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SUMMARY We identify three major components of spatial variation in plot errors from eld experiments and extend the two-dimensional spatial procedures of Cullis and Gleeson (1991) to account for them. The components are non-stationary large scale (global) variation across the eld, stationary variation within the trial (natural variation or local trend) and(More)
ASReml estimates variance components under a general linear mixed model by residual maximum likelihood (REML) The authors gratefully acknowledge the Grains Research and Development Corporation of Australia for their financial support. We thank the Qld Department of Primary Industries & Fisheries and the NSW Department of Primary Industries for permitting(More)
Approximate standard errors (ASE) of variance components for random regression coefficients are calculated from the average information matrix obtained in a residual maximum likelihood procedure. Linear combinations of those coefficients define variance components for the additive genetic variance at given points of the trajectory. Therefore, ASE of these(More)
ASReml-S estimates variance components under a general linear mixed model by residual maximum likelihood (REML) The authors gratefully acknowledge the Grains Research and Development Corporation of Australia for their financial support. We thank the Qld Department of Primary Industries and Fisheries and the NSW Department of Primary Industries for(More)
After estimation of eeects from a linear mixed model, it is often useful to form predicted values for certain factor/variate combinations. This process has been well-deÿned for linear models, but the introduction of random eeects means that a decision has to be made about the inclusion or exclusion of random model terms from the predictions, including the(More)
Eventing competitions in Great Britain (GB) comprise three disciplines, each split into four grades, yielding 12 discipline-grade traits. As there is a demand for tools to estimate (co)variance matrices with a large number of traits, the aim of this work was to investigate different methods to produce large (co)variance matrices using GB eventing data. Data(More)
The possibility of breeding for uniform individuals by selecting animals expressing a small response to environment has been studied extensively in animal breeding. Bayesian methods for fitting models with genetic components in the residual variance have been developed for this purpose, but have limitations due to the computational demands. We use the(More)
ASReml-R estimates variance components under a general linear mixed model by residual maximum likelihood (REML) The authors gratefully acknowledge the Grains Research and Development Corporation of Australia for their financial support. We thank the Qld Department of Primary Industries and Fisheries and the NSW Department of Primary Industries for(More)
Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the(More)