Scholarlink

Objectives <p>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.</p> Data description <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Genomes to fields 2024 maize genotype by environment prediction competition

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月9日

查看全文
  • 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

摘要

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.