Knowledge-guided machine learning for improving crop yield projections of waterlogging effects under climate change

作者：Linchao Li, Qinsi He, Matthew Tom Harrison, Yu Shi, Puyu Feng, Bin Wang, Yajie Zhang, Yi Li, De Li Liu, Guijun Yang, Meixue Zhou, Qiang Yu, Ke Liu · 发表于：Resources Environment and Sustainability · 年份：2024 · DOI：10.1016/j.resenv.2024.100185 · 被引用次数：21 · 研究领域：Plant responses to water stress、Flood Risk Assessment and Management、Hydrology and Drought Analysis

Extreme precipitation poses a significant threat to crop production, often underestimated by process-based models. State-of-the-art models also struggle with high-resolution spatial applications due to process complexity. Here, we developed a Knowledge-Guided Machine Learning (KGML) framework that integrates machine learning with a waterlogging-enabled APSIM (Agricultural Production Systems sIMulator) to simulate wheat yield change under climate change in the Yangtze River Basin, China. Using transfer learning, this KGML framework transferred waterlogging processes to eight gridded crop models, enabling more accurate yield projections. We found that KGML could accurately replicate the behavior of the improved APSIM model under waterlogging conditions, achieving an R 2 of 0.83 and an RMSE of 272.3 kg/ha for yield loss simulations. Soil properties were identified as the primary factors influencing yield losses under waterlogging, highlighting the importance of optimizing soil conditions to mitigate the adverse impacts of excessive water. Across different scenarios, the improved crop model ensembles projected greater crop yield losses compared to the original simulated outputs, with additional losses (compared to the historical period) around 5.9%–7.3% during the two periods. Although global climate models were the primary source of uncertainty in T1 (2029–2059), crop models contributed more to uncertainty in T2 (2069–2099). The improved ensemble reduced uncertainty from crop mo...