Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, 32611, USA.
Department of Geological Sciences and WK Kellogg Biological Station, Michigan State University, East Lansing, MI, 48824, USA.
Glob Chang Biol. 2017 Jun;23(6):2464-2472. doi: 10.1111/gcb.13530. Epub 2016 Nov 10.
Many of the irrigated spring wheat regions in the world are also regions with high poverty. The impacts of temperature increase on wheat yield in regions of high poverty are uncertain. A grain yield-temperature response function combined with a quantification of model uncertainty was constructed using a multimodel ensemble from two key irrigated spring wheat areas (India and Sudan) and applied to all irrigated spring wheat regions in the world. Southern Indian and southern Pakistani wheat-growing regions with large yield reductions from increasing temperatures coincided with high poverty headcounts, indicating these areas as future food security 'hot spots'. The multimodel simulations produced a linear absolute decline of yields with increasing temperature, with uncertainty varying with reference temperature at a location. As a consequence of the linear absolute yield decline, the relative yield reductions are larger in low-yielding environments (e.g., high reference temperature areas in southern India, southern Pakistan and all Sudan wheat-growing regions) and farmers in these regions will be hit hardest by increasing temperatures. However, as absolute yield declines are about the same in low- and high-yielding regions, the contributed deficit to national production caused by increasing temperatures is higher in high-yielding environments (e.g., northern India) because these environments contribute more to national wheat production. Although Sudan could potentially grow more wheat if irrigation is available, grain yields would be low due to high reference temperatures, with future increases in temperature further limiting production.
世界上许多灌溉春小麦地区也是贫困高发地区。在贫困地区，温度升高对小麦产量的影响尚不确定。本研究利用来自两个关键灌溉春小麦地区（印度和苏丹）的多模式集合构建了一个结合模型不确定性量化的粮食品温响应函数，并将其应用于世界所有灌溉春小麦地区。印度南部和巴基斯坦南部的小麦种植区因温度升高而导致的产量大幅减少与高贫困人口数量相吻合，表明这些地区是未来粮食安全的“热点”。多模式模拟产生了一个线性绝对产量下降与温度升高的关系，其不确定性随地点的参考温度而变化。由于绝对产量的线性下降，在低产环境（例如印度南部、巴基斯坦南部和苏丹所有小麦种植区的高参考温度地区）中相对减产幅度更大，这些地区的农民将受到温度升高的最大冲击。然而，由于低产和高产地区的绝对产量下降大致相同，因此在高产环境（例如印度北部）中，由于温度升高导致的国家粮食产量减少的贡献更大，因为这些地区对国家小麦生产的贡献更大。尽管苏丹如果有灌溉条件，可能会种植更多的小麦，但由于参考温度较高，粮食产量将较低，未来温度的升高将进一步限制产量。
