ArticleBMC research notes2020
Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets.
Article in BMC research notes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review.Frontiers in plant science · 2021Pooled it
- Dissecting genetic variance structure and evaluating genomic prediction models for single-cross hybrids derived from Stiff Stalk and Non-Stiff Stalk maize heterotic groups.G3 (Bethesda, Md.) · 2026Article
- Critical evaluation of the theory and practice of feed-forward neural networks for genomic prediction.G3 (Bethesda, Md.) · 2026Article
- A reaction norm for flowering time plasticity reveals physiological footprints of maize adaptation.G3 (Bethesda, Md.) · 2025Article
- Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Article
- Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.bioRxiv : the preprint server for biology · 2024Article
- TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.Plant communications · 2024Article
- Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize.Genetics · 2024Article
- Current challenges and future of agricultural genomes to phenomes in the USA.Genome biology · 2024Article
- Using the Pearson's correlation coefficient as the sole metric to measure the accuracy of quantitative trait prediction: is it sufficient?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
- Article
- Yield prediction through integration of genetic, environment, and management data through deep learning.G3 (Bethesda, Md.) · 2023Article
- Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions.G3 (Bethesda, Md.) · 2023Article
- Genomic prediction and association mapping of maize grain yield in multi-environment trials based on reaction norm models.Frontiers in genetics · 2023Article
- learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data.G3 (Bethesda, Md.) · 2022Article
- Association mapping across a multitude of traits collected in diverse environments in maize.GigaScience · 2022Article
- Predicting phenotypes from genetic, environment, management, and historical data using CNNs.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Resources for image-based high-throughput phenotyping in crops and data sharing challenges.Plant physiology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
54 authors.
Funding
Abstract
objectivesAdvanced 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.
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What OpenQuestion holds
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.