Combining randomized field experiments with observational satellite data to assess the benefits of crop rotations on yields
Combining randomized field experiments with observational satellite data to assess the benefits of crop rotations on yields
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DOI：
10.1088/1748-9326/ac6083
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发表时间：
2022-04-01
影响因子：
6.7
通讯作者：
Lobell, David B.
中科院分区：
文献类型：
--
作者：
Kluger, Dan M.;Owen, Art B.;Lobell, David B.
随着气候变化威胁农业生产力和全球粮食需求的增加，更好地了解哪些农场管理措施将在各种气候条件下最大限度地提高作物产量非常重要。为了评估农业实践的有效性，研究人员经常求助于随机田间实验，这些实验对于确定因果效应是可靠的，但通常范围有限，因此缺乏外部有效性。最近，研究人员还利用了来自卫星和其他来源的大型观测数据集，这可能导致因混淆变量或系统测量误差而产生偏见的结论。由于实验和观测数据集具有互补的优势，在本文中，我们提出了一种在同一分析中使用实验和观测数据相结合的方法。作为一个案例研究，我们专注于在美国中西部的玉米（玉米）和大豆产量轮作的因果关系。我们发现，在均方根误差方面，我们的混合方法比单独使用实验数据和26%，比单独使用观测数据在预测轮换对玉米产量的影响的任务中，在举行了实验地点更好地执行13%。此外，基于我们的方法的因果估计表明，轮作对玉米产量的好处是较低的年份和地点与高温，而轮作对大豆产量的好处是较高的年份和地点与高温。特别是，我们估计轮作对玉米产量的好处（和大豆产量）为0.85 t ha（-1）（0.24吨公顷（-1）），平均温度最高的五分之一，1.03吨公顷（-1）（0.21 t ha（-1）），温度最低的五分之一平均为1.19 t ha（-1）（0.16 t ha（-1））。温度和轮作效益之间的这种关联与玉米-大豆轮作对大豆产量的效益在很大程度上是由害虫压力减少驱动的假设是一致的，而玉米-大豆轮作对玉米产量的效益在很大程度上是由氮的可用性驱动的。
With climate change threatening agricultural productivity and global food demand increasing, it is important to better understand which farm management practices will maximize crop yields in various climatic conditions. To assess the effectiveness of agricultural practices, researchers often turn to randomized field experiments, which are reliable for identifying causal effects but are often limited in scope and therefore lack external validity. Recently, researchers have also leveraged large observational datasets from satellites and other sources, which can lead to conclusions biased by confounding variables or systematic measurement errors. Because experimental and observational datasets have complementary strengths, in this paper we propose a method that uses a combination of experimental and observational data in the same analysis. As a case study, we focus on the causal effect of crop rotation on corn (maize) and soybean yields in the Midwestern United States. We find that, in terms of root mean squared error, our hybrid method performs 13% better than using experimental data alone and 26% better than using the observational data alone in the task of predicting the effect of rotation on corn yield at held-out experimental sites. Further, the causal estimates based on our method suggest that benefits of crop rotations on corn yield are lower in years and locations with high temperatures whereas the benefits of crop rotations on soybean yield are higher in years and locations with high temperatures. In particular, we estimated that the benefit of rotation on corn yields (and soybean yields) was 0.85 t ha(-1) (0.24 t ha(-1)) on average for the top quintile of temperatures, 1.03 t ha(-1) (0.21 t ha(-1)) on average for the whole dataset, and 1.19 t ha(-1) (0.16 t ha(-1)) on average for the bottom quintile of temperatures. This association between temperatures and rotation benefits is consistent with the hypothesis that the benefit of the corn-soybean rotation on soybean yield is largely driven by pest pressure reductions while the benefit of the corn-soybean rotation on corn yields is largely driven by nitrogen availability.