Precrop Payoffs: Causal machine learning reveals large but variable yield benefits of crop rotation in major breadbaskets

TL;DR

This study uses causal machine learning and satellite data to analyze how crop rotation benefits vary with crop sequence and weather, providing insights for sustainable agriculture amid climate change.

Contribution

It introduces a causal machine learning approach to quantify crop rotation effects across different sequences and weather conditions, extending beyond randomized trials.

Findings

Most common precrop choices are highly beneficial.

Switching to diverse rotations often yields small or negative effects.

Precrop benefits increase under wetter and warmer conditions.

Abstract

Building sustainable food systems that are resilient to climate change will require improved agricultural management and policy. One common practice that is well-known to benefit crop yields is crop rotation, yet there remains limited understanding of how the benefits of crop rotation vary for different crop sequences and for different weather conditions. To address these gaps, we leverage crop type maps, satellite data, and causal machine learning to study how precrop effects on subsequent yields vary with cropping sequence choice and weather. Complementing and going beyond what is known from randomized field trials, we find that (i) for those farmers who do rotate, the most common precrop choices tend to be among the most beneficial, (ii) the effects of switching from a simple rotation (which alternates between two crops) to a more diverse rotation were typically small and sometimes even negative, (iii) precrop effects tended to be greater under rainier conditions, (iv) precrop effects were greater under warmer conditions for soybean yields but not for other crops, and (v) legume precrops conferred smaller benefits under warmer conditions. Our results and the methods we use can enable farmers and policy makers to identify which rotations will be most effective at improving crop yields in a changing climate.