Agronomy and Horticulture Department

 

Authors

Naser AlKhalifah, Iowa State University
Darwin A. Campbell, Iowa State University
Celeste M. Falcon, University of Wisconsin
Jack M. Gardiner, Iowa State University
Nathan D. Miller, University of Wisconsin
Maria Cinta Romay, Cornell University
Ramona Walls, University of Arizona
Renee Walton, Iowa State University
Cheng-Ting Yeh, Iowa State University
Martin Bohn, University of Illinois at Urbana-Champaign
Jessica Bubert, University of Illinois at Urbana-Champaign
Edward S. Buckler, Cornell University
Ignacio Ciampitti, Kansas State University
Sherry Flint-Garcia, USDA, Agricultural Research Service
Michael A. Gore, Cornell University
Christopher Graham, South Dakota State University
Candice Hirsch, University of Minnesota
James B. Holland, USDA, Agricultural Research Service
David Hooker, University of Guelph
Shawn Kaeppler, University of WisconsinFollow
Joseph Knoll, USDA, Agricultural Research Service
Nick Lauter, Iowa State University
Elizabeth C. Lee, University of Guelph
Aaron Lorenz, University of Nebraska - LincolnFollow
Jonathan P. Lynch, Pennsylvania State University
Stephen P. Moose, University of Illinois at Urbana-Champaign
Seth C. Murray, Texas A&M University
Rebecca Nelson, Cornell University
Torbert Rocheford, Purdue University
Oscar Rodriguez, University of Nebraska - LincolnFollow
James C. Schnable, University of Nebraska-LincolnFollow
Brian Scully, USDA, Agricultural Research Service
Margaret Smith, Cornell University
Nathan Springer, University of Minnesota
Peter Thomison, The Ohio State University
Mitchell Tuinstra, Purdue University
Randall J. Wisser, University of Delaware
Wenwei Xu, Texas A&M AgriLife Research
David Ertl, Iowa Corn Growers AssociationFollow
Patrick S. Schnable, Iowa State UniversityFollow
Natalia De Leon, University of WisconsinFollow
Edgar P. Spalding, University of WisconsinFollow
Jode Edwards, USDA, Agricultural Research ServiceFollow
Carolyn J. Lawrence-Dill, Iowa State UniversityFollow

Date of this Version

2018

Citation

BMC Res Notes (2018) 11:452

Comments

© The Author(s) 2018.

Open access

https://doi.org/10.1186/s13104-018-3508-1

Abstract

Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available.

Data description: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.

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