong>of</strong> Genetics and Plant Breeding<br />
Acharya N.G. Ranga Agricultural University, Hyderabad – 500 030<br />
Date <strong>of</strong> Receipt : 03.11.2012 Date <strong>of</strong> Acceptance : 12.12.2012<br />
ABSTRACT<br />
<strong>The</strong> present investigation on genetic variability, heritability and character association in large F 2<br />
population<br />
<strong>of</strong> sweet sorghum was carried out at Directorate <strong>of</strong> Sorghum <strong>Research</strong>, Rajendranagar, Hyderabad. F 1<br />
was generated<br />
during 2010 kharif and second filial generation in the following season. <strong>The</strong> mean and variance in respect <strong>of</strong> 14<br />
quantitative characters in F 2<br />
population indicated wide range <strong>of</strong> variability for most <strong>of</strong> the traits. However, variability<br />
range was low for the traits like nodes per plant, stem girth, brix per cent and total soluble sugars. <strong>The</strong> distribution<br />
pattern <strong>of</strong> this F 2<br />
population revealed complementary interaction in the inheritance <strong>of</strong> days to 50% flowering, days to<br />
maturity, plant height, total biomass, fresh stalk yield, grain yield, brix per cent, juice yield, juice extraction per cent,<br />
total soluble sugars, sugar yield and bioethanol yield, while inheritance <strong>of</strong> nodes per plant and stem girth exhibited<br />
duplicate type <strong>of</strong> epistasis. Most <strong>of</strong> these characters except days to 50% flowering and days to maturity exhibited<br />
high heritability coupled with moderate to high genetic advance as per cent <strong>of</strong> mean indicating predominance <strong>of</strong><br />
additive gene action in their genetic control. Further, correlation studies in F 2<br />
generation revealed significant and<br />
positive correlation <strong>of</strong> fresh stalk yield with total biomass, grain yield, plant height, nodes per plant, stem girth, days<br />
to 50% flowering and days to maturity, while sugar yield with juice yield, fresh stalk yield, total biomass, grain yield,<br />
total soluble sugars, brix per cent, bioethanol yield and juice extraction per cent. <strong>The</strong>se correlated traits can be<br />
effectively utilized in formulating indirect selection schemes. While path analysis studies revealed maximum positive<br />
direct effect <strong>of</strong> total soluble sugars and juice yield on sugar yield.<br />
<strong>The</strong> genetic improvement <strong>of</strong> quantitative<br />
characters in a crop species depends upon heritability<br />
pattern <strong>of</strong> the trait in question, nature and amount <strong>of</strong><br />
variability present in the existing germplasm.<br />
Moreover sweet sorghum is also not an exception.<br />
Knowledge on the genetic advance that is expected<br />
by applying selection pressure to a segregating<br />
population is useful in designing effective breeding<br />
programme. Evaluation <strong>of</strong> these segregating<br />
progenies helps in estimation <strong>of</strong> various genetic and<br />
non-genetic components <strong>of</strong> variance. <strong>The</strong> study <strong>of</strong><br />
variability provides an opportunity for selecting the<br />
desirable genotypes. Heritability is a fraction <strong>of</strong><br />
variance in phenotypic expression that arises from<br />
genetic effects. <strong>The</strong> nature <strong>of</strong> the selection units<br />
and sampling errors also influences greatly the<br />
magnitude <strong>of</strong> heritability. <strong>The</strong> estimates <strong>of</strong> heritability<br />
in segregating generations help to know genetic<br />
variance, genotype - environment interaction and the<br />
progress expected from selection.<br />
<strong>The</strong> fresh stalk yield and sugar yield in sweet<br />
sorghum, as in other crops, is a complex quantitative<br />
character and its expression depends upon its<br />
component characters. <strong>The</strong> knowledge on the<br />
relative contribution <strong>of</strong> different yield components and<br />
their direct and indirect impact towards sugar yield<br />
is <strong>of</strong> immense value in selection <strong>of</strong> superior<br />
genotypes. Keeping the situations present in the<br />
foregoing paragraphs in view, the present investigation<br />
was under taken to study genetic variability,<br />
heritability and character association in sweet<br />
sorghum.<br />
MATERIALS AND METHODS<br />
<strong>The</strong> material for this experiment comprised<br />
<strong>of</strong> F 2<br />
population <strong>of</strong> a cross derived from parents (27<br />
B with SSV 84) having low and high sugar content<br />
developed at Directorate <strong>of</strong> Sorghum <strong>Research</strong>,<br />
Rajendranagar, Hyderabad during kharif 2010. <strong>The</strong><br />
F 1<br />
plants <strong>of</strong> this cross were grown during rabi 2010 -<br />
11 and selfed to produce the F 2<br />
seeds, which were<br />
evaluated during summer 2012. <strong>The</strong> F 2<br />
segregating<br />
generations were grown in plots with twenty rows each<br />
in three separate blocks. <strong>The</strong>se plants were sown in<br />
plots <strong>of</strong> twenty rows spaced 45 cm apart with a plant<br />
spacing <strong>of</strong> 15 cm with 2 - 3 seeds per hill in each row<br />
<strong>of</strong> 4 mt length. Thinning was done to retain one<br />
healthy plant per hill at 15 and 25 days after sowing.<br />
All the recommended package <strong>of</strong> practices was<br />
followed to raise a good and healthy crop.<br />
email: vemanraddi@gmail.com<br />
35
GENETIC VARIABILITY, HERITABILITY AND CHARACTER ASSOCIATION STUDIES<br />
RESULTS AND DISCUSSION<br />
<strong>The</strong> descriptive statistics and genetic<br />
variability parameters with respect to fourteen<br />
quantitative characters in F 2<br />
population <strong>of</strong> the cross<br />
‘27 B × SSV 84’ is presented in Table 1. <strong>The</strong>se<br />
descriptive statistics, unravels basic idea <strong>of</strong> the<br />
breeding material. <strong>The</strong> characteristics <strong>of</strong> this F 2<br />
population in respect <strong>of</strong> various quantitative<br />
characters as indicated by these first and second<br />
degree statistics are discussed below.<br />
Wide range <strong>of</strong> variability was present for the<br />
traits such as plant height, total biomass, fresh stalk<br />
yield, grain yield, juice yield, juice extraction per cent,<br />
sugar yield and bioethanol yield <strong>of</strong> this cross <strong>of</strong> sweet<br />
sorghum studied as indicated by their respective<br />
mean and variances. Existence <strong>of</strong> moderate<br />
variability was observed for days to 50% flowering<br />
and days to maturity. However, the variability range<br />
was low for the traits like nodes per plant, stem girth,<br />
brix per cent and total soluble sugars.<br />
<strong>The</strong> study <strong>of</strong> distribution properties such as<br />
co-efficients <strong>of</strong> skewness (third degree statistic) and<br />
kurtosis (fourth degree statistic) provides insight about<br />
the nature <strong>of</strong> gene action and number <strong>of</strong> genes<br />
controlling the traits, respectively. All the studies<br />
reported nature <strong>of</strong> genetic control <strong>of</strong> quantitative traits<br />
in sorghum is based on first degree (gene effects<br />
through generation mean analysis) and second degree<br />
(components <strong>of</strong> genetic variances through diallel, line<br />
× tester analysis, etc.) statistics. Skewness and<br />
kurtosis are greater than first and second degree<br />
statistics which reveal interaction genetic effects. <strong>The</strong><br />
skewed distribution <strong>of</strong> a trait in general suggests that<br />
the trait is under the control <strong>of</strong> non-additive gene<br />
action, especially epistasis and influenced by<br />
environmental variables (Kimberg and Bingham, 1998<br />
and Roy, 2000). Positive skewness is associated<br />
with complementary interaction and negative<br />
skewness is associated with duplicate (additive ×<br />
additive) gene interactions predominantly in the same<br />
directions. Complete ambi-directional epistasis<br />
however produces kurtosis while distribution stays<br />
symmetrical around mean. <strong>The</strong> genes controlling the<br />
trait with skewed distribution tend to be predominantly<br />
dominant irrespective <strong>of</strong> whether they have increasing<br />
or decreasing effects on the expression <strong>of</strong> the trait.<br />
<strong>The</strong> traits with leptokurtic and platykurtic<br />
distribution are controlled by fewer and a large number<br />
<strong>of</strong> genes, respectively. Kurtosis is negative or close<br />
to zero in the absence <strong>of</strong> gene interactions and is<br />
positive in the presence <strong>of</strong> gene interactions. <strong>The</strong><br />
inference on the relative number <strong>of</strong> genes and nature<br />
<strong>of</strong> genetic control <strong>of</strong> different traits in F 2<br />
generation<br />
<strong>of</strong> this sweet sorghum cross is discussed below.<br />
Platykurtic and positively skewed distribution<br />
suggested the involvement <strong>of</strong> relatively large number<br />
<strong>of</strong> segregating genes with dominance based<br />
complementary type <strong>of</strong> interaction in the inheritance<br />
<strong>of</strong> days to 50% flowering, days to maturity, plant<br />
height, total biomass, fresh stalk yield, grain yield,<br />
juice yield, juice extraction per cent and sugar yield<br />
in ‘27 B × SSV 84’ cross. Maximizing the genetic<br />
gain in respect <strong>of</strong> these traits with positively skewed<br />
distribution requires intense selection from the<br />
existing variability.<br />
<strong>The</strong> inheritance <strong>of</strong> nodes per plant and stem<br />
girth recorded negatively skewed platykurtic<br />
distribution which indicates that these traits are<br />
governed by large number <strong>of</strong> dominant genes with<br />
duplicate type <strong>of</strong> epistasis. <strong>The</strong>se traits have evolved<br />
with dominance and dominance based duplicate<br />
epistasis which helps to protect the individual plants<br />
from deleterious alleles arising from existing<br />
variability (Roy, 2000).<br />
<strong>The</strong> leptokurtic and positively skewed<br />
distribution for traits such as brix per cent, total<br />
soluble sugars and bioethanol yield suggested the<br />
involvement <strong>of</strong> relatively fewer number <strong>of</strong> segregating<br />
genes with dominance based complementary<br />
interaction in the inheritance <strong>of</strong> these traits. To<br />
achieve maximum genetic gain in respect <strong>of</strong> these<br />
traits needs intense selection.<br />
Estimation <strong>of</strong> variability parameters in a<br />
population is a pre-requisite for breeding programme<br />
aimed at improving yield, quality and other important<br />
characters under consideration. Unless a major<br />
portion <strong>of</strong> variation is heritable, attempts to improve<br />
characters by selection would be futile. <strong>The</strong>refore, it<br />
is necessary to have information on both PCV and<br />
GCV, so that the heritability, which helps the breeder<br />
to predict the expected genetic advance possible by<br />
selection for characters, can be computed. According<br />
to Johnson et al. (1955), heritability estimates along<br />
with genetic gain would be more useful than the former<br />
alone in predicting the effectiveness <strong>of</strong> selection.<br />
<strong>The</strong>refore it is essential to consider the predicted<br />
genetic advance along with heritability estimate as a<br />
tool in selection programme for better efficiency.<br />
36
VEMANNA et al<br />
<strong>The</strong> range in mean values does not reflect<br />
the total variance in the material studied. Hence,<br />
actual variance has to be estimated for the characters<br />
to know the extent <strong>of</strong> existing variability. However<br />
absolute values <strong>of</strong> phenotypic and genotypic variance<br />
cannot be used for comparing the degree <strong>of</strong> variability<br />
in different characters because the characters differ<br />
in the unit <strong>of</strong> measurement. Hence, the co-efficient<br />
<strong>of</strong> variation (PCV and GCV) which is calculated by<br />
considering the respective means have been used<br />
for the comparisons.<br />
In the present study, the range <strong>of</strong> variability<br />
was quite high for most <strong>of</strong> the characters studied<br />
except days to 50% flowering, days to maturity,<br />
nodes per plant, stem girth, brix per cent and total<br />
soluble sugars, which exhibited low to moderate<br />
amount variability. This indicates ample scope for<br />
the improvement <strong>of</strong> highly variable characters, which<br />
were generated by segregation and recombination,<br />
whereas, it may not be equally effective for a<br />
character, which exhibited narrow range <strong>of</strong> variability.<br />
In general, PCV values were relatively higher<br />
than GCV values which is coupled with negligible<br />
differences between them, indicates less<br />
environmental influence on most <strong>of</strong> the traits except<br />
nodes per plant, stem girth, grain yield, brix per cent,<br />
total soluble sugars, sugar yield and bioethanol yield.<br />
Days to 50% flowering and days to maturity<br />
exhibited lower values <strong>of</strong> GCV and PCV. This was in<br />
conformity with the reports <strong>of</strong> Rajappa (2009). High<br />
heritability coupled with low genetic advance as per<br />
cent <strong>of</strong> mean exhibited by this cross, indicated<br />
predominant role <strong>of</strong> non-additive gene action for these<br />
traits and this result is in accordance with the reports<br />
<strong>of</strong> Sankarapandian (2002). Patil et al. (1996),<br />
Sankarapandian (2002), Unche et al. (2008a) and<br />
Kachapur and Salimath (2009) also reported high<br />
heritability for this trait.<br />
Plant height exhibited high values <strong>of</strong> GCV<br />
and PCV but differences between them was relatively<br />
narrow indicating less influence <strong>of</strong> environment in the<br />
expression <strong>of</strong> this trait. <strong>The</strong> high broad sense<br />
heritability (97.42%) coupled with high genetic<br />
advance as per cent <strong>of</strong> mean (48.37%) indicated<br />
predominant role <strong>of</strong> additive gene action in its genetic<br />
control. <strong>The</strong> results <strong>of</strong> heritability and genetic advance<br />
in the present study is in total agreement with the<br />
reports <strong>of</strong> Sankarapandian et al. (1996), Umakanth<br />
et al. (2004), Sandeep et al. (2009a) and Rajappa<br />
(2009).<br />
37<br />
Nodes per plant and stem girth registered<br />
moderate PCV and GCV values with negligible<br />
difference between them indicating less influence <strong>of</strong><br />
environment on these traits. <strong>The</strong> results <strong>of</strong> PCV and<br />
GCV were in agreement with earlier report <strong>of</strong> Rajappa<br />
(2009). <strong>The</strong>se traits exhibited high heritability coupled<br />
with moderate and high genetic advance expressed<br />
as per cent <strong>of</strong> mean, respectively indicating role <strong>of</strong><br />
additive gene action in there genetic control. Results<br />
<strong>of</strong> the present study are in corroborative with the<br />
earlier reports <strong>of</strong> Sankarapandian et al. (1996),<br />
Krishnakumar et al. (2004), Rajappa (2009) and<br />
Sandeep et al. (2009a). Reliability can be placed on<br />
these traits for selection <strong>of</strong> segregants owing to its<br />
high heritability coupled with high genetic advance.<br />
Total biomass per plant recorded higher<br />
values <strong>of</strong> PCV and GCV with negligible difference<br />
between them indicating less influence <strong>of</strong> environment<br />
on the expression <strong>of</strong> trait <strong>of</strong> interest. <strong>The</strong> high<br />
estimates <strong>of</strong> broad sense heritability (97.04%) and<br />
genetic advance expressed as per cent <strong>of</strong> mean<br />
(93.74%) in this cross indicating preponderance <strong>of</strong><br />
additive gene action in the genetic control <strong>of</strong> this trait.<br />
Similar results were earlier reported by Unche et al.<br />
(2008a) with respect to heritability and genetic<br />
advance indicating efficiency <strong>of</strong> simple selection in<br />
deriving desirable segregants.<br />
Fresh stalk yield registered higher values <strong>of</strong><br />
PCV and GCV with negligible difference between<br />
them, indicating less influence <strong>of</strong> environment on the<br />
expression <strong>of</strong> this trait. Higher values for this trait<br />
were earlier reported by Sankarapandian et al. (1996)<br />
and Rajappa (2009). <strong>The</strong> broad sense heritability and<br />
genetic advance estimates were also higher<br />
indicating usefulness <strong>of</strong> this trait in selection <strong>of</strong><br />
desirable segregants due to its genetic control by<br />
additive gene action. This is in accordance with the<br />
earlier observations made by Sankarapandian et al.<br />
(1996), Krishnakumar et al. (2004), Patel et al. (2006),<br />
Unche et al. (2008a), Sandeep et al. (2009a) and<br />
Rajappa (2009).<br />
Grain yield registered higher values <strong>of</strong> PCV<br />
and GCV with conspicuous difference between them<br />
indicating high environmental influence. Higher values<br />
for this trait were earlier reported by Sankarapandian<br />
et al. (1996) and Rajappa (2009). <strong>The</strong> broad sense<br />
heritability and genetic advance estimates were also<br />
higher indicating usefulness <strong>of</strong> this trait in selection<br />
<strong>of</strong> desirable segregants due to its genetic control by<br />
additive gene action.
GENETIC VARIABILITY, HERITABILITY AND CHARACTER ASSOCIATION STUDIES<br />
Brix per cent registered has considerable<br />
environment influence. However, high heritability and<br />
moderate genetic advance expressed as per cent <strong>of</strong><br />
mean was recorded revealing the major role <strong>of</strong><br />
additive gene action in genetic control <strong>of</strong> this trait.<br />
<strong>The</strong>se results were in accordance with the reports <strong>of</strong><br />
Sankarapandian (2002), Sandeep et al. (2009a) and<br />
Rajappa (2009) for heritability and genetic advance,<br />
but contradicts with the results obtained by<br />
Sankarapandian et al. (1996) and Rajappa (2009) with<br />
respect to GCV and PCV estimates who reported<br />
relatively higher values. Higher heritability and<br />
genetic advance for this trait indicate effectiveness<br />
<strong>of</strong> simple direct selection in improvement<br />
programmes.<br />
Juice yield registered higher PCV and GCV<br />
values with negligible difference between them,<br />
indicating less influence <strong>of</strong> environment on the<br />
expression <strong>of</strong> this trait. This also recorded high broad<br />
sense heritability (98.27%) coupled with high genetic<br />
advance as per cent <strong>of</strong> mean (123.06%) indicating<br />
predominant role <strong>of</strong> additive gene action in the genetic<br />
control <strong>of</strong> this trait. Higher values for this trait was<br />
earlier reported by Rajappa (2009) for PCV and GCV<br />
values and by Sankarapandian et al. (1996),<br />
Sankarapandian (2002), Kachapur and Salimath<br />
(2009) and Rajappa (2009) for heritability and genetic<br />
advance. This trait can be considered as a potential<br />
for improvement by simple selection owing to high<br />
heritability and genetic advance.<br />
Juice extraction per cent exhibited higher<br />
values <strong>of</strong> PCV and GCV with narrow difference<br />
between them, indicating less influence <strong>of</strong><br />
environment on the expression <strong>of</strong> the trait. This is<br />
coupled with high heritability (95.03%) and genetic<br />
advance as per cent <strong>of</strong> mean (43.80%) indicating<br />
predominant role <strong>of</strong> additive gene action. Similar<br />
results with respect to heritability and genetic<br />
advance were reported earlier by Sankarapandian<br />
(2002) and Sandeep et al. (2009a).<br />
Total soluble sugars exhibited moderate<br />
value <strong>of</strong> PCV and low value <strong>of</strong> GCV with negligible<br />
differences between them indicating less influence<br />
<strong>of</strong> environment on the expression <strong>of</strong> this trait. Broad<br />
sense heritability and genetic advance estimates were<br />
also higher. Moderate variability <strong>of</strong> this trait coupled<br />
with high heritability and genetic advance indicate<br />
higher scope for further improvement through simple<br />
selection procedures.<br />
Moderate value <strong>of</strong> PCV and low value <strong>of</strong> GCV<br />
were registered by bioethanol yield coupled with high<br />
broad sense heritability and moderate genetic<br />
advance expressed as per cent <strong>of</strong> mean indicating<br />
major role <strong>of</strong> additive gene action in the genetic<br />
control <strong>of</strong> this trait.<br />
Sugar yield registered higher values <strong>of</strong> PCV<br />
and GCV compared to all other traits under study<br />
with considerable difference between them indicating<br />
substantial environmental influence on the expression<br />
<strong>of</strong> this trait, which is reflected in relatively higher broad<br />
sense heritability (95.16%) and high genetic advance<br />
expressed as per cent <strong>of</strong> mean (123.47%) indicating<br />
major role <strong>of</strong> additive gene action in the genetic<br />
control <strong>of</strong> this trait. Result <strong>of</strong> the present study is in<br />
conformity with the earlier reports <strong>of</strong> Krishnakumar<br />
et al. (2004) and Patel et al. (2006) with respect to<br />
heritability and genetic advance. Though heritability<br />
and genetic advance indicate scope for simple direct<br />
selection to be effective for this trait, actual gain would<br />
entirely depend on its intrinsic association with its<br />
attributing traits.<br />
Correlation Coefficients<br />
<strong>The</strong> correlation co-efficients among the<br />
selected characters related to fresh stalk yield and<br />
sugar yield in F 2<br />
population <strong>of</strong> ‘27 B × SSV 84’ sweet<br />
sorghum cross were estimated; results were<br />
tabulated in Table 2 and 3 and briefly described in<br />
the following paragraphs.<br />
Association <strong>of</strong> fresh stalk yield with its<br />
component characters Fresh stalk yield per plant<br />
was significantly and positively associated with total<br />
biomass per plant, grain yield per plant, plant height,<br />
nodes per plant, stem girth, days to 50% flowering<br />
and days to maturity. Similar trends were evident<br />
from the studies <strong>of</strong> Hapase and Repale (1999), Nahar<br />
et al. (2002), Krishnakumar et al., (2004), Singh and<br />
Khan (2004), Kadian and Mehta (2006), Patel et al.<br />
(2006) and Unche et al. (2008b).<br />
Among the fresh stalk yield attributing<br />
characters, positive and significant association was<br />
noticeable between days to 50% flowering with days<br />
to maturity, plant height, grain yield per plant, nodes<br />
per plant, total biomass per plant and stem girth; plant<br />
height with nodes per plant, total biomass per plant,<br />
stem girth and grain yield per plant; nodes per plant<br />
with total biomass per plant, grain yield per plant and<br />
stem girth; stem girth with total biomass per plant<br />
and grain yield per plant; total biomass per plant with<br />
38
VEMANNA et al<br />
grain yield per plant. <strong>The</strong>se findings were in<br />
confirmation with the findings <strong>of</strong> Manickam and Das<br />
(1994), Ganesh et al. (1995), Verma et al. (1999) and<br />
Kachapur and Salimath (2009). However, positive and<br />
significant association <strong>of</strong> days to flowering and plant<br />
height observed in the present study contradicts with<br />
the report <strong>of</strong> Manickam and Das (1994) who observed<br />
negative significant correlation <strong>of</strong> days to flowering<br />
with plant height and plant height with stem girth.<br />
Thus, to improve the fresh stalk yield in<br />
sweet sorghum it is important to select the plants<br />
with relatively higher plant height, total biomass, stem<br />
girth, nodes per plant and days to maturity, as these<br />
traits had direct relation with fresh stalk yield as<br />
indicated by their positive and significant association.<br />
Hence, fresh stalk yield can be increased by following<br />
indirect selection using above associated traits in<br />
sweet sorghum.<br />
Association <strong>of</strong> sugar yield with its<br />
component characters Association between sugar<br />
yield was positive and highly significant with juice<br />
yield per plant, fresh stalk yield per plant, total<br />
biomass per plant, grain yield per plant, total soluble<br />
sugars, brix per cent, bioethanol yield per plant and<br />
juice extraction per cent. <strong>The</strong> results <strong>of</strong> the present<br />
investigation were in corroborative with Mallikarjun<br />
et al. (1998), Hapase and Repale (1999), Verma et<br />
al. (1999), Singh and Khan (2004), Kadian and Mehta<br />
(2006) and Unche et al. (2008b).<br />
Association among sugar yield attributing<br />
characters <strong>The</strong> association <strong>of</strong> total biomass with<br />
fresh stalk yield per plant, juice yield per plant, grain<br />
yield per plant, brix per cent, total soluble sugars<br />
and bioethanol yield per plant; fresh stalk yield per<br />
plant with juice yield per plant, grain yield per plant,<br />
brix per cent, total soluble sugars and bioethanol yield<br />
per plant; grain yield per plant with juice yield per<br />
plant; brix per cent with total soluble sugars,<br />
bioethanol yield per plant, juice extraction per cent<br />
and juice yield per plant; juice yield per plant with<br />
juice extraction per cent, total soluble sugars and<br />
bioethanol yield per plant; juice extraction per cent<br />
with total soluble sugars and bioethanol yield per<br />
plant; total soluble sugar with bioethanol yield per<br />
plant were positive and significant. <strong>The</strong> reports <strong>of</strong><br />
Ganesh et al. (1995), Singh and Khan (2004), Kadian<br />
and Mehta (2006), Kachapur and Salimath (2009),<br />
Unche et al. (2008b) and Sandeep et al. (2010) were<br />
in agreement with the above results.<br />
<strong>The</strong> results on association <strong>of</strong> sugar yield with<br />
its attributing traits indicated importance <strong>of</strong> juice yield,<br />
fresh stalk yield, total biomass, grain yield, total<br />
soluble sugars, brix per cent, bioethanol yield and<br />
juice extraction per cent in improving sugar yield as<br />
these traits had direct relation with sugar yield.<br />
Hence, improvement in these traits automatically<br />
improve sugar yield. Thus, the above correlated traits<br />
can be effectively utilized in formulating indirect<br />
selection schemes.<br />
Path analysis<br />
<strong>The</strong> correlation estimates are the sum total<br />
<strong>of</strong> direct effect and indirect effects <strong>of</strong> an independent<br />
character on a dependent character and it is quite<br />
obvious that the correlation (positive or negative) may<br />
be <strong>of</strong> small magnitude and non-significant in spite <strong>of</strong><br />
its direct effect and/or some <strong>of</strong> the indirect effects<br />
are operating in the opposite direction. <strong>The</strong>refore, path<br />
analysis is required to partition the correlation value<br />
<strong>of</strong> independent characters on dependent character<br />
into direct and indirect effects so as to get a correct<br />
picture <strong>of</strong> the association <strong>of</strong> characters. Hence, path<br />
co-efficient analysis was carried out to know the<br />
direct and indirect effects <strong>of</strong> the component<br />
characters on sugar yield and the results are<br />
presented in Table 4.<br />
<strong>The</strong> results <strong>of</strong> path analysis <strong>of</strong> component<br />
characters <strong>of</strong> sugar yield indicated maximum positive<br />
direct effect <strong>of</strong> total soluble sugars and juice yield<br />
on sugar yield whereas bioethanol yield, fresh stalk<br />
yield and total biomass has very high, moderate and<br />
low negative direct effect, respectively on sugar yield.<br />
However, all the traits exhibited moderate to high<br />
positive indirect effect via juice yield and total soluble<br />
sugars. <strong>The</strong> indirect effect via total biomass and fresh<br />
stalk yield is negative and low to moderate while juice<br />
extraction per cent is negligible and negative. <strong>The</strong>se<br />
results were in accordance with the earlier reposts <strong>of</strong><br />
Mallikarjun et al. (1998), Hapase and Repale (1999)<br />
and Kachapur and Salimath (2009). In general, the<br />
results revealed that the indirect contribution <strong>of</strong> the<br />
characters viz., total biomass, fresh stalk yield and<br />
juice extraction per cent via juice yield resulted in<br />
their positive correlation with sugar yield.<br />
39
GENETIC VARIABILITY, HERITABILITY AND CHARACTER ASSOCIATION STUDIES<br />
40
VEMANNA et al<br />
41
GENETIC VARIABILITY, HERITABILITY AND CHARACTER ASSOCIATION STUDIES<br />
REFERENCES<br />
Ganesh, S., Khan, A. K. F., Suresh, M and Senthil,<br />
N., 1995. Character association for alcohol<br />
yield in sweet sorghum. <strong>The</strong> Madras<br />
Agricultural <strong>Journal</strong>. 82: 361-363.<br />
Hapase, R. S and Repale, J. M., 1999. Variability,<br />
correlation and path analysis in sugarcane. In:<br />
Proceedings <strong>of</strong> the 61 st Annual Convention <strong>of</strong><br />
the Sugar Technologists Association <strong>of</strong> India,<br />
New Delhi, India, 7-9 September: pp. 130-141.<br />
Johnson, H. W, Robinson, H. F and Comstock, R.<br />
E., 1955. Estimates <strong>of</strong> genetic and<br />
environmental variability in soybean.<br />
Agronomy <strong>Journal</strong>. 47: 314- 318.<br />
Kachapur, R. M and Salimath, P. M., 2009. Genetic<br />
studies on correlation and character<br />
association in sweet sorghum [Sorghum<br />
bicolor (L.) Moench]. Green Farming. 2: 343-<br />
346.<br />
Kadian, S. P and Mehta, A. S., 2006. Correlation<br />
and path analysis in sugarcane. Indian <strong>Journal</strong><br />
<strong>of</strong> Agricultural <strong>Research</strong>. 40: 47-51.<br />
Kimbeng, C. A and Bingham, E. T., 1998. Population<br />
improvement in Lucerne (Medicago sativa L.)<br />
components <strong>of</strong> inbreeding depression are<br />
different in original and improved populations.<br />
Australian <strong>Journal</strong> <strong>of</strong> Experimental Agriculture.<br />
38: 831-836.<br />
Krishnakumar, Singh, P. K and Singh, J. R. P., 2004.<br />
Genetic variability and character association<br />
in subtropical clones <strong>of</strong> Sugarcane<br />
(Saccharum complex hybrid). Indian Sugar.<br />
54: 189-198.<br />
Mallikarjun, H., Khanure, S. K and Kachapur, M. D.,<br />
1998. Correlation and path analysis for juice<br />
quality parameters in sweet sorghum<br />
genotypes. <strong>The</strong> Madras Agricultural <strong>Journal</strong>.<br />
85: 207-208.<br />
Manickam, S and Das, L. D. V., 1994. Character<br />
association and path analysis in forage<br />
sorghum. Mysore <strong>Journal</strong> <strong>of</strong> Agricultural<br />
Sciences. 28: 116-119.<br />
Nahar, S. M. N., Khaleque, M. A and Miah, M. A.,<br />
2002. Correlation, path co-efficient and<br />
construction <strong>of</strong> selection index in sugarcane.<br />
Pakistan Sugar <strong>Journal</strong>. 17: 2-10.<br />
Patel, K. C., Patel, A. I., Mali, S. C., Patel, D. U and<br />
Vashi, R. D., 2006. Variability, correlation and<br />
path analysis in sugarcane (Saccharum spp.).<br />
Crop <strong>Research</strong>. 32: 213-218.<br />
Patil, F. B., Gadekar, D. A and Bhoite, A. G., 1996.<br />
Variability studies in forage sorghum. <strong>Journal</strong><br />
<strong>of</strong> Maharashtra Agricultural Universities.<br />
Z21: 330-332.<br />
Rajappa, P. V., 2009. Morphological and AFLP marker<br />
based genetic diversity in sweet sorghum<br />
working germplasm. M. Sc. (Agri.) <strong>The</strong>sis<br />
submitted to University <strong>of</strong> Agricultural<br />
Sciences, Bangalore.<br />
Roy, D., 2000. Analysis <strong>of</strong> skewness and kurtosis.<br />
In: Plant breeding – <strong>The</strong> Analysis and<br />
Exploitation <strong>of</strong> Variation. Narosa Publishing<br />
House. New Delhi. India. pp 300-304.<br />
Sandeep, R. G., Gururaja Rao, M. R., Chikkalingaiah<br />
and Shivanna, H., 2009a. Assessment <strong>of</strong><br />
variability for grain yield, ethanol yield and their<br />
attributing characters in germplasm<br />
accessions <strong>of</strong> sweet sorghum [Sorghum<br />
bicolor (L.) Moench]. Mysore <strong>Journal</strong> <strong>of</strong><br />
Agricultural Sciences. 43: 472-476.<br />
Sandeep, R. G., Gururaja Rao, M. R., Chikkalingaiah<br />
and Shivanna, H., 2010. Association and path<br />
analysis for ethanol yield in sweet sorghum<br />
[Sorghum bicolor (L.) Moench]. Mysore <strong>Journal</strong><br />
<strong>of</strong> Agricultural Sciences. 44: 28-30.<br />
Sankarapandian, R., 2002. Variability studies in two<br />
groups <strong>of</strong> hybrid populations in forage sorghum.<br />
Forage <strong>Research</strong>. 28: 46-48.<br />
Sankarapandian, R., Rajarathinam, S and Muppidathi,<br />
N., 1996. Genetic variability, correlation and<br />
path co-efficient analysis <strong>of</strong> jaggery yield and<br />
related attributes in sweet sorghum.<br />
<strong>The</strong> Madras Agricultural <strong>Journal</strong>. 83: 628-631.<br />
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Umakanth, A. V., Madhududhana, R and<br />
Madhvilatha, K., 2004. Variability, character<br />
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<strong>The</strong> Andhra Agricultural <strong>Journal</strong>. 51: 333-336.<br />
Unche, P. B., Misal, M. B., Borgaonkar, S. B.,<br />
Chavan, B. D and Sawant, D. R., 2008b.<br />
Correlation studies in sweet sorghum [Sorghum<br />
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Plant Sciences. 3: 69-72.<br />
Unche, P. B., Misal, M. B., Borgaonkar, S. B.,<br />
Godhawale, G. V., Chavan, B. D and Sawant,<br />
D. R., 2008a. Genetic variability studies in<br />
sweet sorghum [Sorghum bicolor (L.) Moench].<br />
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43
J.Res. <strong>ANGRAU</strong> 41(1) 39-41, 2013<br />
ESTRUS SYNCHRONIZATION RESPONSE AND FERTILITY RATE FOLLOWING<br />
TREATMENT WITH PGF 2<br />
α AND GnRH IN ACYCLIC LACTATING ONGOLE COWS<br />
K.VENKATA RAMANA, K.SADASIVA RAO, K.SUPRIYA and N.RAJANNA<br />
Department <strong>of</strong> Veterinary Gynaecology and Obstetrics, College <strong>of</strong> Veterinary Science,<br />
Sri Venkateswara Veterinary University, Rajendranagar – 500 030<br />
Date <strong>of</strong> Receipt : 26.12.2012 Date <strong>of</strong> Acceptance : 04.02.2013<br />
ABSTRACT<br />
Estrus synchronization response and fertility following Ovsynch and double prostaglandin injection protocols<br />
in post partum Ongole cows was studied. A total <strong>of</strong> 50 Ongole parous cows above 60 days postpartum were divided<br />
in to two treatment groups consisting <strong>of</strong> 22 cows in each and 6 cows in control group. <strong>The</strong> follicular dynamics were<br />
monitored every day by ultrasonography till the ovulation. <strong>The</strong> emergence <strong>of</strong> follicular wave was observed on 1.33,<br />
3-4 days in Ovsynch and double PG groups, respectively. <strong>The</strong> size <strong>of</strong> dominant follicle found to be 12.48 ± 0.57 mm<br />
in Ovsynch and control group whereas 10.00 ± 0.78 mm in double PG group. In Ovsynch group estrus response was<br />
noticed in 100 per cent cows and estrus was recorded after 50 to 80 hours <strong>of</strong> PGF 2<br />
á injection with the duration <strong>of</strong><br />
16.28 ± 2.36 hrs While in double PG the duration <strong>of</strong> estrus and mean estrous cycle length recorded as 14.20 ± 2.56<br />
hrs and 21.50 ± 0.21 days, respectively. <strong>The</strong> ovulations were noticed after 1.50 ± 0.22 days <strong>of</strong> 2 nd GnRH injection<br />
with conception rate <strong>of</strong> 54.54 per cent in Ovsynch. <strong>The</strong> mean service period found to be 81.18 ± 1.62 days in<br />
lactating multiparous Ongole cows. It may be concluded that double injection <strong>of</strong> prostaglandin has better conception<br />
rate than Ovsynch protocol and both the treatments reduced the service period around 80 days when compare with<br />
control group, there by reduced the calving interval in lactating Ongole cows.<br />
Ongole breed <strong>of</strong> cattle is dual purpose (milk<br />
and draught) cattle, due to their adaptability traits,<br />
superior production capacity and high disease<br />
resistance under harsh tropical conditions. However,<br />
certain reproductive impediments like long service<br />
period which results in to long calving intervals,<br />
nocturnal incidence <strong>of</strong> estrus with shorter duration<br />
are limiting the economic use <strong>of</strong> this cattle breed.<br />
<strong>The</strong> postpartum ovarian inactivity could be due to<br />
suckling induced inhibition <strong>of</strong> the LH surge (Gumen<br />
et al, 2003 and Naidu et al, 2007).Hence, an attempt<br />
was made to study the follicular dynamics during the<br />
postpartum and to enhance the fertility with PGF 2 α<br />
and GnRH hormonal protocols in lactating Ongole<br />
cows.<br />
MATERIALS AND METHODS<br />
A total <strong>of</strong> fifty postpartum lactating Ongole<br />
cows in their 2 nd to 5 th lactation maintained under<br />
standard feeding and management stationed at Cattle<br />
Project, Live Stock <strong>Research</strong> Station, Lam Farm,<br />
Guntur were included in this study. <strong>The</strong> cows were<br />
randomly divided in to two groups consisting <strong>of</strong> 22<br />
cows in each treatment group and six cows in control<br />
group. Estrus synchronization was performed by<br />
Ovsynch protocol (Treatment-I) i.e., day 0 Receptal<br />
10 g i.m, day 7 Lutalyse 25 mg i.m and again on<br />
day 9 Receptal 10 g i.m. Where as in double injection<br />
<strong>of</strong> prostaglandin group (Treatment-II) the first<br />
injection (Lutalyse 25 mg i.m) was given on day ‘0’<br />
and 2 nd injection was given on day 12 th . <strong>The</strong><br />
inseminations were carried out on observed estrus<br />
in both the groups. <strong>The</strong> non returned cows were<br />
examined for pregnancy per rectally between day 60<br />
to 70 post insemination.<br />
Ovarian structures in six cows in each<br />
treatment groups and control group were monitored<br />
daily by using a real time B-mode ultrasound scanner<br />
with a trans rectal linear-array transducer from day<br />
‘0’ <strong>of</strong> GnRH injection to till the end <strong>of</strong> induced estrus<br />
with ovulation to assess the fate <strong>of</strong> first wave<br />
dominant follicle and the emergence <strong>of</strong> subsequent<br />
wave. <strong>The</strong> scanning was also done in control group<br />
and ovarian changes and estrus response was<br />
recorded and compared with treatment groups during<br />
the treatment period. <strong>The</strong> data was analyzed by<br />
Minitab ®(16) (2012) s<strong>of</strong>tware.<br />
email: kvr_vetgyn@yahoo.in<br />
44
RAMANA et al<br />
RESULTS AND DISCUSSION<br />
In Ovsynch protocol the first injection <strong>of</strong><br />
GnRH is designed to induce ovulation and formation<br />
<strong>of</strong> a new or accessory corpus luteum and a new<br />
follicular wave. In the present investigation<br />
ultrasound scanning <strong>of</strong> ovaries was done every day<br />
after injection <strong>of</strong> GnRH induced ovulation and till the<br />
formation <strong>of</strong> corpus luteum. However, follicular wave<br />
emerge was noticed in all the six cows on day<br />
1.33+0.21. <strong>The</strong> dominant follicle resulted from this<br />
wave emergence grew up to 12.48+0.57 mm by day<br />
7.6 with a growth rate <strong>of</strong> 1.66+0.26 mm per day. This<br />
is in agreement with the reports <strong>of</strong> Mishra et al.(2002)<br />
and Souza et al. (2006) in Sahiwal cows with GnRH.<br />
However, Krishna Mohan et al. (2010) reported much<br />
lesser size (9.17 + 0.27 mm) dominant follicle in<br />
Sahiwal cows.<br />
All the cows with a corpus luteum which were<br />
administered with luteolytic dose (25 mg <strong>of</strong> Dinoprost<br />
Tromethamine) on day 7 were in estrus within<br />
2.33+0.21 days. <strong>The</strong> dominant follicle <strong>of</strong> all the six<br />
cows was ovulated with the second dose <strong>of</strong> GnRH<br />
on day 9 <strong>of</strong> the luteal phase. <strong>The</strong> ovulation recorded<br />
was 100 per cent with GnRH treatment which is in<br />
agreement with Gumen et al. (2003). This could be<br />
due to larger size <strong>of</strong> the dominant follicle measuring<br />
more than 9 mm will potentially ovulate in the GnRH<br />
treatment animals (Adams 1992). Ultrasound<br />
monitoring <strong>of</strong> the ovulated cows had shown a corpus<br />
luteum <strong>of</strong> 16.50+0.30 mm on day 10 following the<br />
induced estrus. Out <strong>of</strong> 22 postpartum lactating cows,<br />
8 cows showed estrus after 1 st GnRH injection and<br />
the cows exhibited normal estrus except two which<br />
had weak estrus following PGF 2 α administration on<br />
day 7 <strong>of</strong> the treatment. Second dose <strong>of</strong> GnRH was<br />
administered on day 9 and all the cows inseminated<br />
at the observed estrus. <strong>The</strong> estrus response recorded<br />
in this study was in agreement with Mialot et al.<br />
(1998) and Vasconcelos et al. (1999). In the present<br />
study the conception rate found to be 54.54+0.36<br />
per cent.<br />
<strong>The</strong> ultrasound scanning <strong>of</strong> PGF 2 α treated<br />
cows revealed a dominant follicle <strong>of</strong> 10.00 + 0.78<br />
mm within 3-4 days after the PGF 2 α administration,<br />
which is in agreement with Alan et al. (2003) and<br />
Naidu et al. (2010) in Nelore cows. Out <strong>of</strong> 22 cows<br />
treated with double injections <strong>of</strong> prostaglandins 18<br />
exhibited estrus within 3.66+0.21 days. <strong>The</strong> time <strong>of</strong><br />
onset <strong>of</strong> estrus following PGF 2 α injection recorded in<br />
this study is corroborating to the findings <strong>of</strong> Alves et<br />
al. (2002) in Gir cows.<br />
In the present study the conception rate<br />
found to be 67.00 +0.26 per cent. Naidu et al. (2006)<br />
has reported higher conception rate (90.0%) than the<br />
present findings in Ongole cows, which might be due<br />
to better synchrony <strong>of</strong> ovulation and fertilization as<br />
the existing follicles were influenced the next wave<br />
<strong>of</strong> follicles during induction. All the treated postpartum<br />
lactating ongole cows in Ovsynch and double PG<br />
groups exhibited 100 and 82.5 per cent <strong>of</strong> estrus,<br />
respectively. <strong>The</strong> reason for varied estrus response<br />
rate between different treatment groups might be due<br />
to the difference in the treatment protocols and due<br />
to, presence <strong>of</strong> too small dominant follicles (< 9 mm)<br />
at the time <strong>of</strong> second PGF 2<br />
injection (Rivera et al.<br />
1998).<br />
In the control group the mean time required<br />
for conception after calving was recorded as<br />
163.60+10.72 (95-397) days, which is less than the<br />
previous studies (Venkateswarulu, 1971; Rao et al,<br />
1985; Acharya & Bhat 1990; Ravi Kiran et al. 1995;<br />
Rao et al. 2001and Naidu et.al,.,.2010). <strong>The</strong> service<br />
period <strong>of</strong> cows in the treatment groups resulted a<br />
reduction <strong>of</strong> approximately 85 days compared to<br />
control group cows.<br />
Hence, it is concluded that the estrus<br />
synchronization with GnRH and PGF2 gives better<br />
results and would reduce the calving to service period<br />
drastically and beneficial to the farmers in dairy<br />
industry.<br />
ACKNOWLEDGEMENT<br />
<strong>The</strong> authors express their sincere gratitude to the<br />
SVVU, Tirupati for providing facilities to carry out<br />
the research work at Cattle Project, Live Stock<br />
<strong>Research</strong> Station, Lam Farm, Guntur.A.P.<br />
45
ESTRUS SYNCHRONIZATION RESPONSE AND FERTILITY RATE<br />
REFERENCES<br />
Acharya and Bhat 1990. Productive and reproductive<br />
traits in Ongole cows. ICAR Bulletin.<br />
Adams, G. P. Matteri R. Kastetic, J. P. Ko, J. C .H<br />
and Ginther, O J 1992. Association between<br />
surges <strong>of</strong> follicle stimulating hormone and the<br />
emergence <strong>of</strong> follicle wave in heifers. <strong>Journal</strong><br />
<strong>of</strong> Reproduction and Fertility 94: 177-178.<br />
Alan Bennett Maia Alexandre Alves Ciro Torres<br />
Reinaldo Jose Mendes Streets Vicente Ribeiro<br />
Rocha Jr Giovanni Ribeiro e Carvalho<br />
Jefferson Ferreira da Fonseca Alberto Neto<br />
Marcatti Anderson George <strong>of</strong> Assisi-2003.<br />
Characteristics <strong>of</strong> follicular dynamics and luteal<br />
regression in cows <strong>of</strong> Gir and Nelore cows after<br />
treatment with cloprostenol. <strong>Journal</strong> <strong>of</strong> Animal<br />
Science. 32(1) 1806-9290.<br />
Alves N G costa E P da Guimaraes J D Silva M R<br />
Zamperlini B Costa F M J Santos A D F<br />
Miranda Neto T 2002. Ovarian activity in<br />
Holstein and crossbreed Holstein x Zebu cows<br />
during two normal estrous cycles.<br />
(Portuguese). Revista brasieira de Zootecnia.<br />
31(2), 627-634. 35 ref.<br />
Gumen A Guenther J N Wiltbank M C 2003. Follicular<br />
size and response to Ovsynch versus<br />
detection <strong>of</strong> estrous in anovular and ovular<br />
lactating dairy cows. <strong>Journal</strong> <strong>of</strong> dairy Science<br />
86 (10) 3184-3194.<br />
Krishna Mohan U K Mishra O P Mishra Singh C<br />
and Prakash B S 2010. Follicular development<br />
pattern in post partum anestrous sahiwal cows<br />
during Ovsynch protocol. IndianVet. <strong>Journal</strong>.,<br />
87: 448-450.<br />
Mialot J P Laumonnier G Ponsart C Fauxpoint<br />
H Barassin F Ponter A A and Deletang F<br />
1999. Postpartum subestrus in dairy cows;<br />
comparison <strong>of</strong> treatment with prostaglandin F2<br />
or GnRH + prostaglandin F2 + GnRH.<br />
<strong>The</strong>riogenology 52 : 901-911.<br />
Mishra O P Khan J R and Awasthi M K 2002<br />
Study <strong>of</strong> follicular dynamics in Sahiwal cows.<br />
Indian <strong>Journal</strong> Animal Reproduction 23: 193.<br />
Naidu G V Babu Rao K 2006 Estrus pattern and<br />
conception rate in postpartum lactating Ongole<br />
cows. Indian <strong>Journal</strong> <strong>of</strong> Animal Reproduction<br />
27 (1) 14 – 17.<br />
Naidu G V Rao A S Rao K B 2007 Progesterone<br />
pr<strong>of</strong>ile in postpartum lactating Ongole (Zebu)<br />
cows. Indian <strong>Journal</strong> <strong>of</strong> Animal Reproduction.<br />
28 (1) : 12-14,<br />
Naidu G V Seshagiri A Babu Rao K 2010.<br />
Progesterone pr<strong>of</strong>ile in postpartum lactating<br />
Ongole (Zebu) cows. Indian <strong>Journal</strong> <strong>of</strong> Animal<br />
Reproduction. 31 (1) :79-80.<br />
Rao K B and Venkata Naidu G 2001 Annual Progress<br />
report <strong>of</strong> the technical programme on Genetic<br />
improvement <strong>of</strong> Ongole breed through<br />
Associate Herd Testing Scheme, <strong>ANGRAU</strong><br />
Rao A V and Venkataramaiah P 1985 Studies on the<br />
effectiveness <strong>of</strong> a smaller dose <strong>of</strong><br />
prostaglandin F2 in increasing the reproductive<br />
efficiency <strong>of</strong> Ongole cattle. Indian Veterinary<br />
<strong>Journal</strong> 67: 528-530<br />
Ravikiran G Rao G N and Jayarama Krishna V<br />
Satyanarayana A 1995. Performance <strong>of</strong> ongole<br />
and crossbred cows under village conditions.<br />
Indian <strong>Journal</strong> <strong>of</strong> Animal Science 65: 782.<br />
Rivera G M Goni C G Chaves M A Ferrero S B and<br />
Bo G A 1998. Ovarian follicular wave<br />
synchronization and induction <strong>of</strong> ovulation in<br />
postpartum beef cows. <strong>The</strong>riogenology 49 :<br />
1365 -1375.<br />
Souza A F Pinheiro V G Ereno R L and Barros M<br />
2006. Synchronization <strong>of</strong> ovulation in<br />
anestrous Nelore cows treated with hormonal<br />
protocol without progesterone or progestagens.<br />
Reproduction fertility and development. 18(2)<br />
115-116.<br />
Vasconcelos J L M Silcox R W Rosa G J M Pursley<br />
J R and Wiltbank M C 1999. Synchronization<br />
rate, size <strong>of</strong> the ovulatory follicle and<br />
pregnancy rate after synchronization <strong>of</strong><br />
ovulation beginning on different days <strong>of</strong> the<br />
estrous cycle in lactating dairy cows.<br />
<strong>The</strong>riogenology 52 : 1067 – 1078.<br />
Venkateswarlu M 1971. Studies on genetic<br />
correlation and inheritance <strong>of</strong> economic<br />
characteristics <strong>of</strong> Ongole cattle, M.Sc <strong>The</strong>sis<br />
submitted to Agra University.<br />
46
J.Res. <strong>ANGRAU</strong> 41(1) 42-46, 2013<br />
A STUDY ON MIGRATION PATTERN OF SHEEP FLOCKS IN TELANGANA<br />
REGION OF ANDHRA PRADESH<br />
N. RAJANNA, M. MAHENDAR and K. VENKATA RAMANA<br />
Department <strong>of</strong> Livestock Production and Management,<br />
Sri Venkateswara Veterinary University, College <strong>of</strong> Veterinary Science,<br />
Rajendranagar, Hyderabad-30.<br />
Date <strong>of</strong> Receipt : 23.11.2012 Date <strong>of</strong> Acceptance : 04.02.2013<br />
ABSTRACT<br />
A survey was carried out to collect information about migration pattern <strong>of</strong> sheep flocks in Telangana region<br />
during 2010-2011. A total <strong>of</strong> 576 farmers were selected by multistage stratified random sampling technique and the<br />
information was collected from them through personal interview. It was revealed the existence <strong>of</strong> twenty traditional<br />
migratory routes in the study area. <strong>The</strong> mean duration and distance <strong>of</strong> migration <strong>of</strong> flocks were 124.3 ± 10.5 days<br />
and 112.2 ± 19.5 km, respectively. <strong>The</strong> migration mostly started in the mid-January and extended up to July. <strong>The</strong><br />
perception <strong>of</strong> farmers about basis for migration was lack <strong>of</strong> grazing resources (90.80%), periodical drought (80.90%),<br />
traditional occupation (77.78%), fields filled with crops (74.65%), disease problem (64.06%), lack <strong>of</strong> feeding resources<br />
(61.81%), lack <strong>of</strong> water resources (30.73%) and heavy rains (23.44%) and ranked them from I to VIII. <strong>The</strong> problems<br />
faced during migration included attack <strong>of</strong> diseases (87.85%), lack <strong>of</strong> shelter for animals (81.60%), theft (74.13%),<br />
restriction <strong>of</strong> entry into other villages (71.18%), lack <strong>of</strong> veterinary facilities (67.88%) predators (23.26%) and abortions<br />
due to stress (13.72%). Thus, knowledge about the migration pattern will help policy makers and planners in making<br />
suitable corrective and remedial measures<br />
Sheep is one <strong>of</strong> the important livestock<br />
species contributing to the livelihood <strong>of</strong> resource poor<br />
farmers in rural areas, particularly that are prone to<br />
drought. It contributes to the farm households not<br />
only by acting as source <strong>of</strong> livelihood and nutritional<br />
security, but also as a moving asset, which can be<br />
liquidated at a times <strong>of</strong> crises within short period.<br />
Andhra Pradesh state is known for its diversified<br />
livestock resources in nine well defined agro climatic<br />
zones. Andhra Pradesh state is divided into three<br />
geopolitical regions viz., Coastal Andhra, Telangana<br />
and Rayalaseema. According to 2008 census sheep<br />
population in Andhra Pradesh are 255.39 lakhs and<br />
ranks first in the country. In Telangana region <strong>of</strong><br />
Andhra Pradesh, sheep rearing largely depended on<br />
grazing under extensive system <strong>of</strong> production.<br />
Whenever the grazing sources degraded, farmers<br />
were compelled to resort migration. During migration<br />
shepherds night shelter their flocks in farmer’s field<br />
and get some payment either in cash or kind in<br />
exchange for leftover <strong>of</strong> sheep manure. However,<br />
information on migration pattern <strong>of</strong> sheep flocks under<br />
field condition is scanty. <strong>The</strong>refore, knowledge about<br />
the migration pattern will help policy makers and<br />
planners in making suitable corrective and remedial<br />
measures. Hence it was felt very essential to study<br />
the migration pattern <strong>of</strong> sheep flocks in Telangana<br />
region <strong>of</strong> Andhra Pradesh .<br />
MATERIALS AND METHODS<br />
<strong>The</strong> study was undertaken in Telangana<br />
region <strong>of</strong> Andhra Pradesh during 2010-2011.<br />
Telangana region was divided into three zones viz.,<br />
Northern Telangana Zone (NTZ), Central Telangana<br />
Zone (CTZ) and Southern Telangana Zone (CTZ) on<br />
the basis <strong>of</strong> the agro-climatic conditions. Multistage<br />
stratified random sampling technique was applied to<br />
select the villages and sheep farmers. In the first<br />
stage two districts from each zone were selected<br />
and in the second stage four mandals from each<br />
district and in the third stage four villages from each<br />
selected mandal were selected based on sheep<br />
population. From each village 6 respondents<br />
possessing sheep were selected randomly for the<br />
present study. Hence, 576 sheep farmers constituted<br />
the study sample. Data on migration practices,<br />
reasons and problems <strong>of</strong> migration were studied from<br />
the respondents by face-to-face interview. Based on<br />
farmers responses frequency and percentages were<br />
calculated and accordingly rankings were given.<br />
email: neeradiraj@gmail.com<br />
47
A STUDY ON MIGRATION PATTERN OF SHEEP FLOCKS IN TELANGANA<br />
Table 1. Months, duration and distance <strong>of</strong> migration <strong>of</strong> sheep flocks in Telangana region<br />
S.No. Zone Tract No. Months <strong>of</strong> migration Duration (days) Distance (Km)<br />
1 NTZ I 15 th Feb- 15 th May 60 60<br />
2 NTZ II 5 th Feb-5 th June 121 25<br />
3 NTZ III 20 th Feb- 20 th June 121 120<br />
4 NTZ IV 15 th Feb- 15 th June 121 55<br />
5 NTZ V 10 th Feb-10 th June 121 80<br />
Mean± SE 108.8± 12.2 68.0 ±15.7<br />
6 STZ VI 5 th April- 5 th July 91 70<br />
7 STZ VII 15 th April- 15 th July 94 200<br />
8 STZ VIII 25 th Dec- 25 th July 213 70<br />
9 STZ IX 5 th April- 5 th July 91 30<br />
10 STZ X<br />
5 th Jan-5 th April &<br />
10 th Aug-10 th Sep<br />
122 20<br />
11 STZ XI 15 th Jan- 15 th Aug 212 232<br />
12 STZ XII 10 th April- 10 th July 92 232<br />
13 STZ XIII 15 th Feb- 15 th April 60 150<br />
14 STZ XIV 5 th Jan-5 th Aug 212 200<br />
15 STZ XV 20 th Feb-20 th June 121 25<br />
16 STZ XVI 10 th Jan-10 th June 151 250<br />
17 STZ XVII 15 th Feb- 15 th June 121 260<br />
Mean± SE 131.6 ±15.4 144.9± 27.51<br />
18 CTZ XVIII 5 th Jan- 5 th April 90 25<br />
19 CTZ XIX 20 th Jan- 20 th June 151 50<br />
20 CTZ XX 20 th Feb- 20 th June 121 90<br />
Mean± SE 120.66± 17.66 55.0± 18.92<br />
Overall Mean± SE 124.3 ± 10.05 112.2 ± 19.5<br />
RESULTS AND DISCUSSION<br />
Sheep rearing largely depended on grazing<br />
under extensive system <strong>of</strong> production. Whenever,<br />
the grazing sources degraded farmers were<br />
compelled to resort migration. <strong>The</strong> migration in local<br />
language (Telugu) known as ‘Valasa’ or ‘Mannem’ is<br />
being performed by farmers who are having large<br />
flock size. <strong>The</strong> migration started between 5 th Feb<br />
and 20th Feb in I to V tracks and between 5 th Jan –<br />
20 th Feb in XVIII to XX tracks, whereas, year round<br />
migration was observed in ST zone due to prevailed<br />
cropping pattern (Table 1). In majority <strong>of</strong> tracks sheep<br />
farmers returned from migration to their native track<br />
between April and June. Thus initiation linked to the<br />
onset <strong>of</strong> dry season and withering <strong>of</strong> surface<br />
vegetation while the termination was based on the<br />
onset <strong>of</strong> monsoon. <strong>The</strong> migrating sheep flock<br />
covered a minimum distance <strong>of</strong> 25 km and maximum<br />
<strong>of</strong> 260 km with a mean <strong>of</strong> 112.2 ± 19.5 km. <strong>The</strong><br />
duration <strong>of</strong> migration ranged from 60 to 213 days<br />
with a mean <strong>of</strong> 124.3 ± 10.5 days. Kumaravelu (2007)<br />
identified 8 major, 10 minor and eleven migratory<br />
tracks, respectively in Andhra Pradesh and Tamil<br />
Nadu. Regarding the onset and return <strong>of</strong> migration<br />
48
RAJANNA et. al.<br />
Table 2. Reasons for migration <strong>of</strong> sheep flock’s as perceived by sheep farmers in Telangana region<br />
49
A STUDY ON MIGRATION PATTERN OF SHEEP FLOCKS IN TELANGANA<br />
Table 3. Problems faced during sheep flock’s migration as perceived by sheep farmers in Telangana region<br />
50
RAJANNA et al<br />
the present findings corroborated with Pattanayak et<br />
al. (2003), Sushilkumar et al. (2003), Arora et al.<br />
(2007) and Gopaldass (2007).<br />
As far as duration and distance covered<br />
Rajapandi, (2005) reported Coimbatore sheep<br />
migrated a distance <strong>of</strong> 100 to 200 km and Kumaravelu<br />
(2007) had described the duration (days) ranged<br />
from 91 to 315 days in southern zone <strong>of</strong> Tamil Nadu<br />
state.<br />
Reason for migration<br />
Majority (90.80%) <strong>of</strong> sheep farmers<br />
perceived lack <strong>of</strong> grazing resources followed by<br />
periodical drought (80.90%), traditional occupation<br />
(77.78), fields filled with crops (74.65%), disease<br />
problem (64.06%), lack <strong>of</strong> feeding resources<br />
(61.81%), lack <strong>of</strong> water resources (30.73%) and<br />
heavy rains (23.44%) as a basis <strong>of</strong> migration and<br />
ranked them from I to VIII, respectively (Table 2) .<br />
Farmers should be encouraged to take up Silvi<br />
Pasture system, controlled grazing and culling <strong>of</strong><br />
unproductive animal as a remedial measure for the<br />
above problems. <strong>The</strong>se results are in conformity with<br />
findings <strong>of</strong> Dorji et. al. (2003) and Saravanakumar et<br />
al. (2003) who reported shortage <strong>of</strong> water and grazing<br />
land and feeding resources, tradition, successive<br />
drought, and disease outbreaks were the reasons <strong>of</strong><br />
migration.<br />
Problems faced during migration<br />
Problems faced by sheep farmers were<br />
ranked in the order <strong>of</strong> attack <strong>of</strong> diseases (87.85%),<br />
lack <strong>of</strong> shelter for animals (81.60%), theft (74.13%),<br />
restriction <strong>of</strong> entry into other villages (71.18%), lack<br />
<strong>of</strong> veterinary facilities (67.88%) predators (23.26%)<br />
and abortions due to stress (13.72%) from I to VII,<br />
respectively. During migration, the shepherds along<br />
with sheep flocks spent most <strong>of</strong> the time in forests,<br />
river belts and remote villages where the veterinary<br />
facilities were not available in time and up to the<br />
mark, leading to disease outbreaks. <strong>The</strong> sheep<br />
farmers allowed animals for penning during night<br />
times. Hence the sheep could not receive any<br />
protection from adverse weather leading to disease<br />
susceptibility. Lack <strong>of</strong> care during lambing and for<br />
new born lambs during migration has lead to lamb<br />
mortality because the lambs also move continuously<br />
without any protection from heat resulting in heat<br />
stress. <strong>The</strong>se findings were corroborated with the<br />
Kuldeepporwal et al. (2006) and Suresh et al. (2008).<br />
REFERENCES<br />
Arora, A. L., Prince, L. L. L and Mishra, A. K. 2007.<br />
Performance evaluation <strong>of</strong> Jaisalmeri sheep<br />
in farmer’s flocks. Indian <strong>Journal</strong> <strong>of</strong> Animal<br />
Sciences: 77 (8)759-762<br />
Dorji, T., Tshering, G., Wangchuk, T., Rege, J. E. O<br />
and Hannote, O. 2003. Indigenous sheep<br />
genetic resources and management in Bhutan.<br />
Animal Genetics Resource information Bulletin<br />
33: 81-91.<br />
Gopaldass.T 2007. Production performance and<br />
management practices <strong>of</strong> Pugal sheep in the<br />
home tract. Indian <strong>Journal</strong> Animal<br />
Sciences.77(8)763-766.<br />
Kuldeepporwal, Karim, S. A., Sisodia, S. L and Singh,<br />
V. K. 2006. Socio-economic survey <strong>of</strong> sheep<br />
farmers in western Rajasthan. Indian <strong>Journal</strong><br />
<strong>of</strong> Small Ruminants 12 (1): 74-81.<br />
Kumaravelu, N. 2007. Analysis <strong>of</strong> sheep production<br />
system in Southern and Northern Zones <strong>of</strong><br />
Tamilnadu. Ph.D <strong>The</strong>sis submitted to Tamil<br />
Nadu Veterinary and Animal Sciences<br />
University, Chennai.<br />
Pattanayak, G. R., Patro, B. N., Das, S. K and Nayak,<br />
S. 2003. Survey and performance evaluation<br />
<strong>of</strong> Ganjam Sheep. Indian <strong>Journal</strong> <strong>of</strong> Small<br />
Ruminants 9(1): 47-49.<br />
Rajapandi, S. 2005. Distribution and management<br />
practices <strong>of</strong> Coimbatore sheep. <strong>The</strong>sis<br />
submitted to Veterinary College and <strong>Research</strong><br />
Institute, Namakkal, Tamil Nadu.<br />
Suresh, A., Gupta, D. C and Mann, J. S. 2008<br />
Constraints in adoption <strong>of</strong> improved<br />
management practices <strong>of</strong> sheep farming in<br />
semi-arid region <strong>of</strong> Rajasthan. Indian <strong>Journal</strong><br />
<strong>of</strong> Small Ruminants 14 (1): 93-98.<br />
Sushilkumar , Sharma R C, Mishra, A. K and Arora<br />
A L 2003. Production performance <strong>of</strong> sheep<br />
and certain management practices in farmer’s<br />
flocks <strong>of</strong> south East Rajasthan. Indian journal<br />
<strong>of</strong> small ruminants, 9(2): 103-105.<br />
51
J.Res. <strong>ANGRAU</strong> 41(1) 47-50, 2013<br />
UTILIZATION OF POULTRY WASTE AN UN-CONVENTIONAL PROTEIN SOURCE<br />
IN SMALL RUMINANT RATIONS<br />
J. NARASIMHA, V.CHINNI PREETHAM AND S.T.VIROJI RAO<br />
All India Co-ordinated <strong>Research</strong> Project on poultry breeding, College <strong>of</strong> Veterinary Science,<br />
Sri Venkateswara Veterinary University, Hyderabad-500030<br />
Date <strong>of</strong> Receipt : 16.08.2012 Date <strong>of</strong> Acceptance :13.12.2012<br />
ABSTRACT<br />
A complete feed containing poultry litter (35%) and other feed ingredients were formulated and processed<br />
in to mash. <strong>The</strong> feed was tested on six each <strong>of</strong> Nellore rams and indigenous bucks in a digestion- cum-metabolism<br />
trial using a completely randomized design to asses the voluntary feed intake and nutrient utilization. <strong>The</strong> voluntary<br />
feed intake <strong>of</strong> DM and the intake per kg DMI was significantly (P
NARASIMHA et al<br />
Table 1. Proximate composition <strong>of</strong> experimental ration and poultry litter (%DM)<br />
Proximate principle Experimental ration (%) Poultry litter (%)<br />
Dry matter 91.25 93.45<br />
Organic matter 80.00 63.21<br />
Crude protein 12.18 15.70<br />
Ether extract 1.87 0.87<br />
Crude fibre 30.17 15.08<br />
Total ash 20.00 36.78<br />
Acid insoluble ash 6.19 12.39<br />
Nitrogen free extract 35.78 31.57<br />
Calcium 0.89 4.57<br />
Phosphorous 0.76 3.7<br />
on various factors like dry matter, season, climatic<br />
condition and type <strong>of</strong> feed (Taneja, 1969; Mehrothra<br />
and Mullick, 1960).<br />
<strong>The</strong>re was no significant difference in<br />
digestibility co-efficient <strong>of</strong> Dry matter, Organic matter,<br />
(Table 2). Similar findings were reported by<br />
(Mallikarjuna, 1989), in sheep and goats fed rations<br />
containing cotton straw, maize cobs for dry matter.<br />
A non-significant difference in digestibilities for Crude<br />
Protein was observed in sheep and goats. Similar<br />
results were reported by (Murthy et al., 1996) when<br />
fed poultry litter and poultry droppings in the pelleted<br />
ration. <strong>The</strong> lower digestibility <strong>of</strong> Crude Protein in the<br />
present experiment may be due to incorporation <strong>of</strong><br />
cotton seed hulls at 40 percent level. <strong>The</strong> cotton seed<br />
hulls contained about 31 percent lint, mostly made<br />
<strong>of</strong> cellulose and about 14.3 percent lignin. It is quite<br />
likely that proteins in the cotton seed hulls are located<br />
in the structural component <strong>of</strong> cell. Structural protein<br />
(Maynard et al., 1981) which may not be available<br />
for the microbes to attack due to high lignin content.<br />
Further the fineness <strong>of</strong> cotton seed hulls during<br />
processing might have also contributed to lower<br />
digestibility due to faster rate <strong>of</strong> passage through the<br />
digestive tract (Keys and Smith., 1984). <strong>The</strong>re was<br />
no significant difference in ether extract and crude<br />
fibre digestibility in the two species <strong>of</strong> sheep and<br />
goats (Table 2). <strong>The</strong> findings are in agreement with<br />
those <strong>of</strong> (Sreedhar et al., 1993).<br />
<strong>The</strong> non-significant difference in Nitrogen free<br />
extract digestibility between sheep and goats<br />
observed in the present study concur with the results<br />
<strong>of</strong> (Murthy et al., 1996) who also reported nonsignificant<br />
difference in Nitrogen free extract<br />
digestibility between sheep and goats fed poultry<br />
litter/poultry droppings based pelleted ration. All<br />
experimental animals were in positive N, Ca, and P<br />
balances indicating that the experimental feed<br />
supplied these nutrients in required quantities to both<br />
species. Sheep retained significantly (P>0.01) higher<br />
N, Ca and P than goats (Table 2). This could be<br />
attributed to higher Dry matter intake in sheep. <strong>The</strong><br />
ration met Digestible crude protein (DCP) and Total<br />
digestible nutrients (TDN) requirements as<br />
recommended by ICAR (1985). <strong>The</strong> results <strong>of</strong> this<br />
study indicate that complete feed containing poultry<br />
litter up to 35 percent level could be utilized for feeding<br />
<strong>of</strong> small ruminants. <strong>The</strong> complete ration formulated<br />
in this study met the nutritive requirement <strong>of</strong> sheep<br />
and goats. Further it was observed that poultry litter<br />
could be used up to 35 percent level in complete<br />
feeds <strong>of</strong> small ruminant rations as an un- conventional<br />
protein source without any adverse effect.<br />
53
UTILIZATION OF POULTRY WASTE AN UN-CONVENTIONAL PROTEIN SOURCE<br />
Table 2. Digestibility <strong>of</strong> nutrients in sheep and goats<br />
Nutrient Sheep Goat Level <strong>of</strong> significance<br />
DMI % BW 5.62±0.23 2.76±0.16 (**)<br />
Water intake L/100kg 13.71±0.55 4.61±0.16 (**)<br />
Per kg DMI 2.56±0.24 1.66±0.13 (**)<br />
Digestibility co-efficients (%)<br />
DM 51.57±0.64 51.53±1.16 NS<br />
OM 56.29±0.15 56.85±0.64 NS<br />
CP 51.39±0.91 51.59±0.89 NS<br />
EE 67.49±1.79 65.41±1.31 NS<br />
CF 51.15±0.35 51.96±0.58 NS<br />
NFE 61.99±0.29 62.54±0.72 NS<br />
N (g/kg) 4.98±0.34 2.56±0.13 (**)<br />
Ca (g/kg) 2.76±0.23 1.59±0.08 (**)<br />
P (g/kg) 2.08±0.14 0.97±0.04 (**)<br />
DCP intake<br />
Per day (g) 67.64±4.64 32.18±2.63 (**)<br />
Per 100kg body weight (kg) 0.35±0.01 0.17±0.13 (**)<br />
TDN intake<br />
Per day (g) 499.26±37.17 240.74±19.20 (**)<br />
Per 100kg body weight (kg) 2.63±0.11 1.03±0.09 (**)<br />
NS- Non significant ; ** Significant (P>0.01)<br />
REFERENCES<br />
AOAC. 2005. Official methods <strong>of</strong> analysis.<br />
Association <strong>of</strong> <strong>of</strong>ficial analytical chemist. 18 th<br />
Ed. Washington DC. USA.<br />
ICAR. 1985. Nutrient requirement <strong>of</strong> livestock and<br />
poultry publications and information division<br />
Indian Council <strong>of</strong> Agricultural <strong>Research</strong>, New<br />
Delhi<br />
Keys,J.E and Smith,L.W. 1984. Effect <strong>of</strong> Ensiling<br />
Corn Stover with Alfalfa on Growth, Intake,<br />
and Digestion by Yearling Dairy Heifers as<br />
Compared with Whole Corn Plant<br />
Silage.<strong>Journal</strong> <strong>of</strong> dairy science 67(9) :1971-<br />
1975<br />
Livestock census, 2003. Department <strong>of</strong> animal<br />
husbandry, dairying and fisheries. Ministry <strong>of</strong><br />
Agriculture, Government <strong>of</strong> India. http: //dahd/<br />
nic.in/census.htm.<br />
Mallikarjuna, G.1989. Studies on the pattern <strong>of</strong><br />
volatile fatty acids concentration in rumen <strong>of</strong><br />
Ssheep and goats and their relative levels with<br />
respect to time after feeding. MVSc <strong>The</strong>sis<br />
submitted to Acharya N.G.Ranga Agricultural<br />
University, Hyderabad.<br />
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NARASIMHA et al<br />
Maynard, L., Lossli,J.K., Horold, F.H and Warner,<br />
R.L.1981.Animal nutrition, 7 th edition, Tata<br />
McGrawHill Publishing company, New Delhi,<br />
India<br />
Mehrothra and Mullick, D.N.1960. Indian <strong>Journal</strong> <strong>of</strong><br />
Veterinary Science and Animal Husbandry<br />
30:30<br />
Murthy, K.S., M.R. Reddy and G.V.N. Reddy. 1996.<br />
Nutritive value <strong>of</strong> supplements containing<br />
poultry droppings/litter for sheep and goats.<br />
Small Ruminant <strong>Research</strong>. 21(2):71-75.<br />
Nageswera Rao,G. 1983. Statistics for Agricultural<br />
sciences. Oxford and IBH Publishing<br />
company, New Delhi<br />
mixture. Indian <strong>Journal</strong> <strong>of</strong> Animal Nutrition 10<br />
(2): 77-81<br />
Talapatra, S.K.,Ray,S.C and Sen, K.C. 1940. <strong>The</strong><br />
analysis <strong>of</strong> mineral constituents in biological<br />
materials. Indian <strong>Journal</strong> <strong>of</strong> Veterinary Science<br />
and A.H. 10:243<br />
Taneja, G.L. 1969. Variation in body temperature,<br />
respiration rate, water intake and body weight<br />
on Marwari sheep during the year. Indian<br />
Veterinary <strong>Journal</strong> 46:49<br />
Venugopal Rao D., Naidu M.M and Raghavan G.V.<br />
1997. Utilisation <strong>of</strong> complete feed containing<br />
poultry droppings in sheep and goats. Indian<br />
Veterinary <strong>Journal</strong> 74:858-861<br />
Sreedhar,C., Reddy, T.J and Raghavan G.V. 1993.<br />
Nutrient availability in goats fed cotton seed<br />
hulls and poultry waste based concentrate<br />
55
J.Res. <strong>ANGRAU</strong> 41(1) 51-55, 2013<br />
POSTPARTUM OVARIAN FOLLICULAR DYNAMICS AND ESTRUS ACTIVITY IN<br />
LACTATING ONGOLE COWS<br />
K. VENKATA RAMANA, K. SADASIVA RAO, K. SUPRIYA and N. RAJANNA<br />
Department <strong>of</strong> Animal Reproduction Gynaecology & Obstetrics, College <strong>of</strong> Veterinary Science,<br />
Sri Venkateswara Veterinary University, Rajendranagar – 500 030<br />
Date <strong>of</strong> Receipt : 06.12.2012 Date <strong>of</strong> Acceptance :28.01.2013<br />
ABSTRACT<br />
<strong>The</strong> objective <strong>of</strong> this study was to characterize early postpartum follicular dynamics in lactating Ongole<br />
cows in relation to their ovarian activity and subsequent reproductive performance using 70 multiparous lactating<br />
Ongole cows. <strong>The</strong> follicles measuring above 6 mm diameter and corpus luteum measuring 8-10 mm were detected<br />
by day 20-25 postpartum period by ultrasonography. <strong>The</strong> first and second wave <strong>of</strong> the dominant follicle emerged on<br />
1.80 + 0.8 and 12.46 + 0.20 days respectively. <strong>The</strong> mean growth rate and size was significant (P
RAMANA et al<br />
with small follicles measuring > 6 mm diameter. In<br />
the ultrasound monitoring follicles measuring above<br />
6 mm diameter and corpus luteum measuring 8-10<br />
mm were detected by day 20-25 postpartum period.<br />
<strong>The</strong> resumption <strong>of</strong> ovarian activity observed in this<br />
investigation is close to the findings <strong>of</strong> Roche et al.<br />
(1992) Henao et al. (2000). <strong>The</strong> follicular size > 6<br />
mm reported in this study is in concurrence with the<br />
observations <strong>of</strong> Krishna mohan et al (2010) and<br />
Satheesh kumar et al (2008). However,<br />
Rajamahendran et al. (1990) reported first postpartum<br />
ovulation as early as 10-15 days postpartum in Bos<br />
indicus cows.<br />
<strong>The</strong> follicular growth pattern monitored using<br />
an ultrasound scanner during the standing estrus in<br />
ten estrus cycles revealed the occurrence <strong>of</strong> seven<br />
two wave and the other three shown three wave<br />
cycles with wave emergence on day 1.80 + 0.80 and<br />
12.46+0.50 for two wave cycles, day 1.52 +0.03, 9.60<br />
+0.40 and 15.20 + 0.37 for three wave cycles,<br />
respectively. <strong>The</strong> two wave pattern (7/10) <strong>of</strong> follicular<br />
growth observed in the present investigation is in<br />
close agreement with the findings <strong>of</strong> Figueiredo et<br />
al. (1997), Mayra et al. 1999), Alan et al. (2003),<br />
Borges et al. (2004), Gaur and Purohit (2007) and<br />
Akter et al. (2010) who have reported predominantly<br />
two wave pattern in the estrus cycle in Nelore or Zebu<br />
cows. On the contrary much shorter intervals were<br />
reported by Borges et al.(2001) in zebu crosses,<br />
Rhodes et al.(1995) in cross breed heifers and Malhi<br />
et al. (2005) in Hereford cows.<br />
<strong>The</strong> mean diameter <strong>of</strong> the first and second<br />
dominant follicles (10.23 +0.40 and 12.30+ 0.36 mm)<br />
<strong>of</strong> the two wave recorded in this study were in close<br />
agreement with the findings <strong>of</strong> Figueiredo et al. (1997)<br />
Mayra et al. (1997) Akter et al. (2010) in Nelore cows.<br />
Contrary to these findings Gaur and Purohit (2007)<br />
observed the lesser average diameter <strong>of</strong> dominant<br />
follicle size <strong>of</strong> 8.02 + 0.38 and 9.10 + 0.24 mm in<br />
Nelore cows for the first and second waves,<br />
respectively. However Krishna Mohan et al. (2010)<br />
also recorded higher diameter <strong>of</strong> (14.6 + 0.12 mm)<br />
than the present study in normal cyclic Sahiwal cows.<br />
It was observed that the first wave dominant follicle<br />
<strong>of</strong> three wave cycles attained a maximum size <strong>of</strong><br />
10.70+0.20 mm with a mean growth rate <strong>of</strong> 0.87+0.03<br />
mm per day and in the second wave <strong>of</strong> the dominant<br />
follicle maximum size was 9.80+0.37 mm with growth<br />
rate <strong>of</strong> 0.77+0.06 per day. <strong>The</strong> third wave maximum<br />
diameter was 12.80+0.37 mm with a mean growth<br />
rate <strong>of</strong> 1.46+0.90 mm per day, which is significantly<br />
bigger and faster in growth rate than the first and<br />
second waves in the same estrous cycle. However,<br />
the growth rate <strong>of</strong> second and third waves were not<br />
significant. This could be due to larger duration <strong>of</strong><br />
dominant higher progesterone and low pulsatile<br />
frequency <strong>of</strong> LH secretion (Wolfenson et al. 2004)<br />
<strong>The</strong> growth rate <strong>of</strong> the dominant follicle recorded in<br />
the present study were in agreement with the findings<br />
<strong>of</strong> Figueiredo et al. (1997). <strong>The</strong> variation in the<br />
diameter and growth rate <strong>of</strong> the dominant follicle<br />
reported could be due to inherent characters <strong>of</strong> the<br />
breeds.<br />
Body score condition (Rhodes et al.1995),<br />
negative energy balance, Insulin like Growth factor,<br />
inhibin and gonadotrophin hormones systemic levels<br />
Rhodes et al. 1997) altered LH pulse frequency and<br />
follicular growth.<br />
In normal two wave cycles, the first wave dominant<br />
follicle did not differ significantly in their size with<br />
ovulatory dominant follicle but it was small in size.<br />
<strong>The</strong> smaller diameter <strong>of</strong> first wave dominant follicle<br />
might be due to the fact that the first wave emerged<br />
during the period <strong>of</strong> higher progesterone production<br />
by the corpus luteum. <strong>The</strong>se findings are in close<br />
agreement with the observations <strong>of</strong> Figueiredo et al.<br />
(1997), Mayra et al. (1999) and Viana et al. (2000) in<br />
Nelore cows.<br />
In the present study the progesterone<br />
concentration observed during the development <strong>of</strong><br />
first follicular wave was between 1.48 to 2.98 ng/ml<br />
which could be a contributing factor for the smaller<br />
size <strong>of</strong> the dominant follicle <strong>of</strong> this first wave (Vinoles<br />
et al. 1999).<br />
Perusal <strong>of</strong> data revealed that, the mean<br />
maximum diameter <strong>of</strong> the ovulatory follicle was larger<br />
than the diameter <strong>of</strong> the dominant follicles <strong>of</strong> previous<br />
waves, which was in agreement with the findings <strong>of</strong><br />
Figueiredo et al. (1997) and Mihm et al. (2006). <strong>The</strong><br />
growth rate <strong>of</strong> ovulatory follicle in two wave cycles<br />
was 0.91+0.88 mm per day, was less than first wave<br />
follicle (0.94+0.80 mm per day), which could be due<br />
to lower plasma concentrations <strong>of</strong> progesterone and<br />
57
POSTPARTUM OVARIAN FOLLICULAR DYNAMICS AND ESTRUS ACTIVITY<br />
presence <strong>of</strong> high FSH receptors (Knopf et al. 1989).<br />
Kulick et al. (2001) opined that the slower average<br />
growth rate <strong>of</strong> the dominant follicle <strong>of</strong> the second in<br />
two wave cycle was compensated by taking a longer<br />
period for reaching maximum diameter and ovulation.<br />
<strong>The</strong> maximum diameter <strong>of</strong> dominant follicles<br />
<strong>of</strong> first and second waves in two wave cycles reached<br />
on 5.13 + 0.63 and 18.50 + 0.50 days and in three<br />
wave cycles, 6.45+ 0.88, 12.89+3.06 and 20.44+1.42<br />
days and regression started on 10.80+ 1.10 in first<br />
wave in two wave cycle and 7.12+ 0.74 and 15.15+<br />
2.26 days in first and second waves <strong>of</strong> three wave<br />
cycle, which were in agreement with the Figueiredo<br />
et al. 1997) and Gaur et al. (2007) in Nellore cows.<br />
Statistical analysis <strong>of</strong> the data <strong>of</strong> diameter<br />
<strong>of</strong> dominant follicle in two wave cycles revealed that<br />
the diameter <strong>of</strong> the first wave dominant follicle was<br />
significantly (P< 0.01) smaller than the second wave<br />
dominant follicle (ovulatory follicle). <strong>The</strong> growth rate<br />
<strong>of</strong> second wave dominant follicle was faster than its<br />
counterpart in the first follicular wave.<br />
<strong>The</strong> regression <strong>of</strong> dominant follicles <strong>of</strong> first<br />
wave in two wave cycle and first and second waves<br />
in three wave cycle occurs between 7-15 days<br />
observed in the present study were similar to the<br />
findings <strong>of</strong> Figueiredo et al. (1997) and Gaur et al.<br />
(2007) in Nellore cows. <strong>The</strong> mean regression rate <strong>of</strong><br />
first wave in two wave cycle was 1.41+0.20 mm per<br />
day and in three wave cycle 1.20 + 0.33 and 1.48 +<br />
0.33 in first and second waves respectively.<br />
<strong>The</strong> mean duration <strong>of</strong> phase <strong>of</strong> regression<br />
<strong>of</strong> dominant follicles in first wave <strong>of</strong> two wave cycles<br />
was recorded as 6.50+ 0.56 days and in three wave<br />
cycles 7.13 + 1.86 and 4.72 + 1.42 days respectively<br />
in first and second waves. <strong>The</strong> mean period <strong>of</strong> static<br />
phase was 1.20±0.26 days in first wave <strong>of</strong> two wave<br />
cycle and 1.80±0.34 and 1.67±0.67 days for first and<br />
second waves <strong>of</strong> three wave cycles, respectively.<br />
<strong>The</strong> mean ovulation in natural estrus in postpartum<br />
lactating Ongole cows was recorded as 20.90 ±0.46<br />
and 22.20±0.37 respectively in two wave and three<br />
wave estrous cycles. <strong>The</strong> mean duration <strong>of</strong> first and<br />
second wave in two wave estrous cycles in<br />
postpartum lactating Ongole cows were 14.20±0.40<br />
and 9.10±0.45, respectively. Where as in three wave<br />
estrous cycles the duration <strong>of</strong> first, second and third<br />
waves were 11.20±0.37, 10.60±0.40 and 6.0±0.31<br />
days, respectively. In the present study, in natural<br />
cycle, the corpus luteum grew to a maximum<br />
diameter <strong>of</strong> 15.93 + 0.37 and 17.8 + 0.37 mm in two<br />
and three wave cycles on the day 13.1+1.50,<br />
14.60+0.56 and remained static up to 14.60+ 1.50<br />
and 15.90+0.45 days <strong>of</strong> the estrous cycles,<br />
respectively.<br />
<strong>The</strong> above results were almost comparable<br />
to that observed by Figueiredo et al. (1997) in Nelore<br />
cows 15.56 + 0.44 and 17.69+0.80 mm in two and<br />
three wave cycles, respectively. But Mayra et al.<br />
(1999) and Rhodes et al.(1997) reported higher size<br />
in zebu cows.<br />
<strong>The</strong> mean period <strong>of</strong> first exhibition <strong>of</strong><br />
postpartum estrus was recorded as 109.50 + 4.56<br />
days. <strong>The</strong> incidence <strong>of</strong> estrus was recorded as 32.50<br />
% and 62.50 % during day and night time respectively.<br />
<strong>The</strong>se findings were in corroboration with the report<br />
<strong>of</strong> Venkat Naidu et al (2010)<br />
It may be concluded that the knowledge<br />
about the follicular dynamics in postpartum lactating<br />
Ongole cows helps in planning and designing <strong>of</strong> the<br />
synchronization protocols and reducing the service<br />
period, inter calving period etc, for improving<br />
reproductive performance.<br />
ACKNOWLEDGEMENT<br />
<strong>The</strong> authors express their sincere gratitude<br />
to the SVVU, Tirupati for providing facilities to carry<br />
out the research work at Cattle Project, Live Stock<br />
<strong>Research</strong> Station, Lam Farm, Guntur.<br />
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postpartum dairy cows using ultrasound<br />
imaging and progesterone pr<strong>of</strong>iles. Animal<br />
Reproduction Science 22 : 171-180.<br />
Rhodes, F.M., A.J. Peterson, P.D. Jolly, W.H.<br />
McMillan, M.Donnison, A. Ledgard, G. Parton<br />
and D.R. Hall, 1997. Bovine ovarian follicle<br />
and oocyte characteristics after emergence <strong>of</strong><br />
the first follicular wave. <strong>The</strong>riogenology, 47:<br />
149 (Abst.).<br />
Rhodes, F.M., De’ath, G., Entwistle, K.W.<br />
1995Animal and temporal effects on ovarian<br />
59
POSTPARTUM OVARIAN FOLLICULAR DYNAMICS AND ESTRUS ACTIVITY<br />
follicular dynamics in Brahman heifers. Animal<br />
Reproduction Science, 38(4). 265-277.<br />
Roche, J. F., Crowe M. A and Bolland, M. P 1992<br />
Postpartum anestrus in dairy and beef cows.<br />
Animal Reproduction Science 28 : 371-378.<br />
Satheshkumar, S., Palanisamy. A., Subramanian.<br />
A., Kathiresan. D., and Ramadass. P. 2008.<br />
Follicular Wave Synchronization using GnRH<br />
Agonist in Jersey Crossbred cows. Indian J.<br />
Anim. Reproduction, 29 (2) 154 – 158.<br />
Venkata Naidu. G, Seshagiri Rao. A and Babu Rao.<br />
K, 2010. Progesterone pr<strong>of</strong>ile in postpartum<br />
lactating Ongole (Zebu) cows. Indian <strong>Journal</strong><br />
<strong>of</strong> Animal Reproduction 31 (1).<br />
Viana, J.H.M.; Ferreira, A., de M.; camargo, L.S, de<br />
A. 2000. Follicular dynamics in zebu cattle.<br />
Pasquisa Agropecuaria Brasileira. 35 (12)<br />
2501-2509. 40 ref.<br />
Vinoles, C., A. Meikle, M. Forsberg and E. Rubianes,<br />
1999. <strong>The</strong> effect <strong>of</strong> subluteal levels <strong>of</strong><br />
exogenous progesterone on follicular<br />
dynamics and endocrine patterns during early<br />
luteal phase <strong>of</strong> the ewe. <strong>The</strong>riogenology, 51:<br />
1351-1361.<br />
Wolfenson, D., G. Inbar, Z. Roth, M. Kaim, A. Bloch<br />
and R.Braw-Tal, 2004. Follicular dynamics and<br />
concentrations <strong>of</strong> steroids and gonadotropins<br />
in lactating cows and nulliparous heifers.<br />
<strong>The</strong>riogenology, 62: 1042-1055.<br />
Zeitoun, M.M., H.F. Rodriguez and R.D. Randel, 1996.<br />
Effect <strong>of</strong> season on ovarian follicular dynamics<br />
in Brahman cows. <strong>The</strong>riogenology, 45: 1577-<br />
1581.<br />
60
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 56-60, 2013<br />
DEVELOPMENT AND EVALUATION OF FIBER ENRICHED KHAKRA<br />
M. KIRTHY REDDY, P. UMADEVI, P.S.S.SAILAJA and APARNA KUNA<br />
Post Graduate <strong>Research</strong> Center, College <strong>of</strong> Home Science,<br />
Acharya N.G Ranga Agricultural University, Rajendra Nagar, Hyderabad-500030<br />
Date <strong>of</strong> Receipt : 12.06.2012 Date <strong>of</strong> Acceptance : 21.11.2012<br />
Dietary fiber components have unique<br />
chemical structures and characteristic physical<br />
properties (e.g., bulk/volume, viscosity, water-holding<br />
capacity, adsorption/binding or fermentability) that<br />
determine their subsequent physiologic behavior<br />
(Schneeman and Tietyen, 1994). Individuals with high<br />
intakes <strong>of</strong> dietary fiber appear to be at significantly<br />
lower risk for developing coronary heart disease,<br />
stroke, hypertension, diabetes, obesity, and certain<br />
gastrointestinal diseases (James et al., 2009).<br />
Khakra is a ready to eat, light crispy, crunchy<br />
flat bread snack <strong>of</strong> Gujarat in western India. It is a<br />
very versatile snack and can be eaten as fat free<br />
chips.Though khakra is gaining importance, the<br />
traditional khakra is made up <strong>of</strong> oil and also it is made<br />
up <strong>of</strong> refined flour which has very low fibre in it which<br />
in turn increases the calorie load. Low fibre and high<br />
fat content in the diets are making people landing in<br />
to various chronic life style disorders like<br />
diabetes,cancer etc. Hence, the present research was<br />
taken up to prepare the khakras with fiber rich<br />
ingredients and without addition <strong>of</strong> any visible fat<br />
thereby making the product therapeutic.<br />
<strong>The</strong> benefits <strong>of</strong> consuming foods rich in fiber<br />
are numerous, ranging from improved large bowel<br />
function to slowed digestion and absorption <strong>of</strong><br />
carbohydrate and fat and reduced risk for certain<br />
diseases (Ali et al. 1982).<br />
Moreover, is<strong>of</strong>lavones contained in soybeans<br />
are effective cancer-preventive agents for lowering<br />
risks <strong>of</strong> various cancers (El Gharras, 2009). Evidence<br />
also points to the beneficial effects <strong>of</strong> soy is<strong>of</strong>lavones<br />
in the prevention <strong>of</strong> cardiovascular disease (El<br />
Gharras, 2009). <strong>The</strong>ir potential health benefits <strong>of</strong> soyis<strong>of</strong>lavones<br />
include prevention <strong>of</strong> osteoporosis via<br />
phytoestrogen effects <strong>of</strong> is<strong>of</strong>lavones, and prevention<br />
<strong>of</strong> neovascularization in ocular conditions (Zhu et al.,<br />
2005).<br />
Table 1. Khakras <strong>of</strong> different formulations<br />
Ingredients Control (T 1) (gms) T 2 (Experimental with<br />
methi leaf powder)<br />
(gms)<br />
T 3 (Experimental<br />
without methi leaf<br />
powder(gms)<br />
Wheat flour 84 - -<br />
Whole wheat flour - 46 48<br />
Defatted soy flour - 15 15<br />
Oats flakes flour - 20 20<br />
Methi leaf powder - 2 -<br />
Green chillies 10 10 10<br />
Ajwain seeds 3 3 3<br />
salt 4 4 4<br />
email: kirthy88@gmail.com<br />
61
DEVELOPMENT AND EVALUATION OF FIBER ENRICHED KHAKRA<br />
<strong>The</strong>re is therefore the need to develop a different<br />
approach to <strong>of</strong>fer the weary consumers the<br />
opportunity to feed on improved formulations with<br />
substantive health benefits from wheat-soy<br />
combinations (Gomez et al., 2003).<br />
Hence, an attempt was made to increase<br />
fibre content <strong>of</strong> khakra with no additional visiblefat.<br />
Product development<br />
Three products T 1 ,<br />
T 2,<br />
T 3<br />
were developed in<br />
various combinations <strong>of</strong> whole wheat flour/oats flake<br />
powder/defatted soy flour/methi leaf powder in<br />
different proportions which were given.<br />
Procedure<br />
Three products were developed using various<br />
combinations <strong>of</strong> whole wheat flour/ oats/ defatted soy<br />
flour/ methi leaf powder in different proportions. Roast<br />
and grind whole wheat, defatted soy bean and oat<br />
flakes in to fine powders. Methi leaves are blanched<br />
at 65 0 c for 2-3 minutes, dried and powered which was<br />
only added in T 2<br />
sample. Mix all the above ingredients<br />
including green chillies, salt and ajwain seeds with<br />
luke warm water. T 1<br />
sample is made up <strong>of</strong> only whole<br />
wheat flour, green chillies, salt and ajwani seeds. All<br />
these are kneaded in to individual doughs containing<br />
various ingredients. Keep it for 15 minutes a side.<br />
Make the dough into small balls <strong>of</strong> weight 7gms each<br />
and hot press them for 45-60 secs.<br />
Sensory evaluation Sensory evaluation was done<br />
to select the most acceptable recipes with 5 point<br />
Hedonic rating scale.<br />
Hedonic scale ratings Like extremely- 5, Like<br />
moderately – 4, Neither like nor dislike – 3, Dislike<br />
moderately – 2, Dislike extremely – 1.<br />
<strong>The</strong> nutrient compositions <strong>of</strong> the recipes were<br />
calculated for protein, fat, fiber, and energy by using<br />
the Nutritive value <strong>of</strong> Indian foods. (Gopalan et al,<br />
2004). <strong>The</strong> completed score cards after the<br />
evaluations were subjected to statistical analysis.<br />
(Snedecor,G.W and Cochran, W.G, 1983)<br />
Table 2 Sensory evaluation <strong>of</strong> khakras<br />
S.No. Colour Taste Flavour Texture Over all<br />
acceptability<br />
Control (T 1 ) 3.55 3.91 3.73 3.45 3.36<br />
T 2 4.18 3.82 4.45 4.55 4.36<br />
T 3 3.91 4.64 3.63 4.09 3.73<br />
Se D 0.261 0.343 0.213 0.384 0.210<br />
CD NS 0.705* 0.436* 0.789* 0.431*<br />
CV 15.804 20.442 12.953 22.876 12.895<br />
* significant NS- Non significant<br />
Khakra <strong>of</strong> three formulations were subjected to the<br />
sensory evaluation and the scores are recorded for<br />
the following attributes colour, taste, flavour, texture<br />
and overall acceptability were presented in Table 2.<br />
Colour<br />
No significant changes were observed<br />
among the scores for colour in which T 2<br />
(4.18%)<br />
scored highest followed by T 3<br />
(3.91%) and lowest<br />
score was recorded in T 1<br />
(3.55%). As per the<br />
observations made by Trongpanich et al. 2001, it<br />
was found that there was no significant difference in<br />
the preferential scores in colour, odor and taste<br />
between the snack samples that contained 5 – 15 %<br />
DFC and the control sample at p < 0.05. However,<br />
adding DFC (Dietary Fiber Concentrate) in the snacks<br />
made up <strong>of</strong> corn grits could improve the snack’s<br />
texture as the texture preferential scores <strong>of</strong> all the<br />
snack samples which contained 10 %DFC were<br />
higher than <strong>of</strong> the control one (Trongpanich et al.,<br />
2001). <strong>The</strong> darker colour <strong>of</strong> the crumbs <strong>of</strong> whole<br />
wheat bread and fortified breads and biscuits have<br />
been reported by several authors (Singh et al., 2000;<br />
62
KIRTHY et al<br />
Akhtar et al., 2008; Serrem et al., 2011). <strong>The</strong> brownish<br />
bread appearance could be directly related to the<br />
increase in fiber content (Hu et al., 2007)T 2<br />
= modified<br />
recipe with 15gms defatted soy flour, 20gms oat<br />
flakes and 2gms methi leaf powder, T 3<br />
= modified<br />
recipe with 15gms defatted soy flour, 20gms oat<br />
flakes. T 1<br />
= control recipe made up <strong>of</strong> whole wheat<br />
flour.<br />
Control (T 1<br />
) T 2<br />
T 3<br />
Taste<br />
Scores for taste <strong>of</strong> khakras recorded<br />
significant changes (p
DEVELOPMENT AND EVALUATION OF FIBER ENRICHED KHAKRA<br />
Table 3. Nutritional evaluation <strong>of</strong> Khakra<br />
S.No. Protein (gms) Fiber (gms) Fat (gms) Energy (Kcal)<br />
Control (T 1 ) 8.3 6.2 0.8 286<br />
T 2 14.7 13.56 1.43 316<br />
T 3 15 13.78 1.47 322<br />
SeD 0.490 2.588 3.657 1.393<br />
CD 1.222 6.456 NS 3.474<br />
CV 4.737 32.859 184.240 0.849<br />
Nutritional quality <strong>of</strong> khakras are tabulated<br />
in Table 3 and are presented in Fig 3. Significant<br />
differences (p
KIRTHY et al<br />
El Gharras H. 2009. Polyphenols: food sources,<br />
properties and applications – a review.<br />
International <strong>Journal</strong> Food Science<br />
Technology. 44: 2512-2518.<br />
Gomez, M., Ronda, F., Blanco, Caballero, P and<br />
Apesteguía A. 2003. Effect <strong>of</strong> dietary fibre on<br />
dough rheology and bread quality. European<br />
Food <strong>Research</strong> Technology. 216: 51-56.<br />
Hu, GH., Yang, F., Ma, Z and Zhou, Q. 2007.<br />
Development <strong>of</strong> <strong>Research</strong> and application <strong>of</strong><br />
rice bran dietary fibre. China Food Addit., 84(5):<br />
80-85.<br />
James., W, Anderson, Pat baird, Richard H Davis J<br />
r, Stefanie ferreri, Mary knudtson, Ashraf<br />
koravm, Valerie waters and Christine l william.<br />
2009. Health benefits <strong>of</strong> dietary fiber. Nutrition<br />
Reviews. 67(4):188–205.<br />
Joel Ndife, L. O. Abdulraheem and U. M. Zakari. 2011.<br />
Evaluation <strong>of</strong> the nutrtional and sensory quality<br />
<strong>of</strong> functional breads produced from whole<br />
wheat and soya bean flour blends. African<br />
<strong>Journal</strong> <strong>of</strong> Food Science Vol. 5(8)466 – 472.<br />
Schneeman B.O and Tietyen. 1994. Dietary fiber.<br />
modern nutrition in health and disease. shills<br />
m. e. olson j. a. shike m. lea and fibiger<br />
philadelphia, pa. 8:89-100.Serrem C, Kock H,<br />
Taylor J. 2011. Nutritional quality, sensory<br />
quality and consumer acceptability <strong>of</strong> sorghum<br />
and bread wheat biscuits fortified with defatted<br />
soy flour. Int. J. Food Science Technology.<br />
46: 74-83.<br />
Singh R, Singh G and Chauhan GS (2000). Nutritional<br />
evaluation <strong>of</strong> soy fortified biscuits. J. Food<br />
Science Technology. 37: 162-164.<br />
Snedecor, G.W and Cochran, W.G.1983.Statistical<br />
Methods Oxford and IBH publishing<br />
company New Delhi.<br />
Trongpanich K, Pracha Boonyasirikool, Sumalai<br />
srikumlaitong, Chowladda taengpook and<br />
Udom kanjanapakornchai. 2001. Feasibility<br />
study on snack production by using dietary<br />
fiber concentrate from soymilk residue. Natural<br />
Science. 35: 188 – 194.<br />
Zhu D, Hettiarachchy N, Horax R, Chen P (2005).<br />
Is<strong>of</strong>lavone contents in germinated soybean<br />
seeds. Plant Foods Human Nutrition. 60: 147-<br />
151.<br />
65
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 61-65, 2013<br />
INFLUENCE OF NITROGEN AND WEED MANAGEMENT ON GROWTH AND YIELD<br />
OF AEROBIC RICE (Oryza sativa L.)<br />
K. SANDYARANI, M. MALLA REDDY, R. UMA REDDY and P.V. RAO<br />
Department <strong>of</strong> Agronomy, Agricultural College,<br />
Acharya N.G. Ranga Agricultural University, Aswaraopet - 507 301<br />
Date <strong>of</strong> Receipt : 13.06.2012 Date <strong>of</strong> Acceptance : 06.11.2012<br />
<strong>The</strong> soil N dynamics and path way <strong>of</strong> nitrogen<br />
losses in dry sown rice system are different from<br />
lowlands and result in different fertilizer nitrogen<br />
recoveries. <strong>The</strong> alternate moist and dry soil conditions<br />
may stimulate nitrification-denitrification processes<br />
in dry sown rice, leading to loss <strong>of</strong> nitrogen through<br />
N 2<br />
and N 2<br />
O (Prasad, 2011). Hence, traditional lowland<br />
rice fertilizer doses may not be optimum for aerobic<br />
rice. Further, aerobic soil conditions and dry tillage<br />
practices, besides alternate wetting and drying are<br />
conducive for germination and growth <strong>of</strong> highly<br />
competitive weeds, which cause grain yield losses<br />
ranging from 50-91%, compared to conventional<br />
production systems (Singh et al., 2006), in which<br />
weeds are suppressed by standing water and<br />
transplanted rice seedlings have a “head start” over<br />
germinating weed seedlings. As the concept <strong>of</strong><br />
aerobic rice in Andhra Pradesh is new, relatively few<br />
insights into weed management and nitrogen<br />
fertilization exist. Hence, the present investigation<br />
was planned out to find out the optimum dose <strong>of</strong><br />
nitrogen and effective weed management practice in<br />
aerobic rice.<br />
Field experiment was conducted during<br />
kharif, 2011 at Regional Agricultural <strong>Research</strong> Station,<br />
Warangal, Andhra Pradesh. <strong>The</strong> soil <strong>of</strong> the<br />
experimental site was sandy loam in texture, medium<br />
in available nitrogen (288 kg ha -1 ), low in available<br />
phosphorus (7.6 kg ha -1 ) and medium in available<br />
potassium (73 kg ha -1 ) with a pH <strong>of</strong> 8.1. <strong>The</strong><br />
experiment was laid out in randomized block design<br />
(factorial concept) with three nitrogen doses viz., 120,<br />
180 and 240 kg ha -1 and four weed management<br />
treatments, viz., pre-emergence application <strong>of</strong><br />
pendimethalin @ 1.2 kg a.i. ha -1 + post-emergence<br />
application <strong>of</strong> pyrazosulfuron ethyl @ 30 g a.i. ha -1 at<br />
25 DAS, mechanical weeding at 20 and 45 DAS,<br />
weed free check and weedy check replicated thrice.<br />
Rice variety ‘WGL-32100’ was sown by dibbling at<br />
30 cm row spacing with solid rows with a seed rate<br />
<strong>of</strong> 40 kg ha -1 . Phosphorus and potash @ 60 and 50<br />
kg ha -1 were applied uniformly as basal in the form <strong>of</strong><br />
single super phosphate and muriate <strong>of</strong> potash,<br />
respectively. Nitrogen was applied in the form <strong>of</strong> urea<br />
as per the treatments in three equal splits, each at<br />
basal, active tillering and panicle initiation stage. A<br />
range <strong>of</strong> mean minimum temperature <strong>of</strong> 19.7 to 26.1<br />
0<br />
C and mean maximum temperature <strong>of</strong> 27.1 to 33.0<br />
0<br />
C was recorded during the crop growth period. A<br />
total rainfall <strong>of</strong> 349.4 mm was received during the<br />
crop season in 26 rainy days. Supplemental irrigation<br />
was given as and when required to maintain the soil<br />
in moist condition. <strong>The</strong> weed density and dry weight<br />
were recorded in each plot using a quadrant <strong>of</strong> 1 m ×<br />
1 m size. <strong>The</strong> data on weed density and dry weight<br />
were subjected to square root transformation before<br />
statistical analysis.<br />
Weed spectrum <strong>of</strong> the experimental field<br />
consisted <strong>of</strong> three groups <strong>of</strong> weeds like grasses,<br />
sedges and broad leaved weeds. <strong>The</strong> observed<br />
sedges were Cyperus rotundus, Fimbristylis<br />
argentea; grasses were Cynodon dactylon,<br />
Echinochloa colona, Dinebra retr<strong>of</strong>lexa, Panicum<br />
javanicum and broad leaved weeds were Corchorus<br />
olitorius, Eclipta alba, Digera arvensis, Cyanotis<br />
axillaris, Psoralea corylifolia, Ammannia baccifera,<br />
Euphorbea geniculata, Phyllanthus niruri, Portulaca<br />
oleracea, Abutilon indicum, Celosia argentia,<br />
Commelina benghalensis, Merremia emarginata,<br />
Gynandropsis pentaphylla and Parthenium<br />
hysterophorus. Among these, broad leaved weeds<br />
were dominant followed by grasses and sedges in<br />
aerobic rice.<br />
email: ksragrico@gmail.com<br />
66
SANDYARANI et al<br />
Weed parameters like weed density, dry<br />
weight, weed control efficiency and weed index were<br />
not significantly influenced by the application <strong>of</strong><br />
different doses <strong>of</strong> nitrogen except weed density at<br />
60 DAS and weed dry weight at 15 DAS and harvest<br />
(Table 1 ). At 60 DAS, the weed density recorded<br />
with 240 kg N ha -1 was significantly higher than 120<br />
kg N ha -1 but was at par with 180 kg N ha -1 . Preemergence<br />
application <strong>of</strong> pendimethalin @ 1.2 kg<br />
a.i. ha -1 followed by pyrazosulfuron ethyl @ 30 g a.i.<br />
ha -1 at 25 DAS registered significantly lower weed<br />
density at all the stages <strong>of</strong> observation compared to<br />
weedy check and mechanical weeding except at 60<br />
DAS where they were at par with each other (Table<br />
1). Similar trend was observed with respect to the<br />
dry weight <strong>of</strong> weeds at 60 DAS. Higher weed control<br />
efficiency was recorded with herbicides than<br />
mechanical weeding at all the stages which led to<br />
lower weed index in the former treatment. <strong>The</strong><br />
interaction between the nitrogen levels and weed<br />
management treatments was significant with respect<br />
to the dry weight <strong>of</strong> weeds at 15 DAS and harvest<br />
only. <strong>The</strong> weed dry weight was significantly higher<br />
with mechanical weeding compared to herbicides<br />
application at all the doses <strong>of</strong> nitrogen both at 15<br />
DAS and harvest. Similarly, the dry weight <strong>of</strong> weeds<br />
significantly increased at 240 kg N ha -1 compared to<br />
120 kg N ha -1 at 15 DAS and 180 kg N ha -1 as well at<br />
harvest except in the weed free treatment. This could<br />
be attributed to vigorous growth and development <strong>of</strong><br />
weeds owing to higher uptake <strong>of</strong> nutrients at higher<br />
rate <strong>of</strong> nitrogen application. Similar results were<br />
reported by Sharma et al. (2007).<br />
Nitrogen removal by the weeds at harvest<br />
was significantly higher at 240 kg N ha -1 over 120 kg<br />
N ha -1 but at par with 180 kg N ha -1 (Table 2). It was<br />
also significantly more with mechanical weeding<br />
compared to herbicidal application which was at par<br />
with weedy check due to higher density and dry weight<br />
<strong>of</strong> weeds in the latter treatment. Singh and Tripathi<br />
(2007) also reported similar results.<br />
Application <strong>of</strong> 240 kg N ha -1 recorded<br />
significantly higher yield attributes and grain yield over<br />
120 kg N ha -1 but at par with 180 kg N ha -1 except the<br />
test weight (Table 2). <strong>The</strong> straw yield was not<br />
significantly different among the different nitrogen<br />
doses. Increased yield under higher nitrogen levels<br />
might be due to adequate nutrient supply which would<br />
have resulted in increased growth and yield<br />
components. Similar findings were reported by<br />
Shekara et al. (2010). Among the weed management<br />
practices, pre-emergence application <strong>of</strong> pendimethalin<br />
@ 1.2 kg a.i. ha -1 followed by post-emergence<br />
application <strong>of</strong> pyrazosulfuron ethyl @ 30 g a.i. ha -1 at<br />
25 DAS recorded significantly higher yield attributing<br />
parameters, grain yield and straw yield over<br />
mechanical weeding and it was comparable with weed<br />
free treatment. <strong>The</strong> increased grain yield was mainly<br />
due to effective control <strong>of</strong> weeds in herbicide applied<br />
plots (Jayadeva et al., 2011). <strong>The</strong> significantly lowest<br />
yield attributing parameters and yield among the<br />
treatments were observed with unweeded check<br />
owing to severe crop-weed competition throughout<br />
crop growth period.<br />
Nitrogen uptake by grain and straw <strong>of</strong> rice<br />
increased significantly with increasing doses <strong>of</strong><br />
nitrogen upto 240 kg N ha -1 (Table 2). Among all the<br />
weed management practices, significantly higher<br />
nitrogen uptake by grain as well as straw was<br />
observed with herbicides compared to weedy check<br />
but at par with mechanical weeding (Table 2).<br />
<strong>The</strong> highest net returns and returns per rupee<br />
invested were obtained with the application <strong>of</strong> 240<br />
kg N ha -1 over other two doses. Among the weed<br />
management practices, weed free check recorded<br />
highest values followed by pre-emergence application<br />
<strong>of</strong> pendimethalin @ 1.2 kg a.i. ha -1 followed by postemergence<br />
application <strong>of</strong> pyrazosulfuron ethyl @ 30<br />
g a.i. ha -1 at 25 DAS. <strong>The</strong>se results corroborate the<br />
findings <strong>of</strong> Jayadeva et al. (2011).<br />
It can be stated that application <strong>of</strong> 180 kg N<br />
ha -1 was found to be optimum for aerobic rice in sandy<br />
loam soils <strong>of</strong> Telangana region and pre-emergence<br />
application <strong>of</strong> pendimethalin @ 1.2 kg a.i. ha -1 followed<br />
by post-emergence application <strong>of</strong> pyrazosulfuron<br />
ethyl @ 30 g a.i. ha -1 at 25 DAS was found to be<br />
effective and economical weed management practice<br />
in aerobic rice during kharif season.<br />
67
INFLUENCE OF NITROGEN AND WEED MANAGEMENT ON GROWTH<br />
Table 1. Influence <strong>of</strong> nitrogen doses and weed management on weed density, weed dry weight, weed control efficiency and weed index in aerobic<br />
rice<br />
Data subjected to square-root (“x+1) transformation; Figures in parentheses are original values.<br />
Pendi.-pendimethalin; py.ethyl-pyrazosulfuron ethyl; M.W-mechanical weeding; DAS-days after sowing<br />
68
SANDYARANI et al<br />
Table 2. Influence <strong>of</strong> nitrogen doses and weed management on yield attributes, yield, nitrogen uptake by crop and weeds and economics in<br />
aerobic rice<br />
Data subjected to square-root (“x+1) transformation; Figures in parentheses are original values.<br />
Pendi.-pendimethalin; py.ethyl-pyrazosulfuron ethyl; M.W-mechanical weeding; DAS-days after sowing<br />
69
INFLUENCE OF NITROGEN AND WEED MANAGEMENT ON GROWTH<br />
REFERENCES<br />
Jayadeva, H.M., Bhairappanavar, S.T., Hugar, A.Y.,<br />
Rangaswamy, B.R., Mallikarjuna, G.B.,<br />
Malleeshappa and Naik, C.C.D. 2011.<br />
Integrated weed management in aerobic rice.<br />
Agricultural Science Digest. 31 (1): 58-61.<br />
Prasad, R. 2011. Aerobic rice Systems. Advance in<br />
Agronomy. 111: 207-246.<br />
Sharma, R.P., Pathak, S.K and Singh, R.C. 2007.<br />
Effect <strong>of</strong> nitrogen and weed management in<br />
direct-seeded rice under upland conditions.<br />
Indian <strong>Journal</strong> <strong>of</strong> Agronomy. 52 (2): 114- 119.<br />
Shekara, B.G., Nagaraju and Shreedhara, D. 2010.<br />
Growth and yield <strong>of</strong> aerobic rice as influenced<br />
by different levels <strong>of</strong> N, P and K in Kaveri<br />
command area. <strong>Journal</strong> <strong>of</strong> Maharashtra<br />
Agricultural Universities. 35 (2): 195-198.<br />
Singh, B., Malik, R.K., Yadav, A and Nandal, D.P.<br />
2006. Weed management in direct seeded rice<br />
under different crop establishment methods.<br />
Extended summaries in the National<br />
Symposium on Conservation Agriculture and<br />
Environment. Varanasi, Uttar Pradesh, 26-28<br />
October 2006. pp.332-333<br />
Singh, K and Tripathi, H.P. 2007. Effect <strong>of</strong> nitrogen<br />
and weed control practices on performance <strong>of</strong><br />
irrigated direct seeded rice (Oryza sativa).<br />
Indian <strong>Journal</strong> <strong>of</strong> Agronomy. 52 (3): 231-234.<br />
70
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 66-69, 2013<br />
EFFECT OF SEED PRIMING ON BIOCHEMICAL CHANGES DURING SEED<br />
STORAGE OF MAIZE (ZEA MAYS L.) HYBRIDS<br />
M. RAM KUMAR, P. S. RAO, V. PADMA and K. V. RADHA KRISHNA<br />
Department <strong>of</strong> Crop Physiology, College <strong>of</strong> Agriculture<br />
Acharya NG Ranga Agricultural University, Rajendranagar, Hyderabad-500 030<br />
Date <strong>of</strong> Receipt : 04.06.2012 Date <strong>of</strong> Acceptance : 08.02.2013<br />
Maize is one <strong>of</strong> the most important cereal<br />
crops in the world. It is widely grown twice a year in<br />
certain agro climatic zones <strong>of</strong> India and the world as<br />
a staple food. Only 20-25 percent quantity <strong>of</strong> the seed<br />
is invariably stored and used by farmers in<br />
subsequent seasons. <strong>The</strong> remaining 75-80 percent<br />
<strong>of</strong> the seed quantity is transported to market during<br />
the first season itself to meet the demand <strong>of</strong> the<br />
farmers. <strong>The</strong> challenging task to overcome problems<br />
in process <strong>of</strong> the seed storage is the seed<br />
deterioration (viz., physiological or biochemical<br />
changes) which ultimately leads to loss <strong>of</strong> its vigour<br />
and viability. Priming is also thought to increase<br />
enzyme activity and counteract the effects <strong>of</strong> lipid<br />
peroxidation (Saha et al., 1990). During priming de<br />
novo synthesis <strong>of</strong> á-amylase is also documented<br />
(Lee and Kim, 2000). Metabolic activities in seeds<br />
increase with á-amylase activity, indicating higher<br />
seed vigour. Rapid and uniform field emergence, are<br />
two essential prerequisites to increase yield, quality<br />
and ultimately pr<strong>of</strong>its in crop production. Uniformity<br />
and percentage <strong>of</strong> seedling emergence <strong>of</strong> directseeded<br />
crops have a major impact on final yield and<br />
quality.<br />
<strong>The</strong> medium vigour (Six month old) seed lots<br />
<strong>of</strong> five maize hybrids viz., AH 122, SMH-9, DHM<br />
111, DHM 115 and DHM 117 having germination up<br />
to minimum seed certification standard (MSCS) were<br />
dried to 9% moisture are used for the study during<br />
2010-11. <strong>The</strong> seeds were subjected to priming at 25 o C<br />
with distilled water (T 1<br />
), PEG - 1.2 MPa (T 2<br />
), KNO 3<br />
@ 0.5% (T 3<br />
) along with untreated control (To). <strong>The</strong><br />
invigoration treatments were given to the seeds<br />
by soaking in same quantity <strong>of</strong> water/aqueous<br />
solution <strong>of</strong> chemical (1: 1 seed and water/solution)<br />
for 18 hours at ambient temperature followed by drying<br />
back to original moisture content under shaded<br />
condition. Immediately after drying the treated seeds<br />
were stored in moisture impervious containers at<br />
ambient condition. <strong>The</strong> seed samples (primed and<br />
unprimed) were drawn at 0, 2, 4 and 6 months after<br />
treatment for studying the enzymatic changes during<br />
storage. <strong>The</strong> fresh seed sample <strong>of</strong> 0.5 g was<br />
homogenized with 5ml <strong>of</strong> chilled citrate buffer (pH<br />
5.0) and centrifuged at 10,000 rpm for 15 minutes.<br />
<strong>The</strong> supernatant was used for assessing the<br />
activity <strong>of</strong> á-amylase as per the procedure described<br />
by Choi et al. (1996). Enzyme extract for peroxidase<br />
was prepared by first freezing the weighed amount<br />
<strong>of</strong> seed sample (1g) in liquid nitrogen to prevent<br />
proteolytic activity followed by grinding with 10 ml<br />
extraction buffer (0.1M phosphate buffer, pH<br />
7.5,containing 0.5 mM EDTA). Brie was passed<br />
through four layers <strong>of</strong> cheese cloth and filtrate was<br />
centrifuged for 20 min. at 15000 G and the supernatant<br />
was used as enzyme extract. <strong>The</strong> enzyme extract<br />
was used for assesing the acivity <strong>of</strong> peroxidase<br />
described by Castillo et al. (1984).<br />
<strong>The</strong> α - amylase activity (mmol g -1 fr wt)<br />
was reported to be significantly different among<br />
treatments and hybrids. <strong>The</strong>re was a significant<br />
interaction between hybrids and treatments at all<br />
duration <strong>of</strong> time after storage (Table 1). Among the<br />
five maize hybrids DHM 117 primed with water<br />
(Hydropriming) recorded maximum value (2.17 mmol<br />
g -1 ) <strong>of</strong> á- amylase activity while unprimed seeds <strong>of</strong><br />
AH 122 recorded least value (1.81 mmol g -1 ).<br />
Significant interaction between hybrids and treatments<br />
at all time intervals <strong>of</strong> germination at all the months<br />
<strong>of</strong> storage was noticed. <strong>The</strong> findings are in conformity<br />
with the studies <strong>of</strong> Sung and Chang (1993); where in<br />
the hydropriming was superior to PEG 6000 in sh-2<br />
corn hybrid and was more effective in improving the<br />
emergence which resulted due to higher á- amylase<br />
and â-amylase activities. Similarly a laboratory<br />
study was conducted by Sathish and<br />
email: sampalrao@gmail.com<br />
71
EFFECT OF SEED PRIMING ON BIOCHEMICAL CHANGES DURING STORAGE<br />
Table 1. Effect <strong>of</strong> seed priming on á-amylase activity (mmol g -1 fr wt) <strong>of</strong> maize hybrids during storage<br />
S.No.<br />
Hybrid ‘0’ Month 2 Months after storage<br />
Treat<br />
ment<br />
Control<br />
HYDRO<br />
PE<br />
G<br />
KNO 3 Mean<br />
Contr<br />
ol<br />
HYDRO PEG KNO 3 Mean<br />
1 AH 122 5.07 5.15 5.10 5.13 5.11 4.95 5.03 4.98 5.00 4.99<br />
2 SMH 9 5.23 5.30 5.27 5.31 5.28 5.14 5.24 5.17 5.21 5.19<br />
3 DHM<br />
117 5.30 5.40 5.34 5.37 5.35 5.22 5.31 5.25 5.27 5.26<br />
4 DHM<br />
111 5.13 5.20 5.14 5.18 5.15 5.01 5.10 5.05 5.07 5.06<br />
5 DHM<br />
115 5.17 5.26 5.20 5.23 5.22 5.07 5.17 5.10 5.14 5.12<br />
Mean 13.93 5.17 5.25 5.21 5.22 5.21 5.08 5.17 5.11 5.14<br />
Hybrid (H)<br />
Treatment<br />
(T)<br />
HxT<br />
Hybrid<br />
(H)<br />
Treatment (T)<br />
S.Em± 0.004 0.004 0.009 0.002 0.002 0.004<br />
CD at 5% 0.013 0.012 0.027 0.006 0.005 0.012<br />
HxT<br />
S.No<br />
. Treat<br />
ment<br />
Hybrid 4 Months after storage 6 Months after storage<br />
Control<br />
HYDRO<br />
PE<br />
G<br />
KNO 3<br />
Mean<br />
Contr<br />
ol<br />
HYDRO<br />
1 AH 122 3.73 3.82 3.77 3.80 3.78 1.81 1.90 1.84 1.87 1.85<br />
2 SMH 9 3.88 4.12 3.97 4.03 4.01 2.02 2.14 2.05 2.07 2.07<br />
PEG<br />
KNO<br />
3<br />
Mean<br />
3 DHM<br />
117 4.01 4.37 4.04 4.07 4.12 2.08 2.17 2.14 2.15 2.14<br />
4 DHM<br />
111 3.81 3.89 3.83 3.85 3.85 1.88 1.97 1.91 1.94 1.92<br />
5 DHM<br />
115 3.86 3.95 3.90 3.93 3.91 1.94 2.11 1.98 2.01 2.03<br />
Mean 11.26 3.87 4.03 3.90 3.93 3.93 1.95 2.10 1.98 2.01<br />
Hybrid<br />
(H)<br />
Treatment<br />
(T)<br />
HxT<br />
Hybrid<br />
H)<br />
Treatment (T)<br />
S.Em± 0.003 0.0032 0.007 0.006 0.005 0.012<br />
CD at 5% 0.010 0.0091 0.020 0.018 0.016 0.036<br />
HxT<br />
Sundareshwaran (2010) who evaluated the influence<br />
<strong>of</strong> seed priming on biochemical parameters <strong>of</strong> fresh<br />
and aged seeds <strong>of</strong> maize hybrid (COH(M) 5) and its<br />
parental lines UMI 285 (female) and UMI 61 (male).<br />
Seeds were soaked in water @ 1% KH 2<br />
PO 4<br />
, 3% KNO 3<br />
and 2 % CaCl 2<br />
solution for 6 hrs. <strong>The</strong> results revealed<br />
that all the seed priming treatments affected the<br />
biochemical activity <strong>of</strong> seeds. However, seed priming<br />
with 1% KH 2<br />
PO 4<br />
for 6 h significantly increased the<br />
protein content, á-amylase and dehydrogenase<br />
activities in both fresh and aged seed lots <strong>of</strong> all the<br />
three genotypes. <strong>The</strong> results are also in conformity<br />
with Sathish and Sundareshwaran (2010) and<br />
Wattanakulpakin (2012).<br />
<strong>The</strong> peroxidase activity was observed at<br />
different months <strong>of</strong> storage (Table 2). <strong>The</strong> hybrid DHM<br />
117 showed maximum peroxidase activity with<br />
hydropriming indicating the treatment is the most<br />
reliable technique for increasing peroxidase activity<br />
there by increasing the vigour during storage. <strong>The</strong><br />
significant interaction between hybrids and treatments<br />
was observed at all periods <strong>of</strong> storage. Priming<br />
72
KUMAR et al<br />
Table 2. Effect <strong>of</strong> seed priming on peroxidase activity (EU/Litre) <strong>of</strong> maize hybrids during storage<br />
S.<br />
No.<br />
Hy<br />
brid<br />
Treatm<br />
ent<br />
1 AH<br />
2 SMH<br />
‘0’ Month 2 Months after storage<br />
Control HYDRO PEG KNO 3 Mean<br />
Con<br />
trol<br />
HYDRO PEG KNO 3 Mean<br />
122 86654.6 87252.0 86854.0 87054.0 86953.6 66346.0 66942.0 66545.0 66746.00 66644.75<br />
9 88376.0 88971.0 88574.0 88771.0 88673.0 67671.0 68272.0 68203.3 68071.00 68054.33<br />
3 DHM<br />
117 88966.0 89566.0 89161.0 89369.0 89265.5 68232.0 68834.0 68435.0 68632.00 68533.25<br />
4 DHM<br />
111 87215.0 87816.0 87417.0 87616.0 87516.0 66545.0 67142.0 66745.3 66943.00 66843.83<br />
5 DHM<br />
115 87983.0 88380.0 87988.0 88183.0 88133.5 67107.0 67706.0 67305.0 67508.00 67406.50<br />
Mean 13.93 87838.8 88397.0 87998.8 88198.6 88108.3 67180.2 67779.2 67446.73 67580.00<br />
Hybrid(H) Treatment(T) HxT<br />
Hybrid(H<br />
)<br />
Treatment(T)<br />
S.Em± 0.77 0.69 1.54 0.52 0.46 1.04<br />
CD (0.05) 2.21 1.98 4.43 1.50 1.34 3.01<br />
HxT<br />
S.No.<br />
Hybri<br />
d<br />
Treat<br />
ment<br />
1 AH<br />
122<br />
2 SMH<br />
9<br />
3 DHM<br />
117<br />
4 DHM<br />
111<br />
5 DHM<br />
115<br />
4 Months after storage 6 Months after storage<br />
Control HYDRO PEG KNO 3 Mean<br />
Con<br />
trol<br />
HYDRO PEG KNO 3 Mean<br />
43105 43702 43306 43508.0 43405.3 17003 17601.3 17202 17407 17303.3<br />
44793 45394 44991 45191.7 45092.3 18694 19293 18894 19097 18994.5<br />
45352 45958 45557 45756.0 45655.8 19256 19857 19456 19652 19555.3<br />
43663 44266 43867 44065.3 43965.3 17564 18165 12433 17961 16530.8<br />
44235 44830 44438 44632.0 44533.8 18128 18729 18328 18524 18427.3<br />
Mean 44229 44830 44431.8 44630.4<br />
Hybrid<br />
(H)<br />
Treatment (T)<br />
HxT<br />
4453<br />
0.5<br />
18129 18729<br />
Hybrid<br />
(H)<br />
17262.<br />
6<br />
Treatment (T)<br />
18528.2<br />
S.Em± 0.85 0.76 1.71 0.65 0.58 1.30<br />
CD at 5% 2.45 2.19 4.91 1.86 1.67 3.74<br />
HxT<br />
treatments showed superior values in comparision<br />
to control. Syed et al., (2010) revealed that the<br />
activity <strong>of</strong> catalase and peroxidase during accelerated<br />
ageing and repair priming treatment <strong>of</strong> maize (Zea<br />
mays L.) seeds. Seed priming with KNO 3<br />
was<br />
performed at different concentrations <strong>of</strong> (0.5, 1, 2.5<br />
and 4%) and seeds were soaked for 8, 12 and 24 h in<br />
each individual concentration and the results showed<br />
that there was significant difference for duration <strong>of</strong><br />
ageing treatment on germination characteristics <strong>of</strong><br />
maize seeds. Increasing ageing duration resulted<br />
higher reduction in germination characteristics. KNO 3<br />
had positive effects on seed germination <strong>of</strong> aged<br />
seed. This was higher in application <strong>of</strong> 0.5% KNO 3<br />
73
EFFECT OF SEED PRIMING ON BIOCHEMICAL CHANGES DURING SEED STORAGE<br />
for 8 h and 2.5% for 24 h. Antioxidant activity <strong>of</strong><br />
aged seeds increased after seed priming treatments.<br />
Seed priming with hormones was more effective than<br />
seed priming with KNO 3<br />
in activation <strong>of</strong> antioxidant<br />
enzymes. It was suggested that using seed<br />
enhancement treatments like seed priming could<br />
improve aged and non-aged seed performance.<br />
REFERENCES<br />
Castillo F I, Penel I and Greppin H, 1984. Peroxidase<br />
release induced by ozone in Sedum album<br />
leaves, Plant Physiology, 74: 846-851.<br />
Choi Y H, Kobayashi M and Sakurai A, 1996.<br />
Endogenous gibberellins A1 level and á-<br />
amylase activity in germinating rice seeds,<br />
<strong>Journal</strong> <strong>of</strong> Plant Growth Regulation, 15: 147-<br />
151.<br />
Lee S S and Kim J H, 2000. Total sugars, á-amylase<br />
activity, and emergence after priming <strong>of</strong> normal<br />
and aged rice seeds, Korean <strong>Journal</strong> Crop<br />
Sciences, 45:108–111<br />
Saha R, Mandal A K, and Basu R N, 1990. Physiology<br />
<strong>of</strong> invigoration treatments in soybean (Glycine<br />
max L.) Seed Science and Technology, 18:<br />
269–276.<br />
Sathish S and Sundareswaran S, 2010. Biochemical<br />
evaluation <strong>of</strong> seed priming in fresh and aged<br />
seeds <strong>of</strong> maize hybrid (COH (M) 5) and its<br />
parental lines, ISSN vol. 4: 2.<br />
Sung F J M and Chang Y H, 1993. Biochemical<br />
activities associated with priming <strong>of</strong> sweet corn<br />
seeds to improve vigor, Seed Science and<br />
Technology 21, 97–105.<br />
Syed, Ata Siadat, Amir Moosavi and Mehran Sharafi<br />
Zadeh, 2012. Effects <strong>of</strong> seed priming on<br />
antioxidant activity and germination<br />
characteristics <strong>of</strong> maize seeds under different<br />
ageing treatment, <strong>Research</strong> <strong>Journal</strong> <strong>of</strong> Seed<br />
Science, 5: 51-62.<br />
Wattanakulpakin, Songsin Photchanachai, Khanok<br />
Ratanakhanokchai, Khin Lay Kyu, Panumart<br />
Ritthichai and Shuichi Miyagawa, 2012.<br />
Hydropriming effects on carbohydrate<br />
metabolism, antioxidant enzyme activity and<br />
seed vigor <strong>of</strong> maize (Zea mays L.), African<br />
<strong>Journal</strong> <strong>of</strong> Biotechnology, 11(15): 3537-3547.<br />
74
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 70-73, 2013<br />
AN ECONOMIC ANALYSIS OF BLACKGRAM IN<br />
GULBARGA DISTRICT OF KARNATAKA<br />
DEEPAK HEGDE, D. V. SUBBA RAO, N. VASUDEV and K. SUPRIYA<br />
College <strong>of</strong> Agriculture, ANGR Agricultural University, Rajendranagar, Hyderabad-500030<br />
Date <strong>of</strong> Receipt : 23.05.2012 Date <strong>of</strong> Acceptance : 04.08.2012<br />
<strong>The</strong> area <strong>of</strong> traditional cultivation <strong>of</strong> black<br />
gram is confined to the South Asia and adjacent<br />
regions (India, Pakistan, Afghanistan, Bangladesh<br />
and Myanmar). About 70 per cent <strong>of</strong> world’s black<br />
gram production comes from India and it is the largest<br />
producer as well as consumer <strong>of</strong> black gram. It<br />
produces about 1.5 million tonnes <strong>of</strong> blackgram<br />
annually from about 2.5 million hectares <strong>of</strong> area with<br />
an average productivity <strong>of</strong> 400 kg per hectare. Black<br />
gram output accounts for about 10 per cent <strong>of</strong> India’s<br />
total pulse production. <strong>The</strong> major producing states<br />
are Andhra Pradesh, Maharashtra, Orissa, Madhya<br />
Pradesh, Tamil Nadu and Uttar Pradesh. <strong>The</strong> study<br />
was under taken in Karnataka with specific objective<br />
to estimate the costs and margins at different stages<br />
in marketing channel <strong>of</strong> blackgram.<br />
Combination <strong>of</strong> purposive and random<br />
sampling techniques was used for selection <strong>of</strong> district,<br />
markets, market functionaries and farmers required<br />
for the study. Two taluks in this district were selected<br />
based on the probability proportional to the area under<br />
the study. Two villages from each taluk were selected<br />
based on area cultivated under pulses and obtained<br />
yield. <strong>The</strong> required primary data were obtained from<br />
25 sample farmers from each village by interview<br />
method making a sample <strong>of</strong> 100 from the district as<br />
a whole. Farmers are categorized into small and large<br />
based on the land holdings.<br />
It was also intended to study the market<br />
functionaries involved at different stages <strong>of</strong> the value<br />
chain <strong>of</strong> pulses, their marketing costs and margins.<br />
Commission agents (15), traders (15), processors<br />
(15), wholesaler (10) and retailers (10) were selected<br />
at random. Secondary data pertaining to the agro<br />
economic features <strong>of</strong> the study area were collected<br />
from tahaseldar <strong>of</strong>fice and Agriculture Department <strong>of</strong><br />
Gulbarga district. Multiple linear regression model was<br />
fitted to analyze the factors influencing the pr<strong>of</strong>it<br />
margin <strong>of</strong> blackgram.<br />
<strong>The</strong> cost <strong>of</strong> cultivation <strong>of</strong> blackgram was<br />
estimated at Rs.27, 671. It increased with the size<br />
<strong>of</strong> holding from Rs. 27,044 for small farmers to Rs.<br />
28,307 for large farmers (Table.1). It was observed<br />
that the operational costs accounted for a major share<br />
in the total costs on all the categories <strong>of</strong> farms. <strong>The</strong><br />
total operational costs were Rs. 22,972, Rs. 24,057<br />
and Rs. 23,493 for small farmers, large farmers and<br />
the sample as a whole respectively. Higher<br />
operational cost <strong>of</strong> large farmers was due to hiring<br />
more human labour and tractor services and incurring<br />
more cost on manures and fertilizers and plant<br />
protection chemicals.<br />
<strong>The</strong> price spread was studied in two<br />
channels. Commission agents were involved in<br />
channel-I while traders were involved in channel-II.<br />
On an average, the producer incurred a cost <strong>of</strong> Rs.<br />
159 per quintal <strong>of</strong> blackgram towards soot, gunny<br />
bag, labour charges, transportation, weighing (Rs.)<br />
and miscellaneous in channel I. Commission agent<br />
incurred a cost <strong>of</strong> Rs. 90 in channel I. In channel II,<br />
blackgram producer incurred a marketing cost <strong>of</strong> Rs.<br />
199. Total marketing cost incurred by the trader was<br />
Rs. 135. Processor’s marketing cost and wholesalers<br />
marketing costs were the same in both the channels.<br />
Total marketing cost incurred by retailer was Rs. 76.<br />
<strong>The</strong> results were in conformity with the findings <strong>of</strong><br />
Banerjee and Palke (2010).<br />
Because <strong>of</strong> more marketing cost incurred by<br />
farmers and more margins obtained by the<br />
middlemen, producers share in consumer’s rupee was<br />
less in channel II (73 per cent) than in channel-I (78<br />
per cent). Similar views were shared by Govind Pal<br />
(2002). Even though the producers share in<br />
consumer’s rupee was less in channel-II when<br />
compared to channel-I, most <strong>of</strong> the farmers in Jewargi<br />
taluk <strong>of</strong> Gulbarga district were selling their produce<br />
in this channel.<br />
email: deepakhegde236@gmail.com<br />
75
AN ECONOMIC ANALYSIS OF BLACKGRAM IN GULBARGA<br />
<strong>The</strong> total costs (Rs. 798) and margins (Rs.<br />
633) were more in channel-II than channel-I, because<br />
<strong>of</strong> which the price spread was more and producers<br />
share in consumer’s price was less in channel II.<br />
Price spread was Rs. 1093 and Rs. 881 in channel I<br />
and II respectively.<br />
Multiple regression model was employed to<br />
study determinants <strong>of</strong> pr<strong>of</strong>it margin per quintal <strong>of</strong><br />
pulses. Out <strong>of</strong> five variables included in the model<br />
three variables significantly explained the variation<br />
in pr<strong>of</strong>it margin per quintal <strong>of</strong> pulses (R 2 = 0.92).<br />
Operating cost <strong>of</strong> production, marketing cost and<br />
gross price received by the producer were found<br />
significantly influencing pr<strong>of</strong>it margin (Table 3).<br />
<strong>The</strong> coefficient <strong>of</strong> determination (R 2 = 0.92)<br />
showed that about 92 percent <strong>of</strong> the variation in pr<strong>of</strong>it<br />
was explained by the variables included in the model.<br />
<strong>The</strong> result showed that, one rupee increase in<br />
operating cost decreases the pr<strong>of</strong>it margin per quintal<br />
by Rs.0.72. Reduction in the marketing cost increases<br />
the pr<strong>of</strong>it margin per quintal <strong>of</strong> blackgram. If the<br />
marketing cost is increased by one rupee, farmer<br />
experiences less pr<strong>of</strong>it margin (Rs.5.66). If the market<br />
price received by the producer is increased by one<br />
rupee, the pr<strong>of</strong>it margin <strong>of</strong> blackgram will be<br />
increased by Rs.0.74. Education level and fixed cost<br />
<strong>of</strong> production were found to be non-significant.<br />
Table 1. Cost <strong>of</strong> cultivation <strong>of</strong> Blackgram (per hectare)<br />
Sl.No<br />
Particulars<br />
Farmers<br />
Small farmer Large farmer Pooled<br />
1.Operational cost (Rs.)<br />
a Human Labour<br />
i. Owned labours<br />
ii. Hired labours<br />
b. Bullock labour<br />
i. Owned labours<br />
7995 8162 8104<br />
5914 2471 5077<br />
2080 5692 3027<br />
5167 5016 5059<br />
3468 3229 3304<br />
ii.<br />
Hired labours<br />
1699 1787 1754<br />
c<br />
Tractor Power<br />
608 1220 819<br />
d. Seed<br />
e. FYM<br />
f. Fertilizers<br />
g.<br />
Plant protection<br />
chemicals<br />
h. Interest on working<br />
capital<br />
Total operational<br />
cost (Rs.)<br />
a. Land Revenue<br />
752 783 773<br />
1750 1560 1643<br />
2998 3082 3052<br />
2200 2660 2507<br />
1503 1574 1537<br />
22972 24057 23493<br />
100 100 100<br />
76
DEEPAK et al<br />
Table 2 Price spread and marketing margin for blackgram (Rs/qtl)<br />
S.No<br />
Particulars<br />
Channel I (Rs/qtl)<br />
Blackgram<br />
Channel II (Rs/qtl)<br />
1 Producer<br />
a) Gross price received 3371 3198<br />
b) Marketing cost 159 199<br />
Net price received 3211 2999<br />
2 Commission Agent<br />
a) Marketing cost 90 --<br />
b) Margin 69<br />
3 Trader<br />
a) Purchase price 3198<br />
b) Marketing cost -- 135<br />
c) Selling price 3450<br />
d) Margin 117<br />
4 Processor<br />
a) Purchase price 3529 3450<br />
b) Marketing cost 126 126<br />
c) Processing cost 199 199<br />
d) Selling price per quintal <strong>of</strong> dal Dall 5790 5790<br />
e)<br />
Selling price <strong>of</strong> per quintal <strong>of</strong> dal<br />
multiplied with conversion factor<br />
(CF)<br />
3821 3821<br />
f) Margin 166 245<br />
5 5 Wholesaler Wholesaler<br />
a)<br />
a)<br />
Purchase<br />
Purchase<br />
price<br />
price<br />
3821<br />
3821<br />
3821<br />
3821<br />
b)<br />
b)<br />
Marketing<br />
Marketing<br />
cost<br />
cost<br />
62<br />
62<br />
62<br />
62<br />
c)<br />
c)<br />
Selling price per quintal <strong>of</strong> dall<br />
Selling price per quintal <strong>of</strong> dall<br />
5950<br />
5950<br />
5950<br />
5950<br />
d)<br />
d)<br />
Selling price <strong>of</strong> per quintal <strong>of</strong> dal<br />
Selling price <strong>of</strong> per quintal <strong>of</strong> dal<br />
multiplied with CF<br />
multiplied with CF<br />
3927<br />
3927<br />
3927<br />
3927<br />
e)<br />
e)<br />
Margin<br />
Margin<br />
106<br />
106<br />
106<br />
106<br />
6<br />
6<br />
Retailer<br />
Retailer<br />
a)<br />
a)<br />
Purchase price<br />
Purchase price<br />
3927<br />
3927<br />
3927<br />
3927<br />
b)<br />
b)<br />
Marketing cost<br />
Marketing cost<br />
76<br />
76<br />
77<br />
77<br />
c)<br />
c)<br />
Selling price per quintal <strong>of</strong> dall<br />
Selling price per quintal <strong>of</strong> dall<br />
6200<br />
6200<br />
6200<br />
6200<br />
77
AN ECONOMIC ANALYSIS OF BLACKGRAM IN GULBARGA<br />
Table 3 Regression estimates <strong>of</strong> determinants <strong>of</strong> pr<strong>of</strong>it margin for blackgram<br />
Y= Pr<strong>of</strong>it margin per quintal <strong>of</strong> blackgram<br />
Variables Coefficients Standard Error t Stat<br />
Intercept<br />
Operating cost <strong>of</strong> production<br />
Fixed cost <strong>of</strong> production<br />
455.30* 176.86 2.57<br />
-0.72** 0.10 -6.85<br />
0.02 0.27 0.06<br />
Marketing cost -5.66 ++ 2.62 -2.16<br />
Market price <strong>of</strong> the produce 0.74** 0.09 8.01<br />
Education level 455.30 176.86 2.57<br />
F value 27.61<br />
R 2 0.92<br />
Adj. R 2 0.89<br />
N 18<br />
++<br />
Significant at 5 % level, * Significant at 5 % level, ** Significant at 1 % level<br />
Note: Operating cost and fixed costs <strong>of</strong> production includes total <strong>of</strong> all components<br />
REFERENCES<br />
Banerjee, Gangadhar and Palke, L. M. 2010.<br />
Economics <strong>of</strong> Pulses Production and<br />
Processing <strong>of</strong> India. National Bank for<br />
Agriculture and Rural Development.<br />
Occasional Paper - 51<br />
Govind Pal. 2002. Marketing <strong>of</strong> gram in Block<br />
Shahabganj; district Chandauli, Uttar Pradesh<br />
(an economic analysis). Indian <strong>Journal</strong> <strong>of</strong><br />
Agricultural Economics. 57(3): 388<br />
78
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 74-78, 2013<br />
GENE ACTION AND COMBINING ABILITY STUDIES IN CHICKPEA<br />
(Cicer arietinum L.)<br />
B. REDDY YAMINI, V. JAYALAKSHMI, B. NARENDRA and P. UMAMAHESHWARI<br />
Department <strong>of</strong> Genetics and Plant Breeding, Agricultural College,<br />
Acharya N.G.Ranga Agricultural University, Mahanandi -518 503<br />
Date <strong>of</strong> Receipt : 07.06.2012 Date <strong>of</strong> Acceptance : 26.12.2012<br />
Chickpea (Cicer arietinum L.) 2n=2x=16, is<br />
the third most important food legume globally,<br />
occupying an area <strong>of</strong> 11.55 m ha with a production <strong>of</strong><br />
10.46 m t (FAO STAT 2010). India is the largest<br />
producer <strong>of</strong> chickpea in the world sharing about 72%<br />
<strong>of</strong> area and production globally and accounts for about<br />
30% and 38% <strong>of</strong> national pulse acreage and<br />
production respectively. Though India is the largest<br />
producer <strong>of</strong> chickpea, the productivity is low (943<br />
kg/ha) compared to other chickpea producing<br />
countries viz., Mexico (1809 kg/ha), Australia (1268<br />
kg/ha) and Ethiopia (1265 kg/ha) and the production<br />
is not adequate to meet the domestic demand.<br />
Consequently India is importing chickpeas every<br />
year. Hence, there is every need to to improve the<br />
productivity potential <strong>of</strong> chickpea through appropriate<br />
breeding strategies. Choice <strong>of</strong> an appropriate breeding<br />
procedure for improving a trait depends mainly on<br />
the nature <strong>of</strong> gene action involved in the inheritance<br />
<strong>of</strong> the character, thus, emphasizing the importance<br />
<strong>of</strong> genetic analysis for yield and its components. In<br />
the present investigation, an attempt has been made<br />
to assess the nature <strong>of</strong> gene effects for yield and its<br />
component characters following the diallel analyses,<br />
so as to design breeding strategies for improvement<br />
<strong>of</strong> chickpea yield potential.<br />
<strong>The</strong> experimental material consisted <strong>of</strong> seven<br />
parents and also twenty-one F 1<br />
s, derived from seven<br />
parental genotypes viz., NBeG-3, JG-11, ICCV 05106,<br />
MNK-1, ICCV 95333, KAK-2 and Vihar, crossed in<br />
diallel fashion excluding reciprocals. <strong>The</strong><br />
experimental material was sown in a Randomized<br />
Block Design with three replications during Rabi 2011-<br />
12. Data were recorded on five competitive randomly<br />
selected plants per replication from each treatment<br />
for seven characters viz., days to 50 per cent<br />
flowering, days to maturity, plant height, number <strong>of</strong><br />
branches per plant, number <strong>of</strong> pods per plant, seed<br />
yield per plant and 100-seed weight. Data is subjected<br />
to combining ability analysis according to Model I<br />
and Method II <strong>of</strong> Griffing (1956).<br />
<strong>The</strong> analysis <strong>of</strong> variance revealed significant<br />
differences among the treatments for all the seven<br />
traits indicating considerable amount <strong>of</strong> variability<br />
thus justifying the use <strong>of</strong> material under study.<br />
Analysis <strong>of</strong> variance for combining ability (Table 1)<br />
revealed significant general combining ability (gca)<br />
and specific combining ability (sca) for all the<br />
characters studied, indicating the importance <strong>of</strong> both<br />
additive as well as non additive genetic components<br />
<strong>of</strong> variation in the expression <strong>of</strong> these attributes.<br />
Importance <strong>of</strong> both types <strong>of</strong> gene effects has been<br />
observed earlier in chickpea for seed yield and related<br />
attributes by Preethi Verma and Waldia (2010),<br />
Bharadwaj et al (2009) and Patil et al (2006).<br />
However, variance components indicated that the<br />
magnitude <strong>of</strong> the non additive (sca) variance was<br />
considerably higher than additive (gca) variance for<br />
all the characters except plant height and 100 seed<br />
weight, indicating the preponderance <strong>of</strong> non additive<br />
genetic effects (dominance and epistasis) in<br />
controlling the expression <strong>of</strong> these characters. <strong>The</strong><br />
predominance <strong>of</strong> non additive gene action was<br />
reported by Sarode et al. (2001) for days to 50 per<br />
cent flowering and days to maturity; Bhardwaj and<br />
Sandhu (2009) for number <strong>of</strong> branches per plant,<br />
number <strong>of</strong> pods per plant and seed yield per plant.<br />
Gupta et al. (2007) reported the importance <strong>of</strong> additive<br />
gene action in inheritance <strong>of</strong> plant height and 100<br />
seed weight.<br />
<strong>The</strong> estimates <strong>of</strong> gca effects (Table 2)<br />
indicated that parents NBeG-3, JG-11, ICCV 05106<br />
and Vihar were good general combiners for number<br />
<strong>of</strong> pods per plant, where as NBeG-3 and JG-11<br />
showed significantly higher gca effects for number<br />
<strong>of</strong> branches per plant. With regard to phenological<br />
email: reddyyamini56@gmail.com<br />
79
GENE ACTION AND COMBINING ABILITY STUDIES IN CHICKPEA<br />
S.<br />
No.<br />
Character<br />
gca<br />
(df=6)<br />
sca<br />
(df=21)<br />
error<br />
(df=54)<br />
² gca<br />
² sca<br />
² gca<br />
/ ²<br />
sca<br />
1 Days to 50% flowering 40.42 ** 12.15 ** 2.43 4.22 9.72 0.43 0.41<br />
2 Days to maturity 9.49 ** 2.00 ** 0.62 0.99 1.38 0.71 0.50<br />
3 Plant height (cm) 70.00 ** 6.30 ** 1.13 7.65 5.17 1.48 0.71<br />
h 2<br />
4<br />
5<br />
Number <strong>of</strong> branches<br />
per plant<br />
Number <strong>of</strong> pods per<br />
plant<br />
37.03 ** 18.75 ** 0.61 4.05 18.14 0.22 0.18<br />
533.32 ** 360.87 ** 1.28 59.12 359.60 0.16 0.25<br />
6 Seed yield per plant (g) 7.16 ** 30.70 ** 0.16 0.78 30.54 0.03 0.05<br />
7 100 seed weight (g) 276.73 ** 20.19 ** 1.35 30.60 18.83 1.62 0.75<br />
* Significant at Pd”0.05, ** Significant at P d”0.01, gca – general combining ability; sca – specific combining<br />
ability; s² gca – variance due to gca ; s² sca – variance due to sca. h 2 - heritability in narrow sense<br />
Table 2. Estimates <strong>of</strong> general combining (gca) effects for seven yield attributes in Chickpea<br />
S.<br />
No.<br />
Genotype<br />
Character<br />
NBeG-3 JG-11<br />
ICCV0<br />
5106<br />
M N<br />
K-1<br />
ICCV9<br />
5333<br />
KAK-2<br />
Vihar<br />
S.Em<br />
<strong>of</strong> Gi<br />
S.Em<br />
<strong>of</strong><br />
Gi-Gj<br />
1<br />
Days to 50<br />
percent Flowering -0.476 -3.03** -2.59 ** 1.97 ** 1.60 ** 0.49 2.04 ** 0.48 0.73<br />
2 Days to maturity -1.00 ** -1.15 ** -0.30 0.85 ** 0.00 -0.185 1.78 ** 0.24 0.37<br />
3 Plant height -2.02 ** -2.75 ** 0.53 4.92 ** 1.91 ** -2.63 ** 0.04 0.33 0.50<br />
4<br />
Number <strong>of</strong><br />
branches per<br />
plant<br />
2.22 ** 2.85 ** -0.01 -3.18 ** -1.08 ** -0.55 * -0.25<br />
0.24 0.37<br />
5<br />
6<br />
7<br />
Number <strong>of</strong> pods<br />
per plant<br />
Seed yield per<br />
plant<br />
100 seed<br />
weight<br />
2.77 ** 12.19 ** 3.16 ** -12.95 ** -3.95 ** -2.22 ** 1.02 ** 0.35 0.53<br />
0.76 ** 0.46 ** -0.71 ** -1.41 ** -<br />
0.56 ** 0.51** 0.96 ** 0.12 0.19<br />
-3.71 ** -6.61 ** -3.34 ** 10.31 ** 3.00 ** -0.38 0.73 * 0.36 0.55<br />
*Significant at Pd” 0.05 , **Significant at Pd”0.01<br />
traits, JG-11 and ICCV 05106 recorded significant<br />
desirable negative gca effects for days to 50 per cent<br />
flowering, while, NBeG-3 and JG-11 for days to<br />
maturity, suggesting that these parents could be good<br />
general combiners for breeding for earliness. <strong>The</strong> high<br />
general combining ability for plant height and 100<br />
seed weight was recorded in MNK-1 and ICCV 95333.<br />
For seed yield per plant the genotypes viz., NBeG-3,<br />
80
YAMINI et al<br />
Table 3. Estimates <strong>of</strong> specific combining (Sca) effects for seven yield attributes in Chickpea<br />
S.<br />
No Cross D 50 F DM PH NB/P NP/P SY/P<br />
100<br />
SW<br />
1 NBeG -3 x JG-11 2.56 -1.77* 2.43* 1.81* -11.74** 3.42** 1.64<br />
2<br />
3<br />
NBeG-3 x ICCV05106 2.44 0.71 -0.52 -4.76** 7.15**<br />
NBeG-3 x MNK-1 -2.44 -1.77* -0.48 1.68* -4.44**<br />
-<br />
4.76**<br />
-<br />
11.79**<br />
-<br />
3.44** -4.88**<br />
4 NBeG-3 x ICCV 95333 1.26 0.75 -0.84 7.77** 31.99** 5.53** 0.6<br />
5 NBeG-3 x KAK-2 1.37 0.27 -1.19 2.05** 4.32** 0.82* 5.52**<br />
6 NBeG-3 x Vihar -3.85** -1.03 2.31* -0.43 3.64** 7.51** 1.27<br />
7 JG-11 x ICCV 05106 2.67 -0.47 1.14 5.53** 23.63** 5.67** 1.35<br />
8 JG-11 x MNK-1 6.11** 0.05 4.57** 1.04 -0.48 4.66** -2.15*<br />
9 JG-11 x ICCV95333 -3.52* 0.23 -4.83** -2.23** -7.2** -4.38** -4.89**<br />
10 JG-11 x KAK-2 -3.41* -1.25 1.55 2.02** 33.29** 6.1** -1.72<br />
11 JG-11 x Vihar -5.3** -1.55* -0.21 -0.06 30.05** 0.96** -0.77<br />
12 ICCV05106 x MNK-1 -2.33 1.86* 0.93 2.1** -0.36 0.66 1.33<br />
13<br />
ICCV05106 X ICCV 95333 -5.96** 0.05 2.87** 1.13 4.66** 0.06 5.85**<br />
14 ICCV05106 x KAK-2 -2.85* 0.9 -1.57 -3.62** -15.4** -0.21 1.83<br />
15 ICCV05106 x Vihar -2.41 -2.4** -0.3 7.63** 20.86** 8.78** -0.57<br />
16 MNK-1 x ICCV 95333 -0.52 -0.44 0.86 -3.08** 0.66 2.33** 4.77**<br />
17 MNK-1 x KAK-2 -3.41* -0.58 -4.61** 5.78** 11.51** 4.47** -6.82**<br />
18 MNK-1 x Vihar 1.7 -0.55 0.79 -5.53** -8.45** -3.95** 2.37*<br />
19 ICCV95333 x KAK-2 3.3* -1.07 1.6 3.69** 4.04** 4.61** 1.4<br />
20 ICCV 95333 x Vihar 1.74 -0.36 -3.35** 0.59 0.69 -0.64 -2.26*<br />
21 KAK-2 x Vihar 2.85* 0.16 2.92** -4.61** -6.99** 1.42 4.38**<br />
S.E <strong>of</strong> Sii-Sjj 1.64 0.83 1.12 0.82 1.19 0.42 1.23<br />
S.E <strong>of</strong> Sij-Sik 2.08 1.05 1.42 1.04 1.51 0.53 1.55<br />
S.E <strong>of</strong> Sij-Skl 1.94 0.98 1.33 0.98 1.41 0.49 1.45<br />
* Significant at Pd” 0.05, ** Significant at Pd” 0.01<br />
D 50 F= Days to 50% flowering, DM= Days to maturity, PH= Plant height, NB/P= Number <strong>of</strong> branches per<br />
plant, NP/P= Number <strong>of</strong> pods per plant, SY/P= Seed yield per plant, 100 SW=100 seed weight.<br />
JG-11 and Vihar exhibits highly significant positive<br />
gca effects indicating that they were good general<br />
combiners for seed yield. So, these parents can be<br />
exploited for the development <strong>of</strong> improved lines <strong>of</strong><br />
chickpea, because high gca effects are mostly due<br />
to additive gene effect or additive x additive<br />
interaction effects which are fixable (Griffing, 1956).<br />
<strong>The</strong> sca effect is an important criterion for<br />
the evaluation <strong>of</strong> hybrids. Among the various gene<br />
interactions contributing towards sca, the additive x<br />
additive type <strong>of</strong> gene interaction is fixable in<br />
segregating generations in self pollinated crops like<br />
chickpea. More over ultimate aim <strong>of</strong> a plant breeder<br />
is to develop potential homozygous lines through<br />
hybridization. <strong>The</strong> cross combinations with significant<br />
desirable sca effects along with mean performance<br />
and gca effects <strong>of</strong> the parents are listed in Table 3.<br />
JG-11 x Vihar had significant desirable sca effects<br />
for seed yield per plant and number <strong>of</strong> pods per plant<br />
81
GENE ACTION AND COMBINING ABILITY STUDIES IN CHICKPEA<br />
and also gave highly significant negative sca effects<br />
for days to 50 per cent flowering and days to maturity.<br />
A few crosses viz., JG-11 x ICCV 95333, JG-11 x<br />
KAK-2, ICCV 05106 x ICCV 95333 and ICCV 05106<br />
x KAK-2 also recorded highly significant sca effects<br />
as well as low mean values for days to 50 per cent<br />
flowering, indicating early maturity and these crosses<br />
involved only one good combiner suggesting additive<br />
x dominance gene effects in these crosses. In these<br />
crosses, additive component present in good<br />
combiners and the complimentary epistatic effects<br />
in F 1<br />
hybrid might work in the same direction to<br />
maximize the desirable effects <strong>of</strong> this trait in<br />
segregants. Promising crosses viz., NBeG-3 x ICCV<br />
95333, JG-11 x ICCV 05106, JG-11 x KAK-2 and<br />
ICCV 05106 x Vihar exhibited significant sca effects<br />
Table 4. Specific combining ability effects, mean performance and general combining ability effects<br />
<strong>of</strong> parents and promising crosses for yield and yield attributes<br />
Character<br />
Days to 50 per<br />
cent flowering<br />
Crosses with high per se<br />
performance and significant<br />
sca effects<br />
JG-11 x ICCV 95333<br />
JG-11 x KAK-2<br />
JG-11 x Vihar<br />
ICCV 05106 x ICCV 95333<br />
ICCV 05106 x KAK-2<br />
sca effects<br />
-3.52*<br />
-3.41*<br />
-5.3**<br />
-5.96**<br />
-2.85*<br />
Per se<br />
performa<br />
nce<br />
35.3<br />
34.3<br />
34.0<br />
33.3<br />
35.3<br />
gca effects <strong>of</strong><br />
parents<br />
G x P<br />
G x P<br />
G x P<br />
G x P<br />
G x P<br />
Days to maturity NBeG-3 x JG-11 -1.77* 88.3 G x G<br />
Plant height JG-11 x MNK-1<br />
4.57** 47.7 P x G<br />
ICCV 05106 x ICCV 95333 2.87** 46.3 P x G<br />
Number <strong>of</strong><br />
branches per<br />
plant<br />
Number <strong>of</strong> pods<br />
per plant<br />
Seed yield per<br />
plant<br />
NBeG-3 x JG-11<br />
NBeG-3 x ICCV 95333<br />
NBeG-3 x KAK-2<br />
JG-11 x ICCV 05106<br />
JG-11 x KAK-2<br />
ICCV 05106 x Vihar<br />
NBeG-3 x ICCV 05106<br />
NBeG-3 x ICCV 95333<br />
JG-11 x ICCV 05106<br />
JG-11 x KAK-2<br />
JG-11 x Vihar<br />
ICCV 05106 x Vihar<br />
NBeG-3 x JG-11<br />
NBeG-3 x ICCV 95333<br />
NBeG-3 x Vihar<br />
JG-11 x ICCV 05106<br />
JG-11 x MNK-1<br />
JG-11 x KAK-2<br />
ICCV 05106 x Vihar<br />
MNK-1 x KAK-2<br />
ICCV 95333 x KAK-2<br />
100 seed weight ICCV 05106 x ICCV 95333<br />
MNK-1 x ICCV 95333<br />
MNK-1 x Vihar<br />
KAK-2 x Vihar<br />
1.81*<br />
7.77**<br />
2.05**<br />
5.53**<br />
2.02**<br />
7.63**<br />
7.15**<br />
31.99**<br />
23.63**<br />
33.29**<br />
30.05**<br />
20.86**<br />
3.42**<br />
5.53**<br />
7.51**<br />
5.67**<br />
4.66**<br />
6.1**<br />
8.78**<br />
4.47**<br />
4.61**<br />
5.85**<br />
4.77**<br />
2.37*<br />
4.38**<br />
27.5<br />
29.5<br />
24.3<br />
29.0<br />
24.9<br />
28.0<br />
58.5<br />
76.3<br />
84.4<br />
88.7<br />
88.7<br />
70.5<br />
20.7<br />
21.8<br />
25.3<br />
21.5<br />
19.8<br />
23.1<br />
25.1<br />
19.6<br />
20.6<br />
38.5<br />
51.1<br />
46.4<br />
37.7<br />
G x G<br />
G x P<br />
G x P<br />
G x P<br />
G x P<br />
P x P<br />
G x G<br />
G x P<br />
G x G<br />
G x P<br />
G x G<br />
G x G<br />
G x G<br />
G x P<br />
G x G<br />
G x P<br />
G x P<br />
G x G<br />
P x G<br />
P x G<br />
P x G<br />
P x G<br />
G x G<br />
G x G<br />
P x G<br />
82
YAMINI et al<br />
coupled with high per se performance for seed yield<br />
and yield attributes like number <strong>of</strong> branches and<br />
number <strong>of</strong> pods per plant. For plant height and 100<br />
seed weight ICCV 05106 x ICCV 95333 (poor x good)<br />
registered significant sca effect with high mean<br />
values. Hence this cross could be exploited for<br />
identifying tall and bold seeded genotypes in the<br />
segregating generations.<br />
<strong>The</strong> results <strong>of</strong> the present investigation<br />
revealed the preponderance <strong>of</strong> non additive gene<br />
action for yield and yield components and therefore<br />
heterosis breeding may be rewarding for improving<br />
chickpea. But the practical production <strong>of</strong> hybrid gram<br />
is not biologically feasible due to small size and<br />
cleistogamous nature <strong>of</strong> the flowers and strong<br />
hybridization barriers (Preethi Verma and Waldia,<br />
2010). In view <strong>of</strong> such problems, Jensen’s (1970)<br />
selective diallel mating system and its modifications<br />
(Frey, 1975) would be utilized for the creation and<br />
isolation <strong>of</strong> recombinants to breed superior chickpea<br />
varieties. <strong>The</strong> crosses NBeG-3 x JG-11 for seed yield<br />
and number <strong>of</strong> branches, NBeG-3 x ICCV 05106, JG-<br />
11 x ICCV 05106 and ICCV 05106 x Vihar for number<br />
<strong>of</strong> pods per plant and NBeG-3 x Vihar and JG-11 x<br />
KAK-2 for seed yield per plant exhibited significant<br />
sca effects coupled with high per se performance<br />
with good x good combiners. Due to additive x additive<br />
effects and their possibility <strong>of</strong> fixation, single plant<br />
selection could be practiced in segregating<br />
generations to isolate purelines from these crosses.<br />
REFERENCES<br />
Bhardwaj, R and Sandhu, J. S. 2009. Components<br />
<strong>of</strong> variance analysis in chickpea. Jounal <strong>of</strong><br />
Food Legumes. 22(4): 254-255.<br />
Bharadwaj, R., Sandhu, J. S., and Gupta, S. K. 2009.<br />
Gene action and combining ability estimates<br />
for yield and other quantitative traits in<br />
chickpea. Indian <strong>Journal</strong> <strong>of</strong> Agricultural<br />
Sciences. 79: 895-900.<br />
Frey, K. J. 1975. Breeding concepts and techniques<br />
for self pollinated crops. Proceedings <strong>of</strong><br />
International workshop on grain legumes,<br />
ICRSAT, Patancheru, India. 257-278.<br />
Griffing, B. 1956. A generalized treatment <strong>of</strong> the use<br />
<strong>of</strong> diallel cross in quantitative inheritance.<br />
Heredity. 10:31-34.<br />
Jensen, N. F. 1970. A diallel selective mating system<br />
for cereal breeding. Crop Science. 10:629-635.<br />
Patil. J. V. Kulkarni, S. S and Gawande, V. L. 2006.<br />
Genetics <strong>of</strong> quantitative characters in chickpea<br />
(Cicer arietinum L.). New Botanist- International<br />
<strong>Journal</strong> <strong>of</strong> Plant Science <strong>Research</strong>. 33:1-4.<br />
Preethi Verma and Waldia, R. S. 2010. Diallel<br />
analysis for nodulation and yield contributing<br />
traits in chickpea. <strong>Journal</strong> <strong>of</strong> Food Legumes.<br />
23 (2): 117-120.<br />
Sarode, N. D. Deshmukh, R. B. Kute, N. S.<br />
Kanawade, D. G and Dhonde, S. R. 2001.<br />
Genetic analysis in chickpea (Cicer arietinum<br />
L.). Legume <strong>Research</strong>. 24:3, 159-163.<br />
Gupta, S. K. Kaur Ajinder and Sandu, J. S. 2007.<br />
Combining ability in desi chickpea. Indian<br />
<strong>Journal</strong> <strong>of</strong> Pulses <strong>Research</strong>. 20(1):22-24.<br />
83
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 79-82, 2013<br />
GENETIC DIVERGENCE IN BRINJAL (Solanum melongena L.)<br />
BALAJI LOKESH, P.SURYANARAYANA REDDY, R.V.S.K.REDDY and N.SIVARAJ<br />
Vegetable <strong>Research</strong> Station, A.R.I, Rajendranagar, Hyderabad-500030<br />
Date <strong>of</strong> Receipt : 07.12.2012 Date <strong>of</strong> Acceptance : 25.01.2013<br />
<strong>The</strong> study was conducted at Vegetable<br />
<strong>Research</strong> Station, Agricultural <strong>Research</strong> Institute,<br />
Rajendranagar, Hyderabad,during rabi season <strong>of</strong><br />
2008-09. Sixty genotypes <strong>of</strong> Brinjal collected from<br />
various agro-climatic regions <strong>of</strong> India by N.B.P.G.R<br />
Regional station, Hyderabad were evaluated in a<br />
Randomized Block Design with two replications for<br />
fourteen quantitative characters. <strong>The</strong> mean data were<br />
analyzed following standard statistical techniques<br />
with the objective <strong>of</strong> studying the nature and<br />
magnitude <strong>of</strong> genetic diversity available in the<br />
germplasm. Genetic diversity is an important factor<br />
for any heritable improvement. Knowledge <strong>of</strong> genetic<br />
diversity is useful for selecting desirable genotypes<br />
from a germplasm for the successful breeding<br />
programme. <strong>The</strong> genetic divergence between<br />
genotypes was estimated using Mahalanobis D 2<br />
statistic (1936).<br />
In the present investigation, <strong>The</strong> D 2 value<br />
was used for the final grouping <strong>of</strong> the genotypes into<br />
eight distinct clusters as presented in Table 1. Cluster<br />
V was the largest cluster consisting <strong>of</strong> 15 genotypes<br />
while cluster VIII consisted <strong>of</strong> single genotype i.e,<br />
MR/04-26. <strong>The</strong> mean inter and intra cluster D and D 2<br />
values (Table 2) suggest that the genotypes within a<br />
cluster are less divergent than those <strong>of</strong> different<br />
clusters. <strong>The</strong> intra cluster and inter cluster D 2 values<br />
ranged from 714.92 to 2452.22 and 2339.92 to<br />
35995.80 respectively. Lowest inter cluster D 2 value<br />
was recorded between cluster I and II (2339.92)<br />
indicating close relationship and similarity for most<br />
<strong>of</strong> the characters <strong>of</strong> the genotypes. Highest inter<br />
cluster D 2 value was recorded between clusters III<br />
and VIII (35995.80) indicating wider genetic diversity<br />
among genotypes in these groups.<br />
<strong>The</strong> percentage contribution <strong>of</strong> each<br />
character towards divergence in Brinjal is presented<br />
in Table 3.Highest contribution towards divergence<br />
was put forth by average fruit weight (44.12%)<br />
followed by plant spread(43.62%), average fruit length<br />
(4.52%), number <strong>of</strong> branches per plant (2.71%),<br />
number <strong>of</strong> flower clusters per plant (2.03%) and<br />
average fruit diameter (1.41%) respectively. This<br />
suggest that in order to select genotypes for<br />
hybridization, the material should be screened for<br />
important traits like average fruit weight, plant<br />
spread, average fruit length, number <strong>of</strong> branches per<br />
plant, number <strong>of</strong> flower clusters per plant and average<br />
fruit diameter. Similar results were reported by Satesh<br />
Kumar et al. (2007).<br />
<strong>The</strong> mean performance <strong>of</strong> genotypes <strong>of</strong><br />
clusters is presented in the Table 4. In calculation <strong>of</strong><br />
cluster means, the superiority <strong>of</strong> a particular genotype<br />
with respect to a given character gets diluted by other<br />
related genotypes that are grouped in the same<br />
cluster which are inferior or intermediary for that<br />
character in question. Hence, apart from selecting<br />
genotypes from the clusters which have high intercluster<br />
distance for hybridization, it is also desirable<br />
to have selection <strong>of</strong> parents based on extent <strong>of</strong><br />
genetic divergence with respect to a particular<br />
character <strong>of</strong> interest. On the basis <strong>of</strong> the mean<br />
performance <strong>of</strong> the characters in each cluster, the<br />
following genotypes were identified as superior for<br />
further genetic studies. <strong>The</strong> genotypes IC-111431 and<br />
IC-203593 were regarded as superior for number <strong>of</strong><br />
flower clusters per plant. MR/04-26 was superior for<br />
days to 50% flowering, average fruit weight, shoot<br />
and fruit borer incidence on fruit and fruit yield per<br />
plant.IC-111352 and IC-111428 were regarded as<br />
superior for number <strong>of</strong> fruits per cluster.IC-13601 and<br />
PSR-11883 were regarded as superior for number <strong>of</strong><br />
fruits per plant. Similar genetic divergence studies<br />
on Brinjal in India have been carried out by many<br />
researchers (Bansal and Mehta 2007, Sherly and<br />
Shanthi 2007, Nandan Mehta and Mayuri Sahu 2009).<br />
email: balajilokesh4@gmail.com<br />
84
LOKESH et al<br />
Table 1. Clustering pattern <strong>of</strong> 60 germplasm accessions <strong>of</strong> Brinjal on the basis <strong>of</strong> Mahalanobis D 2<br />
statistics<br />
Cluster<br />
No <strong>of</strong><br />
genotypes<br />
Genotypes<br />
I 5 IC-089876-B,IC-111384,IC-111431,IC-111468,IC-203593<br />
II 11 IC-104086,IC-345309,IC-89910,PSR-11836,IC-13637,<br />
IC-111461-B,IC-111308,AR04-131,IC-111317,<br />
IC-345255,IC-135056<br />
III 5 IC-245335,DBT/OR-37,MR/04-02,PSR 11773,MR/04-81<br />
IV 11 IC-13601,IC-135920,IC-90930,IC-256208,IC-137751,<br />
PSR-11883,IC-111086,IC-136280,IC-127024,AR/04-132,IC-111071<br />
V 15 IC-111352,IC-111356,IC-383119,PSR-11891,IC-99649,<br />
IC-256150,IC-90767,IC-111444,IC-111074,IC-112851,<br />
IC-111428,IC-089905,IC-112755,MR/04-94,IC-136278<br />
VI 8 IC-136006,IC-136245,IC-135934,IC-136088,<br />
IC-104086,AR/04-145,MR/04-88,IC-90087<br />
VII 4 IC-135955,IC-74204,AR/04-477,IC-11404<br />
VIII 1 MR/04-26<br />
Table 2. Average intra and inter cluster D 2 and D values for eight clusters in 60 germplasm accessions<br />
<strong>of</strong> Brinjal<br />
Cluster I II III IV V VI VII VIII<br />
I 714.92<br />
2339.92<br />
6693.23<br />
6998.93<br />
3349.35<br />
8799.52<br />
4568.94<br />
32737.66<br />
(26.73)<br />
(48.37)<br />
(81.81)<br />
(83.65)<br />
(57.87)<br />
(93.80)<br />
(67.59)<br />
(180.93)<br />
II 1414.06<br />
3047.26<br />
5279.60<br />
3970.72<br />
10954.37<br />
8873.22<br />
31216.35<br />
(37.60)<br />
(55.20)<br />
(72.66)<br />
(63.01)<br />
(104.66)<br />
(94.19)<br />
(176.68)<br />
III 2452.22<br />
7563.46<br />
8260.76<br />
17611.32<br />
16941.25<br />
35995.80<br />
(49.51)<br />
(86.96)<br />
(90.88)<br />
(132.70)<br />
(130.15)<br />
(189.72)<br />
IV 1937.25<br />
3064.44<br />
5211.95<br />
8650.18<br />
14041.89<br />
(44.01)<br />
(55.35)<br />
(72.19)<br />
(93.00)<br />
(118.49)<br />
V 1701.21<br />
3914.04<br />
3986.40<br />
18843.24<br />
(41.24)<br />
(62.56)<br />
(63.13)<br />
(137.27)<br />
VI 1451.12<br />
(38.09)<br />
3899.03<br />
(62.44)<br />
10777.12<br />
(103.81)<br />
VII 1299.78<br />
(36.05)<br />
23498.27<br />
(153.29)<br />
VIII 0.00<br />
(0.00)<br />
<strong>The</strong> figures in the parenthesis are D values<br />
85
GENETIC DIVERGENCE IN BRINJAL<br />
Table 3. Percent contribution <strong>of</strong> characters towards diversity in Brinjal germplasm<br />
S.No Character Percent<br />
contribution<br />
1 Plant height (cm) 0.23<br />
2 Plant spread(cm 2 ) 43.62<br />
3 Number <strong>of</strong> branches per plant 2.71<br />
4 Days to 50% flowering 0.00<br />
5 Number <strong>of</strong> flower clusters per plant 2.03<br />
6 Number <strong>of</strong> flowers per cluster 0.00<br />
7 Number <strong>of</strong> fruits per cluster 0.00<br />
8 Fruit length (cm) 4.52<br />
9 Fruit diameter (cm) 1.41<br />
10 Fruit weight (g) 44.12<br />
11 Number <strong>of</strong> fruits per plant 0.23<br />
12 Shoot and fruit borer incidence on shoot (%) 0.00<br />
13 Shoot and fruit borer incidence on fruit (%) 0.28<br />
14 Yield/ plant (kg) 0.85<br />
REFERENCES<br />
Bansal, S and Mehta, A. K. 2007. Genetic divergence<br />
in brinjal (Solanum melongena L.) Haryana<br />
<strong>Journal</strong> <strong>of</strong> Horticultural Sciences 36(3/4): 319-<br />
320.<br />
Mahalanobis, P. C. 1936. On the generalized distance<br />
in statistics. Proceedings <strong>of</strong> National Institute<br />
<strong>of</strong> Sciences, India 12: 49- 55.<br />
Nandan Mehta and Mayuri Sahu. 2009. Genetic<br />
divergence in brinjal (Solanum melongena L.)<br />
International <strong>Journal</strong> <strong>of</strong> Plant Sciences,<br />
Muzaffarnagar 4(1): 123-124.<br />
Satesh Kumar, Singh, A. K, Sharma, J. P and Neerja<br />
Sharma. 2007. Genotype clustering in brinjal<br />
(Solanum melongena L.) using D 2 statistic.<br />
Haryana <strong>Journal</strong> <strong>of</strong> Horticultural Sciences<br />
36(1/2): 95-96.<br />
Sherly, J and Shanthi, A. 2007. Diversity studies in<br />
brinjal. Haryana <strong>Journal</strong> <strong>of</strong> Horticultural<br />
Sciences 36(1/2): 162-163.<br />
86
LOKESH et al<br />
Table 4. Mean values <strong>of</strong> clusters for fourteen characters in 60 brinjal germplasm accessions<br />
87
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 82-87, 2013<br />
RELATIONSHIP BETWEEN PROFILE OF BENEFICIARY FARMERS AND THE<br />
SOCIO-ECONOMIC IMPACT OF IRRIGATED AGRICULTURE MODERNIZATION<br />
AND WATER BODIES RESTORATION AND MANAGEMENT (IAMWARM)<br />
PROJECT IN PUDUKKOTTAI DISTRICT<br />
G. ABIRAMI, B.VIJAYABHINANDANA and T. GOPI KRISHNA<br />
Department <strong>of</strong> Agricultural Extension, Agricultural College,<br />
Acharya N.G Ranga Agricultural University, Bapatla- 522 101<br />
Date <strong>of</strong> Receipt : 10.09.2012 Date <strong>of</strong> Acceptance : 27.12.2012<br />
<strong>The</strong> project Irrigated Agriculture<br />
Modernization and Water Bodies Restoration and<br />
Management (IAMWARM) was introduced in 2007<br />
and funded by World Bank. Its objective was to<br />
improve irrigation service delivery including<br />
adaptation <strong>of</strong> modern water-saving irrigation<br />
technologies and ultimately to ensure food security<br />
and improved farm incomes. Keeping this in view,<br />
present study was proposed to study the socioeconomic<br />
impact <strong>of</strong> IAMWARM project on beneficiary<br />
farmers in Pudukkottai district <strong>of</strong> Tamil Nadu in the<br />
year 2012. <strong>The</strong> implications <strong>of</strong> the study would be<br />
useful to the project <strong>of</strong>ficials, implementing<br />
authorities, funding agencies concerned, for<br />
extending project benefits to the farming community.<br />
A study was undertaken purposively in<br />
Pudukkottai district <strong>of</strong> Tamil Nadu as this project was<br />
first implemented in Pudukkottai district under first<br />
phase sub-basin <strong>of</strong> the project during the year 2007.<br />
Thus, it gave sufficient time interval to study the<br />
impact. Four taluks were selected randomly and three<br />
villages from each taluk were selected randomly. Ten<br />
beneficiaries from each village were selected randomly<br />
using simple random sampling procedure, thus<br />
making a total sample <strong>of</strong> 120 beneficiary farmers.<br />
Ex-post facto research design was followed. Data<br />
was collected through interview schedule from the<br />
beneficiary farmers <strong>of</strong> the project covering all aspects<br />
<strong>of</strong> the socio-economic impact. To convert the data<br />
into meaningful findings some statistical tools were<br />
used. viz. a) Frequency and Percentage analysis b)<br />
Correlation analysis c) Multiple regression analysis<br />
and d) class interval.<br />
a) Socio-economic impact <strong>of</strong> the project<br />
Socio-economic impact <strong>of</strong> the project in SRI<br />
technique was studied with eleven variables namely,<br />
knowledge, adoption, income, asset acquisition, yield,<br />
water use efficiency, participation in the project<br />
activities, labour use, social participation, cost <strong>of</strong><br />
cultivation, and empowerment. By adding and<br />
averaging the scores <strong>of</strong> all the items, the individual<br />
score for socio-economic impact was worked out. It<br />
was used to categorize the respondents into three<br />
groups based on the class interval (exclusive) method<br />
as low, medium and high level <strong>of</strong> socio-economic<br />
impact.<br />
Table 1. Distribution <strong>of</strong> respondents according to socio-economic impact<br />
S.No<br />
Category<br />
Beneficiary Farmers<br />
N=120<br />
Frequency<br />
Percentage<br />
1. Low (49-62) 30 25.00<br />
2. Medium (63-76) 56 46.67<br />
3. High (77-90) 34 28.33<br />
** - significant at 0.01 level <strong>of</strong> probability<br />
email: abarangi@gmail.com<br />
88
ABIRAMI et al<br />
Socio-economic Impact: A cursory look at<br />
the Table 1 indicates that 46.67 per cent <strong>of</strong> beneficiary<br />
farmers had medium level <strong>of</strong> socio-economic impact,<br />
followed by high (28.33%) and low level <strong>of</strong> socioeconomic<br />
impact (25.00%). <strong>The</strong> result was the<br />
cumulative effect <strong>of</strong> all the factors contributed to the<br />
socio-economic impact. <strong>The</strong> reason for high level <strong>of</strong><br />
socio-economic impact might be particularly due to<br />
majority <strong>of</strong> the farmers obtained fine knowledge about<br />
SRI and adopted SRI technique fairly in their field.<br />
As a result they received more income and asset<br />
acquisition. Medium level <strong>of</strong> socio-economic impact<br />
might be due to partial adoption <strong>of</strong> technology and<br />
inefficient management <strong>of</strong> labour. That results in more<br />
cost <strong>of</strong> cultivation. Low level <strong>of</strong> socio-economic<br />
impact might be due to poor adoption & SRI<br />
technology.<br />
b) Relationship between pr<strong>of</strong>ile <strong>of</strong> beneficiary<br />
farmers and the socio-economic impact<br />
An attempt has been made to find out the<br />
association between independent variables and<br />
dependent variables through correlation coefficient<br />
(r) values. <strong>The</strong> results are presented in Table 2.<br />
Table 2. Correlation coefficient between pr<strong>of</strong>ile <strong>of</strong> beneficiary farmers and the socio-economic impact<br />
N= 120<br />
S. No Independent Variables ‘r’ values<br />
1. Age<br />
2. Education<br />
3. Land Holding<br />
4. Farming Experience<br />
5. Information sources utilization<br />
6. Training Received<br />
7. Economic Motivation<br />
8. Scientific Orientation<br />
9. Innovativeness<br />
10. Risk Orientation<br />
-0.2119 NS<br />
0.4014**<br />
0.3870**<br />
0.3537**<br />
0.3793**<br />
0.4815**<br />
0.5241**<br />
0.6113**<br />
0.5927**<br />
0.6084**<br />
** Significant at 0.01 level <strong>of</strong> probability NS = Non Significant<br />
<strong>The</strong> results presented in the Table 2 clearly<br />
indicate that almost all computed ‘r’ values <strong>of</strong><br />
education, land holding, farming experience,<br />
information sources utilization, training received,<br />
economic motivation, scientific orientation,<br />
innovativeness and risk orientation with socioeconomic<br />
impact were found positively significant<br />
relationship at 0.01 level <strong>of</strong> probability. Whereas, age<br />
with socio-economic impact had non-significant and<br />
negative relationship.<br />
From this study it could be concluded that<br />
higher the education, higher the land holding, higher<br />
the experience in farming, higher the information<br />
sources utilization, higher the training received,<br />
higher the economic motivation, higher the scientific<br />
orientation, higher the innovativeness and higher the<br />
risk orientation, the higher would be the socioeconomic<br />
impact.<br />
<strong>The</strong> probable reason for age, not influencing<br />
the socio-economic impact can be explained that<br />
89
RELATIONSHIP BETWEEN PROFILE OF BENEFICIARY FARMERS & IMPACT<br />
adoption <strong>of</strong> innovation (in this context adoption <strong>of</strong><br />
SRI technique) in Indian agriculture does not differ<br />
from non-adoption with respect to age, it depends on<br />
how worthy the innovation is to a farmer for efficient<br />
crop production. This trend was also noticed by<br />
Mohammad et al (2009).<br />
<strong>The</strong> reason for positive and significant<br />
correlation <strong>of</strong> education with socio-economic impact<br />
might be due to the fact that, the education widens<br />
horizons <strong>of</strong> the individual to get information from<br />
various sources. This seems to be inter-related with<br />
farmers to bring changes in their personal,<br />
psychological orientation, to adopt new ideas,<br />
practices and technologies and motivate the farmers<br />
towards achieving high socio-economic impact. This<br />
result was in agreement with the results <strong>of</strong> Manoj<br />
(2008) and Suresh and Rameshbabu (2008).<br />
<strong>The</strong>re was positive and significant<br />
relationship between land holding and socio-economic<br />
impact. This might be due to the fact that land holding<br />
provides the economic base for the farmers to<br />
practice new agricultural technologies. Land holding<br />
also provides regulated impetus to make optimum<br />
utilization <strong>of</strong> resources on farm for achieving<br />
maximum pr<strong>of</strong>its. Similar results were reported by<br />
Rameshbabu (2002) and Reddy et al. (2007).<br />
It was found that farming experience had<br />
positive and significant relationship with socioeconomic<br />
impact. This might be due to the fact that<br />
experience is the best teacher, farmer having more<br />
experience in farming, irrespective <strong>of</strong> age would know<br />
the difficulties and problems in farming better than<br />
less experienced and who seek for new alternative<br />
farm practices and adopt new production technologies.<br />
This result was in conformity with the results <strong>of</strong><br />
Thyagarajan (2004) and Reddy et al. (2007).<br />
Positive and significant relationship was<br />
noticed between information sources utilization and<br />
socio-economic impact. This might be due to the fact<br />
that different information sources utilization updated<br />
the farmers with new production technologies and<br />
motivate them to adopt it to improve their pr<strong>of</strong>its.<br />
This finding was in tune with the results <strong>of</strong> Reddy et<br />
al. (2007) and Mohammad et al. (2009).<br />
From the Table 2. it was clear that there was<br />
positive and significant relationship between training<br />
received and socio-economic impact. This might be<br />
due to the fact that training is one <strong>of</strong> the means by<br />
which desired changes in knowledge and skills could<br />
be attained. An individual who receives training<br />
become more knowledgeable, skilful and develop<br />
rationale and adopt improved farming practices led<br />
to have more socio-economic impact. This might be<br />
the reason for above result. <strong>The</strong> result was in<br />
agreement with the results <strong>of</strong> Basawarajaiah (2001).<br />
Economic motivation was found to be positively<br />
and significantly associated with the socio-economic<br />
impact. <strong>The</strong> reason could be that the farmers with<br />
more economic motivation would be oriented towards<br />
more information sources utilization; risk bearing that<br />
might help them to adopt new production technologies.<br />
This finding was in line with the findings <strong>of</strong> Manoj<br />
(2008) and Suresh and Rameshbabu (2008).<br />
<strong>The</strong> results furnished in the Table 2 indicated<br />
that there was positive and significant relationship<br />
between scientific orientation and socio-economic<br />
impact. It might be due to the reason that the farmers<br />
having more scientific orientation would gather more<br />
information from authentic sources like Krishi Vigyan<br />
Kandra, TNAU scientists, etc., and think rationally<br />
before applying into the field conditions, get the higher<br />
production and pr<strong>of</strong>its. Similar results were reported<br />
by Manoj (2008) and Mohammad et al. (2009).<br />
<strong>The</strong>re was positive and significant relationship<br />
between innovativeness and socio-economic impact<br />
(Table 2). This might be due to the fact that farmers<br />
who are relatively earlier in adopting new agricultural<br />
innovations would orient towards more risk taking,<br />
more scientific orientation, maintain higher social<br />
status. <strong>The</strong>ir earliness to adopt innovations would<br />
have resulted in higher socio-economic impact in<br />
terms <strong>of</strong> increasing higher yields and income. This<br />
result was in conformity with the results <strong>of</strong> Damodaran<br />
(2007) and Manoj (2008).<br />
<strong>The</strong> correlation between risk orientation and<br />
socio-economic impact was positive and significant<br />
(Table 2). It could be inferred from the finding that<br />
higher the risk orientation, the higher would be the<br />
90
ABIRAMI et al<br />
socio-economic impact. This might be due to the fact<br />
that higher risk oriented farmers adopt the innovations<br />
and get more yield and higher income. Hence, such<br />
type <strong>of</strong> relation existed in the study. This finding was<br />
in line with the findings <strong>of</strong> Chandrasekhar et al. (2005)<br />
and Manoj(2008).<br />
c) Multiple Linear Regression <strong>of</strong> selected<br />
independent variables with socio-economic<br />
impact.<br />
An attempt was made to find out the amount <strong>of</strong><br />
contribution made by the independent variables in<br />
explaining the variation in the dependent variable<br />
through multiple linear regression. <strong>The</strong> results are<br />
presented in Table 3.<br />
Table 3. Multiple regression analysis <strong>of</strong> Pr<strong>of</strong>ile <strong>of</strong> beneficiary farmers and the socio-economic impact<br />
S. No Variables<br />
Regression<br />
coefficient (B)<br />
Standard error<br />
‘t’ value<br />
1. Age<br />
-6.3303 0.7039 -8.9927**<br />
2. Education<br />
0.7092 0.3255 2.1784NS<br />
3. Land Holding<br />
0.4241 0.4370 0.9704NS<br />
4. Farming Experience<br />
4.6575 0.6232 7.4733**<br />
5. Information sources utilization -0.4021 0.0695 -5.7790**<br />
6. Training Received<br />
3.3325 0.5538 6.0164**<br />
7. Economic Motivation<br />
1.4766 0.2322 6.3584**<br />
8. Scientific Orientation<br />
0.3394 0.1763 1.9248NS<br />
9. Innovativeness<br />
0.7704 0.1235 6.2369**<br />
10. Risk Orientation<br />
1.7744 0.2070 8.5691**<br />
R 2 = 0.861; NS = Non Significant;<br />
** Significant at 0.01 level <strong>of</strong> probability<br />
<strong>The</strong> Ten independent variables with the<br />
socio-economic impact <strong>of</strong> the project taken on<br />
Multiple Linear Regression Analysis gave the R 2 (Coefficient<br />
<strong>of</strong> multiple determination) value <strong>of</strong> 0.861. It<br />
indicates that all the selected independent variables<br />
put together contributed 86.10 per cent <strong>of</strong> the total<br />
variation in the socio-economic impact <strong>of</strong> the project<br />
by the beneficiary farmers, leaving the rest to<br />
extraneous factors. <strong>The</strong> independent variables viz.,<br />
farming experience, training received, economic<br />
motivation, innovativeness and risk orientation<br />
contributed significantly to the socio-economic impact<br />
<strong>of</strong> the project. <strong>The</strong> variable scientific orientation was<br />
not having significant value, but the value is near to<br />
significant t –value (1.980272). So, it also could be<br />
considered as significant<br />
REFERENCES<br />
Baswarajaiah, V.2001. Impact <strong>of</strong> Edira Watershed<br />
Development Programme on farm families in<br />
Mahaboobnagar District <strong>of</strong> Andhra Pradesh.<br />
M. Sc. (Ag.) <strong>The</strong>sis submitted to Acharya N<br />
G Ranga Agricultural University, Hyderabad,<br />
India.<br />
91
RELATIONSHIP BETWEEN PROFILE OF BENEFICIARY FARMERS & IMPACT<br />
Chandrasekhar, V., Gangadharappa, N.R and<br />
Suresha, S.V. 2005. Knowledge level farmers<br />
about selected technological interventions in<br />
TAR-IVLP. Mysore <strong>Journal</strong> <strong>of</strong> Agricultural<br />
Sciences. 39 (3): 410-414.<br />
Damodaran, C. 2007. Irrigation management and<br />
socio-economic changes among Cauvery old<br />
delta farmers. M.Sc. (Ag.) <strong>The</strong>sis submitted<br />
to Acharya N G Ranga Agricultural University,<br />
Hyderabad, India.<br />
Manoj, A. 2008. Impact <strong>of</strong> Krishi Vigyan Kendra on<br />
farmers Srikakulam district <strong>of</strong> Andhra<br />
Pradesh. M. Sc. (Ag.) <strong>The</strong>sis submitted to<br />
Acharya N G Ranga Agricultural University,<br />
Hyderabad, India.<br />
Ca Mohammad Ajaz-ul-Islam, Masoodi, N.A.,<br />
Masoodi, T.H and Gangoo, S.A. 2009.<br />
Awareness and participation <strong>of</strong> beneficiaries<br />
in social forestry programme in Baramulla<br />
district <strong>of</strong> Kasmir valley. Indian <strong>Journal</strong> <strong>of</strong><br />
Social <strong>Research</strong>. 50(4): 353-364.<br />
Rameshbabu, C. 2002. Effectiveness <strong>of</strong> Indo-Dutch<br />
Operational <strong>Research</strong> project on drainage and<br />
water management for salinity control in<br />
Prakasam district <strong>of</strong> A.P. M. Sc. (Ag.) <strong>The</strong>sis<br />
submitted to Acharya N G Ranga Agricultural<br />
University, Hyderabad, India.<br />
Reddy, P.T.S., Prabhakar, K and Gidda Reddy, P.<br />
2007. Analysis <strong>of</strong> influence <strong>of</strong> selected<br />
independent variables on knowledge <strong>of</strong> rice<br />
farmers on Eco-friendly technologies. <strong>Journal</strong><br />
<strong>of</strong> <strong>Research</strong> <strong>ANGRAU</strong>. 35 (2): 31-37.<br />
Suresh, T.V and Ramesh Babu, C.H. 2008. Extent<br />
<strong>of</strong> participation <strong>of</strong> farmers in Sujala<br />
Kalinganahalli Halla Watershed Project,<br />
Andhra Agricultural journal. 55(3): 405-407.<br />
Thyagarajan, S. 2004. Rice production technology –<br />
adoption and constraints. Indian <strong>Journal</strong> <strong>of</strong><br />
Extension Education. 40 (3&4): 44-47.<br />
92
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 88-92, 2013<br />
CONSTRUCTION OF SELECTION INDICES FOR F 2<br />
POPULATION<br />
DERIVED FROM CROSSES BETWEEN<br />
GRAIN SORGHUM × SWEET SORGHUM [Sorghum bicolor (L.) Moench]<br />
VEMANNA IRADDI, T. DAYAKAR REDDY, A. V. UMAKANTH, CH. RANI,<br />
D. VISHNU VARDHAN REDDY and M. H. V. BHAVE<br />
Department <strong>of</strong> Genetics and Plant Breeding<br />
Acharya N.G. Ranga Agricultural University, Hyderabad – 500 030<br />
Date <strong>of</strong> Receipt : 06.11.2012 Date <strong>of</strong> Acceptance : 12.12.2012<br />
<strong>The</strong> selection indices by discriminant<br />
function analysis were constructed based on the data<br />
<strong>of</strong> a population <strong>of</strong> 800 F 2<br />
plants developed by<br />
crossing three grain sorghum genotypes viz., 27 B,<br />
ICSB 38 and 296 B as a female parent and four sweet<br />
sorghum genotypes viz., SSV 84, SSV 74, URJA<br />
and NSSV 13 as a male parent. Majority <strong>of</strong> selection<br />
indices were found to be more efficient than straight<br />
selection based on sugar yield alone. <strong>The</strong> selection<br />
index consisting <strong>of</strong> six character combination viz.,<br />
sugar yield, total biomass, fresh stalk yield, brix per<br />
cent, juice yield and total soluble sugars was more<br />
effective with higher relative efficiency. While,<br />
selection based on five characters combinations viz.,<br />
sugar yield, total biomass, fresh stalk yield, brix per<br />
cent and juice yield as well as four character<br />
combination viz., total biomass, fresh stalk yield, brix<br />
per cent and juice yield were also equally effective<br />
in selection <strong>of</strong> plants for maximum sugar yield.<br />
However, selection index comprising six and five<br />
character combinations are <strong>of</strong> little importance in<br />
selection process as it includes derived parameters<br />
such as sugar yield and total soluble sugars. In this<br />
regard it is suggested to go for four character<br />
combination which also manifested maximum relative<br />
efficiency coupled with higher genetic advance.<br />
<strong>The</strong> practical or economic value <strong>of</strong> a plant is<br />
affected by several traits. Since, majority <strong>of</strong> the<br />
economic traits are polygenically inherited and their<br />
expression is subjected to varying degrees <strong>of</strong><br />
fluctuations due to environmental factors, eventually<br />
direct selection may not be useful for such characters.<br />
Efficiency <strong>of</strong> selection under such circumstances can<br />
sometimes be improved by taking into consideration<br />
simultaneously the phenotypic values <strong>of</strong> a number<br />
<strong>of</strong> plant attributes which are correlated with the<br />
genotypic values (high heritability) <strong>of</strong> the characters<br />
under consideration.<br />
<strong>The</strong> material for the present study comprised<br />
<strong>of</strong> 800 F 2<br />
population <strong>of</strong> sweet sorghum crosses<br />
derived from parents having low and high sugar<br />
content developed at Directorate <strong>of</strong> Sorghum<br />
<strong>Research</strong>, Rajendranagar, Hyderabad. <strong>The</strong>se<br />
populations were developed by crossing the<br />
contrasting parents (27 B with SSV 84, ICSB 38 with<br />
SSV 74, 296 B with URJA and 27 B with NSSV 13)<br />
through hand emasculation and pollination during<br />
kharif 2010 and the F 1<br />
plants <strong>of</strong> the two crosses were<br />
grown during rabi 2010 - 11 and selfed to produce the<br />
F 2<br />
seeds, which were evaluated during summer 2012.<br />
<strong>The</strong> technique <strong>of</strong> discriminant function<br />
developed by Fisher (1936) was adopted to know the<br />
true genotypic worth <strong>of</strong> yield and its components and<br />
to have computational formulae for construction <strong>of</strong><br />
selection indices which when applied to select plants<br />
can bring about effective improvement in yield<br />
compared to straight selection for yield. Smith (1936)<br />
has illustrated the use <strong>of</strong> discriminant function in plant<br />
selection.<br />
Formulation <strong>of</strong> selection indices through<br />
discriminant function analysis Selection indices<br />
were formulated in F 2<br />
populations <strong>of</strong> sweet sorghum<br />
considering sugar yield and its five component<br />
characters which had high correlation with sugar yield.<br />
Among six characters, sugar yield (X 1<br />
) was<br />
considered as dependent character, while other<br />
characters viz., total biomass (X 2<br />
), fresh stalk yield<br />
(X 3<br />
), brix per cent (X 4<br />
), juice yield (X 5<br />
) and total soluble<br />
sugars (X 6<br />
) were considered as independent<br />
variables. In order to select plants with high sugar<br />
yield, discriminant functions were computed with<br />
email: vemanraddi@gmail.com<br />
93
CONSTRUCTION OF SELECTION INDICES FOR F 2<br />
POPULATION<br />
different sets <strong>of</strong> characters and the efficiency <strong>of</strong> each<br />
index was compared with direct selection for sugar<br />
yield and other combination <strong>of</strong> characters (Table 1).<br />
A maximum <strong>of</strong> five component characters<br />
which exhibited high significant and positive<br />
correlation with sugar yield were used in F 2<br />
populations<br />
for construction <strong>of</strong> different selection indices. <strong>The</strong><br />
expected genetic advance was computed for each<br />
<strong>of</strong> the indices at five per cent selection intensity.<br />
<strong>The</strong> relative efficiency <strong>of</strong> all the indices was computed<br />
considering the relative efficiency <strong>of</strong> sugar yield as<br />
100 per cent. <strong>The</strong> estimated values <strong>of</strong> genetic<br />
advance and relative efficiency for each combination<br />
<strong>of</strong> character in F 2<br />
population have been tabulated in<br />
Table 1 and briefly discussed below.<br />
Sixty three different selection indices were<br />
formulated based on various combinations <strong>of</strong> six<br />
characters considered for construction <strong>of</strong> selection<br />
indices. Among these, higher relative efficiency <strong>of</strong><br />
2756.36 coupled with high genetic advance (90.42)<br />
was exhibited by combination involving all the six<br />
traits (including sugar yield) <strong>of</strong> X 1<br />
+ X 2<br />
+ X 3<br />
+ X 4<br />
+ X 5<br />
+ X 6<br />
, followed by the combination <strong>of</strong> five traits viz.,<br />
X 1<br />
+ X 2<br />
+ X 3<br />
+ X 4<br />
+ X 5<br />
which recorded relative efficiency<br />
<strong>of</strong> 2734.65 with high genetic advance <strong>of</strong> 89.70<br />
compared to other combinations.<br />
Among single character, X 2<br />
was highly<br />
efficient with relative efficiency <strong>of</strong> 814.80 compared<br />
to the direct selection based on sugar yield (X 1<br />
) whose<br />
relative efficiency was taken as 100. Whereas, among<br />
two character combinations, maximum relative<br />
efficiency <strong>of</strong> 1661.36 was observed for the<br />
combination <strong>of</strong> X 2<br />
+ X 3<br />
traits with high genetic<br />
advance <strong>of</strong> 54.50, followed by X 2<br />
+ X 5<br />
traits with a<br />
relative efficiency <strong>of</strong> 1287.74 and genetic advance<br />
<strong>of</strong> 42.24. However, in case <strong>of</strong> three character<br />
combinations, the combination involving X 2<br />
+ X 3<br />
+ X 5<br />
exhibited higher relative efficiency <strong>of</strong> 2090.98 coupled<br />
with high genetic advance <strong>of</strong> 68.59.<br />
Among four character combinations, X 1<br />
+ X 2<br />
+ X 3<br />
+ X 5<br />
followed by X 2 + X 3 + X 4 + X 5 and X 2 + X 3 + X 5<br />
+ X 6<br />
exhibited high relative efficiencies <strong>of</strong> 2702.34,<br />
2455.56 and 2451.75 coupled with genetic advance<br />
<strong>of</strong> 88.64, 80.55 and 80.42, respectively.<br />
Since, sugar yield and total soluble sugars<br />
are estimated or derived characters, more emphasis<br />
during selection should be given on the directly<br />
measurable characters to get the accurate results.<br />
Hence, the combinations without sugar yield and total<br />
soluble sugars are considered for indirect selection<br />
for sugar yield. Among them, X 2<br />
+ X 3<br />
+ X 4<br />
+ X 5<br />
combination exhibited maximum relative efficiency<br />
<strong>of</strong> 2455.56 per cent with genetic advance <strong>of</strong> 80.55.<br />
Path coefficient analysis revealed the<br />
intricacies <strong>of</strong> yield components while discriminant<br />
function is useful in knowing the extent <strong>of</strong><br />
improvement that can be effected in yield by selecting<br />
plants based on different combination <strong>of</strong> component<br />
characters. Smith (1936) opined that, selection index<br />
is the basis in considering the correlated characters<br />
for higher efficiency in selection for yield. When yield<br />
is associated with other characters, indirect selection<br />
through such traits is sometimes likely to be better<br />
than straight selection for yield. But, when the number<br />
<strong>of</strong> characters associated with yield is large, it<br />
becomes difficult to select simultaneously for all<br />
these characters. Under such circumstances,<br />
selection indices formulated by involving different<br />
combinations <strong>of</strong> characters with appropriate<br />
weightage to each character help in making the<br />
selection procedure easy.<br />
In the present study, selection index<br />
involving six character combination viz., sugar yield,<br />
total biomass, fresh stalk yield, brix per cent, juice<br />
yield and total soluble sugars was more effective with<br />
higher relative efficiency. While, selection based on<br />
five characters combinations viz., sugar yield, total<br />
biomass, fresh stalk yield, brix per cent and juice<br />
yield as well as four character combination viz., total<br />
biomass, fresh stalk yield, brix per cent and juice<br />
yield were also equally effective in selection <strong>of</strong> plants<br />
for maximum sugar yield. Similar results <strong>of</strong> increased<br />
efficiency by inclusion <strong>of</strong> yield as one <strong>of</strong> the<br />
component in formulating selection indices were<br />
reported by Mahadevappa and Ponnaiya (1967) in<br />
pearlmillet, Paroda and Joshi (1970) in wheat, Agrawal<br />
et al. (1978) in dwarf rice, Rahangdale et al. (1987) in<br />
upland rice, Mannur et al. (1991) in soybean, Mathur<br />
and Gupta (1992) in niger, Singh and Khan (1998)<br />
and Nahar et al. (2002) in sugarcane.<br />
When indirect selection scheme excluding<br />
sugar yield in formulating index is to be followed, the<br />
index involving combination <strong>of</strong> four characters viz.,<br />
94
VEMANNA et al<br />
Table 1. Discriminant functions, their genetic advance and relative efficiency over straight selection<br />
for sugar yield in F 2<br />
generation <strong>of</strong> the sweet sorghum crosses<br />
Sl. No. Discriminant Function GA RE (%)<br />
1 Y = 0.23X 1 3.28 100.00<br />
2 Y = 0.12X 2 26.73 814.80<br />
3 Y = 0.14X 3 23.77 724.67<br />
4 Y = 0.36X 4 0.95 28.92<br />
5 Y = 0.17X 5 13.67 416.72<br />
6 Y = 0.36X 6 0.83 25.28<br />
7 Y = 2.31X 1 + 0.01X 2 33.97 1035.68<br />
8 Y = 1.42X 1 + 0.05X 3 28.04 854.85<br />
9 Y = 0.06X 1 + 1.62X 4 4.92 150.08<br />
10 Y = 6.44X 1 – 0.94X 5 22.82 695.72<br />
11 Y = 0.06X 1 + 1.80X 6 4.80 146.39<br />
12 Y = 0.65X 3 – 0.26X 2 54.50 1661.36<br />
13 Y = 0.07X 2 + 8.45X 4 33.29 1015.02<br />
14 Y = 0.57X 5 – 0.02X 2 42.24 1287.74<br />
15 Y = 0.07X 2 + 9.59X 6 33.18 1011.37<br />
16 Y = 0.10X 3 + 5.75X 4 27.49 838.08<br />
17 Y = 0.05X 3 + 0.36X 5 36.67 1117.85<br />
18 Y = 0.10X 3 + 6.50X 6 27.39 834.97<br />
19 Y = 6.49X 4 + 0.04X 5 19.27 587.57<br />
20 Y = 0.47X 4 + 0.25X 6 1.78 54.23<br />
21 Y = 0.04X 5 + 7.36X 6 19.15 583.66<br />
22 Y = 3.15X 1 – 0.32X 2 + 0.52X 3 61.51 1875.03<br />
23 Y = 0.79X 1 + 0.04X 2 + 8.07X 4 37.17 1133.16<br />
24 Y = 16.09X 1 + 0.16X 2 – 2.71X 5 56.51 1722.60<br />
25 Y = 0.79X 1 + 0.04X 2 + 9.15X 6 37.05 1129.41<br />
26 Y = 0.02X 1 + 0.10X 3 + 7.16X 4 30.97 944.15<br />
27 Y = 15.19X 1 + 0.29X 3 – 2.84X 5 51.37 1566.14<br />
28 Y = 0.02X 1 + 0.10X 3 + 8.12X 6 30.86 940.82<br />
29 Y = 4.41X 1 + 3.81X 4 – 0.65X 5 24.30 740.65<br />
30 Y = 0.06X 1 + 1.36X 4 + 0.65X 6 5.75 175.32<br />
31 Y = 4.40X 1 – 0.65X 5 + 4.29X 6 24.16 736.68<br />
95
CONSTRUCTION OF SELECTION INDICES FOR F 2<br />
POPULATION<br />
Sl. No. Discriminant Function GA RE (%)<br />
34 Y = 0.61X 3 – 0.31X 2 + 15.62X 6 63.10 1923.78<br />
35 Y = 0.05X 2 + 14.19X 4 + 0.10X 5 50.80 1548.63<br />
36 Y = 0.07X 2 + 7.56X 4 + 1.41X 6 33.99 1036.31<br />
37 Y = 0.05X 2 + 0.11X 5 + 16.13X 6 50.66 1544.42<br />
38 Y = 0.15X 3 + 13.04X 4 – 0.09X 5 44.76 1364.64<br />
39 Y = 0.10X 3 + 5.14X 4 + 1.09X 6 28.13 857.65<br />
40 Y = 0.15X 3 – 0.09X 5 + 14.83X 6 44.64 1360.77<br />
41 Y = 4.70X 4 + 0.04X 5 + 2.43X 6 20.10 612.67<br />
42 Y = 0.13X 1 – 0.32X 2 + 0.62X 3 + 14.89X 4 66.73 2034.16<br />
43 Y = 25.21X 1 – 0.22X 2 + 0.81X 3 – 4.64X 5 88.64 2702.34<br />
44 Y = 0.14X 1 – 0.32X 2 + 0.62X 3 + 16.95X 6 66.61 2030.74<br />
45 Y = 13.16X 1 + 0.15X 2 + 4.80X 4 – 2.24X 5 57.71 1759.40<br />
46 Y = 0.82X 1 + 0.04X 2 + 7.36X 4 + 1.11X 6 37.91 1155.67<br />
47 Y = 13.19X 1 + 0.15X 2 – 2.25X 5 + 5.35X 6 57.59 1755.58<br />
48 Y = 13.73X 1 + 0.28X 3 + 3.26X 4 – 2.55X 5 52.37 1596.68<br />
49 Y = 0.06X 1 + 0.10X 3 + 6.05X 4 + 1.57X 6 31.65 964.76<br />
50 Y = 13.76X 1 + 0.28X 3 – 2.55X 5 + 3.61X 6 52.26 1593.20<br />
51 Y = 4.57X 1 + 3.75X 4 – 0.68X 5 + 0.26X 6 25.16 766.93<br />
52 Y = 0.74X 3 – 0.35X 2 + 21.70X 4 – 0.17X 5 80.55 2455.56<br />
53 Y = 0.61X 3 – 0.31X 2 + 10.07X 4 + 4.58X 6 63.86 1946.72<br />
54 Y = 0.74X 3 – 0.35X 2 – 0.17X 5 + 24.72X 6 80.42 2451.75<br />
55 Y = 0.05X 2 + 11.34X 4 + 0.11X 5 + 3.61X 6 51.56 1571.89<br />
56 Y = 0.14X 3 + 10.01X 4 – 0.09X 5 + 3.81X 6 45.48 1386.50<br />
57<br />
58<br />
59<br />
60<br />
61<br />
Y = 21.88X 1 – 0.23X 2 + 0.81X 3 + 5.40X 4 –<br />
4.12X 5<br />
Y = 0.17X 1 – 0.32X 2 + 0.62X 3 + 10.78X 4 +<br />
5.02X 6<br />
Y = 21.93X 1 – 0.23X 2 + 0.81X 3 – 4.12X 5 +<br />
6.02X 6<br />
Y = 13.31X 1 + 0.15X 2 + 8.60X 4 – 2.27X 5 –<br />
4.17X 6<br />
Y = 13.89X 1 + 0.28X 3 + 6.57X 4 – 2.57X 5 –<br />
3.61X 6<br />
89.70 2734.65<br />
67.39 2054.36<br />
89.59 2731.24<br />
58.50 1783.51<br />
53.12 1619.52<br />
GA = Genetic advance; RE = Relative efficiency<br />
96
VEMANNA et al<br />
total biomass, fresh stalk yield, brix per cent and<br />
juice yield which exhibited maximum relative<br />
efficiency, same could be considered for selection<br />
schemes. Bhat and Shariff (1994) in finger millet,<br />
Patil et al. (1997) in sunflower and Khulbe and Pant<br />
(1999) in mustard obtained similar results <strong>of</strong><br />
increased efficiency through indirect selection using<br />
different combination <strong>of</strong> characters excluding yield<br />
in selection index. Hence, it can be inferred that the<br />
maximum gain using indirect selection schemes<br />
could be achieved using highly correlated characters<br />
like total biomass, fresh stalk yield, brix per cent and<br />
juice yield, which is further confirmed by path coefficient<br />
as well as discriminant function analysis.<br />
REFERENCES<br />
Agrawal, R. K., Lal, I. P and Richharia, A. K., 1978.<br />
Note on selection indices and path coefficients<br />
in semi dwarf rice varieties. Indian <strong>Journal</strong> <strong>of</strong><br />
Agricultural Science. 48: 58-60.<br />
Bhat, B. V and Shariff, R. A., 1994. Selection criteria<br />
in Finger millet (Eleusine coracana Gaertn.).<br />
Mysore <strong>Journal</strong> <strong>of</strong> Agricultural Sciences.<br />
28: 5-7.<br />
Fisher, R. A., 1936. <strong>The</strong> use <strong>of</strong> multiple<br />
measurements to taxonomic problems. Annals<br />
<strong>of</strong> Eugenics. 7: 87-104.<br />
Khulbe, R. K and Pant, D. P., 1999. Selection indices<br />
in Indian mustard. [Brassica juncea (L.) Czern<br />
& Coss]. Crop Improvement. 26: 109-111.<br />
Mahadevappa, M and Ponnaiya, B. W. X., 1967.<br />
Discriminant functions in the selection <strong>of</strong> pearl<br />
millet (Pennisetum typhoids Stapf and Hubb.)<br />
population for grain yield. <strong>The</strong> Madras<br />
Agricultural <strong>Journal</strong>. 54: 211-222.<br />
Mannur, D. M., Salimath, P. M., Patil, S. S and<br />
Parameshwarappa, R., 1991. Genetic studies<br />
in interspecific crosses <strong>of</strong> soybean Glycine<br />
max (L.) Merill × Glycine formosa. Indian<br />
<strong>Journal</strong> <strong>of</strong> Genetics and Plant Breeding. 51:<br />
471-475.<br />
Mathur, R. K and Gupta, S. C., 1992. Discriminant<br />
function analysis in Niger (Guizotia abyssinica<br />
Cass.). Crop <strong>Research</strong>. 5: 164-165.<br />
Nahar, S. M. N., Khaleque, M. A and Miah, M. A.,<br />
2002. Correlation, path co-efficient and<br />
construction <strong>of</strong> selection index in sugarcane.<br />
Pakistan Sugar <strong>Journal</strong>. 17: 2-10.<br />
Paroda, R. S and Joshi, A. B., 1970. Correlations,<br />
path co-efficients and the implication <strong>of</strong><br />
discriminate function for selection in wheat<br />
(Triticum aestivum). Heredity. 25: 383-392.<br />
Patil, B. R., Rudraaradhya, M and Basappa, H., 1997.<br />
Construction <strong>of</strong> selection indices for varietal<br />
selection in sunflower (Helianthus annuus L.).<br />
<strong>Journal</strong> <strong>of</strong> Oilseed <strong>Research</strong>. 14: 172-174.<br />
Rahangdale, S. L., Khoragade, P. W and Raut, S.<br />
K., 1987. Construction <strong>of</strong> selection indices for<br />
varietal selection in upland Rice. <strong>Journal</strong> <strong>of</strong><br />
Maharashtra Agricultural Universities. 12: 223-<br />
224.<br />
Singh, S. P and Khan, A. Q., 1998. Selection indices<br />
for cane yield in sugarcane. Indian <strong>Journal</strong> <strong>of</strong><br />
Genetics and Plant Breeding. 58: 353-357.<br />
Smith, H. F., 1936. A discriminant function for plant<br />
selection. Annals <strong>of</strong> Eugenics. 7: 240-250.<br />
97
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 93-95, 2013<br />
EVALUATION OF PERFORMANCE OF DENDROBIUM ORCHID HYBRIDS<br />
B. GOPALA RAO, P.T.SRINIVAS and M.H.NAIK<br />
Department <strong>of</strong> Horticulture, Sri Venkateswara Agricultural College,<br />
Acharya N.G. Ranga Agricultural University , Tirupati- 517 502<br />
Date <strong>of</strong> Receipt : 26.07.2012 Date <strong>of</strong> Acceptance : 24.09.2012<br />
Dendrobium is one <strong>of</strong> the largest diverse<br />
genera <strong>of</strong> orchids, very popular among orchids<br />
throughout the world. In India, majority <strong>of</strong> commercial<br />
orchid farms are located in Tamil Nadu, Kerala,<br />
Karnataka and Agency areas <strong>of</strong> Andhra Pradesh.<br />
Dendrobium hybrids viz., Sonia-17, Emma White,<br />
New Wanee, Pampodour Blue Magic and Flame are<br />
bearing population in India. Increasing demand with<br />
insufficient supply <strong>of</strong> orchid flowers in international<br />
trade <strong>of</strong>fer scope for making India a major exporter,<br />
having maximum genetic resources, varied climatic<br />
zones, availability <strong>of</strong> trained man power, lower cost<br />
<strong>of</strong> production as compared to other orchid growing<br />
countries. Very few studies are reported on the<br />
screening <strong>of</strong> orchids. However, the present<br />
investigation was carried out to evaluate Dendrobium<br />
hybrids viz., Sonia-17, Emma White, New Wanee,<br />
Pampodour, Blue Magic and Flame for export<br />
purpose.<br />
<strong>The</strong> study was conducted in the orchid farm<br />
Natural Synergies Limited, Nathanallur village,<br />
Kancheepuram district situated between 12 0 52’ N<br />
latitude and 79 0 51’ E longitude at an altitude <strong>of</strong> 102<br />
m MSL. Six hybrids <strong>of</strong> Sonia-17, Emma White, New<br />
Wanee, Pampodour, Blue Magic and Flame were<br />
screened for their performance grown under 75 per<br />
cent shade net. Growth parameters studied were plant<br />
height, number <strong>of</strong> shoots per plant, number <strong>of</strong> leaves<br />
per plant, number <strong>of</strong> spikes per plant, length <strong>of</strong> spike,<br />
number <strong>of</strong> florets per spike, length <strong>of</strong> floret pedicel,<br />
and longevity <strong>of</strong> spikes on plant and in the vase.<br />
Observations on vegetative growth parameters were<br />
recorded at 60, 120 and 180 days and floral characters<br />
at 130 and 195 days after planting. <strong>The</strong> experiment<br />
was laid out in Completely Randomized Block Design<br />
(CRD) with 4 replications and 6 treatments.<br />
Among the hybrids studied ‘Blue Magic’<br />
produced maximum plant height (56.61 cm) and<br />
hybrid ‘Emma White’ produced minimum plant height<br />
(41.54 cm), while others showed intermediate heights.<br />
<strong>The</strong> hybrid ‘Flame’ had produced maximum number<br />
<strong>of</strong> leaves per plant (12.87) and hybrid ‘New Wanee’<br />
recorded minimum number <strong>of</strong> leaves (6.44) per plant<br />
(Table 1). <strong>The</strong> hybrid ‘Flame’ showed distinguishable<br />
difference in the spike number, length <strong>of</strong> spike, and<br />
number <strong>of</strong> florets per spike from others (Table 2).<br />
<strong>The</strong> number <strong>of</strong> spikes recorded was maximum in the<br />
hybrid ‘Flame’ (3.68). <strong>The</strong> results showed that<br />
increased number <strong>of</strong> spikes had positive and<br />
significant relation with the leaves and shoots. <strong>The</strong><br />
hybrid ‘Flame’ excelled other hybrids with a spike<br />
length <strong>of</strong> 57.96 cm, while hybrid ‘Pompadour’ recorded<br />
spike length <strong>of</strong> 48.39 cm followed by ‘Blue Magic’<br />
(40.51 cm) and ‘New Wanee’ (37.86 cm), whereas<br />
‘Emma White’ and ‘Sonia 17’ recorded minimum spike<br />
length <strong>of</strong> 32.49 and 33.16 cm (Fig. 1A). Maximum<br />
number <strong>of</strong> florets per spike (15.58) was recorded in<br />
‘Flame’, which might be due to production <strong>of</strong> longest<br />
spikes. Whereas minimum number <strong>of</strong> florets per spike<br />
was recorded in ‘New Wanee’ (7.55) and ‘Sonia 17’<br />
(7.60), maximum floret pedicel length was recorded<br />
in ‘Emma White’ and ‘Blue Magic’. <strong>The</strong> vase life study<br />
<strong>of</strong> different hybrids revealed that the cultivars ‘Sonia<br />
17’ (18.38 days) and ‘Flame’ (17.96 days) recorded<br />
longer vase life, whereas shorter vase life was<br />
observed in ‘Pampodour’ (6.45 days) in tap water<br />
(Table 2)(Fig. 1B). <strong>The</strong> variation observed among<br />
different hybrids in respect <strong>of</strong> different above<br />
parameters may be attributed to genetic differences<br />
in the hybrids studied. <strong>The</strong> above findings <strong>of</strong> the<br />
present investigation are in agreement with those <strong>of</strong><br />
Betonio (1966) and Chandrappa (2003).<br />
Based on the above results, it appeared that<br />
Flame’s performance is better than others, though<br />
its vase life is comparatively less but almost equal<br />
to that <strong>of</strong> Sonia 17. Hence it is recommended to grow<br />
‘Flame’ for commercial and export purposes.<br />
email: gopal_hort@yahoo.co.in<br />
98
RAO et al<br />
Table 1. Plant height, number <strong>of</strong> leaves and shoots per plant <strong>of</strong> different Dendrobium hybrids (at 180<br />
days after planting) grown under shade net<br />
Hybrid Plant height (cm) No. <strong>of</strong> leaves/plant No. <strong>of</strong> shoots/plant<br />
Sonia 17 43.90 9.17 5.08<br />
Emma White 41.54 8.87 6.68<br />
New Wanee 43.52 6.44 7.55<br />
Pampodour 49.15 10.62 6.91<br />
Blue Magic 56.61 11.22 6.22<br />
Flame 52.85 12.87 7.92<br />
CD at 5% 2.01 0.23 0.35<br />
Table 2. Spike yield, quality and vase life (at 195 days) <strong>of</strong> different Dendrobium hybrids grown under<br />
shade net.<br />
Hybrid<br />
No. <strong>of</strong> spikes /<br />
plant<br />
No. <strong>of</strong> florets /<br />
spike<br />
Spike<br />
length (cm)<br />
Floret<br />
pedicel<br />
length (cm)<br />
Vase life in<br />
tap water<br />
(days)<br />
Sonia 17 2.96 7.60 33.16 3.75 18.38<br />
Emma White 2.46 11.76 32.49 4.07 13.84<br />
New Wanee 2.02 7.55 37.86 3.29 9.24<br />
Pampodour 2.09 13.45 48.39 3.68 6.45<br />
Blue Magic 3.28 10.23 40.51 3.80 14.88<br />
Flame 3.68 15.58 57.96 4.03 17.96<br />
CD at 5% 0.36 1.16 2.59 0.20 0.88<br />
99
EVALUATION OF PERFORMANCE OF DENDROBIUM ORCHID HYBRIDS<br />
Fig 1. Spike quality and vase life (in tap water) <strong>of</strong> different Dendrobium hybrids grown under shade<br />
net.<br />
REFERENCES<br />
Betonio, G.I. 1966. Germplasm collection and<br />
evaluation <strong>of</strong> different Anthurium cultivars.<br />
<strong>Journal</strong> <strong>of</strong> crop science. 20: 12.<br />
and flowering in Anthurium. Ph.D. <strong>The</strong>sis<br />
submitted to University <strong>of</strong> Agricultural<br />
Sciences, Bangalore.<br />
Chandrappa. 2003. Evaluation and effect <strong>of</strong> media,<br />
bi<strong>of</strong>ertilizer and growth regulators on growth<br />
100
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 96-100, 2013<br />
PROFILE CHARACTERISTICS OF SUGARCANE FARMERS IN<br />
CHITTOOR DISTRICT OF ANDHRA PRADESH<br />
S. RAMALAKSHMI DEVI, P. V. SATYA GOPAL, V.SAILAJA and S.V. PRASAD<br />
Department <strong>of</strong> Extension Education, S.V. Agricultural college, Tirupati-517502<br />
Date <strong>of</strong> Receipt : 26.09.2012 Date <strong>of</strong> Acceptance : 11.01.2013<br />
Sugarcane is the world’s largest crop and is<br />
grown in over 110 countries. In 2009, an estimated<br />
1,683 million metric tons were produced worldwide<br />
which amounts to 22.4% <strong>of</strong> the total world agricultural<br />
production by weight (FAO, 2009). India ranks second<br />
in cane area and sugar production after Brazil. <strong>The</strong><br />
states <strong>of</strong> Uttar Pradesh, Maharashtra, Karnataka,<br />
Tamil Naidu and Andhra Pradesh together produce<br />
nearly 90 per cent <strong>of</strong> the cane and sugar in the<br />
country. Andhra Pradesh ranks fifth in sugar cone<br />
area <strong>of</strong> the country with a share <strong>of</strong> 4.83 per cent.<br />
<strong>The</strong> average production <strong>of</strong> Andhra Pradesh is about<br />
20.30 million tons contributing to 5.83 per cent <strong>of</strong><br />
the total production <strong>of</strong> the country. In Andhra<br />
Pradesh, the major sugarcane growing districts in<br />
Telangana, coastal Andhra and Rayalaseema regions<br />
are Nizamabad, Visakhapatnam and Chittoor districts<br />
respectively.<br />
<strong>The</strong> significant contribution <strong>of</strong> researchers,<br />
extension functionaries and farming community plays<br />
pivotal role in achieving the above success. On one<br />
side, the researchers developed sustainable<br />
technologies to meet the production requirements <strong>of</strong><br />
the farmers followed by effective dissemination <strong>of</strong><br />
technologies by the extension functionaries so as to<br />
bring the technologies to the farmers for adoption.<br />
On the other side, the farming community is<br />
successfully adopting those technologies so as to<br />
increase the productivity levels <strong>of</strong> sugarcane. As the<br />
farmers are the key contributors <strong>of</strong> production, the<br />
present study was taken up to study the pr<strong>of</strong>ile<br />
characteristics <strong>of</strong> sugarcane farmers.<br />
Ex-post-facto research design was adopted<br />
for the study. <strong>The</strong> investigation was carried out in<br />
Chittoor district <strong>of</strong> Rayalaseema region. Four<br />
mandals were selected in Chittoor district purposively<br />
that have highest area under sugarcane. From each<br />
mandal 3 villages were selected purposively. From<br />
each village 10 sugarcane farmers were selected<br />
randomly thus making a total <strong>of</strong> 120 respondents .<br />
<strong>The</strong> data were collected by personal interview method<br />
through structured interview schedule.<br />
<strong>The</strong> sugarcane farmers were distributed into<br />
different categories based on their selected pr<strong>of</strong>ile<br />
characteristics and were presented in the Table 1<br />
and interpreted through frequencies, percentages,<br />
mean and standard deviation.<br />
Age<br />
Majority (57.50%) <strong>of</strong> the sugarcane farmers<br />
belonged to middle age category followed by young<br />
(31.66%) and old age (10.83%) categories. <strong>The</strong><br />
probable reason for distribution might be that the<br />
agriculture in the present situation has been perceived<br />
as a pr<strong>of</strong>itable enterprise in particular as sugarcane<br />
is one <strong>of</strong> the remunerative crops for the farmers.<br />
Middle age and young age farmers were motivated<br />
to cultivate sugarcane by adopting latest production<br />
technologies and obtaining good returns.. Hence the<br />
above trend was observed. It is in conformity with<br />
Reddy (1997) and Gowda et al., (2011).<br />
Education<br />
Majority (90.00%) <strong>of</strong> the respondents were<br />
educated, 4.17 per cent were under can read and<br />
write only category and only 5.00 per cent were<br />
illiterates. <strong>The</strong> probable reason for the above<br />
distribution might be that, as education was gaining<br />
importance for the past three decades and brought<br />
out awareness among the farming community about<br />
the functional literacy. Majority <strong>of</strong> the sugarcane<br />
farmers were under middle and young age lead to<br />
the proper educational status among the farming<br />
community. It is in conformity with Latha (2002) and<br />
Gowda (2011).<br />
email: shilpamohan5050@gmail.com<br />
101
PROFILE CHARACTERISTICS OF SUGARCANE FARMERS<br />
Table 1. Pr<strong>of</strong>ile characteristics <strong>of</strong> sugarcane farmers N=120<br />
S.No<br />
AGE<br />
Category Frequency Percentage MEAN S.D.<br />
1. Young (56 years) 13 10.83<br />
- -<br />
Total 120 100.00<br />
EDUCATION<br />
1 Illiterate 6 5.00<br />
2. Can read and write only 5 4.17<br />
3. Primary school 9 7.50<br />
4. Middle school 26 21.67<br />
5. High school 49 40.83<br />
- -<br />
6. Intermediate 15 12.50<br />
7. College level 10 8.33<br />
Total 120 100.00<br />
FARM SIZE<br />
1. Marginal farmer 4 3.33<br />
2. Small farmer 65 54.17<br />
3. Big farmer 51 42.50<br />
Total 120 100.00<br />
FARMING EXPERIENCE<br />
- -<br />
1. Low 19 15.83<br />
2. Medium 83 69.17<br />
3. High 18 15.00<br />
Total 120 100.00<br />
EXTENSION CONTACT<br />
1. Low 17 14.17<br />
2. Medium 76 63.33<br />
3. High 27 22.50<br />
Total 120 100.00<br />
TRAININGS UNDERGONE<br />
1 Low 37 30.83<br />
2 Medium 60 50.00<br />
3 High 23 19.17<br />
Total 120 100.00<br />
SOCIAL PARTICIPATION<br />
1 Low 14 11.67<br />
2 Medium 81 67.50<br />
3 High 25 20.83<br />
Total 120 100.00<br />
MASS MEDIA EXPOSURE<br />
1. Low 13 10.83<br />
2. Medium 83 69.17<br />
3. High 24 20.00<br />
Total 120 100.00<br />
ACHIEVEMENT MOTIVATION<br />
102<br />
25.35 11.12<br />
9.99 2.20<br />
1.825 1.0262<br />
10.75 3.3487<br />
8.65 2.17
DEVI et al<br />
S.No<br />
Category Frequency Percentage MEAN S.D.<br />
2. Medium 71 59.17<br />
3. High 23 19.16<br />
Total 120 100.00<br />
SCIENTIFIC ORIENTATION<br />
1. Low 22 18.33<br />
2. Medium 74 61.67 11.88 2.73<br />
3. High 24 20.00<br />
Total 120 100.00<br />
MANAGEMENT ORIENTATION<br />
1. Low 15 12.50 35.72. 6.39<br />
2. Medium 86 71.67<br />
3. High 19 15.83<br />
Total 120 100.00<br />
INNOVATIVENESS<br />
1. Low 20 16.67 17.64 4.52<br />
2. Medium 79 65.83<br />
3. High 21 17.50<br />
Total 120 100.00<br />
Farm size<br />
It is evident from the Table 1 that 54.17 per cent <strong>of</strong><br />
the sugarcane farmers were small followed by big<br />
farmers (42.50%) and marginal farmers (3.33%). <strong>The</strong><br />
probable reason might be that, sugarcane as a<br />
commercial crop need to be grown in large farms so<br />
as to take up required farm management practices<br />
and also to cope up with the post harvest management<br />
such as transporting to the sugar factories or taking<br />
up jaggery preparation. It might be very difficult to<br />
take up all such activities under small holding<br />
conditions with half to one acre <strong>of</strong> land which involve<br />
high investment leading to less pr<strong>of</strong>itability. It is in<br />
conformity with findings <strong>of</strong> Pandya (1995).<br />
Farming experience<br />
From Table 1 it is evident that 69.17 per cent<br />
<strong>of</strong> the sugarcane farmers had medium farming<br />
experience followed by low (15.83%) and high farming<br />
experience (15.00%).<strong>The</strong> probable reason might be<br />
that as majority <strong>of</strong> the farmers belong to middle age<br />
group and also there was awareness among the<br />
farming community about the education which made<br />
them to enter into farming after completing their<br />
education. It is in conformity with Roy (2005) and<br />
Reddy (1997).<br />
Extension contact<br />
From Table 1 it could be seen that 63.33 percent <strong>of</strong><br />
the respondents were having medium extension<br />
contact followed by low (22.50%) and high (14.17%)<br />
extension contact. <strong>The</strong> probable reason f might be<br />
that as the sugarcane crop is mainly grown under<br />
the supervision <strong>of</strong> sugar factories and also the majority<br />
<strong>of</strong> sugarcane farmers were educated, the farmers<br />
always seek for timely extension support from factory<br />
<strong>of</strong>ficials, agricultural <strong>of</strong>ficers and the scientists for<br />
their day to day farm operations for better<br />
productivity. It is in conformity with Gattu (2001) and<br />
Roy (2005).<br />
Trainings undergone<br />
From Table 1 it could be seen that majority<br />
<strong>of</strong> the respondents have undergone no. <strong>of</strong> medium<br />
trainings undergone (50.00%) followed by low<br />
(30.83%) and high (19.17%) number <strong>of</strong> trainings. This<br />
might be due to the fact that trainings are the tools<br />
for upgrading the knowledge and skills in a particular<br />
area <strong>of</strong> operation. As sugarcane is the major<br />
103
PROFILE CHARACTERISTICS OF SUGARCANE FARMERS<br />
commercial crop, training on different technologies<br />
will help the farmers in taking up the farm operations<br />
in more viable and economical ways. On the other<br />
side lack <strong>of</strong> awareness on the importance <strong>of</strong> training<br />
and also non availability <strong>of</strong> time to attend the training<br />
programmers might have contributed for the above<br />
trend. It is in conformity with Reddy (1997) and Roy<br />
(2005).<br />
Social participation<br />
From Table 4.7 and Fig 4.7 it could be seen<br />
that majority <strong>of</strong> the respondents were having medium<br />
social participation (67.50%) followed by high<br />
(20.83%) and low (11.67%) levels <strong>of</strong> social<br />
participation. <strong>The</strong> probable reason for the above trend<br />
might be that, being a member <strong>of</strong> society everybody<br />
needs to work together co operatively to achieve<br />
higher returns. As sugarcane is one <strong>of</strong> the major<br />
commercial crops involve year round investment right<br />
from land preparation, selection <strong>of</strong> setts to the final<br />
transportation <strong>of</strong> sugarcane to the factories. <strong>The</strong> need<br />
<strong>of</strong> being a member or <strong>of</strong>fice bearer in such societies<br />
which directly involve in farming operations <strong>of</strong><br />
sugarcane is essential for taking up appropriate and<br />
timely operations in farm production. It is in<br />
conformity with Reddy (1997).<br />
Mass media exposure<br />
Majority <strong>of</strong> the respondents were having<br />
medium mass media exposure (69.17%) followed by<br />
high (20.00%) and low(10.83%) levels <strong>of</strong> mass media<br />
exposure. <strong>The</strong> probable reason for this trend might<br />
be due to the fact that, as majority <strong>of</strong> sugarcane<br />
farmers are young and middle aged and ninety five<br />
per cent <strong>of</strong> sugarcane farmers were educated had<br />
inclination towards better utilization <strong>of</strong> different mass<br />
media such as radio, T.V, news papers so as to take<br />
up modern technologies in sugarcane production. <strong>The</strong><br />
farmers with illiteracy and higher age might not be<br />
utilizing the mass media because <strong>of</strong> their personal<br />
and psychological limitations. It is in conformity with<br />
Reddy (1997) and Sangeetha (2004).<br />
Scientific Orientation<br />
More than half (61.66%) <strong>of</strong> the respondents<br />
had medium scientific orientation followed by high<br />
(20.00%) and low (18.33%) <strong>of</strong> scientific orientation.<br />
<strong>The</strong> probable reason might be that there were ample<br />
number <strong>of</strong> technologies developed by the scientists<br />
and disseminated among the farming community<br />
leading to successful adoption <strong>of</strong> those technologies.<br />
This might be because <strong>of</strong> higher scientific orientation<br />
among the sugarcane farmers to adopt those<br />
technologies as per the recommendations <strong>of</strong> the<br />
scientists for better returns. <strong>The</strong> education level,<br />
extension contact and mass media exposure directly<br />
contributes for the scientific orientation among the<br />
sugarcane farmers. Less scientific orientation for few<br />
farmers might be due to complexity <strong>of</strong> the<br />
technologies and illiteracy <strong>of</strong> the farming community.<br />
It is in conformity with Reddy (1997).<br />
Management Orientation<br />
Perusal <strong>of</strong> the Table 1 reveals that majority<br />
(71.67%) <strong>of</strong> sugarcane farmers had medium<br />
Management Orientation followed by high (15.83%)<br />
and low (12.50%) management Orientation. This<br />
might be due to the fact that sugarcane crop requires<br />
better management at each and every stage <strong>of</strong> its<br />
operations to get high net pr<strong>of</strong>it. Year round and timely<br />
decisions are essential to cope up with the<br />
environmental and human resource management.<br />
Morever people might be adopting age old practices<br />
(traditional way) without proper resource<br />
management. It is in conformity with Reddy (1997).<br />
Innovativeness<br />
Findings from Table 1 show that majority<br />
(65.83%) <strong>of</strong> the respondents had medium<br />
innovativeness followed by high (17.50%) and low<br />
(16.67%) levels <strong>of</strong> innovativeness. <strong>The</strong> possible<br />
reason might be that the farmers with higher<br />
education, extension contact and mass media<br />
exposure were able to update their knowledge and<br />
skills time to time and ready to accept the new<br />
technologies in their farming. How ever illiterates and<br />
resource poor farmers might be lacking awareness,<br />
knowledge and risk taking ability to adopt such<br />
technologies. It is in conformity with Hemanth (2002)<br />
and Gangadhar (2009) findings.<br />
<strong>The</strong> results <strong>of</strong> present study indicated that<br />
majority <strong>of</strong> sugarcane farmers are middle and young<br />
aged with required educational qualification and there<br />
is every chance <strong>of</strong> motivating them towards adopting<br />
sugarcane production technologies so as to enhance<br />
sugarcane productivity and also net income. As<br />
104
DEVI et al<br />
extension contact and mass media exposure were<br />
the major pillars for diffusing information to farming<br />
community there is every scope to improve these<br />
two components so as to utilize the extension<br />
personnel and mass media for strengthening their<br />
knowledge and skills. Psychological variables <strong>of</strong><br />
sugarcane farmers i.e. Social participation, scientific<br />
orientation, management orientation and innovativeness<br />
were found to be medium and this indicates that these<br />
variables are the inherent igniters for the human beings.<br />
Hence, there is need to organize training programmes,<br />
group discussions, brain storming sessions, exposure<br />
visits etc. for sugarcane farmers and provide a platform<br />
to utilize these variables effectively in sugarcane<br />
cultivation.<br />
REFERENCES<br />
Gangadhar, M.M. 2009. Communication factors and<br />
entrepreneurial behavior <strong>of</strong> cotton growers.<br />
<strong>Journal</strong> <strong>of</strong> <strong>Research</strong>, <strong>ANGRAU</strong>, Hyderabad.<br />
31(3):62-67.<br />
Gattu, K.C. 2001. Production constraints <strong>of</strong> turmeric<br />
cultivation in Karimnagar district <strong>of</strong> Andhra<br />
Pradesh. M.Sc. <strong>The</strong>sis submitted to Acharya<br />
N G Ranga Agricultural University, Hyderabad.<br />
Gowda, T.A., Babu, C.R., Naidu, G.R and Rao, V.S.<br />
2011. Pr<strong>of</strong>ile characteristics <strong>of</strong> sugarcane<br />
growers in Mandya district <strong>of</strong> Karnataka. <strong>The</strong><br />
Andhra Agricultural <strong>Journal</strong>. 58(2):236-239.<br />
Hemanth, K. B. 2002. A study on attitude, knowledge<br />
and adoption <strong>of</strong> recommended practices by<br />
oriental tobacco farmers in Chittoor District <strong>of</strong><br />
Andhra Pradesh. M.Sc. (Ag.) <strong>The</strong>sis, Acharya<br />
N G Ranga Agricultural University, Hyderabad.<br />
Latha, S.M. 2002. A study on knowledge and<br />
adoption <strong>of</strong> integrated pest management<br />
practices in cotton by farmers in Kurnool<br />
district <strong>of</strong> Andhra Pradesh. M.Sc. (Ag.) <strong>The</strong>sis<br />
submitted to Acharya N G Ranga Agricultural<br />
University, Hyderabad.<br />
Pandya R.D. 1995. Entrepreneurial behavior <strong>of</strong><br />
sugarcane farmers. <strong>Journal</strong> <strong>of</strong> Extension<br />
Education. 6(4): 1299-1301.<br />
Reddy, S. 1997. Information Management Behaviour<br />
(IMB)-An analysis <strong>of</strong> sugarcane research,<br />
extension and client system. Ph.D.. (Ag.)<br />
<strong>The</strong>sis. Acharya N G Ranga Agricultural<br />
University, Hyderabad.<br />
Roy, S. 2005. A study on the sustainability <strong>of</strong><br />
sugarcane cultivation in Vishakhapatnam<br />
district <strong>of</strong> Andhra Pradesh. Ph.D.. (Ag.) <strong>The</strong>sis<br />
submitted to Acharya N G Ranga Agricultural<br />
University, Hyderabad<br />
Sangeetha, V. 2004. Training needs <strong>of</strong> cotton growers<br />
<strong>of</strong> Madurai district <strong>of</strong> Tamilnadu. M.Sc. (Ag.)<br />
<strong>The</strong>sis submitted to Acharya N G Ranga<br />
Agricultural University, Hyderabad.<br />
105
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 101-104, 2013<br />
A STUDY ON DIFFUSION STATUS OF SYSTEM OF RICE INTENSIFICATION (SRI)<br />
IN ANDHRA PRADESH<br />
K. NIRMALA and R. VASANTHA<br />
Department <strong>of</strong> Agricultural Extension<br />
College <strong>of</strong> Agriculture, Rajendranagar, <strong>ANGRAU</strong>, Hyderabad – 500 030<br />
Date <strong>of</strong> Receipt : 14.06.2012 Date <strong>of</strong> Acceptance : 27.09.2012<br />
Any efforts that successfully reduce the<br />
water allocation for rice even by 20 to 30 per cent<br />
will help in averting both the food and water crises<br />
as farmers can continue to grow more rice with less<br />
water.<br />
Frequent drought over the past 10 years has<br />
left the rice farmers <strong>of</strong> Andhra Pradesh in doldrums.<br />
Andhra Pradesh experienced severe drought in 1999-<br />
2000, characterized by water shortages, falling<br />
groundwater levels and increased risk <strong>of</strong><br />
contamination <strong>of</strong> surface water. Drought, followed by<br />
low rainfall (534 mm annual rainfall) in the south-west<br />
and north-east monsoons during 1999 was<br />
exacerbated by groundwater extraction. Agricultural<br />
production was seriously reduced in kharif 1999.<br />
<strong>The</strong>reafter, the thrust for conservative water-usage<br />
became the major concern for scientists and farmers.<br />
Depleted water resources, stagnated rice productivity,<br />
the growing importance <strong>of</strong> organic agriculture,<br />
increased production costs and the need for better<br />
utilization <strong>of</strong> family labour among small and marginal<br />
farmers, calls for a shift in cultivation practices. <strong>The</strong><br />
System <strong>of</strong> Rice Intensification (SRI) <strong>of</strong>fers a way to<br />
not only reduce the demand for water while growing<br />
irrigated rice, but also <strong>of</strong> simultaneously increasing<br />
rice production. SRI was introduced in Andhra<br />
Pradesh in kharif 2003 in all 22 districts <strong>of</strong> the state<br />
by Acharya N.G. Ranga Agricultural University<br />
(<strong>ANGRAU</strong>). Since 2003, <strong>ANGRAU</strong> and State<br />
Department <strong>of</strong> Agriculture has taken several initiatives<br />
to promote SRI in Andhra Pradesh (www. sriindia.net,2009).<br />
Today, India has one <strong>of</strong> the largest numbers<br />
<strong>of</strong> SRI farmers in the world. Official record indicates<br />
that SRI diffused first to Tamil Nadu State, followed<br />
by Andhra Pradesh (Prasad, 2006). Though Andhra<br />
Pradesh was the first to start large scale promotion<br />
<strong>of</strong> SRI, but no substantial area could be covered<br />
during the last few years. Even after 9-10 years <strong>of</strong><br />
introduction <strong>of</strong> SRI technology among farmers <strong>of</strong><br />
Andhra Pradesh, the pace <strong>of</strong> spread <strong>of</strong> technology is<br />
not rapid.<br />
Hence the present study was conceived to<br />
know the status <strong>of</strong> SRI in terms <strong>of</strong> diffusion and<br />
adoption across the selected villages and mandals<br />
<strong>of</strong> Mahaboobnagar district.<br />
<strong>The</strong> present study was conducted in<br />
Mahaboobnagar district as it has highest cultivated<br />
area under SRI during 2008-09. Ex-post facto research<br />
design was followed. A sample <strong>of</strong> 120 SRI cultivating<br />
farmers from 12 villages <strong>of</strong> four mandals <strong>of</strong> the<br />
district was selected randomly. Measurement <strong>of</strong><br />
diffusion status was done under three dimensions<br />
i.e, Diffusion Status, spread <strong>of</strong> SRI in selected<br />
villages (secondary data) and adopter categories.<br />
Diffusion status <strong>of</strong> System <strong>of</strong> Rice<br />
Intensification was operationalised as the extent <strong>of</strong><br />
spread <strong>of</strong> SRI technology among the farmers from<br />
2006-07 to 2010-11. Diffusion status was measured<br />
with the help <strong>of</strong> developed schedule comprising <strong>of</strong><br />
various items that are pretested. <strong>The</strong> score obtained<br />
by a respondent on all items <strong>of</strong> diffusion status were<br />
added to get total score. Based on total scores<br />
obtained, respondents were grouped into 3 categories<br />
<strong>of</strong> low, medium and high according to equal class<br />
interval method.<br />
<strong>The</strong> second dimension i.e. spread <strong>of</strong> SRI in<br />
selected villages was studied in terms <strong>of</strong> number <strong>of</strong><br />
farmers adopting and number <strong>of</strong> acres. Year - wise<br />
data was collected starting from 2006-07 to 2010-<br />
2011 (5 years) from secondary sources such as<br />
Department <strong>of</strong> Agriculture and NGOs.<br />
<strong>The</strong> third dimension i.e. adopter categories<br />
was studied by categorizing adopters into five<br />
categories based on the criteria <strong>of</strong> innovativeness or<br />
email: drankamarajug@yahoo.com<br />
106
NIRMALA and VASANTHA<br />
earliness in adoption i.e. the degree to which an<br />
individual or others unit <strong>of</strong> adoption is relatively earlier<br />
in adopting new ideas than other members <strong>of</strong> social<br />
system.<br />
<strong>The</strong> year <strong>of</strong> adoption was taken as criteria to<br />
determine the earliness <strong>of</strong> respondents in adoption<br />
<strong>of</strong> SRI. Data on number <strong>of</strong> respondents adopting SRI<br />
for the first time is collected year wise for five years,<br />
starting from 2006-07 to 2010-11 and accordingly the<br />
respondents were grouped into five adopter categories<br />
viz innovators, early adopters, early majority, late<br />
majority and laggards. <strong>The</strong> data was tabulated and<br />
depicted graphically.<br />
Distribution <strong>of</strong> respondents according to their<br />
diffusion status on SRI is depicted in Fig 1. <strong>The</strong><br />
probable reason for medium to low diffusion status<br />
<strong>of</strong> SRI among farming community may be because<br />
<strong>of</strong> inherent problems associated with SRI cultivation<br />
such as nonavailability <strong>of</strong> skilled labour, organic<br />
manures, difficulties in land levelling and weeding<br />
cono weeder, gaps in research and extension, heavy<br />
rains in kharif etc.<br />
If the above problems are overcome by<br />
research and extension agencies by taking necessary<br />
steps, then there is a possibility for increase in area<br />
under SRI in the district.<br />
High<br />
12.50%<br />
Low<br />
35.00%<br />
Medium<br />
52.50%<br />
Fig 1. Diffusion status <strong>of</strong> SRI technology<br />
Table 2. Comparision between total Acreage under Rice and SRI<br />
S.no Year Total<br />
Acreage<br />
under<br />
Rice<br />
Acreage<br />
under<br />
SRI<br />
% Total<br />
Number <strong>of</strong><br />
Farmers<br />
SRI<br />
Farmers<br />
%<br />
1 2006-07<br />
2 2007-08<br />
3 2008-09<br />
4 2009-10<br />
5 2010-11<br />
Total<br />
1964 6 0.30 982 15 1.52<br />
2244 63 2.80 748 101 13.50<br />
2777 245 8.82 925.67 238 25.71<br />
3438 166 4.82 1719 190 11.05<br />
3129 149 4.76 1564.5 172 10.99<br />
13552 629 4.64 5939.17 716 12.05<br />
107
A STUDY ON DIFFUSION STATUS OF SYSTEM OF RICE INTENSIFICATION (SRI)<br />
<strong>The</strong> number <strong>of</strong> farmers cultivating SRI and<br />
acreage under SRI is compared with total number <strong>of</strong><br />
rice farmers and rice acreage in the selected villages.<br />
Percentages were calculated.<br />
<strong>The</strong> percentage <strong>of</strong> SRI acreage over total<br />
Rice acreage is 0.30 per cent in 2006 -07, 2.80 per<br />
cent in 2007-08, 8.82 per cent in 2008-09, 4.82 per<br />
cent in 2009-10 and 4.76 per cent in 2010-11.Whereas<br />
percentage <strong>of</strong> farmers adopting SRI over total Rice<br />
farmers is 1.52 per cent in 2006 -07, 13.50 per cent<br />
in 2007-08, 25.71 per cent in 2008-09 , 11.05 per<br />
cent in 2009-10 and 10.99 per cent in 2010-11.<br />
Results are depicted graphically in figure 1 and 2.<br />
<strong>The</strong> secondary sources reported an<br />
increasing trend both in area and number <strong>of</strong> farmers<br />
from the year 2006 (year <strong>of</strong> inception <strong>of</strong> SRI) to 2009,<br />
afterwards a gradual decline is clearly evident in the<br />
acreage and number <strong>of</strong> farmers adopting SRI in<br />
selected villages. <strong>The</strong> present study showed similar<br />
trend with respect to cumulative frequency reported<br />
by Karthik and Manjunatha (2010).<br />
Percentage adoption <strong>of</strong> SRI<br />
50<br />
45<br />
40<br />
35<br />
30<br />
25<br />
20<br />
15<br />
10<br />
5<br />
0<br />
Early majority<br />
Late majority<br />
Laggards<br />
Early adopters<br />
Innovators<br />
2006-2007 2007-2008 2008-2009 2009-2010 2010-2011<br />
Years<br />
Fig.2. Categorisation <strong>of</strong> adopter on the basis <strong>of</strong> earliness in adoption (innovativeness)<br />
Table 3. Distribution <strong>of</strong> the respondents based on their earliness in adoption (innovativeness) <strong>of</strong> SRI<br />
technology<br />
S.No Year Adopter category N %<br />
1 2006-2007 Innovators 2 1.66<br />
2 2007-2008 Early adopters 11 9.17<br />
3 2008-2009 Early majority 44 36.67<br />
4 2009-2010 Late majority 40 33.33<br />
5 2010-2011 Laggards 23 19.17<br />
Total 120 100.00<br />
Table 3 indicates that during 2006-07, (year<br />
<strong>of</strong> SRI inception in the Mahaboobnagar district) SRI<br />
was adopted by only a few members i.e only 1.66<br />
percent, who are termed as innovators. As SRI is a<br />
new technology it was adopted only by a small<br />
percent <strong>of</strong> respondents. During the first year, the<br />
respondents (innovators) who are having high<br />
extension contacts and sources <strong>of</strong> information<br />
108
NIRMALA and VASANTHA<br />
adopted this technology. In 2007- 08 there was a<br />
little increase in number <strong>of</strong> respondents adopting SRI<br />
i.e. from1.66 to 9.17 percent, the probable reasons<br />
could be they might have got convinced by seeing<br />
SRI performance in innovators fields or there may<br />
be increase in availability <strong>of</strong> implements or<br />
organisation <strong>of</strong> good number <strong>of</strong> demonstrations and<br />
improved extension contacts with concerned<br />
scientists etc. must have motivated the respondents<br />
to adopt SRI. During 2008- 09, there was a rapid<br />
increase in number <strong>of</strong> farmers adopting SRI i.e. from<br />
9.17 to 36.67 percent, the probable reason could be<br />
in order to prevent depletion <strong>of</strong> ground water<br />
resources, which generally happens with conventional<br />
rice cultivation, Government has announced<br />
incentives to promote SRI in the form <strong>of</strong> providing<br />
machinery and inputs at subsidised prices which has<br />
shot up area under SRI. In 2009- 10, there was a<br />
little decrease in adoption <strong>of</strong> SRI i.e from 36.67 to<br />
33.33 percent, the probable reason may be problems<br />
with labour In 2010- 11 there was a drastic decrease<br />
in adoption <strong>of</strong> SRI due to intensified problems with<br />
labour, non availability <strong>of</strong> inputs, difficulties in water<br />
management, non availability <strong>of</strong> organic inputs, land<br />
levelling, weeding operations which has reduced SRI<br />
cultivation. Study reported a large majority <strong>of</strong><br />
respondents under early and late majority (70%).<br />
Similar findings were reported by Prasad (1997).<br />
<strong>The</strong> curve (Fig.2 ) obtained on Adopter<br />
categories is an incomplete bell shaped curve.<br />
According to Rogers (2003) adoption <strong>of</strong> an innovation<br />
usually follows a normal bell shaped curve when<br />
plotted over a time. Since the time tested period is<br />
short in the present study, the curve could not be a<br />
complete bell shaped curve. If the study was carried<br />
out for longer period <strong>of</strong> time as done by Rogers then<br />
there is a possibility to obtain a complete bell shaped<br />
curve for SRI cultivation also. Similar results were<br />
reported by Ryan and Gross (1943) in hybrid seed<br />
corn in Iowa.<br />
<strong>The</strong> status <strong>of</strong> diffusion <strong>of</strong> SRI is medium to low<br />
in spite <strong>of</strong> multifarous efforts <strong>of</strong> government the<br />
aggregate area under SRI is not to the expectations.<br />
Though some farmers are able to continue this method<br />
and reap benefits, some others have adopted SRI<br />
for one season or two seasons and have discontinued<br />
it, some others appreciated the method but did not<br />
adopt it. Lack <strong>of</strong> perception accuracy and operational<br />
difficulties might have discouraged farmers to<br />
continue SRI. Keeping in view <strong>of</strong> benefits <strong>of</strong> SRI,<br />
the government has to take measures to increase<br />
its diffusion by popularising the benefits <strong>of</strong> SRI<br />
through interpersonal and mass communication<br />
media, announcing incentives in the form <strong>of</strong> supply<br />
<strong>of</strong> organic manures, subsidised markers and<br />
conoweeders. Funds should be earmarked to<br />
innovative farmers and NGO’s who were interested<br />
in developing modified implements, varieties or<br />
methods in SRI that definitely helps in increasing<br />
area under SRI.<br />
REFERENCES<br />
Karthik, K. B and Manjunatha, B. N. 2010. Adoption<br />
<strong>of</strong> hybrid paddy seed production technologies<br />
in Mandya District. Mysore <strong>Journal</strong> <strong>of</strong><br />
Agricultural Sciences, 44 (4): 863-865.<br />
Prasad, S.C. 2006. System <strong>of</strong> Rice intensification in<br />
India: Innovation History and Institutional<br />
Challenges. WWF- ICRISAT Dialogue on<br />
Water, Food and Environment, Patancheru,<br />
Hyderabad. http:// wassan.org/Sri/documents/<br />
Shambu-SRI. pdf (21 July 2011)<br />
Prasad, S.V. 1997 A critical analysis <strong>of</strong> diffusion and<br />
adoption <strong>of</strong> production recommendations <strong>of</strong><br />
rainfed castor in Nalgonda district <strong>of</strong> Andhra<br />
Pradesh. Ph.D <strong>The</strong>sis Acharya N G Ranga<br />
Agricultural University, Hyderabad.<br />
Rogers, E. M. 2003. Diffusion <strong>of</strong> Innovations. 5 th ed.<br />
New York, London, Toronto, Sydney.<br />
Singapore: Free Press.<br />
Ryan, Bryce and Neal C. Gross 1943. “<strong>The</strong> Diffusion<br />
<strong>of</strong> Hybrid Seed Corn in Two Iowa<br />
Communities”, Rural Sociology, 8:15-24.<br />
RS(E) Website: www. Sri-india.net, 2009.<br />
109
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 105-108, 2013<br />
CORRELATION AND PATH COEFFICIENT ANALYSIS FOR YIELD AND<br />
PHYSIOLOGICAL ATTRIBUTES IN RICE (Oryza sativa L.) HYBRIDS UNDER<br />
SALINE SOIL CONDITIONS<br />
M.SUDHARANI, P.RAGHAVA REDDY, G.HARIPRASAD REDDY and CH.SURENDRA RAJU<br />
Seed <strong>Research</strong> and Technology Centre, Rajendranagar, Hyderabad-500030<br />
Date <strong>of</strong> Receipt : 21.09.2012 Date <strong>of</strong> Acceptance : 09.11.2012<br />
Correlation studies and path coefficient<br />
analysis were undertaken to know the inter<br />
relationship <strong>of</strong> yield components and physiological<br />
parameters related to salt tolerance and their<br />
usefulness in selection programmes under salt stress.<br />
In the present investigation 28 rice hybrids<br />
derived by crossing eight genotypes (RPBio-226,<br />
Swarna , CSR-27, CSR-30, CST-7-1, CSRC(S)7-1-<br />
4, SR26-B and CSRC(S)5-2-2-5 in half diallel manner<br />
were utilized based on their reaction to salinity<br />
tolerance and were evaluated during kharif, 2010<br />
under salt affected soils <strong>of</strong> Agricultural <strong>Research</strong><br />
Station, Machilipatnam. Seedlings <strong>of</strong> 30 days old were<br />
transplanted in the main field having electrical<br />
conductivity <strong>of</strong> 7.9 dS/m and pH <strong>of</strong> 7.7 following<br />
randomized block design with three replications. <strong>The</strong><br />
recommended agronomic, cultural and plant<br />
protection measures were followed in conducting the<br />
experiment. Genotypic and phenotypic correlation<br />
coefficients were calculated among eight parents<br />
using the formulae suggested by Al-Jibouri et al.<br />
(1958) and their significance was tested by using the<br />
‘r’ table values (Fisher and Yates, 1963) at n-2<br />
degrees <strong>of</strong> freedom, where ‘n’ denotes the number <strong>of</strong><br />
treatments used in the calculation.<br />
To estimate the direct and indirect effects<br />
<strong>of</strong> the yield components on the yield, the statistical<br />
tool employed was path coefficient analysis as<br />
suggested by Wright (1921) and illustrated by Dewey<br />
and Lu (1959). <strong>The</strong> path coefficients were obtained<br />
by solving the ‘p’ normal equations following the<br />
matrix method given by Singh and Chowdhary (1985).<br />
In the present investigation, the genotypic and<br />
phenotypic correlations amongst the traits followed<br />
almost similar trend <strong>of</strong> association, the former being<br />
a little higher in most <strong>of</strong> the cases, indicating the<br />
existence <strong>of</strong> a strong inherent association between<br />
the characters. Further, dissecting these associations<br />
as direct and indirect effects through path analysis<br />
showed direct contribution <strong>of</strong> each component trait<br />
on yield and indirect effect it has through association<br />
on other component traits.<br />
<strong>The</strong> yield component viz., plant height<br />
(0.5847), number <strong>of</strong> tillers plant -1 (0.7789), number <strong>of</strong><br />
productive tillers plant -1 (0.5753), panicle length<br />
(0.8353), panicle weight (0.5500), number <strong>of</strong> filled<br />
grains panicle -1 (0.7809), spikelet fertility per cent<br />
(0.7190), 1000-grain weight (0.5399), root/shoot ratio<br />
(0.4694) and harvest index (0.8128) were significantly<br />
and positively correlated with grain yield (Table 1) in<br />
rice hybrids tested under saline conditions. On the<br />
other hand Na + /K + ratio and SPAD chlorophyll meter<br />
readings exhibited significant negative association<br />
with grain yield, while the effect <strong>of</strong> days to 50 per<br />
cent flowering was non-significant. <strong>The</strong> findings <strong>of</strong><br />
earlier researchers viz., Bala (2001) for plant height;<br />
Zeng and Shannon (2000), Natarajan et al. (2005) for<br />
number <strong>of</strong> tillers plant -1 ; Ravindra Babu (1996),<br />
Natarajan et al. (2005) for number <strong>of</strong> productive tillers<br />
plant -1 ; Bala (2001) for panicle length; Buu and Tuan<br />
(1991), Ravindra Babu (1996), Natarajan et al. (2005)<br />
for number <strong>of</strong> filled grains panicle -1 ; Natarajan et al.<br />
(2005) for 1000-grain weight ; Sajjad (1990) and Balan<br />
et al. (1999) for harvest index were in line with the<br />
present readings. However, Asch et al. (2000)<br />
reported strong negative association <strong>of</strong> Na + /K + ratio<br />
with grain yield which is in agreement with the present<br />
findings. Under saline soil conditions plant height,<br />
number <strong>of</strong> tillers plant -1 , productive tillers plant -1 ,<br />
panicle length, panicle weight, number <strong>of</strong> filled grains<br />
panicle -1 , spikelet fertility per cent, SPAD values and<br />
test weight showed strong positive association with<br />
grain yield plant -1 under stressed environment.<br />
email: madugula.sudharani@yahoo.com<br />
110
SUDHARANI et al<br />
Table 1. Genotypic (r g<br />
) and phenotypic (r p<br />
) correlation coefficients among grain yield, its components and physiological traits in F 1<br />
hybrids <strong>of</strong> rice<br />
under saline soils<br />
* Significant at p=0.05; ** Significant at p=0.01;<br />
PH (cm): Plant height; DFF: Days to 50% flowering; TT: Number <strong>of</strong> tillers plant -1 ; PT: Number <strong>of</strong> productive tillers plant -1 ; PL (cm): Panicle length; PW(g): Panicle<br />
weight;<br />
NFGP -1 : Number <strong>of</strong> filled grains panicle -1 ; SF (%): Spikelet fertility per cent; TW (g): 1000-grain weight; GY (g): Grain yield (g plant -1 ); SES: SES for visual salt injury;<br />
RSR: Root /shoot ratio; HI (%): Harvest index per cent; Na + /K + R: Sodium Potassium ratio; SPAD: SPAD chlorophyll meter reading.<br />
111
CORRELATION AND PATH COEFFICIENT ANALYSIS FOR YIELD<br />
Table 2. Genotypic (G) and phenotypic(P) direct and indirect effects among grain yield, its components and physiological traits in F 1<br />
hybrids <strong>of</strong> rice<br />
under saline soils<br />
Residual effect =0.08005<br />
PH (cm): Plant height; DFF: Days to 50% flowering; TT: Number <strong>of</strong> tillers plant -1 ; PT: Number <strong>of</strong> productive tillers plant -1 ; PL (cm): Panicle length; PW(g):<br />
Panicle weight;<br />
NFGP -1 : Number <strong>of</strong> filled grains panicle -1 ; SF (%): Spikelet fertility per cent; TW (g): 1000-grain weight; GY (g): Grain yield (g plant -1 ); SES: SES for visual salt<br />
injury;<br />
RSR: Root /shoot ratio; HI (%): Harvest index per cent; Na + /K + R: Sodium Potassium ratio; SPAD: SPAD chlorophyll meter reading.<br />
112
SUDHARANI et al<br />
At genotypic level, number <strong>of</strong> total tillers plant -1<br />
(1.0876) exhibited highest positive effect on yield<br />
(Table 2), while substantial magnitude <strong>of</strong> positive<br />
direct effect was also exerted by spikelet fertility<br />
(0.4417). On the other hand, moderate direct effects<br />
were shown by SES for visual salt injury (0.2692)<br />
and root shoot ratio (0.2927). <strong>The</strong> direct effects <strong>of</strong><br />
productive tillers plant -1 (-0.5877), panicle length<br />
(-0.3681) and panicle weight (-0.3495) were high, but<br />
negative, while moderate influence in the same<br />
direction was exhibited by SPAD chlorophyll meter<br />
readings (-0.2290). <strong>The</strong> direct positive effects on yield<br />
were reported for number <strong>of</strong> grains panicle -1 (Sajjad,<br />
1990), harvest index (Tripathi et al., 2011) and<br />
productive tillers plant -1 (Natarajan et al., 2005 and<br />
Tripathi et al., 2011). <strong>The</strong>refore, more emphasis may<br />
be given to spikelet fertility per cent and number <strong>of</strong><br />
tillers plant -1 while executing selections under saline<br />
soil conditions.<br />
<strong>The</strong> results <strong>of</strong> present investigation indicate<br />
selection under saline condition would be effective<br />
for number <strong>of</strong> total tillers per plant, spikelet fertility<br />
per cent as they showed significant positive<br />
association as well as direct effect on yield. Similarly,<br />
selecting the plants with low Na + /K + ratio would help<br />
for yield improvement along with salt tolerance as<br />
this trait showed significant negative association as<br />
well as negative direct effect on grain yield under<br />
stressed conditions Hence, these traits may be<br />
prioritized for developing ideotype(s) for saline<br />
environment.<br />
REFERENCES<br />
Al-Jibouri, H.A., Miller, P.A and Robinson, H.F. 1958.<br />
Genotypic and environmental variances and<br />
co-variances in an upland cotton cross <strong>of</strong><br />
interspecific origin. Agronomy <strong>Journal</strong>. 50: 633-<br />
636.<br />
Asch, F., Dingkunn, M., Dorffling, K and Miezank.<br />
2000. Leaf K/N ratio predicts salinity induced<br />
yield loss in irrigated rice. Euphytica. 113: 109-<br />
118.<br />
Balan, A., Muthiah, A.R and Boopathi, S.N.M.R.<br />
1999. Genetic variability, character association<br />
and path coefficient analysis in rainfed rice,<br />
under alkaline condition. Madras Agricultural<br />
<strong>Journal</strong>. 86 (1/3): 122-124.<br />
Bala, A. 2001. Genetic variability, association <strong>of</strong><br />
characters and path coefficient analysis <strong>of</strong><br />
saline and alkaline rice genotypes under rainfed<br />
condition. Madras Agricultural <strong>Journal</strong>. 88(4-<br />
6): 356-357.<br />
Buu, C.B and Tuan, T.M. 1991. Genetic study in the<br />
F 2<br />
crosses for high grain quality. International<br />
Rice <strong>Research</strong> Newletter. 16: 11.<br />
Dewey, D.R and Lu, K.N. 1959. Correlation and path<br />
coefficient analysis <strong>of</strong> components <strong>of</strong> crested<br />
wheat grass seed production. Agronomy<br />
<strong>Journal</strong>. 51: 515-518.<br />
Fisher. R.A and Yates, F. 1963. Statistical Tables<br />
for Biological, Agricultural and Medical<br />
<strong>Research</strong> (6 th Edition), Hafner Publishing<br />
Company, New York,<br />
Natarajan, S.K., Saravanan, S., Krishnakumar, S and<br />
Dhanalakshmi, R. 2005b. Interpretations on<br />
association <strong>of</strong> certain quantitative traits on<br />
yield <strong>of</strong> rice (Oryza sativa L.) under saline<br />
environment. <strong>Research</strong> <strong>Journal</strong> <strong>of</strong> Agriculture<br />
and Biological Sciences. 1(1): 101-103.<br />
Ravindra Babu, V. 1996. Study <strong>of</strong> genetic parameters,<br />
correlations and path coefficient analysis <strong>of</strong><br />
rice (Oryza sativa L.) under saline conditions.<br />
Annals <strong>of</strong> Agricultural <strong>Research</strong>. 17(4): 370-<br />
374.<br />
Sajjad, M.S. 1990. Correlations and path coefficient<br />
analysis <strong>of</strong> rice under controlled saline<br />
environment. Pakistan <strong>Journal</strong> <strong>of</strong> Agricultural<br />
<strong>Research</strong>. 11(3): 164-168.<br />
Singh, P. K and Chaudhary, B. D.1985. Biometrical<br />
Methods in Quantitative Genetic Analysis (1 st<br />
Edition), Kalyani Publishers, New Delhi, India.<br />
Tripathi, S., Verma, O.P., Dwived, D.K., Yadavendra<br />
Kumar., Singh, P.K and Verma, G.P. 2011.<br />
Association studies in Rice (Oryza Sativa L.)<br />
hybrids under saline alkaline environment.<br />
Environment and Ecology. 29(3) 1557-1560.<br />
Wright, S. 1921. Correlation and causation. <strong>Journal</strong><br />
<strong>of</strong> Agricultural <strong>Research</strong>. 20: 557-585<br />
Zeng, L and Shannon, M.C. 2000. Salinity effects on<br />
seedling growth and yield components <strong>of</strong> rice.<br />
Crop Science. 40: 996-1003.<br />
113
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 109-113, 2013<br />
GENETIC DIVERGENCE STUDIES FOR YIELD AND PHYSIOLOGICAL<br />
ATTRIBUTES IN GROUNDNUT (Arachis hypogaea L.)<br />
D. NIRMALA, V. JAYALAKSHMI, B. NARENDRA and P. UMAMAHESHWARI<br />
Deptartment <strong>of</strong> Genetic & Plant Breeding, Agricultural College, <strong>ANGRAU</strong>, Mahanandi – 518 503<br />
Date <strong>of</strong> Receipt : 07.06.2012 Date <strong>of</strong> Acceptance : 26.12.2012<br />
Selection <strong>of</strong> genotypes from the available<br />
genetic variation is crucial for any crop improvement<br />
programme. Estimating genetic diversity available in<br />
the existing germplasm provides clue to the choice<br />
<strong>of</strong> most desirable parents for use in hybridization<br />
programmes. Selections based on physiological traits<br />
that confer water use efficiency have been suggested<br />
for improving drought tolerance in Groundnut. In the<br />
present investigation an attempt was made to identify<br />
most diverse groundnut genotypes for practical plant<br />
breeding programmes utilizing physiological traits like<br />
SPAD Chlorophyll Metre Reading (SCMR), specific<br />
leaf area, crop growth rate (CGR), relative growth rate<br />
(RGR) etc.<br />
Thirty genotypes <strong>of</strong> Groundnut were<br />
evaluated during Kharif 2011 at Agricutural College<br />
Mahanandi, A.P. <strong>The</strong> experimental material was<br />
procured from the Groundnut Breeding Station,<br />
RARS, Tirupati comprising <strong>of</strong> diverse breeding<br />
material generated in All India Coordinated Groundnut<br />
Improvement Programme. <strong>The</strong> experiment was laid<br />
out in a Randomized Block Design replicated thrice.<br />
Each genotype in a replication was grown in two rows<br />
<strong>of</strong> 4.2 m length with a spacing <strong>of</strong> 30 cm between the<br />
rows and 10 cm within a row. All the recommended<br />
package <strong>of</strong> practices were followed to raise a good<br />
crop. Observations were recorded on five randomly<br />
chosen plants in each genotype in a replication for<br />
19 characters. <strong>The</strong> data collected was analyzed as<br />
per the standard procedures described by<br />
Mahalanobis’s (1936) and Rao (1952).<br />
Analysis <strong>of</strong> variance for both quantitative and<br />
physiological traits in all the 30 genotypes under study<br />
revealed significant differences for all the characters<br />
indicating the wealth <strong>of</strong> variability available in the<br />
germplasm. Further the data was subjected to D 2<br />
analysis and the results were presented in<br />
Table 1 to 3.<br />
Based on D 2 analysis all the 30 genotypes<br />
were grouped into 14 clusters with a variable number<br />
<strong>of</strong> entries in each cluster revealing the presence <strong>of</strong> a<br />
considerable amount <strong>of</strong> genetic diversity in the<br />
material (Table 2). Cluster I had maximum number<br />
<strong>of</strong> 10 genotypes followed by cluster II with 6<br />
genotypes and cluster X with 3 genotypes. Remaining<br />
all other clusters possessed one genotype each.<br />
Cluster I alone had one-third <strong>of</strong> the total genotypes<br />
studied indicating that the genotypes under study had<br />
narrow genetic diversity among them. Similarity in<br />
the base population from which they have been<br />
evolved might be the cause <strong>of</strong> genetic uniformity.<br />
However, the uni-directional selection potential for<br />
one particular character or a group <strong>of</strong> linked traits in<br />
several places may produce similar phenotypes which<br />
can be aggregated into one cluster irrespective <strong>of</strong><br />
geographical diversity. Sudhir Kumar et al (2010),<br />
Awatade (2007) and Garajappa et al. (2005) reported<br />
that there is no correlation between genetic diversity<br />
and geographical diversity in the groundnut genotypes<br />
studied by them.<br />
Average inter cluster and intra cluster D 2<br />
values among the 30 genotypes were furnished in<br />
Table 2. <strong>The</strong> maximum intra cluster distance was<br />
recorded for cluster X (6.31) followed by cluster II<br />
(5.13) and cluster I (4.95) revealing substantial<br />
diversity within the clusters. Maximum inter-cluster<br />
values were observed between cluster III and cluster<br />
XII (12.35) followed by cluster V and cluster XIII<br />
(12.10) indicating maximum divergence between the<br />
genotypes included in these clusters.<br />
Cluster means for all the traits were given in<br />
Table 2. Cluster means for different characters<br />
indicated that none <strong>of</strong> the clusters contained genotype<br />
with all the desirable characters and so recombinant<br />
breeding between genotypes <strong>of</strong> different clusters is<br />
needed. Cluster XII showed higher cluster means for<br />
email: veera.jayalakshmi@gmail.com<br />
114
NIRMALA et al<br />
weight <strong>of</strong> pods per plant (17.67), kernel weight per<br />
plant (12.93) and plant height (67.00) whereas, cluster<br />
XIV showed highest mean values for number <strong>of</strong><br />
mature pods per plant (19.20) and number <strong>of</strong> sound<br />
mature kernels per plant (31.6). Cluster X showed<br />
high mean values for harvest index (47.0), shelling<br />
out turn (82.18) and the genotypes <strong>of</strong> the clusters<br />
XIII possessed high mean value for 100 seed weight<br />
(59.33) and number <strong>of</strong> primary branches per plant<br />
(6.67).<br />
Among various traits studied, the highest<br />
contribution (Table 3) towards divergence was found<br />
for number <strong>of</strong> secondary branches per plant (29.89%)<br />
followed by CGR at 75 DAS to harvest (18.39%) CGR<br />
at 30-75 DAS (10.57%), 100 seed weight (8.51%),<br />
plant height (8.51%), SCMR (6.9%) and harvest index<br />
(5.75%). <strong>The</strong> manifestation <strong>of</strong> genetic diversity due<br />
to number <strong>of</strong> secondary branches per plant was<br />
reported by Muralidharan and Manivannan (2004),<br />
Garajappa et al. (2005), Dolma et al. (2010) and<br />
Pavan kumar (2010) for harvest index, Sonone et al.<br />
(2011) for plant height and 100 seed weight. <strong>The</strong>se<br />
results corroborate with the findings <strong>of</strong> present study.<br />
<strong>The</strong> data on inter cluster distances and per<br />
se performance <strong>of</strong> genotypes were used to select<br />
genetically diverse and agronomically superior<br />
genotypes. <strong>The</strong> genotypes exceptionally good for one<br />
or more characters seemed to be more desirable.<br />
On this basis, CAUG-1, CSMG 2006-6, LGN 123, R-<br />
2001-2, and TCGS 150 were selected. Inter crossing<br />
<strong>of</strong> divergent groups would lead to greater opportunity<br />
for crossing over and realizing hidden potential<br />
variability by disrupting the undesirable linkages. <strong>The</strong><br />
progenies obtained from such diverse genotypes<br />
provides a greater scope for isolating transgressive<br />
segregants in advanced generations particularly in<br />
segmental allotetraploid like groundnut. Hence these<br />
genotypes could be utilized in a multiple crossing<br />
programme to recover desirable transgressive<br />
segregants.<br />
Table 1. Distribution <strong>of</strong> 30 genotypes <strong>of</strong> groundnut in different clusters (Tocher’s method)<br />
Cluster No. No. <strong>of</strong><br />
Genotype(s)<br />
genotypes<br />
I 10 TCGS 876, ICGV 00351, Tirupati 4, TPT 25, TPT 1, ICGV 91114,<br />
Narayani, DH 218, TCGS-913 ,TPT-2<br />
II 6 TCGS 901A, UG 6, K 1392, TCGS 901 A, GPBD 4, PBS 30086<br />
III 1 CSMG 2006-6<br />
IV 1 TCGS-584<br />
V 1 TCGS-150<br />
VI 1 CTMG 7<br />
VII 1 TG 68<br />
VIII 1 CSMG 2006-6<br />
IX 1 RTNG 2<br />
X 3 K 1463, Greeshma, Bheema<br />
XI 1 J 71<br />
XII 1 CAUG 1<br />
XIII 1 LGN 123<br />
XIV 1 R-2001-2<br />
115
GENETIC DIVERGENCE STUDIES FOR YIELD<br />
Table 2. Average inter cluster distances formed by 30 genotypes <strong>of</strong> groundnut<br />
116
NIRMALA et al<br />
Table 3. Cluster means for 19 characters in 30 genotypes <strong>of</strong> groundnut (Mahalanobis’s D 2 method)<br />
Clus<br />
ter<br />
No.<br />
PH = Plant height; PB = Number <strong>of</strong> primary branches per plant; SB = Number <strong>of</strong> secondary branches per plant; MP= Number <strong>of</strong> mature pods per plant; IMMP<br />
= Number <strong>of</strong> immature pods per plant; SMK = Number <strong>of</strong> sound mature kernel; WOP = Weight <strong>of</strong> pods per plant; KW = Kernel weight per plant; SO = Shelling<br />
out turn; 100SW =100-seed weight; HI = Harvest index; SLA = Specific Leaf Area; SCMR = SPAD chlorophyll meter reading; O.C = Oil content; CGR1 = Crop<br />
Growth Rate at 30 DAS to 75 DAS; CGR2 = Crop Growth Rate at 75 DAS to harvest; RGR1 = Relative Growth Rate at 30 DAS to 75 DAS; RGR 2 = Relative<br />
Growth Rate at 75 DAS to harvest.<br />
117
GENETIC DIVERGENCE STUDIES FOR YIELD<br />
REFERENCES<br />
Awatade, S.M. 2007. Genetic variability, characters<br />
association, path analysis and genetic<br />
diversity in groundnut (Arachis hypogaea L.)<br />
M.Sc. <strong>The</strong>sis submitted to Dr. BSKKV, Dapoli.<br />
Dolma, T., Sekhar, M.R and Reddy, K.R. 2010.<br />
Genetic divergence studies in Groundnut<br />
(Arachis hypogaea L.). <strong>Journal</strong> <strong>of</strong> Oilseeds<br />
<strong>Research</strong> 27:2, 158-160.<br />
Garajappa, Dasaradha Rami Reddy, C., Naik, K.S.S<br />
and Srinivasa Rao, V. 2005. Genetic<br />
divergence in groundnut (Arachis hypogaea L.).<br />
<strong>The</strong> Andhra Agricultural <strong>Journal</strong> 52(3&4): 424-<br />
436.<br />
Mahalanobis P. C 1936. On the generalized distance<br />
in statistics. Proceedings <strong>of</strong> National Academy<br />
<strong>of</strong> Sciences in India 2: 49-55.<br />
Muralidharan, V and Manivannan, N. 2004. D 2<br />
analysis in groundnut. Legume <strong>Research</strong><br />
27(4): 302-304.<br />
Pavan Kumar, C 2010. Genetic divergence in<br />
groundnut (Arachis hypogaea L.). M.Sc.<br />
<strong>The</strong>sis submitted to Acharya N.G.Ranga<br />
Agricultural University.<br />
Rao, C.R.V 1952. Advanced statistical methods in<br />
biometrical research. John Wiley and Sons<br />
Inc., New York, pp.236-272.<br />
Sonone, N.G., Thaware, B.L., Bhave, S.G., Jadhav,<br />
B.B., Joshi, G.D and Dhekale J. S. 2011.<br />
Multivariate studies in groundnut (Arachis<br />
hypogaea L.). <strong>Journal</strong> <strong>of</strong> Oilseeds <strong>Research</strong><br />
28 (1): 24-28.<br />
Sudhir Kumar, I., Venkstaravana, P. and Marappa,<br />
N. 2010. Genetic divergence <strong>of</strong> new germ<br />
plasm and advanced breeding lines <strong>of</strong><br />
Groundnut (Arachis hypogaea L.) studied<br />
under late Kharif situation. Legume <strong>Research</strong><br />
33(2):124-127.<br />
118
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 114-115, 2013<br />
INFLUENCE OF METHODS OF IRRIGATION ON PLANT GROWTH, YIELD,<br />
FLOWER QUALITY AND VASE LIFE IN DENDROBIUM ORCHID HYBRID<br />
SONIA-17 UNDER SHADE NET<br />
B. GOPALA RAO, P.T. SRINIVAS and M.H.NAIK<br />
Department <strong>of</strong> Horticulture, Sri Venkateswara Agricultural College,<br />
Acharya N.G. Ranga Agril. University, Tirupati-517502<br />
Date <strong>of</strong> Receipt : 26.07.2012 Date <strong>of</strong> Acceptance : 27.09.2012<br />
Dendrobium is the biggest genera in the<br />
Orchidaceae family and commands a major share<br />
in foreign exchange. It is a water loving plant and<br />
needs irrigation every day. Crops, in the absence <strong>of</strong><br />
definite recommendation are irrigated with different<br />
methods which have definite effects on the growth,<br />
development and performance. In such situation<br />
standardization <strong>of</strong> method <strong>of</strong> irrigation is necessary<br />
in realising the highest benefit <strong>of</strong> irrigation and save<br />
both cost and water. In view <strong>of</strong> this, the present<br />
investigation was under taken to study the effect <strong>of</strong><br />
method <strong>of</strong> irrigation on vegetative and floral<br />
characteristics and longevity <strong>of</strong> Dendrobium hybrid<br />
Sonia-17.<br />
<strong>The</strong> experiment was conducted at orchid<br />
farm, Nathanallur village, Kancheepuram district,<br />
Tamil Nadu during the year 2006. <strong>The</strong>re were four<br />
treatments replicated five times in a completely<br />
randomized block design. <strong>The</strong> Dendrobium hybrid<br />
Sonia-17 owing to its superiority for yield and quality<br />
was used for the study. <strong>The</strong> following are the<br />
treatment details: T1: Mist irrigation, T2: Drip<br />
irrigation, T3: Hose irrigation, T4: Hand watering<br />
(control). Observations on various parameters like<br />
plant height, number <strong>of</strong> leaves per plant, number <strong>of</strong><br />
shoots per plant, and number <strong>of</strong> spikes per plant,<br />
length <strong>of</strong> spike, number <strong>of</strong> florets per spike, floret<br />
pedicel length, longevity <strong>of</strong> spike on the plant and in<br />
the tap water (vase life) were recorded at 180 and<br />
195 days after planting respectively.<br />
Among the four methods <strong>of</strong> irrigation<br />
included, plants grown under mist recorded maximum<br />
height (50.34 cm). Mist increases the humidity in<br />
the green house, thus it retains mist in both container<br />
and growing media which might enhanced the plant<br />
height (Table 1). Misting increased plant growth and<br />
yield by decreasing the canopy temperature and<br />
increasing the relative humidity (Stalmakh et al.,<br />
1986),whereas lowest height <strong>of</strong> plants was recorded<br />
in hand watering (37.96 cm) which is on par with hose<br />
irrigation (38.68 cm),other growth parameters like<br />
number <strong>of</strong> leaves per plant (11.09), number <strong>of</strong> shoots<br />
per plant (6.63) were maximum in mist irrigated<br />
plants, whereas minimum in hand watering<br />
(8.84),(5.19). Gislerod and Nelson (1989) and Gislerod<br />
and Mortenson (1990) obtained similar results in<br />
Chrysanthemum and Begonia.<br />
Floral characters such as number <strong>of</strong> spikes<br />
per plant (2.91), length <strong>of</strong> spike (38.84 cm), number<br />
<strong>of</strong> florets per spike (9.99) and floret pedicel length<br />
(4.31 cm) were maximum under mist (Table 1), while<br />
hand watering recorded minimum number <strong>of</strong> spikes<br />
per plant (2.28),spike length (28.30 cm), number <strong>of</strong><br />
florets per spike (5.43) and length <strong>of</strong> floret pedicel<br />
(3.58 cm), this might be due to increase in production<br />
source (leaves) and nutrients which might have helped<br />
in better synthesis <strong>of</strong> carbohydrates. Number <strong>of</strong><br />
flowers and flower buds significantly increased with<br />
relative humidity in Saintpaulia (Mortensen., 1986).<br />
Longevity <strong>of</strong> flower spike on the plant was<br />
the highest under mist irrigation (94 days), whereas<br />
the lowest under hand watering (76 days). Vase life<br />
<strong>of</strong> flower spike in tap water was maximum from mist<br />
irrigated plants (21 days) and hand watered plants<br />
(control) recorded minimum vase life (18 days)<br />
(Fig 1). Nair and Sujatha (2004) stated that orchids<br />
require 60-80 per cent humidity to perform their best<br />
bloom.<br />
Hence, based on the above results, mist<br />
irrigation under shade net is ideal for successful<br />
production <strong>of</strong> growth and blooms in the Orchid.<br />
email: gopal_hort@yahoo.co.in<br />
119
INFLUENCE OF METHODS OF IRRIGATION ON PLANT GROWTH<br />
Table 1. Growth (at 180 DAP) and Flowering (at 195 DAP) characteristics <strong>of</strong> Dendrobium Orchid hybrid<br />
Sonia-17 grown under different methods <strong>of</strong> irrigation under shade net<br />
Treatments<br />
T1: Mist<br />
irrigation<br />
T2: Drip<br />
irrigation<br />
T3: Hose<br />
irrigation<br />
T4: Hand<br />
watering<br />
(control)<br />
Plant<br />
height<br />
(cm)<br />
Number<br />
<strong>of</strong> leaves<br />
/plant<br />
Number<br />
<strong>of</strong><br />
shoots<br />
/plant<br />
Number<br />
<strong>of</strong> spikes<br />
/plant<br />
Number<br />
<strong>of</strong> florets<br />
/Spike<br />
Spike<br />
length<br />
(cm)<br />
Floret<br />
pedicel<br />
length (cm)<br />
50.3 11.0 6.6 2.9 9.9 38.8 4.3<br />
40.4 9.4 6.1 2.5 6.7 28.4 3.7<br />
38.6 9.0 5.6 2.5 5.9 29.6 3.8<br />
37.9 8.8 5.1 2.2 5.4 28.3 3.5<br />
CD at 5% 1.6 0.3 0.3 0.1 0.7 1.2 0.4<br />
Fig 1. Longevity <strong>of</strong> spikes on the plant and in the vase at room temperature <strong>of</strong> Dendrobium Orchid<br />
hybrid Sonia-17 as influenced by method <strong>of</strong> irrigation<br />
REFERENCES<br />
Gislerod, H. R and Nelson, P. V. 1989. <strong>The</strong><br />
interaction <strong>of</strong> relative air humidity and carbon<br />
dioxide enrichment in the growth <strong>of</strong><br />
Chrysanthemum x Morifolium Ramat. Scientia<br />
Horticulturae 38: 305-313.<br />
Gislerod, H. R and Mortenson, L. M. 1990. Effect <strong>of</strong><br />
relative humidity and nutrient concentration<br />
on nutrient uptake and growth by Begonia x<br />
Hiemalis Fotsch. Schwabeland’. Horticultural<br />
Science 25(5): 524-526.<br />
Mortensen, L. M. 1986. Effect <strong>of</strong> relative humidity<br />
on growth and flowering <strong>of</strong> some green house<br />
plants. Scientia Horticulture. 29(4): 301-307.<br />
Nair, S. A and Sujatha, 2004. Growing orchids,<br />
prospects and advances, Floriculture Today<br />
9(7):27-28.<br />
Stalmakh, E.A., N.D. Cherenkov and M.A. Kuzin,<br />
1986. Mist irrigation is effective.<br />
Kart<strong>of</strong>eliovoshchi, 3: 17.<br />
120
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 116-120, 2013<br />
STUDY ON PESTICIDE RESIDUES OF SELECTED VEGETABLES GROWN IN<br />
NORTH COASTAL ZONE OF ANDHRA PRADESH<br />
Y. PUNYAVATHI and V. VIJAYALAKSHMI<br />
College <strong>of</strong> Home Science, Saifabad, Hyderabad - 500004<br />
Date <strong>of</strong> Receipt : 11.06.2012 Date <strong>of</strong> Acceptance : 21.01.2013<br />
An attempt was made to estimate the<br />
pesticide residues in various vegetables (before and<br />
after processing) grown in north coastal zone <strong>of</strong><br />
Andhra Pradesh. <strong>The</strong> fresh vegetable samples <strong>of</strong><br />
brinjal, bitter gourd and tomato were collected<br />
randomly from three farmers in each <strong>of</strong> the three<br />
districts (Visakhapatnam, Vijayanagaram and<br />
Srikakulam). <strong>The</strong> pesticide residue content <strong>of</strong> the<br />
selected whole and processed vegetables (soaked<br />
in 3% salt water for 15 min) were analyzed for the<br />
pesticides namely chlorophyriphos, quinalphos,<br />
endosulfan, acephate, monocrotophos and<br />
carb<strong>of</strong>uran.<br />
Pesticide residue was determined by the<br />
method <strong>of</strong> Sharma (2007). Non-ionic residues were<br />
extracted with acetone / water, and the residues from<br />
were separated aqueous acetone to dichloromethane<br />
/ hexane phase. <strong>The</strong> traces <strong>of</strong> dichloromethane were<br />
removed and made up to the final extract with<br />
acetone/ hexane. Organophosphate residues were<br />
determined directly by gas chromatography with<br />
ECD/NPD Detector.<br />
<strong>The</strong> level <strong>of</strong> pesticide residue in all the<br />
selected whole and processed vegetables were below<br />
detectable level (BDL). <strong>The</strong> chromatogram <strong>of</strong> the<br />
samples are given in the figures 1-6. Reddy et al.<br />
(1998) also found that the level <strong>of</strong> pesticide residue<br />
in vegetables sampled from Srikakulam was below<br />
Maximum Residue Limits (MRL).<br />
In field studies conducted in India, the<br />
maximum initial deposits <strong>of</strong> cypermethrin,<br />
fenvalerate and deltamethrin applied to cabbage at<br />
50, 50 and 12 g a.i./ha were 0.34, 0.96 and 0.25 mg/<br />
kg, respectively on heads, and 1.34, 0.08 and 0.30<br />
mg/ kg, respectively on leaves. <strong>The</strong>se values were<br />
within the maximum residue limits <strong>of</strong> 2 mg/ kg for<br />
cypermethrin and fenvalerate on lettuce heads, and<br />
0.5 mg/ kg for deltamethrin on leafy vegatables. Most<br />
<strong>of</strong> the insecticide residues were found on the outer<br />
leaves and it was concluded that the residue levels<br />
found do not constitute any health hazard to<br />
consumers (Singh et al. 1992). Chahal et al. (1992)<br />
investigated the persistence <strong>of</strong> residue <strong>of</strong> endosulfan<br />
and fenvelerate on okra and found that the residues<br />
required a holding period <strong>of</strong> 1-3 days to become safe<br />
for consumption.<br />
Persistence <strong>of</strong> fluvalinate and the safe interval<br />
between the last application and the harvest <strong>of</strong> brinjal,<br />
okra, cauliflower and cabbage were determined at<br />
two dose rates in field experiments in India. Residues<br />
at 30 g a.i./ha persisted for 7, 10 and 15 days on<br />
brinjal, okra, cabbage and cauliflower respectively.<br />
A post application holding period before harvest <strong>of</strong><br />
one day was suggested for these crops after<br />
treatment with fluvalinate (Agnihotri et al. 1992).<br />
Bordia and Gupta (1992) reported initial deposits <strong>of</strong><br />
1.17, 12.80, 29.27 and 3.23 mg/kg when 0.05%<br />
monocrotophos, 0.1% carbaryl, 0.07% endosulfan<br />
and 0.05% fenitrothion, respectively, were applied in<br />
foliar application to cauliflowers in the field. Residues<br />
<strong>of</strong> monocrotophos, endosulfan and fenitrothion fell<br />
to about 50% in 3 days and those <strong>of</strong> carbaryl in 7<br />
days. Residues <strong>of</strong> monocrotophos, carbaryl and<br />
fenitrothion were below the level <strong>of</strong> detection in 15<br />
days, and those <strong>of</strong> endosulfan in 21 days indicating<br />
that they have to be harvested only after 15 days /<br />
21 days respectively after application <strong>of</strong> pesticides.<br />
After 4 days tomatoes contained 1.02, 0.41 and 1.46<br />
ppm pr<strong>of</strong>en<strong>of</strong>os, pirimiphos-methyl and<br />
methamidophos, respectively. Tomatoes were<br />
considered to be safe for human consumption 1 day<br />
after treatment with pirimiphos-methyl and 8 days<br />
after treatment with pr<strong>of</strong>en<strong>of</strong>os or methamidophos<br />
( Abdalla et al. 1993).<br />
<strong>The</strong> residues <strong>of</strong> most commonly used<br />
pesticides (endosulfan, cypermethrin, dimethoate,<br />
email: punyavathi.45@gmail.com<br />
121
STUDY ON PESTICIDE RESIDUES OF SELECTED VEGETABLES<br />
monocrotophos and mancozeb) on vegetables grown<br />
in India were observed by Dethe et al. (1995).<br />
Detectable levels <strong>of</strong> residues <strong>of</strong> endosulfan,<br />
cypermethrin, dimethoate, monocrotophos and<br />
mancozeb were observed in 33.3% samples <strong>of</strong><br />
tomatoes (endosulfan, dimethoate and<br />
monocrotophos), 73.3% samples <strong>of</strong> brinjal<br />
(endosulfan, cypermethrin , fenvalerate, quinalphos,<br />
dimethoate and monocrotophos), 14.3% samples <strong>of</strong><br />
okra (endosulfan), 88.9% samples <strong>of</strong> cabbage<br />
( endosulfan, fenvalerate and dimethoate) and 100%<br />
<strong>of</strong> cauliflower ( endosulfan, fenvalerate, dimethoate,<br />
cypermethrin, and monocrotophos). However, the<br />
levels <strong>of</strong> pesticide residues were below the prescribed<br />
MRLs.<br />
Moreover, pesticide residues in all the selected<br />
vegetables were below detectable level (BDL) in both<br />
whole and processed vegetables which reflects that<br />
the pesticides used were highly volatile and required<br />
holding period was taken to harvest the product.<br />
6.0<br />
5.5<br />
5.0<br />
4.5<br />
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28<br />
Fig 1. Chromatogram <strong>of</strong> multiple residues in whole Brinjal<br />
7.0<br />
6.5<br />
6.0<br />
5.5<br />
5.0<br />
4.5<br />
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28<br />
Fig 2. Chromatogram <strong>of</strong> multiple residues in processed Brinjal<br />
122
PUNYAVATHI and VIJAYALAKSHMI<br />
6.5<br />
6.0<br />
5.5<br />
5.0<br />
4.5<br />
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28<br />
Fig 3. Chromatogram <strong>of</strong> multiple residues in whole Bitter gourd<br />
7.0<br />
6.5<br />
6.0<br />
5.5<br />
5.0<br />
4.5<br />
4.0<br />
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28<br />
Fig 4. Chromatogram <strong>of</strong> multiple residues in processed Bitter gourd<br />
123
STUDY ON PESTICIDE RESIDUES OF SELECTED VEGETABLES<br />
100<br />
80<br />
60<br />
40<br />
20<br />
0<br />
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28<br />
Fig 5. Chromatogram <strong>of</strong> multiple residues in whole Tomato<br />
800<br />
600<br />
400<br />
200<br />
0<br />
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28<br />
Fig 6. Chromatogram <strong>of</strong> multiple residues in processed Tomato<br />
124
PUNYAVATHI and VIJAYALAKSHMI<br />
REFERENCES<br />
Abdalla, E. F., Sammour, E. A., Abdallah, S. A and<br />
Ei-Sayed, E. I. 1993. Persistence <strong>of</strong> some<br />
organophosphate residues on tomato and bean<br />
plants. Bulletin <strong>of</strong> Faculty <strong>of</strong> Agricultural<br />
University Cario, Egypt. 44 (2): 465-476.<br />
Agnihotri, N. P., Gajbhiye, V. T., Rai, S and<br />
Srivastava, K. P. 1992. Persistence and safe<br />
waiting period <strong>of</strong> fluvalinate on some<br />
vegetables. Indian <strong>Journal</strong> <strong>of</strong> Entomology. 54:<br />
299.<br />
Bordia, J. S and Gupta, H. C. L. 1992. Residues <strong>of</strong><br />
monocrotophos, carbaryl, endosulfan and<br />
fenitrothion in cauliflower. Indian <strong>Journal</strong> <strong>of</strong><br />
Entomology. 54 (2): 230-232.<br />
Chahal, K. K., Singh, B and Singh, P. P. 1992.<br />
Persistence <strong>of</strong> endosulfan and fenvalerate on<br />
okra fruits. Indian <strong>Journal</strong> <strong>of</strong> Ecology. 19 (2):<br />
196-199.<br />
Dethe, M. D., Kale, V. D and Rane, S. D. 1995.<br />
Pesticide residues in/on farm gate samples<br />
<strong>of</strong> vegetables. Pest management in<br />
Horticultural Eco system. 1: 49-53.<br />
Reddy, D. J., Rao, B.N., Sultan, M. A. and Reddy K.<br />
N. 1998. Pesticide residues in farm gate<br />
vegetables. <strong>Journal</strong> <strong>of</strong> <strong>Research</strong> <strong>ANGRAU</strong>. 26:<br />
6-10.<br />
Sharma, K. K. 2007. Pesticide Residue Analysis<br />
Manual. ICAR, Directorate <strong>of</strong> information and<br />
publications <strong>of</strong> Agriculture, New Delhi.<br />
Singh, B., Singh, P. P., Bhattu, R.S and Kalra, R. I.<br />
1992. Residues <strong>of</strong> some synthetic Pyrethroid<br />
insecticides on cabbage. Pesticide Residue<br />
<strong>Journal</strong>. 4 (2): 134-141.<br />
125
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 121-123, 2013<br />
A STUDY ON THE KNOWLEDGE LEVEL OF FARMERS ON RECOMMENDED TEA<br />
CULTIVATION PRACTICES IN NEPAL<br />
KESHAV KATTEL, R. VASANTHA and M. JAGAN MOHAN REDDY<br />
Department <strong>of</strong> Agricultural Extension, College <strong>of</strong> Agriculture<br />
Rajendranagar, ANGR Agricultural University, Hyderabad-500 030<br />
Date <strong>of</strong> Receipt : 30.06.2011 Date <strong>of</strong> Acceptance : 29.10.2011<br />
Inspite <strong>of</strong> the suitable climate and other<br />
conducive factors, the tea growers in Nepal are not<br />
able to fully reap the benefits <strong>of</strong> this highly export<br />
oriented crop. This has resulted in a stagnation <strong>of</strong><br />
the area <strong>of</strong> tea cultivation. <strong>The</strong> yields are particularly<br />
lower than the adjoining regions <strong>of</strong> India coupled with<br />
poor quality <strong>of</strong> the produce. Against this background,<br />
a research was conducted to assess the level <strong>of</strong><br />
knowledge <strong>of</strong> the farmers on recommended tea<br />
cultivation practices and provide insights on the<br />
reasons for the underperformance <strong>of</strong> the Nepal’s tea<br />
industry.<br />
An exploratory research design was adopted<br />
for the study. As tea cultivation is confined to Jhapa<br />
and Ilam, these two districts were selected for the<br />
study. Jhapa represents the Terai or plains while Ilam<br />
represents the hilly area. Three Village Development<br />
Committees (VDCs) from each district were randomly<br />
picked and 15 farmers from each VDC were selected<br />
thus making a total <strong>of</strong> 90 respondents. A well prepared<br />
and pretested interview schedule consisting <strong>of</strong> all the<br />
recommended practices on tea cultivation was<br />
prepared by consulting the experts in the field. <strong>The</strong><br />
knowledge schedule consisted <strong>of</strong> 32 items on tea<br />
cultivation made in the form <strong>of</strong> multiple choices, fill<br />
in the blanks and true (or) false statements. <strong>The</strong><br />
collected data were analyzed using equal class<br />
intervals, frequencies and percentages.<br />
Majority <strong>of</strong> the respondents were <strong>of</strong> the<br />
middle age group (50%) and educated up to school<br />
level (81%). <strong>The</strong>y had low experience in tea<br />
cultivation (48%) and were semi-medium farmers<br />
(33%). Fifty per cent <strong>of</strong> the respondents had medium<br />
socio-economic status. Majority <strong>of</strong> them had low<br />
extension contact (46%) with the extension agencies<br />
and did not receive any training (39%). In case <strong>of</strong><br />
market intelligence, majority <strong>of</strong> them had medium<br />
market intelligence (60%). A considerable percentage<br />
<strong>of</strong> respondents reported that labour availability was<br />
difficult for tea cultivation (57%) and they were not<br />
timely available (69%). Majority <strong>of</strong> the respondents<br />
expressed that the inputs were readily available (41%)<br />
but not available timely (59%). Majority <strong>of</strong> the<br />
respondents (57%) had utilized loans from the lending<br />
agencies. Majority <strong>of</strong> the respondents had medium<br />
risk orientation (56%), innovativeness (60%) and<br />
achievement motivation (59%).<br />
Majority <strong>of</strong> the respondents (60%) had<br />
medium knowledge followed by low (18%) and high<br />
(16%) level <strong>of</strong> knowledge on recommended tea<br />
cultivation practices. To get a better insight on the<br />
level <strong>of</strong> knowledge <strong>of</strong> respondents on various items<br />
<strong>of</strong> the tea cultivation practices an item analysis on<br />
tea cultivation practices was done as depicted in<br />
Table 1 below.<br />
<strong>The</strong> item analysis revealed that cent per cent<br />
<strong>of</strong> the respondents had knowledge on the items such<br />
as ideal time for pruning and the recommended shade<br />
trees for tea cultivation. A large majority <strong>of</strong> the<br />
respondents (>80%) had knowledge on ideal soil,<br />
best climate, recommended mulches for tea<br />
cultivation, interval between two irrigations, optimum<br />
distance between the plants, reasons for the<br />
occurrence <strong>of</strong> collar canker disease, best time for<br />
planting, plucking cycles for different seasons,<br />
recommended types <strong>of</strong> planting, use <strong>of</strong> urea in pits<br />
at the time <strong>of</strong> planting and good winter and early<br />
spring rainfall improves yield.<br />
Nearly, 50 to 80 percent <strong>of</strong> the respondents<br />
had knowledge on optimum pH <strong>of</strong> soil for tea<br />
cultivation, selection <strong>of</strong> planting material with at least<br />
email: kattel_k@yahoo.com<br />
126
KATTEL et al<br />
Table 1. Item Analysis <strong>of</strong> respondents knowledge on recommended Tea cultivation practices<br />
N=90<br />
S.No.<br />
Knowledge Items<br />
Correct<br />
Response<br />
Percentage<br />
Incorrect<br />
Response<br />
Percentage<br />
1 Ideal soil for tea cultivation 88 12<br />
2 <strong>The</strong> optimum soil pH for tea cultivation 60 40<br />
3 <strong>The</strong> minimum number <strong>of</strong> clones that should be grown for<br />
tea cultivation in a given area 29 71<br />
4 Recommended types <strong>of</strong> mulches for tea cultivation 91 9<br />
5 <strong>The</strong> optimum number <strong>of</strong> plants per hectare <strong>of</strong> land 74 26<br />
6 Symptoms <strong>of</strong> boron deficiency 12 88<br />
7 What should be the height <strong>of</strong> Permanent frame? 76 24<br />
8 Reasons for occurrence <strong>of</strong> Collar Canker disease 92 8<br />
9 Optimum days between two consecutive irrigation 82 18<br />
10 <strong>The</strong> ideal time for pruning 100 0<br />
11 <strong>The</strong> depth <strong>of</strong> ground water table for tea cultivation 74 26<br />
12 <strong>The</strong> percentage <strong>of</strong> total area that one single clone variety<br />
should occupy 0 100<br />
13 <strong>The</strong> clone to seed ratio 19 81<br />
14 <strong>The</strong> optimum distance for planting 83 17<br />
15 <strong>The</strong> ideal depth <strong>of</strong> pits for planting 30 70<br />
16 <strong>The</strong> symptoms <strong>of</strong> Potassium deficiency 21 79<br />
17 Two types <strong>of</strong> planting generally recommended in tea<br />
cultivation 94 6<br />
18 Chemical for the control <strong>of</strong> Black rot 19 81<br />
19 <strong>The</strong> concentration for foliar application <strong>of</strong> Zinc Sulphate 33 67<br />
20 <strong>The</strong> plucking cycle for flush season and for other seasons 90 10<br />
21 <strong>The</strong> planting material should have at least 12 good mature<br />
buds 76 24<br />
22 Application <strong>of</strong> N and P fertilizers in 2 or more splits 77 23<br />
23 Symptoms <strong>of</strong> blister blight on tea 59 41<br />
24 Recommended shade trees for tea cultivation 100 0<br />
25 <strong>The</strong> best time for planting <strong>of</strong> tea is April to June 91 9<br />
26 Moderately hot and humid conditions are best for tea<br />
cultivation 90 10<br />
27 Spraying <strong>of</strong> insecticides or fungicides indiscriminately<br />
increases the incidence <strong>of</strong> mites 56 44<br />
28 Good rainfall during winter and early spring improves tea<br />
yield 93 7<br />
29 Correct dose <strong>of</strong> application <strong>of</strong> Hexaconazole 78 22<br />
30 Urea should not be applied in the pit at the time <strong>of</strong> planting 82 18<br />
31 Various types <strong>of</strong> bio-fertilizers for tea cultivation 37 63<br />
32 Various types <strong>of</strong> bio-pesticides for tea cultivation 56 44<br />
12 good mature buds, the optimum number <strong>of</strong> plants<br />
per hectare, the height <strong>of</strong> the permanent frame,<br />
various types <strong>of</strong> bio pesticides, the use <strong>of</strong> fertilizers<br />
in splits, depth <strong>of</strong> ground water table, doses <strong>of</strong><br />
127
A STUDY ON THE KNOWLEDGE LEVEL OF FARMERS ON TEA CULTIVATION<br />
hexaconazole, incidence <strong>of</strong> mites population due to<br />
indiscriminate sprayings and symptoms <strong>of</strong> blister<br />
blight disease.<br />
Less than 50 per cent <strong>of</strong> the respondents<br />
had knowledge on minimum number <strong>of</strong> clones required<br />
in an area, the clone to seed ratio that should be<br />
maintained, symptoms <strong>of</strong> boron and potassium<br />
deficiency in plants, control <strong>of</strong> diseases such as black<br />
rot, recommended depth <strong>of</strong> pits for planting, various<br />
types <strong>of</strong> bio fertilizers and Zinc sulphate concentration<br />
for foliar application. Surprisingly, none <strong>of</strong> the<br />
respondents had knowledge on the percentage <strong>of</strong> total<br />
area that one single clone variety should occupy in a<br />
given area <strong>of</strong> field.<br />
<strong>The</strong> items which demand high technical<br />
knowledge or advices from experts such as<br />
knowledge on nutrient deficiency symptoms, clone<br />
to seed ratio, chemical for black rot control were not<br />
answered by more than 80 per cent <strong>of</strong> the respondents<br />
and 100 per cent <strong>of</strong> them had no knowledge on the<br />
percentage <strong>of</strong> area that a single clone variety should<br />
occupy. Unfortunately, these are the yield<br />
determining factors in tea cultivation and non adoption<br />
<strong>of</strong> which leads to low yields.<br />
REFERENCES<br />
Anonymous. 2010. Smarika: Tea and C<strong>of</strong>fee. National<br />
Tea and C<strong>of</strong>fee Development Board. New<br />
Baneshwor, Kathmandu.<br />
Thapa, Ajit N. S. 2005. Concept paper on study <strong>of</strong><br />
Nepalese tea industry- Vision 2020. Nepal Tree<br />
Crop Global Development Alliance (NTCGDA),<br />
Winrock International, Baneshwor,<br />
Kathmandu, Nepal.<br />
Verma Pramod, Sonalika Gupta and D. K. Sharma.<br />
2010. Economic analysis <strong>of</strong> Himachal tea<br />
industry- A study <strong>of</strong> co-operative factories and<br />
tea planters. <strong>Journal</strong> <strong>of</strong> Plantation Crops.<br />
38(3):194-200.<br />
128
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 124-126, 2013<br />
AN ECONOMIC ANALYSIS OF VALUE ADDITION TO COTTON<br />
E. RADHIKA, R. VIJAYA KUMARI and D.V. SUBBA RAO<br />
Department <strong>of</strong> Agricultural Economics, College <strong>of</strong> Agriculture<br />
ANGR Agricultural University, Rajendranagar, Hyderabad – 500 030<br />
Date <strong>of</strong> Receipt : 15.06.2012 Date <strong>of</strong> Acceptance : 18.12.2012<br />
<strong>The</strong> processing <strong>of</strong> cotton is a business which<br />
is undertaken for the purpose <strong>of</strong> value addition to the<br />
produce. <strong>The</strong> value addition to cotton takes place at<br />
three main stages <strong>of</strong> processing viz., ginning,<br />
spinning and weaving.<br />
<strong>The</strong> ginning <strong>of</strong> cotton is important for value<br />
addition to the produce, as the spinning mills accept<br />
cotton only after ginning. Indian ginning industry is<br />
very large in size and is spread over a large area in<br />
cotton growing states. An attempt was made to study<br />
the cost and returns, value addition in case <strong>of</strong> cotton<br />
ginning and spinning industries and also the benefit<br />
cost analysis, which gives an intensified idea about<br />
the pr<strong>of</strong>itability condition the unit.<br />
<strong>The</strong> study was undertaken in Adilabad and<br />
Guntur districts <strong>of</strong> Andhra Pradesh. <strong>The</strong> study is<br />
based on primary data (for the year 2010-2011)<br />
collected from ginning and spinning units. A<br />
multistage random sampling technique was employed<br />
for the selection <strong>of</strong> sample units. 20 ginning units<br />
and 5 spinning units were selected for the study. <strong>The</strong><br />
data collected was subjected to analysis through<br />
tabular analysis.<br />
On an average, the total cost incurred in the<br />
processing <strong>of</strong> kapas to lint worked out to Rs. 4630.87<br />
per quintal <strong>of</strong> kapas. It is worth noting that the total<br />
variable cost (Rs. 4545.75 per quintal) formed a<br />
substantial component (98.2%) <strong>of</strong> the total cost <strong>of</strong><br />
processing <strong>of</strong> kapas to lint. <strong>The</strong> total fixed cost being<br />
Rs. 85.12 per quintal, accounted for only 1.8 per cent<br />
<strong>of</strong> the total cost <strong>of</strong> processing. In the total fixed cost<br />
(Rs. 85.12), salaries to permanent staff (Rs. 33.56)<br />
found to be the major component and accounted for<br />
0.72 per cent <strong>of</strong> total processing cost followed by<br />
interest on fixed capital, insurance and depreciation<br />
on building and machinery, which accounted for 0.43<br />
per cent, 0.34 per cent and 0.2 per cent <strong>of</strong> the total<br />
fixed cost respectively. Licence fee and taxes<br />
together accounted for 0.12 per cent <strong>of</strong> the total cost<br />
<strong>of</strong> processing <strong>of</strong> kapas to lint. Of the total variable<br />
costs, the cost <strong>of</strong> raw material (Rs. 4238 / quintal)<br />
accounted for 91.51 per cent <strong>of</strong> the total processing<br />
cost, followed by interest on working capital (Rs.<br />
216.75 / quintal) accounting for 4.68 per cent,<br />
electricity charges (Rs. 49.97 / quintal) accounting<br />
for 1.08 per cent and wages to casual labour (Rs.<br />
22.3 / quintal) for 0.48 per cent. <strong>The</strong> repair and<br />
maintenance (Rs. 15.91 / quintal) and telephone<br />
charges (Rs. 2.82 / quintal) altogether accounted for<br />
0.45 per cent <strong>of</strong> the total cost incurred in the<br />
processing <strong>of</strong> kapas. <strong>The</strong>se results are in line with<br />
the results <strong>of</strong> Mundinamani (2000) and Dodamani<br />
(2007).<br />
Table 1. Returns from processing <strong>of</strong> kapas to lint (for one quintal <strong>of</strong> kapas ginned)<br />
S.No<br />
email: etttediredhikareddy@gmail.com<br />
Particulars<br />
129<br />
Amount<br />
(Rs)<br />
1 Returns from main product(lint) 3978.81<br />
2 Returns from by-product (seed ) 991.6<br />
3 Gross returns 4970.41<br />
4 Raw material cost (kapas) 4238<br />
5 Value addition 732.41<br />
6 Processing cost 392.87<br />
7 Net value addition 339.54<br />
8 Benefit-cost ratio 1.86
AN ECONOMIC ANALYSIS OF VALUE ADDITION TO COTTON<br />
<strong>The</strong> processing <strong>of</strong> one quintal <strong>of</strong> kapas<br />
resulted in 33 kg <strong>of</strong> lint, 65 kg <strong>of</strong> seed and 2 kg <strong>of</strong><br />
waste. <strong>The</strong> gross returns obtained from ginning one<br />
quintal <strong>of</strong> kapas were Rs. 4970.41 <strong>of</strong> which the returns<br />
from main product (lint) were Rs. 3978.81 and that<br />
from byproduct (seed) were Rs. 991.6. Cost <strong>of</strong> raw<br />
material (kapas) was Rs. 4238 with a value addition<br />
to the product in the process being Rs. 732.41 and<br />
cost <strong>of</strong> processing accounting Rs. 392.87. <strong>The</strong> net<br />
value added as a result <strong>of</strong> processing <strong>of</strong> kapas to lint<br />
was Rs. 339.54 per quintal <strong>of</strong> kapas processed. <strong>The</strong><br />
benefit cost ratio worked out to 0.86 in kapas<br />
processing. Similar results were observed with<br />
Mundinamani (2000) and Dodamani (2007).<br />
Among all the costs incurred in marketing <strong>of</strong><br />
ginned cotton, the maximum cost was incurred on<br />
packing material Rs.30.36 per quintal, accounting for<br />
37.47 per cent <strong>of</strong> the total cost <strong>of</strong> marketing. As the<br />
packing material was very important in case <strong>of</strong> lint<br />
marketing, it constituted the maximum percent. This<br />
was followed by sales tax, miscellaneous costs and<br />
selling expenditure, which accounted for 25.02 per<br />
cent, 20.33 per cent and 17.18 per cent <strong>of</strong> the total<br />
cost <strong>of</strong> marketing <strong>of</strong> one quintal lint, respectively.<br />
<strong>The</strong>se results are on par with the results <strong>of</strong><br />
Shivakumar et al. (2001).<br />
<strong>The</strong> average total cost incurred in the<br />
processing <strong>of</strong> lint to yarn was Rs.17201.64 per quintal,<br />
<strong>of</strong> which the total variable cost was Rs.15497.16<br />
forming the major component (90.09%) <strong>of</strong> the total<br />
cost <strong>of</strong> processing <strong>of</strong> lint. <strong>The</strong> total fixed cost being<br />
Rs.1704.48 per quintal accounted for 9.91 per cent<br />
<strong>of</strong> the total cost <strong>of</strong> processing. Of the variable cost<br />
the cost <strong>of</strong> raw material (Rs.12000/quintal) accounted<br />
for 69.76 per cent followed by electricity charges<br />
(9.82%) and interest rate on working capital (5.72%).<br />
<strong>The</strong> cost on wages to casual labour, repair and<br />
maintenance, <strong>of</strong>fice maintenance, telephone charges<br />
together accounted for 4.76 per cent <strong>of</strong> the total cost<br />
<strong>of</strong> processing.<br />
In the total fixed cost (Rs.1704.48),<br />
depreciation amounts Rs.1427.06 (8.27%), which was<br />
found to be major component. This was followed by<br />
salary to permanent staff i.e., Rs.176.9 (1.02%) and<br />
interest on fixed capital, insurance, taxes, license<br />
fee, together accounting 0.58 per cent <strong>of</strong> the total<br />
cost <strong>of</strong> lint to yarn. <strong>The</strong>se results are in line with<br />
Mundinamani (2000) and Shivakumar et.al (2001).<br />
<strong>The</strong> processing <strong>of</strong> one quintal <strong>of</strong> lint on an average<br />
yielded 73 kg <strong>of</strong> yarn and 27 kg <strong>of</strong> waste material.<br />
<strong>The</strong> gross returns obtained from processing (spinning)<br />
<strong>of</strong> one quintal <strong>of</strong> lint were Rs.20435, which comprised<br />
<strong>of</strong> mainly returns from yarn (Rs.18250) and wastage<br />
(Rs.2185). <strong>The</strong> value addition in the process was<br />
Rs.8435. <strong>The</strong> net value added as a result <strong>of</strong><br />
processing <strong>of</strong> lint to yarn was Rs.5933.36 per quintal<br />
<strong>of</strong> lint processed. <strong>The</strong> resultant benefit-cost ratio was<br />
1.62.<br />
Table 2. Returns in processing <strong>of</strong> lint to yarn (for one quintal <strong>of</strong> lint spinned)<br />
S.No Particulars Amount (Rs)<br />
1 Returns from main product(yarn) 18250<br />
2 Returns from wastage 2185<br />
3 Gross returns 20435<br />
4 Raw material cost (kapas) 12000<br />
5 Value addition 8435<br />
6 Processing cost 5201.64<br />
7 Net value addition 5933.36<br />
8 Benefit-cost ratio 1.62<br />
Among all the costs incurred in marketing <strong>of</strong><br />
spinned cotton, the maximum cost was incurred on<br />
packing material, Rs.275.48 per quintal, accounting<br />
for 47.9 per cent <strong>of</strong> the total cost <strong>of</strong> marketing. This<br />
was followed by commission charges Rs.246.45<br />
(42.85%) and export expenses, yarn sale expenses,<br />
yarn freight together accounting for 9.22 per cent <strong>of</strong><br />
the total cost <strong>of</strong> marketing <strong>of</strong> one quintal yarn. <strong>The</strong>se<br />
results are in line with the results <strong>of</strong> Shivakumar et<br />
al. (2001).<br />
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RADHIKA et al<br />
Thus, the results <strong>of</strong> analysis indicated that an<br />
additional value to the extent <strong>of</strong> Rs.2297.54 was<br />
created in the course <strong>of</strong> processing cotton kapas in<br />
to yarn. <strong>The</strong> breakup <strong>of</strong> the same at different levels<br />
<strong>of</strong> processing i.e., at ginning was Rs.339.54 (14.78%)<br />
and spinning Rs.1958 (85.22%).<br />
Table 3. Total net value addition to one quintal <strong>of</strong> cotton by processing<br />
S.No Stage <strong>of</strong> processing Net value addition (Rs) Per cent<br />
1 Ginning 339.54 14.78<br />
2 Spinning 1958 85.22<br />
Total net value addition to cotton 2297.54 100<br />
REFERENCES<br />
Dodamani, M.T and Kunnal L.B. 2007. Value addition<br />
to organically produced naturally- coloured<br />
cotton under contract farming. Agricultural<br />
Economics <strong>Research</strong> Review. 20:521-528.<br />
Mundinamani, R.M. 2000. An economic analysis <strong>of</strong><br />
value addition to Cotton in Gadag district.<br />
M.Sc. (Ag) <strong>The</strong>sis submitted to University <strong>of</strong><br />
Agricultural Sciences, Dharwad.<br />
Shivakumar, S., Sonnad, J.S and Basavaraj, H.<br />
2001. Economics <strong>of</strong> cotton ginning and<br />
pressing in Bellary District. Agricultural<br />
marketing. 43(4): 9-12.<br />
131
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 127-131, 2013<br />
DEVELOPMENT OF PHYTOSTEROL ENRICHED FLAVOURED MILK<br />
M. PENCHALA RAJU , ANURAG CHATHURVEDI and APARNA KUNA<br />
Department <strong>of</strong> Food Technology, Post Graduate and <strong>Research</strong> Centre,<br />
Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad-500 030<br />
Date <strong>of</strong> Receipt : 21.06.2012 Date <strong>of</strong> Acceptance : 01.02.2013<br />
Phytosterol potency in decreasing serum low<br />
density lipoprotein (LDL) cholesterol levels and thus<br />
in protecting against cardiovascular diseases, has<br />
led to the development <strong>of</strong> functional foods enriched<br />
with plant sterols. At present, several functional food<br />
product types such as spreadable fats, yoghurts and<br />
milk, with free phytosterols or phytosteryl fatty acid<br />
esters or phytostanyl fatty acid esters added at high<br />
levels are available in the market especially in several<br />
European countries (Laakso, 2005).<br />
When phytostanols and phytosterols are<br />
included in the diet in sufficient amounts, i.e. 2–3<br />
g/d, they efficiently decrease serum cholesterol<br />
concentration by reducing the absorption <strong>of</strong><br />
cholesterol from the digestive tract. <strong>The</strong> average<br />
reduction in total cholesterol is 10%, and 15% in LDL<br />
cholesterol. No changes occur in serum HDL<br />
cholesterol or triacylglycerol concentrations (Katan<br />
et al., 2003). In addition to the blood cholesterollowering<br />
effect, phytosterols have shown the following<br />
activities in animals:anti-cancer properties (with a<br />
beneficial effect upon the inhibition <strong>of</strong> colon cancer<br />
development) (Awad et al., 2003) and<br />
antiatherosclerotic, anti-inflammatory (Bouic, 2001)<br />
and anti-oxidative effects (van Rensburg et al., 2000).<br />
Sterols make up the largest proportion <strong>of</strong> the<br />
unsaponifiable fraction <strong>of</strong> lipids. Plant fats and oils<br />
contain phytosterols as naturally occurring<br />
constituents. <strong>The</strong> most important natural sources <strong>of</strong><br />
plant sterols in human diets are oils and margarines,<br />
although they are also found in a range <strong>of</strong> seeds,<br />
legumes, vegetables and unrefined vegetable oils.<br />
Cereal products are a significant source <strong>of</strong> plant<br />
sterols, their contents, expressed on a fresh weight<br />
basis, is higher than in vegetables (Phillips et al.,<br />
2005).<br />
<strong>The</strong> exact mechanism by which phytosterols<br />
decrease serum cholesterol levels is not completely<br />
understood, but several theories have been proposed.<br />
One <strong>of</strong> them suggests that cholesterol in the intestine,<br />
already marginally soluble, is precipitated into a<br />
nonabsorbable state in the presence <strong>of</strong> added<br />
phytosterols and stanols (Jones and AbuMweis,<br />
2009). Another theory is based on the fact that<br />
cholesterol must enter bile-salt and phospholipidcontaining<br />
‘mixed micelles’ in order to pass through<br />
intestinal cells and to be absorbed into the<br />
bloodstream. Moreover, phytosterols may modulate<br />
the action <strong>of</strong> key transporters involved in cholesterol<br />
absorption. Cholesterol absorption is a very important<br />
physiological mechanism that regulates cholesterol<br />
metabolism (Rozner and Garti, 2006). Phytosterols<br />
may reduce cholesterol absorption by competing with<br />
cholesterol for incorporation into the bile salts micelles<br />
or for uptaking <strong>of</strong> cholesterol by enterocytes through<br />
Neiman Pick C1 Like1 (NPC1L1) transporter. In<br />
addition, phytosterols may enhance cholesterol<br />
excretion back into the intestinal lumen through the<br />
adenosine triphosphate binding cassette G 5<br />
(ABCG5) and G 8 9ABCG8) transporters. Phytosterol<br />
could also prevent esterification <strong>of</strong> the free cholesterol<br />
into cholesterol esters and thus it’s assembling into<br />
the chylomicrons. As a result <strong>of</strong> reducing cholesterol<br />
absorption by phytosterols, the cholesterol synthesis<br />
rate increase, but the net effect is a reduction in LDLcholesterol<br />
levels (Jones and AbuMweis, 2009).<br />
Phytosterol have been shown to inhibit the uptake <strong>of</strong><br />
both dietary and endogenously produced (biliary)<br />
cholesterol from intestinal cells. Such inhibition<br />
results in a decrease in serum total and LDLcholesterol<br />
levels. Levels <strong>of</strong> HDL cholesterol and<br />
triglycerides do not appear to be affected by dietary<br />
phytosterol consumption (AbuMweis et al., 2008). <strong>The</strong><br />
email: mpraju05@gmail.com<br />
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RAJU et al<br />
estimated daily dietary intakes <strong>of</strong> plant sterols among<br />
different populations range from 160 to 400 mg.<br />
(Berger et al., 2004). <strong>The</strong> average intakes <strong>of</strong><br />
phytosterols have been estimated between 140 and<br />
360 mg/day in Finland and 163 mg/day in the United<br />
Kingdom. (Piironen et al., 2004).<br />
<strong>The</strong> amount <strong>of</strong> phytosterols we consume<br />
from the foods is not sufficient for attaining health<br />
benefits. Hence, incorporation <strong>of</strong> phytosterols in the<br />
foods consumed can benefit in reducing the plasma<br />
cholesterol levels and reduce the risk <strong>of</strong> coronary<br />
artery diseases. <strong>The</strong>re is a lot <strong>of</strong> research evidence<br />
showing that maximum cholesterol lowering benefit<br />
is achieved with phytosterols at doses <strong>of</strong> 2-3 gm per<br />
day (Hallikainen et al., 2000, Jones et al, 2000, Maki<br />
et al., 2001). Phytosterols might also protect against<br />
certain types <strong>of</strong> cancers such as colon, breast &<br />
prostate. (Awad and Fink, 2000).<br />
<strong>The</strong> enrichment <strong>of</strong> foods such as margarines<br />
with phytosterols is one <strong>of</strong> the recent developments<br />
in functional foods to enhance the cholesterol<br />
lowering ability <strong>of</strong> traditional food products<br />
(Anonymous, 2005). <strong>The</strong> phytosterol milk products<br />
inhibit the uptake <strong>of</strong> cholesterol in intestinal Caco-2<br />
cells in vitro. Plant sterols in the milk matrix get<br />
stabilized using a proprietary crystal retardation and<br />
emulsification system (Poteau et al., 2003).<br />
Flavoured milk is the milk in which little<br />
flavour and colour has been added to make it more<br />
palatable. <strong>The</strong> most common flavour for flavored milk<br />
is chocolate, with cocoa powder as ingredient. Other<br />
common flavours for flavoured milk include<br />
strawberry, banana and c<strong>of</strong>fee. Less commonly use<br />
flavors that are available are lime, malt, mango,<br />
papaya, root beer, tropical fruits and vanilla. With<br />
the exception <strong>of</strong> chocolate milk, many <strong>of</strong> these flavors<br />
are artificial. Flavoured milk should contain milk fat<br />
percent equal to the minimum legal requirement<br />
prescribed for that milk. Nowadays, chocolate<br />
flavoured milk, fruit flavoured milk and many more<br />
varieties are more popular in the market.<br />
Objectives <strong>of</strong> the investigation<br />
· To standardize and develop phytosterol<br />
incorporated flavoured milk.<br />
· To study the acceptability <strong>of</strong> the developed<br />
products by sensory evaluation.<br />
· To estimate the physico-chemical and nutritional<br />
properties <strong>of</strong> developed product.<br />
<strong>The</strong> study was planned and conducted in the<br />
department <strong>of</strong> Food Technology, Post Graduate and<br />
<strong>Research</strong> Centre, Acharya N.G. Ranga Agricultural<br />
University, Rajendra Nagar, Hyderabad. For the<br />
present investigation, toned milk, cocoa powder,<br />
stabilizer (CMC-Carboxy Methyl Cellulose),<br />
Phytosterol powder was procured from Reducol TM<br />
Original Powder ( Forbes medi – Tech lnc) USA based<br />
company. Three products (flavoured milk) namely T1,<br />
T2, T3 and one control were prepared and<br />
standardized. Phyotsterol powder was incorporated<br />
in test samples at various levels as shown in<br />
Table 1.<br />
Table 1. Composition <strong>of</strong> different flavoured milk preparations<br />
Ingredients used<br />
Flavoured milk<br />
T1 T2 T3 Control<br />
Toned milk 500ml 500ml 500ml 500ml<br />
Cocoa powder 4g 5g 6g 4g<br />
Sugar 35g 45g 55g 35g<br />
Stabilizer(CMC) 0.2g 0.3g 0.2g 0.2g<br />
Phytosterol powder 2g/100ml 2.5g/100ml 3g/100ml -<br />
Flavoured milk was prepared by mixing all<br />
ingredients which are mentioned in the Table 1.<strong>The</strong><br />
milk (toned milk) was preheated to 60°C/1 minute<br />
and homogenized at 2500 psi at 55-60ºC/1 min and<br />
then clarified. To the warm milk, the desired amount<br />
<strong>of</strong> cocoa-mix, sugar and stabilizer were slowly added<br />
and stirred so as to dissolve them properly. <strong>The</strong> cocoa<br />
powder was added in the form <strong>of</strong> syrup, and the<br />
stabilizer in the form <strong>of</strong> solution. <strong>The</strong> mixture was<br />
then pasteurized at 71ºC/30 minutes, cooled rapidly<br />
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DEVELOPMENT OF PHYTOSTEROL ENRICHED FLAVOURED MILK<br />
to 5°C. <strong>The</strong> phytosterol enriched flavoured milk was<br />
prepared by incorporating the phytosterols into the<br />
flavoured milk. <strong>The</strong> phytosterols powder was<br />
incorporated into the flavoured milk at various levels<br />
as shown in Table 1. Phytosterol powder was added<br />
(2g, 2.5 and 3g /100ml), homogenized at 2000 psi at<br />
room temperature per 2 minutes, bottled by using<br />
glass bottles and kept under refrigeration (5ºC) until<br />
used. <strong>The</strong> received milk was homogenized to prevent<br />
or delay the rising <strong>of</strong> cream. It could be homogenized<br />
after addition <strong>of</strong> cocoa and sugar, but this increases<br />
sedimentation. Stabilizer was added to delay or<br />
prevent settling cocoa particles and also preventing<br />
cream rising.<br />
<strong>The</strong> developed products were subjected to<br />
organoleptic evaluation for parameters like colour/<br />
appearance, flavour, taste, texture/consistency and<br />
overall acceptability by twelve (12) trained panel<br />
members from Post Graduate and <strong>Research</strong> Centre,<br />
Acharya N.G. Ranga Agricultural University, Rajendra<br />
Nagar, Hyderabad. A score card with five point<br />
hedonic scale was used for the purpose. Organoleptic<br />
scores for different parameters <strong>of</strong> sensory evaluation<br />
are presented in Table 2. As per the mean score<br />
obtained the experimental product T1 was rated higher<br />
than experimental products T2 and T3. <strong>The</strong> colour,<br />
taste, flavour and overall acceptability <strong>of</strong> the<br />
experimental product T1 did not differ much from the<br />
control. When compared to T1 (2g/100ml), phytosterol<br />
powder per cent was high in experimental products<br />
T2 (2.5g/100ml) and T3 (3g/100ml). As a result all<br />
the variables scored lower than the control and T1.<br />
<strong>The</strong> overall acceptability scores was observed in<br />
which T1 (4.5) scored higher followed by control (4.4)<br />
and lowest score was observed for T2 and T3 (3.7%).<br />
After product development and acceptability studies,<br />
it was observed that the product T1 (combination <strong>of</strong><br />
toned milk, cocoa, sugar, stabilizer (500:4: 35:0.2)<br />
and phytosterol powder (2g/100ml)) was found to be<br />
superior in all aspects. Hence T1 product was selected<br />
for further study.<br />
Table 2. Mean scores obtained for control and experimental products<br />
S.No.<br />
Variables<br />
Products<br />
Control T1 T2 T3<br />
1 Colour/appearance 4.3±0.8 4.4±0.8 3.6±0.6 3.6±0.6<br />
2 Flavour 4.0±0.6 4.2±0.7 4.0±0.5 3.9±0.7<br />
3 Taste 4.1±0.8 4.2±0.7 4.0±0.5 3.8±0.7<br />
4 Texture/consistency 4.0±0.7 4.0±0.7 3.5±0.5 3.4±0.5<br />
5 Overall acceptability 4.4±0.6 4.5±0.6 3.7±0.4 3.7±0.4<br />
Note: Values are expressed as mean ± SD.<br />
Control: Flavoured milk with out phytosterols<br />
T1: Phytosterols (2g) enriched flavoured milk,<br />
T2: Phytosterols (2.5g) enriched flavoured milk,<br />
T3: Phytosterols (3g) enriched flavoured milk.<br />
Experimental product T1 and control<br />
samples were subjected to Physico- chemical and<br />
nutritional analysis. Physico- chemical and nutritional<br />
composition <strong>of</strong> the phytosterol enriched flavoured milk<br />
(T1) and control samples are presented in Table 3.<br />
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RAJU et al<br />
Table 3. Physico-chemical and nutritional parameters <strong>of</strong> experimental and control products (per 100g<br />
sample)<br />
Parameter<br />
Samples<br />
T1<br />
Control<br />
Fat (g) 3.0 3.1<br />
SNF (g) 12.0 12.4<br />
Specific gravity 1.043 1.044<br />
Total solids (g) 14.52 15.50<br />
Acidity (% <strong>of</strong> lactic acid) 0.18 0.18<br />
pH 6.6 6.5<br />
Protein (g) 2.98 2.96<br />
Calcium (mg) 137.37 137.47<br />
Phosphorus (mg) 123.54 123.34<br />
Carbohydrates (g) 4.60 4.64<br />
Sugars (g) 6.32 6.37<br />
<strong>The</strong>re was a slight variation between the<br />
experimental and control values <strong>of</strong> Fat, SNF, Total<br />
solids, Specific gravity, Acidity and pH. Protein,<br />
calcium, Phosphorus, carbohydrate (CHO) and sugars<br />
content <strong>of</strong> control and experimental samples are<br />
almost similar .Physico-chemical and nutritional<br />
parameters <strong>of</strong> the experimental product did not differ<br />
much from the control. <strong>The</strong> amounts <strong>of</strong> phytosterol<br />
we consume from the foods are not sufficient for<br />
attaining health benefits. So incorporation <strong>of</strong><br />
phytosterols in the foods consumed can benefit in<br />
reducing the plasma cholesterol levels and reduce<br />
the risk <strong>of</strong> coronary artery diseases. Plant sterols<br />
have gained a prominent position in strategies to lower<br />
CHD (Coronary Heart Diseases) risk because <strong>of</strong> their<br />
serum LDL-Cholesterol lowering effects. So that the<br />
phytosterol enriched flavoured milk can be used as<br />
a good vehicle for reducing plasma cholesterol in<br />
hypercholesterolemic subjects. We can also use the<br />
incorporation <strong>of</strong> phytosterol enriched flavoured milk<br />
into a balanced diet represents a practical dietary<br />
strategy in the management <strong>of</strong> serum cholesterol<br />
levels.<br />
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AbuMweis, S.S., Barake, R and Jones, P. 2008. Plant<br />
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Anonymous.2005. Phytosterol esters (plant sterol and<br />
stanol esters). Available: http://www.ifst.org/<br />
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Awad, A.B and Fink, C.S. 2000. Phytosterols as<br />
anticancer dietary components: evidence and<br />
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2127-2130.<br />
Awad, A.B., Roy, R and Fink, C.S. 2003. Betasitosterol,<br />
a plant sterol, induces apoptosis<br />
and activates key caspases in MDA-MB-231<br />
human breast cancer cells. Oncology reports.<br />
10: 497-500.<br />
Berger, A., Jones, P.J.H and Abumweis, S.S. 2004.<br />
Plant sterols: factors affecting their efficacy<br />
and safety as functional food ingredients.<br />
Lipids in Health and Disease. 3:5.<br />
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Bouic, P.J. 2001. <strong>The</strong> role <strong>of</strong> phytosterols and<br />
phytosterolins in immune modulation: a review<br />
<strong>of</strong> the past 10 years. Current opinion in clinical<br />
nutrition and metabolic care. 4: 471–475.<br />
Hallikainen M.A., Sarkkinen, E.S and Uusitupa, M.I.J.<br />
2000. Plant stanol eaters affect<br />
serumcholesterol concentrations <strong>of</strong><br />
hypercholesterolemic men and women in a<br />
dose dependent manner.<strong>Journal</strong> <strong>of</strong> Nutrition.<br />
130(4): 767-776.<br />
Jones, P.J.H and Abumweis, S.S. 2009. Phytosterol<br />
as functional food ingredients: linkages to<br />
cardiovascular disease and cancer. Current<br />
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12: 147-151.<br />
Jones P.J., Raeini-Sarjaz, M and Ntanios, F.Y. 2000.<br />
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cholesterol kinetics by phytosterol versus<br />
phytostanol esters. <strong>Journal</strong> <strong>of</strong> Lipid <strong>Research</strong>.<br />
41(5): 697-705.<br />
Katan, M.B., Grundy, S.M., Jones, P., Law, M.,<br />
Miettinen, T and Paoletti, R. 2003. Efficacy<br />
and safety <strong>of</strong> plant stanols and sterols in the<br />
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Laakso, P. 2005. Analysis <strong>of</strong> sterols from various<br />
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Maki, K.C., Davidson, M.H and Umporowicz, D.M.<br />
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Phillips, K.M., Ruggio, D.M and Ashraf-Khorassani,<br />
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Groux, M.J.A., Sagalowicz, L and Berger, A.<br />
(2003). Non-esterified plant sterols solubilized<br />
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the effects <strong>of</strong> cholesterol, beta-sitosterol, betasitosterol<br />
glucoside, dehydroe piandrosterone<br />
sulphate and melatonin on in vitro lipid<br />
peroxidation. Metabolic brain disease. 15(4):<br />
257-265.<br />
136
<strong>Research</strong> Notes<br />
J.Res. <strong>ANGRAU</strong> 41(1) 132-134, 2013<br />
CORRELATION AND PATH ANALYSIS FOR YIELD AND ITS<br />
COMPONENTS IN RICE (Oryza Sativa L.)<br />
C.MANIKYA MINNIE, T.DAYAKAR REDDY and CH.SURENDER RAJU<br />
Department <strong>of</strong> Genetics and Plant Breeding, College <strong>of</strong> Agriculture,<br />
Acharya N.G.Ranga Agricultural University, Rajendranagar, Hyderabad-500030<br />
Date <strong>of</strong> Receipt : 07.02.2012 Date <strong>of</strong> Acceptance : 07.01.2013<br />
Path analysis measures the direct and<br />
indirect effects <strong>of</strong> independent variables on dependent<br />
variables. Knowledge <strong>of</strong> cause and effect relationship<br />
makes selection more effective. In the present<br />
investigation an attempt was made to find out the<br />
effects <strong>of</strong> component traits on grain yield <strong>of</strong> rice.<br />
<strong>The</strong> experimental material comprising <strong>of</strong> 81<br />
genotypes (including 3 checks) obtained from<br />
different sources was cultivated during kharif, 2008<br />
at Rice section farm, Agriculture <strong>Research</strong> Institute,<br />
Rajendranagar, Hyderabad. All the genotypes were<br />
sown separately in the nursery on raised beds. Thirty<br />
days old seedlings <strong>of</strong> each genotype were<br />
transplanted in five rows <strong>of</strong> 6 m length by adopting a<br />
spacing <strong>of</strong> 15 cm between plants and 15cm between<br />
rows in a Randomized Block Design replicated twice.<br />
Recommended agronomic practices and plant<br />
protection measures for raising a healthy crop were<br />
taken up during experiment. Five plants <strong>of</strong> each<br />
genotype in each replication selected randomly from<br />
central rows were used to record data. <strong>The</strong> mean<br />
values were considered for statistical analysis.<br />
Genotypic correlations in general were high<br />
as compared to their phenotypic correlations<br />
indicating strong inherent association between the<br />
characters which might be due to masking or<br />
modifying effects <strong>of</strong> environment.<br />
<strong>The</strong> correlation analysis indicated that grain<br />
yield was significantly associated with number <strong>of</strong><br />
productive tillers per plant, plant height, number <strong>of</strong><br />
grains per panicle and panicle length. Similar kind <strong>of</strong><br />
association was reported by Satish Chandra et al.<br />
(2009) for number <strong>of</strong> productive tillers per plant,<br />
number <strong>of</strong> grains per panicle and panicle length and<br />
Madhavi Latha (2002) for plant height.<br />
<strong>The</strong> data indicated that days to 50 percent<br />
flowering and 1000 gram weight had no association<br />
with grain yield.<br />
<strong>The</strong> characters that showed positive and<br />
significant association with grain yield could be<br />
considered as criteria for selection for yield<br />
improvement as these were mutually and directly<br />
associated with grain yield.<br />
Days to 50 per cent flowering had significant<br />
positive association with number <strong>of</strong> grains per<br />
panicle. Similar result was reported by Madhavi Latha<br />
(2002).<br />
Plant height registered positive and<br />
significant association with number <strong>of</strong> productive<br />
tillers per plant, panicle length, 1000 grain weight and<br />
number <strong>of</strong> grains per panicle, indicating that the<br />
increase in panicle length and number <strong>of</strong> grains per<br />
panicle and 1000 grain weight can be possible with<br />
an increase in plant height. Similar results were also<br />
obtained by Janardhan et al. (2001) for number <strong>of</strong><br />
productive tillers per plant, panicle length and number<br />
<strong>of</strong> grains per panicle and Yogameenakshi et al. (2004)<br />
for 1000 grain weight.<br />
Number <strong>of</strong> productive tillers per plant was<br />
positively and significantly correlated with number <strong>of</strong><br />
grains per panicle and panicle length. <strong>The</strong>se results<br />
are in consonance with the earlier findings <strong>of</strong><br />
Janardhanam et al. (2001). It showed positive<br />
correlation with 1000 grain weight.<br />
Panicle length had high significant positive<br />
association with 1000 grain weight and number <strong>of</strong><br />
grains per panicle. Similar results were also obtained<br />
by Yogameenakshi et al. (2004).<br />
email: minnie_chitturi@yahoo.co.in<br />
137
CORRELATION AND PATH ANALYSIS FOR YIELD AND ITS COMPONENTS<br />
Path coefficient analysis was used to<br />
compute direct and indirect effects <strong>of</strong> six characters<br />
on grain yield. <strong>The</strong> characters number <strong>of</strong> productive<br />
tillers per plant, panicle length and number <strong>of</strong> grains<br />
per panicle with positive direct effects on yield could<br />
be considered as major yield contributing characters<br />
in rice. <strong>The</strong>se findings were in agreement with the<br />
reports made by Karad and Pol (2008) for number <strong>of</strong><br />
productive tillers per plant and panicle length and<br />
Satish Chandra et al. (2009) for number <strong>of</strong> grains per<br />
panicle.<br />
On the contrary negative direct effects were<br />
existed by days to 50 per cent flowering, plant height<br />
and 1000 grain weight. Gupta et al. (1998) also<br />
recorded similar kind <strong>of</strong> negative direct effect <strong>of</strong> these<br />
traits on yield.<br />
<strong>The</strong> characters, panicle length, number <strong>of</strong><br />
grains per panicle showed positive indirect effect<br />
through number <strong>of</strong> productive tillers per plant on grain<br />
yield. Similar results were obtained by Gupta et al.<br />
(1998). <strong>The</strong> panicle length exhibited positive indirect<br />
effect through number <strong>of</strong> productive tillers per plant<br />
and number <strong>of</strong> grains per panicle. Number <strong>of</strong><br />
productive tillers per plant, 1000 grain weight and<br />
panicle length showed positive indirect effect on grain<br />
yield through number <strong>of</strong> grains per panicle. <strong>The</strong>se<br />
results were in accordance with the results obtained<br />
by Madhavilatha (2002).<br />
<strong>The</strong> characters days to 50 per cent flowering,<br />
plant height and 1000 grain weight were not only<br />
having negative direct effect on grain yield but also<br />
had negative association with the yield contributing<br />
characters. This was in consonance with the results<br />
<strong>of</strong> Gupta et al. (1998).<br />
Path analysis revealed the importance <strong>of</strong> the<br />
characters, number <strong>of</strong> productive tillers per plant,<br />
panicle length and number <strong>of</strong> grains per panicle in<br />
influencing the grain yield. Hence, selection should<br />
be practiced for these characters in order to isolate<br />
superior plant types for improvement <strong>of</strong> grain yield.<br />
Table 1.Estimation <strong>of</strong> correlation coefficients between yield and yield attributing characters<br />
Character<br />
Days to<br />
50 %<br />
flowering<br />
Plant<br />
Height<br />
(cm)<br />
Number <strong>of</strong><br />
productive<br />
tillers per<br />
plant<br />
Panicle<br />
Length<br />
(cm)<br />
Number <strong>of</strong><br />
grains per<br />
panicle<br />
1000 Grain<br />
Weight (g)<br />
Grain<br />
Yield/<br />
Plant (g)<br />
Days to 50% P 1.0000 0.0622 0.0434 0.0992 0.2648** -0.2933** -0.0001<br />
Flowering G 1.0000 0.0727 0.0109 0.0987 0.2590** -0.3024** -0.0181<br />
Plant Height P 1.0000 0.3113** 0.2865** 0.1525 0.1620* 0.3197**<br />
(cm) G 1.0000 0.5046** 0.2958 ** 0.1606* 0.1845* 0.3964**<br />
Number <strong>of</strong> P 1.0000 0.1010 0.2048** -0.0175 0.8501**<br />
productive<br />
tillers/plant<br />
G 1.0000 0.1508 0.2512** 0.0233 1.0014**<br />
Panicle P 1.0000 0.2383** 0.3725** 0.1047<br />
Length (cm) G 1.0000 0.2546** 0.4021** 0.1599*<br />
Number <strong>of</strong> P 1.0000 -0.3270** 0.2483**<br />
grains/panicle G 1.0000 -0.3425** 0.2711**<br />
1000 Grain<br />
Weight<br />
(g)plant<br />
P 1.0000 -0.0329<br />
G 1.0000 -0.0157<br />
* Significant at 5% level ** Significant at 1% level
MINNIE et al<br />
Table 2. Estimates <strong>of</strong> genotypic direct and indirect effects between yield and yield contributing<br />
characters<br />
Character<br />
Days to 50%<br />
Flowering<br />
Plant Height<br />
(cm)<br />
Number <strong>of</strong><br />
productive<br />
tillers/plant<br />
Panicle Length<br />
(cm)<br />
Number <strong>of</strong><br />
grains/panicle<br />
1000 Grain<br />
Weight (g)<br />
Days to<br />
50 %<br />
flowering<br />
Plant<br />
Height (cm)<br />
Number <strong>of</strong><br />
productive<br />
tillers per<br />
plant<br />
Panicle<br />
Length<br />
(cm)<br />
Number <strong>of</strong><br />
grains per<br />
panicle<br />
1000<br />
Grain<br />
Weight<br />
(g)<br />
Grain<br />
Yield/<br />
Plant (g)<br />
-0.0418 -0.0109 0.0116 0.0067 0.0008 0.0156 -0.0181<br />
-0.0030 -0.1504 0.5389 0.0199 0.0005 -0.0095 0.3964**<br />
-0.0005 -0.0759 1.0680 0.0102 0.0008 -0.0012 1.0014**<br />
-0.0041 -0.0445 0.1611 0.0674 0.0008 -0.0207 0.1599*<br />
-0.0108 -0.0242 0.2682 0.0172 0.0031 0.0176 0.2711**<br />
0.0126 -0.0278 0.0249 0.0271 -0.0011 -0.0515 -0.0157<br />
Bold = direct effects Residual effect = -0.1520<br />
REFERENCES<br />
Gupta, J. C., Kotoch, P. C., Kaushik, R. P and<br />
Sharma, S. L. 1998. Cause and effect<br />
relationship <strong>of</strong> yield and its components under<br />
cold stress condition in rice (Oryza sativa L.).<br />
Indian <strong>Journal</strong> <strong>of</strong> Agricultural Sciences. 68 (1)<br />
: 13-15<br />
Janardhanam, V., Nadarajan, N and Jebaraj, S. 2001.<br />
Correlation and path analysis in rice (Oryza<br />
sativa L.). Madras Agricultural <strong>Journal</strong>. 88 :<br />
719-720<br />
Karad, S. R and Pol, K. M. 2008. Character<br />
association, genetic variability and path<br />
coefficient analysis in rice (Oryza sativa L.).<br />
International <strong>Journal</strong> <strong>of</strong> Agricultural Sciences.<br />
4 (2) : 663-666<br />
Madhavilatha, L. 2002. Studies on genetic divergence<br />
and isozyme analusis on rice (Oryza sativa<br />
L.). M.Sc. (Ag). <strong>The</strong>sis submitted to Acharya<br />
N. G. Ranga Agricultural University,<br />
Hyderabad.<br />
Satish Chandra, B., Dayakar Reddy, T., Ansari, N.<br />
A and Sudheer Kumar, S. 2009. Correlation<br />
and path analysis for yield and yield<br />
components in rice (Oryza sativa L.).<br />
Agricultural Science Digest. 29 (1) : 45-47<br />
Yogameenakshi, P., Nadarajan, N and<br />
Anbumalarmathi, J. 2004. Correlation and path<br />
analysis on yield and drought tolerant attributes<br />
in rice (Oryza sativa L.) under drought stress.<br />
Oryza. 41 (3&4) : 68-70
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7. <strong>The</strong> manuscript should accompany the declaration certificate and subscription enrolment form.<br />
8. <strong>The</strong> authors should accept the editorial / references comments until the quality <strong>of</strong> the article is improved.<br />
9. <strong>The</strong> revised manuscript should be submitted in duplicate along with a compact disk.<br />
10. DD may be drawn in favour <strong>of</strong> “Managing Editor, <strong>Journal</strong> <strong>of</strong> <strong>Research</strong>, <strong>ANGRAU</strong>” Payable at Hyderabad.<br />
SUBSCRIPTION TARIFF<br />
ANNUAL<br />
Individual : Rs. 300/- author<br />
Institution : Rs. 1200/-<br />
LIFE<br />
Individual (till retirement) : Rs. 1200/-<br />
Reprints Charges : Rs. 100/- per page<br />
1. Publications : <strong>The</strong> Editor - <strong>Journal</strong> <strong>of</strong> <strong>Research</strong> <strong>ANGRAU</strong> O/o Principal Agricultural Information<br />
Officer, AI&CC and <strong>ANGRAU</strong> Press, ARI Campus, Rajendranagar, Hyderabad.<br />
2. Publications : <strong>The</strong> DD should be mailed to the Managing Editor - <strong>Journal</strong> <strong>of</strong> <strong>Research</strong>, <strong>ANGRAU</strong><br />
- Press Agricultural <strong>Research</strong> Institute, Rajendranagar, Hyderabad - 500 030, A.P.<br />
least in short forms if they are lengthy, but not abbreviated as T 1<br />
, T 2<br />
and T 3<br />
etc. <strong>The</strong> weights and<br />
measures should be given in the metric system following the latest units eg. kg ha -1, kg ha -1 cm, mg<br />
g -1 , ds m -1 , g m -3 , C mol kg -1 etc.<br />
Typing : <strong>The</strong> article should be typed in 12pt font on A 4<br />
size paper leaving a margin <strong>of</strong> 10 cm on all<br />
sides. <strong>The</strong>re should be a single line space between the rows in abstract and double line in rest.<br />
Note : Latest issue <strong>of</strong> the <strong>Journal</strong> may be consulted. Further details can be obtained from the book<br />
“Editors style Manual, edn 4. American Institute <strong>of</strong> Biological Sciences, Washington DC”.<br />
URL : http://www.angrau.ac.in/Publications.aspx<br />
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