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limited rainfall (so-called convective rainfall event), the interpolation for precipitation does
not give plausible results (Zeuner & Kleinhenz, 2007, Zeuner & Kleinhenz, 2008, Zeuner &
Kleinhenz, 2009). Precipitation data with a high spatial resolution may be obtained from
radar measurements.
Using these spatial input parameters for the currently available disease forecast models
should lead to accurate forecasting for areas in-between two or more distant MSs. With the
use of GIS, daily spatial risk maps for diseases and pests can be created in which the spatial
and the temporal process of first appearance and regional development are documented
(Fig.4). These risk maps may lead to improved control and a reduction in fungicide use.
3.1 Storage
In order to store the results of interpolation, a grid was laid out over Germany. At present,
the Governmental Crop Protection Services (GCPS) use about 570 MSs to represent an
agricultural area of aprox. 200.000 km
2
, or an average of one MS per 350 km
2
. With the new
GIS method, grid cells have a size of 1 km
2
and, after interpolation, are represented by
virtual weather stations (Liebig & Mummenthey, 2002)
3.2 Spatial data of temperature and relative humidity
For the interpolation of temperature and relative humidity the multiple regression method
was chosen because it gave the best results by the shortest calculation time of all tested
interpolation methods. The first calculations with the four interpolation methods (Inverse
Distance Weighted, Spline, Kriging and Multiple Regression) showed that deterministic
interpolation methods were not suitable. The general purpose of multiple regressions (the
term was first used by Pearson, 1908) is to learn more about the relationship between several
independant or predictor variables and a dependant or criterion variable. MR is an
interpolation method that allows simultaneous testing and modelling of multiple
independant variables (Cohen
, et al.
, 2003). Parameters that have an influence on
temperature and relative humidity, e.g. elevation, slope, aspect, can therefore be tested
simultaneously. MR uses matrix multiplication and only variables with a defined minimum
influence that will be included into the model. The result of MR is a formula (x = const +
A1*const1 + A2*const2+ A3*const3+…+ Ax*const) which allows a calculation of a parameter
set for each grid cell from which independent variables are known (Zeuner, 2007).
temperature [°C]
relative humidity [%]
year
2003
2004
2005
2006
2003
2004
2005
2006
CoD
96%
96%
99%
98%
94%
96%
95%
92%
mean dev.
0.0
0.0
0.0
0.1
0.3
0.1
0.1
-0.6
maximum
4.4
4.1
4.3
4.7
19.6
32.6
21.6
21.2
minimum
-3.8
-4.5
-4.5
-4.1
-18.9
-21.9
-22.8
-22.8
t-test
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
Table 1. Validation of data on temperature and relative humidity; deviation between
calculated values and measured data with MR (n = 92160 hours, n.s. = not significant)
To validate the results of the interpolation, 13 MSs were ignored in the interpolation process.
After interpolation, the deviation between calculated values and measured data of these
stations was compared. The study was conducted from January to August in the years 2003
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