Information Technology Reference
In-Depth Information
Like the BBCH growth stages of the cereals also the BBCH stages of the lupin are not strictly
arithmetically dependant. Also in this case another validation criterion was needed. Again
the deviation in days between the ontogenesis in field and the simulated ontogenesis was
compared. If the deviation between the both dates was ±7 days the prognosis was rated as
“correct”. Otherwise the prognosis was too early or too late (Tab. 13).
In total 229 data sets were analysed by this validation method for the BBCH-stages: Start of
flowering (BBCH 61) and end of flowering (BBCH 69). BBCH 61 achieved a hit rate of 86.0%
correct forecasts, 14.0% of the dates were simulated too late. Concerning the BBCH 69 it was
simulated correctly in 75.5% of all cases, 22.7% of the simulations were too early and in 1.7%
of the cases the simulated BBCH was too late.
5. Conclusions
Decision support systems in plant protection need plausible and complete meteorological
data as main input. Meteorological data on the one hand are provided by the German
meteorological service. On the other hand several states in Germany built up their own
meteorological networks. These states use the software AgmedaWin for import,
management, presentation, evaluation and export of the measured data. Core of the
program is a flexible import module which facilitates the import of files with different
formats from all types of weather stations by describing the structure of the files with
import profiles. Several algorithms are integrated in AgmedaWin to ensure plausibility and
completeness of the data. The program also includes a module to compare data of
corresponding stations. With an XML-based export interface the data are transferred from
AgmedaWin to the internet system www.isip.de where all data are stored and used as input
for the decision support systems. Furthermore the unprocessed meteorological data can be
evaluated in www.isip.de or downloaded as files in different formats by external users.
The plausibility and completeness of meteorological data as main input for the models is the
most important pre-condition to get correct prognosis results. However by using
meteorological data of weather stations a good prognosis is only reached in the scope of a
weather station. That is the reason why the ZEPP developed a new technology based on
Geographic Information Systems (GIS). With the help of GIS it is possible to obtain results
with higher accuracy for disease and pest simulation models. The influence of geographical
factors on temperature and relative humidity were interpolated with GIS methods getting
meteorological data for every km
2
in Germany. The parameter precipitation was taken by
radar measured precipitation data and the results of all measured meteorological data were
used as input for the simulation models. The output of these models is presented as spatial
risk maps in which areas of maximum risk of the disease outbreak, infection pressure or
pest appearances are displayed. The modern presentation methods of GIS lead to an easy
interpretation and will furthermore promote the use of the system by farmers.
Finally the validation of a simulation model is a critical point in the development of the
model itself. Unfortunately, there is no set of specific tests or decision-making algorithms
which can determine the best method to validate a model. The subjective methods are
certainly more intuitive and provide easy answers with easy interpretations. In this case, the
decision for the method depends on the experience of the one who validates the model. It is
important to know, for example, what weight has to be indicated to the over- and especially
to the underestimation of the results of the model. Careful attention must be payed to the
quality of data which is available for the validation. They should certainly be adequate in
Search WWH ::
Custom Search