A5035656200- Genetics and Plant Breeding
- Genetic and phenotypic traits in livestock
- Wheat and Barley Genetics and Pathology
- Genetic Mapping and Diversity in Plants and Animals
- Optimal Experimental Design Methods
- Ruminant Nutrition and Digestive Physiology
- Pasture and Agricultural Systems
- Soil Geostatistics and Mapping
- Soil Carbon and Nitrogen Dynamics
- Fish Ecology and Management Studies
- Crop Yield and Soil Fertility
- Marine and fisheries research
- Agronomic Practices and Intercropping Systems
- Statistical Methods and Bayesian Inference
- Horticultural and Viticultural Research
- Fish Biology and Ecology Studies
- Plant Pathogens and Fungal Diseases
- Sugarcane Cultivation and Processing
- Statistical Methods and Inference
- Soil and Unsaturated Flow
- Genetic and Environmental Crop Studies
- Economic and Environmental Valuation
- Legume Nitrogen Fixing Symbiosis
- Weed Control and Herbicide Applications
- Ichthyology and Marine Biology
University of Wollongong
2015-2024
Australian Bureau of Statistics
2014-2024
New South Wales Department of Primary Industries
1981-2023
Tamworth Hospital
1985-2020
Commonwealth Scientific and Industrial Research Organisation
2001-2015
Australian National University
2012
Wagga Wagga Base Hospital
2001-2010
The University of Western Australia
2010
Charles Sturt University
2009
Gansu Agricultural University
2008
A strategy of using an average information matrix is shown to be computationally convenient and efficient for estimating variance components by restricted maximum likelihood (REML) in the mixed linear model. Three applications are described. The motivation algorithm was estimation analysis wheat variety means from 1,071 experiments representing 10 years 60 locations New South Wales. We also apply designed incomplete block spatial field experiments.
SUMMARY In designed experiments and in particular longitudinal studies, the aim may be to assess effect of a quantitative variable such as time on treatment effects. Modelling effects can complex presence other sources variation. Three examples are presented illustrate an approach analysis cases. The first example is experiment growth cows under factorial structure where serial correlation variance heterogeneity complicate analysis. second involves calibration optical density concentration...
The analysis of series crop variety trials has a long history with the earliest approaches being based on ANOVA methods. Kempton (1984) discussed inadequacies this approach, summarized alternatives available at that time and noted all these could be classified as multiplicative models. Recently, mixed model have become popular for trials. There are numerous reasons their use, including ease which incomplete data (not varieties in trials) can handled ability to appropriately within-trial...
Summary. The recommendation of new plant varieties for commercial use requires reliable and accurate predictions the average yield each variety across a range target environments knowledge important interactions with environment. This information is obtained from series trials, also known as multi-environment trials (MET). Cullis, Gogel, Verbyla, Thompson (1998) presented spatial mixed model approach analysis MET data. In this paper we extend to include multiplicative models effects in...
The one-dimensional spatial analysis procedure proposed by Gleeson and Cullis (1987, Biometrics 43, 277-288) is extended to two dimensions using the subclass of separable lattice processes model errors. Residual maximum likelihood estimation models described diagnostics for testing adequacy are derived. Results from 24 sets uniformity data indicate frequent need a two-dimensional even when plot shape highly rectangular. These results also potential gain rather than row + column analysis. An...
Summary Geostatistical estimates of a soil property by kriging are equivalent to the best linear unbiased predictions (BLUPs). Universal is BLUP with fixed‐effect model that some function spatial co‐ordinates, or more generally other secondary predictor variable when it called external drift. A problem in universal find variance for random variation, since empirical variograms estimated from data method‐of‐moments will be affected both variation and represented fixed effects. The...
The major aim of crop variety evaluation is to predict the future performance varieties. This paper presents routine statistical analysis data from late‐stage testing varieties in Australia. It uses a two‐stage approach for analysis. individual trials current year are analysed using spatial techniques. resultant table variety‐by‐trial means combined with tables previous years form an overall mixed model Weights allow being estimates varying accuracy. In view predictive analysis, effects and...
A spatial analysis of field experiments is proposed which takes account association between neighbouring plots. The residual maximum likelihood (REML) method Patterson and Thompson (1971, Biometrika 58, 545-554) used to estimate parameters a general neighbour model, can be expressed as an autoregressive moving average (ARMA) model. Three data sets are analysed (i) highlight the need for model selection procedure, (ii) illustrate differing results incomplete block effect including treated...
Modeling of cultivar × trial effects for multi-environment trials (METs) within a mixed model framework is now common practice in many plant breeding programs. The factor analytic (FA) parsimonious form used to approximate the fully unstructured genetic variance–covariance matrix MET data. In this study, we demonstrate that FA generally best fit across range data sets taken from early generation program. addition, superiority achieving most aim METs, namely selection superior genotypes....
Factor analytic mixed models for national crop variety testing programs have the potential to improve industry productivity through appropriate modelling and reporting growers of by environment interaction. Crop are conducted in many countries world-wide. Within each program, data combined across locations seasons, analysed order provide information assist choosing best varieties their conditions. Despite major advances statistical analysis multi-environment trial data, such methodology has...
Abstract An advanced and widely used method of analysis for multi-environment trial data involves a linear mixed model with factor analytic (FA) variance structures the variety by environment effects. This can accommodate unbalanced data, that is, not all varieties in environments, it allows use pedigree information appropriate accommodation individual experimental designs, most importantly FA structure effects is parsimonious regularly results good fit to data. The provides accurate...
Abstract ContextA series of unprovoked shark attacks on New South Wales (Australia) beaches between 2013 and 2015 triggered an investigation new emerging technologies for protecting bathers. Traditionally, bather protection has included several methods capture, detection and/or deterrence but often relied environmentally damaging techniques. Heightened environmental awareness, including the important role sharks in marine ecosystem, demands techniques from attack. Recent advances...
A fully efficient approach for the analysis of multi-environment early stage variety trials is considered that accommodates a general spatial covariance structure errors each trial. The simultaneously produces best linear unbiased predictors genotype and by environment interaction effects residual maximum likelihood estimates parameters variance components. Two motivating examples are presented analyzed, results suggest previous approximate analyses can seriously affect estimation genetic...
Summary The general linear model encompasses statistical methods such as regression and analysis of variance ( anova ) which are commonly used by soil scientists. standard ordinary least squares (OLS) method for estimating the parameters is a design‐based that requires data have been collected according to an appropriate randomized sample design. Soil often obtained systematic sampling on transects or grids, so OLS not appropriate. Parameters can be estimated from systematically sampled...
Exploring and exploiting variety by environment (V × E) interaction is one of the major challenges facing plant breeders. In paper I this series, we presented an approach to modelling V E in analysis complex multi-environment trials using factor analytic models. paper, develop a range statistical tools which explore context. These include graphical displays such as heat-maps genetic correlation matrices well so-called E-scaled uniplots that are more informative alternative classical biplot...
Summary The statistical analysis of late‐stage variety evaluation trials using a mixed model is described, with one‐ or two‐stage approaches to the analysis. Two sets trials, from Australia and UK, were used provide realistic scenarios for simulation study evaluate different methods This showed that one‐stage approach gave most accurate predictions performance overall within each environment, across range models, as measured by mean squared error prediction realized genetic gain. A weighted...