Integrated phenology and climate in rice yields prediction using machine learning methods

文献类型： 外文期刊

第一作者： Guo, Yahui

作者： Guo, Yahui;Fu, Yongshuo;Hao, Fanghua;Zhang, Xuan;Wu, Wenxiang;Wu, Wenxiang;Jin, Xiuliang;Bryant, Christopher Robin;Bryant, Christopher Robin;Senthilnath, J.

作者机构：

关键词： Early mature rice; Machine learning (ML) methods; Multiple linear regression (MLR); Rice yield prediction; Phenology

期刊名称：ECOLOGICAL INDICATORS （ 影响因子：4.958； 五年影响因子：5.846 ）

ISSN： 1470-160X

年卷期： 2021 年 120 卷

页码：

收录情况： SCI

摘要： Rice (Oryza sativa L.) is a staple cereal crop and its demand is substantially increasing with the growth of the global population. Precisely predicting rice yields are of vital importance to ensure the food security in countries like China, where rice accounts for one-fifth of the total agricultural production. Previous studies found that the rice yields had been significantly impacted by climate change. In addition, phenological variables were found to be important factors concerning rice yields due to its fundamental role in carbon allocation between plant organs, but its impacts on rice yields were seldom evaluated. In this study, eleven combinations of phenology, climate and geography data were tested to predict the site-based rice yields using a traditional regression-based method (MLR, multiple linear regression), and more advanced three machine learning (ML) methods: back-propagation neural network (BP), support vector machine (SVM) and random forest (RF). The results showed that ML methods were more precise than MLR method. The combination using the integrated phenology, climate during growing season and geographical information was better for yields predictions than other combinations across the ML methods, e.g. the difference RMSE (R-2) between prediction and observed rice yields were 800 (0.24), 737 (0.33), and 744 (0.31) kg/ha for BP, SVM and RF, respectively. The SVM had achieved the highest precisions in yield predictions and the phenological variables substantially improved the accuracy of yield predictions, and the relative importance of phenological variables were even similar as climatic variables. We highlight the phenology and climate need to be accurately represented in the crop models to improve the accuracy in rice yield prediction under climate change conditions using integrated ML methods.

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