ArticleThe plant genome2025
Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi-environment genomic prediction models incorporating spectral and thermal information.
Article in The plant genome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Effectiveness of low-density high-throughput marker platform and easy-to-measure traits for genomic prediction of biomass yield in oat (Avena sativa L.).The plant genome · 2026Article
- Beyond static snapshots: predicting dynamic, explainable intermediate phenotypes for climate-resilient crop breeding.Frontiers in plant science · 2026Article
- Integration of physiological and remote sensing traits for improved genomic prediction of wheat yield.The plant genome · 2025Article
- Enhancing prediction accuracy of key biomass partitioning traits in wheat using multi-kernel genomic prediction models integrating secondary traits and environmental covariates.The plant genome · 2025Article
- Enhancing genomic-based forward prediction accuracy in wheat by integrating UAV-derived hyperspectral and environmental data with machine learning under heat-stressed environments.The plant genome · 2025Article
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20 authors.
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Abstract
Enhancing predictive modeling accuracy in wheat (Triticum aestivum) breeding through the integration of high-throughput phenotyping (HTP) data with genomic information is crucial for maximizing genetic gain. In this study, spanning four locations in the southeastern United States over 3 years, models to predict grain yield (GY) were investigated through different cross-validation approaches. The results demonstrate the superiority of multivariate comprehensive models that incorporate both genomic and HTP data, particularly in accurately predicting GY across diverse locations and years. These HTP-incorporating models achieve prediction accuracies ranging from 0.59 to 0.68, compared to 0.40-0.54 for genomic-only models when tested under different prediction scenarios both across years and locations. The comprehensive models exhibit superior generalization to new environments and achieve the highest accuracy when trained on diverse datasets. Predictive accuracy improves as models incorporate data from multiple years, highlighting the importance of considering temporal dynamics in modeling approaches. The study reveals that multivariate prediction outperformed genomic prediction methods in predicting lines across years and locations. The percentage of top 25% lines selected based on multivariate prediction was higher compared to genomic-only models, indicated by higher specificity, which is the proportion of correctly identified top-yielding lines that matched the observed top 25% performance across different sites and years. Additionally, the study addresses the prediction of untested locations based on other locations within the same year and in new years at previously tested locations. Findings show the comprehensive models effectively extrapolate to new environments, highlighting their potential for guiding breeding strategies.
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