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DOI | 10.1088/1748-9326/ab7b24 |
Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models | |
Leng, Guoyong1,2; Hall, Jim W.2 | |
2020-04-01 | |
发表期刊 | ENVIRONMENTAL RESEARCH LETTERS |
ISSN | 1748-9326 |
出版年 | 2020 |
卷号 | 15期号:4 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; England |
英文摘要 | Pervious assessments of crop yield response to climate change are mainly aided with either process-based models or statistical models, with a focus on predicting the changes in average yields, whilst there is growing interest in yield variability and extremes. In this study, we simulate US maize yield using process-based models, traditional regression model and a machine-learning algorithm, and importantly, identify the weakness and strength of each method in simulating the average, variability and extremes of maize yield across the country. We show that both regression and machine learning models can well reproduce the observed pattern of yield averages, while large bias is found for process-based crop models even fed with harmonized parameters. As for the probability distribution of yields, machine learning shows the best skill, followed by regression model and process-based models. For the country as a whole, machine learning can explain 93% of observed yield variability, followed by regression model (51%) and process-based models (42%). Based on the improved capability of the machine learning algorithm, we estimate that US maize yield is projected to decrease by 13.5% under the 2 degrees C global warming scenario (by similar to 2050 s). Yields less than or equal to the 10th percentile in the yield distribution for the baseline period are predicted to occur in 19% and 25% of years in 1.5 degrees C (by similar to 2040 s) and 2 degrees C global warming scenarios, with potentially significant implications for food supply, prices and trade. The machine learning and regression methods are computationally much more efficient than process-based models, making it feasible to do probabilistic risk analysis of climate impacts on crop production for a wide range of future scenarios. |
英文关键词 | climate change crop yield machine learning statistical model crop model |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000530345300001 |
WOS关键词 | 2 DEGREES-C ; CLIMATE-CHANGE ; WHEAT YIELD ; MAIZE PRODUCTION ; UNITED-STATES ; RICE YIELDS ; IMPACTS ; TEMPERATURE ; TRENDS ; WATER |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/279285 |
专题 | 气候变化 |
作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China; 2.Univ Oxford, Environm Change Inst, Oxford OX1 3QY, England |
推荐引用方式 GB/T 7714 | Leng, Guoyong,Hall, Jim W.. Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models[J]. ENVIRONMENTAL RESEARCH LETTERS,2020,15(4). |
APA | Leng, Guoyong,&Hall, Jim W..(2020).Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models.ENVIRONMENTAL RESEARCH LETTERS,15(4). |
MLA | Leng, Guoyong,et al."Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models".ENVIRONMENTAL RESEARCH LETTERS 15.4(2020). |
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