| Records |
| Author |
Ruiz-Ramos, M.; Trnka, M. |
Title  |
Riesgos asociados a los eventos extremos meteorológicos para la producción de trigo en Europa |
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Report |
| Year |
2015 |
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Tierras |
Abbreviated Journal |
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224 C6 - |
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92-98 |
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CropM |
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MA @ admin @ |
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2077 |
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| Author |
Hoffmann, H.; Ewert, F. |
| Title |
Review on scaling methods for crop models |
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Report |
| Year |
2015 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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| Volume |
6 |
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Pages |
D-C3.1 |
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| Abstract |
Agricultural systems cover a range of organisational levels and spatial and temporal scales. To capture multi-scale problems of sustainable management in agricultural systems, Integrated assessment modelling (IAM) including crop models is often applied which require methods of scale changes (scaling methods). Scaling methods, however, are often not well understood and are therefore sources of uncertainty in models. The present report summarizes scaling methods as developed and applied in recent years (e.g. in SEAMLESS-IF and MACSUR) in a classification scheme based on Ewert et al. (2011, 2006). Scale changes refer to different spatial, temporal and functional scales with changes in extent, resolution, and coverage rate. Accordingly, there are a number of different scaling methods that can include data extrapolation, aggregation and disaggregation, sampling and nested simulation. Comparative quantitative analysis of alternative scaling methods are currently under way and covered by other reports in MACSUR and several publications (e.g. Ewert et al., 2014; Hoffmann et al., 2015; Zhao et al., 2015). The following classification of scaling methods assists to structure such analysis. Improved integration of scaling methods in IAM may help to overcome modelling limitations that are related to high data demand, complexity of models and scaling methods considered. No Label |
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2094 |
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| Author |
Dalgaard, T.; Kjeldsen, C.; Graversgard, M. |
| Title |
Review of regional scale models in the EU and methods commonly used when modelling outcomes of the implementation of the climate change mitigation policies |
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Report |
| Year |
2015 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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6 |
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Pages |
D-L4.1 |
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Management of Nitrogen (N) losses and the related greenhouse gas emissions is one of the most important environmental issues related to agriculture. This report shows examples of an integrated model tool, developed to quantify the N-dynamics at the complex interface between agriculture and the environment, and quantify effects of different management practices. Based on results from the EU funded research projects NitroEurope (www.NitroEurope.eu) and MEAscope (www.MEA-scope.org), examples from the quantification of farm N-losses in European agricultural landscapes are demonstrated. Applications of the dynamic whole farm model FASSET (www.FASSET.dk), and the Farm-N tool (www.farm-N.dk/FarmNTool) to calculate farm N balances, and distribute the surplus N between different types of N-losses (volatilisation, denitrification, leaching), and the related greenhouse gas emissions, show significant variation between landscapes and management practices. Moreover, significant effects of the nonlinearities, appearing when integrating over time, and scaling up from farm to landscape, are demonstrated. Finally, perspectives for stakeholder involvement is included and general recommendations for landscape level management of farm related nitrogen and greenhouse gas fluxes are made, and discussed in relation to ongoing research in the European research projects. No Label |
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MA @ admin @ |
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2110 |
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| Author |
Haas, E. |
| Title |
Responses of soil N2O emissions and nitrate leaching on climate input data aggregation: a biogeochemistry model ensemble study |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-20 |
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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 |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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Approved |
no |
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MA @ admin @ |
Serial |
2135 |
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| Author |
Scholten, M.C.T. |
| Title |
Research and innovation for a competitive and sustainable animal production sector in a climate changing Europe: linking up MACSUR with Animal Task Force |
Type |
Journal Article |
| Year |
2015 |
Publication |
Advances in Animal Biosciences |
Abbreviated Journal |
Advances in Animal Biosciences |
| Volume |
6 |
Issue |
01 |
Pages |
1-2 |
| Keywords |
animal health; livestock; climate smart production; integration |
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English |
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2040-4700 |
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LiveM, ft_macsur |
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no |
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MA @ admin @ |
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4717 |
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