INRA, Centre Nouvelle-Aquitaine-Poitiers, UR4 (UR P3F - Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères), CS80006, 86600 Lusignan, France
INRA, Centre Nouvelle-Aquitaine-Poitiers, UR4 (UR P3F - Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères), CS80006, 86600 Lusignan, France.
G3 (Bethesda). 2020 Jan 7;10(1):89-107. doi: 10.1534/g3.119.400809.
In a context of increasing environmental challenges, there is an emerging demand for plant cultivars that are adapted to cultivation in species mixture. It is thus pressing to look for the optimization of selection schemes to grow species mixtures, and especially recurrent selection schemes which are at the core of the improvement of many plant species. We considered the case of two populations from different species to be improved by recurrent selection for their performances in mixture. We set up an analytical model of performances in mixture. We expressed the expected responses of the performances in mixture to one cycle of selection in the case of a Reciprocal Mixture Ability selection scheme and of two parallel selection schemes aiming to improve General Mixture Abilities or performances in pure stands. We numerically compared these selection schemes when half-sib or topcross progeny families of selection candidates are tested in mixture. Selection in pure stands appeared efficient within a limited range of genetic correlations between pure stand performance and mixture model effects. The Reciprocal Mixture Ability selection scheme was expected to be less efficient than parallel selections for General Mixture Ability in some situations. The last option enables to control the ratio of expected responses of species contributions to the mixture performance without bias when using selection indices. When more than two species are be improved for their performances in mixture, the advantage of parallel selections for General Mixture Ability is even more marked, providing that compensation trends between species are not too prevalent.
在环境挑战日益加剧的背景下,人们对适应混种栽培的植物品种的需求日益增长。因此,迫切需要寻找优化的选择方案来种植物种混合物,特别是反复选择方案,这是许多植物物种改良的核心。我们考虑了通过反复选择来提高两个来自不同物种的种群在混合物中的表现的情况。我们建立了一个在混合物中表现的分析模型。我们表达了在 Reciprocal Mixture Ability 选择方案的情况下,一个选择周期对混合物中表现的预期响应,以及两种旨在提高一般混合物能力或纯系表现的平行选择方案。当在混合物中测试选择候选者的半同胞或顶交后代家族时,我们对这些选择方案进行了数值比较。在纯系表现与混合物模型效应之间的遗传相关性有限的范围内,纯系选择表现出效率。在某些情况下,与平行选择相比,Reciprocal Mixture Ability 选择方案预计对一般混合物能力的效率较低。最后一种选择方案允许在使用选择指数时,无偏地控制物种对混合物性能的预期贡献的响应比例。当需要为混合物中的表现而改进两个以上的物种时,对于一般混合物能力的平行选择的优势更加明显,前提是物种之间的补偿趋势不是太普遍。