Brian R. Cullis

发表

Statistics for the Australian Grains Industry Technical Report Series Progress Report 2011-2012 for SAGI-2 project

Brian R. Cullis, B. Cullis, 2012 .

Multi-phase variety trials using both composite and individual replicate samples: a model-based design approach

Brian R. Cullis, Alison B. Smith, David G. Butler, 2014, The Journal of Agricultural Science.

Combined analysis of 08/09 and 2010 experiments

Brian R. Cullis, David G. Butler, Daryl J. Mares, 2010 .

The Design of Early-Stage Plant Breeding Trials Using Genetic Relatedness

Brian R. Cullis, Alison B. Smith, David G. Butler, 2020, Journal of Agricultural, Biological and Environmental Statistics.

(Co)Variance Structures for Linear Models in the Analysis of Plant Improvement Data

Brian R. Cullis, Robin Thompson, A. R. Gilmour, 1998, COMPSTAT.

Experimental design and analysis for variety trials.

Kaye E. Basford, Brian R. Cullis, E. R. Williams, 1996 .

Spatial analysis of field experiments : an extension to two dimensions

Brian R. Cullis, B. Cullis, A. C Gleeson, 1991 .

The design and analysis of multi-phase plant breeding experiments

Brian R. Cullis, B. Cullis, Alison B. Smith, 2006, The Journal of Agricultural Science.

On the design of early generation variety trials with correlated data

Brian R. Cullis, B. Cullis, Alison B. Smith, 2006 .

Genotype by Environment Variance Heterogeneity in a Two-Stage Analysis

Brian R. Cullis, Arunas Verbyla, Alison B. Frensham, 1997 .

Effects of otter boards on catches of an Australian penaeid trawl

Brian R. Cullis, David J. Sterling, Matt K. Broadhurst, 2012 .

Multivariate whole genome average interval mapping: QTL analysis for multiple traits and/or environments

Brian R. Cullis, B. Cullis, A. Verbyla, 2012, Theoretical and Applied Genetics.

Comparison of small-scale and large-scale mixing characteristics: Correlations between small-scale and large-scale mixing and extensional characteristics of wheat flour dough

Brian R. Cullis, B. Cullis, G. Mann, 2008 .

Prediction in linear mixed models

Brian R. Cullis, Sue J. Welham, Robin Thompson, 2004 .

Accounting for natural and extraneous variation in the analysis of field experiments

Brian R. Cullis, A. R. Gilmour, Arunas Verbyla, 1997 .

The analysis of QTL by simultaneous use of the full linkage map

Brian R. Cullis, Robin Thompson, Robin Thompson, 2007, Theoretical and Applied Genetics.

Factor analytic mixed models for the provision of grower information from national crop variety testing programs

Brian R. Cullis, B. Cullis, Alison B. Smith, 2014, Theoretical and Applied Genetics.

The statistical analysis of quality traits in plant improvement programs with application to the mapping of milling yield in wheat

Brian R. Cullis, B. Cullis, Alison B. Smith, 2001 .

Joint Modeling of Spatial Variability and Within-Row Interplot Competition to Increase the Efficiency of Plant Improvement

Brian R. Cullis, Robin Thompson, Robin Thompson, 2011 .

REML Estimation of Multiplicative Effects in Multienvironment Variety Trails

Brian R. Cullis, Beverley J. Gogel, B. Cullis, 1995 .

The design and analysis of variety trials using mixtures of composite and individual plot samples

Brian R. Cullis, Robin Thompson, Alison B. Smith, 2011 .

Barley malting quality: are we selecting the best?

Brian R. Cullis, J. F. Panozzo, A. B. Smith, 2003 .

Spatial analysis of multi-environment early generation variety trials

Brian R. Cullis, Robin Thompson, Arunas Verbyla, 1998 .

Productivity and sustainability of a spring wheat-field pea rotation in a semi-arid environment under conventional and conservation tillage systems

Brian R. Cullis, Guangdi Li, M. J. Robertson, 2008 .

Anisotropic Matérn correlation and spatial prediction using REML

Brian R. Cullis, B. Cullis, A. Verbyla, 2007 .

Residual maximum likelihood (REML) estimation of a neighbour model for field experiments

Brian R. Cullis, B. Cullis, A. C Gleeson, 1987 .

Efficiency of neighbour analysis for replicated variety trials in Australia

Brian R. Cullis, B. Cullis, A. C Gleeson, 1989, The Journal of Agricultural Science.