BMC Research Notes (Jul 2023)
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. Knoll,
- John 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
Affiliations
- Dayane Cristina Lima
- Department of Agronomy, University of Wisconsin – Madison
- Jacob D. Washburn
- USDA-ARS Plant Genetics Research Unit
- José Ignacio Varela
- Department of Agronomy, University of Wisconsin – Madison
- Qiuyue Chen
- Department of Crop and Soil Sciences, North Carolina State University
- Joseph L. Gage
- Department of Crop and Soil Sciences, North Carolina State University
- Maria Cinta Romay
- Institute for Genomic Diversity, Cornell University
- James Holland
- USDA-ARS Plant Science Research Unit
- David Ertl
- Iowa Corn Promotion Board
- Marco Lopez-Cruz
- Department of Epidemiology and Biostatistics, Institute for Quantitative Health Science and Engineering, Michigan State University
- Fernando M. Aguate
- Department of Epidemiology and Biostatistics, Institute for Quantitative Health Science and Engineering, Michigan State University
- Gustavo de los Campos
- Department of Plant, Soil and Microbial Sciences, Department of Epidemiology and Biostatistics, Institute for Quantitative Health Science and Engineering, Michigan State University
- Shawn Kaeppler
- Department of Agronomy, University of Wisconsin – Madison
- Timothy Beissinger
- Department of Crop Science, Center for Integrated Breeding Research, University of Göttingen
- Martin Bohn
- University of Illinois at Urbana-Champaign
- Edward Buckler
- USDA-ARS and Cornell University
- Jode Edwards
- USDA ARS CICGRU
- Sherry Flint-Garcia
- USDA-ARS Plant Genetics Research Unit
- Michael A. Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University
- Candice N. Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota
- Joseph E. Knoll
- USDA-ARS Crop Genetics and Breeding Research Unit
- John McKay
- Department of Agricultural Biology, Colorado State University
- Richard Minyo
- Department of Horticulture and Crop Science, College of Food, Agricultural, and Environmental Sciences, Ohio State University
- Seth C. Murray
- Department of Soil and Crop Sciences, Texas A&M University
- Osler A. Ortez
- Department of Horticulture and Crop Science, Ohio State University
- James C. Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln
- Rajandeep S. Sekhon
- Department of Genetics and Biochemistry, Clemson University
- Maninder P. Singh
- Department of Plant, Soil and Microbial Sciences, Michigan State University
- Erin E. Sparks
- Department of Plant and Soil Sciences, University of Delaware
- Addie Thompson
- Department of Plant, Soil and Microbial Sciences, Michigan State University
- Mitchell Tuinstra
- Department of Agronomy, Purdue University
- Jason Wallace
- Department of Crop & Soil Sciences, University of Georgia
- Teclemariam Weldekidan
- Department of Plant and Soil Sciences, University of Delaware
- Wenwei Xu
- Texas A&M University
- Natalia de Leon
- Department of Agronomy, University of Wisconsin – Madison
- DOI
- https://doi.org/10.1186/s13104-023-06421-z
- Journal volume & issue
-
Vol. 16,
no. 1
pp. 1 – 3
Abstract
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.
Keywords
