Competition for land

Phil Trans R Soc B (2010) 365 2941ndash2957

doi101098rstb20100127

Review

Autho

While ththe viewand do

One conworld in

Competition for landPete Smith1 Peter J Gregory2 Detlef van Vuuren3

Michael Obersteiner4 Petr Havlık4 Mark Rounsevell5

Jeremy Woods6 Elke Stehfest3 and Jessica Bellarby1

1Institute of Biological and Environmental Sciences University of Aberdeen 23 St Machar DriveAberdeen AB24 3UU UK

2Scottish Crops Research Institute (SCRI) Invergowrie Dundee DD2 5DA UK3Netherlands Environmental Assessment Agency (PBL) PO Box 303 3720 Bilthoven The Netherlands4International Institute for Applied Systems Analysis (IIASA) Schlossplatz 1 2361 Laxenburg Austria

5Centre for the Study of Environmental Change and Sustainability (CECS) School of GeosciencesUniversity of Edinburgh Drummond Street Edinburgh EH8 9XP UK

6Centre for Environmental Policy Faculty of Natural Sciences Imperial College LondonSouth Kensington London SW7 2AZ UK

A key challenge for humanity is how a future global population of 9 billion can all be fed healthily andsustainably Here we review how competition for land is influenced by other drivers and pressuresexamine land-use change over the past 20 years and consider future changes over the next 40 years

Competition for land in itself is not a driver affecting food and farming in the future but is anemergent property of other drivers and pressures Modelling studies suggest that future policydecisions in the agriculture forestry energy and conservation sectors could have profound effectswith different demands for land to supply multiple ecosystem services usually intensifying competitionfor land in the future

In addition to policies addressing agriculture and food production further policies addressing theprimary drivers of competition for land (population growth dietary preference protected areas forestpolicy) could have significant impacts in reducing competition for land Technologies for increasingper-area productivity of agricultural land will also be necessary Key uncertainties in our projectionsof competition for land in the future relate predominantly to uncertainties in the drivers and pressureswithin the scenarios in the models and data used in the projections and in the policy interventionsassumed to affect the drivers and pressures in the future

Keywords competition for land land use agriculture forestry policy

1 INTRODUCTIONThe UK Foresight Global Food and Farming FuturesProject is considering how a future global populationof 9 billion can all be fed healthily and sustainably(Foresight 2009) The project has identified 19 lsquodri-versrsquo (with subcategories) affecting food and farmingin the future one of which is competition for landThe purpose of this review is to examine competitionfor land and to consider the direct and indirect press-ures and drivers affecting it The scope of the review isglobal and the time scale considered is the past 20years and the next 40 years (1990ndash2050)

In addition to agriculture use is included forforestry non-food crops and protected areas for

r for correspondence (petesmithabdnacuk)

e Government Office for Science commissioned this reviews are those of the author(s) are independent of Governmentnot constitute Government policy

tribution of 23 to a Theme Issue lsquoFood security feeding the2050rsquo

2941

biodiversity as well as use of land for bioenergy andland degradationrestoration The impact ofpolicy on influencing each of these factors is discussedin sect2c

We summarize the quantitative information onchanges in land use and land quality over the last 20years both globally and disaggregated according tothe major regions of the world The most recent syn-thesis of agricultural land-use change was conductedfor the International Assessment of AgriculturalKnowledge Science and Technology for Development(IAASTD) particularly the chapter lsquoOutlook onagricultural changes and its driversrsquo dealing withland-use and land-cover change (van Vuuren et al2008) That study collated projections from the Landuse and cover change synthesis book (Alcamo et al2005) the scenarios from the Global ScenariosGroup (Raskin et al 2002) IPCC Special Report onEmissions Scenarios (SRES) (IPCC 2000) the Mil-lennium Ecosystem Assessment (MEA 2005)UNEPrsquos Global Environment Outlook (UNEP 2002)and some models from the EMF-21 study of the

This journal is q 2010 The Royal Society

competition for land

mdash technology

societal trends

institutional factors

mdash land distributionmdash land tenure securitymdash land-use policies

mdash institutional capacitiesmdash governance

mdash regulations and degree of illegality

natural causes land transition

mdash hurricanes mdash crops and pastures mdash urban sprawl mdash road building

mdash oil and mining

mdash forest clear-cutting (eg pulp paper)

mdash natural fires mdash pests mdash floodsmdash water availability mdash global warming

land degradation mdash logging mdash induced fires mdash over-grazing mdash firewood over-harvestingmdash defaunation

mdash population growthmdash agricultural intensificationmdash dietary preference mdash non-food goods and servicesmdash urbanization mdash economic developmentmdash migration patternsmdash cultural factors

mdash trademdash macroeconomics

mdash market failures

mdash infrastructure investments mdash commodity price and demand changes

drivers (underlying causes)

socio-economic and technology factors

pressures (direct causes)

Figure 1 Conceptual analysis framework for competition for land drivers and pressures Adapted from Contreras-Hermosilla

(2000)

2942 P Smith et al Review Competition for land

Energy Modelling Forum (eg Kurosawa 2006 vanVuuren et al 2006) We expand on that synthesis byadding more recent studies in sectsect4 and 5 In these sec-tions we present projections of land-use change to2050 and examine the impact of changes in non-agri-cultural policy (eg forest and protected land policy)on competition for land We briefly examine theassumptions upon which the projections are basedand identify the main areas of uncertainty

We conclude by assessing and ranking the mostimportant external factors that may affect the landavailable for agriculture between now and 2050 andby discussing future needs to reduce uncertainties inthese projections

2 FACTORS AFFECTING COMPETITIONFOR LANDAlthough competition for land has been identified as adriver affecting land use food and farming by the Fore-sight Global Food and Farming Futures Project it isactually an emergent property of a range of other driversand pressures Figure 1 presents a conceptual frame-work for analysing drivers and related pressures ofcompetition for land at different geographical scales

In understanding interrelated causes for competitionfor land we distinguish between drivers and pressuresPressures represent direct causes the visible motivationsfor competition for land (right-hand side of figure 1)Drivers (underlying causes) for competition are factorsof higher causal order that determine the degree ofthe actual direct pressures (left-hand side of figure 1)

Phil Trans R Soc B (2010)

(see Chomitz amp Gray 1996 Kaimowitz et al 1998Geist amp Lambin 2002 Wunder 2003 Niesten et al2004 Rudel et al 2005 on these different drivers andpressures S Klappa 1999 unpublished data)

We do not attempt to review the drivers and press-ures in detail here since they are covered by the otherdriver reviews in this issue In sect2a however we discuss afew drivers and pressures to demonstrate how theyimpact upon land use through their impact on competitionfor land

(a) Population growth agricultural

intensification and dietary preference

The growth in the human population from about 3 bil-lion in 1960 to 68 billion in 2010 coupled withincreased income and changes in diet has beenaccompanied by substantial increases in crop andanimal production (27-fold for cereals 16-fold forroots and tubers and fourfold for meat Foresight2009) This increase will need to be maintained if theprojected population of 9 billion by 2050 is to be sus-tained Past increases in crop production have occurredas a result of both extensification (altering natural ecosys-tems to produce products) and intensification(producing more of the desired products per unit areaof land already used for agriculture or forestry) Of theworldrsquos 134 billion ha land surface about 3 billion hais suitable for crop production (Bruinsma 2003) andabout one-half of this is already cultivated (14 billion hain 2008) The remaining potentially cultivatable land iscurrently beneath tropical forests so it would be undesir-able to convert this to agricultural land because of the

Table 1 Projected contributions () to increased crop

production between 199799 and 2030 Adapted fromBruinsma (2003)

land areaexpansion

increase incroppingintensity

yieldincrease

all developingcountries

21 12 67

Sub-Saharan

Africa

27 12 61

Near EastNorthAfrica

13 19 68

Latin America

and Caribbean

33 21 46

South Asia 6 13 81East Asia 5 14 81

Table 2 Changes in global forest areas as a function of

country income groups From World Bank (1994) asreported by Hannink (1997)

current median rate of forestreduction

World Bank income group yr21 halving time (years)

low 2080 90lower middle 2060 120

upper middle 2055 131high thorn020 360 (doubling time)world 2060 120 years

Review Competition for land P Smith et al 2943

effects on biodiversity conservation greenhouse gasemissions regional climate and hydrological changesand because of the high costs of providing the requisiteinfrastructure Therefore increased yield and a highercropping intensity will need to be the main driverbehind future growth in food production (Bruinsma2003) Table 1 shows that according to the projectionof Bruinsma extensification will still contribute signifi-cantly to crop production in Sub-Saharan Africa(27) and Latin America and the Caribbean (33)There is almost no land available for expansion of agri-culture in South and East Asia and the Near EastNorth Africa (and there may be loss of agriculturalland to urban development) so that intensification isexpected here to be the main means of increasing pro-duction (Gregory et al 2002 Bruinsma 2003)

The main means to intensify crop production will bethrough increased yields per unit area together with asmaller contribution from an increased number ofcrops grown in a seasonal cycle As cereal production(wheat maize and rice) has increased from 877 milliontonnes in 1961 to 2342 million tonnes in 2007 theworld average cereal yield has increased from135 t ha21 in 1961 to 335 t ha21 in 2007 Simul-taneously per capita arable land area has decreasedfrom 0415 ha in 1961 to 0214 ha in 2007 (Foresight2009) Put another way had the increases in yield ofthe last 40ndash50 years not been achieved almost threetimes more land would have been required to producecrops to sustain the present population land that asindicated above does not exist unless unsuitable forcropping Without changes in productivity the growingpopulation would have led to an even greater expansionin agricultural area than observed and competition forland would have been greatly intensified

There have also been substantial changes in humanfood consumption reflected in dietary and nutritionalchanges over recent decades (Schmidhuber 2003)There is an increasing demand for livestock productsparticularly in developing countries (Smith et al2007) and given the lower efficiency of livestock pro-ducts compared with the direct consumption of vegetalmatter (Stehfest et al 2009) an increasing proportionof livestock products in the diet is expected to increasecompetition for land


(b) Non-food goods and services

While agricultural production for food consumption isone of the predominant land-use activities across theglobe land is also used for the production of timberfibre energy and landscape amenities as well asbeing consumed by urbanization

(i) Forest products and fibreHistorically the production of forest products hasgrown rapidlymdashand again in the future a furtherincrease is necessary (upto 2030 by 14 per annumfor sawnwood and 3 for paper and wood-basedpanels FAO 2009a) But worldwide the area offorest and woodland has decreased over the pastdecade (FAO 2009ab Foresight 2009) mostly at theexpense of agricultural expansion However regionaldifferences in forest areas and timber production arestark with declines occurring in developing countriesbut forest expansion in developed countries (table 2)

The different trends between developed and devel-oping countries arise from a number of factors thatreflect competition with other land uses

mdash Wood substitutes Developed countries have replacedthe use of wood as a source of fuel and inconstruction

mdash Agricultural expansion Demand for agricultural pro-ducts has been growing only modestly in developedcountries but rapidly in developing countries

mdash Trade patterns Developing countries tend to exportprimary products

mdash Public services Forests have amenity value in devel-oped countries and subsequently are oftenprotected from deforestation through policy

While the area of forest in developed countries isincreasing only slightly demand for wood productshas fuelled deforestation in other parts of the worldThe global production of fibre crops has almostdoubled between 1961 and 2007 (Foresight 2009)but the land area used to produce these crops hasdeclined by about 10 per cent over the same period(FAOSTAT 2010) This reflects the increase inglobal consumption of fibre goods but also theincreased productivity (yields) of fibre crops Thearea declines suggest that the competition betweenfood and fibre production may be decreasing


(ii) Energy cropsThe growth of crops for bioenergy has been high-lighted as a potential competitor for land with foodcrops It is noteworthy though that the area occupiedby bioenergy and its by-products in 2004 was only14 Mha compared with 1500 Mha of crops (ieabout 1 of the total cropped area) and 4500 Mhaof pastures worldwide (IEA 2006) While the reasonsfor growing crops for bioenergy are complex the useof land for them is likely to increase in the future(FAO 2009b) An important issue for competitionfor land is the potential clearing of new land for bio-mass crops Using biomass for energy is likely tohave both positive and negative competitive effectson food production and therefore on land withnational and regional policies beginning to reflectdiffering components of these inter-linkages Withglobal oil stocks becoming increasingly threatened(UKERC 2009) fossil fuel prices will inevitably con-tinue to rise and alternative sources of energy willbe needed not least to maintain agricultural yieldsBioenergy is likely to fill a significant part of this emer-ging energy gap for agriculture which in turn willrequire more integrated energyagricultureland-usepolicies to circumvent adverse impacts of competitionfor land

(iii) Amenity activities and biological conservationAn increasing trend in some parts of the world is theuse of land for amenity activities andor biologicalconservation This includes recreational uses such aspublic parks golf courses and other sports facilitiesas well as the conservation of traditional landscapesfor their aesthetic cultural or natural heritage valueLand competition between amenity and other usesdepends strongly on geographical location with stron-ger pressures for amenity use occurring on land near tourban centres However many cultural landscapes aremulti-functional being used for example for food ortimber production as well as offering amenity servicesSetting aside land for amenity or conservation poten-tially increases competition for land on the remainingarea which we return to in sectsect4 and 5

(c) Land and soil degradation

Degradation of soil and land through inappropriateuse or the addition of pollutants has been a topic ofconcern for many decades because of the potentialimpact on biodiversity and the availability of land forthe human population to feed itself Degradation ofland intensifies competition for land since it reducesthe quantity of land suitable for a range of uses suchas food production ISRIC (1991) produced a worldmap of human-induced soil degradation based onthe knowledge of 250 experts from six continentsshowing that of the 115 billion ha of vegetated land15 per cent was degraded Erosion was the main pro-cess of degradation and about 20 per cent of theagricultural land worldwide was moderately degradedand 6 per cent strongly degraded (Oldeman 1994)A more recent global assessment of land degradation(ISRIC 2008) identifies 24 per cent of land as degrad-ing mainly in Africa (south of the equator) SE Asia


and southern China North and Central Australiathe Pampas and parts of the boreal forest in Siberiaand North America Although cropland occupiesonly 12 per cent of land area almost 20 per cent ofthe degrading land is cropland with forests alsoover-represented (28 of area but 42 of degradingland) Some 16 per cent of the land area is improvingincluding cropland rangeland and forests Overall theassessment shows the importance of natural cata-strophic phenomena and human management indriving degradation with the latter also instrumentalin speeding up rehabilitation

Agriculture almost always results in stresses beingapplied to land (for example by reducing organicmatter returns to soils or the imposition of a physicalstress such as tillage) but the properties of some soilsallow them to recover naturally and rapidly whileothers may require amendments (eg inputs of fertili-zer) or other physical interventions to regain theirproductive ability (Greenland amp Szabolcs 1994) Byreducing degradation rates or increasing rates of landrehabilitation competition for land in areas containingdegraded land could be reduced (Debeljak et al 2009)

(d) The role of policy

Agricultural policy in many developed countries isdominated by protectionism established throughtrade tariffs and producer support (subsidies) Subsi-dies affect land-use decisions by influencing the typesof land-use strategies adopted by a land manager Sofor example farmers will only grow crops for whichthey receive financial support through direct paymentIn this sense subsidies tend to limit competition forland Subsidies also distort markets on a global scaleand influence the competitiveness of agricultural landuse in other regions of the world Conversely policy lib-eralization often leads to land-use diversification asseen for example in New Zealand following the 1984agricultural policy reforms (MacLeod amp Moller2006) which overnight led to the complete removalof production subsidies (Smith amp Montgomery 2004)In doing so however a liberalized land-use policy islikely to increase competition between land uses

Pressure from the World Trade Organizationamong other drivers has in part led the governmentsof the developed world to move away from pro-duction-related support to new policy directionsbased on rural development or environmental protec-tion Policies such as the Less Favoured Area schemein Europe for example were designed with the objec-tive of protecting agricultural land use in areas with acompetitive disadvantage usually because of physicallimitations such as topography or climate By pre-serving the status quo of traditional agriculturallandscapes such policies limit or remove entirely thecompetition between alternative land uses Other pol-icies such as the European agri-environment schemescompensate farmers for managing their land to highenvironmental protection standards The commontheme in rural development and environmental pro-tection policies however is the support of farmerincomes and this leads to the maintenance of currentland-use practices that limits land competition

ndash150 ndash100 ndash50 0 50 100

global(a)

(b)

North AmericaLatin America and Caribbean

EuropeAfrica

AsiaOceania

European Unionformer Soviet Unionnorthwestern Europe

UKSub-Saharan Africa

Nile catchmentChinaIndia

Brazil

Mha

ndash20 ndash10 0 10 20 30 40

globalNorth America

Latin America and CaribbeanEuropeAfrica

AsiaOceania




Brazil

Figure 2 (a) Absolute and (b) percentage changes (of total agricultural and forestwood area) in forestwood and agriculturalareas 1990ndash2007 globally and in different world regions (a) Green bars forest and wood (Mha) purple bars agricultural

land (Mha) (b) Blue bars forest and wood () brown bars agricultural land () Adapted from FAOSTAT (2010)


Competition for land is associated with deforesta-tion owing to agricultural expansion while at thesame time expansion of forests is leading to compe-tition with other land uses Furthermore permanentforest clearing is associated with the loss of manyother ecosystem services Thus deforestation is notonly a phenomenon of competition for land per sebut is also important in considering the wider conceptof competition for ecosystem services

3 OBSERVED GLOBAL TRENDS IN LANDUSE 1990ndash2010Since 1960 agricultural area has increased from justunder 45 billion ha to just over 49 billion ha in 2007(FAOSTAT 2010) During the last 20 years there hasbeen an overall increase in agricultural area from 486billion ha in 1990 but showing some fluctuations


with the greatest area of 498 billion ha recorded in2001 Figure 2 shows the absolute and percentagechange in agricultural and forestwoodland area forthe world and for each world region 1990ndash2007

As described in sect2 the close to tripling of global foodproduction since 1960 has largely been met throughincreased food production per unit area For exampleBruinsma (2003) suggests that 78 per cent of theincrease in crop production between 1961 and 1999was attributable to yield increases and 22 per cent toexpansion of harvested area Land use has thereforechanged despite smaller changes in land cover

While yield increases have outpaced increases inharvested area in most regions the proportions varyFor example 80 per cent of total output growth wasderived from yield increases in South Asia comparedwith only 34 per cent in Sub-Saharan Africa In indus-trial countries where the amount of cultivated land has


Ph

Box 1 Models used for examining land-use change and competition for land in this review

The IMAGE Integrated Assessment Model (MNP 2006) is a modelling framework often applied in the field of land-useland-cover change because it is able to provide a geographically explicit description of land use The land-useland-coverdescription of IMAGE can be coupled to other models such as the global trade analysis project (GTAP) model or theIMPACT model The rule-based allocation of IMAGE accounts for crop productivity and other suitability factorssuch as proximity to existing agricultural land and water bodies Bioenergy crops are grown on land other than thatrequired for food production forests nature reserves and urban areas (MNP 2006)

The macro-economic EPPA model was developed to examine climate and energy policy applications Future scenariosare driven by economic growth using the GTAP data as base information simulating the economy recursively at 5-yearintervals Five land types are considered to be interchangeable without restrictions as long as conversion costs are met(submodel EPPA-PCCR Pure Conversion Cost Response EPPA-PCCRN normalized PCCR and EPPA-OLSRObserved Land Supply Response) Reversion to the natural state occurs under no costs with any prior investmentbeing fully depreciated

The MiniCAM model is also an environmental integrated assessment model Land is allocated between different categoriesaccording to its expected profitability This is determined by the productivity of the respective product product price the rentalrate of the land and the non-land cost of production Managed and unmanaged ecosystems are interchangeable according to theabove

Quickscan is a spreadsheet-based model constructed to analyse the bioenergy potential under consideration of key driversand correlations The study considered here identified the consumption of animal products as a key factor for agriculture landuse and examined alternative production systems The resulting spared land is considered to be available for bioenergy pro-duction Therefore the study considered only had the land categories lsquobioenergyrsquo and lsquopasturersquo

The GRAPE model is a macro-economic model of climate change Land is allocated according to food demand of thepopulation and the land-use rent that takes carbon costs (external costs caused by energy systems land use and land-usechange) into account

For the MESSAGE-MACRO framework integration of agriculture and forestry sectors has been achieved through linkagesto the DIMA and AEZ-BLS models as described in Riahi et al (2007) While land requirements for bioenergy supply and CO2

mitigation via forest-sink enhancement are based on the sensitivity analysis of the DIMA model the AEZ-BLS framework pro-vides inputs with respect to agricultural land expansion

GLOBIOM integrates the agricultural bioenergy and forestry sectors Changes in the demand on the one side and prof-itability of the different land-based activities on the other side are the major determinants of land-use change Spatiallyexplicit land-use suitability and respective productivities as well as environmental effects are taken into account (Havlıket al in press)

The IMPACT model projects global food supply food demand and food security to the year 2020 and beyondDemand is determined by prices income and population growth From cropland and urban land projections only crop-land is considered in this analysis

been stable or declining increased output was derivedpredominantly through the development and adoptionof agricultural knowledge science and technologywhich has served to increase yields and cropping inten-sity (van Vuuren et al 2008) The role of land-usechange and adoption of agricultural knowledgescience and technology have therefore varied greatlybetween regions In some regions particularly inLatin America the abundance of land has slowed theintroduction of new technologies (van Vuuren et al2008)

4 PROJECTED GLOBAL TRENDS IN LANDUSE 2010ndash2050(a) Changes in land use

The previous sections have shown that land-usechanges are a result of the interaction of a variety of dri-vers and pressures In particular population growth anda shift towards more meat-intensive diets have in thepast contributed to an increasing demand for agricul-tural land These factors are expected to continue tobe important in the future although trends will differin time and across regions Historically the demandfor more agricultural production has been partly com-pensated by technological advances and improvingtechnology will determine whether yields will continueto improve in the future

The complexity of the interactions between differentdrivers necessitates the use of scenario studies using

il Trans R Soc B (2010)

models of land resources and land use to analyse theconsequences of particular trends and policies Thereis a variety of studies and a range of models for addres-sing these issues Box 1 gives an overview of the mostcommonly used models for such analysis at the globalscale For a review of land-use change scenarios at theregional scale see Alcamo et al (2006) Busch (2006)and de Chazal and Rounsevell (2009)

Future land-use trends are described as part ofstudies that look into long-term agriculture trends(such as the projections published by the Food andAgriculture Organization of the United Nations (FAO)and International Food Production Research Institute(IFPRI) In addition studies focusing on agriculturaltrade increasingly tend to describe the relationshipsbetween trade flows and land use Finally integratedassessment models used for examining global environ-mental change and climate change are increasinglyapplied to investigate how climate policies might inter-act with land-use change The type of models used inthese different areas vary greatly ranging from modelsderived from the economic tradition (general equili-brium models eg GTAP-type models) to partialagricultural-economy models (like IMPACT) andmodels that focus mostly on the interaction of econ-omic activity and biophysical indicators (eg theIMAGE and GLOBIOM model box 1) General equi-librium models account for the economic linkages ofthe land-use sector with the rest of the economy andallow for assessment of income generation owing to

Table 3 Overview of studies considered in this review

study focus model(s) scenarios

IPCC-SRES(IMAGE)

providing different trajectories for globalenvironmental change (especially climate

change)

IMAGE A1 B1 A2 B2

MillenniumEcosystemAssessment

providing contrasting futures with respect tothe future of ecological services

IMAGEIMPACT

Global OrchestrationTechnogarden AdaptingMosaic Order from Strength

GEO-4 providing different trajectories for global

environmental problems

IMAGE

IMPACT

Markets First Policy First

Security First SustainabilityFirst

IAASTD describing alternative future for agriculturewith focus on the role of agricultural

technology and knowledge

IMAGEIMPACT

reference scenario and variants

FAO projections exploring most likely developments foragriculture

IMAGE reference scenarios in subsequentstudies

Stehfest exploring impact of different consumptionbehaviour on land use

IMAGE healthy diet

IFPRI projections exploring most likely development foragriculture

IMPACT mdash

MIT studies exploring land-use implications of a globalbiofuel industry

EPPA-PCCRN refpolicyPCCROLSR

Wise et al (2009b)Gillingham et al(2008)

exploring relationships between climatepolicy and land use

MiniCAM

Smeets et al (2007) exploring potential for bioenergy QuickscanKosugi et al (2009) exploring the effect of internalization of

external costs into the model on land-useresults

GRAPE

IIASA GreenhouseGas InitiativeScenarios

providing different trajectories for globalenvironmental change with focus onclimate mitigation

BLSDIMAMESSAGE

A2r B2 B1

Havlık et al (in press) exploring relationship between bioenergyclimate policy and land use

GLOBIOM updated baseline


land-use activities Another strength of these models istheir consistent description of agriculture trade Partialequilibrium models allow for detailed study of agricul-tural production of different crops and within differentregions Moreover some of these models are also ableto represent specific land-use-related policies Bio-physically based models allow the relationshipbetween environmental parameters (productionpotential based on soils and climate land cover)land use and agriculture to be studied Within thescope of this paper we will not be able to reviewthe complete literature of land-use scenarios insteadwe will focus on a few noteworthy projections(table 3) while in table 4 we provide some details onthe selected models emphasizing how these modelshandle land use For full details the reader is advisedto consult the references given

(i) Studies on land useThe most widely used agricultural projections are thoseof FAO and IFPRI IFPRI uses the IMPACT model asthe basis of its projections The methods underlyingthe FAO projections are more diverse utilizing bothmodels and expert consultations Both studies considermostly agricultural markets and thus do not fully coverland-use projections The scenario projections in theGlobal Environmental Outlook-4 (UNEP 2007) theMillennium Ecosystem Assessment (MEA 2005) and


the IIAASTD study (van Vuuren et al 2008) all focusedon the relationship between environmental change andthe agriculture sector In these studies a combination ofthe IMAGE model and IFPRIrsquos IMPACT model wasused to define the scenarios The scenarios of theother studies look at more specific cases in regard to cli-mate policy and biofuel potential The general trendscommon to the scenarios considered here show anincrease in land for bioenergy crops and livestockwith forest and other lands decreasing The exceptionshere are scenarios implementing a carbon tax and alower meat diet where more land is converted back tounmanaged forest The scenarios considered by theIMAGE model and those used in a wider range ofstudies are given in tables 5 and 6 respectivelyTable 7 shows the different land categories consideredby each of the models we compare in this section

(ii) Changes in consumptionGlobal food production is projected to increase drivenby population growth and changes in diet (sect2a) Theincrease in production is somewhat slower than inthe past as a result of a slowdown in populationgrowth Diets are projected to become more meat-intensive with annual per capita meat consumptionincreasing The growth in production of cereals overthe 2000ndash2050 period based on a range of assess-ments varies between 43 and 60 per cent (figure 3)

Table 4 Overview of models considered in this study

tool type developed at reference

IMAGE (Integrated Model toAssess the Global

Environment)

integrated assessment model National Institute for Public Healthand the Environment (RIVM)

and the NetherlandsEnvironmental AssessmentAgency (MNP)

MNP (2006)

EPPA (Emission Predictionand Policy Analysis)

recursive-dynamic multi-regional computable general

equilibrium model

MIT Joint Program on the Scienceand Policy of Global Change

Gurgel et al(2007)

MiniCAM integrated assessment model Joint Global Research Institute Wise et al(2009ab) andGillingham et al(2008)

Quickscan bottom-up Excel spreadsheetmodel

Corpernicus Institute forSustainable Development andInnovation

Smeets et al(2007)

GRAPE (Global Relationship

Assessment to ProtectEnvironment)

integrated (bottom-up) model

to assess the globalenvironment

Japan Kosugi et al(2009)

MESSAGE (Model for EnergySupply Strategy Alternativesand their General

Environmental impact)

integrated assessmentmodelling framework

International Institute for AppliedSystems Analysis (IIASA)

Riahi et al (2007)

GLOBIOM (Global BiomassOptimization Model)

recursive dynamic multi-regional partial equilibriumbottom-up model foragriculture forestry and

bioenergy


Havlık et al(in press)

IMPACT (International Modelfor Policy Analysis ofAgricultural Commoditiesand Trade)

partial-equilibrium agriculturalmodel for crop and livestockcommodities

International Food Policy ResearchInstitute (IFPRI)

Rosegrant et al(2001 2008)


The differences are relatively small since estimates ofconsumption growth are mostly driven by the increasein the global population (which shows relatively littlevariation between the different scenarios in 2050)An increasing share of cereals will be used as animalfeed to supply the rapidly growing demand for live-stock products As incomes increase demand foranimal products also increases This trend which hasbeen empirically established in all regions is assumedto continue in the scenarios of the three groups ofstudies considered here As a result meat demand isprojected to increase at a greater rate than the globalpopulation and diets are projected to become moremeat-intensive For instance the IFPRI calculationsshow annual per capita meat consumption increasingon average from 90 kg per person per year to over100 kg between 2000 and 2050 in high-incomecountries and from around 25 kg to nearly 45 kg perperson per year in low-income countries during thesame period This trend is relevant for land usesince animal products require much more land thancrops On average the production of beef proteinrequires several times more amount of land thandoes the production of vegetable proteins such as cer-eals (Stehfest et al 2009) While meat currentlyrepresents only 15 per cent of the total global humandiet approximately 80 per cent of the agriculturalland is used for animal grazing or the production offeed and fodder for animals (FAO 2006) It shouldbe noted that this includes extensive grasslands in


areas where other forms of agriculture would beextremely challenging Interestingly future meat pro-duction varies considerably more than future cerealproduction among the scenarios (figure 3) sincedifferent scenarios show much more divergence inper capita meat consumption than for per capitacereal consumption Some studies have looked intothe consequences of reducing consumption of live-stock products with proteins being substituted byadditional consumption of pulses (Stehfest et al2009) and shown that far less land would be requiredfor agriculture under such extreme scenarios

(iii) CroplandThe actual demand for cropland in the future dependson the balance between increases in agriculturaldemand and increases in yield improvement Histori-cally yield improvements (approx 80) have beenmore important in increasing production than expan-sion of agricultural land (approx 20 see sectsect2 and 3for more details) As a result agricultural areas haveexpanded by about 5 per cent since 1970 Scenariosshow a very large variation in the expected develop-ment in cropland (figures 4ndash6) The 2050projections for cropland increase range from as lowas 6 per cent (eg the Technogarden scenario of theMEA) to an increase of more than 30 per cent(such as for the SRES A2 scenario and one of thescenarios of the EPPA model numbers represent the

Table 5 Scenario descriptions of studies using IMAGE derivations

scenario abbreviation as

used in figures description

SRES

A1 high economic growth and rate of innovations environmental issues get addressedA2 self-reliance and preservation of local identitiesB1 assumes continuing globalization and economic growth and a focus on the environmental

and socialmdashimmaterialmdashaspects of lifeB2 local solutions to economic social and environmental sustainability

MA (Millennium Assessment)GO (Global Orchestration) globalized with emphasis on economic growthOS (Order from Strength) regionalized with emphasis on security

TG (Technogarden) globalized with emphasis on green technologyAM (Adapting Mosaic) regionalized with emphasis on local adaptation and flexible governance

GEO4

MF (Markets First) focus on markets not only to deliver economic advances but also social and environmentalimprovements

SecF (Security First) focus on security issues in a strongly regionalized worldSusF (Sustainability First) focus on sustainability issues integrating environmental and social concerns at the heart of

development decisions at every level of scalePF (Policy First) focus on global coordinated corrections to the lsquoMarket Firstrsquo scenario without changing the

underlying paradigm emphasizing economic growth

OECD EObaseline no new policies450 ppm stabilization of greenhouse gas to 450 ppm by 2100

IAASTDbaseline slowly declining rates of growth in agricultural researchhigh AKST higher crop yield and livestock number growth

IMAGE-FAOreference reference meat diet

Stehfest et al (2009)

healthy diet lsquohealthy eatingrsquo recommendations implemented globally (reducing meat consumption andincreasing consumption of vegetables)


60 interval of the literature) The average increase isaround 10ndash20 (see also van Vuuren et al 2008)In general models with a stronger focus on physicalparameters tend to project somewhat lower growthrates than models with a more macro-economicorientation (figure 6)

The slightly lower contribution (on average) fromthe expansion of crop area can be attributed to increas-ing land scarcity and reduced growth of the globalpopulation The decreasing quality of land broughtinto production however may mean that a greaterpercentage of gains in total production will need tobe found from crop area expansion than has histori-cally been the case (as indicated in MEA 2005)Even in the two scenarios with little global expansionof cropland a considerable expansion of arable landstill occurs in Africa Latin America and partly inAsia but this is compensated for by a decrease inarable area in temperate zones Across the assess-ments the area in crop production increases from14 billion ha (or 10 of Earthrsquos land surface) to upto 23 billion ha As indicated by FAO this expansionis within the scope of total land available for crop pro-duction (Bruinsma 2003) The fact that theassessments considered here agree on a rather flexiblecontinuous response of the agricultural system todemand increases is interesting as more scepticalviews have also been expressed An important


implication however is further loss of the area avail-able for unmanaged ecosystems (figures 4 and 7)

(iv) Animal husbandry and pasturesIncreases in meat production will occur through anumber of means including changes that lead to intensi-fied production systems such as more efficientconversion of feed into animal products and viaexpansion of land use for livestock (figure 6) Previousscenarios indicate that most of the increases in worldlivestock production will occur in developing countries(Bouwman et al 2005) For grazing systems this meansthat some intensification is likely to occur Considerableintensification is likely in mixed systems with furtherintegration of crop and livestock in many places Stronggrowth is implied for confined livestock production sys-tems In the FAO scenario for instance at least 75 percent of the total growth is in confined systems althoughthere are likely to be strong regional differences (eg lessgrowth of these systems in Africa Bruinsma 2003)This is a continuation of historic trends The majorexpansion in industrial systems has been in the pro-duction of pigs and poultry as they have shortreproductive cycles and are more efficient than ruminantsin converting feed concentrates (cereals) into meatIndustrial enterprises now account for 74 per cent of theworldrsquos total poultry production 50 per cent of pig

Table 6 Descriptions of all scenarios considered in this review not included already in table 5

scenario abbreviation as used

in figures description

EPPA-PCCR -PCCRN and -OLSR

refBAU business as usual with no attempt to control greenhouse gaspolicy global effort to control greenhouse gas emissions starting with the Kyoto protocolmdashreflects

a path whereby developed countries would reduce emissions by 50 by the year 2050MiniCAM Wise et al (2009a)

ref future estimates of crop productivity are applied to terrestrial products until 2030 then a

rate of 025 per yearFFICT Fossil Fuel and Industrial Emissions Carbon Tax regimeUCT Universal Carbon Tax regime

Gillingham et al (2008)

B2 implements SRES B2 scenarioB2_550 as above with implementation of a mitigation policy to achieve atmospheric CO2 of

550 ppmv by 2095

Quickscan Smeets et al (2007)system 1 mixed animal production rainfed agriculturesystem 2 mixed animal production rainfed and irrigated agriculturesystem 3 landless animal production rainfed and irrigated agriculturesystem 4 landless animal production very high crop production technology rainfed and irrigated

agriculture

GRAPE Kosugi et al (2009)GRAPE (B2) economic cost of environmental impact in a case of successful internalization of externalities

MESSAGE Riahi et al (2007)A2r based on SRES A2 with a lower population growth

GLOBIOM Havlık et al (in press)updated baseline the published baseline was updated in several aspects where the major ones are macro-

economic drivers and bioenergy projections from POLES scenario for Copenhagencommunication Introduction of bioenergy poly-production higher land-use changeflexibility including cropland expansion to grassland and other natural land and non-zeroexogenous input neutral crop productivity growth (05 pa)


meat and 68percent of eggs (FAO 20062009ab)At thesame time a trend to more confined systems for cattle hasbeen observed with a consequent rapid increase indemand for cereal- and soy-based animal feeds (thesetrendsare included in theprojectionsdiscussed inthepre-vious section see Delgado et al 1999)

For grazing land the range of 2050 scenario projec-tions ranges between a 5 per cent contraction to a 25per cent increase (60 interval) Most studies showan increase of 10 per cent or less The IAASTD base-line for instance projects an almost constant grazingarea (van Vuuren et al 2008) These numbers arelower than for croplands representing the generalview that croplands are expected to grow faster thanthe grazing area driven by a further intensification oflivestock production systems (and despite the rapidgrowth in meat consumption) The vast area of landused for animal husbandry also means that somestudies looking into alternative pathways for land useoften identify a large potential for reduction hereeither by low-meat diets (Stehfest et al 2009) orintensification (Smeets et al 2007)

(v) Total land balanceObviously the total demand for agricultural area arisesfrom trends in cropland and grassland Studies showdiverging trends (figure 6) but there are also somecommon characteristics First of all almost all studies


show an expansion in 2020 and 2050 of the area forcropland and grassland (as already noted in the pre-vious sections) Second in most studies expansionof grassland area or cropland area represents themost dominant expansion category in 2020 by 2050in some studies however bioenergy also becomesimportant (especially EPPA MiniCAM and Quicks-can) As indicated earlier cropland expansion isgenerally more important than expansion of grasslandbut there are some noteworthy exceptions (GEO4 andEPPA in 2020) In nearly all studies both forest areaand other areas (savannah natural grasslands etc)decline The lowest numbers of land-use change arereported for the MEA (2005) scenarios theIAASTD scenario the IMAGE representation of theFAO baseline and the MiniCAM reference Some ofthese scenarios include high levels of technologychange (Global Orchestration Technogarden andhigh AKST) High rates of land-use change arereported for several of the EPPA and MiniCAM scen-arios It should be noted that figure 6 represents aglobal picture Much more change may happen atthe regional level A considerable expansion of arableland still occurs in Africa Latin America and partlyin Asia but this is compensated for by a decrease inharvested area in temperate zones

An important implication however is further loss ofthe area available for unmanaged ecosystems This isalready shown in figure 6 figure 7 shows the remaining

Table 7 Comparison of land categories used in different models Land categories in italics are used in figure 6 Plus symbol

100 match with used land category

land category IMAGE EPPA MiniCAM GRAPE GLOBIOM MESSAGE

bioenergy thorn thorn thorn na nab na

agricultural landcropland thorn thorn wheat corn fibre

crop misccrop oil crop

other grainsugar crop riceother arableland

thorn thorn thorn

pasture grass and fodder crop thorn pasture and

fodder cropunmanagedpasture

grassland grassland intensive

grazingpasture

forestmanaged forest regrowth forest

(timber)thorn thorn only total

forestplantation

forestmanagedforest

only totalforest

unmanagedforest

Regrowth forest(abandoning)wooded tundraboreal forest coolconifer temperate

mixed anddeciduous forestwarm mixedtropical woodlandtropical forest

thorn thorn only totalforest

thorn only totalforest

other grasslandsteppescrubland savannah

natural grassland grassland andshrubland

withinotherland

other naturalvegetation

extensivegrazingnon-

vegetatedland

included inrespectivemodel but not

considered inthis review

desert ice tundra tundra wetlandsdesert andbuilt-up areas

are notexplicitlyrepresented inthe model

deserta urbanatundraa

urban build-upland water

aOnly in Wise et al (2009b)bBioenergy is included within plantation forest and cropland


natural areas globallymdashbut again it should be noted thatthese global figures hide underlying regional trends Ingeneral across the assessments total natural areasdecline by about 0ndash20 This includes so-called base-line projections but also scenarios that focus more onthe projection of ecosystem services such as the MEArsquosTechnogarden scenario or the Sustainability First scen-ario of GEO4 There are only a few studies that havelooked at incremental switches in management systemssuch as those to semi-natural forest management (egHavlık et al in press) and changes in grassland manage-ment A great impact on land-use change can also comefrom carbon incentives as demonstrated by Wise et al(2009a) For example the scenario examined by Wiseet al (2009a) in which (i) it is assumed that greenhousegas emissions of the energy system are regulated and (ii)there is no regulation of emission from land-use changeaccording to MiniCAM work will result in massive


land-use change towards bioenergy and crops In con-trast a policy that targets all potential greenhouse gasemissions (also from land use) can lead to preservationof woodland A similar trend can be observed from theGRAPE model which also takes carbon cost intoaccount In fact these studies suggest that carbontaxing could have an impact on changing diet via theinduced prices of meat

Ever-increasing competition for land may endangerthe integrity of currently protected areas which arelocated and classified in the World Database on Pro-tected Areas (UNEP-WCMC 2009) Most modelstudies discussed above either assume projected areasto be constant or even ignore this category as a specialland category There is one major exception which isthe Sustainability First scenario as part of UNEPrsquosGEO4 Based on a minimum share of protected landby biome category this study assumes that projected

(a) (b)pr

oduc

tion

(mill

ion

tonn

e)

4000 800

600

400

200

0

3000

2000

1000

01960 1980 2000 2020 2040 1960 1980 2000 2020 2040

Ag 1530 (FAO 2003) Ag 3050 (FAO 2006) OECDFAO 2008 () OECDFAO 2008

IFPRI 2001 IFPRI 2009IAASTD 2009

MA-GOMA-TGMA-OSMA-AM

Figure 3 Trend in global production of (a) cereals and (b) meat according to various assessments MA scenarios are from Car-penter amp Pingali (2005) the OECDFAO study has been included with (asterisk) and without biofuels IFPRI 2009 is reportedby Msangi amp Rosengrant (2009)

crop

are

a (m

illio

n km

2 )

25

20

15

10

5

01970 1980 1990 2000 2010 204020302020 2050

MA-GO

IAASTD baselineFAO (IMAGE representation)

MA-TGMA-OSMA-AM

Figure 4 Change in crop area in various assessments(IAASTD projection includes land for bioenergy crops)Grey area indicates 20ndash80th percentile literature range

MA-GO

IAASTDFAO (IMAGE)

MA-TGMA-OSMA-AM

1980 2000 20402020

5

10

15

gras

slan

d ar

ea (

mill

ion

km2 )

20

25

30

35

40

45

0

Figure 5 Projected change in grazing area in various assess-ments Grey area indicates 20ndash80th percentile literaturerange


area would need to increase from 2009 to 2030 by up toapproximately 400 Mha worldwide Many of these areasmay not enter into strong competition with other landuses while some are clearly at the forest frontier

5 UNCERTAINTYUncertainties in projecting land use have a range ofsources including the level of understanding of theunderlying causal relationships (ie lsquowhat is knownabout driving forces their impacts and interdependen-ciesrsquo) the degree of complexity of underpinningsystemrsquos dynamics (ie lsquohow do driving forces impactsand their respective feedbacks lead to emergingnonlinear system dynamicsrsquo) the degree of pathdependency (ie lsquoto what degree does the currentsystem state and past trends determine future develop-mentsrsquo) the level of uncertainty introduced by thetime horizon (ie lsquohow far into the futurersquo) or evensurprises and unpredictable future developmentsSome of these phenomena follow known random pro-cesses while others cannot be explored well enough aswe lack anticipative capacity For a more complete dis-cussion of different types of uncertainties and their


consequences for methods to explore the future seevan Vuuren (2007)

This section serves to illustrate some of these uncer-tainties inherent in future projections of land use andof competition for land and how these are criticallydependent upon future policies on forest protectionand bioenergy supply and future trends in agriculturalproduct preferences and consumption

Given that there is substantial uncertainty abouthow different drivers will evolve and how they willimpact upon the competition for land here we illus-trate the impact of uncertainty by presenting resultsof eight selected changes of drivers between 2020and 2030 The analysis presented here was carriedout using the GLOBIOM model (table 4 and box 1Havlık et al in press) over a short timeframe toreduce the level of uncertainty introduced by thetime period considered Four uncertainty domainswere identified for the quantitative modelling analysison a global scale biofuel meat and wood demandand infrastructure development In total eight alterna-tive scenarios were modelled under these fouruncertainty domains since the biofuel scenariosincluded five variants differentiated by the expected

2020

2050

ref

policy

ref

FFCT

UCT

B2

B2

A2r

baseline

B2_550

system1

system2system3system4

A1

Mha Mha

A2B1B2

GOOSTGAM

MFSecFSusFPF

baseline450 ppm

baselinehigh_AKST

reference

healthy_diet

4000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

04000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

0

SRE

SM

AG

EO

4

IMA

GE

IMA

GE

IM

PAC

TIM

AG

E

IMPA

CT

IMA

GE

L

EIT

AP

IMA

GE

IM

PAC

TIMAGE

IMAGE

PCCRN

PCCROLSR

PCCRN

PCCROLSR

GRAPE

MESSAGE

GLOBIOM

OE

CD

EO

MIT

-stu

dy

EPP

AM

iniC

AM

Min

iCA

M

Wis

e G

illin

gham

Smee

ts

Qui

cksc

anIA

ASTD

Kosug

i

Riahi

Havlik

IMAGE-F

AO

Stehfes

t

Fig

ure

6

Glo

bal

lan

d-u

sech

an

ge

by

2020

an

d2050

for

dif

fere

nt

mod

els

an

dsc

enari

os

(see

tab

les

5an

d6

for

ab

bre

via

tion

s)

Chan

ge

giv

enas

abso

lute

chan

ge

rela

tive

to2000

wit

hth

eex

cepti

on

of

Min

iCA

M(b

ase

year

2005)

an

dG

RA

PE

(base

year

2010)

wh

ere

this

was

the

nea

rest

availab

leye

ar

Tab

le7

det

ails

the

lan

dca

tegori

esfo

rth

ed

iffe

ren

tm

od

els

Bro

wn

bio

fuel

ora

nge

crop

yellow

past

ure

light

gre

en

man

aged

fore

st

dark

gre

en

un

man

aged

fore

st

red

oth

er



Table 8 Policy shock scenarios used in the GLOBIOM

model analysis

scenario name description

baseline POLES scenario for Copenhagen

communication 83 of biofuel intotal transport energy in 2030

biofuelsmdashportfolio BIOF1 frac14 15 share of biofuels intotal transport energy in 2030 in theform of a mix of all three types of

biofuels (first-generation biodieseland ethanol and second-generationbioethanol)

biofuelsmdashethanol BIOF2 frac14 15 share of biofuels in

total transport energy in the form offirst-generation ethanol only in 2030

biofuelsmdashbiodiesel BIOF3 frac14 15 share of biofuels intotal transport energy in the form offirst-generation biodiesel only in

2030biofuelsmdashfirst

generationBIOF4 frac14 15 share of biofuels in

total transport energy in 2030 fromfirst generation (mix of biodieseland bioethanol) only

biofuelsmdashsecondgeneration

BIOF5 frac14 15 share of biofuels intotal transport energy in 2030 fromsecond generation only

wood WOOD frac14 overall additional increaseof 15 in demand for wood in

2030meat MEAT frac14 overall additional increase of

10 for meat in 2020 and 15 in2030

infrastructure INFRA frac14 transportation costs will

decrease by 10 in emergingeconomies and 5 in developingregions by 2030

natu

ral a

rea

(mill

ion

km2 )

10

20

30

70

60

50

40

01970 1980 1990 2000 2010 204020302020 2050

GOIAASTDFAO (IMAGE)

TGOSAM

Figure 7 Remaining natural area according to projections

from various assessments (deserts and ice areas are notincluded) Grey area indicates 20ndash80th percentile literaturerange


biofuel mix (table 8) To assess uncertainty in thisanalysis the policy shock (table 8) was incorporatedin the baseline for each scenario separately and themodel was re-run with the new assumptions

The scenarios were defined in such a way that anyexpansion of cropland would occur at the cost offorest land in order to have a lsquopurersquo measure of thedegree of competition for land Under this scenariospecification the GLOBIOM model considers onlydrivers of deforestation coming from agriculture orbioenergy production We consider that the modeloperates under the constraint of a fixed total landarea and allocates land use according to the economiccompetitiveness of different land-use activities De-forestation is used as a measure of the degree ofcompetition for land and is itself costly The cost ofavoiding deforestation is equal to the differencebetween the cost of deforestation itself and theincome from agricultural production that wouldoccur on that land subsequently if it were deforestedand used for agriculture (opportunity cost) Underavoided deforestation the degree of competition forland is mitigated at the cost of land-use intensificationand reduced consumption (Havlık et al in press)

Figure 8 presents the global deforested area whichserves as a proxy for the degree of competition forland between 2020 and 2030 in Mha The red line dis-plays the baseline scenario The biofuel scenarios 1 3and 4 and the meat policy shock scenario causemore deforestation These scenarios are associatedwith agricultural land expansion owing to additionalproduction of commodities Improvement of infra-structure in emerging and developing economies onthe one hand leads to higher pressure on natural eco-systems on the frontier and on the other handincreases global productivity of agricultural productionand will therefore reduce land expansion in the longterm The infrastructure scenario leads to some3 Mha more deforestation compared with the baselineThe result for the wood scenario is very close to base-line results causing 035 Mha less for additional woodconsumption since the relative value of forest increasesThe only scenario that leads to less deforestation is thefifth biofuel scenario in which second-generation


biofuels are used This is associated with afforestationactivities using high-yielding short-rotation forestsThis policy shock scenario leads to a reduction indeforestation of more than 5 Mha over the period2020ndash2030 when compared with the baseline

The scenarios demonstrate the range of impacts asingle biofuel production policy shock can exert ondeforestation depending on the type of biofuelproduction technology used Further sources of uncer-tainty lie in the resolution and quality of the landcategory considered Many studies do not distinguishbetween managed and unmanaged forest and do notconsider conversion to short-rotation coppice as defor-estation Therefore in terms of net deforestationnatural forest can be converted in such models toshort-rotation coppice without showing land-usechange In the scenarios presented here deforestationis defined as conversion of unmanaged natural forestto cropland The development of different foresttypes was tracked separately For example short-rotation plantations were only allowed to expand intocropland and grassland and therefore could onlyindirectly lead to deforestation through croplandexpansion elsewhere into unmanaged forest Increas-ing forest management intensity does not lead todeforestation Lower deforestation in the second-generation biofuel and WOOD scenario is due to the

110

8076721

6732

9813

8758

6264 6627

9260

7062

100

90

80

70

60Mha

50

40

30

20

10

0BIOF1 BIOF2 BIOF3 BIOF4 BIOF5 wood meat INFRA

scenario

Figure 8 Global deforested area owing to expansion of agricultural land between 2020 and 2030 (Mha) Red line baseline


increased value of managed forest reducing deforesta-tion as described above However the increased valueof forest management leads to higher conversion ofunmanaged forest to managed forests using semi-natural forest management practices Another sourceof uncertainty arises from the models themselves Allmodels provide an imperfect representation of realityand rely on the availability and quality of input dataand additional assumptions For example in GLO-BIOM there is no explicit link assumed betweenincreased animal production and grassland demandConsequently the MEAT scenario will overestimatethe degree of deforestation owing to the restrictivegrassland assumptions It is important to be aware ofthese inherent uncertainties when dealing with futureprojections Improved models data and more sophisti-cated scenarios will allow this uncertainty to bereduced in the future but projections of futurepolicy impact will always contain a degree ofuncertainty

6 CONCLUDING REMARKSWe have shown that competition for land in itself isnot a driver affecting food and farming in the futurebut is an emergent property of other drivers and press-ures There is considerable uncertainty overprojections of intensity of competition for land in thefuture and the regional distribution of this compe-tition Modelling studies show that future policydecisions in the agriculture forestry energy and con-servation sectors could have profound effects withdifferent demands for land to supply multiple ecosys-tem services usually intensifying competition for landin the future

Given the need to feed 9 billion people by themiddle of this century and increasing competitionfor land to deliver non-food ecosystem services it isclear that per-area agricultural productivity needs tobe maintained where it is already close to optimal orincreased in the large proportion of the world whereit is suboptimal It remains a challenge to deliverthese increased levels of production in a way thatdoes not damage the environment and compromiseother ecosystem services (Royal Society 2009)


In summary in addition to policies addressing agri-culture and food production further policiesaddressing the primary drivers of competition forland (population growth dietary preference protectedareas forest policy) could have significant impacts inreducing competition for land Technologies forincreasing per-area productivity of agricultural landwill also be necessary Key uncertainties in our projec-tions of competition for land in the future relatepredominantly to uncertainties in the drivers andpressures within the scenarios uncertainties in themodels and data used in the projections and thepolicy interventions assumed to affect the drivers andpressures in the future Though price has been usedas an indicator of land scarcity and therefore compe-tition for land the development of other indicators toassess the intensity for competition for land is in itsinfancy and the development of new metrics willadvance our understanding of competition for landin the future

This work was supported in part by the UK Foresight GlobalFood and Farming Futures Project The work of PS JWMO JB and PH contributes to the NERC-QUEST-funded QUATERMASS project and of MO PH andPS contributes to the EU FP7 project CC-TAME PS isa Royal Society-Wolfson Research Merit Award holderThe authors also thank the researchers who kindly supplieddata for the creation of figure 6 in sect4mdashA Gurgel for theEPPA data J Edmonds and Steve Smith for MiniCAMdata and T Kosugi for the GRAPE data

REFERENCESAlcamo J et al 2005 Changes in ecosystem services and

their drivers across the scenarios In Ecosystems andhuman well-being vol 2 (eds S R Carpenter P PingaliE Bennett amp M Zurek) Millennium Ecosystem Assess-ment pp 297ndash373 Washington DC Island Press

Alcamo J Kok K Busch G Priess J Eickhout B

Rounsevell M D A Rothman D amp Heistermann M2006 Chapter 8mdashSearching for the future of land scen-arios from the local to global scale In Land-use and landcover change local processes global impacts Global

Change IGBP Series pp 137ndash156 Berlin GermanySpringer-Verlag


Bouwman A F Van der Hoek K W Eickhout B ampSoenario I 2005 Exploring changes in world ruminantproduction systems Agric Syst 84 121ndash153 (doi10

1016jagsy200405006)Bruinsma J 2003 World agriculture towards 20152030 an

FAO perspective London UK Earthscan PublicationsBusch G 2006 Future European agricultural landscapesmdash

what can we learn from existing quantitative land-use

scenario studies Agric Ecosyst Environ 114 121ndash140(doi101016jagee200511007)

Carpenter S amp Pingali P 2005 Millennium ecosystem assess-mentmdashscenarios assessment Washington DC Island Press

Chomitz K M amp Gray D A 1996 Roads land use anddeforestation a spatial model applied to Belize WorldBank Econ Rev 10 487ndash512

Contreras-Hermosilla A 2000 The underlying causes offorest decline Occasional Paper Number 30 Center for

International Forestry Research (CIFOR) 25p JakartaIndonesia CIFOR

Debeljak M Kocev D Towers W Jones M GriffithsB S amp Hallett P D 2009 Potential of multi-objectivemodels for risk-based mapping of the resilience character-

istics of soils demonstration at a national level Soil UseManage 25 66ndash77 (doi101111j1475-2743200900196x)

De Chazal J amp Rounsevell M D A 2009 Land-use andclimate change within assessments of biodiversity loss a

review Global Environ Change 19 306ndash315 (doi101016jgloenvcha200809007)

Delgado C Rosegrant M Steinfeld H Ehui S ampCourbois C 1999 Livestock to 2020mdashthe next food

revolution Food Agriculture and Environment Discus-sion Paper 28 Rome Italy IFPRI FAO and ILRI

FAO 2006 World agriculture towards 20302050 Prospects forfood nutrition agriculture and major commodity groupsInterim Rep Rome Italy Global Perspective Studies

Unit FAOFAO 2009a State of the worldrsquos forests 2009 Rome Italy

FAOFAO 2009b The state of food and agriculture 2009 Rome

Italy FAO

FAOSTAT 2010 See httpfaostatfaoorgsite339defaultaspx (Accessed 25th June 2010)

Foresight 2009 Global Food and Farming Futures SeehttpwwwforesightgovukOurWorkActiveProjectsFoo-dandFarmingFuturesFoodandfarmingProjectHomeasp

Geist H J amp Lambin E F 2002 Proximate causes andunderlying driving forces of tropical deforestationBioScience 52 143ndash150 (doi1016410006-3568(2002)052[0143PCAUDF]20CO2)

Gillingham K T Smith S J amp Sands R D 2008 Impactof bioenergy crops in a carbon dioxide constrained worldan application of the MiniCAM energy-agriculture andland use model Mitig Adapt Strat Global Change 13675ndash701 (doi101007s11027-007-9122-5)

Greenland D J amp Szabolcs I (eds) 1994 Soil resilience andsustainable land use Wallingford UK CAB International

Gregory P J et al 2002 Environmental consequences ofalternative practices for intensifying crop productionAgric Ecosyst Environ 88 279ndash290 (doi101016

S0167-8809(01)00263-8)Gurgel A Reilly J M amp Paltsey S 2007 Potential land use

implications of a global biofuels industry J Agric FoodInd Org 5 2ndash9 (doi1022021542-04851202)

Hannink D M 1997 Principles and applications of economicgeography economy policy and environment New YorkNY John Wiley amp Sons

Havlık P et al In press Global land-use implications of firstand second generation biofuel targets Energy Policy(doi101016jenpol201003030)


IEA 2006 World energy outlook 2006 Paris France Inter-national Energy Agency

IPCC (Intergovernmental Panel on Climate Change) 2000

Special Report on emission scenarios understanding and mod-elling technological change Cambridge UK IPCC andCambridge University Press

ISRIC 1991 Global Assessment of Human-Induced Soil Degra-dation (GLASOD) Wageningen The Netherlands

International Soil Reference and Information CentreISRIC 2008 Global Assessment of Land Degradation and

Improvement (GLADA) ISRIC Report 200801 Wagen-ingen The Netherlands International Soil Reference

and Information CentreKaimowitz D Erwidodo Ndoye O Pachero P amp

Sunderlin W 1998 Considering the impact of structuraladjustment policies on forests in Bolivia Cameroon andIndonesia Unasylva 49 57ndash64

Kosugi T Tokimatsu K Kurosawa A Itsubo N YagitaH amp Sakagami M 2009 Internalization of the externalcosts of global environmental damage in an integratedassessment model Energy Policy 37 2664ndash2678(doi101016jenpol200902039)

Kurosawa A 2006 Multigas mitigation an economic analy-sis using GRAPE Model Multigas mitigation and climatepolicy Energy J Special Issue I 275ndash288

MEA (Millennium Ecosystem Assessment) 2005 Ecosystemsand human well-being scenarios Washington DC Island

PressMacLeod J amp Moller H 2006 Intensification and diversifi-

cation of New Zealand agriculture since 1960 anevaluation of current indicators of land use change

Agric Ecosyst Environ 115 201ndash218 (doi101016jagee200601003)

MNP 2006 Integrated modelling of global environmentalchange an overview of IMAGE 24 Bilthoven TheNetherlands Netherlands Environmental Assessment

Agency (MNP)Msangi S amp Rosengrant M 2009 World agriculture in a

dynamically-changing environment IFPRIrsquos long-termoutlook for food and agriculture under additionaldemand and constraints Food and Agriculture Organiz-

ation of the United Nations Economic and SocialDevelopment Department Expert Meeting on How tofeed the World in 2050

Niesten E Ratay S amp Rice R 2004 Achieving biodiversityconservation using conservation concessions to comp-

lement agroforestry In Agroforestry and biodiversityconservation in tropical landscapes (eds G SchrothG Fonseca G Harvey C Gascon H Vasconcelos ampA Izac) pp 135ndash150 Washington DC Island Press

Oldeman L R 1994 The global extent of soil degradationIn Soil resilience and sustainable land use (eds D JGreenland amp I Szabolcs) pp 99ndash118 WallingfordUK CAB International

Raskin P Banuri T Gallopin G Gutman P Hammond

A Kates R amp Swart R 2002 Great transition Thepromise and lire of the times ahead Boston MA StockholmEnvironment Institute

Riahi K Grubler A amp Nakicenovic N 2007 Scenarios oflong-term socio-economic and environmental develop-

ment under climate stabilization Technol ForecastingSoc Change 74 887ndash935 (doi101016jtechfore200605026)

Rosegrant M W Paisner M S amp Meijier S 2001 Long-term prospects for agriculture and the resource base The

World Bank Rural Development Family Rural Develop-ment Strategy Background Paper 1 Washington DCThe World Bank

Rosegrant M W Zhu T Msangi S amp Sulser T 2008Global scenarios for biofuels impacts and implications


Rev Agric Econ 30 495ndash505 (doi101111j1467-9353200800424x)

Royal Society 2009 Reaping the benefits science and the

sustainable intensification of global agriculture RSPolicy document 1109 issued October 2009 RS1608ISBN 978-0-85403-784-1

Rudel T K Coomes O T Moran E Archard FAngelsen A Xu J amp Lambin E 2005 Forest tran-

sitions towards a global understanding of land usechange Global Environ Change 15 23ndash31 (doi101016jgloenvcha200411001)

Schmidhuber J 2003 The outlook for long-term changes in

food consumption patterns concerns and policy optionsPaper prepared for the FAO Scientific Workshop on Globaliza-tion of the Food System Impacts on Food Security andNutrition Oct 8ndash10 Rome Italy FAO

Smeets E M W Faaij A P C Lewandowski I M amp

Turkenburg W C 2007 A bottom-up assessment andreview of global bio-energy potentials to 2050 ProgEnergy Combust Sci 33 56ndash106 (doi101016jpecs200608001)

Smith W amp Montgomery H 2004 Revolution or evolution

New Zealand agriculture since 1984 GeoJournal 59107ndash118 (doi101023BGEJO00000199693849682)

Smith P et al 2007 Policy and technological constraints toimplementation of greenhouse gas mitigation options inagriculture Agric Ecosyst Environ 118 6ndash28 (doi10

1016jagee200606006)Stehfest E Bouwmann L van Vuuren D den Elzen

M G J Eikhout B amp Kabat P 2009 Climate benefitsof changing diet Climate Change 95 83ndash102 (doi10

1007s10584-008-9534-6)UKERC 2009 UK Energy Research Centre See http

wwwukercacuksupporttiki-indexphpUNEP 2002 Global environment outlook 3 (GEO-3) Nairobi

UNEP and London Earthscan


UNEP 2007 Global environment outlook 4 (GEO-4) NairobiUNEP London UK Earthscan

UNEP-WCMC 2009 World Database on Protected Areas(WDPA) Annual Release 2009 (web download version)February 2009 The WDPA is a joint product ofUNEP and IUCN See httpwwwwdpaorgAnnualReleaseaspx

van Vuuren D P 2007 Energy systems and climate policy

Long-term scenarios for an uncertain future PhDthesis Utrecht University The Netherlands

van Vuuren D P Eickhaut B Lucas P L amp den ElzenM 2006 Long-term multi-gas scenarios to stabilise radia-

tive forcingmdashexploring costs and benefits within anintegrated assessment framework Energy J (Spec Iss)3 201ndash234

van Vuuren D P et al 2008 Outlook on agricultural changeand its drivers In International assessment of agriculturalscience and technology development (eds B McIntyreH Herren J Wakhungu amp R T Watson) pp 495ndash590 Global report Washington DC Island Press

Wise M A Calvin K Thomson A Clarke LBond-Lamberty B Sands R Smith S J Janetos A amp

Edmonds J 2009a Implications of limiting CO2

concentrations for land use and energy Science 3241183ndash1186 (doi101126science1168475)

Wise M A Calvin K V Thomson A M Clarke L EBond-Lamberty B Sands R D Smith S J Janetos

A amp Edmonds J 2009b The implications of limitingCO2 concentrations for agriculture land use land-usechange emissions and bioenergy US Department ofEnergy Report Washington DC USDE

World Bank 1994 World Development Report 1994 OxfordUK Oxford University Press

Wunder S 2003 Oil wealth and the fate of the forest Acomparative study of eight tropical countries pp 432London UK Routledge

competition for land

mdash technology

societal trends

institutional factors

mdash land distributionmdash land tenure securitymdash land-use policies

mdash institutional capacitiesmdash governance

mdash regulations and degree of illegality

natural causes land transition

mdash hurricanes mdash crops and pastures mdash urban sprawl mdash road building

mdash oil and mining

mdash forest clear-cutting (eg pulp paper)

mdash natural fires mdash pests mdash floodsmdash water availability mdash global warming

land degradation mdash logging mdash induced fires mdash over-grazing mdash firewood over-harvestingmdash defaunation

mdash population growthmdash agricultural intensificationmdash dietary preference mdash non-food goods and servicesmdash urbanization mdash economic developmentmdash migration patternsmdash cultural factors

mdash trademdash macroeconomics

mdash market failures

mdash infrastructure investments mdash commodity price and demand changes

drivers (underlying causes)

socio-economic and technology factors

pressures (direct causes)

Figure 1 Conceptual analysis framework for competition for land drivers and pressures Adapted from Contreras-Hermosilla

(2000)


Energy Modelling Forum (eg Kurosawa 2006 vanVuuren et al 2006) We expand on that synthesis byadding more recent studies in sectsect4 and 5 In these sec-tions we present projections of land-use change to2050 and examine the impact of changes in non-agri-cultural policy (eg forest and protected land policy)on competition for land We briefly examine theassumptions upon which the projections are basedand identify the main areas of uncertainty

We conclude by assessing and ranking the mostimportant external factors that may affect the landavailable for agriculture between now and 2050 andby discussing future needs to reduce uncertainties inthese projections

2 FACTORS AFFECTING COMPETITIONFOR LANDAlthough competition for land has been identified as adriver affecting land use food and farming by the Fore-sight Global Food and Farming Futures Project it isactually an emergent property of a range of other driversand pressures Figure 1 presents a conceptual frame-work for analysing drivers and related pressures ofcompetition for land at different geographical scales

In understanding interrelated causes for competitionfor land we distinguish between drivers and pressuresPressures represent direct causes the visible motivationsfor competition for land (right-hand side of figure 1)Drivers (underlying causes) for competition are factorsof higher causal order that determine the degree ofthe actual direct pressures (left-hand side of figure 1)


(see Chomitz amp Gray 1996 Kaimowitz et al 1998Geist amp Lambin 2002 Wunder 2003 Niesten et al2004 Rudel et al 2005 on these different drivers andpressures S Klappa 1999 unpublished data)

We do not attempt to review the drivers and press-ures in detail here since they are covered by the otherdriver reviews in this issue In sect2a however we discuss afew drivers and pressures to demonstrate how theyimpact upon land use through their impact on competitionfor land

(a) Population growth agricultural

intensification and dietary preference

The growth in the human population from about 3 bil-lion in 1960 to 68 billion in 2010 coupled withincreased income and changes in diet has beenaccompanied by substantial increases in crop andanimal production (27-fold for cereals 16-fold forroots and tubers and fourfold for meat Foresight2009) This increase will need to be maintained if theprojected population of 9 billion by 2050 is to be sus-tained Past increases in crop production have occurredas a result of both extensification (altering natural ecosys-tems to produce products) and intensification(producing more of the desired products per unit areaof land already used for agriculture or forestry) Of theworldrsquos 134 billion ha land surface about 3 billion hais suitable for crop production (Bruinsma 2003) andabout one-half of this is already cultivated (14 billion hain 2008) The remaining potentially cultivatable land iscurrently beneath tropical forests so it would be undesir-able to convert this to agricultural land because of the



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10

5

01970 1980 1990 2000 2010 204020302020 2050

MA-GO


MA-TGMA-OSMA-AM


MA-GO

IAASTDFAO (IMAGE)

MA-TGMA-OSMA-AM

1980 2000 20402020

5

10

15

gras

slan

d ar

ea (

mill

ion

km2 )

20

25

30

35

40

45

0









2020

2050

ref

policy

ref

FFCT

UCT

B2

B2

A2r

baseline

B2_550

system1


A1

Mha Mha

A2B1B2

GOOSTGAM

MFSecFSusFPF

baseline450 ppm

baselinehigh_AKST

reference

healthy_diet

4000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

04000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

0

SRE

SM

AG

EO

4

IMA

GE

IMA

GE

IM

PAC

TIM

AG

E

IMPA

CT

IMA

GE

L

EIT

AP

IMA

GE

IM

PAC

TIMAGE

IMAGE

PCCRN

PCCROLSR

PCCRN

PCCROLSR

GRAPE

MESSAGE

GLOBIOM

OE

CD

EO

MIT

-stu

dy

EPP

AM

iniC

AM

Min

iCA

M

Wis

e G

illin

gham

Smee

ts

Qui

cksc

anIA

ASTD

Kosug

i

Riahi

Havlik

IMAGE-F

AO

Stehfes

t

Fig

ure

6

Glo

bal

lan

d-u

sech

an

ge

by

2020

an

d2050

for

dif

fere

nt

mod

els

an

dsc

enari

os

(see

tab

les

5an

d6

for

ab

bre

via

tion

s)

Chan

ge

giv

enas

abso

lute

chan

ge

rela

tive

to2000

wit

hth

eex

cepti

on

of

Min

iCA

M(b

ase

year

2005)

an

dG

RA

PE

(base

year

2010)

wh

ere

this

was

the

nea

rest

availab

leye

ar

Tab

le7

det

ails

the

lan

dca

tegori

esfo

rth

ed

iffe

ren

tm

od

els

Bro

wn

bio

fuel

ora

nge

crop

yellow

past

ure

light

gre

en

man

aged

fore

st

dark

gre

en

un

man

aged

fore

st

red

oth

er




model analysis



















natu

ral a

rea

(mill

ion

km2 )

10

20

30

70

60

50

40

01970 1980 1990 2000 2010 204020302020 2050

GOIAASTDFAO (IMAGE)

TGOSAM










110

8076721

6732

9813

8758

6264 6627

9260

7062

100

90

80

70

60Mha

50

40

30

20

10


scenario

































Italy FAO












































































Environment)



MNP (2006)



equilibrium model


Gurgel et al(2007)




Smeets et al(2007)










Riahi et al (2007)



bioenergy















SRES





GEO4


























550 ppmv by 2095


agriculture



















thorn thorn thorn




grazingpasture



forestplantation

forestmanagedforest

only totalforest

unmanagedforest







withinotherland



vegetatedland













(a) (b)pr

oduc

tion

(mill

ion

tonn

e)

4000 800

600

400

200

0

3000

2000

1000

01960 1980 2000 2020 2040 1960 1980 2000 2020 2040





crop

are

a (m

illio

n km

2 )

25

20

15

10

5

01970 1980 1990 2000 2010 204020302020 2050

MA-GO


MA-TGMA-OSMA-AM


MA-GO

IAASTDFAO (IMAGE)

MA-TGMA-OSMA-AM

1980 2000 20402020

5

10

15

gras

slan

d ar

ea (

mill

ion

km2 )

20

25

30

35

40

45

0









2020

2050

ref

policy

ref

FFCT

UCT

B2

B2

A2r

baseline

B2_550

system1


A1

Mha Mha

A2B1B2

GOOSTGAM

MFSecFSusFPF

baseline450 ppm

baselinehigh_AKST

reference

healthy_diet

4000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

04000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

0

SRE

SM

AG

EO

4

IMA

GE

IMA

GE

IM

PAC

TIM

AG

E

IMPA

CT

IMA

GE

L

EIT

AP

IMA

GE

IM

PAC

TIMAGE

IMAGE

PCCRN

PCCROLSR

PCCRN

PCCROLSR

GRAPE

MESSAGE

GLOBIOM

OE

CD

EO

MIT

-stu

dy

EPP

AM

iniC

AM

Min

iCA

M

Wis

e G

illin

gham

Smee

ts

Qui

cksc

anIA

ASTD

Kosug

i

Riahi

Havlik

IMAGE-F

AO

Stehfes

t

Fig

ure

6

Glo

bal

lan

d-u

sech

an

ge

by

2020

an

d2050

for

dif

fere

nt

mod

els

an

dsc

enari

os

(see

tab

les

5an

d6

for

ab

bre

via

tion

s)

Chan

ge

giv

enas

abso

lute

chan

ge

rela

tive

to2000

wit

hth

eex

cepti

on

of

Min

iCA

M(b

ase

year

2005)

an

dG

RA

PE

(base

year

2010)

wh

ere

this

was

the

nea

rest

availab

leye

ar

Tab

le7

det

ails

the

lan

dca

tegori

esfo

rth

ed

iffe

ren

tm

od

els

Bro

wn

bio

fuel

ora

nge

crop

yellow

past

ure

light

gre

en

man

aged

fore

st

dark

gre

en

un

man

aged

fore

st

red

oth

er




model analysis



















natu

ral a

rea

(mill

ion

km2 )

10

20

30

70

60

50

40

01970 1980 1990 2000 2010 204020302020 2050

GOIAASTDFAO (IMAGE)

TGOSAM










110

8076721

6732

9813

8758

6264 6627

9260

7062

100

90

80

70

60Mha

50

40

30

20

10


scenario

































Italy FAO












































































SRES





GEO4


























550 ppmv by 2095


agriculture



















thorn thorn thorn




grazingpasture



forestplantation

forestmanagedforest

only totalforest

unmanagedforest







withinotherland



vegetatedland













(a) (b)pr

oduc

tion

(mill

ion

tonn

e)

4000 800

600

400

200

0

3000

2000

1000

01960 1980 2000 2020 2040 1960 1980 2000 2020 2040





crop

are

a (m

illio

n km

2 )

25

20

15

10

5

01970 1980 1990 2000 2010 204020302020 2050

MA-GO


MA-TGMA-OSMA-AM


MA-GO

IAASTDFAO (IMAGE)

MA-TGMA-OSMA-AM

1980 2000 20402020

5

10

15

gras

slan

d ar

ea (

mill

ion

km2 )

20

25

30

35

40

45

0









2020

2050

ref

policy

ref

FFCT

UCT

B2

B2

A2r

baseline

B2_550

system1


A1

Mha Mha

A2B1B2

GOOSTGAM

MFSecFSusFPF

baseline450 ppm

baselinehigh_AKST

reference

healthy_diet

4000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

04000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

0

SRE

SM

AG

EO

4

IMA

GE

IMA

GE

IM

PAC

TIM

AG

E

IMPA

CT

IMA

GE

L

EIT

AP

IMA

GE

IM

PAC

TIMAGE

IMAGE

PCCRN

PCCROLSR

PCCRN

PCCROLSR

GRAPE

MESSAGE

GLOBIOM

OE

CD

EO

MIT

-stu

dy

EPP

AM

iniC

AM

Min

iCA

M

Wis

e G

illin

gham

Smee

ts

Qui

cksc

anIA

ASTD

Kosug

i

Riahi

Havlik

IMAGE-F

AO

Stehfes

t

Fig

ure

6

Glo

bal

lan

d-u

sech

an

ge

by

2020

an

d2050

for

dif

fere

nt

mod

els

an

dsc

enari

os

(see

tab

les

5an

d6

for

ab

bre

via

tion

s)

Chan

ge

giv

enas

abso

lute

chan

ge

rela

tive

to2000

wit

hth

eex

cepti

on

of

Min

iCA

M(b

ase

year

2005)

an

dG

RA

PE

(base

year

2010)

wh

ere

this

was

the

nea

rest

availab

leye

ar

Tab

le7

det

ails

the

lan

dca

tegori

esfo

rth

ed

iffe

ren

tm

od

els

Bro

wn

bio

fuel

ora

nge

crop

yellow

past

ure

light

gre

en

man

aged

fore

st

dark

gre

en

un

man

aged

fore

st

red

oth

er




model analysis



















natu

ral a

rea

(mill

ion

km2 )

10

20

30

70

60

50

40

01970 1980 1990 2000 2010 204020302020 2050

GOIAASTDFAO (IMAGE)

TGOSAM










110

8076721

6732

9813

8758

6264 6627

9260

7062

100

90

80

70

60Mha

50

40

30

20

10


scenario

































Italy FAO



















































































550 ppmv by 2095


agriculture



















thorn thorn thorn




grazingpasture



forestplantation

forestmanagedforest

only totalforest

unmanagedforest







withinotherland



vegetatedland













(a) (b)pr

oduc

tion

(mill

ion

tonn

e)

4000 800

600

400

200

0

3000

2000

1000

01960 1980 2000 2020 2040 1960 1980 2000 2020 2040





crop

are

a (m

illio

n km

2 )

25

20

15

10

5

01970 1980 1990 2000 2010 204020302020 2050

MA-GO


MA-TGMA-OSMA-AM


MA-GO

IAASTDFAO (IMAGE)

MA-TGMA-OSMA-AM

1980 2000 20402020

5

10

15

gras

slan

d ar

ea (

mill

ion

km2 )

20

25

30

35

40

45

0









2020

2050

ref

policy

ref

FFCT

UCT

B2

B2

A2r

baseline

B2_550

system1


A1

Mha Mha

A2B1B2

GOOSTGAM

MFSecFSusFPF

baseline450 ppm

baselinehigh_AKST

reference

healthy_diet

4000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

04000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

0

SRE

SM

AG

EO

4

IMA

GE

IMA

GE

IM

PAC

TIM

AG

E

IMPA

CT

IMA

GE

L

EIT

AP

IMA

GE

IM

PAC

TIMAGE

IMAGE

PCCRN

PCCROLSR

PCCRN

PCCROLSR

GRAPE

MESSAGE

GLOBIOM

OE

CD

EO

MIT

-stu

dy

EPP

AM

iniC

AM

Min

iCA

M

Wis

e G

illin

gham

Smee

ts

Qui

cksc

anIA

ASTD

Kosug

i

Riahi

Havlik

IMAGE-F

AO

Stehfes

t

Fig

ure

6

Glo

bal

lan

d-u

sech

an

ge

by

2020

an

d2050

for

dif

fere

nt

mod

els

an

dsc

enari

os

(see

tab

les

5an

d6

for

ab

bre

via

tion

s)

Chan

ge

giv

enas

abso

lute

chan

ge

rela

tive

to2000

wit

hth

eex

cepti

on

of

Min

iCA

M(b

ase

year

2005)

an

dG

RA

PE

(base

year

2010)

wh

ere

this

was

the

nea

rest

availab

leye

ar

Tab

le7

det

ails

the

lan

dca

tegori

esfo

rth

ed

iffe

ren

tm

od

els

Bro

wn

bio

fuel

ora

nge

crop

yellow

past

ure

light

gre

en

man

aged

fore

st

dark

gre

en

un

man

aged

fore

st

red

oth

er




model analysis



















natu

ral a

rea

(mill

ion

km2 )

10

20

30

70

60

50

40

01970 1980 1990 2000 2010 204020302020 2050

GOIAASTDFAO (IMAGE)

TGOSAM










110

8076721

6732

9813

8758

6264 6627

9260

7062

100

90

80

70

60Mha

50

40

30

20

10


scenario

































Italy FAO
















































































thorn thorn thorn




grazingpasture



forestplantation

forestmanagedforest

only totalforest

unmanagedforest







withinotherland



vegetatedland













(a) (b)pr

oduc

tion

(mill

ion

tonn

e)

4000 800

600

400

200

0

3000

2000

1000

01960 1980 2000 2020 2040 1960 1980 2000 2020 2040





crop

are

a (m

illio

n km

2 )

25

20

15

10

5

01970 1980 1990 2000 2010 204020302020 2050

MA-GO


MA-TGMA-OSMA-AM


MA-GO

IAASTDFAO (IMAGE)

MA-TGMA-OSMA-AM

1980 2000 20402020

5

10

15

gras

slan

d ar

ea (

mill

ion

km2 )

20

25

30

35

40

45

0









2020

2050

ref

policy

ref

FFCT

UCT

B2

B2

A2r

baseline

B2_550

system1


A1

Mha Mha

A2B1B2

GOOSTGAM

MFSecFSusFPF

baseline450 ppm

baselinehigh_AKST

reference

healthy_diet

4000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

04000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

0

SRE

SM

AG

EO

4

IMA

GE

IMA

GE

IM

PAC

TIM

AG

E

IMPA

CT

IMA

GE

L

EIT

AP

IMA

GE

IM

PAC

TIMAGE

IMAGE

PCCRN

PCCROLSR

PCCRN

PCCROLSR

GRAPE

MESSAGE

GLOBIOM

OE

CD

EO

MIT

-stu

dy

EPP

AM

iniC

AM

Min

iCA

M

Wis

e G

illin

gham

Smee

ts

Qui

cksc

anIA

ASTD

Kosug

i

Riahi

Havlik

IMAGE-F

AO

Stehfes

t

Fig

ure

6

Glo

bal

lan

d-u

sech

an

ge

by

2020

an

d2050

for

dif

fere

nt

mod

els

an

dsc

enari

os

(see

tab

les

5an

d6

for

ab

bre

via

tion

s)

Chan

ge

giv

enas

abso

lute

chan

ge

rela

tive

to2000

wit

hth

eex

cepti

on

of

Min

iCA

M(b

ase

year

2005)

an

dG

RA

PE

(base

year

2010)

wh

ere

this

was

the

nea

rest

availab

leye

ar

Tab

le7

det

ails

the

lan

dca

tegori

esfo

rth

ed

iffe

ren

tm

od

els

Bro

wn

bio

fuel

ora

nge

crop

yellow

past

ure

light

gre

en

man

aged

fore

st

dark

gre

en

un

man

aged

fore

st

red

oth

er




model analysis



















natu

ral a

rea

(mill

ion

km2 )

10

20

30

70

60

50

40

01970 1980 1990 2000 2010 204020302020 2050

GOIAASTDFAO (IMAGE)

TGOSAM










110

8076721

6732

9813

8758

6264 6627

9260

7062

100

90

80

70

60Mha

50

40

30

20

10


scenario

































Italy FAO









































































(a) (b)pr

oduc

tion

(mill

ion

tonn

e)

4000 800

600

400

200

0

3000

2000

1000

01960 1980 2000 2020 2040 1960 1980 2000 2020 2040





crop

are

a (m

illio

n km

2 )

25

20

15

10

5

01970 1980 1990 2000 2010 204020302020 2050

MA-GO


MA-TGMA-OSMA-AM


MA-GO

IAASTDFAO (IMAGE)

MA-TGMA-OSMA-AM

1980 2000 20402020

5

10

15

gras

slan

d ar

ea (

mill

ion

km2 )

20

25

30

35

40

45

0









2020

2050

ref

policy

ref

FFCT

UCT

B2

B2

A2r

baseline

B2_550

system1


A1

Mha Mha

A2B1B2

GOOSTGAM

MFSecFSusFPF

baseline450 ppm

baselinehigh_AKST

reference

healthy_diet

4000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

04000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

0

SRE

SM

AG

EO

4

IMA

GE

IMA

GE

IM

PAC

TIM

AG

E

IMPA

CT

IMA

GE

L

EIT

AP

IMA

GE

IM

PAC

TIMAGE

IMAGE

PCCRN

PCCROLSR

PCCRN

PCCROLSR

GRAPE

MESSAGE

GLOBIOM

OE

CD

EO

MIT

-stu

dy

EPP

AM

iniC

AM

Min

iCA

M

Wis

e G

illin

gham

Smee

ts

Qui

cksc

anIA

ASTD

Kosug

i

Riahi

Havlik

IMAGE-F

AO

Stehfes

t

Fig

ure

6

Glo

bal

lan

d-u

sech

an

ge

by

2020

an

d2050

for

dif

fere

nt

mod

els

an

dsc

enari

os

(see

tab

les

5an

d6

for

ab

bre

via

tion

s)

Chan

ge

giv

enas

abso

lute

chan

ge

rela

tive

to2000

wit

hth

eex

cepti

on

of

Min

iCA

M(b

ase

year

2005)

an

dG

RA

PE

(base

year

2010)

wh

ere

this

was

the

nea

rest

availab

leye

ar

Tab

le7

det

ails

the

lan

dca

tegori

esfo

rth

ed

iffe

ren

tm

od

els

Bro

wn

bio

fuel

ora

nge

crop

yellow

past

ure

light

gre

en

man

aged

fore

st

dark

gre

en

un

man

aged

fore

st

red

oth

er




model analysis



















natu

ral a

rea

(mill

ion

km2 )

10

20

30

70

60

50

40

01970 1980 1990 2000 2010 204020302020 2050

GOIAASTDFAO (IMAGE)

TGOSAM










110

8076721

6732

9813

8758

6264 6627

9260

7062

100

90

80

70

60Mha

50

40

30

20

10


scenario

































Italy FAO









































































2020

2050

ref

policy

ref

FFCT

UCT

B2

B2

A2r

baseline

B2_550

system1


A1

Mha Mha

A2B1B2

GOOSTGAM

MFSecFSusFPF

baseline450 ppm

baselinehigh_AKST

reference

healthy_diet

4000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

04000

3000

2000

1000

ndash100

0

ndash200

0

ndash300

0

ndash400

0

0

SRE

SM

AG

EO

4

IMA

GE

IMA

GE

IM

PAC

TIM

AG

E

IMPA

CT

IMA

GE

L

EIT

AP

IMA

GE

IM

PAC

TIMAGE

IMAGE

PCCRN

PCCROLSR

PCCRN

PCCROLSR

GRAPE

MESSAGE

GLOBIOM

OE

CD

EO

MIT

-stu

dy

EPP

AM

iniC

AM

Min

iCA

M

Wis

e G

illin

gham

Smee

ts

Qui

cksc

anIA

ASTD

Kosug

i

Riahi

Havlik

IMAGE-F

AO

Stehfes

t

Fig

ure

6

Glo

bal

lan

d-u

sech

an

ge

by

2020

an

d2050

for

dif

fere

nt

mod

els

an

dsc

enari

os

(see

tab

les

5an

d6

for

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yellow

past

ure

light

gre

en

man

aged

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st

dark

gre

en

un

man

aged

fore

st

red

oth

er




model analysis



















natu

ral a

rea

(mill

ion

km2 )

10

20

30

70

60

50

40

01970 1980 1990 2000 2010 204020302020 2050

GOIAASTDFAO (IMAGE)

TGOSAM










110

8076721

6732

9813

8758

6264 6627

9260

7062

100

90

80

70

60Mha

50

40

30

20

10


scenario

































Italy FAO










































































model analysis



















natu

ral a

rea

(mill

ion

km2 )

10

20

30

70

60

50

40

01970 1980 1990 2000 2010 204020302020 2050

GOIAASTDFAO (IMAGE)

TGOSAM










110

8076721

6732

9813

8758

6264 6627

9260

7062

100

90

80

70

60Mha

50

40

30

20

10


scenario

































Italy FAO









































































110

8076721

6732

9813

8758

6264 6627

9260

7062

100

90

80

70

60Mha

50

40

30

20

10


scenario

































Italy FAO




























































































Italy FAO









































































Competition for land

Documents

Transcript of Competition for land