Research Article | Open Access

Volume 2025 |Article ID 100007 | https://doi.org/10.1016/j.plaphe.2025.100007

Integrating crop models, single nucleotide polymorphism, and climatic indices to develop genotype-environment interaction model: A case study on rice flowering time

Jinhan Zhang,^1,4 Shaoyuan Zhang,^1,4 Yubin Yang,² Wenliang Yan,¹ Xiaomao Lin,³ Lloyd T. Wilson,² Bing Liu,¹ Leilei Liu,¹ Liujun Xiao,¹ Yan Zhu,¹ Weixing Cao,¹ and Liang Tang ¹

¹National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Ministry of Education, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
²Texas A&M AgriLife Research Center at Beaumont, USA
³Department of Agronomy, Kansas State University, 2108 Throckmorton Plant Sciences Center, Manhattan, KS 66506, USA
⁴These authors contributed equally to this work

Abstract

Genotype-environment interaction (G × E) models have potential in digital breeding and crop phenotype prediction. Using genotype-specific parameters (GSPs) as a bridge, crop growth models can capture G × E and simulate plant growth and development processes. In this study, a dataset containing multi-environmental planting and flowering data for 169 genotypes, each with 700K single nucleotide polymorphism (SNP) markers was used. Three rice growth models (ORYZA, CERES-Rice, and RiceGrow), SNPs, and climatic indices were integrated for flowering time prediction. Significant associations between GSPs and quantitative trait nucleotides (QTNs) were investigated using genome-wide association study (GWAS) methods. Several GSPs were associated with previously reported rice flowering genes, including DTH2, DTH3 and OsCOL15, demonstrating the genetic interpretability of the models. The rice models driven by SNPs-based GSPs showed a decrease in goodness of fit as reflected by increased root mean square errors (RMSE), compared to the traditional model calibration. The predictions of crop model were further modified using the machine learning (ML) methods and climate indicators. The accuracy of the modified predictions were comparable to what was achieved using the traditional calibration approach. In addition, the Multi-model ensemble (MME) was comparable to that of the best individual model. Implications of our findings can potentially facilitate molecular breeding and phenotypic prediction of rice.