Constrained AMMI Model: Application to Polish Winter Wheat Post-Registration Data

Crop Science

Rodrigues, Paulo C.^2¹  Paderewski, Jakub^1² 

[1] CAST, Faculty of Natural Sciences, Univ. of Tampere, Tampere, Finland, Dep. of Statistics, Federal Univ. of Bahia, Salvador, Brazil^2;Dep. of Experimental Design and Bioinformatics, Warsaw Univ. of Life Sciences, Nowoursynowska 159, 02-766 Warsaw, Poland^1

关键词: AMMI;    additive main effects and multiplicative interaction;    C-AMMI;    constrained additive main effects and multiplicative interaction;    C-PCA;    constrained principal component analysis;    C(G)-AMMI;    genotypes constrained additive main effects and multiplicative interaction;    C(GE)-AMMI;    constrained genotype and environment additive main effects and multiplicative interaction;    C(E)-AMMI;    environments constrained additive main effects and multiplicative interaction;    EPC;    environment interaction principal component score;    G × L × M × Y;    genotype × location × management × year;    GE;    genotype × environment;    GPC;    genotype interaction principal component score;    MET;    multienvironment trial;    PC;    principal component;    PCA;    principal component analysis;    RMSPD;    root mean square prediction difference;   

DOI  :  10.2135/cropsci2017.06.0347

学科分类：农业科学（综合）

来源: Crop Science

【 摘 要 】

Constrained principal component analysis (C-PCA) describes a two-dimensional data table and assumes a linear dependence of the principal component scores on known additional parameters (i.e., explanatory matrices). In this study, we used C-PCA to generalize the additive main effects and multiplicative interaction (AMMI) model and propose the constrained AMMI model. The constrained AMMI model is interpreted and illustrated when (i) only the environmental principal component parameters have an explanatory data matrix, (ii) only the genotype principal component parameters have an explanatory data matrix, and (iii) both types of parameters have explanatory data matrices. The cross-validation procedure is adapted for model diagnosis. Data for winter wheat (Triticum aestivum L.) genotype × location × management × year grain yield, recorded in Poland from multienvironment trials conducted in the post-registration variety testing system, were analyzed and used for model comparison.

【 授权许可】

CC BY