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Dono, G., Cortignani, R., Dell’Unto, D., Doro, L., Lacetera, N., Mula, L., et al. (2014). Productive and economic adaptation of Mediterranean agriculture to climate change (Produktive und wirtschaftliche Anpassung der mediterranen Landwirtschaft an den Klimawandel). In Jahrbuch der ÖGA (Vol. 24, pp. 213–222).
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Leolini, L., Moriondo, M., De Cortazar-Atauri, I., Ruiz-Ramos, M., Nendel, C., Roggero, P. P., et al. (2017). Modelling different cropping systems (Vol. 10).
Abstract: Grapevine is a worldwide valuable crop characterized by a high economic importance for the production of high quality wines. However, the impact of climate change on the narrow climate niches in which grapevine is currently cultivated constitute a great risk for future suitability of grapevine. In this context, grape simulation models are considered promising tools for their contribution to investigate plant behavior in different environments. In this study, six models developed for simulating grapevine growth and development were tested by focusing on their performances in simulating main grapevine processes under two calibration levels: minimum and full calibration. This would help to evaluate major limitations/strength points of these models, especially in the view of their application to climate change impact and adaptation assessments. Preliminary results from two models (GrapeModel and STICS) showed contrasting abilities in reproducing the observed data depending on the site, the year and the target variable considered. These results suggest that a limited dataset for model calibration would lead to poor simulation outputs. However, a more complete interpretation and detailed analysis of the results will be provided when considering the other models simulations.
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Grosz, B., Dechow, R., Hoffmann, H., Zhao, G., Constantin, J., Raynal, H., et al. (2015). The implication of input data aggregation on upscaling of soil organic carbon changes. MACSUR Science Conference.
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Hoffmann, H., Gang, Z., Van Bussel, L. G. J., Enders, A., Specka, X., Sosa, C., et al. (2014). Sensitivity of crop models to spatial aggregation of soil and climate data..
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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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