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Hoveid, Ø. |
| Title |
A prototype stochastic dynamic equilibrium model of the global food system |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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5 |
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Sp5-24 |
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The risks of food consumption are primarily linked to those of food production due to stochastic weather. Other sources of risk are associated with break-down of food trade or transport for weather or political reasons. Hopefully, future cures against increased risk due to climate change may be found with new agricultural technologies, systems of storage from favorable to unfavorable periods, more flexible trade-arrangements between favorable and unfavorable places. However, in the short run one has to rely on the available technology, storage facilities and trade agreements. With a realistic model of the stochastic global food system, it should be possible to measure risks of certain extreme unfavorable events.A realistic case will have countries with different climate in different growing seasons. Markets will be open for trade at a number of points per year, in which decisions of production, storage, trade and consumption can be coordinated as a static equilibrium. Determinants of this equilibrium are the weather up to this date reflected in the state of crops, the available harvested stocks and the decision-maker’s preferences. With a global stochastic process of weather, a stochastic sequence of equilibria follows. 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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no |
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MA @ admin @ |
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2139 |
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| Author |
Hoveid, Ø. |
| Title |
Prototype of stochastic equilibrium model of the food system |
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Report |
| Year |
2015 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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| Volume |
6 |
Issue |
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Pages |
D-T2.5 |
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Food security is an issue of risk. If climate change is not responded to with diet, technology and/or policy changes, it may lead to reduced food security for the world population, in particular the poorer part which in longer periods may not afford to purchase food in sufficient quantity and quality. In order to improve the situation, certain policy changes may be required. In some cases are policy recommendations relatively obvious, while in other cases a deeper insight in the stochastic dynamics of food supply and storage is required to assess the consequences of policy proposals. The relatively obvious part is that farmers need be responsive in periods of low total production, so that sufficient supply restores quickly. Moreover, trade should allow local shortages to be covered. Many national policies with the goal of self-sufficiency aim in the opposite direction with stable prices and production and relatively less flexibility in production. The stochastic dynamics of food supply can be analysed in more detail with a dynamic stochastic general equilibrium model (DSGE). Although agriculture by nature is about taking decisions under uncertainty, quantitative stochastic dynamic models for policy analysis in agriculture have not yet emerged. The contribution in MACSUR is a formalization of a class of DSGE-s based on representation of biological processes managed with regard to outcomes due to uncertain nature. No Label |
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2115 |
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| Author |
Holman, I. |
| Title |
Identifying where future landuse allocation in Europe is robust to climate and socio-economic uncertainty |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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5 |
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Sp5-23 |
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The spatial distribution of future European landuse will be influenced by yield changes arising from climate change and changes in profitability as a consequence of socio-economic change (arising from changing food demand; prices; technology etc). To understand how these factors affect future land use allocation, a modelling system has been set up to predict agricultural land use across the EU under any scenario set of climate and socio- and techno-economic data. Metamodels of crop and forest yields, and optimal cropping and profit are derived from the outputs of the IMPEL, GOTILWA+, SFARMODand WaterGAP models. Profitability of each possible land use is modelled across the EU, assuming that use will change to the most profitable in the timescale being considered (2050). Land use in a grid is then allocated based on profit, with minimum profit thresholds set for intensive agriculture (arable or grassland), extensive agriculture, managed forest and finally unmanaged forest or unmanaged land. The European demand for food as a function of population, imports, food preferences and bioenergy, is a production constraint, as is irrigation water available. The model iterates prices until demand is satisfied (or cannot be met) and basin water usage for irrigation is not more than is available.This presentation describes the application of the modelling system across future climate change uncertainty space (as given by 60 combinations of downscaled 10’x10’ gridded climate outputs from 5 Global Climate Models, 3 climate sensitivities and 4 emissions scenario) under both baseline and four future socio-economic scenarios to identify those areas of Europe in which the spatial allocation of agricultural landcovers are robust to this uncertainty. 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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no |
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MA @ admin @ |
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2138 |
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Höglind, M.; the partners of LiveM task L1.3 |
| Title |
Bringing together grassland and farm scale modelling. Part 1. Characterizing grasslands in farm scale modelling |
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Report |
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2017 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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10 |
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Pages |
L1.3-D |
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This report provides an overview of how grasslands are represented in six different farmscale models represented in MACSUR. A survey was conducted, followed by a workshop in which modellers discussed the results of the survey, and identified research challenges and knowledge gaps. The workshop was attended by grassland as well as livestock specialists. The investigated models differed largely with respect to how grasslands were represented, e.g. as regards weather and management factors accounted for, spatial and temporal resolution, and output variables. All models had grassland modules that simulate DM yield and herbage N content (or crude protein (CP) content = N content x 6.25). Many models also simulate P content, whereas only one simulate K content. About half of the model simulate herbage energy value and/or herbage fibre content and fibre and/or dry matter digestibility. Critical input data required from grassland models to simulate ruminant productivity and GHG emissions at farm scale was identified by the workshop participants. The different types of input data required were ranked in order of importance as regards their influence on important system outputs. For simulation of ruminant productivity and GHG emissions, herbage DM yield was ranked as the most important input variable from grassland models, followed by CP content together with at least one variable describing herbage fibre characteristics. These findings suggest that work on improving the ability of the current grassland models with respect to simulation of fibre/energy should be prioritized in farm-scale modelling aiming at quantifying livestock production and GHG emissions under different management regimes and climate conditions. More work is also needed on model evaluation, a task that has not been prioritized yet for some models. |
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LiveM |
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MA @ admin @ |
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4957 |
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| Author |
Hoffmann, H.; Ewert, F. |
| Title |
Review on scaling methods for crop models |
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Report |
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2015 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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| Volume |
6 |
Issue |
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Pages |
D-C3.1 |
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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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MA @ admin @ |
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2094 |
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