Decision Support Systems in Agriculture: Administration of Meteorological Data, Use of Geographic Information Systems(GIS) and Validation Methods in Crop Protection Warning Service - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 349

subjective, comparing the forecasting dates of the maximum abundance of egg cluster and
young larvae with field observations. The model output was considered correct when the
forecast was within an interval of one week compared to the observed date (Tab. 11).

maximum abundance of
egg clusters young larvae
% correct % too early/late n % correct % too early/late n
Germany 91 9 33 87 13 38
Italy 100 0 6 100 0 6
Austria 71 29 7 86 14 7
Poland 100 0 2 100 0 2
Mean 90.5 7.5 93.25 6.75
Table 11. Results of SIMLEP3 subjective validation in several European countries (1999-
2004): share of correct forecasts (%).
In general, SIMLEP3 results were very satisfying. The first occurrence of young larvae in
most of the cases was predicted correctly. Nevertheless, differences between forecasting and
observed date ranging from 18 days too early up to 10 days too late were registered. Good
results were also obtained for the prediction of maximum egg cluster occurrence.
Throughout Germany, Poland, Austria and Italy the mean share of correct forecasts given
from SIMLEP3 (both egg clusters and young larvae) amounted to about 92%. In Austria, the
share of correct predictions was the lowest (approx. 70%) and in Germany the share of
correct predictions exceeded 90%. Maximum occurrences of young larvae predictions were
correct in about 93% of the cases on the European scale. Again, optimum results were
obtained in Italy and Poland. In Austria and Germany, the share of correct forecasts
exceeded 85%. Subjective validation efforts showed that SIMLEP3 is able to give correct
forecasts for the most important development stages of L. decemlineata with respect to
control efforts. The validation also demonstrates the possibility to expand the use of the
model throughout Europe.

country

4.4 Validation of ontogenesis models
Ontogenesis models are ontogenetic models which simulate the development of crops
expressed as BBCH growth stages (Hack , et al. , 1992) over time. SIMONTO-models are
based on the modelling approaches of CERES-Wheat (USA) (Gabrielle , et al. , 1998) and
ONTO-models (Germany) (Wernecke & Claus, 1996). The ontogenetic progress in
SIMONTO is reflected by a developmental rate which is a function of temperature and
photoperiod. Parameters for the different models (winter oilseed rape and winter cereals)
were estimated by employing the Monte-Carlo-method (Falke , et al. , 2006, Roßberg , et al. ,
2005). More than 13800 single observations of BBCH growth stages for winter cereals from
2003 to 2008 were available for the model validation. In the first step of the statistical
validation, the observed BBCH growth stages are simply linear regressed with the model. A
high r 2 (0.88) suggests a good correlation between the data. Both regression parameters, a
and b, are significant but the concordance correlation coefficient of 0.92 demonstrates a good
agreement between the data. The model is apparently good to simulate the reality.
Unfortunately, the BBCH growth stages are not strictly arithmetical dependant. Some stages
could appear very early in the season and stay constant for a long time. The simple
arithmetic difference between two BBCH growth stages can be minimal but the difference in