Integrated phenology and climate in rice yields prediction using machine learning methods
文献类型：期刊论文
| 作者 | Guo, Yahui1; Fu, Yongshuo1; Hao, Fanghua1; Zhang, Xuan1; Wu, Wenxiang2,3 ; Jin, Xiuliang4; Bryant, Christopher Robin5,6; Senthilnath, J.7
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| 刊名 | ECOLOGICAL INDICATORS
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| 出版日期 | 2021 |
| 卷号 | 120页码:11 |
| 关键词 | Early mature rice Machine learning (ML) methods Multiple linear regression (MLR) Rice yield prediction Phenology |
| ISSN号 | 1470-160X |
| DOI | 10.1016/j.ecolind.2020.106935 |
| 通讯作者 | Fu, Yongshuo(yfu@bnu.edu.cn) ; Wu, Wenxiang(wuwx@igsnrr.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | BACKPROPAGATION NEURAL-NETWORKS ; PAST 3 DECADES ; RANDOM FOREST ; CROP YIELD ; WHEAT YIELD ; CONJUGATE-GRADIENT ; LINEAR-REGRESSION ; CARBON ALLOCATION ; GROWTH DURATION ; BIOMASS GROWTH |
| 资助项目 | National Key Research and Development Program of China[2017YFA06036001] ; General Program of National Nature Science Foundation of China[31770516] ; 111 Project[B18006] ; Fundamental Research Funds for the Central Universities[2018EYT05] |
| WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000591880800002 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key Research and Development Program of China ; General Program of National Nature Science Foundation of China ; 111 Project ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/136969]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Fu, Yongshuo; Wu, Wenxiang |
| 作者单位 | 1.Beijing Normal Univ, Coll Water Sci, Beijing 100875, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 3.Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China 4.Chinese Acad Agr Sci, Inst Crop Sci, Key Lab Crop Physiol & Ecol, Minist Agr, Beijing 100081, Peoples R China 5.Univ Guelph, Sch Environm Design & Rural Dev, Guelph, ON N1G 2W5, Canada 6.Univ Montreal, Geog, Montreal, PQ H2V 2B8, Canada 7.ASTAR, Inst Infocomm Res, Singapore 138632, Singapore |
| 推荐引用方式 GB/T 7714 | Guo, Yahui,Fu, Yongshuo,Hao, Fanghua,et al. Integrated phenology and climate in rice yields prediction using machine learning methods[J]. ECOLOGICAL INDICATORS,2021,120:11. |
| APA | Guo, Yahui.,Fu, Yongshuo.,Hao, Fanghua.,Zhang, Xuan.,Wu, Wenxiang.,...&Senthilnath, J..(2021).Integrated phenology and climate in rice yields prediction using machine learning methods.ECOLOGICAL INDICATORS,120,11. |
| MLA | Guo, Yahui,et al."Integrated phenology and climate in rice yields prediction using machine learning methods".ECOLOGICAL INDICATORS 120(2021):11. |
入库方式： OAI收割
来源：地理科学与资源研究所
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