Integrated assessment of agricultural systems (SEAMLESS)
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Integrated assessment of agricultural systems (SEAMLESS)
- 1. Integrated assessment of agricultural systems; On integrated science and science integration Martin van Ittersum Frank Ewert, Thomas Heckelei, Floor Brouwer, Johanna Alkan Olsson, Erling Andersen, Jan Erik Wien, Jacques Wery Acknowledgement: all SEAMLESS colleagues
- 2. Food prices
- 5. Common challenges for research … Multi-dimensional analysis Multi-scale analysis Global Continental Economic Environmental Natural National Institutional Regional Social Farm Field
- 6. What does research have at hand to analyse? Methods and databases targeted at specific processes or scales: Market Farming systems Cropping systems …………… which are ….. developed for a specific purpose often poorly re-used difficult to link for integrated studies not readily used for integrated assessment of indicators Fragmentation, gaps, lack of integration!
- 7. Aims of SEAMLESS project Overcoming fragmentation in research models and data in Europe for integrated assessment of agricultural systems Better informed impact assessment of new agricultural and environmental policies To advance: Consistent micro-macro analysis Consistent economic, environmental, social and institutional analysis Re-use of research tools for a range of issues
- 8. C. Components A. Methodology for IA B. Application D. Science and impact Outline of presentation
- 9. C. Components A. Methodology for IA B. Application D. Science and impact Outline of presentation
- 10. Integrated assessment procedure Users/stakeholders Problem definition Scenario description Indicator selection development Modelling Modelling Definition of simulation experiment Model selection and composition Parameterization and simulation Post - modelling Post-modelling Post - model Post analysis Visualization of results Documentation/ communication communication Data and knowledge base Pre - modelling Pre modelling
- 11. Pre-modelling Global Market Market Continent/ Society Society country Global Global economy economy Link to GTAP AgriAgricultural cultural sector sector Technology Technology EXPAMOD Midi Midi Pyrenees Pyrenees 80 60 Global Global trade trade CAPRI CAPRI Policy Policy 100 Region Post-modelling Modelling … … 40 20 Farm Field 0 -20 Initial (2001) Mixed Mixed farm farm Baseline Baseline Nitrate type type & ND (2013) Structural change Farms Farms (2013) directive (2013) Economy Economy Wheat Wheat Arable Arable farm farm type type Potato Potato Maize Maize Natural Natural resources resources … … … … FSSIM FSSIM Climate Climate APES APES
- 12. C. Components A. Methodology for IA B. Application D. Science and impact Outline of presentation
- 13. Trade liberalization - WTO proposal
- 15. Baseline versus WTO policy scenario Export subsidies EU set to zero Agricultural tariff reductions WTO proposal (according to December 6th 2008 agricultural modalities) with – withou t policy to be assessed baseline 2013 2003 effect of autonomous developments = impact policy
- 16. Model chain Data of NUTS-2 and EU CAPRI EXPAMOD Data of farms in 13 regions (out of 300 regions in EU) FSSIM APES Agricultural sector model - EU NUTS-2 and EU indicators Extrapolate farm to EU Bio-economic farm model Farm and regional indicators Agricultural production & externalities
- 17. Price decline due to WTO proposal: EU vs World
- 18. WTO – change in agricultural income (%) Income declines in all EU27 regions; Losses vary between 1 and 16%; average decline 5% Marcel Adenäuer and Marijke Kuiper
- 19. Decrease in average farm income by region (%) Marcel Adenäuer and Marijke Kuiper
- 20. Decrease in average farm income by farm type (%) Marcel Adenäuer and Marijke Kuiper
- 21. WTO – change in nitrate leaching (%) Farm types in Midi Pyrenees Arable-cereal W vs Baseline TO -2 % +6% Maize area ↓ ↑ Peas area ↓ ↓ Rape area ↓ ↑ Soya area ↑ ↑ Sunflower area -1.0 W vs Baseline TO Nitrate leaching -2.0 Arable-other 0 ↓ -0.0 Hatem Belhouchette and Kamel Louhichi
- 22. C. Components A. Methodology for IA B. Applications D. Science and impact Outline of presentation
- 23. Scales and Dimensions of SD Globe Globe GTAP Earth System Earth System Country/ Country/ Continent Continent CAPRI LABOUR EXPAMOD Region Region Landscape Landscape Structural change Landscape Evaluation SLE Farm Farm FSSIM-AM Field Field PICA FSSIM-MP APES Indicator Framework Biophysical Biophysical Bio-Economic Bio-Economic Social/ Social/ Institutional Institutional
- 24. Scales and Dimensions of SD Globe Globe GTAP Earth System Earth System Country/ Country/ Continent Continent CAPRI LABOUR EXPAMOD Region Region Landscape Landscape Structural change Landscape Evaluation SLE Farm Farm FSSIM-AM Field Field PICA APES Biophysical Biophysical FSSIM-MP Bio-Economic Bio-Economic Social/ Social/ Institutional Institutional
- 25. Simulating cropping systems Weather Outputs: Soil water Agro-forestry 1. Yields 2. Externalities: Simulation engine C-Nitrogen Grasses Agricultural management Vineyard/ orchard APES Dynamic Cropping System model Nitrogen - Crops - Pesticides - Erosion - Pesticides GHGs
- 26. Simulating farm responses - FSSIM FSSIM-Agricultural Management (AM) Activities: inputs-outputs FSSIM-Mathematical Programming (MP) Farm objective: profit – risk Resource constraints Policy constraints Bio-economic farm model Farm layout Farm income and costs Externalities
- 27. Agricultural sector: CAPRI (EU) programming model Supply250 Regional optimisation models Combination of and multi commodity model Quantities Prices University of Bonn Markets Multi-commodity spatial market model with 18 regional aggregates and all EU MS
- 28. Micro-macro analysis: Upscaling farm type - market FSSIM Price Supply response to response price and policy changes on Farm level EXPAMOD Extrapolation to regional supply elasticities and non- sample regions Aggregation weights Structural change CAPRI Regional supply elasticities Calibration of regional supply models to this supply response Scenario analysis based on new supply response Price changes
- 30. Integrated database Data: Two important features: Climate and soils Farmtype data (FADN) Common spatial framework Agricultural management(!) Common farm typology Policy Trade Regional typologies Indicators (model output)
- 31. SEAMLESS Spatial framework The hierarchical framework combines: Administrative regions Climatezones Farm resources Policies Trade Climate Agri-environmental zones Soil data Farm type allocation Survey data on farm management
- 32. SEAMLESS farm typology The typology combines: Farm size Farm specialisation Land use Intensity
- 33. An example of mapping farm types to AEnZs Density of low-intensity farms in agri-environmental zones
- 34. Linking models, data and indicators Methodological linkage: e.g. scaling in time and space Semantic linkage: ontology Technical linkage: OpenMI Connecting people!
- 35. Scales and Dimensions of SD Globe Globe GTAP Earth System Earth System Country/ Country/ Continent Continent CAPRI LABOUR EXPAMOD Region Region Landscape Landscape Structural change Landscape Evaluation SLE Farm Farm FSSIM-AM Field Field PICA APES Biophysical Biophysical FSSIM-MP Bio-Economic Bio-Economic Social/ Social/ Institutional Institutional
- 36. Structural Change Component Objective: Method: Forecast regional shares of farm types Markov chain analysis Data: FADN 3 size classes 10 specialisations = 30 farm types Andrea Zimmermann et al., 2009
- 37. Structural Change component Legend Legend < -3 low -3 - < 0 moderate >= 0 high Annual rates of farm number change 2003-2013 [%] Mobility of farms across farm types [index] Andrea Zimmermann
- 38. SEAMLESS Landscape Explorer Baseline Scenario Griffon and Auclair Policy Scenario
- 39. C. Components A. Methodology for IA B. Application D. Science and impact Outline of presentation
- 40. On integrated science SEAMLESS: one approach to Integrated Assessment Benefits: allows to structure the development of IA tools in components using advances of science focusing on parts of the system a degree of flexibility for range of applications Limitations for specific problems: details of some components not always needed does ‘generic’ approach allow adequate system representation: • relevant feedback mechanisms and interactions captured?
- 41. On integrated science High data demand – three routes: statistical sampling (micro-macro upscaling: Bezlepkina et al.) science-based rules to ‘generate’ crucial but missing data (agro-management data: Oomen et al.) European data (soils, weather, farm: Andersen et al.) Questions: trade-off between integration and flexibility? scaling methods to be further tested forecasting farm responses integration of (agro-)ecosystems services
- 42. Science integration •NUI Galway •UEvora •JRC Mali: IER USA: UVM Interdisciplinary Disciplinary C Interdisciplinary •WU •Alterra •LEI •PRI •LU •LUEAB Integrative Scientists Disciplinary B Disciplinary A Interdisciplinary •UMB •UBER •ZALF Interdisciplinary •UBONN •SGGW •ILE ASCR •VUZE IT Scientists •INRA •CIRAD •IAMM •Cemagref •CRA •JRC •IDSIASUPSI •AntOptima Disciplinary D •UoC •UNEW UEDIN •UNIABDN •
- 43. Beyond the project SEAMLESS Association Overcoming fragmentation Maintenance, extension and dissemination Continue the network role Open source New research projects Science Testing and application • High(er) price scenario
- 44. The use of computerized tools in IA Problem solving stages Contextualisation Network building Model types Sterk, Van Ittersum and Leeuwis, 2009 Role of models Matching process:
- 46. Thank you for your attention
Notes de l'éditeur
- Impact of policies beyond scale at which implemented Impact of policies beyond targeted domain
- Average change in market prices of product groups with the WTO proposal (% change to baseline). Source: SEAMCAP. Prices in the EU decline more than in the rest of the world with meat experiencing the most notable drop. Within the average for meat the strongest decline (-12 %) is observed for beef. The major price decreases for meat area however may not materialize due to the use of sensitive products by the EU. Denominating meat tariff lines as sensitive would greatly limit the tariff reductions although a quota would need to be opened to compensate third countries for their limited increase in market access.
- Regional change in agricultural income with the WTO proposal (% change to baseline). Source: SEAMCAP
- Average farm income changes by region with the WTO scenario (% change to baseline, in brackets number of farm type models per region). Source: FSSIM
- It shows the observed and predicted development of farm numbers of 2 production orientation classes (arable and dairy farms) in 2 size classes (M for medium and L for large farms). In the observation period (1990-2003) one can see that the number of medium sized dairy farms decreases drastically, whereas the number of large dairy farms increases. The number of medium and large arable farms slightly increases until and starts to decrease after 2000. Decreasing farm numbers are predicted for medium dairy farms and medium and large arable farms. The number of large dairy farms is predicted to further increase, although at a lower pace.
- 1. The annual rates of farm number decrease had already been very large during the observation period (1990-2003) and these rates are predicted to continue into the future. This counts for the red regions in France, Spain, Portugal and Germany. 2. Compared to other European countries, structural change in terms of decreasing farm numbers started late (in 1997 or later) in the UK and in Italy, but therefore the rate of change was very fast. These highly negative growth rates of the last observation years are predicted to continue into the future. In fact, the question why even similar (in terms of farm structure) European regions react so differently is part of the ongoing research for my thesis. I will let you know if I find a better explanation (in terms of causing factors) within the next two weeks. The map on the left shows the predicted annual rates of change for total farm numbers (aggregated over all farm types). For the regions in red high farm number losses are predicted, yellow means moderate losses, and for the green regions no change or even small positive growth rates are expected. - The map on the right serves as regional comparison of the mobility of farms across farm types. In red regions farms are likely to change the farm types, in yellow regions a moderate number of farms changes the farm type, and in green regions farms are not very likely to change their farm type.
- Meta-modelling Baysian networks Etc. allows to structure the development of IA tools in relatively independent components using advances of science focusing on parts of the system a degree of flexibility for range of applications
- Component-based approach allows structuring the workforce and international collaboration The teams developing components can work relatively independent and may consist of specialists with sufficient integrative skills A team of adequate size needs to have the necessary conceptual and methodological skills for integration and linkage of components Linkage of components puts high demand on state-of-the-art information technology (IT) which is not present in all teams Experience of SEAMLESS showed the crucial importance of a team with unprecedented interdisciplinary skills and willing to invest in IT There is a third dimension: Integrative scientists, IT specialists and Collaboration amongst 30 teams