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
RELATIONSHIP BETWEEN PROFILE OF BENEFICIARY FARMERS & IMPACT adoption of innovation (in this context adoption of SRI technique) in Indian agriculture does not differ from non-adoption with respect to age, it depends on how worthy the innovation is to a farmer for efficient crop production. This trend was also noticed by Mohammad et al (2009). The reason for positive and significant correlation of education with socio-economic impact might be due to the fact that, the education widens horizons of the individual to get information from various sources. This seems to be inter-related with farmers to bring changes in their personal, psychological orientation, to adopt new ideas, practices and technologies and motivate the farmers towards achieving high socio-economic impact. This result was in agreement with the results of Manoj (2008) and Suresh and Rameshbabu (2008). There was positive and significant relationship between land holding and socio-economic impact. This might be due to the fact that land holding provides the economic base for the farmers to practice new agricultural technologies. Land holding also provides regulated impetus to make optimum utilization of resources on farm for achieving maximum profits. Similar results were reported by Rameshbabu (2002) and Reddy et al. (2007). It was found that farming experience had positive and significant relationship with socioeconomic impact. This might be due to the fact that experience is the best teacher, farmer having more experience in farming, irrespective of age would know the difficulties and problems in farming better than less experienced and who seek for new alternative farm practices and adopt new production technologies. This result was in conformity with the results of Thyagarajan (2004) and Reddy et al. (2007). Positive and significant relationship was noticed between information sources utilization and socio-economic impact. This might be due to the fact that different information sources utilization updated the farmers with new production technologies and motivate them to adopt it to improve their profits. This finding was in tune with the results of Reddy et al. (2007) and Mohammad et al. (2009). From the Table 2. it was clear that there was positive and significant relationship between training received and socio-economic impact. This might be due to the fact that training is one of the means by which desired changes in knowledge and skills could be attained. An individual who receives training become more knowledgeable, skilful and develop rationale and adopt improved farming practices led to have more socio-economic impact. This might be the reason for above result. The result was in agreement with the results of Basawarajaiah (2001). Economic motivation was found to be positively and significantly associated with the socio-economic impact. The reason could be that the farmers with more economic motivation would be oriented towards more information sources utilization; risk bearing that might help them to adopt new production technologies. This finding was in line with the findings of Manoj (2008) and Suresh and Rameshbabu (2008). The results furnished in the Table 2 indicated that there was positive and significant relationship between scientific orientation and socio-economic impact. It might be due to the reason that the farmers having more scientific orientation would gather more information from authentic sources like Krishi Vigyan Kandra, TNAU scientists, etc., and think rationally before applying into the field conditions, get the higher production and profits. Similar results were reported by Manoj (2008) and Mohammad et al. (2009). There was positive and significant relationship between innovativeness and socio-economic impact (Table 2). This might be due to the fact that farmers who are relatively earlier in adopting new agricultural innovations would orient towards more risk taking, more scientific orientation, maintain higher social status. Their earliness to adopt innovations would have resulted in higher socio-economic impact in terms of increasing higher yields and income. This result was in conformity with the results of Damodaran (2007) and Manoj (2008). The correlation between risk orientation and socio-economic impact was positive and significant (Table 2). It could be inferred from the finding that higher the risk orientation, the higher would be the 90
ABIRAMI et al socio-economic impact. This might be due to the fact that higher risk oriented farmers adopt the innovations and get more yield and higher income. Hence, such type of relation existed in the study. This finding was in line with the findings of Chandrasekhar et al. (2005) and Manoj(2008). c) Multiple Linear Regression of selected independent variables with socio-economic impact. An attempt was made to find out the amount of contribution made by the independent variables in explaining the variation in the dependent variable through multiple linear regression. The results are presented in Table 3. Table 3. Multiple regression analysis of Profile of beneficiary farmers and the socio-economic impact S. No Variables Regression coefficient (B) Standard error ‘t’ value 1. Age -6.3303 0.7039 -8.9927** 2. Education 0.7092 0.3255 2.1784NS 3. Land Holding 0.4241 0.4370 0.9704NS 4. Farming Experience 4.6575 0.6232 7.4733** 5. Information sources utilization -0.4021 0.0695 -5.7790** 6. Training Received 3.3325 0.5538 6.0164** 7. Economic Motivation 1.4766 0.2322 6.3584** 8. Scientific Orientation 0.3394 0.1763 1.9248NS 9. Innovativeness 0.7704 0.1235 6.2369** 10. Risk Orientation 1.7744 0.2070 8.5691** R 2 = 0.861; NS = Non Significant; ** Significant at 0.01 level of probability The Ten independent variables with the socio-economic impact of the project taken on Multiple Linear Regression Analysis gave the R 2 (Coefficient of multiple determination) value of 0.861. It indicates that all the selected independent variables put together contributed 86.10 per cent of the total variation in the socio-economic impact of the project by the beneficiary farmers, leaving the rest to extraneous factors. The independent variables viz., farming experience, training received, economic motivation, innovativeness and risk orientation contributed significantly to the socio-economic impact of the project. The variable scientific orientation was not having significant value, but the value is near to significant t –value (1.980272). So, it also could be considered as significant REFERENCES Baswarajaiah, V.2001. Impact of Edira Watershed Development Programme on farm families in Mahaboobnagar District of Andhra Pradesh. M. Sc. (Ag.) Thesis submitted to Acharya N G Ranga Agricultural University, Hyderabad, India. 91
- Page 39 and 40: 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
- Page 47 and 48: J.Res. ANGRAU 41(1) 42-46, 2013 A S
- 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 71 and 72: Research Notes J.Res. ANGRAU 41(1)
- Page 73 and 74: KUMAR et al Table 2. Effect of seed
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- Page 77 and 78: DEEPAK et al Table 2 Price spread a
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- 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: ABIRAMI et al Socio-economic Impact
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- 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
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- 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
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- 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
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- 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
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- Page 139 and 140: MINNIE et al Table 2. Estimates of
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
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