Genomes to Fields 2022 Maize genotype by Environment Prediction Competition

Area of Science:

• Agricultural Science
• Genetics
• Data Science

Background:

• The Genomes to Fields (G2F) Initiative generates valuable datasets for maize breeding.
• Understanding genotype by environment interactions is crucial for improving crop yields.

Purpose of the Study:

• To develop predictive models for maize grain yield using the 2022 Maize GxE project data.
• To leverage historical and publicly available data for enhanced prediction accuracy.

Main Methods:

• Utilized a comprehensive dataset including phenotypic, genotypic, soil, weather, and environmental covariates from 2014-2022.
• Data encompassed 45 diverse field trial locations.
• Dataset was curated for quality and uniformity.

Main Results:

• Models were developed to predict grain yield in maize hybrids.
• The competition facilitated the advancement of predictive modeling techniques in agriculture.

Conclusions:

• The G2F 2022 Maize GxE Prediction Competition successfully advanced the development of predictive models for maize grain yield.
• The collaborative nature and data sharing within the G2F Initiative are vital for agricultural research.