ArticleBMC research notes2018
Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets.
Article in BMC research notes, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- metaGE: Investigating genotype x environment interactions through GWAS meta-analysis.PLoS genetics · 2025Pooled it
- MegaLMM improves genomic predictions in new environments using environmental covariates.Genetics · 2025Article
- Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize.Genetics · 2024Article
- Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments.Frontiers in plant science · 2024Article
- Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America.Nature communications · 2023Article
- New insights into trait introgression with the look-ahead intercrossing strategy.G3 (Bethesda, Md.) · 2023Article
- Identification of QTNs, QTN-by-environment interactions for plant height and ear height in maize multi-environment GWAS.Frontiers in plant science · 2023Article
- learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data.G3 (Bethesda, Md.) · 2022Article
- Predicting phenotypes from genetic, environment, management, and historical data using CNNs.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.G3 (Bethesda, Md.) · 2021Article
- Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks.Frontiers in plant science · 2021Article
- QTL Map of Early- and Late-Stage Perennial Regrowth inFrontiers in plant science · 2021Article
- Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2020Article
- Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets.BMC research notes · 2020Article
- Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project.Frontiers in genetics · 2020Article
- Applying FAIR Principles to Plant Phenotypic Data Management in GnpIS.Plant phenomics (Washington, D.C.) · 2019Article
- Computational aspects underlying genome to phenome analysis in plants.The Plant journal : for cell and molecular biology · 2019Review
- Assessing the Impact of Yield Plasticity on Hybrid Performance in Maize.Physiologia plantarumArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
44 authors.
Funding
Abstract
objectivesCrop 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.