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- ItemEffectivity and Cost Efficiency of a Tax on Nitrogen Fertilizer to Reduce GHG Emissions from Agriculture(Basel : MDPI AG, 2020) Meyer-Aurich, Andreas; Nadi Karatay, Yusuf; Nausediene, Ausra; Kirschke, DieterThe use of nitrogen (N) fertilizer substantially contributes to greenhouse gas (GHG) emissions due to N2O emissions from agricultural soils and energy-intensive fertilizer manufacturing. Thus, a reduction of mineral N fertilizer use can contribute to reduced GHG emissions. Fertilizer tax is a potential instrument to provide incentives to apply less fertilizer and contribute to the mitigation of GHG emissions. This study provides model results based on a production function analysis from field experiments in Brandenburg and Schleswig-Holstein, with respect to risk aversion by calculating certainty equivalents for different levels of risk aversion. The model results were used to identify effective and cost-efficient options considering farmers’ risk aversion to reduce N fertilizer, and to compare the potential and cost of GHG mitigation with different N fertilizer tax schemes. The results show that moderate N tax levels are effective in reducing N fertilizer levels, and thus, in curbing GHG emissions at costs below 100 €/t CO2eq for rye, barley and canola. However, in wheat production, N tax has limited effects on economically optimal N use due to the effects of N fertilizer on crop quality, which affect the sale prices of wheat. The findings indicate that the level of risk aversion does not have a consistent impact on the reduction of N fertilizer with a tax, even though the level of N fertilizer use is generally lower for risk-averse agents. The differences in N fertilizer response might have an impact on the relative advantage of different crops, which should be taken into account for an effective implementation of a tax on N fertilizer.
- ItemHeat stress risk in European dairy cattle husbandry under different climate change scenarios – uncertainties and potential impacts(Göttingen : Copernicus, 2019) Hempel, Sabrina; Menz, Christoph; Pinto, Severino; Galán, Elena; Janke, David; Estellés, Fernando; Müschner-Siemens, Theresa; Wang, Xiaoshuai; Heinicke, Julia; Zhang, Guoqiang; Amon, Barbara; del Prado, Agustín; Amon, ThomasIn the last decades, a global warming trend was observed. Along with the temperature increase, modifications in the humidity and wind regime amplify the regional and local impacts on livestock husbandry. Direct impacts include the occurrence of climatic stress conditions. In Europe, cows are economically highly relevant and are mainly kept in naturally ventilated buildings that are most susceptible to climate change. The high-yielding cows are particularly vulnerable to heat stress. Modifications in housing management are the main measures taken to improve the ability of livestock to cope with these conditions. Measures are typically taken in direct reaction to uncomfortable conditions instead of in anticipation of a long-term risk for climatic stress. Measures that balance welfare, environmental and economic issues are barely investigated in the context of climate change and are thus almost not available for commercial farms. Quantitative analysis of the climate change impacts on animal welfare and linked economic and environmental factors is rare. Therefore, we used a numerical modeling approach to estimate the future heat stress risk in such dairy cattle husbandry systems. The indoor climate was monitored inside three reference barns in central Europe and the Mediterranean regions. An artificial neuronal network (ANN) was trained to relate the outdoor weather conditions provided by official meteorological weather stations to the measured indoor microclimate. Subsequently, this ANN model was driven by an ensemble of regional climate model projections with three different greenhouse gas concentration scenarios. For the evaluation of the heat stress risk, we considered the number and duration of heat stress events. Based on the changes in the heat stress events, various economic and environmental impacts were estimated. The impacts of the projected increase in heat stress risk varied among the barns due to different locations and designs as well as the anticipated climate change (considering different climate models and future greenhouse gas concentrations). There was an overall increasing trend in number and duration of heat stress events. At the end of the century, the number of annual stress events can be expected to increase by up to 2000, while the average duration of the events increases by up to 22 h compared to the end of the last century. This implies strong impacts on economics, environment and animal welfare and an urgent need for mid-term adaptation strategies. We anticipated that up to one-tenth of all hours of a year, correspondingly one-third of all days, will be classified as critical heat stress conditions. Due to heat stress, milk yield may decrease by about 2.8 % relative to the present European milk yield, and farmers may expect financial losses in the summer season of about 5.4 % of their monthly income. In addition, an increasing demand for emission reduction measures must be expected, as an emission increase of about 16 Gg of ammonia and 0.1 Gg of methane per year can be expected under the anticipated heat stress conditions. The cattle respiration rate increases by up to 60 %, and the standing time may be prolonged by 1 h. This causes health issues and increases the probability of medical treatments. The various impacts imply feedback loops in the climate system which are presently underexplored. Hence, future in-depth studies on the different impacts and adaptation options at different stress levels are highly recommended.
- ItemViticulture in the Laetanian Region (Spain) during the Roman Period: Predictive Modelling and Geomatic Analysis(Basel : MDPI AG, 2020) Stubert, Lisa; Oliveras, Antoni Martín i; Märker, Michael; Schernthanner, Harald; Vogel, SebastianGeographic information system (GIS)-based predictive modelling is widely used in archaeology to identify suitable zones for ancient settlement locations and determine underlying factors of their distribution. In this study, we developed predictive models on Roman viticulture in the Laetanian Region (Hispania Citerior-Tarraconensis), using the location of 82 ancient wine-pressing facilities or torcularia as response variables and 15 topographical and 6 socio-economic cost distance datasets as predictor variables. Several predictor variable subsets were selected either by expert knowledge of similar studies or by using a semi-automatization algorithm based on statistical distribution metrics of the input data. The latter aims at simplifying modelling and minimizing the necessity of a priori knowledge. Both approaches predicted the distribution of archeological sites sufficiently well. However, the best prediction performance was obtained by an expert knowledge model utilizing a predictor variable combination based on recommendations on viticulture by Lucius Junius Moderatus Columella, the prominent ancient Roman agronomist. The results indicate that the accessibility of a location and its connectivity to trade routes and distribution centres, determined by terrain steepness, was decisive for the settlement of viticultural facilities. With the knowledge gained, the ancient cultivated area and number of wine-pressing facilities needed for processing the vineyard yields were extrapolated for the entire study region.
- ItemDynamics of rural livelihoods and rainfall variability in Northern Ethiopian Highlands(Amsterdam [u.a.] : Elsevier, 2019) Adamseged, Muluken E.; Frija, Aymen; Thiel, Andreas[No abstract available]