Transcript Slide 1

Multivariate analysis of turfgrass
cell wall components and
relationship with black cutworm
larval develpment
S.C. Hong and R.C. Williamson
The Dept. Entomology
Univ. Wisconsin-Madison
Objectives
To examine multi-dimensional relationship
between cell wall components of
turfgrasses and black cutworm larval
development
To compare multivariate analyses
Turfgrass species and cultivars
Creeping bentgrass (cbe)
Reveillie (tbr): The hybrid of Kentucky bluegrass and
Texas bluegrass
Kentucky bluegrass
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–
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–
–
Challenger (chl)
Julia (jla)
Midnight (mid)
Monopoly (mono)
South Dakota (sd)
Young vs. Old based on planting dates
– Young: less than 60 d
– Old: greater than 365 d
Cell wall components
Dry matter
Acid detergent fiber (adf)
Neutral detergent fiber (ndf)
Lignin
Ash
Total nitrogen
Leaf toughness
Larval weight (lw) from non-choice feeding
assay
Multivariate analysis
Fisher’s discriminant analysis
Cluster analysis
– Hierarchical clustering method
– Kruskal’s non-metric multidimensional
scaling
Factor analysis
Fisher’s discriminant analysis
Objective:
– To describe graphically the different features
of observations from populations
– To classify turfgrasses into groups based on
collected variables.
Cluster analysis
Exploratory data analysis tool
– Sorting different objects into groups in a
way that the degree of association
between two objects is maximal.
Kruskal’s non-metric multidimensional
scaling (MDS)
Hierarchical clustering method (HCM)
Kruskal’s non-metric
multidimensional scaling
Hierarchical clustering method
Factor analysis
Objective:
– To discover if the observed variables can be
explained in terms of a much smaller number
of variables called ‘factors’
– To fix collinearity
Regression analysis using the result of
factor analysis
Factor analysis
Rotated estimated factor loadings and communality by principal component (PC)
Factor1
Factor2
Communalities
Dry matter
ADF
NDF
Lignin
-0.087
0.824
0.880
0.802
0.918
0.430
0.348
-0.092
0.85
0.86
0.89
0.65
Ash
Total nitrogen
Leaf toughness
-0.275
-0.733
0.653
-0.648
-0.059
0.566
0.5
0.54
0.75
Factor analysis
ntmlw: log-transformed
larval weight
ntmfa1: new data
calculated from the first
factor loadings and cell
wall data
ntmfa2: new data
calculated from the
second factor loadings
and cell wall data
Factor analysis
Source
Coefficient
T value
p-value
Linear model 1
Factor 1
Factor 2
-0.15342
-0.06544
-5.213
-1.000
<0.0001
0.324
Linear model 2
Factor 1
-0.16917
-6.807
<0.0001
Fitted model
Log(LW) = 2.44 – 0.17 fa1
Adjusted R-square 0.5251
Summary
Discriminant and Cluster analysis
– Useful to describe graphically the features
of cell wall data
– Discriminant analysis and two cluster
analyses show similar grouping pattern.
Group one: intratypes
Group two: Young vs. old
Summary
Factor analysis
– Remedy for collinearity among some
variables (ADF, NDF, lignin, and leaf
toughness)
– Fitted model with larval weight has a
negative coefficient for factor 1 (fa1).
Acknowledgements
United States Golf Association
Amber Klawitter
Dow AgroSciences