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Mixed linear model methodology in plant breeding
Abstract
The purpose of this research was to determine potential applications of Mixed Linear Model Methodology (MLMM) to plant breeding problems. The common linear model based on ordinary least squares is widely utilized in plant breeding. If some effects are defined as random, the linear model is extended, generating the mixed model. In an experiment involving comparisons between 72 S$\sb1$ lines evaluated in 48 incomplete blocks and two plant densities, smaller standard errors for comparisons between lines were obtained when genotypes were defined as random compared to standard errors for adjusted means obtained when genotypes were defined as fixed. Mixed linear model was found to have use in analysis of early generation yield trials. An Unbalanced Incomplete Block Design (UIBD), which utilized as replicated entries a small sample of the total entries to be tested, was used to evaluate 312 S$\sb1$ lines of maize, using only 72 of lines as replicated entries. The UIBD required only 60% of the total number of plots which would have required in the more common incomplete block design in which all entries would be replicated. Utilization of MLMM made the analysis possible. Mixed linear model methodology applies to genotypic by environmental interaction (GEI) problem. Two data sets of maize grain yield evaluated in a series of environments were analyzed. Two predictable functions were obtained using MLMM in which genotypes were defined as random. A comparison was made between results obtained using MLMM and those obtained using Eberhart-Russell stability parameters (E-R) and Wricke's ecovalence W$\sb{\rm i}$. MLMM and its predictable functions (BLUP) were found to provide additional information on GEI problem compared to the information obtained with E-R and W$\sb{\rm i}$, by providing specific prediction of GEI effects for each genotype. MLMM appears to have value in breeding programs where genotypic and genotypic by environmental interaction effects are of great concern.
Subject Area
Agronomy|Plant propagation|Biostatistics
Recommended Citation
Rodriguez-Herrera, Sergio Alfredo, "Mixed linear model methodology in plant breeding" (1992). ETD collection for University of Nebraska-Lincoln. AAI9308193.
https://digitalcommons.unl.edu/dissertations/AAI9308193