STATISTICAL-ANALYSIS OF A YIELD TRIAL
STATISTICAL-ANALYSIS OF A YIELD TRIAL
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DOI：
10.2134/agronj1988.00021962008000030002x
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发表时间：
1988-05-01
期刊：
影响因子：
2.1
通讯作者：
GAUCH, HG
中科院分区：
文献类型：
--
作者：
ZOBEL, RW;WRIGHT, MJ;GAUCH, HG
产量试验常常同时具有显著的主效应和显著的基因型倍。环境（GE）交互作用。对于这种数据结构，传统的统计分析并不总是有效的：通常的方差分析（ANOVA）仅仅具有相加模型，将GE相互作用确定为来源，但不分析它；另一方面，主成分分析（PCA）是一个多重模型，因此不包含可加性基因型或环境主效应的来源；线性回归（LR）分析只能在模式适合特定回归模型的地方有效地分析交互项。拟合不适当的统计模型来产生试验数据的结果是，相互作用可能被宣布为不显著，尽管更适当的分析将发现相互作用的农学重要和统计显著模式。因此，农学家和植物育种家可能没有察觉到重要的相互作用效应。本文将上述三种传统模型与可加性主效应和乘法相互作用（AMMI）模型进行了比较，并对大豆[Glycine max (L.)]进行了分析。稳定。产量试验方差分析无法检测到显著的相互作用成分，主成分分析无法识别和分离显著的基因型和环境主效应，LR仅占相互作用平方和的一小部分。另一方面，AMMI分析揭示了一个高度显著的相互作用成分，具有明确的农艺意义。由于ANOVA， PCA和LR是更完整的AMMI模型的子案例，AMMI为可能具有基因型的产量试验提供了更合适的第一次统计分析。环境交互。然后可以使用AMMI分析来诊断特定子案例是否提供了更合适的分析。除了双向数据结构外，AMMI没有特定的实验设计要求。
Yield trials frequently have both significant main effects and a significant genotype .times. environment (GE) interaction. Traditional statistical analyses are not always effective with this data structure: the usual analysis of variance (ANOVA), having a merely additive model, identifies the GE interaction as a source but does not analyze it; principal components analysis (PCA), on the other hand is a multiplicate model and hence contains no sources for additive genotype or environment main effects; and linear regression (LR) analysis is able to effectively analyze interaction terms only where the pattern fits a specific regression model. The consequence of fitting inappropriate statistical models to yield trial data is that the interaction may be declared nonsignificant, although a more appropriate analysis would find agronomically important and statistically significant patterns of the interaction. Therefore, agronomists and plant breeders may fail to perceive important interaction effects. This paper compares the above three traditional models with the additive main effects and multiplicative interaction (AMMI) Model, in an analysis of a soybean [Glycine max (L.) Merr.] yield trial. ANOVA fails to detect a significant interaction component, PCA fails to identify and separate the significant genotype and environment main effects, and LR accounts for only a small portion of the interaction sum of squares. On the other hand, AMMI analysis reveals a highly significant interaction component that has clear agronomic meaning. Since ANOVA, PCA, and LR are sub-cases of the more complete AMMI model, AMMI offers a more appropriate first statistical analysis of yield trials that may have a genotype .times. environment interaction. AMMI analysis can then be used to diagnose whether or not a specific sub-case provides a more appropriate analysis. AMMI has no specific experimental design requirements, except for a two-way data structure.