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DOI	10.1016/j.agrformet.2020.108124
	Mapping the sensitivity of agriculture to drought and estimating the effect of irrigation in the United States, 1950-2016
	Lu, Junyu; Carbone, Gregory J.; Huang, Xiao; Lackstrom, Kirsten; Gao, Peng
通讯作者	Lu, JY
来源期刊	AGRICULTURAL AND FOREST METEOROLOGY
ISSN	0168-1923
EISSN	1873-2240
出版年	2020
卷号	292
英文摘要	Drought is a devastating natural hazard posing great threats to agriculture. Identifying the spatial pattern of agricultural sensitivity to drought can provide scientific information for decision-makers to prepare droughts, allocate resources, and mitigate impacts. Here, we use long-term state- and county-level crop data for the 10 major crops: corn grain, soybeans, hay, spring wheat, winter wheat, cotton, corn silage, sorghum, barley, and rice in the United States from 1950 to 2016. First, we perform a correlation analysis between crop yield anomalies and two drought indices (Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI)) to identify the sub-seasonal pattern of agricultural sensitivity to drought stress. SPEI performs better than SPI. For most crops, the sensitivity to drought increases in the early period, peaks at the critical months, and then declines. July is the most critical month for crop growth for most crops. Among all crops, soybean and corn grain are most sensitive to drought. Second, we develop an Agriculture Drought Sensitivity Index (ADSI) to quantitatively measure the sensitivity of agriculture to drought stress based on the statistical relationship between the ten major crops and SPEI. We demonstrate that there exists a very strong spatial correspondence between higher sensitivity to drought and the lower percentage of acres irrigated, and vice versa. Also, for those regions with limited irrigation, the sensitivity is higher in arid/semi-arid regions and lower in humid regions in summer. Third, given the importance of irrigation, an analysis of covariance (ANCOVA) shows that the irrigated crop yields have much higher long-run mean yields than non-irrigated crop yields. Fourth, to investigate how irrigation affects drought sensitivity, a panel data regression model shows that the responses of crop growth to drought are nonlinear for all crops. Non-irrigated crops are more sensitive to droughts than the irrigated crops, particularly in severe drought conditions. This provides quantitative incentive to use irrigation as an important adaptation and coping strategy to mitigate the drought impacts on agriculture in the US.
英文关键词	Drought risks mitigation Gridded standardized precipitation evapotranspiration index (SPEI) Gridded standardized precipitation index (SPI) Analysis of covariance (ANCOVA) Panel data regression model
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000561954900013
WOS关键词	PLANT WATER-STRESS ; TIME-SCALES ; CROP YIELD ; PRECIPITATION ; INDEXES ; CLIMATE ; GROWTH
WOS类目	Agronomy ; Forestry ; Meteorology & Atmospheric Sciences
WOS研究方向	Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/325776
作者单位	[Lu, Junyu] Arizona State Univ, Sch Community Resources & Dev, Phoenix, AZ 85004 USA; [Lu, Junyu] Hainan Univ, Hainan Univ Arizona State Univ Joint Int Tourism, Haikou 570004, Hainan, Peoples R China; [Carbone, Gregory J.; Lackstrom, Kirsten; Gao, Peng] Univ South Carolina, Dept Geog, Columbia, SC 29208 USA; [Huang, Xiao] Univ Arkansas, Dept Geosci, Fayetteville, AR 72701 USA
推荐引用方式 GB/T 7714	Lu, Junyu,Carbone, Gregory J.,Huang, Xiao,et al. Mapping the sensitivity of agriculture to drought and estimating the effect of irrigation in the United States, 1950-2016[J],2020,292.
APA	Lu, Junyu,Carbone, Gregory J.,Huang, Xiao,Lackstrom, Kirsten,&Gao, Peng.(2020).Mapping the sensitivity of agriculture to drought and estimating the effect of irrigation in the United States, 1950-2016.AGRICULTURAL AND FOREST METEOROLOGY,292.
MLA	Lu, Junyu,et al."Mapping the sensitivity of agriculture to drought and estimating the effect of irrigation in the United States, 1950-2016".AGRICULTURAL AND FOREST METEOROLOGY 292(2020).