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With climate change threatening agricultural productivity and global fooddemand increasing, it is important to better understand which farm managementpractices will maximize crop yields in various climatic conditions. To assessthe effectiveness of agricultural practices, researchers often turn torandomized field experiments, which are reliable for identifying causal effectsbut are often limited in scope and therefore lack external validity. Recently,researchers have also leveraged large observational datasets from satellitesand other sources, which can lead to conclusions biased by confoundingvariables or systematic measurement errors. Because experimental andobservational datasets have complementary strengths, in this paper we propose amethod that uses a combination of experimental and observational data in thesame analysis. As a case study, we focus on the causal effect of crop rotationon corn (maize) and soy yields in the Midwestern United States. We find that,in terms of root mean squared error, our hybrid method performs 13% better thanusing experimental data alone and 26% better than using the observational dataalone in the task of predicting the effect of rotation on corn yield atheld-out experimental sites. Further, the causal estimates based on our methodsuggest that benefits of crop rotations on corn yield are lower in years andlocations with high temperatures whereas the benefits of crop rotations on soyyield are higher in years and locations with high temperatures. In particular,we estimated that the benefit of rotation on corn yields (and soy yields) was0.84 t/ha (0.23 t/ha) on average for the top quintile of temperatures, 1.02t/ha (0.20 t/ha) on average for the whole dataset, and 1.18 t/ha (0.15 t/ha) onaverage for the bottom quintile of temperatures.

Combining randomized field experiments with observational satellite data to assess the benefits of crop rotations on yields