State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, HoHai University, Nanjing 210098, Jiangsu, China; Glenn Department of Civil Engineering, Clemson University, 202 Lowry Hall, Clemson, SC 29634, USA.
Glenn Department of Civil Engineering, Clemson University, 202 Lowry Hall, Clemson, SC 29634, USA.
Sci Total Environ. 2020 Dec 15;748:141431. doi: 10.1016/j.scitotenv.2020.141431. Epub 2020 Aug 1.
Climate variability controls crop yield variability with impacts on food security at the local, regional and global levels. This study uses non-parametric elasticity to investigate the sensitivity of crop yields of the top four global crops (wheat, rice, maize, and soybean) to three climate variables (precipitation (PRE), potential evapotranspiration (PET), and mean air temperature (TMP)). Trends and serial correlations exist in both climate variables and crop yields over the study period (1961 to 2014). To overcome this limitation, the Trend Free Pre-Whitening (TFPW) method was applied. Crop yields are most sensitive to TMP globally. But the exact sensitivity varies across continents. The highest sensitivity regions are located in parts of the Southeast Asia. Wheat yields are more sensitive to TMP in Western Europe and Northern America, whereas maize has higher sensitivity to TMP for regions located in South America and parts of Eastern and Western Africa. Soybean is more sensitive in North and South America. The elasticities of wheat and rice yields to TMP are negative in most of the regions (i.e. increased TMP decreases yield), whereas maize witnessed positive and soybean witnessed mixed positive and negative signals depending on the region. PRE has lower influence on crop yields. The non-parametric elasticity concept is a simple and an efficient approach that complements the existing linear models methods used to detect climate change impacts on crop yields and can be used to investigate the future consequences of climate change on local to global scale agricultural production.
气候变化会影响粮食安全，这种影响在地方、区域和全球层面都存在。本研究使用非参数弹性来研究全球四大主要作物（小麦、水稻、玉米和大豆）的产量对三种气候变量（降水（PRE）、潜在蒸散量（PET）和平均气温（TMP））的敏感性。在研究期间（1961 年至 2014 年），气候变量和作物产量都存在趋势和序列相关性。为了克服这一限制，应用了趋势自由预白化（TFPW）方法。全球范围内，作物产量对 TMP 最敏感。但确切的敏感性因大陆而异。敏感性最高的地区位于东南亚部分地区。在西欧和北美，小麦产量对 TMP 的敏感性更高，而在南美洲和东非和西非部分地区，玉米对 TMP 的敏感性更高。大豆在北美和南美更敏感。在大多数地区，小麦和水稻产量对 TMP 的弹性为负（即 TMP 升高会降低产量），而玉米则表现出正弹性，大豆则根据地区表现出正弹性和负弹性混合信号。PRE 对作物产量的影响较小。非参数弹性概念是一种简单有效的方法，补充了用于检测气候变化对作物产量影响的现有线性模型方法，可用于研究气候变化对地方到全球规模农业生产的未来影响。
