ArticlePDF Available

Abstract and Figures

A study was conducted to determine suitable soil properties as soil quality indicators, using factor analysis in order to evaluate the effects of land use change on loessial hillslope soils of the Shastkola District in Golestan Province, northern Iran. To this end, forty surface soil (0-30 cm) samples were collected from four adjacent sites with the following land uses systems: (1) natural forest, (2) cultivated land, (3) land reforested with olive, and (4) land reforested with Cupressus. Fourteen soil chemical, physical, and biological properties were measured. Factor analysis (FA) revealed that mean weight diameter (MWD), water stable aggregates (WSA), soil organic matter (SOM), and total nitrogen (TN) were suitable for assessing the soil quality in the given ecosystem for monitoring the land use change effects. The results of analysis of variance (ANOVA) and mean comparison showed that there were significant (P< 0.01) differences among the four treatments with regard to SOM, MWD, and sand content. Clearing of the hardwood forest and tillage practices during 40 years led to a decrease in SOM by 71.5%. Cultivation of the deforested land decreased MWD by 52% and increased sand by 252%. The reforestation of degraded land with olive and Cupressus increased SOM by about 49% and 72%, respectively, compared to the cultivated control soil. Reforestation with olive increased MWD by 81% and reforestation with Cupressus increased MWD by 83.6%. The study showed that forest clearing followed by cultivation of the loessial hilly slopes resulted in the decline of the soil quality attributes, while reforestation improved them in the study area.
Content may be subject to copyright.
J. Agr. Sci. Tech. (2011) Vol. 13: 727-742
Assessing Impacts of Land Use Change on Soil Quality
Indicators in a Loessial Soil in Golestan Province, Iran
S. Ayoubi1*, F. Khormali2, K. L. Sahrawat3, A. C. Rodrigues de Lima4
A study was conducted to determine suitable soil properties as soil quality indicators,
using factor analysis in order to evaluate the effects of land use change on loessial hillslope
soils of the Shastkola District in Golestan Province, northern Iran. To this end, forty
surface soil (0-30 cm) samples were collected from four adjacent sites with the following
land uses systems: (1) natural forest, (2) cultivated land, (3) land reforested with olive,
and (4) land reforested with Cupressus. Fourteen soil chemical, physical, and biological
properties were measured. Factor analysis (FA) revealed that mean weight diameter
(MWD), water stable aggregates (WSA), soil organic matter (SOM), and total nitrogen
(TN) were suitable for assessing the soil quality in the given ecosystem for monitoring the
land use change effects. The results of analysis of variance (ANOVA) and mean
comparison showed that there were significant (P< 0.01) differences among the four
treatments with regard to SOM, MWD, and sand content. Clearing of the hardwood
forest and tillage practices during 40 years led to a decrease in SOM by 71.5%.
Cultivation of the deforested land decreased MWD by 52% and increased sand by 252%.
The reforestation of degraded land with olive and Cupressus increased SOM by about
49% and 72%, respectively, compared to the cultivated control soil. Reforestation with
olive increased MWD by 81% and reforestation with Cupressus increased MWD by
83.6%. The study showed that forest clearing followed by cultivation of the loessial hilly
slopes resulted in the decline of the soil quality attributes, while reforestation improved
them in the study area.
Keywords: Factor analysis, Land use change, Reforestation, Soil quality.
1 Department of Soil Science, College of Agriculture, Isfahan University of Technology, 84156-83111,
Isfahan, Islamic Republic of Iran.
* Corresponding author, e-mail:
2 Department of Soil Science, College of Agriculture, Gorgan University of Agricultural Sciences and
Natural Resources, Gorgan, Islamic Republic of Iran.
3 International Crop Research Institute for the Semi Arid Tropics (ICRISAT), Patancheru 502 324, Andhra
Pradesh, India.
4 Farm Technology Group, Wageningen University, P. O. Box: 17, 6700 AA Wageningen, The Netherlands.
Environmental degradation caused by
inappropriate land use is a worldwide
problem that has attracted attention in
sustainable agricultural production systems
(Pierce and Larson, 1993; Zink and Farshad,
1995; Hurni, 1997; Hebel, 1998; Sanchez-
Maranon et al., 2002; Vagen et al., 2006;
Khormali and Nabiollahy, 2009). During the
recent decades, soil quality concept has
emerged and is used to assess land or soil
quality under various systems (Doran and
Parkin, 1994; Karlen et al., 1997; de Lima et
al., 2008). Soil quality essentially means
“the capacity of a soil to function” (Larson
and Pierce, 1991; Doran and Parkin, 1994;
Karlen et al., 1997).
Larson and Pierce (1991) outlined five soil
functions that may be used as the criteria for
judging the soil quality: to hold and release
water to plants, streams, and subsoil; to hold
_______________________________________________________________________ Ayoubi et al.
and release nutrients and other chemicals; to
promote and sustain root growth; to
maintain suitable soil biotic habitats; and to
respond to management and resist
degradation. It is suggested that, for
practical purposes, soil quality can be used
to judge impact on crop yield, erosion,
ground and surface water status and quality,
food and air quality (Wang et al., 2003).
The capacity of the soil to function can be
determined by soil physical, chemical, and
biological properties, also termed as soil
quality indicators (Shukla et al., 2006; Wang
and Gong, 1998). Soil properties that are
responsive to the change in the land use
dynamics on a short-term are considered as
suitable soil quality indicators (Carter et al.,
1998). A soil quality indicator is a
measurable soil property that affects the
capacity of a soil to perform a specified
function (Karlen et al., 1997). For evaluation
of soil quality, it is desirable to select
indicators that are directly related to soil
quality. If a set of attributes is selected to
represent the soil functions and if the
appropriate measurements are made, the
data may be used to assess the soil quality
(Heil and Sposito, 1997).
A large body of information is now
available that clearly shows that severe
decline in soil quality occurs along with
increased soil erosion as a result of
agricultural activities following
deforestation (Sigstad et al., 2002).
Hajabbasi et al. (1997) showed that
deforestation and clear cutting of the forest
in central Zagrous mountains (western Iran)
resulted in a lower soil quality and,
consequently, decreased productivity.
Ellingson et al. (2000) quantified soil N
dynamics: mineralization and nitrification
rates in response to the change in land use
from forest to pasture. However, they
represented the high-end extreme as a large
proportion of the above ground forest
biomass was consumed by anthropogenic
fires. Land use changes, especially
cultivation of deforested land, may rapidly
diminish soil quality. As a result, severe
degradation in soil quality may lead to a
permanent degradation of land productivity
(Kang and Juo, 1986; Nadri et al., 1996;
Islam et al., 1999; Islam and Weil, 2000b).
Due to an increasing demand for firewood,
timber, pasture, food, and residential
dwelling, the hardwood forests are being
degraded or converted to cropland at an
alarming rate in the hilly regions of Golestan
Province, during the last few decades. The
forest coverage in this province has
decreased by 32.2% (from 18 to 12.2 million
ha) in the last 30 years (Kiani et al., 2003).
This conversion of natural forest to other
uses, such as cultivation, has created serious
problems and is a main cause of the annual
destructive flooding in this area (Mosaedi,
2003; Ajami et al., 2006).
The study region is located in north-facing
slopes of Alborz Mountain Ranges and was
covered with hardwood forests of Parotia
persica and Carpinus betulus up to 40 years
ago. The parent material in the lower hill
slopes of Golestan Province are composed
of loess materials, which are very
susceptible to soil erosion and need to be
properly managed (Kiani et al., 2003).
While signs of rill, gully, and even landslide
erosion patterns induced by improper
conservation practices in the deforested land
are evident on the hill slopes (Ayoubi,
2005), degraded land has been reclaimed by
reforestation with Olea europea and
Cupressus arizonica by local farmers and
governmental organizations, during the last
30 years.
Although there are a lot of data available
on soil properties due to land use change,
little information is available for the soils
developed on the loess material in the semi-
arid region. No attempt has been made to
generate minimum data set to evaluate soil
quality changes following the deforestation
and reforestation. The objectives of this
study were to: (1) generate a minimum data
set (MDS) on soil quality indicators using
factor analysis and (2) evaluate the changes
in the selected soil quality indicators in
response to land use changes.
Assessment of Soil Quality in a Loessial Soil _____________________________________
Figure 1. Location of the study site in north of Iran.
Description of the Study Area
The study area is located between 36° 24ََ
and 38° 5ََ northern latitudes, and 53° 51ََ
and 56° 14ََ eastern longitudes, 10 km east
of Gorgan City, in northern Iran (Figure 1).
The parent material is composed mainly of
loess material, highly sensitive to erosion
and has a hilly physiographic landform with
20-25% slope. The average annual rainfall is
560 mm and occurs mainly from October to
April. The annual average temperature at the
site is 14.9ºC. The average elevation of the
hillslope is 320 m above sea level.
According to Soil Taxonomy (Soil Survey
Staff, 2006), the soil moisture and
temperature regimes are xeric and thermic.
The hill slopes of the study area
have been generally covered with hardwood
dominated by Parotia persica and Carpinus
betulus trees. The selected site on the steep
slopes was opened by clear cutting and
converted to farmlands, about 40 years ago.
In some areas, the reforestation with
Cupressus arizonica and Olea europea was
introduced by local farmers and
governmental organizations during the last
30 years. Details of the selected land uses
are given in Table 1. The soils of the study
area are classified as Mollisols and
Inceptisols (Soil Survey Staff, 2006) with
textures ranging from silt and silt loam to
silty clay loam in the surface of different
land uses.
The study included four adjacent land
parcels under different uses at the Shastkola:
(1) natural hardwood forest, (2) cultivated
land, (3) reforested land with Olea europea,
and (4) reforested land with Cupressus
arizonica, as in Figure 1.
Soil Sampling and Pretreatments
Surface soil samples from 0-30 cm depth
were collected in April 2005 from forty
randomly selected points in the four adjacent
land parcels, using a hand auger. In total,
160 samples were collected, air-dried and
passed through a 2 mm sieve to remove
stones, roots, and large organic residues
before conducting analyses for chemical and
physical characteristics. In order to measure
soil microbial respiration rate, 40 fresh and
undisturbed soil samples were taken from
each land parcel.
_______________________________________________________________________ Ayoubi et al.
Table 1. Description of the site under different land uses on losseial soil in the Gorgan Province, northern
Land use Soil classification
(USDA, 2006)
Age of
Natural Forest Typic Calcixerolls 10-25 Loess Native Back slope-
Foot slope
Cultivated land Typic Haploxerepts 10-20 Loess 40 years Back slope-
Foot slope
Reforested( Olea) Typic Haploxerepts 10-20 Loess 10 years Back slope-
Foot slope
Reforested(Cupressus) Typic Haploxerepts 10-25 Loess 30 years Back slope-
Foot slope
Analyses of Soil Samples
Physical Properties
The soil samples collected by a cylindrical
metal sampler (core diameter 100 mm), were
oven-dried at 105° C for 24 hours and
weighed to calculate bulk density (Blake and
Hartage, 1986). Particle size distribution was
determined by the Bouyoucos hydrometer
method (Gee and Bauder, 1986). The wet
sieving method of Angers and Mehuys
(1993) was used with a set of sieves of 2.0,
1.0, 0.5, 0.25 and 0.1 mm diameter.
Approximately, 50 g of soil sieved through
4.6 mm was put on the first sieve of the set
and gently moistened to avoid a sudden
rupture of soil aggregates. The set was
sieved in distilled water at 30 oscillations
per minute for 10 minutes and the resistant
aggregate on each sieve were dried at 105°C
for 24 hours, weighted and corrected for
sand fraction to obtain the proportion of the
true aggregates. The mass of < 0.1 mm
fraction was obtained by difference. The
method of van Bevel (1949) as modified by
Kemper and Rosenau (1986) was used to
determine water stable aggregates (WSA)
and MWD.
The WSA % was calculated using
Equation (1) as follows:
)( )( ×
WSA (1)
Where M (a+s) is the mass of resistant
aggregates plus sand (g), Ms is the mass of
the sand fraction alone (g), and Mt is the
total mass of the sieved soil (g). The MWD
was determined as follows:
Where MWD is the mean weight diameter
of water stable aggregates, Xi is the mean
diameter of each size fraction (mm), and Wi
is the proportion of the total sample mass in
the corresponding size fraction after
deducing the mass stone as indicated above.
Soil erodibility factor i.e. K factor in the
Universal Soil Loss Equation, was
calculated according to Wischmeier and
Smith (1978). Available water holding
capacity (AWHC) was determined as the
difference between field capacity and
permanent wilting point (Klute and Dirksen,
1986). Water retention at field capacity (-
33kPa) and at permanent wilting point (-
1500 kPa) were determined using high-range
pressure plate extractor (Soil Moisture
Equipment Corp) equipped with a ceramic
Chemical Properties
Soil pH was measured in saturated soil
using glass electrode (Mclean, 1982) and
electrical conductivity (EC) was measured in
the saturated paste using conductivity meter
(Rhoades, 1982). Calcium carbonate
(CaCO3) was measured by the Bernard’s
calcimetric method (Chaney and Slonim,
1982). Soil organic matter (SOM) was
Assessment of Soil Quality in a Loessial Soil _____________________________________
determined using a wet combustion method
(Nelson and Sommers, 1982) and total
nitrogen (TN) was determined by the
Kjeldahl method (Bremner and Mulvaney,
Biological Properties
Microbial respiration rate (MR) was
measured by the closed bottle method of
Anderson (1982). Soil samples (moistened
to about 30% of filed capacity) were
transferred to a bottle with a glass test tube
containing an alkali solution (1.0N NaOH);
the bottle was closed and maintained at 25ºC
for seven days. The trapped CO2 was
calculated as a function of soil respiration by
titration of the contents of the test tube with
HCl after BaCl2 pretreatment
Statistical Analysis
Descriptive statistics in the form of mean,
standard deviation (SD), minimum,
maximum, median, coefficient of variation
(CV), distribution of normality, range,
skewness and kurtosis were determined
(Wendroth et al., 1997). The CV was used to
describe the amount of variability for each
soil parameter. Pearson linear correlations
among various soil parameters were
calculated using SPSS software (Swan and
Sandilands, 1995) and were used to establish
relationships among the soil variables.
Factor analysis was used to group the 14
soil variables into factors based on the
correlation matrix of the variables using
FACTOR module and the principal
component analysis method of factor
extraction in SPSS software (Brejda et al.,
2000). Principal component analysis was
used as the method of factor extraction
because it required no prior estimates of the
amount of variation of each soil variable that
would be explained by the factors. The
maximum number of factors possible is 14,
which is equal to the number of variables.
Only factors with eigen value >1 were
retained (Brejda et al., 2000). Also, one-way
ANOVA and mean comparison using
Duncan’s test were conducted using the
SPSS software.
Statistical Descriptions
Summary of the measured soil properties
including mean, median, standard deviation,
coefficient of variation, range, skewness and
kurtosis coefficients, are given in Table 2. The
descriptive statistics of soil data suggested that
they were all normally distributed because the
skewness values were within the range of -1 to
+1 (Swan and Sandilands, 1995) (Table 2).
Some researchers, however, have suggested
that, in disturbed ecosystems, some soil
variables show skewed distributions (Nael et
al., 2004; Wang et al., 2003). Skewness values
of soil properties in the cultivated land showed
low deviation from normal distribution.
Coefficient of variation for all of the variables
was low, with the highest and lowest CV’s
related to sand (0.29-0.51) and pH (0.01-0.03),
respectively. In general, the CV values for the
selected soil properties of the cultivated land
were lower than those reported in the
literature, probably due to the homogenizing
effect of the long-term cultivation under
similar soil management practices. This
finding is also in accordance with those
reported by Paz Gonzalez et al. (2000).
Factor Analysis
The linear correlation analysis of the 14 soil
attributes, which represent soil physical,
chemical, and biological properties for the
study area, showed a significant correlation
among 77 of the 91 soil attribute pairs (P<
0.01, and P< 0.05) (Table 3). Statistically
significant positive correlations were
obtained for the total nitrogen versus SOM,
and MWD versus WSA (r> 0.90).
_______________________________________________________________________ Ayoubi et al.
Table 2. Summary of the statistics for selected soil physical, chemical, and biological properties in all
land uses in Golestan Province, Northern Iran (N= 40).
Variable Unit Land
Mean Min Max Median S.D CV Range Skewness Kurtosis
NFh 10.5 4.8 26.4 9 2.3 0.22 21.6 0.7 3.0
CLi 37.0 7.2 65 36.7 19.2 0.51 57.8 -0.5 -0.80
ROj 25.3 14 45 23 9.9 0.39 31 0.8 0.06
RCk 13.6 5.2 25 12.6 6.3 0.46 19.8 -0.14 1.88
NF 77.3 63.1 86.4 78.5 6.65 0.09 23.3 -0.8 1.50
CL 40.8 15.5 71.3 39.8 17 0.41 55.8 -0.5 -0.80
RO 56.6 32.7 66.6 59.9 11.4 0.20 33.9 -1.0 -0.09
RC 54.4 43 64.8 58.2 5.9 0.10 21.8 -1.0 1.88
NF 12.2 8.0 20.5 10.5 4.0 0.33 12.5 0.9 0.68
CL 22.2 11.5 37.5 19.5 8.4 0.38 26 1.0 0.15
RO 18.1 15.5 31.5 19.5 4.8 0.26 16 1.0 1.87
RC 32 18 38 29.9 5.7 0.18 20 -0.4 0.80
NF 1.24 1.03 1.49 1.25 0.13 0.10 0.46 0.15 -1.24
CL 1.53 1.42 1.66 1.54 0.07 0.04 0.25 0.08 -0.98
RO 1.47 1.18 1.55 1.36 0.1 0.07 0.37 0.23 -0.71
g cm-3
RC 1.36 1.31 1.64 1.45 0.09 0.06 0.33 -0.02 -0.79
NF 0.12 0.08 0.19 0.15 0.03 0.23 0.11 0.99 1.01
CL 0.36 0.23 0.44 0.39 0.09 0.25 0.21 -0.99 0.67
RO 0.23 0.18 0.28 0.19 0.07 0.30 0.10 0.03 1.50
K-factor -
RC 0.24 0.16 0.26 0.23 0.06 0.25 0.10 -0.87 -0.34
NF 0.19 0.13 0.21 0.17 0.02 0.10 0.08 -0.98 1.02
CL 0.11 0.07 0.13 0.09 0.03 0.27 0.06 -0.8 1.33
RO 0.15 0.08 0.18 0.16 0.03 0.20 0.10 0.23 0.32
% Vol
RC 0.16 0.09 0.20 0.17 0.02 0.12 0.11 0.11 -0.54
NF 92 78 95 93 32.2 0.35 17 0.99 2.50
CL 54 34 64 56 21.6 0.40 30 0.02 0.50
RO 67 59 72 62 16.7 0.25 13 0.11 0.99
RC 78 71 85 72 23.4 0.30 14 0.06 1.10
NF 2.42 1.7 3.03 2.4 0.4 0.16 0.41 -0.28 -0.78
CL 1.16 0.14 1.65 1.17 0.26 0.22 0.81 0.24 -0.83
RO 2.10 1.3 2.73 2.2 0.46 0.21 1.43 -0.46 -0.86
RC 2.13 1.68 2.59 2.13 0.25 0.11 0.91 -0.42 0.76
SOMe % NF 6.45 5.07 7.53 6.36 0.65 0.1 2.46 -0.5 -0.47
CL 1.84 0.94 2.81 1.91 0.54 0.29 1.87 0.04 -0.79
RO 2.75 1.56 3.82 2.81 0.64 0.23 2.26 -0.09 -0.61
RC 3.17 1.79 4.65 3.15 0.64 0.2 2.77 -0.08 0.16
pH -Log[H+] NF 7.21 6.9 7.4 7.2 0.12 0.01 0.5 -0.64 -0.05
CL 7.61 7.41 7.33 7.63 0.1 0.01 0.32 0.77 -0.56
RO 7.53 7.28 7.8 7.63 0.14 0.02 0.52 0.13 0.99
RC 7.29 6.86 7.68 7.3 0.2 0.03 0.82 -0.27 1.0.1
EC dS/m NF 1.1 0.54 1.95 0.87 0.32 0.29 1.4 0.7 2.9
CL 1.01 0.51 1.77 0.83 0.37 0.37 1.26 0.91 0.04
RO 1.2 0.74 1.99 1.0 0.38 0.32 1.25 0.56 -0.95
RC 0.99 0.74 1.62 1.17 0.23 0.19 0.87 0.002 -0.52
TNf % NF 0.92 0.72 1.08 0.91 0.09 0.10 0.35 -0.06 -0.46
CL 0.28 0.13 0.4 0.27 0.07 0.25 0.27 -0.05 -0.79
RO 0.39 0.22 0.55 0.40 0.09 0.23 0.32 -0.09 -0.61
RC 0.45 0.26 0.65 0.45 0.09 0.20 0.4 -0.09 0.93
CaCO3 % NF 4.16 2.4 6.8 4.2 1.15 0.27 4.4 0.63 0.04
CL 14.59 12 16.85 13.26 1.25 0.09 4.85 0.93 0.87
RO 13.87 11.11 15.78 15.2 1.27 0.09 4.76 -0.57 -0.21
RC 10.04 7.96 11.76 10.03 0.8 0.07 3.8 -0.54 2.25
MRg (mg CO2 g-1 soil day-1) NF 0.75 0.7 0.79 0.74 0.02 0.02 0.09 0.33 -0.25
CL 0.24 0.19 0.3 0.24 0.028 0.11 0.11 0.37 0.35
RO 0.42 0.38 0.49 0.42 0.02 0.04 0.11 0.06 0.92
RC 0.31 0.19 0.3 0.24 0.03 0.11 0.11 0.37 0.35
a Bulk Density; b Available Water Holding Capacity; c Water Stable Aggregate; d Mean Weight Diameter; e
Soil Organic Matter; f Total Nitrogen; g Microbial Soil Respiration Rate; h Natural Forest; i Cultivated land; j
Reforested with Olive, k Reforested with Cupressus.
Assessment of Soil Quality in a Loessial Soil _____________________________________
The highest negative correlation was
obtained for sand versus silt (r= -0.89).
Results showed that there was a high
correlation among physical properties such
as BD, MWD, and WSA, and among the
various chemical properties such as SOM
and the measured soil respiration (MR)
(Table 3). BD was negatively correlated
with most of the soil properties, unlike WSA
and MWD, which were positively correlated
with other soil characteristics. The findings
by Islam and Weil (2000a) showed similar
trend in the correlation coefficients for soil
If soil sampling and analyses are properly
conducted, the results should collectively
show the land use effects (Wang et al.,
2003). Attributes selected for assessment of
soil characteristic induced by land use
change must ideally account for most, if not
all, of the variances. For the 14 soil
properties measured, a maximum of 14
factors might explain the total variance of
each factor that was defined as eigenvalue
(Swan and Sandilands, 1995). An eigenvalue
plot allows identification of the significant
factors that collectively represent the major
proportions of the total variability.
Factors 1, 2, and 3 are the most significant
factors in explaining the system variance
compared to the remaining factors. The first
three factors have eigenvalues more than 1
(Table 4). The factors with eigenvalue> 1,
were retained, since eigenvalue< 1 indicated
that the factor could explain less variance
than the individual attribute (Shukla et al.,
2006). The first factor (Factor 1) explained
50.79% of the total variance. The second
factor accounted for a further 15.86% of the
total variance. Factors 1, 2, and 3
collectively accounted for 76.28% of the
total variance. The inclusion of the next
factor increased the cumulative variance by
7.08% up to 83.36%.
A factor, as an array of variables, holds
contributions (in the forming of loadings or
weights) from all of the selected 14
properties. The weights (loadings) for the
first three factors are illustrated in Table 4.
The magnitude of the eigenvalues was used
_______________________________________________________________________ Ayoubi et al.
Table 4. Proportion of variance, initial eigenvalues and communality estimates for soil properties in the
0-30 cm soil layer under different land uses in loessial soils of Golestan povince, nrthern Iran.
soil attributes Factor Communality estimates
1 2 3
SAND -0.67663 -0.28967 -0.5702 0.99
SILT 0.768511 -0.10632 0.472002 0.87
CLAY -0.31862 0.839708 0.112359 0.36
BDa -0.76813 0.207423 0.107715 0.49
MWDb 0.821633 0.270165 -0.09152 0.99
K factor -0.75879 -0.0473 0.471154 0.61
WSAc 0.821014 0.270342 -0.09107 0.99
AWHCd 0.597841 0.490413 0.27465 0.39
CaCO3 -0.63891 -0.57709 0.154535 0.53
SOMe 0.881894 -0.35664 -0.00204 0.99
MRf 0.837238 -0.4714 -0.07693 0.84
ECg 0.224255 0.382505 -0.65648 0.05
pH -0.61194 -0.17775 -0.04432 0.31
TNh 0.881818 -0.35665 -0.00191 0.99
Initial eigenvalue 7.11 2.22 1.34 -
Variance% 50.79 15.86 9.63 -
Cumulative variance% 50.79 66.65 76.28 -
a Bulck density; b Mean Weight Diameter; c Water Satble Aggregates; d Available Water Holding Capacity;
e Soil Organic Matter; f Microbla Respiration; g Electrical conductivity, h Total Nitrogen.
as a criterion for interpreting the relationship
between soil properties and factors. Soil
properties were assigned to a factor for
which their eigenvalues were the highest.
Factor 1 explained 50.79% of the total
variance with a high positive loading (>
0.85) from MWD, TN, WSA, and SOM
(Table 4). Factor 1 included negative
loading from sand and clay contents, BD, K
factor, CaCO3, and pH (Figure 2). The high
positive loading from MWD, TN, WSA, and
SOM were the results of the statistically
significant correlation coefficients among
the characteristics selected for the study
(Table 3).
Factor 2 explained 15.85% of the total
variance with high negative loading (-0.43)
from clay content, MWD and WSA and high
positive loading (> 0.4) from MR, TN and
SOM (Table 4). It also had a moderate
positive loading from MR (0.43), TN (0.49),
and SOM (0.49) resulting from significant
correlation among MR, TN, and SOM
(Table 3). Factor 3 had high positive loading
from sand content (0.72) and negative
loading from silt content (-0.52), K factor (-
0.32), and clay content (-0.28).
The relative importance of each soil
attribute, in terms of its contribution to all of
the factors, is judged by its communality
value, a value that indicates the residual
variance of the attribute in comparison to a
critical convergence value of confidence
(Joreskog, 1977). If the residual variance is
less than the convergence value, the
corresponding communality of the attribute
is equal to 1. The three factors explained
nearly 99% of variance in sand content,
SOM, TN, WSA, and MWD; >84% in silt
content and MR; > 60% in K factor; > 50%
in CaCO3; < 50% in BD, clay content,
AWHC, pH, and EC (Table 4). A high
proportion of communality estimate
suggests that a high portion of variance was
explained by the factor; therefore, it would
get higher preference over a low
communality estimate (Shukla et al., 2006).
Thus, EC was the least important attribute
Assessment of Soil Quality in a Loessial Soil _____________________________________
Figure 2. Loading plot indicating associations of soil properties to Factors 1 and Fcator 2 in the area studied.
Table 5. Effects of selected land uses on the
factor scores in the 0- 30 cm soil layer depth,
Golestan province, northern Iran.
Land use Factor 1 Factor 2 Factor 3
NF 0.36 a* -0.56 c -1.23 c
CL -0.45 c 0.03 b -0.13 a
RO 0.03 b 0.14 a -0.59 b
RC 0.02 b 0.19 a -0.49 b
* a, b,… letter indicate significant differences
(P<0.01) among treatments based on
Duncan’s mean test.
a Natural forest; b Cultivated Land; c
Reforested with Olive, d Reforested with
due to the lowest communality estimate.
Mean score for Factor 1 was higher under
natural forest than under cultivated land;
whereas the score was not significant
between land reforested with olive or with
Cupressus land use (Table 5). Factors 2 and
3 had significant differences among natural
forest, cultivated land, and reforested
treatments. Land use affects the mean score,
which is consistent with the results from the
analysis of variance among the most
appropriate soil properties as discussed in
the following section.
Selection of the suitable soil properties for
monitoring land use change should consider
the properties that account for the most
variability. Such data set would have a few
soil properties for the practical assessment
of soil quality. Ideally, the selected
properties should be easy to measure and the
results should be reproducible (Wang et al.,
2003). Based on the results of factor analysis
and communality values, the properties that
explained the greatest proportion of the total
variance in the present study included sand
content, SOM, TN, WSA, and MWD. These
soil characteristics seem to be the suitable
parameters for assessing the effects of land
use pattern on soil degradation in the study
region. Since SOM was highly correlated to
TN, and WSA and MWD were also strongly
correlated among themselves. To optimize
the number of indicators, it is suggested to
use SOM and MWD in addition to sand as
the parameters for assessing the soil quality
as affected by land use change.
Effects of Land Use Change on the
Selected Soil Properties
Sand Content (Indicator of Soil Erosion)
The conversion of forest into cropland is
known to deteriorate soil physical properties
and making the land more susceptible to
erosion since macro-aggregates are
Factor 1
Factor 2
_______________________________________________________________________ Ayoubi et al.
disturbed (Çelik, 2005). Soil erosion can
modify soil properties by reducing soil
depth, changing soil texture, and by loss of
nutrients and organic matter (Foster, 2001).
Loss of organic matter is expected to
destabilize soil aggregates and,
consequently, the finer particles are
transported by erosion. Sand content is a
physical parameter affected by soil erosion
and, hence, can be measured and used as an
indicator for evaluating soil degradation
under different land use systems.
The results of ANOVA indicated that
there were significant (P< 0.001) differences
among the four land parcels studied (Table
6). The highest and the lowest sand contents
were found in the cultivated land and natural
forest, respectively. The results of the
multiple comparison test (Duncan’s method)
confirmed that there were significant
differences (P< 0.01) between mean values
of sand content in the natural forest,
cultivated land and land reforested with
Olea europea. There was no significant
difference in sand content between the plot
of natural forest and that reforested with
Cupressus arizonica.
The parent material of the selected site
under different land uses is loess deposit
containing mainly silt size particles, almost
completely homogenous within the depth of
the profile. Therefore, considering the short
distances between the studied land
parcels(shorter than 100 m), it is suggested
that the variability in the particle size
distribution is mainly due to the effects of
the different land uses and not different
parent materials.
The sites are located on steep slopes and
cultivation is mainly done along the slope
without implementing conservation
practices. Therefore, over the last 40 years,
the finer soil particles have been selectively
removed by erosion, thereby increasing the
proportion of the coarser particles in the soil,
as also suggested by Wang et al. (2006).
These processes have led to significant
increase in the percentage of sand content
(+252%) compared to the plot under natural
forest on the same slopes. But, the
reforestation of steep slopes during the last
30 years has reduced the loss of fine
particles; consequently, the percentage
increase in the sand contents were 141% and
29.5% in the land reforested by Cupressus
and olive, respectively, as compared to the
natural forest.
According to Ajami et al. (2006), clay
content decreased from 38.8% to 20% in the
surface horizons after deforestation and
cultivation of loessial soils of the Golestan
Province, northern Iran. In contrast, the
percentage of sand content increased 1.5 to 2
times following deforestation and silt
content also increased from 55% to 70% in
the parcel under cultivation. Islam and Weil
(2000a) indicated that the cultivated soils in
Bangladesh were considerably lower in silt
and lower in clay compared to the adjacent
soils under natural forest, most likely as a
result of preferential removal of silt by
accelerated water erosion in the monsoon
Soil organic matter (SOM) has been
reported as the most powerful indicator for
assessing soil potential productivity in
different regions of the world under varied
land uses and managements (Shukla et al.,
2006; Ajami et al., 2006; Kiani et al., 2003).
The results of ANOVA showed that there
were significant differences among the
studied land parcels (Table 6). The mean
comparisons using Duncan’s test indicated
that there was significant (P< 0.01)
difference in SOM among the four land uses
studied, especially between the natural forest
(6.45%) and the cultivated land (1.84%)
(Table 7). Evrendilek et al. (2004) showed
that deforestation and subsequent cultivation
decreased organic matter by 48.8%. Also,
other studies have shown that there were
significant differences in SOM content of
the soils under cultivation and mature
woodland (Chidumayo and Kwibisa, 2003;
Kiani et al., 2003; Ajami et al., 2006;
Khormali et al., 2006).
Assessment of Soil Quality in a Loessial Soil _____________________________________
Table 6. The result of analysis of variance (ANOVA) for selected soil properties under different land
uses all treatments, Golestan province, northern Iran.
Sum of squares df Mean square F P-value
SAND Between groups 21876.01 3 7292 58.86 0.001
within groups 19448.57 157 123.87
total 41324.58 160
SOMa Between groups 402.31 3 134.1 355.16 0.001
within groups 59.28 157 0.37
total 461.6 160
MWDb Between groups 26.66 3 8.88 86.01 0.001
within groups 16.22 157 0.1
total 42.89 160
a Soil Organic Matter, b Mean Weghit Diameter.
Table 7. Comparison of mean values of selected soil parameters under different land uses using
Duncan’s test, Goletan province, northern Iran (Duncan’s method).
Land use
Soil property Unit NFa CLb ROc RCd
Sand % 10.5c* 37.0a 25.3b 13.6c
SOMe % 6.45a 1.84c 2.75b 3.17b
MWDf Mm 2.42a 1.16b 2.10a 2.13a
*a, b, c, … indicate significant differences (P< 0.01) among treatments based on Duncan’s
mean test.
a Natural forest; b Cultivated Land; c Reforested with Olive; d Reforested with Cupressus; e Soil
Organic Matter, f Mean Weghit Diameter.
In this study, deforestation and cultivation of
land decreased SOM by 71.5% (Table 7).
Disturbance can alter soil temperature,
moisture, and aeration, and, thus, increase the
decomposition rate of SOM. SOM in the
forested land was higher than in the cultivated
parcel, since the soil in the first case was not
tilled or exposed to erosion. Probably, the loss
of SOM combined with greater sand content
and poorer aggregation resulted in higher bulk
density (23.4% increase) under cultivation
compared to the natural forest.
The continuous use of heavy farm
machineries can further aggravate the loss of
SOM through erosion. Similar results were
reported by Hajabbasi et al. (1997) and Çelik
(2005) who showed that deforestation and
subsequent tillage practices resulted in 20.0%
and 7.9% increase in bulk density of the
surface soil in the central Zagros Mountain
Range in Iran and southern highlands of
Turkey, respectively. This is also consistent
with the findings of other researchers (Vagen
et al., 2006; Rasiah et al., 2004; Kiani et al.,
2003). Organic matter is greatly influenced by
the land use change on the hillslope soils with
loess parent material.
In the studies by Kiani et al. (2003) and
Ajami et al. (2006), it was shown that, by the
conversion of land use from forest to
cultivation on the loess hill-slope soils of
Golestan Province, the soil organic carbon
decreased, respectively, from 4% to 1.3% and
from 7.2% to 1.2%, ,.Consequently, due to the
significant role of SOM in soil erodibility, the
K factor of the cultivated land increased by
66.7% compared to the value found for the
natural forest. Çelik (2005) reported that soil
erodibility factor of the cultivated soil was 2.4
times higher than that of the forest soil.
Reforestation of degraded land with Olea
europea and Cupressus arizonica increased
the SOM by 49.5% and 72.3%, respectively,
compared to the cultivated land; and there
_______________________________________________________________________ Ayoubi et al.
Figure 3. Mean comparisons of different
classes of aggregates in four land uses (NF:
Natural forest, RC: Reforested with Cupressus;
RO: Reforested with Olive, CL: Cultivated
land) (a, b, c, …letters indicate significant
differences among treatments based on
Duncan’s mean test, the treatments with the
same letter are not significantly different at P<
were significant differences between the
reforested and the cultivated soils (Table 7).
These results are consistent with those
observed for the surface soils following
afforestation (Ritcher et al., 1999; Paul et al.,
2002). Moreover, following an increase in the
SOM in the land reforested by olive and
Cupressus, BD decreased to 1.47 and 1.36 g
cm-3, respectively, (Table 2) while the soil
erodibility factor (K factor) decreased by
36.1% and 33.3% compared to the cultivated
Because of the abovementioned effects of
SOM, natural forest soils had more TN,
AWHC, and MR as compared to the cultivated
soils (Table 2). Evrendilek et al. (2004) also
suggested that cultivation decreased the total
soil porosity, soil respiration rate, and nutrient-
retention capacity.
The mean weight diameter (MWD) of soil
aggregates was significantly (P< 0.001)
different among the four land uses (Table 6).
Duncan’s test showed that there were
significant differences (P< 0.01) between soils
under natural forest (2.42 mm) and under
cultivation (1.16 mm) (Table 7).
Aggregate stability depends on the
interaction between primary particles and
organic constituents to form stable aggregates,
which are influenced by various factors related
to soil environmental conditions and
management practices (Elustondo et al.,
1990). SOM plays a key role in the formation
and stabilization of soil aggregates (Lu et al.,
1998). Loss of soil organic carbon with
cultivation is related to the destruction of
macro-aggregates. There was a highly
significant correlation (0.86) between SOM
and MWD (Table 3).
The differences observed in the percentages
of the stable aggregates under various land
uses likely resulted from the differences in the
quality and quantity of SOM. Caravaca et al.
(2004) indicated that aggregate stability of
cultivated soils was significantly lower (mean
40%) than that of forested soils (mean 82%).
Findings of Çelik (2005) also indicated that
cultivation caused 61 and 52% decrease in the
MWD in the 0-10 cm and 10-20 cm soil layers,
respectively. The higher aggregation in the
forested soils might have protected SOM from
decomposition by microbial activity (Çelik,
2005; Evrendilek et al., 2004).
Figure 3 shows the distribution of the
aggregate size classes. Distribution of soil
aggregates differed significantly among
different land uses. The cultivated soils had
significantly (P< 0.01) higher mass of
aggregates in the smaller diameter classes (0.1-
0.25 mm) than the other land uses. In the 2-4.6
mm class, however, the forest soils showed
greater mass of aggregates than the cultivated
soils. The small aggregate size was found to be
a useful indicator of soil degradation.
Reforestation with olive and Cupressus in the
study area increased the proportion of larger
aggregates and reduced those of smaller ones
The physical, chemical, and biological
characteristics of soils under four land uses
were measured and suitable soil quality
indicators were selected using factor analysis.
The first three factors explained about 76% of
the total variance. Communality estimates for
these three factors and correlation studies
Land use
Assessment of Soil Quality in a Loessial Soil _____________________________________
showed that the most suitable indicators were
MWD, SOM, and sand content to evaluate soil
quality following land use change. The
clearing and cultivation of forest lands resulted
in the degradation of soil properties compared
to the soils under well-stocked natural forest,
Olea europea and Cupressus arizonica
reforestation. SOM and MWD size were
reduced and sand content (as indicator of soil
erosion) was increased. Reforestation with
Olea europea and Cupressus arizonica
indicated that planting of well-adapted and
fast-growing trees can gradually improve the
soil quality and rehabilitate the degraded lands.
Therefore, greater attention is needed to
conserve the soils on the hilly slopes by
preventing deforestation and through
reclamation of degraded land by establishing
appropriate forest and orchard plantations.
1. Abdi, H. 2003. Factor Rotations in Factor
Analyses. In: "Encyclopedia for Research
Methods for the Social Sciences", (Eds.):
Lewis-Beck, M., Bryman, A. and Futing, T..
Sage, Thousand Oaks (CA), PP. 792-795.
2. Ajami, M., Khormali, F., Ayoubi, S. and
Omrani, R. A. 2006. Changes in Soil Quality
Attributes by Conversion of Land Use on a
Loess Hillslope in Golestan Province, Iran. 18th
International Soil Meeting (ISM) on Soil
Sustaining Life on Earth, Maintaining Soil and
Technology Proceedings, Soil Science Society
of Turkey, PP. 501-504.
3. Anderson, J. P. E. 1982. Soil Respiration. Part
2. In: "Method of Soil Analysis: Chemical
Analysis", (Eds.): Miller, R. H. and Keeney, D.
R.. Soil Science Society of America, Madison,
WI, PP. 831-872.
4. Andews, S. S., Karlen, D. L. and Mitchel J. P.
2002. A Comparison of Soil Quality Indexing
Methods for Vegetable Production Systems in
the Northern California. Agric. Eco. Syst.
Environ., 90: 24-45.
5. Angers, D. A. and Mehuys, G. R. 1993.
Aggregate Stability to Water. In: "Soil
Sampling and Methods of Analysis", (Ed.):
Carter, M. R.. Lewis Publication, Boca Raton,
PP. 651-657.
6. Ayoubi, S. 2005. Soil Attributes Prediction
Using Soil-landscape Model in Hilly Region of
Azadshahr District, Golestan Province,
Northern Iran. Gorgan University of
Agricultural Sciences and Natural Resources
Publication, GUASNR Press, P. 54.
7. Bachmann, G. and Kinzel, H. 1992.
Physiological and Ecological Aspects of the
Interactions between Plant Roots and
Rhizosphere Soil. Soil Biol. Biochem., 24:
8. Blake, G. R. and Hartge, K. H. 1986. Bulk
Density. Part I. In: "Methods of Soil Analysis",
(Ed.): Klute, A.. ASA, Monograph, No. 9,
Madison, WI. PP. 363-376.
9. Brejda, J. I., Moorman, T. B., Karlen, D. L.
and Dao, T. H. 2000. Identification of
Regional Soil Quality Factors and Indicators. I.
Central and Southern High Plains. Soil Sci.
Soc. Am. J., 64: 2115-2124.
10. Bremner, J. M. and Mulvaney, C. S. 1982.
Nitrogen Total. Part II. In: "Methods of Soil
Analysis", (Ed.): Bu Buxton, D. R.. ASA,
Monograph, No.9, 2nd Edition, Madison, WI,
PP. 595- 624.
11. Caravaca, F., Lax, A. and Albaladjeo, J. 2004.
Aggregate Stability and Characteristics of
Particle Size Fractions in Cultivated and Forest
Soils of Semiarid Spain. Soil Till. Res., 78: 83-
12. Carter, M. R., Gregorich, E. G., Angers, D.
A., Donald, R. G., Bolinder, M. A., 1998.
Organic C and N Storage and Organic C
Fractions in Adjacent Cultivated and
Forested Soils of Eastern Canada. Soil Till.
Res. 47:253-261.
13. Çelik, I. 2005. Land Use Effects on Organic
Matter and Physical Properties of Soil in a
Southern Mediterranean Highland of Turkey.
Soil Till. Res., 83: 270- 277.
14. Chaney, R. and Slonim, S. 1982.
Determination of Calcium Carbonate Content
in Soils: Geotechnical Properties Behavior and
Performance of Calcareous Soils. In: "ASTM
STP, Vol: 777. American Society for Testing
and Materials" Demars, K. R., Chaney,
R.(Eds.), , Philadelphia, PP. 3-15.
15. Chidumayo, E. N. and Kwibisa, L. 2003.
Effect of Deforestation on Grass Biomass and
Soil Nutrient Status in Miombo Woodland,
Zambia. Agri. Ecosys. Environ., 96: 97-105.
16. Doran, J. W., Parkin, T. B., 1994. Defining
Soil Quality for a Sustainable Environment
In: “Defining and Assessing Soil Quality
(Eds.): Doran, J. W., Coleman, D. C.,
Bezdicek, D. F., Stewart, B. A., SSSA.
Special Publication, no. 35.
_______________________________________________________________________ Ayoubi et al.
17. Ellingson, L., Kauffman, J. B., Cummings, D.
L., Sanford Jr, R. L. and Jaramillo, V. J. 2000.
Soil N Dynamics Associated with
Deforestation, Biomass Burning, and Pasture
Conversion in a Mexican Tropical Dry Forest.
Forest Ecol. Manag., 137: 41-51.
18. Elustondo, J., Angers, D. A., Laverdiere, M. R.
and N’dayegamiye, A. 1990. Influence de la
Culture de Mais et de la Prairie sur l’Aregation
et la Matiere Organique de Sept Soils de
Quebec. Can. J. Soil Sci., 70: 395-403.
19. Evrendilek, F., Çelik, I. and Kilic, S. 2004.
Changes in Soil Organic Carbon and other
Physical Soil Properties along Adjacent
Mediterranean Forest, Grassland, and
Cropland Ecosystems in Turkey. J. Arid
Environ., 59: 743-752.
20. Foster, G. R. 2001. Keynote: Soil Erosion
Prediction Technology for Conservation
Planning. In: "Sustaining Global Farm",
(Eds.): Stott, D. E., Mohtar R. H. and
Steinhardt, G. C., PP. 847-851.
21. Gee, G. W. and Bouder, J. W. 1986. Particle
Size Analysis. In: "Methods of Soil Analysis",
(Ed.): Klute, A., Part I. ASA, Monograph, No.
9. 2nd Edition, Madison, WI. PP. 337-382.
22. Hajabbasi, M. A., Jalalain, A. and
Karimzadeh, H. 1997. Deforestation Effects on
Soil Physical and Chemical Properties.
Lordegan, Iran, Plant Soil, 190: 301-308.
23. Hebel, A. 1998. Soil Degradation-diagnosis,
Appraisal and Reversing Measure.
Introduction. P. 1-2. In:"Toward Sustainable
Land Use", (Ed.): Blume, H. P. et al., Vol. I,
Adv. Geo. Ecol. 31. Catena Verlag,
Reiskirchen, Germany. PP?
24. Heil, D. and Sposito, G. 1997. Chemical
Attributes and Processes Affecting Soil
Quality. In: "Soil Quality for Crop Production
and Ecosystem Health", (Eds.): Gregorich, E.
G. and Carter, M. R.. Elsevier, Amsterdam,
PP. 59-79.
25. Hurni, H. 1997. Concepts of Sustainable Land
Management. ITC. J., 3(4): 210-215.
26. Islam, K. R. and Weil, R. R. 2000a. Land Use
Effects on Soil Quality in a Tropical Forest
Ecosystem of Bangladesh. Agriculture Ecosys.
Environ., 79: 9-16.
27. Islam, K. R. and. Weil, R. R. 2000b. Soil
Quality Indicator Properties in Mid-Atlantic
Soils as Influenced by Conservation
Management. J. Soil Water Conser., 55: 2269-
28. Islam, K. R., Kamaluddin, M., Bhuiyan, M. K.
and Badruddin, A. 1999. Comparative
Performance of Exotic and Indigenous Forest
Species for Tropical Semi-Evergreen
Degraded Forest Land Reforestation in
Bangladesh. Land Dedrag. Dev., 10: 241-249.
29. Joreskog, K. 1977. Factor Analysis by Least
Squares and Maximum Likelihood Method,
volume III, In:" Statistical Methods for Digital
Computers", (Eds.): Enslein, K., Rolston, D. E.
and Wilf, H.. Wiley, New York, NY.
30. Kang, B. T. and Juo, A. S. R. 1986. Effect of
Forest Clearing on Soil Chemical Properties
and Crop Performance. In: "Land Clearing
and Development in the Tropics", (EdS.): Lal,
R., Sanchez, P. A. and Cummings, Jr., R. W..
Belkema, Rotterdam, PP. 383-394.
31. Karlen, D. L., Mausbach, M. J., Doran, J.
W., Cline, R. T., Harris, R. F., Schuman, G.
E., 1997. Soil Quality: A Concept Definition
and Framework for Evaluation. Soil Sci. Soc.
Am. J. 90:644–650.
32. Kemper, W. D. and Rosenau, R. C. 1986.
Aggregate Stability and Size Distribution. Part
I. In: "Methods of Soil Analysis", (Ed.): Klute,
A. ASA, 2nd Edition, Monograph, No. 9.,
Madison, WI. PP. 687-734.
33. Khormali, F., Ajami, M. and Ayoubi, S. 2006.
Genesis and Micromorphology of Soils with
Loess Parent Material as Affected by
Deforestation in a Hillslope of Golestan
Province, Iran. International Soil Meeting on
Soils Sustaining Life on Earth, May 22-26,
Şanliurfa-Turkey. PP. 149-151.
34. Khormali, F. and Nabiollahi, K. 2009.
Degradation of Mollisols in Western Iran as
Affected by Land Use Change. J Agri. Sci.
Tech., 11: 363-374.
35. Kiani, F., Jalalian, A., Pashae, A. and
Khademi, H. 2003. Effects of Deforestation on
Selected Soil Quality attributes in Loess-
derived Land Forms of Golestan Province,
Northern Iran. Proceeding of the 4th
International Iran and Russian Conference,
Shahrekord, PP. 546-550.
36. Klute, A. and Dirksen, C. 1986. Hydraulic
Conductivity and Diffusivity. Part 1. In:
"Methods of Soil Analysis", (Ed.): Klute, A..
2nd Edition, Agronomy Monograph, Vol. 9.,
American Society of Agronomy, Madison, WI,
PP. 687-734.
37. Larson, W. E. and Pierce, F. G. 1991.
Conservation and Enhancement of Soil Quality
in Evaluation for Sustainable Land
Management in the Developing World.
International Borad for Soil Research and
Assessment of Soil Quality in a Loessial Soil _____________________________________
Managemen,t IBSRAM Proceeding 12(2), Vol.
2, Bangkok, Thailand.
38. Larson, W. E. and Pierce, F. G. 1994. The
Dynamic of Soil Quality as a Measure of
Sustainable Management. In: "Defining Soil
Quality for a Sustainable Environment",
(Eds.): Doran, J. W. et al. Soil Sci. Soc. Am.,
Spec. Publ., No. 35., ASA, Madison, WI, PP.
39. Likens, G. B. Bormann, F. H., Johnson, N. M.
and Fisher, D. W. 1970. Effects of Forest
Cutting and Herbicide Treatment on Nutrient
Budgets in the H Brook Watershed-ecosys.
Ecol. Monogr., 40: 23-41.
40. Lima, A. C. R., Hoogmoed, W. and Brussaard,
L. 2008. Soil Quality Assessment in Rice
Production Systems: Establishing a Minimum
Data Set. J. Environ. Qual., 37: 623-630.
41. Lu, G., Sakagami, K., Tanaka, H. and
Hamada, R. 1998. Role of Soil Organic Matter
in Stabilization of Water Aggregates in Soils
under Different Types of Land Use. Soil Sci.
Plant. Nutr., 44: 147-155.
42. Mclean, E. O. 1982. Soil pH and Lime
Requirement. Part II. In: "Methods of Soil
Analysis ", (Ed.): Page, A. L. 2nd Edition, ASA,
Monograph, No. 9, Madison, WI, PP. 199-223.
43. Mosaedi, A. 2003. Study of Factors Increasing
Flood Damages in the North of Iran on August
2001 and 2002. European Geophysical
Society. Geophys. Res. Abst. Vol. 5, 03945.
44. Nadri, S., Cocheri, G. and Dell' Agnola, G.
1996. Biological Activity of Humus. In:
"Humic Substances in Terrestrial Ecosystems",
(Ed.): Piccolo, A.. Elsevier, Amsterdam, PP.
45. Nael, M., Khademi, H. and Hajabassi, M. A.
2004. Response of Soil Quality Indicators and
Their Spatial Variability to Land Degradation
in Central Iran. Appl. Soil Ecol., 27: 221-232.
46. Nelson, D. W. and Sommers, L. E. 1982. Total
Carbon, Organic Carbon, and Organic Matter.
Part II. In: "Methods of Soil Analysis", (Ed.):
Buxton, D. R.. 2nd Edition, ASA, Monograph,
No. 9, Madison, WI, PP. 539-579.
47. Patrick, J. H. and Smith, D. W. 1975. Forest
Management and Nutrient Cycling in Eastern
Hardwoods. USDA For. Serv. Res. Pap. ME-
4. Northeast For. Exp. Stat. Broomal, PA.
48. Paul, K. I., Polglase, P. J., Nyakuengama J. G.
and Khanna, P. K. 2002. Change in Soil
Carbon Following Afforestation. Forest Ecol.
Manag., 168: 241-257.
49. Paz-Gonzalez, A., Viera, S. R. and Toboada
Castro, M. T. 2000. The Effect of Cultivation
on the Spatial Variability of Selected
Properties of an Umbric Horizon. Geoderma,
97: 273-292.
50. Pierce, F. J. and Larson, W. E. 1993.
Developing Criteria to Evaluate Sustainable
Land Management.p:7-14. In: "Proceeding of
the 8th International Soil Management
Workshop: Utilization of Soil Survey
Information for Sustainable Land Use", (Ed.):
Kimble, J. M. Oregon, California, and Nevada.
USDA, Soil Conservation Service, National
Soil Survey Center, Lincoln, NE.
51. Rasiah, V., Florentine, S. K., Williams, B. L.
and Westbrooke, M. E. 2004. The Impact of
Deforestation and Pasture Abandonment on
Soil Properties in the West Tropics of
Australia. Geoderma, 120: 35-45.
52. Rhoades, J. D. 1982. Soluble Salts. Part II. In:
"Methods of Soil Analysis", (Ed.): Page, A. L.
2nd Edition, ASA, Monograph, No. 9, Madison,
WI, PP.167-179.
53. Ritcher, D. D., Markewitz, D., Trumbore, S. E.
and Wells, C. G. 1999. Rapid Accumulation
and Turnover of Soil Carbon in a Re-
establishing Forest. Nature, 400: 56- 58.
54. Sanchez Maranon, M., Soriano, M., Delgado,
G. and Delgado, R. 2002. Soil Quality in
Mediterranean Mountain Environment. Soil
Sci. Soc. Am. J., 66: 948-958.
55. Shukla, M. K., Lal, R. and Ebinger, M. 2006.
Determining Soil Quality Indicators by Factor
Analysis. Soil Till. Res., 87: 194-204.
56. Sigstad, E., Begas, M. A., Amoroso, M. J. and
Garcia, C. I. 2002. Effects of Deforestation on
Soil Microbial Activity. Themochimica Acta,
394: 171-178.
57. Soil Survey Staff. 2006. Keys to Soil
Taxonomy. US, Department of Agriculture,
Natural Resources Conservation Service.
58. Swan, A. R. H. and Sandilands, M. 1995.
Introduction to Geological Data Analysis.
Blackwell, London. 446 PP.
59. Vagen, T. G., Andrianorofanomezana, M. A.
A. and Andrianorofanomezana, S. 2006.
Deforestation and Cultivation Effects on
Characteristics of Oxisols in the Highlands of
Madagascar. Geoderma, 131: 190-200.
60. van Bevel, C. H. M. 1949. Mean Weight
Diameter of Soil Aggregate as a Statistical
Index of Aggregation. Soil Sci. Soc. Am. Proc.,
14: 20-23.
61. Wander, M. M. and Bollero, G. A. 1999. Soil
Quality Assessment of Tillage Impact in
Illinois. Soil Sci. Soc. Am. J., 63: 961-971.
_______________________________________________________________________ Ayoubi et al.
62. Wang, X., Liu, M., Liu, S. and Liu, G. 2006.
Fractal Characteristics of Soils under Different
Land-use Pattern in the Arid and Semiarid
Region of the Tibetan Platue, China.
Geoderma, 134: 56-61.
63. Wang, Z., Chang, A. C., Wu, L. and Crowley,
D. 2003. Assessing the Soil Quality of Long-
term Reclaimed Wastewater-irrigated
Cropland. Geoderma, 114: 261-278.
64. Wendroth, O., Reynold, W. D., Vieira, S. R.,
Reichardt, K. and Wirth, S. 1997. Statistical
Approaches to the Analysis of Soil Quality
Data. In: "Soil Quality for Crop Production
and Ecosystem Health", (EdS.): Gregorich, E.
G. and Carter, M. R.. Elsevier, Amsterdam,
PP. 247-276.
65. Wischmeier, W. H. and. Smith, D. D. 1978.
Predicting Rainfall Erosion Losses. A Guide to
Conservation Planning. USDA, Agr. Res.
Serve. Handbook, 537 PP.
66. Zink, J. A. and Farshad, A. 1995. Issues of
Sustainability and Sustainable Land
Management. Can. J. Soil. Sci., 75: 407-412.
نﺎﺘﺳا ﻲﺴﻟ يﺎﻬﻛﺎﺧ رد ﺖﻴﻔﻴﻛ يﺎﻫ ﺺﺧﺎﺷ يور ﻲﺿارا يﺮﺑرﺎﻛ ﺮﻴﻴﻐﺗﺮﺛا ﻲﺑﺎﻳزرا
نﺎﺘﺴﻠﮔناﺮﻳا ،
ش .ف ،ﻲﺑﻮﻳا .ك ،ﻲﻟﺎﻣﺮﺧ .ل .ا و ،تاواﺮﻫﺎﺳ .س .ﺎﻤﻴﻟد ﺰﮕﻳردور
رﻮﻈﻨﻣ ﻪﺑ ﻪﻌﻟﺎﻄﻣ ﻦﻳا ﻚﻴﻨﻜﺗ ﻚﻤﻛ ﻪﺑ كﺎﺧ ﺖﻴﻔﻴﻛ يﺎﻫ ﺺﺧﺎﺷ يور ﻲﺿارا يﺮﺑرﺎﻛ ﺮﻴﻴﻐﺗ ﺮﺛا ﻲﺑﺎﻳزرا
را رد ﺎﻫرﻮﺘﻛﺎﻓ ﻪﻳﺰﺠﺗﺖﺳا هﺪﺷ مﺎﺠﻧا نﺎﺘﺴﻠﮔ نﺎﺘﺳا يﻼﻛ ﺖﺼﺷ ﻪﻘﻄﻨﻣ يرﻮﻫﺎﻣ ﻪﭙﺗ ﻲﺿا . رﻮﻈﻨﻣ ﻦﻳا ﻪﺑ40
ﻲﺤﻄﺳ ﻖﻓا زا كﺎﺧ ﻪﻧﻮﻤ)30-0ﺮﺘﻣ ﻲﺘﻧﺎﺳ ( ﻞﻣﺎﺷ يﺮﺑرﺎﻛ رﺎﻬﭼ زا)1 ( ،ﻲﻌﻴﺒﻃ ﻞﮕﻨﺟ)2 ( ﺖﺸﻛ ﻲﺿارا
،هﺪﺷ)3 ( و نﻮﺘﻳز ﺎﺑ هﺪﺷ يرﺎﻛ ﻞﮕﻨﺟ ﻲﺿارا)4 ( وﺮﺳ ﺎﺑ هﺪﺷ يرﺎﻛ ﻞﮕﻨﺟ ﻲﺿارا) ًﺎﻌﻤﺟ160ﻪﻧﻮﻤ (
ادﺮﺑﺪﻳدﺮﮔ ﺖﺷ . دراﺪﻧﺎﺘﺳا يﺎﻬﺷور ﻪﺑ كﺎﺧ يﺎﻫ ﻪﻧﻮﻤﻧ يور ﻲﻜﻳژﻮﻟﻮﻴﺑ و ﻲﻳﺎﻴﻤﻴﺷ ،ﻲﻜﻳﺰﻴﻓ ﻪﻳﺰﺠﺗ هدرﺎﻬ
ﺖﻓﺮﻳﺬﭘ ترﻮﺻ ﻲﻫﺎﮕﺸﻳﺎﻣزآ . ﺎﻫ ﻪﻧاﺪﻛﺎﺧ ﺮﻄﻗ ﻲﻧزو ﻦﻴﮕﻧﺎﻴﻣ ﻪﻛ داد نﺎﺸﻧ ﺎﻫرﻮﺘﻛﺎﻓ ﻪﻳﺰﺠﺗ ﺞﻳﺎﺘﻧ(MWD) ،
بآ رد راﺪﻳﺎﭘ يﺎﻫ ﻪﻧاﺪﻛﺎﺧ ﺪﺻرد(WSA) كﺎﺧ ﻲﻟآ هدﺎﻣ راﺪﻘﻣ ،(SOM) ﻞﻛ تزا و (TN) ﻦﻳﺮﺘﻬ
ﺪﻧدﻮﺑ ﻲﺿارا يﺮﺑرﺎﻛ ﺮﻴﻴﻐﺗ ﺮﺛا نداد نﺎﺸﻧ ياﺮﺑ ﻪﻌﻟﺎﻄﻣ درﻮﻣ ﻪﻘﻄﻨﻣ رد كﺎﺧ ﺖﻴﻔﻴﻛ ﻲﺑﺎﻳزرا يﺎﻫ ﺺﺧﺎﺷ .
لﺎﻤﺘﺣا ﺢﻄﺳ رد ﻪﻛ داد نﺎﺸﻧ ﺎﻫ ﻦﻴﮕﻧﺎﻴﻣ ﻪﺴﻳﺎﻘﻣ و ﺲﻧﺎﻳراو ﺰﻴﻟﺎﻧآ ﺞﻳﺎﺘ99 ﻲﺳرﺮﺑ درﻮﻣ رﺎﻤﻴﺗ رﺎﻬﭼ ﻦﻴﺑ ﺪﺻرد
ﻦﻴﺑMWD , SOMدراد دﻮﺟو يراد ﻲﻨﻌﻣ فﻼﺘﺧا ﻦﺷ راﺪﻘﻣ و . و ﻪﻘﻄﻨﻲﻌﻴﺒﻃ نﺎﺘﺧرد ﻞﻣﺎﻛ ﻊﻄ
رد رﺎﻛ و ﺖﺸﻛ40 ﺶﻫﺎﻛ ﻪﺑ ﺮﺠﻨﻣ ﻪﺘﺷﺬﮔ لﺎﺳ 5/71 %ﺖﺳا هﺪﺷ ﻲﻟآ هدﺎﻣ . ﺶﻫﺎﻛ ﺚﻋﺎﺑ رﺎﻛ و ﺖﺸﻛ
1/52 % راﺪﻘMWD ﺶﻳاﺰﻓا ﺚﻋﺎﺑ و ،252 %ﺖﺳا هﺪﺷ ﻦﺷ راﺪﻘ . هﺪﺷ ﺐﻳﺮﺨﺗ ﻲﺿارا دﺪﺠﻣ يرﺎﻛ ﻞﮕﻨﺟ
ﺶﻳاﺰﻓا ﺚﻋﺎﺑ ﺐﻴﺗﺮﺗ ﻪﺑ وﺮﺳ و نﻮﺘﻳز ﺎﺑ5/49 % و3/72 %دﺎﻣ راﺪﻘ هﺪﻳدﺮﮔ ﻲﻋارز ﻲﺿارا ﺎﺑ ﻪﺴﻳﺎﻘﻣ رد ﻲﻟآ ه
ﺖﺳا . راﺪﻘﻣ ﻦﻴﻨﭽﻤMWD ﺐﻴﺗﺮﺗ ﻪﺑ وﺮﺳ و نﻮﺘﻳز ﺎﺑ هﺪﺷ ﺖﺸﻛ ﻲﺿارا رد 81 و 6/83 ﻪﺑ ﺖﺒﺴﻧ ﺪﺻرد
ﺖﺳا ﻪﺘﻓﺎﻳ ﺶﻳاﺰﻓا ﻲﻋارز ﻲﺿارا . و ﺖﺸﻛ نآ ﻊﺒﺗ ﻪﺑ و ﻞﮕﻨﺟ ﻞﻣﺎﻛ ﻊﻄﻗ ﻪﻛ داد نﺎﺸﻧ ﻖﻴﻘﺤﺗ ﻦﻳا ﻲﻠﻛ ﺞﻳﺎﺘﻧ
ﺚﻋﺎﺑ ﻲﺴﻟ يرﻮﻫﺎﻪﭙﺗ ﻲﺿارا يور ﺪﺘﻤﻣ رﺎﻛ يرﺎﻛ ﻞﮕﻨﺟ ﻪﻜﻴﻟﺎﺣ رد ﺖﺳا هﺪﺷ كﺎﺧ ﺖﻴﻔﻴﻛ ﺶﻫﺎﻛ
ﺖﺳا هﺪﻴﺸﺨﺑ دﻮﺒﻬﺑ ار كﺎﺧ ﺖﻴﻔﻴﻛ ﻲﺿارا ﻦﻳا دﺪﺠﻣ.
... While, the lowest (3.64 % and 4.04 %) moisture was observed under abandoned land which was non-significant than the rest of the land uses (Table 3.1.1). Similar results were also obtained by Selassie and Ayanna (2013) and Ayoubi et al., (2011) who found out that forest land (natural forest) soil has more moisture content than agriculture (cultivated) land (agriculture) soils. ...
... al., (2004) who found forest soils more acidic as compared to agricultural soils. The same result was also observed by Ayoubi et al., (2011) who found forest land (natural forest) soils more acidic than agriculture (cultivated) land (agricultural) soils. Similarly, Selassie and Ayanna (2013) also reported the same result who found forest land (natural forest) soils more acidic than agriculture (cultivated) land (agriculture) soils. ...
... Similar results were reported by Kizilkaya and Dengiz (2010) who found out that forest land (natural forest) has more electrical conductivity as compared to agriculture (cultivated) land (agricultural) soils. Ayoubi et al. (2011) also obtained similar results natural forest has more electrical conductivity than agriculture (cultivated) land (agricultural) soils. Gholami (2013) also observed similar results where the electrical conductivity of agriculture (cultivated) land (agricultural) soils was more as compared to abandoned land (abandoned) soils. ...
Full-text available
A study conducted in the Selaqui area of Dehradun, Uttarakhand, India was to assess and compare the changes in the physical and chemical properties of soils under different land-use viz. natural forest soil, agricultural (cultivated) soil, abandoned soil, and industrial soil. Soil samples collected randomly from the four sites with three replications for each land-use system, at two varied depth levels (0-15 cm and 15-30 cm). Results of this study indicated that the forest land recorded the highest moisture content, electrical conductivity, total nitrogen, organic carbon, available phosphorous and available potassium while the higher pH and calcium content were observed in an abandoned land and industrial land use respectively. However, the lowest moisture content, electrical conductivity, total nitrogen, and calcium content were recorded from abandoned land use; the pH from industrial land use and the available phosphorous and available potassium from agriculture (cultivated) land use.
... The total plant available water was the greater in the natural forest land use systems followed by grass and horticultural land use systems might be due to the higher content of soil organic carbon that provided larger surface area for absorption and retention of water molecules (Materchera, and Mkhabela, 2001). This result is agreement with the finding of Ayoubi (2011), which stated that the natural forest land use soils have more plant available water holding capacity as compared to the cultivated land use system. ...
Jour Pl Sci Res 38 (1) 2022 Climate Change and Health and Ayush: Curcuma and COVID-19 Heat waves hit both poles at once. Planet earth is warming and climate change is affecting. “Temperature records were smashed in Antarctica last week: one weather station recorded temperatures that were 40! above normal. At the same time, it is 30! warmer than average at the North Pole. “They are opposite seasons. You don’t see the North and the South [Poles] both melting at the same time,” says ice scientist Walt Meier. (�colorado-arctic-antarctica-eda 9ea 8704108bdab2480 fa2cd4b6e34?utm_source= Nature+Briefing&utm_ campaign=9faf4287e9-briefing-dy-20220321&utm_ medium=email&utm_term=0_c9dfd39373-9faf4287e 9-45318994). “Not a good sign when you see that sort of thing happen,” said University of Wisconsin meteorologist Matthew Lazzara. Although both Lazzara and Meier said what happened in Antarctica is probably just a random weather event and not a sign of climate change, but if it happens again or repeatedly then it might be something to worry about and part of global warming, they said. In my own experience as student in 1965-1967 period, Jaipur the pink city (the capital and largest city of the Indian state of Rajasthan) was so cool that its maximum temperature in summer was about 37o C even in May and June. The highest temperature was 42o C and within two or three days rain splashes will come. Jaipur was surrounded by hills and almost on all sides and hills were lush green after the rain. Ramgarh dam as source of water for entire city, used to overflow in rainy season and Jaipur had a population of around a lac or so in 1947. A single bus of Kamal Co used to run between stations (some 6 km from city) and walled city during early days of freedom. However, Jaipur and Udaipur (another city in the state of Rajasthan, India) or for that matter any city of the world is facing climate change but there is no concern in the present generation of politicians, scientists, medical and health practitioners as if nothing is happening. UN bodies are more of an ornamental structures. Any solutions to global warming mitigation are not spread or accepted even by those nations who will be most affected by climate change the poorest of the poor. CO26 is just another meeting and IPCC reports are just a number. Jaipur temperatures or for that matter all temperature across India are reaching 6 degrees higher than normal. It is almost 40 to 42o C in most cities of Rajasthan. The Journal of Plant Science Research is doing its part in understanding the phenomenon and providing solutions. Can you believe idea of plant-based vaccine that too from a plant commonly use as vegetable in India? Yes, this week it’s official that Medicago’s homegrown, plant-based COVID-19 vaccine has been approved by Health Canada ( news/health/medicago-s-homegrown-plant-based�covid-19-vaccine-approved-by-health-canada�1.6362745?utm_source=Nature+Briefing&utm_campaign =8f 4263cf0e-briefing-dy-20220315&utm_ medium =email&utm_term=0_c9dfd3). The shots use Medicago’s plant-derived, virus-like particles — which resemble the coronavirus behind COVID-19 but don’t contain its genetic material — and also contain an adjuvant from GSK to help boost the immune response. Curcuma longa (Turmeric) has shown promising response to prevents many diseases including current global severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and neurodegenerative disorders. Curcuma longa (Haldi) a plant-based Ayurveda medicine and its content Curcumin has shown effectiveness in help fighting COVID-19 (see review in this issue by Singh et al.). Some of the other topics covered in this issue include peach cultivation in Shimla, Himachal Pradesh, India. Physical properties of soil of Aravalli hills of Rajasthan; Leaf and Sheath Blight disease of Maize Caused Rhizoctonia solani; Peptone induced pigment (a natural pigment in textile and food industry) production in Ganoderma lucidum. Algae, the principle primary producers are photosynthetic thallophytes, usually are microscopic, unicellular and colonial or multicellular. The maintenance of a healthy canal ecosystem The Journal of Plant Science Research ii Editorial depends on the abiotic properties of water and the biological diversity of ecosystem. Large scale industrialization has caused concern regarding the pollution of water. Jayasree et al. (this issue) provides ecological Study of Blue Green Algae of Canal Waters of Kerala. As Editor-in-Chief of JPSR, I feel honoured to have contributions from distinguished scientists from India and abroad. I would expect that authors follow guidelines and devote more time in articulating their ideas and discussing them in detail based on the results obtained or review of literature. The most important point in each paper remains what is the “Take home lesson”. Being UGC Care indexed journal we have added responsibility and our acceptance rate is around 70 percent. Professor Govindjee from University of Illinois at Urbana-Champaign, USA, Professor Yau from USA, Professor Ogita from Japan and Professor N. K. Dube from BHU Varanasi provide support in screening the manuscripts. Our editorial, Ms. Shyaloo and Ms. Princee Singh, Prints Publications team left no stone unturned to provide you this issue. Prof. Ashwani Kumar Editor-in-chief Alexander von Humboldt Fellow (Germany)
... This may partly be due to inappropriate usage of land with high preference for nonagricultural usage of quicker economic returns (Senjobi, 2007). However, since the main consequences of inappropriate land use changes are land degradation and soil quality deterioration through loss of vegetative cover, soil organic matter as well as biota life (Ayoubi et al., 2011), therefore, ascertaining the nature and properties of soils becomes an indispensable measure for monitoring soil quality and ensuring best uses of soil resources for optimum crop production (Aiboni, 2001). ...
Full-text available
A clear understanding of the nature and properties of soils is indispensable for monitoring soil quality potential. This study investigated soil chemical variations and its potentials at different elevations. Two soil series (Apomu and Iseyin) at three varying elevations: Lower slope-LS (103.84-120.94 m), Middle slope-MS (120.94-135.91 m) and Upper slope-US (135.91-164.57 m) were studied. Twelve profile pits were dug, described and sampled for chemical analysis following standard methods. Soil quality indices (pH, organic carbon, total nitrogen, cation exchange capacity, electrical conductivity, extractable P, exchangeable bases and acids) were assessed. Data were subjected to analysis of variance and correlation, while Duncan Multiple range Test was used to separate means at p < 0.05. Results showed that soil pH (6.6-7.5) was near neutral at both series and across the elevations, while electrical conductivity (EC) (1-11 dS/m) ranged from slightly to strongly alkaline. Organic carbon was erratic throughout the profile and ranged from 0.25-3.29%. Total nitrogen was < 1% in all the soils and low values were obtained for exchangeable bases. Generally, cation exchange capacity (CEC) was < 6 cmollkg, while base saturation ranged from 51.73-95.29%. Soil available phosphorus (0.83-4.80 ppm) was low across the series. There were significant differences (p < 0.05) among soil chemical properties within each series and across the three elevations. Soil pH was significant (p < 0.01) and negatively correlated with exchangeable acidity at Apomu. The soil quality in this study was generally considered low based on the measured parameters, hence the need for soil fertility amelioration approaches.
... There is a negative correlation between BD and GWC, and % sand and % silt (Table 2). Similar indirect relationship between BD and GWC was also reported by Asiedu et al. (2013), while negative correlation between % sand and % silt concurs with similar results by Ayoubi et al. (2011), Ganiyu (2018, and Ganiyu et al. (2019). Negative moderate correlation at 5% level also exists between % sand and % clay (− 0.593*). ...
Full-text available
Samples of contaminated top soil (0–30 cm) and uncontaminated soil (control) from two locations in Precambrian basement complex area were analyzed to assess the effects of single and mixed oil contaminants on the physico-chemical and thermal properties of soils. Pearson’s correlation and analysis of variance (ANOVA) were used to study the interrelationships of the studied parameters as well as variation of studied soil characteristics under the different oil contaminants, respectively. Results showed insignificant impact of pollutant(s) on the textural class of contaminated soils. The highest and lowest mean soil resistivity (SR) values were found in petrol-contaminated and mixed surfactants (shampoo + conditioner) -contaminated soils, respectively. The least values of mean specific heat capacity (SHC), heat capacity (HC), and soil water diffusivity (SWD) were found in soils contaminated by mixed surfactants-contaminants. However, mixed mineral oils (petrol + diesel + engine oil) and mixed vegetable oils (palm oil + groundnut oil) had mean SHC values lower than those of control soils at the two sampling locations. The mixed surfactants-polluted soil is characterized by lowest mean bulk density (BD) and highest mean gravimetric water content (GWC) while lowest mean GWC and highest mean SWD characterized engine oil-contaminated soils. The analysis of variance (ANOVA) result revealed significant variation in % sand at 5% level (p < 0.05) for petroleum-derived contaminants but no significant differences in mean values of all analyzed soil properties under vegetable oil contaminants.
... Land management techniques out of the ordinary can result in a loss of soil nutrients and soil health, hurting agricultural, food security (Perveen et al., 2010). The global issue of environmental degradation resulting from poor land use has generated interest in sustainable agriculture production strategies (Ayoubi et al., 2011). Soil productivity and long-term viability are dependent on a dynamic balance of Physico-chemical, and biochemical qualities (Somasundaram et al., 2013). ...
Full-text available
The variability and status of micronutrients are very important for crop production there for so a sound knowledge of micronutrients is more important. The rapid agricultural change has been reported in South Asian countries.Today change takes place in a single direction from natural ecosystem to artificial ecosystem. Therefore, this study was conducted for the effects of different land-use systems on soil properties,i.e. electrical conductivity (EC), pH, and micronutrients. The study area was located at Norman E. Borlaug Crop Research Centre, G.B. Pant University of Agriculture and Technology, Pantnagar, which lies at 29°N latitude, 79°3E longitudes, and 243.84 m above the mean sea level altitude. The randomized complete block design, including different treatments with three replications for soil depth (0-20cm), was used in this experiment.The treatment were selected for study micronutrient content in soil that T 1 (rice-potato-okra), T 2 (rice-pea (vegetable)-maize), T 3 (sorghum multi-cut (fodder)-yellow Sarson-black gram), T 4 (rice-wheat-green gram), T 5 (rice-berseem + oat + mustard (fodder)-maize + cowpea (fodder)), T 6 (guava + lemon), S 7 (poplar + turmeric), T 8 (eucalyptus + turmeric), T 9 (fallow (uncultivated land)). The highest value of micronutrients content Zn(2.19mg kg-1), Fe(33.37mg kg-1) Cu(5.77mg kg-1)Mn (7.46mg kg-1). Among the different treatmentT 9 fallow (uncultivated) treatment were obtained significantly lowest value of micronutrients content Zn (0.71mg kg-1), Fe (13.45mg kg-1) Cu (3.47mg kg-1) Mn (5.74mg kg-1). According to this finding soil under agro-forestry-based treatment was found better with respect to soil properties followed by crops and the fellow treatment. India's population is increasing at an increasing rate, due to which the demand for food is also increasing. To meet this demand, we need huge production which we get using chemical fertilizers in crops. Chemical fertilizers were adversely are influenced on the soil health, production potential, water pollution, increase the environmental challenges, land degradation, increase problematic soil areas, and loss of soil biodiversity. These all are the challenges related to monocropping in a particular area. Resultant most fertile soil becomes unproductive, Therefore, the solution to this problems is that we should bring different land uses systems into practice, which will develop in the soil health and the quality of the soil. On the basis of result of the this experiment, we can say that more soil health was found in the crop with forestry base treatments, as well as it was found that with this type of system, we can also generate additional income like different products from different systems. which is sold in different markets It has been concluded from this experiment that the soil EC and micronutrients were observed more in the crop and forestry-based land uses systems because the forestry system increases the amount of organic carbon and organic matter resultant increase the soil micronutrients. The forestry system improves other properties of the soil such as physical, chemical, and biological properties, resultant in increasing the productivity and health of the soil.
... Abandoned grassland is one of the most essential vegetation restoration types, accounting for about 42% of the total vegetation area of the Loess Plateau [3]. In recent decades, vegetation restoration has provided large potential carbon sequestration and accumulation and has played a crucial role in regulating the carbon cycle and mitigating climate change [4,5]. Soil microbial carbon use efficiency (CUE), defined as the ratio between the carbon (C) allocated to growth and the C taken up by microorganisms, is a pivotal index for controlling C cycling in terrestrial ecosystems [6], which ultimately determines soil C storage. ...
Full-text available
Soil microbial carbon use efficiency (CUE) plays a crucial role in terrestrial C cycling. However, how microbial CUE responds to nitrogen addition and its mechanisms in soil aggregates from abandoned grassland systems remains poorly understood. In this study, we designed a nitrogen (N) addition experiment (0 (N0), 10 (N1), 20 (N2), 40 (N3), 80 (N4) kg N ha−1yr−1) from abandoned grassland on the Loess Plateau of China. Subsequently, the enzymatic stoichiometry in soil aggregates was determined and modeled to investigate microbial carbon composition and carbon utilization. The vegetation and soil aggregate properties were also investigated. Our research indicated that soil microbial CUE changed from 0.35 to 0.53 with a mean value of 0.46 after N addition in all aggregates, and it significantly varied in differently sized aggregates. Specifically, the microbial CUE was higher and more sensitive in macro-aggregates after N addition than in medium and micro-aggregates. The increasing microbial CUE in macro-aggregates was accompanied by an increase in soil organic carbon and microbial biomass carbon, indicating that N addition promoted the growth of microorganisms in macro-aggregates. N addition significantly improved the relative availability of nitrogen in all aggregates and alleviated nutrient limitation in microorganisms, thus promoting microbial CUE. In conclusion, our study indicates that soil microbial CUE and its influencing factors differ among soil aggregates after N addition, which should be emphasized in future nutrient cycle assessment in the context of N deposition.
Vegetation restoration is one of the principal strategies for ecosystem recovery in degraded land of fragile regions, which is an important driving factor for soil fertility and elemental circulation. While the relationship between revegetation and soil C–N–P stoichiometry remains unclear. To evaluate the relationships between vegetation restoration and soil C–N–P stoichiometry, the distribution of soil C, N, and P within 0–30 cm soil depth under five typical artificial restored vegetation types on the Loess Plateau was analyzed and the influencing factors were evaluated. The results showed that soil C, N, and P contents were relatively lower at the study site than the mean values for topsoil in China. Compared with other vegetation types (Populus simonii Carr., Pinus tabuliformis Carr., and Caragana korshinskii Kom.), Medicago Sativa L. and Stipa bungeana Trin. helped improve soil fertility better; the soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) contents within the 0–30 cm soil layer respectively maximized under Stipa bungeana Trin. (3.30 g kg⁻¹), Medicago Sativa L. (0.34 g kg⁻¹), and Medicago Sativa L. (0.41 g kg⁻¹). The values of soil C/N, C/P, and N/P for the five vegetation types were 9.50–11.85, 15.36–21.47, and 1.29–1.90, respectively. The contents of SOC and TN under the five vegetation types were significantly (P < 0.001) affected by soil depth and vegetation type (P < 0.001) and decreased with increasing soil depth. However, the TP content was significantly (P < 0.001) affected by vegetation type and not by soil depth. Considering the better adaptability of native species, native herb vegetation types should be considered first for ecological restoration in semiarid continental climate zones.
Full-text available
Potentially toxic metals in soils are a threat to food security and harmful to human because it enters the food chain through crop uptake. Hence, it is critical to know the levels of potentially toxic metals in soils due to agricultural land use patterns and how they can be removed from the soil. This review discussed the effect of different land-use patterns on heavy metal accumulation, their removal using biochar. A desktop review which employed PRISMA was used to put together information from peer-reviewed papers including journal articles, books, thesis and reports. It was shown that potentially toxic metals mainly found in the soil include; As, Cu, Cd, Zn, Cr, Co, Ni, Sb, Hg, Th, Pb, Si, and Se. The sources of these potentially toxic metals accumulation in soils were organic and inorganic fertilizer application, irrigation, pesticides and weedicides application and atmospheric deposition. However, different land-use patterns (greenhouse field, vegetable field soils, forest field, and maize field soil) had a significant accumulation of heavy metals (Cr, Ni, Cu, As, Cd, and Zn) due to increasing crop yield through the application of fertilizers and pesticides. Biochar was found to be effective in the removal of 18 to 40% of these potentially toxic metals from the soil. The mechanisms of removal were; precipitation, physical sorption, complexation, and ion exchange and electrostatic interaction. It can be concluded that biochar applied solely or in addition to compost has strong stability to remove heavy metals accumulated in soils due to land use patterns.
Rubber plantation establishment is a common land use that can profoundly impact plant diversity and soil properties in tropical forest ecosystems, although empirical data on such effects are scarce in tropical Africa. In this study, we examined the effects of this land use on tree species diversity and soil physicochemical properties of the Awudua Forest in Ghana. We surveyed 60 25 m × 25 m plots, 20 in each of three land uses (i.e., primary forest, secondary forest and rubber plantation) of the study area. Within each plot, we identified and enumerated all trees (≥ 10 cm diameter at breast height, dbh at 1.3 m) and collected soil samples at three depths (i.e., 0-10 cm, 10-20 cm and 20-30 cm) for the determination of soil physicochemical properties (bulk density, particle size distribution, pH, OC, TN, AP, K, Mg, Ca). Soil physicochemical properties; sand, OC, AP, K, Mg, Ca in the rubber plantation decreased by 4.4%, 24.9%, 40.0%, 16.7%, 50.0% and 20.2% respectively, whereas the primary forest recorded decreases in OC (42.2%), TN (25%), AP (71.1%), K (69%), Mg (59.6%), Ca (27%) from the surface to the sub layers. Results showed significantly lower tree species richness (S, 12), diversity (H', 0.37), evenness (E, 0.43), and basal area (BA, 2.86 m²/ha) in the rubber plantation compared to those of the secondary forest (36, 1.10, 0.85 and 0.93 m² ha⁻¹, respectively) and the primary forest (46, 2.28, 0.97, and 3.32 m² ha⁻¹, respectively). Non-metric multidimensional scaling ordination also revealed distinct tree species composition among the land uses. Terminalia ivorensis (IVI = 7.1%), Musanga cecropoides (28.91%) and Hevea brasiliensis (72.03%) were the most dominant species in the primary forest, secondary forest and plantation respectively. Soil OC, TN, K, AP, Mg and bulk density differed among the land uses, but no differences were observed in pH and Ca. The findings confirmed the strong negative impacts of rubber plantation land use on plant diversity and soil properties, suggesting the need for mitigation measures such as afforestation and offsetting to ensure sustainable utilization and conservation of biodiversity in the reserve.
Full-text available
The present study was conducted to investigate changes in soil physical and chemical properties and organic carbon storage at different stages of sugarcane growth (Plant, Ratoon 1, Ratoon 2, Ratoon 3, and Ratoon 4) in some sugarcane plantations in southern parts of Khuzestan Province, Iran. For this purpose, a number of soil profiles were dug in the sugarcane fields and 30 sites were selected and studied in a completely randomized design with five treatments (i.e. sugarcane growth stages) and six replications. The results of this study showed that long-term cultivation of sugarcane and different stages of growth in soils of Sugarcane Cultivation and Industry Company caused changes in physicochemical properties and soil carbon storage. Indeed, abundant irrigation and leaching and cultivation management of sugarcane reduced the salinity and sodium in soil solution. Also, different growth stages changed the physical and chemical properties of the soil. Different stages of sugarcane growth reduced electrical conductivity (EC), dissolved sodium in the soil, the available potassium (K), and increased the amount of bulk density (Bd) in the soil. There was no significant difference in the amount of organic matter (OM) and soil acidity (pH) of sugarcane fields at different stages of growth. Finally, in order to prevent possible negative consequences and depletion of soil nutrients, especially potassium, it is necessary to periodically study the complete properties of the soils and evaluate these changes, appropriate management methods can be performed to maintain soil quality. The results showed that difference in soil carbon sequestration was significant (p<5%) in the treatments, such that the highest carbon sequestration was in Raton 3 (28.84 tons/ha) and the lowest in Raton 4 (15.50 tons/ha). The reason for the higher amount of carbon storage can be attributed to more vegetation and, therefore, more plant debris, which reduce evaporation from the soil surface, a positive effect on vegetation, especially in arid and semi-arid regions. In general, optimal management of sugarcane fields plays an important role in improving atmospheric carbon sequestration capacity.
Outlines of engineering activities of Soil Conservation Service with particular emphasis on engineering aspects of new Watershed Protection and Flood Prevention Program, Public Law 566.
An aggregate is a group of primary particles that cohere to each other more strongly than to other surrounding soil particles. Most adjacent particles adhere to some degree. Therefore, disintegration of the soil mass into aggregates requires imposition of a disrupting force. Stability of aggregates is a function of whether the cohesive forces between particles withstand the applied disruptive force.