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The Study on the Analysis of Genotype Environment Interaction Using Different Statistical Models
|關鍵字:||genotype environment interaction;基因型與環境交感效應;A.N.O.V.A.;regression model;principal components analysis model;AMMI model;partial least square model;biplot;變異數分析;回歸模型;主成分分析模型;AMMI模型;淨最小平方法模型;雙軸圖||出版社:||農藝學系||摘要:||
回歸分析模型中，使用Perkins 和 Jinks 回歸模型與Eberhart 和 Russell回歸模型分析交感效應，結果兩種模型得到的結果相同。回歸模型所計算的變異佔處理變異的88.61 ﹪，但是就交感效應方面來說，只佔了交感效應15.66 ﹪的變異。在包括六個區域的試驗中，發現品系5為最穩定的品系。
Cultivars grown in multi-environments react differently to environmental changes. This differential response of cultivars from one environment to another is called genotype ×environment interaction. There are additive main effect and nonadditive genotype ×environment interaction in the regional trial data. Applying an efficient statistical method to analyze genotype ×environment interaction can explore how the cultivars were affected by the environments. This study used the yield data provided by T.A.R.I. which includes five lines grown in six environments in 1998. It was shown that not only the ability of ANOVA model, regression model, PCA model, and AMMI model for interpreting if the interaction effect, but also shown how covariables affect cultivars and environments by using PCA model, AMMI model, and PLS model.
The results showed that line effect, environment effect, and interaction effect were significant in the ANOVA model. The line effect and environment effect explained 41.19 and 45.31% of the treatment variation, respectively. It was also shown that there was highly variation within lines and within environments. The interaction sum of squares explains 13.5% of treatment variation by using 69% degrees of freedom. The ANOVA model only partitions line effect, environment effect, and interaction effect. It could not intepret how lins and environments were affected by interaction.
The Perkins and Jinks model and Eberhart and Russell model were used to analyze the genotype ×environment interaction in regression models. It was appeared that two models had the same result, and both of them explained about 88.61% of the treatment variation. However, only 15.66% of the interaction variation were explained. It was found that line 5 was the most stable among all tested lines in the regional trial.
The PCA model interpreted 97.65% of treatment variation. It was found that line 3 was the most stable and line 5 was the sub-stable. The explicit relationship between lines and environments and the relationship of covariables between lines and between environ- ments were not found because the first two components only contain the main effect in the PCA model.
The AMMI model intepreted 99.51% of treatment variation, and accounted for 95.4% of interaction variation. It was also found that line 3 was the most stable and line 5 was the sub-stable. The relationship between lines and environments and identified major en- vironmental (or line's) covariables that caused interactions between lines and between environments were explained clearly in the AMMI model.
There were excellent results in interpreting relationship between lines and environ- ments and the relationship of covariables between lines and between environments in PLS model, especially in line covariables. Although the relationship between environ- mental covariables and interaction was not clearly, however, it was easily found that the relationship between line covariables and yield using the biplot.
The ability in interpreting variation of AMMI model is the best. The PLS model clearly describes the relationship between lines and environments and the relationship of covariables between lines and between environments. However, the AMMI model is a quick and efficient statistical model without considering covariables.
|Appears in Collections:||農藝學系|
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