Agriculture, Ecosystems and Environment 131 (2009) 229–239

Relating soil carbon fractions to land use in sloping uplands innorthern Thailand

Supathida Aumtong a,b, Jakob Magid a,*, Sander Bruun a, Andreas de Neergaard a

a Department of Agriculture and Ecology, Faculty of Life Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg, Denmarkb Department of Soils and Environmental Resources, Faculty of Agricultural Production, Maejo University, Chiang Mai, Thailand

A R T I C L E I N F O

Article history:

Received 6 October 2008

Received in revised form 20 January 2009

Accepted 21 January 2009

Available online 4 March 2009

Keywords:

Soil carbon

Soil carbon fractions

Carbon storage

Land-use change

Soil use and management

Ultisols

Sloping uplands

A B S T R A C T

In this study from Khun Samun Watershed in northern Thailand we investigate land use and soil carbon

relationships in 99 sloping upland soils that pertain to soil degradation/conservation and carbon storage/

loss. Approximately 35% of the variability in total soil carbon could be explained by the available

quantitative and semi-quantitative information, primarily clay content and CEC, and to a lesser degree

by factors concerning land management. When categorical information such as land-use class and

location was included, the explanatory power increased to 65% for total soil carbon, whereas for

particulate and soluble carbon fractions the model predicted 71–95% of the total variability. The water

soluble carbon fractions were high in systems with input of high C-to-N ratio material, whereas the

permanganate oxidizable fraction was somewhat increased in the predominantly anoxic wetland rice

site, and in fruit tree plantations. Coarse particulate organic matter carbon was strongly affected by land

use. A causal relationship between land use and soil carbon or carbon fractions cannot be firmly

established since historical data from these soils are lacking, but the significance of the various carbon

fractions is discussed. It is proposed that the communities have selected land for particular purposes

based on suitability, i.e. perceived inherent quality, accessibility and possibly other factors. Thus, the best

land is apt to be used for the cultivation of fruit trees, teak and annual crops, leaving the poorest land for

secondary forest. This study did not indicate that the cultivation of land is rapidly degrading the soil

quality, either by carbon content, carbon fractions, or the profile gradients of total carbon or particulate

carbon through the topsoil (0–20 cm). Profile carbon gradients were significantly explained by land-use

class, but not by the position in the landscape which indicates that erosion is not an overriding force.

While the secondary forest had the strongest profile gradient of total carbon, it was not much higher than

fallow and fruit tree plantations, and only significantly different from wetland rice, which had a very

small gradient due to the frequent ploughing of the soil. The similarities in profile carbon gradients is

ascribed to the actual land management, where planting of crops is commonly done by punching holes in

the otherwise undisturbed topsoil, and subsequent weeding is done by manual slashing or with

herbicides, thus effectively forming a zero-till practice.

� 2009 Elsevier B.V. All rights reserved.

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1. Introduction

Sloping uplands are geographically the most extensive ecosys-tems in Southeast Asia (Garrity, 1993) covering up to 180 millionhectares, thus forming a global land resource unit. Despite thelarge areas of land associated with these soils, only a limitednumber of researchers have examined the land use and carbon (C)stocks in these areas (Forsyth, 1994; Tanaka et al., 1997, 1998a,b,2001; Funakawa et al., 1997a,b; Schmidt-Vogt, 1998; Moller et al.,2000, 2002, 2005; Post and Kwon, 2000; Guo and Gifford, 2002).

* Corresponding author. Tel.: +45 35333491; fax: +45 35333460.

E-mail address: jma@life.ku.dk (J. Magid).

0167-8809/$ – see front matter � 2009 Elsevier B.V. All rights reserved.

doi:10.1016/j.agee.2009.01.013

Although the Thai Governments Royal Forestry and Land Devel-opment Departments have extensive databases of soil propertiesover lowland parts of Thailand, much less detailed information isavailable about the upland areas, which are mostly inhabited byethnic tribes, traditionally under relatively extensive shiftingcultivation (Schmidt-Vogt, 1998; Tanaka et al., 2001). Apart frombeing a natural resource in its own right, the upland areascontributions to environmental services from nature (Costanzaet al., 1997) are relatively unknown. Shifting (or swidden)cultivation as practised in these areas has always been hotlydebated in the literature and analysis of the coupled human-environment interactions characterising this land-use system hasfound a natural place within sustainability science (Chowdhuryand Turner, 2006). Shifting cultivation is still frequently criticized

Fig. 1. Location of study area Khun Samun Watershed, northern Thailand.

Table 1Land-use categories in the study area.

Land use Abbreviation Characteristics

Least disturbed forest (LDF) Mixed deciduous forests in which

nontimber forest products (NTFPs)

are harvested by the villagers.

Secondary conservation

forests

(SCF) 25–45 years old, much of the plant

cover is composed of bamboo and

have nearly 50% closed canopies.

Some villagers harvest NTFPs.

Fruit tree plantations (FTP) Mostly lychee (Litchi chinensis Sonn.),

which are now 10–30 years old.

Fallow areas (FA) Covered with bush and grass; free of

agricultural activities for 1–3 years.

Afforestated areas (AFF) (1) Young 10–12-year old teak areas

(Tectona grandis L.). (2) 15–17-year

old areas of teak and mixed native

tree species.

Wetland rice (WR) Approximately 30 years old,

wetland rice is grown in Bann

Kasai village for household

consumption.

Mixed crop land (MCL) Planted to upland rice for

subsistence and maize for livestock

feed, annually or rotated.

S. Aumtong et al. / Agriculture, Ecosystems and Environment 131 (2009) 229–239230

for being environmentally destructive and keeping rural people inpoverty despite the many studies showing that in many places andsituations it is the most rational economic and environmentalchoice for millions of the poorest farmers in the world (e.g. Thrupp,1997; Schmidt-Vogt, 1998; Fox, 2000).

In this study, we investigate some characteristics of the landuse and soil C relationships that pertain to soil degradation/conservation, and C storage/loss, and specifically focus on somemethods that have been suggested as being appropriate forassessing biologically meaningful fractions of soil carbon, andearly indicators of long-term trends. A major difficulty whenestablishing relationships between land use and soil C isvariability, both spatial and temporal, of most of the drivingvariables, even at the microscale. Soil C has often been used as anindicator of soil quality (Janzen, 2005). However, soil C does notreflect the effects of changes in land use in a particularlysensitive manner because it changes relatively slowly. Thus, aplethora of methods have been devised to assess C fractions thatare more sensitive to changes in land use (Chan et al., 2002;Haynes, 2005). However, these methods have mainly been testedunder controlled experimental conditions either in field orlaboratory experiments. Extrapolating the results from thesestudies to fuzzy landscape or regional scales with all thevariability in soil properties and vegetation is not straightfor-ward. Therefore, there is a very large gap between the type ofknowledge obtained from experimental studies and the knowl-edge that can be gained by applying the methods on landscape orregional scales.

Local field research trials, georeferenced soil archives or soilquality databases that would assist in an assessment of the impactof land-use change in the area could not be found. Even wheresuch information is available, there are limitations in terms of thevariety of conditions encompassed and consistency with farmerpractices. Thus, the relationship between land use and soil organicC and organic C fractions in real land-use systems wasinvestigated. With the limited information available it washypothesized that soil organic C and nutrient content woulddecrease with increasing intensity of land use. It was furtherhypothesized that examining selected organic C fractions wouldenable a better understanding of the short to medium term Cdynamics.

2. Materials and methods

2.1. The study region

The study was carried out in Khun Samun Watershed (229 km2,188540–198030N; 1008320–1008400E) in Nan Province, northernThailand (Fig. 1). Hilly and mountainous landforms occupy morethan 85% of the area, with a small proportion of flat land in the riverbeds. Elevation ranges from 300 to 800 m a.s.l. The mean annualrainfall and monthly temperature in the area from 1993 to 2006was about 1400 mm and about 22–29 8C, respectively. The mean ofminimum and maximum monthly air temperature were approxi-mately 16 and 37 8C.

2.2. Land-use classes

The study region was divided into 7 land-use classes (LUC).The land-use classes are least disturbed forest (LDF), secondaryforest (SCF), fruit tree plantations (FTP), fallow areas (FA),afforestation areas (AFF), wetland rice (Oryza sativa L.) (WR) andmixed crop (MCL). The land-use classes are described in Table 1.Apart from the least disturbed forest and wetland rice, all theseland classes are presumably part of a longer term shiftingcultivation cycle.

2.3. Replicated volume specific sampling of 56 pedons (0–120 cm)

from 18 sites

The study area consisted of 3 villages in Khun SamunWatershed (Bann Huey Hai, Bann Huey Ra Pee, Bann La Bou Ya)

Table 2Semi-quantitative intensity indicators scored on the basis of farmer interviews.

Land-use intensity indicator Scorea

Years utilized 0 = no use for >40 years,

10 = frequent and recent use

Frequency of burning 0 = not burned for >40 years,

10 = frequently and recently

burned

Tillage intensity 0 for zero tilling, 10 for

intensive tilling

Fertilization intensity 0 for no chemical fertilizer,

10 for frequent and relatively

intensive chemical fertilization

a The scores were used for PLS regressions of soil quality indicators (SOC, POXC,

CPOM) on land-use intensity indicators and soil properties (clay content and CEC).

S. Aumtong et al. / Agriculture, Ecosystems and Environment 131 (2009) 229–239 231

(Fig. 1). In May 2003, samples were collected from each land-useclass (total 18 sites), taken up to 120 cm depth (0–5, 5–10, 10–20,20–30, 30–50, 50–70, 70–90 and 90–120 cm) in 2 cylinders perdepth (5 cm diameter, 5 cm depth). At each site, three pits weredug. The total number of soil samples from 18 sites was 432 (i.e.18 � 3 replications � 8 soil depths).

2.4. Composite volume specific sampling of topsoil from 82 sites

representing known land use

This study area consisted of 4 villages (Bann Huey Ra Pee, BannLa Bou Ya, Bann Huey Pook and Bann Ka Sai) (Fig. 1). In May 2004,10 m � 5 m plots were sampled from 82 sites in all. From each site,a composite soil sample of 8–10 subsamples were collected fromeach land-use class. Samples were taken at three depths (0–5, 5–10and 10–20 cm) at 34 sites and at four depths (0–2.5, 2.5–5.0, 5.0–10.0 and 10.0–20.0 cm) at 48 sites.

2.5. Soil classification

Classification of soil included a field morphology study of soiland laboratory analyses of soil samples collected from 8 pedons.The soils physical, chemical and mineralogical properties wereanalysed according to standard techniques. Pedon analysis in soilpits was carried out at each site including a detailed profiledescription according to the FAO guidelines (FAO, 1990).

2.6. Soil analysis

Unless otherwise stated, soil analyses were conducted accord-ing to the procedures described in Anderson and Ingram (1993).Volume specific soil samples were taken to the laboratory, airdried, weighed, ground and sieved through a 2 mm mesh sieve.CEC was determined with ammonium acetate (pH 7) and theMicro-Kjeldahl method. Exchangeable cations (Ca, Mg and K) wereextracted with ammonium acetate (1N NH4OAc) and the contentsof various cations (Ca, Mg and K) were determined by atomicabsorption emission spectrophotometry. Texture was determinedusing a Bouyoucos hydrometer after dispersion of soil with 2 ghexametaphosphate. Soil with a C content of >1% was allowed toreact with 30% hydrogen peroxide (20 ml added to 50 g soil and125 ml water) prior to dispersion. Soil pH (KCl) was determined byusing a pH meter with a soil-to-KCl ratio of 1:2.5.

2.7. C content and C fractions

Total soil organic C content was determined after sieving(0.5 mm) by the wet combustion method described in Nelson andSommers (1982). Five C fractions were evaluated: permanganateoxidizable C (POXC), water soluble C (WSC), hot water soluble C(HWSC), coarse and fine particulate organic matter C (CPOM-C andFPOM-C). Both permanganate oxidizable C (POXC) and the watersoluble C (WSC) fractions have been ascribed as indicators of labileC (Blair et al., 1995; Ghani et al., 2003), while particulate organicmatter fractions are indicators of undecomposed organic tissues,predominantly of plant origin.

Permanganate oxidizable C was determined using a modifica-tion of Blair et al. (1995): 3 g of air-dried soil was passed through a0.5 mm sieve with 20 ml neutral solution of 0.05 M KMnO4. Thesoil-KMnO4 suspensions were shaken at 200 rpm for 15 min atroom temperature in screw cap polycarbonate centrifuge tubes.After shaking, the tubes were centrifuged at 2500 rpm for 15 minto separate the soil particles from the solution. These were thentransferred, with 0.20 ml of the clear solution, into a glass tube,diluted with 10.0 ml de-ionized water, using a vortex for completemixing. The absorbance was measured with a spectrometer at

550 nm of light and compared to an absorbance reading on astandard curve. Water soluble C (Ghani et al., 2003) was extractedfrom 3.0 g of dried soil and passed through a 2 mm sieve and wasweighed into 50 ml polypropylene centrifuge tubes. These wereextracted by adding 20 ml of distilled water for 30 min, on an end-over-end shaker at 20 rpm, centrifuged for 20 min at 5000 rpm andall supernatant from that was filtered through a membrane filterinto a separate vial for C analysis (Nelson and Sommers, 1982). Hotwater soluble C (HWSC) was extracted with hot water (80 8C) using1:5 ratio for 16 h in a water bath (Ghani et al., 2003). Organic C inthe centrifuged extracts was analysed by dichromate oxidation.

Coarse and fine particulate organic carbon: 30 g whole soil wasdispersed with 100 ml of 5 g L�1 sodium hexametaphosphate for16 h in a shaker at 20 revolutions per minute (rpm), and the slurrywas poured onto an assembly of 1, 0.250 and 0.053 mm sieves.After wet sieving, and crushing of aggregates on the 1 mm sieve,the materials from the 0.250 and 0.053 mm sieves were oven-driedat 50 8C, weighed and ground, and subsequently measured forcontent of organic C (Nelson and Sommers, 1982).

2.8. Land-use intensity indicators

Village key respondents were interviewed to get an overallpicture of land use and major historical dynamics. Samplinglocations were identified and characterised using farmer inter-views as well as personal observation and mapping. Only siteswhere the owner could account for the land use for at least the past25 years were included. After selection of sampling locations, fieldowners were interviewed on present and historical farmingpractices using a semi-structured guideline. Using a rough semi-quantitative approach the intensity of use of fields and secondaryforests was scored by the researcher, based on the interview data.The scheme in Table 2 was used to score the intensity for thefollowing land-use intensity indicators ‘years utilized’, ‘frequencyof burning’, ‘tillage intensity’ and ‘fertilization intensity’.

2.9. Statistical analysis

Data from 0 to 20 cm depth in the replicated volume specificsampling of 56 pedons from 18 sites were averaged on a site basis.Since the LDF was only represented by one site, this data set wasexcluded from statistical analysis. Data from the remaining 17 siteswere merged with the data set representing the composite volumespecific sampling of topsoil from 82 sites representing known landuse, therefore data from 99 sites were available representing 0–20 cm. The stock of C and nutrients was calculated by multiplyingthe soil density, the concentration and the thickness, for eachsampled layer. Thus organic C, C fractions and nutrient storages in0–20 or 0–120 cm were obtained by summing of stocks in eachlayer. Fractions of C were expressed as a percentage of total C forpresentation and further statistical analysis.

Fig. 2. The results of PLS calibrations of the total amount of C (left panel), POM-C (central panel) and POM-C expressed as a percentage of total C (right panel) in the 0–20 cm

soil layer. In each panel, the upper figure shows the percentage of explained variance as a function of the number of components included in the model, while the lower figure

shows the X loadings ( ) and Y loadings ( ) of variables used in the model on the first two components of the model.

S. Aumtong et al. / Agriculture, Ecosystems and Environment 131 (2009) 229–239232

Partial least square (PLS) regressions were used to correlate thesoil C and C fractions with land-use intensity indicators (scores foryears utilized, frequency of burning, tillage intensity, fertilizationintensity) and soil properties (clay content and CEC, as well as allother measured parameters which proved to have no explanatorypower). The PLS regressions were performed with the programUnscrambler� version 8.0 (CAMO, Norway). The effect of LUC ontotal C and C fractions was tested with multiple regressions usingthe SAS� general linear model procedure using LUC, clay contentand CEC. In an extended model ‘Village’ was introduced as a classvariable and the class variable LUC was nested into ‘Village’.

Profile gradient analysis was performed on the verticaldistribution of soil C and CPOM-C to test the effect of land useand position in the landscape on the gradient formed. Anexponential model was fitted to Q (soil C and CPOM-C):

Q ¼ Q0e�kd (1)

where d is the soil depth, Q0 is the value of Q at the surface and k isthe steepness of the gradient. The effect of land-use class on thedevelopment of the gradients in soil quality indicators parameterswere tested by a one-way ANOVA of the parameter k obtained inthe different locations similar to the one used for analysing total Cand CPOM-C.

3. Results

3.1. Soil classification

Morphology studies indicated that all soils were ultisols. Sevenprofiles were further classified as Hyperalic Alisols (Humic) whileone was an Endogleyic Luvisol (Chromic).

3.2. Partial least squares analysis

The PLS regressions showed that, in general, less than 35% of thevariation in the soil C and C fractions in the upper 20 cm soil couldbe explained by the other measured soil properties and the

qualitatively scored management information available. PLS fortotal C content (SOC) in the upper 20 cm could predict 34% of thevariation using two components (Fig. 2a). The first component,explaining 31% of the variation, is correlated with the soilproperties, i.e. clay content and CEC, and both of these propertieswere positively correlated with total C content. In contrast, thesecond component explains about 3% of the variation and iscorrelated mainly with land-use indicators such as age and tillageintensity (Fig. 2b). Beyond the two components, the explainedvalidation variance decreases indicating that the extra componentsmainly are noise components, and the additional data on pH andavailable nutrients did not contribute to further explanation.

Analysis of the on-site variability in the replicated volumespecific sampling of 56 pedons from 18 sites showed a variance of9% in the SOC content. This means that, in principle, it should bepossible to predict 91% of the variation if all the necessaryinformation about soil properties and land-use intensity wasavailable. The fact that only 34% of the variation was explainedwith the land use and soil parameters indicates that there wasinsufficient information to characterise these factors.

The results of a PLS regression of POM-C content is shown inFig. 2c. Four outliers with an unduly large influence on the modelwere removed. The regression only explains 9% of the variance inthe POM-C content. The first component is mainly correlated withthe soil properties and explains most of the variation, while thesecond component that is correlated with both soil properties andland-use indicators only explains another 0.5%. There was, thus, noindication that POM-C correlated well with the land-use intensityindicators. The same result was found when performing PLSregression of the fine and coarse fractions of POM-C. The POM-Ccontent can also be expressed as a fraction of total SOC (POM-C/SOC). Again 4 outliers with an unduly large influence on the modelwere removed, after which the PLS regression explained 29% of thevariation with two components (Fig. 2e). The first component,explaining 27% of the variance, was highly correlated with soilproperties (Fig. 2f). This indicates that the main reason why thevariation in POM-C/SOC could be explained was because the SOCcontent could be explained.

Table 3Multiple regressions using the type III sum of squares that are each adjusted for all other effects in the model, regardless of order.

Dependent variablea Limited model Extended modelb

R2 LUC Clay CEC R2 Village LUC (village) Clay CEC

Total C 0.41 n.s. *** ** 0.64 * * * **

POXC 0.23 ** * n.s. 0.71 *** n.s. n.s. n.s.

fPOXC 0.25 *** n.s. * 0.64 *** n.s. n.s. n.s.

HWSC 0.33 *** ** * 0.93 *** *** n.s. n.s.

fHWSC 0.37 *** *** n.s. 0.95 *** *** n.s. n.s.

CPOM-C 0.27 ** n.s. n.s. 0.73 * *** n.s. n.s.

fCPOM-C 0.39 *** n.s. n.s. 0.73 ** *** n.s. n.s.

FPOM-C 0.33 n.s. ** * 0.44 * *** n.s. n.s.

fFPOM-C 0.23 n.s. ** * 0.33 n.s. n.s. * n.s.

n.s.: not significant.a The letter ‘f’ in front of dependent variables denotes transformation relative to total carbon of the following: permanganate oxidizable carbon (POXC), hot water soluble

carbon (HWSC), coarse particulate organic carbon (CPOM-C) and fine particulate organic carbon (FPOM-C).b In the extended model ‘Village’ is introduced as a class variable, and the class variable land-use category (LUC) nested into ‘Village’.* P < 0.05.** P < 0.05.*** P < 0.001.

Fig. 3. Carbon stocks in 54 pedons (0–120 cm depth) by land-use type: FA, fallow agriculture; FTP, fruit tree plantation; LDF, least disturbed forest; MCL, mixed crop land; SCF,

secondary conservation forest. Error bars represent standard error of means; n (number of pedons characterised) in soft brackets, 3 pedons were dug at each site.

Fig. 4. Carbon stocks in topsoil (0–20 cm depth) by land-use type from 99 locations:

AFF, afforested area; FA, fallow agriculture; FTP, fruit tree plantation; MCL, mixed

crop land; SCF, secondary conservation forest; WR, wetland rice. Error bars

represent standard error of means; n (number of locations sampled) in soft

brackets.

S. Aumtong et al. / Agriculture, Ecosystems and Environment 131 (2009) 229–239 233

3.3. Multiple linear regressions

Table 3 presents results from analyses including LUC with CECand clay content that were shown to be dominantly importantusing the PLS regression. The explained variance (R2) rangedbetween 0.23 and 0.41 for the various C fractions. LUC was clearlysignificant except for SOC and FPOM-C. Further, in an extendedmodel the village associated with the sampling sites wasintroduced as a class variable, and LUC was nested into ‘Village’.This resulted in a much improved explained variance (R2) up to0.95 for the various C fractions. In this case LUC became significantfor total C and fine particulate organic carbon, but was renderedinsignificant for permanganate oxidizable carbon. Concomitantly,clay and CEC became less significant as explanatory variables. InPOXC, WSC, HWSC and CPOM-C the explained variance (R2) wasimproved by the normalization relative to total C in the limitedmodel, while when village was taken into account this pattern wasless pronounced. This indicates that taking the total C into accountin the limited model compensated for the lack of place-boundinformation.

3.4. C storage

The results of Fig. 3 show the C storage in the whole pedon soil (0–120 cm) or in the subsoil from the replicated pedon (20–120 cm) of18 sites. The average storage ranged from 18.1 to 23.4 kg m�2 in thefull pedons and 12.4–17.2 kg m�2 in the subsoils according to land-

use class. C storage in cultivated soils tended to be higher thansecondary and least disturbed forest areas (Fig. 3).

The top soils (0–20 cm) from 99 sites are shown in (Fig. 4). TheSOC storage at surface soil ranged from 4.7 to 6.1 kg m�2 with

Fig. 5. Soluble carbon fractions in topsoil (0–20 cm depth) by land-use type from 99 locations: AFF, afforested area; FA, fallow agriculture; FTP, fruit tree plantation; MCL,

mixed crop land; SCF, secondary conservation forest; WR, wetland rice. Error bars represent standard error of means; n (number of locations sampled) in soft brackets.

S. Aumtong et al. / Agriculture, Ecosystems and Environment 131 (2009) 229–239234

wetland rice containing the lowest average C content. These soilsare ploughed with small tractors to a depth of about 20 cm. Cstorage was generally highest in the less disturbed, cultivated orrecently cultivated soils (AFF, FTP and FA, Fig. 4).

3.5. Bulk density

The average of bulk density of wetland rice soil was higher thanother land-use types although no significant differences werefound (data not shown). The bulk density varied between 1.31 and1.53 g cm�3. Bulk density increased approximately linearly withdepth. In the 56 pedons sampled to 120 cm depth the correlation to50 cm was R2 = 0.26 (P < 0.0001).

3.6. Coarse and fine particulate organic matter (POM) at 0–20 cm

The average amount of coarse POM ranged from 1.4 to 8.2% oftotal C (Fig. 5a) with the greatest proportion in the tilled wetlandrice soil followed by the relatively undisturbed forest soils. Thepercentage of coarse POM was substantially lower in the cultivatedbut untilled soils, with the lowest content in the recently afforestedsoils. The average fine C POM ranged from 6.2 to 14.5% of total C invarious land-use classes, and closely resembled the coarse POMvariations (Fig. 5b).

3.7. Permanganate oxidizable C (POXC)

The average permanganate oxidizable C content ranged from 11to 18% of total C between various land-use classes with fruit treeplantations and wetland rice yielding the highest and fallowagriculture yielding the lowest content (Fig. 6a). The differences

Fig. 6. POM-C fractions in topsoil (0–20 cm depth) by land-use type from 99 locations: AF

land; SCF, secondary conservation forest; WR, wetland rice. Error bars represent stand

between land-use classes were significant with wetland riceyielding the most and fallow agriculture the least.

3.8. Water soluble and hot water soluble C

The amount of water soluble C ranged from 0.15 to 0.58% oftotal C from various land-use classes, whereas the amount of hotwater soluble C ranged from 0.55 to 1.7% of total carbon.Differences between the classes were significant (Fig. 6b and c),but in contrast to the permanganate oxidizable carbon, secondaryconservation forest and fallow agriculture yielded the most andafforested and wetland rice areas the least.

3.9. Profile gradient analysis

Gradients with depth in soil C and coarse POM are shown inFigs. 7 and 8. They were negative in all cases, and were sensitive toland use. The shallowest gradient was found in the tilled wetlandrice soils. Gradients were not significantly affected by the positionof the site in the undulating landscape, whether on the top, side ortoe slopes.

4. Discussion

The analysis of the on-site variability of the replicated volumespecific sampling of 56 pedons indicated that only about 9% of thevariation of SOC in the full dataset should be impossible to explain.However, a large part of the variability was inexplicable on thebasis of the available data. Thus, while soil quality relatedparameters such as CEC and clay content were positivelycorrelated and explained 31% of total variation, the land-use

F, afforested area; FA, fallow agriculture; FTP, fruit tree plantation; MCL, mixed crop

ard error of means; n (number of locations sampled) in soft brackets.

Fig. 7. Concentration gradients (positive figures indicate decreasing concentrations with depth) of SOC and CPOM-C in topsoil (0–20 cm depth) by land-use type from 99

locations: AFF, afforested area; FA, fallow agriculture; FTP, fruit tree plantation; MCL, mixed crop land; SCF, secondary conservation forest; WR, wetland rice. Error bars

represent standard error of means; n (number of locations sampled) in soft brackets.

Fig. 8. Concentration gradients (positive figures indicate decreasing concentrations with depth) of SOC and CPOM-C in topsoil (0–20 cm depth) by landscape position from 99

locations. Error bars represent standard error of means; n (number of locations sampled) in soft brackets.

S. Aumtong et al. / Agriculture, Ecosystems and Environment 131 (2009) 229–239 235

intensity indicators (fertilization, burning, years of cultivation,tillage) were only able to explain 3% of total variation (Fig. 2a andb). This low explanatory power is perhaps surprising, but mayreflect two opposing causes. On one hand, it may be argued that theintensity of land use is very low compared to the levels knownfrom high input systems. On the other hand, the accuracy of theinformation is based only on farmer interviews, and not supportedindependently, e.g. by aerial photographs and/or satellite images(McDonagh et al., 2001; Birch-Thomsen et al., 2007). The degree ofexplanation was improved to 41% when introducing the classvariable LUC and further to 64% when taking in the ‘village’ as aclass variable (Table 3). Even larger improvements were seen formost of the C fractions assessed, except for fine particulate organiccarbon.

The location ‘village’ is an integration of place-bound statevariables and drivers such as the mineralogy, micro-climate andsignificant local historical occurrences whether natural (e.g.vegetation) or anthropogenic (management). Although commonas well as geostatistical knowledge implies that the location is acrucial determinant of soil quality within the landscape, it issurprising how substantial the improvement became in overallexplanation of variance (up to 60%) by the introduction of thisvariable. We are not aware of publications showing similar results,but this could be important for future assessments of soil Crelationships to land use in sloping uplands (but see next section).

Significant correlation between LUC and SOC as well as soil Cfractions does not indicate a straight forward effect of land use onsoil quality. It might as well be an indication that the land was usedfor a particular purpose due to its inherent qualities. This is inaccordance with previous studies from the region, showing thatfallow periods were shorter on more fertile soils (Roder et al.,

1995). The important land-use classes within each village areawere sampled with the view to establish soil C relationships at thecatchment level. It was not our intention to conduct a replicatedstudy across villages, where indeed land uses were quite variableperhaps due to both landscape factors and ethnic preferences. Sucha strategy could be devised by limiting the number of land-useclasses, but even then a test of land-use effects on soil quality couldbe confounded, for several reasons. For example, in 1 village thedistance between accessible fields could be a determining factor,while in another the soil quality could be an overriding factor. Asfar as we understand these systems, the only way to firmlyestablish a causal relationship between land use and soil quality isto study several locations over a sufficient time span.

4.1. C stocks

The amount of information available on the relation between Cstocks and land-use change is quite restricted in humidsubtropical–tropical areas. In a meta-study employing data from74 publications, 16 were from tropical parts of Africa and southAmerica, but none from Asia (Guo and Gifford, 2002). They foundthat broadleaf plantations did not affect soil C stocks when plantedon native forests or pastures, whereas a substantial reduction wasfound when native forests or pastures were replaced with annualcrops (42–59%) or pine plantations (12–15%). Furthermore, theyfound that the transition from cropland to secondary forestsincreased C stocks by 53% on average. A few studies from the regionhave investigated the effect of land use on C stocks, mainly thetransition from low-input shifting cultivation to more intensifiedland uses (Roder et al., 1995; Tanaka et al., 1998b, 2001). Tanaka(2001) showed a significant effect of cultivation on both total and

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labile C stocks, and concluded that more permanent cultivationwould require substantial C amendments to soil in order tomaintain its fertility. In contrast, Roder et al. (1995) found a verypoor correlation between fallow length and soil C (as well as riceyields), consistent with other work from the region (Mertz et al.,2008). A general problem when comparing data from the literatureis the inconsistency in sampling depth, ranging from 25 to>150 cm, and perhaps more importantly that many studies onlyinclude the top soil layers, which may show a quite differentpicture than the full profile (de Neergaard et al., 2008).

Determining SOC changes is difficult both due to the intrinsicspatial variability and the fact that small changes in a large massare to be measured. In a meta-study of C storage potentials, Postand Kwon (2000) quantified rates of storage on establishment offorest upon annually cultivated land from 30 publications to muchless than 100 g C m�2 year�1, on average 33.2 g C m�2 year�1. Mostof the cited studies were to 50 cm depth or less. Changes of soils Cdue to land degradation are reportedly rapid. For example severelosses of soil C (40–50%) has been reported from a number of tilledmaize based African farming systems over 10–30 years ofcultivation (McDonagh et al., 2001; Zingore et al., 2005; Birch-Thomsen et al., 2007). Studies from Asian hill-farming systemshave also reported significant decreases of soil C and nutrientsfrom cultivation (Roder et al., 1997; Gafur et al., 2000; Wairiu andLal, 2003).

Our observations are partly in contrast with the aforemen-tioned meta-studies and strongly in contrast with the studies ofAfrican farming systems, as well as our initial hypothesis that soilorganic C would decrease with increasing intensity of land use.However, several studies from more comparable farming systems,as the shifting cultivation of hill rice in Southeast Asia, have shownmuch more limited effects of cultivation on soil quality indicatorsincluding soil C (Forsyth, 1994; Bruun et al., 2006; Ziegler et al.,2007; de Neergaard et al., 2008). While rather high C stocks on thefruit tree plantations and afforested areas were found, the contentsfound on the fallow area and mixed crop land were not muchlower. This rather small difference may well be due to the actuallocal land management practices by the ethnic tribes in the slopingareas, where planting of crops is commonly done by punchingholes in the otherwise undisturbed topsoil and subsequentweeding is done with manual slashing or with herbicides, thuseffectively forming a zero-till practice. Addition of moderateamounts of fertilizer to the utilized land may contribute toincreased productivity and increased formation of organic matter.This falls well within the previously cited farming systems analysisfrom the region by other researchers, who generally agree that thetraditional farming systems have large potential for soil conserva-tion, compared to more mechanised land uses.

The higher C content in the utilized areas is most likely due tohuman preference. We propose that the communities haveselected land for particular purposes based on suitability, i.e.perceived inherent quality, accessibility and possibly other factors,leaving the poorest land for secondary forest, as also shown byRoder et al. (1995). This two-way interaction between land use andland quality is a fundamental challenge to catchment-level studies.This may be a significant contributor to the observation that thecausal relationships between land use and soil quality derivedfrom controlled studies are difficult to extrapolate to field andregional scale.

4.2. C fractions and their relations to land use

Particulate organic C is mainly derived from plant material, andonly to a limited extent derived from soil macro-fauna. Previousstudies have shown that the age of POM-C is recent to intermediate(Christensen, 1992; Magid et al., 2002; Bruun et al., 2008).

Microscopic examination revealed that substantial amounts ofmore or less intact carbonized plant remnants may remain afterburning (Magid et al., 2002), which is only degrading very slowly(Forbes et al., 2006; Bruun et al., in press). It is commonly assumedthat POM-C is highly sensitive to land-use changes compared tototal soil organic C (Haynes, 2005). However the PLS analysis didnot show any relationship between POM-C and soil properties andland-use intensity (Fig. 2c and d), There could be numerous reasonsfor this. POM-C is likely not to be sensitive to inherent soilproperties but only to land use. SOC was mainly explained by soilproperties and therefore POM-C would also be impossible toexplain if the land-use intensity indicators that have been used areinadequate due to the quality of the interview data. However, it isnoteworthy that the papers that report high sensitivity of POM-Cto land-use change generally involve large differences in tillageintensity. Of all the systems included in the current study onlywetland rice cultivation implied actual breaking and turning of thesoil matrix.

The POM-C-to-SOC ratio was clearly negatively correlated withthe soil properties clay content and CEC, while the managementindicators spread out evenly along the second component (Fig. 2f),which was only able to explain about 2% of the variation. This canbe expected since increasing CEC and clay content both willincrease the stabilization potential for degraded (nonparticulate)organic substances, microbial residues and microorganisms.Furthermore, the explanatory power of the PLS analysis was muchincreased compared to analysis of POM-C on its own (Fig. 2e and f).It is proposed that factoring out the SOC variability will removesome of the unaccounted for environmental and inherent soilproperty factors that control overall organic matter productivity ofthe system.

Secondary forest and wetland rice areas had the highest contentof coarse particulate organic matter 6.7–8.2% of total C as opposedto 1.4–3.6% in the other land uses (Fig. 5). Although they hadslightly lower content of total C this difference is substantial andindicates that the dynamics of organic matter turnover may besomewhat different. The secondary forest areas are the oldestestablished vegetation, a factor that could explain a high coarsePOM content. For the wetland rice it may be that frequent cropcycles, as well as tilling stubble into the soil increases the content,while the predominant anoxic conditions could act as a stabilizingfactor. The fine POM generally reflects the coarse POM content.However, the afforested areas, which clearly had the lowest coarsePOM content (1.6%), had an intermediate to high fine POM content(8.6% of SOC). This could reflect a higher degradability of the coarsePOM.

4.3. Soluble and oxidizable C fractions

There is much emphasis in the literature on the relationshipbetween solubility and ‘lability’ in the attempt to find the most‘active’ fraction of a given soil component, i.e. organic C (Blair et al.,1995; Ghani et al., 2003), or soil inorganic and organic phosphorus(Hedley et al., 1982). Even though the solubility vs. labilityrelationship is well established in aqueous systems, it is moretenuous in soil. Microorganisms colonize only a tiny fraction of thesoil pore spaces giving rise to the claim that soils are mostly‘deserts’ (Adu and Oades, 1978; Vanveen and Kuikman, 1990) anddiffusion barriers and sorption processes limit the transfer ofsoluble substances. Therefore, it is known that in zones of highbiological activity, relatively insoluble fractions of C may bemobilized (de Neergaard and Magid, 2001).

Ghani et al. (2003) stated that the water soluble fraction is asensitive indicator of labile organic matter and that the hot watersoluble fraction especially could be used as an integrated measureof soil quality. Blair et al. (1995) dubbed the permanganate

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oxidizable C as the labile C fraction ‘C–L’ that has subsequentlybeen used in numerous studies. The absolute magnitude of thesefractions varies between 0.2 and 0.6% (water soluble), 0.6–1.7%(hot water soluble) and 12–18% (POXC) of total soil carbon. Whilewater soluble and hot water soluble C are closely correlated,permanganate oxidizable C reflects land use in a different andsomewhat contradictory manner (Fig. 5). Therefore while allmethods are claimed to be central to understanding the most labileand active fraction – they do not reflect similar dynamics. Wetlandrice fields, which are predominantly anoxic and fruit treeplantations had the highest fraction of permanganate oxidizablecarbon, but low content of water and hot water soluble carbon. Onthe other hand, secondary forests and fallow agricultural land hadhigh content of water soluble and rather low content of POXC.

We hypothesized that examining selected organic C fractionswould enable a better understanding of the short to medium termC dynamics. Based on our understanding of the methods, it seemsplausible that there is an association between high POXC and‘fertile’ organic matter (Blair et al., 2006a,b). On the other handHWSC and WSC is associated with high content of solublecarbohydrates (Ghani et al., 2003), which might be more likelyto be present in systems with higher C-to-N ratio organic input (FAand SCF), and a more nutrient limited turnover of carbon. The POXCfraction represents a large part of the SOC compared to HWSC, andis probably less rapidly responsive to changes in the soilenvironment. Therefore, the POXC is more likely to be anintegrative measure of longer term changes.

4.4. Indications of land degradation

From more recent literature, it is apparent that the generalnotion that swidden is unsustainable and detrimental (Harwood,1996; Brady, 1996; Devendra and Thomas, 2002; Rasul and Thapa,2003; Rasul et al., 2004) and can only be sustainable at lowintensity levels (Juo and Manu, 1996) is increasingly challenged(Kendawang et al., 2004; Bruun et al., 2006; Ziegler et al., 2007; deNeergaard et al., 2008; Mertz et al., 2008). The narratives of fertilityloss and massive erosion are not always mirrored in an apparentimpact on soil quality. Particularly in comparison with other land-use types – e.g. various plantations and intensified agriculture –the traditional farming systems may have a significantly lowerenvironmental impact than suggested in textbooks (Forsyth, 1994;Rerkasem and Rerkasem, 1995; Tanaka et al., 1998b). In a study ofthe impacts of swidden on forests in northern Thailand, Schmidt-Vogt (1998) argues that the common belief that swidden is adegrading land use, causing species loss, soil erosion, and watershortages, is highly simplistic. An extensive review demonstratedthat the reported literature on yields as functions of fallow lengthprovides little quantitative support of the ‘‘system breakdown’’theory that has formed the public image of swidden (Mertz, 2002).In both Thailand and Malaysia a deagrarization over the last decadehas taken place due to economic development, but also in anumber of places a replacement with more intensive plantations.However, the academic challenge would be to compare theenvironmental services – including food production – and theenvironmental impacts of varying farming systems along anintensification gradient. Unfortunately, no such comparison hasbeen published, but based on the reviewed literature it is ouropinion that the low-input farming systems may comparefavourably with more intensive land uses.

This study does not indicate that the cultivated land isdegrading relative to the secondary forest land, either by Ccontent (Figs. 2 and 3), or the profile gradients of total C andparticulate C through the topsoil (0–20 cm; Figs. 7 and 8). The factthat gradients are significantly explained by land-use class but notby position in the landscape indicates that erosion generally is not

an overriding force. While the secondary forest has the strongestgradient of total C (Fig. 7) it is not much higher than fallow (FA) andfruit tree plantations, and only significantly different from wetlandrice, which has a very small gradient due to the frequent ploughingof the soil.

In the lowland communities and administrative bodies inThailand it is often assumed that hill tribe farmers are causingmassive erosion by their farming activities. A study based on 137Csmeasurements of soil erosion and farmers perceptions to indicateland degradation amongst shifting cultivators in northern Thai-land, (Forsyth, 1994) found the influence of upland agriculture onerosion to be overstated, and to be highly dependent on naturallyformed gullies on strongly sloping land. The notion that hill tribefarmers are causing erosion at a massive scale is primarily based onthe perception that the larger rivers are carrying increasingsediment loads. Relating localised estimates of erosion to sub-catchment or catchment scale is difficult due to sedimentationstorage at the base of slopes and valley floors, etc. (Dickinson andCollins, 1998). Due to the scale effect, the amount of sediment thatexits a catchment, in proportion to the field soil loss rate, typicallydecreases from 0.1 to 0.01 for small catchments, to majorcatchments and large river systems, respectively (Stocking,1996). During this study in an adjoining area to the Forsyth(1994) study 10 years hence, it was observed that ditches alongsidenewly built tarmac roads traversing the area would be highlyconducive to long distance transport of sediments delivered frombarren road side land.

4.5. Interpretation of soil functioning and soil processes in fuzzy

systems

This work illustrates the difficulties associated with establish-ing appropriate controls for examining land-use impacts on soilquality, in landscapes that are moderately or strongly influencedby human activities. The ‘hedging approach’, in which a nearbypatch of land is designated as ‘undisturbed’ or ‘virgin’ may beproblematic, since there may well be a soil fertility/soil qualityrelated difference in the particular patch that has been set aside,apart from other rationales (e.g. holy ground, conservation area). Itwas found that the secondary conservation forest generally hadlower C content than the agricultural land. It may be speculatedthat this is due to human actions, but since soil archives are lackingit is not clear whether these have occurred during the timberextraction period (1960–1970s) or are due to unsustainableagricultural practises in subsequent years.

The ideal of a measurable ‘labile’ or ‘active’ C fraction that couldtell farmers and administrators with reasonable accuracy whetherthe soil is in a better or worse state is difficult to achieve, evenunder controlled conditions (Andren et al., 2008), and is notidentified in this study of variable land-use systems. However, asdiscussed above the differences in land use are reflected in therelative distribution of C in the various fractions. It would,therefore, seem appropriate to normalize measurements of Cfractions relative total C in future studies. It is not possible todisentangle a complex issue as sustainability of land use within acomplex landscape of variable land-use systems using simplemeasurements. While they may serve as supportive indicators inintegrated work, this study illustrates that long-term (>30 years)monitoring with periodic sampling of soil from fixed locationsmust be established to enable a well documented assessment inthe future.

5. Conclusion

Low C contents were found in secondary forest and a highercontent in most utilized areas. Rather than a functional relation

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between vegetation and C storage, this is ascribed to farmerpreference for land with higher C content. Thus, this studyindicated that the secondary forest land is degraded relative to thecultivated land. Topsoil profiles of C and particulate organic matterC were affected by land use but unaffected by slope position inlandscape, indicating minor effects of erosion.

Analysing distribution rather than the absolute C content infractions improved explanation of variance presumably byremoving some of the unaccounted for environmental andinherent soil property factors that control overall organic matterproductivity of the system. Including the location ‘village’ as anintegral of place-bound state variables and drivers in the statisticalanalysis substantially improved the overall explanation ofvariance.

The water soluble C fractions were high in systems with input ofhigh C-to-N ratio material, whereas the permanganate oxidizablefraction was somewhat increased in the predominantly anoxicwetland rice site, and in fruit tree plantations. Coarse particulateorganic matter C was strongly affected by land use.

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