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surrounding a weather station. Examples of these models currently used by the GCPS are
CERCBET1, ERYBET1, UROBET1 and RAMUBET1 used to predict the appearance of
Cercospora leaf spot ( Cercospora beticola ) (Roßberg , et al. , 2000), powdery mildew ( Erysiphe
betae ), rust ( Uromyces betae ) and ramularia ( Ramularia beticola ) (Racca , et al. , 2010a) on sugar
beet, SIMCOL1 for the lupin anthracnose ( Colletotrichum lupini ) (Racca & Tschöpe, 2010),
SIMPHYT1 and SIMBLIGHT1 for the potato leaf blight ( Phytopthora infestans ) (Kleinhenz , et
al. , 2007), SIMPEROTA1 for blue mold disease of tobacco ( Peronospora tabacina ) (Racca , et al. ,
2007). An example for pests is the SIMLEP1-Start model to forecast the appearance of the
hibernating adults of Colorado potato beetle ( Leptinotarsa decemlineata ) (Jörg , et al. , 2007).
All type 1 models can be validated by using statistical and/or subjective validation
methods. Some of these methods will be described below with a few examples.
CERCBET1 is a model which is able to forecast the appearance of Cercospora leaf spot on
sugar beet fields (Roßberg , et al. , 2000, Rossi & Battilani, 1986, Rossi & Battilani, 1991).
The subjective validation of CERCBET1 took place retrospectively with monitoring - data of
the years 1995 to 2008 in all German sugar beet growing areas.
The validation was made by comparing
the date of the first appearance forecasted by the model and observed in the field;
the forecasted and observed date when 50% of the fields in one region are infected..
This date represents the distribution of the disease in several fields in one region, the
50th percentiles. At this point the probability to detect a Cercospora infection in a field
is very high and the disease had been established in this region. For the validation the
available data are grouped in “regions” near a representative weather station. To detect
the distribution of the infected field, only regions with surveys in more than four sugar
beet fields were taken into consideration.
In any case the forecasting was considered:
correct - when the difference between the forecasted and the observed date was in a
range of one week (± 7 days);
early/late - when the difference between the forecasted and the observed date was
bigger than one week (> ± 7 days).
The subject of the validation method in this case is to consider a period of ± 7 days correct
for these kind of model results. We consider that the data for the validation derive from
regional monitoring arranged by the GCPS. This monitoring is done weekly. So, one week of
delay or one week of earlier forecast is acceptable for this model.
The result of the validation is summarised in Table 2.
first appearance 50 % infected fields
too early 31.80% 20.14%
correct 64.66% 72.08%
too late 3.53% 7.77%
Table 2. CERCBET1: Subjective validation in Germany. Data from 1995 to 2008 (n=283)
Concerning the first appearance, in about 64% of the forecasts the model was able to predict
the disease appearance correct, in 32% the date was too early and in 4% of the cases the
forecasted date of disease appearance was too late. The model shows more accuracy in the
prediction of the 50% infected field date. Analyzing the results of the validation, the trend of
the model to anticipate the occurrence of disease can be identified (Tab.2). This trend can
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