ArticlePloS one2020
Predicting biomass of rice with intermediate traits: Modeling method combining crop growth models and genomic prediction models.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genome-Enabled Prediction Methods Based on Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2022Pooled it
- Review
- Enhancing alfalfa breeding through genomic prediction with exotic germplasm resources.BMC plant biology · 2026Article
- Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Enhancing winter wheat prediction with genomics, phenomics and environmental data.BMC genomics · 2024Article
- Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits.PloS one · 2024Article
- Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field.Nature communications · 2023Article
- Upcycling rice yield trial data using a weather-driven crop growth model.Communications biology · 2023Article
- Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives.Plant communications · 2022Review
- Genomic Prediction: Progress and Perspectives for Rice Improvement.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Integration of Crop Growth Models and Genomic Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Genomic prediction modeling of soybean biomass using UAV-based remote sensing and longitudinal model parameters.The plant genome · 2021Article
- Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize.Frontiers in plant science · 2021Article
- Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction.Frontiers in plant science · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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Abstract
Genomic prediction (GP) is expected to become a powerful technology for accelerating the genetic improvement of complex crop traits. Several GP models have been proposed to enhance their applications in plant breeding, including environmental effects and genotype-by-environment interactions (G×E). In this study, we proposed a two-step model for plant biomass prediction wherein environmental information and growth-related traits were considered. First, the growth-related traits were predicted by GP. Second, the biomass was predicted from the GP-predicted values and environmental data using machine learning or crop growth modeling. We applied the model to a 2-year-old field trial dataset of recombinant inbred lines of japonica rice and evaluated the prediction accuracy with training and testing data by cross-validation performed over two years. Therefore, the proposed model achieved an equivalent or a higher correlation between the observed and predicted values (0.53 and 0.65 for each year, respectively) than the model in which biomass was directly predicted by GP (0.40 and 0.65 for each year, respectively). This result indicated that including growth-related traits enhanced accuracy of biomass prediction. Our findings are expected to contribute to the spread of the use of GP in crop breeding by enabling more precise prediction of environmental effects on crop traits.
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