Using the case study of a sterile-type chilling disaster during the head-flowering phase of rice,this study presents a novel approach to constructing vulnerability curves that can overcome data limitations while also taking into account crop growth mechanisms.Meteorological data during 1990-2010 was used to generate sterile-type chilling scenarios at county scale,estimated rice yield losses through combining one crop model(MCWLA)and machine learning(XGBoost)method,finally developed sterile-type chilling vulnerability curves for each main rice-planting zone in China and estimated long-term historical(1961-2010)yield loss caused by sterile-type chilling disasters.The results showed that:(1)Machine learning could effectively reproduce the estimation ability of crop model(RRMSE<6%,R2>0.93).(2)The sterile-type vulnerability decreased with decreasing latitude,and was weaker in growing seasons for late rice than that in early rice.(3)The historical yield loss was higher for single rice(1224kg·ha-1)than for double rice(early rice:868kg·ha-1;late rice:807kg·ha-1).
维普
万方数据