ArticleFrontiers in plant science2020
Use of Contemporary Groups in the Construction of Multi-Environment Trial Datasets for Selection in Plant Breeding Programs.
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 27 citations in OpenAlex.
- From family trials to genomic mate allocation: statistical and genomic strategies to accelerate sugarcane genetic improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Randomization across breeding cohorts improves the accuracy of conventional and genomic selection.The plant genome · 2026Article
- Optimizing resource allocation inFrontiers in plant science · 2026Article
- Multi-environment trials data analysis: linear mixed model-based approaches using spatial and factor analytic models.Frontiers in research metrics and analytics · 2025Article
- A framework for simulating genotype-by-environment interaction using multiplicative models.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Genomic selection for genotype performance and stability using information on multiple traits and multiple environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Breeding crops for drought-affected environments and improved climate resilience.The Plant cell · 2023Review
- Extending the breeder's equation to take aim at the target population of environments.Frontiers in plant science · 2023Article
- Plant Variety Selection Using Interaction Classes Derived From Factor Analytic Linear Mixed Models: Models With Independent Variety Effects.Frontiers in plant science · 2021Article
- Information Based Diagnostic for Genetic Variance Parameter Estimation in Multi-Environment Trials.Frontiers in plant science · 2021Article
- Evidence for the Application of Emerging Technologies to Accelerate Crop Improvement - A Collaborative Pipeline to Introgress Herbicide Tolerance Into Chickpea.Frontiers in plant science · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant breeding programs use multi-environment trial (MET) data to select superior lines, with the ultimate aim of increasing genetic gain. Selection accuracy can be improved with the use of advanced statistical analysis methods that employ informative models for genotype by environment interaction, include information on genetic relatedness and appropriately accommodate within-trial error variation. The gains will only be achieved, however, if the methods are applied to suitable MET datasets. In this paper we present an approach for constructing MET datasets that optimizes the information available for selection decisions. This is based on two new concepts that characterize the structure of a breeding program. The first is that of "contemporary groups," which are defined to be groups of lines that enter the initial testing stage of the breeding program in the same year. The second is that of "data bands," which are sequences of trials that correspond to the progression through stages of testing from year to year. MET datasets are then formed by combining bands of data in such a way as to trace the selection histories of lines within contemporary groups. Given a specified dataset, we use the A-optimality criterion from the model-based design literature to quantify the information for any given selection decision. We demonstrate the methods using two motivating examples from a durum and chickpea breeding program. Datasets constructed using contemporary groups and data bands are shown to be superior to other forms, in particular those that relate to a single year alone.
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