Global Change Biology (2004) 10, 844–862, doi: 10.1111/j.1529-8817.2003.00766.x
Pasture degradation in the central Amazon: linking
changes in carbon and nutrient cycling with remote
sensing
G R E G O R Y P. A S N E R *w , A L A N R . T O W N S E N D z, M E R C E D E S M . C . B U S T A M A N T E § ,
G A B R I E L A B . N A R D O T O } and L Y D I A P. O L A N D E R *
*Department of Global Ecology, Carnegie Institution of Washington, Stanford, CA 94305, USA, wDepartment of Geological and
Environmental Sciences, Stanford University, Stanford, CA 94305, USA, zInstitute for Arctic and Alpine Research and Department
of EPO Biology, University of Colorado, Boulder, CO, USA, §Departamento de Ecologia, Universidade de Brasilia, Brasilia-DF,
Brasil, }Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, Piracicaba, SP, Brazil
Abstract
The majority of deforested land in the Amazon Basin has become cattle pasture, making
forest-to-pasture conversion an important contributor to the carbon (C) and climate
dynamics of the region. However, our understanding of biogeochemical dynamics in
pasturelands remains poor, especially when attempting to scale up predictions of C cycle
changes. A wide range of pasture ages, soil types, management strategies, and climates
make remote sensing the only realistic means to regionalize our understanding of
pasture biogeochemistry and C cycling over such an enormous geographic area.
However, the use of remote sensing has been impeded by a lack of effective links
between variables that can be observed from satellites (e.g. live and senescent biomass)
and variables that cannot be observed, but which may drive key changes in C storage
and trace gas fluxes (e.g. soil nutrient status). We studied patterns in canopy biophysical–
biochemical properties and soil biogeochemical processes along pasture age gradients on
two important soil types in the central Amazon. Our goals were to (1) improve our
understanding of the plot-scale biogeochemical dynamics of this land-use change, (2)
evaluate the effects of pasture development on two contrasting soil types (clayey Oxisols
and sandy Entisols), and (3) attempt to use remotely sensed variables to scale up the sitespecific variability in biogeochemical conditions of pasturelands.
The biogeochemical analyses showed that (1) aboveground and soil C stocks
decreased with pasture age on both clayey and sandy soils, (2) declines in plant
biomass were well correlated with declines in soil C and with available phosphorus (P)
and calcium (Ca), and (3) despite low initial values for total and available soil P,
ecosystem P stocks declined further with pasture age, as did a number of other nutrients.
Spectral mixture analysis of Landsat imagery provided estimates of photosynthetic
vegetation (PV) and non-photosynthetic vegetation (NPV) that were highly correlated
with field measurements of these variables and plant biomass. In turn, the remotely
sensed sum PV 1 NPV was well correlated with the changes in soil organic carbon and
nitrogen, and available P and Ca. These results suggest that remote sensing can be an
excellent indicator of not only pasture area, but of pasture condition and C storage,
thereby greatly improving regional estimates of the environmental consequences of such
land-use change.
Keywords: Amazon basin, base cations, Brazil, calcium, deforestation, land-use change, nitrogen,
nutrient cycling, pastures, phosphorus, tropical forest
Received 31 October 2002; revised version received and accepted 4 February 2003
Correspondence: G. P. Asner, Carnegie Institution, Stanford
University 260 Panama Street Stanford, CA 94305, USA,
tel. 1 1 650 325 1521, fax 1 1 650 325 6857,
e-mail: [email protected]
844
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Introduction
Land use is a well-recognized agent of ecological
change in humid tropical regions of the world (Reiners
et al., 1994; Nepstad et al., 1999; Houghton et al., 2000;
Achard et al., 2002). In the Brazilian Amazon basin,
approximately 8–15 000 km2 or about 1–2% of tropical
forest is cleared each year (Fearnside & Barbosa, 1998;
Houghton et al., 2000), most of which becomes cattle
pasture (Moran et al., 1994; Fearnside, 1996). Forest-topasture conversion is driven by a combination of social,
economic, and ecological factors that vary in relative
importance at local, municipal, and states levels (Moran
et al., 1994; Scatena et al., 1996; Fujisaka & White, 1998;
Nepstad et al., 2001a). These factors create a large,
complex regional mosaic of pasture age states, with
associated variations in vegetation composition, productivity, and biogeochemical condition (Uhl et al.,
1988; Correa & Klaus, 1989). Although gross deforestation estimates for the Brazilian Amazon are improving
(e.g. Skole & Tucker, 1993; Laurance et al., 2002), spatial
and temporal variations in pasture condition relative to
biogeochemical processes are poorly understood. Our
knowledge of the interactive role of land use, vegetation dynamics, and biogeochemical cycles that determine the functioning of managed lands must therefore
improve as pastures continue to expand throughout the
Amazon, increasing their contribution to the regional
carbon and climate system (Fearnside & Barbosa, 1998;
Houghton et al., 2000; Lawton et al., 2001).
Forest-to-pasture conversion is undertaken by landholders who fell trees (often selling the merchantable
timber to sawmills), burn the slash, and plant grasses.
Pastures are usually subjected to relatively low grazing
intensities (Dias-Filho et al., 2001), and ranch managers
may use fire periodically to clear woody successional
plants and weeds (Uhl et al., 1988). Due to its high cost,
fertilizer is rarely used to boost pasture productivity
(Kristensen et al., 2000; Dias-Filho et al., 2001). Despite
efforts to maintain pasture productivity, progressive
degradation is often reported to occur after about 5–15
years of pasture use (Nepstad et al., 1991; Davidson
et al., 1995; Fearnside & Barbosa, 1998). Degradation –
defined here as a loss of grass (forage) productivity – has
been linked to soil nutrient impoverishment, increased
acidity and compaction (Buschbacher et al., 1988; Asner
et al., 1999; McGrath et al., 2001). However, only the
proximate causes are known, and the spatio-temporal
dynamics of pasture degradation remain unclear.
In part, our limited understanding of pasture
biogeochemistry results from a scarcity of data in
comparison with the diversity of soil types, management strategies, and climate regimes across which such
land-use changes occur. Upland (terra firme) soils of the
r 2004 Blackwell Publishing Ltd, Global Change Biology, 10, 844–862
845
Amazon basin are often highly weathered and nutrient
poor Oxisols and Ultisols, but significant areas of other
soil types exist, some of which can be even poorer in
nutrients than the widespread Oxisols and Ultisols,
while still others are nutrient rich (Sanchez et al., 1982).
Forest ecosystems in the Amazon occur across considerable variability in soil type and nutrient status (e.g.
Vitousek & Sanford, 1986), and conversion of those
systems does not occur in equal proportion to the total
soil areas. Moreover, given the prevalence of highly
weathered soils, nutrient limitation in primary forest
may vary among different nutrients and the relative
intensity of such limitation is likely to change along
land-use gradients (Davidson et al., in press). Unfortunately, very few studies of pasture chronosequences
have reported data on the full suite of nutrients – N, P,
Ca, Mg, K, and others – that may be important in
determining the spatial and temporal dynamics of
pasture production and degradation in the Amazon
basin (McGrath et al., 2001).
A 10 000 km2 region of the Central Amazon near the
city of Santarém and the confluence of the Amazon and
Tapajós Rivers typifies the soil variation that occurs
throughout the basin. Highly weathered, acidic, and
relatively nutrient poor Oxisols are most common here,
but a significant fraction of the upland area is
dominated by sandy, nutrient poor Entisols (Parrotta
et al., 1995; Silver et al., 2000). Biogeochemical and
hydrological characteristics of forest ecosystems differ
along the soil texture gradients found in this region
(Silver et al., 2000), and large areas of cleared forest exist
across all soil types (Asner et al., 2003). The full regional
effects of this soil textural mosaic on patterns of pasture
productivity and degradation remains poorly known.
Recently, Asner et al. (1999) and Townsend et al. (2002)
found evidence for significant differences in P cycling in
the two soil types, with clear implications for pasture
sustainability and secondary succession.
Our knowledge of the pasture biogeochemical dynamics is still incomplete at the plot level, and we also
lack the necessary ability to extrapolate biogeochemical
information to larger scales. For example, a major
limitation to regional ecological studies is a lack of
quantitative data on surface biophysical conditions at
spatial scales ranging from plot (within-pasture) to
landscape (between pastures) levels. Observed changes
in plant canopy properties such as leaf area index (LAI),
live/dead biomass, and productivity could serve as
important conduits to belowground processes such as
nutrient dynamics and soil hydrology (McGrath et al.,
2001). Such observations could provide a means to
collect information on pasture biogeochemistry
throughout a region using remote sensing. The strength
of these connections is still unclear, but initial studies
846 G . P. A S N E R et al.
demonstrate the potential for remote monitoring of
pasture condition and degradation (Asner et al., 1999).
Remotely sensed measurements of green LAI are
common in grassland ecosystems (e.g. Wylie et al.,
2002), but remote determination of green leaf and nonphotosynthetic vegetation (NPV) area indices
(LAI 1 NPVAI) has been more recent and preliminary
(e.g. Asner et al., 1999). It remains unknown how closely
these remote sensing measurements track changes in
pasture canopy biomass and productivity – two
vegetation properties closely linked to soil carbon and
nutrient characteristics. Moreover, it is not certain if and
how any of these plant canopy characteristics relate to
the suite of nutrient (e.g. N, P, base cation) changes
likely to occur in aging pastures.
We report a study focused on long-term changes in
canopy biophysical and ecosystem biogeochemical
properties of pastures on two distinct soil types (clayey
Oxisols and sandy Entisols) in the central Amazon
basin. We studied both the biogeochemical mechanisms
regulating observed changes in pasture carbon (C)
stocks, and the linkages between vegetation properties
and soil processes. We also determined the strength of
the relationship between satellite estimates of canopy
condition and field-measured biogeochemical dynamics. This study builds off previous studies by
Asner et al. (1999) and Townsend et al. (2002) by
substantially extending and adding analyses of soil
chemistry, plant biophysical and biochemical changes,
and remote sensing at landscape scales.
Methods
Study region and field sites
The field sites comprised two pasture chronosequences
located on ranches south of Santarém, Pará and the
Amazon River, and east of the Tapajós River (3116 0 S
54156 0 W; 318 0 S 54140 0 W; Fig. 1). Mean annual rainfall is
2000 mm, most of which occurs between January and
Figure 1 (Left) Landsat image showing locations of study pastures bordering the Tapajós National Forest, south of the city of Santarém,
central Amazon basin; (Right) Zoom images of pasture study sites with GPS boundaries shown.
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May, and mean annual temperature is 25 1C. All sites
are on upland terra firme that has pockets of relic
depositional surfaces, creating a mix of clay-rich
Oxisols and highly sandy Entisols (Parrotta et al.,
1995; Silver et al., 2000). We established one chronosequence of pastures on the Oxisols dating 2, 7, and 15
years since conversion at the start of the study, and the
other chronosequence (1-, 7-, and 15-year old) on the
sandy Entisols (Table 1). Three of the sites (1 year
Entisol, 2 years and 7 years Oxisols) were dominated by
the common pasture grass Brachyaria brizantha; the
other three sites were dominated by another common
grass, Pennesetum clandestinum. All pastures were
formed from primary forest.
All but the two youngest pasture sites had been
burned since conversion as a means for controlling
woody pioneer species, with one post-clearing burn in
each of the two 7-year old pastures, and two such burns
in the two oldest pastures (Table 1). Cattle stocking
rates ranged from 0.25 and 0.5 head of cattle ha1 yr1,
which is low but typical of the central Amazon basin
(Dias-Filho et al., 2001). Topographic relief at all sites is
minimal, and none of the pastures have been fertilized.
The sites were visited in August 1997, June 1998, and
November 1998. Field data on aboveground vegetation
condition and soil samples for detailed laboratory
analysis were collected in 1997. The 1998 field campaigns were timed to take place at the beginning (June)
Table 1
847
and end (November) of the dry season that occurs
throughout the region. These visits focused on in situ
measurements of vegetation condition (biomass, phenology, etc.) and soil nutrient availability, as detailed
below. Throughout the study, all vegetation measurements, soil collections, and in situ soil assays were
conducted along randomly selected 100 m transects
within each pasture.
Vegetation studies
Vegetation measurements included plant area index
(PAI), live : senescent vegetation ratio, aboveground
biomass (AGB), plant canopy height, and specific leaf
area (SLA). PAI was measured using a plant canopy
analyzer (Licor LAI-2000; Licor Inc., Lincoln, NE, USA)
according to the method detailed by Asner et al. (1998).
PAI measurements were collected under diffuse sky
conditions – either at dawn or dusk or on overcast days
– as required for this instrument (Welles & Norman,
1991). Pasture grass areas measured for PAI were
clipped in 1 m2 plots (n 5 50), separated into live and
senescent fractions, and weighed fresh. Samples were
then oven dried at 60 1C for 36 h, reweighed for calculation of percent dry matter, and SLA was determined.
Sub-samples from biomass harvests were assayed for
carbon and nutrient concentrations. Total C and N were
measured using a combustion–reduction elemental
Land-use, vegetation, and soil characteristics of sites comprising the pasture age gradients
Pasture age–soil type
Clay
Sand
Young
Medium
Old
Young
Medium
Old
Land-use
Year cleared*
Time since clearing (years)*,w
Size (ha)z
Cattle (# ha1 yr1)w
Burns (# since clearing)w
1996
1–2
160.1
0.25
0
1991
6–7
9.3
0.5
1
1982
15–16
7.3
0.25
2
1997
0.5–1.5
4.3
0.0 (ungrazed)
0
1991
6–7
5.1
0.5
1
1982
15–16
4.7
0.25
2
Vegetation
Dominant species
Canopy height (m)
B. brizantha
1.1–2.2
B. brizantha
0.5–0.75
P. clandestinum
0.1–0.2
B. brizantha
0.75–1.5
P. clandestinum
0.1–0.2
P. clandestinum
0.05–0.1
Soils
Texture (sand/silt/clay)
pH
Bulk density (g cm3)
Exch Al (meq 100 g 1 soil)
Exch Fe (meq 100 g1 soil)
37/3/60
5.3
1.2
0.27
1.50
39/2/59
5.4
1.5
0.16
0.83
40/5/55
5.0
1.5
0.21
1.34
89/4/7
5.6
1.4
0.07
0.82
92/2/5
5.6
1.4
0.07
2.01
91/4/5
5.3
1.4
0.15
1.27
*Verified using multi-temporal Landsat imagery collected in 1982, 1986, 1991,1997.
w
Determined by interviewing ranch owners.
Determined using differentially-corrected global positioning system (GPS) measurements.
z
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848 G . P. A S N E R et al.
analyzer (Carlo-Erba, Inc., Milan, Italy). Foliar P was
determined using a colorimetric autoanalyzer (OI Analytical, Inc., College Start, TX, USA) following hot-acid
digestion. Base cation concentrations were determined
using a flame atomic absorption analyzer (PerkinElmer, Inc., Wellesley, MA, USA). Foliar resorption
efficiency was calculated by ratioing nutrient concentrations of standing senescent to live (green) leaves.
Soil studies
During the first field campaign (8/1997), 10 soil
samples per site were collected from 0 to 10 cm depth
along random intervals within 100 m transects. Soils
were air dried, sieved at 2 mm, and transported to the
University of Brasilia, Brazil. Bulk density was determined using an excavation method in which the
volume of removed soil is measured by replacing it
with a known volume of sand, and the soil removed is
dried and weighed. Sub-samples for total organic C and
N were ground to a fine powder and analyzed using a
combustion–reduction elemental analyzer (CE Elantech, Inc., Lakewood, NJ, USA). Total P analyses were
performed by digesting 5 g of sieved, air-dried soil in
H2SO4 and H2O2 (Parkinson & Allen, 1975); the
digested solution was then analyzed for phosphate
concentrations using a flow-injection autoanalyzer (OI
Analytical, Inc.). Exchangeable base cations were determined by atomic adsorption following extraction in 1 M
ammonium acetate solution, and modified-Hedley-P
fractions were determined according to methods
described in Tiessen & Moir (1993) and Townsend et
al. (2002). Because higher bulk densities are often found
in increasingly older pastures due to soil compaction,
total soil values for all elements in pasture sites were
adjusted as described by Veldkamp (1994).
Ion exchange resin bags were employed for in situ
assessment of soil nutrient availability. Ten grams of
mixed cation–anion exchange resin were placed at
10 cm depth in the soil in June and November 1998,
coinciding with the beginning and end of the dry
season. Thirty resin bags were placed at 3 m intervals
along transects in each pasture, then recovered following 14-day incubations. Resin bags were removed,
stored cool and transported to the lab for analysis.
Nutrients were extracted from the resin bags using 2 N
HCl; the supernatant was stored at 4 1C until analysis.
Phosphate (PO4), ammonium (NH4–N) and nitrate
(NO3–N) were measured using a colorimetric autoanalyzer (OI Analytical, Inc.), and base cations were
assayed using atomic-absorption spectroscopy (PerkinElmer, Inc.).
Differences in P sorption and desorption were
evaluated on composited soil samples (0.33 g) from
each site (n 5 3 composites of 10 samples each).
Samples were placed in sterilized polypropylene
centrifuge tubes and 10 mL of 0.02 M KCl solution
containing 5 and 10 ppm KH2PO4 was added. One to
two drops of toluene were added to suppress microbial
activity and the suspensions were shaken (200 rpm
continuous 6.8 g) at room temperature for 0, 10, and
30 min and 2, 8, and 24 h. At the end of each period, the
soil suspensions were centrifuged and filtered through
a Whatmann 42 filter paper. The concentration of PO4 in
solution was determined by the molybdate-ascorbic
acid method (Murphy & Riley, 1962) using a flowinjection autoanalyzer (OI Analytical, Inc.). The amount
of P sorbed was calculated from the difference between
the amount of P added and the amount remaining in
solution.
To evaluate P desorption, 10 mL of 0.02 M KCl solution was added to the tubes containing the soil plus the
‘entrained’ solution after the 30 min and 24 h sorption
times. The amount of entrained solution was calculated
by weighing the tubes before and after the supernatant
filtration. One to two drops of toluene were added and
the suspensions were shaken for 30 min and 24 h. The
suspensions were centrifuged, filtered, and the P concentration of the supernatant solution was determined.
Remote sensing analysis
A Landsat Thematic Mapper (TM) image was acquired
on August 27, 1997 over the region containing the
pasture age–texture chronosequences. Image pre-processing included atmospheric correction, geo-rectification, and ground-target calibration, all documented
by Asner et al. (in press a). The resulting image
provided ground-calibrated reflectance measurements
in 28 m 28 m pixels, with geo-locational error of
10 m. Each pasture site was delineated in the image
using differentially corrected global positioning system
(GPS) points collected in the field. The locations of GPSlocated pasture areas are shown in Fig. 1 (dotted lines).
Fractional surface cover of live-photosynthetic vegetation (PV), senescent NPV and bare soil were estimated
in each Landsat image pixel using spectral mixture
analysis (Asner et al., in press a). The spectral mixture
analysis provided estimates of PV, NPV, and bare soil
covers within each image pixel, along with statistical
uncertainty estimates for each cover-type. The model,
AutoMCU, is based on an algorithm originally used
with hyperspectral observations (Asner, 1997; Asner &
Lobell, 2000; Asner & Heidebrecht, 2002), but adapted
for use with multi-spectral Landsat imagery (Asner
et al., 2003). AutoMCU uses three spectral endmember ‘bundles’, derived from field measurements
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and satellite imagery, to decompose each image pixel
using the following equation:
rðlÞpixel ¼ S½Ce rðlÞe ¼ ½Cpv rðlÞpv þCsoil rðlÞsoil
þCnpv rðlÞnpv þe
849
ments of live and senescent biomass to determine if
these remotely sensed variables could be used for
quantitative monitoring of pastures in the Amazon.
ð1Þ
where r(l)e is the reflectance of each land-cover
endmember (e) at wavelength l, and e is a residual or
error term. The sub-pixel cover fractions (Ce) are PV,
NPV, and bare soil. Solving for Ce therefore requires
that the observations (in this case, reflectance or r(l)pixel
contain enough information to solve a system of linear
equations, each of the form in Eqn (1) but at a different
wavelength (l).
The accuracy of the estimated sub-pixel cover
fractions (Cpv, Cnpv, Csoil) relies heavily upon the
selection of endmember reflectances (rpv, rnpv, rsoil),
thus we developed reflectance bundles as detailed by
Asner et al. (2003). Briefly, we collected the spectral data
during visits in 8/97, 6/98, 9/98, 11/98, and 7/99 from
more than 60 cleared areas representing old and young
cattle pastures in different stages of senescence, four
distinct soil types ranging from red/yellow clay latisols
to alluvial sands, 41 species of early successional
woody plants, seven crops species, and another 11
unidentified roadside forbs. A full optical range
spectroradiometer (Analytical Spectral Devices Inc.,
Boulder, CO, USA) was used to collect the field
reflectance data. The data were then organized to
isolate spectra representing the purest mixtures of PV,
NPV and bare soil covers; all other spectra were
discarded. The data were then convolved to Landsat 5
TM channels using published spectral response functions (RSI 1999).
Satellite-derived estimates of surface PV, NPV, and
bare soil were compared with field-based measure-
Results
Vegetation changes
The total amount of live and senescent foliar area or
PAI, decreased with pasture age on both clay and sand
soils (Fig. 2). Decreases were more pronounced on sand
(64%) than on clay (45%) soils in the first 5–6 years of
pasture use. However, decreases in PAI to the oldest
pasture age sites (15 years) were 67% on both clay and
sand soils. These PAI trends were consistent during
repeated visits in the early (June), middle (August), and
late (November) dry season. However, the partitioning
of PAI into live (LAI) and senescent (NPVAI) fractions
did vary substantially throughout the season (Fig. 2).
For example, the young clay and sand pastures had
high LAI : NPVAI ratios of 2–32 during the June and
August visits, but much lower ratios at the end of the
dry season (0.2–0.3). There was little variation in the
LAI : NPVAI ratio (0.2–1.4) for the older pasture sites
throughout the dry season.
PAI was highly correlated with AGB for both B.
brizantha (r2 5 0.79, Po0.05) and P. clandestinum
(r2 5 0.83, Po0.01) (Fig. 3). Differences between the
two relationships resulted from species-specific differences in SLA, which were fairly constant within a
species across the pasture age/soil texture gradient
(Table 2). Applying the general equation for all data
combined (AGB 5 57 PAI 1 27; r2 5 0.78), we found
that AGB decreased 51% from young-to-medium age
pastures and again by 50% for medium-to-old pastures
Figure 2 Leaf area index (LAI; black bars) and non-photosynthetic vegetation area index (NPVAI; grey bars) results for clay and sand
pasture chronosequences. Results from three measurement dates are shown. Other summary results for plant area index
(PAI 5 LAI 1 NPVAI) and aboveground biomass (AGB; g C m2) are given as means ( standard deviation) at top.
r 2004 Blackwell Publishing Ltd, Global Change Biology, 10, 844–862
850 G . P. A S N E R et al.
on clay soils (Fig. 2). Similarly, AGB decreased 52% and
57% on sand soils when going from young-to-medium
and from medium-to-old age pastures, respectively.
Foliar nutrient concentrations differed by pasture age
and soil texture (Table 2). In live foliage, N concentrations decreased with pasture age on clays and sands in
all cases except for the transition from medium-to-old
clay pasture. In contrast, foliar P concentrations only
decreased when going from medium-to-old clay pastures. Whereas foliar Mg and K showed few clear
trends with pasture age or soil texture, foliar Ca
concentrations decreased by 27% from young-to-medium and 40% from medium-to-old clay pastures. The
largest percentage change in nutrient concentration was
found for foliar N in grasses from sandy soils. We also
found that foliar P concentrations on the youngest sand
site were about the same as that of the oldest clay site.
Nutrient concentrations of standing senescent foliage
followed trends similar to those of live material (Table
2). Foliar P, Ca, and Mg were inter-correlated, while
foliar N and K showed no correlation with other
nutrients (Table A1).
Calculations using live and senescent foliar nutrient
concentrations indicated that resorption efficiencies
generally increased with pasture age for N, P, and base
cations (Table 2). P resorption efficiency was consistently highest among all nutrients. Across the chronosequence of clay sites, P resorption did not increase as
dramatically as for N (11% vs. 41% relative change,
respectively). However, P resorption increased (25%
relative) much more so than for N (12%) on sand sites.
The N : P ratio of live foliage decreased substantially
from 19.4 to 14.0 (28%) and from 25.1 to 20.6 (18%)
Figure 3 Relationships between plant area index (PAI 5 LAI 1 NPVAI) and aboveground biomass for the major graminoid species found in pasture study areas.
Table 2
Foliar characteristics of vegetation collected along pasture age gradients on two soil types
Pasture age–soil type
Clay soils
Young
Vegetation property
Specific leaf area
(SLA; cm2 g1)
Foliar nutrients in live
vegetation (%)
Foliar nutrients in senescent
vegetation (%)
Mean foliar resorption
efficiency (%)
Foliar nutrient ratios
B. brizantha
P. clandestinum
N
P
Ca
Mg
K
N
P
Ca
Mg
K
N
P
Ca
Mg
K
N:P
Ca : P
26.8
–
1.75
0.09
0.48
0.28
1.60
0.68
0.07
0.28
0.15
0.90
39
73
58
55
56
19.4
5.3
(3.6)a
(0.10)a
(0.01)a
(0.02)a
(0.02)a
(0.06)a
(0.01)a
(0.01)a
(0.01)a
(0.01)a
(0.02)a
Sand soils
Medium
21.3
–
1.16
0.09
0.35
0.26
2.12
0.44
0.07
0.22
0.15
1.25
35
76
63
58
59
14.0
3.9
(2.9)a
(0.04)b
(0.01)a
(0.02)b
(0.03)a
(0.1 1)b
(0.03)b
(.01)b
(0.02)b
(0.01)a
(0.03)b
Old
–
12.2
1.12
0.06
0.21
0.17
1.80
0.62
0.05
0.14
0.10
1.10
55
81
65
59
61
18.7
3.5
Young
Medium
Old
23.1 (4.1)a
–
9.3 (1.5)b
1.44 (0.05)b
0.07 (0.01)a
0.16 (0.02)a
0.16 (0.02)b
2.63 (0.09)b
0.58 (0.02)a
0.05 (0.01)a
0.10 (0.01)a
0.09 (0.01)a
1.42(0.03)b
40
71
61
55
54
20.6
2.3
–
12.8
0.87
0.07
0.15
0.21
1.40
0.40
0.05
0.09
0.13
0.88
46
76
61
60
63
12.4
2.1
b
(2.1)
(0,05)b
(0.01)b
(0.01)c
(0.02)b
(0.08)b
(0.02)a
(0.01)b
(0.01)c
(0.01)b
(0.02)c
1.76
0.07
0.15
0.20
1.30
0.73
0.04
0.08
0.10
0.72
41
61
53
51
55
25.1
2.1
(0.06)a
(0.02)a
(0.01)a
(0.01)a
(0.04)a
(0.02)a
(0.01)b
(0.01)a
(0.01)a
(0.01)a
(1.9)b
(0.04)c
(0.01)a
(0.01)a
(0.03)a
(0.04)a
(0.01)c
(0.01)a
(0.01)a
(0.02)b
(0.01)c
Values given are means with standard deviations in parentheses. Superscript letters (a, b,y) within each row of the table indicate
statistical differences within a soil type (Po0.05 or higher confidence level using ANOVA).
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A M A Z O N PA S T U R E B I O G E O C H E M I S T R Y A N D R E M O T E S E N S I N G
from young-to-medium age pastures on clay and sand
soils, respectively (Table 2). While the trend continued
for the medium-to-old sand pastures (40%), N : P
increased ( 1 34%) from medium-to-old sites on clay
soils. In contrast, the Ca : P ratio of live foliage
decreased significantly throughout the chronosequence
on clay sites (11% to 26%), but displayed variable
trends on the sand sites (9% to 1 9%).
Applying the nutrient concentrations as nutrient : C
ratios to the AGB carbon (AGB 0.48) estimates (Fig. 2)
yielded clear negative trends in AG nutrient mass with
pasture age and soil texture (Fig. 4). The most
pronounced decreases were found for canopy P
(84%) and Ca (89%) on clay sites over the 15–16year chronosequence period. The largest observed
decreases on sand pasture sites were found for canopy
P (80%) and N (91%). Among all nutrients, canopy P
reached the lowest levels on the oldest sand sites
(0.08 0.005 g m2). Canopy phosphorous content in
the youngest sand site was similar to that of the
medium-age clay pasture, or 0.43 0.09 and
0.33 0.02 g m2 (t-tests; P 5 0.21), respectively (Fig.
4). Similar comparisons were apparent for Ca, Mg, and
K. Over the entire period represented by this chronosequence, canopy N decreased more on sand (91%)
than on clay (85%) soils. In contrast, canopy P, Ca, and
Mg decreased more rapidly on clay than on sand sites.
Soil studies
Soil organic carbon (SOC) was 50% lower in the
youngest sand than in the youngest clay site (Table 3).
851
However, losses resulted in comparable SOC stocks
between the medium-age clay and young sand sites
(both 2.7 kg m2). Carbon losses in the clay sites were
more than double that of the sands during the initial
5–6 years of pasture use, but losses were similar on the
two soil types (9% vs. 11%) when going from
medium (5–6 years)-to-old age (15–16 years) states.
Total soil N and P also decreased with pasture age, both
showing the greatest losses in the initial 5–6 years of
land use. Whereas soil organic N (SON) pools initially
decreased by 50% in both clay and sand pastures,
further losses were not evident during the latter 10-year
period of the chronosequence. Soil P losses were lower
than for SON in the initial 5–6 years, averaging 22% and
31% decreases on clays and sands, respectively; but
they were higher relative to SON in the latter period
(6% and 13% on clay and sand, respectively).
Overall, there was relatively high inter-correlation
between SOC, SON, and total soil P levels among the
sites (Table A2), though soil organic P (SOP) often
showed distinctly different patterns than those in SOC
and SON (see below). As one might expect, SOC
content was inversely correlated with soil bulk density
(r 50.82, P 5 0.04), and soil P content was positively
correlated with soil clay content (r 5 0.93, P 5 0.01).
Other correlation tests between SOC, SON, soil P, bulk
density, and clay were insignificant (Table A2).
Laboratory extracts of plant-available forms of soil
P and Ca indicated major decreases in the first 5–6
years of pasture use (Table 3). Extractable P and Ca
decreased 46–50% and 35–68%, respectively, in clay and
sand soils. P and Ca levels decreased further from
Figure 4 Aboveground nutrient contents along pasture age and soil texture gradients. Error bars show standard deviations. All
differences among pastures are statistically significant at the P 5 0.01.
r 2004 Blackwell Publishing Ltd, Global Change Biology, 10, 844–862
852 G . P. A S N E R et al.
Table 3
Soil properties from pasture age gradient on two soil types
Pasture age–soil type
Clay
Soil property
Sand
Young
Basic characteristics (n 5 10 per pasture)
Soil C (%)
4.35 (0.1)b
Soil N (%)
0.32 (0.02)a
Total organic C (kg m2)
5.20 (0.4)a
2
Total organic N (kg m )
0.40 (0.05)a
2
Total P (g m )
20.9 (1.2)a
Medium
2.28
0.19
2.7
0.2
16.3
(0.04)b
(0.01)b
(0.02)b
(0.02)b
(1.0)b
Soil extractables (n 5 10)
P (mg L1)
Ca (meq 100 g1 soil)
Mg (meq 100 g1 soil)
K (meq100 g1 soil)
13.8
4.3
3.9
2.4
(3.9)a
(0.4)a
(0.5)a
(0.4)a
7.5
2.8
2.8
1.8
(0.4)b
(0.1)b
(0.2)b
(0.3)b
In situ resin bags (mg L1; n 5 60)
PO4
N–NH4
N–NO3
N (total)
K
Mg
Ca
0.36
0.24
1.10
1.34
10.6
3.5
1.63
(0.12)a
(0.04)a
(0.04)a
(0.03)b
(2.3)a
(0.85)a
(0.78)a
0.05
0.18
0.15
0.33
10.1
2.6
2.31
(0.01)b
(0.02)b
(0.01)b
(0.02)b
(3.3)a
(0.75)a
(0.85)a
Modified Hedley-P fractions (mg g1 soil; n 5 3)
8.6 (3.5)
Resin Pi*
Bicarbonate Pi
2.9 (1.7)
4.7 (0.9)
Bicarbonate Po*
8.0 (2.9)
1 M HCl
NaOH Pi
25.5(9.1)
NaOH Po
22.6 (9.8)
HCl Pi
30.2 (2.7)
HCl Po
11.1 (0.6)
Residue
37.4 (2.3)
7.5
1.8
3.9
1.0
9.6
20.3
38.2
12.2
33.8
(0.4)
(0.1)
(0.3)
(0.1)
(0.7)
(1.2)
(0.5)
(0.7)
(1.5)
Old
2.02
0.15
2.4
0.2
15.3
Young
(0.1)c
(0.02)c
(0.02)c
(0.03)b
(0.9)b
7.8
2.1
7.5
1.4
11.5
31.0
28.8
14.9
34.82
Old
1.85
0.12
2.7
0.2
10.8
(0.1)a
(0.02)a
(0.03)a
(0.04)a
(0.6)a
1.54
0.09
2.2
0.1
7.5
(0.2)b
(0.04)b
(0.02)b
(0.02)b
(0.05)b
1.4
0.08
2.0
0.1
6.5
(0.04)c
(0.01)b
(0.02)b
(0.04)b
(0.08)b
6.8
2.5
0.6
0.4
(1.2)a
(0.2)a
(0.2)a
(0.1)a
3.4
0.8
0.8
0.4
(0.8)b
(0.2)b
(0.1)a
(0.04)a
1.4
0.7
0.8
0.3
(0.1)c
(0.1)b
(0.1)b
(0.02)a
(0.01)b
(0.03)a
(0.02)c
(0.02)c
(3.2)a
(0.33)a
(0.18)a
0.07
0.20
0.86
1.06
3.3
1.8
0.56
(0.01)a
(0.02)a
(0.02)a
(0.02)b
(1.0)b
(0.61)a
(0.24)a
0.06 (0.01)b
0.20 (0.02)a
0.13 (0.01)b
0.33 (0.01)b
1.3(0.6)b
0.28 (0.07)b
0.20 (0.03)b
0.03
0.28
1.05
1.32
0.71
0.25
0.05
(0.01)c
(0.01)b
(0.03)c
(0.02)c
(0.32)c
(0.04)b
(0.01)c
(1.1)
(0.8)
(0.8)
(0.2)
(0.9)
(2.5)
(1.4)
(2.2)
(1.6)
6.8
2.5
4.9
1.1
10.9
12.3
15.3
3.6
22.7
(1.2)
(0.4)
(0.4)
(0.2)
(1.5)
(0.5)
(0.6)
(0.1)
(1.0)
3.4
0.6
3.4
1.3
10.1
13.5
9.1
3.2
14.8
1.4
0.2
2.8
0.9
3.5
11.4
6.9
3.0
9.6
(0.1)
(0.1)
(0.2)
(0.1)
(0.6)
(1.0)
(0.3)
(0.4)
(0.8)
7.7(0.1)b
1.4 (0.1)c
2.4 (0.5)b
1.0 (0.1)b
0.04
0.32
0.49
0.80
10.6
2.5
2.24
Medium
(0.8)
(0.2)
(0.3)
(0.4)
(2.5)
(1.2)
(0.4)
(0.5)
(0.6)
Values given are means with standard deviations in parentheses. Within each table row, superscript letters (a, b, y) indicate
statistical differences within a soil type and at the Po0.05 or higher confidence level using t-tests.
*Pi, inorganic P; Po, organic P.
medium-to-old sand pastures (59% P, 12% Ca),
while P levels were similar between medium- and oldage clay pastures (Table 3). Extractable Mg and K levels
decreased with increasing pasture age on clay sites, but
mostly in the initial 5–6 years of land use. No
differences were observed for either nutrient along the
sand soil chronosequence.
In situ resin bags provide a unique indicator of
inorganic nutrient levels in soil solution during the
selected incubation period. Results presented here are
combined by sampling date, as nutrient levels were not
found to vary significantly over time (t-tests). Available
P decreased 86% and 14% on the young-to-medium age
clay and sand pastures, respectively (Table 3). While
available P did not decrease further with pasture age on
clay soils, it did significantly decrease on sand soils to
the lowest measured levels of 0.03 ( 0.01) mg L1
(P 5 0.01). In situ resin assays indicated initial decreases
of NH4–N and NO3–N on both soil types, followed by
increases as pastures further aged to 15 years (Table
3). Resin Ca, Mg, and K did not differ with pasture age
on clay soils, but almost universally decreased along
the sand pasture chronosequence (Table 3).
We focused additional attention on P dynamics of the
pasture systems because (1) little is known about them
along pasture age and textural gradients in the
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Amazon, and (2) a wide variety of our soil and plant
measurements indicated consistent decreases in the P
capital of the pasture systems with increasing age.
These measurements – including foliar P resorption,
total soil P, extractable P, and in situ resin P – together
highlighted the central role that phosphorus losses may
be playing in determining the productivity and condition of pastures in the region.
Modified-Hedley-P fractionations of the soils were
presented in detail by Townsend et al. (2002), but are
summarized here briefly for use in determining the
nutrient dynamics pertinent to the present study. Thus
for reference, we include the original soil P fraction
results in Table 3, but combine these results into three
pools: labile inorganic (Pi), organic (Po), and recalcitrant (Pr) for further analysis. On both clay and sand
soils, Hedley-Pi fractions decreased with increasing
pasture age, a result consistent with field-based resin P
measurements (Fig. 5). In fact, these two indicators of
inorganic P availability were highly correlated (r 5 0.85,
P 5 0.03; Table A4). This suggests that laboratory Pi
fractionation studies are a viable surrogate for in situ
PO4 assays (e.g. resin bags, in situ incubations, etc.), the
latter of which tend to be logistically challenging in
remote areas. Both Hedley-Pi and in situ PO4 levels
decreased more substantially in clay soils over time, but
P levels were already very low in sand soils (Fig. 5); we
note, for example, that total soil P values along the sand
sequence were markedly lower than any other total soil
P values of which we are aware in the literature.
Organic P (Hedley-Po) levels declined slightly, then
rose significantly along the clay-pasture chronosequence (Fig. 5), a result possibly indicating decreased
microbial mineralization of Po (Townsend et al., 2002).
853
In contrast, Hedley-Po decreased steadily in sand soils
with increasing pasture age, but again, the declines in
Po were far less dramatic than those in Pi or Pr. The
unexpected declines in Pr seen in both pastures (Fig. 5)
may have contributed to the observed Po patterns
(Townsend et al., 2002). When both soil types are
considered together, there was a significant correlation
between Hedley-Po and -Pr across the pasture age–soil
texture gradient (r 5 0.83, P 5 0.04; Table A4).
The P sorption analyses showed that the youngest
clay and sand pasture soils generally had the highest
sorption capacity, and that sorption capacity decreased
with pasture age (Fig. 6a and c). All soils showed
increasing P sorption with increasing time exposed to
the P source in solution. Trends were generally similar
for both the 5 and 10 ppm sorption studies with the
exception that soils from the young clay site showed no
increase in sorption capacity with increasing sorption
time. On a relative basis, the sands showed the greatest
increases in P sorbed over time. Soils from young-,
medium-, and old-age sand pastures showed P sorption
increases of 111%, 365%, and 1134%, respectively, from
0 to 24 h in the 5 ppm experiments (Fig. 6c). In
comparison, P sorption increases were 39%, 139%,
and 149% in the chronosequence of clay soils. Although
P sorption was higher in the young pasture soils,
increases in sorption over 24 h were always greater for
the oldest pasture soils (sand or clay).
Additional P analyses showed decreasing desorption
capacity in clay soils from young- to medium-age
pastures, but no further decreases in the oldest pasture
soils (Fig. 6b and d). The P desorption characteristics of
the sand soils were more variable and without a clear
trend over time. However, P desorption did increase
Figure 5 Changes in soil 1 aboveground carbon stocks in relation to P indices derived from Modified-Hedley assays and field-based
resin bag studies. Hedley fractions from Table 3 are grouped by inorganic (Pi), organic (Po), and recalcitrant (Pr) forms. All trends shown
are significant (Po0.01) except for clay-soil changes in Hedley-Pr and sand-soil changes in Hedley-Po.
r 2004 Blackwell Publishing Ltd, Global Change Biology, 10, 844–862
854 G . P. A S N E R et al.
Figure 6 Changes in (a) phosphate sorption and (b) phosphate desorption at 5 ppm initial solution P concentration for soils collected
along the pasture age and soil texture gradients. (c and d) Same analyses but at 10 ppm initial solution P concentration.
much more over time in the clay soils, which showed
33%, 73%, and 109% increases in desorption from
30 min to 24 h for young, medium, and old pastures,
respectively (Fig. 6b). In contrast, assays of sand soils in
the same experiment had P desorption increases of only
24%, 6%, and 22%.
Remote sensing studies
Satellite-derived surface cover fractions of PV, NPV,
and bare soil varied substantially with pasture age and
soil texture (Fig. 7a). The youngest sites on clay and
sand soils had the highest PV fractions (0.94 and 0.80,
respectively), but also the lowest NPV and bare soil
values. The dominance of PV fractions is seen as red
tones in the spectral mixture analysis images shown in
Fig. 7c and d. The medium-age clay and sand sites
showed the highest NPV (0.35–0.41) and moderate PV
(0.28–0.30) fractions (blue tones in Fig. 7c and d),
whereas the oldest sites had the highest bare soil (0.29–
0.39) and lowest PV (0.16–0.18) values (green tones).
Variation in PV cover was low in the youngest clay site
(SD 5 0.01), as depicted in the small error bar in Fig. 7a
and the consistent red tones in Fig. 7c. Greater spatial
variability of PV (SD 5 0.08) and NPV (SD 5 0.05) was
found in the youngest sand pasture (Fig. 7a and d).
Other medium- and old-age pastures showed moderate
spatial variability in all cover constituents except for the
low variation of PV in the medium and old sand
pastures.
Both PV and NPV surface cover fractions were highly
correlated with field LAI and NPVAI measurements
(r2 5 0.91, Po0.05; Fig. 7b). Spatial variability in all four
variables was of generally similar magnitude as well.
The fact that satellite PV fraction tracked field LAI
measurements, and likewise for NPV fraction vs.
NPVAI, implies that the horizontal cover of vegetation
scales linearly with the vertical density of the plant
canopies. That is, pastures with higher PV 1 NPV cover
contain plants with higher LAI 1 NPVAI or biomass
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855
Figure 7 (a) Sub-pixel surface cover fractions for photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil
derived from Landsat spectral mixture analysis. Error bars show standard deviations. (b) Leaf area index (LAI) and NPV area index
(NPVAI) from field measurements vs. fractional PV and NPV cover from Landsat. (c) Color composite image showing Landsat spectral
mixture analysis of young clay-soil pasture. (d) Same as (c) but for other five pasture types.
(Fig. 2). Hence, the satellite estimates of PV 1 NPV
cover are a good proxy for AGB, which in turn, was a
good indicator of pasture degradation and condition.
Discussion
Pasture degradation and vegetation–soil carbon stocks
In the most thorough review article to date, McGrath
et al. (2001) synthesized data from over 100 studies
relating land use to soil C, N, P, and a variety of other
soil properties in Amazônia. Their results emphasized
several critical points regarding the fate of soil
resources immediately following forest-to-pasture conversion, and to a lesser extent, during the years of
pasture use following deforestation. With specific
regard to SOC dynamics, McGrath et al. (2001) found
that (1) SOC responses to pasture development vary
r 2004 Blackwell Publishing Ltd, Global Change Biology, 10, 844–862
dramatically from net gains (Feigl et al., 1995; Neill et al.,
1997) to no change (Hughes et al., 2000; Nepstad et al.,
2001b) to losses (Moraes et al., 1996; Koutika et al., 1997),
(2) soil C : N ratios tend to increase as pastures increase
in age; and most importantly to our study, (3) the effects
of forest-to-pasture conversion on SOC dynamics
cannot be understood without knowledge of ‘how
pasture productivity differs across Amazônia’. Their
review also highlights the scarcity of pasture age
gradient studies, and no mention is made of crossing
such studies with environmental variables such as soil
texture, which varies widely throughout the Amazon
basin.
Our study focused on the aboveground and soil
organic C changes that occur on two distinct soil types
as pastures increase in age and degrade. We then
related observed changes in C stocks to a variety of
nutrient properties of the vegetation and soil. Finally,
856 G . P. A S N E R et al.
we sought to link remotely sensed indicators of pasture
condition, biomass and degradation with measured
changes in C and nutrient dynamics.
Our results indicated that both AGB and soil organic
C stocks decreased across 15 years pasture chronosequences on two soil types, but that the patterns of
change were often significantly different between the
two soils. For example, we found that AGB decreased
roughly 50% faster on the sand than on the clay soil
chronosequence (Fig. 2). In contrast, the initial 5-year
decrease in SOC stocks was greater in clay soils, while
the long-term (15 years) rate of change was relatively
similar between soil types (Fig. 5, Table 3). Soil C : N
ratios did not change along the clay pasture chronosequence, averaging values of 12.4–13.5; however, soil
C : N ratios did increase along the sand pasture
gradient, going from 15.4 to 17.0 to 17.5 for young,
medium, and old pastures, respectively (paired ANOVA,
Po0.05). These differences in clay and sand pastures
suggest that different biogeochemical processes are
causing observed decreases in pasture condition and
productivity. Clearly, both pasture age and soil texture
are important determinants of carbon storage following
pasture establishment (Table A5).
Carbon–nutrient interactions
In comparison with some other pasture age gradient
studies (e.g. Neill et al., 1997), our chronosequences
showed clear trends of plant and soil C loss over time;
however, the rates differed based on soil texture. A
variety of vegetation and soils assays indicated that
several key nutrients also decreased with pasture use,
and again, that these decreases varied by soil texture.
Some of the clearest indicators of nutrient-induced
limitation of vegetation production included (1) decreased concentrations and increased resorption efficiencies of foliar nutrients (Table 2), (2) decreased
aboveground nutrient stocks (Fig. 4), and (3) strong
correlations between vegetation biomass and both the
short- and long-term indicators of soil nutrient status.
Foliar N, P, and Ca concentrations decreased with
pasture age and were well correlated with changes in
plant biomass (r 5 0.78–0.81, Po0.05; Table A1). While
changes in foliar concentrations alone may not indicate
long-term nutrient impoverishment, increases in N, P,
and Ca resorption efficiency do suggest a timeintegrated physiological adjustment by the vegetation
to changing resource conditions. There were, however,
notable correlations between several foliar nutrients (N,
P, Ca, Mg; Table A1), and thus a single given nutrient
limitation could not be inferred.
The short-term indicators of soil nutrient availability
are the most biologically relevant in terms of current
pasture conditions and productivity. These indicators –
namely, laboratory extracts and in situ resin bag
incubations – showed that (1) available P was extremely
low in both clay and sand soils and that P availability
decreased sharply with pasture age, (2) N availability
appeared more variable in these systems, and (3) the
availability of Ca, Mg, and K mostly decreased with
increasing pasture age, but especially so in sand soils
(Table 3). Combined with the foliar results, these
findings strongly suggest that P and/or base cations
(especially Ca) were in short supply in these pastures
and that they constrain plant productivity as pastures
increase in age. However, striking decreases in foliar
N : P with pasture age also suggested that N could
become limiting to productivity over time. Further
research is needed to test this hypothesis.
In the long-term, significant changes in soil C, N, and
P pool sizes and turnover rates may have more lasting
effects on both available nutrient pools and vegetation
dynamics. Soil C, N, and P all decline with pasture age,
and the declines in SOC were highly correlated with
declines in AGB (r 5 0.94, P 5 0.01) and SON (r 5 0.89,
P 5 0.02) (Table A2), suggesting decreasing plant inputs
to the soil with pasture age. Total soil P pools were less
correlated with AGB (r 5 0.73, P 5 0.10), but this is not
surprising as different fractions of the soil P pool
responded in markedly different ways along both the
texture and age gradients (Townsend et al., 2002).
Overall, the P capital of the ecosystem is clearly
declining along both age sequences, and given the very
low rates of new P inputs to ecosystems, such losses
may exert the most lasting constraints on the carbon
balance of degraded pasturelands. In addition,
although Ca inputs from the atmosphere are much
greater than those for P (Graham & Duce, 1979;
Chadwick et al., 1999), plant requirements for Ca are
also substantially larger than P demands (cf. Aber &
Melillo, 1991). Thus, the strong decreases in available
Ca we observed with pasture age and their correlation
with AGB suggest that land-use effects on base cation
cycles may also exert long-term controls over carbon
stocks.
As reported by Townsend et al. (2002), the strong
decline in P fractions assumed to be highly recalcitrant
and largely immobile was a surprising result of our
studies (Fig. 5), and one which has now been seen in
both our sites and in chronosequences in Rondonia
(Garcia-Montiel et al., 2000). The results suggest that the
effects of forest–pasture conversion on soils, which can
include changes in pH, redox status, organic matter
content, and soil compaction, may combine to significantly alter the geochemical interactions that contribute
to P occlusion, and may render these pools much more
available than previously thought. Our observed
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changes in P sorption dynamics with pasture age
support this hypothesis, in that both sorption and
desorption of P decreased in strength with increasing
pasture age (Fig. 6). In addition, decreased sorption and
desorption was well correlated with decreased SOC
pools (r 5 0.89, P 5 0.02).
In the shorter term, changes in soil properties that
allow recalcitrant P pools to be released may also
increase P availability to the biota, thus providing a
pulse of fertility. However, given that we saw significant overall declines in soil P, in the long term these
changes appear to cause further P impoverishment in
systems that are already very low in total and available
P prior to any land use. This statement is particularly
relevant in the sandy soils, where total soil P in even the
youngest pasture were lower than any values reported
in a comprehensive review of soil P fractions across
multiple soil types (Cross & Schlesinger, 1995), and
then declined markedly with pasture age. Sandy soils
such as those in our study sites do not represent the
dominant soil type throughout the Amazon, but they
may be more important to land-use dynamics than their
overall area would suggest because such soils are
typically located near sources of water, and thus are
desirable locations for land conversion and use.
Despite the wide range of vegetation and soil
analyses conducted in this pasture texture–age chronosequence study, a definitive determination of which
nutrients most limit pasture productivity requires a
multi-factorial nutrient fertilization study. Such studies
are logistically difficult in remote areas such as the
central Amazon and were well beyond the scope of our
study. However, our ensemble of observations does
indicate that the total soil nutrient base decreases
substantially with increasing pasture age. We also
know that pastures developed on sandy soils have
lower vegetation and soil C stocks initially and fast
rates of C loss in comparison with similar pastures on
clay soils. These soil texture-based differences in stored
C parallel a similar dynamic of much lower initial
nutrient stocks and high rates of nutrient loss on sands
vs. clays.
Nutrient losses appear to differ substantially by soil
type. In general, soil P and Ca stores and availability
appear to decrease the most in clay pastures, while
N decreases the most in sand pastures. However, P
and Ca (and other cations) are already very low at
the beginning of the chronosequence of sandy sites,
thus losses in the sands may be functionally more
critical. Hence we cannot be sure which nutrients drive
the system downward in total resource base and
productivity and which nutrients simply equilibrate
(via leaching losses, soil-matrix stabilization or
excess uptake by plants) to the decrease in total
r 2004 Blackwell Publishing Ltd, Global Change Biology, 10, 844–862
857
nutrient capital of the system over time. Our findings
strongly indicate that no single nutrient solely mediates
the process of pasture degradation and C storage
decreases.
Remote sensing of pasture change
Regardless of the biogeochemical contributions to
pasture degradation, C cycle/land-use studies in the
Amazon are most challenged by the spatial and
temporal variability of human activities and the
attendant ecological changes. Previous studies of
pasture C and nutrient dynamics show highly variable
trends, often opposite in sign, even at local scales (e.g.
McGrath et al., 2001). We do not know how the
increasing mosaic of pastures, in their various age
states and topo-edaphic positions, functions as a
regional contributor to the C cycle. Remote sensing is
the only way to regionalize our understanding of the C
cycle in the Amazon, but few connections have been
made between remotely sensed variables and actual
pasture conditions. While many studies have focused
on the area of pasturelands using Landsat or similar
satellite technologies (e.g. Skole & Tucker, 1993), no
large-scale assessments have focused on the functioning of pastures and thus the biogeochemical processes
regulating C storage changes. The synthesis of McGrath
et al. (2001) emphasizes that knowledge of the spatial
and temporal changes in pasture conditions would go a
long way toward understanding biogeochemical dynamics, especially nutrient cycling that regulates
pasture productivity.
The spectral mixture analysis of Landsat TM imagery
from August 1997 provided estimates of PV, NPV, and
bare soil covers within each image pixel (Fig. 7). Subpixel cover fractions of PV 1 NPV were highest in the
young clay and sand sites, and they were lowest in the
oldest pastures (Fig. 7a). There was an opposite trend of
increasing bare soil fraction with increasing pasture
age. There was also a tendency for increased spatial
variability of the three surface constituents in the
medium-age sites (Fig. 7d). These results for PV and
NPV fractions were well correlated with field measurements of leaf and NPV area indices (LAI, NPVAI) (Fig.
7b), the latter of which were highly correlated with
AGB (Fig. 3).
Together these findings show that spatial and
temporal changes in pasture AGB and live : senescent
biomass ratios can be remotely quantified using Landsat imagery with the appropriate analytical technique.
The remotely sensed variables were also highly
correlated with a variety of foliar and soil nutrient
indicators, including leaf N, P, and Ca as well as soil
organic C and N, and available P and base cation
858 G . P. A S N E R et al.
stocks. As stated, while the individual nutrient cycling
processes responsible for variations in pasture biomass
cannot be pinpointed, remote sensing of live 1 senescent biomass does provide wholly new insight to the
time-dependence of biogeochemical changes in these
systems. Moreover, remote sensing provides a window
into the spatial variability of carbon and nutrient
dynamics, an issue that we plan to address in future
work.
Conclusions
Our study sought to understand temporal and soil
textural controls over pasture C dynamics in the central
Amazon. This study also provided the first multinutrient analysis of pasture degradation along a 15year chronosequence on two contrasting soil types. We
initially focused on the aboveground and soil organic C
changes that occur as pastures increase in age and
degrade. Causes of the observed changes in C stocks
were then interpreted relative to short-term (e.g. foliar
concentrations, available nutrients) and long-term (e.g.
organic matter, soil P fractions) indicators of nutrientmediated changes. Finally, we linked remotely sensed
indicators of pasture condition, biomass and degradation with measured changes in C and nutrient
dynamics. From these efforts, we are able to draw the
following conclusions:
AGB and soil carbon storage decreased with pasture
age on both soil types. Initial C stocks were lower
and C loss rates higher in pastures established on
sandy soils than on clay soils.
Short-term indicators of nutrient availability indicate that P was very low in both clay and sand soils
and that P availability decreased with pasture age.
N availability was more variable and did not
correlate well with aboveground C or soil organic
matter (C, N) stocks. Base cation (Ca, Mg, K)
availability decreased with pasture age, especially
in sandy soils.
Long-term indicators of nutrient status indicate that
N and P become increasingly scarce in pasture soils
over time. Changes in soil inorganic, organic, and
recalcitrant P pools differed by soil type, but
suggested unexpected lability of recalcitrant, inorganic P fractions and decreased organic P mineralization with pasture age on both soil types.
No single nutrient is likely to limit pasture
productivity and biomass accumulation in aging
pastures, and the nutrients exerting the greatest
control over carbon balance may vary with both age
and soil type. Overall, a suite of nutrients forming a
resource capital decreases with pasture age on both
soil types, but more so in sandy soils.
Spectral mixture analysis of Landsat imagery
provides estimates of photosynthetic vegetation
and non-photosynthetic vegetation (PV 1 NPV) that
are highly correlated with field measurements of
leaf and NPV area indices (LAI 1 NPVAI). Because
LAI 1 NPVAI is highly correlated with AGB, the
remote sensing analyses are an excellent indicator of
pasture condition and carbon storage.
Remote sensing measurements of PV 1 NPV are
highly correlated with soil organic C and N stocks
as well as short-term indicators of nutrient availability such as foliar N, P, and Ca, and in situ soil P
and Ca availability.
Remote sensing may be a viable approach to
estimate pasture condition, carbon storage, and
biogeochemical status at the regional level in the
Amazon basin. We will further test the approach
presented in this paper in a future top-down study
of pasture biomass and soil nutrient status.
Although some inferences regarding nutrient controls
over C storage changes could be made, the study raises
additional questions requiring our attention before we
can fully understand the role of biogeochemical
processes determining the land-use legacy resulting
from continual expansion of pasturelands in the
Amazon basin. Some of these remaining questions
are: How common is it for aboveground and soil C
stocks to decrease in pastures over time, and on what
timescales do these changes take place? Is there any one
nutrient that more often regulates the rates and patterns
of C losses in pasture systems, or do a suite of nutrients
exert control, with the relative importance of a given
element varying with pasture age? How can the spatial
and temporal vegetation information afforded by
remote sensing observations best be used to understand
human and topo-edaphic controls over C cycle processes throughout the region? These questions and
others should continue to guide future efforts to
improve our knowledge of the ecology and biogeochemistry of land-use systems in the Amazon.
Acknowledgements
We thank G. Cardinot, C. Cleveland, B. Constance, and V. Morris
for assistance with field and laboratory analyses. We thank R.
Martin, E. Davidson, and two anonymous reviewers for editorial
assistance with the manuscript. This work was supported by
NASA New Investigator Program grant NAGW-5253 (LBA ND10), NASA New Investigator Program grant NAG5-8709, and
NASA New Millenium Program grant NCC5-480 (LBA LC-13).
This is CIW Department of Global Ecology publication 24.
r 2004 Blackwell Publishing Ltd, Global Change Biology, 10, 844–862
A M A Z O N PA S T U R E B I O G E O C H E M I S T R Y A N D R E M O T E S E N S I N G
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Table A1
Foliar P
Appendix A.
See Tables A1–A5.
Correlation coefficients relating aboveground (AG) biomass and foliar nutrient concentrations
AG biomass
Foliar N
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Foliar N
Foliar P
Foliar Ca
Foliar Mg
Foliar K
0.81
0.05
0.78
0.04
0.40
0.43
0.80
0.05
0.32
0.54
0.81
0.05
0.72
0.11
0.24
0.65
0.92
0.01
0.87
0.03
0.10
0.85
0.09
0.87
0.00
1.00
0.01
0.99
0.28
0.59
Foliar Ca
Foliar Mg
Statistically significant values (Po0.05) are show with r values in bold type.
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A M A Z O N PA S T U R E B I O G E O C H E M I S T R Y A N D R E M O T E S E N S I N G
Table A2
861
Correlation coefficients relating aboveground (AG) biomass and basic soil properties
AG biomass
Clay
BD
pH
SOC
SON
Total P
0.44
0.38
0.77
0.07
0.04
0.94
0.21
0.69
0.61
0.20
0.16
0.76
0.94
0.01
0.57
0.24
0.82
0.04
0.10
0.86
0.89
0.02
0.70
0.12
0.67
0.15
0.24
0.64
0.96
0.01
0.73
0.10
0.93
0.01
0.33
0.52
0.42
0.41
0.81
0.05
0.91
0.01
Clay
BD
pH
SOC
SON
Statistically significant values (Po0.05) are show with r values in bold type. SOC and SON are soil organic C and N, respectively.
Table A3
Correlation coefficients relating aboveground (AG) biomass and field (in situ) resin bag studies
AG biomass
Resin P
Resin NH4
Resin NO3
Resin Ca
Resin Mg
Resin K
0.91
0.01
0.43
0.40
0.08
0.88
0.30
0.56
0.50
0.31
0.34
0.52
0.27
0.61
0.21
0.70
0.15
0.79
0.27
0.60
0.69
0.13
0.63
0.18
0.01
0.98
0.15
0.78
0.84
0.04
0.46
0.36
0.44
0.38
0.17
0.75
0.09
0.87
0.97
0.01
0.93
0.01
Resin P
Resin NH4
Resin NO3
Resin Ca
Resin Mg
Statistically significant values (Po0.05) are show with r values in bold type.
Table A4
Correlation coefficients relating aboveground (AG) biomass and soil P indices
AG biomass
Resin P
Resin P
Hedley-Pi
Hedley-Po
Hedley-Pr
0.91
0.01
0.89
0.02
0.85
0.03
0.16
0.77
0.22
0.67
0.57
0.24
0.58
0.23
0.51
0.30
0.80
0.06
0.83
0.04
Hedley-Pi
Hedley-Po
Statistically significant values (Po0.05) are show with r values in bold type. Pi, inorganic P; Po, organic P; Pr, recalcitrant P.
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862 G . P. A S N E R et al.
Table A5
Clay
pH
Exch Al
Exch Fe
Correlation coefficients relating basic soil properties and soil P indices.
pH
Exch Al
Exch Fe
Resin P
Hedley-Pi
Hedley-Po
Hedley-Pr
0.61
0.20
0.79
0.12
0.65
0.24
0.19
0.71
0.02
0.97
0.34
0.57
0.45
0.37
0.06
0.91
0.72
0.17
0.21
0.68
0.69
0.13
0.17
0.75
0.80
0.11
0.00
0.99
0.88
0.02
0.79
0.06
0.68
0.21
0.08
0.88
0.96
0.01
0.44
0.38
0.76
0.14
0.29
0.58
Statistically significant values (Po0.05) are show with r values in bold type. Pi, inorganic P; Po, organic P; Pr, recalcitrant P.
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