ABSTRACT
Two models, the Universal Soil Loss Equation (USLE) and the Soil Loss Estimation Model of the Southern Africa Model (SLEMSA), were adopted to assess soil losses and identify the most effective land use for mitigating erosion. The land use systems considered were forest (FF), cassava plot (CP), grassland fallowed (GF), and oil palm plantation (OP).The experimental design was a Balanced Replicated Fixed Design (BRFD) under a two-factor factorial experiment where land use and soil depth were the factors involved. Four spots within a land use were randomly selected for sample collection. Within a spot, disturbed (auger) and undisturbed samples were randomly collected from four different points at two depths (0-15 and 15-30 cm) and replicated in four different locations for a good spread across the state. A total of 256 sampling units were collected and taken to the laboratory. Standard procedures were followed for sample analysis, including rainfall, topography, erosion control measures, and vegetation data generation. Data obtained were inputted into the USLE and SLEMSA models for soil loss determination with analysis of variance for the BRFD used for data analysis. Least significant difference at a 5% probability level detected mean differences, while correlations, regressions, and Python programming established relationships between soil properties and soil loss. A T-test compared the two models, and principal component analysis (PCA) identified key soil properties influencing land use-specific soil loss. Soil under GF had the highest sand content (830.894 g/kg) but the lowest silt (66.792 g/kg) and clay (103.542 g/kg) contents whereas FF soil had the lowest sand content (743.683 g/kg) but the highest silt (86.075 g/kg) and clay (173.398 g/kg) contents. Textural classes of the area varied ranging from loamy sand to sandy loam. Soil organic matter (SOM) content in the area was generally high with the soil of the FF land use containing the highest. Consequently, other properties such as soil bulk density, total porosity, moisture characteristics and aggregate stability also varied. The USLE prediction ranged from 0.610 to 14.943 t/ha/yr while SLEMSA prediction was from 3.031 to 23.928 t/ha/yr. The two models indicated FF as the most effective in soil loss mitigation while PP was the least, with rankings as FF > GF > CP > OP. Soils higher in colloidal materials and with good vegetation cover had better stability and thus resulted to lower soil loss as seen in FF. The USLE model predicted lower soil loss with higher precision and consistency than SLEMSA and thus more suited for the CPS soils of Akwa Ibom State. PCA indicated that sand, silt, coarse sand, SOM, available water content, field capacity, saturated hydraulic conductivity, pH, bulk density, and total porosity were the important components influencing soil losses. The study showed that FF and GF soils were the most effective in erosion control of the CPS soil, indicating the impact of land use on soil loss. Land use practices such as afforestation, plantation cropping and fallowing are recommended for enhancing soil stability and regeneration of CP, GF and OP soils.
TABLE OF CONTENTS
Title Page ii
Declaration iii
Dedication iv
Certification v
Acknowledgement vi
Table of contents viii
List of Tables xii
List of Figures xv
Abstract xvi
CHAPTER 1: INTRODUCTION 1
CHAPTER 2: LITERATURE
REVIEW 7
2.1 Soils of
Akwa Ibom State 7
2.1.1. Common land use practices in Akwa Ibom
State 7
2.2 Soil Erosion
and Erodibility 9
2.2.1. Mechanism of soil loss 10
2.2.2. Soil erosion by water 11
a.
Sheet erosion: 12
b. Rill erosion 12
c. Gully erosion 12
2.2.3.
Soil erosion by wind 13
a. Suspension 13
b. Saltation 13
c. Surface creep 14
2.2.4.
Factors controlling the rate of soil erosion 14
a.
Climate erosivity 15
b. Soil erodibility 16
c, Topography 17
d.
Conservation measures and cover practice 18
2.3. Parent Materials and
Soil Loss 18
2.4. Erodibility
and Selected Soil Properties 21
2.4.1. Soil texture 21
2.4.2. Soil structure 23
2.4.3. Aggregate stability 25
2.4.4 Soil bulk density and porosity 26
2.4.5. Soil
biodiversity 27
2.4.6. Soil
moisture characteristics 28
2.4.6.1 Moisture
constants 29
a.
Saturation 30
b. Field capacity 30
c. Permanent wilting point 30
d. Available water content 30
2.4.6.2
Saturated hydraulic conductivity (Ksat) 32
2.4.6.3
Permeability and infiltration rate 33
2.4.7 Organic matter content 35
2.4.8 Soil pH 37
2.4.9 Exchange properties 38
2.5. Soil Degradation by Water 40
2.6.
Erosion Prediction Models 41
2.6.1. Universal soil loss equation
(USLE) 43
2.6.2 The
revised universal soil loss equation (RUSLE) 45
2.6.3. Water erosion prediction
project (WEPP) 46
2.6.4. The soil loss estimation model for Southern Africa
(SLEMSA) 47
2.7.
Soil Loss Assessment in Different Land Use Types 48
CHAPTER 3: MATERIALS AND METHODS 52
3.1. Location
and Biophysical Environment 52
3.2 Soil Sampling and Preparations 54
3.3 Soil
Analyses 56
3.3.1 Physical properties 56
3.3.2 Chemical properties 58
3.4. Selection of Models and Soil
Erodibility Determination 59
3.4.1.
The universal soil loss equation (USLE) 59
3.4.2.
The soil loss estimation model for Southern Africa (SLEMSA) 61
3.5 Analysis of
Data 63
CHAPTER 4: RESULTS AND DISCUSSION 64
4.1. Particle Size Distribution 64
4.2 Moisture
Retention Characteristics 71
4.3 Bulk
Density, Total Porosity and Saturated Hydraulic Conductivity 75
4.4. Aggregate Stability of the Soils 83
4.5. Organic
Matter (SOM) and pH of the Soils 87
4.6. Erodibility
Factor of the Soils Studied Based on USLE (Ku) and SLEMSA
(Ks) Models 91
4.7. Soil Loss
Prediction Using USLE (A) and SLEMSA (Z) Models 93
4.8.
Influence of Some Soil Properties on Soil Loss as Estimated by USLE (A)
and
SLEMSA (Z) Models. 96
4.9.
Python-Based Models Relating Estimated Soil Losses of the USLE and SLEMSA in
the Different Land Use Systems with Selected Soil Properties 108
4.11.
Comparison of the Soil Loss Predictions of the USLE and SLEMSA Models 124
4.11.1. Comparison of soil loss estimated by
USLE and SLEMSA in the
different land use systems studied 124
4.11.2. Qualitative comparison of soil loss
predictions of USLE and
SLEMSA models 127
CHAPTER 5:
CONCLUSION AND RECOMMENDATIONS 129
REFERENCES 132
APPENDICES 154
LIST
OF TABLES
Values for selected Cover Conditions and Cultural Practices for West Africa adapted for both Models. 60
Structural
Class Indices of Soils 60
Permeability Class Indices of Soils 61
Slope Classifications
values 62
Effects of Land
Use and Soil Depth on Textural Properties of the Soils
Studied. 65
Effects of Land Use and Soil
Depth Interactions on Textural Properties of the Soils 68
Effects of Land
Use and Soil Depth on SC, FC, AWC and PWP of the Soils
Studied 72
Effects of Land
Use and Soil Depth Interactions on SC, FC, AWC and PWP of the Soils Studied 72
Effect of Land Use and Soil Depth
on BD, TP and Ksat of the Soils Studied. 76
Effects of Land Use and Soil
Depth Interactions on BD, TP and Ksat of the Soils 78
Correlation of Soil Properties
across the Different Land Use Systems 80
Effect of Land Use and Soil Depth
on MWD, %CFI and %DR of the Soils Studied. 84
Effects of Land Use and Soil
Depth Interactions on MWD, %CFI and %DR of the Soils 84
Effect of Land
Use and Soil Depth on OM and pH of the Soils Studied 88
Effects of Land Use and Soil
Depth Interactions on OM and pH of the Soils 90
Comparison of
Erodibility Factors and Soil Loss Estimates of the USLE and SLEMSA Models for
the Different Land Use Types 92
Principal
Component Analysis of Properties that Mostly Affect A and Z in FF Soil 115
Principal
Component Analysis of Properties that Mostly Affect A and Z in CP Soil 118
Principal
Component Analysis of Properties that Mostly Affect A and Z in GF Soil 120
Principal
Component Analysis of Properties that Mostly Affect A and Z in OP Soil 122
Comparison
of Soil Loss Values Estimated Using the USLE and SLEMSA Models Based on the
Different Land Use Types. 124
Comparison of Soil Loss Estimations of USLE
(A) and SLEMSA (Z) Models of Soils Using T-test 126
Qualitative
Comparison of Soil Loss Estimates of USLE and SLEMSA Models of the Different
Land Use Types in the Study Area. 128
Estimation
of Annual Soil Loss Limits of Southern Nigeria 128
LIST
OF FIGURES
Map
showing the Study Areas and Locations of the Land Use Types 53
Regression of Soil Loss as Predicted by the USLE and SLEMSA
Models on Sand Contents of the Soil. 97
Regression of Soil Loss as Predicted by the USLE and SLEMSA
Models on Silt Contents of the Soil. 99
Regression of
Soil Loss as Predicted by the USLE and SLEMSA Models on Clay Contents of the Soil 100
Regression of
Soil Loss as Predicted by the USLE and SLEMSA Models on C/sand Contents of the
Soil 102
Regression of
Soil Loss as Predicted by the USLE and SLEMSA Models on F/sand Contents of the
Soil 104
Regression of
Soil Loss as Predicted by the USLE and SLEMSA Models on SOM Contents of the Soil 107
CHAPTER
1
INTRODUCTION
Soil erosion, recognized as a prominent catalyst for
land degradation, stands out as a critical global ecological concern in the
contemporary era. It consistently ranks among the primary mechanisms
responsible for the deterioration of land quality (Oldeman et al., 1991).This
challenge plays an essential role in the productivity of agroecosystems and
remains fundamental to ensuring food security (Amundson
et al., 2015). Okorafor et al. (2017) reported a significant
reduction in the availability of farmlands for agricultural production and
construction activities due to soil losses caused by erosion.
A rough calculation on a global scale of the current
rates of soil degradation due to water erosion suggested that only about 60
years of topsoil is left (WEF, 2012).
This implies that the current rate of soil loss as occasioned by water erosion
would have completely degraded arable soils and reduced agricultural production
within the next 50 years.
The type, rate and severity of soil erosion/loss in
an area depend on different factors including precipitation, topography, soil
characteristics, vegetation/land cover changes, cropping systems and land
management practices (Lal, 2001; Szilassi et
al., 2006 Mohammad and Adam, 2010; Amana et al., 2012; Yang, 2014). Szilassi et al. (2006) opined land use as the most important factor
influencing soil erosion.
Land use alterations cause changes in soil properties
and thus productivity overtime (Braimoh and Vlek, 2004). Land use change from
one type such as deforestation, forest degradation, increase in
croplands, plantation establishment, etc., to another often has adverse effects on soil characteristics such as soil texture, SOM, aggregate stability,
permeability, and soil biodiversity (Bewket and Stroosnijder, 2003; Szilassi et al., 2006; Martinez-Mena et al., 2008; Emadi et al., 2009; Rutgers et al., 2009; Kalu et al., 2015).
Over the past
decades, there has been an observed increase in the rate of land conversion from
one land use type to another (Lambin and Meyfroidt, 2011; Chen et al., 2014). Reports showed that between
1980 and 2000, more than 55% of new agricultural lands across the tropics were
developed by clearing the natural forests (Gibbs et al., 2010), an indication of a higher rate of land use change. This
level of change has the capability to alter soil biogeochemical properties
which will result in increased soil erosion, and overall reduction in soil
health (Gochin and Asgan, 2008; Zhou et
al. 2008; Mohammad and Adam 2010; Hairiah et al. 2011). This may arise as a result of the inability of the
ecologically sensitive components of, especially the tropical soil, to buffer
the effect of intensive agricultural practices (Islam and Weil, 2000) within
the repetitive time of usage. Studies
conducted in Nigeria on the effect of land use on the environment showed that
over 70% of the 17 specific ecosystem services such as carbon sequestration and
storage, oxygen production, soil formation and fertility, water regulation and
filtration, nutrient cycling, pollination, habitat provision for wildlife,
erosion control and slope stabilization, etc., has been lost due to the
conversion of natural land into agricultural use in the region (Ejaro and
Abdullahi, 2013; Jibril and Liman, 2014; Chukwuocha, 2015). MEA
(2005) identified unwise land use choices and inappropriate crop and/or
soil management practices as the major drivers of increasing soil erosion.
Land features,
as well as inherent soil properties such as topography, texture, structure,
organic matter content, permeability, soil cations, and soil biology among
others, are known to influence soil loss in various dimensions in an area. (Toy and Terrence. 2002; Anon, 2004; Zhang et al., 2004; Mahmoodabadi and Rafahi, 2007; Hairiah et al., 2011; Kusumandari, 2014; Kalu et al., 2015; Oguike and Udo, 2016). These
properties are intricately linked to the composition and characteristics of the
parent materials on which the soil is formed, forming the foundation for the
soil's physical and chemical attributes (Irmak
et al., 2007; Ahukaemere et al., 2016). Therefore, the parent material on which a soil is formed
confers on the soil specific characteristics and thus its ability to resist or
succumb to soil loss. This is why the texture and mineralogy of coastal plain
sands (CPS) bear the imprints of quartz arenite which is not rich in most plant
growth nutrients, dominantly sandy and coarse textured (Chikezie et al., 2009). Studies on the influence
of parent material on the soils of the coastal plain sand and sandstones in the
southeastern Nigeria abound (Anderson, 1988; Osher and Buol 1998; Cerda, 2002; Yesilonis
et al., 2008).
About one sixth
of the land area in the world has been reported to be affected by soil
degradation with about 55.6% of the affected area damaged by water erosion
(Hurni et al., 2008). In Sub-Saharan Africa, soil erosion accounts
for about 77% of land degradation and threatens about 22% of the
arable land (Unah, 2020). In Nigeria, over 22.8% of the total land mass surface is affected by erosion (Fubara, 2012). In the southern states,
about 25,000 hectares of land are lost annually to erosion menace (Abraham et al., 2019). Thus, soil erosion
is a prevailing global problem that urgently needs to be solved (Cai et al., 2007; Li et al., 2016) else the food production system, environmental
security and social life is under an imminent threat.
The challenge of
accelerated land degradation due to soil loss has been acknowledged to be more
acute in tropical Africa than in non-tropical areas (Sanchez et al., 1982; Lal, 1994; Lal, 2001; deGraffenried
and Shepherd, 2009). This could be attributed to
the high erosivity of rains commonly experienced in the tropical region
(Lal, 1985). High intensity rains are particularly damaging especially when the
vegetation cover is poor (Marc and Richard 2009; Wang et al., 2016). Other factors such as agricultural practices, high
erosion-risk soils, over population, lack of appropriate policies and over
reliance on subsistence crop farming (Sanchez et al., 2003) as well
as lack of capacity to control and
restore degraded soils (Ringius et al.,
1996), commonly seen in sub Saharan Africa, could also add up to the challenge.
While rainfall and/or
wind are considered the driving factors of soil erosion, the factor that
significantly hinders soil displacement by rain or wind is land cover or
vegetation cover (Wijitkosum, 2012). Therefore, the reduction in vegetation
cover can increase soil erosion. This relationship is the reason why vegetation
cover and land use have been widely included in soil erosion studies (Zhou et al. 2008; Solaimani et al. 2009; Su et al. 2010).
One of the
common methods of predicting soil loss in an area is by the use of scientific
models (Merritt et al.,
2003; Morgan and Nearing, 2011; Nearing, 2013). Such models include the
Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation
(RUSLE), Water Erosion Prediction Project erosion model (WEPP), The Soil Loss
Estimation Model for Southern Africa (SLEMSA), etc. These models take into
account various data sources such as rainfall intensity, soil type, vegetation
cover, topography, soil structural properties (Pandey et al., 2021), etc., to generate a more accurate estimates (Anejionu et al.,
2013). Scientific models can also be used to simulate different
scenarios, such as changes in land use or management practices, and predict the
potential impact on soil loss (Pandey et
al., 2021). This can be useful for
developing conservation strategies to reduce erosion and improve soil health
(Auerswald et al., 2014).
Coastal-Plain-Sand
(CPS) soil is an important component of the tropical ecosystem in Akwa Ibom
State, serving as the bedrock for agriculture, infrastructure, and
environmental sustainability. This area is located in a highly susceptible
agro-ecological zone of Nigeria, where acute soil erosion is prevalent due to
the region's heavy rainfall and the high erodibility of its soils, especially
during the rainy seasons. These conditions pose a significant challenge to the
stability and long-term productivity of the soils. Therefore, there is a
pressing need for a comprehensive understanding and effective solutions to
ensure consistent food security, enhance water resources, promote biodiversity,
facilitate carbon sequestration, and promote sustainable environmental management
(Mol and Keesstra, 2012; Keesstra et al.,
2016; Novara et al., 2016).
Factors such as
inherent soil characteristics of the CPS soil, land use types practiced in the
area, climatic forces, and topography, as well as management techniques,
intensify the vulnerability of these soils to erosion processes (rill, sheet,
gully erosion, and sediment transportation), leading to land deterioration as
experienced in the area.
The absence of
accurate predictions specific to the region's unique soil composition and land
uses pose a substantial challenge. Consequently, there is an urgent need to
bridge this gap by implementing model-based predictions to comprehensively
understand and mitigate soil losses in this vulnerable ecosystem (Sadeghi et al., 2007; Rahim et al., 2016). Documenting the extent of soil erosion through
modeling will be crucial for formulating an acceptable land use plan for
agricultural development vis-a-vis environmental sustainability in the area.
Although widely
recognized models such as the USLE and the RUSLE have been applied in the area,
the choice of the USLE model and the recently developed SLEMSA originally
designed for the Southern African Region was to determine if SLEMSA can be
adapted for accurate soil loss predictions in the area as well as evaluate its
performance against USLE model under different land use scenarios.
The
justification of this research lies in the critical need to address the
escalating soil loss challenges in the coastal-palin-sand soils of Akwa Ibom
State. The application of modeling techniques in this work aligns with the global
trend of employing technology to tackle environmental challenges.
The outcomes of
this study also have the potential to inform future research endeavours and
policies aimed at mitigating soil erosion in areas with similar geological and
ecological characteristics. The work will also help farmers, planners, and
environmental agencies in conserving soil, preserving agriculture, and
safeguarding infrastructure as well as improves the understanding of intricate
relationship between land use, soil properties, and soil loss in
coastal-plain-sand soils thus its significance.
Therefore, the research work aimed to contribute to scientific knowledge
and practical solutions required to mitigate erosion in coastal plain sand
soils by predicting soil losses under different land uses using the USLE and
SLEMSA models.
The main objective of this study is to predict the soil loss of soils of
coastal plain sand in Akwa Ibom State as influenced by land use.The specific
objectives of the study were to:
i.
assess the variations in
some physico-chemical properties of the soils among land uses;
ii.
predict the soil losses
of the area based on land use using USLE and SLEMSA models;
iv.
compare the USLE and
SLEMSA models for the determination of soil losses;
v.
develop models that
relate predicted soil loss to texture and organic matter.
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