Application of mixed model and spatial analysis methods in multi-environmental and agricultural field trials.
Loading...
Date
2015
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Agricultural experimentation involves selection of experimental materials,
selection of experimental units, planning of experiments, and collection of
relevant information, analysis and interpretation of the results. An overall
work of this thesis is on the importance, improvement and efficiency of variety
contrast by using linear mixed mode with spatial-variance covariance compare
to the usual ANOVA methods of analysis. A need of some considerations on the
recently widely usage of a bi-plot analysis of genotype plus genotype by
environment interaction (GEE) on the analysis of multi-environmental crop
trials. An application of some parametric bootstrap method for testing and
selecting multiplicative terms in GGE and AMMI models and to show some
statistical methods for handling missing data using multiple imputations
principal component and other deterministic approaches.
Multi-environment agricultural experiments are unbalanced because several
genotypes are not tested in some environments or missing of a
measurement from some plot during the experimental stage. A need for
imputation of the missing values sometimes is necessary. Multiple
imputation of missing data using the cross-validation by eigenvector method
and PCA methods are applied. We can see the advantage of these methods
having easy computational implementation, no need of any distributional or
structural assumptions and do not have any restrictions regarding the pattern
or mechanism of missing data in experiments.
Genotype by environment (G×E) interaction is associated with the differential
performance of genotypes tested at different locations and in different years,
and influences selection and recommendation of cultivars. Wheat genotypes
were evaluated in six environments to determine the G×E interactions and
stability of the genotypes. Additive main effects and multiplicative interactions
(AMMI) was conducted for grain yield of both year and it showed that grain
yield variation due to environments, genotypes and (G×E) were highly
significant. Stability for grain yield was determined using genotype plus
genotype by environment interaction (GGE) biplot analysis. The first two
principal components (PC1 and PC2) were used to create a 2-dimensional GGE
biplot. Which-won where pattern was based on six locations in the first and five
locations in the second year for all the twenty genotypes? The resulting pattern
is one realization among many possible outcomes, and its repeatability in the
second was different and a future year is quite unknown. A repeatability of
which won-where pattern over years is the necessary and sufficient condition
for mega-environment delineations and genotype recommendation.
The advantages of mixed models with spatial variance-covariance structures,
and direct implications of model choice on the inference of varietal
performance, ranking and testing based on two multi-environmental data sets
from realistic national trials. A model comparison with a ᵪ2-test for the trials in
the two data sets (wheat and barley data) suggested that selected spatial
variance-covariance structures fitted the data significantly better than the
ANOVA model. The forms of optimally-fitted spatial variance-covariance,
ranking and consistency ratio test were not the same from one trial (location) to
the other. Linear mixed models with single stage analysis including spatial
variance-covariance structure with a group factor of location on the random
model also improved the real genotype effect estimation and their ranking. The
model also improved varietal performance estimation because of its capacity to
handle additional sources of variation, location and genotype by location
(environment) interaction variation and accommodating of local stationary
trend. The knowledge and understanding of statistical methods for analysis of
multi-environmental data analysis is particularly important for plant breeders
and those who are working on the improvement of plant variety for proper
selection and decision making of the next level of improvement for country
agricultural development.
Description
Doctor of Philosophy in Statistics. University of KwaZulu-Natal, Pietermaritzburg 2015.
Keywords
Environmental impact analysis--Statistical methods., Plant breeding--Statistical methods., Agriculture--Experimentation--Statistical methods., Crop yields., Multi-environmental field trials., Agricultural field trials., Theses--Statistics.