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. Tol 
2

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 Conclusions ..................................................................................................... 14 

7. References .............................................................................................................................. 16 

 Figure 1. Crop sector land rent allocation among AEZs: world total ....................... 21 

 Figure 2. Distribution of crop sector land rent within each AEZ: world total .......... 21 

 Figure 3. USDA estimated cash rents for cropland and pasture, by state ................. 22 

 Figure 4. U.S. cropland rents, 2001 US$ million: 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 forestry sector, world total, v6.0 ...................... 27 

 Table 4. U.S. per hectare land rent: GTAP v.s. Mendelsohn et al ............................ 28 

 Table 5. GTAP agriculture per hectare land rent, unit: 2001 US$/ha ....................... 29 
3

AN INTEGRATED GLOBAL LAND USE DATA BASE FOR CGE ANALYSIS OF 

CLIMATE POLICY OPTIONS

Huey-Lin Lee, Thomas W. Hertel, Steven Rose and Misak Avetisyan 

1. Introduction 

 The preceding two chapters1 of this volume have discussed physical and economic data bases 

for global agriculture and forestry, respectively. These form the foundation for the integrated, 

global land use data base discussed in this chapter. However, in order to utilize these data for 

global CGE analysis, it is first necessary to integrate 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 current version, benchmarked to the year 2001 

(Dimaranan, 2007). This fits nicely with the global 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 base of Sohngen et al. outlined in Chapter 32

. 

 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 will be the land rents associated with a given 

economic activity, taking place in a given Agro-Ecological 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. 41 
4

available, it is not grouped by detailed use or AEZ. 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 supplies = 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, according to the information provided in the previous 

two chapters3

. 

 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. physically 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 production over the course of the entire year, not just one season. 

 

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 GTAP Working Papers No. 40 and No. 41 
5

 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 growing 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., CH4 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 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 cropspecific physically cultivated area. In those cases where cultivated area is preferred (e.g., soil 

N2O and soil CO2 emissions), we can make side calculations 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 24

 and 35). 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 the GTAP sectoral land rents into 18 AEZs (Lca). 

For region r, 

 

4

 GTAP Working Paper No. 40 

5

 GTAP Working Paper No. 41 
6

{/ ,

,

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: 

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

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

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

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

Hia 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. The 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 LGP temperate AEZs: 11 and 12, then AEZs 9, 8 

and finally tropical AEZ 6. The values of land rents generated in the boreal zones are an order of 

magnitude smaller, and essentially negligible for the shortest growing periods. 

 Figure 1 offers a visualization of the cropland 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 value 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 share-based breakout of total land rents in each of 

the Agro-Ecological Zones, world wide. In this figure, the totals all sum to 100% and we simply 

see the relative economic importance of each crop within 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 contribution 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), dairy production (rmk), wool (wol) and non-ruminants 

(oap = pigs and poultry). In the case of non-ruminants, 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 production, crops and forestry. Of course there is 

indirect competition, insofar as increased production of poultry, for example, will boost the feed 

requirements and hence increase the demand for land in feed grains. However, we capture this 

competition via the intermediate demand for feed in non-ruminant production in the CGE model. 

 

6

 Since we assume the "oap" sector does not use land directly we must take away GTAP land rents in this sector and 

augment payments to labor and capital by the amount of these 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, as well as the 

agriculture-wide labor/capital ratio, we scale up land rents of the other agriculture sectors (i.e., crop sectors, “ctl”, 

“rmk” and “wol” sectors) by amounts summing 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 the SAGE pasture land cover hectares of the 18 

AEZs, to split the GTAP livestock sectors' land rents into 18 AEZs. Since we do not have 

independent estimates of land used for dairy production vs. land used for cattle, sheep and goats, 

the aggregate land rents within each of these sectors are shared out across AEZs in the same 

proportions for each of the sectors. (I.e., if AEZ 8 has 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 relatively high levels of livestock 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. 

4. GTAP Forest Land Rent Data By 18 AEZs 

 We now turn to the estimation of GTAP-consistent land rents in the forestry sector, based 
9

on the work of Sohngen et al. reported in Chapter 37

. 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 aggregate supply response in the forestry sector (see 

Hertel and Tsigas, 2002). However, it has no firm 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 total 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. (Inaccessible land will not generate land rents, by 

definition.) Accessible forest land, by management 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 estimates of Gouel and Hertel (2006). Furthermore, for 

incorporation into the GTAP data base, the land rental share in total costs must be less than the 

value-added share. Not surprisingly, this is violated 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 area, product sales or price. Rather than making 

somewhat arbitrary allocations on a region-by-region basis, we choose the following approach. 

First, compute the share of land rents in value-added at a global level. In the version 6 data base, 

 

7

 GTAP Working Paper No. 41 
10

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 forestry 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 have 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 land rents for the U.S. so far. Therefore, we present here the 

comparison of per hectare land rents between the GTAP land rent data and the observed cash land 

rent only for the US, using data published by the U.S. Department of Agriculture (USDA). 

 Figure 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 cropland 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 rents8

. 

 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 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 Table 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 production, we see crop land rents varying between 

$177/ha. and $244/ha., with livestock land rents in the range of $21 - $30/ha. For all crop land in 

the US, the estimate is $205/ha. – somewhat higher 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 relative 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 data 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 per 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, Mendelsohn’s estimates can be mapped to AEZs and 

this has been done. This mapping gives us a basis of comparison for the distribution of land rents 

across Agro-Ecological Zones. Here we see greater differences between the two data bases, with 

the Mendelsohn et al. cropland estimates ranging from $68/ha. in AEZ7 to $196/ha. in AEZ10. 

Their pastureland rental estimates range from $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 broad pattern of relative land 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 grains 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 more 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 something about the extremely high per hectare land rents 

in the boreal AEZs – particularly AEZs 15 and 16. Firsly, note that the total area involved is very 

small (column A in Table 4), so this is not a serious problem in the aggregate. Nonetheless, it is 

worthwhile considering why this problem arises. To do so, return to the methodology for 
13

constructing the GTAP-AEZ data base. 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 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 infrastructure in order for the land to be 

productive. Indeed, without these improvements, land productivity would be very low. So these 

gross errors for the boreal zone are largely a function of our inability to separate returns to capital 

and land in greenhouse production. In order to overcome this problem, we need a more direct 

approach to the estimation of land rents in agriculture. A natural approach would be to build on 

the work of Mendelsohn et al. (2005), applying their estimated land rents by AEZ to the total 

hectares in the land use data base. However, to date, we have only been able to obtain these data 

for the US. Once such a data base becomes available 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 independent data with which to validate the GTAP-AEZ land rental 

estimates for countries other than the US, it is still instructive to examine the pattern of land rents 

across these countries. The total farm land rent, agricultural land area and implied average land 

rental rates for each of the 87 GTAP-AEZ regions 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 than $1/ha. in Botswana, which is sparsely 
14

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 $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 agriculture and forestry, as highlighted in the two 

preceding chapters9

. However, even with these data in hand, incorporation of this information 

into a globally consistent, general equilibrium framework poses significant challenges. This 

chapter has outlined one approach to this problem. 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 work of Mendelsohn et al. progresses, there may be 

greater scope to draw on this research to construct 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 distribution 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 

 

99 GTAP Working Papers No. 40 and No. 41 
15

management challenges for the CGE models and likely remains a few years off for the majority 

of CGE analyses at the global level. In the meantime, some experimentation with further 

disaggregation of AEZs (e.g., using shorter Lengths of Growing Periods) would be useful. 
16

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21

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

GTAP crop sector land rent (2001, USD million)

1 pdr

2 wht

3 gro

4 v_f

5 osd

6 c_b

7 pfb

8 ocr

Figure 1. Crop sector land rent allocation among AEZs: world total 

0%

20%

40%

60%

80%

100%

1 AEZ12 AEZ23 AEZ34 AEZ45 AEZ56 AEZ67 AEZ78 AEZ89 AEZ9

10 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 pdr

Figure 2. Distribution of crop sector land rent within each AEZ: world total 
22

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 45

Cash 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.pdf 
23

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 land rents, 2001 US$ million: GTAP v.s. Mendelsohn et al. 
25

Table 1. 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 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 134920 
26

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 228397 
27

Table 3. GTAP land rents: VFM of forestry 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 80641 
28

Table 4. U.S. per hectare land rent: 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 hectare land rent, unit: 2001 US$/ha. 

GTAP regions 

GTAP VFM 

unit: million 2001 

USD 

SAGE Land cover 

unit: 1000ha. 

Average per ha. land 

rent 

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 52
30

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 7
31

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