Fernandes Filho Claudio Carlos, Lima Barrios Sanzio Carvalho, Santos Mateus Figueiredo, Nunes Jose Airton Rodrigues, do Valle Cacilda Borges, Jank Liana, Rios Esteban Fernando
Sugarcane Technology Center, Piracicaba, SP 13400-970, Brazil.
Embrapa Beef Cattle, Campo Grande, MS 79106-550, Brazil.
G3 (Bethesda). 2025 Mar 18;15(3). doi: 10.1093/g3journal/jkae306.
Genotype selection for dry matter yield (DMY) in perennial forage species is based on repeated measurements over time, referred to as longitudinal data. These datasets capture temporal trends and variability, which are critical for identifying genotypes with desirable performance across seasons. In this study, we have presented a random regression model (RRM) approach for selecting genotypes based on longitudinal DMY data generated from 10 breeding trials and three perennial species, alfalfa (Medicago sativa L.), guineagrass (Megathyrsus maximus), and brachiaria (Urochloa spp.). We also proposed the estimation of adaptability based on the area under the curve and stability based on the curve coefficient of variation. Our results showed that RRM always approximated the (co)variance structure into an autoregressive pattern. Furthermore, RRM can offer useful information about longitudinal data in forage breeding trials, where the breeder can select genotypes based on their seasonality by interpreting reaction norms. Therefore, we recommend using RRM for longitudinal traits in breeding trials for perennial species.
多年生牧草物种干物质产量（DMY）的基因型选择基于随时间的重复测量，即纵向数据。这些数据集捕捉了时间趋势和变异性，这对于识别在不同季节具有理想表现的基因型至关重要。在本研究中，我们提出了一种随机回归模型（RRM）方法，用于根据来自10个育种试验以及苜蓿（Medicago sativa L.）、几内亚草（Megathyrsus maximus）和臂形草（Urochloa spp.）这三种多年生物种产生的纵向DMY数据来选择基因型。我们还提出了基于曲线下面积的适应性估计和基于曲线变异系数的稳定性估计。我们的结果表明，RRM总是将（协）方差结构近似为自回归模式。此外，RRM可以为牧草育种试验中的纵向数据提供有用信息，育种者可以通过解释反应规范根据基因型的季节性来进行选择。因此，我们建议在多年生物种的育种试验中对纵向性状使用RRM。
