Genomes to fields 2022 maize genotype by environment prediction competition

Authors: LIMA, DAYANE - University Of Wisconsin; Washburn, Jacob; VARELA, JOSE - University Of Wisconsin; CHEN, QIUYUE - North Carolina State University; GAGE, JOSEPH - North Carolina State University; ROMAY, MARIA - Cornell University; Holland, Jim - Jim; ERTL, DAVID - Iowa Corn Promotion Board; LOPEZ-CRUZ, MARCO - Michigan State University; AGUATE, FERNANDO - Michigan State University; DE LOS CAMPOS, GUSTAVO - Michigan State University; KAEPPLER, SHAWN - University Of Wisconsin; BEISSINGER, TIMOTHY - University Of Gottingen; BOHN, MARTIN - University Of Illinois; Buckler, Edward - Ed; Edwards, Jode; Flint-Garcia, Sherry; GORE, MICHAEL - Cornell University; HIRSCH, CANDICE - University Of Minnesota; Knoll, Joseph - Joe; MCKAY, JOHN - Colorado State University; MINYO, RICHARD - The Ohio State University; MURRAY, SETH - Texas A&M University; ORTEZ, OSLER - The Ohio State University; SCHNABLE, JAMES - University Of Nebraska; SEKHON, RAJANDEEP - Clemson University; SINGH, MANINDER - Michigan State University; SPARKS, ERIN - University Of Delaware; THOMPSON, ADDIE - Michigan State University; TUINSTRA, MITCHELL - Purdue University; WALLACE, JASON - University Of Georgia; WELDEKIDAN, TECLEMARIAM - University Of Delaware; XU, WENWEI - Texas A&M University; DE LEON, NATALIA - University Of Wisconsin

Submitted to: BMC Research Notes
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/28/2023
Publication Date: 7/17/2023
Citation: Lima, D.C., Washburn, J.D., Varela, J.I., Chen, Q., Gage, J.L., Romay, M.C., Holland, J.B., Ertl, D., Lopez-Cruz, M., Aguate, F.M., De Los Campos, G., Kaeppler, S., Beissinger, T., Bohn, M., Buckler IV, E.S., Edwards, J.W., Flint Garcia, S.A., Gore, M.A., Hirsch, C.N., Knoll, J.E., Mckay, J., Minyo, R., Murray, S.C., Ortez, O.A., Schnable, J., Sekhon, R.S., Singh, M.P., Sparks, E.E., Thompson, A., Tuinstra, M., Wallace, J., Weldekidan, T., Xu, W., De Leon, N. 2023. Genomes to fields 2022 maize genotype by environment prediction competition. BMC Research Notes. 16: Article 148. https://doi.org/10.1186/s13104-023-06421-z.
DOI: https://doi.org/10.1186/s13104-023-06421-z

Interpretive Summary: Yield prediction across different cultivars and environmental conditions is critical to agricultural efficiency and improvement, but requires large data sets and sophisticated prediction models. This publications describes and makes publicly available a large dataset which has been purposefully created, curated, and used in a recent world-wide yield prediction competition. Making the data set available to the public will allow it to be used by researcher's, computer scientists, and hobbyists for further development of agricultural prediction solutions.

Technical Abstract: Objectives: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (G x E) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize G x E 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 G x E project within the G2F Initiative. 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 G x E is a collaborative project and all the data generated becomes publicly available. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.