Summary Through enviromics, precision breeding leverages innovative geotechnologies to customize crop varieties to specific environments, potentially improving both crop yield and genetic selection gains. In Brazil's four southernmost states, data from 183 distinct geographic field trials (also accounting for 2017–2021) covered information on 164 genotypes: 79 phenotyped maize hybrid genotypes for grain yield and their 85 nonphenotyped parents. Additionally, 1342 envirotypic covariates from weather, soil, sensor‐based, and satellite sources were collected to engineer 10 K synthetic enviromic markers via machine learning. Soil, radiation light, and surface temperature variations remarkably affect differential genotype yield, hinting at ecophysiological adjustments including evapotranspiration and photosynthesis. The enviromic ensemble‐based random regression model showcases superior predictive performance and efficiency compared to the baseline and kernel models, matching the best genotypes to specific geographic coordinates. Clustering analysis has identified regions that minimize genotype‐environment (G × E) interactions. These findings underscore the potential of enviromics in crafting specific parental combinations to breed new, higher‐yielding hybrid crops. The adequate use of envirotypic information can enhance the precision and efficiency of maize breeding by providing important inputs about the environmental factors that affect the average crop performance. Generating enviromic markers associated with grain yield can enable a better selection of hybrids for specific environments.

中文翻译：

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通过环境标记工程基于 GIS 的玉米杂交种 G × E 建模


摘要通过环境学，精准育种利用创新的岩土技术来定制适合特定环境的作物品种，从而有可能提高作物产量和遗传选择收益。在巴西最南端的四个州，来自 183 个不同地理田间试验的数据（也涵盖了 2017-2021 年）涵盖了 164 个基因型的信息：79 个表型玉米杂交基因型的谷物产量及其 85 个非表型亲本。此外，还收集了来自天气、土壤、基于传感器和卫星来源的 1342 个环境型协变量，以通过机器学习设计 10 K 合成环境标记。土壤、辐射光和地表温度变化显着影响差异基因型产量，暗示包括蒸散和光合作用在内的生态生理调节。与基线和内核模型相比，基于环境集成的随机回归模型展示了卓越的预测性能和效率，将最佳基因型与特定地理坐标相匹配。聚类分析已确定最小化基因型与环境 (G × E) 相互作用的区域。这些发现强调了环境组学在培育特定亲本组合以培育新的高产杂交作物方面的潜力。充分利用环境型信息可以通过提供有关影响作物平均表现的环境因素的重要输入来提高玉米育种的精度和效率。生成与谷物产量相关的环境标记可以更好地选择适合特定环境的杂交品种。