© The Authors, 2024, Published by the Universidad del Zulia*Corresponding author: jperezn@chapingo.mx
Keywords:
Geographic Information Systems,
Watershed management
QGIS Smart-Map
Soil and water conservation practices
Carlos Arturo Aguirre-Salado
2
Alejandro Ismael Monterroso-Rivas
3
Rev. Fac. Agron. (LUZ). 2024, 41(1): e244101
ISSN 2477-9407
DOI: https://doi.org/10.47280/RevFacAgron(LUZ).v41.n1.01
Crop production
Associate editor: Dr. Jorge Vilchez-Perozo
University of Zulia, Faculty of Agronomy
Bolivarian Republic of Venezuela
Abstract
Understanding the stocks of Soil Organic Carbon (SOC) and elucidating
the variables inuencing its spatial distribution within a small watershed
are imperative for advancing targeted climate change mitigation strategies,
specically directed toward soil and water conservation. The selection of
this watershed is predicated upon its three-decade-long implementation of
diverse soil and water conservation practices. Therefore, the objective of this
study was to analyze land use, vegetation cover, slope and soil and water
conservation practices (SCWP) as factors that inuence the variability and
spatial distribution of soil organic carbon in a small basin in the Mixteca Alta
region of the state of Oaxaca. Mexico. Soil samples (77) were collected to
determine SOC storage. These samples were interpolated using the QGIS
Smart-Map plugin to obtain a spatial COS predictive model. Thematic
maps were generated for each factor. Areal statistics, Pearson’s correlation
and principal component analysis (PCA) were performed to explain COS
variability. The results in the variability of SOC with respect to vegetation
cover and land use, showed adult pine plantations with the highest value of
SOC with 36.8 t.ha
-1
, followed by seasonal agriculture with 28.8 t.ha
-1
. The
most eective management practice for storing COS was the stone terrace
with 35.0 t.ha
-1
. Our results indicate that vegetation cover and land use
complemented by soil and water conservation practices are the main drivers
of SOC storage in small watersheds.
Factors regarding the spatial variability of soil organic carbon in a Mexican small watershed
Factores relacionados con la variabilidad espacial del carbono orgánico del suelo en una microcuenca
Mexicana
Fatores
relativos
à
variabilidade
espacial
do
carbono
orgânico
do
solo
em
uma
pequena
bacia
hidrográca Mexicana
Olimpya Talya Aguirre-Salado
1
Joel Pérez-Nieto
1*
1
Autonomous University of Chapingo, Department of Crop
Science.
Km
38.5
Hw.
Mexico-Texcoco,
Chapingo.
Postal
Code 56230. Texcoco, State of Mexico, Mexico.
2
Autonomous
University
of
San
Luis
Potosi,
Faculty
of
Engineering.
Av.
Dr.
Manuel
Nava
8,
Zona
Universitaria.
Postal
Code
78290.
San
Luis
Potosi,
San
Luis
Potosi,
Mexico.
3
Autonomous
University
of
Chapingo,
Department
of
Soil
Science.
Km
38.5
Hw
Mexico-Texcoco,
Chapingo.
Postal
Code 56230. Texcoco, State of Mexico, Mexico.
Received: 15-08-2023
Accepted: 12-11-2023
Published: 15-12-2023
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2024, 41(1): e244101. January-March. ISSN 2477-9407.2-6 |
Resumen
Comprender los niveles del Carbono Orgánico del Suelo (COS)
y las variables que controlan su distribución en una pequeña cuenca
permitirá promover estrategias de mitigación contra el cambio
climático orientadas a la conservación de suelo y agua. La selección
de esta cuenca se basa en la implementación durante tres décadas
de diversas prácticas de conservación de suelo y agua. Por ello, el
objetivo de este estudio fue analizar el uso de la tierra, la cubierta
vegetal, la pendiente y las prácticas de conservación del suelo y
agua como factores que inuyen en la variabilidad y la distribución
espacial del carbono orgánico del suelo en una pequeña cuenca
en la región de la Mixteca Alta del estado de Oaxaca, México. Se
tomaron 77 muestras de suelo para determinar el almacenamiento
de COS. Se realizó la interpolación de las observaciones de COS
utilizando el complemento QGIS Smart-Map para obtener un modelo
predictivo COS espacial. Se generaron mapas temáticos para cada
factor. Se realizaron análisis estadísticos por área, correlación de
Pearson, y análisis de componentes principales (PCA) para explicar
la variabilidad espacial de COS. Los resultados en la variabilidad del
COS con respecto a la cobertura vegetal y el uso de la tierra, mostraron
a las plantaciones de pino adulto con el mayor valor de COS con
36,8 t.ha
-1
, seguido de la agricultura de temporal con 28,8 t.ha
-1
. La
práctica de gestión más ecaz para almacenar COS fue la terraza de
piedra con 35,0 t.ha
-1
. Los resultados indican que la cobertura vegetal
y el uso de la tierra complementados con prácticas de conservación
del suelo y agua son los principales impulsores del almacenamiento
de COS en pequeñas cuencas hidrográcas.
Palabras clave: sistemas de información geográca, manejo de
cuencas, QGIS Smart-Map, prácticas de conservación de suelo y
agua.
Resumo
Compreender os stocks de Carbono Orgânico do Solo (COS) e
elucidar as variáveis que inuenciam a sua distribuição espacial dentro
de uma pequena bacia hidrográca são imperativos para o avanço
de estratégias especícas de mitigação das alterações climáticas,
especicamente dirigidas à conservação do solo e da água. A seleção
desta bacia hidrográca baseia-se na implementação, ao longo de
três décadas, de diversas práticas de conservação do solo e da água.
Portanto, o objetivo deste estudo foi analisar o uso do solo, a cobertura
vegetal, a declividade e as práticas de conservação do solo e da água
(SCWP) como fatores que inuenciam a variabilidade e a distribuição
espacial do carbono orgânico do solo em uma pequena bacia na
região de Mixteca Alta do estado de Oaxaca. México. Amostras de
solo (77) foram coletadas para determinar o armazenamento de SOC.
Essas amostras foram interpoladas usando o plugin QGIS Smart-Map
para obter um modelo preditivo espacial de COS. Foram gerados
mapas temáticos para cada fator. Estatísticas de área, correlação de
Pearson e análise de componentes principais (ACP) foram realizadas
para explicar a variabilidade do COS. Os resultados na variabilidade
do SOC em relação à cobertura vegetal e uso do solo, mostraram as
plantações de pinus adulto com o maior valor de SOC com 36,8 t.ha
-1
,
seguidas pela agricultura sazonal com 28,8 t.ha
-1
. A prática de manejo
mais ecaz para armazenamento de COS foi o terraço de pedra com
35,0 t.ha
-1
. Nossos resultados indicam que a cobertura vegetal e o uso
da terra complementados por práticas de conservação do solo e da
água são os principais impulsionadores do armazenamento de SOC
em pequenas bacias hidrográcas.
Palavras-chave: sistemas de Informação Geográca, gestão de bacia
hidrográca, QGIS Smart-Map, práticas de conservação do solo e da
agua.
Introduction
Since 2011, concentration of carbon dioxide (CO
2
) in the
atmosphere have increased, reaching an annual average of 410 ppm
(IPCC,2021). Soil has the largest reserves of terrestrial organic carbon;
current estimates of the global stock of soil organic carbonrange from
1,500 to 2,400 Pg C, according to Lal et al. (2021). Soil organic carbon
(SOC) is the C that remains in the soil after partial decomposition
of all added organic residues and is produced by living organisms
(Lefèvre, 2017).SOCplays a critical role in climate change mitigation
and food security (Wang et al., 2020), and its distribution is spatially
and temporally variable (Wiesmeier et al., 2019). The variability and
spatial distribution of SOC is partly controlled by environmental
conditions such as vegetation cover and land use (Borůvka et al.,
2022; Yescas et al., 2018).
Determining the variables that control soil organic carbon
distribution at the small watershed scale is important for planning
and implementing appropriate soil and water conservation practices
(SWCP). These practices are used to reduce soil erosion, but also
to retain large amounts of organic carbon in the same sediments
to reduce greenhouse gas emissions to the atmosphere (Mekonnen
and Getahun, 2020). Considering the intense land degradation that
aects almost half of Mexico’s territory, the Mexican government
has implemented various public policies with subsidies, under which
landowners have implemented land conservation practices. However,
these impacts have not been evaluated in terms of carbon storage
(Cotler et al., 2015). In this sense, this study aims to provide reliable
quantitative data that will allow decision and policymakers to further
promote SWCP in Mexico.
The objective of this study was to evaluate the eects of land use,
vegetation cover, slope, and soil and water conservation practices on
the variability and spatial distribution of soil organic carbon (SOC)
in a small watershed in the Mixteca Alta region of Oaxaca State,
Mexico.
Material and methods
Study area
The study area is a 44.6 ha small watershed, known as “El Arenal”
located in the High Mixteca region in the municipality of San Miguel
Tulancingo, state of Oaxaca, México, between coordinates 97°27’
W, 17°45’ N, at 2,200 m above sea level (gure 1). The climate of
the study area is temperate (Cw
0
). The precipitation is 544.7 mm
per year and the average temperature is 15.9 ºC. The study area is
characterized by steep slopes, low vegetation cover and erosion. In
this small watershed, there are soil and water conservation practices
such as land terraces, stone terraces, stone dams, gabion dams,
ditches, reforestation with pines and contour furrows.This study
area was chosen because it is representative, since it has a variety of
soil and water conservation practicesimplemented in the watershed
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Aguirre-Salado et al. Rev. Fac. Agron. (LUZ). 2024 40(1): e244101
3-6 |
during the last 30 years, which allows comparisons of soil organic
carbon estimates.
Figure 1. Location of study area.
The study area is a small watershed with 44.6 ha, called “El
Arenal”, located in the municipality of San Miguel Tulancingo,
Oaxaca, México. The yellow and red dots correspond to the 77 COS
samples collected in the eld.
Preparation of the SOC map
The creation of the map SOC was divided into six steps: a)
selection of sampling sites, b) soil sampling, c) determination of
SOC, d) data statistics, e) interpolation map, and f) validation map,
as indicated below:
a) Selection of sampling sites: a manual digitization of a Sentinel-2
satellite image from 26/09/2018 was performed to distinguish land
uses at 10 m spatial resolution. The digitized land use polygons were
used to design a stratied simple random sample (Gruijter et al.,
2006), taking into account that SOC varies spatially due to vegetation
cover and land use.
b) Soil sampling: a single soil sample of one kilogram from the
supercial layer (0 to 30 cm) was collected from the selected site
or its immediate vicinity (Borůvkaet al., 2022; Yescas et al., 2018;
Pazet al., 2016). Land use and management, hydrologic condition,
and soil-water conservation practices were recorded at each site. The
total number of sampling sites was 77 (gure 1).
c) Determination of SOC: The soil samples collected were
analysed in the laboratory to obtain organic carbon in g.kg
-1
according
to the method of Walkley and Black (1934), the bulk density in g.cm
-3
was determined by the paran method and the percentage of rock
fractionation that represents particles > 2 mm with respect to a known
volume. Subsequently, the SOC expressed in t.ha
-1
was determined
according to the formula used by Nabiollahi et al. (2021) proposed by
Penman et al. (2003):
SOC = OC• Bulk Density • Depth •Coarse Fragments• 10 (1)
Where: SOC = the soil organic carbon stock for soil of interest in
t.ha
-1
; OC = concentration of organic carbonin g / kg; Bulk Density =
the mass of soil sample per volume in the ne soil fractionin g.cm
-3
; Depth
= sampling depth or thickness or soil layerin m; Coarse Fragments =
1 – (% volume of coarse fragments / 100); the nal multiplier of 10
is introduced to convert units to t.ha
-1
. These values measured in the
laboratory will be called the observed SOC.
d) Data statistics: The statistical values that characterize the
sample were analysed and extracted by means of its measures of
centrality, position and dispersion, with the statistical panel tool in
QGIS. Also, distribution pattern analysis was performed to determine
if the points have aclusteringor dispersion pattern, with the QGIS
Nearest NeighbourIndex (NNI) tool (Ose, 2018).
e) Interpolation map: the sample was divided into 70 % training
data and 30 % validation data using the random selection module in
QGIS. The method used for interpolation was developed and proposed
by Pereira et al. (2022) in the Smart-Map Plugin Tool, installed from
the QGIS Plugin Repository. This tool uses Machine Learning (ML)
algorithms. The area-weighted average of the predicted SOC will be
called the estimated SOC.
f) Validation map: The accuracy of the prediction was evaluated
by comparing the estimated values with the actual observations at
validation points Z (xi) according to Boubehziz et al. (2020).
Factors related to SOC
Land Use
For the classication of satellite imagery, a free and open-source
plugin for QGIS was used, developed by Luca Congedo and known
as Semi-Automatic Classication Plugin (SCP). Sentinel-2 satellite
imagery data with a spatial resolution of 10 m, taken September 26,
2018 was used to create the land use map.
Vegetation cover
Soil Adjusted Vegetation Index (SAVI) was used for
determiningthe percentage of vegetation cover using the method
proposed by Bingfang and Qiangzi (2004) which assumes that each
pixel receives two signals, one coming from soil and the other from
vegetation. The formula for calculating vegetation cover is as follows:
(2)
Where: % CV is the percentage of vegetation cover; SAVI is the
Soil Adjusted Vegetation Index observed in the pixel; SAVI
bs
is the
Soil Adjusted Vegetation Index of a pixel withbare soil and SAVI
veg
corresponds of a pixel completely covered with vegetation.
Slope
The slope of the terrain in percentage, was calculated with the
Slope tool using QGIS and a digital elevation model (DEM) with a
spatial resolution of 15 m (INEGI, 2020); the output slope dataset
was classied according to Jahn et al. (2006) into the following
categories: at (0-1 %), very gently sloping (1-2 %), gently sloping
(2-5 %), sloping (5-10 %), strongly sloping (10-15 %), moderately
steep (15-30 %), steep (30-60 %), and very steep (> 60 %). Next, we
used the Prole tool, a QGIS add-on that allows us to draw lines on
the elevation base map and create elevation proles.
Soil and water conservation practices (SWCP)
SWCP wererecordedfor the 77 samplingsites in the eld. Sixty-
nine sampling points were within the small watershed; 33 of them had
SWCP, while 36 had no practice (the remaining 8 points are outside
the small micro watershed and their value was that they were used to
correctly interpolate the SOC map outside the boundaries). Eachsite
was characterized with its respective SWCP. Land management
practices (recorded at the point level) were spatialized by Thiessen
polygons. These polygons were used to assign 0 to locations without
land conservation practices and 1 to locations with land conservation
practices. Pearson’s correlation coecient was estimated to examine
the relationship between soil conservation practices and SOC.
1
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2024, 41(1): e244101. January-March. ISSN 2477-9407.4-6 |
Principal Component Analysis (PCA)
A principal component analysis (PCA) was performed to decipher
the grouping of environmental variables that explain variability in
the small watershed. The ACP was conducted in ArcMap 10.8, with
the variables in raster format: SOC land use, vegetation cover, soil
conservation practices, and slope. According to Figueroa et al. (2018),
the principal components with the highest eigenvalues explain the
largest percentage of variability.
Results and discussion
Descriptive statistics of observed SOC
The results ofdescriptive statistics of the observed soil organic
carbon (t.ha
-1
) at 0–30 cm depth indicates aminimum value: 1.58,
maximum value: 84.72, range: 83.14, mean value: 25.74, standard
deviation: 18.70 and variation coecient: 73, these results show the
SOC magnitudesashighly variable and heterogeneous. On the other
hand, the results of the analysis of the distribution pattern estimated
with the QGIS nearest neighbour analysis tool exposes an average
observed distance of 76.85, an average expected distance of 59.99
anda NNI of 1.28. These results exhibit two facts, 1) the average
observed distance is greater than expected and 2) a clustered pattern
(NNI >1). This variability is explained because samples were obtained
at an average distance of 90 m, which can vary with respect to the
position of the slope, land use, vegetation cover, and management.
Yescas et al. (2018) observed that the behaviour of the variability in
the SO Cis mainly due to land use.
Spatial estimation of SOC
The map of SOC in t.ha
-1
resulted from interpolation with the
machine learning algorithm implemented in the Smart Map plugin is
depicted in gure 2.The cross-validation show a mean prediction error
(ME) of 0.98 t.ha
-1
, a root-mean-square prediction error (RMSE) of
3.770 t.ha
-1
and a high value of R
2
(0.96). This R
2
indicated a strong
correlation between predictors and observed SOC. The RMSE was
lower than the one obtained by Yescaset al. (2018) of 4.69 t.ha
-1
with
ordinary kriging (KO) model. This implies a better t of the model
ML with respect to the KO.The total content of SOC predicted in the
small watershed was obtained by (previously rescaling to the spatial
resolution of the raster dataset) summing up all corresponding pixels
of the study area totalling 101,826 t.ha
-1
in 44.2 ha, while the average
SOC was 23.77 t.ha
-1
. Paz et al. (2016) associated this value to areas
without apparent vegetation and xeric scrub. In this case, our study
area has all forms of water erosion in addition to low vegetation cover.
On the map, the areas marked with blue are those with the lowest
COS content and the red are the areas with the highest storage. The
cross-validation resulted ina strong correlation between predictors
and observed SOC whit a R
2
(0.96).
Factors that inuence SOC storage in the small watershed
Figure 2. Map SOC Prediction (t.ha
-1
) and cross-validation
results.
Figure 3. Environmental variables cross-correlated to soil organic
carbon. a) Vegetation and land use, b) Vegetation cover
(%), c) Slope (%), and d) Soil and water conservation
practices.
Vegetation and land use
Variability of SOC at each site was partially controlled by land use
and vegetation (Table 1). The largest carbon stocks were associated
with areas of adult pine plantations; however, they represented only
1.32 ha of the study area. Despite, this value represents a small area
within the small watershed, this result is still outstanding. This is
because if we would want to increase the carbon storage in soil, an
eective way to do this, is by planting trees. Second, rainfed agriculture
showed the highest SOC value in the study area (estimated: 25.6 t.ha
-1
,
observed: 28.8 t.ha
-1
), distributed over 15.6 ha, which emphasizes
the importance of rainfed agriculture for soil carbon storage. Third,
thorny scrub showed similar results to rainfed agriculture (estimated:
24.4 t.ha
-1
, observed: 25.9 t.ha
-1
). The spatial variability of vegetation
and land use along the small watershed is shown in gure 3a.
Table 1. SOC variability by land use.
Land use
Area SOC
observed
value
SOC
estimated
value
Total
content of
SOC in the
watershed
Reference
value
Paz et al.
(2016)
(ha) (t.ha
-1
) (t) (t.ha
-1
)
Adult pine Plantation 1.2 36.8 29.6 3,544.6 65.8
Rainfed agriculture 15.6 28.8 25.6 39,654.1 46
Thorn scrub 14.4 25.9 24.4 35,186.1 28
Gallery vegetation 10 17.6 20.1 20,100.6 32.9
Bare soil 3 6.4 10.8 3,340.5 19
Water 0.4 0 0 0 0
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Aguirre-Salado et al. Rev. Fac. Agron. (LUZ). 2024 40(1): e244101
5-6 |
Vegetation cover
The 74 % of the study area is dominated by sites with vegetation
cover less than 50 %, and areas with vegetation cover greater than
75 % account for only 3.7 %. According to our results, the greater
the vegetation cover, the greater the storage of SOC. The areas with
cover > 75 % have an estimated average of 30 t.ha
-1
as adult pine
plantation, those with values of vegetation cover less than 50 %
have an estimated average of 21.1 t.ha
-1
, which explains the average
data for the small watershed. These results are consistent with those
of Nabiollahi et al. (2021), which indicate that the loss of natural
vegetation cover leads to a reduction of SOC. Therefore, to increase
soil carbon reserves, it is necessary to increase vegetation cover. The
spatial distribution of vegetation cover along the small watershed is
shown in Figure 3b.
Slope and prole analyses on the small watershed
There are signicant dierences between SOC and the dierent
slope percentages. The average results for each slope category are
as follows: at (13.4 t.ha
-1
), very gently sloping (21.4 t.ha
-1
), gently
sloping (25.8 t.ha
-1
), sloping (24.1t.ha
-1
), strongly sloping (20.2 t.ha
-1
),
moderately steep (21.5 t.ha
-1
), steep (28.5 t.ha
-1
). Our results showed
no signicant relationship between slope and soil organic carbon
content in accordance whit Gadisa and Hailu (2020) and Bai and
Zhou (2019). This can be explained by the result obtained from cross-
sectional proleconducted in the lower part of the small watershed
(Figure 4). Here it can be seen that regardless of the slope category,
the SOC value is varying according to land use. In this sense, the
prole shows an agricultural use with values up to 49 t.SOC.ha
-1
with
a slope of 10-15 %, which contrasts in this prole with 9 t.SOC.ha
-1
of the bare soil in the same slope category.
was the gabion lter dam with 5.1 t.ha
-1
. This value was unexpected
because Mekonnen & Getahun (2020) found that gabion dam trapped
106.29 t.ha
-1
in 5104 m
3
sediment. However, this can be explained
because water that ows into the gabion dam probably washes soil,
leaches the SOC or transports it out the reservoir. Therefore, it is
suggested to analyse the sediment trapped in these sediment storage
dams at dierent depths.
Table 3 shows the observed SOC values at the sampled sites
in relation to soil depth and SWCP. Severely degraded areas and
with no soil are associated with lower SOC storage and lack of soil
management practices. Sites deeper than 30 cm, on the other hand,
were associated with the presence of SWCP.The dierence between
the observed and estimated values is due to the fact that the area
calculated comes from the geometry of the Thiessen polygons, which
in turn correspond to the spatial distribution of the sample points.
Table 2. SOC variability respect to soil and water conservation
practices.
Thematic
Class
Soil and water conservation
practices
SOC Estimated
Value (t.ha
-1
)
SOC Observed
Value (t.ha
-1
)
1 Land terrace 25.3 27.3
2 Adult pine plantation 26.6 29.0
3 Stone terrace 29.6 35.0
4 Furrowed to the contour 26.7 21.4
5 Gabion lter dam 8.6 5.1
6
Accommodated stone lter
dam
20.1 17.5
7
Reforestation with pine and
inltration trench
25.0 32.1
8
MIAF (Corn and young fruit
trees)
14.8 8.4
9
None (Absence of soil and
water conservation practices)
22.3 21.2
Table 3. SOC variability with respect to the depth of the sampled
soil.
Soil Depth
(cm)
SOC Observed Value
(t.ha
-1
)
Soil and water conservation practices
3 1.6 None
5 2.3 None
15 10.3 None
20 15.9 None – Pine Plantation
25 32.6 None – Pine Plantation
30 27.0
Land terrace, Reforestation with pine,
Stone terrace, Furrowed to the contour,
Gabion lter dam, Accommodated stone
lter dam, Reforestation with pine and
inltration trench, MIAF
Principal Component Analysis
The principal components (PCs) captured the variability of
the original variables in the following proportions: PC
1
(49.34 %),
PC
2
(40.65 %), PC
3
(9.88 %), PC
4
(0.09 %) and PC
5
(0.02 %). The
accumulative value for the rst three components was 99.8%. When
analysed the weights of the principal components in the matrix of
Figure 4. Prole analyses the lower part in small watershed,
regarding elevation, slope, land use and SOC storage. The
variation in soil organic carbon (SOC) is not determined by the slope
but rather by vegetation and land use.
Soil and water conservation practices (SWCP)
Table 2 shows that stone terrace was the soil management
practice that stored the most carbon in the surface layer (rst 30
cm), with 35 t.ha
-1
. Second, aforestation with adult pines combined
with an inltration trench with 32.1 t.ha
-1
. The management practice
equivalent to bare soil was the combination of fruit trees interspersed
with corn, commonly known in Mexico as MIAF (corn and fruit trees
grown simultaneously); these trees are still in their early stages of
growth and are planted with a spacing of 8 meters between rows. The
soil management method that stored the least SOC in the topsoil layer
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Aguirre-Salado et al. Rev. Fac. Agron. (LUZ). 2024 40(1): e244101
6-6 |
eigenvalues and eigenvectors, PC
1
revealed that the COS variable
(0.50) and the vegetation cover variable (0.86) were directly and
proportionally related to the component, as they had a positive sign
in the loading. PC
2
disclosed that the COS variable (-0.86) and the
vegetation cover variable (0.50) were representative but with inverted
signs in this relationship. Meanwhile, in PC
3
, it was shown that
only the Slope variable (0.99) was representative in a directly and
proportionally related manner to that component. Furthermore, the
Pearson’s correlation coecient obtained to examine the relationship
between the four explaining variables (i.e., land use, vegetation cover,
conservation practices and slope) and SOC was 0.16, 0.08, 0.06 and
0.04, respectively. These values align with the ndings of Yescas
et al. (2018), Bai and Zhou (2019), and Gadisa and Hailu (2020),
supporting the notion that land use and vegetation cover primarily
inuence SOC variability, while slope carries a lower weight.
Conclusion
The analysis of observed and estimated SOC in a small
watershed revealed signicant variability and heterogeneity. The
SOC distribution pattern was successfullymodeledwith spatial
interpolation and subsequently related to four explaining variables
includingland use, vegetation cover, conservation practices and slope.
Soil and water conservation practices played a crucial role, enhancing
SOC stock by preventing soil erosion. To safeguard SOC reserves, it
is crucial to enhance vegetative cover and supplement land use with
SWCP. Through these measures, not only can erosion be eectively
managed, but they also play a pivotal role in curbing CO
2
emissions,
thereby mitigating the impact of global warming.
Literature cited
Bai, Y., and Zhou, Y. (2020). The main factors controlling spatial variability
of soil organic carbon in a small karst watershed, Guizhou Province,
China. Geoderma, 357, 113938–113938. https://doi.org/10.1016/J.
GEODERMA.2019.113938
Bingfang, W., and Qiangzi, L. (2004). Crop area estimation using remote sensing on
two-stage stratied sampling. International Society for Photogrammetry
and Remote Sensing (ISPRS), 20, 12-23. https://bit.ly/3SVuXYb
Borůvka, L., Vašát, R., Šrámek, V., Hellebrandová, K. N., Fadrhonsová, V., Sáňka,
M., Pavlů, L., Sáňka, O., Vacek, O., Němeček, K., Nozari, S., & Sarkodie,
V. Y. O. (2022). Predictors for digital mapping of forest soil organic
carbon stocks in dierent types of landscape. Soil and Water Research,
17(2), 69–79. https://doi.org/10.17221/4/2022-SWR
Boubehziz, S., Khanchoul, K., Benslama, M., Benslama, A., Marchetti, A.,
Francaviglia, R., & Piccini, C. (2020). Predictive mapping of soil organic
carbon in Northeast Algeria. Catena, 190. https://doi.org/10.1016/J.
CATENA.2020.104539
Cotler, H., Cram, S., Martínez Trinidad, S., & Bunge, V. (2015). Conservation
practices assessment in forest soils of Mexico: The case of the
ditches. Investigaciones geográcas, (88), 6-18. https://doi.org/10.14350/
rig.47378
De Gruijter, J., Brus, D. J., Bierkens, M. F., & Knotters, M. (2006). Sampling
for Natural Resource Monitoring. Sampling for Natural Resource
Monitoring. https://doi.org/10.1007/3-540-33161-1
Figueroa Jáuregui, M. D. L., Martínez Menez, M. R., Ortiz Solorio, C. A., &
Fernández Reynoso, D. (2018): Inuence of forming factors on soil
properties in the Mixteca, Oaxaca, Mexico. Terra Latinoamericana, 36(3),
287–299. https://doi.org/10.28940/terra.v36i3.259
Gadisa, S., and Hailu, L. (2020). Eect of Level Soil Bund and FaynaJuu on
soil physico-chemical properties, and farmers adoption towards the
practice at Dale Wabera district, western Ethiopia. American Journal
of Environmental Protection, 9(5), 107–20. https://doi.org/10.11648/j.
ajep.20200905.12
Jahn, R., Blume, H. P., Asio, V. B., Spaargaren, O., & Schad, P. (2006). Guidelines
for soil description. FAO: 9–12. Recovered from: https://www.fao.org/3/
a0541e/a0541e.pdf
INEGI, National Institute of Statistics and Geography. (2017). Continuum of
Mexican Elevations 3.0 (CEM 3.0). Recovered from:https://www.inegi.
org.mx/app/geo2/elevacionesmex/
IPCC, 2021: Summary for Policymakers. In: Climate Change (2021). The Physical
Science Basis. Contribution of Working Group I to the Sixth Assessment
Report of the Intergovernmental Panel on Climate Change. Cambridge
University Press, Cambridge, United Kingdom and New York, NY, USA,
pp. 3−32. Recovered from: https://bit.ly/47BPype
Lal, R., Monger, C., Nave, L., & Smith, P. (2021). The role of soil in regulation
of climate. Philosophical Transactions of the Royal Society B, 376(1834),
20210084. https://doi.org/10.1098/rstb.2021.0084
Lefèvre, C., Rekik, F., Alcantara, V., & Wiese, L. (2017).Soil organic carbon:
the hidden potential. Food and Agriculture Organization of the United
Nations. Rome, Italy. FAO, 1–7. Recovered from: https://www.fao.org/3/
i6937e/i6937e.pdf
Leroux, L., Congedo, L., Bellón, B., Gaetano, R., & Bégué, A. (2018). Land
Cover Mapping Using Sentinel-2 Images and the Semi-Automatic
Classication Plugin: A Northern Burkina Faso Case Study. QGIS
and Applications in Agriculture and Forest, 2: 119–151. https://doi.
org/10.1002/9781119457107.ch4
Mekonnen, M., & Getahun, M. (2020). Soil conservation practices contribution in
trapping sediment and soil organic carbon, Minizr watershed, northwest
highlands of Ethiopia. Journal of Soils and Sediments, 20(5), 2484–2494.
https://doi.org/10.1007/s11368-020-02611-5
Nabiollahi, K., Shahlaee, S., Zahedi, S., Taghizadeh-Mehrjardi, R., Kerry, R., &
Scholten, T. (2021): Land Use and Soil Organic Carbon Stocks—Change
Detection over Time Using Digital Soil Assessment: A Case Study from
Kamyaran Region, Iran (1988–2018). Agronomy (11) 597–597. https://
doi.org/10.3390/AGRONOMY11030597
Ose, K. (2018). Introduction to GDAL Tools in QGIS. QGIS and generic tools,
(1): 19–65. https://doi.org/10.1002/9781119457091.ch2
Paz Pellat, F., Argumedo Espinoza, J., Cruz Gaistardo, C. O., Etchevers, J. D.,
& de Jong, B. (2016). Spatial and temporal distribution of soil organic
carbon in terrestrial ecosystems in Mexico. Terra Latinoamericana,
34(3), 289–310. https://www.scielo.org.mx/pdf/tl/v34n3/2395-8030-
tl-34-03-00289.pdf
Penman, J., Gytarsky, M., Hiraishi, T., Krug, T., Kruger, D., Pipatti, R.,Buendia,
L., Miwa, K.,Ngara, T., Tanabe, k., & Wagner, F. (2003).Good practice
guidance for land use, land-use change and forestry. Institute for Global
Environmental Strategies (IGES) for the IPCC, Japan, 3.42–3.43. https://
bit.ly/49TLAcL
Pereira, G. W., Valente, D. S. M., Queiroz, D. M. de, Coelho, A. L. de F., Costa,
M. M., & Grift, T. (2022). Smart-Map: An Open-Source QGIS Plugin
for Digital Mapping Using Machine Learning Techniques and Ordinary
Kriging. Agronomy 2022, Vol. (12), 1350–1350. https://doi.org/10.3390/
AGRONOMY12061350
Walkley, A. & Black, C.A. (1934). An examination of the Degtjare method for
determining soil organic matter and a proposed modication of chromic
acid titration method: Soil Science, 37, 29–38. https://bit.ly/3sNZEUJ
Wang, L., Li, Z., Wang, D., Liao, S., Nie, X., & Liu, Y. (2022). Factors controlling
soil organic carbon with depth at the basin scale. CATENA, (217),
106478–106478. https://doi.org/10.1016/j.catena.2022.106478
Wiesmeier, M., Urbanski, L., Hobley, E., Lang, B., von Lützow, M., Marin-Spiotta,
E., Kögel-Knabner, I. (2019). Soil organic carbon storage as a key function
of soils-A review of drivers and indicators at various scales. Geoderma,
333, 149-162.https://doi.org/10.1016/j.geoderma.2018.07.026
Yescas-Coronado, P., Álvarez-Reyna, V. de P., Segura-Castruita, M. Á., García-
Carrillo, M., Hernández-Hernández, V., & González-Cervantes, G.
(2018). Spatial variability of organic and inorganic carbon in the soil
in the Comarca Lagunera, Mexico. Bulletin of the Mexican Geological
Society, 70(3), 591–610. https://doi.org/10.18268/BSGM2018V70N3A2