AGRICULTURAL TECHNOLOGY ADOPTION BY MARGINAL AND SMALL FARMERS IN KARNATAKA
Authors:   CMR University, Bangalore, Karnataka, India, B Basavaraj , Annapoorna M. S. ,   CMR University, Bangalore, Karnataka, India
Published: April 2023

Adoption of new technology in agriculture paves a way for meeting the ever-increasing food demand by the growing population. However, despite the evident benefits of new technologies, farmers either don’t adopt them or in many a case, it takes long years to adopt. Thus, the present study was undertaken to understand the factors that influence the farmers in technology adoption. The study focused on understanding the various factors influencing the technology adoption, the extent of crop diversification, the pattern of resource use efficiency. Further an attempt was made to study the interdependence among these factors. The total sample size was 612 famers consisting of 306 technology adopters and 306 technology non-adopters. Study was conducted in Raichur for paddy, Yadgir for cotton, Kalaburagi for jowar and redgram, Chitradurga for groundnut, and Belagavi for sugarcane. The selection of crops and area was based on the area under the crops and production. Details on socio economic characteristics, cropping pattern, input use were collected from the farmers. Level of education was relatively higher among the adopters. Thus, providing informal education to the village population can enhance technology adoption. Secondary analysis of the data on area, production and productivity revealed that only cotton had registered significant growth in area (19%) in Yadgir, while the remaining crops had registered negative growth rate in their respective sampling areas. With respect to production, redgram exhibited significant growth rate of five per cent in Kalaburagi, while all other crops registered either non-significant or negative growth rate. With respect to yield, redgram (4%) and jowar (4%) shows significant growth rate in Kalaburagi. Thus, research in this line leading to the improvement in the yields are required. Further, Tobit estimates revealed that education level, farm size, credit availability, experience in farming, owning a mobile phone, cooperative membership significantly influenced technology adoption among the farmers. Analysis of resource use efficiency revealed that the farmers were over utilizing plant protection chemicals, and fertilizers whereas underutilizing the FYM and machine labor which implies that there is a scope for enhancing the input levels of these resources for increased profit. The over utilization of the resources shows that the expenditure on such inputs has exceeded the optimum level. With respect to crop diversification, high level of crop diversification was witnessed among the adopters whereas among the non-adopters it was relatively lesser. Highest diversification was seen among adopter category of redgram (0.33), groundnut (0.33) and sorghum (0.34). Further Seemingly Unrelated Regression model (SUR) was employed to analyze the interdependence between technology intervention in agriculture, crop diversification and production efficiencies in cultivation of crops. The results of SUR model reveal in general across crops, education level, farm size, experience in farming were seen to have positively influenced crop diversification, resource use efficiency, adoption of best practices suggested by SAU/KVK, and adoption of improved seeds and chemical application. Thus, overall, the study reveals that crop diversification and resource use efficiency was comparatively higher among the adopters than the non-adopters. Various factors leading to the adoption of technology among the adopters was also identified. Thus, role of extension agents must be further strengthened as they highly influence the rural farmers in incorporating advanced technologies into farming. Easy credit access to the farmers must be made possible as this can help farmers from escaping the debt trap. Thus, by understanding the rural scenario at the micro level, the issues of lower productivity, coupled with low income can be successfully addressed. Keywords: CAGR, Growth Rates, Technology Adoption, Crop Diversification, Resource Use efficiency, Tobit Model, Logistic Regression, Cobb Douglas, SUR(Seemingly Unrelated Regression), Herfindahl Index and Marginal & Small Farmers.