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Changes in soil organic carbon stocks and soilquality: land-use system effects in northern EthiopiaG. Girmay a & B. R. Singh ba Department of Land Resources Management and Environmental Protection , MekelleUniversity , P.O. Box 231, Mekelle , Ethiopiab Department of Plant and Environmental Science , Norwegian University of LifeScience , N–1432 , Ås , NorwayPublished online: 02 Mar 2012.

To cite this article: G. Girmay & B. R. Singh (2012) Changes in soil organic carbon stocks and soil quality: land-usesystem effects in northern Ethiopia, Acta Agriculturae Scandinavica, Section B - Soil & Plant Science, 62:6, 519-530, DOI:10.1080/09064710.2012.663786

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ORIGINAL ARTICLE

Changes in soil organic carbon stocks and soil quality: land-use systemeffects in northern Ethiopia

G. GIRMAY1 & B. R. SINGH2

1Department of Land Resources Management and Environmental Protection, Mekelle University, P.O. Box 231, Mekelle,

Ethiopia, 2Department of Plant and Environmental Science, Norwegian University of Life Science, N�1432 As, Norway

AbstractIn Tigray, Ethiopia, land degradation is a dominant environmental problem and hence the regional government hasundertaken restoration measures on degraded soils since 1991. The present study was aimed to assess the impact of landuses and soil management practices on soil properties, and consequently on soil quality of degraded soils. The catchmentsselected were Maileba and Gum Selassa, and land uses included cultivated (CL), grazing (GL), plantation (PA) and areaexclosure (AE). Replicated soil samples were collected from topsoil and profiles of four land-use types in both catchments.Soils in area exclosure showed higher soil organic carbon (SOC), total N and extractable K than grazing land, cultivatedland and plantation area mainly at 0�40 cm soil depth. Estimated soil organic carbon stock at Maileba in 0�40 cm depthvaried between 54 to 74 Mg C ha�1, being lowest in cultivated land and highest in area exclosure, and the soil organiccarbon stock in area exclosure represents 63% of total carbon stock stored in the profile. Soil organic carbon stock (0�40cm) at Gum Selassa ranged between 33 to 38 Mg C ha�1, being higher in cultivated land and lower in plantation area. Soilquality index (SQI) of area exclosure (0.794) at Maileba and cultivated land (0.721) at Gum Selassa scored highest amongall land uses, and the order was area exclosure�grazing land�plantation area�cultivated land at Maileba and cultivatedland�grazing land�plantation area at Gum Selassa, highlighting the effectiveness of area exclosure in restoring soil qualityof degraded soils.

Keywords: Land use, soil management, soil properties, Tigray.

Introduction

The long history and recent intensification of agri-

culture in Ethiopia has led to soil degradation

through land-use changes (Kebrom and Hedlund

2000, Gete and Hurni, 2001, Woldeamlak 2002)

and the subsequent soil quality deterioration (Mu-

lugeta et al. 2004, 2005a). Many studies in Ethiopia

(Woldeamlak and Stroosnijder 2003, Mulugeta and

Itanna 2004, Mulugeta et al. 2005a) and elsewhere

(Guo and Gifford 2002, Shrestha et al. 2004,

Awasthi et al. 2005) have reported that conversion

of forest land into arable land with the aim of

expanding cultivated land has caused land degrada-

tion and often result in accelerating soil erosion

(Warkentin 1995), nutrient depletion (Gong et al.

2006), soil organic matter (SOM) reduction (Mulu-

geta et al. 2005b) and soil quality degradation

(Solomon et al. 2000). Deforestation results in

historical significant loss of both soil organic carbon

(SOC) and inorganic carbon (SIC) worldwide (Lal

2002) and the impact is more pronounced on the

topsoil (Sombroek et al. 1993). As a result, SOC

vary considerably both along with land-use types and

soil depths. But apart from land use, the level of

SOC is also determined by many factors such as

climatic factors (e.g. temperature and moisture

regime) and edaphic factors (e.g. parent materials,

soil drainage, texture etc.). The SOC accumulation

is higher, for example, on higher altitude due to low

degrees of decomposition associated with low tem-

perature and high rainfall (Lal 2008).

Land use and management affects the SOC and

nutrient in the soil. Restoration and management of

degraded land with various conservation measures or

disturbing virgin lands may significantly contribute

Correspondence: B. R. Singh, Department of Plant and Environmental Science, Norwegian University of Life Science, P.O. Box 5003, N�1432 As, Norway

E-mail: balram.singh@umb.no

Acta Agriculturae Scandinavica Section B � Soil and Plant Science, 2012; 62: 519�530

(Received 9 December 2011; revised 31 January 2012; accepted 1 February 2012)

ISSN 0906-4710 print/ISSN 1651-1913 online # 2012 Taylor & Francis

http://dx.doi.org/10.1080/09064710.2012.663786

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to enhance or degrade soil quality (Lal 2002, Singh

and Lal 2005). Doran and Parkin (1994) define soil

quality as the ‘capacity of a soil to function, within

ecosystems boundaries, to sustain biological produc-

tivity, improve environmental quality and support

human and plant health’. Soil quality cannot be

measured directly but inferred indirectly by measur-

ing soil physical and chemical properties which serve

as quality indicators (Diack and Stott 2001). How-

ever, soil properties do have different degrees of

influence on soil quality. As a result an assessment of

integrated soil quality index (SQI) on the basis of the

weighted contribution of each soil property may

serve better in quantifying soil quality indicator for

different land uses (Diack and Stott 2001, Awasthi

et al. 2005).

The state of Tigray is characterized by high

environmental degradation induced by improper

use of land resources that has occurred for many

decades (e.g. Dereje et al. 2002, Mekuria et al.

2007). To address these problems various environ-

mental rehabilitation programmes that include plan-

tations of exotic and indigenous species, and

introducing area exclosure (i.e. closing area to allow

natural regeneration of forest without the interfer-

ence of animals and humans) have been implemen-

ted on degraded lands (Descheemaeker et al. 2006a,

2006b, Mekuria et al. 2007). However, little is

known about the impact of these land uses and

management situations on carbon and nutrient

status in the study area. Therefore, the increasing

human disturbance and past deforestation on one

hand and the restoration measures underway on the

other hand makes Tigray state a typical and inter-

esting area for integrated assessment of soil proper-

ties and soil quality changes in relation to land use

and soil management. However, such studies are

rare in Ethiopia in general and that of Tigray in

particular where the level of land alteration is

supposedly huge and land degradation is severe.

Therefore the objectives of the present study were to:

(1) quantify the amount and distribution of soil

C stocks and major nutrients on soil surface and

profile of different land uses and (2) determine soil

quality indices for different land uses and soil

management based on some selected soil properties.

Materials and methods

Study area

The study areas, Gum Selassa and Maileba, are

located respectively at Hintalo Wajerat and Hagre

Selam district of Tigray region. Gum Selassa catch-

ment is located at 13815?N and 39832?E. It has a

surface area of 23.5 km2 that lies on elevation ranges

of 2100�2160 m a.s.l. Similarly Maileba catchment

lies at 13814?N and 39815?E. The catchment has an

area of 17.3 km2 and elevation varies between 2300

to 2830 m a.s.l. Location of the study area is given in

Figure 1.

Both study areas have annual precipitation of 400�700 mm, and mean daily maximum and minimum

temperatures that vary between 20�30 8C and

8�15 8C, respectively. In both sites rainfall mainly

occurs between June�September and temperature

gets lower in September�November. Despite some

micro-climate variation, no significant climate dif-

ference exists between these two sites that could

importantly bring variation in soil properties alone.

The dominant lithology of both study areas are

composed of largely Agulae shale, Antalo limestone,

dolerite and basalt. Silicified lacustrine deposit layers

locally occur in between basalt layers at Maileba as

well. Van de Wauw et al. (2008) studied soil-

landscape relations for the Maileba catchment.

Dominant soils at Gum Selassa area are Cambi-

sols, Leptosols, Vertisols, Luvisols and Calcisol

developed from shale and limestone. At Maileba

major soils include Vertisols, Cambisols, Regosols

and Fluvisols.

Vegetation cover is low in both areas and largely it

remains only in small protected areas. Natural

vegetation inside the area exclosures is dominated

by Acacia etbaica and Euclea schimperi. Eucalyptus

camaldulens is the major species on plantation sites,

but other tree species such as Schinus molle also exist

to some extent. Understorey vegetation in area

exclosure and plantation area contains diverse spe-

cies of grasses (e.g. Chloris gayana Kunth, Pennisetum

petiplare (Hochst) Chiov, Cymbopogon caesius, Cynodon

dactylon) and shrubs (e.g. Acokanthera schimperi,

Lantana viburnoides, Calpurnia aurea, Maytenus

arbutifolia). Most of the understorey vegetation is

palatable for livestock and hence serves as sources of

feed for livestock through the cut-and-carry system,

although sometimes there is also illegal grazing

inside these protected areas.

Soil sampling and laboratory analysis

Selected sites for this research contain four dominant

land-use types consisting of cultivated land (CL),

grazing land (GL), plantation area (PA) and area

exclosure (AE) at Maileba and three land-use types

at Gum Selassa (all with the exception of area

exclosure). At both sites land uses exist close to

each other. Our intention in this research was to

study the variation in soil nutrient content and soil

quality with respect to differences in land-use types

and hence land use was the main factor considered.

520 G. Girmay & B. R. Singh

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From each land use, replicated soil samples were

collected from both catchments in 2007. The first

batches of samples were collected from surface soils

(0�20 cm) using auger replicated three to five times.

A total of 25 soil samples were analysed for organic

carbon (OC), total N, available P (Pav), available K

(Kav), sand, silt and clay for assessing the general

properties of each land use in each catchment. The

second batches of samples were collected from

profiles dug to a depth of 80 cm. In each land-use

type three to five randomly chosen profiles were dug

and samples collected from 0�20, 20�40, 40�60 and

60�80 cm depths. The numbers of profiles were 3, 5,

4 and 3 in AE, CL, GL and PA, respectively at

Maileba, and 4, 3 and 3 in CL, GL and PA,

respectively at Gum Selassa. Hard rock was found

in some of the profile after 40 cm as a result all

profiles did not have the same depth. A total of 94

samples were collected and analysed for OC, total N,

Pav and Kav. Simultaneously, soil samples for

determining bulk density (BD) were also collected

using core samplers with a volume of 100 cm3 from

each depth of the profiles as well as from the surface

soils.

The soils collected from the field were air dried,

lightly ground and passed through a 2-mm sieve

Figure 1. Location of the study area.

Land use effects on soil quality 521

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prior to physicochemical analysis. Textural analysis

was performed using Boycous hydrometer method

after the organic matter was removed by hydrogen

peroxide (H2O2) and the soil was dispersed by

sodium hexametaphosphate (Na6P6O18). Walkley�Black oxidation method for SOC and digital pH

meter for pH were used for their determination.

Olsen method (Olsen and Sommers 1982) for Pav,

flame photometer (Black et al. 1965) for Kav, and

Kjeldahl method (Bremner and Mulvaney 1982) for

total N were used for soil analysis. Bulk density was

determined after core samples were dried in an oven

at 105 8C for 24 hours. Percentage of pore space (soil

porosity) was computed from bulk density result and

a particle density of 2.65 Mg m�3 as suggested for

mineral soil (Brady 2002).

Calculation of carbon stocks

Soil carbon stock (g C m�2) for each sample depth

was computed using the following equation

Carbon stock ¼ d � BD � OC � CFst�10 (1)

Where d�soil layer thickness (cm); BD�bulk

density (g cm�3) of each sample depth, OC�carbon concentration (g kg�1) of each soil sample,

CFst (%)�correction factor for fraction of

fragments�2 mm and the multiplication factor 10

is a value obtained when units of all variables

are multiplied. It results in carbon stock in g

C m�2�10.

Carbon stock for each layer of the dominant land

use was calculated by multiplying the C stock

obtained by equation 1 by the total area covered by

a particular land use. Subsequently, C stock in each

soil layer thickness was summed up to determine

total C stock contained up to 80 cm depth for each

land-use type. Finally, total C at a watershed level

(Mg) was obtained as a summation of C up to 80-cm

depth obtained from all land-use types assuming that

the mean values of the parameters involved in the

calculation of carbon stock from different land uses

represent the entire catchment. Difference in soil

bulk density caused due to difference in land use or

cover affects the calculation of carbon stock by

influencing the amount of soil sampled from the

same soil depth (Solomon et al. 2002). Such

differences in bulk density should be adjusted,

particularly if the variation among the different

land uses or covers is statistically significant. In the

present study the variation was not significant among

all land uses and even on those land uses that showed

significant difference, the p value is marginal. Hence

the influence was small and no correction measure

was taken for bulk density.

Selection of indicators and minimum data sets

Selection of minimum data sets (MDS) of indicators

was based on expert opinion by weighting according

to the relative importance of each function in

maintaining sustainable crop production in the study

area. Minimum dataset for SQI consists of pH, BD,

C, N, C/N, P and K, and these indicators were also

suggested by others (e.g. Doran and Parkin 1996,

Andrews et al. 2002, Awasthi et al. 2005, Tiwari

et al. 2006).

Calculation of soil quality index

Soil quality index (SQI) reflects the relative quality

of soils available under different land-use types. It is

a useful tool to assess sustainability of soil resources

that are managed and used in different land-use

systems. However, different nutrients do have dif-

ferent roles in maintaining soil quality and as such

SQI was calculated by selected soil factor member-

ships and their weights following a procedure similar

to that of Tiwari et al. (2006), Awasthi et al. (2005)

and Fu et al. (2004).

SQI ¼Xn

i

Wi � QðXiÞ (2)

Where n�number of observations, Wi�the weight

vector of ith soil quality factor and Q(Xi)�member-

membership value of each soil quality factor. The

values Q(Xi) were calculated by ascending or des-

cending functions using the equations given below,

respectively.

QðXiÞ ¼ ðXij�XminÞ=ðXmax�XminÞ (3)

QðXiÞ ¼ ðXmax�XiÞ=ðXmax�XminÞ (4)

Where Xij is the value of the soil physical and

chemical properties that were selected for the soil

quality. Xmax and Xmin are the maximum and

minimum value of ith soil property. Based on

appropriate ranges for individual soil properties

favourable for plant growth and results of our

laboratory analysis, two classes of functions were

identified: (1) ‘More is better’ and (2) ‘Less is

better’. Accordingly, we have chosen ‘More is better’

for C, N, C/N, P and K, and values were arranged in

ascending order to compute Q(Xi) with equation 3.

While for pH and BD, ‘Lower is better’ was chosen

and values were arranged in descending order to

compute Q(Xi) using equation 4.

The membership values, obtained using equation

3 and 4, of the selected soil quality factor were

subjected to principal component analysis (PCA) to

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determine the weights for each soil indicator (Fu

et al. 2004, Awasthi et al. 2005, Tiwari et al. 2006).

Wi ¼ Ci=Xn

i

Ci (5)

Where Ci is the component capacity score coefficient

of soil quality factor i, obtained and calculated from

PCA. Finally SQI was calculated as a summed

product of membership values Q(Xi) and the weights

of the selected soil quality indicators. To evaluate soil

quality (i.e. improvement or deterioration) under the

different land-use types, SQI was classified into five

arbitrary classes as: (i) excellent (SQI�0.80), (ii)

good (SQI�0.60�0.80), (iii) at risk (SQI�0.40�0.60), (iv), degraded (SQI�0.20�0.40) and (v)

severely degraded (SQIB0.20) (Awasthi et al. 2005).

Statistical analysis

Analysis of variance (ANOVA) was made using

general linear model procedure of Minitab statistical

software to test the significant of mean difference on

soil properties. Multiple mean comparison test was

carried out to distinguish mean significant difference

among treatments using Tukey test at a significance

level of 5%.

Results

Surface soil properties under different land use types

In Table I, mean values of soil properties of surface

soil (0�20 cm) and their membership for different

land-use types is given. Area exclosure at Maileba

site gave higher values for most measured soil

properties than the other land-use types involved in

the same area. At Maileba, soils were higher in clay

content, OC, Pav and Kav than at Gum Selassa, and

the mean value for BD varied from 1.12 to 1.34 g

cm�3, higher in grazing land (GL) and lower in area

exclosure (AE). Also OC varied from 1.16 to 1.93%,

total N from 0.11 to 0.20%, and available K from

169.6 to 460.3 mg kg�1 being highest in AE and

lowest in cultivated land (CL) in all cases. However,

at Gum Selassa the mean BD is higher in PA (1.33 g

cm�3) and lower in CL (1.2 g cm�3). Similarly,

available K was higher in plantation area (PA).

Nevertheless, OC, total N and Pav was higher in

CL and lower in PA.

SOC and major nutrient distribution under different land

uses and soil depths

Both land uses (p�0.05) and soil depth (p�0.001)

had a significant effect on BD. On the other hand,

Table I. Mean soil properties (0�20 cm), standard error of means (SEM) and membership values (Q(Xi) of different land uses. OC: Soil

organic carbon; N: total nitrogen; C/N: carbon-nitrogen ratio; Pav: available phosphorus; Kav: available potassium; BD: Bulk density; n:

number of samples; CL: cultivated land; GL: grazing land; PA: plantation area (Eucalyptus); AE: area exclosure.

Particle size distribution

Site

Land

uses Values

Sand

(%)

Silt

(%)

Clay

(%)

Porosity

(%)

pH (1:2.5

H2O)

OC

% N %

C/N

ratio

Pav mg

kg�1

Kav mg

kg�1

BD g

cm�3

Maileba CL

(n �5)

Mean 27.0 33.6 40.1 52.1 7.6 1.2 0.1 10.8 12.5 169.6 1.3

SEM 2.9 1.7 3.3 0.0 0.2 0.1 0.0 1.0 2.4 14.4 0.0

Q(Xi) 0.3 0.0 0.0 1.0 0.0 0.0 0.3

GL

(n �4)

Mean 31.9 26.4 41.7 49.9 7.8 1.7 0.2 10.0 30.0 381.8 1.3

SEM 2.4 2.0 3.2 0.0 0.3 0.2 0.0 1.6 11.5 30.9 0.0

Q(Xi) 0.0 0.7 0.7 0.4 1.0 0.7 0.0

PA

(n �3)

Mean 20.7 29.9 40.4 52.8 7.4 1.6 0.2 9.7 20.9 288.2 1.3

SEM 1.1 1.8 2.7 0.2 0.2 0.3 0.0 1.0 2.1 30.5 0.2

Q(Xi) 0.6 0.6 0.6 0.2 0.5 0.4 0.4

AE

(n �3)

Mean 41.6 27.1 31.3 57.7 7.2 1.9 0.2 9.4 24.2 460.3 1.1

SEM 3.9 1.8 3.1 0.1 0.1 0.2 0.0 0.2 2.6 125.2 0.1

Q(Xi) 1.0 1.0 1.0 0.0 0.7 1.0 1.0

Gum

Selassa

CL

(n �4)

Mean 41.8 27.8 30.3 54.6 7.5 1.0 0.1 10.8 17.1 191.5 1.2

SEM 12.5 3.8 10.1 0.1 0.4 0.1 0.0 3.2 1.2 40.5 0.1

Q(Xi) 0.0 0.9 1.0 0.9 1.0 0.0 1.0

GL

(n �3)

Mean 48.4 37.5 14.1 53.6 8.3 1.0 0.1 11.1 14.1 194.1 1.2

SEM 1.2 0.7 1.5 0.1 0.5 0.2 0.0 1.9 1.3 48.9 0.1

Q(Xi) 1.0 1.0 0.0 1.0 0.1 0.0 0.7

PA

(n �3)

Mean 49.0 29.1 21.9 49.8 7.8 0.8 0.1 9.1 13.7 270.3 1.3

SEM 6.2 2.7 1.9 0.1 0.6 0.1 0.0 0.4 1.4 97.8 0.1

Q(Xi) 0.6 0.0 0.0 0.0 0.0 1.0 0.0

Land use effects on soil quality 523

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OC significantly varied with soil depth (p�0.007),

but not with land-use types (Table II). Yet, Pav and

BD showed significant difference among land uses.

However, the difference in Pav was significant only

between cultivated and grazing lands (p�0.0034),

and in BD between grazing land and area exclosure

(p�0.0477) (Table III). Depth-wise, OC differed

significantly between 0�20 cm and 60�80 cm

(p�0.0067). Also BD at 60�80 cm depth was

significantly higher than in all soil layers above it.

On the other hand, sites show significant difference

for most measured soil properties (OC, total N, BD

and C/N). On both sites, OC decreases with depth

with a minor exception observed in CL at Maileba

site, wherein an increase up to 60-cm depth was

witnessed (Table IV). Among the land uses, OC

distribution above 60-cm depth was highest in AE.

At Gum Selassa, OC level is lower in PA followed by

GL. In both sites BD varies between 1.1�1.7 g

cm�3, being lower in the surface layer but tends to

increase with depth across all land uses. Bulk density

was lowest in AE and highest in GL. The ANOVA

result also indicated significant difference between

AE and GL. Bulk density significantly varies with

depths as well. Eucalyptus-dominated plantations at

Gum Selassa have low OC and higher BD than the

same land use at Maileba likely because of degraded

site condition prior to closing as well as low input

from understorey and vegetation cover. This will

have implications for the moisture retention and

aeration of the soils for proper growth of life forms.

Profile of SOC stocks

The soils under the different land-use types showed

significant difference in SOC stocks at Maileba but

not at Gum Selassa (Table V). At Maileba mean

SOC stock within 0�20 cm depth varied between 26

to 43 Mg C ha�1, significantly lower in cultivated

land and higher in area exclosure (pB0.05). Soil

organic carbon stock largely declined with depth at

Maileba, particularly in area exclosure and planta-

tion area. On the other hand, SOC stock was

significantly higher in grazing land than in AE and

PA at 60�80 and 40�60 cm soil depth respectively.

But on both depths no significant difference in SOC

stock was observed between grazing land and

cultivated land-use types. At Gum Selassa no sig-

nificant difference was observed among the different

land-use types and depths. Between sites SOC stock

did show variation and was generally higher at

Maileba than at Gum Selassa.

SQI for different land-use types

The Q(Xi) values for each indicator computed by

equations 2 and 3 are shown in Table I. The first

component score gave a cumulative percentage of

72%, sufficient to explain soil quality variations

among land uses and hence to compute Wi. The

weight of the soil quality indicator reflects the

relative importance of each indicator in explaining

soil quality. As such OC at Maileba (Wi�0.172)

and BD (Wi�0.168) at Gum Selassa showed higher

scores. The first four indicators with highest weight

scores according to their decreasing importance

includes: OC, N, C/N, and K at Maileba, and BD,

K, OC and P at Gum Selassa (Figure 2(a) and (c)).

Result of the integrated SQI revealed that AE at

Maileba (SQI�0.79) and CL (SQI�0.72) at Gum

Selassa scored highest in their respective area (Figure

2(b) and (d)). Grazing land scored intermediate but

with relatively similar SQI value in both sites.

According to the SQI classification, cultivated land

(CL) at Maileba and plantation area (PA) at Gum

Selassa falls into ‘degraded’ class, whereas plantation

area at Maileba and grazing land (GL) at both sites

fall into ‘at risk’ class. On the other hand, area

Table II. Result of general linear model analysis for major soil

properties under four soil depths obtained from different land

uses and two sites (pB0.05).

Land uses Site Soil depth

Soil

property

F

(df�3) P

F

(df�1) P

F

(df�3) P

OC (%) 1.56 0.204 31.48 0.000* 4.27 0.007*

N (%) 1.07 0.368 6.57 0.012* 1.00 0.399

Pav (mg

kg�1)

4.33 0.007* 3.17 0.078 0.36 0.784

Kav (mg

kg�1)

0.19 0.903 1.00 0.323 2.68 0.056

BD (g

cm�3)

2.66 0.053* 3.95 0.050* 10.29 0.000*

C/N ratio 1.35 0.263 5.11 0.026* 0.11 0.956

* Indicates significant differences at pB0.05. For abbreviations

see Table I.

Table III. Results for Tukey paired wise multiple comparison test

of soil properties for different land uses and soil depths (pB0.05).

Soil property Significant paired wise comparison p-value

Land uses

Pav (mg kg�1) CL and GL 0.003

BD (g cm�3) AE and GL 0.047

Depths

OC (%) 0�20 cm and 60�80 cm 0.006

BD (g cm�3) 0�20 cm and 60�80 cm 0.000

20�40 cm and 60�80 cm 0.000

40�60 cm and 60�80 cm 0.005

For abbreviations see Table I.

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Table IV. Depth- wise distribution of mean (9 SEM) of soil properties under different land uses at Maileba and Gum Selassa sites.

Maileba Gum Selassa

Soil Property Depth AE CL GL PA CL GL PA

OC (%) 0�20 1.990.2 1.290.1 1.790.2 1.690.3 1.090.1 1.090.2 0.890.1

20�40 1.790.2 1.290.2 1.690.1 1.590.3 0.890.1 0.890.2 0.890.2

40�60 1.490.4 1.590.2 1.090.5 0.790.1 0.890.3 0.890.2 0.790.2

60�80 0.590.1 1.190.3 1.090.3 0.790.1 0.790.2 0.790.2 0.890.0

N (%) 0�20 0.290.0 0.190.0 0.290.0 0.290.0 0.190.0 0.190.0 0.190.0

20�40 0.290.0 0.190.0 0.290.0 0.290.0 0.190.0 0.190.0 0.190.0

40�60 0.190.0 0.190.0 0.190.3 0.190.0 0.190.0 0.190.0 0.190.0

60�80 0.190.0 0.190.0 0.190.0 0.190.0 0.19 0.0 0.190.0 0.190.0

C/N ratio 0�20 9.490.2 10.891.1 10.091.6 9.791.0 10.893.3 11.191.9 9.190.4

20�40 9.690.4 13.791.8 9.891.3 9.790.6 7.890.5 9.591.5 7.790.3

40�60 10.791.4 11.190.6 8.893.8 10.591.0 7.590.4 10.091.7 8.891.6

60�80 10.392.2 9.991.3 15.691.2 8.590.7 9.690.8 10.493.0 7.990.2

Pav (mg kg�1) 0�20 24.292.6 12.592.4 30.0911.5 20.992.1 17.191.2 14.191.1 13.791.2

20�40 21.691.3 8.992.8 31.9919.2 22.890.9 16.390.6 17.391.2 14.492.1

40�60 19.393.2 14.892.3 28.5918.8 14.591.1 14.291.6 17.693.6 15.89 3.0

60�80 14.691.8 11.093.1 37.1926.7 13.891.1 13.592.6 13.892.7 12.59 0.8

Kav (mg kg�1) 0�20 460.39216.8 169.6942.2 381.8963.1 288.2952.8 191.5940.5 194.1984.7 270.3969.4

20�40 221.0959.4 130.4927.0 203.9930.8 231.7974.6 139.4946.0 181.7987.8 93.1917.4

40�60 215.8979.6 111.1931.6 331.8978.2 180.4946.9 170.1945.2 179.5984.1 90.7923.0

60�80 89.8928.0 127.9956.1 143.2951.4 163.8983.3 168.3939.1 187.6990.1 190.6991.2

BD (g cm�3) 0�20 1.190.1 1.190.0 1.390.0 1.39 0.2 1.290.1 1.290.0 1.390.0

20�40 1.290.0 1.390.1 1.490.0 1.390.1 1.390.1 1.390.1 1.490.1

40�60 1.290.1 1.390.0 1.490.0 1.390.1 1.590.1 1.490.1 1.490.1

60�80 1.490.1 1.590.0 1.539 0.1 1.490.1 1.690.1 1.790.1 1.590.1

For abbreviations see Table I.

Table V. Profile level carbon stocks at different land use types and two sites.

Maileba Gum Selassa

Soil depths (cm)

Land

uses

Mean SOC pool

(Mg ha�1)

Area

(ha)

Total SOC stocks

(Mg)

Mean SOC pool

(Mg ha�1)

Area

(ha)

Total SOC stocks

(Mg)

0�20 AE 43(a) 13.6 585

PA 36(ab) 50.8 1829 17(a) 18.5 315

GL 33(ab) 188.0 6204 19(a) 338.7 6435

CL 26(b) 1083.4 28�168 21(a) 1877.3 39�423

20�40 AE 31(a) 13.6 422

PA 32(a) 50.8 1626 17(a) 18.5 315

GL 32(a) 188.0 6016 16(a) 338.7 5419

CL 29(a) 1083.4 31�419 17(a) 1877.3 31�914

40�60 AE 31(a) 13.6 422

PA 15(b) 50.8 762 16(a) 18.5 296

GL 26(a) 188.0 4888 16(a) 338.7 5419

CL 35(a) 1083.4 37�919 22(a) 1877.3 41�301

60�80 AE 12(b) 13.6 163

PA 17(ab) 50.8 864 19(a) 18.5 352

GL 42(a) 188.0 7898 17(a) 338.7 5758

CL 28(ab) 1083.4 30�335 19(a) 1877.3 35�669

Total carbon stock

(Mg C)

159�516 172�615

Values of SOC pool followed by the same letter (in parentheses) on a column under each soil depths are not statistically different at pB0.05.

For abbreviations see Table I.

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exclosure (AE) at Maileba and cultivated land at

Gum Selassa are of ‘good’ class.

Discussion

SOC and major nutrient distribution with depth under

different land uses

Variation in SOC and nutrient distribution with

depth are the result of interaction of complex

processes such as land management, biological

cycling, leaching, illuviation, soil erosion, weathering

of minerals, atmospheric deposition, application of

fertilizers and FYM.

In this study, the OC and major nutrients

decreased with depth (with minor exceptions) but

only showed significant difference in OC between

the top (0�20 cm) and bottom (60�80 cm) soil

depths (Table III). Total N, OC and Kav distribu-

tions in 0�40 cm are higher in soils of area

exclosures, although not statistically significant

than the other land uses, mainly because of the

restoration of natural vegetation and subsequent

increase above and below ground biomass inputs.

Area exclosures are often established on marginally

degraded land where soil erosion has removed

topsoil and provide little service to local community.

Hence closing off the area from human and animal

interference allows regeneration of natural vegetation

and increase in vegetation composition (Dereje et al.

2002) and this in turn has increased litter fall

(Descheemaeker et al. 2006a) leading to increased

SOM in surface soil (Descheemaeker et al. 2006b,

Mekuria et al. 2007). A study by Mekuria et al.

(2007) on a nearby area reported that soils of closed

area had significantly higher SOM, total N, Pav

exchangeable base and CEC than in open grazing

land adjacent to it. The fact that PA and AE are often

established on degraded soils makes the land-use

types to have low SOC and major nutrients at a

deeper profile. Contrary to this, soils of CL have

more SOC at 40�60 cm depth at Maileba (Table

IV). Translocation of SOC through the action of

leaching or effect of deep-rooting crops may operate

in these soils � a process responsible for accumula-

tion of SOC in deeper depth. However, translocation

of dissolved OC through the soil profile is more

pronounced in areas with high precipitation whereas

drier areas experienced less and have more accumu-

lation in the upper 20 cm. In our case precipitation is

somewhat higher at Maileba than Gum Selassa and

this might have contributed to the transport and

accumulation of OC in the lower part of the profile.

A study by Mulugeta and Itanna (2004) reported

that in a drier climate in the Rift valley of Ethiopia,

50% of soil C is retained in the upper 20 cm of the

soil while in humid and cool soils they found more

Figure 2. Weight for soil quality indicators (Wi) and soil quality index (SQI) for different land uses at Maileba (a and b) and at Gum Selassa

(c and d), respectively. BD: bulk density; pH: soil pH; P: available P; K: available K; C/N: carbon-nitrogen ratio; N: total nitrogen, OC: soil

organic carbon; CL: cultivated land; GL: grazing land; PA: plantation area (Eucalyptus); AE: area exclosure.

526 G. Girmay & B. R. Singh

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even distribution across the profile. Increasing SOC,

on the other hand, in the upper layer of cultivated

lands are often associated with the agricultural

practices such as tillage, FYM, fertilizer application

and crop residue while in forest soils leaf litter and

root litter inputs play a major role. Grazing modifies

soil properties indirectly by influencing plant species

composition (Burke 1999) mainly due to removal of

above-ground biomass with subsequent effect on

reducing litter addition to soils that are supposedly

important for soil nutrient conservation and cycling

(Solomon et al. 2000).

Reduction in SOM, total N and Pav was reported

in free grazing land (Girma 1998, Mekuria et al.

2007). In this study no significant difference was

observed in SOC, N and Kav among the different

land-use types, but Pav showed significantly higher

value (p�0.0034) in grazing land compared with

cultivated land (Table III). Other studies, however,

found significantly higher SOC up to 60 cm in

heavily grazed land compared with light or no

grazing due to the addition of SOC through animal

excrements (Franzluebbers et al. 2001). Hence,

livestock can exert both beneficial and detrimental

effects on a grazed field that can be explained in

terms of improving or degrading soil nutrients

depending on number of livestock and grazing

intensity. In the present study, the fact that no

significant difference exists in SOC among the

different land-use types and that Pav is significantly

higher on GL compared with CL indicates the

significance of other inorganic sources of P such as

weathering of P containing minerals. The higher Pav

content in grazing land than CL, AE and PA also

suggests that trees in closed and PA as well as crops

in arable field may extract more Pav and immobilize

a higher proportion of it as P pool on their biomass

than grazing species do.

Profile of soil organic carbon stocks

Land use and associated soil managements are

important variables that were hypothesized to bring

difference in SOC stocks. The estimated SOC stock

was higher in area exclosure (AE) and plantation

area (PA) in the first two soil layers (0�40 cm) at

Maileba compared with the lower depths. For

example, AE and PA stored 63% and 67.9% of total

SOC stock, respectively in the 0�40 cm depths. At

Gum Selassa, soils at plantation area did not show

the same trend. Dominant trees in the plantation are

Eucalyptus species and there are varied opinions

about their ecological effect. Soil organic carbon

increases when Eucalyptus plantations are established

on degraded soils whereas on newly cleared sites they

have an adverse effect on soil quality (Zerfu 2002).

Plantation at Gum Selassa did not show improve-

ment in SOC stock when compared with grazing

land (GL) and cultivated land (CL). The rate of C

accumulation in soils and biota are determined

among others by landscape position (Lal 2008,

Nelson et al. 2008). As such PA is situated on a

relatively steep area prone to high soil erosion that

results in lower OC and higher BD. Carbon storage

in forest soils is affected by forest type and soil

quality. Also SOC accretion in mineral soils is age

and tree species dependent (Lemma et al. 2006).

Hence, the lower OC accumulation in plantation

area at Gum Selassa is primarily related to poor soil

quality prior to its establishment and the subsequent

low litter input. The site was relatively degraded with

areas with many visible outcrop rocks on the surface

even at the present time. Being closer to a village

there are also human and animal intrusions that

severely damage the physical stand of trees as well as

grass cover which might also contribute to lower

SOM supply.

At Maileba, SOC stock is significantly lower in CL

(26 Mg C ha�1) than in AE (43 Mg C ha�1), PA

(36 Mg C ha�1) and GL (33 Mg C ha�1) at 0�20

cm depth, whereas at Gum Selassa no significant

difference was observed (Table V). Such difference

between sites could be attributed to difference in

landscape position, soil management, previous crop-

ping history and local biophysical variation. Lower

SOC at Maileba in CL reflects the severity of land

degradation, whereas higher SOC stock in AE

indicates the importance of such restoration mea-

sures in addressing SOC depletion in the area.

Hence AEs are effective means of soil C sequestra-

tion in degraded area as shown by the increased C

accumulation in the topsoil compared with the lower

depths. For example, SOC in AE at 0�20 cm layer

exceeds by 21, 12 and 12 Mg C ha�1 than in 60�80

cm, 40�60 cm and 20�40 cm depths, respectively.

Taking the entire 80 cm soil depth, the SOC stock

varied from the minimum of 100 Mg C ha�1 under

plantation area to the maximum of 133 Mg C ha�1

under grazing land at Maileba, and from the mini-

mum of 68 Mg C ha�1 under grazing land to the

maximum of 79 Mg C ha�1 under cultivated land at

Gum Selassa. These estimates for all land uses and

both sites are within a range of estimates (42.9�234.6 Mg C ha�1) found for Andosols, Nitosols

and Solanchaks for 60 cm depth in southern

Ethiopia (Mulugeta and Itanna 2004) as well as

global average estimate for tropical Aridosols (42 Mg

C ha�1), Ultisol (83 Mg C ha�1) and Oxisols (97

Mg C ha�1) for 100 cm soil depths reported by

Kimble et al. (1990). The higher mean SOC stock in

grazing land compared with the other land uses at

Maileba could be due to higher annual turnover of

Land use effects on soil quality 527

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organic matter from dying grass roots despite heavy

grazing. Grass roots decompose faster than tree roots

and hence contribute higher organic matter to soils

(Guo and Gifford 2002). Another possibility is the

effect of animal excrement. But this was not reflected

in the case of Gum Selassa.

At a catchment level, the dominant land uses

involved in this study account for 77% and 95% of

the total catchment area at Maileba and Gum

Selassa, respectively. The total SOC stock stored

(up to 80 cm) by all land-use types was estimated to

be 159, 516 Mg C at Maileba and 172, 615 Mg C at

Gum Selassa, of which 48% was found in 0�40 cm

soil depth in both sites. The relative distribution of

SOC between top layer soils and the layers below are

comparable with results found in Bale Mountain,

Ethiopia (Fantaw et al. 2006). The amount of SOC

stock in the 0�40 cm soil depth for area exclosure

and plantation area at Maileba account for 63�68%

indicating the importance of topsoil layers of these

land-use types acting as good sources of carbon sink

but also the potential for large amount of CO2

emission upon conversion and mismanagement.

Nevertheless, as these land uses cover a small part

of the catchment (on both sites), their overall

contribution on SOC stock at a catchment scale is

very small (B5%). Cultivated lands and grazing

land, on the other hand, constitute the largest area

coverage in the catchments contributing to about

80% and 16% of SOC stock, respectively at Maileba,

and 86% and 13% at Gum Selassa. These results

demonstrate the need for policy priority and man-

agement systems focusing toward cultivated land

and grazing land to ensure sustainable land manage-

ment and enhance SOC sequestration in terrestrial

ecosystems.

Soil Quality Index and land uses

Several researchers (e.g. Larson and Pierce 1994,

Andrews et al. 2002) have pointed out that SOC is

the most important indicator of soil quality because

it determines many of the physical, chemical and

biological soil properties (Wang et al. 2003). Simi-

larly, BD influences agriculture by restricting air and

water movement, and has been recognized as a key

attribute of soil quality indicator (e.g. Andrews et al.

2002, Fu et al. 2004, Awasthi et al. 2005).

The calculated SQI varied between 0.27 to 0.79 at

Maileba and 0.22 to 0.72 at Gum Selassa (Figure

2(b) and (d)). Cultivated land at Maileba and

plantation area at Gum Selassa scored least SQI

and categorized into ‘degraded’ class suggesting that

these land-use types are under threat and require

immediate soil restoration and conservation mea-

sures for sustainable productivity. Conversely, higher

SQI found in area exclosure at Maileba and in

cultivated land at Gum Selassa highlights soils under

these land-use types are better off regarding soil

functioning and soil health (Andrews et al. 2003).

These results demonstrate the extent to which soils

under different land-use types differ in quality as

influenced by level of input received from land use

itself and from external sources such as fertilizer and

manure inputs by human being. Also soils respond

differently to management depending on inherent

properties and the surrounding landscape. Given

that area exclosures increase above- and below-

ground biomass and that SOC and nutrients have

improved, it is logical to assume higher SQI. How-

ever, higher SQI in cultivated land at Gum Selassa is

something unexpected. Considering that soils under

GL, CL and PA are situated under the same

environment and climate conditions, we believe

that the farmland has received fertilizer and manure

prior to sampling and this has improved soil proper-

ties leading to higher SQI. Its relative location on a

flat landscape might have also benefited CL to

receive transported soils from upper areas. Measure-

ment and inclusion of more physical, chemical and

biological soil properties such as soil structure,

infiltration, water-holding capacity, soil respiration,

microbial biomass C and N, potentially mineraliz-

able N and soil aggregate stability could improve the

result of SQI.

This study showed how management choices

affect soil quality. In Tigray despite the fact that

the area covered with plantations, particularly with

Eucalyptus trees, is expanding with the sole objective

of improving degraded soils and increasing economic

benefits, soils of both study sites were of poor quality.

This suggests that either Eucalyptus trees were

originally planted on degraded soils and as such

could not restore fertility of the soil or the tree has an

inherently degradative effect. In view of benefiting

soil resources, this study demonstrates that

Eucalyptus plantation is not a promising alternative

and cannot be a good substitute for indigenous trees

that grow either through plantation or re-vegetate

naturally by closing off the area from human and

livestock interference. On the other hand, area

exclosures are effective in terms of improving soil

quality of degraded soils in Tigray as confirmed in

this study as well as other studies in Tigray (Dereje

et al. 2002, Descheemaeker et al. 2006a, Mekuria et

al. 2007) and hence the regional government should

give emphasis and policy priority toward expanding

area exclosure in the efforts of improving environ-

mental degradation.

528 G. Girmay & B. R. Singh

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Acknowledgements

The authors are greatly indebted to the DCG-water

harvesting and WHO-SAREC projects based at

Mekelle University for providing financial support.

We also appreciate Mekelle University for providing

laboratory service to carry out soil analysis. Finally

the senior author received financial support from

Lannekassen and is grateful for that.

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