quantifying the climate-change consequences of shifting land use between forest and agriculture

Science of the Total Environment xxx (2013) xxx–xxx

STOTEN-14372; No of Pages 11

Contents lists available at SciVerse ScienceDirect

Science of the Total Environment

j ourna l homepage: www.e lsev ie r .com/ locate /sc i totenv

Quantifying the climate-change consequences of shifting land use between forestand agriculture

Miko U.F. Kirschbaum ⁎, Surinder Saggar, Kevin R. Tate, Kailash P. Thakur, Donna L. GiltrapLandcare Research, Private Bag 11052, Palmerston North, New Zealand

H I G H L I G H T S G R A P H I C A L A B S T R A C T

► We quantified radiative forcing of landuse change, incl. CO2, CH4, N2O andalbedo.

► CO2 is responsible for 50% of forcingand CH4 and N2O for 25% each.

► Albedo changes negate forcing ofgreenhouse gases by 20–25%.

► Accounting for wood products makeslittle difference.

► Pastures have greater greenhouse im-pact than cropping land. Forest is best.

⁎ Corresponding author at: Landcare Research, PrivatE-mail address: [email protected]

0048-9697/$ – see front matter © 2013 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.scitotenv.2013.01.026

Please cite this article as: Kirschbaum MUagriculture, Sci Total Environ (2013), http

Def (s

heep

)

Def (d

airy

, no

WP)

Def (d

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, w. W

P)Def

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-800

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CH4

Carbon

Albedo

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Article history:Received 30 September 2012Received in revised form 7 January 2013Accepted 8 January 2013Available online xxxx

Keywords:AlbedoCO2

Greenhouse gasMethaneNitrous oxideRadiative forcing

Land-use change between forestry and agriculture can cause large net emissions of carbon dioxide (CO2), andthe respective land uses associated with forest and pasture lead to different on-going emission rates of methane(CH4) and nitrous oxide (N2O) and different surface albedo. Here, we quantify the overall net radiative forcingand consequent temperature change from specified land-use changes.These different radiative agents cause radiative forcing of different magnitudes and with different time profiles.Carbon emission can be very high when forests are cleared. Upon reforestation, the former carbon stocks can beregained, but the rate of carbon sequestration is much slower than the rate of carbon loss from deforestation. Aproduction forest may undergo repeated harvest and regrowth cycles, each involving periods of C emission andrelease.Agricultural land, especially grazed pastures, have much higher N2O emissions than forests because of theirgenerally higher nitrogen status that can be further enhanced through intensification of the nitrogen cycleby animal excreta. Because of its longevity in the atmosphere, N2O concentrations build up nearly linearlyover many decades. CH4 emissions can be very high from ruminant animals grazing on pastures. Becauseof its short atmospheric longevity, the CH4 concentration from a converted pasture accumulates for only afew decades before reaching a new equilibrium when emission of newly produced CH4 is balanced by theoxidation of previously emitted CH4. Albedo changes generally have the opposite radiative forcing fromthose of the GHGs and partly negate their radiative forcing. Overall and averaged over 100 years, CO2 istypically responsible for 50% of radiative forcing and CH4 and N2O for 25% each. Albedo changes can negatethe radiative forcing by the three greenhouse gases by 20–25%.

© 2013 Elsevier B.V. All rights reserved.

e Bag 11052, Palmerston North 4442, New Zealand. Tel.: +64 6 353 4902; fax: +64 6 353 4801..nz (M.U.F. Kirschbaum).

rights reserved.

F, et al, Quantifying the climate-change consequences of shifting land use between forest and://dx.doi.org/10.1016/j.scitotenv.2013.01.026

http://dx.doi.org/10.1016/j.scitotenv.2013.01.026

mailto:[email protected]


http://www.sciencedirect.com/science/journal/00489697


2 M.U.F. Kirschbaum et al. / Science of the Total Environment xxx (2013) xxx–xxx

1. Introduction

Table 1Critical constants to define the global C cycle model used here. Note that thenewer version of the Bern model uses only four pools whereas older versionsused five pools. The new parameters follow Joos et al. (2012).

Pool fi τi (yr)

Pool 1 0.218 –

Pool 2 0.229 381.3Pool 3 0.285 34.78Pool 4 0.269 4.12

Climate change, which is now recognised as a major threat to thefuture of society and the environment, is mainly caused by increasesin the concentration of the three principal greenhouse gases carbondioxide (CO2), methane (CH4) and nitrous oxide (N2O) (Forster et al.,2007), with variability in solar output, volcanic eruptions and ENSOcycles adding to short-term variability (Lean, 2010). The greenhousegas (GHG) warming attributable to an increasing concentration ofthese gases is partly negated by increases in the reflectivity (albedo)of the Earth (Forster et al., 2007; Boisier et al., 2012). Albedo changesconsist of changes of both the Earth's surface and the atmosphere, espe-cially through cloud formation, which may change due to increasingaerosol load in the atmosphere from escalating air pollution and fromchanges to the hydrological cycle (Forster et al., 2007; Bala et al., 2007).

Mature forests can contain significant amounts of carbon (C), anddeforestation has been a significant contributor to past (Houghton,1999) and on-going (Denman et al., 2007) CO2 emissions. These Cemissions can potentially be reversed through reforestation of previouslydeforested land. The non-CO2 greenhouse gases CH4 and N2O are largelyassociated with agricultural activities, especially livestock farming(Denman et al., 2007) so that land-use change indirectly plays a largerole in their emissions through the subsequent land use. Land-use changealso leads to changes in the albedo of the surface, with its own conse-quent radiative forcing (Kirschbaum et al., 2011; Bright et al., 2012).

Past analyses of the combined GHG impact of alternative land-useoptions have typically just multiplied the net emission flux of differentgases by their respective greenhouse warming potentials (e.g. Scheer etal., 2008; Bernoux et al., 2010). More sophisticated assessment proce-dures that explicitly account for the atmospheric longevity of the differentGHGs have only recently begun to be employed (e.g. Anderson-Teixeiraand DeLucia, 2011; Bright et al., 2012). At present, there are few agreedprocedures for quantifying the role of the timing of GHG emissions foroverall impact assessments, and different developing approaches for lifecycle assessment use a variety of different assumptions and conventions(Brandão et al., 2013).

Albedo changes have recently begun to be explicitly included inanalyses of the net climate change consequences of land-use change(Jackson et al., 2008; Schwaiger and Bird, 2010; West et al., 2010;Kirschbaum et al., 2011; Bright et al., 2012; Anderson-Teixeira et al.,2012). Such inclusion is warranted as albedo changes cause radiativeforcing in much the same way as GHGs, and for some regions of theworld, principally those with extended periods of snow cover, the ra-diative forcing of albedo changes can be as significant as the radiativeforcing of greenhouse gases (e.g. Betts, 2000).

Here, we provide a quantitative assessment of radiative forcingand resultant global temperature changes that follow from a change inland use, involving shifts between forest and agricultural land use. Forthese land-use changes, we separately quantify the radiative contribu-tions of CO2, CH4, N2O and direct albedo changes. For want of a betterterm, we refer to the three greenhouse gases and albedo collectivelyas ‘radiative agents’. Each of the three GHGs has their own emissionprofiles associated with land-use change, and they have different lon-gevities in the atmosphere. Changes in albedo occurmore or less instan-taneously with changes in land-cover, and they can be readily reversed,with immediate consequences for radiative forcing. These factors areincluded in the analysis. Ultimately, an overall global warming effectof specified land-use changes is calculated that includes a term for thethermal inertia of the global climate system.

In this analysis, we aim to provide a globally relevant analysis,quantified with specific numeric examples as case studies. Specificnumbers for these case studies are largely drawn from New Zealand.Land use and land-use change contribute a large part to New Zealand'stotal greenhouse gas emissions, with CH4 and N2O contributing abouthalf of the country's gross emissions while reforestation offsets aboutone third of gross emissions (MfE, 2011; Kirschbaum et al., 2012). A

Please cite this article as: Kirschbaum MUF, et al, Quantifying the climagriculture, Sci Total Environ (2013), http://dx.doi.org/10.1016/j.scitot

detailed quantification of emissions associated with land use andland-use change is therefore more important for New Zealand than formost other countries. The present work follows on from the quantifica-tion briefly given by Kirschbaum et al. (2012), but includes a more com-prehensive quantification of the key components and a more detaileddescription of the underlying model and assumptions.

2. Calculating radiative forcing and temperature changes

2.1. Radiative forcing of CO2

To assess the ultimate effects of C storage in trees, it is necessary toconsider interactions with the global C cycle. In essence, C stored intrees lowers the atmospheric CO2 concentration,which, in turn, reducesCO2 uptake by the natural pools of the global C cycle, especially theoceans. The atmospheric CO2 concentration is therefore reduced byless than the amount of C stored in trees, and that difference increasesover time. The opposite occurs when C is released from storage in thebiosphere — the atmospheric increase is greatest immediately afterthe release, but then trends back down over time. The radiative effectof the removal or addition of a certain amount of C by trees therefore re-duces with time after the removal or addition.

The C cycle feed-back can be described by considering the C absorp-tion into four notional global C pools, representing pools such as theatmosphere, deep and shallow oceans, and the undisturbed biosphere(Meier–Reimer and Hasselmann, 1987; Wigley, 1991; Joos et al.,2012), that have different exchange time constants. When C is addedto the atmosphere, all of it initially resides in the atmosphere but overtime is transferred from the atmosphere into these various pools.Atmospheric concentrations consequently change as the C in thesepools changes towards regaining their effective equilibria.

Following a C addition, ΔB, to the atmosphere due to land clearingor removal by a biospheric sink, the atmospheric CO2 concentration attime t was, therefore, described as:

Ca ¼ f1 þ f2 exp –t=τ2ð Þ þ f3 exp –t=τ3ð Þ þ f4 exp –t=τ4ð Þ½ �ΔB ð1Þ

where f1 ... f4 and τ2 ... τ4 are the critical constants of the notional Cpools as per Table 1 (Joos et al., 2012). At time 0, immediately afterany C addition to the atmosphere, the exponential terms are allexp(0)=1, and since the four fractions (f1 ... f4) sum to 1, Ca=ΔB, allemitted C resides in the atmosphere. After a very long time period,the exponential terms approach zero and the remaining atmosphericamount becomes Ca=f1 ΔB. When there are multiple C additions, thecalculations in Eq. 1 are repeated for each addition with different ‘t’values set for each C addition/removal as the respective times sincethose additions/removals occurred. The overall atmospheric amount isthe sum of the calculated values for each individual addition/removal.

From these amounts of C (in tC), a change in atmospheric concen-tration, Δ[C] (in μmol mol−1), was calculated based on the ratio ofatmospheric C content and C concentration given by Joos et al. (2012)as:

Δ C½ � ¼ Ca= 2:123·109� �

: ð2Þ

ate-change consequences of shifting land use between forest andenv.2013.01.026


3M.U.F. Kirschbaum et al. / Science of the Total Environment xxx (2013) xxx–xxx

Radiative forcing due to CO2 changes in the atmosphere, Rc (W m−2),was calculated as:

Rc ¼ 5:35 ln 1þ Δ C½ �ð Þ= C½ �f g ð3Þ

where [C] is the background atmospheric CO2 concentration andΔ[C] thechange in atmospheric CO2 concentration that is investigated here. Theconversion 5.35 (W m−2) relates a mole fraction change of CO2 to radia-tive forcing (Harvey et al., 1997; Ramaswamy et al., 2001). It correspondsto amid-range climate sensitivity of about 3.75 W m−2 for doubling CO2

concentration.

2.2. Radiative forcing of CH4 and N2O

With CH4 emissions, it is necessary to include the atmosphericbreakdown of CH4 in the atmosphere because CH4 has a relativelyshort atmospheric life time. As for CO2, the radiative forcing of a unitof emitted CH4 is greatest immediately after its release, but over time,its effect diminishes as it is gradually broken down by atmosphericand (to a small extent) soil oxidation:

dM=dt ¼ Fm–M=τm ð4Þ

where Fm is the net flux of CH4 to the atmosphere, M is the amount (intCH4 attributable to a specific land use) present in the atmosphere andτm is the time constant for CH4 removal, which is treated here as a con-stant. In principle, there are further possible interactions between CH4

and other gases in the atmosphere or the concentration of hydroxylradicals that could change the atmospheric life time of CH4 in the atmo-sphere (Ramaswamy et al., 2001; Monteil et al., 2011). However, theseinteractions are not included here.

The net flux Fm (tCH4 ha−1 yr−1) is here taken to be the amountadded from a particular activity of interest, such as for enteric fermenta-tion fromone paddock in one year. Following Ramaswamy et al. (2001),τm is taken to be 12 years.When CH4 is generated through biological re-actions, every molecule of CH4 generated also removes one molecule ofC from the global C cycle and thereby reduces the atmospheric CO2 con-centration by that molecule.

When CH4 is oxidised it effectively increases the atmospheric CO2

concentration irrespective of whether CH4 originated from biogenicor fossil origin. In the oxidation of CH4, there are also a number of otherintermediate compounds being produced (e.g. Boucher et al., 2009), butwe assume here that they are all ultimately oxidised to CO2 and thefurther time delay in that conversion is not included. In the presentcalculations we deal only with biogenic CH4. We have therefore in-cluded the reduction in CO2 upon the generation of CH4 and a laterincrease in CO2 when CH4 is oxidised. This adds a minor adjustmentincluded for completeness.

From this amount of CH4 (in tCH4), a change in atmospheric concen-tration, Δ[M], can be calculated based on the conversion term of Joos etal. (2012) and the respective molecular weights of C and CH4 as:

Δ M½ � ¼ M= 16=12ð Þ·2:123·109h i

: ð5Þ

For N2O the calculations are similar to those for CH4, except thatN2O has a much longer atmospheric life time. Hence, it was calculatedas:

dN=dt ¼ Fn–Na=τn ð6Þ

where Fn is the net flux of N2O (in tN2O(N) ha−1 yr−1) to the atmo-sphere, Na is the amount present in the atmosphere, and τn is thetime constant for N2O removal. As for CH4, the quantity Na is takento be the amount added from a particular activity of interest. Follow-ing Ramaswamy et al. (2001), τn it is taken to be 120 years.


The change in atmospheric concentration, Δ[N], was calculatedas for CH4 above, based on the molecular weights of C and N,keeping in mind that N2O fluxes are usually quantified in units ofnitrogen, as:

Δ N½ � ¼ Na= 28=12ð Þ·2:123·109h i

: ð7Þ

The radiative forcing of CH4, Rm, and N2O, Rn, were calculated basedon the simplified expressions given by Ramaswamy et al. (2001):

Rm ¼ 0:036ffiffiffiffiffiffiffiM½ �

p−

ffiffiffiffiffiffiffiffiffiffiM0½ �

p� �þ f M0½ �; N0½ �ð Þ−f M½ �; N0½ �ð Þ ð8Þ

Rn ¼ 0:12ffiffiffiffiffiffiffiN½ �

p−

ffiffiffiffiffiffiffiffiffiN0½ �

p� �þ f M0½ �; N0½ �ð Þ−f M0½ �; N½ �ð Þ ð9Þ

where [M0] and [N0] are the background CH4 and N2O concentrations,[M] and [N] are the increased concentrations due to the additionalquantities of CH4 andN2O added to the atmosphere due to the activitiesstudied here and f([M],[N]) is a function that describes the interactiveeffects of CH4 and N2O due to their overlapping absorption bands. It isgiven by Ramaswamy et al. (2001) as:

f M;Nð Þ ¼ 0:47 ln 1þ 2:01·10−5 M½ �· N½ �ð Þ0:75 þ 5:31·10−15 M½ � M½ �· N½ �ð Þ1:52h i

:

ð10Þ

2.3. Radiative forcing of albedo

Following Kirschbaum et al. (2011), daily local radiative forcingdue to a change in albedo, ΔRd, was calculated as:

ΔRd ¼ Q s↓Δα 1–aatmð Þ ð11Þ

where Qs↓ is total daily downward solar radiation received at the sur-face, Δα is the annually-averaged difference in albedo between twodifferent land-use types integrated over the whole short-wave spec-trum, and aatm is the globally averaged proportion of short-wave radi-ation absorbed by the atmosphere which is usually estimated as 20%(Kiehl and Trenberth, 1997).

Hence, the radiative forcing of an albedo change, ΔRα, was calcu-lated as:

ΔRα ¼ 106ΔRd

s·Að12Þ

where 106 converts from MJ to joules, s is the number of seconds in aday (86,400), and A is the surface area of the Earth (510·1012 m2 or510 million km2).

Albedo-based radiative forcing is a direct function of the differencein albedo between two land-use types. This, in turn, is strongly affectedby the extent of snow cover, when albedo changes between land-usetypes are most pronounced, and by the average annual radiation re-ceived at specific locations (Betts, 2000).

Average annual incoming radiation changes with latitude andwith the extent of cloudiness. Across the globe, mean daily radiationmean daily radiation received at ground level, and averaged over awhole year, ranges from about 10 MJ m−2 d−1 in polar regions toabout 20 MJ m−2 d−1 near the equator (e.g. Stanhill and Cohen,2001). Desert regions in low latitudes may receive an even higher radia-tion load due to the general cloud-free conditions experienced for mostof the year. Near the equator there is also little seasonality, but furtherpoleward there is an increasingly distinct seasonal cycle that can rangefrom complete darkness in winter to daily radiation receipt in summerthat is similar to that in equatorial regions.



Table2

Activityda

taus

edforthescen

ariosinve

stigated

here.N

umbe

rsin

bracke

tsarenu

mbe

rsinclud

edforav

oide

dem

ission

s.

Scen

ario

CH4from

pasture

(kgCH

4ha

−1yr

−1 )

N2O

from

pasture

[kgN2O

(N)h

a−1yr

−1 ]

Timeconstant

(N2O

emission

s)CH

4ox

idation

(kgCH

4ha

−1yr

−1 )

Timeconstant

(CH4ox

idation)

SoilC

(tCha

−1 )

Timeconstant

(SOCch

ange

)W

oodprod

ucts

(%of

a-gbiom

ass)

Timeconstant

(woo

dprod

ucts

decay)

Forest

Agric.

Deforestto

shee

p15

05

3−

10−

11

0–

0–

Deforestto

dairy

240

103

−10

−1

10

–0

–

Deforestto

crop

ping

02

3−

10−

11

−20

200

–

Deforestw

ithwoo

dprod

ucts

240

103

−10

−1

10

–41

.725

Reforest

shee

pto

indige

nous

(−15

0)(−

5)1

−10

−1

500

–0

–

Reforest

crop

ping

toindige

nous

0(−

2)1

−10

−1

5020

200

–

Reforest

dairyto

indige

nous

(−24

0)(−

10)

1−

10−

150

0–

0–

Reforest

toex

otics

(−15

0)(−

5)1

−5

−1

50−

1020

5025


For the present case studies, we used a mean annual incident solarradiation (Qs) of 15 MJ m−2 d−1. Based on Betts (2000), Breuer et al.(2003) and Kirschbaum et al. (2011), we used an albedo differencebetween forest and agricultural land use of 7% (e.g. albedo of 0.13for forests and 0.20 for pastures and cropland), which are typicalvalues under snow-free conditions.

It was further assumed that albedo changes upon deforestation areessentially instantaneous, and that albedo changes upon reforestationoccur linearly over a period of 10 years as the tree canopy gradually ex-pands (Kirschbaum et al., 2011). It is recognised that for slow-growingboreal forests, it may take several decades for forest canopies to reachrepresentative forest albedo values (e.g. Bright et al., 2012).

2.4. Surface temperature

Radiative forcing leads to changes in surface temperatures, and re-cent work has shown that the radiative forcing of different radiativeagents leads to global surface warming with different warming effica-cies (e.g. Hansen et al., 2005; Forster et al., 2007). A climate sensitivityof 0.5 K m2 W−1 was assumed here (Harvey, 2000), which was thenmultiplied by respective warming efficacies of different agents, whichby definition, are expressed relative to that of CO2 (Forster et al.,2007). Temperature changes lag behind any radiative forcing, whichcan be described by one or several time constants. These issues anddifferent options were discussed and briefly explored by Kirschbaum(2003a) and, in line with the approach adopted then, a single time con-stant of 10 years was used here so that:

dT=dt ¼ 0:5Σ εiΔRi;t

� �–ΔTt–1

� �=10 ð13Þ

where ΔRi,t is the radiative forcing of each radiative agent i at time t at-tributable to the land-use change, εi is the warming efficacy of eachradiative agent and ΔTt−1 is temperature change at time t−1 rela-tive to the temperature at time 0 which is taken here to be the timewhen land-use change is first initiated. The constant 0.5 is the climatesensitivity.

We used warming efficacies of 1 for CO2 (by definition), 1.1 for CH4

(Hansen et al., 2005; Berntsen et al., 2005), 1.04 for N2O (Hansen et al.,2005) and 1.0 for radiative forcing from albedo changes (Hansen et al.,2005; Forster et al., 2007). Forster et al. (2007) showed that warmingefficacies are close to unity for the longer-lived greenhouse gases andsurface albedo changes, but efficacies can deviate more strongly fromunity for radiative agents with more pronounced uneven regional oraltitudinal distribution patterns. They may also range more widely ifother indirect effects, such as the effect of CH4 on tropospheric ozone(e.g. Hansen et al., 2005), or effects other than the direct radiativeeffects are included for vegetation changes (Davin et al., 2007; Boisieret al., 2012). However, while the quantification of such indirect effectsis still scientifically uncertain, we have opted to restrict our analysis tothe more certain direct effects.

2.5. Land-use change scenarios

We have considered a range of land-use scenarios and assessedthe effects of land-use change on albedo and the fluxes of CO2, CH4,N2O, and their respective removals from the atmosphere through natu-ral processes. This resulted in changes in atmospheric concentrationsfor each GHG attributable to the change in land use. We expressed theeffect of each radiative agent through their respective radiative forcingper hectare converted.We also calculated the resultant change in globaltemperature attributable to the radiative forcing and their respectivewarming efficacies of all agents combined.

These examples give specific case studies, and key assumptions foreach are given below and in Table 2. These examples aim to be fairlyrepresentative for the range of possible land-use changes, with the

Please cite this article as: Kirschbaum MUF, et al, Quantifying the climate-change consequences of shifting land use between forest andagriculture, Sci Total Environ (2013), http://dx.doi.org/10.1016/j.scitotenv.2013.01.026



specific numbers each representing one specific case study. However,the specific examples remain valid only within the context of their spe-cific assumptions, and different global regions or land-use andmanage-ment conditions would require their own specific quantifications.

For all deforestation scenarios, it was assumed that above-groundtree biomass was completely removed in the year of harvest, either bybeingburnt or by beingused forwoodproducts (see below).We assumedan initial live biomass plus forest floor litter mass of 150 tC ha−1

(Table 2). This figure is a good average figure for tropical forests al-though itwould behigh for forests frommost temperate or boreal regions(Kirschbaum et al., 2012). It is also a reasonable figure for New Zealand'sindigenous forests (Tate et al., 1997;Hall et al., 2001; Coomes et al., 2002).

We assumed 20% of the total C in the live biomass+litter C poolswas contained in coarse roots (Mokany et al., 2006). Coarse roots areoften neglected in deforestation studies, yet they can constitute animportant fraction of total site C stocks (Kirschbaum et al., 2008;Dean et al., 2012; Wang et al., 2012). They tend to be quantitativelyimportant and usually remain in the ground for extended periodswhile above ground C is generally burnt or removed.

Veryfine roots tend to decomposewithin a year or two, depending ontheir diameter and chemical composition, with decomposition rates de-creasing as root sizes increase (Silver and Miya, 2001). For coarse roots,Chen et al. (2001) reported decay time constants of 15–50 years for arange of forest species growing in the Pacific North West of Canada, andOlajuyigbe et al. (2011) reported decay time constants of 25–30 yearsfor Sitka spruce growing in Ireland. Garrett et al. (2012) reported muchfaster decay rates for Pinus radiata of 3–10 years in New Zealand, butup to 30 years at a very poorly drained site. In line with these studies,we used exponential decay constants of 5 years for plantations and20 years for indigenous forests.

We also assumed that soil C stocks donot changewith the conversionfrom indigenous forest to pasture (e.g. Murty et al., 2002; Tate et al.,2005), but that 20 tC ha−1 is lostwhen forests are converted to cropping(Mann, 1986; Murty et al., 2002). We assumed that this loss can bereversed when cropping land is converted back to indigenous forest(Guo and Gifford, 2002), and that 10 tC ha−1 is lost when pasture isconverted to exotic forest (Guo and Gifford, 2002). We assumed thatall these changes in soil C occurred linearly over 20 years (Murty etal., 2002; Paul et al., 2002).

Biomass growth in reforested stands was calculated with theRichards equation (Cooper, 1983; Kirschbaum, 2003a) as:

B ¼ Bmax 1−e−rt� �n ð14Þ

where B is total biomass (both above and below ground), Bmax is thetotal asymptotically reached biomass, t is time since forest plantingand r and n are parameters. The parameter n was set to 1.2 for all for-est types, and r was set to 0.02 for indigenous and 0.075 for exoticplantation forests (Kirschbaum and Watt, 2011). Maximum biomass,Bmax, was set to 200 tC ha−1 for indigenous forests and 250 tC ha−1

for exotic pine forests.A value of 250 tC ha−1 is a large amount of biomass by global

standards (Kirschbaum et al., 2012), but appropriate and consistentwith observed growth rates of P. radiata under generally favourablegrowing conditions in New Zealand (Kirschbaum and Watt, 2011).The figures for Bmax are larger than biomass estimates used in the de-forestation scenarios because we assumed that most forests had notattained their maximum biomass at the time of deforestation. Tosimulate the effect of multiple rotations, a rotation length of 25 yearswas assumed, which is a typical rotation length in New Zealand andAustralia butwould be very short rotation lengths for northern temperateor boreal regions.

For scenarios with the inclusion of wood products, we included anadditional pool to representwoodproducts,with C fromharvestedwoodtransferred to the wood-products pool rather than being immediately


released to the atmosphere. Cwas eventually emitted to the atmospherewhen wood products reached the end of their service life as well. Thechange in wood products, dWi/dt, at time i,Wi, was calculated as:

dWi=dt ¼ hBf –Wi–1=tw ð15Þ

where h is the fraction of total biomass that can be harvested and usedfor wood products, Bf is the total amount of biomass harvested, Wi−1 isthe wood products pool in the previous year, and τw is a time constantfor the decay of wood products. This equation assumes an exponentialloss of C from the wood products pool. It was assumed that 50% ofwoody biomass from plantations, and 41.7% from indigenous forests,could be used for long-lived wood products (h=0.5) which were thenlost exponentially with an exponential time constant of 25 years,which is a typical time constant for structural timber (e.g. Mathieuet al., 2012). Even shorter time constants could be appropriate forpaper or furniture uses.

For CH4 emissions, we used emissions rates of 150 kg CH4 ha−1 yr−1

for sheep and 240 kg CH4 ha−1 yr−1 for dairy pastures. This primarilyconsists of emissions fromenteric fermentation and smaller contributionsfrom faecal dry matter and animal effluent (Lassey, 2007; Kirschbaumet al., 2012).

We also included a small amount of CH4 oxidation by soils, set to1 kg CH4 ha−1 yr−1 for pastures and cropland, 5 kg CH4 ha−1 yr−1

for forest plantations and 10 kg CH4 ha−1 yr−1 for indigenous forests(Saggar et al., 2008; Kirschbaum et al., 2012). We further assumedthat the CH4 oxidation potential of forests is lost immediately follow-ing deforestation, and that after reforestation, it changes linearly over50 years to attain respective forest values (Smith et al., 2000).

Various modelling approaches have been used to estimate agricul-tural N2O emissions (Chen et al., 2008), displaying large regional var-iations in estimated rates (e.g. Del Grosso et al., 2009). Part of thisvariation is associated with soil and climatic factors, but the widerange of emission rates is primarily a function of different fertiliserapplication rates. The IPCC Tier 1 default methodology suggeststhat 1% of added fertiliser and of atmospherically deposited N isemitted as N2O (IPCC, 2006) which was reduced from the figure of1.25% used in earlier inventories (IPCC, 1997). For grazing land,these figures are further increased by emissions from dung and urine(2% emitted based on IPCC emission factors) and from nitrate leachedoff site (2.5%).

Based on New Zealand and international data (Del Grosso et al.,2009; Kirschbaum et al., 2012), we used N2O emission rates of2 kg N2O(N)ha−1 yr−1 for cropland, 5 kg N2O(N)ha−1 yr−1 fromsheep-grazed and 10 kg N2O(N)ha−1 yr−1 from dairy-grazed pas-tures. Emissions from forests were assumed to be 0. We also assumedthat the change from pasture to forest occurred instantaneously, butthat it takes 3 years of linear change to attain agricultural values.

Key data for all the scenarios used are summarised in Table 2 togeth-er with some other relevant information. In the reforestation scenarios,we included a credit for avoided agricultural emissions. These avoidedemissions are not included in the time courses of future emissions,but are included in our summary information where we average the ef-fect of land-use change over the subsequent 100 years.

3. Results and discussion

For a number of case studies, we have presented figures showingthe resulting radiative forcing and temperature changes over the100 years following a land-use change. We did not include figuresfor some scenarios because they involved changes in fewer importantradiative agents and the patterns were obvious through other figureswith similar land-use changes and where more radiative agents wereactively involved. The results of all scenarios are shown in the finalsummary table that gives the average effects over 100 years followingland-use change.



0 20 40 60 80 100

0

200

400

600

0 20 40 60 80 100

0

400

800

1200

Time since deforestation (years)

Rad

iati

ve f

orc

ing

(10-1

2 W

m-2

ha-1

)

albedoN2O

CH4

CO2

Totala)

Temperature

b)

(10-12 K

ha

-1)

Fig. 2. Radiative forcing (a) and temperature (b) changes resulting from conversion ofa forest to dairy-grazed pastures with consideration of wood products.


The first case study dealt with the conversion of forest to sheep- ordairy-grazed pasture (Fig. 1). Assumptions for C and albedo changeswere the same for both conversions, but dairy-grazed pastures wereassumed to have significantly higher emissions of CH4 and N2O thansheep-grazed pastures (Saggar et al., 2008; Kirschbaum et al., 2012).

Radiative forcing from deforestation and use of land for agricul-ture was dominated by C emissions. Net radiative forcing from CO2

was greatest immediately after clearing (based on the assumptionof an immediate release of above-ground C to the atmosphere). Radi-ative forcing then reduced over time as feedbacks via the global Ccycle reduced the amount of CO2 remaining in the atmosphere.

Methane emissions further added to the warming effect of defor-estation. The contribution started slowly at first, but it accumulatedas emissions were added year after year. Because of the short atmo-spheric life time of CH4, the radiative forcing of CH4 plateaued afterabout 20 years. After 100 years, the radiative forcing of CH4 releasedfrom a dairy-grazed pasture was similar to that of CO2 remaining inthe atmosphere.

The radiative forcing of N2O built up even more slowly than that ofCH4, but it continued to accumulate nearly linearly for 100 years (andbeyond). With its long life time in the atmosphere, it continued toaccumulate without reaching equilibrium over the 100-year horizonanalysed here. Accumulations over 100 years meant that the radiativeforcing of N2O released from a dairy-grazed pasture were approxi-mately equal to that of CH4 and CO2 over a 100-year release period,but it will exceed the radiative forcing of both CO2 and CH4 if land ismaintained as pasture for more than 100 years.

The effect of the three GHGs was compensated to a small extent bythe cooling effect of albedo changes — the lower radiation absorptionby a grass sward compared to that of a forest canopy. Overall, forsheep pasture, the net radiative forcing of the four agents consideredtogether due to deforestation quickly reached a maximum and thenunderwent only small changes as the diminishing effect of CO2 wasbalanced by the increasing effect of CH4 in the short term and N2O inthe longer term (Fig. 1a). For dairy pasture, because of the higher CH4

and N2O emissions, the increasing contributions from those gases ex-ceeded the reducing contribution from CO2 so that radiative forcing con-tinued to increase slightly over the 100 years under pasture (Fig. 1b).

Because of thermal inertia in the Earth system, temperature changeslagged behind radiative forcing, and temperature increased for about20 years in the sheep pasture before plateauing and remaining fairlyconstant for the remainder of the 100-year analysis period (Fig. 1c). Inthe dairy system, warming continued to increase slightly throughoutthe 100-year analysis period (Fig. 1d). The warming effects were calcu-lated here as about 300 and 500x10–12 K ha–1 in the sheep and dairysystems, respectively implying that the conversion of 1 billion ha of for-est to grazing land could raise global mean temperature by 0.3 to 0.5 °C.

Comparing sheep- and dairy-grazed pastures, the effects of C releaseand albedo changes were the same for both because it was assumed that

0 20 40 60 80

0

500

1000

Time since defore

Rad

iati

ve f

orc

ing

(10-1

2 W

m-2

ha-1

) a) sheep pasture

albedo

CH4

Total

0 20 40 60 800

200

400

600c)

Temperature

Fig. 1. Radiative forcing (a, b) and global temperature changes (c, d) as a result of deforestat


the original forest and the subsequent pasture had the same properties.However, rates of both CH4 and N2O emissions were distinctly higherfromdairy- comparedwith sheep-grazed pastures. These two gases con-sequently contributed a greater share of the total emission load fromdairy- compared with sheep-grazed pastures, and total radiative forcingand greenhouse warming was greater from dairy than sheep-grazedpastures.

When wood products were also considered, it reduced theradiative forcing from the initial C release from about 700 to400x10–12 W m–2 ha–1 (cf. Figs. 1b and 2). However, as woodproducts then decayed themselves, the benefit of prolonged C storagediminished. After 100 years, there were still some wood products left(about 2% with an exponential time constant of 25 years), yet after100 years, the radiative forcing of CO2 was actually 5% greater than forthe case without wood products. This was because the higher initialCO2 concentration caused by early release of all CO2 also stimulatedfaster removal of CO2 from the atmosphere through natural processes.When CO2 release to the atmosphere was delayed through CO2 storageinwood products, the rate of C uptake by the oceans and other natural Cpools was reduced for some time.

It is generally observed that delaying CO2 released through anyprocess such as storage in wood products, leads to subsequently in-creased atmospheric CO2 concentrations compared with what theywould have beenwithout the temporary storage. Ultimate temperatureincreases are therefore higher with temporary C storage in woodproducts, but on the positive side, the temperature increases are de-layed and cumulative warming is reduced (Kirschbaum, 2003a, 2003b).

When pastures were reforested, the radiative forcing from a CO2

change was negative as growing forests absorbed CO2 from the

0 20 40 60 80 100

0

500

1000

station (years)

b) dairy pasture

N2O

CO2

(10-12 K

ha

-1)

0 20 40 60 80 1000

200

400

600d)

ion of 1 ha to sheep (a, c) or dairy (b, d) pasture. Simulation details are given in Table 2.



0 20 40 60 80 100-200

0

200

0 20 40 60 80 100

-400

-200

0

200

Time since reforestation (years)

Rad

iati

ve f

orc

ing

(10-1

2 W

m-2

ha-1

) albedo

CH4

CO2

Total

Temperature

(10-12 K

ha

-1)

a)

b)

Fig. 3. Radiative forcing (a) and temperature (b) changes resulting from reforestationof sheep pasture with indigenous forest.


atmosphere and thereby reduced its concentration and radiative forcing(Fig. 3). Because forest growth was slow relative to the rate at which Ccan be released into the atmosphere upon harvesting or burning aforest, it took time for the benefit of C sequestration to be fully realised.

Over the first 10 years, the benefit from CO2 changes was approxi-mately negated by the warming effect of decreasing albedo of forests.After 10 years, the albedo of forest canopies reached its final value, andthe CO2 effect began to dominate.

There was also a small benefit from increasing CH4 oxidation inthe soils of re-establishing forests. A more substantial benefit arosethrough the cessation of CH4 and N2O emissions from agriculturalactivities, and the overall benefit of reforestation was considerablygreater if those avoided emissions were also considered (see below).

When forests were replanted with commercial exotic forests, theirgrowth rates were much higher than for indigenous forests, and it be-came necessary to also consider the C gains and losses over successiverotations (Fig. 4). Exotic forests also tended to lead to some loss of soilC (Guo and Gifford, 2002; Tate et al., 2005), which reduced the overallbenefit of tree plantings.

The initial gain from C accumulations tended to be higher for com-mercial plantation (Fig. 4a) than for replanted indigenous forests(Fig. 3), but that gain was lost when the forest was cut at the end ofthe first rotation if one considered only C stocks in the forest. Thereplanted forest even led to some overall positive radiative forcingfor some years after forest harvesting. That was due first to the lossof some soil C and second due to feedbacks in the natural C cycle.While C was stored in trees, the atmospheric concentration of CO2

was reduced through that C storage and, there was consequently lessremoval of C from the atmosphere than there would be without the

0 20 40 60 80

-500

0

500

Time since refor

Rad

iati

ve f

orc

ing

(10-1

2 W

m-2

ha-1

) a) without wood products

CO2

0 20 40 60 80-300-200-100

0100

c)

Fig. 4. Radiative forcing (a, b) and temperature (c, d) changes after reforestation


temporary C storage. When C was released again, it resulted in a higheratmospheric CO2 concentration than it would have been without thetemporary C storage (Kirschbaum, 2003a, 2003b).

When C storage in wood products was also considered, it greatlydampened the extent of the ups and downs related to harvesting,and the net effect remained one of overall negative radiative forcingeven immediately after harvesting. However, the effect of includingwood products was only moderate because even products that wereconsidered long-lived (e.g., housing frames) tended todecay over decadesand thus released C over the period that is relevant for climate-changeconsiderations. Feed-backs via the global C cycle made the overall neteffect of delayed C emission even less effective.

Fig. 5 provides a summary of the effect of land-use change averagedover 100 years, a period considered to be a reasonable time horizonover which to assess the integrated impact of land-use change. Acrossthe different land-use change scenarios, changes in C, CH4 and N2O allhave appreciable effects over the integration horizon. Similarly, albedochanges cause radiative forcing opposite to that of the GHGs and appre-ciable negates the effect of those gases.

Across the different scenarios of deforestation to pastures, approx-imately half of the warming effect of deforestation is due to C, withCH4 and N2O contributing one quarter each. Albedo changes counterthe effect slightly by about 20–25%, keeping in mind that albedo canhave a larger effect in regions with extended snow cover, whereas theparameterisation used here assumed snow cover to play no significantrole.

For conversion to cropping, CH4 plays only a minor role, and N2O issignificantly less important than for conversions to pastures. CO2-basedradiative forcing, in contrast, is slightly larger than for conversions topasture because losses of soil C add to biomass changes. Overall, be-cause of the smaller contributions from CH4 and N2O, the radiativeforcing for conversion to cropping is considerably smaller than forconversion to pasture (Fig. 5).

Radiative forcing from reforestation is largely the converse of radi-ative forcing from deforestation provided that a credit for avoidedCH4 and N2O emissions is included in the calculations. For assessingthe overall benefit of reforestation, it is therefore critically importantwhether credits for the cessation of CH4 and N2O emissions are included.Apart from a small flux of CH4 oxidation, the fluxes of CH4 and N2O aregenerally low in forest systems (Saggar et al., 2008; Price et al., 2010;Kirschbaum et al., 2012). However, if one includes a credit for the cessa-tion of the large agricultural emissions, then the contributions of CH4 andN2O become sizeable and more than double the overall greenhousebenefit of reforestation (Fig. 5).

The time courses of deforestation and reforestation differ forCO2-based radiative forcing since forest growth is relatively slow com-pared to the speed with which C can be released following deforestation.The C loss from deforestation can therefore not be fully reversed over

0 20 40 60 80 100

-500

0

500

estation (years)

b) incl. wood productsalbedo

Temperature

Total

(10-12 K

ha

-1)

0 20 40 60 80 100-300-200-1000100

d)

CH4

of a pasture without (a, c) and with (b, d) consideration of wood products.



100-

year

ave

rag

e ra

dia

tive

forc

ing

(10

-12

W m

-2 h

a-1)

-800

-400

0

400

800

1200

N2O

CH4

Carbon

Albedo

Def (s

heep

)

Def (d

airy

, no

WP)

Def (d

airy

, w. W

P)

Def (c

ropp

ing)

Ref (s

heep

)Ref

(dai

ry)

Ref (c

ropp

ing)

Ref (e

xotic

no

WP)

Ref (e

xotic

w. W

P)

Tem

per

atu

re c

han

ge

(10-1

2 K

ha-1

)

-400

-200

0

200

400

a)

b)

Fig. 5. Radiative forcing (a) and temperature changes (b) of various land-use transitionsaveraged over 100 years. The calculations of reforestation scenarios include a credit foravoided methane and nitrous oxide emissions.


100 years even with the reasonably fast tree-growth rates assumed here.For slower tree growth, as might be expected in boreal regions (e.g.Bright et al., 2012), it would take even longer for lost C from defores-tation to be regained, and the imbalance of CO2-based radiative forc-ing between deforestation and reforestation would be even greater.

Because of the relatively slow accumulation of C, the effect of albe-do changes also becomes relatively more important under reforesta-tion than deforestation, and it can negate between 33% and 50% ofthe greenhouse benefit of C sequestration. That is a significant contribu-tion and warrants the inclusion of albedo in any overall assessment.

In contrast, it was relatively unimportant whether wood productswere included or omitted even with the assumption of fairly long ser-vice life (25 years) of wood products. This was especially pronouncedfor deforestationwherewood productswere generated on only a singleoccasion. For an exotic plantation with multiple harvests, inclusion ofwood products does have a greater effect, but even then the effect isnot large and would be even smaller for wood products with a shorterservice life.

Overall, across the different scenarios, deforestation contributedbetween 400 and 860x10–12 W m–2 ha–1 averaged over the following100 years, while reforestation could reduce radiative forcing by 307–766x10–12 W m–2 ha–1 leading to warming of 178-398x10–12 W m–2

ha–1 for the deforestation and cooling of 134–356x10–12 W m–2 ha–1

for the reforestation scenarios (Fig. 5). This means that land-use changeof 1 billion ha globally could change global temperatures by about0.1–0.4 °C. It confirms the role of land-use change in contributing toglobal warming as well as its potential to contribute to climate changemitigation.

4. General discussion

In this work, we quantified the contribution from different radiativeagents affected by land-use change. They are principally the threeGHGs, CO2, CH4 and N2O. We also included albedo as it has radiativeforcing properties comparable to those of the GHGs (Forster et al.,


2007). Of those agents, CO2was themost important owing to the releaseof a large amount of C during deforestation itself. It was relatively lessimportant for reforestation because forest growth takes many decades,or even centuries, whereas accumulated C can be released much faster,especially when fire is involved. Hence, trends shown for deforestationdo not present mirror images of patterns for reforestation. While defor-estation is reversible, in principle, full reversibility can take up to severalcenturies, depending on forest-growth rates in different global regions.

The other agents are primarily affected by the land use practisedafter the initial change in land use. Annual N2O emission rates tendto be small, but can nonetheless make an important contribution toradiative forcing due to its atmospheric persistence and its high specificradiative forcing. Because of its slow break-down in the atmosphere, itscontribution accumulates almost linearly over the length of time thatland is under agricultural use (Figs. 1, 2).

In contrast to N2O emissions, CH4 can be emitted at much higherrates. Its radiative forcing is similar to that of N2O, but because of itsrelatively short atmospheric longevity, its forcing does not accumu-late like that of N2O. Instead, it reaches a plateau after a few decadeswith the break-down of CH4 already in the atmosphere balancing theaddition of new CH4 from on-going agricultural activity (Figs. 1, 2).

In addition to the contribution by the respective GHGs, the directradiative contribution from albedo changes of the land surface is increas-ingly being recognised as an equally valid and important contributor tothe net radiative balance of the Earth (Jackson et al., 2008; Schwaigerand Bird, 2010; Kirschbaum et al., 2011; Bright et al., 2012). Its contribu-tion is only moderate at temperate latitudes, especially in comparisonwith the C contribution of fast-growing forests, but it can attain greaterimportance where forest growth is slow, such as where reforestationinvolves more slowly growing indigenous forests (cf. Figs. 3, 4). It isalso more important in boreal regions where an extensive snow covergreatly increases the albedo differences of different land cover types(Betts, 2000).

Albedo changes also retain their radiative forcing indefinitely whilethe concentration of GHGs and their radiative forcing are eventually re-duced through natural break-down processes or transfer to the deepoceans. The radiative forcing attributable to albedo differences of differ-ent land-cover types thus persists for as long as the different land coversaremaintained. This has the advantage that as amitigation option, it can,in principle, be implemented with immediate effect on radiative forcing.

Differences between the different radiative agents include differencesnot only in the magnitude of effects, but also in their timing because oftheir very different atmospheric longevities (Ramaswamy et al., 2001).CO2 has the most complex interactions with the environment, withabout half of emitted C being removed from the atmosphere withinseveral decades after uptake by shallow- and intermediate-depthoceanwaters (Wigley, 1991; Joos et al., 2012). Another quarter of emittedCO2, however, continues to reside in the atmosphere for centuries, and afinal quarter stays in the atmosphere indefinitely.

As discussed by Brandão et al. (2013), most analyses of theclimate-change consequences of land-use change tend to ignore thesemore complex timing issues and simply calculate the contribution by dif-ferent radiative agents by multiplying the emission of different gases bytheir respective greenhouse warming potentials (e.g. Scheer et al., 2008;Bernoux et al., 2010; Nemecek et al., 2012). Our analysis also confirmsthe short-coming of the commonly used approaches as the respective in-fluences of the different radiative agents change over time even if landuse remains the same (see Figs. 1–4).

Greenhouse warming potentials are derived by summing the radia-tive forcing attributable to different gases over 100 years (or over anotherselected integration horizon). This makes no distinction between thecontribution of one gas, like CH4, where most radiative forcing is con-centrated in the decades following its emission compared with that ofother gases, like CO2 and N2O, where the radiative forcing is moreevenly spread over the full 100 years. This means that CH4 contributesto warming over the initial years to decades after its release, but it




ceases to contribute to warming over later decades when the globaltemperature might reach dangerously high levels. The other gases con-tribute relatively less to short-term warming but add more at laterstages at just the time when temperatures are higher and may reachmore damaging levels.

Which time course would be more desirable, or more damaging?The answer depends in part on a valuation of the relative importanceof different climatic impacts, with impacts directly related to the tem-perature in future years made worse by a delay in emissions (whichwill lead to higher ultimate temperature increases) whereas impactsquantified through the cumulative effect of raised temperature benefitfromadelay in emissions (Kirschbaum, 2003a, 2003b). Or, put differently,current CH4 emissionswill make little contribution to the actual warmingin 100 years, whereas current N2O emissionswill do so. Even current CH4

emissions will, however, contribute to the cumulative warming up to2100 in much the same way as N2O emissions will.

Our work aimed to illustrate the climate-change effects of represen-tative land-use change scenarios. It does not provide formally averagedemission rates across the globe. Nonetheless, it is warranted to ask howrepresentative our chosen scenarios are.

The largest uncertainty lies in area-based estimates of net emis-sions of the three greenhouse gases and albedo. C losses can varywidely across the world with the size of cut forests, both systematically(Kindermann et al., 2008; Kirschbaum et al., 2012) and with the pecu-liarities of individual stands. If C stock changes are calculated based onthe actual C stocks present at the time of conversion (rather than onaverage C stocks over typical forest rotations), it can obviously varywidely between maximal amounts representative of a mature forestand minimal amounts shortly after a forest might have been harvested,or destroyed through natural processes such as wildfires or storms.

Forest regrowth similarly varies widely across the globe, with verylow growth rates in cold or dry regions (Kindermann et al., 2008) togrowth rates asmuch as 20 tC ha−1 yr−1 in intensivelymanaged euca-lypt plantations in Brazil (Stape et al., 2010). These differences wouldlead to corresponding differences in the C losses or gains attributableto land-use change.

Nitrous oxide emissions can similarly vary by more than an orderof magnitude for systems with similar land use, principally as a func-tion of fertiliser additions, or more generally, as a function of a site'sfertility status (Del Grosso et al., 2009; Kirschbaum et al., 2012).Methane emissions are ubiquitous from ruminant animals, with thepercentage of ingested feed released as CH4 being a fairly conservative5–6% (Kelliher and Clark, 2012). Rates per area of land can consequentlyvary greatly with the productivity of land and resultant stocking num-bers and feed intake.

Albedos of different vegetation types can vary up to about two-fold(Breuer et al., 2003), and these differences can be further accentuatedwhen snow covers the ground (Betts, 2000). Radiative forcing can befurther affected through differences in short-wave radiation receiptthat can vary more than two-fold across the globe. We have also onlyconsidered the primary, directly surface-albedo based effects but donot consider secondary effects via perturbations of the hydrologicalcycle and changes in surface roughness (e.g. Bala et al., 2007; Pielkeet al., 2011; Boisier et al., 2012). Some of these secondary effectscan be of comparable magnitude to the direct radiatively based effects(Boisier et al., 2012), butmostly constitute forcing in the opposite direc-tion to the direct radiative effects, with their importance increasingfrom boreal to tropical regions (Bala et al., 2007).

Assessment procedures that explicitly accounted for the atmospher-ic longevity of the different GHGs have been used in some recent assess-ments (e.g. Anderson-Teixeira and DeLucia, 2011). It is warranted forsuch more detailed assessments so that the climate change impacts ofland-use change can be more fully assessed, including not only themagnitude of its contribution but also its evolution over time.

Scenario analyses point to significant future demand for agri-cultural land, owing to on-going population growth and a shift to


meat-rich diets, with attendant increases in net greenhouse gasemissions (e.g. Powell and Lenton, 2012). At the same time, there arelarge differences in the energy conversion efficiency of net primary pro-duction to consumed food (Powell and Lenton, 2012), or in terms ofgreenhouse gas generation per unit of food produced (Crosson et al.,2011; Sejian et al., 2011). Emission rates per unit of food productionare particularly high in countries with low animal production efficien-cies (Gerber et al., 2011; Zervas and Tsiplakou, 2012).

Greenhouse gas emissions can be quantified per unit of land area, aswas done in the present work. This corresponds to the usual measure-ment and accounting units. However, agricultural emissions are tiedto the production of agricultural commodities so that emissions perunit of production become important. Global food demand is increasingbecause of population growth and shifting dietary choices (Powell andLenton, 2012), and the scope for modifying those trends is limited.The scope for reducing agricultural emissions is also limited as manyyears of research have generated few practical ways of reducing CH4

(e.g. Buddle et al., 2011) or N2O (e.g. Luo et al., 2010) emissions forexisting production systems.

For reducing global emissions, this points to a role and importanceof greater focus and concentration on improvements in greenhousegas reductions per unit of produce. There is probably greater scope toincrease food production per unit of land rather than reduce the emis-sion of greenhouse gases per unit of land (e.g. Gerber et al., 2011). Itwould thus be targeting the ultimate driver of emissions rather thanthe proximal ones related to the emissions from specific units of land.

The mitigation of climate change has so far mainly concentrated onreducing the emissions of CO2. This has been warranted because of thevarious radiative agents, CO2 makes by far the greatest contribution toclimate change (Forster et al., 2007). Nonetheless, other radiativeagents also make sizeable contributions towards the Earth's climatechange load, and an optimal response strategy for climate change miti-gation should concentrate not primarily on the largest contributor, buton the contributor where the most cost-effective mitigation is possible,and land use and land-use change may well present opportunities forcost-effective mitigation. Devising an optimal response strategy thattargets themost cost-effective mitigation options requires a quantifica-tion of the various options, and the present work aimed to contributetowards that.

Acknowledgements

Wewould like to thank Martin Persson for access to his comparableand alternative calculation routines for calculating radiative forcing, andAnne Austin, Anne-Gaelle Ausseil, David Whitehead and two anony-mous referees for many useful comments on this paper.

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quantifying the climate-change consequences of shifting land use between forest and agriculture

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