Genomes to Fields 2022 Maize genotype by Environment Prediction Competition

Dayane Cristina Lima, Jacob D. Washburn, José Ignacio Varela, Qiuyue Chen, Joseph L. Gage, Maria Cinta Romay, James Holland, David Ertl, Marco Lopez-Cruz, Fernando M. Aguate, Gustavo de los Campos, Shawn Kaeppler, Timothy Beissinger, Martin Bohn, Edward Buckler, Jode Edwards, Sherry Flint-Garcia, Michael A. Gore, Candice N. Hirsch, Joseph E. KnollJohn McKay, Richard Minyo, Seth C. Murray, Osler A. Ortez, James C. Schnable, Rajandeep S. Sekhon, Maninder P. Singh, Erin E. Sparks, Addie Thompson, Mitchell Tuinstra, Jason Wallace, Teclemariam Weldekidan, Wenwei Xu, Natalia de Leon

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number148
JournalBMC Research Notes
Issue number1
StatePublished - Dec 2023


  • Grain yield
  • Maize
  • Root mean squared error

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology


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