Assessment of future drought sensitivity shift associated with crop yield loss risk in the Anthropocene

Abstract

ABSTRACT This study proposed a framework using artificial neural networks (ANNs), the designed residual regression method, and probabilistic indexing, for drought sensitivity shift analysis, associated with future yield loss risk in Anthropocene. The temporal assessment of the drought index (standardized precipitation evapotranspiration index, SPEI) illustrated prevalent occurrence of mild to moderate drought with a fluctuating trend. Furthermore, the optimal ANN model, with R2 = 0.86 for training and 0.7 for testing, was used to project yield for future analysis. The projected spatial changes in drought sensitivity patterns suggest most of the area will experience an increased sensitivity to drought in the far future (2036–2050). The estimated probability loss under the different risk levels defined by the yield reduction rate was 6.2–55.5%. Furthermore, the spatial distribution of the designed risk index (IR) revealed the districts with the highest probability of vulnerability and showed a probable increase in future vulnerability.