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The Role of Climate Covariability on Crop Yields in the Conterminous United States

机译:在美国本土气候协变对作物产量的作用

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摘要

The covariability of temperature (T), precipitation (P) and radiation (R) is an important aspect in understanding the climate influence on crop yields. Here, we analyze county-level corn and soybean yields and observed climate for the period 1983–2012 to understand how growing-season (June, July and August) mean T, P and R influence crop yields jointly and in isolation across the CONterminous United States (CONUS). Results show that nationally averaged corn and soybean yields exhibit large interannual variability of 21% and 22%, of which 35% and 32% can be significantly explained by T and P, respectively. By including R, an additional of 5% in variability can be explained for both crops. Using partial regression analyses, we find that studies that ignore the covariability among T, P, and R can substantially overestimate the sensitivity of crop yields to a single climate factor at the county scale. Further analyses indicate large spatial variation in the relative contributions of different climate variables to the variability of historical corn and soybean yields. The structure of the dominant climate factors did not change substantially over 1983–2012, confirming the robustness of the findings, which have important implications for crop yield prediction and crop model validations.
机译:温度(T),降水(P)和辐射(R)的协变性是理解气候对农作物产量影响的重要方面。在这里,我们分析了县级玉米和大豆的产量,并观察了1983-2012年期间的气候,以了解生长季节(6月,7月和8月)意味着T,P和R共同和孤立地影响着整个玉米种植区。州(CONUS)。结果表明,全国平均水平的玉米和大豆单产年际差异较大,分别为21%和22%,其中35%和32%分别由T和P显着解释。通过包含R,可以解释两种作物的变异性额外增加5%。使用偏回归分析,我们发现忽略T,P和R之间协变量的研究可以大大高估农作物产量对县域范围内单一气候因素的敏感性。进一步的分析表明,不同气候变量对历史玉米和大豆产量变化的相对贡献的空间变化很大。在1983-2012年期间,主要气候因子的结构没有实质性变化,这证实了研究结果的稳健性,这对作物产量预测和作物模型验证具有重要意义。

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