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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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Angelova, D. (2014). The state-contingent approach to production and choice under uncertainty: usefulness as a basis for economic modeling (Vol. 3). FACCE MACSUR Reports, 3.
Abstract: The state-contingent approach developed by Chambers and Quiggin (2000) constitutes an attractive blend of a theory of production analysis under uncertainty and a theory of decision-making under uncertainty.One of the goals of this contribution is to introduce the reader to the approach by outlining its contents while comparing and contrasting it to related theories. With respect to production analysis: an emphasis is made on the ability of the approach to deliver well defined cost functions corresponding to stochastic production technologies. With respect to decision-making under uncertainty: the comparison with other theories consistent with a rational agent emphasizes the production theoretical basis of the state-contingent approach.It is the author’s belief that appropriately categorizing the state-contingent approach serves the primary goal of this work: to explore its usefulness as a basis for economic modeling. Some challenges regarding an empirical implementation are discussed: challenges in estimating the parameters of a state-contingent technology representation in general, as well as challenges arising from the fact that the approach is constructed around the argument pioneered by Leonard J Savage: that probabilities underlying economic decision-making are inherently subjective.(The financial support of ScienceCampus Halle is gratefully acknowledged.) No Label
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Kersebaum, K., & C,. (2014). Results of uncalibrated model runs available (ROTATIONEFFECTS) (Vol. 3).
Abstract: The study ROTATIONEFFECT aims to compare the output of different models simulating field data sets with multi-year crop rotations including different treatments. Data sets for 5 locations in Europe were distributed to 19 interested modeller groups comprising a total of 201 crop growth seasons. In a first step only minimal information for calibration were provided to the modellers. In total 14 modelling teams sent their “uncalibrated” results as single-year calculations and/or calculations of rotation depending on the capability of the model. 7-10 models were capable to run the rotations as continuous runs. Up to 12 models provided single year simulations of at least one crop. Comparing results of models which provided both single year and continuous runs, show a little lower root mean square error for the continuous rotations runs. Cereal crop yields were generally better simulated than tuber/beet yields. Additionally, the models’ response to various treatments (irrigation/rainfed, nitrogen level, CO2 level, residue management/ tillage, catch crops) were compared to observed differences. First indicators of model performance have been developed and presented at international conferences. No Label
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Kersebaum, K., & C. (2015). Model intercomparison for calibrated models (Vol. 4).
Abstract: The study ROTATIONEFFECT aims to compare the output of different models simulating field data sets with multi-year crop rotations including different treatments.Within the first Step (1a2a) data sets (comprising a total of 301 crop growth seasons) for 5 locations in Europe were distributed to 15 interested modeller groups.For this step only minimal information for calibration were provided to the modellers. In total 15 modelling teams sent their “uncalibrated” results as single-year calculations and/or continuous calculations of rotation depending on the capability of the model. Results have been evaluated and the paper submitted (European Journal of Agronomy).Now, within the 2nd step (1b2b) modellers were provided with more information on the crop for the calibration of models. Again, results of calibrated runs were collected.6 models were capable to run the rotations as continuous runs and another set of 6 models provided single year simulations.A first overview of the improvement of predictions due to calibration has been produced. Result files have been uploaded to the web platform for CropM results at Aarhus University (Work package C2 – data management). No Label
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Rusu, T. (2014). Impact of Climate Change on Crop Land and Technological Recommendations for the Main Crops in Transylvanian Plain, Romania (Vol. 3).
Abstract: The Transylvanian Plain (TP) is an important agricultural production area of Romania that is included among the areas with the lowest potential of adapting to climate changes in Europe. Thermal and hydric regime monitoring is necessary to identify and implement measures of adaptation to the impacts of climate change. Soil moisture and temperature regimes were evaluated using a set of 20 data logging stations positioned throughout the plain. Each station stores electronic data regarding ground temperature at 3 depths (10, 30, 50 cm), humidity at a depth of 10 cm, air temperature (at 1 m) and precipitation. For agricultural crops, the periods of drought and extreme temperatures require specific measures of adaptation to climate changes. During the growing season of crops in the spring (April – October) in the southeastern, southern, and eastern escarpments, precipitation decreased by 43.8 mm, the air temperature increased by 0.37°C, and the ground temperature increased by 1.91°C at a depth of 10 cm, 2.22°C at a depth of 20 cm and 2.43°C at a depth of 30 cm compared with values recorded for the northern, northwestern or western escarpments. Water requirements were ensured within an optimal time frame for 58.8-62.1% of the spring row crop growth period, with irrigation being necessary to guarantee the optimum production potential. The biologically active temperature recorded in the TP demonstrates the need to renew the division of the crop areas reported in the literature. No Label
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