{"title":"Comparison of additive main effect–multiplicative interaction model and factor analytic model for genotypes ordination from multi-environment trials","authors":"Cecilia I. Bruno, Mónica Balzarini","doi":"10.1002/agj2.21591","DOIUrl":null,"url":null,"abstract":"<p>An additive main effects and multiplicative interaction (AMMI) model is used to explore the genotype × environment interaction (GEI) in complete multi-environmental trials. This model orders genotypes (G) according to their performance across environments (E) on a vectorial plane generated by the first two axes of a principal component analysis (AMMI-biplot). Alternatively, interaction terms can be regarded as random effects, which can be predicted from linear mixed models using a factor analytic (FA) covariance structure for the GEI terms. Here, an FA-biplot was obtained by plotting the G and E scores derived from the FA mixed model with complete and incomplete data. The aim of this work was to compare AMMI-biplot with FA-biplot for balanced data and then show the impact of the imbalance on the FA-biplot. The G ordinations were assessed in four scenarios generated using datasets of 3 consecutive years obtained from comparative wheat trials conducted under a complete random block design in different environments across the Argentine network of cultivar assessment. For each scenario, G with the lowest performance in the third year were deleted, one by one, from all sites to generate a scenario with missing G. Although we used different statistical procedures to obtain AMMI-biplot and FA-biplot, they showed the same interaction pattern in the case of up to 50% of G dropped from all E in the last year of the multiyear trials. We conclude that the FA-biplot yields a robust G ordination even when with incomplete datasets.</p>","PeriodicalId":7522,"journal":{"name":"Agronomy Journal","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agronomy Journal","FirstCategoryId":"97","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/agj2.21591","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0
Abstract
An additive main effects and multiplicative interaction (AMMI) model is used to explore the genotype × environment interaction (GEI) in complete multi-environmental trials. This model orders genotypes (G) according to their performance across environments (E) on a vectorial plane generated by the first two axes of a principal component analysis (AMMI-biplot). Alternatively, interaction terms can be regarded as random effects, which can be predicted from linear mixed models using a factor analytic (FA) covariance structure for the GEI terms. Here, an FA-biplot was obtained by plotting the G and E scores derived from the FA mixed model with complete and incomplete data. The aim of this work was to compare AMMI-biplot with FA-biplot for balanced data and then show the impact of the imbalance on the FA-biplot. The G ordinations were assessed in four scenarios generated using datasets of 3 consecutive years obtained from comparative wheat trials conducted under a complete random block design in different environments across the Argentine network of cultivar assessment. For each scenario, G with the lowest performance in the third year were deleted, one by one, from all sites to generate a scenario with missing G. Although we used different statistical procedures to obtain AMMI-biplot and FA-biplot, they showed the same interaction pattern in the case of up to 50% of G dropped from all E in the last year of the multiyear trials. We conclude that the FA-biplot yields a robust G ordination even when with incomplete datasets.
在完整的多环境试验中,采用加法主效应和乘法交互作用(AMMI)模型来探索基因型与环境的交互作用(GEI)。该模型根据基因型(G)在主成分分析(AMMI-biplot)前两个轴生成的矢量平面上不同环境(E)中的表现对基因型(G)进行排序。另外,交互作用项也可被视为随机效应,可通过线性混合模型使用因子分析(FA)协方差结构对 GEI 项进行预测。在这里,通过绘制完整和不完整数据的 FA 混合模型得出的 G 和 E 分数,得到了 FA 双曲线图。这项工作的目的是将 AMMI-biplot 与平衡数据的 FA-biplot 进行比较,然后显示不平衡对 FA-biplot 的影响。在阿根廷栽培品种评估网络的不同环境中,采用完全随机区组设计进行了小麦比较试验,利用连续 3 年获得的数据集生成了四种情景,对 G 排序进行了评估。虽然我们使用了不同的统计程序来获得 AMMI-双图和 FA-双图,但它们在多年试验的最后一年从所有 E 中删除多达 50%的 G 的情况下显示出相同的交互模式。我们的结论是,即使在数据集不完整的情况下,FA-biplot 也能得到可靠的 G 排序。
期刊介绍:
After critical review and approval by the editorial board, AJ publishes articles reporting research findings in soil–plant relationships; crop science; soil science; biometry; crop, soil, pasture, and range management; crop, forage, and pasture production and utilization; turfgrass; agroclimatology; agronomic models; integrated pest management; integrated agricultural systems; and various aspects of entomology, weed science, animal science, plant pathology, and agricultural economics as applied to production agriculture.
Notes are published about apparatus, observations, and experimental techniques. Observations usually are limited to studies and reports of unrepeatable phenomena or other unique circumstances. Review and interpretation papers are also published, subject to standard review. Contributions to the Forum section deal with current agronomic issues and questions in brief, thought-provoking form. Such papers are reviewed by the editor in consultation with the editorial board.