ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2022
Leveraging probability concepts for cultivar recommendation in multi-environment trials.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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
9 citing papers in PubMed, 27 citations in OpenAlex.
- Leveraging probabilistic models to enhance soybean cultivar recommendation in Zimbabwe.BMC plant biology · 2026Article
- Enhanced Bayesian model for multienvironmental selection of winter hybrids maize: assessing grain yield using 'ProbBreed'.Plant methods · 2025Article
- Strategic risk analysis for the selection of stable and high-potential maize genotypes in multi-environment trials.PloS one · 2025Article
- Probabilistic ranking of plant cultivars: stability explains differences from mean rank.Frontiers in plant science · 2025Article
- Factor analytic selection tools and environmental feature-integration enable holistic decision-making in Eucalyptus breeding.Scientific reports · 2024Article
- GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- ProbBreed: a novel tool for calculating the risk of cultivar recommendation in multienvironment trials.G3 (Bethesda, Md.) · 2024Article
- Article
- Genomic prediction for complex traits across multiples harvests in alfalfa (Medicago sativa L.) is enhanced by enviromics.The plant genome · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
key messageWe propose using probability concepts from Bayesian models to leverage a more informed decision-making process toward cultivar recommendation in multi-environment trials. Statistical models that capture the phenotypic plasticity of a genotype across environments are crucial in plant breeding programs to potentially identify parents, generate offspring, and obtain highly productive genotypes for target environments. In this study, our aim is to leverage concepts of Bayesian models and probability methods of stability analysis to untangle genotype-by-environment interaction (GEI). The proposed method employs the posterior distribution obtained with the No-U-Turn sampler algorithm to get Hamiltonian Monte Carlo estimates of adaptation and stability probabilities. We applied the proposed models in two empirical tropical datasets. Our findings provide a basis to enhance our ability to consider the uncertainty of cultivar recommendation for global or specific adaptation. We further demonstrate that probability methods of stability analysis in a Bayesian framework are a powerful tool for unraveling GEI given a defined intensity of selection that results in a more informed decision-making process toward cultivar recommendation in multi-environment trials.
Indexed as
Identifiers
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.