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Piayda, A. (2015). The FACCE-ERA-Net Plus project “Climate smart Agriculture on Organic Soils” (CAOS) (Vol. 5).
Abstract: The FACCE-ERA-Net Plus project “Climate smart Agriculture on Organic Soils” (CAOS) focuses on farmed organic soils, hotspots of vulnerability and GHG emissions in Europe. We propose to use wet organic soils as risk insurance in dry periods on farm/regional level, while water and soil management assures trafficability in wet conditions. Wet management systems abate peat degradation and therefore foster higher infiltration rates and ease subirrigation. Economically, wetness-adapted crops with stable yield quantity/quality for food, feed and bioenergy are needed. Convincing farmers and decision makers of profitable and resilient wet management systems on organic soils under climate change needs proof by on-farm experiments, historical evidence and bi-directional involvement.Overall, we aim to generate knowledge of climate smart agricultural system design on organic soils adapted to regional European conditions. CAOS will provide and distribute evidence that active management with control of groundwater levels, improved trafficability and alternative high productivity crops improves yield stability/quality and climate change resilience while mitigating GHG emissions and improving soil/water quality. We hypothesize that the strong potential for adaptation to increased climatic variability on farmed organic soil will facilitate mitigation of the largest GHG source from agriculture in Central/Northern Europe. At MACSUR conference, we present the project concept and first results. No Label
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Schmidhuber, J. (2015). The Food Equation”: Taking a long/term View on World Agriculture, Climate Change and Food Security (Vol. 4).
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Özkan, S. (2015). The greenhouse gas emissions intensity of herds with mastitis (Vol. 5).
Abstract: Mastitis is an inflammatory disease of milking cows, causing production and economic losses in dairy farms. The main pathogens causing majority of the intramammary infections are Staphylococcus aureus, Streptococcus dysgalactiae, Escherichia coli and coagulase-negative Staphylococci. Here, we analysed the effect of mastitis on herd parameters such as milk yield, feed intake, replacement rate, gross margin and greenhouse gas emissions. The data were collected from the Norwegian Dairy Herd Recording System between 2010 and 2012. The farm data were recorded from 20 farms in Norway, based on health, fertility and breeding characteristics. SimHerd, a computer simulation model was used to estimate the impact of the observed levels of mastitis on herd parameters which were then fed into a whole farm model, HolosNor, to calculate the greenhouse gas emissions on the farm. The standard values provided in the SimHerd except for mastitis occurrence were applied in the scenario simulations. A further study is planned to parameterize each herd with specific herd characteristics in SimHerd so that herd specific estimates of the effect of mastitis on greenhouse gas emissions can be performed. No Label
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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
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Mittenzwei, K. (2015). The importance of climate and policy uncertainty in Norwegian agriculture (Vol. 5).
Abstract: The paper addresses future climate and policy uncertainty for agricultural production and food security in Norway. The two crop simulation models, CSM-CERES-Wheat and, the LINGRA model, were used to determine the impact of climate change on grain yield of spring wheat, and harvest security and biomass yield of timothy, an important forage grass in Northern Europe, respectively. Harvestable yield distributions from the crop models were fed into a stochastic version of the economic sector model Jordmod. Distributions of the rates of agricultural subsidies rates were assessed based on past policy changes and prospective reforms. The model was used to assess the effects of both climate and policy uncertainty on agricultural production, land use, and national food security. Jordmod is comprised of a supply module in which stochastic profits for more than 300 regional farms are maximized and a deterministic market module which maximizes social welfare in the agricultural sector. Socio-economic scenarios were developed around the level of ambition of Norwegian agricultural policy makers. The model results were contrasted with the deterministic results based on average yield and payment rates. The innovation of this paper lays in assessing the combined effects of future climate and policy uncertainty for the agricultural sector in Norway. It also highlights the potential errors made by neglecting these types of uncertainty in economic modelling. No Label
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