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The subjective validation simply consists of the comparison of the simulated disease
incidence with data, recorded in the field (fig. 7). The simulation is decided to be correct
when the simulated disease incidence ranges between the confidence interval of the
recorded disease incidence. Overestimation is given when the simulated value overrides the
highest level of the confidence interval in opposition to underestimation when the simulated
value is under the lowest level of the confidence interval. Validation was done for both
models and for each leaf layer F (flag leaf) to F-3 (Tab.5).
leaf layer
PUCREC -winter rye PUCTRI -winter wheat
under. corr. over. under. corr. over.
F 8 74 18 0 82 18
F 8 74 18 0 82 18
F-1 2 86 12 0 76 24
F-2 6 84 10 0 71 29
F-3 0 80 20 0 76 24
Table 5. Validation of PUCREC (n=51) and PUCTRI (n=37) - Share (%) of underestimated,
correct and overestimated leaf rust epidemics on different leaf layers (2001 - 2005) (under.=
Underestimation, corr. = correct, over.=overestimation)
According to the subjective validation the field data and the model results coincided well. In
most of the cases (from 71 to 86%), the disease incidence progress was correctly simulated
(Tab. 5). In a few cases PUCREC underestimated (2 - 8%) or overestimated (10 - 20%) the
epidemic progress of
P. recondita
. For winter wheat a considerable share of overestimations
occurred (18 - 29%). This means that epidemics simulated by PUCTRI started earlier and
progressed faster than observed in the field. For winter wheat no underestimations could be
observed.
The statistical validation was done with two parametric (regression analysis, hypothesis
test) and one non-parametric test (Kolmogorov-Smirnov).
The simulated disease incidence (dependant variable) is simply linear correlated with the
recorded data (independant variable). The “null hypothesis” demonstrates that “a”
(intercept of the regression line) is equal to 0 and “b” (slope of regression) is equal to 1
(tested using the Student t-test) (Tab.6).
PUCREC -winter rye
PUCTRI -winter wheat
Leaf
layer
Regression parameters
Regression parameters
Kolm.-Smirn.
Kolm.-Smirn.
t-a
t-b
t-a
t-b
ns
*
ns
*
ns
*
ns
*
ns
*
ns
*
F
93
7
59
41
96
4
95
5
90
10
95
5
F-1
91
9
77
23
98
2
94
6
87
13
94
6
F-2
91
9
68
32
96
4
100
-
100
-
92
8
F-3
88
12
79
21
91
9
100
-
67
33
100
-
Table 6. Validation of PUCREC (n=51) and PUCTRI (n=37) - regression analysis and
Kolmogorov-Smirnov test (2001 - 2005), share (%) of the significance. (t-a: hypothesis t-test
for regression intercept, t-b: hypothesis t-test for regression slope, Kol.Smirn.= Kolmogorov-
Smirnov test, n.s. = not significant, * = significant with p<0.05)
The statistical validation gave very satisfactory results with both (parametric and non-
parametric) methods. The high number of non-significant cases of the regression parameters
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