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.

From 1988 to July 2019 more than 100 review articles were published, including opinion papers and book chapters, that focused on potential climate change effects on plant pathogens and the future crop disease risks. Therefore, an overview of them is presented herein, particularly helpful for beginners and non‐experts in climate change biology research. Specifically, this overview contributes to a faster and more convenient identification of appropriate review articles, for example, related to a certain crop, pathogen, plant disease or country of interest. However, not all important crops, pathogens, diseases and countries are considered specifically and in‐depth in any of these review articles, suggesting that there are still research gaps prevalent, which are also highlighted herein. Nevertheless, the overview suggests that researchers are increasingly busy and successful in summarizing the fragmented information spread throughout the international literature. Consequently, they are providing ‘step‐by‐step’ a comprehensive, in‐depth, and continuously updated knowledge platform on potential climate change effects on plant pathogens and the respective crop disease risks in the future, although some aspects will, by nature, be repeated.

Drosophila suzukii is an invasive polyphagous pest of wild and cultivated soft‐skinned fruits, which can cause widespread economic damage in orchards and vineyards. The simulation and prediction of D. suzukii's population dynamics would be helpful for guiding pest management. Therefore, we reviewed and summarized the current knowledge on effects of air temperature and relative humidity on different life cycle parameters of D. suzukii. The literature summary presented shows that high oviposition rates can occur between 18 and 30 °C. Temperatures between 16 and 25 °C resulted in fast and high egg‐to‐adult development success of more than 80%. Oviposition and adult life span were positively affected by high relative humidity; however, the factor humidity is so far rarely investigated. We assume that this is one reason why relative humidity usually is not considered in modelling approaches, which are summarized herein. The high number of recently published research articles on D. suzukii's life cycle suggests that there is already a lot of knowledge available on its biology. However, there are still considerable research gaps mentioned in the literature, which are also summarized herein. Nevertheless, we conclude that sufficient temperature data in the literature are suitable to understand and predict population dynamics of D. suzukii, in order to assist pest management in the field.

As a result of increasing cultivation of corn and potatoes, the polyphagous larvae of the click beetles (Coleoptera: Elateridae), called wireworms, become a problem in agriculture (Parker and Howard 2001). The hypothesis that the vertical distribution of wireworms depends on soil moisture, soil temperature and soil type had to be verified. In field experiments, investigations on wireworm activity in relation to soil moisture and soil temperature were carried out over a period of 2 years. Bait traps were buried in soil, and the appearance of larvae was recorded during the seasons. In laboratory, the optimum soil moisture for larvae was tested with four soil types. Correlations between the percentage of observed wireworms and soil moisture were analysed. The results were taken as the basis for the prediction model SIMAGRIO‐W (SIMulation of the larvae of AGRIOtes (Wireworms)), which appraises the risk of damages on field culture caused by wireworms in relation to soil moisture and soil temperature. With logistic and Gaussian regressions, a first approach of a prediction model was developed. One output of the model displays the risk for damages in form of a binary response, which identifies two risk classes (risk and no risk). A second output displays for four soil types the percentage of appeared wireworms in relation to soil moisture, starting with an undefined amount of wireworms on a field. With a R² from 0.81 to 0.89, the percentage of occurred wireworms could be calculated well. The correlations were significant in all tested soil types (P ≤ 0.05). With data collected in 2010 and 2011, an independent validation was carried out to get information about the predictions quality of the developed model SIMAGRIO‐W. The hit rate was validated within two classes, risk and no risk. With correct results in over 85% of the cases, the class was predicted correctly.

Cercospora leaf spot (CLS) poses a high economic risk to sugar beet production due to its potential to greatly reduce yield and quality. For successful integrated management of CLS, rapid and accurate identification of the disease is essential. Diagnosis on the basis of typical visual symptoms is often compromised by the inability to differentiate CLS symptoms from similar symptoms caused by other foliar pathogens of varying significance, or from abiotic stress. An automated detection and classification of CLS and other leaf diseases, enabling a reliable basis for decisions in disease control, would be an alternative to visual as well as molecular and serological methods. This paper presents an algorithm based on a RGB‐image database captured with smartphone cameras for the identification of sugar beet leaf diseases. This tool combines image acquisition and segmentation on the smartphone and advanced image data processing on a server, based on texture features using colour, intensity and gradient values. The diseases are classified using a support vector machine with radial basis function kernel. The algorithm is suitable for binary‐class and multi‐class classification approaches, i.e. the separation between diseased and non‐diseased, and the differentiation among leaf diseases and non‐infected tissue. The classification accuracy for the differentiation of CLS, ramularia leaf spot, phoma leaf spot, beet rust and bacterial blight was 82%, better than that of sugar beet experts classifying diseases from images. However, the technology has not been tested by practitioners. This tool can be adapted to other crops and their diseases and may contribute to improved decision‐making in integrated disease control.