The Soil Organic Carbon In Particle-size Separates

e c o l o g i c a l e n g i n e e r i n g 3 4 ( 2 0 0 8 ) 300–310

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The soil organic carbon in particle-size separates under different regrowth forest stands of north eastern Costa Rica Juan J. Jiménez a,∗ , Rattan Lal a , Ricardo O. Russo b , Humberto A. Leblanc b a b

School of Environment and Natural Resources, The Ohio State University, 2021 Coffey Road, Columbus, OH 43210, USA EARTH University, Guácimo, Limón, Costa Rica

a r t i c l e

i n f o

a b s t r a c t

Article history:

Despite the importance of the secondary forest (SF) in tropical areas, few studies have quan-

Received 19 March 2008

tified the soil organic carbon (SOC) pool in Costa Rica. Most of the studies conducted to date

Received in revised form 1 July 2008

in this country have focused mainly on changes in the soil C pool following conversion of

Accepted 3 July 2008

forests to pastures, which is the predominant land use in the tropics. The aim of this study was to measure SOC concentration and pool in particle-size fractions down to 50 cm depth in four SF stands regenerating from different intensities of prior land use in loamy sand

Keywords:

and sandy loam soils of northeast Costa Rica: (i) a gallery forest (GF), (ii) a 15-year-old SF

Soil organic carbon

enriched with commercially planted native trees (15SF), (iii) a 25-year-old SF (25SF), and (iv)

Secondary forests

an abandoned Theobromma cacao plantation >60 years old (60SF). Additional objectives were

Particle-size fraction

(1) to determine the relationship of SOC concentration with selected physical and chemical

Soil aggregation

soil properties, and (2) to establish the key determinants of the depth distribution of SOC in

Tropical soils

order to identify meaningful trends in the SOC pool. The SOC pool was highest under the 60SF (221.4 Mg C ha−1 ) followed by the 15SF (212.1 Mg C ha−1 ), the 25SF (195.9 Mg C ha−1 ) and the lowest in the GF (183.5 Mg C ha−1 ). The SOC concentration decreased significantly from 59.7 to 94.1 g kg−1 in the 0–10 cm layer down to 31.0 to 45.5 g kg−1 in the 40–50 cm layer in all forest stands. The fine silt + clay fraction contained the highest values of SOC concentration in all forest stands. Soil texture and the age of the SF were identified as the main factors that explained the variability in SOC. The age of SF stand influenced the distribution of size class aggregates and SOC. © 2008 Elsevier B.V. All rights reserved.

1.

Introduction

Carbon (C) sequestration is defined as the biotic process whereby the atmospheric CO2 is transferred into a longlived C pool (Lal, 2004). The soil organic carbon (SOC) pool is about 2.6 times the biotic pool (Post et al., 1990; Eswaran et al., 1993) and twice the atmospheric pool. Hence, SOC pool plays an important role in climate change processes by acting either as source or sink of atmospheric CO2 . As much as 75% of the antecedent SOC pool can be

∗

lost following conversion of natural ecosystems to agroecosystems in tropical regions due to intensive tillage practices that increase decomposition and litter removal (Lal, 2005). This leads to decline in soil quality and plant productivity and sometimes to important soil degradation processes as loss of soil structure and aggregate stability, fertility decline and beneficial soil organism depletion. Thus, severe reduction in land productivity promotes tropical deforestation, contributing to continuous anthropogenic C emissions.

Corresponding author. Present address: Instituto Pirenaico de Ecología-CSIC, Avda. Regimiento Galicia s/n, E-22700 Jaca, Huesca, Spain. E-mail addresses: [email protected], juanjo [email protected] (J.J. Jiménez). 0925-8574/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ecoleng.2008.07.001

e c o l o g i c a l e n g i n e e r i n g 3 4 ( 2 0 0 8 ) 300–310

The forest area in Central America decreased at a rate of 350 × 103 ha year−1 during the period 1990–2005 (FAO, 2005). Costa Rica, being once almost totally forested (Keogh, 1984), has experienced severe deforestation between 1950 and 1984 (Hartshorn et al., 1982; Sader and Joyce, 1988). The forest area of Costa Rica covers 46.8% of the total land area, and comprises 180 × 103 ha of primary forest (no disturbance) and 1319 × 103 ha and 888 × 103 ha of modified natural and seminatural forest, respectively (FAO, 2005). Secondary forests (SF) are becoming an extended land use in Costa Rica and many other tropical countries in Central America, due mainly to anthropogenic disturbances such as logging and conversion of forests to agricultural lands (Skole and Tucker, 1993). The SFs are important since they can act as important carbon sinks and may offset C losses resulting from deforestation and land use change. The Neotropical SF varies in relation to the type, intensity and duration of agricultural practices (Fearnside and Guimaraes, 1996; Guariguata and Ostertag, 2001), which affects plant community structure (Fernandes and Sanford, 1995). In Costa Rica, data on the SOC pool in SF following abandonment of the previous land use are lacking. In other tropical sites these studies have been conducted by Hughes et al. (1999) and Rhoades et al. (2000). In previously deforested areas the SOC pool may either increase (Silver et al., 2000), decrease (Uhl and Jordan, 1984) or be unaltered with forest age (Hughes et al., 1999). While some research information is available on the aboveground biomass accumulation and productivity of commercial tree plantations in Costa Rica and SF (Montagnini, 2000; Russell et al., 2004), data on the SOC pool and its distribution within particle-size aggregates in SF are lacking. Recent estimates of the total C pool (organic + inorganic) in forests of Costa Rica are given at 112, 81 and 21 Pg (1 Pg = 1015 g) for above, belowground and dead woody biomass, respectively (FAO, 2005). Unfortunately, no SOC pool estimates exist before land use change occurred in the area, although studies conducted in nearby areas as the Sarapiquí region in northeastern Costa Rica have reported a SOC pool of 87.1 Mg C ha−1 (Powers, 2004). Therefore our main objective was to quantify the SOC pool and detect its variation in different processes of forest regeneration, as it is expected that forest regrowth leads to positive or negative variations both soil C concentration and pool (Uhl and Jordan, 1984; Hughes et al., 1999; Silver et al., 2000). Soil structure and distribution of size class aggregates can be disrupted by disturbance (Wigginton et al., 2000). Carbon accumulation can be facilitated through the formation of a well soil macro-aggregated structure and it is expected that the age of recovery has an influence in the distribution of soil aggregates and SOC. A range of factors affects the formation of stable aggregates as land use, management, soil mineralogy, texture, quantity and quality of the organic matter (OM) incorporated, diversity and abundance of soil macrofauna (Jiménez and Lal, 2006). The OM in the soil can be occluded within aggregates and thus be protected against the mineralization process as it is not readily available for micro-organism attack. We thus assessed soil structure and particle-size aggregates in relation to C concentration. This is important in the context of SOC formation and stability, as C is more protected from mineral-

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ization processes in large aggregates (Six et al., 1998; Jiménez and Lal, 2006). Our hypothesis was that under secondary forests established in loamy sand and sandy loam soils both the management and age of the SF contribute to differences in the SOC pool. Therefore, our general objective in this study was to assess the SOC pool and its distribution within size aggregates and particle-size fractions down to 50 cm depth in TSF of northeastern Costa Rica and elucidate key determinants of the depth distribution of SOC related to some selected soil properties by means of multivariate ordination methods.

2.

Material and methods

2.1.

Study site

The study was conducted at EARTH University (10◦ 10 N and 83◦ 37 W; 64 m a.s.l.) at the confluence of the “Parismina” and “Destierro” rivers, in the Caribbean lowlands of Limón Province, Costa Rica, during July 2005. Climate zone of the region is classified as premontane, wet forest basal belt ˜ transition (Bolanos and Watson, 1993). Topography is flat to undulating with mean annual rainfall of 3464 mm, which is evenly distributed with peaks observed during June, July, August, November, and December. The mean annual temperature is 25.1 ◦ C (iso-hyperthermy). The average annual relative humidity is 87%. Soils at the study site, classified as Inceptisols (Typic Dystropept), are moderate to very low in soil fertility. Soil pH (H2 O, 1:1) ranged from 4.9 to 5.9 and texture from loamy sand in the upper soil layers to sandy loam in the sub-soil layers (Table 1). Four forest stands with no thinning after establishment and located in the same soil unit with no slopes were selected for this study: a) The “Tigre” reserve (60SF): This is a 10-ha SF derived from an old abandoned cacao (Theobroma cacao L.) plantation that has regenerated over >60 years (indicated by the presence of lianas of ca. 6–7 cm Ø). The current regeneration is similar to the primary forest. Mean stand basal area (G) at the study site was 10.9 + 3.5 m2 ha−1 and mean tree density was 237 + 62 trees ha−1 >10 cm DBH (mean DBH = 24.2 cm). Luehea seemanii, Castilla elastica, Hampea appendiculata, Bursera simarouba, Laetia procera, Simarouba amara, Vochysia ferruginea, Vitex cooperi and Zanthoxylum kellermanii are part of the floristic composition on this forest. Plant species that are typical during the first succession stages, e.g., Cecropia obtusifolia and Heliconia sp., are not abundant. b) A 25-year-old regrowth forest (25SF): This is a 5-ha SF characterized by the presence of pioneer plants, i.e., H. appendiculata, C. obtusifolia, Ochroma pyramidale and abundant understory vegetation (e.g., Heliconia spp.). Other species in this forest were Cedrela odorata, Inga sp., L. seemannii, Rollinia microcephala, and Virola sebifera. Mean stand basal area at the time of the study was 25.9 + 3.2 m2 ha−1 and mean tree density was 450 + 172 trees ha−1 >10 cm DBH (mean DBH = 27.2 cm) (R. Russo data). c) The “Charral” (15SF): This is a 29.5-ha 15-year-old SF that was enriched 10 years ago with native trees such as H.

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Table 1 – Average soil organic carbon (SOC) concentration and bulk density in the different soil layers analyzed in the SF stands in north eastern Costa Rica Depth (cm)

Forest stand GF SOC (g kg−1 )

0–10 10–20 20–30 30–40 40–50

59.7 48.1 37.8 33.1 31.0

± ± ± ± ±

18.0 a 21.8 b 13.5 c 11.2 d 10.0 e

15SF

d (Mg m−3 ) 0.79 0.99 0.93 0.96 0.98

± ± ± ± ±

0.2 0.3 0.3 0.2 0.2

SOC (g kg−1 ) 67.1 50.9 44.4 40.9 40.0

± ± ± ± ±

16.0 a 10.1 b 9.3 c 11.2 d 10.0 d

25SF

d (Mg m−3 ) 0.83 0.89 0.90 1.01 0.91

± ± ± ± ±

0.1 0.04 0.1 0.3 0.3

SOC (g kg−1 ) 76.8 56.6 52.9 47.0 44.3

± ± ± ± ±

19.4 a 4.4 b 5.4 c 4.7 d 4.5 d

60SF

d (Mg m−3 ) 0.74 0.70 0.68 0.70 0.74

± ± ± ± ±

0.1 0.1 0.1 0.1 0.1

SOC (g kg−1 ) 94.1 65.8 54.8 50.1 45.4

± ± ± ± ±

12.9 a 8.1 b 7.1 c 9.8 d 10.8 e

d (Mg m−3 ) 0.63 0.71 0.76 0.77 0.78

± ± ± ± ±

0.1 0.1 0.1 0.1 0.1

Different letters indicate significant differences at P < 0.05 (Tukey’s HSD test). Standard deviation within parentheses.

alchorneoides. Mean stand basal area at the time of the study was 17.7 + 8.3 m2 ha−1 and mean tree density was 525 + 170 trees ha−1 >10 cm DBH (mean DBH = 18.3 cm). (R. Russo data). C. obtusifolia, H. appendiculata, O. pyramidale and Ficus weckleana are also common in this forest. Because of the proximity of agricultural lands, cultivated plant species were also present: Musa sp. and Erythrina costaricensis. The presence of timber species, e.g., V. sebifera, H. alchorneoides, Carapa guianensis, C. odorata, Chloroleucon mangense and Cordia alliodora, indicates that enrichment in this area has influenced the composition and structure of the vegetation (Russo and Leblanc, unpubl.) d) A gallery forest (GF): Close to the 15SF stand we selected a gallery forest. Mean stand basal area (G) at the time of the study was 11.8 + 5.1 m2 ha−1 and mean tree density was 283 + 75 trees ha−1 >10 cm DBH (mean DBH = 23.05 cm) (R. Russo data). Heliocarpus appendiculatus, H. appendiculata, Zygia longifolia, C. alliodora, Ficus werckleana, Pentaclethra macroloba, Inga oerstediana, Nectandra sinuata, R. microcephala, Trophis racemosa, Virola koschnyi, Protium costaricense, Sterculia apetala, Chloroleucon eurycyclum, Cecropia spp., Ficus spp. and Guarea spp. are also common in this forest. Sites were carefully selected for comparisons based on the time elapsed since perturbation and management, e.g., sowing more productive trees that are normally used in the region.

2.2.

Soil sampling

gate separation and sieved through a 8 mm sieve to remove roots, coarse debris and gravels. These samples were airdried for subsequent aggregate analyses, and carefully packed for shipment to The Ohio State University for further lab analyses. Litter in the soil surface was collected manually from 0.5 m × 0.5 m metal frames prior to excavation of the pit. Litter was oven-dried at 60 ◦ C for 72 h.

2.3.

Soil physical properties

Soil bulk density (d ) was measured in all five layers by the core method (Blake and Hartge, 1986) using metal cylinders of 5 cm × 5 cm. The core sample was taken in the middle of each layer, transferred to a labelled plastic bag and carried to the lab for weight measurements. A small quantity of soil was oven-dried at 105◦ C for 48 h to calculate moisture content and report d on a dry basis. Another sub-sample of 50 g air-dried soil was used for aggregate size fractionation by dry sieving (Kemper and Rosenau, 1986), and shaking mechanically the nest of sieves for 30 min to obtain 6 aggregate size fractions: >4.75 mm, 4.75–2.0 mm, 2.0–1.0 mm, 1.0–0.5 mm, and 0.5–0.250 mm and <0.250 mm. The data obtained were used to calculate the mean weight diameter (MWD) and the aggregate size class distribution using the formula by Kemper and Rosenau (1986):

MWD =

n 

x¯ i mi

and the aggregate fraction

i=1

At each selected forest stand four pits of 1 × 0.5 m2 were dug out to collect soil samples down to 50 cm depth in 10 cm depth increments. Although sampling could have been performed down to 1 m depth, time and logistics constrained the collection of soil samples to the top 50 cm. Due to the possible spatial autocorrelation of C concentration in soil at short distances, i.e., <25 m as shown in some studies (Cerri et al., 2004; Powers and Veldkamp, 2005), four soil pits were separated 100 m to reduce autocorrelation between samples. Precautions were taken to minimize soil and site disturbance. Samples were transferred into plastic bags and gently crumbled manually to break the aggregates along planes of weakness while at field moisture content, and air-dried for several days. Samples were dropped onto a hard surface to facilitate aggre-

(mi ) =

Msieve i Mtotal sample

where x¯ i is the mean diameter of each aggregate fraction; Msieve i is the dry mass of the particles retained on the sieve i; Mtotal sample is the dry mass of the initial total sample. The mean diameter of each size class aggregate was considered as the average opening size of the two sieves retaining the specific aggregate. Although dry sieving yields higher mass of large aggregates compared to wet sievings, as reported in other studies (Balabane and Plante, 2004), the former is also a commonly used procedure in assessing the degree of soil aggregation. The remaining soil was sieved at 2 mm and stored for further analysis.

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2.4 1.9 1.8 1.7 1.8 2.4 2.3 1.7 1.9 2.2 3.1 2.8 2.1 2.6 1.9 42.4 34.0 24.1 24.0 22.6 46.0 33.7 31.5 28.1 30.2 43.0 41.1 28.6 31.8 38.9 63.0 53.4 36.9 43.7 31.8 21.2 18.4 17.1 14.8 15.1 21.2 17.2 17.6 14.9 15.7 21.2 23.5 20.1 17.7 18.7 18.3 14.1 16.9 13.2 14.6 28.7 35.4 39.5 38.8 40.0 25.6 33.0 33.9 36.0 34.9 26.1 28.1 34.7 34.5 28.6 14.3 19.5 28.9 29.2 36.4

25SF GF

15SF

25SF

60SF

GF

15SF

25SF

60SF

GF

15SF

25SF

60SF

GF

15SF

MWD >2000 ␮m 1000–2000 ␮m 250–1000 ␮m

60SF

7.7 12.3 19.3 22.3 22.3 7.3 16.1 17.0 21.0 19.2 9.6 7.3 16.6 16.0 13.7

Data were log transformed (ln x + 1) to reduce asymmetry of data before analysis when assumption of normality was not achieved by the Kolmogorov–Smirnov test (Lilliefors, 1967). A two-way ANOVA was used to determine significant differences with forest type and depth as the fixed main effects for d , size class aggregates, MWD, C and N concentrations, and C:N ratio. When significant differences appeared, the Tukey’s honestly significant difference (HSD) test at ˛ = 0.05 was used for mean comparisons between groups. The t-test was used to detect significant changes in the total SOC pool between the TSFs. The Sigmastat package was used to perform all analyses and Sigmaplot software for graphics.

4.4 13.0 17.2 13.9 17.2

Statistical analyses

0–10 10–20 20–30 30–40 40–50

2.6.

25SF

(1)

15SF

×D(m) × 10−3 Mg kg−1 × 104 m2 ha−1

GF

C poollayer (Mg ha−1 ) = Clayer (kg Mg−1 ) × d layer (Mg m−3 )

<250 ␮m

Concentrations of C and N in soil were determined by the dry combustion method for each aggregate size fraction by using a CN Analyzer (Elementar Vario EL, Germany). The HCl test was performed to detect the presence of carbonate C in the samples. Since all samples tested negatively, total C was considered organic C. Sub-samples of aggregate size separates that were obtained from the dry sieving method were ground in a mortar and passed through a 200-␮m sieve. Any visible roots and plant debris >1 mm were removed since we were only interested in C and N concentration in the mineral soil fraction. The SOC pool, expressed as Mg ha−1 for a specific depth, was computed by multiplying the SOC concentration (kg Mg−1 ) with bulk density (Mg m−3 ), depth (m) and area (104 m2 ha−1 ). The SOC pool was calculated with the formula (1) by summing up the C pool in each soil layer (Batjes, 1996):

Size class aggregates

2.5. Aggregate-associated carbon and nitrogen concentrations

Soil depth (cm)

The use of physical fractionation methods has increased in order to study the factors involved in the associations between soil mineralogy and SOC differing in composition, structure and function (Christensen, 1992, 2001; Cambardella and Elliot, 1994). These methods expose OM that is physically protected in aggregates. However, no attempt has been made in the study of SOC concentration in different particle-size fractions of SF. A composite sample of ca. 50 g of the remaining <2 mm air-dried soil was dispersed in 50 ml 0.5 M Na-hexametaphosphate and 75 ml deionized water for 18 h and stirred mechanically in a multi-mixer machine for 20 min. The mixture was then sieved through a nest of sieves of 250, 105, 53, and 20 ␮m to retain the coarse sand (105–200 ␮m), fine sand (53–105 ␮m), coarse silt (20–53 ␮m) and fine silt + clay (<20 ␮m) fractions in beakers that were oven-dried at 60 ◦ C for 72 h. The <20 ␮m fraction was later flocculated with MgCl2 and allowed to settle before discarding the supernatant.

60SF

Simplified particle-size fractionation

Table 2 – Distribution of particle-size aggregates after dry sieving and mean weight diameter (MWD) of aggregates in the four SF stands

2.4.

2.2 2.0 1.5 1.5 1.4

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Since four soil samples were collected in each forest stand, thus pseudo-replicates, a specific multivariate ordination technique was employed to identify the main sources of variation and significance between land uses and ecosystems, the between-within principal component analysis (PCA). A first PCA identifies the sources of variability based on the groups (forest stands) that are responsible in the definition of axes. This between-class PCA focuses on the between groups’ differences (see Dolédec and Chessel, 1989 for more details). Statistical significance was tested with a Monte Carlo randomisation (10,000 simulations) procedure (Manly, 1991). This analysis was later followed by a within-class PCA in order to focus on the remaining variability after the group effect (forest stand) is removed. All centers of groups are placed at the origin of the factorial plane while sampling units are scattered with the maximal variance around the origin (Dolédec and Chessel, 1991). The discriminant analysis module included in the ADE4 software package was used (Thioulouse et al., 1997).

3.

Results

3.1.

Soil physical properties

gates for the main fixed factor SF type (ANOVA, P < 0.01), while no significant differences were observed either for depth or the interaction (Table 3). However, differences were not statistically significant (Table 3). Differences in size class aggregate distribution were statistically significant for aggregates in the size range of 0.25–0.50 mm (P < 0.05) and in the size range of 0.50–1 mm (P < 0.001), 1–2 mm (P < 0.01) and >4.75 mm (P < 0.001) (Table 3). The distribution of aggregates >4.75 mm was significantly different between the GF and the rest of SF stands (P < 0.05).

3.2.

SOC concentration (ground soil)

The highest SOC concentration was measured under the 60SF and the lowest in the GF (P < 0.001; Fig. 1). The SOC concentration decreased significantly with increase in depth in all forest stands and ranged from 59.7 to 94.1 g kg−1 in the 0–10 cm layer up to 31.0–45.5 g kg−1 in the 40–50 cm layer under the GF and the 60SF, respectively (Fig. 1). Significant differences in SOC concentration were observed for all the main factors and their interaction (ANOVA, P < 0.001). The SOC concentration was not significantly different among the aggregate size fractions collected after dry sieving in each SF stand (Fig. 1).

3.3. SOC concentration in particle-size fractions (wet sieving)

The soil bulk density in the GF and in the 15SF was significantly higher than that obtained in the 25 and 60SF. Average values for soil d ranged from 0.6 to 1.0 Mg m−3 in the 60SF and 15SF, respectively (Table 1). Significant differences were found for the main factor SF stand (ANOVA, P < 0.001), but no significant differences were observed either for depth (ANOVA, P = 0.346) or the interaction (ANOVA, P = 0.970). There were no significant differences in the d obtained at the different soil layers for all treatments (HSD Tukey test). The distribution of size class aggregates and MWD is indicated in Table 2. The percentage of aggregates <0.250 mm increased with depth with values ranging from 5 to 20% (Table 2). On the contrary, for aggregates larger than 1 mm and especially for those >2 mm the opposite was found. In general, when aggregate distribution was examined by depth, 40–60% of aggregates were larger than 2 mm in the 0–10 cm layer in all SF stands, and thus average MWD decreased with depth. Significant differences appeared in the MWD of aggre-

The SOC concentration within particle-size fractions of wetsieved aggregates <250 ␮m decreased with increasing soil depth (Fig. 2). The highest SOC concentrations were observed in the fine silt + clay fraction (<20 ␮m) in all SF evaluated, whereas the coarse-sand (105–200 ␮m) and fine-sand fractions (53–105 ␮m) contained less SOC concentration. In the GF the SOC concentration was higher in the fine silt + clay fraction than in the rest of particle-size fractions, probably due to an effect of fine silt + clay enrichment from alluvial depositions, although differences were not statistically significant. Compared to the average values of SOC obtained by grinding the soil, the <20 ␮m fraction of <250 ␮m aggregates yielded the highest percentage (30–35% of C), whereas the coarsesand fraction contributed 17–20% of the SOC concentrations (Table 4). The SOC concentration comprises both the light fraction and the particulate organic matter (POM), since no

Table 3 – Two-way ANOVA for aggregate size class distribution and MWD (mm) in the four SF stands evaluated, with forest type and sampling depth as main fixed factors Source of variation

d.f.

MWD

Size class aggregates <0.25

**

0.25–0.50 *

0.50–1.00 ***

1.00–2.00 **

SF type (A) Depth (B) A×B MS residual

3 4 12 60

5.218 0.144 NS 0.583 NS 0.41

1.286 NS 0.232 NS 0.172 NS 67.51

3.844 0.364 NS 0.438 NS 35.64

7.261 0.210 NS 1.033 NS 14.57

4.636 0.716 NS 0.349 NS 17.01

MS total

79

0.43

57.13

35.30

17.53

17.43

The F-ratio is indicated for each variable. NS, not significant. ∗

P < 0.05. P < 0.01. ∗∗∗ P < 0.001. ∗∗

2.00–4.75

>4.75

1.268 NS 0.557 NS 0.243 NS 106.65

6.177*** 0.535 NS 0.793 NS 66.09

93.08

75.45

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Fig. 1 – The SOC concentration (g kg−1 ) in the different aggregate size fractions (ground soil) in four forest systems. Different letters indicate significant differences for average SOC concentration (ANOVA, P < 0.001); for each soil layer differences between size class aggregates within the same SF stand were not significant (HSD Tukey test, P < 0.05).

chemical treatment or flotation technique was employed to remove the organic debris.

3.4.

N concentrations and C:N ratio

Total N concentration (%) decreased significantly with increase in depth in all SF stands (Table 5). Significant differences were observed in the mean N concentration among forest stands (ANOVA, P < 0.001), depth (ANOVA, P < 0.001) and the interaction (ANOVA, P < 0.001). Except in the GF and the CHSF, total N concentration differed significantly among forest stands for 0–10, 10–20 and 30–40 cm soil depths (HSD Tukey test, P < 0.05). There were no significant differences in N

Table 4 – Percentage of C in particle-size class aggregates <250 ␮m after fractionation of the mineral soil (average of all depths) Secondary forest <20 Gallery SF 15SF 25SF 60SF

34.7 29.2 30.5 34.0

Particle-size class fraction (␮m) 20–53 53–105 105–200 23.2 26.0 25.7 27.1

23.8 24.4 23.8 21.6

18.3 20.4 19.9 17.2

concentration between the 25SF and the 60SF for 20–30 cm depth. Regarding the C:N ratio, significant differences were observed for the fixed main factors and also for the interaction (ANOVA, P < 0.001). The C:N ratio ranged between 10 and 11 in all secondary forest stands (Table 5). Within particle-size fractions, the highest C:N ratio was measured in the fine silt + clay fraction, i.e., 7.5 in the first soil layer and increased with depth (data not shown).

3.5.

Litter and SOC pool

The highest litter accumulation was observed in the 15SF (Table 6) with 1.6 ± 0.2 kg m−2 and decreased in the order 60SF (1.08 ± 0.05 kg m−2 ), 25SF (1.0 ± 0.08 kg m−2 ), and the GF (0.41 ± 0.01 kg m−2 ). The amount of litter collected (expressed in Mg C ha−1 ) in the four forest stands was significantly different (t-test, P < 0.05). The highest SOC pool was measured under the 60SF and the lowest in the GF, similar to that obtained in the 25SF (Table 6). Differences were significant at P < 0.05 (t-test). The SOC pool was not evenly distributed through the soil profile. In all SF stands 50% of the total SOC pool (50 cm depth) was in the top 20 cm (Table 6).

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Fig. 2 – The distribution of SOC concentration (g kg−1 ) with soil depth in the particle-size class fractions <200 ␮m collected from wet sieving in the four SF stands.

3.6.

Multivariate analysis

The first two axes of the between-class PCA explained 84.0% of the total variability (Fig. 3a). Axis I explained 66.1% of variability and discriminated soil samples with high values of C in all fractions, although to a lesser extent the <20 ␮m fraction, and the distribution of aggregates <0.5 mm and percentage of sand were high in opposition to those variables related to physical soil properties, i.e., MWD, aggregates >4.75 mm

and bulk density. This axis showed differences in soil characteristics among the four forest stands. The second axis retained from the between-class PCA explained 17.9% of variation. It clearly separated percentage of clay, 1–2 mm aggregates and C:N ratio from pH. Axis II was interpreted as the management effect of the SF stands. The projection of objects (ecosystems) onto the factorial plane defined by the first two axes (Fig. 3c) was significant (P = 0.0026; Montecarlo permutation test). The GF was characterized by a high proportion

Table 5 – Average N concentration (%) and C:N ratio in the four SF studied Depth (cm)

0–10 10–20 20–30 30–40 40–50

GF

15SF

25SF

60SF

N (%)

C:N

N (%)

C:N

N (%)

C:N

N (%)

C:N

0.64 a, A 0.52 b, A 0.40 c, A 0.34 d, A 0.32 d, A

9.8 a, A 9.1 c, A 9.3 bc, A 9.8 a, A 9.9 a, A

0.65 a, A 0.51 b, A 0.43 c, B 0.36 d, A 0.35 d, B

10.4 b, B 9.9 c, B 10.4 b, B 11.2 a, B 11.3 a, B

0.74 a, B 0.59 b, B 0.54 c, C 0.42 d, B 0.38 e, C

10.4 c, B 9.6 e, C 9.9 d, C 11.1 b, BC 11.5 a, C

0.95 a, C 0.68 b, C 0.52 c, C 0.46 d, C 0.41 e, D

9.9 c, A 9.7 d, C 10.5 b, B 10.9 a, C 10.9 a, D

Values followed by the same letter within a column or a row are not statistically different at P < 0.05 (HSD Tukey test). Lower letters indicate significant differences between soil depth within the same site, and capital letters between sites within the same soil layer.

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Table 6 – The SOC pool and litter (Mg C ha−1 ) in the four SF stands SOC pool (Mg C ha−1 )

Depth (cm) Litter (Mg C ha−1 ) 0–10 10–20 20–30 30–40 40–50 Total in soil

GF

15SF

2.1 44.7 a 44.9 a 33.3 b 30.4 b 30.2 b

8.0 54.3 a 45.3 b 39.2 c 39.0 c 34.3 d

183.5 a

212.1 ab

25SF

60SF

5.0 55.5 a 39.5 b 35.5 c 32.8 d 32.6 d

5.4 59.6 a 46.1 b 41.8 b 38.5 c 35.4 c

195.9 a

221.4 b

Fig. 4 – Within-class PCA showing (a) ordination of soil variables in the factorial plane, (b) “eigenvalues” diagram and (c) ordination of soil samples with all centroids (groups or ecosystems) at the origin of the factorial plane; ordination of samples by depth (i) and by soil texture (ii).

of >4.75 mm aggregates and MWD, and the 60SF by high SOC concentration. Regarding the within-class PCA, the first and second axes of the within-class PCA (Fig. 4a) accounted for 81.4% of the inertia. Axis I (66.1% of variability explained) showed an opposition between those soil samples with a large proportion of small aggregates, high bulk density and pH in opposition to those samples with high C concentrations in all fractions and MWD. Axis II (15.3%) separated samples with high percentage of sand in contrast to those with high C:N ratio and percentage of clay and silt. The projection of SF stands onto the factorial plane defined by both axes in the within-class PCA is given in Fig. 4a. The ecosystem effect is removed in the within-class PCA by placing all centers of gravity in the axes origin of the factorial maps, while the sampling units (soil from each land use at different depth) are scattered, with the maximal variance around the origin (Fig. 4c, i and ii). Fig. 3 – Results of the between-class principal component analysis (PCA) showing (a) ordination of variables in the factorial plane, (b) “eigenvalues” diagram and (c) projection of the soil samples onto the factorial plane defined by axes I and II. The circles represent the centroids of each cloud of points for each ecosystem. Variables are defined as follows: Bd = bulk density; MWD = mean weight diameter of aggregates; Ccosa = C in coarse-sand fraction; Cfisa = C in fine-sand fraction; Ccosi = C in coarse silt fraction; Cs + c = C in the fine silt and clay fraction.

4.

Discussion and conclusions

4.1.

SOC pool and comparison with other studies

The SOC pool (0–30 cm) under forest vegetation in humid northeastern Costa Rica was estimated at 87.1 and 80.5 Mg ha−1 (Powers, 2004; Powers and Veldkamp, 2005). In the southern area of Costa Rica, Krishnaswamy and Richter (2002) reported a SOC pool under forest of 79.5 Mg C ha−1 in the

308

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top 30 cm. These values are much lower than those reported in the present study, i.e., from 130.5 to 147.5 Mg C ha−1 (Table 6). In Costa Rica, the SOC pool in SF of 10–15 years old was 21 Mg C ha−1 compared to 16 Mg C ha−1 in the 0–10 cm depth under pastures (Reiners et al., 1994). In our study, the SOC pool was higher and increased with the age of the SF, from 56.1 to 60.1 Mg C ha−1 (0–10 cm) in the 15SF and 60SF, respectively (Table 5). The SOC pool in the 15SF was higher (Table 5) than that reported by Russell et al. (2004) in soils from La Selva Biological station with a maximum of 40.5 Mg C ha−1 . The SOC pool in a T. cacao and Erythrina poeppigiana shade tree system ranged from 115 to 140 Mg C ha−1 (0–45 cm) over a 9-year period (Fassbender, 1998). In our study the SOC pool in the 60SF (old T. cacao plantation) has likely increased prior to the abandonment of this land use. In other tropical areas as in the Brazilian Amazon basin, the SOC pool (0–30 cm depth) in agricultural land of less than 10 years old was reported at 27.4–62.0 and 36.8–39.1 Mg C ha−1 by Neill et al. (1997) and Fearnside and Barbosa (1998), respectively. These values are lower than that obtained in our study (Table 6). In Puerto Rico, the SOC pool (0–50 cm) under forest and reforested sites derived from pastures was estimated at 99.0 and 95.2 Mg C ha−1 , respectively (Silver et al., 2004). In Los Tuxtlas, Mexico, the total SOC pool (1 m depth) reported in SF from 6 months to 50 years old remained stable along the succession and averaged 207 Mg C ha−1 (Hughes et al., 1999). In Central Amazonia, the total SOC pool (0–10 cm) in SF was estimated at 43.3 Mg C ha−1 (Hughes et al., 2000). Rhoades et al. (2000) observed SOC pool increased by 1.9 Mg ha−1 year−1 , and the total SOC pool attained the antecedent level within 20 years that under TSF of lower montane Ecuador (South America). The review of literature revealed that reforestation of abandoned agricultural land, mainly pastures, results in a slow increase of the SOC pool. Regarding the gallery forests in our study, the SOC pool in the GF was higher, i.e., 183.5 Mg C ha−1 , than that reported in other tropical sites. For example, in a study conducted by Veneklass (pers. commun.) in the eastern plains of Colombia, the average SOC down to 1 m depth in the Yucao watershed was estimated at 133 Mg C ha−1 . In the dry tropical forest of Guanacaste (Costa Rica), the SOC under a gallery forest has been reported at 66.1 Mg C ha−1 (Jiménez et al., 2008, this issue).

4.2.

Determinants of SOC accumulation in SF

Forest type, soil texture, litter production, root inputs, the time elapsed since the perturbation and intensity of the previous land use are factors responsible for variability in soil’s response to perturbation (Allen, 1985; Brown and Lugo, 1990; Neill et al., 1997; Russell et al., 2004). In our study, plant diversity, i.e., understory vegetation and trees, which is highest in the 60SF (Russo, unpubl. results), and litter production are important factors affecting the SOC concentration. Data from temperate agroforestry systems have shown that the SOC pool was not increased from the tree row compared with the center after 13 years of alley cropping, although a positive trend was observed (Oelbermann and Voroney, 2007). Several studies have revealed that soil type and clay content are strong determinants of the SOC concentration and

pool. Desjardins et al. (2004) observed higher SOC concentrations in clayey Ferralsols (more than 80% of kaolinite) in Central Amazonia (ca. 6.0 kg C m−2 ) than in sandy–clayey Acrisols from Eastern Amazonia (ca. 3.0 kg C m−2 ). Several authors reported similar values of SOC pool for clayey Oxisols and sandy Ultisols in the Amazonia (Choné et al., 1991; Desjardins et al., 1994; Koutika et al., 1997; Neill et al., 1997). The SOC pool (0–20 cm) estimated in volcanic soils of Martinique was 80.3 and 109.3 Mg C ha−1 in soils with 27 and 36% silt + clay fraction, and 69.7 to 82.9 Mg C ha−1 in soils with ca. 65% of silt + clay (Feller et al., 2001). In our study there is an inverse relationship, i.e., while clay content increases, the SOC concentration decreases with soil depth (Fig. 4a). Textural analysis also showed that the percentage of sand in the soils studied was higher than 80%. In other words, even if the highest SOC concentrations were associated with the fine silt + clay fractions in the present study, the SOC in the sand-size fraction play an important role in the area and merits further research. Higher fine root biomass has been observed in SFs compared with tree plantations of similar age (Cuevas et al., 1991), and sometimes similar to old-growth forests (Cavelier et al., 1996). Plant diversity provides different qualities of OM that may increase the SOC pool. Studies on signatures of OM in order to identify their origin with near infrared spectroscopy (NIRS) (Joffre et al., 2001) can be relevant to address these questions and elucidate the contribution of plant litter and roots to the SOC pool. Further studies are needed to assess litter and fine roots contribution to the total SOC pool in addition to the contribution of black carbon or charcoal. The study area was previously occupied by humans before the arrival of Europeans and evidence of fire use exists.

4.3.

SOC concentration and aggregate size distribution

The distribution of size class aggregates is an indicator of soil quality, i.e., soil structure. In general, the topsoil was more aggregated than the deeper layers. Aggregates larger than 2 mm were more abundant in the topsoil than in the deeper soil layers and thus average MWD decreased with increase in depth (Table 2). It seems that aggregate distribution is affected by forest regeneration since there was an increase in the proportion of aggregates <0.250 mm with increasing age of the SF, whereas the opposite was observed for aggregates >2 mm. The consequence of this pattern is that an important proportion of SOC was occluded within large aggregates in SFs of less than 20 years old, while aggregates of <1 mm size are important for SOC accumulation in the more aged SF. A higher aggregation in the topsoil can be the result of higher root density and soil biological activity that has been favored by the time elapsed since the recovery of the SF stands. In fact, diplopods and earthworms were normally observed in high numbers in the older SFs contributing to aggregate stability in this ecosystem. Carbon in the soil can be stabilized within aggregates (Six et al., 1998). In the present study the pattern observed in SOC concentration indicated uniformity between size class aggregates (dry sieving) and a slight increase in aggregates <250 ␮m (Fig. 1). Therefore, in the soils studied under different SF stands, the increase of SOC concentration with decrease in

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particle-size fraction is an indication of relative C enrichment in the <250 ␮m size aggregates. These results are in accordance with those reported by Lehmann et al. (1998) and Neufeldt et al. (1999). Conversely, data provided by Angers and Giroux (1996) indicated that C was uniformly distributed regardless of the separation of primary particles. On the contrary, increases in SOC concentration with increase in aggregate size have been reported by several authors (Gijsman, 1996; Wigginton et al., 2000; Conant et al., 2004). In our study, the fact that fine silt + clay fraction (<20 ␮m) in the <250 ␮m aggregates contained the highest concentration of SOC (Fig. 2) revealed that this C is physically protected and occluded within larger aggregates. The SOC concentration in this fraction was much higher compared to values obtained by grinding the soil for all depths (Fig. 1). In the GF, a higher enrichment in SOC concentration was observed in the fine silt + clay fraction than in the rest of particle-size fractions, although no significant differences were observed. The fact that the soil is of alluvial nature could have influenced deposition of clay and sand within the soil profile, and thus affect SOC accumulation in particle-size fractions. Finally, no estimates of SOC sequestration are provided in this study due to differences regarding land use history and intensity of previous land use in the 15SF, 25SF and 60SF stands and because they do not constitute a true chrono-sequence. However, a clear trend was observed, the older the TSF the higher the SOC pool (Table 6). It is possible that C accumulation has been favored by the well aggregated soil structure observed in the older SF stands. This confirmed that the age of recovery influenced the distribution of size class aggregates and SOC pool. Although reforested areas may differ in plant community composition from mature forests (Lugo, 1992), they constitute an important ecosystem for soil C sequestration, especially when the total area of SF currently exceeds the area of primary forest (FAO, 2005).

Acknowledgements This work was funded by the US Department of Energy. We are grateful to Juan Orlando for his assistance in the field, and to C. Hernández and H. Arrieta for kindly permitting the use of lab facilities at EARTH during field work, and Y. Raut for his help at OSU lab. The first author thanks “Fundación ARAID” (Spain) for financial support during manuscript preparation.

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