Integrating Crop Growth Models and Genomic Prediction to Improve Flowering Time Forecasting Across Novel Environments and Breeding Lines

Abstract

Abstract Genomic selection has accelerated genetic gain in many breeding programs worldwide but genotype-by-environment-by-management (GxExM) hampers further progress for systems where these interactions are important and not well represented in the training data. Process-based crop growth models (CGMs), which encode physiological relationships between plants and their environments, can extrapolate to novel conditions but cannot directly leverage genomic information. Coupling these complementary approaches in the Crop Growth Model–Whole Genome Prediction (CGM-WGP) framework addresses both limitations, yet applications in horticultural crops remain scarce. In this study, we apply CGM-WGP to predict flowering time in broccoli ( Brassica oleracea var. italica ) and common bean ( Phaseolus vulgaris L.), two horticultural species with contrasting physiological responses to temperature and photoperiod. Genotype-specific thermal time requirements and photoperiod parameters were jointly estimated with genome-wide marker effects and predictions were compared against a Reaction Norm Genomic Best Linear Unbiased Prediction (RN-GBLUP) benchmark across four cross-validation scenarios of increasing predictive difficulty. RN-GBLUP achieved the highest accuracy under sparse-testing scenarios where training data covered all target environments, while CGM-WGP outperformed RN-GBLUP when predicting untested environments and untested genotype-environment combinations (broccoli: Pearson r = 0.66, RMSE = 9.4 days; bean: r = 0.86, RMSE = 5.2 days). These results demonstrate that CGM-WGP can be applied to horticultural crops using genome-wide markers alone, but without requiring prior identification of quantitative trait loci. CGM-WGP also provides a modular foundation that can be extended to predict the timing of other developmental transitions and output traits such as biomass and yield.

Plant phenotyping relevance

開花期という植物形質を対象に、CGM-WGPによる予測手法を適用し、RN-GBLUPとの交差検証で精度比較・評価しており、形質推定ワークフローが研究の中心である。

Code and data availability

The supplied blocks describe broccoli and bean flowering-time phenotyping and the CGM-WGP analysis, but contain no public dataset, image, code, or model deposit. The 'Data Availability' section is cut off at the heading with no URL or repository named. Weather data sources (FAWN) and prior bean datasets are cited works

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