EFS12- Book of abstracts - Contact
EFS12- Book of abstracts - Contact
EFS12- Book of abstracts - Contact
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SESSION 5: DISEASE CONTROL AND FORECASTING MODELS<br />
Forecasting helps to target DON toxin testing<br />
T. Kaukoranta, V. Hietaniemi, P. Parikka<br />
MTT Agrifood Research Finland, 31600 Jokioinen, Finland<br />
E-mail: timo.kaukoranta@mtt.fi<br />
We assessed the potential gain from predicting the risk <strong>of</strong> DON toxin for<br />
increasing the efficiency <strong>of</strong> actual toxin testing <strong>of</strong> cereals. Monitoring data on toxin<br />
concentrations in spring cereals were linked to gridded weather data and varietal<br />
susceptibility. Logistic regression models predicting probability <strong>of</strong> moderately high<br />
(>400/500 pbb) and high (>1000 ppb) toxin concentration from weekly mean<br />
temperatures and duration <strong>of</strong> high humidity around flowering and harvesting were<br />
constructed. The observed proportions <strong>of</strong> cases above 400/500 ppb were 8, 8,<br />
26%, and <strong>of</strong> above 1000 ppb were 5, 4, 15%, in spring wheat barley and oat<br />
(n=183, 252, 432), respectively. Using the prediction as a guide, the chance <strong>of</strong><br />
finding positives above 400/500 ppb in the population changed to 80, 14, and<br />
37%, above 1000 ppb to 56, 11, and 31% in wheat, barley and oat respectively. In<br />
smaller populations (n=130, 174, 339), where also total occurrence <strong>of</strong> DON<br />
producing species Fusarium graminearum and F. culmorum was used as a<br />
predictor, the chances <strong>of</strong> finding positives above 400/500 ppb were 85, 68, and<br />
39%, above 1000 ppb 89, 75%, and 77% in wheat, barley and oat, respectively.<br />
Using solely the information on the occurrence <strong>of</strong> the DON producers, the<br />
chances <strong>of</strong> finding positives above 400/500 ppb were 41, 52, and 66%, 1000 ppb<br />
67, 75%, and 77%. Prediction based <strong>of</strong> spatially rather coarse gridded weather<br />
data points out very effectively high risk regions and fields <strong>of</strong> spring wheat.<br />
Though useful, the prediction is less powerful with barley and oat. Using<br />
information on the occurrence <strong>of</strong> DON producing species improves strongly the<br />
power <strong>of</strong> the prediction. Its usefulness will further be further enhanced with<br />
improving spatial resolution.<br />
Keywords: Fusarium, mycotoxin, forecasting<br />
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