Accurate estimation of crop yield predictions is crucial for food security under climate change. This paper proposes a data-driven crop model combining process-based and data-driven modeling advantages. The model tracks daily biomass accumulation to estimate final grain yield. Using US Corn Belt data (1981-2020), the model achieved a 7.16% relative root-mean-square-error of average yield in 2020 and demonstrated the ability to detect genotype-environment interactions, potentially aiding farmers in optimizing seed selection.
Publisher
Communications Biology
Published On
Apr 21, 2023
Authors
Yanbin Chang, Jeremy Latham, Mark Licht, Lizhi Wang
Tags
crop yield prediction
data-driven modeling
biomass accumulation
food security
genotype-environment interactions
US Corn Belt
seed optimization
Related Publications
Explore these studies to deepen your understanding of the subject.