1 

Towards An Integrated Land Use Data Base for Assessing 

the Potential for Greenhouse Gas Mitigation 

By 

Huey-Lin Lee,* Thomas W. Hertel,***

Brent Sohngen, ***and Navin Ramankutty****

GTAP Technical Paper No.25 

December, 2005

 

*

 The authors thank Professor Claudia Kemfert, Dr. Truong Truong, Professor Bruce McCarl, Dr. John Reilly, and 

colleagues at the Australian Bureau of Agricultural and Resource Economics (ABARE) for their review and 

comments on the documentation. We would also like to thank the U.S. Environmental Protection Agency (US-EPA) 

Climate Analysis Branch for funding this effort to produce the data. In particular, Dr. Steven Rose has provided 

valuable guidance and critical input without which this document would have been far from complete. We also thank 

Francisco de la Chesnaye of the US-EPA for his leadership and patience and for inspiring us to undertake this work. 

The authors thank Professor Gerald Shively in the Department of Agricultural Economics, Purdue University, for his 

assistance in reviewing and finalizing this documentation. Finally, we would like to acknowledge the pioneering 

work of the late Roy Darwin, who was the first researcher to implement a detailed land use data base in the GTAP 

framework. His dedication to this work provides continuing inspiration to those of us working on the problem of 

global land use and climate change. All errors and omissions remain the responsibility of the authors alone. * Center for Global Trade Analysis (GTAP), Dept. of Agricultural Economics, Purdue University. Email: 

HLEE1@purdue.edu ** Center for Global Trade Analysis (GTAP), Dept. of Agricultural Economics, Purdue University. Email: 

hertel@purdue.edu *** Dept. of Agricultural, Environmental and Development Economics, Ohio State University. Email: 

sohngen.1@osu.edu **** Center for Sustainability and the Global Environment (SAGE), Institute for Environmental Studies, University of 

Wisconsin-Madison. Email: nramanku@wisc.edu 
2

Towards An Integrated Land Use Database for Assessing 

the Potential for Greenhouse Gas Mitigation 

GTAP Technical Paper No. 25 

Huey-Lin Lee, Thomas Hertel, Brent Sohngen, and Navin Ramankutty 

Abstract 

This paper describes the GTAP land use data base designed to support integrated 

assessments of the potential for greenhouse gas mitigation. It disaggregates land use by 

agro-ecological zone (AEZ). To do so, it draws upon global land cover data bases, as well 

as state-of-the-art definition of AEZs from the FAO and IIASA. Agro-ecological zoning 

segments a parcel of land into smaller units according to agro-ecological characteristics, 

including: precipitation, temperature, soil type, terrain conditions, etc. Each zone has a 

similar combination of constraints and potential for land use. In the GTAP-AEZ data 

base, there are 18 AEZs, covering six different lengths of growing period spread over 

three different climatic zones. Land using activities include crop production, livestock 

raising, and forestry. In so doing, this extension of the standard GTAP data base permits a 

much more refined characterization of the potential for shifting land use amongst these 

different activities. When combined with information on greenhouse gas emissions, this 

data base permits economists interested in integrated assessment of climate change to 

better assess the role of land use change in greenhouse gases mitigation strategies. 

Keywords: 

Land Use, Agro-Ecological Zoning, Integrated Assessment, Greenhouse Gas Mitigation. 

Prologue 

This document reports on construction of an analytical data base to support policy 

analyses related to land use and land use change, in particular as they relate to potential 

climate policies. While this report focuses on land use, it is intended to be read in 

conjunction with the companion report on the emissions data base. Both of these reports 

represent ongoing work aimed at further improvements. Further updates will be posted on 

the GTAP website when available: 

http://www.gtap.agecon.purdue.edu/databases/projects/Land_Use_GHG/default.asp
3 

Table of Contents: 

1. Introduction............................................................................................................................... 6

1.1 Background and Motivation............................................................................................ 6

1.2 Data products of this project ........................................................................................... 7

2. The AEZ-identified GTAP Land Use Data............................................................................... 9

2.1 Agro-Ecological Zoning.................................................................................................. 9

2.2 The GTAP Land Use Data Base ................................................................................... 10

2.2.1 Overview of the GTAP land use data .............................................................. 10

2.2.2 Cropland and Timberland Data Inputs............................................................. 10

2.2.2.1 Land cover and cropland data from SAGE .......................................... 11

Key Assumptions and Procedures ................................................................. 16

Definition of AEZs in the SAGE data ........................................................... 16

The SAGE Global Land Cover Data ............................................................. 20

Crop harvested area ....................................................................................... 20

Harvested area vs. physically cultivated area: Which is appropriate?........... 20

Estimating crop yields from FAO data.......................................................... 28

2.2.2.2 Timberland data ................................................................................... 38

Preparation of the timberland data for GTAP................................................ 38

Caveats and Limitations of the Forestry Data ............................................... 41

Data items derived from DGTM for GTAP use ............................................ 41

2.2.3 GTAP AEZ land rents...................................................................................... 48

2.2.3.1 Development of GTAP land rent data.................................................. 48

2.2.3.2 GTAP cropland rent data by 18 AEZs ................................................. 48

2.2.3.3 GTAP livestock sector land rent data by 18 AEZs .............................. 54

2.2.3.4 Preserving country-specific total valued-added of agriculture in GTAP.

 .............................................................................................................. 56

2.2.3.5 GTAP forest land rent data by 18 AEZs .............................................. 56

3. Validation of the GTAP AEZ land rent data........................................................................... 58

4. Concluding remarks and future research directions................................................................ 70

References ................................................................................................................................... 71

Appendix A. Sectors and region mappings in the GTAP version 6 data base .................... 74
4

List of Tables: 

Table 1. Summary of all database products from this project.................................................... 9

Table 2. Definition of global agro-ecological zones used in GTAP ........................................ 17

Table 3. Mapping of crops between SAGE and GTAP data.................................................... 22

Table 4. Cropland use (harvested area): China, 2001 (unit: 1000 hectare).............................. 23

Table 5. SAGE Land Cover Data: China, ca. 1992 (unit: 1000 hectare)................................. 27

Table 6. Mapping from FAO crops to SAGE crops ................................................................ 30

Table 7. Definition of FAO AEZs ........................................................................................... 30

Table 8. Mapping from FAO AEZs to GTAP AEZs ............................................................... 30

Table 9. SAGE crop yield data: China (unit: ton per 1000 hectare) ........................................ 37

Table 10. Summary of the SAGE land cover/use data set provided to GTAP .......................... 38

Table 11. DGTM coniferous timberland area data of the U.S.: AEZ by age (unit: 1000 hectare)

 ................................................................................................................................... 44

Table 12. DGTM timberland area data of the U.S.: AEZ by timber types (unit: 1000 hectare) 46

Table 13. GTAP crop sector land rents: VFM, world total, v6.0 (unit: million US Dollar)...... 51

Table 14. GTAP livestock sector land rents: VFM, world total, v6.0 (unit: million US Dollar)55

Table 15. GTAP land rents: VFM of all land-based sectors, world total, v6.0 (unit: million US 

Dollar) ................................................................................................................................... 57

Table 16. U.S. per hectare land rent: GTAP v.s. Mendelsohn et al. .......................................... 62

Table 17. GTAP agriculture per hectare land rent, unit: 2001 US$/ha...................................... 65

Table 18. Summary output of regression: average per ha. Land rent v.s. %PSE....................... 68

Table 19. Summary output of regression: average per ha. Land rent v.s. economic and physical 

variables .................................................................................................................................. 69

Table A1. Sectors in the GTAP version 6 data base and activity description ............................ 74

Table A2. The 87 countries/regions in the GTAP 6 data base and mapping to world 

counttries/territories ................................................................................................................ 78
5

List of Figures: 

Figure 1. The GTAP Land Use Matrix...................................................................................... 10

Figure 2. The global distribution of croplands ca. 1992 from Ramankutty and Foley (1998).. 13

Figure 3. SAGE global land cover map (the original 15 classes have been merged to 4 classes 

used in GTAP) ........................................................................................................................ 14

Figure 4. The global distribution of grazing lands ca. 1992 from Foley et al. (2003).............. 15

Figure 5. A global map of length of growing periods (LGP).................................................... 18

Figure 6. The SAGE global map of the 18 AEZs ..................................................................... 19

Figure 7. Distribution of cropland use (harvested area): China, 2001 ...................................... 24

Figure 8. The SAGE global land cover distribution by LGP .................................................... 25

Figure 9. Crop-specific ratio of yield in each AEZ to the total yield, average over the 94 

countries with FAO data ......................................................................................................... 32

Figure 10. A regression across all countries and all crops of yields in each rainfed AEZ to total 

rainfed yields........................................................................................................................... 34

Figure 11. Distribution of global total harvested area and global average yields across LGPs for a 

few sample crops..................................................................................................................... 35

Figure 12. Graphical depiction of methods used to obtain values in the GTAP forestry dataset 39

Figure 13. DGTM U.S. coniferous timberland area distribution: AEZ by age ........................... 45

Figure 14. Distribution of DGTM U.S. timberland area data: AEZ by timber types.................. 47

Figure 15. Crop sector land rent allocation among AEZs: world total ....................................... 52

Figure 16. Distribution of crop sector land rent within each AEZ: world total .......................... 53

Figure 17. Livestock sector land rent allocation among AEZs: world total................................ 55

Figure 18. USDA estimated cash rents for cropland and pasture, by state ................................. 61

Figure 19. U.S. cropland rents, 2001 US$ million: GTAP v.s. Mendelsohn et al....................... 63

Figure 20. U.S. pasture land rents, 2001 US$ million: GTAP v.s. Mendelsohn et al. ................ 64
6

1. Introduction 

1.1 Background and Motivation 

The main goal of the EPA sponsored GTAP project (hereafter, GTAP/EPA project) is to develop 

a land-use and greenhouse gas (GHG) emissions database for use in global computable general 

equilibrium (CGE) models aimed at assessing the economic costs of climate change policy. This 

multi-year project began in January 2002 and has now been completed. 

 Growing research demands for integrated assessment of GHG issues have motivated 

construction of a combined database of land use and GHG emissions for use with CGE models. 

Many economic analyses of climate policies use CGE models of the global economy to track 

GHG emissions to their source, to evaluate the costs of mitigation, and to assess the spill-over 

effects of GHG policies via international trade and inter-sectoral interactions. The GTAP model is 

a building block for many of the global CGE models currently in use today. With a database 

covering inputs/outputs and bilateral trade of 57 commodities2

 (and producing industries) of each 

87 countries/regions3

, GTAP is able to capture both the sectoral interactions within the domestic 

economy as well as international trade effects of climate change policy. 

 The Global Trade Analysis Project (GTAP) has filled an important need in the integrated 

assessment community by providing regular updates of world-wide input-output and bilateral 

trade data sets with significant disaggregation of regions and sectors, plus energy volume data. 

GTAP began as a database and modeling framework to assess the global implications of trade 

policies (Hertel, 1997). However, over the past decade, through a series of grants from the U.S. 

Department of Energy (US-DOE) and the U.S. Environmental Protection Agency (US-EPA), 

GTAP has become increasingly central to analyses of the global economic consequences of 

attempts to mitigate greenhouse gas emissions. The first step in this direction involved integrating 

the International Energy Agency’s database on fossil fuel consumption into GTAP. When 

coupled with CO2 emissions coefficients, this permits researchers to more accurately estimate 

changes in economic activity and fossil-fuel-based emissions in the wake of policies aimed at 

curbing CO2 emissions. 

 In the GTAP/EPA project documented here, we extend the GTAP database to allow it to 

support analyses of terrestrial sequestration and greenhouse gas emissions and mitigation from 

sources across the global economy as well as the linkage between land use and net GHG 

emissions from agriculture and forestry. While this report focuses on the land use portion of the 

data base, the companion report documents the inclusion of CO2 emissions, terrestrial 

sequestration, and non-CO2 greenhouse gases emissions data—covering methane (CH4), nitrous 

oxide (N2O), and the fluorinated gases (HFC-134a, CF4, HFC-23, and SF6) from all sources. 

These CO2 and non-CO2 GHG emissions and sequestration are indirectly linked to the underlying 

economic drivers of emissions, which are faithfully represented in the core GTAP database. 

 Land use, land-use change and forestry (LULUCF) activities have been perceived as a 

relatively cost-effective option to mitigate climate change due to the rapid buildup of greenhouse 

gases in the atmosphere. LULUCF may contribute to abatement of emissions by increasing 

 

2

 See Table A1 in Appendix A for sector coverage of the GTAP version 6 data base and the description of the sectoral 

activities. 3

 See Table A2 in Appendix A for world countries/territories and their mapping to the 87 countries/regions covered in 

the GTAP version 6 data base. 
7

carbon storage in forests and soils (the so-called sinks: enhancing afforestation and forest 

management, while curbing deforestation, and soil management). Article 3 of the Kyoto Protocol 

makes provision for the Annex I parties to take into account removals and emissions due to 

LULUCF activities since 1990 (e.g., afforestation, reforestation, deforestation and other agreed 

land use changes) to meet their commitment targets of greenhouse gas emission abatement. In the 

seventh Conference of the Parties (COP7) to the UNFCCC held in Marrakesh, 

October/November 2001, the parties finally agreed to include land-based carbon sequestration in 

their 2008-2012 GHG emissions reduction targets. The COP9, held in Milan, December 2003, 

has reached consensus for the rules of accounting for LULUCF projects in the Clean 

Development Mechanism (CDM) for the first commitment period (2008-2012) of the Kyoto 

Protocol. Along with such policy commitments, research on Integrated Assessment (IA) of 

climate change has recently been advancing towards the LULUCF embraced analysis. 

 At an inaugural project workshop, held at MIT in September, 2002 (GTAP Website, 2002), 

co-sponsored by the U.S. Environmental Protection Agency (US-EPA), Massachusetts Institute 

of Technology (MIT), and the Center for Global Trade Analysis (GTAP), the idea of identifying 

agro-ecological zoning in the GTAP model was sparked in the discussion among the participating 

experts. The recognition of various agro-ecological zones (AEZ) is believed to be a more realistic 

approach to modeling land use and land use change in GTAP, whereby land is mobile between 

crop, livestock and forestry sectors within AEZ’s. In the standard GTAP model, land is assumed 

to be transformable between uses of crop growing, livestock raising, or timber production, 

regardless of climatic or soil constraints. The fact is that most crops can only grow on lands with 

particular temperature, moisture, soil type, land form, etc. The same concern arises for land use 

by the livestock and the forestry sectors. Lands that are suitable for growing wheat may not be 

suitable for rice cultivation. The introduction of agro-ecological zoning in GTAP helps to better 

inform the issue of land mobility and sharpens the focus on competition among alternative land 

uses within AEZs. 

 This report is the first of three reports for this project. It details the land use and land cover 

data base. The second report covers the associated greenhouse gases (GHG) emissions database. 

The third report introduces a CGE framework to illustrate how the AEZ distinguished land and 

GHG emissions/sequestration data could be incorporated in computable general equilibrium 

models for analyses of climate change related land use and land use change. 

1.2 Data products of this project 

This project has resulted in the following products: 

1. land cover data: physical area (thousands of hectares), ca. 1992, of 7 land cover types, in 160 

countries/regions, by 18 agro-ecological zones; 

2. crop(land) use data: harvested area (thousands of hectares) for the year 2001, covering 19 crop 

types, grown in 160 countries/regions, by 18 agro-ecological zones; 

3. crop yield data: production (metric tons) per thousand hectare of harvested area, of year 2001, 

of 19 crop types, grown in 160 countries/regions, by 18 agro-ecological zones; 

4. timberland area data: timberland area (thousands of hectares), of circa 1990 – 2000, of three 

tree species in various management types, in 124 countries/regions, by 18 agro-ecological 

zones, and by 10-year tree age classes; 

5. timberland marginal land rent data: 2000 US$ per hectare per year, of various management 

types in 124 countries/regions; 
8

6. forest carbon stock data: million metric tons of CO2, from the year 2000, of 3 tree species in 

various management types, in 124 countries/regions, by 18 agro-ecological zones, and by 10-

year tree age classes; 

7. GTAP-compatible land rent (header "VFM" in the GTAP input-output database) data: 2001 

US$, of agriculture and forest sectors (totaling 13) in 87 regions, by 18 agro-ecological 

zones; 

8. soil carbon stock data: see Table 1 below for details. 

9. GTAP CO2 emissions data: giga-grams of CO2 emissions from the year 2001 due to 

combustion of fossil fuels (domestically-produced and imported) by all GTAP sectors (57) in 

66 regions of GTAP database version 6.0 and 78 regions of GTAP database version 5.44

; 

10.CO2 emissions from non-combustion sources for 2001 (e.g., cement manufacturing), and 

11.GTAP non-CO2 GHG (CH4, N2O, and fluorinated gases, including ozone depleting 

substances) emissions data: tera-gram CO2-equivalents, for 2001 of years 1990, 1995 and 

2000, by all emitting GTAP sectors (57) in all GTAP regions (66)5

. 

The first seven of these data products are discussed in this report. The final two are covered in the 

companion report on greenhouse gas emissions. 

 We follow the FAO’s global agro-ecological zoning concept (FAO, 2000; Fischer et al, 2002) 

to identify lands located in eighteen different agro-ecological zones. In section 2, we introduce 

the AEZ-identified land use data and how they are used to produce the GTAP AEZ-specific land 

use database. Section 3 focuses on the validation of the GTAP AEZ land rent data. Here, we 

compare the average land rents in the U.S. agriculture sector, as implied by combining the GTAP 

land rent data with the hectares in the land cover data base, with directly observed cash land rent 

data published by the U.S. Department of Agriculture (USDA). We also compare land rents by 

AEZ with hedonic estimates of land rents from Mendelsohn et al. (2005). This report concludes 

in section 4 with a summary and an overall evaluation of the data base. 

 

4

 The GTAP database version 6.0, including energy volume data, is released to subscribers Spring 2005. We will 

recompile the CO2 data of 2001 for version 6.0's 87 regions in subsequent emissions data updates so as to match the 

sectors, regions, and benchmark year as in the GTAP version 6.0 database. 5

 In compiling the EPA supplied non-CO2 emissions data to be matched up with GTAP sector and region aggregation, 

we used some value shares derived from the GTAP input-output database. At the time when we compiled the CH4

and N2O data, the available GTAP input-output database was version 5.0, which has 66 regions and the benchmark 

year is 1997. The GTAP database version 6.0 was publicly released in the summer of 2005. Like CO2 emissions data 

update, we plan to recompile the 2001 CH4, N2O, and F-gases data for version 6.0's 87 regions for consistency 

reasons in the future. 
9

Table 1. Summary of all database products from this project 

Data Year Dimensions Comment

Land cover ca. 1992 7 land cover types, 160 global 

regions, by 18 AEZs 

Land activity data 

Crop harvested acreage and 

yields 2001 19 crops, 160 regions, by 18 AEZs

Forest acreage ca. 1990 – 2000

3 tree species, country specific 

management types, 124 regions; by 

18 AEZs 

10-year tree 

age classes 

in Sohngen 

data 

GTAP AEZ land rents ("VFM") 2001 13 crop, livestock, and forest 

sectors, 87 regions, by 18 AEZs

Emissions/sequestration (ALL SECTORS - land-using and other)

Forest carbon stock 2000

3 tree species, country specific 

management types, 124 regions; by 

18 AEZs 

10-year tree 

age classes 

in Sohngen 

data 

Soil carbon stock ca. 1990 – 2000 7 land cover types, 160 global 

regions, by 18 AEZs 

CO2 emissions from energy fossil 

fuel combustion 2001 57 sectors, 87 regions, domesticallyproduced and imported

Other CO2 emissions 2001 57 sectors, 87 regions, domesticallyproduced and imported

Non-CO2 GHG emissions (CH4, 

N2O, fluorinated gases, ODS) 2001 57 sectors, 87 regions

2. The AEZ-identified GTAP Land Use Data 

2.1 Agro-Ecological Zoning 

In constructing the GTAP land use database, we adopt the FAO/IIASA convention of agroecological zoning that grew out of pioneering work by the Food and Agriculture Organization 

(FAO) of the United Nations and the International Institute for Applied Systems Analysis 

(IIASA). Their Land Use and Land Cover (LUC) project resulted in an agro-ecological zoning 

methodology that has been steadily refined over the past 20 years (Fischer et al., 2002; Fischer et 

al., 2000). For global AEZ data, this method is considered State of the Art. Agro-ecological 

zoning refers to segmentation of a parcel of land into smaller units according to agro-ecological 

characteristics, e.g., moisture and temperature regimes, soil type, landform, etc. In other words, 

each zone has a similar combination of constraints and potentials for land use. The FAO/IIASA 

agro-ecological zoning methodology provides a standardized framework for characterizing 

climate, soil and terrain conditions pertinent to agricultural production (FAO and IIASA, 2000). 

 We focus on the “Length of Growing Period” (LGP) data from the IIASA/FAO Global AgroEcological Zones (GAEZ) database. Fischer et al. (2000) derived the length of growing period by 

combining climate, soil, and topography data with a water balance model and knowledge of crop 

requirements. The “length of growing period” (LGP) refers to the period during the year when 

both soil moisture6

 and temperature are conducive to crop growth. The concept of “length of 

growing period” (LGP) is brought in to differentiate the agro-ecological zones by attainable crop 

productivity. Thus, in a formal sense, LGP refers to the number of days within the period of 

temperatures above 5°C when moisture conditions are considered adequate for crop production 

(FAO, 2000). 

 

6

 Soil moisture is a function of precipitation, soil type, topography, etc. 
10

2.2 The GTAP Land Use Data Base 

We introduce in section 2.2.1 the overview of the GTAP land use database as proposed at the 

2002 MIT workshop (GTAP Website, 2002). To build this land use database, we used cropland 

and timberland data provided by Dr. Navin Ramankutty of the Center for Sustainability and 

Global Environment (SAGE), University of Wisconsin-Madison and Dr. Brent Sohngen of Ohio 

State University, respectively. The data inputs from these two sources are described in section 

2.2.2 (including land cover data and land use data). Details on how the data inputs are derived to 

support the construction of the GTAP land use database are described in sections 2.2.2.1 

(cropland) and 2.2.2.2 (timberland). In sections 2.2.3.2, 2.2.3.3, and 2.2.3.5, we describe how we 

compile the GTAP AEZ-distinguished land rent data for crop sectors, livestock sectors, and 

forestry, based on these two sources of data inputs. 

2.2.1 Overview of the GTAP land use data 

Figure 1 shows the format of the GTAP land use data that was originally proposed at the 2002 

MIT workshop (GTAP Website, 2002). For each region, we identify land located in various agroecological zones (the rows in Figure 1) and the uses (sectors or activities) of land (the columns in 

Figure 1). 

 Land use activities in region r 

AEZs Crop1 .... CropN Livestock1 .... LivestockH Forest1 .... Forestv

AEZ1 

.... 

.... 

AEZM 

Total 

Figure 1. The GTAP Land Use Matrix 

 Land used by the GTAP land-based sectors—i.e., crops, livestock and forestry sectors—are 

distinguished by agro-ecological zones (across the rows in Figure 1). At any one point in time, for 

a given climate regime, the total endowment of each AEZ land type (row sum) is fixed. That is, 

land is not assumed to be mobile across AEZs. That is the purpose of the definition. However, in 

the context of a general equilibrium model, the allocation across land-using sectors will vary 

based on relative returns. 

2.2.2 Cropland and Timberland Data Inputs 

The GTAP AEZ-specific land use data are compiled from two sources. The first source includes 

global land cover and cropland data, provided by Dr. Navin Ramankutty of the Center for 

Sustainability and Global Environment (SAGE), University of Wisconsin-Madison (Ramankutty 

et al., 2005). Specifically, the following data items are provided: 

(a) land cover data: physical area (thousands of hectares), ca. 1992, of 7 land cover types, in 

160 countries/regions, by 18 agro-ecological zones; 
11

(b) (crop)land use data: harvested area (thousands of hectares) of year 2001, of 19 crop types, 

grown in 160 countries/regions, by 18 agro-ecological zones; 

(c) crop yield data: production (ton) per thousand hectares of harvested area, of year 2001, of 

19 crop types, grown in 160 countries/regions, by 18 agro-ecological zones. 

 The second source includes global timber land area and forest carbon stock data, provided by 

Dr. Brent Sohngen of Ohio State University (Sohngen and Tennity, 2004). Specifically, the 

following data items are acquired from Dr. Sohngen: 

(a) timberland area data: timberland area (thousands of hectares), of circa 1990 – 2000, of 

three tree species (coniferous, broadleaf, and mixed) in various management types, 

located in 124 countries/regions, by 18 agro-ecological zone, and by 10-year tree age 

classes; 

(b) timberland marginal land rent data: 2000 US$ per hectare per year, of various 

management types in 124 countries/regions; 

(c) forest carbon stock data: million metric tons of CO2, of year 2000, of three tree species in 

various management types, in 124 countries/regions, by 18 agro-ecological zones, and by 

10-year tree age classes. 

We introduce these two sources of data—abbreviated, respectively, as SAGE and DGTM7

 data 

hereafter—in sections 2.2.2.1 and 2.2.2.2. 

2.2.2.1 Land cover and cropland data from SAGE 

The Center for Sustainability and the Global Environment (SAGE) at the University of Wisconsin 

has been developing global databases of contemporary and historical agricultural land use and 

land cover. SAGE has chosen to focus on agriculture because it is clearly the predominant land 

use activity on the planet today, and provides a vital service—i.e., food—for human societies. 

 SAGE has developed a “data fusion” technique to integrate remotely-sensed data on the 

world’s land cover with administrative-unit-level inventory data on land use (Ramankutty and 

Foley, 1998; Ramankutty and Foley, 1999). The advent of remote sensing data has been 

revolutionary in providing consistent, global, estimates of the patterns of global land cover. 

However, remote sensing data are limited in their ability to resolve the details of agricultural land 

cover from space. Therein lies the strength of the ground-based inventory data, which provide 

detailed estimates of agricultural land use practices. However, inventory data are limited in not 

being spatially explicit, and are plagued by problems of inconsistency across administrative units. 

The “data fusion” technique developed by SAGE exploits the strengths of both the remotelysensed data as well as the inventory data. 

 Using SAGE’s methodology, Ramankutty and Foley (1998)—RF98 hereafter—developed a 

global data set of the world’s cropland distribution for the early 1990s (Figure 2). This was 

accomplished by integrating the Global Land Cover Characteristics (GLCC; Loveland et 

 

7

 DGTM refers to the Dynamic Global Timber Model (described originally in Sedjo and Lyon (1990) and expanded in 

Sohngen et al. (1999), Sohngen and Sedjo (2000), and Sohngen and Mendelsohn (2003)), which is the source for 

much of the forestry data. 
12

al.(2000)) database at 1 km resolution (derived from the Advanced Very High Resolution 

Radiometer (AVHRR) instrument), with comprehensive global inventory data (at national and 

subnational levels) of cropland area. The resulting data set, at a spatial resolution of 5 min (~10 

km) in latitude by longitude, describes the percentage of each 5 min grid cell that is occupied by 

croplands. Leff et al. (2004) further disaggregated the RF98 dataset to derive the spatial 

distribution of 19 crop types of the world (18 major crops and one “other crop” type (see Table 3 

for a list); maps of individual crops not shown – see Leff et al. (2004) for detailed maps). 

Ramankutty and Foley (1999)—RF99 hereafter—compiled historical inventory data on cropland 

areas to extend the global croplands data set back to 1700 (figures not shown). RF99 also derived 

a global data set of potential natural vegetation (PNV) types; this data set describes the spatial 

distribution of 15 natural vegetation types that would be present in the absence of human 

activities (Figure 3). Furthermore, global data sets of the world’s grazing lands (Figure 4) and 

built-up areas (not shown), representative of the early 1990 period, were also developed recently 

(National Geographic Maps, 2002; Foley et al., 2003). 

 The SAGE data sets described above are being used for a wide array of purposes, including 

global carbon cycle modeling (McGuire et al., 2001), analysis of regional food security 

(Ramankutty et al., 2002b), global climate modeling (Bonan, 1999; Brovkin et al., 1999; Bonan, 

2001; Myhre and Myhre, 2003), and estimation of global soil erosion (Yang et al., 2003). They 

also formed part of the BIOME300 effort, initiated by two core projects—LUCC (Land Use and 

Land Cover Change) and PAGES (Past Global Changes) of the International GeosphereBiosphere Programme (IGBP). In other words, they are a widely recognized, and widely used 

data set of global agricultural land use. 

 The SAGE land cover and agricultural land use data form the core of the GTAP land cover 

and land use database. In addition to the SAGE data, to derive information on crop yields and 

irrigation, some ancillary data were obtained from the Food and Agriculture Organization (FAO). 

In the subsequent section, we describe the procedure used to adapt the SAGE data and the 

ancillary data to derive land use information for GTAP. 
Figure 2. The global distribution of croplands ca. 1992 from Ramankutty and Foley (1998) 
Figure 3. SAGE global land cover map (the original 15 classes have been merged to 4 classes used in GTAP
Figure 4. The global distribution of grazing lands ca. 1992 from Foley et al. (2003) 
16

Key Assumptions and Procedures 

In order to supply the necessary data for this specification of GTAP, the spatially-explicit land 

use data sets from SAGE must be aggregated to match up with the format of the GTAP land use 

data (see Figure 1). The following developments were required: 

(1) development of global Agro-Ecological Zones for deriving sub-national information on 

land endowments; 

(2) mapping data to match GTAP crop sectors; 

(3) deriving yield (and production) data for the crop sectors; and 

(4) mapping spatial SAGE data to AEZs by nation. 

These developments are described in detail below. 

Definition of AEZs in the SAGE data 

SAGE derived 6 global lengths of growing period (LGPs) by aggregating the IIASA/FAO GAEZ 

data into 6 categories of approximately 60 days each LGP: (1) LGP1: 0-59 days, (2) LGP2: 60-

119 days, (3) LGP3: 120-179 days, (4) LGP4: 180-239 days, (5) LGP5: 240-299 days, and (6) 

LGP6: more than 300 days. These 6 LGPs roughly divide the world along humidity gradients, 

and is generally consistent with previous studies in global agro-ecological zoning (Alexandratos, 

1995). They are calculated as the number of days with sufficient temperature and 

precipitation/soil moisture for growing crops. These six LGPs are plotted by 0.5 degree grid cell 

for the world in Figure 5. The colors range from white (shortest LGP) to red (longest LGP). The 

red tends to be concentrated in the tropics, but not exclusively. The white zones are found in the 

arctic, the deserts and in the mountain regions. 

 In addition to the LGP break-down, the world is subdivided into three climatic zones—

tropical, temperate, and boreal—using criteria based on absolute minimum temperature and 

Growing Degree Days, as described in Ramankutty and Foley (1999). Table 2 details definition 

of global agro-ecological zones used in the GTAP land use database, with the first six AEZs 

corresponding to tropical climate, the second six to temperate and the last six to boreal. Within 

each climate grouping, the AEZs progress from short to long LGPs. 

 A global map of 18 AEZs has been developed by overlaying the 6 categories of LGPs with the 

3 climatic zones. Figure 6 shows this 18-AEZ global map by 0.5 degree grid cell. The red shades 

in the map denote tropical AEZs, with the more intense shades denoting longer growing periods. 

The green shading denotes temperate AEZs, whereby the darker greens also communicate a 

longer LGPs. Finally, the boreal climate is portrayed by blue shading. 

 The beauty of this AEZ approach is that we can simulate shifts in AEZs as a function of 

changing climate. Furthermore, one could potentially define a suite of feasible land uses within 

each AEZ, which although infeasible under current conditions could become feasible under future 

conditions. 
17

Table 2. Definition of global agro-ecological zones used in GTAP 

LGP in days Moisture regime Climate zone GTAP class 

Tropical AEZ1 

Temperate AEZ7 

0-59 Arid 

Boreal AEZ13 

Tropical AEZ2 

Temperate AEZ8 

60-119 Dry semi-arid 

Boreal AEZ14 

Tropical AEZ3 

Temperate AEZ9 

120-179 Moist semi-arid 

Boreal AEZ15 

Tropical AEZ4 

Temperate AEZ10 

180-239 Sub-humid 

Boreal AEZ16 

Tropical AEZ5 

Temperate AEZ11 

240-299 Humid; 

Boreal AEZ17 

Tropical AEZ6 

Temperate AEZ12 

>300 days Humid; year-round growing season 

Boreal AEZ18 
Figure 5. A global map of length of growing periods (LGP) 
Figure 6. The SAGE global map of the 18 AEZs 
20

The SAGE Global Land Cover Data 

A map of global land cover, representative of ca. 1992, was first derived by overlaying the SAGE 

global data set of potential natural vegetation (Figure 3), over the present-day global maps of 

croplands8

 (see Figure 2), grazing lands (see Figure 4), and built-up areas. The resulting map was 

overlain with the global AEZ map, to calculate land cover by country, for each of the 18 AEZs 

Figure 5 is a summary chart with global total numbers as a function of the 6 LGPs, aggregated 

across tropical, temperate, and boreal zones for clarity of the figure. 

Crop harvested area 

The SAGE land use data provides information on crop areas (Leff et al., 2004—LEFF04 

hereafter). The global distribution of major crops were derived by compiling crop harvested area 

statistics from national and sub-national sources, estimating the proportions of harvested area of 

each crop to total harvested area, and then redistributing it using the RF98 croplands map 

described above. 

Harvested area vs. physically cultivated area9

: Which is appropriate? 

The original proposal for splitting the GTAP sectoral land rents into AEZs, involved the 

conversion of harvested area data from SAGE to physically cultivated area data due to the 

concern of multiple cropping. This poses a problem due to the absence of a global data set on 

multiple cropping and/or crop-specific, physically cultivated area. However, upon further 

reflection, it became clear that, for purposes of disaggregating land rents in GTAP, we do not 

really need crop-specific physically cultivated area data. In the GTAP Input-Output data, land 

rents are generated from the activity (or use) on the given parcel of land during the calendar year. 

Therefore, we are interested in the value of the land in production over the course of the entire 

year, not just one season. 

 Consider the case of a farmer in Southern China who grows early double-crop rice from 

March to July, and then grows "catch crops" (fast growing crops, e.g., vegetables) in the rest of 

the calendar year. Now the GTAP Input-Output data identify sectors in terms of crops (e.g., the 

paddy rice sector, the cereal grain sector, the oil seeds sector, etc.), not hectares of land, per se. So 

the land rents of the crop sectors should accrue to the harvested area, by crop. In this particularly 

example, we allocate the land rent generated due to the growing of paddy rice to the GTAP paddy 

 

8

 SAGE cropland data follows the FAO definition of croplands, which includes arable land: land under temporary crops 

(double-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and 

kitchen gardens and land temporarily fallow (less than five years); and permanent crops: land cultivated with crops 

that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee and rubber; 

this category includes land under flowering shrubs, fruit trees, nut trees and vines, but excludes land under trees 

grown for wood or timber. SAGE pasture land data also follows FAO definition of permanent pasture: land used 

permanently (five years or more) for herbaceous forage crops, either cultivated or growing wild (wild prairie or 

grazing land). 9

 The difference between harvested area and physical cultivated area is related to multiple cropping. With harvested 

area, land that is double cropped or triple cropped is counted two or three times respectively. This variable is 

normally reported in national statistics for specific crops. Physical cultivated area represents the physical area of 

land used for cultivation, without double or triple accounting for multiple cropping. This variable is normally not 

reported, but can be inferred if the extent of multiple cropping is known. “Cropland area” is also reported by 

national statistics (see footnote 8), and only accounts for physical land area. However, it also includes fallow land 

and temporary pasture land, and therefore cannot be used to infer physical cultivated area. Even if that were not a 

problem, cropland area aggregates all the crops, and therefore cannot be used to infer crop-specific physical 

cultivated area. 
21

rice sector, and allocate the land rents generated due to the growing of vegetables to the GTAP 

vegetables sector. Thus, while the harvest-based land rents can be allocated to GTAP sectors, the 

physically cultivated-based land rents cannot. 

 A final argument in favor of working with harvested acres is due to the fact that land based 

emissions (e.g., CH4 emissions from paddy rice cultivation) are mostly tied to the harvested area 

(IPCC 1996 Guidelines). Fertilizer use is normally proportional to harvested area. So, we 

conclude that harvested area is a useful, as well as a practical basis for developing the GTAP land 

use data, rather than the crop-specific physically cultivated area. Soil N2O and soil CO2

emissions are tied to cultivated area and crop cycles, not harvested area. For these emissions, we 

can directly use the SAGE cropland area data. 

 Next, we introduce how SAGE estimates harvested area and yield of cropland for the GTAP 

land use database. Because the RF98 croplands and LEFF04 crop area maps represent the 

“physical” cultivated area on the ground, the distribution of crop harvested area required a 

conversion to from harvested to physical area on the ground. Therefore, we first recalibrate the 

LEFF04 data, against the national crop harvested area statistics from FAOSTAT (FAO, 2004), to 

obtain harvested area by AEZ. The approach used can be described as follows. 

 Let 

A ′ LEFF (i,mc) be the LEFF04 crop area for pixel i and major crop mc. Note that the 

original LEFF04 data sets are gridded, at 0.5 degree resolution in latitude by longitude. We 

recalibrate the LEFF04 data to the FAOSTAT harvested area data AFAO (l,mc,tref ) as follows: 

 

ALEFF (i,mc) = A ′ LEFF (i,mc) x

AFAO (l,mc,tref )

ALEFF (i,mc)

i∈l

∑ , 

where l = countries in FAOSTAT, i ∈ l , and tref is the reference time period = 2001 (for 

consistency with GTAP version 6.0). 

 The recalibrated LEFF04 data are then overlain with: 1) the global AEZ map; and 2) political 

boundaries, and aggregated to derive harvested areas of 19 crops for all nations of the world, for 

18 AEZs within each nation. 

Let this aggregated data be represented by ALEFF (l,mc,z), where, 

l is the country, mc is one of 19 LEFF04 major crops, and z = one of 18 AEZs. 

This can then be mapped onto GTAP’s 8 crop sectors using the mapping in Table 3: 

 

ALEFF (l,mc,z)→ AGTAP (l,s,z), where s is one of 8 GTAP crop sectors. 

 In this first release of the GTAP land use database, we encountered a problem in mapping 

from SAGE crops to the GTAP crop sectors. As it would take some time to fix the mapping 

problem in the SAGE data—the basis which we used for the AEZ splitting—we have come up 

with a discretionary solution to this problem and have planned to fix it in the next release of the 

GTAP land use data base. We explain the mapping problem below, followed by a discretionary 

solution we adopted in the first release of the database. 

 In the GTAP input-output data base, agriculture sectors are defined by reference to the Central 

Product Classification (CPC), developed by the Statistical Office of the United Nations (United 

Nations, 1991). Based on this concordance, we mapped potato, cassava, and pulses to the 
22

vegetable and fruits sector ("v_f") of GTAP. However, in the SAGE data, fruits and vegetables 

were not classified as a separate category, but were aggregated with "Others" (see Table 6). As 

such, we were not able to separate out vegetables and fruits from the SAGE data to map to the 

"v_f" sector of GTAP. Similarly the SAGE crops poorly mapped to the GTAP other crops (“ocr”) 

sector. 

 Before updated data is provided by SAGE, we developed a discretionary solution to fix this 

problem. We used the AEZ shares of the aggregate production of the SAGE crops that are 

mapped to GTAP's "v_f" and "ocr" sectors to split the AEZs of both the "v_f" and the "ocr" 

sectors. We plan to fix this crop mapping discrepancy in the next release when we receive the 

AgroMAPS (FAO/IFPRI/SAGE/CIAT, 2003) data from SAGE. The newly available AgroMAPS 

data set capitalizes on sub-national production data and offers, for the first time, a global data set 

with spatially explicit production information. SAGE is developing new global crop maps for the 

Year 2000 using AgroMAPS, and will define many new crop categories that will be consistent 

with the GTAP crop sectors. 

 Table 4 shows the cropland distribution for China, as provided by SAGE. This table contains 

the harvested area data. Figure 7 charts the distribution of cropland use across AEZ (as from data 

in Table 4) It indicates that most of the crops in China are grown in temperate area (AEZs 7 to 

12).

Table 3. Mapping of crops between SAGE and GTAP data 

SAGE No. SAGE code GTAP No. GTAP code Description

1 barley 3 gro Cereals grain n.e.c.

2 cassava 4 v_f Vegetables, fruit, nuts

3 cotton 7 pfb Plant-based fibres

4 groundnuts 5 osd Oil seeds

5 maize 3 gro Cereals grain n.e.c.

6 millet 3 gro Cereals grain n.e.c.

7 oilpalm 5 osd Oil seeds

8 others 8 ocr Crops n.e.c.

9 potato 4 v_f Vegetables, fruit, nuts

10 pulses 4 v_f Vegetables, fruit, nuts

11 rape 5 osd Oil seeds

12 rice 1 pdr Paddy rice

13 rye 3 gro Cereals grain n.e.c.

14 sorghum 3 gro Cereals grain n.e.c.

15 soy 5 osd Oil seeds

16 sugar beet 6 c_b Sugar cane, sugar beet

17 sugar cane 6 c_b Sugar cane, sugar beet

18 sunflower seeds 5 osd Oil seeds

19 wheat 2 wht Wheat

Reference: Concordance, HS96 to GSC rev. 2: concordance between the 1996 edition 

of the Harmonized System and revision 2 of the GTAP sectoral classification.

http://www.gtap.agecon.purdue.edu/resources/download/582.txt
Table 4. Cropland use (harvested area): China, 2001 (unit: 1000 hectare) 

China cropland (Unit: 1000ha)

1 2 3 4 5 6 

Paddy rice Wheat Cereal grains

Vegetables/fruits/

nuts Oil seeds Sugar cane/beet

Plan

fi

AEZ1 0.00 0.00 0.00 0.00 0.00 0.00 AEZ2 0.00 0.00 0.00 0.00 0.00 0.00 AEZ3 0.00 0.00 0.00 0.00 0.00 0.00 AEZ4 66.84 4.96 2.67 8.22 15.97 1.82 AEZ5 76.44 8.61 10.10 10.01 17.68 2.57 AEZ6 2516.49 57.54 263.01 319.21 661.87 270.44 AEZ7 94.06 1406.27 752.65 223.94 426.05 40.25 65AEZ8 917.33 4277.84 7144.95 1198.28 3949.04 175.02 43AEZ9 977.46 4317.75 6562.54 1249.27 3417.01 86.15 95AEZ10 1066.33 2586.44 3745.75 1030.96 2347.44 73.58 42AEZ11 4151.19 4849.99 2898.51 1310.12 2913.02 95.13 106AEZ12 18806.93 4440.91 5142.22 2982.33 7526.94 898.25 83AEZ13 60.29 1067.65 332.39 155.95 461.65 19.21 41AEZ14 57.44 692.48 158.98 84.10 368.17 4.74 1AEZ15 177.89 666.10 338.27 120.48 427.85 11.58 AEZ16 164.59 280.98 167.78 56.89 119.55 7.54 AEZ17 10.78 6.56 9.76 2.97 3.77 0.93 AEZ18 0.00 0.00 0.00 0.00 0.00 0.00 Total 29144.04 24664.06 27529.56 8752.70 22656.01 1687.21 480
AEZ1

AEZ3

AEZ5

AEZ7

AEZ9

AEZ11

AEZ13

AEZ15

AEZ17Paddy rice

Vegetables/fruits/nut

Plant-based fibre

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000 Cropland use - harvested area (1000ha) Padd

WheaCereaVegeOil seSugaPlant-Crops 

Figure 7. Distribution of cropland use (harvested area): China, 2001 
000000E+0

0200E+60400E+60600E+60800E+61000E+61200E+61400E+61600E+61800E+6

LGP1 LGP2 LGP3 LGP4 LGP5 

Area (ha)

Forest Savanna/Grassland Shrubland Cropland Pasture Built-up land O

Figure 8. The SAGE global land cover distribution by LGP 
26

 The land cover data sets (Figure 8) show that forests dominate in LGP3 (120-179 days) and 

LGP6 (> 300 days), corresponding primarily to boreal forests and tropical rainforests, 

respectively. Shrub lands and pastures dominate in LGP1 (the driest AEZ) and their areas 

decrease as the AEZs get more humid. Savanna/grasslands are distributed fairly uniformly across 

the six aggregated AEZs. Croplands are distributed with slightly higher proportions in LGP3 and 

LGP4 (i.e., in areas that are not too dry, but are not heavily forested) (see Ramankutty et al.

(2002) for a study on climatic constraints on cropland distribution). Built-up lands predominate in 

LGP4 and LGP5 (also the temperate regions of the world; (Small, 2003), but their total area is 

very small. The “other land” category, which includes tundra, desert, and polar desert/rock/ice, is 

dominant in LGP1 (with some additional area in LGP2), as would be expected. Table 5 shows the 

SAGE land cover data of China as an example. 
Table 5. SAGE Land Cover Data: China, ca. 1992 (unit: 1000 hectare) 

Unit: 1000ha 1 Forest 2 SavnGrasslnd 3 Shrubland 4 Cropland 5 Pastureland 6 Builtupland 7 Othe

1 AEZ1 0.00 0.00 0.00 0.00 0.00 0.00 2 AEZ2 0.00 0.00 0.00 0.00 0.00 0.00 3 AEZ3 0.00 0.00 0.00 0.00 0.00 0.00 4 AEZ4 41.71 0.00 0.00 209.13 15.41 19.87 5 AEZ5 851.99 0.00 0.00 252.34 29.40 12.75 6 AEZ6 3850.93 818.89 0.00 8168.40 1755.42 51.52 7 AEZ7 380.19 2846.51 15712.78 5423.19 76431.86 112.55 9358 AEZ8 12535.79 8204.33 1517.63 30451.23 38891.08 328.87 9 AEZ9 23488.61 3236.56 12.93 31329.95 9846.51 312.30 1

10 AEZ10 20113.39 2344.85 1708.09 18806.62 6113.10 167.57 11 AEZ11 29123.68 3085.58 10722.81 33162.16 9531.92 151.34 12 AEZ12 57965.89 5751.70 183.69 75797.70 17176.50 357.77 13 AEZ13 206.34 2299.57 3440.05 3643.21 84475.91 4.68 43214 AEZ14 924.79 2714.68 100.37 2126.64 67327.57 2.22 7515 AEZ15 18073.58 3061.28 90.28 2751.87 33442.67 21.87 2216 AEZ16 890.81 2431.72 1291.78 1425.86 12072.31 1.28 17 AEZ17 0.00 297.67 0.00 61.43 453.60 0.08 18 AEZ18 0.00 0.00 0.00 0.00 0.00 0.00 

Total 168447.70 37093.35 34780.42 213609.72 357563.25 1544.66 1469
28

Estimating crop yields from FAO data 

The FAO provided GTAP with estimates of harvested area, yield, and production, for 94 

developing countries, for several FAO agro-ecological zones (including an FAO AEZ labeled 

“irrigated”). These unpublished data were developed based on primary data obtained in the 

1970s, and has been periodically updated since then based on observed aggregates (Jelle 

Bruinsma, personal communication, 2003). So while the FAO data are not reliable for direct 

estimation of today’s yields, they are the only available data on relative yields by AEZ within 

countries. We have therefore chosen to use the FAO data as a provisional measure, until 

improved estimates become available in the future. Here we describe how we adapted the FAO 

data for our purpose. 

A. Derive yields from FAO data for 94 developing countries 

The FAO data were provided for 6 different agro-ecological zones (Table 7; see also 

Alexandratos (1995)), defined slightly differently from our AEZs, and for 34 different crops. We 

therefore had to match the FAO AEZs and crops with GTAP’s 18 AEZs and 19 LEFF04 crops. 

 FAO reports yields separately for four rainfed AEZs (AT1, AT2, AT3, AT4+AT5), one AEZ 

with fluvisol/gleysol soils (AT6+AT7; naturally flooded soils), and one irrigated AEZ (denoted 

“Irrigated Land”). In other words, FAO has separated out irrigation and the occurrence of 

naturally flooded soils into separate AEZs. In this study, we choose to treat AEZs as a climate 

only constraint (including the influence of soil moisture), and therefore irrigation and/or 

fluvisols/gleysols can occur within each AEZ. As a result, we needed to repartition the irrigated 

and AT6+AT7 yields into the rainfed zones to estimate the total yields for each AEZ. This 

procedure is described below. 

 We first mapped from the 34 FAO crops to the 19 LEFF04 major crops, based on the mapping 

given in Table 6 (harvested-area weighted averages were calculated when multiple FAO crops 

mapped into one LEFF04 crop). 

 Let 

YFAO,RF (n,mc, fz) be the FAO reported yield for the four rainfed AEZs ‘fz’, for nation 

‘n’, and crop ‘mc’, where, 

mc = one of 19 LEFF04 major crops, 

fz = FAO AEZs AT1, AT2, AT3, AT4+AT5 (Table 7), 

‘RF’ refers to rainfed. 

 Let 

YFAO (n,mc) be the national yield from FAO for each crop (harvested area weighted 

average of all 6 zones). We calculated national rainfed yields for each crop, 

 

YFAO,RF ( ) n,mc =

YFAO,RF ( ) n,mc, fz x AFAO,RF ( ) n,mc, fz

fz= AT1

AT 4+ AT5

∑

AFAO,RF ( ) n,mc, fz

fz= AT1

AT 4+ AT 5

∑

, 

where 

AFAO,RF ( ) n,mc, fz = harvested area data from FAO, corresponding to the yield data. 
29

 Then, we estimated total yield (rainfed plus irrigated plus fluvisol/gleysol) for each of the 

FAO AEZs, AT1, AT2, AT3, & AT4+AT5, by applying the ratio of national total yield to 

national rainfed yield to each AEZ, 

 

Y ′ ′ FAO (n,mc, fz) = YFAO,RF (n,mc, fz) x

YFAO (n,mc)

YFAO,RF (n,mc)

, if YFAO,RF (n,mc) > 0. 

As an average across all countries, the national total yield is ~50% greater than rainfed yields for 

rice. This is reasonable because paddy rice is heavily irrigated, and irrigated yields are higher 

than rainfed yields. The national total yield to rainfed yield ratios for cassava and oilpalm is 1.0 

because they are not irrigated at all. 

 This yield is then adjusted to match FAOSTAT national statistics, 

 

YFAO (n,mc, fz) = Y ′ ′ FAO (n,mc, fz) x

Y FAO (n,mc)

YFAO (n,mc)

, if YFAO,RF (n,mc) > 0, 

where 

 

Y FAO (n,mc) = FAOSTAT national statistic on crop yield. 

If total rainfed yield is zero (i.e., FAO reports that for a particular crop and country, the crop is 

entirely irrigated or found in the gleysol/fluvisol AEZ), then we simply repartition the nationallevel FAOSTAT yields using an estimated global average of the proportion of yield in each AEZ 

to total yield. This is described in greater detail below in the next section (Note that the 

estimation of average yields in the next section is executed prior to the calculation below for zero 

total rainfed yields). 

 

YFAO (n,mc, fz) = Y FAO ( ) n,mc x

1

N

YFAO (n,mc, fz)

YFAO ( ) n,mc

, if YFAO,RF (n,mc) = 0

n=1

N

∑ , 

where 

N = total number of countries with YFAO (n,mc, fz) > 0 and YFAO (n,mc) > 0. The summation 

in the above equation is only performed when both numerator and denominator are non-zero. 
30

Table 6. Mapping from FAO crops to SAGE crops 

No. FAO crops No. SAGE crops No. FAO crops No. SAGE crops 

1 WHEA 19 Wheat 17 CITR 8 Others 

2 RICE 12 Rice 18 FRUI 8 Others 

3 MAIZ 5 Maize 19 OILC 8 Others 

4 BARL 1 Barley 20 RAPE 11 Rape 

5 MILL 6 Millet 21 PALM 7 Oil palm 

6 SORG 14 Sorghum 22 SOYB 15 Soy 

7 OTHC 13 Rye 23 GROU 4 Groundnuts 

8 POTA 9 Potato 24 SUNF 18 Sunflower 

9 SPOT 8 Others 25 SESA 8 Others 

10 CASS 2 Cassava 26 COCN 8 Others 

11 OTHR 8 Others 27 COFF 8 Others 

12 BEET 16 Sugar beet 28 TEAS 8 Others 

13 CANE 17 Sugar cane 29 TOBA 8 Others 

14 PULS 10 Pulses 30 COTT 3 Cotton 

15 VEGE 8 Others 31 FIBR 8 Others 

16 BANA 8 Others 32 RUBB 8 Others 

Table 7. Definition of FAO AEZs 

FAO AEZ Class Moisture regime 

(LGP in days) 

Description 

AT1 75-119 Dry semi-arid 

AT2 120-179 Moist semi-arid 

AT3 180-269 Sub-humid 

AT4 270+ Humid 

AT4+AT5 AT5 120+ Marginally suitable land in moist semi-arid, sub-humid, humidclasses 

AT6 Naturally flooded Fluvisols/gleysols AT6+AT7 AT7 Naturally flooded Marginally suitable fluvisols/gleysols 

Irrigated Land Irrigated Irrigated 

Table 8. Mapping from FAO AEZs to GTAP AEZs 

FAO AEZs GTAP AEZs 

Estimated (see text) AEZ1, AEZ7, AEZ13 

AT1 AEZ2, AEZ8, AEZ14 

AT2 AEZ3, AEZ9, AEZ15 

AT3 AEZ4, AEZ10, AEZ16 

AT4+AT5 AEZ5, AEZ11, AEZ17 

AT4+AT5 AEZ6, AEZ12, AEZ18 

AT6+AT7 No separate AEZ (see text) 

Irrigated Land No separate AEZ (see text) 
31

B. Estimate yields for countries without FAO data 

As FAO data were available for only 94 countries, we estimated information on yield variation 

across AEZ for the remaining countries using averages calculated over the 94 countries and 

applying them to the national statistics for the remaining countries10. Note that we did not 

average the yields themselves, but rather the proportion of yield in each AEZ to national yields. 

The formula used is as follows. 

 For each country ‘m’, without FAO data by AEZ, 

 

YFAO (m,mc, fz) = Y FAO ( ) m,mc x

1

N

YFAO (n,mc, fz)

YFAO ( ) n,mc n=1

N

∑ , 

where 

 

Y FAO (m,mc) = FAOSTAT national statistic on crop yield, and 

N = total number of countries with YFAO (n,mc, fz) > 0 and YFAO (n,mc) > 0. The summation 

in the above equation is only performed when both numerator and denominator are non-zero. The 

results in Figure 9 show that generally yields are highest in the AT3 AEZ. 

 

10 Averaging across all 94 countries may introduce biases. For example, the 94 countries are developing countries, and 

not representative of developed country yield variations across AEZ. In future versions, proxy data for averaging 

may be selected based on similarity in climates, as well as socio-economic conditions. 
Figure 9. Crop-specific ratio of yield in each AEZ to the total yield, average over the 94 countries with FAO 
33

C. Merge the data and adjust for consistency with SAGE harvested area 

The yields from the 94 countries are merged with the estimated yields for the remaining 

countries, 

 

YFAO (l,mc, fz)= YFAO (n,mc, fz)UYFAO (m,mc, fz). 

FAO does not report yields for GTAP AEZ1, AEZ7, and AEZ13 (see Table 8). Furthermore, 

often the FAO yield data and the recalibrated LEFF04 harvested area data are inconsistent, with 

FAO reporting non-zero yields even though recalibrated LEFF04 reports zero harvested areas, 

and conversely, FAO reporting zero yields while LEFF04 reports non-zero harvested area. In all 

of these cases, we adjusted the FAO yield data to match the recalibrated LEFF04 harvested area 

data. 

 We first mapped the FAO yield data from FAO’s AEZs to GTAP AEZs based on Table 8. To 

fill in gaps in FAO yield data (i.e., zero reported yields when recalibrated LEFF04 harvested area 

is non-zero), we estimate yields using a regression across all countries and all crops of yields in 

each rainfed AEZ to total rainfed yields (Figure 10)11. For GTAP AEZ1, AEZ7, and AEZ13 (0-

60 day LGP, with no data reported by FAO), we assumed that yield is one-tenth of the total 

rainfed yield for the corresponding crop and country (the value of 0.1 is arbitrary, but meant to 

represent a small yield in these arid AEZs; because not much is grown in these AEZs (see Figure 

11), this assumption shouldn’t have significant influence on the final results). In other words, 

⎩

⎨

⎧

= =

←

0,if ( , , ) 0

( , , ), based on Table 7

( , , ) A l mc z

Y l mc fz Y l mc z

LEFF

FAO

FAO

 

If YFAO (l,mc,z) = 0 & ALEFF ( ) (l,mc,z) ≠ 0 , YFAO (l,mc,z) = α z YFAO,RF (l,mc) YFAO (l,mc)

YFAO,RF (l,mc)

, 

where 

 

α z = α fz = 0.10 for AEZ1, AEZ7, and AEZ13; 

 = 0.50 for AT1; 

 = 0.87 for AT2; 

 = 1.30 for AT3; and 

 = 0.82 for AT4+AT5 (based on Figure 10, and Table 8). 

 Figure 11 shows the distribution of global total harvested area and global average yields 

across LGPs for a few sample crops. Note that the climatic zones are not differentiated in this 

figure (i.e., tropical, temperate, and boreal zones are not separated). While rice and soy dominate 

in humid climates, cassava is grown in intermediate-to-humid climates, maize and pulses are 

mostly grown in intermediate climates, while millet dominates in semi-arid climates. 

 

11 This averaging is done across all crops to maintain a sufficiently large sample size for the regression. For now, this is 

used here simply as a consistency checker, and therefore will not bias the final results very much. Future versions 

should consider establishing this relationship for individual crop types. 
Figure 10. A regression across all countries and all crops of yields in each rainfed AEZ to total rainfed yields 
Figure 11. Distribution of global total harvested area and global average yields across LGPs for a few sample cr
36

D. Recalibrate the yield data to Year 2001 and map to GTAP crop sectors 

Finally, because the recalibrated harvested area data by AEZ are derived from LEFF04, and the 

yield data are from FAO, the re-calculated national yields will change. Also, the yields need to 

be calibrated to the reference period of 2001. We do this as follows:

 

Y(l,mc,z) = YFAO (l,mc,z) *

PFAO (l,mc,tref )

YFAO (l,mc,z) * ALEFF (l,mc,z)

z

∑ , 

where 

 

PFAO (l,mc,tref ) = the FAOSTAT national production for tref = 2001. 

 This data can then be mapped onto GTAP’s 8 crop sectors: 

 

Y(l,mc,z)→YGTAP (l,s,z) (see Table 3 for mapping) 

 Table 9 uses China as an example to show the crop yield data estimated by SAGE from the 

above described procedure. 
Table 9. SAGE crop yield data: China (unit: ton per 1000 hectare) 

Unit:ton/1000ha 1 barley 2 maize 3 millet 4 rice 5 rye 6 sorghum 7 wheat 8 cassava 9 potat 10 sugarb 11 sugarc 12 pulses 13 grnuts 14 rape 15 oilpalm 16 soy 

1 AEZ1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 AEZ2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 AEZ3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 AEZ4 5290 8850 2871 7153 2069 5344 4871 22483 19262 0 46149 1710 3132 2017 20109 2105 AEZ5 4508 5233 1340 6262 1403 3506 4506 17783 15297 45885 61071 1528 3049 1820 15469 1996 AEZ6 4508 5233 1340 6262 1403 3506 4506 17783 15297 0 61071 1528 3049 1820 15469 1997 AEZ7 443 571 195 660 183 468 464 1809 1510 4413 0 154 306 176 1547 208 AEZ8 2325 2062 1534 3301 913 2338 2322 9043 7552 22065 0 772 1531 930 7734 1009 AEZ9 3868 4967 1852 5743 1588 4067 5015 15735 12609 38393 53035 1343 2664 1230 13458 17710 AEZ10 5290 8850 2871 7153 2069 5344 4871 22483 19262 41116 46149 1710 3132 2017 20109 21011 AEZ11 4508 5233 1340 6262 1403 3506 4506 17783 15297 45885 61071 1528 3049 1820 15469 19912 AEZ12 4508 5233 1340 6262 1403 3506 4506 17783 15297 45885 61071 1528 3049 1820 15469 19913 AEZ13 443 571 195 660 183 468 464 1809 1510 4413 6096 154 306 176 1547 2014 AEZ14 2325 2062 1534 3301 913 2338 2322 9043 7552 22065 30480 772 1531 930 7734 10015 AEZ15 3868 4967 1852 5743 1588 4067 5015 15735 12609 38393 53035 1343 2664 1230 13458 17716 AEZ16 5290 8850 2871 7153 2069 5344 4871 22483 19262 41116 46149 1710 3132 2017 20109 21017 AEZ17 4508 5233 1340 6262 1403 3506 4506 17783 15297 45885 61071 1528 3049 1820 15469 19918 AEZ18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 
38

Summary of the SAGE data 

SAGE combined several global land use data sets to derive land use information at the national 

level, by 18 different AEZs (Table 10) for use in the GTAP land use database. In particular, 

SAGE utilized global land use/land cover data sets developed in-house, with the following 

features: (1) spatially-explicit maps of croplands, pastures, built-up areas, and potential natural 

vegetation, (2) covering 18 major crops (plus Other crops) and 18 agro-ecological zones, (3) 

observing national boundaries, and (4) derived from and made consistent with the FAO data of 

harvested area, yield, and production for 34 crops grown in 94 developing nations. As described 

earlier, these data sets are synthesized, adjusted for consistency, and calibrated to the year 2001. 

Table 10. Summary of the SAGE land cover/use data set provided to GTAP 

Category Items Variables (Units) Specifications Reference 

Period 

Land Cover Forest, 

Savanna/Grassland, 

Shrubland, 

Cropland, 

Pasture, 

Built-up land, and 

Other land 

Area (1000ha) 160 countries 

18 AEZs within 

each country 

ca. 1992 

Major Crops 19 LEFF04 crops Harvested Area (1000ha); 

Yield (kg/ha) 

 2001 

2.2.2.2 Timberland data 

This section introduces the timberland data provided in GTAP. The data is described in more 

detail in Sohngen and Tennity (2004). The data were originally compiled for use in a Dynamic 

Global Timber market Model (hence forth DGTM) as described in Sohngen et al. (1999), 

Sohngen and Sedjo (2000), and Sohngen and Mendesohn (2003). The description below presents 

general information on the types of data included in the GTAP dataset. Readers are urged to 

review Sohngen and Tennity (2004) and the data at the following website for more detailed 

information: 

http://aede.osu.edu/people/sohngen.1/forests/GTM/index.htm

Preparation of the timberland data for GTAP 

Two types of data are obtained from the DGTM described in Sohngen et al. (1999), including 

forestland inventories for different timber types in 9 regions of the world, and economic 

parameters associated with each of these timber types. The 9 regions included in the global 

timber model are: North America, South and Central America, Europe, the Former Soviet Union, 

China, Asia-Pacific, India, Oceania (Australia and New Zealand), and Africa. The two types of 

data, land area inventories and economic (& biophysical growth and carbon) parameters, are 

disaggregated to different levels of detail. The forestland area data are disaggregated to show 

inventories of timber types in different agro-ecological zones within a country, while the 

economic parameters are disaggregated only to the timber type level for specific countries. The 

methods used to disaggregate the data are shown in Figure 12. 
(3) Regional estimates

of forestland area by timber

type (M1 – M14) and age class.

(Sohngen et al., 1999)

Country estimates

of land area 

in timber types (M1 – M14)

and age classes

Regiona

of econom

in timber ty

from Sohng

Countr

of econom

in timber ty

Timber type M1 

area

In AEZ 1 - 18

Timber type M14

area

In AEZ 1 - 18

.........

(4) Agro-ecologicalZones from Ramankuttty & Foley (1999)

Forest Inventory Data Economic 

(1) Forest area 

(Ramankutty & Foley, 1999) and ecosystem types from BIOME3 (Haxeltine & Prentice, 1996) combined for each country

(2) Results from step (1) 

linked to total forestland area from FAO (2003)

Figure 12. Graphical depiction of methods used to obtain values in the GTAP forestry dataset
40

 The method for disaggregating the forestland area data is as follows (the left hand side of 

Figure 12). The model in Sohngen et al. (1999) was originally developed so that a number of 

timber types were linked to spatial distribution of ecosystems presented in the BIOME3 model 

(Haxeltine and Prentice, 1996). The term "timber types" refers to aggregations of similar species 

that occur within a given region that have similar growth and market characteristics. In some 

regions, a single timber type may be defined for each ecosystem type, while in other regions, 

multiple timber types may be defined for each ecosystem type. For example, in some developed 

countries, substantial additional detail is available from local inventory sources to break down 

forests located in ecosystem types defined by BIOME3 into a large number of timber types. In 

particular, North America and Europe have more timber type classifications than most other 

regions due to the availability of data sources at the regional and country level. 

 In order to take the classification of timber types in the global timber model and disaggregate 

those into specific timber types in particular countries, three steps are taken. First, the BIOME3 

model is overlain with a global forest area dataset from Ramankutty and Foley (1999) to estimate 

the proportion of forestland residing in each timber type in each country. Second, the proportion 

of forestland in each timber type is then applied to the total forestland estimate from FAO (2003) 

to estimate the area of forestland in each timber type in each country. Third, the proportion of 

forestland in each age class and timber type for the region is then applied to the country level 

estimates of the area of different timber types to determine the age class distribution within the 

country. 

 For the most part, age class distributions are available only for developed countries and for the 

large developing countries. They were originally combined into a global dataset in Sohngen et al. 

(1999) using a range of inventory sources. One important limitation of the data on age classes is 

that each country that collects age-class data uses different sampling techniques and methods. 

For example, they handle mixed-age stands differently, or they classify forests into maturity 

classes that are only broadly linked to age. The resulting estimates of age class distributions 

provided in this document are therefore estimates based on judgments made in Sohngen et al. 

(1999). For countries where FAO (2003) is identified as the inventory source, no age class 

information is available (except for plantations), and these age classes are arbitrarily assigned age 

classes. Specifically, we have assigned all these forests into a single age class – typically 50 or 

100 years. 

 In addition to having country level data sources for age class information, additional data on 

the distribution of species is also available for some countries. For instance, for Europe, North 

America, and countries of the Former Soviet Union, additional information on hardwood and 

softwood types within each ecosystem type is available, so that hardwoods and conifers can be 

considered separately. The methods used here ensure that the total land area in forests in each 

country is consistent with FAO (2003), but total forestland has been disaggregated to different 

timber types using the timber types in the global timber model, BIOME3, and other local data 

sources where additional data are available. 

 The steps taken provide estimates of the area of land in different types of forests and age 

classes for each country. In this dataset, however, the area of forestland in different agroecological zones (AEZ’s) is also estimated. The AEZ map from Ramankutty and Foley (1999) 

was overlain on the map of ecosystem types from BIOME3, to generate an estimate of the 

proportion of land in each ecosystem type that resides in each AEZ. As a fourth step, these 

proportions were used to allocate the timber types in each country to AEZ’s. Because we do not 
41

have specific age class information on AEZ’s, each age class is proportionally allocated by total 

area to the respective AEZ’s for a timber type. 

 The second type of data relates to parameters that can be used by modelers, such as economic 

parameters (i.e., prices and costs of harvesting), parameters to calculate biomass growth, or 

carbon sequestration, and other parameters. These data are obtained from the global timber 

model, but they are disaggregated only to the timber type level for each country. It was not 

possible to further disaggregate these parameters to AEZ’s, given that data on productivity, 

prices, etc. is generally not available in a globally consistent database at the AEZ level. 

Consequently, economic parameters are available only for timber types. For each timber type in 

a specific country, there is a corresponding timber type in one of the 9 major regions in the global 

timber model. The parameters for the corresponding timber type from the global timber model are 

used for each timber type in each country. 

Caveats and Limitations of the Forestry Data 

There are several caveats that should accompany the forestry data. First, there will be more 

timber price variation across countries in reality than reflected in this data. The reason for this is 

that the prices and quality adjustment factors for prices were originally developed for a global 

model that aggregates the world into just 9 regions: North America, South and Central America, 

Europe, the Former Soviet Union, China, Asia-Pacific, India, Oceania (Australia and New 

Zealand), and Africa. Within each of these regions, there will surely be price differentials that are 

not reflected here. For modelers interested in global analyses, the price differentials contained in 

this data set appears are adequate for purposes of making broad comparisons across the major 

producing regions of the world. However, modelers seeking to use the data for more selected, 

national analyses involving countries within a particular region may consider adjusting the prices 

used for timber with more recent data from the FAOSTAT database (FAOSTAT, 2004). 

 A second, and analogous issue, is that there are surely larger differences in forest productivity 

across countries than reflected in this data set. The reasons are similar to those described above 

for prices: The productivity (i.e. merchantable yield) of timber types was originally estimated so 

that it could be applied to large areas of timber in the nine regions of the model in Sohngen et al. 

(1999). The same parameters have been applied to all timber types in each country located in a 

particular region. Thus, the productivity estimates may fail to reflect important differences in 

specific countries. Unlike price data, however, there are no global databases with country 

specific parameters for the timber yield functions; hence it is not possible at this point to make 

further corrections to the data for specific countries. 

 A third qualification is that, in addition to providing country specific data, the data on 

forestland areas has been further disaggregated to specific AEZ's. Thus, the dataset provides an 

estimate of the quantity of timber in a timber type in each agro-ecological zone. While the 

overall estimates of forestland areas in specific agro-ecological zones conform to the aggregate 

estimates from Ramankutty and Foley (1999), the dataset only provides economic information on 

the general timber type, not a specific set of parameters for each timber type and agro-ecological 

zone combination. There are reasons to believe that the same timber type might have different 

productivity in different agro-ecological zones (i.e. oaks grow at different rates in different 

ecological zones), but it was not possible with this data to estimate those differences. 

Data items derived from DGTM for GTAP use 

The information drawn from this work and provided for use in this and future versions of the 
42

GTAP land use data base include the following items, disaggregated across 124 

countries/regions: 

1. Basic economic and biophysical data on timber types within the region; 

2. Inventory data on the hectares of land in each timber type class12 (M1 up to M14), 10-year age 

class (where age class information is available), and AEZ; 

3. Information on carbon in each timber type, age class, and AEZ – derived from data drawn 

from items 1 and 2. 

 Note that all of these data are described fully in Sohngen and Tennity (2004). The timber 

types, which are country-specific combinations of management and timber species, are 

designated M1 through M14. Only the United States has 14 timber types. All other regions have 

fewer types. The main reason for this is that substantial information is available for forest 

economic modeling within the United States, so disaggregated data for this region has been 

developed more extensively. It is generally not possible to compare timber types across different 

countries in different regions, for example, M1 in the United States is not the same forest type as 

M1 in Argentina. 

 Timber types within general regions can be compared across countries, with the exception of 

the "developed, large, and other countries" category. The countries included in this dataset, as 

well as the general regions to which they are assigned are shown in Table A1 of Sohngen and 

Tennity (2004). Briefly, the general regions are: 

(1) Africa 

(2) Central Asia 

(3) Southeast Asia 

(4) Europe 

(5) Central and South America 

(6) Developed, Large, and Other Countries. 

 Note that these 6 "regions" differ from the 9 regions used in the Sohngen et al. (1999) model, 

from which the data are derived. The grouping of regions above is purely for convenience. 

Researchers using the data can feel free to group the data in different ways that make economic 

and ecological sense. 

 For the readers' information, the differences between the above categories and the 9 regions 

used in Sohngen et al (1999) are briefly described. First, Sohngen et al. (1999) did not originally 

use data for Central Asia, so these data were added to this data set. Second, Southeast Asia above 

includes India, which was included as a separate region in Sohngen et al. (1999). Third, category 

(6), "Developed, Large, and Other Countries" includes data for several of the regions originally in 

Sohngen et al. (1999), such as North America (Canada/U.S.), Russia, Oceania (Australia/New 

Zealand), and China. Category (6) also includes some other countries not included in the original 

data set, like North and South Korea, and Japan. 

 Economic data for each timber type is provided in year 2000 US $. The values are obtained 

from the global timber model developed by Sedjo and Lyon (1990) and Sohngen et al. (1999). 

 

12 See Tables A2 – A6 in Sohngen and Tennity (2004) for detailed description of the timber type classes. 
43

The inventories (of circa 1990 – 2000) for each of the timber types have been further 

disaggregated into AEZ’s using the methods described above. As noted, it is currently not 

possible to take the economic data associated with each forest type and present specific estimates 

of economic parameters for each AEZ. 

 To facilitate modeling of different global timber markets, each timber type in each country 

was mapped into one of three timber species categories—coniferous, broadleaf, or mixed. Table 

11 shows the DGTM coniferous timberland area data of the U.S., by 18 AEZs and 10 classes of 

age. Note that coniferous timberland is spread across both temperate (AEZ7 – 12) and boreal 

(AEZ 13 – 16) lands, with the largest concentration arising in the long season, temperate AEZ12, 

dominant in the Southeastern United States, as well as coastal areas in the Northwestern US 

(recall Figure 6). This is followed in total land area by coniferous timberland in AEZ 14, which is 

found largely in Alaska, but also in some of the Rocky Mountain range. 

 Figure 13 charts the distribution of DGTM's U.S. coniferous timberland area data by AEZ and 

age class (as from Table 11). This shows that the age class distribution between 0 and 90 is 

relatively uniform in most cases. The large spike in the age category of 100 and above reflects the 

predominance of old growth forests – particularly in the boreal zone. 

 Table 12 shows the U.S. all-species timberland acreage data, by 18 AEZ and 14 timber types 

(i.e., management type coupled with tree species). This is a relatively sparse matrix, indicating 

that management types are somewhat specialized by AEZ. Another view of these data is offered 

by Figure 14 which charts the distribution of DGTM's U.S. coniferous timberland area data, by 

AEZ and management type (from Table 12). 

 The economic and biophysical data (i.e., "Data Output") includes fundamental economic 

values associated with forestry activity and carbon sequestration for the particular timber types, 

e.g., land rents, management costs, timber prices, forest area and area change, yields, production, 

growth parameters, and carbon accounting values. The data are provided only for the timber types 

identified as being relevant for each country. The values for particular timber types in countries 

within a particular region are similar because the data have been obtained from the rather 

aggregate, global timber model. 
Table 11. DGTM coniferous timberland area data of the U.S.: AEZ by age (unit: 1000 hectare) 

Unit: 1000ha 1 AGE_10 2 AGE_20 3 AGE_30 4 AGE_40 5 AGE_50 6 AGE_60 7 AGE_70 8 AGE_80 9 AGE_90 

1 AEZ1 0 0 0 0 0 0 0 0 0 2 AEZ2 0 0 0 0 0 0 0 0 0 3 AEZ3 0 0 0 0 0 0 0 0 0 4 AEZ4 0 0 0 0 0 0 0 0 0 5 AEZ5 0 0 0 0 0 0 0 0 0 6 AEZ6 0 0 0 0 0 0 0 0 0 7 AEZ7 573.2 511.9 399.5 423.1 459.6 463.7 525.7 502.7 768.4 8 AEZ8 383.8 314.5 251.4 266.8 297.4 323 393.2 391.4 665.8 9 AEZ9 74 74.4 70.7 84.9 92.6 98.9 104.9 91.9 152.9 10 AEZ10 721.6 801.1 773.5 842 787.3 659.7 547.9 324.9 214 11 AEZ11 856.1 732.7 834.5 696.7 440.7 220.2 213.1 40.3 17.9 12 AEZ12 5387.5 4442.1 5328 4337.4 2537.5 1099.3 1120.7 0 0 13 AEZ13 201.6 304 293 297.3 290.2 278.4 304.9 290.5 599 14 AEZ14 699.3 1284.4 1282.6 1291.4 1228.4 1145.5 1220.3 1147.9 2490.1 15 AEZ15 916.1 1795.1 1810.8 1819.7 1718.6 1589.7 1679 1572.8 3463.8 16 AEZ16 24.9 34.3 32.4 33 32.7 31.8 35.3 33.9 68.1 17 AEZ17 0 0 0 0 0 0 0 0 0 18 AEZ18 0 0 0 0 0 0 0 0 0 Total 9838.2 10294.5 11076.4 10092.3 7885.1 5910.1 6145.2 4396.2 8440 
1 AEZ1

3 AEZ3

5 AEZ5

7 AEZ7

9 AEZ9

11 AEZ11

13 AEZ13

15 AEZ15

17 AEZ171 AGE_10

4 AGE_40

7 AGE_70

0 10 AGE_100

1000

2000

3000

4000

5000

6000

7000 U.S. coniferous timberland area (1000ha)

1 AGE_2 AGE_3 AGE_4 AGE_5 AGE_6 AGE_7 AGE_8 AGE_9 AGE_10 AGE

Figure 13. DGTM U.S. coniferous timberland area distribution: AEZ by age 
Table 12. DGTM timberland area data of the U.S.: AEZ by timber types (unit: 1000 hectare) 

Unit: 1000ha 1 M1 2 M2 3 M3 4 M4 5 M5 6 M6 7 M7 8 M8 9 M9 10 M10 11 M11 12 M12 1

1 AEZ1 0 0 0 0 0 0 0 0 0 0 0 0 2 AEZ2 0 0 0 0 0 0 0 0 0 0 0 0 3 AEZ3 0 0 0 0 0 0 0 0 0 0 0 0 4 AEZ4 0 0 0 0 0 0 0 0 0 0 0 0 5 AEZ5 0 0 0 0 0 0 0 0 0 0 0 0 6 AEZ6 0 0 0 0 0 0 0 0 0 0 0 0 7 AEZ7 827.7 0 0 0 1766.5 0 1898.1 1139 0 0 0 0 8 AEZ8 0 0 0 0 1472.1 0 1752 949.2 0 0 0 0 9 AEZ9 0 0 0 46.3 229 142.2 511 147.6 0 134 92.3 85.7 10 AEZ10 2648.7 400.9 754 390.8 81.8 1200.4 511 52.7 1253.9 1131.3 9226.7 8566.4 11 AEZ11 496.6 1202.7 2261.9 0 65.4 0 0 42.2 3761.6 0 12825.1 11907.3 12 AEZ12 0 8419 15833.5 0 0 0 0 0 26331.2 0 1845.3 1713.3 13 AEZ13 0 0 0 0 310.8 0 3431.1 200.4 0 0 0 0 14 AEZ14 0 0 0 0 310.8 0 16060.4 200.4 0 0 0 0 15 AEZ15 0 0 0 1.9 32.7 5.7 23068.6 21.1 0 5.4 0 0 16 AEZ16 0 0 0 0 49.1 0 365 31.6 0 0 0 0 17 AEZ17 0 0 0 0 0 0 0 0 0 0 0 0 18 AEZ18 0 0 0 0 0 0 0 0 0 0 0 0 Total 3973 10022.6 18849.4 439 4318.2 1348.3 47597.2 2784.2 31346.7 1270.7 23989.4 22272.7 

Note: M1-M8 are softwood; M9-M11 are hardwood; M12-M14 are mixed forest. 
1 AEZ1

3 AEZ3

5 AEZ5

7 AEZ7

9 AEZ9

11 AEZ11

13 AEZ13

15 AEZ15

17 AEZ17

1 M1

3 M3

5 M5

7 M7

9 M9

11 M11

13 M1

0

5000

10000

15000

20000

25000

30000 U.S. timberland area (1000ha)

Figure 14. Distribution of DGTM U.S. timberland area data: AEZ by timber types
48

2.2.3 GTAP AEZ land rents 

2.2.3.1 Development of GTAP land rent data 

Land rents represent a very important part of the GTAP-AEZ data base. This is because the 

GTAP model, as with all general equilibrium models, is expressed in terms of value flows. 

Therefore, prices are used to weight all underlying quantities – be they tons of wheat, numbers of 

workers, or hectares of land—prior to aggregation and incorporation in the economic model. As 

such this is a critical section of this paper. Unfortunately, it is also a very difficult problem. 

Ideally, where an active land rental market is present, we could observe land rents, by use and 

AEZ. It would then be a simple matter of multiplication (land rents/ha. * total ha.) in order to 

estimate land rents in each activity/AEZ. However, such data are not readily available in most 

countries, and where it is available, it is not grouped by detailed use or AEZ. 

 Furthermore, when it comes to estimating agricultural land rents by economic activity, the 

GTAP data base reflects the common level of ignorance of the agricultural economics profession. 

Because of the difficulty in allocating inputs to different activities within the agricultural sector 

(most farms are multi-product enterprises), all estimates of aggregate cost shares are for the entire 

farm sector, not for individual crops or livestock activities. Therefore, the only relevant piece of 

data offered by GTAP is total land rents in agriculture, at the national level. For simplicity, all 

farm sectors in the GTAP data base inherit the same share of land in total value-added (i.e. the 

payments to land, labor and capital). However, there is nothing sacred about this assumption, and 

it would appear that we should alter that assumption for present purposes, in light of the fact that 

we have observations on total hectares of land in various activities. 

 In the following sections, we describe how we allocate the GTAP land rents across AEZs. As 

our data sources differ for crops and livestock, we discuss the associated procedures first for 

crops (section 2.2.3.2) and then for livestock (section 2.2.3.3). In section 2.2.3.4, we describe how 

we adjusted sectoral value-added to preserve the estimates of primary factor shares from 

literature, given that we assumed in section 2.2.3.3 the indirect use of land by non-ruminant 

sectors. Forest land rent allocation is described in section 2.2.3.5. 

2.2.3.2 GTAP cropland rent data by 18 AEZs 

We split the GTAP sectoral land rents into 18 AEZs according to the AEZ-specific production 

shares as derived from the data provided by SAGE and DGTM. Table 3 shows the mapping 

between SAGE’s 19 crops to GTAP’s 8 crops. The following formula is used to split the GTAP 

sectoral land rents into 18 AEZs (Lca). For region r, 

Lca = Lc* [ ∑i∈SAGECROPS=c

Pi*Yia*Hia / ∑a∈AEZS ∑i∈SAGECROPS=cPi*Yia*Hia ], 

 c∈CROPS; i∈SAGECROPS; a∈AEZS. (1) 

where 

Lca is the land rent accrued to GTAP crop sector c in AEZ a; 

Lc is the original land rent of GTAP crop sector c, that is, with no AEZ distinction; 
49

Pi is the per-ton price of SAGE’s crop i; 

Yia is the yield (ton/1000ha) of SAGE’s crop i in AEZ a, with Yia = Qia

Hia; and 

Hia is the harvested area of SAGE’s crop i in AEZ a. 

Set SAGECROPS contains SAGE’s 19 crops; 

Set CROPS contains GTAP’s 8 crop types, which are more aggregated than SAGE’s. 

The mapping between SAGECROPS (index i) and CROPS (index c) is given in Table 3. The 

∑i∈SAGECROPS=c operator in (1) aggregates over disaggregated SAGE's crop i, thereby creating the 

corresponding aggregated GTAP's crop c. 

Additional note the following: The per-ton crop price (Pi) is invariant to AEZs 

Data source of Lc = coefficients VFM for land from the GTAP database; 

Data source of Pi = 2001 crop prices from the FAOSTAT database; 

Data source of Yia = SAGE, as described earlier; and 

Data source of Hia = SAGE, as described earlier. 

Discretionary solution to the mapping inconsistency for “Other crops” 

As noted earlier in section 2.2.2.1, in this first release of the GTAP land use database, we 

encountered a problem in mapping between SAGE crops and the GTAP crop sectors of 

vegetables and fruits (“v_f”) and other crops (“ocr”). We have come up with a discretionary 

solution to this problem and have planned to fix it in the next release of the GTAP land use data 

base. 

 We used the AEZ shares of the aggregate production of the SAGE crops that are mapped to 

GTAP's "v_f" and "ocr" sectors to split the AEZs of both the "v_f" and the "ocr" sectors. We plan 

to fix this crop mapping discrepancy in the next release when we receive the AgroMAPS 

(FAO/IFPRI/SAGE/CIAT, 2003) data from SAGE. 

Some special treatment for Belgium and Luxembourg 

Note that we moved the land rents of the crop sectors of Belgium and Luxembourg to be the 

capital rentals of the corresponding sectors. This is to make their crop sector land rents consistent 

with the SAGE data which show zero harvested area. The same adjustment is made to the "pfb" 

sector of Japan. For some individual GTAP countries that SAGE does not have data—i.e., 

Taiwan ("twn") and Hong Kong ("hkg")—we use the AEZ shares of Viet Nam ("vnm") as the 

proxies to split their cropland rents into 18 AEZs. None of these consistency adjustments are 

likely to make a large difference in the global results associated with land use changes in the 

wake of policies aimed at climate change mitigation. 

An illustration of the GTAP crop sector land rents by 18 AEZs 

Table 13 shows world total value-added, including land rents (header "VFM", from v6.0 

database) for the GTAP crop sectors (sectors 1 to 8) split into 18 AEZs. The data show that most 
50

of the world's crops (value-basis) are grown in the tropical and temperate AEZs (AEZs 1 - 12). 

The largest total crop land rents, and estimated $58,786 million, are generated on AEZ 10 – 

temperate climate with LGP of 180 – 240 days. This is followed by the longer LGP temperate 

AEZs: 11 and 12. The values of land rents generated in the boreal zones are an order of 

magnitude smaller, and essentially negligible for the shortest growing period (presumably 

production in greenhouses generates most of the value of production and hence land rents in these 

zones).13

 Figure 15 offers a visualization of the cropland rent allocation among AEZs, as seen in Table 

13. This reveals some interesting points about specific crops. For example, we see that paddy rice 

("pdr") is mostly grown in AEZs with longer LGPs (e.g., AEZs 3 – 6, and 10 - 12). Vegetables, 

fruits and nuts ("v_f") are a high value crop and therefore dominate the total land rents picture in 

most of the AEZs. This can be explained by their shorter cultivation period, which allows for 

multiple cropping, the widespread irrigation of fruit and vegetable production, as well as the 

potential for greenhouse production. The dominance of the "v_f" sector in the total cropland rent 

distribution within each AEZ is further emphasized in Figure 16, which shows a share-based 

breakout of total land rents in each of the Agro-Ecological Zones, world wide. 

 

13 The issue of greenhouse production raises some interesting and important questions regarding our imputation of land 

rents. The use of greenhouses is clearly an attempt to circumvent the natural limitations presented by a given AEZ. 

This requires substantial investment, the cost of which is confounded with land rents in our present analysis. This 

highlights the need for independent estimates of land rents, by AEZ. We hope to incorporate such estimates in future 

versions of this data base. 
Table 13. GTAP crop sector land rents: VFM, world total, v6.0 (unit: million US Dollar) 

Unit: million USD 1 pdr 2 wht 3 gro 4 v_f 5 osd 6 c_b 7 pfb 1 AEZ1 37 297 133 801 58 43 69 2 AEZ2 75 402 252 942 526 94 249 3 AEZ3 1678 1922 855 4636 3313 1696 1103 4 AEZ4 3316 672 1103 6292 1419 984 475 5 AEZ5 3984 137 1294 5600 752 766 316 6 AEZ6 5097 140 1932 9445 918 1126 152 7 AEZ7 190 479 232 989 184 109 213 8 AEZ8 754 3024 1538 6623 1086 323 525 9 AEZ9 830 3453 2705 8396 2062 620 763 10 AEZ10 3219 8610 12469 18481 4048 944 881 11 AEZ11 4070 5065 4926 13611 2767 381 823 12 AEZ12 4147 1730 1842 17413 2178 562 1037 13 AEZ13 3 47 11 64 11 3 14 14 AEZ14 16 358 125 725 47 13 8 15 AEZ15 77 991 803 2097 290 35 7 16 AEZ16 40 273 245 449 82 16 5 17 AEZ17 2 2 1 14 0 0 0 18 AEZ18 0 0 0 1 0 0 0 19 UnSkLab 34221 23133 25714 156766 21103 10094 9083 20 SkLab 368 629 831 3357 636 232 203 21 Capital 9972 8775 10394 45817 10470 4713 4319 22 NatlRes 0 0 0 0 0 0 0 Total 72092.95 60138.31 67404.52 302516.91 51950.37 22752.57 20246.77 14
1 AEZ1

3 AEZ3

5 AEZ5

7 AEZ7

9 AEZ9

11 AEZ11

13 AEZ13

15 AEZ15

17 AEZ171 p d r

4 

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000 G T A P c ro p s e c to rs la n d re n t (2 0 0 1 U S D , m illio n )

Figure 15. Crop sector land rent allocation among AEZs: world total 
0%

20%40%60%80%

100%

1 AEZ1

2 AEZ2

3 AEZ3

4 AEZ4

5 AEZ5

6 AEZ6

7 AEZ7

8 AEZ8

9 AEZ9

10 AEZ10

11 AEZ11

12 AEZ12

13 AEZ13

14 AEZ14

15 AEZ15

16 AEZ16

Figure 16. Distribution of crop sector land rent within each AEZ: world total 
54

2.2.3.3 GTAP livestock sector land rent data by 18 AEZs 

There are four primary livestock production sectors in the GTAP data base: ruminants (ctl = 

cattle, sheep and goats), dairy production (rmk), wool (wol) and non-ruminants (oap = pigs and 

poultry). Only the first two of these sectors are assumed to use land directly in their production 

process. Since wool production is a by-product of the ruminants sector (sheep and goat 

production), this does not use land directly. In the case of non-ruminants, the justification is a bit 

more complex, since these animals may roam freely on the farm in the case of low-intensity 

production. However, by their very nature, what they consume has already been produced using 

land somewhere else in the system (e.g., feedgrains). As production intensifies, these animals are 

confined to a facility which is more nearly akin to a manufacturing sector than a land-using 

sector. Therefore, we abstract from the direct competition for land between non-ruminant 

production, ruminant production, crops and forestry. Of course there is indirect competition, 

insofar as increased production of poultry, for example, will boost the feed requirements and 

hence increase the demand for land in feed grains. However, we capture this competition via the 

intermediate demand for feed in non-ruminant production. 

 In order to estimate land rents by AEZ for the crops sectors, we capitalized on SAGE’s 

estimates of crop harvested area by AEZ, as well as the relative yield estimates from the FAO. 

However, in the case of the livestock sectors, we do not have a similar allocation of production by 

AEZ. Therefore, we are forced to resort to a different approach. From the land cover data base, 

we know how much total grazing land there is in each AEZ. To this, we seek to add an estimate 

of the relative productivity of these different hectares of land in all types of ruminant production 

across AEZs. The most natural thing is to use an index of crop yields as a predictor of land 

productivity in forage. Since there is no single “forage crop” sector in our data base, we use the 

average yield of GTAP coarse grains sector, i.e., the "gro" sector, of the country/region, 

multiplied by the SAGE pasture land cover hectares of the 18 AEZs, to split the GTAP livestock 

sectors' land rents into 18 AEZs.14 Since we do not have independent estimates of land used for 

dairy production vs. land used for cattle, sheep and goats, the aggregate land rents in these sectors 

are divided across AEZs in equal proportions. 

A summary of the GTAP livestock sector land rents by 18 AEZs 

Table 14 shows world total land rents (header "VFM", from v6.0 database) of the two GTAP 

land-using livestock sectors (sectors 9 and 11) split into 18 AEZs. As with crop production, the 

largest total land rents are in AEZ 10. However, the distribution of land rents across AEZs is now 

much more even, as shown in Figure 17. Note that the tropical AEZs 3 – 6, and the boreal AEZs 

14 and 15 show relatively high levels of land rents, worldwide. This reflects the fact that livestock 

production is more amenable to the shorter growing seasons and sometimes more adverse 

circumstances characterized by these AEZs. In short, livestock production appears to be more 

tolerant to severe climate conditions. 

 When the AgroMAPS (FAO/SAGE/IFPRI, CIAT, 2003) data, mentioned earlier, becomes 

available, we will be able to extract data on forage crop areas and yields. Therefore, in the next 

version of this database, we plan to update our livestock land rents. 

 

14 For Belgium and Luxembourg, we use the Netherlands ("nld") data as proxy for the AEZ split due to a lack of data 

for the former countries. 
55

Table 14. GTAP livestock sector land rents: VFM, world total, v6.0 (unit: million 

US Dollar) 

Unit: million USD 9 ctl 10 oap 11 rmk 12 wol Total

1 AEZ1 83 0 161 0 244

2 AEZ2 208 0 643 0 850

3 AEZ3 227 0 3066 0 3293

4 AEZ4 849 0 1970 0 2819

5 AEZ5 1446 0 894 0 2340

6 AEZ6 1362 0 767 0 2130

7 AEZ7 1149 0 1130 0 2280

8 AEZ8 1750 0 2522 0 4272

9 AEZ9 1109 0 1342 0 2451

10 AEZ10 2593 0 4560 0 7153

11 AEZ11 1455 0 2084 0 3539

12 AEZ12 1105 0 810 0 1915

13 AEZ13 152 0 122 0 274

14 AEZ14 663 0 932 0 1596

15 AEZ15 1134 0 1444 0 2578

16 AEZ16 429 0 509 0 938

17 AEZ17 13 0 9 0 22

18 AEZ18 1 0 1 0 2

19 UnSkLab 30127 58281 36372 3668 128447

20 SkLab 1140 1303 1376 73 3892

21 Capital 24095 54798 18304 3309 100506

22 NatlRes 0 0 0 0 0

Total 71090 114382 79020 7049 271540

0

1000

2000

3000

4000

5000

6000

7000

8000

1 AEZ12 AEZ23 AEZ34 AEZ45 AEZ56 AEZ67 AEZ78 AEZ89 AEZ9

10 AEZ1011 AEZ1112 AEZ1213 AEZ1314 AEZ1415 AEZ1516 AEZ1617 AEZ1718 AEZ18

Million USD

12 wol

11 rmk

10 oap

9 ctl

Figure 17. Livestock sector land rent allocation among AEZs: world total 
56

2.2.3.4 Preserving country-specific total valued-added of agriculture in GTAP 

The GTAP input-output data base has imposed from available literature country-specific 

estimates of value-added shares for all of agriculture (including crops and livestock; see Chapter 

18.C of Dimaranan and McDougall (2002)). As such, we would like to preserve the value-added 

shares of agriculture for each GTAP region. 

 As alluded to in section 2.2.3.3, we assumed "oap" and "wol" sectors do not use land directly. 

Therefore, we took away land rents of the two sectors and augmented their capital rental by the 

amount of their land rents to keep the total costs of the two sectors correspond to their total 

revenue. To preserve the country-specific shares of agriculture value-added, we scaled up land 

rents of the other agriculture sectors (i.e., crop sectors, "ctl" and "rmk" sectors) by amounts 

summing up to the total land rents of the two non-ruminants sectors. Again, to preserve the zeroprofit condition, we scaled down capital rentals of the other agriculture sectors accordingly. 

2.2.3.5 GTAP forest land rent data by 18 AEZs 

The standard GTAP database treats agricultural land rents as distinct from forestry land rents. The 

latter are classified as natural resource rents (see Section 18.C of Chapter 18 in Dimaranan and 

McDougall, 2002). Accordingly, we reallocate the “natural resource” rent in forestry to become 

simply land rents. We split the forestry land rent into 18 AEZs according to the rental shares by 

AEZ. We derive the AEZ-specific forestry land rent shares from the DGTM data of timberland 

marginal land rent (by tree type and by country), multiplied by timberland area by tree type, by 

age, by AEZ, and by country. 

 Table 15 shows world total land rents (header "VFM", from v6.0 database) of the GTAP 

forest sector ("13 frs") split into 18 AEZs. 
57

Table 15. GTAP land rents: VFM of all land-based sectors, world total, v6.0 (unit: 

million US Dollar) 

Unit: million USD 13 frs

1 AEZ1 30

2 AEZ2 24

3 AEZ3 191

4 AEZ4 479

5 AEZ5 507

6 AEZ6 1141

7 AEZ7 100

8 AEZ8 118

9 AEZ9 564

10 AEZ10 1667

11 AEZ11 1059

12 AEZ12 2008

13 AEZ13 15

14 AEZ14 299

15 AEZ15 720

16 AEZ16 190

17 AEZ17 1

18 AEZ18 0

19 UnSkLab 32898

20 SkLab 914

21 Capital 37713

22 NatlRes 0

Total 80641
58

3. Validation of the GTAP AEZ land rent data 

The construction of the GTAP AEZ land rent data has involved a host of different assumptions. 

So it is natural to ask: how does this compare with observed land rents, when the rents themselves 

are divided by the hectares of observed land cover? Ideally we would like to do this for all 

regions in the GTAP data base. However, we only have the data of observed land rents for the 

U.S. so far. Therefore, we present here the comparison of per hectare land rents between the 

GTAP land rent data and the observed cash land rent only for the US, using data published by the 

U.S. Department of Agriculture (USDA). 

 Figure 18 shows the data available from the USDA for crop and pasture land rents by state, as 

well as the national average. Note that there is tremendous variation in cropland rents – with the 

highest figures in the states where irrigated cropland is predominant. Indeed, the cropland data for 

Arizona (AZ), Washington (WA) and California (CA) cover only irrigated land. The overall 

average U.S. cropland cash rent is $175/hectare, while the average pasture land cash rent is 

$23/hectare—about 13% of cropland rents15. To compare these estimates to those implied by the 

GTAP-AEZ data base, we must first perform some intermediate calculations. This is done in 

Table 16. The first pair of columns in this table report total land rents, by AEZ, in the modified 

GTAP data base, for both crops and livestock. In order to compute land rents per hectare, we 

must divide these figures by the SAGE land cover data in the second pair of columns in Table 16. 

This yields the estimated land rents per hectare, by AEZ, reported in columns E and F. Based on 

these estimates, the highest land rents per hectare occur for AEZ 10. These are $226/ha. and 

$76/ha., for crops and grazing, respectively. Not surprisingly, the lowest per hectare land rents 

arise on AEZs 7 and 13 – the shortest growing period land. Here, average crop land rents are just 

$21/ha. and pasture land rents are just $5/ha. For the entire cropland and grazing land in the US, 

these figures are $164/ha. and $20/ha., respectively. These estimates compare very favorably with 

USDA’s cash rent figures of $175/ha. and $23/ha., respectively. 

 It is also of interest to consider the relative land rents for crop and livestock activities within a 

given AEZ. This ratio is reported in the next column of Table 16 (G). Here, note that the overall 

ratio of pasture land rents to crop land rents in our data base is 0.12 – remarkably close to the 

USDA ratio of 0.13. However, this varies widely across AEZ’s in the GTAP-AEZ data base. The 

lowest value is 0.10 in AEZ 16 and the highest value is 0.40 in AEZ 11. 

 In light of the fact that we do not have cash rents for the US, by AEZ, it is useful to compare 

our estimates of land rents to those from another source. Towards this end, the latter columns of 

Table 16 report per hectare land rents from Mendelsohn et al. (2005) for the United States. Their 

aggregate land rents for the entire US are a bit lower than the GTAP-AEZ and USDA estimates: 

$147/ha. for crops and $13/ha. for grazing. The ratio of relative land rents in grazing vs. crops 

estimated by Mendelsohn et al. (2005) is also lower than the USDA one, at just 0.09 (vs. 0.13 for 

USDA and 0.12 for GTAP-AEZ). 

 However, unlike the USDA estimates, Mendelsohn’s estimates can be mapped to AEZs and 

this has been done. This mapping gives us a basis of comparison for the distribution of land rents 

across Agro-Ecological Zones. Here we see much greater differences between the two data bases. 

These are also illustrated in Figures 19 – 20 in order to facilitate discussion. Begin with the 

estimate of total crop land rents, by AEZ (Figure 19). The largest absolute discrepancy arises on 

 

15 This USDA cash land rent finding was provided by Alla Golub. 
59

AEZ 12, temperate climate with the maximum LGP. Here, the GTAP-AEZ data base estimates 

nearly twice as much land rent being generated as does Mendelsohn et al. (2005). By contrast, the 

latter authors’ estimates are higher for AEZ 11. Overall, these total land rent estimates aren’t too 

different. This is reassuring, particularly in light of the relatively weak basis for estimating AEZ 

yield differentials in the GTAP-AEZ data base. (Recall that the FAO data base used to obtain 

these differential yield estimates was rather dated and furthermore only included developing 

countries.) 

 Of course, once we divide these land rents by total hectares in each AEZ, the picture is 

somewhat different. Now the biggest discrepancies arise in those AEZs with very low levels of 

overall land rents. To see this, consider the Mendelsohn et al. (2005) per hectare land rents 

reported in columns H and I of Table 17. According to these authors, the highest average crop 

land rental rates arise in AEZs 10 and 11, amounting to nearly $200/ha. The lowest average rental 

rates arise in the boreal zone – AEZs 13 – 16, where rents range from $30 - $37/ha. Mendelsohn 

et al.’s pasture land rents range from $4/ha. in the boreal zone to more than $47/ha. in AEZ’s 10 

and 11. 

 Now compare these figures from Mendelsohn et al. (2005) to those reported in columns E and 

F derived from the GTAP-AEZ data base. Here, the highest crop land rents arise in the boreal 

zone! This is clearly absurd, as are the rental rates per hectare in this zone. However, before the 

reader gives up on this data base altogether, it should be noted that these AEZs are relatively 

unimportant in the overall economic picture. When combined, they account for just about half a 

percent of total crop land rents and about one percent of pasture land rents. So this gross error is 

confined to a relatively small part of the data base. 

 To understand why this problem might arise, consider how the GTAP-AEZ data base is 

constructed. We start with information on land use, by AEZ. This is combined with estimates of 

total land rents, based on the average share of land rents in crop production for the entire United 

States, and relative yields from the FAO data, in order to infer total land rents by hectare. Not 

surprisingly, there is a fair amount of high value vegetable production undertaken in the boreal 

zone (primarily Alaska) – presumably under greenhouses. This leads to a relatively high apparent 

land rent. Yet the total number of hectares is small. So the implied per hectare land rent is very 

high. However, this is not really a proper estimate of land rents, as the greenhouse-based 

production requires considerable infrastructure in order for the land to be productive. Indeed, 

without these improvements, land productivity would be very low. So these gross errors for the 

boreal zone are largely a function of our inability to separate returns to capital and land in 

greenhouse production. 

 In order to overcome this problem, we need a more direct approach to the estimation of land 

rents in agriculture. A natural approach would be to build on the work of Mendelsohn et al.

(2005), applying their estimated land rents by AEZ to the total hectares in the land use data base. 

However, to date, we have only been able to obtain these data for the US. We understand that 

Mendelsohn et al. (2005) are working on several other countries, and this will greatly facilitate 

more extensive validation of the data base. However, even having data for four or five countries 

is insufficient for building a global data base. We are, however, optimistic that improvements in 

our approach to estimating productivity by AEZ will improve the precision of our overall 

estimates of the distribution of land rents across AEZs. This will be discussed in greater detail 

below. 

 While we do not have independent data with which to validate the GTAP-AEZ land rental 

estimates for countries other than the US, it is still instructive to examine the pattern of land rents 
60

across these countries. The total land rent, land area and average land rents for each of the 87 

GTAP-AEZ regions are reported in Table 17, in descending order (highest to lowest per hectare 

land rents). The results are broadly as expected. The highest land rents arise in the densely 

populated, high income countries of East Asia: Korea, Japan and Hong Kong, followed by the 

smaller, high income countries of Europe (e.g., Switzerland). The lowest land rents per hectare 

arise in Sub-Saharan Africa – amounting to scarcely more than $1/ha. in Botswana, which is 

sparsely populated and dominated by the Kalahari Desert – an arid area, much of which is 

extensively grazed by livestock. Australia – a continent dominated by desert and extensive 

grazing as well, is not far behind at $6/ha. average land rent. 

 As a simple validity check on this set of average national land rents, we attempt to explain the 

cross-section variation via a simple regression model. The independent variables are as follows: 

income (measured by GDP/capita), population density (population/ha.), intensity of agricultural 

subsidies (Producer Support Estimate rate16 – for OECD and some European countries only), 

share of land in grazing, and share of land in each of the 18 AEZs. As we only have PSE rates for 

27 OECD/European countries, we put PSE rate variable in a separate regression model. Just this 

one variable has remarkable explanatory power with respect to variation in per hectare land rents 

across OECD countries as shown in the results reported in Table 18. The adjusted R-squared is 

0.50 and the T-statistic is 5.5. Table 19 reports the regression results encompassing all regions in 

the data base. Here, we see that higher population density is the most significant variable 

contributing to higher average per hectare land rent. The other variables are not significant – 

perhaps due to multi-collinearity of the AEZ shares (a temperate region tends to have high shares 

in all the temperate AEZs). However, the combined explanatory power of these variables is still 

quite high – with an adjusted R-squared of 0.59. Overall, it appears that much of the variation in 

land rents across countries can be explained by just a few simple economic and physical 

variables. This is reassuring. However, given the ambitious nature of this exercise, there are 

surely a number of countries for which these estimates do not make sense – either due to 

peculiarities in their agricultural structure, or due to mis-estimation of the fundamental inputs to 

this exercise (e.g., the size of the crops sectors in the residual regions for which no national inputoutput data are available). We hope that the frequency of such occurrences will diminish as we 

improve the underlying data bases and replace key assumptions with observed data. 

 

16 PSE rate data of 2001 are derived from "Agricultural Policies in OECD Countries: Monitoring and Evaluation", 2003 

edition, OECD. 
US Cash Rents (irrigated and/or non-irrigated), 2001

DE MD

NJ

NY

PA

MI

MN

WI

IL

IN

IA

MO

OH

KS

NE

ND SD

KY

NC

TN

VA

WV

AL FL

GA

SC

AR LA MS

OK TX

 AZ

CO

ID

MT

UT

CA

 PA

 MN

 WI IL IA

 MO

 KS NE ND SD

 NC TN VA AL FL GA LA MS OK TX CO MT NM UT WY CA

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

0

100200300400500600700800

0 5 10 15 20 25 30 35 

Cash rents per hectare, $

Cropland Pasture Cropland average Pasture average

Figure 18. USDA estimated cash rents for cropland and pasture, by state Note: AZ, WA and CA: only irrigated cropland. Source: Agricultural Cash Rents, 2001. http://usda.mannlib.cornell.edu/reports/nassr/other/plrbb/rent0701.pdf 
Table 16. U.S. per hectare land rent: GTAP v.s. Mendelsohn et al. 

Pasture/Cropland perha. land rent ratio

Pasture/Cropland pha. land rent ratio

(A) (B) (C) (D) (E) (F) (G) (H) (I) (J) 

= (F)/(E) = (I)/(H) 

Cropland Pastureland Cropland Pastureland Cropland Pastureland Cropland Pastureland 

AEZ1AEZ2AEZ3AEZ4AEZ5AEZ6AEZ7 589.70 874.06 28104.32 166232.30 20.98 5.26 0.25 67.76 7.79 0.11 AEZ8 3587.08 1456.16 27864.80 41457.14 128.73 35.12 0.27 95.43 11.06 0.12 AEZ9 2564.66 340.25 14996.95 6434.79 171.01 52.88 0.31 132.41 17.38 0.13 AEZ10 15558.51 1507.08 68842.63 19768.97 226.00 76.23 0.34 196.46 41.82 0.21 AEZ11 6237.91 576.95 43875.98 10097.77 142.17 57.14 0.40 191.16 46.50 0.24 AEZ12 7233.18 247.86 33195.84 4484.70 217.89 55.27 0.25 112.20 37.13 0.33 AEZ13 36.62 26.67 1757.56 5406.68 20.84 4.93 0.24 29.99 4.06 0.14 AEZ14 116.05 28.42 591.40 847.03 196.24 33.55 0.17 32.11 3.94 0.12 AEZ15 26.23 1.11 53.20 22.33 492.95 49.53 0.10 36.89 5.16 0.14 AEZ16 10.78 0.59 16.71 9.27 645.20 63.78 0.10 32.11 12.35 0.38 AEZ17AEZ18All land 35960.73 5059.15 219299.41 254760.99 163.98 19.86 0.12 147.11 13.16 0.09 

GTAP land rent (VFM),unit: million 2001 US$

SAGE land cover, unit:1000 ha.

Derived per ha. Land rent,unit: 2001 US$/ha.

Mendelsohn, unit:2001 US$/ha.
590

3587

2565

15559

6238

7233

37 116 2

1904

2659

1986

13525

8387

3725

53 19 

0

2000400060008000

10000120001400016000

AEZ7 AEZ8 AEZ9 AEZ10 AEZ11 AEZ12 AEZ13 AEZ14 A

GTAP.Cropland Men

Figure 19. U.S. cropland rents, 2001 US$ million: GTAP v.s. Mendelsohn et al. 
874

1456

340

1507

577

248

27 28 

1295

459

112

827

470

167

22 3 

0

200400600800

1000120014001600

AEZ7 AEZ8 AEZ9 AEZ10 AEZ11 AEZ12 AEZ13 AEZ14 A

GTAP.Pastureland Men

Figure 20. U.S. pasture land rents, 2001 US$ million: GTAP v.s. Mendelsohn et al. 
65

Table 17. GTAP agriculture per hectare land rent, unit: 2001 US$/ha. 

GTAP regions 

GTAP VFM 

unit: million 2001 

USD 

SAGE Land cover 

unit: 1000ha. 

Average per ha. land 

rent 

7 Korean, republic of 8727.24 2515.08 3469.96 

5 Hon Kong 402.77 220.87 1823.60 

52 Switzerland 1566.46 1219.04 1285.00 

6 Japan 8384.37 7616.70 1100.79 

48 Netherlands 1094.72 1768.47 619.02 

42 Germany 7396.19 14641.29 505.16 

40 Finland 792.36 1769.18 447.87 

46 Italy 5597.91 13007.80 430.35 

37 Austria 1000.76 2526.40 396.12 

13 Singapore 85.32 217.21 392.78 

19 Sri Lanka 1661.36 4239.57 391.87 

51 Sweden 646.81 1862.95 347.20 

53 Rest of EFTA 632.86 2058.37 307.46 

41 France 7395.18 25998.61 284.45 

35 Rest of Free Trade Area of 

the Americas 1209.28 4312.38 280.42 

39 Denmark 935.56 3665.28 255.25 

12 Philippines 4487.29 17641.20 254.36 

17 Bangladesh 2662.20 11756.34 226.45 

43 United Kingdom 3397.84 15795.49 215.11 

57 Croatia 533.27 2499.22 213.38 

18 India 45882.52 226113.69 202.92 

44 Greece 1154.00 6117.42 188.64 

14 Thailand 4812.68 25581.40 188.13 

59 Czech Republic 796.70 4311.75 184.77 

56 Bulgaria 1243.38 7539.03 164.93 

10 Indonesia 8343.95 58131.04 143.54 

63 Romania 1943.69 14164.36 137.22 

49 Portugal 639.68 5008.84 127.71 

45 Ireland 691.00 5447.46 126.85 

62 Poland 2940.05 25290.60 116.25 

65 Slovenia 87.64 757.42 115.71 

15 Viet Nam 1749.19 15453.50 113.19 

 ...continued
66

Table 17 (continued) 

GTAP regions 

GTAP VFM 

unit: million 2001 

USD 

SAGE Land cover 

unit: 1000ha. 

Average per ha. land 

rent 

50 Spain 3386.45 30052.01 112.69 

60 Hungary 753.85 6706.12 112.41 

36 Rest of the Caribbean 813.82 7312.26 111.30 

75 Rest of North Africa 2914.64 26714.68 109.10 

34 Central America 2191.61 20323.64 107.84 

27 Venezuela 2333.34 23555.98 99.05 

64 Slovakia 202.04 2185.15 92.46 

16 Rest of Southeast Asia 2587.31 29481.13 87.76 

22 U.S.A. 41019.88 474060.41 86.53 

25 Colombia 2359.21 28362.74 83.18 

4 China 46744.18 571172.97 81.84 

11 Malaysia 1021.59 12511.83 81.65 

20 Rest of South Asia 8862.91 112527.95 78.76 

23 Mexico 8570.87 111875.31 76.61 

55 Albania 134.14 1764.63 76.01 

38 Belgium 144.73 2255.82 64.16 

31 Chile 1490.83 23517.14 63.39 

54 Rest of Europe 497.84 8194.44 60.75 

21 Canada 3648.39 70851.79 51.49 

26 Peru 1356.20 29244.66 46.37 

74 Tunisia 213.46 4906.43 43.51 

73 Morocco 629.78 14749.47 42.70 

66 Estonia 71.50 1880.34 38.02 

32 Uruguay 476.65 13893.66 34.31 

71 Turkey 1724.26 56340.18 30.60 

86 Uganda 329.31 11617.70 28.35 

2 New Zealand 443.47 17214.21 25.76 

28 Rest of Andean Pact 978.93 42690.42 22.93 

29 Argentina 3445.84 155308.95 22.19 

68 Lithuania 121.89 5601.60 21.76 

72 Rest of Middle East 4190.93 194967.42 21.50 

3 Rest of Oceania 158.73 8107.85 19.58 

33 Rest of South America 349.26 20282.88 17.22 

67 Latvia 46.08 2871.48 16.05 

 ...continued
67

Table 17 (continued) 

GTAP regions 

GTAP VFM 

unit: million 2001 

USD 

SAGE Land cover 

unit: 1000ha. 

Average per ha. land 

rent 

9 Rest of East Asia 1943.24 124609.77 15.59 

70 Rest of Former Soviet 

Union 5654.56 383581.80 14.74 

30 Brazil 3442.64 245458.43 14.03 

69 Russia 3638.02 300957.28 12.09 

79 Malawi 67.26 5839.10 11.52 

81 Tanzania 464.91 43306.47 10.74 

77 South Africa 590.25 82239.61 7.18 

1 Australia 3060.66 452506.93 6.76 

83 Zimbabwe 173.77 25749.54 6.75 

87 Rest of Sub-Saharan Africa 2841.21 473251.20 6.00 

85 Madagascar 155.33 33593.74 4.62 

84 Rest of Southern African 

Development Community 235.57 84103.17 2.80 

80 Mozambique 104.25 42741.14 2.44 

82 Zambia 87.91 43670.49 2.01 

78 Rest of South African 

Customs Union 61.42 53253.77 1.15 

76 Botswana 40.12 37302.88 1.08 

8 Taiwan 1770.05 N/A N/A 

24 Rest of North America 6.60 N/A N/A 

47 Luxembourg 34.92 N/A N/A 

58 Cyprus 24.35 N/A N/A 

61 Malta 10.66 N/A N/A 

Total 297515.83 5104516.50 
68

Table 18. Summary output of regression: average per ha. Land rent v.s. %PSE 

Regression Statistics 

Multiple R 0.734 

R2 0.538 

Adjusted R2

 0.500 

Standard Error 542.397 

Observations 27 

 

ANOVA 

 df SS MS F Significance 

F 

Regression 1 8919760.734 8919760.734 30.319 0.000 

Residual 26 7649056.565 294194.483 

Total 27 16568817.298 

 

 Coefficients Standard Error t Stat P-value Lower 95% Upper 

95% 

Lower 

95.0% 

Upper 

95.0% 

Intercept 0.000 #N/A #N/A #N/A #N/A #N/A #N/A #N/A 

%PSE 16.810 3.053 5.506 0.000 10.535 23.086 10.535 23.086 
69

Table 19. Summary output of regression: average per ha. Land rent v.s. economic 

and physical variables 

Regression Statistics 

Multiple R 0.824 

R2 0.679 

Adjusted R2

 0.591 

Standard Error 289.221 

Observations 82 

 

ANOVA 

 df SS MS F Significance 

F 

Regression 16 11666037.747 729127.359 9.298 0.000 

Residual 66 5520833.087 83648.986 

Total 82 17186870.834 

 

 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 

95.0% 

Upper 

95.0% 

Intercept -289.880 1012.790 -0.286 0.776 -2311.981 1732.220 -2311.981 1732.220 

GDP/POP -0.004 0.004 -0.781 0.437 -0.012 0.005 -0.012 0.005 

Pop. Dens. 

(p/ha) 82.083 8.612 9.531 0.000 64.889 99.278 64.889 99.278 

pasture/tot 470.140 322.779 1.457 0.150 -174.309 1114.588 -174.309 1114.588 

2 AEZ1_2 258.033 1129.114 0.229 0.820 -1996.316 2512.381 -1996.316 2512.381 

3 AEZ3 -382.758 1168.463 -0.328 0.744 -2715.670 1950.153 -2715.670 1950.153 

4 AEZ4 373.910 1071.907 0.349 0.728 -1766.221 2514.041 -1766.221 2514.041 

5 AEZ5 -103.638 1043.337 -0.099 0.921 -2186.729 1979.452 -2186.729 1979.452 

6 AEZ6 103.805 1022.880 0.101 0.919 -1938.442 2146.051 -1938.442 2146.051 

8 AEZ7_8 13.621 1026.233 0.013 0.989 -2035.320 2062.562 -2035.320 2062.562 

9 AEZ9 164.372 1030.236 0.160 0.874 -1892.561 2221.305 -1892.561 2221.305 

10 AEZ10 329.943 1032.872 0.319 0.750 -1732.252 2392.138 -1732.252 2392.138 

11 AEZ11 495.887 1044.616 0.475 0.637 -1589.757 2581.531 -1589.757 2581.531 

12 AEZ12 -120.675 1084.263 -0.111 0.912 -2285.476 2044.127 -2285.476 2044.127 

14 AEZ13_14 -59.063 1109.736 -0.053 0.958 -2274.722 2156.596 -2274.722 2156.596 

15 AEZ15 959.109 1107.731 0.866 0.390 -1252.548 3170.766 -1252.548 3170.766 

18 AEZ17_18 0.000 0.000 65535.000 #NUM! 0.000 0.000 0.000 0.000 
70

4. Concluding remarks and future research directions 

This paper described how the GTAP land use database is constructed with source data developed 

at the Center for Sustainability and Global Environment (SAGE), University of WisconsinMadison, for the crop and livestock sectors; and The Ohio State University, for the forest sector 

and forest carbon stock. For the GTAP land use database, we draw inspiration from Darwin 

(1999) and follow the FAO fashion of agro-ecological zoning (FAO, 2000; Fischer et al, 2002) to 

identify land located in 18 agro-ecological zones (AEZs)—six AEZs supporting various lengths 

of growing days each located in three different climatic zones. 

 As with other pioneering data base efforts, this one suffers from many flaws which we hope to 

address over time, as the data base matures. Fortunately, the stage is set for remedying one of the 

greatest of these flaws – the absence of AEZ-specific productivity estimates. The newly available 

AgroMAPS data set (FAO/IFPRI/SAGE/CIAT, 2003) capitalizes on sub-national production data 

and offers, for the first time, a global data set with spatially explicit production information. By 

combining this with the SAGE land use data set, we will be able to replace a key set of 

assumptions (that of relative productivity differences across AEZs) with real data. This will be a 

great advance and should help refine our estimates of the distribution of land rents across AEZs 

and activities. 

 When combined with the data base on GHG emissions and sequestration (Lee et al., 

forthcoming), which correlates emissions with GTAP economic activity, the land use and 

emissions data bases will permit economists interested in Integrated Assessment (IA) of climate 

change to better assess the role of land use change in GHG mitigation strategies. 
71

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74

Appendix A. Sectors and region mappings in the GTAP 

version 6 data base 

Table A1 shows the list of sectors in the GTAP version 6 (Dimaranan and McDougall, 2005 

forthcoming) data base and the description of sector activities. Table A2 shows the list of the 87 

regions covered in the GTAP version 6 data base and the mapping of the 226 world 

countries/territories to the 87 countries/regions. 

Table A1. Sectors in the GTAP version 6 data base and activity description 

No. Code Description

1 pdr Rice, not husked 

Husked rice 

2 wht Wheat and meslin 

3 gro Maize (corn) 

Barley 

Rye, oats 

Other cereals 

4 v_f Vegetables 

Fruit and nuts 

5 osd Oil seeds and oleaginous fruit 

6 c_b Plants used for sugar manufacturing 

7 pfb Raw vegetable materials used in textiles 

8 ocr Live plants; cut flowers and flower buds; flower seeds and fruit seeds; vegetable seeds 

Beverage and spice crops 

Unmanufactured tobacco 

Cereal straw and husks, unprepared, whether or not chopped, ground, pressed or in the form 

of pellets; swedes, mangolds, fodder roots, hay, lucerne (alfalfa), clover, sainfoin, forage 

kale, lupines, vetches and similar forage products, whether or not in the form of pellets 

Plants and parts of plants used primarily in perfumery, in pharmacy, or for insecticidal, 

fungicidal or similar purposes 

Sugar beet seed and seeds of forage plants 

Other raw vegetable materials 

9 ctl Bovine cattle, sheep and goats, horses, asses, mules, and hinnies, live 

Bovine semen 

10 oap Swine, poultry and other animals, live 

Eggs, in shell, fresh, preserved or cooked 

Natural honey 

Snails, live, fresh, chilled, frozen, dried, salted or in brine, except sea snails; frogs’ legs, 

fresh, chilled or frozen 

Edible products of animal origin n.e.c. 

Hides, skins and furskins, raw 

Insect waxes and spermaceti, whether or not refined or coloured 

 ...to be continued 
75

Table A1 (continued) 

No. Code Description 

11 rmk Raw milk 

12 wol Raw animal materials used in textile 

13 for Forestry, logging and related service activities 

14 fsh Hunting, trapping and game propagation including related service activities 

Fishing, operation of fish hatcheries and fish farms; service activities incidental to fishing

15 col Mining and agglomeration of hard coal 

Mining and agglomeration of lignite

16 oil Extraction of crude petroleum and natural gas (part) 

Service activities incidental to oil and gas extraction excluding surveying (part) 

Mining and agglomeration of peat

17 gas Extraction of crude petroleum and natural gas (part) 

Service activities incidental to oil and gas extraction excluding surveying (part)

18 omn Mining of uranium and thorium ores 

Mining of metal ores 

Other mining and quarrying

19 cmt Meat of bovine animals, fresh or chilled 

Meat of bovine animals, frozen 

Meat of sheep, fresh or chilled 

Meat of sheep, frozen 

Meat of goats, fresh, chilled or frozen 

Meat of horses, asses, mules or hinnies, fresh, chilled or frozen 

Edible offal of bovine animals, swine, sheep, goats, horses, asses, mules or hinnies, fresh, 

chilled or frozen 

Fats of bovine animals, sheep, goats, pigs and poultry, raw or rendered; wool grease 

20 omt Meat of swine, fresh or chilled 

Meat of swine, frozen 

Meat and edible offal, fresh, chilled or frozen, n.e.c. 

Preserves and preparations of meat, meat offal or blood 

Flours, meals and pellets of meat or meat offal, inedible; greaves 

Animal oils and fats, crude and refined, except fats of bovine animals, sheep, goats, pigs and 

poultry 

21 vol Soya-bean, ground-nut, olive, sunflower-seed, safflower, cotton-seed rape, colza and mustard 

oil, crude 

Palm, coconut, palm kernel, babassu and linseed oil, crude 

Soya-bean, ground-nut, olive, sunflower-seed, safflower, cotton-seed, rape, colza and 

mustard oil and their fractions, refined but not chemically modified; other oils obtained 

solely from olives and sesame oil, and their fractions, whether or not refined, but not 

chemically modified 

Maize (corn) oil and its fractions, not chemically modified 

Palm, coconut, palm kernel, babassu and linseed oil and their fractions, refined but not 

chemically modified; castor, tung and jojoba oil and fixed vegetable fats and oils (except 

maize oil) and their fractions n.e.c., whether or not refined, but not chemically modified 

Margarine and similar preparations 

Animal or vegetable fats and oils and their fractions, partly or wholly hydrogenated, interesterified, re-esterified or elaidinised, whether or not refined, but not further prepared 

Cotton linters 

Oil-cake and other solid residues resulting from the extraction of vegetable fats or oils; flours 

and meals of oil seeds or oleaginous fruits, except those of mustard; vegetable waxes, except 

triglycerides; degras; residues resulting from the treatment of fatty substances or animal or 

vegetable waxes 

 ...to be continued
76

Table A1 (continued) 

No. Code Description 

22 mil Dairy products 

23 pcr Rice, semi- or wholly milled 

24 sgr Sugar 

25 ofd Prepared and preserved fish 

Prepared and preserved vegetables 

Fruit juices and vegetable juices 

Prepared and preserved fruit and nuts 

Wheat or meslin flour 

Cereal flours other than of wheat or meslin 

Groats, meal and pellets of wheat 

Cereal groats, meal and pellets n.e.c. 

Other cereal grain products (including corn flakes) 

Other vegetable flours and meals 

Mixes and doughs for the preparation of bakers’ wares 

Starches and starch products; sugars and sugar syrups n.e.c. 

Preparations used in animal feeding 

Bakery products 

Cocoa, chocolate and sugar confectionery 

Macaroni, noodles, couscous and similar farinaceous products 

Food products n.e.c. 

26 b_t Beverages 

Tobacco products 

27 tex Manufacture of textiles 

Manufacture of man-made fibres 

28 wap Manufacture of wearing apparel; dressing and dyeing of fur 

29 lea Tanning and dressing of leather; manufacture of luggage, handbags, saddlery, harness and 

footwear 

30 lum Manufacture of wood and of products of wood and cork, except furniture; manufacture of 

articles of straw and plaiting materials 

31 ppp Manufacture of paper and paper products 

Publishing of books, brochures, musical books and other publications 

Publishing of newspapers, journals and periodicals 

Publishing of recorded media 

Other publishing (photos, engravings, postcards, timetables, forms, posters, art reproductions, 

etc.) 

Printing and service activities related to printing 

Reproduction of recorded media 

32 p_c Manufacture of coke oven products 

Manufacture of refined petroleum products 

Processing of nuclear fuel 

33 crp Manufacture of basic chemicals 

Manufacture of other chemical products 

Manufacture of rubber and plastics products 

34 nmm Manufacture of other non-metallic mineral products 

35 i_s Manufacture of basic iron and steel 

Casting of iron and steel 

36 nfm Manufacture of basic precious and non-ferrous metals 

Casting of non-ferrous metals

37 fmp Manufacture of fabricated metal products, except machinery and equipment

...to be continued 
77

Table A1 (continued) 

No. Code Description

38 mvh Manufacture of motor vehicles, trailers and semi-trailers

39 otn Manufacture of other transport equipment

40 ele Manufacture of office, accounting and computing machinery 

Manufacture of radio, television and communication equipment and apparatus

41 ome Manufacture of machinery and equipment n.e.c. 

Manufacture of electrical machinery and apparatus n.e.c. 

Manufacture of medical, precision and optical instruments, watches and clocks

42 omf Manufacturing n.e.c. 

Recycling

43 ely Production, collection and distribution of electricity

44 gdt Manufacture of gas; distribution of gaseous fuels through mains 

Steam and hot water supply

45 wtr Collection, purification and distribution of water

46 cns Construction

47 trd Sales, maintenance and repair of motor vehicles and motorcycles; retail sale of automotive 

fuel 

Wholesale trade and commission trade, except of motor vehicles and motorcycles 

Non-specialized retail trade in stores 

Retail sale of food, beverages and tobacco in specialized stores 

Other retail trade of new goods in specialized stores 

Retail sale of second-hand goods in stores 

Retail trade not in stores 

Repair of personal and household goods 

Hotels and restaurants

48 otp Land transport; transport via pipelines 

Supporting and auxiliary transport activities; activities of travel agencies

49 wtp Water transport

50 atp Air transport

51 cmn Post and telecommunications

52 ofi Financial intermediation, except insurance and pension funding 

Activities auxiliary to financial intermediation 

53 isr Insurance and pension funding, except compulsory social security 

54 obs Real estate activities 

Renting of transport equipment 

Renting of other machinery and equipment 

Renting of personal and household goods n.e.c. 

Computer and related activities 

Research and development 

Other business activities 

55 ros Recreational, cultural and sporting activities 

Other service activities 

Private households with employed persons 

56 osg Public administration and defence; compulsory social security 

Education 

Health and social work 

Sewage and refuse disposal, sanitation and similar activities 

Activities of membership organizations n.e.c. 

Extra-territorial organizations and bodies 

57 dwe Dwellings 

Note: n.a. = not available; n.e.c. = not elsewhere classified. 

Source of table: Chapter 3 in Dimaranan and McDougall (2005 forthcoming). 
78

Table A2. The 87 countries/regions in the GTAP 6 data base and mapping to world 

counttries/territories 

No. Code Name Member Regions (226) Code 

1 AUS Australia Australia AUS 

2 NZL New Zealand New Zealand NZL 

3 XOC Rest of Oceania American Samoa ASM 

 Cook Islands COK 

 Fiji FJI 

 French Polynesia PYF 

 Guam GUM 

 Kiribati KIR 

 Marshall Islands MHL 

 Micronesia, Federated States of FSM 

 Nauru NRU 

 New Caledonia NCL 

 Norfolk Island NFK 

 Northern Mariana Islands MNP 

 Niue NIU 

 Palau PLW 

 Papua New Guinea PNG 

 Samoa WSM 

 Solomon Islands SLB 

 Tokelau TKL 

 Tonga TON 

 Tuvalu TUV 

 Vanuatu VUT 

 Wallis and Futuna WLF 

4 CHN China China CHN 

5 HKG Hong Kong Hong Kong HKG 

6 JPN Japan Japan JPN 

7 KOR Korea Korea, Republic of KOR 

8 TWN Taiwan Taiwan TWN 

9 XEA Rest of East Asia Macau MAC 

 Mongolia MNG 

 Korea, Democratic People’s Republic of PRK 

10 IDN Indonesia Indonesia IDN 

11 MYS Malaysia Malaysia MYS 

12 PHL Philippines Philippines PHL 

13 SGP Singapore Singapore SGP 

14 THA Thailand Thailand THA 

15 VNM Viet Nam Viet Nam VNM 

 ...to be continued

 
79

 Table A2 (continued) 

No. Code Name Member Regions (226) Code 

16 XSE Rest of Southeast Asia Brunei Darussalam BRN 

 Cambodia KHM 

 Lao People’s Democratic Republic LAO 

 Myanmar MMR 

 Timor Leste TLS 

17 BGD Bangladesh Bangladesh BGD 

18 IND India India IND 

19 LKA Sri Lanka Sri Lanka LKA 

20 XSA Rest of South Asia Afghanistan AFG 

 Bhutan BTN 

 Maldives MDV 

 Nepal NPL 

 Pakistan PAK 

21 CAN Canada Canada CAN 

22 USA United States of America United States of America USA 

23 MEX Mexico Mexico MEX 

24 XNA Rest of North America Bermuda BMU 

 Greenland GRL 

 Saint Pierre and Miquelon SPM 

25 COL Colombia Colombia COL 

26 PER Peru Peru PER 

27 VEN Venezuela Venezuela VEN 

28 XAP Rest of Andean Pact Bolivia BOL 

 Ecuador ECU 

29 ARG Argentina Argentina ARG 

30 BRA Brazil Brazil BRA 

31 CHL Chile Chile CHL 

32 URY Uruguay Uruguay URY 

33 XSM Rest of South America Falkland Islands (Malvinas) FLK 

 French Guiana GUF 

 Guyana GUY 

 Paraguay PRY 

 Suriname SUR 

34 XCA Central America Belize BLZ 

 Costa Rica CRI 

 El Salvador SLV 

 Guatemala GTM 

 Honduras HND 

 Nicaragua NIC 

 Panama PAN 

 ...to be continued

 
80

 Table A2 (continued) 

No. Code Name Member Regions (226) Code 

35 XFA Rest of Free Trade Area of the 

Americas 

Antigua & Barbuda ATG 

 Bahamas BHS 

 Barbados BRB 

 Dominica DMA 

 Dominican Republic DOM 

 Grenada GRD 

 Haiti HTI 

 Jamaica JAM 

 Puerto Rico PRI 

 Saint Kitts and Nevis KNA 

 Saint Lucia LCA 

 Saint Vincent and the Grenadines VCT 

 Trinidad and Tobago TTO 

 Virgin Islands, U.S. VIR 

36 XCB Rest of the Caribbean Anguilla AIA 

 Aruba ABW 

 Cayman Islands CYM 

 Cuba CUB 

 Guadeloupe GLP 

 Martinique MTQ 

 Montserrat MSR 

 Netherlands Antilles ANT 

 Turks and Caicos TCA 

 Virgin Islands, British VGB 

37 AUT Austria Austria AUT 

38 BEL Belgium Belgium BEL 

39 DNK Denmark Denmark DNK 

40 FIN Finland Finland FIN 

41 FRA France France FRA 

42 DEU Germany Germany DEU 

43 GBR United Kingdom United Kingdom GBR 

44 GRC Greece Greece GRC 

45 IRL Ireland Ireland IRL 

46 ITA Italy Italy ITA 

47 LUX Luxembourg Luxembourg LUX 

48 NLD Netherlands Netherlands NLD 

49 PRT Portugal Portugal PRT 

50 ESP Spain Spain ESP 

51 SWE Sweden Sweden SWE 

52 CHE Switzerland Switzerland CHE 

 ...to be continued

 
81

 Table A2 (continued) 

No. Code Name Member Regions (226) Code 

53 XEF Rest of EFTA Iceland ISL 

 Liechtenstein LIE 

 Norway NOR 

54 XER Rest of Europe Andorra AND 

 Bosnia and Herzegovina BIH 

 Faroe Islands FRO 

 Gibraltar GIB 

 Macedonia, the former Yugoslav Republic of MKD 

 Monaco MCO 

 San Marino SMR 

 Serbia and Montenegro SCG 

55 ALB Albania Albania ALB 

56 BGR Bulgaria Bulgaria BGR 

57 HRV Croatia Croatia HRV 

58 CYP Cyprus Cyprus CYP 

59 CZE Czech Republic Czech Republic CZE 

60 HUN Hungary Hungary HUN 

61 MLT Malta Malta MLT 

62 POL Poland Poland POL 

63 ROM Romania Romania ROM 

64 SVK Slovakia Slovakia SVK 

65 SVN Slovenia Slovenia SVN 

66 EST Estonia Estonia EST 

67 LVA Latvia Latvia LVA 

68 LTU Lithuania Lithuania LTU 

69 RUS Russian Federation Russian Federation RUS 

70 XSU Rest of Former Soviet Union Armenia ARM 

 Azerbaijan AZE 

 Belarus BLR 

 Georgia GEO 

 Kazakhstan KAZ 

 Kyrgyzstan KGZ 

 Moldova, Republic of MDA 

 Tajikistan TJK 

 Turkmenistan TKM 

 Ukraine UKR 

 Uzbekistan UZB 

71 TUR Turkey Turkey TUR 

 ...to be continued

 
82

 Table A2 (continued) 

No. Code Name Member Regions (226) Code 

72 XME Rest of Middle East Bahrain BHR 

 Iran, Islamic Republic of IRN 

 Iraq IRQ 

 Israel ISR 

 Jordan JOR 

 Kuwait KWT 

 Lebanon LBN 

 Palestinian Territory, Occupied PSE 

 Oman OMN 

 Qatar QAT 

 Saudi Arabia SAU 

 Syrian Arab Republic SYR 

 United Arab Emirates ARE 

 Yemen YEM 

73 MAR Morocco Morocco MAR 

74 TUN Tunisia Tunisia TUN 

75 XNF Rest of North Africa Algeria DZA 

 Egypt EGY 

 Libyan Arab Jamahiriya LBY 

76 BWA Botswana Botswana BWA 

77 ZAF South Africa South Africa ZAF 

78 XSC Rest of South African Customs Union Lesotho LSO 

 Namibia NAM 

 Swaziland SWZ 

79 MWI Malawi Malawi MWI 

80 MOZ Mozambique Mozambique MOZ 

81 TZA Tanzania Tanzania, United Republic of TZA 

82 ZMB Zambia Zambia ZMB 

83 ZWE Zimbabwe Zimbabwe ZWE 

84 XSD Rest of Southern African Development 

Community 

Angola AGO 

 Congo, the Democratic Republic of the COD 

 Mauritius MUS 

 Seychelles SYC 

85 MDG Madagascar Madagascar MDG 

86 UGA Uganda Uganda UGA 

 ...to be continued

 

 

 

 

 

 

 
83

 Table A2 (continued) 

No. Code Name Member Regions (226) Code 

87 XSS Rest of Sub-Saharan Africa Benin BEN 

 Burkina Faso BFA 

 Burundi BDI 

 Cameroon CMR 

 Cape Verde CPV 

 Central African Republic CAF 

 Chad TCD 

 Comoros COM 

 Congo COG 

 Cote d'Ivoire CIV 

 Djibouti DJI 

 Equatorial Guinea GNQ 

 Eritrea ERI 

 Ethiopia ETH 

 Gabon GAB 

 Gambia GMB 

 Ghana GHA 

 Guinea GIN 

 Guinea-Bissau GNB 

 Kenya KEN 

 Liberia LBR 

 Mali MLI 

 Mauritania MRT 

 Mayotte MYT 

 Niger NER 

 Nigeria NGA 

 Reunion REU 

 Rwanda RWA 

 Saint Helena SHN 

 Sao Tome and Principe STP 

 Senegal SEN 

 Sierra Leone SLE 

 Somalia SOM 

 Sudan SDN 

 Togo TGO 

Source of table: Chapter 3 in Dimaranan and McDougall (2005 forthcoming).