Drought impact-based forecasting of crop yield in India

The reliable prediction of drought impacts on crop yield in India poses a significant challenge due to the complex interactions of climatic variables, systems vulnerabilities and impacts propagation. Addressing this challenge requires advanced methods, such as impact-based forecasting, to account for these complexities. In this study, we leveraged remote sensing-based vegetation indicators as proxies for crop yield, along with multiple drought indices across various accumulation periods, to establish a robust indicator-impact relationship. A cluster analysis was performed to group districts, followed by a comparative evaluation of various machine-learning algorithms (Random Forest, XGBoost, Artificial Neural Network) to assess their efficacy in predicting crop yield impacts on a subseasonal-to-seasonal scale. We finally evaluated the accuracy of predicting the crop yield impacts based on drought indices computed from ECMWF’s seasonal forecast system SEAS5.

Our analysis highlights the importance of key predictors, uncovers seasonal trends and spatio-temporal patterns in indicator-impact relationships, and marks a pioneering effort in comparing diverse machine-learning algorithms for establishing an impact-based forecasting model at lead times of 1 to 6 months. As such, these findings offer valuable insights into the dynamics of drought impacts on crop yield, providing a monitoring tool and a foundational basis for implementing targeted drought mitigation actions within the agricultural sector. This research contributes to advancing the understanding of impact-based forecasting models and their practical application in addressing the challenges associated with drought impacts on crop yield in India.