外文摘要：Timely and reliable regional crop yield forecasting before harvest is critical for managing climate risk, adjusting agronomic management, and making food trade policy. Although various methods exist for crop yield forecasting, including process -based crop models and machine learning techniques, the potential of integrating these methods for early -season yield forecasts has not been well investigated. In this study, we proposed a hybrid framework for crop yield forecasting that firstly assimilated leaf area index and soil moisture into a crop model and then combined the data -assimilated crop model with machine learning techniques to improve the prediction skill further. The proposed framework was applied to winter wheat yield forecasting in the North China Plain during 2009-2015. We found that the assimilation significantly enhances wheat yield estimates, achieving additional ACC = 0.27, MAPE = 6.12 %. Incorporating weather forecasts enabled reliable winter wheat yield forecasts up to 1-3 months in advance, achieving ACC = 0.69, MAPE = 7.79 %. Furthermore, integrating the assimilated crop model with machine learning techniques improved the forecasting further, achieving ACC = 0.97 and MAPE = 1.74 %. The proposed framework for crop yield forecasting can be adapted to other crops and regions and has great potential in developing food security early warning system at a regional scale.
外文关键词：machine learning;Data assimilation;crop modelling;early warning system;Extreme climate
作者：Zhang, Jing;Zhang, Zhao;Tao, Fulu;Zhuang, Huimin;Cheng, Fei;Han, Jichong;Luo, Yuchuan;Zhang, Liangliang;Cao, Juan;He, Bangke;Xu, Jialu
作者单位：Chinese Acad Sci;Univ Chinese Acad Sci;Beijing Normal Univ
期刊名称：AGRICULTURAL AND FOREST METEOROLOGY
期刊影响因子：0.0
出版年份：2024
出版刊次：347
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台