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Assessing the Genotype-by-Environment G×E Interaction in Desi Chickpea via the Bayesian Additive Main Effects and Multiplicative Interaction Model.
- Published in:
- Agriculture; Basel, 2024, v. 14, n. 2, p. 215, doi. 10.3390/agriculture14020215
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- Article
Enhancing Genomic Prediction Models for Forecasting Days to Maturity in Soybean Genotypes Using Site-Specific and Cumulative Photoperiod Data.
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- Agriculture; Basel, 2022, v. 12, n. 4, p. N.PAG, doi. 10.3390/agriculture12040545
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- Article
Development of a Genomic Prediction Pipeline for Maintaining Comparable Sample Sizes in Training and Testing Sets across Prediction Schemes Accounting for the Genotype-by-Environment Interaction.
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- Agriculture; Basel, 2021, v. 11, n. 10, p. 932, doi. 10.3390/agriculture11100932
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- Article
Prediction Strategies for Leveraging Information of Associated Traits under Single- and Multi-Trait Approaches in Soybeans.
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- Agriculture; Basel, 2020, v. 10, n. 8, p. 308, doi. 10.3390/agriculture10080308
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- Article
Coupling day length data and genomic prediction tools for predicting time-related traits under complex scenarios.
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- Scientific Reports, 2020, v. 10, n. 1, p. 1, doi. 10.1038/s41598-020-70267-9
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- Article
Sparse testing designs for optimizing predictive ability in sugarcane populations.
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- Frontiers in Plant Science, 2024, p. 1, doi. 10.3389/fpls.2024.1400000
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- Article
Incorporation of Soil-Derived Covariates in Progeny Testing and Line Selection to Enhance Genomic Prediction Accuracy in Soybean Breeding.
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- Frontiers in Genetics, 2022, v. 13, p. 1, doi. 10.3389/fgene.2022.905824
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- Article
Improving predictive ability in sparse testing designs in soybean populations.
- Published in:
- Frontiers in Genetics, 2023, p. 1, doi. 10.3389/fgene.2023.1269255
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- Article