White Paper Sustainable Land Management programme / GLUES:

Models and Scenarios

Das vorliegende „white paper“ versucht die Arbeiten, die das Wissenschaftliche

Begleitvorhaben GLUES im Bereich Modelle und Szenarien leisten möchte, zu

konkretisieren.

Dieser Entwurf stellt eine erste Arbeitsgrundlage für die kommende gemeinsame Arbeit

mit den Regionalprojekten dar und wird durch den Input des Kick-offs „Nachhaltiges

Landmanagement“ in Bonn modifiziert.

Bei Rückfragen wenden Sie sich bitte an Frau Ruth Delzeit, ruth.delzeit@ifw-kiel.de

Models and scenarios

Tamara Avellan4, Benjamin Bodirsky1, Ruth Delzeit2, Thomas Heckelei3, Christoph Heinzeller4, Gernot Klepper2, Hermann Lotze-Campen1, Wolfgang Lucht1, Wolfram Mauser4, Alexander Popp1, Sibyll Schaphoff1, Leila Shamsaifar3 1Potsdam Institute for Climate Impact Research (PIK), PO Box 60 12 03, 14412 Potsdam, Germany2 Kiel Earth Institute, Hindenburgufer 66, 24104 Kiel, Germany3 Institute for Food and Resource Economics, University of Bonn, Nussallee 21, 53115 Bonn, Germany4 Department of Geography, Ludwig-Maximilians Universität Munich, Luisenstraße 37 / III / 428, 80333 Munich, Germany

1. Aim and ScopeThis paper aims to serve as basis for an exchange between the GLUES Work Packages

3 and 4 and the regional projects on global data sets on long term and midterm

scenarios. This exchange should on the one hand consist of the provision of global data

sets under different scenarios from GLUES to the regional projects, whereas global data

sets will be provided through the Geodata Infrastructure (GDI). On the other hand, since

models applied by the GLUES partners and the regional projects work on different

regional scales and might have different assumptions and drivers, a comparison of the

model results and a validation of regional results simulated by the global models is

another field of exchange. Within GLUES, the models used to simulate mid term and

long term scenarios do not run only on different temporal, but also on different spatial

scales. Therefore, in order to describe steps to make global data sets consistent, the

paper additionally provides an overview on the modelling activities applied for

developing global data sets with different models.

2. General overview Scenarios of alternative plausible futures have been increasingly used in environmental

change assessments as a means of exploring potential consequences of socioeconomic

change on the environment.

One important contribution of GLUES is to support regional projects in their efforts in

modeling and impact assessment of land use change on greenhouse gas emissions and

ecosystem services. GLUES will apply different models to derive climate change

scenarios and biophysical impacts, to explore future pathways of the land use system

and to undertake structured analysis of complex interactions within the land system.

While assessing consistent regional and spatially-explicit scenarios, these models will

take account for the global context, as local and regional demands can be met in

spatially unconnected regions through international trade (Erb et al. 2009).

Most environmental foresight studies including the climate change scenarios of the

Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions

Scenarios (Nakicenovic et al. 2000) and the Millennium Ecosystem Assessment (MEA

2005) have used explorative storylines to provide scenarios of alternative plausible

futures. The ultimate objective of such storylines is to assess the variation in possible

futures and to provide insights into the magnitude and uncertainty of future changes.

Explorative storylines can finally be used for communication in the scientific community

and with stakeholders (Van der Heijden, 2005) in an attempt to extract policy

implications.

Within GLUES, a set of alternative scenarios will be developed that explores contrasting

trajectories within this scenario framework.

In the following sections we introduce models used within GLUES to simulate scenarios,

possible parameter spaces for the construction of scenarios, and expected outputs. To

construct these scenarios, explorative scenarios with associated deviations based on

differences in the underlying drivers will be adapted, elaborating the underlying

qualitative storylines. This contribution has two temporal facets: Medium-term (to 2030)

and long term (to 2100) scenarios as will be described in the following sections:

3. Medium-term scenarios (WP 3)

3.1 Objective

Besides land use dynamics that are driven by long term trends such as population

growth and climate change, there are some short term factors which are policy driven

and vary over time. Therefore, the aim of WP 3 “Medium-Term Projections” is to

combine long and short term trends in order to provide activities of the regional projects

on modelling and impact assessment with a common set of scenarios for global land use

change on a mid term time scale. The scenarios will consider important feedbacks of

agricultural markets, climate, economy and the related land use. Since factors

influencing land use dynamics are located in interdisciplinary research fields, three

different types of models are applied to produce and simulate drivers of mid term

scenarios.

3.2 Models

PROMET

In order to understand where agriculture takes place nowadays and how much is

produced, global models have been applied in the past ten years to examine global land

cover, climate variability, soil properties, extension of irrigation and ultimately the

extension and amount of harvest of agricultural crops. However, the scale at which these

models, due to technical limitations, have been run is mainly insufficient to truly be able

to extrapolate useful information about the productivity of the land stretches. With the

PROMET model (PROcess of radiation Mass and Energy Transfer) (Mauser & Bach,

2009) we therefore, contrary to current models, propose to study a large number of

statistically selected points (synonymous with a site on which a crop growth) on the

global land surface that are representative for a) the local climatic condition, b) the soil

properties of the region. PROMET is a physically based, spatially distributed process

model that simulates a wide variety of land surface categories including crop growth.

The included dynamic plant module in PROMET calculates the site specific biomass for

each relevant crop and makes inferences about maximum attainable yields and yield

gaps possible.

Outcomes of the approach is to furnish the global trade models (DART & CAPRI) with

biophysically maximum attainable yields of economically relevant crops/crop aggregates

(see Appendix A), in order to understand the role of climate and soil on crop production.

We will do so by first creating a global suitability classification of the relevant crops

(triggered by climate, soil and terrain) and randomly selecting representative areas from

this. Using the site-specific soil and climate values, we will then apply PROMET on these

areas and calculate potential maximum yields for each crop. An aggregation of these

results into the accorded regions (see Appendix B) will provide the final output result as

an input for the trade models.

CAPRI

The Common Agricultural Policy Regionalised Impact Analysis (CAPRI) Model1 has

been developed by a Europe-wide network of researchers under the lead of the Institute

for Food and Resource Economics at the University of Bonn. Its main objective is to

support decision making on reforms of the Common Agricultural Policy of the EU.

Global, trade related feedbacks have received increasing attention in recent years

CAPRI is a global agricultural sector model which is divided into two major modules, an

EU supply and a global market module: The supply module covers EU27 as well as

Norway, Western Balkans and Turkey. Regional disaggregation takes place at level 2 of

the Nomenclature of Statistical Territorial Units (NUTS, 280 regions)2. The NUTS 2

system divides the territory of the EU into a hierarchical system of administrative

regions. Within these regions, optimization models for up to ten representative farm

types determine land allocation to crop production activities and animal production

levels.3

The market module is a partial, spatial, global multi-commodity model for agricultural

products and includes 60 countries in 28 trade blocks for 47 products. It is characterized

as partial, because non-agricultural factor and product markets and some agricultural

products such as flowers are excluded. It is spatial, as bilateral trade flows and related

trade policy instruments and transportation costs between territorially explicit trade

blocks are included. 4

Data in CAPRI are stored in the GDX format. As CAPRI is a GAMS-based system, the

GDX format enables the use of an interface to pass data in or out more rapidly. Along

with a native interface definition, data can then be exchanged with applications.

For the GLUES project, the explicit representation of land use of agricultural production

activities already implemented in the EU supply module will be integrated into the supply

specification at the global scale with an alternative approach compatible with the

structure of the market module. In addition, the currently specified regions will be

aggregated and mapped to 23 regions. This development will enable simulations of

global agricultural land use changes depending on medium term scenarios. Scenario 1 Website: http://www.capri-model.org/index.htm2 Website: http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/introduction3 See Annex for a list of output, income indicators, policy variables and processed products in the database

as well as a list for aggregated farm types in the supply model.4 See Annex for a list regions and databases of further elements in the market model.

variables currently include exogenous determinants of demand and supply such as

population, shifts in preferences, income, input prices, all potentially differentiated by

trading blocks. The explicit integration of land at the global level will be accompanied by

allowing for climate dependent yield developments.

DART

At the Kiel Earth Institute, the Dynamic Applied Regional Trade (DART) model, a

recursive dynamic computable general equilibrium (CGE) model of the world economy,

covering multiple sectors and regions has been developed. It simulates the

repercussions on economic activity between the different world regions and countries

and can be used to analyse the world-wide feedback effects of economic policies across

different sectors and regions (see Springer 2002, Klepper et al. 2003, Kretschmer et al.

2008). Hence it computes likely impacts outside the land use sectors and derives

welfare effects of different policy scenarios (see section 3.3).

The model’s primary factors are labour, capital and land and it is based on the GTAP7

data set of the Global Trade Projects. For the analysis of land use change, it is

calibrated to an aggregation of 23 regions (see Appendix B), and consequently model

outputs are values (changes compared to a base year) for each region. Each regional

economy comprises five energy sectors, sixteen agricultural sectors that include the

most important energy crops (wheat, maize, sugar cane, four types of oil seeds), and

three industrial production sectors. An explicit representation of land is included into

DART via the introduction of Agro-Ecological-Zones (AEZs). In each region there are up

to 18 AEZs, which differ along growing period and climatic zones. The AEZs enter as

inputs into national production functions for each land-using sector.

The major exogenous drivers of the model dynamics are changes in the labour force, the

rate of labour productivity growth, changes in human capital, the savings rate, the gross

rate of return on capital, and thus the endogenous rate of capital accumulation.

The agricultural sector is represented in a less detailed way compared to the CAPRI

model, but the advantage of DART is that it endogenously considers all sectors of the

world economy. Thus, by taking into account both agricultural and energy markets and

their interactions, in the GLUES project, DART is applied to generate a set of global

projections of agricultural markets and related land use. Thereby, parameters driving the

demand side such as population dynamics and shifts in nutrition are linked with regional

economic growth dynamics and their world market repercussions. As in CAPRI, data are

stored in GDX format.

The model outputs are summarised in the following section.

3.3 Global data sets and parameter spaces

All data are generated for the 23 regions; therefore, they are average values for these

world regions. Some data might be available in a more disaggregated resolution, since

CAPRI is very detailed for the European Union. Global data sets, produced for midterm

scenarios are:

GLUES Activities in this context (WP 3)

o In a dialogue with the regional projects, GLUES will develop global scenarios with the following parameters to be discussed:

o An exogenous parameter in DART and CAPRI is population growth. In DART population growth is taken from the PHOENIX model, which is in line with OECD projections. If of interest for regional projects, it could be changed for scenario analysis.

o Impacts of climate change on the maximum attainable yield.

o Climate policies: different green house gas reduction goals can be implemented into DART

o Biofuel policies: we can e.g. assume different countries to implement policies to support biofuels (for example EU target on 10% biofuels by 2020)

o Change in food consumption behaviour: we can assume a world with current food preferences or scenarios assuming, for example, more meat and milk consumption, or in contrast a more vegetarian diet

o We can assume a scenario with and without agricultural production in protected areas or areas with high biodiversity.

o The output of the models under these different scenario settings are global data sets which GLUES can provide to the regional projects. These are:

o Maximum attainable yield of the specified crops per GLUES region (see section 3.2),

o Yield gaps of the specified crops per GLUES region (see section 3.2),

o Land use (in production value and hectare) for the GLUES regions

o In many regional models, parameters on development of global market prices of e.g. agricultural goods or energy goods are exogenously given. GLUES can provide these data to the regional projects at the level of aggregation of the GLUES regions.

o Changes in the average Gross Domestic Product by GLUES region under different scenario settings can be used to measure welfare effects of different policy settings. This also provides another category of input data for the regional projects.

4. Long-term scenarios (WP 4)WP4 will help to assess global-scale, yet regionally explicit quantitative scenarios of

factors that are likely to co-determine regional trajectories of land use change under

policies that consider long-term global sustainability objectives and trade-offs such as

climate change impacts, adaptation and mitigation. To construct these scenarios,

explorative scenarios with associated deviations based on differences in the underlying

drivers will be adapted, elaborating the underlying qualitative storylines. The quantitative

trends for the main driving forces will either be elaborated or compiled from existing

sources (i.e. MEA 2005, Nakicenovic 2000).

First, temperature stratified climate scenarios will be made available at a spatial

resolution of 0.5 x 0.5 degrees for the climate parameters temperature, precipitation and

cloudiness. The range of IPCC AR4 scenarios and updates of these as well as more

recent scenarios as they become available in the lead-up to the AR5, will be transformed

for impact, mitigation and adaptation research in the regional consortia.

Second, we will apply different simulation models and methodologies to derive a set of

drivers (such as the impact of global warming on yield changes and freshwater

availability), consequences (such as greenhouse gas emissions) and patterns of

potential future land use under specified interregional optimization and sustainability

criteria using an internally consistent framework (see Fig. 1 for an overview):

LPJmL

The prime eco-physiological model that will be employed is the long-established,

internationally recognized, global-scale, spatially and temporally explicit biogeochemical

process model of natural and agricultural vegetation LPJmL (Sitch et al. 2003, Gerten et

al. 2004, Bondeau et al. 2007). It is able to simulate the transient changes in carbon and

water stocks and fluxes in response to land use change and climate change, the specific

phenology and seasonal CO2 fluxes of agricultural-dominated areas, and the production

of crops and grazing land as well as the potential of biomass plantation of the second

generation within grid cells of 0.5 degree resolution. Crops are represented by 12 crop

functional types and biomass plants by 1 temperate tree ( e.g poplars and willows) and 1

tropical tree (e.g. Eukalyptus) and a C4 grass (e.g. Miscanthus). Crops, grazing land and

land for biomass production can be either rainfed or irregated. This allows the global and

regional quantification and differentiation of irrigation water use and rainwater use from

agricultural products, including biomass production for bioenergy use. External drivers

are climate parameters as temperature, precipitation, cloudiness and wet days, these

data, used for the historical period, are provided by the Climatic Research Unit Datasets

- CRU TS 3.00 (Mitchel and Jones, 2005) and by atmospheric carbon dioxide content.

The individual cover fraction per gridcell are prescribed by a land use data set and soil

texture parameters are derived from the FAO database (FAO, 1991).

MAgPIE

The global land-use optimization model MAgPIE (Lotze-Campen et al. 2008, Popp et al.

2010) simulates future transitions of the landuse sector. The model works on a time

step of 10 years in a recursive dynamic mode. The optimized land-use pattern from one

period is taken as the initial land constraint in the next period.The model inputs are both

socio-economic parameters like population, income or production costs (labour,

chemicals and other capital from GTAP) and biophysical information like yield-levels or

water requirements from LPJmL. The model features 20 cropping and 5 livestock

sectors, and uses depending on data-availability either 0.5° grid-based data sets or

regional parameters for 10 world regions. The objective function of MAgPIE is to

minimize total cost of production for a given amount of regional food and bioenergy

demand. Feed for livestock is produced as a mixture of grain, green fodder produced on

crop land and pasture. The model simulates trajectories for the agricultural sector and

determines endogenously cropping patterns, trade flows, land-expansion and increases

in future crop-yields. The direct link of MAgPIE to the LPJml model allows for an

integration of biophysical constraints into an economic decision-making process and

thus provides a straightforward link between monetary and physical units as well as

processes, producing insights into the internal use value of resource constraints.

The following products will be produced and made available to the regional projects

through the Geodata Infrastructure (GDI) (see Fig. 1 for an overview):

(S0) Temperature-scaled Climate Change Scenarios

(S1) Scenarios of calorie demand for different demographic, GDP and lifestyle

trajectories (regions-based)

(S2) Scenarios of (potential) agricultural yield development under climate change (by

temperate increase, GCM, 0.5° resolution, 2000-2100)

(S3) Scenarios of macrohydrological freshwater availability (0.5° resolution and for river

basins, monthly 2000-2100)

(S4) Scenarios of 2nd generation bioenergy demand (regional [EJ]) and production (10-

yearly, 0.5° resolution, 2000-2100)

(S5) Biome composition shifts (in the form of change metrics, to be used as a top-level

indicators of shifts in ecosystem services) under climate and land use change (0.5°

resolution, annually 2000-2100)

(S6) Scenarios of potential future land use patterns (0.5° resolution, 10-yearly time slices

2000-2100)

(S7) Scenarios of potential (implied) change in global agricultural trade (including

bioenergy) as a consequence of the land use scenarios produced (regions, 10-yearly

time slices 2000-2100)

(S8) Scenarios of shadow prices for environmental resources (within 10 macroeconomic

regions, with selected regional resolution to the pixel level, 10-yearly time slices 2000-

2100)

(S9) Scenarios of greenhouse gas emissions (CH4, N2O, CO2) from land use

(agriculture) and land use change (e.g. deforestation) under (0.5° resolution, 10-yearly

time slices 2000-2100)

Fig. 1: Overview of long-term scenarios; yellow boxes describe drivers of the bio-

geochemical cycle in LPJmL, brown boxes exogenous socio-economic drivers, green

boxes consequences of climate change, blue and pink boxes patterns and

consequences of land use.

5. Consistency of models and dataBoth mid- and long-term scenarios divide into biophysical (PROMET, LPJmL) and

economic models (CAPRI, DART, MAgPIE). The main link between both model-types

consists in the yield level of crops, which is passed on from the biophysical to the

economic models. The mid-term models have to upscale yield levels from statistically

selected points to the respective regions, while the MAgPIE model can use the grid

based output from LPJml.

The two mid-term economic models will be harmonised with respect to the

representation of land in order to increase consistency of the models. Whether and how

the data sets of mid-term and the long-term models shall be harmonised has not been

decided yet. The economic models use different modelling approaches: CAPRI is a

partial equilibrium model, DART a computable general equilibrium model and MAgPIE a

partial optimisation model.

6. ReferencesBondeau, A.; Smith, P. C.; Zaehle, S.; Schaphoff, S.; Lucht, W.; Cramer, W.; Gerten, D.; Lotze-Campen, H.; Müller, C.; Reichstein, M.; Smith, B. (2007): Modelling the role of

agriculture for the 20th century global terrestrial carbon balance. Global Change Biology 13(3): 679-706.

Erb K, Krausmann F, Lucht W, Haberl H 2009 Embodied HANPP Mapping the spatial disconnect between global biomass production and consumption Ecological Economics, 692 328-334

FAO: The digitized soil map of the world, Food and Agriculture Organization of the United Nations, Rome, Italy, 1991.

Gerten, D., Schaphoff, S., Haberlandt, U., Lucht, W., Sitch, S., 2004. Terrestrial vegetation and water balance: hydrological evaluation of a dynamic global vegetation model. Journal of Hydrology 286, 249–270.

Klepper, G., S. Peterson, K. Springer (2003): DART97: A Description of the Multi-regional, Multi-sectoral Trade Model fort he analysis of Climate Policies. Kiel Working Paper 1149.

Kretschmer, B., S. Peterson, A. Ignaciuk (2008): Integrating Biofuels into the DART Model. Kiel Working Papers 1472.

Lotze-Campen, H., Müller, C., Bondeau, A., Rost, S., Popp, A., Lucht, W., 2008. Global food demand, productivity growth and the scarcity of land and water resources: a spatially explicit mathematical programming approach. Agricultural Economics 39 (3), 325–338.

Mauser, W., Bach H. (2009): PROMET – a Physically Based Hydrological Model to Study the Impact of Climate Change on the Water Flows of Medium Sized, Mountain Watersheds, J. Hydrol., 376(2009)362-377, DOI:10.1016/j.hydrol.2009.07.046

Millennium Ecosystem Assessment (MA). 2005. Millennium ecosystem assessment synthesis report. Island Press, Washington, D.C., USA.

Mitchell and Jones, 2005: An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatology, 25, 693-712, Doi: 10.1002/joc.1181.

Nakicenovic, N., J. Alcamo, G. Davis, B. de Vries, J. Fenhann, S. Gaffin, K. Gregory, A. Grübler, T. Y. Jung, T. Kram, E. la Rovere, L. Michaelis, S. Mori, T. Morita, W. Pepper, H. Pitcher, L. Price, K. Riahi, A. Roehrl, H.-H. Rogner, A. Sankovski, M. E. Schlesinger, P. R. Shukla, S. Smith, R. J. Swart, S. van Rooijen, N. Victor, and Z. Dadi. 2000. Special report on emissions scenarios. Cambridge University Press, Cambridge, UK.

Popp A, Lotze-Campen H and Bodirsky B 2010 Food consumption, diet shifts and associated non-CO2 greenhouse gas emissions from agricultural production. Global Environmental Change 20 451-462

Sitch, S., Smith, B., Prentice, I., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J., Levis, S., Lucht, W., Sykes, M., Thonicke, K., Venevsky, S., (2003). Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology 9 (2), 161–185.

Springer, K. (2002): Climate Policy in an Globalizing World: A CGE Model with Capital Mobility and Trade. Kieler Studien. Springer, Berlin.

Van der Heijden, K. (2005). "Scenarios: The Art of Strategic Conversation," 2nd/Ed. Wiley, Chichester, UK.

Annex A: List of crops

Crop

Barley

Groundnut

s

Maize

Millet

Oats

Paddy

Rice

Palm Oil

Rapeseed

s

Rye

Sorghum

Soybeans

Sugar

cane

Wheat

Annex B

List of regions (WP3 – midterm scenarios)

EU (7) Non-EU (16)DEU Germany NA New Zealand, AustraliaGBR UK, Ireland CAN CanadaSCA Finland, Sweden, Denmark USA USAFRA France BRA BrazilBEN Belgium, Netherlands,

LuxemburgPAUC Paraguay, Argentina, Uruguay, Chile

MED Spain, Portugal, Italy, Greece, Malta, Cyprus

LAM Rest of Latin America

REU Austria, Estonia, Latvia, Lithuania, Poland, Hungary, Slovakia, Slovenia, Czech Republic, Romania, Bulgaria

JPN JapanRUS RussiaFSU Rest of Former Soviet Union & Rest of

EuropeCPA ChinaIND IndiaSEA Cambodia, Laos, Tailand, Vietnam,

Burma, BangladeshMAI Malaysia, IndonesiaMEA Middle East, North AfricaAFR Sub-Saharan AfricaPAS Rest of the World

List of MAgPIE regions (WP4 – longterm scenarios)Sub-Saharan Africa

AFR Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo (Dem Republic), Congo(Republic), Côte d'Ivoire, Djibouti, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, The, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, Somalia, South Africa, Sudan, Swaziland, Tanzania, Togo, Uganda, Western Sahara, Zambia, Zimbabwe,

CambodiaCentrally planned Asia

CPA China, Hong Kong, Laos, Mongolia, Taiwan, Viet Nam

Europe(incl. Turkey)

EUR Albania, Austria, Belgium-Luxembourg, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Falkland Islands (U.K.), Finland, France, Germany, Greece, Greenland, Hungary, Iceland, Ireland, Italy, Kerguelen (F.S.A.T.), Latvia, Lithuania, Luxembourg, Macedonia, Former Yugoslavia, Montenegro, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, Yugoslavia (Fed Rep of) Former Soviet Union (FSU) Armenia, Azerbaijan, Republic of, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Republic of, Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan

Former Soviet Union

FSU Armenia, Azerbaijan, Republic of, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Republic of, Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan

Latin America

LAM Argentina, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, French Guiana, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Suriname, Trinidad, Uruguay, Venezuela

Middle East/North Africa

MEA Algeria, Egypt, Iran, Islamic Rep of, Iraq, Israel, Jordan, Kuwait, Lebanon, Libyan Arab Jamahiriya, Morocco, Oman, Qatar, Saudi Arabia, Syrian Arab Republic, Tunisia, United Arab Emirates, Yemen

North America

NAM Canada, Puerto Rico, United States of America

Pacific OECD PAO Australia, Japan, New ZealandPacific Asia PAS Brunei, Fiji, Indonesia, Korea (Dem People's Rep), Korea,

Republic of, Malaysia, New Caledonia, Papua New Guinea, Philippines, Singapore, Solomon Islands, Thailand, Vanuatu

Southern Asia

SAS Afghanistan, Bangladesh, Bhutan, India, Myanmar, Nepal, Pakistan, Reunion, Sri Lanka

CAPRI Supply Module: Output, Input, income indicators, policy variables and processed products in the data base

Group Activity Code

Outputs

Cereals Soft wheatDurum wheatRye and Meslin Barley Oats Paddy rice Maize Other cereals

SWHE DWHE RYEM BARL OATS PARI MAIZ OCER

Oilseeds Rape Sunflower Soya Olives for oil Other oilseeds

RAPE SUNF SOYA OLIV OOIL

Other annual crops Pulses Potatoes Sugar beet Flax and hemp Tobacco Other industrial crops

PULS POTA SUGB TEXT TOBA OIND

Vegetables Fruits Other perennials

Tomatoes Other vegetablesApples, pear & peaches Citrus fruits Other fruits Table grapes Table olives Table wine Nurseries Flowers Other marketable crops

TOMA OVEG APPL CITR OFRU TAGR TABO TWIN NURS FLOW OCRO

Fodder GrasFodder maize Fodder root crops Fodder root crops Straw

GRAS MAIF OFAR ROOF STRA

Marketable products from animal product

Milk from cows Beef Pork meat Sheep and goat meat Sheep and goat milk Poultry meat Other marketable animal products

COMI BEEF PORK SGMT SGMI POUM OANI

Intermediate products from animal production

Milk from cows for feedingMilk from sheep and goat cows for feeding Young cows Young bulls Young heifers

COMF SGMF

YCOW YBUL YHEI

Young male calves Young female calves Piglets Lambs Chicken

Nitrogen from manure Phosphate from manure Potassium from manure

YCAM YCAF YPIG YLAM YCHI

MANN MANP MANK

Other Output from EAA Renting of milk quota Agricultural services

RQUO SERO

Inputs

Mineral and organic fertiliser Seed and plant protection

Nitrogen fertiliser Phosphate fertiliser Potassium fertiliser Calcium fertiliser Seed Plant protection

NITF PHOF POTF CAOF SEED PLAP

Feeding stuff Feed cereals Feed rich protein Feed rich energy Feed based on milk products Gras Fodder maize Other Feed from arable land Fodder root crops Feed otherStraw

FCER FPRO FENE FMIL FGRA FMAI FOFA FROO FOTHFSTRA

Young animal Other animal specific inputs

Young cow Young bull Young heifer Young male calf Young female calf Piglet Lamb Chicken Pharmaceutical inputs

ICOW IBUL HEI ICAM ICAF IPIG ILAM ICHI IPHA

General inputs Maintenance machineryMaintenance buildings Electricity Heating gas and oil Fuels Lubricants Water Agricultural services input Other inputs

REPM REPB ELEC EGAS EFUL ELUB WATR SERI INPO

Income indicators Production value Total input costs Gross value added at producer prices Gross value added at basic pricesGross value added at market prices plus CAP premiums

TOOU TOIN GVAP GVAB MGVA

Activity level Cropped area, slaughtered heads or herd size

LEVL

Policy variables Relating to activities

Premium ceiling Historic yield

PRMC HSTY

Premium per ton historic yield Set-aside rate Premium declared below base area/herd Premium effectively paid Premium amount in regulation Type of premium application Factor converting PRMR into PRMD Ceiling cut factor

PRET SETR PRMD

PRME PRMR APPTYPE APPFACT CEILCUT

Processed products Rice milled Molasse Starch Sugar Rape seed oil Sunflower seed oil Soya oil Olive oil Other oil Rape seed cake Sunflower seed cake Soya cake Olive cakes Other cakes Gluten feed from ethanol production Biodiesel Bioethanol Palm oil Butter Skimmed milk powder Cheese Fresh milk products Creams Concentrated milk Whole milk powder Whey powder Casein and caseinates Feed rich protein imports or byproducts Feed rich energy imports or byproducts

RICE MOLA STAR SUGA RAPO SUNO SOYO OLIO OTHO RAPC SUNC SOYC OLIC OTHC GLUE BIOD BIOE PLMO BUTT SMIP CHES FRMI CREM COCM WMIO WHEP CASE FPRI FENI

Source: CAPRI Model Documentation

CAPRI Supply module: Aggregated farm types used for impact assessmentCode Description Farm type included

A10 Specialist COP (other than rice) or various field crops

133,144

A13 Specialist Rice or Rice & COP 132,133

A14 Root crops 141,142

A23 Permanent crops & vegetables 143,201,202,203,311,312,313,314,321,322,323,330,340

A41 Dairy 411,412,431

A42 Cattle fattening & rairing 421,422,432

A44 Sheep & goats 441,442,443,444

501 Specialist pigs 501

A52 Specialist poultry 502,503

A60 Field crops diversified 601,602,603,604,605,606

A70 Livestock diversified 711,712,721,722,723

A80 Livestock & crops diversified 811,812,813,814,821,822,823

999 Various

Source: CAPRI modeling system

CAPRI Market Module: Regional BreakdownCountry/Country

aggregateCode Components with own

behavioural functionsCountry name Covered by

programming models in supply module?

European Union 15, broken down into Member States (Luxembourg aggregated with Belgium)

EU015000 AT000000BL000000DK000000DE000000EL000000ES000000FI000000FR000000IR000000IT000000NL000000PT000000SE000000UK000000

AustriaBelgium/LuxDenmarkGermanyGreeceSpainFinlandFranceIrlandItalyNetherlandsPortugalSwedenUnited Kingdom

Yes

European Union 10, broken down into Member States

EU010000 CY000000

CZ000000

EE000000

HU000000

LT000000

LV000000

MT000000

SI000000

SK000000

PL000000

CyprusCzech RepublicEstoniaHungaryLithuaniaLatviaMaltaSloveniaSlovakiaPoland

Yes

Norway NO000000 Norway Yes

Bulgaria & BUR BG000000 Bulgaria Yes

Romania RO000000 Romania

Other mediterranean countries

MED TUNALGEGYISR

TunisiaAlgeriaEqypt Israel

No

Turkey TUR Yes

Morocco MOR MOR No

Western Balkan countries

WBA HR000000 CS000000MO000000KO000000AL000000BA000000

MK000000

Croatia SerbiaMontenegroKosovoAlbaniaBosnia & Herzegov.TFYR Macedonia

Yes

Rest of Europe REU No

Russia, Belarus & Ukraine RBU No

United States of America USA No

Canada CAN No

Mexico MEX No

Venezuela VEN No

Argentina ARG No

Brazil BRA No

Chile CHL No

Uruguay URU No

Paraguay PAR No

Bolivia BOL No

Rest of South America RSA No

Australia & New Zealand ANZ No

China CHN No

India IND No

Japan JAP No

Least Developed Countries LDC No

ACP Countries which are not

ACP No

LDCs

Rest of World ACP No

Source: CAPRI modeling system

CAPRI Market Module: Data Sources Based on

Bi-lateral trade flows FAOSTAT

Items of the market balances for countries not covered by the supply model. (production, feed demand, processing demand, human consumption)

FAOSTAT

Most favorite nation tariffs and data for TRQs and bilateral agreements AMAD data base, EU legislation

Source: CAPRI modeling system