start
Differences
This shows you the differences between two versions of the page.
Next revision | Previous revisionLast revisionBoth sides next revision | ||
start [2019/10/09 19:46] – created 134.110.15.150 | start [2020/04/25 13:53] – matsz | ||
---|---|---|---|
Line 1: | Line 1: | ||
- | $a^2 + b^2 = c^2$ | + | ======Introduction====== |
+ | |||
+ | ===== Structure of the documentation ===== | ||
+ | |||
+ | 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 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, | ||
+ | |||
+ | **Figure 1: General structure of the CAPRI model** | ||
+ | |||
+ | {{: | ||
+ | |||
+ | The CAPRI modelling system itself consists of specific data bases, a methodology, | ||
+ | |||
+ | The data bases exploit wherever possible // | ||
+ | |||
+ | The economic model builds on a // | ||
+ | |||
+ | 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' | ||
+ | |||
+ | CAPRI contains two modules, market and supply, which interact (see Figure 1). | ||
+ | |||
+ | The //supply module// consists of independent aggregate non linear programming models representing activities of all farmers at regional or farm type level captured by the Economic Accounts for Agriculture (EAA). The models optimize regional agricultural income, given the prices for inputs and outputs, subsidy levels and other policy measures. These models are a kind of hybrid approach, as they combine a Leontief-technology for variable costs covering a low and high yield variant((The two technological alternatives (for most activities), | ||
+ | |||
+ | Around 55 agricultural inputs produced in about 60 activities are covered in the supply module. The activities include inputs to crop and livestock production from other sectors and intermediate inputs produced by the farms such as feed and young animals. The models capture in high detail the premiums paid under CAP, include NPK balances and a module with feeding activities covering nutrient requirements of animals. | ||
+ | |||
+ | Main constraints outside the feed block are arable and grassland – which are treated as imperfect substitutes -, and potential policy restrictions (set-aside obligations, | ||
+ | |||
+ | Market equilibria are calculated by iterations between the supply module and the market module. | ||
+ | |||
+ | The market module for marketable agricultural outputs is a //spatial, non-stochastic global multi-commodity// | ||
+ | |||
+ | 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 analysis at global, EU and national scale. | ||
+ | |||
+ | 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 for supply and feed demand are calibrated to the results of the regional aggregate programming models aggregated to Member State level. Solving the market modules then delivers new prices. A weighted average of the prices from past iterations then defines the prices used in the next iteration of the supply module. Equally, in between iterations, CAP premiums are re calculated to ensure compliance with national ceilings and crop yields may respond to changing market prices. | ||
+ | |||
+ | 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, | ||
+ | |||
+ | CAPRI allows for //modular applications// | ||
+ | |||
+ | // | ||
+ | |||
+ | More information about the CAPRI model, including technical documentation, | ||
+ | |||
+ | ===== CAPRI uses the GAMS software ===== | ||
+ | |||
+ | |||
+ | To solve the large-scale, | ||
+ | |||
+ | 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 the results. Without GAMS, you can view and analyse scenario results from previous scenario runs, but not run new simulations with CAPRI. | ||
+ | |||
+ | 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. | ||
+ | |||
+ | ===== The network ===== | ||
+ | |||
+ | Methodological development, | ||
+ | |||
+ | 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. | ||
+ | |||
+ | ===== CAPRI development and applications ===== | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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, | ||
+ | |||
+ | 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”, | ||
+ | |||
+ | 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. | ||
+ | |||
+ | From 2006-2012 CAPRI participated in the LIFE funded EC4MACS((See http:// | ||
+ | |||
+ | In line with the shift of the CAP focus towards sustainability, | ||
+ | |||
+ | GHG abatement options have also been investigated in two studies by the JRC (IES, Ispra((See https:// | ||
+ | |||
+ | 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). | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | Sustainability in its various facets has been the topic driving model developments and extensions that are likely to be pursued in the next years. | ||
+ | |||
+ | * 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, | ||
+ | |||
+ | * 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:// | ||
+ | |||
+ | * 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, | ||
+ | |||
+ | * 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:// | ||
+ | |||
+ | Apart from the wide area of sustainability aspects of trade modelling have also been repeatedly at the heart of targeted model improvements, | ||
+ | |||
+ | 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, | ||
+ | |||
+ | 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). | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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). | ||
+ | |||
+ | 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 the series of past FP and H2020 projects and. Furthermore the DG-JRC (IPTS, Seville and IES Ispra) has actively contributed to improvements and extensions in various components of the system and also stimulated system development with a continuous flow of new research questions and matching projects. Since a number of years recurring demand for up-to-date and long run projections on the part of DG CLIMA is contributing to some regularity in the updating process for data base and projections. Nonetheless the CAPRI network faces the common problem of the commons such that the update process for documentation is in risk to lag behind the moving target of the current code. Readers identifying missing or obsolete sections are therefore invited to contact any of the authors. |
start.txt · Last modified: 2022/11/07 10:23 by 127.0.0.1