Factors Influencing Adoption of Climate Resilient Agricultural Technologies in Andhra Pradesh, India
A. Sandhya Neelima
Department of Agricultural Economics, Agricultural College, Bapatla, Acharya NG Ranga Agricultural University (ANGRAU), Guntur, Andhra Pradesh, India.
C. A. Rama Rao *
Section of Design and Analysis, ICAR-Central Research Institute for Dryland Agriculture (CRIDA), Hyderabad, Telangana, India.
K. N. Ravi Kumar
Department of Agricultural Economics, Agricultural College, Rajamahendravaram, ANGRAU, Guntur, Andhra Pradesh, India.
V. Srinivasa Rao
Department of Statistics and Computer Applications, Agricultural College, Naira, ANGRAU, Guntur, Andhra Pradesh, India.
M. Rama Devy
Department of Agricultural Extension Education, Agricultural College, Bapatla, ANGRAU, Guntur, Andhra Pradesh, India.
*Author to whom correspondence should be addressed.
Abstract
In Andhra Pradesh, climate change may negatively impact crop yields and variability, especially in rainfed agricultural areas, which account for 46 per cent of the total cultivated area. Besides drought-prone areas like the districts in the Rayalaseema region of Andhra Pradesh, even the state's coastal districts are prone to cyclones and floods. In this context, farmers have to adopt Climate Resilient Agricultural (CRA) technologies to mitigate the negative impacts of climate change. Certain factors influence the farmer's decision to adopt these technologies. In this context, this study investigated the factors influencing the adoption of CRA technologies in the Srikakulam and Anantapur districts of Andhra Pradesh by employing a logistic regression model. Primary data was collected from 300 purposively selected farmers comprising of 240 adopters and 60 non-adopters from both the districts. The results revealed that education (6.2%), farming experience (7.8%), family size (5.1%), annual farm income (4.8%), access to climate information (9.8%) and access to extension contact (16.5%) significantly influenced the adoption of CRA technologies in Srikakulam district. Similarly, in Anantapur district age of the farmer (0.7%), education (6.6%), annual farm income (5.2%), access to climate information (9.0%), access to extension contact (17.6%) and membership in organisation (7.1%) significantly influenced the adoption of CRA technologies. Therefore, the results indicated that it is necessary to set up an appropriate institutional structure to provide climate information, extension services and non-formal education to farmers on the benefits of CRA technologies for the wide spread adoption of CRA technologies.
Keywords: Adopters, climate change, CRA technologies, determinants, logistic regression model, non-adopters