Genomes to fields 2024 maize genotype by environment prediction competition

Qiuyue Chen; Jacob D. Washburn; Dayane Cristina Lima; Maria Cinta Romay; Joseph L. Gage; James B. Holland; Alencar Xavier; Seth C. Murray; David Ertl; Marco Lopez-Cruz; Gustavo de los Campos; Fernando M. Aguate; Timothy M. Beissinger; Martin O. Bohn; Edward S. Buckler; Jode Edwards; Sherry A. Flint-Garcia; Michael A. Gore; Candice N. Hirsch; Shawn M. Kaeppler; Aida Z. Kebede; Joseph E. Knoll; John K. McKay; Richard Minyo; Osler A. Ortez; Jonathan W. Reneau; James C. Schnable; Rajandeep S. Sekhon; Maninder P. Singh; Erin E. Sparks; Addie M. Thompson; Mitchell R. Tuinstra; Jason Wallace; Wenwei Xu; Natalia de Leon

doi:10.1186/s13104-026-07629-5

Genomes to fields 2024 maize genotype by environment prediction competition

Qiuyue Chen
, Jacob D. Washburn
, Dayane Cristina Lima
, Maria Cinta Romay
, Joseph L. Gage
, James B. Holland
, Alencar Xavier
, Seth C. Murray
, David Ertl
, Marco Lopez-Cruz
, Gustavo de los Campos
, Fernando M. Aguate
, Timothy M. Beissinger
, Martin O. Bohn
, Edward S. Buckler
, Jode Edwards
, Sherry A. Flint-Garcia
, Michael A. Gore
, Candice N. Hirsch
, Shawn M. Kaeppler

Aida Z. Kebede, Joseph E. Knoll, John K. McKay, Richard Minyo, Osler A. Ortez, Jonathan W. Reneau, James C. Schnable, Rajandeep S. Sekhon, Maninder P. Singh, Erin E. Sparks, Addie M. Thompson, Mitchell R. Tuinstra, Jason Wallace, Wenwei Xu, Natalia de Leon

Research output: Contribution to journal › Comment/debate › peer-review

3 Scopus citations

Abstract

Objectives: The genomes to fields (G2F) 2024 Maize Genotype by Environment (GxE) Prediction Competition challenged participants to develop and submit their best performing models to predict grain yield for the 2024 maize GxE project field trials, using G2F data collected from 2014 to 2023 and other publicly available data. Data description: The G2F Maize GxE Project is a collaborative effort, with all generated data made publicly available. The resource presented here includes the training and test datasets used for the G2F 2024 Maize GxE Prediction Competition. Specifically, data collected from 2014 to 2023 served as the training set to predict grain yield in the 2024 test set. The dataset comprises phenotypic, genotypic, soil, weather, and environmental covariate data, along with metadata describing environments (year-location combinations). It has been curated and lightly filtered for quality control and to ensure consistent naming across years. Competitors also had access to readme files that describe the structure and content of the datasets.

Original language	English (US)
Article number	113
Journal	BMC Research Notes
Volume	19
Issue number	1
DOIs	https://doi.org/10.1186/s13104-026-07629-5
State	Published - Dec 2026
Externally published	Yes

Bibliographical note

Publisher Copyright:
© The Author(s) 2026.

Keywords

Grain yield
GxE
Maize (Zea mays ssp. mays)
Prediction

PubMed: MeSH publication types

Journal Article

Publisher link

10.1186/s13104-026-07629-5

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Chen, Q., Washburn, J. D., Lima, D. C., Romay, M. C., Gage, J. L., Holland, J. B., Xavier, A., Murray, S. C., Ertl, D., Lopez-Cruz, M., de los Campos, G., Aguate, F. M., Beissinger, T. M., Bohn, M. O., Buckler, E. S., Edwards, J., Flint-Garcia, S. A., Gore, M. A., Hirsch, C. N., ... de Leon, N. (2026). Genomes to fields 2024 maize genotype by environment prediction competition. BMC Research Notes, 19(1), Article 113. https://doi.org/10.1186/s13104-026-07629-5

Chen, Q, Washburn, JD, Lima, DC, Romay, MC, Gage, JL, Holland, JB, Xavier, A, Murray, SC, Ertl, D, Lopez-Cruz, M, de los Campos, G, Aguate, FM, Beissinger, TM, Bohn, MO, Buckler, ES, Edwards, J, Flint-Garcia, SA, Gore, MA, Hirsch, CN, Kaeppler, SM, Kebede, AZ, Knoll, JE, McKay, JK, Minyo, R, Ortez, OA, Reneau, JW, Schnable, JC, Sekhon, RS, Singh, MP, Sparks, EE, Thompson, AM, Tuinstra, MR, Wallace, J, Xu, W & de Leon, N 2026, 'Genomes to fields 2024 maize genotype by environment prediction competition', BMC Research Notes, vol. 19, no. 1, 113. https://doi.org/10.1186/s13104-026-07629-5

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title = "Genomes to fields 2024 maize genotype by environment prediction competition",

abstract = "Objectives: The genomes to fields (G2F) 2024 Maize Genotype by Environment (GxE) Prediction Competition challenged participants to develop and submit their best performing models to predict grain yield for the 2024 maize GxE project field trials, using G2F data collected from 2014 to 2023 and other publicly available data. Data description: The G2F Maize GxE Project is a collaborative effort, with all generated data made publicly available. The resource presented here includes the training and test datasets used for the G2F 2024 Maize GxE Prediction Competition. Specifically, data collected from 2014 to 2023 served as the training set to predict grain yield in the 2024 test set. The dataset comprises phenotypic, genotypic, soil, weather, and environmental covariate data, along with metadata describing environments (year-location combinations). It has been curated and lightly filtered for quality control and to ensure consistent naming across years. Competitors also had access to readme files that describe the structure and content of the datasets.",

keywords = "Grain yield, GxE, Maize (Zea mays ssp. mays), Prediction",

author = "Qiuyue Chen and Washburn, \{Jacob D.\} and Lima, \{Dayane Cristina\} and Romay, \{Maria Cinta\} and Gage, \{Joseph L.\} and Holland, \{James B.\} and Alencar Xavier and Murray, \{Seth C.\} and David Ertl and Marco Lopez-Cruz and \{de los Campos\}, Gustavo and Aguate, \{Fernando M.\} and Beissinger, \{Timothy M.\} and Bohn, \{Martin O.\} and Buckler, \{Edward S.\} and Jode Edwards and Flint-Garcia, \{Sherry A.\} and Gore, \{Michael A.\} and Hirsch, \{Candice N.\} and Kaeppler, \{Shawn M.\} and Kebede, \{Aida Z.\} and Knoll, \{Joseph E.\} and McKay, \{John K.\} and Richard Minyo and Ortez, \{Osler A.\} and Reneau, \{Jonathan W.\} and Schnable, \{James C.\} and Sekhon, \{Rajandeep S.\} and Singh, \{Maninder P.\} and Sparks, \{Erin E.\} and Thompson, \{Addie M.\} and Tuinstra, \{Mitchell R.\} and Jason Wallace and Wenwei Xu and \{de Leon\}, Natalia",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2026.",

year = "2026",

month = dec,

doi = "10.1186/s13104-026-07629-5",

language = "English (US)",

volume = "19",

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issn = "1756-0500",

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TY - JOUR

T1 - Genomes to fields 2024 maize genotype by environment prediction competition

AU - Chen, Qiuyue

AU - Washburn, Jacob D.

AU - Lima, Dayane Cristina

AU - Romay, Maria Cinta

AU - Gage, Joseph L.

AU - Holland, James B.

AU - Xavier, Alencar

AU - Murray, Seth C.

AU - Ertl, David

AU - Lopez-Cruz, Marco

AU - de los Campos, Gustavo

AU - Aguate, Fernando M.

AU - Beissinger, Timothy M.

AU - Bohn, Martin O.

AU - Buckler, Edward S.

AU - Edwards, Jode

AU - Flint-Garcia, Sherry A.

AU - Gore, Michael A.

AU - Hirsch, Candice N.

AU - Kaeppler, Shawn M.

AU - Kebede, Aida Z.

AU - Knoll, Joseph E.

AU - McKay, John K.

AU - Minyo, Richard

AU - Ortez, Osler A.

AU - Reneau, Jonathan W.

AU - Schnable, James C.

AU - Sekhon, Rajandeep S.

AU - Singh, Maninder P.

AU - Sparks, Erin E.

AU - Thompson, Addie M.

AU - Tuinstra, Mitchell R.

AU - Wallace, Jason

AU - Xu, Wenwei

AU - de Leon, Natalia

PY - 2026/12

Y1 - 2026/12

N2 - Objectives: The genomes to fields (G2F) 2024 Maize Genotype by Environment (GxE) Prediction Competition challenged participants to develop and submit their best performing models to predict grain yield for the 2024 maize GxE project field trials, using G2F data collected from 2014 to 2023 and other publicly available data. Data description: The G2F Maize GxE Project is a collaborative effort, with all generated data made publicly available. The resource presented here includes the training and test datasets used for the G2F 2024 Maize GxE Prediction Competition. Specifically, data collected from 2014 to 2023 served as the training set to predict grain yield in the 2024 test set. The dataset comprises phenotypic, genotypic, soil, weather, and environmental covariate data, along with metadata describing environments (year-location combinations). It has been curated and lightly filtered for quality control and to ensure consistent naming across years. Competitors also had access to readme files that describe the structure and content of the datasets.

AB - Objectives: The genomes to fields (G2F) 2024 Maize Genotype by Environment (GxE) Prediction Competition challenged participants to develop and submit their best performing models to predict grain yield for the 2024 maize GxE project field trials, using G2F data collected from 2014 to 2023 and other publicly available data. Data description: The G2F Maize GxE Project is a collaborative effort, with all generated data made publicly available. The resource presented here includes the training and test datasets used for the G2F 2024 Maize GxE Prediction Competition. Specifically, data collected from 2014 to 2023 served as the training set to predict grain yield in the 2024 test set. The dataset comprises phenotypic, genotypic, soil, weather, and environmental covariate data, along with metadata describing environments (year-location combinations). It has been curated and lightly filtered for quality control and to ensure consistent naming across years. Competitors also had access to readme files that describe the structure and content of the datasets.

KW - Grain yield

KW - GxE

KW - Maize (Zea mays ssp. mays)

KW - Prediction

UR - https://www.scopus.com/pages/publications/105033472417

UR - https://www.scopus.com/pages/publications/105033472417#tab=citedBy

U2 - 10.1186/s13104-026-07629-5

DO - 10.1186/s13104-026-07629-5

M3 - Comment/debate

C2 - 41664105

AN - SCOPUS:105033472417

SN - 1756-0500

VL - 19

JO - BMC Research Notes

JF - BMC Research Notes

IS - 1

M1 - 113

ER -

Genomes to fields 2024 maize genotype by environment prediction competition

Abstract

Bibliographical note

Keywords

PubMed: MeSH publication types

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