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Related Experiment Video

Updated: Jul 23, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

Genomes to Fields 2022 Maize genotype by Environment Prediction Competition.

Dayane Cristina Lima¹, Jacob D Washburn², José Ignacio Varela³

¹Department of Agronomy, University of Wisconsin - Madison, Madison, WI, 53706, USA. dclima@wisc.edu.

BMC Research Notes

|July 17, 2023

Summary

This summary is machine-generated.

The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition focused on predicting grain yield. Models were developed using extensive genotypic, phenotypic, and environmental data from field trials.

Keywords:

Grain yield Maize Root mean squared error

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Published on: February 14, 2020

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.