2023 article
Genomes to Fields 2022 Maize genotype by Environment Prediction Competition
Lima, D. C., Washburn, J. D., Varela, J. I., Chen, Q., Gage, J. L., Romay, M. C., … Leon, N. (2023, July 17). BMC RESEARCH NOTES, Vol. 16.
author keywords: Grain yield; Maize; Root mean squared error
MeSH headings : Phenotype; Zea mays / genetics; Genotype; Genome, Plant / genetics; Edible Grain / genetics
open access via bmcresnotes.biomedcentral.com (publisher PDF)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: August 7, 2023
Abstract Objectives The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data. Data description This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.