Predicting the influence of extreme temperatures on grain production in the Middle-Lower Yangtze Plains using a spatially-aware deep learning model

Abstract

Grain crops are vulnerable to anthropogenic climate change and extreme temperature events. Despite this, previous studies have often neglected the impact of the spatio-temporal distribution of extreme temperature events on regional grain outputs. This research focuses on the Middle-Lower Yangtze Plains and aims to address this gap as well as to provide a renewed projection of climate-induced grain production variability for the rest of the century. The proposed model performs significantly superior to the benchmark multilinear grain production model. By 2100, grain production in the MLYP is projected to decrease by over 100 tons for the low-radiative-forcing/sustainable development scenario (SSP126) and the medium-radiative-forcing scenario (SSP245), and about 270 tons for the high-radiative-forcing/fossil-fueled development scenario (SSP585). Grain production may experience less decline than previously projected by studies using Representative Concentration Pathways. This difference is likely due to a decrease in coldwave frequency, which can offset the effects of more frequent heatwaves on grain production, combined with alterations in supply-side policies. Notably, the frequency of encoded heatwaves and coldwaves has a stronger impact on grain production compared to precipitation and labor indicators; higher levels of projected heatwaves frequency correspond with increased output variability over time. This study emphasizes the need for developing crop-specific mitigation/adaptation strategies against heat and cold stress amidst global warming.

Keywords

• Extreme temperature event
• NEX-GDDP-CMIP6
• Grain production model
• Convolutional autoencoder
• Random forest
• Shared socioeconomic pathway