Multi-environment evaluation and genomic prediction of agronomic traits in the southern US rice genepool

Authors: LAPORTE, MARY-FRANCIS - University Of California, Davis; HU, HAIXIAO - University Of California, Davis; SOLOMON, WALTER - Mississippi State University; KNAPP, STEVEN - University Of California, Davis; RUNCIE, DANIEL - University Of California, Davis; Edwards, Jeremy; McClung, Anna; DIEPENBROCK, CHRISTINE - University Of California, Davis

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/5/2026
Publication Date: 3/30/2026
Citation: Laporte, M., Hu, H., Solomon, W., Knapp, S.J., Runcie, D.E., Edwards, J., McClung, A.M., Diepenbrock, C.H. 2026. Multi-environment evaluation and genomic prediction of agronomic traits in the southern US rice genepool. The Plant Genome. https://doi.org/10.1002/tpg2.70222.
DOI: https://doi.org/10.1002/tpg2.70222

Interpretive Summary: Maintaining and improving rice yields requires understanding how genetic factors and growing conditions influence rice growth, productivity, and adaptability. Use of genomic prediction (predicting traits from genomic data) can be used to accelerate the breeding of new rice varieties. The inclusion of data from multiple traits and the use of simulations of weather, soil, and management practices can enhance the accuracy of genomic prediction. In this study, a large collection of rice varieties grown across three southern states was analyzed to identify genetic and environmental effects on key rice traits, including plant height, number of seed-bearing shoots, total seed weight, flowering time, and maturity. Analysis of genetic structure revealed differences and similarities among breeding programs. Results showed that multi-trait genomic prediction improved accuracy compared to traditional single-trait predictions. These findings will help breeders more rapidly and accurately select high-performing rice varieties, potentially increasing yields and profitability. Improved rice varieties contribute to stable production, benefiting farmers, local economies, and consumers.

Technical Abstract: The southern US is responsible for 80% of the country’s production of rice and half of this is exported to other countries. Understanding genotypic and environmental factors impacting the historical performance of rice (Oryza sativa L.) is important for directing research efforts to optimize production of this globally important crop. A set of 452 rice cultivars including globally diverse historical parents and advanced japonica breeding lines from southern US breeding programs were phenotyped in 2008 for 8 agronomic traits in Arkansas, Louisiana, and Mississippi. These were also genotyped using a Single Nucleotide Polymorphism set optimized for genomic prediction/selection. Genotypic and phenotypic data were analyzed via clustering techniques, principal component analysis, and Finlay-Wilkinson regression. Single-trait Genomic Best Linear Unbiased Prediction, multi-trait genomic prediction (via mega-scale linear mixed models; MegaLMM), and crop growth modeling (CERES-Rice in the Decision Support System for Agrotechnology Transfer) were used to predict/simulate traits on a per-plant basis. We found that contemporary germplasm from the four southern state breeding programs were highly interrelated and distinct from progenitor indica and temperate japonica cultivars. Genomic predictive abilities were high and largely consistent across environments for seed number per panicle, tiller number, and plant height. Although predictive abilities were lower for seed weight, that trait was correlated with seed number per panicle (r = 0.919) and predictive ability was higher for both traits in a multi-trait prediction framework. Furthermore, including data from the two major genotypic clusters had no penalty on predictive ability. The data and analyses presented herein could inform future genomic and phenotypic investigations and applied breeding in the southern U.S. rice germplasm pool.