Impact of Climate Factors on Tamil Nadu’s Food Grain Yield: An ARDL Approach

This study examines the impact of climate factors on Tamil Nadu’s food grain productivity and also agriculture gross value added, spanning the period from 1990–91 to 2022–23 using the Autoregressive Distributed Lag (ARDL) model. The bounds test reveals that minimum temperature, maximum temperature, and rainfall exhibit a long-term cointegration relationship with Gross State Value Added and the Yield rate of Total Food Grain. The Autoregressive Distributed Lag model results show that previous years’ rainfall and minimum temperature have a significant positive impact on both Gross State Value Added and Total Food Grain Yield. On the other hand, the previous year’s maximum temperature has a significant adverse effect on Total Food Grain Yield. The model’s adjusted R-squared values suggest that climate variables explain approximately 63.19 per cent of the variation in GSVA and 75.25 per cent of the variation in food grain yield. These findings offer crucial insights for policymakers seeking to design targeted interventions that mitigate the adverse effects of climatic variability which is worsened by both natural and human activities.