Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany.
Laboratoire des Sciences du Climat et de l'Environnement, Institut Pierre-Simon Laplace (IPSL), Gif sur Yvette, France.
Glob Chang Biol. 2017 Nov;23(11):4750-4764. doi: 10.1111/gcb.13738. Epub 2017 Jun 1.
Quantifying the influence of weather on yield variability is decisive for agricultural management under current and future climate anomalies. We extended an existing semiempirical modeling scheme that allows for such quantification. Yield anomalies, measured as interannual differences, were modeled for maize, soybeans, and wheat in the United States and 32 other main producer countries. We used two yield data sets, one derived from reported yields and the other from a global yield data set deduced from remote sensing. We assessed the capacity of the model to forecast yields within the growing season. In the United States, our model can explain at least two-thirds (63%-81%) of observed yield anomalies. Its out-of-sample performance (34%-55%) suggests a robust yield projection capacity when applied to unknown weather. Out-of-sample performance is lower when using remote sensing-derived yield data. The share of weather-driven yield fluctuation varies spatially, and estimated coefficients agree with expectations. Globally, the explained variance in yield anomalies based on the remote sensing data set is similar to the United States (71%-84%). But the out-of-sample performance is lower (15%-42%). The performance discrepancy is likely due to shortcomings of the remote sensing yield data as it diminishes when using reported yield anomalies instead. Our model allows for robust forecasting of yields up to 2 months before harvest for several main producer countries. An additional experiment suggests moderate yield losses under mean warming, assuming no major changes in temperature extremes. We conclude that our model can detect weather influences on yield anomalies and project yields with unknown weather. It requires only monthly input data and has a low computational demand. Its within-season yield forecasting capacity provides a basis for practical applications like local adaptation planning. Our study underlines high-quality yield monitoring and statistics as critical prerequisites to guide adaptation under climate change.
量化天气对产量变化的影响对于当前和未来气候异常下的农业管理至关重要。我们扩展了现有的半经验建模方案，以实现这种量化。我们为美国和其他 32 个主要生产国的玉米、大豆和小麦建模了产量异常，这些异常是通过年际差异来衡量的。我们使用了两个产量数据集，一个来自报告的产量，另一个来自遥感推断的全球产量数据集。我们评估了模型在生长季节内预测产量的能力。在美国，我们的模型可以解释至少三分之二（63%-81%）的观测到的产量异常。其样本外性能（34%-55%）表明，当应用于未知天气时，它具有稳健的产量预测能力。当使用遥感衍生的产量数据时，样本外性能较低。天气驱动的产量波动的份额在空间上有所不同，估计系数与预期相符。在全球范围内，基于遥感数据集的产量异常的解释方差与美国相似（71%-84%）。但是样本外性能较低（15%-42%）。这种性能差异可能是由于遥感产量数据的缺陷所致，因为当使用报告的产量异常时，它会减小。我们的模型允许对几个主要生产国的产量进行长达 2 个月的可靠预测，直到收获前。一项额外的实验表明，假设温度极端没有重大变化，平均变暖会导致适度的产量损失。我们的结论是，我们的模型可以检测天气对产量异常的影响，并预测未知天气下的产量。它只需要每月的输入数据，并且计算需求低。它的季节内产量预测能力为本地适应规划等实际应用提供了基础。我们的研究强调了高质量的产量监测和统计作为指导气候变化下适应的关键前提条件。
