ArticleThe plant genome2025
Enhancing prediction accuracy of key biomass partitioning traits in wheat using multi-kernel genomic prediction models integrating secondary traits and environmental covariates.
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 5 papers.
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Who cites it
5 citing papers in PubMed.
- Improving grain yield prediction in Southern US oat germplasm using genomics information and environmental covariates.The plant genome · 2026Article
- Deciphering the genetic basis of yield components in wheat by integrating hyperspectral-based phenomes.Plant phenomics (Washington, D.C.) · 2026Article
- LSTM-attention-guided graph neural networks for integrated genotype-Environment modeling in maize yield prediction.PLoS computational biology · 2026Article
- Optimizing biomass partitioning in wheat using UAV-based hyperspectral phenomic and genomic prediction: kernel-based and machine learning approaches.Frontiers in plant science · 2026Article
- 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
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Authors and funding
9 authors.
Funding
Abstract
Achieving significant genetic gains in grain yield (GY) in wheat (Triticum aestivum L.) requires optimization of the key biomass partitioning traits such as spike partitioning index (SPI) and fruiting efficiency (FE). However, traditional manual phenotyping of these traits is labor-intensive and destructive, making it unsuitable for evaluating large germplasm panels. This study developed genomic prediction models to estimate these traits using diverse statistical methods while enhancing predictive ability (PA) by integrating environmental covariates (ECs) and secondary traits. A panel of 341 soft wheat elite lines was evaluated for biomass partitioning and yield-related traits from 2022 to 2024 in Citra, FL. Genomic best linear unbiased predictor (GBLUP) and Bayesian methods performed similarly or better than machine learning models for SPI, harvest index (HI), and GY. On the other hand, random forest models performed better in predicting effective tillers m
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Registered trials
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