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Potentielle forklarende variabler for udbytte i forskellige
miljer
Hans PinnschmidtDanmarks JorgbrugsForskningAfdeling for
PlantebeskyttelseForskning Center Flakkebjerg4200
[email protected]
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Background
BAROF WP1 data: multivariate measurements on 86 spring barley
genotypes in 10 environments (2 years: 2002 & 2003, 3 sites:
Flakkebjerg, Foulum, Jyndevad, 2 production systems: ecological
& conventional)[email protected]
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variables: yield 1000 grain weight grain protein contents culm
length date of emergence growth duration mildew severity rust
severity scald severity net blotch severity disease diversity weed
cover broken panicles & culms lodgingparameters: raw data
mean/median/max./min. rank/relative values main effects interaction
slopes raw data adjusted for E/G main effects/slopes (residuals)
IPCA scoresSD/variance}derive information on general properties,
specificity, stability/variability [email protected]
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Objectives
Multivariate characterisation of genotypes with emphasis on
yield-related [email protected]
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Statistical methods
Non-linear Canonical Correlation Analysis (NCCA): an optimal
scaling procedure suited for handling multivariate data of any kind
of scaling (numerical/quantitative, ordinal, nominal).
Multiple Regression Analysis (MRA)[email protected]
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Non-linear Canonical Correlation Analysis (NCCA)
data treatment: quantitative variables (vm) were converted into
ordinal variables with n categories (v11 ... v1n, ..., vm1 ...
vmn)[email protected]
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Characterisation of environments
based on data adjusted for G main effects (=
residuals)[email protected]
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Flakkebjerg 2003:high yield, net blotch & panicle
breakage;low mildew & lodging
Flakkebjerg 2002: high rust & 1000 grain weight; late
sowingFoulum 2002 conventional & Jyndevad 2003 ecological:high
mildew & lodging; low yield % net blotchJyndevad 2002
ecological:low yield, 1000 grain weight,weed infestation, protein
[email protected]
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Characterisation of genotypes
based on data adjusted for E main effects (= residuals)
[email protected]
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[email protected]
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Characterisation of genotypes in individual environments based
on:
actual yield datadisease main effects (ME) of Gs environmental
disease variability (SD) of Gs (= standard deviation of E adjusted
data)[email protected]
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Flakkebjerg 2003:high yield, net blotch & panicle
breakage;low mildew & [email protected]
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[email protected] 4 (sq. root)dimension 6 (sq.
root)high yield; low net blotch ME & SDshort strawhigh rust ME
& SDlong strawlow yield; high net blotch ME & SD
Flakkebjerg 2003: high yield, net blotch & panicle breakage;
low mildew & lodging
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Foulum 2002 conventional & Jyndevad 2003 ecological:high
mildew & lodging; low yield & net
[email protected]
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[email protected] 1 (sq. root)dimension 5 (sq.
root)low yield;high mildew & net blotch ME & SD low mildew
ME & SDhigh yield
Jyndevad 2003 ecological: high mildew & lodging; low yield
& net blotch
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Multiple Regression Analysis (MRA)
dependent variables: yield (actual, E-adj. G mean &
SD)independent variables: E-adj. G mean & SD of disease
severity, weed infestation, growth duration, culm lengthcriteria:
Pin/out = 0.05/0.10; Fin/out = 3,84/2.71; tolerance =
[email protected]
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Variables must pass both tolerance and minimum tolerance tests
in order to enter and remain in a regression equation. Tolerance is
the proportion of the variance of a variable in the equation that
is not accounted for by other independent variables in the
equation. The minimum tolerance of a variable not in the equation
is the smallest tolerance any variable already in the equation
would have if the variable being considered were included in the
analysis. If a variable passes the tolerance criteria, it is
eligible for inclusion based on the method in effect.
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Mean versus standard deviation of environment-adjusted yield of
spring barley genotypes; BAROF
[email protected]
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[email protected]
G mean E-adj. yield
Results of regressing mean environment-adjusted yield of spring
barley genotypes on variables representing E-adjusted mean &
standard deviation of genotype properties.
variableBSE of Bbetasig. tsig. F
constant0.2730.2220.223
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Observed versus estimated mean environment-adjusted yield of
spring barley genotypes; BAROF
[email protected]
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[email protected]
G mean E-adj. yield
Results of regressing mean environment-adjusted yield of spring
barley genotypes on variables representing E-adjusted mean &
standard deviation of genotype properties.
variableBSE of Bbetasig. tsig. F
constant0.2730.2220.223
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Observed versus estimated standard deviation of
environment-adjusted yield of spring barley genotypes; BAROF
[email protected]
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Yield of spring barley genotypes versus main effect yield of the
environment; BAROF [email protected]
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[email protected]
yield across E summary
Results of regressing yield of spring barley genotypes on yield
main effect of the environment and on variables representing
E-adjusted mean & standard deviation of genotype
properties.
variableBSE of Bbetasig. tsig. F
constant5.2770.0980.000
E main0.9950.0190.8060.0000.649
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Yield of spring barley genotypes estimated based on yield main
effect of environment and E-adjusted mean & standard deviation
of genotype property variables (disease severity, weed infestation,
culm length, growth duration); analysis across environments; BAROF
[email protected]
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[email protected]
yield by E summary
Results of regressing yield of spring barley genotypes on
variables representing E-adjusted mean & standard deviation of
genotype properties.
environmentvariableconventionalecological
BSE of Bbetasig. tsig. FBSE of Bbetasig. tsig. F
Flakkebjerg 2002constant5.5430.0420.0005.0250.0460.000
mildew severity,
mean-0.0700.021-0.3370.0010.1030.001-0.1110.027-0.4740.0000.059
net blotch severity, mean-0.1540.042-0.4340.0000.127
rust severity, mean-0.2110.083-0.2890.0140.1800.000
Flakkebjerg 2003constant6.0750.3310.0005.2990.3020.000
mildew severity,
mean-0.2460.056-0.6710.0000.088-0.2390.050-0.7280.0000.116
net blotch severity,
mean-0.2170.063-0.3920.0010.154-0.1650.058-0.3320.0060.169
culm length, mean-0.0340.013-0.2480.0100.211
rust severity,
SD-0.9150.248-0.3500.0000.258-0.7570.226-0.3230.0010.220
mildew severity,
SD0.6790.2720.3640.0150.3030.0000.4970.2470.2970.0480.251
growth duration, mean-0.2110.095-0.2140.0300.2780.000
Foulum 2002constant5.2640.2460.0005.7550.2690.000
mildew severity,
mean-0.2550.031-0.9920.0000.312-0.1690.036-0.7180.0000.129
net blotch severity,
mean-0.1280.036-0.3300.0010.437-0.1430.040-0.4020.0010.195
rust severity, mean-0.2330.083-0.2940.0060.475
rust severity,
SD-0.5260.161-0.2870.0020.511-0.6280.157-0.3730.0000.241
mildew severity,
SD0.4570.1470.3500.0030.5360.4320.1710.3600.0130.291
growth duration, mean-0.1170.058-0.1520.0460.573
culm length,
SD-0.0710.033-0.1610.0370.5910.000-0.0760.037-0.1880.0450.3180.000
Foulum 2003constant5.7310.1480.0005.1830.2400.000
mildew severity,
mean-0.2590.030-0.7800.0000.274-0.2650.040-0.8910.0000.223
net blotch severity,
mean-0.2010.043-0.4000.0000.378-0.2450.046-0.5450.0000.333
rust severity,
SD-0.5610.189-0.2370.0040.422-0.6630.181-0.3120.0000.396
growth duration, mean-0.2650.082-0.2670.0020.462
scald severity, mean-0.1290.061-0.1780.0360.4850.000
mildew severity, SD0.4990.1960.3290.0130.4340.000
Jyndevad 2002aconstant3.3840.0770.000
mildew severity, mean-0.0740.015-0.4970.0000.080
net blotch severity, mean-0.1030.022-0.4590.0000.240
rust severity, SD-0.2090.098-0.1980.0360.272
growth duration, mean-0.0920.042-0.2080.0310.3030.000
Jyndevad 2002bconstant4.3670.0340.000
mildew severity, mean-0.1090.020-0.5870.0000.130
net blotch severity, mean-0.1270.031-0.4530.0000.214
rust severity, mean-0.1590.062-0.2760.0130.2620.000
Jyndevad 2003constant5.0060.0350.000
mildew severity, mean-0.2100.019-0.8160.0000.562
net blotch severity, mean-0.0760.028-0.1950.0090.5910.000
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Yield of spring barley genotypes estimated based on E-adjusted
mean & standard deviation of genotype property variables
(disease severity, weed infestation, culm length, growth duration);
analysis by environment; BAROF
[email protected]
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Conclusions & outlook
NCCA: intuitive method good for visualising the main features in
multivariate data of various scales useful for obtaining an overall
synoptic orientation of G properties and E characteristics soft
systems approach
MRA: hard systems approach synoptic view neglected
Mildew & net blotch had highest yield-related effect,
although not always functional (especially in
MRA!)[email protected]
