Premium
Analyzing Variety by Environment Data Using Multiplicative Mixed Models and Adjustments for Spatial Field Trend
Author(s) -
Smith Alison,
Cullis Brian,
Thompson Robin
Publication year - 2001
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2001.01138.x
Subject(s) - multiplicative function , ammi , variety (cybernetics) , multivariate statistics , computer science , variance (accounting) , mixed model , field (mathematics) , set (abstract data type) , spatial analysis , data set , additive model , statistics , data mining , mathematics , machine learning , artificial intelligence , gene–environment interaction , mathematical analysis , biochemistry , accounting , genotype , pure mathematics , business , gene , programming language , chemistry
Summary. The recommendation of new plant varieties for commercial use requires reliable and accurate predictions of the average yield of each variety across a range of target environments and knowledge of important interactions with the environment. This information is obtained from series of plant variety trials, also known as multi‐environment trials (MET). Cullis, Gogel, Verbyla, and Thompson (1998) presented a spatial mixed model approach for the analysis of MET data. In this paper we extend the analysis to include multiplicative models for the variety effects in each environment. The multiplicative model corresponds to that used in the multivariate technique of factor analysis. It allows a separate genetic variance for each environment and provides a parsimonious and interpretable model for the genetic covariances between environments. The model can be regarded as a random effects analogue of AMMI (additive main effects and multiplicative interactions). We illustrate the method using a large set of MET data from a South Australian barley breeding program.