| Home | [31–40] << 41 42 43 44 45 46 47 48 49 50 >> [51–60] |
|
Haas, E. (2015). Responses of soil N2O emissions and nitrate leaching on climate input data aggregation: a biogeochemistry model ensemble study (Vol. 5).
Abstract: Numerical simulation models are increasingly used to estimate greenhouse gas (GHG) emissions at site to regional scales and are outlined as the most advanced methodology (Tier 3) for national emission inventory in the framework of UNFCCC reporting.Low resolution simulations needs less effort in computation and data management, but details could be lost during data aggregation associated with high uncertainties of the simulation results. This aggregation effect and its uncertainty will be propagated with the simulations. This paper aims to study the aggregation effects of climate and soil input data on soil N2O emissions and nitrate leaching by comparing different biogeochemistry models. We simulated two 30-year cropping systems (winter wheat and maize monocultures) under nutrient-limited conditions. Input data (climate and soil) was based on a 1 km resolution aggregated on resolutions of 10, 25, 50, and 100. In the first step, the soil data was kept homogenous using representative soil properties while climate data was used on all different scales. In the second step, the climate data was kept homogeneous while soil initial data was used on all different scales. Finally in the third step we have used spatially explicit climate and soil data on all different scales. We analyzed the N2O emissions per unit of crop yield as well as the nitrate leaching on the annual average as well as on daily resolution to study pulsing events for all scenarios and on all scales. The study presents an analysis of the influence of data aggregation.The study gives an indication on adequate spatial aggregation schemes in dependence on the scope of regionalization studies addressing the quantification of losses of reactive nitrogen from managed arable systems. No Label
|
|
Gutzler, C., Helming, K., Balla, D., Dannowski, R., Deumlich, D., Glemnitz, M., et al. (2015). Agricultural land use changes – a scenario-based sustainability impact assessment for Brandenburg, Germany. Ecological Indicators, 48, 505–517.
Abstract: Decisions for agricultural management are taken at farm scale. However, such decisions may well impact upon regional sustainability. Two of the likely agricultural management responses to future challenges are extended use of irrigation and increased production of energy crops. The drivers for these are high commodity prices and subsidy policies for renewable energy. However, the impacts of these responses upon regional sustainability are unknown. Thus, we conducted integrated impact assessments for agricultural intensification scenarios in the federal state of Brandenburg, Germany, for 2025. One Irrigation scenario and one Energy scenario were contrasted with the Business As Usual (BAU) scenario. We applied nine indicators to analyze the economic, social and environmental effects at the regional, in this case district scale, which is the smallest administrative unit in Brandenburg. Assessment results were discussed in a stakeholder workshop involving 16 experts from the state government. The simulated area shares of silage maize for fodder and energy were 29%, 37% and 49% for the BAU, Irrigation, and Energy scenarios, respectively. The Energy scenario increased bio-electricity production to 41% of the demand of Brandenburg, and it resulted in CO2 savings of up to 3.5 million tons. However, it resulted in loss of biodiversity, loss of landscape scenery, increased soil erosion risk, and increased area demand for water protection requirements. The Irrigation scenario led to yield increases of 7% (rapeseed), 18% (wheat, sugar beet), and 40% (maize) compared to the BAU scenario. It also reduced the year-to-year yield variability. Water demand for irrigation was found to be in conflict with other water uses for two of the 14 districts. Spatial differentiation of scenario impacts showed that districts with medium to low yield potentials were more affected by negative impacts than districts with high yield potentials. In this first comprehensive sustainability impact assessment of agricultural intensification scenarios at regional level, we showed that a considerable potential for agricultural intensification exists. The intensification is accompanied by adverse environmental and socio-economic impacts. The novelty lies in the multiscale integration of comprehensive, agricultural management simulations with regional level impact assessment, which was achieved with the adequate use of indicators. It provided relevant evidence for policy decision making. Stakeholders appreciated the integrative approach of the assessment, which substantiated ongoing discussions among the government bodies. The assessment approach and the Brandenburg case study may stay exemplary for other regions in the world where similar economic and policy driving forces are likely to lead to agricultural intensification. (C) 2014 The Authors. Published by Elsevier Ltd.
|
|
Gutierrez, L., Piras, F., & Roggero, P. P. (2015). A global vector autoregression model for the analysis of wheat export prices. American Journal of Agricultural Economics, 97(5), 1494–1511.
Abstract: Food commodity price fluctuations have an important impact on poverty and food insecurity across the world. Conventional models have not provided a complete picture of recent price spikes in agricultural commodity markets, and there is an urgent need for appropriate policy responses. Perhaps new approaches are needed to better understand international spill-overs, the feedback between the real and the financial sectors, as well as the link between food and energy prices. In this article, we present the results from a new worldwide dynamic model that provides the short and long-run impulse responses of the international wheat price to various real and financial shocks.
|
|
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.
|
|
Grosz, B. (2015). The implication of input data aggregation on upscaling of soil organic carbon changes (Vol. 5).
Abstract: In regionalization studies the spatial resolution of driving data is often restricted by data availability or limited computational capacity. Method and level of spatial driver aggregation in upscaling studies are sources of uncertainty and might bias aggregated model results. The suitability of upscaled model results using aggregated driving data depends on both the sensitivity of the model to these model drivers and the scale of interest to which the model output will be aggregated. An important component of soil plant atmosphere systems is the soil organic matter content influencing GHG emissions and the soil fertility of croplands.The implications of driver aggregation schemes on different system properties of croplands have been examined in a scaling exercise within the joint research project MACSUR. In this study, meteorological driving data and data on soil properties on several aggregation levels have been used to calculate the organic carbon change of cropland soils of North Rhine-Westphalia with an ensemble of biogeochemical models.The results of this scaling exercise show that the aggregation of meteorological data has little impact on modeled soil organic carbon changes. However, model uncertainty increases slightly with decreasing scale of interest from NUTS 2 level to smaller grid cell size. Conversely, the aggregation of soil properties resulted in high uncertainty ranges constraining the predictable scale of interest for all models. The study gives an indication on adequate spatial aggregation schemes in dependence on the scope of regionalization studies addressing soil organic carbon changes. No Label
|