A crop loss-related forecasting model for sclerotinia stem rot in winter oilseed rape

Sclerotinia stem rot (SSR) is an increasing threat to winter oilseed rape (OSR) in Germany and other European countries due to the growing area of OSR cultivation. A forecasting model was developed to provide decision support for the fungicide spray against SSR at flowering. Four weather variables-air temperature, relative humidity, rainfall, and sunshine duration-were used to calculate the microclimate in the plant canopy. From data reinvestigated in a climate chamber study, 7 to 11 degrees C and 80 to 86% relative humidity (RH) were established as minimum conditions for stem infection with ascospores and expressed as an index to discriminate infection hours (Inh). Disease incidence (DI) significantly correlated with Inh occurring post-growth stage (GS) 58 (late bud stage) (r(2) = 0.42, P /= Inh(i). Historical field data (1994 to 2004) were used to assess the impact of agronomic factors on SSR incidence. A 2-year crop rotation enhanced disease risk and, therefore, lowered the infection threshold in the model by a factor of 0.8, whereas in 4-year rotations, the threshold was elevated by a factor 1.3. Number of plants per square meter, nitrogen fertilization, and soil management did not have significant effects on DI. In an evaluation of SkleroPro with 76 historical (1994 to 2004) and 32 actual field experiments conducted in 2005, the percentage of economically correct decisions was 70 and 81%, respectively. Compared with the common practice of routine sprays, this corresponded to savings in fungicides of 39 and 81% and to increases in net return for the grower of 23 and 45 euro/ha, respectively. This study demonstrates that, particularly in areas with abundant inoculum, the level of SSR in OSR can be predicted from conditions of stem infection during late bud or flowering with sufficient accuracy, and does not require simulation of apothecial development and ascospore dispersal. SkleroPro is the first crop-loss-related forecasting model for a Sclerotinia disease, with the potential of being widely used in agricultural practice, accessible through the Internet. Its concept, components, and implementation may be useful in developing forecasting systems for Sclerotinia diseases in other crops or climates.