Runoff, Sediment Loss and the Attenuating Effectiveness of Vegetation Parameters in the Rainforest Zone of Southeastern Nigeria

Abstract

The research was conducted to assess the pace of sediment loss in deserted 3-, 5- and 10-year-fallow traditional farmlands, as well as cultivated farmlands, in a remote forested zone in southern Nigeria. During the 2012 rainy and cropping season, field measurements of sediment and runoff caused by rainfall were carried out. Pearson’s correlation revealed that crown cover positively and significantly correlated with runoff on the cultivated farmland (r = 0.652, p < 0.01). The results showed that the vegetation characteristics assessed on the different fallows explained 73.1%, 89.9%, 53.7% and 86.7% of the runoff variations. In addition, Pearson’s correlation demonstrated that girth explained sediment loss on the 5-year fallow (r = 0.807, p < 0.01), while a strong positive and significant association existed between sediment loss and crown cover on the farmland plot (r = 0.835, p < 0.01). The vegetation components were mutually responsible for 48.4%, 84.3%, 95.1% and 85.9% of the changes in sediment enrichment on the 5-year-, 10-year-, 3-year-fallow and cultivated farmland, respectively. The study found that mature/older fallows had a more substantial attenuating impact on soil erosion control than younger fallows.

1. Introduction

Across the globe, soil erosion remains a key ecological problem involving the detachment, transportation and deposition of weathered debris triggered by denudational agents of water and wind [1]. During the process of erosion, soil particles are demolished by raindrops, which is the basis for the dissolution of soil particles in water [2]. After which, runoff generated from rainwater conveys soil particles or aggregates, resulting in sediment loss [3]. The dissolution of soil particles is continuous in the absence of adequate surface cover in terms of vegetation. The presence of vegetation helps to minimize the rate of sediment enrichment in runoff. As such, vegetation’s floristic and structural composition is crucial in regulating earth’s processes and systems, particularly in the rainforest ecosystem. However, human activities such as farming have changed vegetation dramatically. The change impacts on the structure and floristic composition of the vegetation, soil erosion and vulnerabilities associated with it [4,5]. The change in vegetation renders the land prone to soil erosion, mainly if the vegetation is not sufficiently established to provide the soil with sufficient cover, resulting in nutrient loss [6,7]. Furthermore, the differences in rainfall intensity and volumes, topography, soil types, and vegetation characteristics all contribute to variations in soil erosion intensity and sediment loss from one month to the next [8,9,10].

During natural nutrient restoration, vegetation suppresses soil erosion, primarily in abandoned farmlands. The efficacy of plant characteristics in avoiding erosion in distinct fallow communities is mainly dictated by the vegetation type and the length of the site’s abandonment [4,11]. The quantity of sediment loss that happens in thinly vegetated fallows, such as recently abandoned farmland, can be massive and substantial which may delay the optimal time for nutrient accretion in the soil. During the period of fallowing, nutrient rich topsoil can be lost to soil erosion, which, if left unchecked, could affect soil fertility resulting in lower crop yields for subsequent farming [12,13]. The washing away of the topsoil and the fertile soil layer could also affect the fertility of the soil and make it unproductive for crop production [14,15].

According to Iwara [4] and Adekiya et al. [16], the enriched portion of the soil that is lost in eroded sediment contains three times the quantity of nutrients needed as the soil is left behind. As a result, nutritional supplies are being depleted which impact on the soil’s overall productivity as well as its capacity to support agricultural production [17,18]. Furthermore, they contended that optimum nutrient input in fallow fields might be prevented and delayed if conservative soil measures are not implemented. Because of the persistent decrease in vital vegetation growth parameters, several lands may be devoured of the essential nutrient requirements for cultivation and maximum output because of the growing human population and shorter fallow periods. The loss of topsoil rich in organic matter weakens the structure of the soil and makes the soil susceptible to soil erosion [7,19]. Butt, the presence of dense land cover potentials, primarily vegetation and herbaceous cover, may reduce the energy of rainwater that results in sediment loss through runoff.

While affirming the effectiveness of vegetation in erosion control, Iwara [4] stated that vegetation reduces the rate of sediment and nutrient loss by providing the soil with a protective cover during the period of nutrient restoration. As a result of its ecological concerns, soil erosion remains a topical issue in the tropics, and this has resulted in several studies yet with varied results and study locations. A look at the burgeoning literature on the subject matter suggests the need for more research on the synergy between soil erosion and vegetation parameters in abandoned farmlands of varying ages. This is apparent because the nexus between soil erosion and vegetation components, mostly in fallows of different ages, has received little research attention over time. Most of the currently available studies focused primarily on cropping systems, plantations, and other land-use-related soil erosion and sediment generation [7,20,21]. These studies only quantified the amount of volume of runoff and sediment loss across diverse land uses without assessing the role of vegetation parameters in soil erosion control. The characteristics of the vegetation and erosional losses (runoff and sediment loss) in uncultivated flora and cultivated farmland in southern Nigeria were the focus of this study. The outcome of the study would enable site-specific sustainable land management techniques to be put in place to control soil erosion and preserve essential soil nutrients. The outcome would also inform farmers of the vegetation components of utmost importance during fallow periods and the need for adequate sustainable land and forest management practices to be put in place.

2. Material and Methods

2.1. Study Area

Agoi-Ekpo in Yakurr Local Government Area southeastern Nigeria is the study location. It is located in the lowland area of southern Nigeria known as the cross-river lowland. The relief of the area is moderate and rises in few areas above the usual level of the surface. The area falls within the humid equatorial climate and has high temperatures, much rain and high relative humidity, all of which are typical of the tropics. Soils of the area are vertosol. According to Iwara [4,7], the parent material and geology are of cretaceous sediments. Vegetation of the area is lush with varied tree species, wildlife, bird, butterflies and other flora species. During data collection, only areas that were similar with respect to the physical environment (rainfall, temperature, topography and slope degree) and soils derived from the same parent rocks were selected. Across the study sites, a runoff plot was constructed on the area of slope not exceeding 3° (i.e., 3 degrees). The reason was to ensure uniformity in runoff velocity.

2.2. Plot Description

The 10-year fallow served as the control (Figure 1 and Figure 2). Like the 3-year and 5-year fallows, the 10-year plot contained vegetation deliberately allowed by farmers to grow for nutrient and biodiversity restoration. The 10-year-old fallow’s most common tree and shrub species were Baphia nitida, Cnesti sferruginea, Sterculia tragacantha, Alchornea cordifolia and Napoleonavogelii. The most common herbaceous species were Paspalum conjugatum, Aspilia Africana and Triumfetta rhomboidea. In the 5-year fallow, the most common herbaceous species were Paspalum conjugatum, Chromolaena odorata, Centrosema pubescens and Costus afer (Figure 3 and Figure 4). The 3-year-old fallow had more stands of herbs than shrubs. Chromolaena odorata, Aspilia Africana, Sidaactua and Melanthera scanders were the most common herbs (Figure 5 and Figure 6). The most common tree/shrub species were Anthonotha macrophylla and Rauvolfia vomitoria. Both 5- and 3-year-aged uncultivated lands contained vegetation that farmers in the area had voluntarily allowed to grow. For the cultivated farmland, after the yam had been harvested and formed part of a continuum which consisted of cassava. The farmers allowed only a few stands of trees to grow among the mostly herbaceous vegetation. The most ecologically dominant tree/shrub species were Alchornea cordifolia and Millettia aboensis, while the most ecologically dominant herbaceous species were Chromolaena odorata and Aspilia africana in the cultivated farmland (Figure 7 and Figure 8).

2.3. Site Sampling and Installation of Runoff Plots

Using historical data obtained from local farmers on land utilization (fallow ages), abandoned farmland of three, five, and ten years old, as well as cultivated farmland, were identified and sampled (Figure 9). Ten randomly selected 20 m × 20 m plots were set up in each identified fallow community. Vegetation information such as girth, tree/shrub herbaceous cover, herbaceous cover and litter depth were gathered from the established plots. Similarly, runoff plots measuring 10 m by 4 m were constructed from a randomly selected plot used for vegetation sampling in each fallow community.

In the 3- and 5-year-old fallow, runoff plots measuring 10 m by 4 m were constructed. Moreover, using GPS, the runoff plots were appropriately geo-referenced. The plots were constructed on an area with slopes of no more than 3° degrees. Runoff amount was measured in liters. After thoroughly stirring in the runoff water, the sediment that had settled at the bottom of the collection container was emptied into a plastic container. Then, the sediment was deposited into polythene bags with labels on them. The sediment was then air-dried and weighed using an electronic balance in grams. Using Vadas’s formula, the runoff units were converted from liters to millimeters [4].

The units of runoff were thereafter converted from liters to millimeters using the formula given by Vadas et al. [22]:

$$\mathrm{Runoff} \left(\mathrm{mm}\right)=\frac{\left[\mathrm{Runoff} \left(\mathrm{L}\right)\right]\times \left[1000 \left({\mathrm{cm}}^3{\mathrm{L}}^{-1}\right)\right]\times \left[10 \left({\mathrm{mm} \mathrm{cm}}^{-1}\right)\right]}{\mathrm{Plot} \mathrm{area} \left({\mathrm{cm}}^2\right)}$$

Here, the plot area in meters (m) was converted to centimeters (cm) by multiplying the values in meters by 100. Therefore, the length of the plot being 10 m becomes 1000 cm, while the width being 4 m becomes 400 cm. Hence, 1000 cm × 400 cm gives 40,000 cm².

This becomes

$$\mathrm{Runoff} \left(\mathrm{mm}\right)=\frac{\left[\mathrm{Runoff} \left(\mathrm{L}\right)\right]\times \left[1000\right]\times \left[10\right],}{400,000 \left({\mathrm{cm}}^2\right)}$$

The sediment loss measured in grams was thereafter converted to kilogram per hectare (kg ha⁻¹) using the formula given by [22]:

$$\mathrm{Sediment} \mathrm{Loss} \left({\mathrm{kg} \mathrm{ha}}^{-1}\right)=\frac{\left[\mathrm{Soil} \mathrm{loss} \left(\mathrm{g}\right)\right]\times \left[\mathrm{Runoff} \left(\mathrm{L}\right)\right]\times \left[{100}^4\left({\mathrm{cm}}^2{\mathrm{ha}}^{-1}\right)\right].}{1000 \left({\mathrm{g} \mathrm{kg}}^{-1}\right)\times \mathrm{Plot} \mathrm{Area} \left({\mathrm{cm}}^2\right)}$$

This becomes:

$$\mathrm{Sediment} \mathrm{Loss} \left({\mathrm{kg} \mathrm{ha}}^{-1}\right)=\frac{\mathrm{Soil} \mathrm{loss} \left(\mathrm{g}\right)\times \mathrm{Runoff} \left(\mathrm{L}\right)\times 100,000,000.}{1000 \times 400,000}$$

2.4. Vegetation Inspection and Assessment

The line intercept method was used to calculate percentages of the crown and basal cover [7,23]. Distance at breadth height (DBH) of 1.3 m above the ground was used to measure tree girth (tree size) [7,24], and all plant species with dbh greater than 0.10 m were measured in terms of girth and tree size. Herbaceous composition was determined using 1 m² subplots [25,26]. On the other hand, the litter depth of each plot was estimated by measuring the quantity of litter with a ruler and expressing it in centimeters. Tievsky [27] described how it was accomplished by lowering a ruler through the ground until it hit a hard/firm surface. All the species of stems in the respective plots were counted to determine tree/shrub species density, which also included cassava. Estimates of herbaceous cover were also made using the line-intercept method [28,29]. This was performed by stretching a 20-m tape over each plot floor and recording any gaps or open places not covered by plants. The percentage of coverage was calculated by subtracting the total covered area from the length of the tape and multiplying it by 100.

2.5. Techniques of Analysis

The field-collected data was analyzed using averages, multiple correlation analysis, Pearson’s correlation, One-Way Analysis of Variance (ANOVA), charts, and tables. Statistical analysis was carried out for the 61 rainstorms that generated runoff across the plots. It should be noted that 77 rainfall events were recorded, but only 61 of the rainstorms generated runoff. In addition, out of the 61 storms recorded in the entire experiment, only 54 storms generated sediment on all the plots.

3. Results

3.1. Vegetation’s Floristic and Structural Composition

Table 1. Summary of vegetation inventory in the study communities ^a.

Vegetation Components	I	II	III	IV
Tree/shrub density	282	35	1313	1681
Mean tree/shrub density	28.20 ± 0.70	3.50 ± 0.22	131.30 ± 1.51	168.10 ± 2.24
Mean crown cover (%)	53.45 ± 1.18	36.48 ± 1.06	74.92 ± 1.03	91.11 ± 0.54
Mean basal cover (%)	7.09 ± 0.14	4.05 ± 0.22	11.17 ± 0.31	17.90 ± 0.32
Mean girth (m)	0.08 ± 0.01	0.12 ± 0.00	0.21 ± 0.01	0.29 ± 0.01
Mean litter depth	0.04 ± 0.00	0.05 ± 0.00	0.04 ± 0.00	0.07 ± 0.00
Mean ground/herbaceous cover (%)	42.15 ± 0.44	91.29 ± 0.68	51.94 ± 0.63	29.64 ± 0.50
Density of herbs	88	215	92	74

^a Values are means ± standard errors; Plot I—Cultivated farmland; Plot II—3-year fallow; Plot III—5-year fallow; Plot IV—10-year fallow.

shows the characteristics of the vegetation on the cultivated farmland and fallow vegetation, and

Table 2. A summary of the ANOVA results for the characteristics of the vegetation at various fallow ages.

Parameters	Mean Values				F-Ratio
	Plot (I)	Plot (II)	Plot (III)	Plot (IV)
Trees/shrubs density	28.20	3.50	131.30	168.10	3216.223 *
Crown cover (%)	53.45	36.48	74.92	91.11	594.350 *
Girth (m)	0.08	0.12	0.21	0.29	149.085 *
Basal cover (%)	7.09	4.05	11.17	17.90	538.754 *
Ground/herbaceous cover (%)	42.15	91.29	51.94	29.64	2171.779 *
litter depth (cm)	0.04	0.05	0.04	0.07	48.939 *
Density of herbs	8.8	21.5	9.2	7.4	227.643 *

* At a 5% confidence level, there is a significant difference in the means; Plot I—Cultivated farmland; Plot II—3-year fallow; Plot III—5-year fallow; Plot IV—10-year fallow.

shows the significance of the plots. There were 1681 trees and shrubs in the 10-year fallow, with 168 stems on average per plot. In the 5-year fallow, 1313 trees and shrubs were counted, having a mean value of about 131 stems in each plot; in the 3-year fallow, 35 shrubs with a mean value of 4 stems per plot were tallied; while in the cultivated farmland, 282 cassava stems/shrubs/trees with an average of 28 stems per plot were enumerated. This suggested that the 10-year plot was densest and most diverse. The density of trees/shrubs varied significantly among the fallows and cultivated farmland (F = 3216.223, p < 0.05). Between fallows and cultivated farmland, the crown cover changed slightly (F = 594.350; while p is <0.05), with the 10-year-fallow plot having the luxuriant and densest cover (Table 2). With mean values of 36.48%, 74.92% and 91.1%, respectively, the 10-year fallow had the highest crown cover percentage, followed by the 5-year fallow and the 3-year fallow. However, the percentage of the herbaceous cover was highest in the 3-year fallow, followed by the 5-year fallow, with mean values of 51.94% and 91.29% due to the dominance of herbaceous species.

The herbaceous cover percentages in the fallows and cultivated farmland varied significantly (F = 2171.779, p < 0.05). Tree girth (or the size of a tree) varied significantly between fallow and cultivated farmland (F = 149.085, p < 0.05) with mean values of 0.29 m, 0.21 m, 0.12 m and 0.08 m for the 10-year, 5-year, 3-year and farmland plots respectively. The 10-year plot’s high girth classes indicate a rapidly regenerating and relatively undisturbed plant community. Plant girth helps to weaken raindrops and acts as a barrier to raindrops and water movement, depending on their sizes. This process facilitates deeper infiltration; hence, tree girth may act as a barrier to the erosive force of rainfall. Moreover, tree height may serve as a barrier against the destructive force of rain. The low girth classes in the 3-year and 5-year-old fallows suggest early successional vegetation with immature stem diameters since shrubs dominated the fallow. The 10-year fallow land had a significantly higher litter depth than the farmland, with averages of 0.07 m and 0.04 m. Additionally, litter depth differed between the 3-year uncultivated plot and the 5-year uncultivated plot, with the 0.05 m measured in the 3-year fallow plot representing relatively higher values.

The 5-year-fallow plot’s history of unintentional bushfires that burned litter and undergrowth may have contributed to its low litter depth. Moreover, basal cover varied significantly among the fallows and cultivated farmland (F = 538.754, p < 0.05), with the 10-year unfarmed plot having the highest mean value of 17.9%, followed by the 5-year fallow (11.17%) and, finally, the farmland (7.1%). Litter depth varied significantly between the fallows and cultivated farmland (F = 48.939, p < 0.05). The quantity of basal cover examined across treatments influences the stems’ ability to collect rainfall and gently release it into the soil. Substantial variation was also observed in basal cover (F = 227.643, p < 0.05). High significant percentages of herb density were observed on the 3- and 5-years-aged uncultivated plots.

3.2. Runoff Losses across Fallows

The amounts of runoff in the plots ranged from 6.63 to 28.84 mm. Again, the 10-year-old fallow had a low runoff amount, with an annual runoff value of 6.63 mm. Figure 10 and Figure 11 show that the annual runoff volumes on the cultivated farmland, 5-years and 3-years uncultivated plots were 15.83 mm, 28.84 mm and 25.30 mm, respectively. Runoff recorded the lowest volume on the 10-year plot, while it had the highest volume on the 5-year plot. The variations in runoff volumes across the treatments demonstrate that vegetation plays different roles in soil erosion control, and the effects tend to be more substantial in mature or older fallows as observed on the 10-year plot. Sediment enrichment varied significantly across the plots (F = 16.089, p < 0.05). The apparent differences in vegetation components across the plots in intercepting and reducing the kinetic force of rainfall may be responsible for the observed variation in runoff volume.

3.3. Losses in Sediment from Fallow Vegetation

This study looked at 54 rainstorms with measurable sediment on all plots. The 5-year-old uncultivated plot had the most significant total sediment losses, with 209.24 kg ha⁻¹ per year, while the 10-year-old fallow plot had the lowest total sediment losses, with 12.43 kg ha⁻¹ per year

Table 3. Descriptive statistics on sediment loss (kg ha⁻¹) across fallow plots.

Fallow Plots	N	Mini	Max	Sum	Mean	Std. Devn
3 year old sediment	54	0	592	2729	50.5	109.3
5 year old sediment	54	0.3	2309.1	11,299.1	209.2	446.7
10 year old sediment	54	0	231	671.0	12.4	36.8
Farmland sediment	54	0.3	1142	6732.7	124.7	209.4

F-value = 6.355; Probability value = 0.000.

revealed that the 3-year-old and cultivated farmland plots produced sediment at an average mean rate of 50.54 kg ha⁻¹ yr⁻¹ and approximately 124.68 kg ha⁻¹ yr⁻¹. The analysis of variance revealed that the amount of eroded soil differed substantially across plots (F = 6.355; while the p < 0.05). This study evaluated 54 rainstorms that produced quantifiable sediments on all the plots, in contrast to the studies by Avwunudiogba [30] and Daura [31], which documented 23 and 43 rainfall circumstances that produced sediment. The sizes of the runoff plots may also be responsible for the variations in measured eroded soil.

3.4. Relationships between Runoff and Vegetation Components

Vegetation impacts on runoff volume [7,32,33]. The Pearson’s correlation results of the relationships between the criterion variable (runoff) on the fallows and cultivated farmland and the predictor variables (crown cover, litter depth, girth, tree/shrub density, basal cover, herbaceous cover, and herb density) are presented in

Table 4. Fallows vegetation components and runoff correlation.

S/No	Correlation Coefficients in the Treatments
	Predictor Variables	10 Year Fallow	5 Year Fallow	3 Year Fallow	Farmland
1	TSD	0.037	−0.271	−0.530	−0.222
2	G	−0.175	0.456	−0.134	−0.159
3	HC	−0.366	−0.251	0.215	0.056
4	CC	−0.363	−0.441	0.411	0.652 +
5	LD	0.365	0.267	−0.054	0.156
6	BC	−0.068	−0.002	0.162	−0.251
7	DH	−0.247	−0.299	0.273	−0.336

⁺ at the 0.05 level, the correlation constant is significant. BC means Basal cover (%); CC means Crown cover (%); DH means Density of herbs. G means Girth (m); HC means Herbaceous cover (%); LD means Litter depth (cm); TSD means Tree/shrub density.

. Vegetation characteristics showed inverse and insignificant associations with runoff volume on the 10-year-, 5-year- and 3-year-fallow plots. The crown cover had a strong and positive correlation with a runoff on the cultivated farmland. On the 10-year fallow plot, a runoff was also found to have low inverse correlation coefficients of −0.068 to −0.366 with basal cover, girth, herb density, crown cover, litter depth and herbaceous cover.

Runoff, tree/shrub density, herbaceous cover, crown cover, basal cover and herb density were inversely correlated on the 5-year-fallow plot. These vegetation variables had correlation coefficients between −0.002 and −0.441. The litter depth, tree/shrub density, and girth of the 3-year-fallow plot all showed an inverse correlation. Runoff was found to have low and negative correlations with tree/shrub density, girth, basal cover and herb density on the farmland plot. These variables had correlation coefficients that ranged from −0.159 to −0.336. These inverse relationships indicate that vegetation characteristics, to a greater extent, substantially reduce runoff.

In addition, the combined effects of seven vegetation variables on the runoff volume generated on the various plots were investigated. On the 10-year, 5-year, 3-year and farmland plots, the joint contribution of these parameters to runoff volume had multiple correlation coefficients (R-values) of 0.773, 0.855, 0.945 and 0.931 (

Table 5. Multiple correlation between vegetation components runoff and sediment loss.

Fallow Plots	Multiple Correlation Values		Multiple Correlation Values
	r-Value (Runoff mm)	R2 (Runoff mm)	r-Value (Sediment kg ha−1)	R2 (Sediment Loss kg ha−1)
10-year fallow	0.733 *	0.537	0.696 *	0.484
5-year fallow	0.855 *	0.731	0.915 *	0.847
3-year fallow	0.948 *	0.899	0.975 *	0.951
Farmland	0.931 *	0.867	0.927 *	0.859

* Significant multiple R-values with a 5% confidence level.

). All the plots have positive correlation coefficients that show that as vegetation components increase, so does runoff volume. On all plots, the vegetation variables were jointly responsible for 53.7, 73.1, 89.9 and 86.7% of the variances in the runoff. Other parameters not considered in the study were responsible for the remaining 46.3, 26.9, 20.1 and 11.1% of the unexplained variances. This suggests that runoff on the various treatments is directly related to vegetation components.

3.5. Relationships between the Components of Vegetation and the Loss of Sediment

The relationship between vegetation and sediment yield is used in erosion models to verify the influence of vegetation that runoff volume and sediment yield decrease with increasing vegetation [7,11].

Table 6. Correlation between sediment loss and vegetation components.

S/No	Correlation Coefficients across the Treatments
	Predictor Variables	10 Year	5 Year	3 Year	Farmland
1	TSD	−0.222	−0.108	−0.534	−0.198
2	G	−0.076	0.807 *	−0.138	0.371
3	HC	−0.089	−0.196	0.058	0.324
4	CC	−0.232	−0.445	0.148	0.835 *
5	LD	0.135	0.035	−0.122	0.153
6	BC	0.049	0.315	0.459	−0.299
7	DH	0.089	−0.413	0.355	−0.380

* Correlation constant is substantial at 0.05 level. BC means Basal cover (%); CC means Crown cover (%); DH means Density of herbs; G means Girth (m); HC means Herbaceous cover (%); LD means Litter depth (cm); TSD means Tree/shrub density.

displays Pearson’s correlation between sediment loss and the vegetation variables. Negative and positive correlations between vegetation components and sediment loss was weak on the 10-year plot. On the 5-year-fallow plot, the amount of sediment yielded was suggestively associated with girth (the tree size). However, it negatively and insignificantly correlated with the density of trees/shrubs, herbaceous cover, crown cover, and herb density. This indicates that tree size (girth) can account for sediment yield on the 5-year-fallow plot (r-Value = 0.807, p < 0.05). Again, sediment yield was insignificantly associated with tree/shrub density, girth and litter depth on the 3-year-fallow plot. Additionally, there was a strong, positive and statistically significant association between sediment loss and crown cover on the cultivated farmland plot (r-Value = 0.835, p < 0.05). The sediment yield was negatively and insignificantly correlated with the density of trees/shrubs, basal cover and density of herbs on the cultivated farmland. Changes in tree/shrub density, girth, herbaceous cover, crown cover, basal cover and herb density are inversely related to sediment loss quantities on the respective plots. The low relationship noticed between vegetation is consistent with the findings of Sanjari et al. [34], who also reported little correlation between the components of vegetation and eroded sediment. According to the correlation result, the density of trees/shrubs significantly influences how much sediment is lost from each plot. All plots showed a negative correlation between this vegetative component and sediment loss.

With multiple correlation coefficients (R-values) of 0.915, 0.975 and 0.927, respectively, the joint contributions of these parameters to sediment loss were high on the 5-year, 3-year and farmland. The result of the joint contribution of vegetation components on sediment loss is shown in Table 5. The fact that all the plots have positive correlation coefficients suggests that as vegetation components increase, so does sediment loss. The cultivated farmland plots and the 5-year, 3-year and vegetation variables were jointly responsible for 84.3%, 95.1% and 85.9%of the variances in sediment yield. The remaining 15.7%, 4.9% and 14.1% of the unexplained variances were attributed to other parameters that were not considered in the study. For the 10-year fallow, the vegetation factors were mutually answerable for 48.4% of the fluctuations in sediment enrichment. In comparison, the leftover 51.6% of the unexplained differences were credited to other ecological variables. Table 5 showed that the multiple correlation coefficients obtained on the 10-year, 5-year, 3-year and cultivated farmland plots were all statistically significant at a 0.05% confidence level.

4. Discussion

The study demonstrates that the well-established and dense canopy structure of the 10-year-old fallow and its developed root system, facilitates infiltration, and absorbs more of the annual rainfall. Cassava farming and the fast development of herbaceous species most notably Chromolaena odorata, are credited to the significant decrease in erosional losses on the developed farmland compared to the uncultivated plot that has been there for five years. Because they allow raindrops to strike directly on the topsoil, the 5-year-old fallow’s open canopy structure and sparse herb density could not provide adequate protection against the denudational effects of rainwater. This supports the results of [35,36], i.e., that areas with low vegetation and herbaceous cover on may generate increased soil erosion. The land’s history of accidental bushfire that burned undergrowth or herbaceous plants may cause the area’s sparse herbaceous cover.

The crown cover on the farmland plot had a positive correlation with runoff volume and sediment loss. At the same time, there was a positive and statistically significant relationship between sediment loss and the 5-year fallow’s girth. There are negative and positive correlations between vegetation components and runoff on all plots. The negative associations signify the parts of the vegetation that reduce raindrops’ destructive force. This agrees with the findings of [37] that vegetation regulates hydrological processes significantly. Moreover, Wu et al. [11,38] noted that the presence and growth of vegetation attributes decreases runoff and sediment loss. Similarly, Wu et al. [11] and Iwara [4] stated that crown cover lessens the negative influence of rainfall on the land and soil by intercepting and holding a significant amount of their energy. However, the cultivated farmland plot’s low crown cover percentage was due to the absence of trees, rendering it ineffective at reducing runoff.

The observed positive association and high erosional losses can be attributed to the abundance of cassava leaves on the cultivated farmland which did not provide sufficient cover to the topsoil. Girth is negatively correlated with runoff and sediment loss on cultivated farmland at ages 10, 3 and 10 years. Depending on its size, plant girth assists in intercepting raindrops and functions as a physical barrier to the passage of water. Additionally, it is a rainwater-absorbing reservoir. This assertion is consistent with that of [38,39,40], who identified tree size, vegetation cover and tree density as the essential plant component influencing water-holding capacity for minimizing soil nutrient leaching. The positive link between girth and sediment loss on the 5-year-old fallow is not unexpected since the bushes’ girth was not sufficiently established to function as a physical barrier against precipitation. Since shrubs dominated the girth diameters, they did not successfully reduce the erosive effects of rainfall/precipitation.

Given the plot’s canopy gaps and low herb density, the negative relationship between crown cover and herb density and sediment loss in the 5-year unplanted land is not surprising. This invariably means that the crown cover percentage, anywhere from 69.4 to 76.4%, was high enough to catch much rainwater. This is in line with what [41] stated: in forested regions, a minimum of 60% forest cover is necessary to prevent significant soil erosion. Even though there were few herbs in the 5-year fallow, they could block and weaken the raindrops’ direct effect. The importance of plants, on the other hand, in deflecting the direct effect of rainfall and the negative connections shown between sediment loss and some features of vegetation, illustrate the role of vegetation in soil erosion control.

Due to the lack of woody trees, the positive and significant correlation between crown cover and sediment loss on the cultivated farmland is expected. Insufficient cover from the cassava leaves could not afford the soil sufficient cover from the vagaries of soil erosion. However, sediment losses negatively correlate with tree/shrub density on all plots. This is to be expected because trees/shrubs develop deep rooting systems that help to control soil erosion by loosening the soil layer to make it easier for water to penetrate. This, in addition, acts as a physical barrier against soil erosion. As a result, the growth of root systems increases the porosity of the topsoil layer, making it easier for water to penetrate. According to Iwara [4], a deep root system loosens the soil and reduces surface runoff. The result of the study is also in line with those of Wu et al. [11] and Sanjari et al. [34], who also found a low correlation between sediment loss and vegetation components.

5. Conclusions

The findings of the study demonstrate that elements of the vegetation play essential roles in reducing erosional losses, but the extent of these roles varies with fallow age. This is because the vital role performed is impacted by fallow age. Many of the vegetation components on the 10-year and 5-year fallows negatively correlate with runoff and sediment loss, indicating that they can substantially reduce sediment losses. In addition, runoff and sediment loss are negatively correlated with the tree/shrub density, girth and litter depth of the 10- and 3-year fallows. These vegetation parts can handle denudational forces. However, the farmland’s crown cover’s low surface cover strongly promotes runoff and sediment loss, and the cassava leaves do not sufficiently protect the soil from rain. The findings reveal that soil erosion can only be controlled to some extent by girth, basal cover and tree/shrub density. As a measure to control soil erosion and its inherent consequences, mulch should be used to cover the soil to protect it in fallows with a sparse crown and herbaceous cover. This will reduce losses and restore soil fertility. In addition, farming operations should only partially eliminate trees from farmlands. This will prevent soil erosion and accelerate ecological restoration following land abandonment.