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Savary, S., Jouanin, C., Félix, I., Gourdain, E., Piraux, F., Brun, F., et al. (2016). Assessing plant health in a network of experiments on hardy winter wheat varieties in France: patterns of disease-climate associations. Eur. J. Plant Pathol., 146, 741–755.
Abstract: A data set generated by a multi-year (2003–2010) and multi-site network of experiments on winter wheat varieties grown at different levels of crop management is analysed in order to assess the importance of climate on the variability of wheat health. Wheat health is represented by the multiple pathosystem involving five components: leaf rust, yellow rust, fusarium head blight, powdery mildew, and septoria tritici blotch. An overall framework of associations between multiple diseases and climate variables is developed. This framework involves disease levels in a binary form (i.e. epidemic vs. non-epidemic) and synthesis variables accounting for climate over spring and early summer. The multiple disease-climate pattern of associations of this framework conforms to disease-specific knowledge of climate effects on the components of the pathosystem. It also concurs with a (climate-based) risk factor approach to wheat diseases. This report emphasizes the value of large scale data in crop health assessment and the usefulness of a risk factor approach for both tactical and strategic decisions for crop health management.
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Zhao, G., Hoffmann, H., van Bussel, L. G. J., Enders, A., Specka, X., Sosa, C., et al. (2015). Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables. Clim. Res., 65, 141–157.
Abstract: We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.
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