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Kuhnert, M. (2015). Impact of climate aggregation over different scales on regional NPP modelling (Vol. 5).
Abstract: In spatial modelling of Net Primary Productivity (NPP), predictability and uncertainty depends on the availability of input data, as well as on the scale of the available data sets. Therefore, the study presented here quantifies the impact of aggregation effect of input data of different scales for a regional modelling approach using 5 different resolutions. As part of this study, the presentation focuses on the impact of the climate aggregation on the simulation of NPP. The effect is investigated on the model results of 11 different crop and biogeochemical models simulating NPP for wheat and maize for the area of the German state of North Rhine-Westphalia. The focus of the study is on the impact of drought effects across the scales considered. The data are analysed on annual time steps we followed two approaches to investigate the impact of water limitation on primary production: First, two model runs, one considers water limitation and the other one ignores the impacts of water limitation on plant production second, an external definition of dry conditions by a drought index, only considering climate data, enables a separation of grid-cells and years with drought impacts, independent of the model internal functions. The results show hardly any difference between the overall average NPP across the scales, but some variability for the impact of extreme weather conditions on the simulated NPP. No Label
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Özkan, Ş., Ahmadi, B. V., Bonesmo, H., Østerås, O., Stott, A., & Harstad, O. M. (2015). Impact of animal health on greenhouse gas emissions. Advances in Animal Biosciences, 6(01), 24–25.
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Holman, I. (2015). Identifying where future landuse allocation in Europe is robust to climate and socio-economic uncertainty (Vol. 5).
Abstract: The spatial distribution of future European landuse will be influenced by yield changes arising from climate change and changes in profitability as a consequence of socio-economic change (arising from changing food demand; prices; technology etc). To understand how these factors affect future land use allocation, a modelling system has been set up to predict agricultural land use across the EU under any scenario set of climate and socio- and techno-economic data. Metamodels of crop and forest yields, and optimal cropping and profit are derived from the outputs of the IMPEL, GOTILWA+, SFARMODand WaterGAP models. Profitability of each possible land use is modelled across the EU, assuming that use will change to the most profitable in the timescale being considered (2050). Land use in a grid is then allocated based on profit, with minimum profit thresholds set for intensive agriculture (arable or grassland), extensive agriculture, managed forest and finally unmanaged forest or unmanaged land. The European demand for food as a function of population, imports, food preferences and bioenergy, is a production constraint, as is irrigation water available. The model iterates prices until demand is satisfied (or cannot be met) and basin water usage for irrigation is not more than is available.This presentation describes the application of the modelling system across future climate change uncertainty space (as given by 60 combinations of downscaled 10’x10’ gridded climate outputs from 5 Global Climate Models, 3 climate sensitivities and 4 emissions scenario) under both baseline and four future socio-economic scenarios to identify those areas of Europe in which the spatial allocation of agricultural landcovers are robust to this uncertainty. No Label
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Ramirez-Villegas, J., Watson, J., & Challinor, A. J. (2015). Identifying traits for genotypic adaptation using crop models. J. Experim. Bot., 66(12), 3451–3462.
Abstract: Genotypic adaptation involves the incorporation of novel traits in crop varieties so as to enhance food productivity and stability and is expected to be one of the most important adaptation strategies to future climate change. Simulation modelling can provide the basis for evaluating the biophysical potential of crop traits for genotypic adaptation. This review focuses on the use of models for assessing the potential benefits of genotypic adaptation as a response strategy to projected climate change impacts. Some key crop responses to the environment, as well as the role of models and model ensembles for assessing impacts and adaptation, are first reviewed. Next, the review describes crop-climate models can help focus the development of future-adapted crop germplasm in breeding programmes. While recently published modelling studies have demonstrated the potential of genotypic adaptation strategies and ideotype design, it is argued that, for model-based studies of genotypic adaptation to be used in crop breeding, it is critical that modelled traits are better grounded in genetic and physiological knowledge. To this aim, two main goals need to be pursued in future studies: (i) a better understanding of plant processes that limit productivity under future climate change; and (ii) a coupling between genetic and crop growth models-perhaps at the expense of the number of traits analysed. Importantly, the latter may imply additional complexity (and likely uncertainty) in crop modelling studies. Hence, appropriately constraining processes and parameters in models and a shift from simply quantifying uncertainty to actually quantifying robustness towards modelling choices are two key aspects that need to be included into future crop model-based analyses of genotypic adaptation.
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Wallach, D., & Rivington, M. (2015). Identification and quantification of differences between models (Vol. 6).
Abstract: A major goal of crop model inter-comparison is model improvement, and an important intermediate step toward that goal is understanding in some detail how models differ, and the consequences of those differences. This report is intended as a first attempt at describing possible techniques for relating differences between model outputs to specific aspects of the models. No Label
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