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- | ====== | + | ======Introduction====== |
+ | ===== Structure of the documentation ===== | ||
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+ | The documentation is structured as follows. Sections [[what_is_capri|What is CAPRI]]-[[CAPRI_uses_the_GAMS_software|CAPRI uses the GAMS software]] give an overview of CAPRI system and its main software, GAMS. Section 1.4 informs about the CAPRI network. Sections 1.5 and 1.6 describe historical development of the model and more recent examples of CAPRI studies. Chapter " | ||
+ | The rest of the document largely follows the workflow of the model: the different steps of building up the national, regional and global data base provide the foundations on which the system rests ([[The_CAPRI_Data_Base|Chapter "The CAPRI Data Base" | ||
+ | ===== What is CAPRI ===== | ||
- | The documentation | + | The Common Agricultural Policy Regional Impact (CAPRI) model is a global partial equilibrium model for the agricultural sector, with a focus on the European Union. It has been designed for ex-ante impact assessment |
- | The rest of the document largely follows | + | **Figure 1: General structure of the CAPRI model** |
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+ | {{: | ||
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+ | The CAPRI modelling system itself consists | ||
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+ | The data bases exploit wherever possible // | ||
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+ | The economic | ||
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+ | CAPRI is designed for scenario analysis. It is a comparative static model, which technically means that the market equilibrium simulated for a given point in time does not involve lags or leads of endogenous variables. If several points in time are simulated, these simulatons may be perfomed therefore in any order or in parallel((This does not hold if land use transitions are simulated for environmental indicators but in a “basic” CAPRI run, these may be switched off.)). Comparative static results are best interpreted as the long run outcome of some scenario, after all adjustments to the new equilibrium are completed. By contrast, dynamic or recursive dynamic models also trace the adjustment path over time, while considering lagged relationships that are ususally critical in adjustment processes. CAPRI simulations start from a so-called baseline, which is a special applicaiton of the model as discussed in a separate chapter of this documention. The CAPRI baseline integrates projections from external sources, typically the Agricultural Outlook published annually by the European Commission' | ||
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+ | CAPRI contains two modules, market and supply, which interact (see Figure 1). | ||
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+ | The //supply module// consists of independent aggregate non linear programming models representing activities of all farmers at regional | ||
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+ | Around 55 agricultural inputs produced in about 60 activities are covered in the supply module. The activities include inputs | ||
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+ | Main constraints outside the feed block are arable and grassland – which are treated as imperfect substitutes -, and potential policy restrictions | ||
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+ | Market equilibria are calculated by iterations between the supply module and the market module. | ||
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+ | The market module for marketable agricultural outputs | ||
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+ | Agricultural supply is modelled in a simpler way than in the supply module, with behavioural functions for supply and feed demand. These are supplemented with other functions for processing, biofuel use, and human consumption. These functions apply flexible functional forms where calibration algorithms ensure full compliance with micro economic theory including curvature. The parameters are synthetic, i.e. to a large extent taken from the literature and other modelling systems.Consumers and traders are represented by economic agents that follow neo-classical micro-economic theory regarding behaviour, which makes it possible to compute welfare effects. Bi lateral trade flows and attached prices are modelled based on the Armington assumptions (Armington 1969). Policy instruments cover (bi lateral) tariffs, the Tariff Rate Quota (TRQ) mechanism and, for the EU, intervention stocks and subsidized exports. This market module delivers prices used in the supply module and allows for market | ||
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+ | As the supply models are solved independently at fixed prices, //the link between the supply and market modules// is based on an iterative procedure. After each iteration, during which the supply module works with fixed prices, the constant terms of the behavioural functions | ||
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+ | Environmental indicators, primarily for nutrient surpluses and greenhouse gas (GHG) emissions, are calculated in CAPRI and may be directly addressed in some scenarios. Regarding nutrient surpluses, the supply module contains nutrient balance equations for nitrogen, phosphorous and potassium. It considers nutrient uptake by crops following a crop growth function, and supply of nutrients from mineral fertilizer, manure, crop residues, and, for nitrogen, atmospheric deposition and fixation. The balances also contain factors for over-fertilization, | ||
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+ | CAPRI allows for //modular applications// | ||
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+ | // | ||
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+ | More information about the CAPRI model, including technical documentation, | ||
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+ | ===== CAPRI uses the GAMS software ===== | ||
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+ | To solve the large-scale, | ||
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+ | Data used or produced by GAMS is generally stored in a file format called GDX (GAMS Data Exchange). CAPRI database and results are stored in gdx files, which can be loaded into the CAPRI Result Viewer in the Graphical User Interface where you can analyse and export | ||
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+ | GAMS solves models using third-party solvers that are linked to GAMS. GAMS comes with a large library of such solvers, most of them specializing in particular types of problems or solution algorithms. CAPRI relies on a particular solver called CONOPT. While CAPRI itself is distributed free of charge for anyone to download and use, GAMS and the solvers such as CONOPT requires a license to work beyond demonstration mode. | ||
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+ | ===== The network ===== | ||
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+ | Methodological development, | ||
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+ | The CAPRI modelling network may be defined as a ‘club’: there are currently no fees attached to its use but the entry in the network is controlled by the current club members. The members have agreed on a distribution of tasks to maintain and update the system. They as well contribute by acquiring new projects, by quality control of data, new methodological approaches, model results and technical solutions, and by organising events such as training sessions and preparing this documentation. It is currently considered if the club constitution needs an update as well. | ||
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+ | ===== CAPRI development and applications ===== | ||
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+ | CAPRI – ‘Common Agricultural Policy Regionalised Impact analysis’ is both the acronym for an EU-wide quantitative agricultural sector modelling system and of the first project centred around it((http:// | ||
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+ | Later, a larger project (EU research FP VI, Nr. 501981: CAPRI-Dynaspat) was conducted under the co-ordination of the team in Bonn to render the system recursive-dynamic, | ||
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+ | A PhD study (Pérez-Dominguez 2005) initiated (non-CO2) GHG accounting and modelling with CAPRI to analyse tradable permits for GHG emissions from agriculture. Subsequently several projects served to improve the representation of trade policies (FP VI, Nr. 502457: “EU MedAgPol”, | ||
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+ | In 2006-2008 a first biofuel coverage in CAPRI has been achieved during an interim stay of Wolfgang Britz at JRC-Ispra which has been expanded in later years leading to follow up studies on bioenergy policies (Blanco et al. 2010, Britz and Delzeit 2013). In 2006-2007 CAPRI made contributions to study “Integrated measures in Agriculture to reduce Ammonia emission” together with MITERRA-Europe (Alterra, Wageningen) and GAINS (IASSA, Laxenburg) which led to an update of the N-cycle description in CAPRI. | ||
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+ | From 2006-2012 CAPRI participated in the LIFE funded EC4MACS((See http:// | ||
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+ | In line with the shift of the CAP focus towards sustainability, | ||
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+ | GHG abatement options have also been investigated in two studies by the JRC (IES, Ispra((See https:// | ||
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+ | The current two level version of land supply derives from a study on agricultural and trade policy reform impacts on land-use across the EU, with a particular focus on land abandonment (Renwick et al. 2012). | ||
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+ | Until summer 2013, again a EU framework project co-ordinated by the team in Bonn called “CAPRI-RD” ensured various updates, and added a layer of regional CGEs, while working on the integration of CAP pillar 2 measures into the system. While the latter have become an essential element of CAP representation in the system, the regional CGEs have not been applied since that time (Schroeder et al. 2015, but this might be also considered the starting point of Wolfgang Britz, the main developper of CAPRI up to 2013, to move more into CGE modelling((See https:// | ||
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+ | Sustainability in its various facets has been the topic driving model developments and extensions that are likely to be pursued in the next years. | ||
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+ | * Beginning with a small explorative study in 2011 several studies led to the development and improvement of a “CAPRI water version” used in various projects((See, | ||
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+ | * GHG accounting and modelling beyond non-CO2 required to address LULUCF effects in projects aiming at a complete coverage of the country area in the UNFCCC classification as well as transitions between those land categories and a closed carbon balance for agricultural areas((This started with an ERA NET project TRUSTEE in 2013 (https:// | ||
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+ | * Several efforts have been undetaken by JRC-Ispra, partly in house, partly in specific projects to achieve a more accurate representation of various environmental indicators. The detailed nutrient flow in CAPRI has been exploited to measure nitrogen footprint of food products in the EU (Leip et al. 2014) and to assess the impacts of European livestock production (Leip et al. 2015). The representation of environmental constraints, | ||
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+ | * Diet shifts of food consumers offer a great potential to achieve environmental relief (as well as health benefits), such that their representation in CAPRI has been improved in the context of various partly ongoing projects((See e.g. https:// | ||
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+ | Apart from the wide area of sustainability aspects of trade modelling have also been repeatedly at the heart of targeted model improvements, | ||
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+ | Two areas of technical developments are also likely to be continued in the future. The first one is the improvement of linkages to the in house JRC model IFM CAP that permits to represent the diversity of CAP restrictions only amenable to modelling at the farm level. As IFM-CAP operates with exogenous prices, it requires prices as model inputs that may be provided by CAPRI. The ongoing SUPREMA project (mentioned in the context of LULUCF modelling already) pursues these linkages while trying to also watch for computational feasibility, | ||
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+ | The historical review has so far focussed on those studies and projects, that left clear marks in the current system as a heritage. In addition, the system was applied to a wide range of numerous different scenarios that often left smaller “traces” in the system but illustrate its capabilities and contributed to improvement in many details that are critical for serious impact assessments. The very first application in 1999 analysed the so called ‘Agenda 2000’ reform package of the CAP. Shortly afterwards, a team at SLI, Lund, Sweden applied CAPRI to analyse CAP reform option for milk and dairy. FAL, Braunschweig looked into the effects of an increase of organic production systems. WTO scenarios as well as scenarios on specific trade agreements were frequnetly untertaken. Moreover, CAPRI was applied to analyse sugar market reform options at regional level, linked to results of the WATSIM and CAPSIM models. In 2003, scenarios dealing with the CAP reform package titled ‘Mid Term Review’ were performed by the team in Bonn (Britz et al. 2003). In the wake of the sugar market reforms various reform options have been investigated (Adenaeuer et al. 2004). | ||
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+ | In 2004 CAPRI was used to generate a baseline in close co-operation with DG Agri match DG Agri’s outlook projections which has become a regular activity. Several studies have been launched in 2007 on particular aspects of the ongoing CAP reform (decoupling project for DEFRA, UK, modulation study by LEI for DG Agri and a milk quota expiry for JRC, IPTS, Seville). The Farm Type version of CAPRI has been used frequently to look at intrasectoral distribution of CAP reform impacts((See e.g. https:// | ||
- | ====== What is CAPRI ====== | + | Several analyses have investigated potential impact of climate change in EU agriculture by introducing changes in crop yields from biophysical models as exogenous shifts. This enables to analyse regional changes in production within the EU while considering market feedback, as well as the role of trade to counterbalance uneven effects of climate change across the world (Delincé et al 2015, Blanco et al. 2017, Pérez Dominguez and Fellmann, 2018). |
- | The Common Agricultural Policy Regional Impact (CAPRI) model is a global partial equilibrium model for the agricultural sector, with a focus on the European Union. It has been designed for ex-ante impact assessment of agricultural, environmental | + | As will be clear from this review the CAPRI system strongly benefitted from EU Commission support in various forms. Most of the initial developments were co financed by DG RSRCH through |
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