1 

## An Integrated Global Land Use Data Base for CGE Analysis of Climate Policy Options*
by

Huey-Lin Lee Center for Global Trade Analysis, Purdue University
Thomas W. Hertel Center for Global Trade Analysis, Purdue University
Steven Rose US Environmental Protection Agency Misak Avetisyan Purdue University

GTAP Working Paper No. 42 2008

* Chapter 4 of the forthcoming book Economic Analysis of Land Use in Global Climate Change Policy , edited by Thomas W. Hertel, Steven Rose, and Richard S.J. Tol2 

### Table of Contents
1. Introduction .................................................................................................................. 3
2. GTAP Cropland Rent Data By 18 AEZs .................................................................................. 4


### 3. GTAP Livestock Sector Land Rent Data By 18 Aezs .............................................................. 7


### 4. GTAP Forest Land Rent Data By 18 AEZs ............................................................................. 8
5. Validation of the GTAP AEZ Land Rent Data



### ................................................................. 10
6. Summary and C onclusions ..................................................................................................... 1 4
7. References .................................................................................................................... .......... 16



### Figure 1. Crop sector land rent a llocation among AEZs: world total ....................... 21 Figure 2. Distribution of cr op sector land rent within each AEZ: world total .......... 21 Figure 3. USDA estimated cash rents fo r cropland and pasture, by state ................. 22 Figure 4. U.S. cropland rents, 2001 US$ m illion: GTAP v.s. Mendelsohn et al ...... 23 Figure 5. U.S. pasture land rents, 2001 US $ million: GTAP v.s. Mendelsohn et al . 24

Table 1. GTAP crop sector land rents: VFM, world total, v6.0 ............................... 25 Table 2. GTAP livestock sector land rents: VFM, world total, v6.0 ......................... 26 Table 3. GTAP land rents: VFM of fo restry sector, world total, v6.0 ...................... 27 Table 4. U.S. per hectare land re nt: GTAP v.s. Mendelsohn et al ............................ 28 Table 5. GTAP agriculture per h ectare land rent, unit: 2001 US$/ha ....................... 293 

### AN INTEGRATED GLOBAL LAND USE DAT A BASE FOR CGE ANALYSIS OF CLIMATE POLICY OPTIONS Huey-Lin Lee, Thomas W. Hertel, Steven Rose and Misak Avetisyan

1. Introduction

The preceding two chapters 1 of this volume have discussed physical and economic data bases for global agriculture and forestry, respectivel y. These form the foundation for the integrated, global land use data base discussed in this chapte r. However, in order to utilize these data for global CGE analysis, it is first necessary to integr ate them into a global, general equilibrium data base. This integration is the subject of the present chapter.
The most widely used, global economic data base for CGE modeling is GTAP: the Global Trade Analysis Project data base. This is released every 2 – 3 years, and, at the time this book was being written, GTAP 6 was the most curre nt version, benchmarked to the year 2001 (Dimaranan, 2007). This fits nicely with the glob al agricultural data bases of Ramankutty et al. (2006) and Monfreda et al. (2006), as they refer to data from several years centered around 2000. The same is true of the forestry data b ase of Sohngen et al. outlined in Chapter 3 2

. The GTAP data base reports estimated economic flows in the year 2001. When it comes to the global land use data base, the relevant flows w ill be the land rents associated with a given economic activity, taking place in a given Agro-E cological Zone (AEZ). Ideally, where an active land rental market is present, we could observe land rents, by use and AEZ. It would then be a simple matter of multiplication (land rents/ha. * total ha.) to obtain land rents in each activity/AEZ. However, such data are not readily available in most countries, and where it is

1 GTAP Working Papers No. 40 and No. 41 2 GTAP Working Paper No. 414 available, it is not grouped by detailed use or A EZ. In addition, since it supports global, economywide analysis, the GTAP data base must satisfy a number of key equilibrium conditions, including: costs = revenues, and national suppli es = domestic uses plus export demands for each good or service produced in all 57 sectors of each of the 87 regions in the version 6 data base, national income is exhausted on national expenditures, and global savings = global investment. Because of these requirements, we cannot simply go into the data base and alter a given set of flows (e.g., returns to land in crop production) without destroying one or more of these equilibrium conditions. For all of these reasons, our approach will instead be one of “sharing out” existing land rents in the GTAP data base, accordi ng to the information provided in the previous two chapters 3

. In the following sections, we describe how we allocate the GTAP land rents across AEZs. As our data sources and procedures differ for crops and livestock, we discuss the associated procedures first for crops and then for livestock. We then describe how we adjust sectoral valueadded to preserve the estimates of primary factor shares from literature, given that we assumed in section the indirect use of land by non-ruminant sectors. Forest land rent allocation is described next, followed by an overview of the final data base, validation, evaluation, and implications for future research.



### 2. GTAP Cropland Rent Data By 18 AEZs Harvested area vs. physi cally cultivated area: While it might seem, at first glance, that physically cultivated area is preferred to harvested area in building this global data base, this is not the case. In the GTAP economic accounts for each country, land rents are generated from the activity (or use) on a given parcel of land during the calendar year. Therefore, we are interested in the value of the land in prod uction over the course of the entire year, not just one season.

3 GTAP Working Papers No. 40 and No. 415 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) over the rest of the calendar year. The GTAP Input-Output data identify sectors in terms of crops (e.g., the paddy rice sector, the cereal grain sector, the oil seeds sector , etc.), not hectares of land, per se. So the land rents of the crop sectors should accrue to the harvested area, by crop. In this particular example, we allocate the land rent generated, due to the growing of paddy rice, to the GTAP paddy rice sector, while we allocate the land rents generated due to the gr owing of vegetables to the GTAP vegetables sector, both within the same AEZ. Thus, while the harvest-based land rents can be allocated to GTAP sectors within a given AEZ, the physically cultivated-based land rents

cannot. An additional argument in favor of working with harvested area is due to the fact that land based emissions (e.g., CH 4 emissions from paddy rice cultivation) are often tied to the harvested area (IPCC 1996 Guidelines). For example, for given soil conditions, prices and crop types, fertilizer use is normally proportional to harvested ar ea. 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 cropspecific physically cultivated area. In those cases where cultivated area is preferred (e.g., soil
N 2 O and soil CO 2 emissions), we can make side calcula tions based on available data on cropland cultivated area. Procedures for Splitting out Land Rents: We split the GTAP sectoral land rents into 18 AEZs according to the AEZ-specific production shares as derived from the data provided by MRF and BTHM (chapters 2 4 and 3 5 ). Recall Table 1 from Chapter 2 which reports the mapping from FAO’s 175 crops (Set FAO ) to GTAP’s 8 crops sectors (Set crops ). This mapping is used in conjunction with the following formula to split th e GTAP sectoral land rents into 18 AEZs (L ca ). For region r,

4 GTAP Working Paper No. 40 5 GTAP Working Paper No. 416 

### {/ ,

, ca c i ia ia i ia ia i FAO c a AEZ i FAO c 

### L L PY H PY H

c crops i FAO ∈= ∈ ∈= 

### = ∀∈ ∈ 

# ∑ ∑∑

where:

ca 

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

c 

### L is the total land rent of GTAP crop sector c, (no AEZ distinction: header VFM in GTAP);

i 

### P is the per-ton price of FAO crop i (invariant to AEZs, sourced from FAOSTAT)

ia 

### Y is the yield (ton/1000ha) of FAO crop i in AEZ a, (sourced from MRF); and

ia 

### H is the harvested area of FAO crop i in AEZ a, (sourced from MRF).

An illustration of the GTAP crop sector land rents by 18 AEZs: Table 1 shows world total value-added, including land rents (header "VFM ", from the GTAP v6.0 database) for the GTAP crop sectors (sectors 1 to 8) split into 18 AEZs. Th e data show that most of the world's crops (value-basis) are grown in the tropical and temperate AEZs (1 - 12). The largest total crop land rents, an estimated $50,416million, are generated in AEZ 10 – temperate climate with LGP of 180 – 240 days. This is followed by the longer LG P temperate AEZs: 11 and 12, then AEZs 9, 8 and finally tropical AEZ 6. The values of land rent s generated in the boreal zones are an order of magnitude smaller, and essentially negligib le for the shortest growing periods. Figure 1 offers a visualization of the cropl and rent allocation among AEZs, by GTAP crop sector. This reveals some interesting points about specific crops. For example, we see that paddy rice ("pdr") is mostly grown in AEZs with longer LGPs (e.g., AEZs 3 – 6, and 11 - 12). Vegetables, fruits and nuts ("v_f") are a high va lue crop sector and therefore dominate the total land rents picture in most of the AEZs. This can be explained by their shorter cultivation period,7 which allows for multiple cropping, the widespread irrigation of fruit and vegetable production, as well as the potential for greenhouse production.
The dominance of the "v_f" sector in the total cropland rent distribution within each AEZ is further emphasized in Figure 2, which shows a sh are-based breakout of total land rents in each of the Agro-Ecological Zones, world wide. In this fi gure, the totals all sum to 100% and we simply see the relative economic importance of each crop w ithin a given AEZ. From this figure we also see the relatively greatly importance of wheat and coarse grains in the boreal AEZs, followed by the temperate zones, with relatively little contri bution from these crops sectors in the tropical Agro-ecological Zones.



### 3. GTAP Livestock Sector Land Rent Data By 18 AEZs
Methodology: There are four primary livestock production sectors in the GTAP data base: ruminants (ctl = cattle, sheep and goats), dair y production (rmk), wool (wol) and non-ruminants (oap = pigs and poultry). In the case of non-rumi nants, we assume that the sector does not use substantial amounts of land directly in production. 6 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 produc tion, crops and forestry. Of course there is indirect competition, insofar as increased produc tion of poultry, for example, will boost the feed requirements and hence increase the demand for land in feed grains. However, we capture this competition via the intermediate demand for feed in non-ruminant productio n in the CGE model.

6 Since we assume the "oap" sector doe s not use land directly we must take aw ay GTAP land rents in this sector and

augment payments to labor and capital by the amount of th ese land rents to keep the total costs of the two sectors correspond to their total revenue. To preserve the country-spe cific shares of agriculture va lue-added, as well as the agriculture-wide labor/capital ratio, we scale up land rents of the other agriculture sector s (i.e., crop sectors, “ctl”, “rmk” and “wol” sectors) by amounts su mming up to the total land rents of the non-ruminants sector, while scaling down payments to labor and capital in these other agriculture sectors accordingly.8 In order to estimate land rents by AEZ for the crops sectors, we capitalized on the MRF data base on crop harvested area and yields, by crop and AEZ. 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 REMF land cover data base, we know how much total grazing land there is in each AEZ. To this, we seek to add an estimate of the relative productivity of these different land in all types of ruminant production across AEZs. The most natural thing is to use an index of crop yields as a predictor of land productivity in forage. Since there is no single “forage crop” sector in our data base, we use the average yield of GTAP coarse grains sector (i.e., "gro") in each the AEZ/country, multiplied by th e SAGE pasture land cover hectares of the 18 AEZs, to split the GTAP livestoc k sectors' land rents into 18 AEZs. Since we do not have independent estimates of land used for dairy pro duction vs. land used for cattle, sheep and goats, the aggregate land rents within each of these sect ors are shared out across AEZs in the same proportions for each of the sectors. (I.e., if AEZ 8 h as 20% of the beef land rents in Australia, it will also have 20% of the dairy land rents in that country.)
A summary of the GTAP livestock sector land rents by 18 AEZs: Table 2 shows world total land rents (header "VFM", from v6.0 database) of the three GTAP land-using livestock sectors: ruminants, dairy and wool, split into 18 AEZs. Note that the distribution of land rents across AEZs is now much more even than was the case with crops. Indeed, AEZ7 has slightly higher land rents than AEZ10, which clearly dominated in the case of crops. Note also that the tropical AEZs 3 – 6, and the boreal AEZs 13 - 15 show re latively high levels of livestock land rents, worldwide. This reflects the fact that livestoc k production is more amenable to the shorter growing seasons, and sometimes more adverse circumstances, characterized by these AEZs.



### 4. GTAP Forest Land Rent Data By 18 AEZs

We now turn to the estimation of GTAP-consis tent land rents in the forestry sector, based9 on the work of Sohngen et al. reported in Chapter 3 7 . In the standard GTAP data base, the land share in forestry is unrealistically small – about 7% of value added. This share of land in forestry was chosen to give a reasonable degree of aggreg ate supply response in the forestry sector (see Hertel and Tsigas, 2002). However, it has no fi rm basis in the measurement of land rents. Therefore, a new estimate is needed. We focus our attention on the share of land rents in total costs (sales) so that the estimate is portable across data sets as well as over time.

In order to estimate the share of land rents in to tal costs, we begin with the total hectares in forestry (see Chapter 3). From this, we must deduct the inaccessible hectares, to obtain the currently accessed land in forestry production. (In accessible land will not generate land rents, by definition.) Accessible forest land, by manageme nt class, is then multiplied by estimated forest land rents, which vary by management class (see above). In order to estimate total costs, we assume that sales are fully exhausted on costs (zero profits – in keeping with the standard CGE model assumptions). Sales are estimated as timber production multiplied by quality adjusted timber price, as obtained from the Global Timber Supply Model (Sohngen and Mendelsohn) . On this basis, Gouel and Hertel (2006) find that, on a global basis, the estimated forest land rental share amounts for 38% of product sales. In equilibrium, land rents should always be less than total sales in all AEZs/regions. However, this is not always the case in the estim ates of Gouel and Hertel (2006). Furthermore, for incorporation into the GTAP data base, the land re ntal share in total cost s must be less than the value-added share. Not surprisingly, this is viol ated even more frequently than the land rents < costs condition. There are many reasons why this might arise, include disequilibrium, as well as mis-estimation of land rents, accessible forest ar ea, product sales or price. Rather than making somewhat arbitrary allocations on a region-by-re gion basis, we choose the following approach. First, compute the share of land rents in value-adde d at a global level. In the version 6 data base,

7 GTAP Working Paper No. 4110 the share of value-added in global forestry costs is 62%, so the share of land in global forestry value-added is 0.38/0.62 = 61%. We then assume this relationship holds in each and every country, so that, given value-added in each region, we can now compute forest land rents at the national level as simply being equal to 61% of this value. We derive the AEZ-specific forestry land rent shares from the Sohngen et al data on timberland land rent (by tree type and by country) , multiplied by timberland area by tree type, by age, by AEZ, and by country. Then we split the for estry land rent into 18 AEZs according to these forestry land rental shares by AEZ. Table 3 shows world total value-added (header "VFM", from v6.0 database) of the GTAP forest sector ("frs") split into 18 AEZs. Note that the longer length of growing period AEZs tend to ha ve higher aggregate land rents in the tropical and temperate zones, but total land area under commercial forestry comes into play as well, and, as with crops, AEZ10 is shown to be a very large source of forestry land rents in the world.



### 5. Validation of the GTAP AEZ Land Rent Data

The construction of the GTAP AEZ land rent data base has involved a host of different assumptions. So it is natural to ask: how does this compare with observed land rents, when the rents themselves are divided by the hectares of observed crop and pasture land cover? Ideally we would like to undertake such a comparison for all regions in the GTAP data base. However, we only have the data of observed la nd rents for the U.S. so far. Therefore, we present here the comparison of per hectare land rents between the GTAP land rent data and the observed cash land rent only for the US, using data published by th e U.S. Department of Agriculture (USDA). Figure 3 shows the data available from the USDA for crop and pasture land rents by state, as well as the national average, in the year 2001. Note that there is tremendous variation in cropland rents – with the highest figures in the states where irrigated cropl and is predominant. Indeed, the cropland data for Arizona (AZ), Washington (WA) and California (CA) cover only irrigated land.11 The overall average U.S. cropland cash rent is $ 175/hectare, while the average pasture land cash rent is $23/hectare

— about 13% of per hectare cropland rents 8

.
To compare these estimates to those implied by the GTAP-AEZ data base, we must first perform some intermediate calculati ons. This is done in Table 4. The first pair of columns in this table report total land rents, by AEZ, in the modified GTAP data base, for both crops and livestock. In order to compute land rents per hectare, we must divide these figures by the REMF land cover data in the second pair of columns in Ta ble 4. This yields the estimated land rents per hectare, by AEZ, reported in columns E and F. Ignoring the boreal AEZs, which show very little area, and are likely influenced by greenhouse pr oduction, we see crop land rents varying between $177/ha. and $244/ha., with livestock land rents in th e range of $21 - $30/ha. For all crop land in the US, the estimate is $205/ha. – somewhat highe r than the $175/ha. estimated by USDA. In the case of grazing land, the aggregate estimate from our data is virtually the same as that of USDA -- $23/ha.

It is also of interest to consider the rela tive land rents for crop and livestock activities within a given AEZ. This ratio is reported in the next column of Table 4. Not surprisingly, the overall ratio of pasture land rents to crop land rents in our da ta base is slightly lower than from USDA, 0.11 vs. the USDA ratio of 0.13, since our cropland rental estimate is higher. However, this varies widely across the temperate AEZ’s in the GTAP -AEZ data base, ranging from 0.09 in AEZ9 to 0.16 in AEZ 11. In light of the fact that we do not have cash rents for the US, disaggregated 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 4 report pe r hectare land rents from Mendelsohn et al. (2005) for the United States. Those authors’ aggregate land rents for the entire US are quite a bit lower than the

8 This USDA cash land rent finding was provided by Alla Golub.12 GTAP-AEZ and USDA estimates: $118/ha. for crops and $12/ha. for grazing. However, the ratio of relative land rents in grazing vs. crops estimated by Mendelsohn et al. (2005) – 0.11 -- is the same as 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. He re we see greater differences between the two data bases, with the Mendelsohn et al. cropland estimates ranging fro m $68/ha. in AEZ7 to $196/ha. in AEZ10. Their pastureland rental estimates range fro m $8/ha. in AEZ7 to $47/ha. in AEZ 11. Figures 4 and 5 report the distribution of total land rents in each AEZ of the US for the two data bases, for crops and livestock, respectively. This is a comparison of the relative importance of the agricultural land endowment, by AEZ for the US. Begin with crop land rents, by AEZ (Figure 4). The first thing to note is that, with the exception of AEZ 11, the GTAP-AEZ land rents are higher for all AEZs. Also, the same broa d pattern of relative la nd rents, by AEZ applies in the two data bases. The largest discrepancy between the two is for AEZ7, the short growing period, temperate AEZ. In the case of livestock land rents, the bulk of the divergence occurs in AEZ7, where Mendelsohn et al. estimate a relatively low land rental rate for pastureland, but the GTAP-AEZ productivity estimate, based on coarse gr ains yield, is much higher. This would appear to highlight a significant weakness in the GTAP-AEZ methodology for imputing relative productivity of land in grazing, across AEZs. A mo re direct measure of productivity would be desirable. (This will be discussed further below. ) The broad pattern of livestock land rents across the other AEZs is similar between the two data bases. Before proceeding, we need to say somethi ng about the extremely high per hectare land rents in the boreal AEZs – particularly AEZs 15 and 16. Fi rsly, note that the total area involved is very small (column A in Table 4), so this is not a ser ious problem in the aggregate. Nonetheless, it is worthwhile considering why this problem arises. To do so, return to the methodology for13 constructing the GTAP-AEZ data base. We start w ith information on land use, by AEZ. This is combined with estimates of total land rents, b ased on the average share of land rents in crop production for the entire United States, and value of output from MRF, in order to infer total land rents by hectare. Not surprisingly, there is a fair amount of high value vegetable production undertaken in the boreal zone (primarily Alaska) – largely under greenhouses. This leads to a relatively high apparent land rent. Yet the total number of hectares is small. So the implied per hectare land rent is very high. However, this is not really a proper estimate of land rents, as the greenhouse-based production requires considerable in frastructure in order for the land to be productive. Indeed, without these improvements, la nd productivity would be very low. So these gross errors for the boreal zone are largely a functi on of our inability to separate returns to capital and land in greenhouse production. In order to ove rcome this problem, we need a more direct approach to the estimation of land rents in agri culture. A natural approach would be to build on the work of Mendelsohn et al. (2005), applying their estimated land rents by AEZ to the total hectares in the land use data base. However, to da te, we have only been able to obtain these data for the US. Once such a data base becomes availa ble for the world as a whole – or even for the majority if the countries in the world, it will clearly be preferable. While we do not have indepe ndent data with 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 rents across these countries. The total farm land rent, ag ricultural land area and implied average land rental rates for each of the 87 GTAP-AEZ region s in GTAP v.6 are reported in Table 5, in descending order (highest to lowest per hectare land rents). The results are broadly as expected. The highest land rents arise in the densely populated, high income countries of East Asia: Hong Kong, Korea and Japan, as well as some of the smaller, high income countries of Europe,including Switzerland, Netherlands and Denmark. The lowest land rents per hectare arise in SubSaharan Africa – amounting to scarcely more th an $1/ha. in Botswana, which is sparsely14 populated and dominated by the Kalahari Desert – an arid area, much of which is extensively grazed by livestock. Australia – a continent domin ated by desert and extensive grazing as well, is not far behind at $4/ha. average land rent.

6. Summary and Conclusions In summary, creating a global land use data base is an ambitious exercise. Fortunately, we now have such data bases available for agricu lture and forestry, as highlighted in the two preceding chapters 9 . However, even with these data in ha nd, incorporation of this information into a globally consistent, general equilibrium framework poses signifi cant challenges. This chapter has outlined one approach to this probl em. It involves taking the aggregate agricultural land rents in the GTAP data base as given and sharing them out based on estimates of productivity (yields) across crops and AEZs. A preferable approach would involve direct observation of land rents, by crop and AEZ, but unfortunately these data are not currently available on a global basis. However, as the wo rk of Mendelsohn et al. progresses, there may be greater scope to draw on this research to constr uct an improved, globally consistent data base of land rents in agriculture and forestry.
In the case of livestock, our estimates could be improved upon by capitalizing on recently available data reporting the geographic dist ribution of livestock output, worldwide (GET REFERENCE FROM NAVIN). This would substitute for our current assumption that the relative productivity of a given AEZ in livestock production, within any given country, is proportional to that AEZ’s productivity in feedstuff production. Finally, there is the more fundamental issue of how the AEZs are defined. Ideally we would like to eliminate AEZ’s and simply work with each country’s land endowment fully disaggregated at the grid-cell level. However, this poses significant computational and data

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Loveland, T.R., B.C. Reed, J.F. Brown, D.O. Ohle n, J. Zhu, L. Yang, and J.W. Merchant. (2000). Development of a Global Land Cover Character istics Database and IGBP DISCover from 1- km AVHRR Data, International Journal of Remote Sensing , 21 (no. 6/7), 1303-1330. McGuire, A.D., S. Sitch, J.S. Clein, R. Dargaville , G. Esser, J. Foley, M. Heimann, F. Joos, J. Kaplan, D.W. Kicklighter, R.A. Meier, J.M. Melillo, B.M. III, I.C. Prentice, N. Ramankutty, T. Reichenau, A. Schloss, H. Tian, L.J. W illiams, and U. Wittenberg. (2001). Carbon balance of the terrestrial biosphere in the twentieth century: Analyses of CO2, climate and land-use effects with four process-based ecosystem models., Global Biogeochemical Cycles , 15 , 183- 206.19 Mendelsohn, R., P. Kurukulasuriya, A. Basist, F. Kogan, and C. Williams. (2005). Climate Analysis with Satellite versus Weather Station Data. Unpublished working paper. Monfreda, C., N. Ramankutty, and Foley, J.A. (2006). Farming the Planet 2: The Geographic Distribution of Crop Areas and Yields in the Year 2000. Mimeo; SAGE: University of Wisconsin Madison. Myhre, G., and A. Myhre. (2003). Uncertainties in radiative forcing due to surface albedo changes caused by land-use changes, Journal of Climate , 16 (10), 1511-1524. National Geographic Maps. (2002). A World Tran sformed, Supplement to National Geographic September 2002, National Geographic Society, Washington, D.C. Ramankutty, N., and J.A. Foley. (1998). Char acterizing Patterns of Global Land Use: An Analysis of Global Croplands Data, Global Biogeochemical Cycles , 12, 667-685. Ramankutty, N., and J.A. Foley. (1999). Estima ting Historical Changes in Global Land Cover: Croplands from 1700 to 1992, Global Biogeochemical Cycles , 13 , 997-1027. Ramankutty, N., J.A. Foley, J. Norman, and K. McSweeney. (2002a). The global distribution of cultivable lands: current patterns and se nsitivity to possible climate change, Global Ecology and Biogeography , 11 (5), 377-392. Ramankutty, N., J.A. Foley, and N.J. Olejniczak. (2002b). People on the land: Changes in Population and Global Croplands During the 20th Century, Ambio , 31 (3) , 251-257. Ramankutty, N., Hertel, T. W., Lee, H.-L. and Rose, S. (2005). Global Land Use and Land Cover Data for Integrated Assessment Modeling. Book chapter for the Snowmass Conference, Snowmass, CO, July 2004. Paper available at the GTAP website: http://www.gtap.agecon.purdue.edu/r esources/res_display.asp?RecordID=1635

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1 pdr 2 wht 3 gro 4 v_f 5 osd 6 c_b 7 pfb 8 ocr



### Figure 1. Crop sector land rent a llocation among AEZs: world total

0% 20% 40% 60% 80% 100%1 AEZ12 AEZ23 AEZ34 AEZ45 AEZ56 AEZ67 AEZ78 AEZ89 AEZ910 AEZ1011 AEZ1112 AEZ1213 AEZ1314 AEZ1415 AEZ1516 AEZ1617 AEZ1718 AEZ18 8 ocr 7 pfb 6 c_b 5 osd 4 v_f 3 gro 2 wht 1 p dr



### Figure 2. Distribution of cr op sector land rent within each AEZ: world total22 US Cash Rents (irrigated and/or non-irrigated), 2001

DE MD NJ NY PA MI MN WI IL IN IA

MO OH

KS NE

ND SD KY
NC TN
VA WV AL FL GA
SC AR LA MS

OK TX AZ

CO ID

MT UT CA

OR WA

PA

MN

WI

IL IA MO KS

NE

ND

SD

NC

TN

VA

AL

FL

GA

LA MS OK

TX

CO

MT

NM

UT

WY

CA 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 0 100 200 300 400 500 600 700 800

0 5 10 15 20 25 30 35 40 45Cash rents per hectare, $

Cropland Pasture Cropland average Pasture average 

### Figure 3. USDA estimated cash rents for cropland and pasture, by state Note: AZ, WA and CA: only irrigated cropland. Source: Agricultural Cash Rents, 2001. http://usda.mannlib.cornell.edu/reports/nassr/other/plrbb/rent0701.pdf23 6267 5441

4017 12241

6442

2975

198 48 9 1 2395 2622 1889 9851

7273

1824

56 11 1 0 0 2000 4000 6000 8000 10000 12000 14000 16000

AEZ7 AEZ8 AEZ9 AEZ10 AEZ11 AEZ12 AEZ13 AEZ14 AEZ15 AEZ16 GTAP. Cropland Mendelsohn. Cropland



### Figure 4. U.S. cropland rents, 2001 US$ million: GTAP v.s. Mendelsohn et al.24 3359

784

146 494 309 155 88 30 2 0 1123

409

95 698 543

262 30 8 1 0 0 500 1000 1500 2000 2500 3000 3500 4000

AEZ7 AEZ8 AEZ9 AEZ10 AEZ11 AEZ12 AEZ13 AEZ14 AEZ15 AEZ16 GTAP. Pastureland Mendelsohn. Pastureland



### Figure 5. U.S. pasture la nd rents, 2001 US$ million: GTAP v.s. Mendelsohn et al.25 

### Table 1. GTAP crop sector land rents: VFM, world to tal, 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 Total
1 AEZ1 200 419 209 1171 127 133 192 301 2751 2 AEZ2 102 180 251 738 914 68 810 607 3669 3 AEZ3 1429 1347 948 3805 2278 1701 563 3081 15151 4 AEZ4 3964 308 1096 5880 1485 908 442 3557 17639 5 AEZ5 4018 100 854 5309 919 652 333 2608 14794 6 AEZ6 3979 88 1156 9212 748 1070 189 4976 21418 7 AEZ7 851 3111 1371 4909 665 394 1222 2551 15075 8 AEZ8 943 4056 1973 8529 1647 353 601 3617 21720 9 AEZ9 529 3475 3835 11513 1912 779 673 3450 26167 10 AEZ10 2349 7378 11286 15274 4192 814 347 8776 50416 11 AEZ11 4794 5225 5905 11819 3148 330 651 5827 37698 12 AEZ12 3905 1129 1601 14891 1674 408 983 2431 27022 13 AEZ13 4 253 67 191 32 19 22 151 737 14 AEZ14 15 139 83 219 42 7 9 109 623 15 AEZ15 51 380 547 693 185 17 5 423 2300 16 AEZ16 9 70 122 110 28 3 2 95 440 17 AEZ17 0 0 0 2 0 0 0 0 3 18 AEZ18 0 0 0 0 0 0 0 0 0 19 UnSkLab 32068 20958 23429 144287 19326 9155 8751 56618 314591 20 SkLab 336 529 718 2853 549 190 195 2008 7377 21 Capital 12749 10805 13011 59377 12011 5277 4771 33732 151733 22 NatRes 0 0 0 0 0 0 0 0 0 Total 72293 59951 68460 300780 51882 22277 20760 13492026 

### Table 2. 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 271 0 194 64 529 2 AEZ2 151 0 492 55 698 3 AEZ3 164 0 2227 143 2534 4 AEZ4 569 0 1111 69 1749 5 AEZ5 1440 0 849 29 2318 6 AEZ6 1129 0 699 41 1869 7 AEZ7 4050 0 3398 448 7896 8 AEZ8 1272 0 1889 221 3382 9 AEZ9 724 0 1056 109 1889 10 AEZ10 1838 0 5042 180 7060 11 AEZ11 1231 0 2271 106 3608 12 AEZ12 948 0 790 212 1950 13 AEZ13 606 0 343 115 1064 14 AEZ14 357 0 527 99 983 15 AEZ15 629 0 1627 150 2406 16 AEZ16 158 0 297 31 487 17 AEZ17 1 0 0 0 2 18 AEZ18 0 0 0 0 0 19 UnSkLab 26517 54219 31236 3481 115453 20 SkLab 938 1106 1063 68 3174 21 Capital 25348 22520 20032 1449 69348 22 NatRes 0 0 0 0 0 Total 68342 77845 75143 7068 22839727 

### Table 3. GTAP land rents: VFM of forest ry sector, world total, v6.0 (unit: million US Dollar) Unit: million USD 13 frs 1 AEZ1 30 2 AEZ2 24 3 AEZ3 196 4 AEZ4 494 5 AEZ5 516 6 AEZ6 1125 7 AEZ7 98 8 AEZ8 116 9 AEZ9 622 10 AEZ10 1703 11 AEZ11 1004 12 AEZ12 2014 13 AEZ13 14 14 AEZ14 322 15 AEZ15 711 16 AEZ16 125 17 AEZ17 2 18 AEZ18 0 19 UnSkLab 32899 20 SkLab 914 21 Capital 37713 22 NatlRes 0 Total 8064128 

### Table 4. U.S. per hectare land re nt: GTAP v.s. Mendelsohn et al.

Pasture/Cropland per ha. land rent ratio Pasture/Cropland per ha. 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 Pastureland AEZ1 AEZ2 AEZ3 AEZ4 AEZ5 AEZ6 AEZ7 6267 3359 35343 144140 177 23 0 68 8 0 2395 1123 AEZ8 5441 784 27477 36932 198 21 0 95 11 0 2622 409 AEZ9 4017 146 14266 5488 282 27 0 132 17 0 1889 95 AEZ10 12241 494 50145 16688 244 30 0 196 42 0 9851 698 AEZ11 6442 309 38048 11674 169 26 0 191 47 0 7273 543 AEZ12 2975 155 16258 7055 183 22 0 112 37 0 1824 262 AEZ13 198 88 1861 7488 106 12 0 30 4 0 56 30 AEZ14 48 30 327 2109 146 14 0 32 4 0 11 8 AEZ15 9 2 24 141 384 16 0 37 5 0 1 1 AEZ16 1 0 2 2 590 0 0 32 12 0 0 0 AEZ17 AEZ18 All land 37639 5368 183753 231718 205 23 0 118 12 0 25922 3169 Mendelsohn, 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.29 

### Table 5. GTAP agriculture per he ctare land rent, unit: 2001 US$/ha. GTAP regions GTAP VFM unit: million 2001 USD SAGE Land cover unit: 1000ha. Average per ha. land rent 5 Hon Kong 413 286 1444 7 Korean, republic of 8619 9495 908 52 Switzerland 1478 4037 366 48 Netherlands 1033 3220 321 13 Singapore 91 310 294 39 Denmark 1262 5216 242 19 Sri Lanka 1674 7072 237 6 Japan 8702 39938 218 42 Germany 7843 36576 214 46 Italy 5776 29358 197 17 Bangladesh 2762 15334 180 38 Belgium 542 3544 153 37 Austria 1229 8159 151 12 Philippines 4433 29996 148 18 India 45305 309374 146 41 France 7693 55032 140 35 Rest of FT of the Americas 1323 9707 136 43 United Kingdom 3585 26429 136 56 Bulgaria 1279 11155 115 59 Czech Republic 905 8018 113 44 Greece 1179 12706 93 62 Poland 2900 31557 92 14 Thailand 4687 51464 91 45 Ireland 713 7992 89 60 Hungary 776 9272 84 63 Romania 1880 24190 78 57 Croatia 454 6357 71 50 Spain 3575 50117 71 49 Portugal 716 10976 65 15 Viet Nam 1719 30607 56 55 Albania 146 2804 52 20 Rest of South Asia 8893 172583 5230 

### Table 5 (continued) GTAP regions GTAP VFM unit: million 2001 USD SAGE Land cover unit: 1000ha. Average per ha. land rent 36 Rest of the Caribbean 598 12108 49 4 China 46984 959967 49 10 Indonesia 8576 179528 48 64 Slovakia 232 4905 47 22 U.S.A. 44235 944153 47 23 Mexico 9024 201568 45 65 Slovenia 104 2364 44 34 Central America 2220 57628 39 11 Malaysia 1214 35898 34 54 Rest of Europe 592 17742 33 40 Finland 1014 34690 29 32 Uruguay 474 19022 25 27 Venezuela 2295 92272 25 71 Turkey 1729 80189 22 16 Rest of Southeast Asia 2483 115475 22 51 Sweden 935 45343 21 25 Colombia 2335 118348 20 31 Chile 1499 85476 18 2 New Zealand 533 31253 17 66 Estonia 80 4681 17 68 Lithuania 133 8307 16 86 Uganda 335 21085 16 53 Rest of EFTA 710 45562 16 73 Morocco 624 43195 14 74 Tunisia 210 15911 13 70 Rest of Form. Soviet Union 6536 498236 13 29 Argentina 3469 281209 12 9 Rest of East Asia 1912 166020 12 26 Peru 1370 132233 10 67 Latvia 59 6441 9 72 Rest of Middle East 4232 527500 8 28 Rest of Andean Pact 994 133998 731 

### Table 5 (continued) GTAP regions GTAP VFM unit: million 2001 USD SAGE Land cover unit: 1000ha. Average per ha. land rent 79 Malawi 67 10247 7 75 Rest of North Africa 2898 494191 6 77 South Africa 662 124641 5 81 Tanzania 483 91472 5 33 Rest of South America 396 85625 5 21 Canada 4459 991138 5 3 Rest of Oceania 178 41579 4 83 Zimbabwe 169 40476 4 30 Brazil 3412 852846 4 1 Australia 3023 784874 4 85 Madagascar 197 61660 3 87 Rest of Sub-Saharan Africa 3483 1422515 2 69 Russia 4033 1689470 2 82 Zambia 98 74768 1 80 Mozambique 101 79791 1 78 Rest of South African Customs Union 66 87524 1 76 Botswana 42 58571 1 84 Rest of Southern African Development Community 230 362519 1 8 Taiwan 1714 0 N/A 24 Rest of North America 7 0 N/A 47 Luxembourg 83 0 N/A 58 Cyprus 27 0 N/A 61 Malta 14 0 N/A Total 307160 13299093