论文标题
优化多种环境作物品种测试中的试验分配给子区域
Optimizing the allocation of trials to sub-regions in multi-environment crop variety testing
论文作者
论文摘要
在多环境试验中对新的农作物品种进行了广泛的测试，以便为农民提供扎实的经验基础。当环境的目标群体较大且异质性时，分为子区域的划分通常是有利的。在设计此类试验时，问题是如何将试验分配给不同的子区域。我们考虑使用线性混合模型的解决方案。我们提出了一种用于计算最佳设计的分析方法，以最佳的线性无偏见预测基因型效应和成对线性对比度，并通过来自印度全国范围内玉米综合试验的真实数据示例来说明获得的结果。结果表明，除了简单的情况（例如复合对称模型）之外，最佳分配取决于嵌套在子区域内的基因型效应的方差 - 协方差结构。
New crop varieties are extensively tested in multi-environment trials in order to obtain a solid empirical basis for recommendations to farmers. When the target population of environments is large and heterogeneous, a division into sub-regions is often advantageous. When designing such trials, the question arises how to allocate trials to the different subregions. We consider a solution to this problem assuming a linear mixed model. We propose an analytical approach for computation of optimal designs for best linear unbiased prediction of genotype effects and pairwise linear contrasts and illustrate the obtained results by a real data example from Indian nation-wide maize variety trials. It is shown that, except in simple cases such as a compound symmetry model, the optimal allocation depends on the variance-covariance structure for genotypic effects nested within sub-regions.
