County-scale crop yield prediction by integrating crop simulation with machine learning models.

ABSTRACT: Crop yield prediction is of great importance for decision making, yet it remains an ongoing scientific challenge. Interactions among different genetic, environmental, and management factors and uncertainty in input values are making crop yield prediction complex. Building upon a previous work in which we coupled crop modeling with machine learning (ML) models to predict maize yields for three US Corn Belt states, here, we expand the concept to the entire US Corn Belt (12 states). More specifically, we built five new ML models and their ensemble models, considering the scenarios with and without crop modeling variables. Additional input values in our models are soil, weather, management, and historical yield data. A unique aspect of our work is the spatial analysis to investigate causes for

SUBMITTER: Sajid SS 

PROVIDER: S-EPMC9742473 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature