气候变化领域集成服务门户(CCPortal): The response of maize, sorghum, and soybean yield to growing-phase climate revealed with machine learning

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DOI	10.1088/1748-9326/ab7b22
	The response of maize, sorghum, and soybean yield to growing-phase climate revealed with machine learning
	L Hoffman A.; R Kemanian A.; E Forest C.
发表日期	2020
ISSN	17489318
卷号	15 期号:9
英文摘要	Accurate representation of crop responses to climate is critically important to understand impacts of climate change and variability in food systems. We use Random Forest (RF), a diagnostic machine learning tool, to explore the dependence of yield on climate and technology for maize, sorghum and soybean in the US plains. We analyze the period from 1980 to 2016 and use a panel of county yields and climate variables for the crop-specific developmental phases: establishment, critical window (yield potential definition) and grain filling. The RF models accounted for between 71% to 86% of the yield variance. Technology, evaluated through the time variable, accounted for approximately 20% of the yield variance and indicates that yields have steadily increased. Responses to climate confirm prior findings revealing threshold-like responses to high temperature (yield decrease sharply when maximum temperature exceed 29 C and 30 C for maize and soybean), and reveal a higher temperature tolerance for sorghum, whose yield decreases gradually as maximum temperature exceeds 32.5 C. We found that sorghum and soybean responded positively to increases in cool minimum temperatures. Maize yield exhibited a unique and negative response to low atmospheric humidity during the critical phase that encompasses flowering, as well as a strong sensitivity to extreme temperature exposure. Using maize as a benchmark, we estimate that if warming continues unabated through the first half of the 21st century, the best climatic conditions for rainfed maize and soybean production may shift from Iowa and Illinois to Minnesota and the Dakotas with possible modulation by soil productivity. © 2020 The Author(s). Published by IOP Publishing Ltd.
语种	英语
scopus关键词	Atmospheric humidity; Climate change; Crops; Decision trees; Grain (agricultural product); Machine learning; Climate variables; Climatic conditions; Extreme temperatures; Maximum temperature; Minimum temperatures; Soil productivity; Soybean production; Temperature tolerance; Atmospheric temperature; climate effect; crop yield; machine learning; maize; physiological response; sorghum; soybean; Glycine max; Zea mays
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/153842
作者单位	Department of Meteorology and Atmospheric Science, Pennsylvania State UniversityPA, United States; Department of Plant Science, Pennsylvania State UniversityPA, United States; Earth and Environmental Systems Institute, Pennsylvania State UniversityPA, United States
推荐引用方式 GB/T 7714	L Hoffman A.,R Kemanian A.,E Forest C.. The response of maize, sorghum, and soybean yield to growing-phase climate revealed with machine learning[J],2020,15(9).
APA	L Hoffman A.,R Kemanian A.,&E Forest C..(2020).The response of maize, sorghum, and soybean yield to growing-phase climate revealed with machine learning.Environmental Research Letters,15(9).
MLA	L Hoffman A.,et al."The response of maize, sorghum, and soybean yield to growing-phase climate revealed with machine learning".Environmental Research Letters 15.9(2020).