Statistical analysis of yield trials by AMMI and GGE
Statistical analysis of yield trials by AMMI and GGE
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DOI：
10.2135/cropsci2005.07-0193
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发表时间：
2006-07-01
期刊：
影响因子：
2.3
通讯作者：
Gauch, Hugh G., Jr.
中科院分区：
文献类型：
--
作者：
Gauch, Hugh G., Jr.
加性主效应与乘性互作（AMMI）模型、基因型主效应与基因型X环境互作（GGE）模型和主成分分析（PCA）是基于奇异值分解（SVD）的统计分析方法，常用于产量试验数据。本文提出了一个系统的比较，使用统计理论和实证调查，同时考虑到目前的做法和最佳做法。使用这些分析的农业研究人员面临两个不可避免的选择。首先是选择一个可视化数据的模型。AMMI无疑是上级的，不是因为统计原因，而是因为农业原因。AMMI将总体变异分为基因型主效应、环境主效应和基因型X环境互作。这三种变异来源为农业研究人员带来了不同的挑战和机遇，因此最好分别处理它们，同时仍然以综合的方式考虑所有三种变异。第二个是选择一个给定的模型家族的成员，以获得预测精度。AMMI、GGE和其他基于SVD的模型族本质上是等效的，但最佳实践需要对每个数据集进行模型诊断，以确定哪个成员预测最准确。做好这两个选择可以让研究人员从他们的数据中提取更多有用的信息，从而提高效率并加速进展。
The Additive Main effects and Multiplicative Interaction (AMMI) model, Genotype main effects and Genotype X Environment interaction (GGE) model, and Principal Components Analysis (PCA) are singular value decomposition (SVD) based statistical analyses often applied to yield-trial data. This paper presents a systematic comparison, using both statistical theory and empirical investigations, while considering both current practices and best practices. Agricultural researchers using these analyses face two inevitable choices. First is the choice of a model for visualizing data. AMMI is decidedly superior, not for statistical reasons, but rather for agricultural reasons. AMMI partitions the overall variation into genotype main effects, environment main effects, and genotype X environment interactions. These three sources of variation present agricultural researchers with different challenges and opportunities, so it is best to handle them separately, while still considering all three in an integrated manner. Second is the choice of a member of a given model family for gaining predictive accuracy. AMMI, GGE, and other SVD-based model families are essentially equivalent, but best practices require model diagnosis for each individual dataset to determine which member is most predictively accurate. Making these two choices well allows researchers to extract more usable information from their data, thereby increasing efficiency and accelerating progress.