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. Tr uong 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 tha nk 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 wit hout which this document would have be en 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 Departme nt of Agricultural Economics, Purdue University, for his assistance in reviewing and finalizing this documentation. Finally, we w ould 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 om issions remain the responsibility of the authors alone.

* Center for Global Trade Analysis (GTAP), Dept. of Agricultural Economics, Pu rdue University. Email:

HLEE1@purdue.edu

** Center for Global Trade Analysis (GTAP), Dept. of Agricultural Economics, Pu rdue University. Email:

hertel@purdue.edu *** Dept. of Agricultural, Environmental and Develo pment Economics, Ohio St ate 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.edu2 

# Towards An Integrated Land Use Database for Assessing the Potential for Greenhouse Gas Mitigation GTAP Technical Paper No. 25

Huey-Lin Lee, Thomas Hertel, Br ent Sohngen, and Navin Ramankutty



# Abstract
This paper describes the GTAP land use da ta 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 fr om the FAO and IIASA. Agro-ecological zoning segments a parcel of land into smaller units according to agro-ecological characteristics, including: precipitation, temperature, soil t ype, terrain conditions, etc. Each zone has a similar combination of constraints and poten tial for land use. In the GTAP-AEZ data base, there are 18 AEZs, cove ring six different lengths of growing period spread over three different climatic zones. Land usi ng activities include crop production, livestock raising, and forestry. In so doing, this extensi on of the standard GTAP data base permits a much more refined characterization of th e 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, Integrat ed Assessment, Greenhouse Gas Mitigation.



# Prologue
This document reports on cons truction of an analytical data base to support policy analyses related to land use and land use cha nge, 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 em issions data base. Both of these reports represent ongoing work aimed at further impr ovements. Further update s will be posted on the GTAP website when available: http://www.gtap.agecon.purdue.edu/databases/projects/Land_Use_GHG/default.asp3 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 cultivat ed 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 .................... 744 List of Tables:

Table 1. Summary of all database pr oducts 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 sector s, 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: averag e 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 da ta base and activity description ............................ 74

Table A2. The 87 countries/regions in the GTAP 6 data base and mapping to world counttries/territories ......................................................................................................... ....... 785 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 cropl and 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. ................ 646 

# 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 da tabase 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 mode ls currently in use today. With a database covering inputs/outputs and bila teral trade of 57 commodities 2 (and producing industries) of each 87 countries/regions 3 , 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 upda tes 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 d ecade, 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 anal yses of the global economic consequences of attempts to mitigate greenhouse gas emissions. The fi rst step in this direction involved integrating the International Energy Agency ’s database on fossil fuel consumption into GTAP. When coupled with CO 2 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 CO 2 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 th is report focuses on the land use portion of the data base, the companion report documents the inclusion of CO 2 emissions, terrestrial sequestration, and non-CO 2 greenhouse gases emissions data—covering methane (CH 4 ), nitrous oxide (N 2 O), and the fluorinated gases (HFC-134a, CF 4 , HFC-23, and SF 6 ) from all sources. These CO 2 and non-CO 2 GHG emissions and sequestration are indirectly linked to the underlying economic drivers of emissions, which are faithfu lly represented in the core GTAP database. Land use, land-use change and forestry (L ULUCF) activities have been perceived as a relatively cost-effective option to mitigate climat e change due to the rapid buildup of greenhouse gases in the atmosphere. LULUCF may contribut e to abatement of emissions by increasing

2 See Table A1 in Appendix A fo r 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 target s of greenhouse gas emission abatement. In the seventh Conference of the Parties (COP 7) 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 acc ounting 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 advanci ng 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 Protec tion 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 cha nge 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 grow ing, 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 th is project. It details th e land use and land cover data base. The second report covers the associ ated 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 incor porated 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 hect ares), 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$ pe r hectare per year, of various management types in 124 countries/regions;8 6. forest carbon stock data: million metric tons of CO 2 , 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 CO 2 emissions data: giga-grams of CO 2 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 a nd 78 regions of GTAP database version 5.4 4 ; 10. CO 2 emissions from non-combustion sources fo r 2001 (e.g., cement manufacturing), and 11. GTAP non-CO 2 GHG (CH 4 , N 2 O, and fluorinated gases, including ozone depleting substances) emissions data: tera-gram CO 2 -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-ecol ogical zoning concept (FAO, 2000; Fischer et al , 2002) to identify lands located in eighteen different ag ro-ecological zones. In section 2, we introduce the AEZ-identified land use data and how they ar e 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. agricu lture 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 Agri culture (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 volum e data, is released to subs cribers Spring 2005. We will

recompile the CO 2 data of 2001 for version 6.0's 87 regions in subse quent emissions data updat es 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-CO 2 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 CH 4 and N 2 O data, the available GTAP input-out put database was version 5.0, whic h has 66 regions and the benchmark year is 1997. The GTAP database version 6.0 was publicly released in the summer of 2005. Like CO 2 emissions data update, we plan to recompile the 2001 CH 4 , N 2 O, and F-gases data for version 6.0's 87 regions for consistency reasons in the future.9 

### Table 1. Summary of all databa se products from this project

Data Year Dimensions Comment

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

re g ions, b y 18 AEZs Land activit y data Crop harvested acreage and y ields 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 re g ions, b y 18 AEZs Emissions/se q uestration ( ALL SECTORS - land-usin g 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

re g ions, b y 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 g ases, 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 Internatio nal Institute for Applied Systems Analysis (IIASA). Their Land Use and Land Cover (LUC) proj ect 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 co nsidered State of the Art. Agro-ecological zoning refers to segmentation of a parcel of la nd into smaller units according to agro-ecological characteristics, e.g., moisture and temperature regi mes, soil type, landform, etc. In other words, each zone has a similar combination of constrai nts 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 w ith a water balance mode l and knowledge of crop requirements. The “length of growing period” (L GP) refers to the period during the year when both soil moisture 6 and temperature are conducive to crop growth. The concept of “length of growing period” (LGP) is brought in to differentia te 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 precip itation, soil type, topography, etc.10 

## 2.2 The GTAP Land Use Data Base

We introduce in section 2.2.1 the overview of th e 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 Ra mankutty of the Center for Sustainability and Global Environment (SAGE), University of Wisconsin-Madison and Dr. Brent Sohngen of Ohio State University, respectively. The data input s from these two sources are described in section 2.2.2 (including land cover data a nd 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 u ses (sectors or activities) of land (the columns in Figure 1).

Land use activities in region r AEZs Crop 1 .... Crop N Livestock 1 .... Livestock H Forest 1 .... Forest v AEZ 1

....

.... AEZ M 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 e ach AEZ land type (row sum) is fixed. That is, land is not assumed to be mobile across AEZs. Th at 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 comp iled from two sources. The first source includes global land cover and cropland da ta, provided by Dr. Navin Ra mankutty 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 hect ares), 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 hect ares 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: timber land area (thousands of hectares), of circa 1990 – 2000, of three tree species (coniferous, broadleaf, a nd 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 CO 2 , 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 DGTM 7 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 Enviro nment (SAGE) at the Un iversity of Wisconsin has been developing global databases of contem porary and historical agricultural land use and land cover. SAGE has chosen to focus on agricu lture 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-leve l inventory data on land use (Ramankutty and Foley, 1998; Ramankutty and Foley, 1999). The advent of remote sensing data has been revolutionary in providing cons istent, global, estimates of th e patterns of global land cover. However, remote sensing data are limited in their ability to resolve the deta ils 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, inve ntory data are limited in not being spatially explicit, and are plagued by prob lems 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 an d Foley (1998)—RF98 he reafter—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 Sedj o (2000), and Sohngen and Mendelsohn ( 2003)), which is the source for much of the forestry data.12 al. (2000)) database at 1 km resolution (deriv ed from the Advanced Very High Resolution Radiometer (AVHRR) instrument), with compre hensive 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 percenta ge 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 majo r 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 na tural vegetation (PNV) types; this data set describes the spatial distribution of 15 natural vegetation types th at would be present in the absence of human activities (Figure 3). Furthermore, global data set s 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 bei ng 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, in itiated by two core projects—LUCC (Land Use and Land Cover Change) and PAGES (Past Global Ch anges) of the International GeosphereBiosphere Programme (IGBP). In other words, th ey are a widely recognized, and widely used data set of global agricultural land use. The SAGE land cover and agricultural land use da ta 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 pro cedure used to adapt the SAGE data and the ancillary data to derive land use information for GTAP.13Figure 2. The global distribution of croplandsca. 1992 from Ramankutty and Foley (1998)14Figure 3. SAGE global land cover map (the original 15 classes have been merged to 4 classes used in GTAP)15Figure 4. The global distribution of grazing landsca. 1992 from Foleyet 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 ma tch 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 peri od (LGPs) by aggregating the IIASA/FAO GAEZ data into 6 categories of approximately 60 da ys 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 studi es 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 worl d 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 GT AP 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 globa l 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, wher eby 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 poten tially 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 AEZ1818Figure 5. A global map of length of growing periods (LGP)19Figure 6. The SAGE global map of the 18 AEZs20 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 croplands 8 (see Figure 2), grazing lands (see Figure 4), and built-up areas. The resulting map was overlain with the global AEZ map, to calculate la nd cover by country, for each of the 18 AEZs Figure 5 is a summary chart with global total num bers 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. ph ysically cultivated area 9 : Which is appropriate? The original proposal for splitting the GTAP s ectoral 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, physi cally 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 ar ea data. In the GTAP Input-Output data, land rents are generated from the activity (or use) on th e given parcel of land during the calendar year. Therefore, we are interested in the value of th e 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 th e 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 m eadows for mowing or pasture, land under market and kitchen gardens and land temporarily fall ow (less than five years); and permanent crops: land cultivated with crops that occupy the land for long periods and need not be replan ted 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 cultivat ed area is related to multiple cropping. With harvested

area, land that is double cropped or triple cropped is coun ted two or three times respec tively. This variable is normally reported in national statistics for specific crops. Ph ysical 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 footnot e 8), and only accounts for phys ical land area. However, it also includes fallow land and temporary pasture land, and therefore cannot be used to in fer physical cultivated area. Even if that were not a problem, cropland area a ggregates all the crops, and therefore cannot be used to infer crop-specific physical cultivated area.21 rice sector, and allocate the land rents generated du e 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 harv ested acres is due to the fact that land based emissions (e.g., CH 4 emissions from paddy rice cultivation) are mostly tied to the harvested area (IPCC 1996 Guidelines). Fertilizer use is normall y 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 N 2 O and soil CO 2 emissions are tied to cultivated area and crop cycl es, 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 di stribution of crop harvested area required a conversion to from harvested to physical area on th e 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

LEF F 

### ( 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 

### A FAO 

### ( l
, mc , t ref 

### ) as follows:



### A

LEFF 

### ( i
, mc ) = ′ 

### A

LEFF 

### ( i
, mc ) x A FAO 

### ( l
, mc , t ref 

### )

A

LEFF 

### ( i
, mc )

i ∈ l 

# ∑ ,

where l = countries in FAOSTAT, 

### i ∈ l , and t ref is the reference time period = 2001 (for consistency with GTAP version 6.0).

The recalibrated LEFF04 data are then overlain w ith: 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 

### A LEF F 

### ( 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 cr op sectors using the mapping in Table 3: 

### A

LEF F 

### ( l
, mc , z ) → A GTA P 

### ( 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 Sta tistical Office of the United Nations (United Nations, 1991). Based on this concordance, we mapped potato, cassava, and pulses to the22 vegetable and fruits sector ("v_f") of GTAP. Howe ver, 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 vegetabl es 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 aggr egate 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 discrep ancy 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-nationa l production data and offers, for the first time, a global data set with spatially explicit producti on 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 dist ribution 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.txt23Table 4. Cropland use (harvested area): China, 2001 (unit: 1000 hectare)China cropland (Unit: 1000ha)1 2 3 4 5 6 78Paddy rice Wheat Cereal grainsVegetables/fruits/nuts Oil seeds Sugar cane/beetPlant-basedfibres Crops N.E.C.AEZ1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00AEZ2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00AEZ3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00AEZ4 66.84 4.96 2.67 8.22 15.97 1.82 0.00 51.62AEZ5 76.44 8.61 10.10 10.01 17.68 2.57 0.15 58.03AEZ6 2516.49 57.54 263.01 319.21 661.87 270.44 1.30 1851.88AEZ7 94.06 1406.27 752.65 223.94 426.05 40.25 654.41 419.42AEZ8 917.33 4277.84 7144.95 1198.28 3949.04 175.02 430.99 4105.76AEZ9 977.46 4317.75 6562.54 1249.27 3417.01 86.15 954.89 5985.30AEZ10 1066.33 2586.44 3745.75 1030.96 2347.44 73.58 427.47 2906.26AEZ11 4151.19 4849.99 2898.51 1310.12 2913.02 95.13 1063.45 7764.89AEZ12 18806.93 4440.91 5142.22 2982.33 7526.94 898.25 835.06 15386.55AEZ13 60.29 1067.65 332.39 155.95 461.65 19.21 413.34 262.92AEZ14 57.44 692.48 158.98 84.10 368.17 4.74 12.12 208.13AEZ15 177.89 666.10 338.27 120.48 427.85 11.58 7.35 292.73AEZ16 164.59 280.98 167.78 56.89 119.55 7.54 8.98 253.31AEZ17 10.78 6.56 9.76 2.97 3.77 0.93 0.23 10.53AEZ18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Total 29144.04 24664.06 27529.56 8752.70 22656.01 1687.21 4809.75 39557.3324

AEZ1 AEZ3 AEZ5 AEZ7 AEZ9 AEZ11 AEZ13 AEZ15 AEZ17Paddy riceVegetables/fruits/nutsPlant-based fibres02000400060008000100001200014000160001800020000Cropland use - harvested area (1000ha)Paddy riceWheatCereal grainsVegetables/fruits/nutsOil seedsSugar cane/beetPlant-based fibresCrops N.E.C.Figure 7. Distribution of croplanduse (harvested area): China, 200125000000E+00200E+60400E+60600E+60800E+61000E+61200E+61400E+61600E+61800E+6LGP1 LGP2 LGP3 LGP4 LGP5 LGP6

Area (ha)ForestSavanna/GrasslandShrublandCroplandPastureBuilt-up landOtherlandFigure 8. The SAGE global land cover distribution by LGP26 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/gr asslands 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 crop land 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 incl udes 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.27Table 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 Otherland Total1 AEZ1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.002 AEZ2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.003 AEZ3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.004 AEZ4 41.71 0.00 0.00 209.13 15.41 19.87 0.00 286.115 AEZ5 851.99 0.00 0.00 252.34 29.40 12.75 0.00 1146.486 AEZ6 3850.93 818.89 0.00 8168.40 1755.42 51.52 0.00 14645.167 AEZ7 380.19 2846.51 15712.78 5423.19 76431.86 112.55 93560.60 194467.698 AEZ8 12535.79 8204.33 1517.63 30451.23 38891.08 328.87 0.00 91928.939 AEZ9 23488.61 3236.56 12.93 31329.95 9846.51 312.30 130.91 68357.7810 AEZ10 20113.39 2344.85 1708.09 18806.62 6113.10 167.57 81.56 49335.1911 AEZ11 29123.68 3085.58 10722.81 33162.16 9531.92 151.34 0.00 85777.5012 AEZ12 57965.89 5751.70 183.69 75797.70 17176.50 357.77 0.00 157233.2513 AEZ13 206.34 2299.57 3440.05 3643.21 84475.91 4.68 43269.18 137338.9414 AEZ14 924.79 2714.68 100.37 2126.64 67327.57 2.22 7563.08 80759.3615 AEZ15 18073.58 3061.28 90.28 2751.87 33442.67 21.87 2288.73 59730.2916 AEZ16 890.81 2431.72 1291.78 1425.86 12072.31 1.28 33.60 18147.3517 AEZ17 0.00 297.67 0.00 61.43 453.60 0.08 0.00 812.7718 AEZ18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Total 168447.70 37093.35 34780.42 213609.72 357563.25 1544.66 146927.67 959966.8128 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-eco logical 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 sin ce then based on observed aggregates (Jelle Bruinsma, personal communication, 2003). So wh ile 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 u se 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 cr ops with GTAP’s 18 A EZs and 19 LEFF04 crops. FAO reports yields separately for four rain fed 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 se parated 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 av erages were calculated when multiple FAO crops mapped into one LEFF04 crop).

Let 

### Y FAO
, 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 

### Y FAO 

### ( 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,



### Y

FAO
, RF 

### n , mc 

# ( )
= Y FAO
, RF 

### n , mc , fz 

# ( ) x A FAO
, RF 

### n , mc , fz 

# ( )

fz = AT 1 AT 4 + AT 5



# ∑

A FAO
, RF 

### n , mc , fz 

# ( )

fz = AT 1 AT 4 + AT 5



# ∑ ,

where 

### A FAO
, 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 ) = Y FAO
, RF 

### ( n , mc , fz ) x Y FAO 

### ( n , mc )
Y

FAO
, RF 

### ( n , mc ) , if Y FAO
, RF 

### ( n , mc ) > 0

.
As an average across all countries, the national tota l yield is ~50% greater than rainfed yields for rice. This is reasonable because paddy rice is heav ily 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,



### Y

FAO 

### ( n , mc , fz ) = ′ ′ Y

FAO 

### ( n , mc , fz ) x Y FAO 

### ( n , mc )
Y

FAO 

### ( n , mc ) , if Y FAO
, 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 th at for a particular crop and country, the crop is entirely irrigated or found in the gleysol/fluviso l 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).



### Y

FAO 

### ( n , mc , fz ) = Y FAO 

### n , mc 

# ( ) x 1
N Y FAO 

### n , mc , fz 

# ( )

Y

FAO 

### n , mc 

# ( ) , if Y FAO
, RF 

### ( n , mc ) = 0

n = 1 N 

# ∑

,

where

N

= total number of countries with 

### Y FAO 

### n , mc , fz 

# ( ) > 0 and 

### Y FAO 

### 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 FA O 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 countri es, 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 countries 10 . Note that we did not average the yields themselves, but rather the pr oportion of yield in each AEZ to national yields. The formula used is as follows. For each country ‘m’, without FAO data by AEZ,



### Y

FAO 

### ( m , mc , fz ) = Y FAO 

### m , mc 

# ( ) x 1
N Y FAO 

### n , mc , fz 

# ( )

Y

FAO 

### n , mc 

# ( )

n = 1 N 

# ∑

,

where



### Y

FA O 

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

N

= total number of countries with 

### Y FAO 

### n , mc , fz 

# ( ) > 0 and 

### Y FAO 

### 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 ac ross AEZ. In future versi ons, proxy data for averaging may be selected based on similarity in c limates, as well as so cio-economic conditions.32Figure 9. Crop-specific ratio of yield in each AEZ to the total yield, average over the 94 countries with FAO data33 C. Merge the data and adjust for cons istency with SAGE harvested area The yields from the 94 countries are merged w ith the estimated yields for the remaining countries,



### Y

FAO 

### ( l
, mc , fz ) = Y FAO 

### ( n , mc , fz )U Y FAO 

### ( m , mc , fz )

. FAO does not report yields for GTAP AEZ1, A EZ7, and AEZ13 (see Table 8). Furthermore, often the FAO yield data and the recalibrated LEFF 04 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 LEF F04 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 reporte d 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 reporte d by FAO), we assumed that yield is one-tenth of the total rainfed yield for the corresponding crop and country (t he 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 infl uence on the final results). In other words,



### ⎩ ⎨ ⎧

= = ← 0 )
, , ( if
, 0 7 Table on based ),
, , ( )
, , ( z mc l A fz mc l Y z mc l Y

LEFF FAO FAO



### If Y FAO 

### ( l
, mc , z ) = 0 & A LEFF 

### ( l
, mc , z ) ≠ 0 

# ( )

, Y FAO 

### ( l
, mc , z ) = α z 

### Y FAO
, RF 

### ( l
, mc ) Y FAO 

### ( l
, mc )
Y

FAO
, 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 th e 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 inte rmediate-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 suffici ently 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 re lationship for individual crop types.34Figure 10. A regression acrossall countries and all crops ofyields in each rainfed AEZ to total rainfed yields35Figure 11. Distribution of global total harvested area and global averageyields across LGPs for a few sample crops36 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 peri od of 2001. We do this as follows:



### Y ( l
, mc , z ) = Y FAO 

### ( l
, mc , z ) * P FAO 

### ( l
, mc , t ref 

### )

Y

FAO 

### ( l
, mc , z ) * A LEFF 

### ( l
, mc , z )

z 

# ∑ ,

where



### P

FAO 

### ( l
, mc , t ref 

### ) = the FAOSTAT national production for t ref = 2001.
This data can then be mapped onto GTAP’s 8 crop sectors:



### Y ( l
, mc , z ) → Y GTA P 

### ( l
, s , z ) (see Table 3 for mapping)
Table 9 uses China as an example to show th e crop yield data estimated by SAGE from the above described procedure.37Table 9. SAGE crop yield data:China (unit: ton per 1000 hectare)Unit:ton/1000ha1 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 17sunfl 18 cotton 19 others1 AEZ1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02 AEZ2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 03 AEZ3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 04 AEZ4 5290 8850 2871 7153 2069 5344 4871 22483 19262 0 46149 1710 3132 2017 20109 2106 2586 0 180545 AEZ5 4508 5233 1340 6262 1403 3506 4506 17783 15297 45885 61071 1528 3049 1820 15469 1993 2494 4453 158466 AEZ6 4508 5233 1340 6262 1403 3506 4506 17783 15297 0 61071 1528 3049 1820 15469 1993 2494 4453 158467 AEZ7 443 571 195 660 183 468 464 1809 1510 4413 0 154 306 176 1547 201 250 461 14838 AEZ8 2325 2062 1534 3301 913 2338 2322 9043 7552 22065 0 772 1531 930 7734 1005 1252 2307 74139 AEZ9 3868 4967 1852 5743 1588 4067 5015 15735 12609 38393 53035 1343 2664 1230 13458 1775 2178 4014 1020510 AEZ10 5290 8850 2871 7153 2069 5344 4871 22483 19262 41116 46149 1710 3132 2017 20109 2106 2586 4885 1805411 AEZ11 4508 5233 1340 6262 1403 3506 4506 17783 15297 45885 61071 1528 3049 1820 15469 1993 2494 4453 1584612 AEZ12 4508 5233 1340 6262 1403 3506 4506 17783 15297 45885 61071 1528 3049 1820 15469 1993 2494 4453 1584613 AEZ13 443 571 195 660 183 468 464 1809 1510 4413 6096 154 306 176 1547 201 250 461 148314 AEZ14 2325 2062 1534 3301 913 2338 2322 9043 7552 22065 30480 772 1531 930 7734 1005 1252 2307 741315 AEZ15 3868 4967 1852 5743 1588 4067 5015 15735 12609 38393 53035 1343 2664 1230 13458 1775 2178 4014 1020516 AEZ16 5290 8850 2871 7153 2069 5344 4871 22483 19262 41116 46149 1710 3132 2017 20109 2106 2586 4885 1805417 AEZ17 4508 5233 1340 6262 1403 3506 4506 17783 15297 45885 61071 1528 3049 1820 15469 1993 2494 4453 1584618 AEZ18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 038 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 u se in the GTAP land use database. In particular, SAGE utilized global land use/land cover data se ts developed in-house, with the following features: (1) spatially-explicit maps of cropla nds, 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 productio n for 34 crops grown in 94 de veloping nations. As described earlier, these data sets are synthesized, adjusted fo r consistency, and calibra ted to the year 2001.



### Table 10. Summary of the SAGE land c over/use data set provided to GTAP

Category Items Variables (Uni ts) 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 timberla nd 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 include d in the GTAP dataset. Readers are urged to review Sohngen and Tennity (2004) and the da ta 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 Centra l 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 (& bioph ysical growth and carbon) parameters, are disaggregated to different levels of detail. Th e 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 th e timber type level for specific countries. The methods used to disaggregate the data are shown in Figure 12.39(3) Regional estimatesof forestland area by timbertype (M1 – M14) and age class.(Sohngen et al., 1999)Country estimatesof land areain timber types (M1 – M14)and age classesRegional estimatesof economic parametersin timber types M1 – M14from Sohngen et al. (1999)Country estimatesof economic parametersin timber types M1 – M14Timber type M1areaIn AEZ 1 - 18Timber type M14areaIn AEZ 1 - 18.........(4) Agro-ecologicalZones fromRamankuttty &Foley (1999)Forest Inventory DataEconomic Parameters(1) Forest area(Ramankutty & Foley,1999) and ecosystemtypes from BIOME3(Haxeltine & Prentice,1996) combined foreach country(2) Results from step (1)linked to totalforestland area fromFAO (2003)Figure 12. Graphical depiction of methods used toobtain values in theGTAP forestry dataset40 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) w as 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 typ es" refers to aggregations of similar species that occur within a given region that have simila r 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 ecosy stem 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 BIOM E3 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 ty pes in the global timber model and disaggregate those into specific timber types in particular count ries, 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 applie d 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 th e 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 im portant 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 differe ntly, or they classify forests into maturity classes that are only broadly linked to age. Th e 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 fo rests into a single age class – typically 50 or 100 years. In addition to having country level data sour ces 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 ava ilable, so that hardwoods and conifers can be considered separately. The methods used here en sure 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 globa l 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, howev er, 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 fro m BIOME3, to generate an estimate of the proportion of land in each ecosystem type that r esides 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 not41 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. Th ese 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 gl obally consistent datab ase 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 sh ould 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 fo r prices were originally developed for a global model that aggregates the world into just 9 regi ons: 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 gl obal 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, mode lers seeking to use the data for more selected, national analyses involving countries within a par ticular region may consider adjusting the prices used for timber with more recent data fr om the FAOSTAT database (FAOSTAT, 2004). A second, and analogous issue, is that there ar e 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 yiel d) 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 ag ro-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 pa rameters 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 the42 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 class 12 (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 fu lly 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 Stat es 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 th e United States is not th e same forest type as M1 in Argentina. Timber types within general regions can be co mpared across countries, with the exception of the "developed, large, and other countries" cat egory. 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 re gions 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) ar e briefly described. First, Sohngen et al. (1999) did not originally use data for Central Asia, so these data were adde d to this data set. Second, Southeast Asia above includes India, which was included as a separate re gion in Sohngen et al. (1999). Third, category (6), "Developed, Large, and Other Countries" includ es data for several of the regions originally in Sohngen et al. (1999), such as North America (Can ada/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 Se djo and Lyon (1990) and Sohngen et al. (1999).

12 See Tables A2 – A6 in Sohngen and Tennity (2004) fo r detailed description of th e 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 d escribed 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 cat egories—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 spr ead across both temperate (AEZ7 – 12) and boreal (AEZ 13 – 16) lands, with the largest concentrati on 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 ar ea 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 DGT M's U.S. coniferous timberland area data, by AEZ and management type (from Table 12). The economic and biophysical data (i.e., "D ata Output") includes fundamental economic values associated with forestry activity and carbo n sequestration for the particular timber types, e.g., land rents, management costs, timber pri ces, forest area and area change, yields, production, growth parameters, and carbon accounting values. Th e 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.44Table 11. DGTM coniferous timberland area dataof 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 10 AGE_100 Total1 AEZ1 0 0 0 0 0 0 0 0 0 0 02 AEZ2 0 0 0 0 0 0 0 0 0 0 03 AEZ3 0 0 0 0 0 0 0 0 0 0 04 AEZ4 0 0 0 0 0 0 0 0 0 0 05 AEZ5 0 0 0 0 0 0 0 0 0 0 06 AEZ6 0 0 0 0 0 0 0 0 0 0 07 AEZ7 573.2 511.9 399.5 423.1 459.6 463.7 525.7 502.7 768.4 1003.3 5631.38 AEZ8 383.8 314.5 251.4 266.8 297.4 323 393.2 391.4 665.8 886 4173.39 AEZ9 74 74.4 70.7 84.9 92.6 98.9 104.9 91.9 152.9 231.1 1076.210 AEZ10 721.6 801.1 773.5 842 787.3 659.7 547.9 324.9 214 368 6040.211 AEZ11 856.1 732.7 834.5 696.7 440.7 220.2 213.1 40.3 17.9 16.6 4068.912 AEZ12 5387.5 4442.1 5328 4337.4 2537.5 1099.3 1120.7 0 0 0 24252.513 AEZ13 201.6 304 293 297.3 290.2 278.4 304.9 290.5 599 1083.5 3942.314 AEZ14 699.3 1284.4 1282.6 1291.4 1228.4 1145.5 1220.3 1147.9 2490.1 4781.7 16571.615 AEZ15 916.1 1795.1 1810.8 1819.7 1718.6 1589.7 1679 1572.8 3463.8 6764.4 2313016 AEZ16 24.9 34.3 32.4 33 32.7 31.8 35.3 33.9 68.1 119.3 445.717 AEZ17 0 0 0 0 0 0 0 0 0 0 018 AEZ18 0 0 0 0 0 0 0 0 0 0 0Total 9838.2 10294.5 11076.4 10092.3 7885.1 5910.1 6145.2 4396.2 8440 15253.8 89331.945

1 AEZ1 3 AEZ3 5 AEZ5 7 AEZ7 9 AEZ9 11 AEZ11 13 AEZ13 15 AEZ15 17 AEZ171 AGE_104 AGE_407 AGE_7010 AGE_10001000200030004000500060007000U.S. coniferous timberland area (1000ha)1 AGE_102 AGE_203 AGE_304 AGE_405 AGE_506 AGE_607 AGE_708 AGE_809 AGE_9010 AGE_100Figure 13. DGTM U.S. coniferous timberland area distribution: AEZ by age46Table 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 13 M13 14 M14 Total1 AEZ1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02 AEZ2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 03 AEZ3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 04 AEZ4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 05 AEZ5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 06 AEZ6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 07 AEZ7 827.7 0 0 0 1766.5 0 1898.1 1139 0 0 0 0 0 0 5631.38 AEZ8 0 0 0 0 1472.1 0 1752 949.2 0 0 0 0 0 0 4173.39 AEZ9 0 0 0 46.3 229 142.2 511 147.6 0 134 92.3 85.7 8.8 81.2 1478.110 AEZ10 2648.7 400.9 754 390.8 81.8 1200.4 511 52.7 1253.9 1131.3 9226.7 8566.4 877.4 8124.4 35220.411 AEZ11 496.6 1202.7 2261.9 0 65.4 0 0 42.2 3761.6 0 12825.1 11907.3 1219.5 11292.9 45075.212 AEZ12 0 8419 15833.5 0 0 0 0 0 26331.2 0 1845.3 1713.3 175.5 1624.9 55942.713 AEZ13 0 0 0 0 310.8 0 3431.1 200.4 0 0 0 0 0 0 3942.314 AEZ14 0 0 0 0 310.8 0 16060.4 200.4 0 0 0 0 0 0 16571.615 AEZ15 0 0 0 1.9 32.7 5.7 23068.6 21.1 0 5.4 0 0 0 0 23135.416 AEZ16 0 0 0 0 49.1 0 365 31.6 0 0 0 0 0 0 445.717 AEZ17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 018 AEZ18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Total 3973 10022.6 18849.4 439 4318.2 1348.3 47597.2 2784.2 31346.7 1270.7 23989.4 22272.7 2281.2 21123.4 191616Note: M1-M8 are softwood; M9-M11 are hardwood; M12-M14 are mixed forest.47

1 AEZ1 3 AEZ3 5 AEZ5 7 AEZ7 9 AEZ9 11 AEZ11 13 AEZ13 15 AEZ15 17 AEZ171 M13 M35 M57 M79 M911 M1113 M13050001000015000200002500030000U.S. timberland area (1000ha)1 M12 M23 M34 M45 M56 M67 M78 M89 M910 M1011 M1112 M1213 M1314 M14Figure 14. Distribution of DGTM U.S.timberland area data: AEZ by timber types48 

## 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 m odels, is expressed in terms of value flows. Therefore, prices are used to weight all underlyi ng quantities – be they t ons 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. Un fortunately, it is also a very difficult problem. Ideally, where an active land rental market is pr esent, we could observe land rents, by use and AEZ. It would then be a simple matter of multipli cation (land rents/ha. * to tal 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 ag ricultural land rents by economic activity, the GTAP data base reflects the common level of i gnorance 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 estimat es 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 agricu lture, 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 hectar es 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 (secti on 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. Th e following formula is used to split the GTAP sectoral land rents into 18 AEZs (L ca ). For region r,

L ca = L c * 

# [ ∑

i ∈ SAGECROPS=c Pi*Yia*Hia 

# / ∑

a ∈ AEZS 

# ∑

i ∈ SAGECROPS=c Pi*Yia*Hia 

# ]

,

c ∈ CROPS; i ∈ SAGECROPS; a ∈ AEZS. (1) where

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

L c is the original land rent of GTAP crop sector c, that is, with no AEZ distinction;49 P i is the per-ton price of SAGE’s crop i;

Y ia is the yield (ton/1000ha) of SAGE’s crop i in AEZ a, with Y ia = Qia Hia ; and

H ia 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 CRO PS (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 (P i ) is invariant to AEZs

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

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

Data source of Y ia = SAGE, as described earlier; and

Data source of H ia = SAGE, as described earlier.

Discretionary solution to the mappi ng inconsistency for “Other crops”
As noted earlier in section 2.2.2.1, in this fi rst 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 (“oc r”). 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 pr oduction 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 ne xt 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 ar ea. 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 u se 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, in cluding 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 most50 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 estim ated $58,786 million, are generated on AEZ 10 – temperate climate with LGP of 180 – 240 days. Th is is followed by the longer LGP temperate AEZs: 11 and 12. The values of land rents gene rated 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 va lue of production and hence land rents in these zones). 13
Figure 15 offers a visualization of the cropl and 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 LG Ps (e.g., AEZs 3 – 6, and 10 - 12). Vegetables, fruits and nuts ("v_f") are a high value crop and th erefore dominate the total land rents picture in most of the AEZs. This can be explained by th eir shorter cultivation pe riod, 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 emphasi zed 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 questio ns regarding our imputation of land

rents. The use of greenhouses is clearly an attempt to circ umvent the natural limitations presente d 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 estimat es of land rents, by AEZ. We hope to incorporate such estimates in future versions of this data base.51Table 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 8 ocr Total1 AEZ1 37 297 133 801 58 43 69 72 15092 AEZ2 75 402 252 942 526 94 249 367 29073 AEZ3 1678 1922 855 4636 3313 1696 1103 3181 183854 AEZ4 3316 672 1103 6292 1419 984 475 4687 189475 AEZ5 3984 137 1294 5600 752 766 316 3473 163216 AEZ6 5097 140 1932 9445 918 1126 152 5940 247507 AEZ7 190 479 232 989 184 109 213 390 27848 AEZ8 754 3024 1538 6623 1086 323 525 1679 155529 AEZ9 830 3453 2705 8396 2062 620 763 2550 2137910 AEZ10 3219 8610 12469 18481 4048 944 881 10134 5878611 AEZ11 4070 5065 4926 13611 2767 381 823 4937 3657912 AEZ12 4147 1730 1842 17413 2178 562 1037 3949 3285913 AEZ13 3 47 11 64 11 3 14 20 17214 AEZ14 16 358 125 725 47 13 8 267 155915 AEZ15 77 991 803 2097 290 35 7 741 504116 AEZ16 40 273 245 449 82 16 5 159 126717 AEZ17 2 2 1 14 0 0 0 1 2118 AEZ18 0 0 0 1 0 0 0 0 119 UnSkLab 34221 23133 25714 156766 21103 10094 9083 66212 34632620 SkLab 368 629 831 3357 636 232 203 2582 883821 Capital 9972 8775 10394 45817 10470 4713 4319 30478 12493722 NatlRes 0 0 0 0 0 0 0 0 0Total 72092.95 60138.31 67404.52 302516.91 51950.37 22752.57 20246.77 141818.78 73892152

1 AEZ1 3 AEZ3 5 AEZ5 7 AEZ7 9 AEZ9 11 AEZ11 13 AEZ13 15 AEZ15 17 AEZ171 p d r4 v_f7 p fb02000400060008000100001200014000160001800020000G 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 )1 p d r2 w h t3 g ro4 v_f5 o sd6 c_b7 p fb8 o crFigure 15. Crop sector land rent allocation among AEZs: world total530%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 17 AEZ17 18 AEZ188 ocr7 pfb6 c_b5 osd4 v_f3 gro2 wht1pdrFigure 16. Distribution of crop sector land rent within each AEZ: world total54 

### 2.2.3.3 GTAP livestock sector la nd 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 a ssumed to use land directly in their production process. Since wool production is a by-produ ct 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 fre ely 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, fo r 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 ea ch AEZ. To this, we seek to add an estimate of the relative productivity of these different hectar es 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 “for age 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 fo r 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 fora ge 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 sp lit 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 80001 AEZ12 AEZ23 AEZ34 AEZ45 AEZ56 AEZ67 AEZ78 AEZ89 AEZ910 AEZ1011 AEZ1112 AEZ1213 AEZ1314 AEZ1415 AEZ1516 AEZ1617 AEZ1718 AEZ18Million USD 12 wol
11 rmk 10 oap
9 ctl



### Figure 17. Livestock sect or land rent allocation among AEZs: world total56 

### 2.2.3.4 Preserving country-specific total va lued-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 agricultu re (including crops and livestock; see Chapter 18.C of Dimaranan and McDougall (2002)). As su ch, 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 amountssumming 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 “n atural 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, worl d 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 8064158 

# 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 w ith 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 onl y 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 re nt 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 cropla nd 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 rents 15 . 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 re port 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 peri od 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 colu mn 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 re nts 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 b it lower than the GTAP-AEZ and USDA estimates: $147/ha. for crops and $13/ha. for grazing. The ra tio 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, Mendels ohn’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 li ght of the relatively weak basis for estimating AEZ yield differentials in the GTAP-AEZ data base. (R ecall 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 th is, consider the Mendelsohn et al. (2005) per hectare land rents reported in columns H and I of Table 17. Accord ing 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 th e 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 rat es 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 va lue vegetable production undertaken in the boreal zone (primarily Alaska) – presumably under greenho uses. This leads to a relatively high apparent land rent. Yet the total number of hectares is sma ll. 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 inabilit y 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 w ould 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 w ith which to validate the GTAP-AEZ land rental estimates for countries other than the US, it is still in structive to examine the pattern of land rents60 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 d escending 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 scarcel y 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 averag e 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 rate 16 – 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 variab le 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 repor ted 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 populat ion 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 combin ed explanatory power of these variables is still quite high – with an adjusted R-squared of 0.59. Ov erall, 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 th e 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 freque ncy of such occurrences will diminish as we improve the underlying data bases and re place key assumptions with observed data.

16 PSE rate data of 2001 are derived from "Agricultural Policies in OECD Countri es: Monitoring and Evaluation", 2003

edition, OECD.61US Cash Rents (irrigated and/or non-irrigated), 2001DEMDNJNYPAMIMNWIILINIAMOOHKSNENDSDKYNCTNVAWVALFLGASCARLA MSOK TXAZCOIDMTUTCAORWAPAMNWIILIAMOKSNENDSDNCTNVAALFLGALA MSOKTXCOMTNMUTWYCA1 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 38 391 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 2601002003004005006007008000 5 10 15 20 25 30 35 40 45

Cash rents per hectare, $CroplandPastureCropland averagePasture averageFigure 18. USDA estimated cash rentsfor cropland and pasture, by stateNote: AZ, WA and CA: only irrigated cropland.Source: Agricultural Cash Rents, 2001. http://usda.mannlib.cornell.edu/reports/nassr/other/plrbb/rent0701.pdf62Table 16. U.S. per hectare land rent: GTAP v.s. Mendelsohn et al.Pasture/Cropland perha. land rent ratioPasture/Cropland perha. land rent ratio(A) (B) (C) (D) (E) (F) (G) (H) (I) (J) (K) (L)= (F)/(E) = (I)/(H) = (H)*(C) = (I)*(D)Cropland Pastureland Cropland Pastureland Cropland Pastureland Cropland Pastureland Cropland PasturelandAEZ1AEZ2AEZ3AEZ4AEZ5AEZ6AEZ7 589.70 874.06 28104.32 166232.30 20.98 5.26 0.25 67.76 7.79 0.11 1904.42 1294.77AEZ8 3587.08 1456.16 27864.80 41457.14 128.73 35.12 0.27 95.43 11.06 0.12 2659.24 458.59AEZ9 2564.66 340.25 14996.95 6434.79 171.01 52.88 0.31 132.41 17.38 0.13 1985.68 111.83AEZ10 15558.51 1507.08 68842.63 19768.97 226.00 76.23 0.34 196.46 41.82 0.21 13524.82 826.81AEZ11 6237.91 576.95 43875.98 10097.77 142.17 57.14 0.40 191.16 46.50 0.24 8387.12 469.59AEZ12 7233.18 247.86 33195.84 4484.70 217.89 55.27 0.25 112.20 37.13 0.33 3724.59 166.53AEZ13 36.62 26.67 1757.56 5406.68 20.84 4.93 0.24 29.99 4.06 0.14 52.71 21.95AEZ14 116.05 28.42 591.40 847.03 196.24 33.55 0.17 32.11 3.94 0.12 18.99 3.33AEZ15 26.23 1.11 53.20 22.33 492.95 49.53 0.10 36.89 5.16 0.14 1.96 0.12AEZ16 10.78 0.59 16.71 9.27 645.20 63.78 0.10 32.11 12.35 0.38 0.54 0.11AEZ17AEZ18All land 35960.73 5059.15 219299.41 254760.99 163.98 19.86 0.12 147.11 13.16 0.09 32260.08 3353.64Mendelsohn, land rent, unit:million 2001 US$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$/ha.63590358725651555962387233371162611190426591986135258387372553192 10200040006000800010000120001400016000AEZ7 AEZ8 AEZ9 AEZ10 AEZ11 AEZ12 AEZ13 AEZ14 AEZ15 AEZ16GTAP.CroplandMendelsohn.CroplandFigure 19. U.S. cropland rents, 2001 US$million: GTAP v.s. Mendelsohn et al.648741456340150757724827 281 112954591128274701672230 002004006008001000120014001600AEZ7 AEZ8 AEZ9 AEZ10 AEZ11 AEZ12 AEZ13 AEZ14 AEZ15 AEZ16GTAP.PasturelandMendelsohn.PasturelandFigure 20. U.S. pasture landrents, 2001 US$ million: GTAP v.s. Mendelsohn et al.65 

### Table 17. GTAP agriculture per he ctare land rent, unit: 2001 US$/ha.

GTAP regions GTAP VFM unit: million 2001 USD SAGE Land cover unit: 1000ha. Avera g e 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 6 117.42 188.64
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
...continued66 

### 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
...continued67 

### 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 5104516.5068 

### Table 18. Summary output of regression: average per ha. Land rent v.s. %PSE
Regression Statistics Multiple R 0.734

R 2 0.538 Adjusted R 2 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 Er ror 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 23.08669 

### Table 19. Summary output of regression: average per ha. Land rent v.s. economic and physical variables
Regression Statistics Multiple R 0.824

R 2 0.679 Adjusted R 2 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 0.00070 

# 4. Concluding remarks and future research directions

This paper described how the GTAP land use data base is constructed with source data developed at the Center for Sustainability and Global E nvironment (SAGE), University of WisconsinMadison, for the crop and livestock sectors; and Th e Ohio State University , for the forest sector and forest carbon stock. For the GTAP land u se 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 z ones (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. Fortunate ly, 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-na tional production data and offers, for the first time, a global data set w ith 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 estimat es 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 w ith GTAP economic activity, the land use and emissions data bases will permit economists interest ed 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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# Appendix A. Sectors and region mappings in the GTAP version 6 data base

Table A1 shows the list of sectors in the GT AP 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 da ta 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; fl ower 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 pro ducts, 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 continued75 

### 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, ba bassu and linseed oil, crude Soya-bean, ground-nut, olive, sunflower-s eed, 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 fracti ons, not chemically modified Palm, coconut, palm kernel, babassu and linseed oil and their fractions, refined but not chemically modified; castor, tu ng 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 preparationsAnimal or vegetable fats and oils and their fractions, partly or wholly hydrogenated, interesterified, re-esterified or el aidinised, whether or not refined, but not further prepared Cotton linters Oil-cake and other solid residues resulting from th e extraction of vegetable fats or oils; flours and meals of oil seeds or oleaginous fruits, ex cept those of mustard; vegetable waxes, except triglycerides; degras; residues resulting from the treatment of fatty substances or animal or vegetable waxes ...to be continued76 

### 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; su gars and sugar syrups n.e.c. Preparations used in animal feeding Bakery products Cocoa, chocolate and sugar confectionery Macaroni, noodle s, 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 wo od 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 prod ucts, except machinery and equipment ...to be continued77 

### 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, accoun ting 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, exce pt of motor vehicles and motorcycles Non-specialized retail trade in stores Retail sale of food, beverages an d 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 continued79 

### 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 continued80 

### 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 an d 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 continued81 

### 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 continued82 

### 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 continued83 

### 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).