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.

Four components of rate‐reducing resistance to Cercospora leaf spot in sugar beet (infection efficiency of conidia RC1, incubation period RC2, size of necrotic spots RC3 and spore yield RC4), previously measured in single infection cycle experiments, were integrated into a model simulating the chain of infection cycles under field conditions, as influenced by weather. To integrate resistance components, variables accounting for infection frequency, incubation period, affected leaf area, and infectiousness – which are computed for a susceptible cultivar – were modified by means of coefficients which reduced (RC1, RC3, RC4) or increased (RC2) them. Outputs obtained by running the model and changing resistance components actually reduced the rate of disease progress and the area under the disease progress curve of epidemics (AUDPC), as happens at field level; therefore, the approach may be considered successful. Changes in single resistance components were closely correlated with changes in AUDPC: improvements in RC1, RC3 or RC4 reduced AUDPC by the same, over the whole range of variation in infection frequency, affected leaf area, and infectiousness; on the contrary, little improvements in RC2 were more effective than stronger ones. When components acted simultaneously, each of them reduced disease progress in proportion to its magnitude; when all components were improved by the same amount, they had about the same effectiveness in slowing the epidemic. Changing more components simultaneously reduced the disease development slightly more than additively. Advantages for plant breeders in improving their selection strategies are outlined.

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.

Attribution studies on recent global warming by Global Climate Model (GCM) ensembles converge in showing the fundamental role of anthropogenic forcings as primary drivers of temperature in the last half century. However, despite their differences, all these models pertain to the same dynamical approach and come from a common ancestor, so that their very similar results in attribution studies are not surprising and cannot be considered as a clear proof of robustness of the results themselves. Thus, here we adopt a completely different, non-dynamical, data-driven and fully nonlinear approach to the attribution problem. By means of neural network (NN) modelling, and analysing the last 160 years, we perform attribution experiments and find that the strong increase in global temperature of the last half century may be attributed basically to anthropogenic forcings (with details on their specific contributions), while the Sun considerably influences the period 1910–1975. Furthermore, the role of sulphate aerosols and Atlantic Multidecadal Oscillation for better catching interannual to decadal temperature variability is clarified. Sensitivity analyses to forcing changes are also performed. The NN outcomes both corroborate our previous knowledge from GCMs and give new insight into the relative contributions of external forcings and internal variability to climate.