Maize genomes to fields (G2F): 2014-2017 field seasons: Genotype, phenotype, climatic, soil, and inbred ear image datasets

Bridget A. McFarland, Naser Alkhalifah, Martin Bohn, Jessica Bubert, Edward S. Buckler, Ignacio Ciampitti, Jode Edwards, David Ertl, Joseph L. Gage, Celeste M. Falcon, Sherry Flint-Garcia, Michael A. Gore, Christopher Graham, Candice N. Hirsch, James B. Holland, Elizabeth Hood, David Hooker, Diego Jarquin, Shawn M. Kaeppler, Joseph KnollGreg Kruger, Nick Lauter, Elizabeth C. Lee, Dayane C. Lima, Aaron Lorenz, Jonathan P. Lynch, John McKay, Nathan D. Miller, Stephen P. Moose, Seth C. Murray, Rebecca Nelson, Christina Poudyal, Torbert Rocheford, Oscar Rodriguez, Maria Cinta Romay, James C. Schnable, Patrick S. Schnable, Brian Scully, Rajandeep Sekhon, Kevin Silverstein, Maninder Singh, Margaret Smith, Edgar P. Spalding, Nathan Springer, Kurt Thelen, Peter Thomison, Mitchell Tuinstra, Jason Wallace, Ramona Walls, David Wills, Randall J. Wisser, Wenwei Xu, Cheng Ting Yeh, Natalia De Leon

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


Objectives: Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014-2017. Data description: Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

Original languageEnglish (US)
Article number71
JournalBMC Research Notes
Issue number1
StatePublished - Feb 12 2020

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 Arkansas Corn and Grain Sorghum Board; the Clemson University, the Colorado Corn Administrative Committee; the Georgia Agricultural Commodity Commission for Corn; the Corn Marketing Program of Michigan; the Illinois Corn Marketing Board; the Iowa Corn Promotion Board; the Iowa State University Plant Sci‑ ences Institute; the Kansas Corn Commission; the Minnesota Corn Research and Promotion Council; National Corn Growers Association; Nebraska Corn Board; the Ohio Corn Marketing Program; the Ontario Ministry of Agriculture, Food, and Rural Affairs; the Texas Corn Producers Board and the Wisconsin Corn Promotion Board. We also acknowledge funding from the National Science Foundation under Grant Numbers #DBI‑0735191 and #DBI‑1265383 to support CyVerse (, #IOS‑1339362 to support phenotyping by JC and SPM, and USDA‑NIFA 2011‑67003‑30342 to RJW, SFG, JH, NL, SM, WX, and NDL. The funders had no role in the design and conduct of the study, data collection, and writing of the manuscript.

Publisher Copyright:
© 2020 The Author(s).


  • Environment
  • Field metadata
  • G × E
  • GBS
  • Genome
  • Genotype
  • Hybrid
  • Inbred
  • Maize
  • Phenotype


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