Download Models and scenarios - Nachhaltiges Landmanagement

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, [email protected]
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,
Germany
2 Kiel Earth Institute, Hindenburgufer 66, 24104 Kiel, Germany
3 Institute for Food and Resource Economics, University of Bonn, Nussallee 21, 53115 Bonn,
Germany
4 Department of Geography, Ludwig-Maximilians Universität Munich, Luisenstraße 37 / III / 428,
80333 Munich, Germany
1. Aim and Scope
This 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) Model 1 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
1 Website: http://www.capri-model.org/index.htm
2 Website: http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/introduction
3 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.
global agricultural land use changes depending on medium term scenarios. Scenario
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
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.
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 (10yearly, 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 20002100)
(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 biogeochemical 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 data
Both 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. References
Bondeau, 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 Multiregional, 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, 693712, 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
Groundnuts
Maize
Millet
Oats
Paddy Rice
Palm Oil
Rapeseeds
Rye
Sorghum
Soybeans
Sugar cane
Wheat
Annex B
List of regions (WP3 – midterm scenarios)
EU (7)
DEU
GBR
SCA
FRA
BEN
MED
REU
Non-EU (16)
Germany
UK, Ireland
Finland, Sweden, Denmark
France
Belgium,
Netherlands,
Luxemburg
Spain, Portugal, Italy, Greece,
Malta, Cyprus
Austria,
Estonia,
Latvia,
Lithuania, Poland, Hungary,
Slovakia, Slovenia, Czech
Republic, Romania, Bulgaria
NA
New Zealand, Australia
CAN
USA
BRA
PAUC
Canada
USA
Brazil
Paraguay, Argentina, Uruguay, Chile
LAM
Rest of Latin America
JPN
RUS
FSU
Japan
Russia
Rest of Former Soviet Union & Rest of
Europe
CPA
IND
SEA
China
India
Cambodia, Laos, Tailand,
Burma, Bangladesh
MAI
MEA
AFR
PAS
Malaysia, Indonesia
Middle East, North Africa
Sub-Saharan Africa
Rest of the World
Vietnam,
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,
Madagascar,
Malawi,
Guinea-Bissau,
Mali,
Mauritania,
Kenya,
Lesotho,
Mauritius,
Liberia,
Mozambique,
Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, Somalia,
South Africa, Sudan, Swaziland, Tanzania, Togo, Uganda, Western
Sahara, Zambia, Zimbabwe,
Cambodia
Centrally
CPA
China, Hong Kong, Laos, Mongolia, Taiwan, Viet Nam
EUR
Albania, Austria, Belgium-Luxembourg, Bosnia and Herzegovina,
planned Asia
Europe
(incl. Turkey)
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
FSU
Union
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
MEA
Algeria, Egypt, Iran, Islamic Rep of, Iraq, Israel, Jordan, Kuwait,
East/North
Lebanon, Libyan Arab Jamahiriya, Morocco, Oman, Qatar, Saudi
Africa
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 Zealand
Pacific 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
Cereals
Soft wheat
Durum wheat
Rye 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 vegetables
Apples, 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
Gras
Fodder 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 feeding
Milk from sheep and goat cows for
feeding
Young cows
Young bulls
Young heifers
COMF
SGMF
Outputs
YCOW
YBUL
YHEI
Young male calves
Young female calves
Piglets
Lambs
Chicken
YCAM
YCAF
YPIG
YLAM
YCHI
Nitrogen from manure
Phosphate from manure
Potassium from manure
MANN
MANP
MANK
Renting of milk quota
Agricultural services
RQUO
SERO
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 other
Straw
FCER
FPRO
FENE
FMIL
FGRA
FMAI
FOFA
FROO
FOTH
FSTRA
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 machinery
Maintenance 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 prices
Gross 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
Premium ceiling
Historic yield
PRMC
HSTY
Other Output from EAA
Inputs
Relating to activities
Processed products
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
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
PRME
PRMR
APPTYPE
APPFACT
CEILCUT
Source: CAPRI Model Documentation
CAPRI Supply module: Aggregated farm types used for impact assessment
Code
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
143,201,202,203,311,312,313,314,321,322,323,330,3
40
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 Breakdown
Country/Country
aggregate
Code
Components with own
behavioural functions
European Union
15, broken down
into Member States
(Luxembourg
aggregated with
Belgium)
EU015000
AT000000
BL000000
DK000000
DE000000
EL000000
ES000000
FI000000
FR000000
IR000000
IT000000
NL000000
PT000000
SE000000
UK000000
European Union
10, broken down
into Member States
EU010000
CY000000
Country name
Austria
Belgium/Lux
Denmark
Germany
Greece
Spain
Finland
France
Irland
Italy
Netherlands
Portugal
Sweden
United Kingdom
Covered by
programming models
in supply module?
Yes
Yes
CZ000000
EE000000
HU000000
LT000000
LV000000
MT000000
SI000000
SK000000
PL000000
Norway
Bulgaria &
Romania
BUR
Cyprus
Czech Republic
Estonia
Hungary
Lithuania
Latvia
Malta
Slovenia
Slovakia
Poland
NO000000
Norway
Yes
BG000000
RO000000
Bulgaria
Romania
Yes
Other
mediterranean
countries
MED
Turkey
TUR
Morocco
MOR
MOR
Western Balkan
countries
WBA
HR000000
CS000000
MO000000
KO000000
AL000000
BA000000
TUN
ALG
EGY
ISR
Tunisia
Algeria
Eqypt
Israel
No
Yes
MK000000
No
Croatia
Serbia
Montenegro
Kosovo
Albania
Bosnia &
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
LDCs
ACP
No
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