Metadata for the papers published under the auspices of the AgMIP/ISI-MIP model intercomparison exercise.

This document provides metadata for the model results reported as part of the Global Economic Model Intercomparison Activity of AgMIP ( as well as the ISI-MIP project ( It describes the full data sets, of which subsets were used in the various papers published from this work. Notes specific to the PNAS paper are included below. For the papers published in Agricultural Economics, Vol. 45, Issue 1, 2014 more documentation and results are available in the supporting information for each of the papers.References are listed at the end of this document.

Table 1. Codes other than crop names and regions

Variable code / Variable name / Units
XPRR / Representative price on international markets / Index (Base Year =1)
XPRX / Weighted average export price / Index (Base Year =1)
XPRP / Weighted average producer price / Index (Base Year =1)
AREA / Harvested area / 1000 ha or index
PROD / Production / 1000 t or Million USD in constant prices
CONS / Total domestic use, irrespective of origin / 1000 t or Million USD in constant prices
FOOD / Consumption for food / 1000 t or Million USD in constant prices
FEED / Consumption for feed / 1000 t or Million USD in constant prices
OTHU / Other use / 1000 t or Million USD in constant prices
EXPO / Export quantity / 1000 t or Million USD in constant prices
IMPO / Import quantity / 1000 t or Million USD in constant prices
NETT / Net exports quantity / 1000 t or Million USD in constant prices
YILD / Yield / t/ha or index (base year = 1)
AEXO / Exogenous change in area / % per annum or index (base year=1)
YEXO / Exogenous change in yield / % per annum or index (base year=1)
CALO / Human calorie intake / kilo calories per capita per day
YELA / Income elasticity / dimensionless
PELA / Price elasticity / dimensionless
GDPT / Gross domestic product / Billion USD in constant base year prices, SSP data version 0.5
POPT / Total population / Million people, SSP data version 0.5
BFSH / Global share of crop used in the production of 1st-generation biofuels / Share (Range 0,1)
BFHA / Global land area used for2nd-generation biofuel feedstocks / 1000 ha

Table 2. Commodity codes and names

Abbreviation / Commodity/Commodity aggregate
AGR / Aggregate of all crops
WHT / Wheat
CGR / Coarse grains
CR5 / Five Crops / Aggregate of WHT,CGR,RIC,SUG,OSD
CRP / All Crops
DRY / Dairy
NRM / Nonruminant meat
PAS / Pasture
RIC / Rice
RUM / Ruminant meat
SUG / Sugar
OSD / Oil seeds

Table 3. Regions

Region code / Region name
AME / Africa and Middle East
ANZ / Australia and New Zealand
BRA / Brazil
CAN / Canada
CHN / China
EUR / Europe
FSU / Former Soviet Union
IND / India
MEN / Middle East and North Africa
NAM / North America (USA+CAN)
OAM / Other Americas (OSA+BRA)
OAS / Other Asia
OSA / Other South and Central America and Caribbean
SAS / Southern Asia (CHN+IND+SEA+OAS)
SEA / South-East Asia
SSA / Sub-Saharan Africa
USA / United States of America
WLD / World

Table 4. Key scenario elements

Scenario identifier for Agricultural Economics special issue / Socioeconomic assumptions / General circulation model / Greenhouse gas emissions pathway / Crop model / CO2 atmospheric concentration assumed by the crop models / Bioenergy policy assumptions / Scenario identifier, PNAS paper
S1 / SSP2 / None / None / None / NA / Model specific / Reference
S2 / SSP3 / None / None / None / NA / Model specific / NA
S3 / SSP2 / IPSL-CM5A-LR / RCP 8.5 / LPJmL / 370 ppm in all periods / Model specific / S1
S4 / SSP2 / HadGEM2-ES / RCP 8.5 / LPJmL / 370 ppm in all periods / Model specific / S2
S5 / SSP2 / IPSL-CM5A-LR / RCP 8.5 / DSSAT / 369 ppm in all periods / Model specific / S3
S6 / SSP2 / HadGEM2-ES / RCP 8.5 / DSSAT / 369 ppm in all periods / Model specific / S4
S7 / SSP2 / None / None / None / NA / 1st-gen. ca. 6ExaJoule; no 2nd-gen. (2050)
S8 / SSP2 / None / None / None / NA / 1st-gen. ca. 6ExaJoule; 2nd-gen. ca. 108EJ (2050) / NA
SSP2 / HadGEM2-ES / RCP 8.5 / pDSSAT / 369 ppm in all periods / Model specific / S5
SSP2 / HadGEM2-ES / RCP 8.5 / EPIC / 369 ppm in all periods / Model specific / S6
SSP2 / HadGEM2-ES / RCP 8.5 / PEGASUS / 369 ppm in all periods / Model specific / S7

Notes: LPJmL – Lund-Potsdam-Jena managed Land Dynamic Global Vegetation and Water Balance Model, DSSAT – Decision Support System for Agricultural Technology. EPIC – Environmental Policy Integrated Climate model. PEGASUS – Predicting Ecosystem Goods And Services Using Scenarios.All GCMs use the greenhouse gas emissions pathway RCP 8.5. The crop models assume CO2 fertilization is constant at 370 ppm throughout the period of the analysis. Effects of increased ozone concentration, increased weather variability, and greater biotic stresses are not included.

Notes specific to the PNAS paper

The data file for this paper is 'data_pnas_isimip_25nov13.csv' and is available for download at both and

Definitions used in the datafile:

Model:economic model name producing the results (see Table S1)

Scen:scenario identifiers for the paper (see Table S2 for correspondence with the AgMIP and ISI-MIP exercises)

Region:region acronym (see aboveTable 3. Regions)
Prod:product category (WHT = wheat, CGR = coarse grain, RIC = rice, OSD = oilseeds)
Year:year of observation
Modtype: model type; partial equilibrium (PE) or computable general equilibrium (CGE). See column 2 of Table S1
GCM:general circulation model used for the input (see Table S2, column 2)
CRM:crop model used for the input (see Table S2, column 3)
Var:variable following AgMIP nomenclature or derived from AgMIP variables

YEXO = exogenous yield

YILD = endogenous yield (renamed YTOT for this paper)

AREA = harvested area

PROD = production

CONS = total demand

XPRP = producer price (renamed PRICE for this paper)

TRSH = defined as follows:

- for all regions except world, TRSH = NETT / PROD(baseline) (net trade divided by baseline production)

- for 'world' region, TRSH = sum abs(NETT) / PROD(baseline) /2 (sum of absolute net trade divided by baseline production divided by two)

Val:results value

- for all variables except TRSH: calculated as deviation from the reference scenario (S1 in the AgMIP nomenclature in 2050 (0.12 = +12% compared to the baseline)

- for TRSH, calculated as a difference between scenario and baseline (equivalent to percentage change to baseline production)

All results from the paper can be reproduced with the R script file 'PNAS_ISI-MIP_online.r' included with the PNAS data file.

Derivation of data used for the PNAS paper from the AgMIP and ISI-MIP raw datasets

Data for this paper were produced from the initial databases of these projects after corrections.

To generate the data from this paper using the raw AgMIP and ISI-MIP datasets, the operations below were performed to prepare the data:

- for all models, the WLD value of YEXO has been recalculated using the regional AREA weight reported by each model for the base year (2005)

- for the MAgPIE model, some missing regions have been allocated the same impact of the reported macro region (SAS -> IND, NAM -> USA, OAM -> BRA, OAM -> OSA)

- the results value for AgMIP variable are all indicated as deviation to their value in the baseline for the year 2050

Papers published based on the AgMIP/ISI-MIP model comparison activities

PNAS special feature paper

Nelson, Gerald C., Hugo Valin, Ronald D. Sands, Petr Havlík, Helal Ahammad, Delphine Deryng, Joshua Elliott, et al. 2013. “Climate Change Effects on Agriculture: Economic Responses to Biophysical Shocks.” PNAS, forthcoming.

Agricultural Economics special issue papers

Lotze-Campen, H., von Lampe, M., Kyle, P., Fujimori, S., Havlík, P., von Meijl, H., Hasegawa, T., Popp, A., Schmitz, C., Tabeau, A., Valin, H., Willenbockel, D., Wise, M. (2014): Impacts of increased bioenergy demand on global food markets: an AgMIP economic model intercomparison. Agricultural Economics 45(1).

Müller, Christoph, and Richard D. Robertson. 2014. “Projecting Future Crop Productivity for Global Economic Modeling.” Agricultural Economics 45 (1).

Nelson, Gerald C., Dominique van der Mensbrugghe, Helal Ahammad, Elodie Blanc, Katherine Calvin, Tomoko Hasegawa, Petr Havlik, et al. 2014. “Agriculture and Climate Change in Global Scenarios: Why Don’t the Models Agree?” Agricultural Economics 45 (1).

Robinson, Sherman, Hans van Meijl, Hugo Valin, and Dirk Willenbockel. 2013. “Comparing CGE and PE Supply-Side Specifications in Models of the Global Food System.” Agricultural Economics 45 (1).

Schmitz, Christoph, Hans van Meijl, Page Kyle, Gerald C Nelson, Shinichiro Fujimori, Angelo Gurgel, Petr Havlik, et al. 2014. “How Much Cropland Is Needed? Insights from a Global Agro-Economic Model Comparison.” Agricultural Economics 45 (1).

Valin, Hugo, Ronald Sands, Dominique van der Mensbrugghe, Gerald C. Nelson, Helal Ahammad, Benjamin Bodirsky, Tomoko Hasegawa, et al. 2014. “Global Economic Models and Food Demand Towards 2050: An Intercomparison.” Agricultural Economics 45 (1).

Von Lampe, Martin, Dirk Willenbockel, Helal Ahammad, Elodie Blanc, Yongxia Cai, Katherine Calvin, Shinichiro Fujimori, et al. 2014. “Why Do Global Long-Term Scenarios for Agriculture Differ? An Overview of the AgMIP Global Economic Model Intercomparison.” Agricultural Economics 45 (1).