Management guidelines for the CAPRI baseline

Transcription

1 Common Agricultural Policy Regional Impact The Rural Development Dimension Collaborative project - Small to medium-scale focused research project under the Seventh Framework Programme Project No.: WP4 Baseline Deliverable: D4.8 Management guidelines for the CAPRI baseline Mihaly HIMICS, Pavel CIAIAN, Benjamin VAN DOORSLAER, Guna SALPUTRA Partner(s): JRC-IPTS Final version: The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.

2 The FP7 project "Common Agricultural Policy Regional Impact The Rural Development Dimension" (CAPRI-RD) aims at developing and applying an operational, Pan-European tool including all Candidate and Potential Candidate countries to analyse the regional impacts of all policy measures under CAP Pillar I and II across a wide range of economic, social and environmental indicators. The project is carried out by a consortium of 10 research organisations, led by Bonn University (UBO). Authors of this report and contact details Names: Partner acronym: Address: Mihály HIMICS, Pavel CIAIAN, Benjamin VAN DOORSLAER, Guna SALPUTRA JRC-IPTS European Commission - Joint Research Centre (JRC) Institute for Prospective Technological Studies (IPTS) Edificio Expo, C/ Inca Garcilaso 3, E Sevilla, Spain pavel.ciaian@ec.europa.eu

3 Table of contents ABBREVIATIONS INTRODUCTION OVERVIEW OF THE CAPRI MODELLING SYSTEM CAPRI BUILDING BLOCKS BUILDING A CONSISTENT DATASET FROM DIVERSE STATISTICAL SOURCES BASELINE PROCESS OF DG AGRI PROJECTIONS OF THE FUTURE STATE OF THE ECONOMY CAPTRD Projections of global commodity balances CALIBRATION OF THE CAPRI MODELLING SYSTEM VALIDATION OF THE CAPRI BASELINE RESULTS GOOD MANAGEMENT PRACTICES FOR THE BASELINE CONCLUDING REMARKS REFERENCES ANNEX 1: CALIBRATION STEPS Page 3 of 32

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5 Abbreviations AGLINK- COSIMO AGMEMOD AMAD CAP CAPRI cif CLC COMEXT COSIMO DG-AGRI EAA EBB epure ESIM FAO FAOSTAT FAPRI fob FSS GDP GLOBIOM GTAP GUI HPD IIASA IFPRI IMPACT JRC JRC-IPTS NMS NUTS OECD PCE PRIMES UN USDA US Recursive-dynamic, Partial Equilibrium, Supply Demand Model of World Agriculture, Developed by the OECD Secretariat in Close Co-operation with Member Countries and Certain Non Member Economies Agricultural Member State Modelling for the EU and Eastern European Countries Agricultural Market Access Database Common Agricultural Policy Common Agricultural Policy Regional Impact Cost, Insurance and Freight Corine Land Cover Intra- and extra-eu Trade Data Commodity Simulation Model Directorate General for Agriculture and Rural Development Economic Accounts for Agriculture European Biodiesel Board European renewable ethanol European Simulation Model Food and Agriculture Organization Statistics Division of the FAO Food and Agricultural Policy Research Institute Free on Board Farm Structure Survey Gross Domestic Product Global Model for Assessment of Competition for Land Use between Agriculture, Bioenergy, and Forestry Global Trade Analysis Project Graphical User Interface Bayesian Highest Posterior Density International Institute for Applied System Analysis International Food Policy Research Institute International Model for Policy Analysis of Agricultural Commodities and Trade Joint Research Centre Joint Research Centre - Institute for Prospective Technological Studies New Member States Nomenclature of Units For Territorial Statistics Organisation for Economic Co-operation and Development Private consumption expenditure deflator EU-wide Energy Model United Nations United States Department of Agriculture United States Page 5 of 32

6 1. Introduction The CAPRI modelling system is designed for comparative static analysis. In its essence it means comparing alternative scenarios to a given baseline. Constructing a baseline, therefore, is an integral part of any policy impact analysis with CAPRI. Building a baseline involves two major steps: (1) A possible future state of the (global) economy needs to be projected and translated into a set of consistent model parameters. This also includes projected values for model-endogenous variables. (2) The modelling system needs to be calibrated to the projection, i.e. the model reproduces the above set of projections including of course the endogenous model variables. The CAPRI modelling system consists of several interlinked sub-modules that might follow different calibration approaches. The supply and demand equations of the global market model, for example, are calibrated by (1) shifting them to projected levels and (2) trimming the elasticities by using econometric estimations and imposing regulatory conditions. The regional supply module on the other hand is calibrated following Positive Mathematical Programming (PMP) techniques, first formulated by Howitt (Howitt 1995) and has been further improved over the last decade (Heckelei and Britz 2005). This report guides the reader through the CAPRI modelling system in order to complete the two steps from above. The text always refers to the relevant code implementation in order to give the reader further insights and to help him putting theory into practice. But given the size of CAPRI this objective can only be fulfilled to a limited extent and only with the aim of giving the reader a good starting point for his own further investigation. Page 6 of 32

7 2. Overview of the CAPRI modelling system CAPRI is a comparative static partial equilibrium model for the agricultural sector developed for policy and market impact assessments from global to regional and even farm type scale. It consists of two main components: a set of mathematical programming models covering the agricultural supply of most European countries (hereinafter referred to as the 'supply module') and a global equilibrium model for agricultural commodity markets (hereinafter referred to as the 'market module'). (Figure 1) (Britz and Witzke, 2012). The market module is a comparative-static, deterministic, partial, spatial, global equilibrium model covering about 75 countries or country aggregates. Based on the Armington approach (Armington, 1969), products are differentiated by origin, enabling to capture bilateral trade flows. The EU is split in three trading blocks: EU15, EU10 and BUR 1 ). EU trade relations are simulated at this geographical aggregation level. On the other hand, each of the EU Member States has an own system of behavioural functions, i.e. supply and demand functions (Britz and Witzke, 2012). The market module is defined by a system of behavioural equations representing agricultural supply, human consumption, multilateral trade relations, feeding balances and the processing industry; all differentiated by commodity and geographical units. The supply module is composed of separate, regional and farm-type, non-linear programming models. The regional programming models are based on a model template assuming profit-maximizing behaviour under technological constraints, most importantly in animal feeding and fertilizer use, but also constraints on inputs and outputs such as young animal, land balances and policies (e.g. set-aside) (Jansson and Heckelei, 2011). The supply module currently covers all individual Member States of the EU-27 and also Norway, Turkey and the Western Balkans broken down to about 280 administrative regions (NUTS 2 level) with up to 10 farm-types in each of the NUTS 2 region (in total 1823 farm-regional models) and more than 50 agricultural products. The challenge is calibrating these two modules relying on different modelling paradigms to the same projected state of the economy. The calibration of the market module requires that market equilibrium conditions are satisfied at calibration point. The quantities of demand and supply functions are dependent on prices and both quantities and prices represent the point to which market module is calibrated. On 1 BUR includes Bulgaria and Romania, the two new Member States joined in 2007 Page 7 of 32

8 the other hand, the regional programming models in the supply module should be calibrated to the projected land use and animal production at the same prices the market module is calibrated to. The calibration requires that first order optimality conditions (marginal revenues equal to marginal costs, all constraints feasible) hold in the calibration point for each of the NUTS 2 or farm-type models. Positive Mathematical Programming (PMP) is applied to close the difference between marginal revenues and marginal costs in the calibration point by introducing nonlinear terms in the objective function to capture other unaccounted costs (e.g. labour, capital) such that optimality conditions are satisfied at defined levels of decision variables (Britz and Witzke 2012). Figure 1: Structure of CAPRI model Source: Britz and Witzke (2012) Page 8 of 32

9 3. CAPRI building blocks Calibration of CAPRI is split in several tools consistently interlinked between them with the aim to facilitate data manipulation, generation of projections of the future state of agricultural economy, calibration to the AGLINK-COSIMO baseline and exploitation of results. The building bocks of CAPRI relevant for the calibration process can be split in four tools: (1) database tool, (2) projections, (3) calibration and scenario simulations, and (4) exploitation of results. The interlinkage between these tools and their specific components are depicted in Figure 2. The main database tools include MS level database CoCo, regional database CAPREG, and global database for world regions. CoCo and CAPREG databases alongside other support data (primarily coming from AGLINK-COSIMO and PRIMES) are key inputs into the trend projection module (CAPTRD). Calibration is done within the CAPMOD module combining information from CAPTRD, global database, and policy data. User friendly results are provided primarily through the Graphical User Interface (GUI) in form of tables, maps and graphs. Page 9 of 32

10 Figure 2: Interlinkages between CAPRI tools DATA Eurostat, FAO, FADN, etc. Database tool CoCo MS data CAPREG Regional data Input allocation Global World data CAPTRD Trend projections Policies Projections CAPMOD Support AGLINK, PRIMES, etc. Calibration and scenario simulations Exploitation of results Tables Maps Graphs Page 10 of 32

11 4. Building a consistent dataset from diverse statistical sources A key process necessary to be conducted prior to the baseline work includes preparation and construction of data. Following the CAPRI structure, the main tools needed for the baseline construction are: 1. COCO 2. CAPREG 3. Global 4. Policy data (policy module including CAP, trade policies, biofuel mandates etc.) The most important source of information for CoCo is EUROSTAT. This in itself creates a consistency in terms of data definitions for EU Member states and selected European countries (which would not be the case using national data sources). Other supporting data sources include: FADN-based estimations (e.g. costs), expert info, farm practice books, FAO, etc. For specific sub-modules (e.g. biofuels, GHGemission accounting, land-use) additional sources are needed as they cannot be retrieved from standard statistical sources. For example, data for biofuels are collected from European Biodiesel Board (EBB), European renewable ethanol (epure), PRIMES model database, FO Licht s World Ethanol and Biofuels Report, COMEXT trade data, AGLINK-COSIMO model, etc. The data base for land use is based on information on land use classes from various sources such as Corine Land Cover (CLC), Farm Structure Survey (FSS), FAO, etc. For the NMS additional data are included such as national data, FAO and Eurostat contractor Ariane. 2 The use of various sources for building CoCo is to impose completeness and consistency of the final database in terms of temporal resolution and coverage of all relevant variables. However, choice of particular database is done in hierarchical steps by giving preference to key statistical sources (e.g. Eurostat). If data in the first best source (e.g. Eurostat) are unavailable, then second best sources are used to fill the gaps for missing data. To combine the various data sources and to ensure consistency of the final database, Bayesian Highest Posterior Density (HPD) approach is applied. The main principle of the HDP is to ensure minimal deviation of the estimated values from the support data, as constructed from the EUROSTAT and non-eurostat statistical sources, subject to consistency constraints (e.g. closed market balances, perfect aggregation from lower to higher regional level) (Britz and Witzke 2012). 2 For more detailed description see Britz and Witzke (2012). Page 11 of 32

12 CAPREG is the regionalized version of CoCo with many important 'add-ons'. The CAPREG database is broken down at NUTS 2 level or to the farm-type level inside NUTS 2. The main data source is REGIO domain of EUROSTAT. The FADN and FSS databases are the primary sources used for further disaggregation of NUTS 2 data to farm-type level. However, due to gaps in regional EUROSTAT data and because of the relatively high aggregation level used especially in the field of crop production, additional sources (e.g. European Fertiliser Manufacturer Association fertiliser data available from FAOSTAT), assumptions and econometric procedures (e.g. HPD, panel data estimators) are applied to close data gaps and to disaggregate data to NUTS 2. The key concept in building the CAPREG data is to obtain regionalised data at the NUTS 2 and farm-type levels by preserving the consistent and complete national data base CoCo. Thus, the aggregation of CAPREG data over regions and farm-types must match the national CoCo values (Britz and Witzke 2012; Gocht and Britz 2011). One of the most important additional calculations in CAPREG is the input allocation module (e.g. fertilizer, nutrient balances, feed, labour) which distributes physical quantities or monetary values of inputs applied to specific agricultural activities. Other 'add-ons' needs include herd sizes and yields disaggregation at regional level (Britz and Witzke 2012). The global database builds a consistent set of (macro)economic data for world regions. It includes data on supply utilisation accounts, bilateral trade flows, as well as data on trade policies (Preferential Agreements, Tariff Rate quotas, export subsidies) and domestic market support instruments (market interventions, subsidies to consumption). Its main statistical sources (for historical data and projections) are FAOSTAT, AGLINK-COSIMO model, Agricultural Market Access Database (AMAD), GLOBIOM (IIASA) and IMPACT (IFPRI). The primary use of the global database is in the market module where global agricultural markets are modelled (Britz and Witzke 2012). The primary focus of CAPRI is to asses the impacts of CAP policy instruments. For this reason, the modelling of EU policies is more detailed and comprehensive than for other regions. Policy data in CAPRI are compiled from various sources. For CAP policies, the main sources are EU regulations and European Commission documents. Both first Pillar 1 measures as well as major ones from Pillar 2 (i.e., Less Favoured Area support, agri-environmental measures, NATURA 2000 support) are included. For non-eu countries policy data are extracted primarily from AMAD database and AGLINK-COSIMO model and include mainly market instruments such as applied and scheduled tariffs, tariff rate quotas or bilateral trade agreements. Page 12 of 32

13 5. Baseline process of DG AGRI DG AGRI annually constructs an outlook for the medium-term developments in agricultural commodity markets in the EU. This outlook permits a better understanding of the markets and their dynamics and also contributes to identify key issues for market and policy developments. Furthermore, the outlook serves as a benchmark for assessing the medium-term impact of future market and policy issues (hence we refer to it as baseline in the following). The model used for the outlook projections is a specific version of AGLINK-COSIMO, a recursive dynamic partial equilibrium model with global coverage. The baseline construction process always tries to build on the latest available market and policy information. Projection results are presented in balance sheets for the main agricultural commodities, with detailed results for the EU. The commodities covered include cereals, oilseeds, sugar, rice, biofuels, meat and dairy markets (Fellmann and Hélaine 2012). Figure 3: Flowchart of the baseline construction process Short-term DG AGRI OECD-FAO Outlook First draft of baseline Macro-economics Baseline week (discussion with DG AGRI market experts) Preliminary baseline Calibration-CAPRI Outlook workshop Uncertainty assessment Final baseline Calibration of CAPRI and ESIM Publication Source: M barek and Londero (2012) Page 13 of 32

14 The process of the DG AGRI s baseline construction is depicted in Figure 3. 3 The starting point is the latest available version of the AGLINK-COSIMO model, which was used for the OECD-FAO Agricultural Outlook 4 of that year. The EU module of AGLINK-COSIMO is then updated and optionally further extended in order to answer EU-specific research questions (e.g. income module for EU farmers). An in-depth discussion of the first results takes place between the modelling and market experts of DG AGRI and the JRC-IPTS during a baseline week in September. After further adjustments, the projections are presented in October at the Commodity Market Development in Europe Outlook workshop, organised by the DG AGRI and JRC-IPTS. In order to identify and quantify the potential variability of the market projections, the results of additional scenarios with alternative assumptions are also presented during the workshop. The workshop gathers highlevel policy makers, modelling and market experts from the EU, the United States and international organisations such as the FAO, OECD, FAPRI and The World Bank. The workshop provides a forum to present and discuss recent and projected developments in the EU agricultural and commodity markets, to outline the reasons behind observed and prospected developments and to draw conclusions on the short/medium term prospects of European agricultural markets in the context of world market developments. Special focus is given to the discussion on the sensitivity of the projected market developments to different settings/assumptions (regarding for example macroeconomic uncertainties, biofuel policies, specific drivers of demand and supply, etc.). Suggestions and comments made during the workshop are taken into account to improve the final version of the outlook, which is then published in the report Prospects for Agricultural Markets and Income in the EU in December each year 5. 3 More detailed information on the general baseline construction process is given in Nii-Naate (Ed.) (2011). 4 The OECD-FAO Agricultural Outlook is available online: 5 Latest available report can be found here: Page 14 of 32

15 6. Projections of the future state of the economy As mentioned in previous section, CAPRI does not generate its own baseline, but the main objective is to calibrate it primarily to the DG AGRI baseline generated by AGLINK-COSIMO model (section 5). However, AGLINK-COSIMO provides baseline results only at EU15, EU12 and EU27 aggregate level as well as it does not cover a full set of non-eu regions and activities available in CAPRI. For this reason, CAPRI needs to supplement AGLINK-COSIMO with internal projections for regional level and for activities not covered by AGLINK-COSIMO model. At the same time, projections from other sources (e.g. FAPRI, FAO) are used to supplement AGLINK- COSIMO data mostly for the non-eu countries. CAPRI projections are generated in two separate processes: (i) within the trend projection module (CAPTRD) and (ii) as part of the calibration of market module. The latter refers to the data balancing problem, aiming at creating consistent commodity balances at the global scale. The estimation framework in CAPTRD guarantees that the internal projections are as close as possible to the AGLINK-COSIMO baseline. The regional supply module is calibrated to the CAPTRD projections. On the other hand, the market module is calibrated to the outcome of the data balancing problem of the global commodity markets CAPTRD CAPTRD (defined in captrd.gms) projects commodity balances and prices for the EU countries, Norway and Western Balkan (countries covered by the supply module). Trend projections are derived by minimizing weighted squared deviation of trend values to support points. The weights reflect the variation of the error terms in the historical trend model and/or defined by the trust level of the supports. The optimization is subject to a set of consistency constraints. CAPTRD integrates a multitude of external information sources and historical trends derived from CoCo and CAPREG. The optimal values can therefore be interpreted as the closest consistent projections to a set of external projections/forecasts and historical trends. More specifically, the a-priori information sources for defining the support points are typically forecasts and projections from national and international organizations (e.g. AGLINK-COSIMO baseline, OECD market outlook, EC prospects for commodity markets). There exists also a built-in possibility in CAPRI to rank the information sources by their reliability, the so called trust level. This involves assigning appropriate weights to the bits of a-priori information in the constructed Bayesian Page 15 of 32

16 estimator. This issue is highly relevant in applied research (e.g. in policy impact analysis) because the uncertainty in projecting different model parameters varies to a great extent. For example, experts might express a high trust with regard to the outlook on cropping areas at country level, contrary to e.g. the net trade position which typically shows big variation over time and therefore difficult to predict. Consistency constraints imposed during the generation of projections link the different information sources by following some logical rules. Constraints ensure consistency of projections between different activities and different regional levels. A simple example is the area balance that links utilized agricultural area and cropping areas, simply stating that summing up the land use of agricultural activities gives back total utilized agricultural area. The consistency constraints are fully described in Britz and Witzke Overall the estimation framework in CAPTRD guarantees that projections are as close as possible to the AGLINK-COSIMO baseline. As AGLINK-COSIMO provides projection results only at EU15 and EU12 levels, these values are used to scale proportionally the CAPRI projections at lower aggregation level such that they are consistent with the AGLINK-COSIMO baseline. These CAPTRD projections are then used as the targets for the simulation year to which supply module is calibrated Projections of global commodity balances Projections of global commodity balances, trade and prices for all market regions are run simultaneously with the calibration (see section 7). Wherever possible, the AGLINK-COSIMO baseline is used to generate global projections. This is complemented by other external information sources: Supply and Utilization Accounts, trade matrices and projections from the FAO; Longer term projections from GLOBIOM (IIASA) and IMPACT (IFPRI); and biofuel related projections from the PRIMES energy model. Projections on commodity balances, trade and prices for all market regions are made consistent simultaneously. Technically, the data balancing problem is solved by the arm\data_cal.gms module. CAPRI has a separate global data preparation module (global.gms) that collects information from different sources and converts it to the format accepted by CAPRI (model specific product definitions, variables etc.). The module constructs a global database for the base year that serves as the basis of projections until the simulation year. The database is stored in a set of.gdx containers (under the folder results\global): Page 16 of 32

17 fao_agg_04.gdx: (1) FAO trade matrices (trade flows) and commodity balances, (2) biofuel-related balances, trade and parameters, (3) technical parameters (elasticities) from the World Food Model tc_04.gdx: transportation cost estimates, the estimation procedure is integrated in global.gms and is based on the difference between cif and fob prices tariffs.gdx: applied and bound rates for specific and ad-valorem tariffs, compiled mainly from the AMAD database (aggregated from HS6 level) f2050_impact.gdx: IMPACT model results mapped to the CAPRI nomenclature longrun_info_fac.gdx: long-term projections, currently until 2050, compiled from different sources, including PRIMES, IFPRI, FAO, etc. In a next step, the arm\data_prep.gms module collects base year information and growth factors for the market module from various sources and stores it in the parameters DATA and p_growthratemarketmodelpos. In order to get a consistent quantity and price framework for the market module calibration, a balancing problem for the global commodity markets (market balancing problem) is set up and solved in the arm\data_cal.gms module. The balancing problem is defined in arm\cal_models.gms and includes the following equations: Balance identities for market balances, for the two-stage Armington demand system and for trade Trade policy mechanisms for public intervention, specific and ad-valorem tariffs, tariff rate quotas, export subsidies and the entry price system of fruits and vegetables Accounting equations along the supply chain, i.e. feeding, processing and biofuel production Price linkages, i.e. prices derived from the equilibrium market prices (producer, consumer, cif, import and Armington prices); processing margins The balancing problem is first solved for the base year. Based on the consistent base year data, prices and quantities are shifted to the simulation year and the balancing problem is solved again. The algorithm for the balancing problem keeps certain variables fixed while gradually relaxes others in order to find a feasible solution. These consistent price and quantity data are then used as the targets for the simulation year to which market module is finally calibrated. Page 17 of 32

18 7. Calibration of the CAPRI modelling system As anticipated in section 2, the supply and market modules of CAPRI are sequentially calibrated in simulation runs (Britz 2008). The calibration of the modules against the given baseline data generated by CAPTRD and 'arm\data_cal.gms' therefore requires a consistent calibration point for the supply and market modules with respect to prices and quantities. However, reflecting the different structure of the modules, the calibration is based on different principles (PMP approach versus calibration constraints for a system of equations). The calibration is executed with the CAPMOD module (Figure 2). 6 The supply modules are calibrated following the methods of Positive Mathematical Programming (PMP), first formulated by Howitt (1995) and improved substantially over the last decades by addressing the early problems of parameter specification and simulation behaviour. For a summary on the methodological developments in PMP modelling, see Heckelei and Britz (2005) and Heckelei, Britz, and Zhang (2012). Technically the calibration happens in two consecutive steps both using the CAPMOD simulation engine in baseline mode (Figure 4). First the market model is calibrated ( Baseline calibration market model in GUI). The data balancing problem of the commodity markets and the actual calibration of the parameters for the behavioural equations are solved in one go. Then the supply model is calibrated to target values including product balances broken down to activity levels, land use, feed demand and producer prices ( Baseline calibration supply models in GUI). Most of the target balances are generated by CAPTRD (see section 6.1) while the producer prices are derived from the same market prices that the market model is calibrated to. 6 For more detailed hands-on on calibration see Annex 1. Page 18 of 32

19 The communication between CAPTRD and the simulation engine of CAPRI is via a set of.gdx containers providing the results of the trend projections (in the folder \results\baseline): AGLINK_for_capmod.gdx: growth factors for the unit values (prices) in the CAPRI supply modules, derived from AGLINK-COSIMO results results_0420.gdx: full result set of the trend estimation procedure, including intermediate steps for debugging purposes. trends_04_20.gdx: trend estimates for the commodity balances and prices (in the simulation year). This is a subset of the full result set stored in results_0420.gdx, containing only the part directly used later by the calibration process Figure 4: CAPRI GUI with the two consecutive steps of the calibration The results of the market model calibration are stored under baseline\data_market*year*.gdx. The unit value prices (UVAG) used in the supply model calibration are prepared in the arm\prep_market.gms module and written to disk in the file baseline\data_uvag*year*.gdx. The internal communication of these prices between supply and market modules are done through the p_pricesiniters parameter. All calibrated parameters are stored in a single.gdx container results\simini\sim_ini_*geog.level*years*.gdx. This data file contains all parameters necessary for initiating a simulation run. The.gdx is created with the module capmod\create_sim_ini_gdx.gms which calls the data preparation, data balancing and market model calibration modules explained above. Page 19 of 32

20 8. Validation of the CAPRI baseline results After the calibration of the CAPRI model, the results need to be checked and validated to ensure reliability and plausibility of the baseline. As CAPRI is calibrated to the AGLINK-COSIMO baseline, first objective of the validation exercise is to check deviation of CAPRI results from the AGLINK-COSIMO results. The desired outcome is that CAPRI results are relatively close to AGLINK-COSIMO results. However, they are not expected to exactly replicate them due to the fact that CAPRI is a significantly more detailed model in terms of regional coverage and disaggregation, activity coverage, EU supply representation, behavioural relationships and CAP policy modelling. Given its higher complexity, the CAPRI model needs to take significantly more interactions and micro and macro constraints (e.g. cost allocation, nutrient balances, policies) into consideration during the calibration compared to AGLINK- COSIMO, which might result in differences in baseline results. Following this logic, the check of CAPRI baseline starts from the EU aggregate level (EU27, EU15, EU-N12) according to the regional resolution available in the AGLINK- COSIMO. This is followed by examination of results at MS level and blocks of other non-eu countries for prices, production level (areas, number of animals), supply/demand (production, domestic use), trade (export, import, net export) and policies. Next important distinction is made between activities available in AGLINK- COSIMO and non-aglink-cosimo activities. The results for non-aglink-cosimo activities are evaluated based on expert opinion or other sources (e.g. the Outlook workshop organised by the DG AGRI and JRC-IPTS, Agricultural Markets Briefs 7 ). A challenge for a CAPRI baseline validation is the high resolution of EU results. CAPRI produces a huge quantity of regional (NUTS 2) and farm-type data which is hard to check mainly due to the facts that AGLINK-COSIMO baseline does not provide results beyond EU aggregate level and that no other comparable studies are available that provide an EU wide baseline at regional or farm-type level. Time and resources limitations further on typically do not allow checking all the disaggregated results and derived indicators. Thus, a detailed expert validation must concentrate more on selected indicators at representative countries, sectors, activities and policy areas, typically chosen according to a specific particular study or scenario analysis for which the baseline is constructed. 7 European Commission (2012): Prospects for the olive oil sector in Spain, Italy and Greece ', Agricultural Markets Briefs. Available at Page 20 of 32

21 9. Good management practices for the baseline Bellow we list some of the main management practices that contribute to smooth implementation of the CAPRI baseline calibration process: Scheduling database updates (including data on agricultural and trade policies) Always run an ex-post calibration. This results in a full set of results for the base year that can be later on directly compared to the baseline for the simulation year) Always run a baseline replication test to check that the calibration process worked corrected. Always run test scenarios and compare the model's output to the expected model behaviour For validation, compare the baseline results with other projections and model outputs in a structured way Document the underlying assumptions of the baseline Page 21 of 32

22 10. Concluding remarks The CAPRI modelling system is designed for comparative static analysis. In its essence it is developed to model alternative scenarios to a given baseline. Constructing a baseline, therefore, is an integral part of any policy impact analysis. CAPRI does not generate its own baseline but the aim is to calibrate it as close as possible to the DG AGRI baseline generated by AGLINK-COSIMO model. Calibration of CAPRI is split in several tools consistently interlinked between them with the aim to facilitate data manipulation, its calibration to the AGLINK-COSIMO baseline and exploitation of results. It includes modules to derive a set of consistent databases for the calibration (Figure 2): Coco and CAPREG for countries covered by the supply module at national, regional and farm type level the global.gms module to compile a consistent database for the global commodity markets a projection tool (CAPTRD) which generates target points for calibrating the supply module; calibration and scenario simulation tool (CAPMOD) calibrates the CAPRI model to target points and allows counterfactual scenario analysis; and the Graphical User Interface (GUI) allows to extract and analyse results. CAPRI is built of two main components: supply module following a template approach and a global market module. Both modules rely on different data sources and calibration approaches. The market module is defined by a system of supply and demand equations. The calibration requires that market equilibrium conditions are satisfied at the calibration point; supply and demand quantities and prices represent the point to which market module is calibrated. The supply module captures in high details EU production structure and with its constraints captures many aspects of the Common Agricultural Policy. The template model of the supply side follows a Positive Mathematical Programming approach assuming profit-maximizing behaviour subject to technological, endowment, policy and agro-economic constraints. JRC-IPTS has a regular calibration exercise which is executed in annual cycles in cooperation with DG AGRI and results are published in a joint DG AGRI JRC-IPTS report Prospects for Agricultural Markets and Income in the EU. Other key use of CAPRI baseline is for scenario analysis of various policies of interest to DG AGRI, Page 22 of 32

23 JRC and other research institutions. Important is to note that CAPRI baseline is usually recalibrated when a policy scenario is analysed in order to take into account the specific needs of the analysed polices, update of data and improvement of modelling of behavioural relationships relevant for the analysed polices. Page 23 of 32

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25 References Blanco-Fonseca, M. (2010) Literature Review of Methodologies to Generate Baselines for Agriculture and Land Use. CAPRI-RD project deliverable D4.1 Britz, W. (2011): An overview on the CAPRI model. Institute for Food and Resource Economics. University of Bonn, Britz, W.; Witzke, H.P. (2012): CAPRI model documentation, Institute for Food and Resource Economics. University of Bonn, < European Commission (2012): Prospects for agricultural markets and income in the EU Available at Fellmann, T., Hélaine, S. (2011): Commodity Market Development in Europe Outlook. October 2011 Workshop Proceedings. JRC Scientific and Technical Reports, European Commission. JRC Fellmann, T., Hélaine, S. (2012): Commodity Market Development in Europe Outlook. Proceedings of the October 2012 Workshop. JRC Scientific and Policy Reports, European Commission. JRC Gocht, A. (2010b): Update of a quantitative tool for farm systems level analysis of agricultural policies (EU FARMS). In: Dominguez, I. P., Cristoiu, A. (eds.). JRC Scientific and Technical Reports (EUR24321EN), IPTS Seville, 92 pp. Gocht, A. and Britz, W. (2011): EU-wide farm types supply in CAPRI - How to consistently disaggregate sector models into farm type models. Journal of Policy Modelling, 33(1) Howitt, R.E. (1995) Positive Mathematical Programming. American Journal of Agricultural Economics 2. 77: M barek, R. and Londero, P. (2012): Commodity Market Development in Europe Outlook workshop, 16/17 October 2012, Brussels. Nii-Naate, Z. (Ed.) (2011): Prospects for Agricultural Markets and Income in the EU. Background information on the baseline construction process and uncertainty analysis. JRC Scientific and Technical Reports, European Commission, Luxembourg. Available at: Heckelei, T. and Britz, W. (2005): Models based on Positive Mathematical Programming: State of the Art and Further Extensions.Conference Paper. 89th European seminar of the European Association of Agricultural Economics. Parma, Italy Page 25 of 32

26 Heckelei, T., Britz, W. and Zhang, Y. (2012): Positive Mathematical Programming Approaches - Recent Developments in Literature and Applied Modelling. Bio-based and Applied Economics. 1(1): Page 26 of 32

27 Annex 1: Calibration steps Bellow are listed main technical steps that need to be performed during the CAPRI calibration process. This work is usually done at IPTS (Sevilla), which developed a set of GAMS routines that automate this process: Before starting the calibration process the user needs to convert the original AGLINK-COSIMO result set (which is usually provided in one table in text file format) into a GAMS-readable format (.gdx file). The files convert_to_gdx.gms and codes_not_mapped.gms have two objectives, namely (1) convert the result set in the right format and (2) perform certain tests on the code mappings between CAPRI and AGLINK-COSIMO. The file convert_to_gdx.gms prepares the AGLINK-COSIMO result sheet in an appropriate format. It also checks for new definitions/codes in the AGLINK- COSIMO nomenclature and outdated ones that are not anymore in use. This checking is important because the AGLINK-COSIMO model is in continuous evolution and its nomenclature can change with the new model releases. The file codes_not_mapped.gms further checks if there are any AGLINK-COSIMO definitions that are in use in CAPRI but not anymore maintained (or changed) in AGLINK-COSIMO. The above GAMS routines create excel files for reporting. If new definitions/codes are identified, a corresponding code update needs to be performed in CAPRI in 'baseline\aglink(year)dgagri_sets.gms' (e.g. create 'baseline\aglink2012dgagri_sets.gms'). This is followed by the corresponding update of mappings between CAPRI and AGLINK-COSIMO definitions/codes in 'baseline\aglink(year)dgagri_mappings.gms' (e.g. create 'baseline\aglink2012dgagri_mappings.gms'). Create files 'global\convert_aglink(year).gms' and 'global\bio_fuel_markets_aglink(year).gms' in '\gams\global' which allows CAPRI to read AGLINK-COSIMO information in the global module (e.g. 'global\convert_aglink2012dgagri.gms' and 'global\bio_fuel_markets_aglink2012dgagri.gms' global\convert_aglink.gms loads in the original AGLINK-COSIMO results; convert them to the CAPRI nomenclature and does corrections on Page 27 of 32

28 commodity balances. The converted AGLINK-COSIMO projections are stored in the parameter p_datamarket for further processing. global\bio_fuel_markets.gms aims to compile a consistent data set for the biofuel markets based on AGLINK-COSIMO and F.O. Licht 8 information. The module calculates balances for the down-stream sector of biofuel production as well. Processing coefficients and extraction rates are harmonized with the ones used in AGLINK-COSIMO. The results are stored in the parameter p_biodat. Cross-check those implicit assumptions that are set manually in the market balancing procedure but should be harmonized with the ones used in the EC baseline. This mainly includes the policy assumptions (tariffs, WTO notifications, institutional prices etc.). The relevant code snippets are in the arm\data_cal.gms module. Select the AGLINK-COSIMO version you would like to use in the following screen, consistent with your calibration. This triggers a change in 'gams\global.gms' by deactivating the includes for the old AGLINK-COSIMO baseline and by activating the new ones, e.g.: $SETGLOBAL AGLINK aglink2012dgagri $SETGLOBAL AGLINK_scen aglink2012dgagri 8 F.O. Licht s World Ethanol and Biofuels Report provides statistical information and projections on global biofuel production and use. Page 28 of 32

29 If CAPRI is calibrated to AGLINK-COSIMO biofuel projections instead of PRIMES, the following options need to be adapted in the GUI.: This corresponds in the code to Update the following file 'captrd\scale_biofuel_to_dgagri(year).gms' (e.g. 'captrd\scale_biofuel_to_dgagri2012.gms') and Activate scale_to_dgagri in the file 'biofuel\bio_trends.gms', for example : $SETGLOBAL ScaleToDGAGRI ON $SETGLOBAL ScaleToDGAGRI DGAGRI2012 $ifi %ScaleToDGAGRI%==DGAGRI2012 $INCLUDE 'CAPTRD\scale_biofuel_to_dgagri2012.gms' Page 29 of 32

30 Implementation of the calibration in the GUI: Database update (this step might be skipped if national and regional data are not updated): CAPREG time series (only NUTS 2) GUI: build database -> Build regional time series CAPREG base year at NUTS 2 and afterwards at Farm type level GUI: build database -> Build regional database GLOBAL database GUI: build database -> Build global database Trend projections (CAPTRD): Run trend projections at MS and then NUTS 2 level GUI: Generate baseline -> Generate trend projections (choose MS and then NUTS 2) Run trend projections at farm-type level (only if interested in farm-type calibration, otherwise this step can be skipped) GUI: Generate baseline -> Generate farm type trends Update or adjust the load_aglink.gms module if necessary; modify variable bounds or equations of the trend models if necessary The load_aglink module has the following functions: Initialize set definitions (for AGLINK-COSIMO) and mappings between the two models nomenclatures; Load in the raw data under the parameter p_aglinkori Restrict the complete AGLINK-COSIMO result set to the EU and calculate balance items that can be later on mapped one-to-one to the CAPRI balances. This includes breaking down EU27 results to EU15/EU12 when necessary; calculating missing balance items etc. This is done in the parameter p_aglink. The content of the p_aglink parameter is mapped to the CAPRI nomenclature and stored under p_aglinktrd. Some additional items are Page 30 of 32

31 calculated, e.g. crop yields, balances for different intensity variants of CAPRI activities (e.g. high yield dairying), demand for biofuel feedstock, cow milk demand. The results of the above calculations are saved under p_result and p_aglinkuvag. The first parameter is the general container for results, the 5 th dimension indicates the source of the information; in this case it is marked with dgagri. The AGLINK-COSIMO information needs also to be scaled in order to match the base year values coming from the CoCo/CAPREG databases. This is done in the sub-module scale_dg_agri_baseline.gms. The results of the scaling algorithm are stored under the flag dgagri1 in the p_result parameter. As already noted above, CAPTRD needs to derive its projections at regional and farm-type level. That means that AGLINK-COSIMO results, which are given at EU15/EU12/EU27 level, must be broken-down to more detailed geographical levels. The trend models of CAPTRD do this job by also integrating further information sources (CoCo, CAPREG, expert information). The trend models are defined in equations.gms, the consistency constraints guarantee a consistent set of projections at all geographical level. Baseline calibration (CAPMOD): MTR_RD Baseline calibration at country level with market module switched ON GUI: Generate baseline -> Baseline calibration (choose MS) MTR_RD Baseline calibration at NUTS 2 with market module switched ON GUI: Generate baseline -> Baseline calibration (choose NUTS 2) Run farm type with market module switched OFF (this step can be skipped if farm-type calibration is not needed) GUI: Generate baseline -> Baseline calibration (choose farm-types) Run baseline scenario (CAPMOD): Run TSTCAL.gms at country level and NUTS 2 level GUI: Run scenario -> Run scenario (choose MS and then NUTS 2) Page 31 of 32

32 Baseline results: Baseline results are located in folder 'results\capmod' (e.g. the 2020 baseline results at NUTS 2 level are stored in the file 'res_2_0420tstcal.gdx') Page 32 of 32