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February 11, 2026BMC Research Notes0 citationsOpen Access

Genomes to fields 2024 maize genotype by environment prediction competition

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QCQiuyue ChenJWJacob D. WashburnDLDayane Cristina Lima

Key Points

  • The central aim is to predict grain yield for maize across different environments using G2F data.
  • Participants developed models using training datasets from 2014 to 2023.
  • The datasets included phenotypic, genotypic, soil, and weather data.
  • Quality control measures ensured data accuracy and consistency.
  • All models were submitted for evaluation in the 2024 GxE Prediction Competition.
  • The use of curated, publicly available data improved prediction accuracy.
  • Grain yield predictions were made for the 2024 test set using trained models.
  • Quality control enhanced the reliability of the datasets for competitors.

Abstract

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. 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.

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/698be001058ab1890a13bbe9https://doi.org/10.1186/s13104-026-07629-5
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