Combining limited multiple environment trials data with crop modeling to identify widely adaptable rice varieties

DATA for the study. Multi-Environment Trials (MET) are conventionally used to evaluate varietal performance prior to national yield trials, but the accuracy of MET is constrained by the number of test environments. A modeling approach was innovated to evaluate varietal performance in a large number of environments using the rice model ORYZA (v3). Modeled yields representing genotype by environment interactions (GEI) were used to classify the target population of environments (TPE) and analyze varietal yield and yield stability. Eight Green Super Rice (GSR) and three check varieties were evaluated across 3796 environments and 14 seasons in Southern Asia. Based on drought stress imposed on rainfed rice, environments were classified into nine TPEs. Relative to the check varieties, all GSR varieties performed well except GSR-IR1-5-S14-S2-Y2, with GSR-IR1-1-Y4-Y1, and GSR-IR1-8-S6-S3-Y2 consistently performing better in all TPEs. Varietal evaluation using ORYZA (v3) significantly corresponded to the evaluation based on actual MET data within specific sites, but not with considerably larger environments. ORYZA-based evaluation demonstrated the advantage of GSR varieties in diverse environments. This study substantiated that the modeling approach could be an effective, reliable, and advanced approach to complement MET in the assessment of varietal performance on spatial and temporal scales whenever quality soil and weather information are accessible. This approach can also be adopted to other crops or other rice producing domains in various locations with available weather information.本研究数据集如下。传统上，多环境试验（Multi-Environment Trials, MET）常被用于国家产量试验开展前的品种表现评估，但多环境试验的准确性受限于测试环境的数量。本研究创新性提出一种建模方法，借助水稻模型ORYZA（v3）对大量环境中的水稻品种表现进行评估。该方法利用表征基因型-环境互作（Genotype by Environment Interactions, GEI）的模拟产量，对目标环境群体（Target Population of Environments, TPE）进行分类，并分析品种产量与产量稳定性。研究团队在南亚地区的3796个环境与14个季节中，对8个绿色超级稻（Green Super Rice, GSR）品种及3个对照品种开展评估。基于雨养水稻所承受的干旱胁迫，研究将环境划分为9个目标环境群体。相较于对照品种，除GSR-IR1-5-S14-S2-Y2外，所有绿色超级稻品种均表现优异；其中GSR-IR1-1-Y4-Y1与GSR-IR1-8-S6-S3-Y2在全部目标环境群体中均持续表现更佳。基于ORYZA（v3）模型的品种评估结果，与特定站点的实际多环境试验数据具有显著相关性，但与覆盖范围更广的环境数据相关性较弱。基于ORYZA模型的评估结果证实了绿色超级稻品种在多样环境中的竞争优势。本研究证实，当可获取高质量土壤与气象信息时，该建模方法可作为一种高效、可靠且先进的手段，补充多环境试验在时空尺度上对品种表现的评估工作。该方法还可推广应用于其他作物，或是拥有可用气象信息的各地区水稻种植领域。