Department of Geosciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
PLoS One. 2021 Nov 18;16(11):e0259180. doi: 10.1371/journal.pone.0259180. eCollection 2021.
Crop yields are sensitive to extreme weather events. Improving the understanding of the mechanisms and the drivers of the projection uncertainties can help to improve decisions. Previous studies have provided important insights, but often sample only a small subset of potentially important uncertainties. Here we expand on a previous statistical modeling approach by refining the analyses of two uncertainty sources. Specifically, we assess the effects of uncertainties surrounding crop-yield model parameters and climate forcings on projected crop yield. We focus on maize yield projections in the eastern U.S.in this century. We quantify how considering more uncertainties expands the lower tail of yield projections. We characterized the relative importance of each uncertainty source and show that the uncertainty surrounding yield model parameters is the main driver of yield projection uncertainty.
作物产量对极端天气事件敏感。提高对预测不确定性的机制和驱动因素的理解有助于改善决策。先前的研究提供了重要的见解,但通常只抽样了潜在重要不确定性的一小部分。在这里,我们通过改进对两个不确定性源的分析,扩展了先前的统计建模方法。具体来说,我们评估了围绕作物产量模型参数和气候强迫不确定性对预测作物产量的影响。我们关注本世纪美国东部的玉米产量预测。我们量化了考虑更多不确定性如何扩大产量预测的低端范围。我们描述了每个不确定性源的相对重要性,并表明产量模型参数的不确定性是产量预测不确定性的主要驱动因素。