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SESSION 5: DISEASE CONTROL AND FORECASTING MODELS
Forecasting helps to target DON toxin testing
T. Kaukoranta, V. Hietaniemi, P. Parikka
MTT Agrifood Research Finland, 31600 Jokioinen, Finland
E-mail: timo.kaukoranta@mtt.fi
We assessed the potential gain from predicting the risk of DON toxin for
increasing the efficiency of actual toxin testing of cereals. Monitoring data on toxin
concentrations in spring cereals were linked to gridded weather data and varietal
susceptibility. Logistic regression models predicting probability of moderately high
(>400/500 pbb) and high (>1000 ppb) toxin concentration from weekly mean
temperatures and duration of high humidity around flowering and harvesting were
constructed. The observed proportions of cases above 400/500 ppb were 8, 8,
26%, and of above 1000 ppb were 5, 4, 15%, in spring wheat barley and oat
(n=183, 252, 432), respectively. Using the prediction as a guide, the chance of
finding positives above 400/500 ppb in the population changed to 80, 14, and
37%, above 1000 ppb to 56, 11, and 31% in wheat, barley and oat respectively. In
smaller populations (n=130, 174, 339), where also total occurrence of DON
producing species Fusarium graminearum and F. culmorum was used as a
predictor, the chances of finding positives above 400/500 ppb were 85, 68, and
39%, above 1000 ppb 89, 75%, and 77% in wheat, barley and oat, respectively.
Using solely the information on the occurrence of the DON producers, the
chances of finding positives above 400/500 ppb were 41, 52, and 66%, 1000 ppb
67, 75%, and 77%. Prediction based of spatially rather coarse gridded weather
data points out very effectively high risk regions and fields of spring wheat.
Though useful, the prediction is less powerful with barley and oat. Using
information on the occurrence of DON producing species improves strongly the
power of the prediction. Its usefulness will further be further enhanced with
improving spatial resolution.
Keywords: Fusarium, mycotoxin, forecasting
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