Geographic Bias of Field Observations of Soil Carbon Stocks with Tropical Land-Use Changes Precludes Spatial Extrapolation

Abstract

Accurately quantifying changes in soil carbon (C) stocks with land-use change is important for estimating the anthropogenic fluxes of greenhouse gases to the atmosphere and for implementing policies such as REDD (Reducing Emissions from Deforestation and Degradation) that provide financial incentives to reduce carbon dioxide fluxes from deforestation and land degradation. Despite hundreds of field studies and at least a dozen literature reviews, there is still considerable disagreement on the direction and magnitude of changes in soil C stocks with land-use change. We conducted a meta-analysis of studies that quantified changes in soil C stocks with land use in the tropics. Conversion from one land use to another caused significant increases or decreases in soil C stocks for 8 of the 14 transitions examined. For the three land-use transitions with sufficient observations, both the direction and magnitude of the change in soil C pools depended strongly on biophysical factors of mean annual precipitation and dominant soil clay mineralogy. When we compared the distribution of biophysical conditions of the field observations to the area-weighted distribution of those factors in the tropics as a whole or the tropical lands that have undergone conversion, we found that field observations are highly unrepresentative of most tropical landscapes. Because of this geographic bias we strongly caution against extrapolating average values of land-cover change effects on soil C stocks, such as those generated through meta-analysis and literature reviews, to regions that differ in biophysical conditions.

Full text

Geographic bias of field observations of soil carbon
stocks with tropical land-use changes precludes
spatial extrapolation
Jennifer S. Powers
a,1
, Marife D. Corre
b
, Tracy E. Twine
c
, and Edzo Veldkamp
b
a
Departments of Ecology, Evolution, and Behavior and Plant Biology, and
c
Department of Soil, Water, and Climate, University of Minnesota, St. Paul,
MN 55108; and
b
Buesgen Institute-Soil Science of Tropical and Subtropical Ecosystems, Georg-August University of Goettingen, 37077 Goettingen, Germany
Edited* by Susan E. Trumbore, University of California, Irvine, CA, and approved March 4, 2011 (received for review November 8, 2010)
Accurately quantifying changes in soil carbon (C) stocks with land-
use change is important for estimating the anthropogenic fluxes
of greenhouse gases to the atmosphere and for implementing
policies such as REDD (Reducing Emissions from Deforestation and
Degradation) that provide financial incentives to reduce carbon di-
oxide fluxes from deforestation and land degradation. Despite
hundreds of field studies and at least a dozen literature reviews,
there is still considerable disagreement on the direction and mag-
nitude of changes in soil C stocks with land-use change. We con-
ducted a meta-analysis of studies that quantified changes in soil C
stocks with land use in the tropics. Conversion from one land use
to another caused significant increases or decreases in soil C stocks
for 8 of the 14 transitions examined. For the three land-use tran-
sitions with sufficient observations, both the direction and mag-
nitude of the change in soil C pools depended strongly on
biophysical factors of mean annual precipitation and dominant soil
clay mineralogy. When we compared the distribution of biophys-
ical conditions of the field observations to the area-weighted dis-
tribution of those factors in the tropics as a whole or the tropical
lands that have undergone conversion, we found that field obser-
vations are highly unrepresentative of most tropical landscapes.
Because of this geographic bias we strongly caution against ex-
trapolating average values of land-cover change effects on soil C
stocks, such as those generated through meta-analysis and litera-
ture reviews, to regions that differ in biophysical conditions.
Organic carbon stored in the world’s soils is the largest ter-
restrial pool of carbon, and is at least three times larger
than the pool of atmospheric carbon dioxide (1–3). It has long
been recognized that land-cover change and management can
alter the amount of organic carbon stored in the soil (4, 5), and
this in turn affects both soil fertility and atmospheric carbon
dioxide (CO
2
) concentrations. Although the contributions of
land-cover change to anthropogenic CO
2
atmospheric emissions
have recently been revised downward (6), the estimated current
annual contribution of 1.2 Pg, or about 12 to 15% of total an-
thropogenic fluxes, is still significant. The terrestrial CO
2
flux
includes emissions from both biomass and soils (7, 8). Continued
improvements in remote sensing allow for ever-better estimates
of both the areal extent of different land uses and above-ground
biomass stocks (9). In contrast, remote sensing is currently not
a reliable option for measuring stocks of soil C. Despite hun-
dreds of field studies and dozens of literature reviews, there is
still considerable disagreement on the direction and magnitude
of changes in soil C stocks with land-use change. Indeed, the
Intergovernmental Panel on Climate Change 2006 Guidelines for
National Greenhouse Gas Inventories states, “the current knowl-
edge remains inconclusive on both the magnitude and direction
of C stock changes in mineral forest soils associated with forest
type, management and other disturbances, and cannot support
broad generalizations”(10).
We conducted a meta-analysis of field studies of land-use
change effects on total soil organic carbon stocks to determine
whether general patterns exist and if including biophysical fac-
tors reduces unexplained variation in observed responses. We
focused on the tropics, as these latitudes account for the bulk of
the current CO
2
emissions from land-cover change (11). We
considered precipitation and clay mineralogy as the most im-
portant biophysical drivers, as precipitation strongly influences
soil C stocks and residence time (1) and, within a precipitation
regime, clay mineralogy is often the most important factor
explaining differences in soil C stocks in tropical regions (12, 13).
Here we show that mean annual precipitation (MAP) and clay
mineralogy affect the direction and magnitude of changes in soil
C stocks with different land-cover changes. However, the dis-
tribution of field observations does not match the distribution of
biophysical factors on an areal basis, and is highly skewed toward
high-precipitation regions with allophanic clay mineralogy. His-
torically, land-conversion activities in the tropics have focused on
high-activity clay soils in lower precipitation regions. Thus, we
strongly caution against extrapolating average values of land-
cover change effects on soil C stocks, such as those generated
through meta-analysis and literature reviews, to regions that dif-
fer in biophysical conditions.
Results
Patterns of Land-Cover Change Effects. Our search of the literature
yielded 837 observations from 80 studies that met our criteria for
inclusion in the database (Dataset S1). Across all sampling
depths, precipitation classes, and clay mineralogy classes, 8 of 14
land-use changes had significant effects on soil C stocks (Fig. 1).
The conversion of forests to shifting cultivation or permanent
crops reduced soil C stocks by an average of 15.4 or 18.5%, re-
spectively. Interestingly, both the conversions of forests to pas-
tures and pastures to secondary forests, which were the two best-
represented land-cover transitions in the database, increased soil
C stocks (Fig. 1). The establishment of perennial tree plantations
on lands that were previously grazed or cropped increased soil C
stocks, but the conversion of unmanaged forests, grasslands, or
savannas to plantations had no effect.
Effects of Biophysical Drivers. Even though many of the patterns of
land-cover change effects are statistically significant, there is still
unexplained variance that may be reduced by including addi-
tional variables in the analyses (14). We reanalyzed the data by
pooling all observations across land-use transitions and stratify-
ing the data into potential drivers that could be readily extracted
Author contributions: J.S.P ., M.D.C., and E.V. designed rese arch; J.S.P. and T.E.T. per-
formed research; J.S.P. and T.E.T. analyzed data; and J.S.P., M.D.C., and E.V. wrote the
paper.
The authors declare no conflict of interest.
*This Direct Submission article had a prearranged editor.
Data deposition: Meta-analysis dataset is available in Dataset S1.
1
To whom correspondence should be addressed. E-mail: powers@umn.edu.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1016774108/-/DCSupplemental.
6318–6322
|
PNAS
|
April 12, 2011
|
vol. 108
|
no. 15 www.pnas.org/cgi/doi/10.1073/pnas.1016774108

from the literature, including mean annual temperature, years
since conversion, species (for transitions to plantations), MAP,
and clay mineral composition (Fig. S1). These analyses identified
two of the major soil-forming factors of MAP (a key facet of
climate) and clay mineral composition (in part inherited from
parent material) as the most important variables that separated
observations into statistically significant groups (Fig. S1). Thus,
subsequent analyses focused on these biophysical drivers.
Observations were grouped into four precipitation regimes based
on MAP (500–1,500 mm, 1,501–2,500 mm, 2,501–3,500 mm, and
>3,501 mm). We chose 500 mm as a minimum cutoff because
there were very few studies in regions with MAP <500. Although
precipitation does vary continuously, dividing the data into more
classes unduly reduces the number of observations per class and
these divisions correspond roughly to life-zone classification
schemes. We further classified observations into three classes of
clay minerals based on reported soil types or characteristics:
allophanic soils dominated by noncrystalline clay minerals that
may stabilize soil C, highly weathered soils dominated by low-
activity clay with low surface area and cation exchange capacity
(CEC), and young to moderately weathered soils dominated by
high-activity clay with high surface area and CEC. Three addi-
tional meta-analyses were conducted for observations from
depths including 0 to 30 cm for the land-cover change transitions
with sufficient data to examine whether soil C dynamics depen-
ded on biophysical variables: the conversions of forest to pasture,
pasture to secondary forest, and forest to crop (Table 1). These
analyses show that the effects of land-use change on soil C stocks
depend upon both precipitation regime and soil clay mineralogy,
and that the interactions between these two drivers are signifi-
cant (Table 1). For example, on allophanic soils, conversion of
forest to pasture reduced soil C stocks, but only in high pre-
cipitation (MAP >3,501 mm) regions. In contrast, forest-to-
pasture conversion increased soil C stocks on soils with low-
activity clay receiving from 1,501 to 2,500 mm of precipitation
annually, but had no effect in regions receiving >2,500 mm.
Secondary forest regeneration on abandoned pastures increased
soil C stocks from 19.0 to 32.6% on soils with low-activity clay,
but had smaller or no effects on other soil types, although we
view the results for pasture-to-forest conversion with extreme
caution because of the limited number of studies (Table 1). Fi-
nally, conversion of forest to crops caused large losses of soil C
stocks under diverse precipitation conditions on soils with allo-
phane and high-activity clay, but no effect on soils with low-activity
clay receiving from 1,501 to 2,500 mm annual precipitation. Re-
gardless of the exact magnitude of increase or decrease in soil C
stock under each combination of clay mineralogy class and
precipitation regime, the most salient result from this analysis is
that the effects of land-use change on soil C stocks may vary as
a function of biophysical drivers. Although we approach con-
clusions drawn from such limited data with caution, what these
data suggest is that extrapolating average stock-change factors
(e.g., Fig. 1) to unmeasured sites across the tropics would be
-30 -20 -10 0 10 20 30 40 50 60 70
percent change in soil C stock
forest to pasture (280; 35)
pasture to secondary forest (126; 9)
forest to crop (98; 18)
pasture to plantaon (84; 11)
forest to plantaon (57; 8)
crop to plantaon (44; 6)
forest to shiing culvaon (40; 5)
savanna to plantaon (27; 2)
savanna to pasture (19; 2)
savanna to crop (18; 2)
crop to forest fa llow (17; 3)
crop to secondary forest (17; 4)
crop to pasture (6; 3)
grassland to plantaon (4; 2)
Fig. 1. Average percentage change in total soil C stocks for different land-
use changes in the tropical region with bootstrapped 95% confidence
intervals. Land-use transitions appear beside means, and are arranged by
decreasing number of observations. The number of observations for each
mean is in parentheses, followed by the number of studies from which the
observations were drawn.
Table 1. Mean values of land-cover change effects on soil carbon contents (including 0- to 30-cm sampling depths), grouped by clay
mineralogy and annual precipitation classes
Clay mineralogy class
Annual precipitation
class (mm)
Mean percent change (lower and
upper 95% bootstrapped confidence intervals)
Number of
observations
Number of
studies
Forest to pasture conversion
Allophane 2,501–3,500 −2.7 (−16.1, 15.0) 7 4
>3,501 −15.8 (−24.5, −7.5) 55
High activity <1,500 16.4 (−1.8, 39.3) 6 3
1,501–2,500 −10.2 (−21.7, −0.8) 73
Low activity 1,501–2,500 26.4 (20.5, 31.9) 79 16
2,501–3,500 1.1 (−13.9, 18.7) 9 5
>3,501 14.1 (−1.0, 29.7) 12 6
Pasture to secondary forest conversion
Allophane 2,501–3,500 4.0 (−5.0, 19.5) 9 1
High activity <1,500 16.5 (6.3, 24.1) 41
1,501–2,500 10.8 (−0.7, 21.9) 15 2
2,501–3,500 −5.0 (−18.6, 6.8) 8 1
Low activity 1,501–2,500 19.0 (0.8, 34.7) 11 4
2,501–3,500 23.6 (4.8, 44.1) 61
>3,501 32.6 (25.9, 39.2) 42
Forest to crop conversion
Allophane <1,500 −36.9 (−46.2, −24.7) 51
2,501–3,500 −41.8 (−50.7, −33.6) 73
High activity 1,501–2,500 30.8 (−40.2, −22.8) 16 3
Low activity 1,501–2,500 −10.4 (−23.3, 7.4) 12 5
Significant transitions, inferred as approximate 95% bootstrapped confidence intervals that do not contain 0, are in bold. Only data from clay mineralogy
and precipitation classes that had at least four observations were included in the analyses.
Powers et al. PNAS
|
April 12, 2011
|
vol. 108
|
no. 15
|
6319
SUSTAINABILITY
SCIENCE

warranted only if the distribution of field observations corre-
sponds to the biophysical conditions in the landscape.
Geographic Bias in the Field Observation Dataset. There is strong
evidence that the distribution of field observations is not repre-
sentative of the distribution of biophysical variables in tropical
regions that affect the magnitude and direction of change in soil C
stocks following land-cover change (Fig. 2 Aand B)(χ
2
= 11,789,
df = 11, P<0.0001). Although the global areal distribution of
biophysical factors in the tropics is skewed toward lower pre-
cipitation areas (500–1,500 mm MAP) with high-activity clay soils
(Fig. 2B), the distribution of field observations is skewed toward
regions with higher precipitation and allophanic clay mineralogy
(Fig. 2A). It is possible that land-conversion activities are biased
toward regions with certain combinations of biophysical factors,
and therefore field studies merely reflect the nonrandom nature
of land conversion. However, when we compared the distribution
of field observations to the distribution of biophysical factors in
tropical lands that have experienced >50% conversion over the
past century or longer, the patterns are even more striking (Fig.
2C). Historically, over 75% of tropical land-use activities have
occurred primarily in drier regions (500–1,500 mm MAP) with
high-activity clay soils, allowing us to reject the hypothesis that the
distribution of biophysical factors represented in the field obser-
vations reflects the typical conditions on managed lands in the
tropics (χ
2
= 12,262, df = 11, P<0.0001).
Discussion
Biophysical Drivers of Land Use-Related Soil C Changes. There is
growing recognition that unless biophysical drivers are explicitly
considered, we will not be able to estimate the consequences of
land-use changes on soil C stocks (15) or predict the effects of
management decisions, such as biochar amendment, to increase
carbon sequestration (16). Precipitation strongly influences plant
production, fluxes of soil C pools, and ultimately total soil C stocks
and residence time (1). The control of clay mineralogy on total soil
C stocks, residence time, and susceptibility of the soil C pool to
land-use change (17, 18) is through mechanisms such as differ-
ential chemical complexation, aggregation, or physical protection
(19). Large-scale quantifications of soil C stocks in tropical regions
show that within a precipitation regime, clay mineralogy is often
the single largest factor explaining differences in soil C stocks
within the landscape or with land-use change (12, 13). These
findings are not unique and date back to the work of Jenny (1941)
(20). What is unique is that we are able to illustrate these effects
(and their interactions) in this pan-tropical database (in contrast to
local- and regional-based studies) of the relationship between
land-use change and soil C stocks.
Using this knowledge in predicting land-use change effects on
soil C stocks is both promising and challenging. It is promising that
stratification along biophysical drivers indeed reduces variance in
the dataset (Table 1) and that methods exist for employing effi-
cient sampling and monitoring schemes (21, 22). The patterns in
the data suggest that extrapolating average stock-change factors
(e.g., Fig. 1) to unmeasured sites across the tropics would result in
large errors of unknown direction and magnitude. Stratification
would strongly reduce this error. The challenge is, however, that
the present database is insufficient for this approach. Even with
our simple stratification of 12 biophysical strata (four annual
precipitation classes by three clay mineralogy classes) only three
land-cover change transitions can be included.
Caveats of Datasets. The database of our meta-analysis was se-
lected using rigorous criteria. This process prevented errors re-
lated to bulk density estimates instead of measurements (23, 24)
and errors related to unclear reference land uses (25). Although
the database is one of the largest used for meta-analysis or reviews
for the tropics, we acknowledge the many limitations of our
datasets. First, the paucity of field observations did not allow us to
evaluate temporal trends in soil C dynamics with land-use change
within biophysical categories. Thus, our analysis assumes that soil
C stocks have reached equilibrium values under current land uses.
Undoubtedly, other factors not incorporated in our analyses also
affect the direction and magnitude of changes in soil C stocks,
including site preparation, fertilization and improved manage-
ment (26), species effects (27), and legacy effects of multiple land-
use transitions. However, we did not stratify according to these
factors, as their effect is less studied and no georeferenced data-
bases of these factors exist that might be used to improve pre-
dictions of soil C stock changes following land-use change.
Second, even for the three land-cover change transitions, for
which data enabled us to examine whether soil C dynamics
depended interactively on biophysical variables, many of the
categories did not have enough observations, whereas one cat-
egory (i.e., forest-to-pasture conversion on soils with low-activity
clay in the 1,501- to 2,500-mm annual precipitation class) (Table 1)
was overrepresented. This finding illustrates that no systematic
effort has been made to sample underrepresented land-use
changes or regions. On the contrary, the nonrandom character of
our database strongly suggests sampling and geographic bias
(see below).
0
10
20
30
40
50
60
70
80
90
500 to 1500 1501 to 2500 2501 to 3500 >3501
percentage of field observaons
mean annual rainfall (mm)
0
10
20
30
40
50
60
70
80
90
500 to 1500 1501 to 2500 2501 to 3500 >3501
percentage of all grid cells
mean annual rainfall (mm)
0
10
20
30
40
50
60
70
80
90
500 to 1500 1501 to 2500 2501 to 3500 >3501
percentage of grid cells with
>50% conversion
mean annual rainfall (mm)
C
A
B
Fig. 2. Numbers of observations or grid cells grouped into 12 biophysical
classes defined by annual precipitation and soil clay mineralogy. (A) Field
observations of soil C changes. (B) Pan-tropical distribution of 1° x 1° grid
cells. (C) Tropical grid cells that have experienced >50% conversion to other
land uses. Gray bars are low activity clay, open bars are high-activity clay,
and black bars are allophanic clays.
6320
|
www.pnas.org/cgi/doi/10.1073/pnas.1016774108 Powers et al.

Third, the majority of field observations are sampled at in-
consistent depths that are typically only above 30 cm. Thus, we
cannot draw reliable conclusions about land-use effects deeper in
the soil profile.
Fourth, the coarse spatial resolution of the global maps of
precipitation, soil type, and land cover likely masks important
spatial heterogeneity (28). For example, because of their high
native fertility, we would expect that agricultural activities would
be preferentially located on allophanic soils. However, the global
soil map only considers the dominant soil types. Consequently,
soils dominated by allophanes (Andosols) appear in very few of
the 1° by 1° grid cells we sampled (and none in the >3,500-mm
precipitation category), even though Andosols cover about 98
million hectares worldwide, or an estimated 1.0% of the total
tropical area (28). This mismatch in spatial scales helps in part to
reconcile our finding that 9% of field observations were located
on high precipitation, allophanic soils, but the global map con-
tained no grid cells with this combination of precipitation and
clay mineralogy composition. Nevertheless, we believe that our
main conclusions are robust to these limitations.
Causes and Consequences of Geographic and Sampling Bias. One of
the strongest conclusions from our analyses is the existence of
geographic bias in the field observations of land-use change
effects relative to biophysical drivers (Fig. 2). What this means is
that we have concentrated our scientific research on regions that
are highly unrepresentative of the tropics as a whole, and are
particularly unrepresentative of the tropical lands that have un-
dergone conversion to other land covers. A likely cause of this
bias is that published scientific research in tropical countries
is disproportionately conducted in countries and locations of
large, internationally funded field stations (e.g., Costa Rica and
Panama) (29). Not only are certain combinations of biophysical
variables undersampled as our data show (e.g., regions with
precipitation <1,500 mm and high-activity clays), but also the
intellectual and scientific infrastructure for conducting research
in certain geographical regions (e.g., Africa) remains underde-
veloped, which should be a cause for global concern (29).
A consequence of unrepresentative sampling is that it pre-
cludes us from extrapolating field observations to the continental
or global scales that are relevant for global biogeochemistry and
policy. For example, in our analyses we obtained the curious
result that both forest-to-pasture and pasture-to-secondary forest
conversions increased soil C contents (Fig. 1). One biological
explanation for this is that productive pastures are unlikely to be
abandoned, which biases the pasture-to-secondary forest con-
version field studies toward low-productivity pastures that likely
lost soil C when they initially were converted from forest. A
second explanation is unequal sampling across the biophysical
driving variables. Of the data that estimate the mean effect of
forest-to-pasture conversion, 62% came from low-activity clay
soils with precipitation between 1,501 and 2,500 mm, which was
the only combination of biophysical factors that yielded increases
in soil C stocks for this land-use conversion (Table 1). In con-
trast, soil C stocks increased when secondary forests grew on
abandoned pastures in four of seven classes of biophysical vari-
ables. In summary, the mean estimated stock-change factors are
highly dependent on the number of observations from each class
of biophysical variables, and the data we have do not allow us to
discriminate between the biological and the sampling bias ex-
planation for this result.
Recommendations. We believe that a relatively simple set of cri-
teria could significantly improve estimates of average soil C
stock-change factors following land-use conversion. First, we
recommend that clear reference land-use and -change trajecto-
ries should be sampled under comparable biophysical conditions,
and sampling should be based on defined depths with measured
soil bulk density and not based on soil horizons. These have been
recurring recommendations from the literature in the past few
decades, but are still commonly neglected, given the number of
studies that we had to exclude from our database because of this
missing critical information. Second, field studies should focus
on areas that are underrepresented in the present database [i.e.,
drier part of the tropics (500–1,500 mm annual precipitation) on
soils dominated by high-activity clay] to amend the present
geographic bias. Third, the present dataset does not include
current, important land-cover changes (i.e., conversion of trop-
ical peatland and savanna to agro-biofuel production) (30, 31),
and detailed quantification of soil C stock changes is missing for
these areas. Finally, we should abandon the idea that we can
extrapolate average values of land-cover change effects on soil C
stocks unless the distribution of field observations corresponds to
the distribution of biophysical conditions in the tropics.
Methods
Literature Review and Meta-Analysis. Published studies located between 28°
35′N and 28°15′S latitude were identified from previous meta-analyses and
reviews (14, 27, 32–34) or from searching online scientific databases. The
majority of the studies were conducted between 23° N and 23° S latitudes
and only a few are considered subtropical. Most of the studies quantified
land-cover change effects by comparing plots on different land uses, as-
suming that soil C stocks were identical before land-cover change (i.e.,
chronosequence and space-for-time substitution designs). The final database
consisted of studies that: (i) reported soil C stocks or information that
allowed us to calculate it (carbon percentages, measured bulk density, and
sampling depth) and excluded studies lacking bulk density or that estimated
it from soil function formulas; (ii) included clear, logical reference sites that
represented the immediate, previous land cover; (iii ) included data on cli-
mate and soils; and (iv) had not been published elsewhere (Dataset S1).
Although it is desirable to compare changes in soil C stocks between land
uses based on common soil mass rather than volume because of compaction
(23, 24), it was not possible for us to correct data for all these studies, as not
all studies we surveyed reported both bulk densities and C concentrations.
Thus, we did not adjust reported data to a common mass, but we used mass-
corrected soil C stock changes when authors expressed them.
Most studies reported data for more than one pair of sites or more than
one soil depth, and the decision of what constitutes an independent ob-
servation from each study can affect the results of meta-analyses (35). Our
approach was to be conservative in what we called “independent observa-
tions.”In the few longitudinal studies we found where the same plots were
sampled repeatedly, we included the data from only the first and most re-
cent sampling period. In studies that sampled many replicate plots over
a landscape, plots with the same age, edaphic conditions, and land use were
pooled together, and compared with the mean value from the reference
sites representing the previous land use. We considered data from different
sampling depths as independent observations in the overall analysis testing
for land-use change effects and comparisons to global distributions of bio-
physical variables, but restricted the three additional meta-analyses that
explored effects of biophysical variables on specific land-use transitions to
surface samples up to 30-cm profile depth so as not to confound any bio-
physical effects with depth effects. Observations were assigned to one of the
following land-cover transitions: forest to pasture, forest to plantation (i.e.,
perennial trees), forest to crop, pasture to secondary forest, pasture to
plantation, crop to plantation, crop to secondary forest, crop to pasture,
savanna to crop, savanna to pasture, or savanna to plantation. We also in-
cluded two types of shifting-cultivation studies: those that compared shift-
ing cultivation to primary forest (forest to shifting cultivation), and those
that compared cropped fields to fallow forest (crop to forest fallow).
Observations were assigned one of three clay mineralogy classes (low-
activity clay, high-activity clay, and allophanic mineralogy) that we inferred
from reported soil classification, CEC, geological substrate, or a combination
of these criteria. In general, soils dominated by low activity clay have a CEC
of <24 cmol
c
·kg
−1
clay or <4 cmol
c
·kg
−1
soil (e.g., Acrisols, Ferralsols, and
Nitisols); soils dominated by high activity clay have a CEC of >24 cmol
c
·kg
−1
clay (e.g., Alisols, Cambisols, Fluvisols, and Luvisols); soils dominated by al-
lophane are typically developed on volcanic ash (e.g., Andosols) (36).
The percent-difference in C stock between plots representing managed
and initial conditions, expressed relative to the initial soil C stock [i.e., (Xc −
Xr)/Xr ×100], was used as the metric of change in soil C (with Xc repre-
senting soil C stock in the current land use, and Xr the reference land use).
Powers et al. PNAS
|
April 12, 2011
|
vol. 108
|
no. 15
|
6321
SUSTAINABILITY
SCIENCE

Following other authors (14, 27), we used nonparametric resampling
methods to generate bias-corrected bootstrapped approximate 95% confi-
dence intervals (CI) from 10,000 randomizations in META-WIN (37), and re-
sponse effects were not weighted by sample size. Observed effect sizes were
considered statistically different from zero if the 95% CI did not include
zero, and land-cover transitions or other categorical grouping factors were
considered different from one another if their 95% CI did not overlap. For
the three most studied land-use transitions (forest to pasture, pasture to
secondary forest, and forest to crop), we assessed how precipitation class
and clay-activity class interactively affected the responses of soil C pools to
land-use change using identical statistical methods, for depths including 0 to
30 cm in the profile (average sampling depth was 14.6 cm).
Geographic Analysis. We tabulated the distribution of average precipitation
conditions and soil clay mineralogy from global databases as follows. A global
map of the 1961 to 1990 annual mean precipitation was derived from the
Climate Research Unit dataset at 1.0° by 1.0° resolution (38) and classified
into four categories: 500 to 1,500 mm, 1,501 to 2,500 mm, 2,501 to 3,500
mm, and >3,501 mm annual precipitation. All oceans, extratropical land
(defining tropical lands as those occurring between 24° N and 24° S latitude),
and tropical lands with mean annual precipitation <499 mm (e.g., the
Sahara Desert) were omitted from the analysis. We used the 1.0° by 1.0°
resolution Food and Agriculture Organization global soil map to generate
a map of soil clay mineralogy (http://data.giss.nasa.gov/landuse/soilunit.
html). To accomplish this, we reclassified the map units into the same three
clay-activity classes used for the literature studies: low-activity clay, high-
activity clay, and allophanic mineralogy. This gridded, classified map was
overlaid onto the precipitation map, and the numbers of grid cells in each
combination of precipitation and clay mineralogy (12 classes total) were
tabulated (n= 2,857 grid cells).
The distribution of field observations that included information on both
precipitation and soil order (n= 837) was compared with the actual area-
weighted distribution of annual precipitation and soil clay mineralogical
conditions in the tropics using a χ
2
test. At the coarse-scale resolution of
global datasets, there are no tropical grid cells in the category of allophanic
mineralogy and annual precipitation >3,501 mm, even though 9.0% of the
field observations come from lands with these conditions. To accommodate
the fact that this precipitation/clay mineralogy class had an expectation of 0,
we assigned it a pseudoexpectation of one and decreased the expected
number of field observations in the 500- to 1,500-mm precipitation/high-
activity clay class from 276 to 275. χ
2
tests are considered robust only when
all of the expected counts are >5, which was not the case for 3 of the 12
precipitation/clay mineralogy categories we analyzed. Nevertheless, the ex-
tremely large χ
2
statistic of 11,789 (df = 11) was highly significant (P<
0.0001) and gives us assurance that the distributions of field observations
and actual precipitation and clay mineralogy conditions are indeed distinct.
To control for the possibility that the distribution of field observations
reflects the conditions of lands that have undergone conversion, we used
a time series of global maps of grazing lands and croplands (the Global
Cropland and Pasture Data from 1700 to 2007) (39) to create a map of all grid
cells in the tropics that have undergone at least 50% conversion in the last
century (including all lands in pasture or cropland before 1900). We used this
land-conversion map as a mask and retabulated the distribution of annual
precipitation/clay mineralogy classes for the 981 grid cells that had un-
dergone >50% conversion (roughly 34% of all tropical lands with annual
precipitation >500 mm). We compared the distributions of field observa-
tions to converted conditions using the χ
2
test described above.
ACKNOWLEDGMENTS. We thank Angelica Pame-Baldos for helping to
assemble the literature database, and Peter Tiffin and two anonymous
reviewers for insightful reviews of the manuscript. This study was part of the
project “Reducing Emissions from Deforestation and Degradation through
Alternative Landuses in Rainforests of the Tropics (REDD-ALERT)”, funded
by the European Community’s Seventh Framework Programme (FP7/2007-
2013) under Grant agreement No. 226310 and by the Norwegian Agency for
Development Cooperation project “Learning from REDD: A Global Com-
parative Analysis.”M.D.C. received funding from the Robert Bosch Foun-
dation, Germany.
1. Amundson R (2001) The carbon budget of soils. Annu Rev Earth Planet Sci 29:535–562.
2. Jobbagy EG, Jackson RB (2000) The vertical distribution of soil organic carbon and its
relation to climate and vegetation. Ecol Apps 10:423–436.
3. Schlesinger WH (1997) Biogeochemistry: An Analysis of Global Change (Academic
Press, San Diego, CA).
4. Blair AW, McLean HC (1917) Total nitrogen and carbon in cultivated land and land
abandoned to grass and weeds. Soil Sci 4:283–294.
5. Greenland DJ, Nye PH (1959) Increases in the carbon and nitrogen contents of tropical
soils under natural fallows. J Soil Sci 10:284–299.
6. van der Werf GR, et al. (2009) CO
2
emissions from forest loss. Nat Geosci 2:737–738.
7. Detwiler RP (1986) Land use change and the global carbon cycle: The role of tropical
soils. Biogeochemistry 2:67–93.
8. Houghton RA (1995) Land-use change and the carbon cycle. Glob Change Biol 1:
275–287.
9. Asner GP (2009) Tropical forest carbon assessment: Integrating satellite and airborne
mapping approaches. Environ Res Lett, 10.1088/1748-9326/4/3/034009.
10. Aalde H, et al. (2006) Forest Land. 2006 IPCC Guidelines for National Greenhouse Gas
Inventories eds Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K (Institute for
Global Environmental Strategies, Japan).
11. Le Quéré C, et al. (2009) Trends in the sources and sinks of carbon dioxide. Nat Geosci
2:831–836.
12. deKoning GHJ, Veldkamp E, Lopez-Ulloa M (2003) Quantification of carbon
sequestration in soils following pasture to forest conversion in northwestern Ecuador.
Global Biogeochem Cy, 10.1029/2003GB002099.
13. Powers JS, Schlesinger WH (2002) Relationships among soil carbon distributions and
biophysical factors at nested spatial scales in rain forests of norteastern Costa Rica.
Geoderma 109:165–190.
14. Guo LB, Gifford RM (2002) Soil carbon stocks and land use change: A meta analysis.
Glob Change Biol 8:345–360.
15. Lopez-Ulloa M, Veldkamp E, deKoning GHJ (2005) Soil carbon stabilization in
converted tropical pastures and forests depends on soil type. Soil Sci Soc Am J 69:
1110–1117.
16. Joseph SD, et al. (2010) An investigation into the reactions of biochar in soil. Aust J
Soil Res 48:501–515.
17. Parfitt RL, Theng BKG, Whitton JS, Shepherd TG (1997) Effects of clay minerals and
land use on organic matter pools. Geoderma 75:1–12.
18. Torn MS, Trumbore SE, Chadwick OA, Vitousek PM, Hendricks DM (1997) Mineral
control of soil organic carbon storage and turnover. Nature 389:170–173.
19. Sollins P, Homann P, Caldwell BA (1996) Stabilization and destabilization of soil
organic matter: Mechanisms and controls. Geoderma 74:65–105.
20. Jenny H (1941) Factors of Soil Formation: A System of Quantitative Pedology
(McGraw-Hill, New York), p 281.
21. Minasny B, McBratney AB (2006) A conditioned Latin hypercube method for sampling
in the presence of ancillary information. Comput Geosci 32:1378–1388.
22. Brown DJ, et al. (2010) Soil organic carbon change monitored over large areas. Eos 91:
441–442.
23. Davidson EA, Ackerman IL (1993) Changes in soil carbon inventories following
cultivation of previously untilled soils. Biogeochemistry 20:161–193.
24. Veldkamp E (1994) Organic carbon turnover in three tropical soils under pasture after
deforestation. Soil Sci Soc Am J 58:175–180.
25. Powers JS (2004) Soil carbon and nitrogen storage following contrasting land-use
transitions in Northeastern Costa Rica. Ecosystems 7:134–146.
26. Fearnside PM, Barbosa RI (1998) Soil carbon changes from conversion of forest to
pasture in Brazilian Amazonia. For Ecol Manage 108:147–166.
27. Berthrong ST, Jobbágy EG, Jackson RB (2009) A global meta-analysis of soil
exchangeable cations, pH, carbon, and nitrogen with afforestation. Ecol Appl 19:
2228–2241.
28. Palm C, Sanchez P, Ahamed S, Awiti A (2007) Soils: A contemporary perspective. Annu
Rev Environ Resour 32:99–129.
29. Stocks G, Seales L, Paniagua F, Maehr E, Bruna EM (2008) The geographical and
institional distribution of ecological research in the tropics. Biotropica 40:397–404.
30. Fargione J, Hill J, Tilman D, Polasky S, Hawthorne P (2008) Land clearing and the
biofuel carbon debt. Science 319:1235–1238.
31. Koh LP, Ghazoul J (2010) Spatially explicit scenario analysis for reconciling agricultural
expansion, forest protection, and carbon conservation in Indonesia. Proc Natl Acad Sci
USA 107:11140–11144.
32. Laganière J, Angers DA, Paré D (2010) Carbon accumulation in agricultural soils after
afforestation: a meta-analysis. Glob Change Biol 16:439–453.
33. McGrath DA, Smith CK, Gholz HL, de Assis Oliveira F (2001) Effects of land-use change
on soil nutrient dynamics in Amazonia. Ecosystems (N Y) 4:625–645.
34. Paul KI, Polglase PJ, Nyakuengama JG, Khanna PK (2002) Change in soil carbon
following afforestation. For Ecol Manage 168:241–257.
35. Hungate BA, et al. (2009) Assessing the effect of elevated carbon dioxide on soil
carbon: A comparison of four meta-analyses. Glob Change Biol 15:2020–2034.
36. IUSS International Union of Soil Sciences Working Group WRB (2007) World reference
base for soil resources 2006, first update 2007, in World Soil Resources Reports No.
103 (Food and Agriculture Organization, Rome, Italy).
37. Rosenberg MS, Adams DC, Gurevitch J (2000) MetaWin: Statistical Software for Meta-
Analysis. Version 2 (Sinauer Associates, Sunderland, Massachusetts).
38. New M, Hulme M, Jones PD (1999) Representing twentieth century space-time climate
variability. Part 1: Development of a 1961-90 mean monthly terrestrial climatology.
J Clim 12:829–856.
39. Ramankutty N, Foley JA (1999) Estimating historical changes in global land cover:
Croplands from 1700 to 1992. Global Biogeochem Cy 13:997–1027.
6322
|
www.pnas.org/cgi/doi/10.1073/pnas.1016774108 Powers et al.