Skip to content
Research Article Open access CC BY 4.0

Factors Influencing Adoption of Climate Resilient Agricultural Technologies in Andhra Pradesh, India

A. Sandhya Neelima, C. A. Rama Rao, K. N. Ravi Kumar, V. Srinivasa Rao, M. Rama Devy

International Journal of Environment and Climate Change · pp. 481–489 · Published 14 Aug 2024

10.9734/ijecc/2024/v14i84368

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.

Adopters climate change CRA technologies determinants logistic regression model non-adopters

References (22)

  1. 1 Impact of Climate Change on Agriculture and Its Mitigation Strategies: A Review [DOI]
  2. 2 Adapting agriculture to climate change in Kenya: Household strategies and determinants [DOI]
  3. 3 Factors Affecting Farmers’ Adaptation Strategies to Environmental Degradation and Climate Change Effects: A Farm Level Study in Bangladesh [DOI]
  4. 4 Indian agriculture and climate sensitivity [DOI]
  5. 5 Multi-model climate change projections for India under representative concentration pathways
  6. 6 Farmers’ Adaptation Strategies to Climate Change and Their Implications in the Zou Department of South Benin [DOI]
  7. 7 The effect of development on the climate sensitivity of agriculture [DOI]
  8. 8 Adaptation and resilience to climate change and variability in north-east Ghana [DOI]
  9. 9 Assessing Climate Smart Agriculture and Its Determinants of Practice in Ghana: A Case of the Cocoa Production System [DOI]
  10. 10 Fostering sustainable agriculture: Do institutional factors impact the adoption of multiple climate-smart agricultural practices among new entry organic farmers in Pakistan? [DOI]
  11. 11 Factors Influencing the Adoption of Climate-Smart Agricultural Practice by Small-Scale Farming Households in Wondo Genet, Southern Ethiopia [DOI]
  12. 12 Climate-Smart Agriculture Technologies and Determinants of Farmers’ Adoption Decisions in the Great Rift Valley of Ethiopia [DOI]
  13. 13 Determinants of climate change adaptation strategies in South India: Empirical evidence [DOI]
  14. 14 How Sensitive is Indian Agriculture to Climate Change? [DOI]
  15. 15 Determinants of Adoption Choices of Climate Change Adaptation Strategies in Crop Production by Small Scale Farmers in Some Regions of Central Ethiopia
  16. 16 A district-level analysis for measuring the effects of climate change on production of rice: evidence from Southern India [DOI]
  17. 17 Impact of climate change on productivity of food crops: a sub-national level assessment for India [DOI]
  18. 18 Determinants and Policy Implications of Farmers’ Climate Adaptation Choices in Rural Cameroon [DOI]
  19. 19 Climatic vulnerability, adoption of climate-resilient technologies, and its socioeconomic-institutional-agroecological determinants [DOI]
  20. 20 Building climate resilience in Indian Farm households: An analysis of National and State Policies and Initiatives [DOI]
  21. 21 Assessing vulnerability and adaptation of agriculture to climate change in Andhra Pradesh
  22. 22 Determinants of Adoption of Climate Smart Agricultural Technologies among Farm Households in Southern Karnataka, India [DOI]

Cited by 2

A Climate-Resilient Agriculture Framework Using Machine Learning and Web-Based Decision Support Systems

Pradeep Gs, Amshu HU, Apoorva Bhagwath · International Journal of Innovative Research in Computer Science & Technology · 2026

Showing 1 of 2 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

45

Page views

0

PDF downloads

5

Outbound clicks

2

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.