Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend
Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend
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DOI：
10.1111/j.0006-341x.2001.01138.x
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发表时间：
2001-12-01
期刊：
影响因子：
1.9
通讯作者：
Thompson, R
中科院分区：
文献类型：
--
作者：
Smith, A;Cullis, B;Thompson, R
为商业用途推荐新的植物品种需要可靠和准确地预测每个品种在一系列目标环境中的平均产量，并了解与环境的重要相互作用。这些信息是从一系列植物品种试验中获得的，也称为多环境试验（MET）。Cullis、Cogel、Verbyla和Thompson（1998年）提出了一种分析MET数据的空间混合模型方法。在本文中，我们扩展了分析，包括在每个环境中的各种影响的乘法模型。乘法模型对应于因子分析的多变量技术中使用的模型。它允许一个单独的遗传方差为每个环境，并提供了一个简约和可解释的模型，环境之间的遗传协方差。该模型可以被视为AMMI（加性主效应和乘性相互作用）的随机效应模拟。我们说明的方法，使用一个大的MET数据集从南澳大利亚大麦育种计划。
The recommendation of new plant varieties for commercial use requires reliable and accurate predictions of the average yield of each variety across a range of target environments and knowledge of important interactions with the environment. This information is obtained from series of plant variety trials, also known as multi-environment trials (MET). Cullis, Cogel, Verbyla, and Thompson (1998) presented a spatial mixed model approach for the analysis of MET data. In this paper we extend the analysis to include multiplicative models for the variety effects in each environment. The multiplicative model corresponds to that used in the multivariate technique of factor analysis. It allows a separate genetic variance for each environment and provides a parsimonious and interpretable model for the genetic covariances between environments. The model can be regarded as a random effects analogue of AMMI (additive main effects and multiplicative interactions). We illustrate the method using a large set of MET data from a South Australian barley breeding program.