Browsing by Subject "Scenario"

Now showing 1 - 2 of 2

Regionalising a soil-plant model ensemble to simulate future yields under changing climatic conditions
(2023) Bendel, Daniela Silke; Streck, Thilo
Models are supportive in depicting complex processes and in predicting their effects. Climate models are applied in many areas to assess the possible consequences of climate change. Even though Global Climate Models (GCM) have now been regionalised to the national level, their resolution of down to 5x5 km2 is still rather coarse from the perspective of a plant modeller. Plant models were developed for the field scale and work spatially explicitly. This requires to make adjustments if they are applied at coarser scales. The regionalisation of plant models is reasonable and advantageous against the background of climate change and policy advice, both gaining in importance. The higher the spatial and temporal heterogeneity of a region, the greater the computational need. The (dis)aggregation of data, frequently available in differing resolutions or quality, is often unavoidable and fraught with high uncertainties. In this dissertation, we regionalised a spatially-explicit crop model ensemble to improve yield projections for winter wheat under a changing climate. This involved upscaling a crop model ensemble consisting of three crop models to the Stuttgart region, which has an area of 3,654 km2. After a thorough parameter estimation performed with a varying number of Agricultural Response Units on a high-performance computing cluster, yield projections up to the year 2100 were computed. The representative concentration pathways of the Intergovernmental Panel on Climate Change (IPCC) RCP2.6 (large reduction of CO2 emissions) and RCP8.5 (worst case scenario) served as a framework for this effort. Under both IPCC scenarios, the model ensemble predicts stable winter wheat yields up to 2100, with a moderate decrease of 5 dt/ha for RCP2.6 and a small increase of 1 dt/ha for RCP8.5. The variability within the model ensemble is particularly high for RCP8.5. Results were obtained without accounting for a potential progress in wheat breeding.
Wirtschaftliche Analyse der Tierhaltungsbetriebe um die Metropole Moskau unter besonderer Berücksichtiung von Aufwands- und Ertragsrisiken
(2017) Droganova, Yulia; Fuchs, Clemens
The slow modernisation of the agricultural sector in the Russian Federation after the USSR era, the adoption and the ratification of the Basel Accords, the accession of Russia to the World Trade Organisation in 2012, and finally the crisis in the Ukraine, followed by the import ban on numerous agricultural, fishery products from the EU, USA, Canada, Australia, Norway in August 2014 are the most significant problems which found their reflection in this dissertation. This lead to an increased interest to analyse livestock farms in the Moscow region in consideration of risks in order to predict their profitable development. The goal of the current research was to identify the impending bankruptcy of the Russian livestock farms as early as possible in order to engage in efficient counter planning. The majority of the livestock farms in the Moscow region are dairy farms, which was why this type of livestock farming became the main topic of research for this thesis. The classification of dairy farms into solvent and insolvent farms is based on the application of the multivariate discriminant analysis, a bankruptcy predicting method that is widely used by many banks in Europe and the USA. The risk factor is taken into account in the empirical model of the dairy farm by setting up the stochastic Monte Carlo simulation with the most important random variables (prices, yields and interest rate) in order to quantitatively measure their influence on the economic profitability of a typical dairy farm. Following the results of the discriminant analysis, questions concerning the validation of this model were be raised. What measures were required for the dairy farms, classified as insolvent to deter bankruptcy? This question was examined using a cash flow model, summaries of relevant data and requirements for an empirical model of the dairy farms were collected through interviews of subject experts. On the basis of reference scenario/status quo scenario, three main scenarios were created: Scenario 1 Re-structuring, scenario 2 Improvement of Management and Marketing Activities, and scenario 3 Risk analysis, whereby the measures from scenarios 1 and 2 were stochastically simulated in the scenario 3 Risk analysis in order to be able to estimate the economic risks. From the data set of 31 farms, five typical model farms were selected: two correctly classified solvent, two correctly classified insolvent, and one, which showed up as a type 1 error in the discriminant analysis. A reference scenario describes the data period based on the average values of operational performance from 2008-2010, and the individualized data from the Russian statistics of 2011-2013 and forms a data basis for the scenarios 1 to 3. Scenario 1a Restructuring under Russian Insolvency Law is counterpoised to scenario 1b Restructuring under German Insolvency Law. Scenario 2a Improvement of Management and Marketing Activities without Investment and scenario 2b Improvement of Management and Marketing Activities with Investment contains measures to improve management and marketing. Labour costs were doubled, maintenance, repair costs as well as some other costs were adjusted; while the milk yields, the weight of the dairy cows, the silage yields and the yields of pastures, meadows have been estimated with a logistic function. Over a planning period of twelve years, the dairy farms classified as solvent maximised the increase of their equity capital in scenario 2b, which represents the best result compared to all other scenarios considered. Firstly, it has shown that an adequate insolvency law should support the restructuring process, secondly that training and education, consulting, motivation of employees through higher wages can lead to a better-combined performance in comparison to restructuring. In scenario 3 Risk Analysis, ten relevant random variables and their volatility were simulated and analysed within the frame conditions of the initial Scenarios 1 and 2. In addition, the target values selected were: equity after tax, equity change per hectare of agricultural area, internal equity interest and profit after tax. The presented results explain how on one hand, an adequate insolvency law can support the restructuring process and lead to reinstate solvency of the dairy farms. On the other hand, these results confirm, that the improvements in management can also lead to significant positive achievements in operational performance as opposed to restructuring. The farm, which belongs to type 1 error in the discriminant analysis, has ranked as a solvent dairy farm over the planning period of twelve years in all the scenarios considered. In this case, it can be concluded that the simulation model in the researched composition with the multivariate discriminant analysis has indirectly served to be applicable for validation purposes of the determined discriminant function.