Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

Naser Alkhalifah, Darwin A. Campbell, Celeste M. Falcon, Jack M. Gardiner, Nathan D. Miller, Maria Cinta Romay, Ramona Walls, Renee Walton, Cheng Ting Yeh, Martin Bohn, Jessica Bubert, Edward S. Buckler, Ignacio Ciampitti, Sherry Flint-Garcia, Michael A. Gore, Christopher Graham, Candice Hirsch, James B. Holland, David Hooker, Shawn KaepplerJoseph Knoll, Nick Lauter, Elizabeth C. Lee, Aaron Lorenz, Jonathan P. Lynch, Stephen P. Moose, Seth C. Murray, Rebecca Nelson, Torbert Rocheford, Oscar Rodriguez, James C. Schnable, Brian Scully, Margaret Smith, Nathan Springer, Peter Thomison, Mitchell Tuinstra, Randall J. Wisser, Wenwei Xu, David Ertl, Patrick S. Schnable, Natalia De Leon, Edgar P. Spalding, Jode Edwards, Carolyn J. Lawrence-Dill

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish (US)
Article number452
JournalBMC Research Notes
Issue number1
StatePublished - Jul 9 2018

Bibliographical note

Funding Information:
We gratefully acknowledge support from: USDA Hatch program funds to mul‑ tiple PIs in this project; the USDA Agricultural Research Service; the Iowa State University Plant Sciences Institute; the Ontario Ministry of Agriculture, Food, and Rural Affairs; the Illinois Corn Marketing Board; the Iowa Corn Promotion Board; the Kansas Corn Commission; the Minnesota Corn Research and Pro‑ motion Council; the Nebraska Corn Board; the Ohio Corn Marketing Program; the Texas Corn Producers Board; and the National Corn Growers Association. We also acknowledge funding from the National Science Foundation under Grant Numbers #DBI‑0735191 and #DBI‑1265383 to support CyVerse (http:// and USDA‑NIFA 2011‑67003‑30342 to SFG, JH, NL, SM, RW, WX, and NDL.

Publisher Copyright:
© 2018 The Author(s).

Copyright 2018 Elsevier B.V., All rights reserved.


  • Breeding
  • Environment
  • Genome
  • Genotype
  • Hybrid
  • Inbred
  • Maize
  • Phenotype
  • Prediction
  • Soil

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