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KEYNOTE LECTURE SESSION 5: DISEASE CONTROL AND FORECASTING MODELS Plant disease prediction using data mining and machine learning: a case study on Fusarium head blight and deoxynivalenol content in winter wheat S. Landschoot 1,2 , K. Audenaert 1,3 , W. Waegeman 2 , B. De Baets 2 , G. Haesaert 1,3 1 Faculty of Applied Bioscience Engineering, University College Ghent, Valentin Vaerwyckweg 1, BE- 9000 Gent, Belgium; 2 KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, BE-9000 Gent, Belgium; 3 Department of Crop Protection, Laboratory of Phytopathology, Ghent University, Coupure links 653, BE-9000 Gent, Belgium. E-mail: sofie.landschoot@hogent.be Crop diseases are one of the major causes of yield loss and hence timely application of remedial measures may combat yield loss to a great extent. Prediction models help in providing prior knowledge of the time and severity of the outbreak of diseases and allow growers to underpin decision making on cultivation techniques and the application of fungicides. Existing prediction models are based on the combined effect of host susceptibility, inoculum strength and meteorological conditions on disease development. The main objective for developing a useful predictive model may be timely accurate predictions, but this must be accomplished using the most simple, easily acquired, and inexpensive data. If the data required to run the model is too expensive to acquire, it is unlikely that the model will be deployed in practice. Fusarium head blight (FHB) results in yield loss and quality loss due to mycotoxins. Given these severe consequences throughout the world, various models for FHB have been developed. Although of great interest, these models cannot directly be transported to a Belgian context. The lack of fit of these models could be explained by regional factors. Therefore, the objective of this research was to implement machine learning techniques to generate field-specific predictions of FHB and deoxynivalenol in winter wheat. To reach this goal various regression techniques were compared. Additionally, the influence of a correct cross-validation strategy, to gain insight in the performance of the models, was studied. In order to deliver the models to growers and industry a web tool was developed. This tool will lead to an integrated pest management and a more sustainable agriculture since the optimal time and dose of fungicides is recommended. It will also have an economic impact by reducing the input of fungicides and a more efficient sampling strategy to guarantee food and feed safety. Keywords: disease forecasting, Fusarium, web tool, winter wheat 75
SESSION 5: DISEASE CONTROL AND FORECASTING MODELS Wheat monitoring in Switzerland: Which cropping factors influence occurrence of Fusarium species and mycotoxins? S. Vogelgsang, I. Bänziger, T. D. Bucheli, F. E. Wettstein, H. R. Forrer Agroscope Reckenholz-Tänikon Research Station ART, 8046 Zurich, Switzerland E-mail: susanne.vogelgsang@art.admin.ch Throughout four years, wheat samples and information on cropping measures were collected from Swiss growers. Wheat grains were examined for incidence of various Fusarium head blight (FHB) causing species, DNA amount of F. graminearum (FG) and content of different mycotoxins. In a total of 527 samples from 16 cantons, three Fusarium species were dominant: FG (64%), followed by F. poae (19%) and F. avenaceum (10%). The mean content of deoxynivalenol (DON) was 650 ppb and 12% of all samples were above the European limit for unprocessed cereals (1’250 ppb). The mean DON content varied between 260 ppb in 2009 and 970 ppb in 2008. There was a significant positive correlation between FG incidence and DON content (r 2 =0.52) and between FG DNA quantity and DON content (r 2 =0.64). The combination of pre-crop maize and conservation tillage versus ploughing resulted in an average DON content of 2’200 ppb or 370 ppb, respectively. The use of other pre-crops further reduced the average DON content to 240 ppb. In samples from the two cropping systems wheat after maize with or without ploughing, zearalenone and nivalenol (NIV) contents were lower but showed a similar pattern as that DON, with means of 208 and 17 ppb for ZEA and to 27 and 16 ppb for NIV, respectively. No significant correlation was found between F. poae incidence and the NIV content. One reason may be the presence of F. graminearum NIV chemotypes in Switzerland. Currently, DNA quantities of F. poae as well as the ratio of FG chemotypes are investigated by PCR analyses. The resulting data combined with in-depth multivariate analysis of various cropping measures will provide a better understanding of the relationship between fungal prevalence and toxin content. This knowledge is crucial to develop sustainable cropping systems with low risks of Fusarium occurrence and contamination of wheat. Keywords: cropping, Fusarium head blight, monitoring, wheat 76
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Bordeaux EFS12 12 th European Fusar
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TABLE OF CONTENTS Welcome from the
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4 SPONSORSHIP The 12 th European Fu
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8 13:00 - 14:00 Lunch 14:00 - 15:00
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10 Session 4: Genetics of Hosts - P
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12 Thursday, May 16 th Session 6: F
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Evidence for birth-and-death evolut
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Future challenges of Fusarium and m
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P35 - Screening deoxynivalenol in o
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P76 - Fusarium verticillioides and
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P113 - Agronomic practices and risk
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de Hoog G. S., 89, 114, 236 de Kler
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BOKU-University of Natural Resource
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touatisihem03@yahoo.fr He Xinyao In
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steve.oliver@bioc.cam.ac. uk Ortega
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Subramaniam Gopal Agriculture and A
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