Towards an integrated global framework to assess the impacts of land use and management change on...

REV I EW

Towards an integrated global framework to assess theimpacts of land use and management change on soilcarbon: current capability and future visionPETE SM ITH * , CHR I ST IAN A . DAV IE S † , S T EPHEN OGLE ‡ , G IUL IANA ZANCHI § ,J E S S ICA BELLARBY * , NE I L B IRD § , ROBERT M . BODDEY ¶ , N IALL P . MCNAMARA* * ,

DAV ID POWLSON † † , ANNETTE COWIE ‡ ‡ , ME INE V AN NOORDWI JK § § , SARAH C .

DAV I S ¶ ¶ , DAN IEL D E B . R ICHTER * * * , L EN KRYZANOWSK I † † † , MARK T . V A N WI JK ‡ ‡ ‡ , § § § ,J UD I TH STUART ¶ ¶ ¶ , AK IRA K IRTON* * * * , DUNCAN EGGAR † † † † , GERALD INE

NEWTON-CROSS * * * * , TAPAN K . ADHYA ‡ ‡ ‡ ‡ and ADEMOLA K. BRAIMOH§§§§

*Institute of Biological and Environmental Sciences, School of Biological Sciences, University of Aberdeen, Aberdeen, AB24 3UU,

UK, †Shell Global Solutions (UK), Shell Technology Centre Thornton, PO Box 1, Chester, CH1 3SH, UK, ‡Natural Resources

Ecology Laboratory and Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523,

USA, §Resources – Institute for Water, Energy and Sustainability, Joanneum Research, A-8010, Graz, Austria, ¶ Embrapa

Agrobiologia, Seropedica, 23890-000, Rio de Janeiro Brazil, **The Centre for Ecology and Hydrology, Lancaster Environment

Centre, Lancaster, LA1 4AP, UK, ††Department of Sustainable Soils and Grassland Systems, Rothamsted Research, Harpenden,

AL5 2JQ, UK, ‡‡National Centre for Rural Greenhouse Gas Research, University of New England, Armidale, NSW 2351,

Australia, §§World Agroforestry Centre (ICRAF) Situ Gede, Sindang Barang, Bogor 16115, PO Box 161, Bogor, 16001, Indonesia,

¶¶Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, ***Environmental Sciences

and Policy, Nicholas School of the Environment, Duke University, Durham, NC 27708, USA, †††Land Use Section, Government

of Alberta Agriculture and Rural Development, Edmonton, T6H 5T6, Alberta Canada, ‡‡‡Plant Production Systems Group,

Wageningen University, Droevendaalsesteeg 1, 6708 PB, Wageningen, NL, §§§International Livestock Research Institute (ILRI),

Box 30709, Nairobi, Kenya, ¶¶¶Soils Policy Team, Defra, Area 3C, Nobel House, 17 Smith Square, London, SW1P 3JR, UK,

****The Energy Technologies Institute, Holywell Building, Holywell Park, Loughborough, LE11 3UZ, UK, ††††BBSRC, PolarisHouse, North Star Avenue, Swindon, SN2 1UH, UK, ‡‡‡‡Central Rice Research Institute, Cuttack, Orissa 753 006, India,

§§§§World Bank, 1818 H Street, NW, Washington DC, USA

Abstract

Intergovernmental Panel on Climate Change (IPCC) Tier 1 methodologies commonly underpin project-scale carbon

accounting for changes in land use andmanagement and are used in frameworks for Life Cycle Assessment and carbon

footprinting of food and energy crops. These methodologies were intended for use at large spatial scales. This can intro-

duce error in predictions at finer spatial scales. There is an urgent need for development and implementation of higher

tier methodologies that can be applied at fine spatial scales (e.g. farm/project/plantation) for food and bioenergy crop

greenhouse gas (GHG) accounting to facilitate decisionmaking in the land-based sectors. Higher tier methods have been

defined by IPCC and must be well evaluated and operate across a range of domains (e.g. climate region, soil type, crop

type, topography), and must account for land use transitions and management changes being implemented. Further-

more, the data required to calibrate and drive the models used at higher tiers need to be available and applicable at fine

spatial resolution, covering the meteorological, soil, cropping system andmanagement domains, with quantified uncer-

tainties. Testing the reliability of themodelswill require data either from siteswith repeatedmeasurements or from chro-

nosequences.We review current global capability for estimating changes in soil carbon at fine spatial scales and present a

vision for a framework capable of quantifying land use change andmanagement impacts on soil carbon, which could be

used for addressing issues such as bioenergy and biofuel sustainability, food security, forest protection, and direct/indi-

rect impacts of land use change. The aim of this framework is to provide a globally accepted standard of carbonmeasure-

ment andmodelling appropriate forGHGaccounting that could be applied at project to national scales (allowing outputs

to be scaledup to a country level), to address the impacts of land use and landmanagement change on soil carbon.

Keywords: land use, land use change, model, monitoring, soil carbon

Received 14 November 2011; revised version received 28 February 2012 and accepted 4 March 2012

Correspondence: Pete Smith, tel. + 44 0 1224 272 702, fax + 44 0 1224 272 703, e-mail: [email protected]

All authors contributed equally to this work

© 2012 Blackwell Publishing Ltd 1

Global Change Biology (2012), doi: 10.1111/j.1365-2486.2012.02689.x

Introduction

Land use changes and soil degradation have played a

significant role in increasing the atmospheric CO2 con-

centration, with estimates of the global historic loss of

carbon (C) from soils varying from 41 Pg C (Houghton

& Skole, 1990) to 55 Pg C (IPCC, 2000). Monitoring soil

C is important for scientific understanding of the

C-cycle and is becoming critical for a variety of policy

objectives such as mitigation of greenhouse gas emis-

sions, food and energy security and biodiversity protec-

tion, and assessing the feedbacks between soil carbon

and climate change. The Intergovernmental Panel on

Climate Change (IPCC, 2006b) has developed standard

methods for estimating soil organic carbon (SOC)

stocks through time, which are used by countries for

reporting to the United Nations Framework Conven-

tion on Climate Change (UNFCCC). The methods are

characterized by flexibility that ranges from a Tier 1

default method prescribed by the IPCC, to methods

that incorporate local information to estimate C stock

changes at a Tier 2 level, or more advanced modelling

and/or measurement-based networks at the Tier 3

level. The simplest Tier 1 method only requires moni-

toring of the land use and management activity that

influences soil C, and spatial data on soil types and

climates. The annual change in SOC is calculated by

multiplying a reference SOC stock by a series of dimen-

sionless factors representing land use or land use

change type, management regime and input of organic

matter; equations, default factor values and C change

rates are provided in the guidelines (IPCC, 2006b).

Lokupitiya & Paustian (2006) reviewed the applica-

tion of IPCC methods in national inventory reporting

to the UNFCCC and found that only about 30% of the

Annex I (i.e. industrialized) countries reported soil C

stock changes for mineral soils, and half of those were

using Tier 1 methods. However, the Tier 1 level is not

necessarily appropriate for assessing soil C in all situa-

tions. This method is intended for application in coun-

tries for which soil C stock change is not a significant

contributor to national emissions or removals, and

countries that have limited capacity to provide coun-

try-specific data. The latter is a common limitation in

non-Annex I (nonindustrialized) countries. The C stock

change rates for the Tier 1 method are derived from a

global dataset of experimental results (Ogle et al.,

2005), but they are highly uncertain for most regional

monitoring applications, particularly in tropical

regions, and stocks under native vegetation are also

uncertain (Batjes, 2011). The IPCC recommends that

Tier 2 or 3 methods be used as a good practice for

inventory reporting, particularly for key sources of

greenhouse gas emissions (IPCC, 2006c). Tier 2 meth-

ods have been used to develop C offset market proto-

cols for soil C sequestration based on soil tillage

practice changes (Goddard et al., 2008; Haak, 2008) that

is currently being used in the Alberta, Canada. Tier 2

and 3 will provide more accurate results, if there are

sufficient data to support their application. Tier 2

methods apply country-specific emissions factors to

the same equations used in Tier 1. Tier 3 methods may

use detailed direct measurement, but are likely to be

unaffordable for most countries using current technol-

ogies for soil C measurement, though new techniques

such as visible and near infrared reflectance spectros-

copy may provide less expensive methods to measure

SOC (Zimmermann et al., 2007). Tier 3 methods based

on modelling are more affordable and offer multiple

advantages, such as the potential for application at a

range of scales from national down to the individual

landholder.

Similar Tiers are used for estimating non-CO2 GHG

fluxes from the land i.e. nitrous oxide (N2O) and meth-

ane (CH4), with Tier 1 methods using activity data (such

as fertilizer use) to estimate emissions. Tier 2 methods

allow regionally specific emission factors (EFs) to be

used with similar activity data to Tier 1, and at Tier 3,

more complex methods can be used (IPCC, 2006b,c; Del

Grosso et al., 2010). Under the 2006 IPCC Guidelines for

National Greenhouse Gas Inventories (IPCC, 2006b),

methods for accounting for soil C change and for GHG

emissions under all land uses are brought together

under the sector of Agriculture, Forestry and Other

Land Use (AFOLU), which aims to harmonize methods

for soil C and GHG accounting across all land uses. In

this paper, we focus on assessing changes in soil C.

Our objective is to review the current availability of

data to support application of the IPCC methods in

various regions of the world. The required data

include spatial climate, soil, vegetation and land

cover and land use and management datasets, and

measurements of soil C stock changes and CO2 fluxes

from various experiments and networks. We discuss

the availability of these data in the context of meth-

ods that can be applied at the three methodological

tiers provided by the IPCC. Given the current capa-

bility, we provide a short and longer term vision for

the future that would allow for soil C estimation

using the greater rigour of the IPCC Tier 2 and 3

methods. Progression to the higher tiers will provide

more accuracy and precision for quantifying the con-

tribution of soils to the global C and nitrogen cycles,

and support a growing number of policies influencing

C stock changes due to land use and management

activity.

© 2012 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2012.02689.x

2 P. SMITH et al.

Spatial data necessary for a soil C monitoring and

accounting framework

Estimating soil C stock changes using Tier 2 or model-

based Tier 3 methods requires country-specific data on

the factors that govern C stock: first, the fundamental

edaphic and climatic factors that determine organic

matter inputs and turnover rates, and second, the

anthropogenic factors that modify their influence, that

is, land management and land use change. Tier 1 meth-

ods utilize simplified relationships between these driv-

ers, provided by the IPCC (2006a,b,c), and therefore

application of Tier 1 methods requires only land use,

soil type, and broad climatic data. Global datasets exist

for some of these factors, but the quality is variable,

and the coverage for some attributes such as land man-

agement is limited to developed countries. In this sec-

tion, we review data availability and the consequences

for application of higher Tier IPCC methods.

Global climate/weather data

IPCC Tier 2 methods require a classification of climate,

while most soil carbon models that could be used in a

Tier 3 method work on a monthly basis and need

monthly weather inputs. Availability of global climate

and monthly weather data has improved dramatically

over the last few years. Several on-line databases are

available, for example WorldClim, NEO and CRU.

WorldClim is a set of global climate layers (climate

grids) with a spatial resolution of a square kilometre

(Hijmans et al., 2005). The database presents spatially

interpolated weather data of the last 50 years and also

projections to the future based on IPCC AR4. NEO also

provides other remotely sensed observation, for exam-

ple concerning vegetation in addition to the most

important climatic variables. Some of these datasets go

back to 1880 and the resolution of these data differs

with the highest spatial resolution of a square kilome-

tre. The CRU dataset is a lower resolution dataset of

only 0.5 degree but supplies data back to 1901. Tempo-

rally more detailed historical climate data from 1971

onwards can be accessed through results of the ERA40

project (Dee et al., 2011). In general, there are sufficient

climate or weather data available for developing a Tier

2 or 3 method for soil C.

Global soil datasets

IPCC Tier 2 methods require taxonomic data to classify

soils into general categories for assignment of EFs. Soils

data are used in most soil carbon models that could be

used in Tier 3 methods to simulate key processes influ-

encing carbon dynamics. In particular, the influence of

texture on microbial decomposition is represented in

many models (e.g. Jenkinson & Rayner, 1977; Parton

et al., 1987). Soils data can also provide information on

soil C stocks which can be useful for Tier 2 methods

(IPCC, 2006b). There are several global datasets that

include estimates of soil C to a depth of 1 m, though

these are not all fully independent from each other.

Generally, there are two different approaches to creat-

ing such datasets: (1) estimation of C stocks under natu-

ral, or mostly undisturbed, vegetation using climate

and ecological life zones (e.g. Zinke et al., 1984; Siltanen

et al., 1997); (2) extrapolation of soil C data from

measurement in soil profiles using soil type (FAO-

UNESCO), e.g. IGBP-DIS, NRCS and WISE, the

Harmonised World Soils Database (HWSD).

The HWSD is the most recent, highest resolution glo-

bal soils dataset available. It was compiled by the Food

and Agriculture Organization of the United Nations

(FAO) and the International Institute for Applied

Systems Analysis (IIASA) and uses vast volumes of

recently collected regional and national soil infor-

mation to supplement the 1 : 5 000 000 scale FAO-

UNESCO Digital Soil Map of the World (FAO/IIASA/

ISRIC/ISS-CAS/JRC, 2009 – see Supporting Informa-

tion, Appendix S1). The HWSD combines the FAO Soil

Map of the World, regional SOTER databases, the Euro-

pean Soils Database, the Northern Circumpolar Soil

Map, the Soil Map of China 1 : 1 Million scale and ver-

sion 2 of the WISE database. It provides soil parameter

estimates for topsoil (0–30 cm) and subsoil (30–100 cm), at (maximum) 30 arc-second resolution aggre-

gated to 16112 soil mapping units (SMUs; Appendix

S1). Work is ongoing to create new global, high-resolu-

tion soil property maps under the GlobalSoilMap.net

project, and could provide an even global better soil

map (Sanchez et al., 2009).

Generally, the soil databases are based on a limited

number of soil profiles and extrapolated to other areas

according to soil type. Climate or land cover and man-

agement are usually not considered so that these data

have high associated uncertainty. These datasets give

the best available estimate at a global scale, but at

regional scale, the use of local data is preferable

Table 1.

Global land use/land cover/vegetation datasets

Land use and land use change probably have the most

significant impact on the C stock changes in managed

land (Houghton et al., 1983; IPCC, 2000). Therefore,

land use is a key variable in any approach to estimating

changes in soil C stocks, regardless of the Tier (IPCC,

2006b). Land cover maps are often used to determine

land use and land use transitions. Freely available land


A GLOBAL FRAMEWORK FOR ESTIMATING SOIL C 3

cover datasets are shown in Table 2 and these were

reviewed by Mccallum et al. (2006).

The most recent land cover project is GLOBCOVER

which uses MERIS image data from 2005 and 2006 and

has a spatial resolution of 300 m. It builds on a thor-

ough understanding of existing datasets (Table 2), with

the aim of updating and complementing these. Cur-

rently the most recent data is from 2009 released at the

end of 2010 as version 2.3. GLOBCOVER has only been

made available recently. Several of the categories in the

GLOBCOVER product are mixtures of different land

uses, which may limit its utility for determining land

use change with high levels of accuracy.

Land cover maps are often used to determine land

use and land use change transitions. For this purpose

Ramankutty & Foley (1998) developed time series of

historical cropland and pasture data from 1700 to 2007,

which has recently been fully revised. Further, there

are a variety of future land use scenarios available to

the year 2100, for example in preparation for the

fifth Assessment Report (AR5) (http://luh.unh.edu/).

As with soils, there have been attempts to produce

harmonized land cover datasets (Jung et al., 2006; Gold-

ewijk et al., 2007; Ramankutty et al., 2008). Jung et al.

(2006) developed the land cover map SYNMAP by syn-

thesizing GLCC-IGBP, GLC2000 and MODIS. This was

specifically designed for C cycling but has not been as

fully validated as the land cover maps it is based on.

Ramankutty et al. (2008) produced a validated map

using the MODIS and GLC2000 land cover products.

These were combined and validated against agricul-

tural statistical data available from 15 990 administra-

tive units, thus providing validation at a local/regional

level. It is also a considerable improvement to an earlier

effort by the same group (Ramankutty & Foley, 1998).

Goldewijk et al. (2007) created a map using the same

approach, but based on GLCC-IGBP and GLC2000 and

using FAO statistics only. A drawback of both of these

maps is that they have the much coarser resolution of

5 min, compared to the 1 km2 of MODIS and GLC2000.

The sources of variability between land cover maps

have been summarized by Jung et al. (2006), which

includes sensor capabilities, raw data processing,

acquisition period, classification system (land cover

legend), selection of input data for classification, classi-

fication procedure and validation of the final product.

The classification system contributes considerably to

differences between the maps as land cover types are

based on different thresholds, so that, for example,

GLC2000 requires a tree cover of 60% whereas MODIS

only requires a tree cover of 15% to assign the land

cover type as forest (Fritz & See, 2008). Consequently,

there will probably be more forest cover when using

MODIS as input, although there is no difference in the

remotely sensed raw data. This is important when

assessing differences among land cover maps. The

impact on the use of different land use and land cover

inputs on the resulting model output needs to be

assessed.

There is no perfect global land cover map as different

regions will be best represented by different products

(Fritz et al., 2008); recent efforts have begun to produce

regional land cover maps, such as FAO’s Africover pro-

ject. The best suited land cover map may also depend

on its purpose. Land cover maps should be evaluated

in conjunction with vegetation biomass C, since this

will be an important parameter in any framework to

estimate changes in total ecosystem C.

Estimates of vegetation biomass and C (which could

be used by some soil C models) are often derived from

land cover datasets. Consequently, any uncertainties

related to land cover will have a direct effect on bio-

mass estimates. Vegetation biomass is greatest in for-

ests. In contrast, only a small proportion of C stock in

croplands is contained in vegetation. Vegetation C is

not only determined by the forest area. Other factors

Table 1 Freely available global soil datasets (see Appendix

S1 for list of acronyms and abbreviations)

Product Year Format

HWSD 2009 0.3 and 1 m depth at 30 arc-second

resolution

WISE ver1 2006 20 cm intervals up to 1 m depth at

5 min resolution

WISE ver3 2005 0.3 and 1 m depth at 30 min resolution

NRCS 2000 1 m depth at 2 min resolution

IGBP-DIS 2000 1 m depth at 5 min resolution

Zinke et al. 1984 Soil profiles

Siltanen et al. 1997 Soil profiles

Table 2 Freely available global land cover/land use/vegeta-

tion cover datasets (see Appendix S1 for list of acronyms and

abbreviations)

Product Year Data type

Goldewijk et al. (2007) 2007 % cropland and pasture

Ramankutty & Foley

(1998)

1700–2007 % cropland and pasture

GLOBCOVER 2007 Landcover

MODIS (MOD44b) 2006 % forest

MODIS (BU) 2000 Landcover

GLC2000 2000 Landcover

FRA2010 2000 % forest

UMD 1992 Landcover

GLCC_IGBP 1992 Landcover


4 P. SMITH et al.

include, for example, selective wood harvest and forest

fragmentation (Houghton, 2005). This alone can make a

large difference as Pinard & Putz (1996) found in

Borneo. Here, average aboveground biomass have

been found to be 400 Mg ha�1 for undisturbed/well

managed forests and 200 Mg ha�1 for degraded/badly

managed forests (Pinard & Putz, 1996). Kindermann

et al. (2008) recently used aggregated results from the

country-level global Forest Resources Assessment from

2005 (FRA 2005) to produce the first half degree global

spatial forest dataset. They integrated NPP, a factor for

human influence, FRA statistics, GLC2000 land cover

and CIESIN land area to map forest biomass and C

stocks (Kindermann et al., 2008) (data available at:

http://www.iiasa.ac.at.Research/FOR). Generally, the

available land cover data is sufficient for Tier 2 and Tier

3 methods. However, the high sensitivity of C stocks

to land use change would make it preferable to use

information at a regional/local scale if available.

Global land management data

Although management does not typically have as large

an impact on soil C stocks as land use change, the influ-

ence is not insignificant and forms the basis for much

of interest in C sequestration and possible credits for

mitigation of greenhouse gas emissions (Smith et al.,

2007). While opportunities exist to improve manage-

ment, accounting for these impacts requires sufficient

activity data on past and current management activity.

Management activity data required for soil C estima-

tion at the Tier 1 and 2 levels include crop type and

rotation; mineral and organic fertilizer usage, tillage

practices, irrigation management, lime applications,

use of bare or vegetated fallows or cover crops in rota-

tions (IPCC, 2006b). The Tier 3 inputs will typically be

more detailed, including factors such as planting and

harvest schedules, specific rates of fertilization and lim-

ing, as well as irrigation timing and rates. Although

some data rich regions, such as the United States (Ogle

et al., 2010) and Europe (Leip et al., 2008), have begun

to compile this information, it can only be inferred at

poor spatial resolution from country-level statistics

(e.g. for nitrogen fertilization rates) or not at all (e.g. till-

age practice) in many less-developed regions. This

remains a large source of uncertainty for developing a

soil C estimation system at the global scale, and a

gap that global organizations such as the UN Food

and Agriculture Organisation (FAO) are keen to fill

(Francesco Tubiello, personal communication); FAO is

supporting WOCAT (World Overview of Conservation

Approaches and Technologies) to develop a global map

of sustainable land management implementation

(Liniger & Critchley, 2007).

Data used for developing and testing Tier 2 and 3

methods and models

Long-term soil experiments

Experimental and monitoring data provide the basis for

our understanding of land use change and manage-

ment impacts on soil C dynamics. Soil C stocks change

in response to the balance between C inputs and out-

puts (Smith & Fang, 2010). The inputs are driven pri-

marily by the net primary production and external

additions of C such as organic amendments, while the

outputs are largely driven by the rate of microbial

decomposition. Our mechanistic understanding of

these processes is based on laboratory and field studies,

but ultimately the latter is needed to parameterize and

evaluate models for the Tier 3 methods, and also to

derive regionally specific EFs for Tier 2 methods (see

examples in Ogle et al., 2005, 2006; Maia et al., 2010a).

Field experiments date from the 19th century and for

the last several decades, results of several dozen of

these field studies have been instrumental to improving

competence and performance of global C models and

in helping to better resolve human effects on soil C stor-

age. These field studies have a limited history of net-

working and collaboration but in the late 20th century,

several attempts were made to promote such interac-

tions (Barnett et al., 1995); most notably with respect to

soil C were the SOMNET and EuroSOMNET programs,

two ambitious attempts to couple long-term observa-

tional data with C-cycle models to promote the science

of C modelling and better resolve agriculture’s role in

contributing to atmospheric CO2 (Smith et al., 1997a,

2000). SOMNET evolved into an on-line, real-time

inventory project with a web-site known as LTSEs,

Long-Term Soil-Ecosystems Experiments, which now

has collected metadata on well over 200 long-term soil

experiments (http://ltse.env.duke.edu/), in an effort

described in Richter et al. (2007).

Two firm conclusions can be reached from a review

of the LTSE inventory and metadata of the world’s soil

field studies (Richter & Yaalon, in press). First is how

valuable the world’s long-term field studies are for our

understanding of soil and of soil’s sustainability. The

second is a much more sobering conclusion. For as

valuable as these field studies are, they can be useful to

the larger global framework for only some questions

about soil C. Nearly all (>80%) of the world’s long-term

field studies address agricultural research questions,

and the preponderance of the field studies test agricul-

tural questions in the temperate zone. Land use change

can have a large influence on both the C inputs and out-

puts to soils, but there are few field experiments evalu-

ating the impact of land use change on soil C, and the



majority have been conducted in North America

(Fig. 1a). Agronomic practices can also influence soil C

stocks, such as crop selection and rotation; tillage

and residue management; nutrient management and

organic amendments; water management (irrigation

and drainage); temporary set-aside of cropland in

reserve; and agroforestry (Smith et al., 2007, 2008a). Till-

age and residue management experiments evaluating

the impact on soil C stocks are more widely distributed

than the land use studies, with relatively high densities

in North America (Paustian et al., 1997), Europe (Smith

et al., 2002), southern South America and eastern

Australia (Fig. 1b). Experiments evaluating the influ-

ence of other agronomic practices are also relatively

common in the same regions (Fig. 1c).

Chronosequence soil C data

Medium-term, and especially long-term, studies are the

gold standard for accurately estimating changes in soil

C stocks and of immense value to parameterize and

evaluate models. They do however have some limita-

tions. Often the crop rotations in the experiments and

their management do not represent on-farm reality and

there are very few experiments undertaken at a scale

that encompasses variation in land form, altitude, soil

type and climatic conditions. For this reason the use of

on-farm studies using chronosequences combined with

appropriate modelling, is another suitable strategy for

assessing soil-C stock changes on the scale of individual

properties or groups of smallholders (e.g. Bayer et al.,

2000; Amado et al., 2001; Machado & Silva, 2001; Freixo

et al., 2002; Sisti et al., 2004; Jantalia et al., 2007; Boddey

et al., 2010).

The chronosequence approach uses a series of sites

on which the current land use has been undertaken for

different time periods, in lieu of repeated measure-

ments over time. Sites are selected that have the same

underlying features (soil type, landscape position) and

the same land use history prior to conversion to the

current land use. Therefore, it is assumed that the C

stocks before the change in/management were identi-

cal. Equivalence of the sites can be tested by examining

similarities in soil texture (e.g. Moraes et al., 1996;

Jantalia et al., 2007), bulk density (S. P. Braz, unpub-

lished data) or 13C natural abundance of C in the profile

as an indicator of the similarity to the original vegeta-

tion (Cerri et al., 1985). However, land use and manage-

ment history prior to the adoption of the crop/

management practice of interest in the chronosequence

can be difficult to determine in some cases, and will

lead to uncertainty when using these data for model

evaluation. Similar limitations arise from other site-

level monitoring approaches, where soil C stock

changes are measured over time along with quantifica-

tion of land use and management activities, but outside

of a controlled experimental environment. Such ‘low-

tech’ approaches are actively being discussed in the

research and policy communities, so need to be consid-

ered. Whilst such approaches do not provide the same

quality of data as do controlled experiments, a wide

distribution of such sites could complement research at

experimental sites, trading data quality for wider

spatial coverage.

Long-term data on ecosystem C fluxes

Another source of experimental data is the FLUXNET

global network of micrometeorological tower sites that

use eddy covariance methods to measure the exchanges

of carbon dioxide, water vapour, and energy between

terrestrial ecosystems and the atmosphere (FLUXNET,

2011). More than 500 tower sites from about 30 regional

networks across five continents are currently operating

on a long-term basis (Fig. 1d). The main aim of FLUX-

NET is to provide information for validating remote

sensing products for net primary productivity (NPP),

evaporation, and energy absorption, but the data have

been used extensively in model calibration (Rastetter

et al., 2010) and model testing (Morales et al., 2005; Wat-

tenbach et al., 2010). The FLUXNET website (FLUX-

NET, 2011) provides information about tower location,

site characteristics, data availability, and where to

obtain the data, and also provides links to the many

publications utilizing FLUXNET data, which have been

used in many global level analyses of terrestrial C

fluxes (e.g. Stoy et al., 2009). FLUXNET data have

improved our understanding of ecosystem C fluxes, but

in this context, these data are most valuable in testing of

soil C models for evaluating net primary production

and carbon inputs to soils that are predicted by the Tier

3 methods. Estimation of soil C stock changes from the

towers is indirect and highly uncertain due to the small

net change in soil C pools relative to the biomass and

litter over the time frame of most flux data currently

available (annual to a few years; Smith et al., 2008b),

and because it can be difficult to attribute measured dif-

ferences to different drivers. Despite the uncertainties

in flux measurements, and the desirability of supple-

mentary measures (SOC change, soil respiration, etc.) at

these sites (Smith et al., 2010a), ecosystem C flux

measurements have proved extremely useful if model

testing and development (e.g. Morales et al., 2005).

Data gaps for long-term data

There are additional experiments in other regions, such

as China (e.g. Yan et al., 2011) and India (Pathak et al.,


6 P. SMITH et al.

(a)

(b)

(c)

(d)

Fig. 1 (a) Distribution of land use change experiments evaluating the impact on soil C stocks. (b) The distribution of tillage experi-

ments evaluating the impacts on soil C stocks. (c) Distribution of experiments evaluating the influence of different agronomic practices

on soil C stocks. (d) Distribution of FLUXNET flux sites.



2011), but there appear to be a few studies in the south-

ern hemisphere, particularly in the tropical latitudes,

and there are also considerably larger uncertainties in

estimating soil C stock changes in these regions, espe-

cially with the Tier 1 method (Ogle et al., 2005; IPCC,

2006b) due to sparse data coverage. Moreover, much of

the expansion in bioenergy and traditional commodity

crop production is likely to occur in these regions

(Smith et al., 2010b). Therefore, the global framework

will need to champion the great need to substantiate

and expand the application of field studies to the devel-

oping world, with contemporary agricultural systems,

and to nonagricultural land uses as well (Richter &

Yaalon, in press). In the best of all worlds, dedicated

field experimentalists with guidance from modellers

and quantitative ecologists could have several dozen

new high-impact field studies with two decades of

observational data by the year 2035. Instead of confin-

ing our understanding largely to soil C dynamics in the

temperate region, by 2035, scientists could substantially

increase the certainties about soil C changes across a

wide range of ecosystems and land uses, from the bor-

eal zone to the tropics, from contemporary agricultural

systems to urban and residential influences.

Data on short-term C fluxes

Short-term in situ experiments concerning the fate of

recently assimilated C to the belowground components

of root, soil and microbial pools can yield valuable data

required for predicting ecosystem respiration fluxes.

Under land use and management change, these pools

are not in equilibrium and an important experimental

challenge therefore is to quantify the residence and tra-

jectory of C in these pools. Many C allocation studies

that have focused on land use change and management

have taken advantage of stable C isotope tracer

approaches to achieve this (e.g. Johnson et al., 2002;

Rangel-Castro et al., 2004; Leake et al., 2006; Wang et al.,

2007; Ward et al., 2009; De Deyn et al., 2011). The most

common field approach is through the exposure of

plants to isotopically enriched 13C in CO2 at ambient

(Ostle et al., 2000; De Deyn et al., 2011) or above ambi-

ent concentrations (Hogberg et al., 2008; Ward et al.,

2009) for several hours in chambers or tents. The photo-

assimilation of 13CO2 during this pulse labelling is then

tracked through soil and plant materials and into respi-

ratory fluxes during the following days to months. The

technique has often been referred to as the ‘13CO2 pulse

chase’ approach due to the intensive nature of the field

sampling that follows the isotope addition. For land

use change and management studies this 13C approach

has primarily been used for grassland and peatland

ecosystems with short stature vegetation (Johnson et al.,

2002; Rangel-Castro et al., 2004; Leake et al., 2006; Wang

et al., 2007; Ward et al., 2009; De Deyn et al., 2011).

However, recent 13C pulse chase experiments on whole

tree (Hogberg et al., 2008; Epron et al., 2011), crowns of

trees (Plain et al., 2009; Dannoura et al., 2011) and large

energy crop grass (Travi et al., 2009) have demonstrated

the application of this technique in taller vegetation

systems.

The short-term 13C tracer approach does not overide

the utility of using long-term monitoring networks or

space-for-time experiments (i.e. chronosequences);

rather, it provides a new level of process information

for calibration and verification of ecosystem C models,

which will be used in Tier 3 methods. The 13C pulse

chase approach provides valuable data for C allocation

to belowground ecosystem components (Epron et al.,

2011); the contribution of photosynthate to heterotro-

phic and autotrophic fluxes (Subke et al., 2009); time

lags between assimilation and soil respiration (Bahn

et al., 2009) and the transfer of C to microbial and fun-

gal pathways (Ostle et al., 2003; Leake et al., 2006). A

potential limitation of the approach, being short term in

nature, is that several hours of 13CO2 exposure may not

introduce a measurable amount of new C into more

recalcitrant soil pools (Carbone et al., 2007; Carbone &

Trumbore, 2007; Kuzyakov, 2011). However, 13CO2

additions over extended periods of time (days to

months in FACE or large chamber experiments) or

using very low and safe levels of radioactive 14CO2

tracers in combination with measurements by Acceler-

ated Mass Spectrometry can override these limitations

(Carbone et al., 2007; Carbone & Trumbore, 2007).

Application of methods for soil C accounting

Existing Tier 2 and Tier 3 methods

IPCC Tier 2 or 3 methods are both possibilities for

advancing soil C accounting. For a Tier 2 method, mea-

surements are typically combined from several sites to

estimate an EF specific to a region or country (e.g. Van-

denbygaart et al., 2004; Ogle et al., 2006; Maia et al.,

2010b). Meta-analytic approaches can be used to derive

factors and estimate uncertainty in the resulting factors

(Maia et al., 2010a). Sufficient measurements are needed

to estimate EFs, and many regions of the world cur-

rently lack the necessary measurements to estimate EFs

representing the influence of land use and management

(Fig. 1a–c).However, experiments could be designed and imple-

mented within the next decade that could form the

basis for advancing Tier 2 methods. Sufficient land use

data are also probably available to support Tier 2 meth-

ods, or could be developed from existing remote


8 P. SMITH et al.

sensing imagery archives. Weather and soils data are

also likely to be available. Land management data are

more problematic because much of these data are not

collected in surveys. As with gaps in experimental data,

surveys could be developed and implemented within

the next decade to provide this information. WOCAT

has developed a standard land management survey

system, and is assembling relevant databases. Expert

knowledge could also be used in the short-term to fill

activity data gaps (IPCC, 2006a; Maia et al., 2010b).

Remote imaging spectroscopy has also shown promise

in estimating crop residue quantity and inferring tillage

intensity (Serbin et al., 2009), and can provide other

information, such as the crop species present, pres-

ence/absence of fallow season cover crops, presence/

absence of irrigation and estimation of above-ground

biomass and productivity, all of which are useful as

inputs for soil C models.

Tier 3 methods are likely to produce more precise

and accurate results, compared to Tier 2 methods,

which in turn are likely more accurate and precise than

Tier 1 methods. For example, Del Grosso et al. (2011)

compared results from Tier 1, Tier 2 and Tier 3 methods

(CENTURY) that were applied to US agricultural lands

(Ogle et al., 2006, 2010), and found that the Tier 3

method using a process-based model produced esti-

mates with a precision of ±16%, compared to ±40% and

±59% for estimates derived with Tier 2 and 1 methods,

respectively. Several process-based models exist that

could be used in a Tier 3 method, such as CENTURY

(Parton et al., 1987; Ogle et al., 2010), RothC (Coleman

& Jenkinson, 1996; Jenkinson & Rayner, 1977), EPIC-

CENTURY (Izaurralde et al., 2007), DNDC (Li et al.,

2003) and many others. Over 10 years ago, Smith (2002)

reviewed 33 SOC models which could be adapted for

use in such a framework, and more models (and deri-

vations of earlier models) have become available since.

Some systems already exist for regional application

such as Austrialia’s Tier 3 national inventory which

uses RothC (Paul & Polglase, 2004), and the GEFSOC

system which uses RothC and CENTURY (Easter et al.,

2007). Many potential Tier 3 models have been tested

for their ability to characterize changes in soil carbon

(e.g. Smith et al., 1997b). Whilst models vary in their

performance, and in the degree of parameterization

they require, many have been shown to reproduce

historical changes effectively (Smith et al., 1997b;

Izaurralde et al., 2007). Data is critical for both model

testing and, where necessary, for model parameteriza-

tion and calibration.

Process-based models rely on measurements for

parameterization and evaluation of model results

(IPCC, 2006a), and measurements can be used to quan-

tify uncertainties in model parameterization and algo-

rithms (e.g. Ogle et al., 2010). Consequently, a sufficient

number of experiments must be available for this pur-

pose and many regions of the world lack the necessary

data at this time (Fig. 1a–c). Assuming model evalua-

tion is possible and the results are adequate, the models

can be used to estimate dynamic EFs that change

through time and space, based on current process-level

understanding of C dynamics. This contrasts with the

Tier 2 method which utilizes experimental data or mod-

els to calculate a country or region-specific emissions

factor, which is then applied across a region or through

time. In contrast to Tier 2, the Tier 3 method requires

considerably more activity data, and this will probably

be the greatest limitation to applying the highest Tier

method. Activity data may include planting and har-

vesting dates, tillage implements and passes, organic

and synthetic fertilization rates, irrigation timing and

rates, in addition to crop types, rotation practices and

other data required for the Tier 2 method. Surveys can

be developed to fill these data gaps with sufficient

resources and planning, as planned under WOCAT

(see section Global land management data).

Advancing Tier 3 methods with models that can learn

Recent years have seen important advances in methods

to improve the estimation of model parameters, and the

quantification of model uncertainty, the latter deter-

mined both by uncertainty in parameters but also in

process descriptions (e.g. Fox et al., 2009). These meth-

ods can be used to advance Tier 3 methods by assessing

the robustness of process-based model outcomes, and

identifying the entry points to improve their perfor-

mance and reliability. Parameter estimation using

inverse modelling (i.e. the process of iteratively adjust-

ing model parameters so that the model approximates,

as closely and consistently as possible, the observed

response of the system under study during some histor-

ical period of time; Vrugt et al., 2005) accommodates

more flexible experimental conditions than typically

available in laboratory experiments and thereby facili-

tates estimating values of soil properties associated to

the scale of interest. These inverse modelling methods

can be completely automated, although at the cost of a

substantial amount of computer power. An important

weaknesses of these calibration methods is that they

attribute all model uncertainty to the model parame-

ters, often without an explicit treatment of input, out-

put, and model structural uncertainties. In the past few

years, ensemble-forecasting techniques based on

sequential data assimilation methods have become

increasingly popular due to their potential ability to

explicitly handle the various sources of uncertainty in

geophysical modelling. Techniques based on the



ensemble Kalman filter (EnKF, Evensen, 1994) have

been suggested as having the power and flexibility

required for data assimilation using nonlinear models

(e.g. Vrugt et al., 2005; Williams et al., 2005). EnKF has

the power to take into account uncertainty in both

parameters and states simulated by the model, and can

therefore result in a more integrated uncertainty analy-

sis of model outcomes and estimation of the robustness

of model predictions. At the same time, account must

be taken of the simplifications associated to the applica-

tion of EnKF (e.g. Rastetter et al., 2010). The current

development of these inverse modelling techniques will

allow the setup of an integrated measurement – model

platform which can identify key regions in the world

for which targeted research plans should be developed

to improve the reliability of model predictions.

Vision for predicting soil C stock changes in the

short and long term

Numerous models are used at a variety of scales to esti-

mate soil C stock changes for research purposes, but

the spatial extent and resolution of the underlying data,

whilst continually improving, is not sufficient for soil C

monitoring in all regions (van Wesemael et al., 2011). It

is not advisable to recommend any one model for a

framework to predict soil C stock changes as there are

many that could be used. Any candidate model should,

however, be tested against multiple data sources (as

described in section Data used for developing and test-

ing Tier 2 and 3 methods and models) and its ability to

capture changes in SOC under various land cover

transitions, land management practices, soil types and

eco-regions should be established. Sensitivity and

uncertainty should be established. A number of model

evaluation exercises have been conducted (e.g. Smith

et al., 1997a,b) so the methods are in place to perform

this task. Processes that current models represent least

well are highly organic soils (Smith et al., 2010c), and

degraded soils such as those that have become saline

(Setia et al., 2011). The climate, soils and land use data

are coming close to adequate spatial extent and resolu-

tion (though some soil types, such as highly organic

soils tend to be less well characterized, for example

with respect to total depth of profile). All datasets are

being improved continually, either through better inte-

gration of fine scale survey data (Sanchez et al., 2009)

by higher resolution and more sophisticated earth

observation products (e.g. MODIS, 2011). However,

management and other high-resolution activity data

are available for only a few data-rich regions. Surveys

can be developed to fill these data gaps with sufficient

resources and planning, and global organizations such

as FAO are keen to begin compiling this information,

for example under the WOCAT project (Liniger &

Critchley, 2007).

Experimental data, chronosequences and flux net-

works are needed to derive EFs for the Tier 2 methods,

in addition to parameterization and evaluation of Tier 3

methods. A number of global data networks on long-

term soil carbon change and ecosystem carbon fluxes

are available to provide a basis for deriving Tier 2 EFs

and a test bed for Tier 3 methods (section Long-term

soil experiments). Within these networks, however,

there is poor coverage for developing countries. The

same regions lack sufficient management and activity

data, and are also the regions with the greatest changes

in land use and management due to expanding biofuels

and increasing demand for food and livestock feed

(Smith et al., 2007). Areas for initial focus might be

those particularly high rates of land use change on high

C soils, such as South East Asian tropical peatlands.

Filling the data gaps in these under-represented regions

can be accomplished with long-term commitment to

measurement of soil organic C stocks in experimental

plots or careful selection of chronosequences. Research

institutes could lead this charge with support from

international and national funding organizations, as

well as foundations with an interest in environmental

issues associated with global change.

We envision that a portable modelling platform

could be developed with capability to implement Tier 2

or 3 methods. This system will require standards for

the development of input datasets, such as climate and

soils data, as well as model code development. Con-

forming to the standards would allow for new data and

models to be easily incorporated into the estimation

system. There is also the possibility to use model-data

fusion techniques to optimize model performance, and

to assimilate new data from diverse sources as they

become available (Williams et al., 2009; Rastetter et al.,

2010; Smith et al., 2011).

With estimates of soil C stock changes that are rea-

sonably accurate and precise, and that account for envi-

ronmental change, policy-makers could develop action

plans in order to enable sustainability criteria to be

measured, monitored and transmitted to stakeholders

in an effective manner at regional, intra-national, and

trans-national scales. The rigour needed to produce

defensible estimates moves beyond the current Tier 1

methods towards the development of the more spa-

tially representative data-sets described here. Depend-

ing on global prioritization and funding, the initial

focus could be on developing these datasets in coun-

tries where the data is poor but the rate of land use and

management change is great, which is largely in devel-

oping countries. Agriculture, environment, and forestry

often have different institutional structures and


10 P. SMITH et al.

funding channels with the implication that data needs

are addressed separately. Success would, however, be

more likely if actions were coordinated across national

(e.g. national agricultural centres of excellence, depart-

ments for foreign and international development) and

transnational (World Bank, UN, IPCC, IEA, G20, World

Economic Forum, etc.) groups. There is potential to

pool resources from several quarters to aid this endeav-

our: Maintenance of SOC is recognized as critical for

sustainable land management, and therefore to manag-

ing land degradation and biodiversity conservation; the

UNCCD and CBD have both identified the need to

monitor soil C as an indicator of sustainable land man-

agement, and the benefits of combining effort and

resources to build capacity for monitoring soil C under

the three major environmental conventions have been

noted (Cowie et al., 2011).

The importance of this endeavour should not be

under-estimated. Without sufficient progress on filling

the gaps identified, it is unlikely that policies and pro-

grams that encourage expansion of biofuel production,

changes in land use and management to meet future

food security demands, or use for conservation, will

account for the impact on soil carbon stocks. At best,

the uncertainties will be much greater, as the alterna-

tive would be a systematic application of Tier 1. The

methods and data necessary to meet this vision are in

various states of readiness, and a blend of public, pri-

vate and development funding, especially in under-

represented regions, would yield great benefits in terms

of our ability to quantify the changes in carbon, that

have confounded our ability to deliver consistent

policies in the past (e.g. Searchinger et al., 2008).

Acknowledgements

We are grateful to the UK Biotechnology and Biological SciencesResearch Council (BBSRC) and the Energy Technologies Insti-tute (ETI) for co-funding the expert meeting at Rothamsted inthe United Kingdom, in March 2011, from which this paperarose. Pete Smith is a Royal Society-Wolfson Research MeritAward holder. We thank Dr Dario Papale for location data ofFluxnet sites used in Fig. 1d and Dr Jim Penman of DECC forhelpful comments on early drafts, and three anonymousreviewers for comments that helped us to improve themanuscript.

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Supporting Information

Additional Supporting Information may be found in theonline version of this article:

Appendix S1. List of acronyms, abbreviations and Weblinks (all URLs accessed 27 February 2012).

Please note: Wiley-Blackwell are not responsible for the con-tent or functionality of any supporting materials suppliedby the authors. Any queries (other than missing material)should be directed to the corresponding author for thearticle.



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