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Climate Smart Agriculture in Drought-Prone Landscapes: Pathways to Resilience, Livelihoods and Sustainable Productivity
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1  Department of Agricultural Economics, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India – 641003
Academic Editor: Sanzidur Rahman

Abstract:

Abstract

Introduction:

Climate change has become a major threat to agricultural sustainability, particularly to drought-prone areas. This paper investigates the adoption of climate-smart agriculture (CSA) technology by smallholder farmers in the Ramanathapuram District of Tamil Nadu and its impact on crop productivity, specifically on paddy cultivation.

Methods:

Data were collected from 180 farm households in 15 villages through structured interviews. Logistic regression and propensity score matching (PSM) methods were used to confirm the influence of socioeconomic factors on adoption of CSA and to assess its impact on yield outcomes.

Results:

The logistic regression results indicated that gender, education, longer experience in farming, the size of farmland, and information from extension services were determinants of CSA practice adoption. Specifically, being a male farmer and having higher school education, farming experience, and access to extension services had a significant positive effect of CSA adoption, and farm size had a negative effect on CSA adoption. In addition, PSM analysis indicated that CSA adaptation led to a significant increase in crop yield. Both matching and linear regression models show that the coefficient of CSA practices on log yield is positive and significant. The R2 value of 0.87 in linear regression model suggests that the model describes a high variation inthe yield.

Conclusions:

These results highlight the role of CSA in improving the resilience and productivity of agriculture in climate-exposed areas. Accordingly, policy interventions should aim at enhancing farmers' education, reinforcing extension, and providing support to smallholders.

Keywords: Climate-Smart Agriculture; Drought-prone; Crop productivity; Propensity score matching; Logistic regression.

 
 
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