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SESSION 5: DISEASE CONTROL AND FORECASTING MODELS P119 - Predicting Deoxynivalenol in Oats under Northern European Conditions T. Börjesson 1 , O. Elen 2 , C. G. Pettersson 1 , T. Persson 2 , H. Eckersten 3 1 Lantmännen, P.O. Box 30192 SE-104 25 Stockholm, Sweden; 2 Bioforsk, Norwegian Institute for Agricultural and Environmental Research, Norway; 3 Swedish University of Agricultural Sciences, SE- 750 07 Uppsala, Sweden. E-mail: thomas.borjesson@lantmannen.com Contamination of Deoxynivalenol (DON), produced by fungi within the genus Fusarium, is an increasing problem in the production of oats in Norway and Sweden. This crop is susceptible to Fusarium-infection especially during the flowering stage. The goal of this study is to adapt and test a dynamic model, which, given an inoculum source, calculates the risk of DON contamination in oats as a function of planting date and the prevailing weather conditions. This model could be used, alone, or in combination with statistical models. The model, including the calculation of DON risk indices, is adapted to oats from previous prediction models of Fusarium infections and DON contamination in wheat (Rossi et al., 2003, Bulletin OEPP/EPPO 33, 421-425; Del Ponte et al., 2005, Fitopatologia Brasileira 30, 634-642). Field experiments with four oat cultivars (cv Axeli, Belinda, Ivory and Kerstin) are carried out at four locations in south-eastern Norway and western Sweden during the period 2012-2014. The appearance of panicles over time and the flowering progression within single panicles are assessed. Daily data of air temperature, precipitation and relative air humidity, are obtained from weather stations near the field experiments. DON is analyzed in samples taken at grain maturity using ELISA (Ridascreen, Rhone.Biopharm, Darmstadt, Germany). Crucial model parameters for the flowering progress of oats, and thus also for the susceptible tissue that is available for an infection at a given time, are calibrated against the experimental data. In addition, different functions to describe the direct impact of weather factors on an infection are evaluated against experimental and weather data. Subsequently, we apply different methods to evaluate the model against DON samples collected from farmers’ fields together with field and management data. Results, so far, indicate that this model could be a useful tool to predict DON in oats in Scandinavia. Keywords: DON, flowering, simulation model, weather impact 213
SESSION 5: DISEASE CONTROL AND FORECASTING MODELS P120 - A hierarchical bayesian approach to predict the risk of Fusarium head blight in wheat J. M. C. Fernandes, M. Nicolau Embrapa Trigo, Rodovia BR 285, km 294, Passo Fundo, RS, 99001-970, Brazil 99001-970 E-mail: mauricio.fernandes@embrapa.br Fusarium head blight (FHB) is a fungal disease of increasing concern to both yield and grain quality. Risk modeling and prediction can be useful in disease management and new data and statistical approaches can improve accuracy of the predictions. Ultimately, management decisions need to be based on the outbreak probability. Currently most approaches for risk assessment rely upon deterministic weather-driven models based on regression techniques. After more than a decade of development and use of a FHB decision support system across Brazil wheat producing areas, an improvement was made taking into account the prediction of airborne inoculum. Thus, motivating us to explore other modeling techniques that could provide a cohesive statistical framework and formally integrate the available information on both disease life cycle and observations. The improved system is based on a Hierarchical Bayesian (HB) method to estimate the probability of FHB outbreaks. Prior distributions of the estimates for each model component were derived from previous studies using MCMC (Markov Chain Monte Carlo) algorithms. A sample-based approach combines inference from different crop/disease cycle processes: spore dispersal, wheat heading and flowering, infection risk and host susceptibility. As result, predictions were consistent with actual epidemic data both from non epidemic and epidemic years. This study contributes with novel statistical approaches to predict FHB risk than can integrate decision support systems to assist on FHB management. Hierarchical Bayesian modeling is demonstrated as both a useful analytical tool for FHB risk management and a natural investigative paradigm for developing and focusing on the release of new wheat cultivars. Keywords: modeling, risk assessment, decision-making 214
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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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ORAL PRESENTATIONS 25
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KEYNOTE LECTURE SESSION 1: FUSARIUM
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SESSION 1: FUSARIUM - GENETICS, GEN
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KEYNOTE LECTURE SESSION 2: SECONDAR
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SESSION 2: SECONDARY METABOLITES -
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KEYNOTE LECTURE 1 SESSION 4: GENETI
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SESSION 4: GENETICS OF HOSTS - PLAN
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KEYNOTE LECTURE 2 SESSION 4: GENETI
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KEYNOTE LECTURE SESSION 5: DISEASE
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SESSION 5: DISEASE CONTROL AND FORE
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SESSION 6: FUTURE CHALLENGE FOR EUR
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SESSION 6: FUTURE CHALLENGE FOR EUR
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POSTER PRESENTATIONS 91
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Page 241 and 242: de Hoog G. S., 89, 114, 236 de Kler
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Page 247 and 248: BOKU-University of Natural Resource
Page 249 and 250: touatisihem03@yahoo.fr He Xinyao In
Page 251 and 252: steve.oliver@bioc.cam.ac. uk Ortega
Page 253 and 254: Subramaniam Gopal Agriculture and A
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