Assessing the Historical and Future Relationship Between Climatic Factors and the Production of Different Crops over India

Abstract

This analysis explores the effects of climate variations over 15 different food and industrial crop productions in India using the econometric model and forecasts these crop outputs using the Artificial Neural Network (ANN) model up to 2030. The unit root test was used to inspect the stationarity time series datasets in agricultural crop productions and climatic variables. The Dynamic Ordinary Least Square (DOLS) and Fully Modified Dynamic Ordinary Least Square (FMOLS) models were used to test the accuracy of the outcomes. The results showed that land productivity declines if the annual mean temperature increases, demonstrating the sensitivity of Indian agricultural crop production to the temperature variance. Meanwhile, results also showed that if the temperature increases, rainfall would become more unstable, triggering a reduction of rice, wheat, tuar, cotton, and tea crop productions. Major crop productions, namely sesamum (til), gram, sugarcane, groundnut, jowar, and bajra would experience a positive impact and subsequently increase slightly up to 2030. The outcomes of the study can add insights into sustainable agricultural crop productions in India, and livelihood patterns, particularly among minor and marginal agricultural families. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.