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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. Advanced approaches, such as impact-based forecasting, become necessary to address the intricate nature of this challenge. 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. We further performed a comparative analysis of various machine-learning algorithms 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 ECMWFs seasonal forecast system SEAS5. Our analysis not only unveils seasonal trends and spatio-temporal patterns in indicator-impact links but also marks a pioneering effort in comparing diverse machine-learning algorithms for establishing an impact-based forecasting model. 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.
Shyrokaya et al. (Mon,) studied this question.