Statistical Analysis of a Yield Trial

Zobel, R.W. and Wright, M.J. and Gauch, Jr, H.J. (1988) Statistical Analysis of a Yield Trial. Agronomy Journal, 80 (3). pp. 388-393.

[img] PDF - Published Version
Restricted to ICRISAT researchers only


Yield trials frequently have both significant main effects and a significant genotype x 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 multiplicative 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 in the interaction. Therefore, agronomists and plant breeders may fail to perceive important interaction effects.

Item Type: Article
Additional Information: Dr S N Nigam research collection - Box No: 20
Uncontrolled Keywords: Additive main effects and multiplicative interaction model, Analysis of variance, Biplot, Linear regression, Glycine mar (L.) Merr., Principal components analysis.
Author Affiliation: USDA-ARS-USPSNL, Dep. of Agronomy and Dep. of Plant Breeding, Cornell Univ., Ithaca, NY 14853-190
Subjects: Statistics and Experimentation > Statistial Methods
Divisions: General
Depositing User: Mr Siva Shankar
Date Deposited: 25 Mar 2013 10:04
Last Modified: 25 Mar 2013 10:04
Official URL:

Actions (login required)

View Item View Item