uncertainty in estimating land use and management impacts on soil organic carbon storage for us...

Uncertainty in estimating land use and managementimpacts on soil organic carbon storage for US agriculturallands between 1982 and 1997

S T E PH EN M . OG L E *, F . J AY B R E I D T w , MAR L EN D . E V E * and K E I TH PAU S T I AN * z*National Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA, wDepartment of Statistics,

Colorado State University, Fort Collins, CO 80523, USA, zDepartment of Soil and Crop Science, Colorado State University, Fort

Collins, CO 80523, USA

Abstract

Uncertainty was quantified for an inventory estimating change in soil organic carbon

(SOC) storage resulting from modifications in land use and management across US

agricultural lands between 1982 and 1997. This inventory was conducted using a

modified version of a carbon (C) accounting method developed by the Intergovern-

mental Panel on Climate Change (IPCC). Probability density functions (PDFs) were

derived for each input to the IPCC model, including reference SOC stocks, land use/

management activity data, and management factors. Change in C storage was estimated

using a Monte-Carlo approach with 50 000 iterations, by randomly selecting values from

the PDFs after accounting for dependencies in the model inputs. Over the inventory

period, mineral soils had a net gain of 10.8TgCyr� 1, with a 95% confidence interval

ranging from 6.5 to 15.3 TgCyr� 1. Most of this gain was due to setting-aside lands in the

Conservation Reserve Program. In contrast, managed organic soils lost 9.4 TgCyr� 1,

with a 95% confidence interval ranging from 6.4 to 13.3 TgCyr� 1. Combining these gains

and losses in SOC, US agricultural soils accrued 1.3TgCyr� 1 due to land use and

management change, with a 95% confidence interval ranging from a loss of 4.4TgCyr� 1

to a gain of 6.9 TgCyr� 1. Most of the uncertainty was attributed to management factors

for tillage, land use change between cultivated and uncultivated conditions, and C loss

rates from managed organic soils. Based on the uncertainty, we are not able to conclude

with 95% confidence that change in US agricultural land use and management between

1982 and 1997 created a net C sink for atmospheric CO2.

Keywords: agroecosystems, carbon sequestration, greenhouse gas mitigation, IPCC, land use change,

uncertainty analysis

Received 8 November 2002; revised version received 2 June 2003 and accepted 3 June 2003

Introduction

One of the most dramatic changes in the global system

resulting from human activity is the rising concentra-

tion of greenhouse gases in the atmosphere (Vitousek

et al., 1997). Consequently, nations have been negotiat-

ing policies over the last decade, to mitigate greenhouse

gas emissions and one of the proposals would allow

countries to use carbon (C) sinks as a way to offset

emissions, based on the Kyoto Protocol (Article 3.3 and

3.4; UNFCCC, 1997). A potential C sink for CO2

emissions is the terrestrial pool of soil organic carbon

(SOC) (Houghton et al., 1983; Schimel, 1995). While SOC

pools are affected by a wide variety of environmental

variables operating across a range of temporal and

spatial scales, human management has been shown to

have an important influence, particularly in highly

managed systems such as agricultural fields (Paustian

et al., 1997a). Moreover, atmospheric CO2 can be

sequestered in agricultural soils with the implementa-

tion of conservation management practices (Kern &

Johnson, 1993; Paustian et al., 1997a, b, 2000; Lal et al.,

1998; Post & Kwon, 2000; Batjes, 2001; Follett, 2001;

Kucharik et al., 2001). Bruce et al. (1999) describe several

practices that promote C sequestration, including aCorrespondence: Stephen M. Ogle, fax 1 970-491-1965,

e-mail: [email protected]

Global Change Biology (2003) 9, 1521–1542, doi: 10.1046/j.1529-8817.2003.00683.x

r 2003 Blackwell Publishing Ltd 1521

reduction in tillage disturbance, intensification of

cropping rotations, improvements in crop yields, and

replacement of annual crops with perennial vegetation.

In general, these practices increase SOC storage by

enhancing C inputs to the soil through improved

productivity and residue management. However some

practices decrease C outputs resulting from decom-

position, such as reducing tillage disturbance, which

enhances soil aggregation and provides limited ‘pro-

tection’ to organic matter from microbial decomposi-

tion (Jastrow, 1996; Six et al., 1998, 2000). In addition,

several national assessments have demonstrated the

potential for C sequestration in agricultural soils

based on the adoption of conservation practices

(Smith et al., 2000a, b; Kucharik et al., 2001; Sperow

et al., 2003).

While the potential exists for agricultural soils to be a

C sink, inventories are needed to estimate the amount

of C sequestration for qualification as a mitigation

practice under international agreements (Subak, 2000;

Houghton, 2001). The Intergovernmental Panel on

Climate Change (IPCC) has developed such a model

for quantifying changes in SOC storage based on land

use and management practices. This method has been

applied to US agroecosystems (Eve et al., 2001a, b, 2002),

but previous inventories did not include rigorous

measures of uncertainty. In fact, the standard IPCC

method does not supply the information needed to

assess uncertainty. Consequently, our objective was to

estimate the change in SOC storage for US agricultural

lands between 1982 and 1997, along with the uncertainty

in those estimates, using the IPCC method. This time

frame was selected to determine baseline trends in net C

fluxes between agricultural soils and the atmosphere

that have resulted from land use and management

change in the recent past. The baseline conditions can be

used to quantify the effectiveness of future management

activity in mitigating greenhouse gas emissions.

Ecological models always have uncertainty in struc-

ture and formulation, and quantifying this uncertainty

provides a measure of the model’s ability to estimate

changes in a system, as well as information about

which inputs lead to the greatest uncertainty (Smith &

Heath, 2001). Without this assessment, it is problematic

for policymakers and resource managers to use model

results for developing government policies and man-

agement programs.

Methods

IPCC method

The IPCC method accounts for a change in SOC stocks

from a reference condition resulting from modifications

in agricultural land use and management (IPCC, 1997).

It is a relatively simple C accounting approach that any

nation, even those with limited technology and

resources, can use to account for net changes in SOC

storage as part of their greenhouse gas reporting. This

method has been used to report a change in SOC

storage due to agricultural land use and management

as part of the US greenhouse gas inventory, which is

compiled under agreements set forth by the UN

Framework Convention on Climate Change (EPA,

2003).

The method estimates changes in the SOC stocks for

the top 30 cm of a mineral soil profile (i.e., excluding

Histosols) over the first 20 years following a shift in

management. Presumably, agricultural management

has its greatest influence over this time frame and

portion of the profile (IPCC, 1997), although this may

limit its usefulness if there are pervasive impacts at

longer time intervals or deeper depths. For organic soils

(i.e., Histosols), the method estimates an annual loss

rate until the organic horizon has disappeared. Organic

soils are typically drained when converted to agricul-

tural land uses, leading to relatively high rates of

oxidation of the well-developed organic horizon (Ar-

mentano & Menges, 1986). Consequently, the method

only considers C losses from these soils. Also, in its

current form, the method does not account for gains

that may occur following wetland restoration.

Overall, the IPCC approach only deals with direct

anthropogenic effects on SOC storage that are verifiable

from land use and management activity records, and

hence its application is a partial accounting approach

(Watson et al., 2000; Houghton, 2003). Additional

changes in SOC storage resulting from other driving

variables (e.g., natural disturbance or climate) are not

considered in this method because their impact on

storage is unlikely to be given credit for mitigation

purposes. In addition, while full C accounting is

desirable, costs and resource availability limit the

current use and practicality of such approaches

(Houghton, 2001). For this analysis, we have focused

on land use change between cultivated croplands and

uncultivated cover types, such as grasslands and

forests, as well as management effects resulting from

changing tillage practices, cropping rotations, and

draining organic soils for agricultural production.

SOC inventory calculations are carried out for

aggregated soil types within climate regions. The

aggregated types include ‘high-activity’ mineral soils

(Vertisols, Mollisols, high base status Alfisols), ‘low-

activity’ mineral soils (Ultisols, Oxisols, acidic Alfisols),

sandy soils (o8% clay and 470% sand), volcanic soils

(Andisols), spodisols (Spodisols), wetland soils (Aquic

suborder), and organic soils (Histosols). Climate

1522 S . M . OG L E et al.

r 2003 Blackwell Publishing Ltd, Global Change Biology, 9, 1521–1542

divisions occurring in the US include wet and dry

zones for subtropical, warm, and cool temperate

regions (Eve et al., 2001a).

For mineral soil types, annual changes in SOC stocks

(TgCyr� 1) are estimated using the equation

dC ¼

PH

h¼1

ðSOCtðhÞ � SOCt�20ðhÞÞ

1�106�20; ð1Þ

where H represents the number of climate regions by

soil types by land use/management systems, SOCt(h)

(MgC) is the amount of SOC in system h at the

beginning of the inventory, and SOCt-20 (h) (MgC) is the

amount at the end of the inventory period. We divided

by 1�106 to convert units from Mg to TgC, and then

divided by 20 to obtain an annual rate of change, as

recommended in IPCC guidelines (IPCC, 1997). SOC (h)

(MgC) is estimated using the equation

SOC ðhÞ ¼ RC � TF � IF � LUC � LA; ð2Þ

where RC is the reference SOC stock (MgCha� 1), TF is

the tillage factor (deals with tillage intensity such as no-

till vs. conventional tillage practices; dimensionless), IF

is the input factor (deals with cropping intensity and

the productivity of various cropping rotations; dimen-

sionless), LUC is the land use change factor (deals with

conversions between cultivated and non-cultivated

conditions; dimensionless), and LA is the land area

(ha).

Eqn (2) is a modified version of the original IPCC

formulation (IPCC, 1997). In the original method, the

reference SOC stock (RC) is based on values from

relatively undisturbed, native ecosystems. We modi-

fied this approach to use conventionally managed

cropland as the reference for estimating SOC stocks,

instead of native conditions, because US pedon

databases provide information that is readily linked to

agricultural management, and SOC stock measure-

ments are more common for soils under cultivated

than native conditions in major agricultural regions of

the US.

C emissions from managed organic soils are com-

puted using the following equation:

CL ¼LR � LA1�106

; ð3Þ

where CL is C loss (TgC), LR is an annual loss rate

factor (MgCha� 1 yr� 1), LA is the land area (ha), and

the quantity is divided by 1�106 to convert from Mg to

TgC.

We estimated annual SOC stock changes in mineral

soils and C losses from managed organic soils between

1982 and 1997, assuming a linear change in C storage.

In other analyses (EPA, 2003), we have estimated stock

changes for 1982 to 1992 and 1992 to 1997. Including the

1992 activity data had little effect on the final values,

however, and so for brevity, we present the simpler

results in this paper. All analyses were conducted using

the Splus 2000 Professional software package, release 3

(Insightful Corporation, Seattle, WA, USA).

Uncertainty analysis: Monte Carlo simulations

Uncertainties in model estimates are a consequence of

imprecision in initial (or reference) values, parameters,

inputs, model formulation, and validation data (Kros

et al., 1993; Klepper, 1997). We considered the first three

types of uncertainty in this analysis. Uncertainty in

model formulation may be explored in the future by

comparisons with other models that have different

constructs and assumptions. In addition, validation

data along with their corresponding uncertainties may

be incorporated into future analyses as they become

available frommonitoring of land use and management

impacts on SOC storage.

As depicted in Fig. 1, we estimated uncertainty using

a Monte Carlo approach by generating multiple outputs

from the model based upon a random selection of input

values from probability density functions (PDFs) (e.g.,

Kros et al. 1993; IPCC 2000; Smith and Heath 2001).

PDFs represent the distributions of uncertain input

values to the IPCC equations, including areas in various

land use/management practices, reference SOC stocks,

and factors representing the management impacts on

SOC storage. We simulated 50 000 runs of the IPCC

model for each climate region by soil type combination,

and then summed outputs for each run to produce an

empirical distribution of 50 000 C change estimates at

the national level. From these 50 000 estimates, we

(Lit. reviewof field trials)

IPCC Factors

Framework for Uncertainty Analysis

National Estimates(95% C.I.)

Land Use /Management History

Reference Carbon Stocks(NSSC Database)

(NRI and CTIC)

IPCCEquation

(50,000 reps.)MonteCarlo

Simulation

Fig. 1 Framework for IPCC uncertainty analysis with the

sources of uncertainty in italics.

UNC E RTA IN T Y ANALY S I S F O R AGR I CU LTURAL SOC S TORAGE 1523


obtained the 2.5 and 97.5 percentiles as a description of

uncertainty (i.e., 95% confidence interval). Values at

these percentiles would change if the entire simulation

experiment were repeated. However, to assess the

precision of using 50 000 replicates, we estimated the

standard errors of the percentiles and constructed 95%

confidence intervals. The range of the confidence

intervals for both percentiles was 0.03. This was

adequate for our application because we were only

reporting one significant digit beyond the decimal; that

is, the Monte-Carlo percentile estimates are unlikely to

change in the reported digits for other simulations with

50 000 runs.

In general, uncertainties about model inputs are not

statistically independent, and a simulation study needs

to deal with dependencies to generate meaningful

results. We considered several key dependencies. First,

we accounted for dependence in land use activity data.

For example, if an estimated land use is too high in one

inventory year, it will also tend to be too high in the

next year because the same survey locations are visited

each survey year in the US to determine land use

patterns (USDA-NRCS, 2000). Likewise, in a given

inventory year, if one estimated land use is too high,

then other estimated land uses must be too low

(otherwise the land use estimates would not add up

to the total land area). These dependencies in un-

certainty are reflected in the simulation by incorporat-

ing the proper variance–covariance matrix of the

estimates for the simulated land uses across time.

Second, we accounted for the dependent uncertainties

in management factors that were estimated in a single

statistical model using a common set of studies, such as

factors for reduced and no-till management (i.e.,

dependence exists because some studies had no-till

and reduced till treatments that were compared with a

single conventional till treatment). Land use change

and cropping input factors were also derived from a

single data set. Third, we accounted for dependence in

the assignment of factor values and land use areas to

climate regions by soil types in the simulation (i.e., id-

entical factor values were assigned to each climate by

soil region and therefore they were completely depen-

dent). Uncertainties for tillage, land use and input fac-

tors were dependent across all climates by soil types

because we used a single PDF to quantify the effect of

these management practices for the entire country.

Similarly, we did not differentiate separate C loss rates

for the dry and wet areas of a climate region (e.g., cool

temperate dry and wet), and therefore there were

dependencies in those rates. In contrast, uncertainties in

land use activity were considered to be independent

across climate by soil types because estimates were based

on separate data sets that were collected in each region.

PDFs

Reference SOC stocks for mineral soils

PDFs were derived for the reference SOC stocks using

pedon data from the National Soil Survey Character-

ization Database (USDA-NRCS, 1997). We selected

pedons representing cultivated agricultural conditions

based on the presence of an Ap horizon at the surface

with no overlying O horizon. In addition, we only

included pedons with measurements to a 30 cm depth

for %OC, textural composition, and gravel content data,

as well as having location information and a taxonomic

description. A total of 2601 pedons met these criteria.

Bulk density values (o2mm fraction) were only

measured for about 1200 of those pedons; the remain-

ing bulk densities were computed based on neural

network calculations described in Lacelle et al. (2001).

We estimated SOC stocks (RC) in Mgha� 1 for each

pedon, using the following equation:

RC ¼ð%OC=100Þ � depth �Db

� ½ð100�%gravelÞ=100� � 10 000;ð4Þ

where %OC is the organic carbon content, depth

is the increment width (m), Db is bulk density for the

o2mm fraction (Mgm� 3), %gravel is the proportion of

gravel in the top 30 cm of the profile, and 10 000 is a

factor for converting m2 to ha. PDFs were normal

densities based on the means and variances of the

pedon data, with truncation to avoid unrealistic values.

The lower limit for truncation was 0MgCha� 1, and

the upper limit for truncation was 450MgCha� 1

(only histosols are likely to accumulate C above this

value).

Pedon locations were clumped in various parts of the

country. Clumping often reduces the statistical inde-

pendence of individual measurements, and can lead to

lower variance estimates than would be justified by the

data. To check for this pattern, data were tested for

spatial autocorrelation in each climate region by soil

type using Moran’s I test. Traditionally, spatial depen-

dence has been ignored in statistical modeling, except

for cases in which investigators have specified spatial

models using geostatistical techniques. Because of the

sparse density of pedon data, however, we could not

specify spatial models to compensate for autocorrela-

tion effects. As an alternative, we inflated variances by

10% for all climates by soil types with significant

P-values from the Moran’s I test. There is no standard

for inflation in such cases, but in further simulations not

reported here, we found that even with a higher

inflation at 50%, the contribution of the reference C

stocks to overall IPCC model uncertainty changed by

less than 1%.

1524 S . M . OG L E et al.


Tillage, input, and land use change factors

To estimate management factors for input, tillage, and

land use change, we conducted a literature search using

the Agricola bibliographic database and the ISI Web of

Science citations database. We selected studies pub-

lished in journal articles and book chapters based on

several criteria. First, we only included studies from the

US and neighboring areas in Canada (with similar soils

and climate as northern portions of the US), assuming

that they would best represent management impacts for

this inventory. Second, we selected studies that either

provided SOC stocks in the paper, or the %C and bulk

density measurements so that we could calculate the

stocks. Requiring bulk density measurements allowed

us to reduce uncertainty in the factors by not having to

approximate values using pedo-transfer functions.

Third, studies had to report the depth of measurement

and time frame over which the management change

had occurred because these variables were used to

estimate factors representative of impacts for the top

30 cm of the soil profile over the first 20 years since a

management change, using a regression analysis.

Input factors quantified the effect of variation in

cropping intensity and crop type on SOC storage, and

were grouped into three general categories. The vast

majority of rotations were part of the medium input

category, which included continuous row crops and

small grains. Rotations with bare summer-fallow were

placed in the low-input category, along with crops

producing low amounts of residue, such as vegetables

and cotton. The high-input category was reserved for

cropping systems that included a year of hay, legumes,

or pasture in rotation, and fields with winter cover

crops or irrigation.

Tillage impacts were based on three general types

that represent a gradient in tillage disturbance, ranging

from the most intensive practices referred to as

conventional tillage (e.g., moldboard plowing), to

moderately intensive practices referred to as reduced

tillage (e.g., ridge tillage, mulch tillage, or chisel

plowing), and the least intensive referred to as no-till.

Land use change factors quantified the effect of

conversions between cultivated and uncultivated con-

ditions. Uncultivated lands included continuous pas-

ture and hayland (i.e., not in rotation with annual

crops), rangeland, and managed forest. In addition, we

estimated a separate land use change factor for areas

that had been set aside from agricultural production in

the Conservation Reserve Program (CRP).

PDFs were derived by fitting the published data in

linear mixed-effect models. The response variable was a

ratio of the SOC stock for the management change

divided by the SOC stock for the reference condition.

This metric is referred to as a response ratio, and is

equivalent to the factor values defined in the IPCC

method (IPCC, 1997). In some cases, ratios were

transformed using a natural log:

ln ðratioÞ ¼ ln ðSOCchangeÞ � ln ðSOC referenceÞ ð5Þ

if the residual analysis demonstrated a violation of

model assumptions. Fixed effects were included for

time and depth, while random effects were assumed for

time series data and multiple depth increments from a

single study, accounting for correlations among those

measurements.

While some studies only considered the effect of

management across a single depth increment, most

included more than one increment in the evaluation,

such as 0–5, 5–10, and 10–20 cm. For studies with single

and multiple depth increments, regressors were formed

from the upper and lower values of each increment by

modeling the instantaneous SOC stock ratio as a

quadratic function of depth, and then integrating and

averaging the quadratic function over the increment.

That is, the average SOC stock ratio for a single

increment was the integral of the quadratic function

from the upper depth to the lower depth, divided by

the thickness of the increment. Integration of the

quadratic function resulted in two regression variables

for each depth increment:

x1¼ ðL2 �U2Þ=ð2 ðL�UÞÞ; ð6Þ

x2¼ ðL3 �U3Þ=ð3 ðL�UÞÞ; ð7Þ

where L represents the lower depth of the increment

(cm) and U represents the upper depth (cm). By using

this approach, no information was lost from the studies

through an aggregation of individual samples or

through interpolation to a standard set of depth

increments.

A quadratic function was chosen to represent the

declining difference in SOC storage between manage-

ment treatments at deeper depths in the profile (i.e., the

effects of agricultural management are typically great-

est at the surface and then diminish with depth). The

rate of decline depended on the strength and nature of

the management impact. In some cases, such as in the

comparison of no-till and conventional till practices, the

difference in SOC storage changes sign across depth,

with more SOC in the no-till treatment near the surface

of the soil at 0–5 cm, but more SOC in the conventional

till treatment near the bottom of the plow layer, which

typically reaches a depth of 20–25 cm. This is due to less

mixing of the soil with no-till management, leading to

greater stratification of C near the soil surface (Anger

et al., 1997). The fit of a quadratic function is well suited

to capture this trend.

PDFs for the management factors were normal

densities with the mean equal to the prediction for



the integrated effect of management in the top 30 cm of

the soil profile over the first 20 years after implementa-

tion. The standard deviation for the PDF was calculated

based on the prediction error variance for that estimate.

We did not synthesize the literature to determine

management factors for pasture and hay land improve-

ments (e.g., irrigation and seeding legumes), contin-

uous perennial/horticultural cropping, and rice

production, because fewer than five US studies were

found meeting our selection criteria for these practices.

Because of relatively high C inputs to the soil for these

practices, the IPCC method assumes that they increase

SOC storage above levels found in typical uncultivated

systems, and a default value of 1.1 is provided in the

IPCC documentation for estimating their impact (IPCC,

1997). A PDF was constructed for the default estimate

with a nominal level of uncertainty based on a 750%

normal distribution around the value (i.e., standard

deviation5 value/4). To calculate SOC storage under

these management systems, we multiplied the land use

change factor (LUC) from Eqn (2), which was estimated

from our literature synthesis, by the additional value of

1.1 from the IPCC documentation.

C loss rates from managed organic soils

PDFs for C loss rates were derived from published

studies in the US and neighboring areas in Canada,

assuming that they would best represent losses for this

inventory. Most studies are based on measures of

subsidence, and while subsidence is related to oxida-

tion of the organic matter, it is not a direct measurement

of C loss. Consequently, we used adjustment factors to

account for the amount of total subsidence that was due

to oxidation, assuming a nominal 750% normal

distribution around those adjustments. The C loss rates

(LR) (MgCha-1 yr� 1) were computed using the follow-

ing equation:

LR ¼ ST � Sox � ð%OC=100Þ �Db � 10000; ð8Þ

where ST is the total subsidence (myr� 1) (rate of land

subsidence following drainage of a wetland for

agricultural production), Sox is the adjustment factor

for the proportion of subsidence attributed to decom-

position of organic matter (percentage), %OC is the

organic C content, Db is the bulk density of the soil

(Mgm� 3), and 10 000 is a factor for converting m2 to ha.

C loss rates falling below 0MgCha� 1 were reassigned

to 0 because we only estimated C losses from managed

organic soils in accordance with the IPCC method (i.e.,

negative loss rates would imply a gain in C using

Eqn (3)).

Loss rates were also needed for soils that have been

drained, but managed as pastures or forests (IPCC

1997). We were unable to find enough articles to model

this effect based on studies conducted in the US.

Therefore, we used the default assumption from the

IPCC method that C losses from organic soils in

managed forest or pastures are about 25% of those

from the annually cropped, organic soils. We assumed a

nominal 750% normal distribution around the 25%

conversion value in order to construct a PDF.

Land use activity data: National Resources Inventory

The majority of the land use and management activity

data were obtained from the National Resources

Inventory (NRI), which has a record of land use history

since 1982 for the conterminous US and Hawaii (Nusser

& Goebel, 1997; USDA-NRCS, 2000). The land base for

the agricultural SOC inventory included all areas from

the NRI that were designated as an agricultural land

use in 1992 or 1997, including high-input cropping

systems, low-input cropping systems, medium-input

cropping system, continuous perennial/horticultural

crops, rice, pasture lands, improved pasture lands

(based on seeding legumes or irrigation as captured

in the NRI), rangelands, and set-aside lands registered

in the CRP (Table 1). Several non-agricultural land uses

were also included in the analysis because they were

transferred from or into agricultural production during

the inventory period. The non-agricultural land uses

included forests, open water, urban, federal land, and

other miscellaneous non-cropland (e.g., barren areas,

feedlots, marshland, and gravel pits).

We computed change in SOC storage for the entire

agricultural land base, with the exception of those areas

that were shifting between agricultural land uses and

urban, open water, or miscellaneous non-cropland. In

previous inventories, it was assumed that these land

uses were similar to native conditions in terms of SOC

storage (Eve et al., 2002). However, the fate of SOC

during conversions to non-agricultural uses is largely

unknown, and so in the current inventory, we adopted

a more conservative approach in dealing with these

land use conversions and assumed no change in SOC

storage.

The NRI uses a stratified, two-stage probability

sampling design (Nusser et al., 1998), in which first-

stage sampling units are land areas (typically quarter-

sections), second-stage sampling units are points within

those land areas, and all units are stratified geographi-

cally to ensure good spatial dispersion. During the time

period of interest for the present study, the sampling

points were inventoried every 5 years. The standard

theory of probability surveys (Cochran, 1977) shows

that uncertainties in the estimates for land areas are

approximately normally distributed, with variances

and covariances computed via standard formulae.

Therefore, we constructed multivariate normal PDFs

1526 S . M . OG L E et al.


for land use to have a mean vector equal to the vector of

total areas for the land use categories in different

years of the inventory, and to have a covariance matrix

equal to the sampling covariances that were compu-

ted from the NRI data. This construction ensures

that dependencies are properly reflected in the simula-

tion, including dependent uncertainties in land use

estimates between the first and last year of the

inventory (due to the panel structure of the NRI), and

dependent uncertainties in land use estimates for a

given inventory year (due to constraints on the total

land area).

Tillage practice activity data: Conservation Technology

Information Center

We used an annual survey conducted by the Conserva-

tion Technology Information Center to estimate the

amount of agricultural land managed with conven-

tional, reduced, and no-tillage (CTIC, 1998). PDFs were

derived for the CTIC data as bivariate normal on a log-

ratio scale, to reflect negative dependence among tillage

classes (e.g., if the percentage of conventional tillage in

a county is high, then the percentage of no-till is

necessarily low) and to ensure that simulated tillage

percentages were non-negative and summed to 100%.

Table 1 Land use/management categories derived from the National Resources Inventory and the corresponding land use/

management systems recognized in the Intergovernmental Panel on Climate Change (IPCC) method (1997)

IPCC categories

Land use/management categories Mineral soils Histosols

Agricultural (cropland and grazing land)

Irrigated crops High-input cropping Cultivated crops

Continuous row crops Medium-input cropping Cultivated crops

Continuous small grains Medium-input cropping Cultivated crops

Continuous row crops and small grains Medium-input cropping Cultivated crops

Row crops in rotation with hay and/or pasture High-input cropping Cultivated crops

Small grains in rotation with hay and/or pasture High-input cropping Cultivated crops

Row crops and small grains in rotation with hay and/or pasture High-input cropping Cultivated crops

Vegetable crops Low-input cropping Cultivated crops

Low residue annual crops (e.g., tobacco or cotton) Low-input cropping Cultivated crops

Small grains with fallow Low-input cropping Cultivated crops

Row crops and small grains with fallow Low-input cropping Cultivated crops

Miscellaneous crop rotations Medium-input cropping Cultivated crops

Continuous rice Rice* No loss

Rice in rotation with other crops Rice* No loss

Continuous perennial or horticultural crops Perennial/horticultural cropping* Pasture/forest

Continuous hay Uncultivated land (general) Pasture/forest

Continuous hay with legumes or irrigation Improved pasture/hayland* Pasture/forest

CRP Set-aside No loss

Rangeland Uncultivated land (general) No loss

Continuous pasture Uncultivated land (general) Pasture/forest

Continuous pasture with legumes or irrigation Improved pasture/hayland* Pasture/forest

Aquaculturew Not estimated Not estimated

Non-agriculturalzForest Uncultivated land (general) Pasture/forest

Federal Uncultivated land (general) No loss

Waterw Not estimated Not estimated

Urban Landw Not estimated Not estimated

Miscellaneousw§ Not estimated Not estimated

*Improved pastures and haylands are treated separately because they have relatively higher amounts of soil organic carbon (SOC)

storage than is typically found for uncultivated lands in a similar climate and soil type.

wAssumed no change in carbon stocks when converting to or from these land uses because of a lack of information about the effect

of current practices on SOC storage.

zSome non-agricultural lands are included in the inventory because those areas were converted to or from an agricultural use in

1992 or 1997.

§Includes a variety of land uses from beaches and marshes to mining and gravel pits.



Specifically, we assumed the log-ratios (ln (no-till/

conventional), ln (reduced-till/conventional)) to have a

bivariate normal distribution, with a positive correla-

tion among counties within states. We estimated

variance components for state- and county-specific

random effects using the method of moments. Then,

we simulated log-ratios as bivariate normal, and

transformed the values back to the original scale. That

is, if X and Y represent the simulated log-ratios, ln(no-

till/conventional), ln(reduced-till/conventional), then

the percentages in each tillage class were

no-till percentage¼100 � ½exp ðXÞ=ð1þexp ðXÞþexp ðYÞÞ�;ð9Þ

reduced-till percentage ¼ 100 � ½exp ðYÞ=ð1þ exp ðXÞþexpðYÞÞ�;

ð10Þ

conventional till percentage ¼ 100=ð1þ exp ðXÞþexpðYÞÞ: ð11Þ

The simulated percentages were always non-negative

and summed to 100% (see Aitchison, 1986, for further

discussion).

We also modified CTIC data to reflect long-term

adoption of no-till management. Currently, CTIC

obtains data on an annual basis and does not represent

long-term trends for individual fields because produ-

cers often rotate tillage practices (Hill, 2001). This is

problematic for using the CTIC tillage data to account

for the impact of no-till on SOC storage. Even a single

pass over an agricultural field with a conventional till

plow can reduce the positive benefit of no-till with

respect to SOC storage, and can take 4–5 years to reach

previous storage levels (Pierce et al., 1994). Conse-

quently, CTIC personnel adjusted the annual estimates

for the long-term adoption of no-till based on expert

opinion (Dan Towery, CTIC, personal communication).

Sources of uncertainty: contribution index

We computed the amount of uncertainty that was

attributed to each individual model input (Fig. 1) using

a contribution index (Smith & Heath, 2001). The

contribution index is based on estimating new con-

fidence intervals using the Monte-Carlo simulation

approach after setting the variance for an individual

model input to 0 (i.e., the mean value for the input was

used in each model run instead of simulating values

from a PDF). The contribution index was calculated

using the following equation:

Index ¼ Range ðiÞPJ

j¼1

ðRange ðfullÞ � Range ðjÞÞ�100; ð12Þ

where J is the total number of model inputs, i is the

input category of interest, Range (i) is the difference

between the high and low value for the confidence

interval with the variance for input (i) set to 0, Range (j)

is the difference between the high and low value in

the confidence interval for input (j), and Range (full) is

the difference between the high and low value in the

confidence interval for the full simulation (i.e., with all

uncertainties included).

Results

Management factors

We found 91 articles meeting the criteria for this

synthesis (Appendix 1). Of those, 46 articles dealt with

tillage, 19 dealt with cropping rotations and intensity,

35 dealt with land use change (i.e., cultivated vs. non-

cultivated including set-asides), and 10 dealt with

cultivating organic soils. Some of these studies reported

data for multiple sites or management practices, and

we included two unpublished studies in this synthesis

(K. Paustian and E. T. Elliott; L. A. Sherrod and G. A.

Peterson, unpublished results).

General land use change from cultivated croplands

into grasslands or forests increased SOC storage in the

top 30 cm of the soil profile after 20 years by a factor

value of 1.370.04 (Table 2). This represented a 30%

increase in SOC over the reference C stock of a

cultivated cropland (RC, Eqn (2)). However, the land

use change factor also accounted for losses of SOC with

plow-out of uncultivated pastures and forests. The

factor was derived in this manner because the IPCC

method does not consider the previous condition of the

land when estimating SOC storage. Rather, SOC storage

is estimated for an ‘equilibrium’ state based on 20 years

of current use (IPCC, 1997). This simplistic approach

was meant to allow inventory calculations with basic

agricultural statistics (i.e., the amount of land in

generalized systems such as conventionally tilled corn,

no-till corn, pasture, etc., without tracking the transi-

tions on specific agricultural fields). Therefore, using

the same factor of 1.3 represented a 23% decline in SOC

storage following plow-out of grasslands and forests.

This estimate was at the low end of values found in

other reviews, which ranged from 20% to 30% (Mann,

1986; Davidson & Ackerman, 1993; Murty et al., 2002).

However, it has typically been found that SOC gains are

slower for recently established grasslands and forests

relative to the losses following plow-out (Burke et al.,

1995; Ihori et al., 1995; Ihori et al., 1995; Reeder et al.,

1998; Baer et al., 2000). Consequently, the relatively low

loss rate from our analysis in comparison to other

1528 S . M . OG L E et al.


reviews was plausible since the factor represented both

types of conversions.

In addition to general land use change, the IPCC

method utilizes a separate factor for land that is set

aside from agricultural production, which has occurred

in the US through enrollment in the CRP. SOC storage

increased on set-aside lands by a factor of 1.270.03

over that found in reference croplands, which repre-

sented a 20% increase in the top 30 cm of the soil after

20 years (Table 2). As expected, the set-aside factor was

smaller than the general land use change factor because

recovery of SOC in land taken out of production is

slower than losses following plow-out, and as pre-

viously noted, the general land use change factor

represented both cases.

Land management also impacted SOC storage, but

varied depending on the practice. Changing from

conventional to no-till management increased storage

by a factor of 1.1370.03 in the top 30 cm after 20 years

(Table 2). In contrast, changing from conventional to

reduced till management had a minimal impact on

storage, estimated at a factor value of 1.0270.03. This

represented only a 2% change in storage for the top

30 cm after 20 years, and the uncertainty ranged below

a factor value of 1, suggesting that reduced tillage did

not always increase the amount of SOC storage relative

to conventionally tilled cropland. As for cropping

practices, changing from medium-input rotations to

high-input rotations increased SOC storage by a factor

of 1.0770.02 in the top 30 cm after 20 years (Table 2).

Changing from medium input to low-input rotations

decreased SOC storage by a factor of 0.9470.01, or 94%

of the amount of C found under the reference condition.

Lastly, C loss rates for cultivated cropland on organic

soils varied by climate, with the highest rates estimated

for the sub-tropical and warm temperate regions at

1473.3MgCha-1 yr� 1 and 1472.5MgCha� 1 yr� 1, re-

spectively, followed by the cool temperate region at

1172.5MgCha� 1 yr� 1 (Table 2).

Reference C stocks

Reference C stocks were computed for each climate

region by soil type, and the highest stocks were

estimated for volcanic soils, followed by spodisols

and wetland soils (Table 3). Not surprisingly, the sandy

soils had the lowest C stocks for most climate regions,

while the high- and low-activity mineral soils had

intermediate values. Across climates, moister regions

tended to have higher C stocks than dry regions, and

cool temperate regions tended to have higher stocks

than warm temperate or sub-tropical regions.

Land use and management trends (1982–1997)

Cultivated cropland declined in the US between 1982

and 1997, including losses in medium-, high-, and low-

input rotations, as well as the amount of area in rice

production (Table 4). Most of this land was set aside in

the CRP, but some cropland was lost to urban

expansion, development of new water bodies (e.g.,

lakes), or converted to miscellaneous non-cropland,

such as feedlots and mining operations. The amount of

land under other agricultural land uses remained

relatively stable, including those designated as range-

land, pasture, and perennial/horticultural cropland.

Conventional tillage was the most common practice

used across US agricultural lands between 1982 and

1997 (Table 5). Reduced tillage was the second most

common practice, ranging from 0% to around 30%

usage, depending on the region of interest and crop

type. No-till was the least common, with virtually 0%

usage in 1982, while climbing to about 12% usage for a

few crop types in select regions by 1997.

Table 2 US factor estimates (7SE) for management impacts

on soil organic carbon (SOC) storage in mineral soils, which

were derived from a synthesis of studies evaluating the effect

of changing management from a conventionally tilled agri-

cultural field with medium-input cropping; the factors

represent the change in SOC storage for the top 30 cm of the

soil profile after 20 years following the management change;

in addition, annual carbon loss rates (7SE) are provided for

the impact of draining organic soils for crop production

Management practices US factors

Land use

Cultivated 1

Uncultivated (general)*w 1.370.04

Set-asides (Conservation Reserve Program)w 1.270.03

Tillage

Conventional till 1

Reduced till 1.0270.03

No-till 1.1370.03

Cropping rotations and intensities

High 1.0770.02

Medium 1

Low 0.9470.01

Carbon loss rate for organic soils (MgCha� 1 yr� 1)

Cool temperate 1172.5

Warm temperate 1472.5

Sub-tropical 1473.3

*The uncultivated factor represents the difference in carbon

storage between the reference condition and the uncultivated

condition, and includes studies tracking changes following

‘plow-out’ as well as after converting land back into

uncultivated uses (i.e., pastures and forest).wRatios are back-transformed values as estimated from a

model fit with ln-transformed data (Eqn (5)).



Annual change in SOC storage (1982–1997)

We estimated that management and land use change

accounted for an average increase of 1.3 TgCyr� 1 in US

agricultural soils between 1982 and 1997, with a 95%

confidence interval ranging from losses of 4.4 TgCyr� 1

to gains of 6.9 TgCyr� 1 (Table 6). Mineral soils gained

C at an average of 10.8 TgCyr� 1, with an uncertainty

range of 6.5–15.3 TgCyr� 1, while managed organic

soils lost C at an average rate of 9.4 TgCyr� 1, with an

uncertainty range of 6.4–13.3 TgCyr� 1.

Among mineral soil types, the range of uncertainty

was greatest for the high-activity mineral soils at

6.8 TgCyr� 1 (i.e., the difference between the high and

low estimate for the 95% confidence interval). High-

activity mineral soils (Vertisols, Mollisols, high base

status Alfisols) are characterized by predominately 2 : 1

clay mineralogy, with a relatively high potential to

stabilize SOC. They are the most common type used for

agricultural production, comprising about 250Mha of

the land base, which was nearly 5 times as much land

area compared with other soils. Due to the large land

area, it was not surprising that high activity soils had

the greatest range of uncertainty among mineral soils

because the majority of calculations were carried out for

agricultural land on this soil type.

In contrast, however, the uncertainty range for

organic soils was 6.9 TgCyr� 1, which exceeded the

ranges for all mineral types, including high-activity

soils. This was surprising considering that organic soils

represented a much smaller portion of the land base,

approximately 1Mha, and therefore had a dispropor-

tionate effect on overall uncertainty relative to the total

amount of land in the inventory (i.e., 386Mha).

In terms of contribution of individual model inputs to

uncertainty, C loss rates for managed organic soils

contributed the largest amount, accounting for 36% of

the overall uncertainty in the inventory (Table 7). The

land use change and tillage factors each contributed an

additional 28% of the remaining uncertainty, while none

of the other model inputs contributed more than 5%.

Table 3 Mean (71SE) and sample size for reference SOC stocks in MgCha� 1 for the top 30 cm of a soil profile (based on data

from the NSSC database: NRCS 1997)

Cold temperate Warm temperate Sub-tropical

Soil type Dry Moist Dry Moist Dry Moist

High-activity mineral 42 (71.4) 65 (71.1) 37 (71.1) 51 (71.0) 42 (72.6) 57 (713.0)

n5 133 n5 526 n5 203 n5 424 n5 26 n5 12

Low-activity mineral 45 (73.0) 52 (72.3) 25 (71.4) 40 (71.2) 39 (74.8) 47 (713.9)

n5 37 n5 113 n5 86 n5 300 n5 13 n5 7

Sandy 24 (74.8) 40 (73.7) 16 (72.4) 30 (72.0) 33 (71.9)* 50 (77.9)

n5 5 n5 43 n5 19 n5 102 n5 186 n5 18

Volcanic 124 (711.4)* 114 (716.7) 124 (711.4)* 124 (711.4)* 124 (711.4)* 128 (715.0)

n5 12 n5 2 n5 12 n5 12 n5 12 n5 9

Spodisols n/aw 74 (76.8) n/aw 107 (78.3) n/aw 86 (76.5)*

n5 13 n5 7 n5 20

Wetland 86 (711.4) 89 (73.6) 48 (73.6) 51 (71.8) 63 (71.9)* 48 (78.4)

n5 4 n5 161 n5 26 n5 300 n5 503 n5 12

*Estimate based on an average for soil type across all climate zones because no data were available to estimate a value for the

individual soil type by climate zone categories.wSpodisols generally do not occur in the dry climate regions.

Table 4 Trends in land use and management for US

agricultural lands between 1982 and 1997 based on NRI

(USDA-NRCS 2000)

Land areas

(106 ha)

IPCC land use/management categories* 1982 1997

Medium-input cropping 87.49 78.27

High-input cropping 22.21 21.74

Low-input cropping 30.96 25.13

Rice 2.71 2.22

Continuous perennial/horticultural

cropping

2.45 2.52

General uncultivated landw 210.04 210.26

Improved pasture and hayland 28.74 28.91

Set-aside (Conservation Reserve Program) 0.00 13.23

Urban, water, and miscellaneous

non-cropland

1.78 4.11

*Corresponding NRI land use categories are given in Table 1.wIncludes rangeland, forest, pasture, and hayland, with the

exception of improved pasture and hayland.

1530 S . M . OG L E et al.


Discussion

On average, the amount of SOC in US agricultural

lands increased between 1982 and 1997 due to land use

and management change. However, the uncertainty

ranged from gains to losses of SOC, and so the

inventory does not support the conclusion that agri-

cultural management created a net C sink for atmo-

spheric CO2.

Our estimated change in SOC of 1.3 TgCyr� 1 is in

the lower portion of the range reported by Pacala et al.

(2001), who suggested that C was sequestered in

agricultural soils at a rate between 0 and 40TgCyr� 1

during the 1980s. For mineral soils, our uncertainty

Table 5 Tillage percentages for no-till (NT), reduced till (RT), and conventional till (CT) cropland across the six climate zones in US

agricultural lands during 1982 and 1997; estimates are based on an annual survey conducted by the Conservation Technology

Center with modifications for long-term adoption of no-till agriculture (Dan Towery, CTIC, February 2001, personal

communication)

1982 1997

NT RT CT NT RT CT

Sub-tropical dry

High- and medium-input cultivation 0 3 97 0 15 85

Low-input cultivation with fallow 0 0 100 0 5 95

Low-input cultivation without fallow 0 3 97 0 10 90

Sub-tropical wet


low-input cultivation with fallow 0 0 100 1 10 89


Warm temperate dry

High and medium-input cultivation 0 0 100 1 15 84



Warm temperate moist




Cool temperate dry




Cool temperate moist




Table 6 Estimated change in SOC storage based on the Monte-Carlo IPCC analysis for soil types; the 95% confidence intervals and

ranges are also provided, along with the land area estimates from the National Resource Inventory

Area Mean carbon flux Upper limit Lower limit

(106 ha) (TgCyr� 1) 95% CI 95% CI Range

High-activity mineral 249 7.2 10.7 3.9 6.8

Low-activity mineral 63 1.7 2.5 1.1 1.4

Sandy 37 0.7 0.9 0.5 0.4

Volcanic o1 o0.1 o0.1 4� 0.1 0.1

Spodisol o1 o0.1 o0.2 4� 0.1 0.3

Wetland 36 1.1 1.9 0.3 1.6

Total for mineral soils 385 10.8 15.3 6.5 8.8

Organic soils 1 � 9.4 � 6.4 � 13.3 6.9

Total (mineral1 organic) 386 1.3 6.9 � 4.4 11.3

UNCERTA IN T Y ANALY S I S F O R AGR I CU LTURAL SOC S TORAGE 1531


range of 6.5–15.3 TgCyr� 1 is more conservative than

Pacala et al. (2001), and this is likely because we did not

account for gradual or incremental changes in manage-

ment, particularly variation in fertilizer application

rates and crop varieties. The IPCC method is a simple

approach based on a categorical system that recognizes

major shifts in land use and management practices

(e.g., fertilized vs. non-fertilized), assessing the impact

during the first 20 years for purposes of crediting

emission offsets, but does not capture additional

changes in SOC resulting from variation in a practice

(e.g., changing fertilizer application rates). Producers

have used fertilizers and improved crop varieties since

the 1950s and 1960s in the US, and most croplands were

managed with these higher-input practices before the

inventory period from 1982 to 1997. For example,

90–100% of corn fields (or rotations with corn) have been

fertilized since the mid-1960s (USDA-ERS, 1994), but the

average rate of N fertilization has increased steadily

during the past few decades into the early 1990s

(USDA-ERS, 1994). While no specific estimate is

available with respect to the change in SOC storage

due to gradually increasing application rates, fertiliza-

tion has been shown to enhance storage by an average

of 7% in long-term experiments, ranging from 7 to 120

years (Paustian et al., 1997a), and it has been estimated

that this practice could sequester an additional

11.8 TgCyr� 1 in US agricultural soils in the future

(Lal et al., 1998). Similarly, organic fertilizers (i.e.,

manures and sewage sludge) have been used to

improve soil fertility, and this practice increased SOC

storage by an additional 4–6 TgCyr� 1 during the 1990s

(EPA, 2003). As with mineral fertilizers, though, much

of the C sequestration presumably occurred on lands

that have been managed with organic fertilizers for

decades, and these gradual increases are not captured

by the IPCC method. In contrast to fertilizer usage, the

types of cultivars planted in US croplands have not

been reported, but it is likely that producers have

consistently used the most productive varieties avail-

able for several decades, thus increasing SOC storage

due to incremental changes in this management

practice.

In addition, Pacala et al. (2001) did not account for

impacts of agricultural management on organic soils,

which have been shown to generate large fluxes of CO2

to the atmosphere (Armentano & Menges, 1986). We

found that the estimated loss of 9.4 TgCyr� 1 from

organic soils counteracted much of the gain in SOC

storage for mineral soils, even though managed organic

soils comprised less than 1% of all US agricultural land.

This large flux of CO2 to the atmosphere is unlikely to

change in the near future unless there is more effort to

conserve the organic horizon, such as by maintaining

water table depths near the rooting zone of crops

(Jongedyk et al., 1950; Shih et al., 1998) or by restoring

agricultural fields into wetlands.

C sequestration in mineral soils was mainly attrib-

uted to setting aside land through the CRP (Eve et al.,

2002). Of approximately 16 million hectares that under-

went land use change, the dominant trend was setting

aside about 13 million hectares from production. This

conversion was estimated to increase SOC storage by

an average of 20% over 20 years. In contrast, conserva-

tion tillage had little effect on SOC storage due to the

low rate of continuous no-till agriculture in the US

(Hill, 2001), even though this practice was estimated to

increase C storage by 13% over 20 years.

The majority of uncertainty using the IPCC method

resulted from estimating C loss rates for managed

organic soils, changes in SOC storage with shifts in land

use, particularly setting aside land in CRP, and the

effect of implementing conservation tillage. Bernoux

et al. (2001) also identified the management factors,

particularly land use change, as a key source of

uncertainty when using the IPCC method to estimate

stock changes in Brazil. The PDFs for land use change

and tillage management factors were estimated from

Table 7 Contribution index (Eqn (12)) for model inputs to overall uncertainty in the inventory results

Upper limit

95% CI

Lower limit

95% CI Range

Difference from

full simulation

Contribution

index (%)

Full simulation 6.9 � 4.4 11.3

Land use (NRI data) 6.8 � 4.2 11 0.3 4.6

Tillage practices (CTIC data) 6.8 � 4.3 11.1 0.2 3.1

Reference carbon stocks 6.9 � 4.4 11.3 o0.01 o1.0

Input factor 6.9 � 4.4 11.3 o0.01 o1.0

Tillage factor 5.9 � 3.6 9.5 1.8 27.7

Land use change factor 5.9 � 3.6 9.5 1.8 27.7

Improved pasture 6.8 � 4.4 11.2 0.1 1.5

Carbon loss rate (organic soils) 6.0 � 3.0 9 2.3 35.4

1532 S . M . OG L E et al.


published studies (Appendix 1). Of course, studies

were not conducted for all or even most of the

combined management systems by climate region and

soil type across the US. The data were scaled to

represent impacts throughout the country in a manner

consistent with the original IPCC method. Reducing

uncertainty will rely upon future studies that improve

the scaling process and possibly revised scaling

procedures that diverge from the original method. In

addition, uncertainty in C loss rates from managed

organic soils may be reduced by directly measuring

CO2 emissions. Currently, loss rates are based on

subsidence data, which reflect not only losses from

decomposition of organic matter but also other impacts

following drainage, such as shrinkage of the drying

organic mat (Weir, 1950). Consequently, we had to

estimate the proportion of total subsidence that is

attributable to oxidation of the organic matter, introdu-

cing greater uncertainty than would presumably occur

with direct CO2 flux estimates.

Although current uncertainties lead us to conclude

that US agricultural soils were not a net C sink for

atmospheric CO2 between 1982 and 1997, these soils

have the potential to sequester considerable amounts of

C in the future beyond the baseline estimates from this

analysis (Lal et al., 1998; Bruce et al., 1999; Sperow et al.,

2003). This could be done through widespread adop-

tion of conservation management, particularly by

increasing the usage of no-till, eliminating fallow in

rotations, increasing the usage of winter cover crops,

and setting aside more of the highly erodible cropland

from cultivation than has currently occurred through

the CRP (Sperow et al., 2003). Using the IPCC method,

Sperow et al. (2003) estimated a potential C sequestra-

tion rate of 83.1 TgCyr� 1 on mineral soils through

widespread adoption of these conservation practices.

This estimate was consistent with other analyses for the

US, Canada, and Europe (Lal et al., 1998; Bruce et al.,

1999; Smith et al., 2000a, b), which have suggested that

the potential for C sequestration greatly exceeds current

rates. Interestingly, C sequestration at a rate of

83.1 TgCyr� 1 would exceed the CO2 emission reduc-

tions from the agricultural sector that would be

required under the Kyoto Protocol (Sperow et al.,

2003). Overall, though, the agricultural sector would

likely remain a net emitter of greenhouse gases even

with higher rates of C sequestration based on a full

accounting of all emissions and sinks involved in farm

operations, particularly CO2 losses from fossil fuel

usage (e.g., Schlesinger, 2000; West & Marland, 2002),

N2O losses from soil management, and CH4 losses from

livestock operations (EPA, 2003).

The current approach to estimate the change in SOC

storage due to land use and management provides

assurance about the magnitude of C sequestration in

agricultural soils, while maintaining the simplicity of

the IPCC method so that it can be applied with a

minimal amount of technology and resources. Of

course, mitigation of greenhouse gases in agricultural

systems will also depend on the net emissions of other

gases, particularly nitrous oxide and methane. Conse-

quently, future advancements should account for these

fluxes and associated uncertainties to address fully the

effectiveness of changing agricultural management for

purposes of mitigating greenhouse gas emissions (e.g.,

Robertson et al., 2000; Smith et al., 2001).

Acknowledgements

We appreciate the comments and recommendations on an earlierdraft of this work by scientists who attended a USDA NationalSoil Carbon Inventory Workshop in June 2002 that was hostedby Ron Follett and John Kimble. We are grateful for theassistance provided by Amy Swan, Kristen Howerton, MarkSperow, Mark Easter, Rich Conant, John Brenner, Johan Six,Kendrick Killian, Steve Williams, Dan Towery, MohammedKalkhan, Sharon Waltman, and Thomas Reinsch. This researchwas supported by the Environmental Protection Agency(Agreement No. 2W-2964-NAEX), USDA Agricultural ResearchService (Agreement No. 58-5402-6-109), and USDA/CSREES(Agreement No. 2001-38700-11092) through funding for theConsortium for Agricultural Soils Mitigation of GreenhouseGases (CASMGS).

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Appendix

1.Publish

edstudiesusedin

theanalysisto

estim

ate

theim

pact

ofagricu

lturalmanagementonSOC

storage

Study

Location

Tim

espan

(years)

Depth

(cm)

Tillage*

Inpu

twLanduse

change

(cultivation)z

Organic

soil

Albert&

Zeasm

an(1953)

Wisconsin

35Gen

eral

�

Angerset

al.(1992)

Len

noxville,Queb

ec3

24Gen

eral

Angerset

al.(1995)

StLam

bert,Queb

ec11

24RT

Angerset

al.(1997)

Charlottetown,P.E.I.

860

RT

Delhi,Ontario

460

NT

Harrington,P.E.I.

860

NT

Harrow,Ontario

1160

NT

LaPocatiere,

Queb

ec6

60NT

Norm

andin,Queb

ec3,

460

RT,NT

Ottaw

a,Ontario

560

NT

Arm

entano(1979)

New

York

60Set-aside

�Baeret

al.(2000)

Gag

ean

dSaline

Counties,NE

1010

Bau

er&

Black

(1981)

GrantCounty,ND

2545.7

RT

Beare

etal.(1994)

Athen

s,GA

1315

NT

Black

andTan

aka(1997)

MortonCounty,ND

691.2

RT,NT

LBordovskyet

al.(1999)

Munday,TX

3,8

10RT

HBowman

&Anderson(2002)

Northeastern

Colorado

415

NT,RT

Gen

eral

Bremer

etal.(1994)

Lethbridge,

Alberta

7,41

30L

Set-aside

Burkeet

al.(1995)

WeldCounty,CO

5310

Gen

eral

Buyan

ovksy

etal.(1987)

Columbia,MO

9650

Gen

eral

Buyan

ovsky&

Wag

ner

(1998)

Columbia,MO

2520

HGen

eral

Cam

bardella

&Elliott(1994)

Sidney,NE

2020

Cam

pbell&

Zen

tner

(1997)

SwiftCurren

t,Saskatch

ewan

10,15,18,24

15L

Cam

pbellet

al.(1991)

Melfort,Saskatch

ewan

3115

H,L

Cam

pbellet

al.(1996)

StewartValley,

Saskatch

ewan

3,7,

1115

NT

L

Cam

pbellet

al.(1997)

IndianHead,

Saskatch

ewan

3015

L

Cam

pbellet

al.(1999)

SwiftCurren

t,Saskatch

ewan

5,9,

13,18

15NT

L

Cam

pbellet

al.(2000)

SwiftCurren

t,Saskatch

ewan

1030

H,L

Carter(1991)


320

RT,NT

Carteret

al.(1988)


320

NT

Carteret

al.(2002)


516

NT

Clappet

al.(2000)

Rosemount,MN

1330

RT,NT

Gen

eral

Collinset

al.(1999)

Lexington,KY

2250

Gen

eral

Coote

&Ram

sey(1983)

Ottaw

a,Ontario

3530

Dav

is&

Engberg(1955)

Michigan

5�

Dick&

Durkalski(1997)

Wooster,OH

3130

NT

HDicket

al.(1997)

Wooster,OH

945

RT,NT

HGen

eral

Doranet

al.(1998)

Sidney,NE

1130

11,22

30,20

NT,RT

(Continued)

U N C E RTA IN T Y ANALY S I S F O R AGR I CU LTURAL SOC S TORAGE 1539


Appendix

1.(C

ont.)

Study

Location

Tim

espan

(years)

Depth

(cm)

Tillage*

Inpu

twLanduse

change

(cultivation)z

Organic

soil

Duiker

&Lal

(1999)

Columbus,

OH

710

RT,NT

Edwardset

al.(1992)

Crossville,AL

1120

NT

Eghballet

al.(1994)

Lincoln,NE

1130

RT,NT

Gen

eral

Follett&

Peterson(1988)

Sidney,NE

16,17

20RT,NT

Gen

eral

Follettet

al.(1997)

Akron,CO

8460

Gen

eral

Sydney,NE

2330

Set-aside

Follettet

al.(2001)

Akron,CO

630

Set-aside

Boley,

OK

1030

Set-aside

Bush

land,TX

830

Set-aside

Columbia,MO

730

Set-aside

Dalhart,TX

830

Set-aside

Dorothy,

MN

730

Set-aside

Glencoe,

MN

930

Set-aside

Indianola,IA

830

Set-aside

Lincoln,NE

630

Set-aside

Man

dan

,ND

1030

Set-aside

Med

ina,

MN

1030

Set-aside

Roseau

,MN

730

Set-aside

Sidney,MT

530

Set-aside

Vinson,OK

1030

Franzlueb

berset

al.(1995)

William

sonCounty,TX

1020

NT

Franzlueb

bers&

Arshad

(1996)

Alberta

4,6

20NT

British

Columbia

7,16

20NT

Franzlueb

berset

al.(1999)

Watkinsville,GA

415

NT

Gen

eral

Frye&

Blevins(1997)

Lexington,KY

5,19

30Set-aside

Geb

hartet

al.(1994)

Atw

ood,KS

5300

Set-aside

Big

Springs,

TX

5300

Set-aside

Colby,

KS

5300

Set-aside

Sem

inole,TX

5300

Set-aside

Valen

tine,

NE

5300

Gen

eral

Gregorich

etal.(1996)

Woodslee,Ontario

3240

Halvorsonet

al.(1997)

Akron,CO

1520

NT

Halvorsonet

al.(2002)

Man

dan

,ND

1230.4

NT

LHan

smey

eret

al.(1997)

Rosemount,MN

47.5

RT,NT

Hao

etal.(2001)

Lethbridge,

Ontario

430

RT

Gen

eral

Harden

etal.(1999)

Tatean

dPan

ola

Coun-

ties,MS

127

40

Harriset

al.(1962)

Indiana

�Hav

lin&

Kissel(1997)

Man

hattan,KS

1115

NT

Hen

drix(1997)

Athen

s,GA

4,5,

6,7,

21NT

8,10,12

Gen

eral

Ihori

etal.(1995)

WeldCounty,CO

5030

Irwin

(1977)

Ontario

35�

Janzen(1987)

Lethbridge,

Alberta

3330

LKarlenet

al.(1994)

Lan

caster,W

I12

5RT,NT

Karlenet

al.(1998)

Nashua,

IA15

20RT,NT

Set-aside

1540 S . M . OG L E et al.


Karlenet

al.(1999)

Butler

Co.,IA

67.5

Set-aside

Hen

ryCo.,IA

27.5

Set-aside

Minnesota

67.5

Set-aside

NorthDak

ota

57.5

Set-aside

Washington

57.5

Lal

etal.(1994)

Wooster,OH

2915

RT,NT

Gen

eral

Lam

bet

al.(1985)

Sidney,NE

1830

Larney

etal.(1997)

Lethbridge,

Alberta

7,8,

1515

RT,NT

LMcC

arty

etal.(1998)

Maryland

320

NT

Mielkeet

al.(1986)

Elw

ood,IL

630

NT

Lincoln,NE

630

NT

Sidney,NE

1230

NT

Waseca,

MN

6,11

30NT

Nyborg

etal.(1995)

Breton,Alberta

1115

NT

Ellerslie,Alberta

1115

NT

Paren

tet

al.(1982)

Queb

ec38

�Pau

stian&

Elliott,unpublish

edresu

lts

Bush

land,TX

12100

LGen

eral

Griffin,GA

1810

0NT

Hickory

Corners,

MI

53100

NT

Gen

eral

HorseshoeBen

d,GA

1425

Gen

eral

Hoytville,OH

29100

NT

IndianHead,

Saskatch

ewan

35100

L

Kutztown,PA

11100

HLethbridge,

Alberta

41100

LGen

eral

Man

hattan,KS

92,17

90RT,NT

Melfort,Saskatch

ewan

3550

LGen

eral

Sidney,NE

22100

South

Charleston,OH

30100

RT,NT

Sterling,CO

7100

NT

Gen

eral

Stratton,CO

7100

Gen

eral

SwiftCurren

t,Saskatch

ewan

2625

Walsh

,CO

7100

NT

LGen

eral

WestLafey

ette,IN

12100

RT,NT

Gen

eral

Wooster,OH

3075

RT,NT

Potter

etal.(1997)

Bush

land,TX

10100

LSet-aside

Potter

etal.(1999)

Burleson,TX

26120

Set-aside

Riesel,TX

60120

Set-aside

Tem

ple,TX

6120

Gen

eral

Rasmussen

&Albrech

t(1997)

Pen

dleton,OR

59,28,40

20RT

LSet-aside

Reeder

etal.(1998)

Arvad

a,W

Y4,

6325

Set-aside

Keeline,

WY

4,33

28Rhotonet

al.(199

3)Auburn,AL

515

NT

Jackson,TN

915

NT

Verona,

MS

515

NT

Watkinsville,GA

1515

NT

Study

Location

Tim

espan

(years)

Depth

(cm)

Tillage*

Inpu

twLanduse

change

(cultivation)z

Organic

soil

(Continued)

U N C E RTA IN T Y ANALY S I S F O R AGR I CU LTURAL SOC S TORAGE 1541


Richardson(1981)

NorthCarolina

50Set-aside

�Robles&

Burke(1998)

Chugwater,W

Y6

5Rojstaczer

&Dev

erel

(1993)

California

70�

Sainju

etal.(2002)

Fort

Valley,

GA

1,2,

3,4,

520

NT

Salinas-G

arciaet

al.(1997)

CorpusChristi,TX

1620

RT,NT

Set-aside

Sherrod&

Peterson,

unpublish

edresu

lts

Sterling,CO

1220

LSet-aside

Stratton,CO

1220

LSet-aside

Walsh

,CO

1220

LShih

etal.(1998)

Florida

54–60

Gen

eral

�Six

etal.(1998)

Sidney,NE

2520

Gen

eral

Six

etal.(2000)

Lexington,KY

2520

NT

Hickory

Corners,

MI

920

NT

Wooster,OH

3320

NT

Gen

eral

Slobodianet

al.(2002)

StDen

is,Saskatch

ewan

49120

Gen

eral

Tiessen

etal.(1982)

Blaine

Lak

e,Saskatch

-ew

an4,

60,90

30Gen

eral

Bradwell,

Saskatch

e-wan

6530

Gen

eral

Sutherland,Saskatch

e-wan

7031

Turner

&Neill(1984)

Louisiana

10Set-aside,

Gen

eral

�Unger

(2001)

Oldham

County,TX

10,78

30Set-aside,

Gen

eral

Swisher

County,TX

10,48

30Set-aside,

Gen

eral

Hutchinson

County,

TX

10,40

30Set-aside,

Gen

eral

Moore

County,TX

10,40

30Set-aside,

Gen

eral

Arm

strongCounty,TX

10,50

30Set-aside,

Gen

eral

BriscoeCounty,TX

10,43

30Set-aside,

Gen

eral

Dallam

County,TX

10,40

30Set-aside,

Gen

eral

CarsonCounty,TX

10,40

30Set-aside,

Gen

eral

Potter

County,TX

10,78

30Gen

eral

Voroney

etal.(1981)

IndianHead,Saskatch

-ew

an70

50

Wan

der

etal.(1998)

DeK

alb,IL

1030

NT

Monmouth,IL

1030

NT

Perry,IL

1030

NT

Wan

niarach

chiet

al.(1999)

Delhi,Ontario

6,14

50NT,RT

Gen

eral

Woods(1989)

WeldCounty,CO

415

Yan

g&

Wan

der

(1999)

Urban

a,IL

1190

RT,NT

Yan

g&

Kay

(2001)

SouthernOntario

1960

NT

Gen

eral

Zhan

get

al.(1988)

Hardin

County,IA

120

20Gen

eral

* RTindicates

reducedtillag

eim

pacts,an

dNTindicates

no-tillim

pacts.

w Hindicates

high-inputrotationsan

dLindicates

low-inputrotations.

z Set-asideindicates

thestudydealtwithim

pacts

forlandremoved

from

agricu

lturalproduction(i.e.,ConservationReserveProgram)an

dGen

eral

deals

withnon-specificland

use

chan

ges

betweencu

ltivated

anduncu

ltivated

conditions.

Appendix

1.(C

ont.)

Study

Location

Tim

espan

(years)

Depth

(cm)

Tillage*

Inpu

twLanduse

change

(cultivation)z

Organic

soil

1542 S . M . OG L E et al.


uncertainty in estimating land use and management impacts on soil organic carbon storage for us...

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