The Journal of Research ANGRAU

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RELATIONSHIP BETWEEN PROFILE OF BENEFICIARY FARMERS & IMPACT Chandrasekhar, V., Gangadharappa, N.R and Suresha, S.V. 2005. Knowledge level farmers about selected technological interventions in TAR-IVLP. Mysore Journal of Agricultural Sciences. 39 (3): 410-414. Damodaran, C. 2007. Irrigation management and socio-economic changes among Cauvery old delta farmers. M.Sc. (Ag.) Thesis submitted to Acharya N G Ranga Agricultural University, Hyderabad, India. Manoj, A. 2008. Impact of Krishi Vigyan Kendra on farmers Srikakulam district of Andhra Pradesh. M. Sc. (Ag.) Thesis submitted to Acharya N G Ranga Agricultural University, Hyderabad, India. Ca Mohammad Ajaz-ul-Islam, Masoodi, N.A., Masoodi, T.H and Gangoo, S.A. 2009. Awareness and participation of beneficiaries in social forestry programme in Baramulla district of Kasmir valley. Indian Journal of Social Research. 50(4): 353-364. Rameshbabu, C. 2002. Effectiveness of Indo-Dutch Operational Research project on drainage and water management for salinity control in Prakasam district of A.P. M. Sc. (Ag.) Thesis submitted to Acharya N G Ranga Agricultural University, Hyderabad, India. Reddy, P.T.S., Prabhakar, K and Gidda Reddy, P. 2007. Analysis of influence of selected independent variables on knowledge of rice farmers on Eco-friendly technologies. Journal of Research ANGRAU. 35 (2): 31-37. Suresh, T.V and Ramesh Babu, C.H. 2008. Extent of participation of farmers in Sujala Kalinganahalli Halla Watershed Project, Andhra Agricultural journal. 55(3): 405-407. Thyagarajan, S. 2004. Rice production technology – adoption and constraints. Indian Journal of Extension Education. 40 (3&4): 44-47. 92
Research Notes J.Res. ANGRAU 41(1) 88-92, 2013 CONSTRUCTION OF SELECTION INDICES FOR F 2 POPULATION DERIVED FROM CROSSES BETWEEN GRAIN SORGHUM × SWEET SORGHUM [Sorghum bicolor (L.) Moench] VEMANNA IRADDI, T. DAYAKAR REDDY, A. V. UMAKANTH, CH. RANI, D. VISHNU VARDHAN REDDY and M. H. V. BHAVE Department of Genetics and Plant Breeding Acharya N.G. Ranga Agricultural University, Hyderabad – 500 030 Date of Receipt : 06.11.2012 Date of Acceptance : 12.12.2012 The selection indices by discriminant function analysis were constructed based on the data of a population of 800 F 2 plants developed by crossing three grain sorghum genotypes viz., 27 B, ICSB 38 and 296 B as a female parent and four sweet sorghum genotypes viz., SSV 84, SSV 74, URJA and NSSV 13 as a male parent. Majority of selection indices were found to be more efficient than straight selection based on sugar yield alone. The selection index consisting of six character combination viz., sugar yield, total biomass, fresh stalk yield, brix per cent, juice yield and total soluble sugars was more effective with higher relative efficiency. While, selection based on five characters combinations viz., sugar yield, total biomass, fresh stalk yield, brix per cent and juice yield as well as four character combination viz., total biomass, fresh stalk yield, brix per cent and juice yield were also equally effective in selection of plants for maximum sugar yield. However, selection index comprising six and five character combinations are of little importance in selection process as it includes derived parameters such as sugar yield and total soluble sugars. In this regard it is suggested to go for four character combination which also manifested maximum relative efficiency coupled with higher genetic advance. The practical or economic value of a plant is affected by several traits. Since, majority of the economic traits are polygenically inherited and their expression is subjected to varying degrees of fluctuations due to environmental factors, eventually direct selection may not be useful for such characters. Efficiency of selection under such circumstances can sometimes be improved by taking into consideration simultaneously the phenotypic values of a number of plant attributes which are correlated with the genotypic values (high heritability) of the characters under consideration. The material for the present study comprised of 800 F 2 population of sweet sorghum crosses derived from parents having low and high sugar content developed at Directorate of Sorghum Research, Rajendranagar, Hyderabad. These populations were developed by crossing the contrasting parents (27 B with SSV 84, ICSB 38 with SSV 74, 296 B with URJA and 27 B with NSSV 13) through hand emasculation and pollination during kharif 2010 and the F 1 plants of the two crosses were grown during rabi 2010 - 11 and selfed to produce the F 2 seeds, which were evaluated during summer 2012. The technique of discriminant function developed by Fisher (1936) was adopted to know the true genotypic worth of yield and its components and to have computational formulae for construction of selection indices which when applied to select plants can bring about effective improvement in yield compared to straight selection for yield. Smith (1936) has illustrated the use of discriminant function in plant selection. Formulation of selection indices through discriminant function analysis Selection indices were formulated in F 2 populations of sweet sorghum considering sugar yield and its five component characters which had high correlation with sugar yield. Among six characters, sugar yield (X 1 ) was considered as dependent character, while other characters viz., total biomass (X 2 ), fresh stalk yield (X 3 ), brix per cent (X 4 ), juice yield (X 5 ) and total soluble sugars (X 6 ) were considered as independent variables. In order to select plants with high sugar yield, discriminant functions were computed with email: vemanraddi@gmail.com 93
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CONTENTS PART I : PLANT SCIENCE Eff
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J.Res. ANGRAU 41(1) 1-4, 2013 EFFEC
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EFFECT OF FOLIAR APPLICATION OF NPK
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J.Res. ANGRAU 41(1) 5-13, 2013 NUTR
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NUTRIENT UPTAKE BY RICE CROP UNDER
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LAKSHMI et al Fig 1. Changes in C/N
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LAKSHMI et al Changes in C/N ratio
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LAKSHMI et al Table 3. Changes in h
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J.Res. ANGRAU 41(1) 20-29, 2013 INF
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PRASAD and PRASADINI of bulk densit
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PRASAD and PRASADINI to as high as
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PRASAD and PRASADINI Table 4 . Infl
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PRASAD and PRASADINI Table 8. Influ
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J.Res. ANGRAU 41(1) 30-38, 2013 GEN
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VEMANNA et al The range in mean val
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VEMANNA et al grain yield per plant
Page 41 and 42: VEMANNA et al 41
Page 43 and 44: VEMANNA et al Singh, S. P and Khan,
Page 45 and 46: RAMANA et al RESULTS AND DISCUSSION
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Page 49 and 50: RAJANNA et. al. Table 2. Reasons fo
Page 51 and 52: RAJANNA et al the present findings
Page 53 and 54: NARASIMHA et al Table 1. Proximate
Page 55 and 56: NARASIMHA et al Maynard, L., Lossli
Page 57 and 58: RAMANA et al with small follicles m
Page 59 and 60: RAMANA et al Characteristics of fol
Page 61 and 62: Research Notes J.Res. ANGRAU 41(1)
Page 63 and 64: KIRTHY et al Akhtar et al., 2008; S
Page 65 and 66: KIRTHY et al El Gharras H. 2009. Po
Page 67 and 68: SANDYARANI et al Weed parameters li
Page 69 and 70: SANDYARANI et al Table 2. Influence
Page 73 and 74: KUMAR et al Table 2. Effect of seed
Page 77 and 78: DEEPAK et al Table 2 Price spread a
Page 81 and 82: YAMINI et al Table 3. Estimates of
Page 83 and 84: YAMINI et al coupled with high per
Page 85 and 86: LOKESH et al Table 1. Clustering pa
Page 87 and 88: LOKESH et al Table 4. Mean values o
Page 89 and 90: ABIRAMI et al Socio-economic Impact
Page 91: ABIRAMI et al socio-economic impact
Page 95 and 96: VEMANNA et al Table 1. Discriminant
Page 97 and 98: VEMANNA et al total biomass, fresh
Page 99 and 100: RAO et al Table 1. Plant height, nu
Page 103 and 104: DEVI et al S.No Category Frequency
Page 105 and 106: DEVI et al extension contact and ma
Page 107 and 108: NIRMALA and VASANTHA earliness in a
Page 109 and 110: NIRMALA and VASANTHA adopted this t
Page 111 and 112: SUDHARANI et al Table 1. Genotypic
Page 113 and 114: SUDHARANI et al At genotypic level,
Page 115 and 116: NIRMALA et al weight of pods per pl
Page 117 and 118: NIRMALA et al Table 3. Cluster mean
Page 123 and 124: PUNYAVATHI and VIJAYALAKSHMI 6.5 6.
Page 125 and 126: PUNYAVATHI and VIJAYALAKSHMI REFERE
Page 127 and 128: KATTEL et al Table 1. Item Analysis
Page 131 and 132: RADHIKA et al Thus, the results of
Page 133 and 134: RAJU et al estimated daily dietary
Page 135 and 136: RAJU et al Table 3. Physico-chemica
Page 139 and 140: MINNIE et al Table 2. Estimates of
Page 141 and 142: Statement about ownership and other
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GUIDELINES FOR THE PREPARATION OF M
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ESSENTIAL REQUIREMENTS FOR CONSIDER
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