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Assam Agricultural University, Jorhat

Assam Agricultural University is the first institution of its kind in the whole of North-Eastern Region of India. The main goal of this institution is to produce globally competitive human resources in farm sectorand to carry out research in both conventional and frontier areas for production optimization as well as to disseminate the generated technologies as public good for benefitting the food growers/produces and traders involved in the sector while emphasizing on sustainability, equity and overall food security at household level. Genesis of AAU - The embryo of the agricultural research in the state of Assam was formed as early as 1897 with the establishment of the Upper Shillong Experimental Farm (now in Meghalaya) just after about a decade of creation of the agricultural department in 1882. However, the seeds of agricultural research in today’s Assam were sown in the dawn of the twentieth century with the establishment of two Rice Experimental Stations, one at Karimganj in Barak valley in 1913 and the other at Titabor in Brahmaputra valley in 1923. Subsequent to these research stations, a number of research stations were established to conduct research on important crops, more specifically, jute, pulses, oilseeds etc. The Assam Agricultural University was established on April 1, 1969 under The Assam Agricultural University Act, 1968’ with the mandate of imparting farm education, conduct research in agriculture and allied sciences and to effectively disseminate technologies so generated. Before establishment of the University, there were altogether 17 research schemes/projects in the state under the Department of Agriculture. By July 1973, all the research projects and 10 experimental farms were transferred by the Government of Assam to the AAU which already inherited the College of Agriculture and its farm at Barbheta, Jorhat and College of Veterinary Sciences at Khanapara, Guwahati. Subsequently, College of Community Science at Jorhat (1969), College of Fisheries at Raha (1988), Biswanath College of Agriculture at Biswanath Chariali (1988) and Lakhimpur College of Veterinary Science at Joyhing, North Lakhimpur (1988) were established. Presently, the University has three more colleges under its jurisdiction, viz., Sarat Chandra Singha College of Agriculture, Chapar, College of Horticulture, Nalbari & College of Sericulture, Titabar. Similarly, few more regional research stations at Shillongani, Diphu, Gossaigaon, Lakhimpur; and commodity research stations at Kahikuchi, Buralikson, Tinsukia, Kharua, Burnihat and Mandira were added to generate location and crop specific agricultural production packages.

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20191
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Subject: Extension Education × Author: Bharadwaj, Akhoy Jyoti × Type: Thesis × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    A STUDY ON THE EXTENT OF DIVERSIFICATION AND LEVEL OF LIVELIHOOD SECURITY OF FARMERS IN THE NORTH BANK PLAINS ZONE OF ASSAM
    (AAU, Jorhat, 2019-07) Bharadwaj, Akhoy Jyoti; Das, P. K.
    The study entitled ‘A Study On The Extent Of Diversification And Level Of Livelihood Security Of Farmers In The North Bank Plains Zone Of Assam’ was conducted with the following objectives: 1. To find out the extent of diversification across different farm size groups 2. To find out the level of livelihood security of farmers across different farm size groups 3. To identify the factors influencing the extent of diversification and level of livelihood security across different farm size groups 4. To identify the constraints in diversification as perceived by the farmers across different farm size groups. The present study was conducted in The North Bank Plains Zone Of Assam. The North Bank Plains Zone consists of 6 districts. Out of these 2 districts was selected randomly viz., Sonitpur and Lakhimpur. A random sampling was followed foe the selection of sub-divisions, ADO circles, AEA elekas and villages. A sample of 160 farmers was selected from the 8 selected villages following a proportionate random sampling technique. The major tool used for collection of primary data in the study was a pretested schedule by personal interview method. The statistical tools used for analysis and interpretation of data included frequency, percentage, mean, standard deviation, coefficient of variation, t-test, multiple correlation coefficient and multiple regression analysis. The two dependent variables included in the study were extent of diversification and level of livelihood security. All together 15 independent variables were included in the study. Findings revealed that 23.12 per cent of the respondents were marginal farmers, 43.13 per cent small and 33.75 per cent medium farmers. Majority of the respondents were middle aged (50.62%) and literate (82.50%) with single type (71.87%) but large (51.25%) large size family and had medium (44.40%) credit seeking behaviour. Majority of the respondents (38.75%) had membership in one organization. Majority of the respondents had medium degree of information exposure (48.12%), medium farm mechanization (70/00%), medium scientific orientation (43.75%), medium risk orientation (54.37), medium economic motivation (56.87%), high innovativeness (37.50%) and medium management efficiency (60.63%). While, 54.05 per cent of the marginal farmers had less favourable attitude towards agricultural diversification, 68.12 per cent of small and 50.00 per cent of the medium farmers had moderately favourable attitude towards agricultural diversification. In this study, the extent of diversification was measured in terms of crop diversification and enterprise diversification across different farm size groups. As regards crop diversification, in case of marginal farmers, majority of the respondents (59.46%) had medium crop diversification followed by 27.03 per cent of them with low and 13.51 per cent of them with high crop diversification. In case of small farmers, majority of the respondents (75.37%) had medium crop diversification followed by 13.04 per cent of them with high and 11.59 per cent of them with low crop diversification. Whereas among the medium farmers, majority of the respondents (53.70%) had medium crop diversification category followed by 33.33 per cent of them with high and 12.97 per cent of them with low crop diversification. In the pooled sample of farmers, majority of the respondents (64.37%) had medium crop diversification followed by 20.00 per cent of them with high and 15.63 per cent with low crop diversification. As regards enterprise diversification, majority of the marginal farmers (64.86%) had medium enterprise diversification followed by 27.03 per cent with low and 8.11 per cent with high enterprise diversification In case of small farmers, majority (66.67%) of the respondents had medium enterprise diversification followed by 17.39 per cent of them with low and 15.94 per cent with high enterprise diversification. Whereas among the medium farmers, majority (51.85%) of the respondents had medium enterprise diversification followed by 29.63 per cent with high and 18.52 per cent with low enterprise diversification. In the pooled sample of farmers, majority (61.25%) of the respondents had medium enterprise diversification followed by 20.00 per cent with low and 18.75 per cent with high enterprise diversification. As regards livelihood security, majority of the marginal farmers (57.76%) had medium level of livelihood security followed by 32.43 per cent with low and 10.81 per cent with high level of livelihood security. In case of small farmers, majority (63.77%) of the respondents had medium level of livelihood security followed by 18.84 per cent of them with low and 17.39 per cent with high level of livelihood security. In case of medium farmers, majority (51.85%) of the respondents had medium level of livelihood security followed by 33.33 per cent with high and 14.82 per cent with low level of livelihood security. In the pooled sample of farmers, majority (58.13%) of the respondents had medium level of livelihood security followed by 21.25 per cent with high and 20.62 per cent with low level of livelihood security. Findings of correlation analysis of crop diversification reveal that, in case of marginal farmers, 9 independent variables and in case of both small and medium farmers 7 independent variables were significantly correlated with the extent of crop diversification. In the pooled sample of farmers, 10 independent variables were significantly correlated with the extent of crop diversification. The findings of regression analysis of crop diversification revealed that, in case of marginal farmers out of 9 independent variables, only 3 variables were found to contribute significantly towards the extent of crop diversification. The variables viz. size of operational land holding, scientific orientation and innovativeness had positive and significant contribution towards extent of crop diversification at 0.05 level. The value of R2 (0.674) indicated that 9 independent variables selected for regression could predict 67.40 per cent of the variation in extent of crop diversification. In regards small farmers, out of 7 independent variables, only 4 variables were found to contribute significantly towards the extent of crop diversification. The variables viz, innovativeness and management efficiency were had positive and significant contribution towards extent of crop diversification at 0.01 level, whereas the variable size of operational land holding and risk orientation had positive and significant contribution towards extent of crop diversification at 0.05 level. The value of R2 (0.787) indicated that 7 independent variables selected for regression could predict 78.70 per cent of the variation in extent of crop diversification. In case of medium farmers, out of 7 independent variables, only 4 variables were found to contribute significantly towards the extent of crop diversification. The variables viz. size of operational land holding, scientific orientation and economic motivation had positive and significant contribution towards extent of crop diversification at 0.01 level, whereas the variable farm mechanization was positively and significantly correlated with extent of crop diversification at 0.05 level. The value of R2 (0.787) indicated that that 7 independent variables selected for regression could predict 74.50 per cent of the variation in extent of crop diversification. In the pooled sample of farmers, out of 10 independent variables, 7 variables were found to contribute significantly towards the extent of crop diversification. The variables viz. size of operational land holding, scientific orientation, risk orientation, economic motivation, innovativeness and management efficiency had positive and significant contribution towards extent of crop diversification at 0.01 level, whereas the variable degree of information exposure had positive and significant contribution towards extent of crop diversification at 0.05 level. The value of R2 (0.813) indicated that 10 independent variables selected for regression could predict 81.30 per cent of the variation in extent of crop diversification. Findings of correlation analysis of enterprise diversification reveal that, in case of both marginal and medium farmers, 8 independent variables and in case of small farmers 12 independent variables were significantly correlated with the extent of enterprise diversification. In the pooled sample of farmers, 9 independent variables were significantly correlated with the extent of enterprise diversification. The findings of regression analysis of enterprise diversification revealed that, in case of marginal farmers out of 8 independent variables, only 3 variables were found to contribute significantly towards the extent of enterprise diversification. The variables viz. size of operational land holding, innovativeness and management efficiency had positive and significant contribution towards extent of enterprise diversification at 0.05 level. The value of R2 (0.829) indicated that 8 independent variables selected for regression could predict 82.90 per cent of the variation in extent of enterprise diversification. In respect of small farmers, out of 12 independent variables, only 5 variables were found to contribute significantly towards the extent of enterprise diversification. The variables viz, education level and attitude towards agricultural diversification had positive and significant contribution towards extent of enterprise diversification at 0.01 level, whereas the variables size of operational land holding, social participation and innovativeness had positive and significant contribution towards extent of enterprise diversification at 0.05 level. The value of R2 (0.831) indicated that 12 independent variables selected for regression could predict 83.10 per cent of the variation in extent of enterprise diversification. In case medium farmers, out of 8 independent variables, only 4 variables were found to contribute significantly towards the extent of enterprise diversification. The variables viz. size of operational land holding, degree of information exposure and attitude towards agricultural diversification had positive and significant contribution towards extent of enterprise diversification at 0.01 level whereas the variable economic motivation had positive and significant contribution towards extent of enterprise diversification at 0.05 level. The value of R2 (0.745) indicated that 7 independent variables selected for regression could predict 74.50 per cent of the variation in extent of enterprise diversification. In the pooled sample of farmers, out of 9 independent variables, 6 variables were found to contribute significantly towards the extent of enterprise diversification. The variables viz., age, degree of information exposure, innovativeness, management efficiency and attitude towards agricultural diversification had positive and significant contribution towards extent of enterprise diversification at 0.01 level whereas, the variable operational land had positive and significant contribution towards extent of enterprise diversification at 0.05 level. The value of R2 (0.813) indicated that 9 independent variables selected for regression could predict 81.30 per cent of the variation in extent of enterprise diversification. Findings of correlation analysis of livelihood security revealed that in case of marginal farmers, 13 independent variables and in case of both small and medium farmers, 7 independent variables were significantly correlated with the level of livelihood security. In respect of pooled sample of farmers, 12 independent variables were significantly correlated with the level of livelihood security. The findings of regression analysis of livelihood security revealed that, in case of marginal farmers out of 13 independent variables, 5 variables were found to contribute significantly towards the level of livelihood security. The variables social participation and innovativeness had positive and significant contribution towards level of livelihood security at 0.01 level whereas, the variables size of operational land holding, risk orientation and management efficiency had positive and significant contribution towards level of livelihood security at 0.05 level. The value of R2 (0.929) indicated that 13 independent variables selected for regression could predict 92.90 per cent of the variation in level of livelihood security. As regards small farmers, the variables size of operational land holding, social participation, economic motivation, innovativeness and management efficiency had positive and significant contribution towards level of livelihood security at 0.01 level. The value of R2 (0.849) indicated that 7 independent variables selected for regression could predict 84.90 per cent of the variation in level of livelihood security. In respect of medium farmers, out of 6 independent variables, 3 variables were found to contribute significantly towards the level of livelihood security. The variables social participation, economic motivation and innovativeness had positive and significant contribution towards level of livelihood security at 0.01 level. The value of R2 (0.776) indicated that 6 independent variables selected for regression could predict 77.60 per cent of the variation in level of livelihood security. In the pooled sample of farmers, out of 12 independent variables, only 5 variables were found to contribute significantly towards the level of livelihood security. The variables social participation, economic motivation, innovativeness and management efficiency had positive and significant contribution towards level of livelihood security at 0.01 level whereas, the variable size of operational land holding had positive and significant contribution towards extent of enterprise diversification at 0.05 level. The value of R2 (0.873) indicated that 12 independent variables selected for regression could predict 81.30 per cent of the variation in level of livelihood security. “Lack of finance to start a new enterprise”, “high cost of labour” and “Lack of information regarding scientific cultivation of crops” were perceived by the marginal farmers as the three most important constraints in diversification. “Lack of finance to start a new enterprise”, “High cost of labour” and “High cost of animal feed” were perceived by both small and medium farmers as the three most important constraints in diversification.