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
Research Notes J.Res. ANGRAU 41(1) 82-87, 2013 RELATIONSHIP BETWEEN PROFILE OF BENEFICIARY FARMERS AND THE SOCIO-ECONOMIC IMPACT OF IRRIGATED AGRICULTURE MODERNIZATION AND WATER BODIES RESTORATION AND MANAGEMENT (IAMWARM) PROJECT IN PUDUKKOTTAI DISTRICT G. ABIRAMI, B.VIJAYABHINANDANA and T. GOPI KRISHNA Department of Agricultural Extension, Agricultural College, Acharya N.G Ranga Agricultural University, Bapatla- 522 101 Date of Receipt : 10.09.2012 Date of Acceptance : 27.12.2012 The project Irrigated Agriculture Modernization and Water Bodies Restoration and Management (IAMWARM) was introduced in 2007 and funded by World Bank. Its objective was to improve irrigation service delivery including adaptation of modern water-saving irrigation technologies and ultimately to ensure food security and improved farm incomes. Keeping this in view, present study was proposed to study the socioeconomic impact of IAMWARM project on beneficiary farmers in Pudukkottai district of Tamil Nadu in the year 2012. The implications of the study would be useful to the project officials, implementing authorities, funding agencies concerned, for extending project benefits to the farming community. A study was undertaken purposively in Pudukkottai district of Tamil Nadu as this project was first implemented in Pudukkottai district under first phase sub-basin of the project during the year 2007. Thus, it gave sufficient time interval to study the impact. Four taluks were selected randomly and three villages from each taluk were selected randomly. Ten beneficiaries from each village were selected randomly using simple random sampling procedure, thus making a total sample of 120 beneficiary farmers. Ex-post facto research design was followed. Data was collected through interview schedule from the beneficiary farmers of the project covering all aspects of the socio-economic impact. To convert the data into meaningful findings some statistical tools were used. viz. a) Frequency and Percentage analysis b) Correlation analysis c) Multiple regression analysis and d) class interval. a) Socio-economic impact of the project Socio-economic impact of the project in SRI technique was studied with eleven variables namely, knowledge, adoption, income, asset acquisition, yield, water use efficiency, participation in the project activities, labour use, social participation, cost of cultivation, and empowerment. By adding and averaging the scores of all the items, the individual score for socio-economic impact was worked out. It was used to categorize the respondents into three groups based on the class interval (exclusive) method as low, medium and high level of socio-economic impact. Table 1. Distribution of respondents according to socio-economic impact S.No Category Beneficiary Farmers N=120 Frequency Percentage 1. Low (49-62) 30 25.00 2. Medium (63-76) 56 46.67 3. High (77-90) 34 28.33 ** - significant at 0.01 level of probability email: abarangi@gmail.com 88
ABIRAMI et al Socio-economic Impact: A cursory look at the Table 1 indicates that 46.67 per cent of beneficiary farmers had medium level of socio-economic impact, followed by high (28.33%) and low level of socioeconomic impact (25.00%). The result was the cumulative effect of all the factors contributed to the socio-economic impact. The reason for high level of socio-economic impact might be particularly due to majority of the farmers obtained fine knowledge about SRI and adopted SRI technique fairly in their field. As a result they received more income and asset acquisition. Medium level of socio-economic impact might be due to partial adoption of technology and inefficient management of labour. That results in more cost of cultivation. Low level of socio-economic impact might be due to poor adoption & SRI technology. b) Relationship between profile of beneficiary farmers and the socio-economic impact An attempt has been made to find out the association between independent variables and dependent variables through correlation coefficient (r) values. The results are presented in Table 2. Table 2. Correlation coefficient between profile of beneficiary farmers and the socio-economic impact N= 120 S. No Independent Variables ‘r’ values 1. Age 2. Education 3. Land Holding 4. Farming Experience 5. Information sources utilization 6. Training Received 7. Economic Motivation 8. Scientific Orientation 9. Innovativeness 10. Risk Orientation -0.2119 NS 0.4014** 0.3870** 0.3537** 0.3793** 0.4815** 0.5241** 0.6113** 0.5927** 0.6084** ** Significant at 0.01 level of probability NS = Non Significant The results presented in the Table 2 clearly indicate that almost all computed ‘r’ values of education, land holding, farming experience, information sources utilization, training received, economic motivation, scientific orientation, innovativeness and risk orientation with socioeconomic impact were found positively significant relationship at 0.01 level of probability. Whereas, age with socio-economic impact had non-significant and negative relationship. From this study it could be concluded that higher the education, higher the land holding, higher the experience in farming, higher the information sources utilization, higher the training received, higher the economic motivation, higher the scientific orientation, higher the innovativeness and higher the risk orientation, the higher would be the socioeconomic impact. The probable reason for age, not influencing the socio-economic impact can be explained that 89
- Page 37 and 38: VEMANNA et al The range in mean val
- 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
- Page 75 and 76: Research Notes J.Res. ANGRAU 41(1)
- Page 77 and 78: DEEPAK et al Table 2 Price spread a
- Page 79 and 80: Research Notes J.Res. ANGRAU 41(1)
- 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: LOKESH et al Table 4. Mean values o
- Page 91 and 92: ABIRAMI et al socio-economic impact
- Page 93 and 94: Research Notes J.Res. ANGRAU 41(1)
- 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 101 and 102: Research Notes J.Res. ANGRAU 41(1)
- 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 119 and 120: Research Notes J.Res. ANGRAU 41(1)
- Page 121 and 122: Research Notes J.Res. ANGRAU 41(1)
- 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 129 and 130: Research Notes J.Res. ANGRAU 41(1)
- 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 137 and 138: Research Notes J.Res. ANGRAU 41(1)
<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
Change language