ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY & 

ENVIRONMENT 
AZOJETE September 2023. Vol. 19(3):519-536 

Published by the Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria.   

Print ISSN: 1596-2490, Electronic ISSN: 2545-5818 
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ORIGINAL RESEARCH ARTICLE 
 

EVALUATION AND COMPARISON OF SOME INFILTRATION MODELS ON 

AGRICULTURAL SOILS OF BOSSO LOCAL GOVERNMENT AREA, NIGER STATE, 

NIGERIA 

J. J. Musa1*, I. A. Kuti1, P. O. O. Dada2, E. A. Otuaro3, P. C. Eze4, and S. O. Nathaniel1 

1Department of Agricultural and Bioresources Engineering, Federal University of Technology, P. M. B. 65, Minna, Nigeria 
2Department of Agricultural and Bioresources Engineering, Federal University of Agriculture, P. M. B. 102, Abeokuta, Nigeria 

3Department of Civil Engineering, Nigeria Maritime University, Okerenkoko, Nigeria 
4Department of Soil Science and Land Management, Federal University of Technology, P. M. B. 65, Minna, Nigeria. 

*Corresponding author’s email address: johnmusa@futminna.edu.ng 

 

1.0 Introduction 

Soil compaction changes the physical properties of soil by increasing its strength and bulk density, 

lowering its porosity, and forcing a narrower pore size distribution within the soil. These changes 

 

ARTICLE 

INFORMATION 
 

  

ABSTRACT 
 

Over the last few decades, researchers have developed a great interest in fabricating and 

synthesising zinc silicate-based glass ceramics. However, using waste materials as 

precursors for the fabrication is yet another milestone in waste management. Thus, this 

study fabricated zinc silicate glass-ceramic using sola lime silica (SLS) glass waste as a 

silicon source. The series of precursor glass in the (𝑍𝑛𝑂)0.6−𝑥 (𝐵2𝑂3)𝑥 (𝑆𝐿𝑆)0.4 the 

conventional solid-state melt-quench technique prepared system through a controlled 

crystallisation process. The glass system’s physical, structural, and optical properties were 

obtained by density measurement, X-Ray diffraction, Fourier Transform Infrared 

Spectroscopy, and Ultraviolet-visible spectroscopy, respectively. The density of the 

(𝑍𝑛𝑂)0.6−𝑥 (𝐵2𝑂3)𝑥 (𝑆𝐿𝑆)0.4  glasses series was observed to be decreasing with the 

increment of 𝑤𝑡. % 𝐵2𝑂3  content. The XRD spectra of the 

(𝑍𝑛𝑂)0.6−𝑥 (𝐵2𝑂3)𝑥 (𝑆𝐿𝑆)0.4  samples with 0 and 0.01 wt.% 𝐵2𝑂3, exhibits major 

diffraction peaks attributed to the ZnO phase in the glass matrix. However, as 𝐵2𝑂3 

content  increased, the precursor glass sample shown by the XRD spectra depicted a 

broad halo characteristic, which reflected the properties of amorphous glass that were 

observed at the composition of 0.05 wt.% 𝐵2. From FTIR spectra, the bands at 500, 688, 

902 and 1243 𝑐𝑚−1 can be associated with stretching vibrations of ZnO4 and Si-O-B 

bending vibrations, stretching vibrations of the B–O bonds in the 𝐵𝑂4 units, and modes 

of boron–oxygen triangular 𝐵𝑂3 units. The intensity of the IR band increases with 

increasing percentage of 𝐵2𝑂3. The UV-Vis analysis of the samples with 0 and 0.01 wt.% 

𝐵2𝑂3  demonstrates crystalline hump and varies at 370 nm. It was observed that the 

addition of B2O3 to the ZnO-SLS glass network caused the glassy amorphous state without 

a sharp absorption edge at 0.05, 0.10, and 0.15 wt.% B2O3.The 

(𝑍𝑛𝑂)0.6−𝑥 (𝐵2𝑂3)𝑥 (𝑆𝐿𝑆)0.4  system  shows the increase in band gap when the 

composition of  𝐵2𝑂3 increased. When 𝑍𝑛𝑂 serves as a modifier, the number of NBOs 

will increase, and this will cause the expansion of glass network. The zinc silicate-based 

glass-ceramic produced has been classified as a semiconductor due to the vast optical 

band gap energy obtained and may have vital potential applications for future LED and 

other optoelectronic lighting devices. 
 
© 2023 Faculty of Engineering, University of Maiduguri, Nigeria. All rights reserved. 

 

Submitted 16 June 2023 

Revised 5 August, 2023 

Accepted 10 August, 2023 

  
 
 
Keywords: 

Bosso 

infiltration models 

Kostiakov 

Philip 

Modified Kostiakov 

US-Soil Conservation 

Service (SCS) 

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impact how air and water circulate through the soil, as well as the capacity of roots to develop 

in it (Sollins and Gregg, 2017; Jury and Stolzy, 2018, Lal et al., 2018). The infiltration rate can be 

affected by changes in how air and water travel through the soil. Higher runoff volume, increased 

flooding potential, and less groundwater recharge within watersheds resulting from a lower 

infiltration rate.  

Infiltration is a critical technique in managing water resources for crop production in irrigated 

and dry agricultural environments. It is a precondition for constructing, analysing and managing 

irrigation systems. Similarly, knowledge of infiltration is required to model, critically evaluate, and 

design management solutions to conserve soil and water resources in dry-farming circumstances. 

Different variables can considerably impact the appropriate evaluation, modelling, and use of 

infiltration data. According to Barros et al. (2014), the activation of hydrological processes such 

as surface runoff, erosion, and solute transport is affected by undulations and spatial 

inhomogeneity of soil, spatial and temporal changes in soil use, and changes in soil water 

infiltration affected by climate change. Soil science, hydrology, irrigation, agriculture, civil 

engineering, environmental engineering and science rely on infiltration (Valiantzas, 2010) for 

irrigation. The hydrological modelling process requires an understanding of infiltration models. 

Along with occurrence time and runoff volume, infiltration regulates water division, water 

redistribution within soils, and even water-deep percolation down to groundwater. According to 

Angelaki et al. (2013), the infiltration rate is influenced by several factors, such as soil moisture 

level, texture, density, and behaviour. 

Researchers have condensed soil infiltration features into various mathematical models (Musa 

and Egharevba, 2009; Al-Janobi et al., 2010; Umar et al., 2021). However, model predictions must 

be proved by proper field verification, with the agreement between observed and anticipated 

values (Ogbe et al., 2011). Water infiltration into the soil can be monitored in the field or 

approximated using mathematical models that are either empirical or theoretically based on 

physical principles. Empirical models can relate model parameters to soil characteristics without 

requiring a physical meaning, which could include factors that are difficult to consider in 

theoretical models to determine their constants (Mirzaee et al., 2014).  

Therefore, the study aims to determine the best model for the soil of built-up areas in Bosso-

Campus of the Federal University of Technology, Minna, Niger State. 

2.0 Materials and Methods 

2.1 Description of the Study Area 

The study area is located between Latitudes 9°39’3.82“N to 9°39’25.90 “N and Longitude 

6°31’27.65 “E to 6°31’27.65 “E at an elevation of 400 m above sea level within Bosso Local 

Government Area of Niger State. The area’s mean annual maximum and minimum temperatures 

are 39.18 °C and 25.86 °C, respectively, while the mean relative humidity and rainfall are 56.28 

% and 130.0 mm, respectively. Bosso has two distinct central climates: rainy and dry seasons. The 

rainy season spans between April and October, while the dry season is between November and 

March.  

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Musa et al: Evaluation and Comparison of some Infiltration Models on Agricultural Soils of Bosso Local Government Area, Niger State, 

Nigeria.  AZOJETE, 19(3):519-536. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng 

Corresponding author’s e-mail address: johnmusa@futminna.edu.ng     521 

 
Figure 1: Map showing selected study area within Federal University of Technology Minna, 

Bosso Campus 

2.2 Methodology 

2.2.1 Soil sampling 

A core sampler collected 15 soil samples from five selected locations at 0-30cm and 30 – 60cm 

depths. The soil samples were placed inside polythene bags and carefully labelled before being 

taken to the laboratory. These soil samples were analysed in the Soil and Water Laboratory of 

the Agricultural and Bioresources Engineering Department, Federal University of Technology, 

Minna. The soil properties tested include soil moisture content, bulk density, and soil texture 

(sand %, silt %, and clay %).  

2.2.1.1 Determination of soil Moisture content  

The soil samples collected from each sample point were oven-dried at 1050C for 24 hours, and 

the moisture content was determined using Equation 1. This is in accordance with the work of 

Umaru et al. (2021). 

𝑀𝐶 (%) =  
𝑊2− 𝑊3

𝑊3− 𝑊1
 𝑋 100           (1) 

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Where Mc is the moisture content of the soil (%); W1 is the weight of the container (g); W2 is 

the weight of the container and sample before drying (g); and W3 is the weight of the container 

and sample after oven drying (g). 

2.2.1.2 Determination of soil bulk density  

The bulk density was calculated using Equation 2 from the mass of oven-dried soil per core 

volume (g/cm3).  

𝜌𝑏(𝑔𝑐𝑚−3) =  
𝑀𝑎𝑠𝑠 𝑜𝑓 𝑜𝑣𝑒𝑛 𝑑𝑟𝑖𝑒𝑑 𝑠𝑜𝑖𝑙 (𝑔)

𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑐𝑜𝑟𝑒 𝑠𝑎𝑚𝑝𝑙𝑒𝑟 (𝑐𝑚3)
        (2) 

2.2.1.3 Particle size analysis 

The hydrometer method determined the particle size distribution of the soil samples collected 

from the study area, which is in accordance with Panagos et al. (2014). The soil textural triangle 

was then used to determine the textural class of the soil samples.  

2.3 Infiltration Rate Estimation Models and Parameters 

The following infiltration models were considered for this study.  

2.3.1 Philip Model  

The infiltration model proposed by Philip includes the soil sorptivity (S) and stable infiltration rate 

(A) from the series of solutions from Richards’ equation (Philip 1957). The two-term infiltration 

equation of Philip is expressed in Equations 3 and 4. 

𝑖(𝑡) =  𝑆𝑡1 2⁄ + 𝐴𝑡           (3) 

The differentiation of equation (1) yields equation 4 for infiltration rate (i). 

𝑖(𝑡) =  (
𝑆𝑡−1 2⁄

2
) + 𝐴           (4) 

Where i(t) represents the infiltration rate and the cumulative infiltration at infiltration time (t), 

respectively, a is related to saturated hydraulic conductivity (Ksat), S is the soil water sorptivity 

term, and t is the time elapsed. 

2.3.2 Kostiakov Model  

Kostiakov proposed one of the earliest empirical infiltration models Hillel, (1982) for estimating 

the infiltration rate, as presented in equation 5 (Kostiakov 1932). 

𝑖(𝑡) =  𝐾𝑡𝑎            (5) 

Where I(t) is the infiltration rate (mm min−1) at time t (min), and a >0 (Kostiakov’s time 

coefficient) and 0 < a < 1. k and a are coefficients, the Kostiakov model parameters depend on 

the soil texture and conditions, including initial moisture content. The integration of equation 5 

gives the expression of cumulative infiltration I (t) in mm, as presented in equation 6. 

𝐼(𝑡) =  (
𝐾

1−𝑎
) 𝑡1−𝑎           (6) 

 

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Musa et al: Evaluation and Comparison of some Infiltration Models on Agricultural Soils of Bosso Local Government Area, Niger State, 

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2.3.4. Modified Kostiakov model 

The Kostiakov-Lewis or Menzencev model (equation 7) was developed to overcome the 

limitations of the initial Kostiakov equation (Sihag et al., 2017).  

𝑖𝑡 =  𝐾𝑡𝑎 +  𝑓0𝑡           (7) 

2.3.5. US- Soil Conservation Service (SCS) model  

The US Department of Agricultural, Natural Resources and Conservation Service summarised 

long-term experimental data as suggested in Kostiakov’s model as complex. Therefore, a 

recommended coefficient of 0.6985 was added to the Kostiakov model to improve its 

performance and practical applicability as in equation 8. 

 𝑖𝑡 =  𝑎𝑡𝑏 + 0.6985          (8) 

Where i(t) represents the cumulative infiltration 

2.4 Statistical Analysis 

The statistical indices used in evaluating the models’ performance are presented in Equations 9 

to 11. 

2.4.1 Root Mean Square Error (RMSE) 

The RMSE for the data obtained was determined using equation 9.  

𝑅𝑀𝑆𝐸 =   √
1

𝑁
∑ (𝑝𝑖 − 𝑚𝑖)𝑖

2𝑁
𝑖=1          (9) 

2.4.2 Coefficient of Determination (R2) 

The models’ efficacy was assessed using the coefficient of determination presented in Equation 

10. 

𝑅2 = 1 − 
√∑ (𝑃𝑖−𝑃)𝑖2𝑁

𝑖=1

√∑ (𝑚𝑖−𝑚)𝑖2𝑛
𝑖=1

          (10) 

2.4.3 Coefficient of Correlation (CC) 

The effectiveness of the numerical predictions of the infiltration models was evaluated using the 

correlation coefficient as presented in Equation 11. 

  𝐶𝐶 =  
𝑧 ∑ 𝑎𝑏− ∑ 𝑎− ∑ 𝑏

√𝑧(∑ 𝑎2)− (∑ 𝑎)2√𝑧(∑ 𝑏2)− (∑ 𝑏)2 
        (11) 

Where n is the total number of cumulative infiltrations, pi is the mean simulated cumulative 

infiltration, and mi is the mean observed cumulative infiltration. The RMSE provides an overall 

idea of the dispersion between measured and predicted cumulative infiltration (Vand et al., 2018; 

Amami et al., 2021). 

 

 

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2.4.4 Single Factor Analysis of Variance (ANOVA) 

The single factor ANOVA is employed to ascertain if there are statistically significant differences 

between the means of two or more independent groups. Most importantly, it tests the null 

hypothesis (H0) 

 𝐻0: μ1 = μ2 =  μ3 =   μ4 … … … = μ𝑘       (12) 

Where μ is the group mean, and k is the number of groups. If the one-way ANOVA returns a 

statistically significant result in an Excel sheet, single-factor ANOVA will provide the value of F, 

F-critical and P values.  

3.0 Results  

Table 1 presents the soil textural classification of the study location with a slight difference 

between the sample collection depths. This is in accordance with the study by DeArmond et al. 

(2019). The soil samples collected from the fifteen plots within the study area at 0–30 cm and 

30–60 cm indicated that the average soil moisture and bulk density ranged from 7.14 to 17.07 % 

and 1.40 to 1.80 g/cm3, respectively.  

Table 1: Physical properties of soil samples collected from the study location 

Samples  
Soil depth 

(cm)  
% Sand % Silt % Clay Textural class 

Moisture 
content 

(%) 

Bulk 
density 

(g/cm3) 

A 
0 - 30 71.00 13.66 15.34 Sandy loam 13.51 1.56 

30 - 60 32.15 53.63 14.22 Silty Loam 14.13 1.26 

B 
0 - 30 76.10 7.20 16.70 Sandy loam 16.64 1.53 

30 - 60 21.65 38.64 39.71 Clay loam 13.85 1.61 

C 
0 - 30 72.40 14.60 13.00 Sandy loam 12.35 1.20 

30 - 60 77.21 15.39 7.40 Loamy sand 8.72 1.80 

D 
0 - 30 83.10 10.25 6.65 Loamy sand 8.43 1.49 

30 - 60 81.25 7.56 11.19 Loamy sand 9.54 1.30 

E 
0 - 30 78.12 7.73 14.15 Sandy loam 12.33 1.58 

30 - 60 58.12 23.44 18.44 Sandy loam 10.37 1.35 

F 
0 - 30 66.76 18.20 15.04 Sandy loam 7.93 1.72 

30 - 60 87.10 2.53 10.37 Loamy sand 6.35 1.52 

G 
0 - 30 71.16 14.22 14.62 Sandy loam 15.77 1.97 

30 - 60 25.18 36.57 38.25 Clay loam 11.10 1.47 

H 
0 - 30 63.15 23.70 13.15 Sandy loam 13.48 1.68 

30 - 60 60.31 22.30 17.57 Sandy loam 12.23 1.60 

I 
0 - 30 80.25 15.32 4.43 Loamy sand 11.65 1.78 

30 - 60 35.23 31.95 32.82 Clay loam 14.66 1.46 

J 
0 - 30 82.28 9.36 8.36 Loamy sand 12.73 1.62 

30 - 60 50.42 21.03 28.55 Sandy clay loam 11.47 1.28 

K 0 - 30 65.90 8.93 25.17 Sandy clay loam 17.26 1.69 

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30 - 60 11.50 49.40 39.10 Silty clay 16.87 1.27 

L 
0 - 30 79.55 6.36 14.09 Sandy loam 12.29 1.42 

30 - 60 25.15 60.75 14.10 Silty Loam 14.64 1.72 

M 
0 - 30 71.40 16.16 12.44 Sandy loam 15.46 1.25 

30 - 60 35.51 30.83 33.66 Clay loam 11.14 1.62 

N 
0 - 30 80.11 5.74 14.15 Sandy loam 12.84 1.95 

30 - 60 65.20 4.28 30.52 Sandy clay loam 12.08 1.65 

O 
0 - 30 68.52 13.38 18.10 Sandy Loam 13.99 1.23 

30 - 60 72.33 3.25 24.15 Sandy clay loam 11.49 1.60 

 

3.1 Infiltration Curves  

The cumulative infiltration for 15 plots is presented in Figure 1 (a to o). The cumulative infiltration 

graph of the observed and predicted (Kostiakov, Philip, Modified Kostiakov, and SCS) was plotted 

against the elapsed time.    

 

   

a.        b. 

   

c.       d. 

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   m.       n 

 
   o. 

Figure 1: Cumulative infiltration for the observed and prediction models 

Table 2 presents field data for the various infiltration rate models considered for this study, while 

Tables 3 and 4 present the average values of the performance evaluation parameters of infiltration 

models and the average results of the single factor ANOVA test for different infiltration models  

Table 2: Parameters of the selected infiltration models for each of the fifteen plots 

Sampled 

Plot 

Kostiakov’s Philip Modified Kostiakov SCS model 

K a S A K A f0 a b 

A 0.790 0.529 0.458 0.055 0.652 0.057 0.099 0.908 0.464 

B 1.020 0.488 0.631 0.047 0.429 0.089 0.122 1.527 0.352 

C 0.801 0.518 0.468 0.047 1.366 0.344 0.017 0.932 0.978 

D 1.235 0.430 0.571 0.042 1.130 0.373 0.031 3.103 0.183 

E 1.193 0.445 0.719 0.028 1.464 0.327 0.028 1.443 0.373 

F 2.987 0.196 0.432 0.048 2.279 0.236 0.667 1.302 0.370 

G 1.047 0.976 0.399 0.057 0.195 0.072 0.102 1.708 0.297 

H 2.665 0.189 0.343 0.051 1.069 0.113 0.065 0.118 0.913 

I 1.178 0.426 0.775 0.012 1.012 0.440 0.010 1.145 0.404 

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J 0.973 0.441 0.646 0.014 1.198 0.012 0.073 0.935 0.418 

K 1.302 0.274 0.300 0.043 1.251 0.007 0.056 1.115 0.252 

L 1.120 0.434 0.826 0.004 1.254 0.305 0.036 1.383 0.352 

M 1.008 0.448 0.109 0.089 1.078 0.197 0.062 2.557 0.183 

N 0.410 0.681 0.495 0.046 0.599 0.181 0.086 1.116 0.410 

O 0.638 0.578 0.799 0.010 0.673 0.407 0.052 1.165 0.404 

 

Table 3: Average values of the performance evaluation parameters of infiltration models  

Model RMSE R2 CC 

Kostiakov  0.6124 0.9849 0.992 

Philip  0.2116 0.9977  0.999 

Modified Kostiakov  0.5262 0.9911 0.996  

SCS 0.9815 0.9821 0.991 

 

Table 4: Average results of the single factor ANOVA test for different infiltration models  

Approaches F P-value f-critical 

Kostiakov Model 0.009249 0.948290 4.090291 

Philip Model 0.00077 0.988993  4.033874 

Mod. Kostiakov Model 0.003814 0.968504 4.090291  

SCS model 0.020821 0.900067 4.090291 

4.0 Discussion 

The quantity of water plants needed from sowing to harvest is the available soil water/moisture 

content (Musa et al., 2020). Soil moisture content is a critical factor influencing various soil 

functions and processes. This function includes plant growth, physical and chemical properties, 

soil biological activity, erosion, and runoff. Managing soil moisture content is critical for promoting 

sustainable agriculture. The result obtained is similar to the works of Uloma et al. (2014) in a 

different location where moisture content ranged between 7.1% to 17.5%. 

Furthermore, it was discovered that a constant infiltration rate was achieved within a minimal 

time at location K, which had a high moisture content. The longer it takes for the soil to establish 

a stable infiltration rate, the lower the infiltration rate and the higher the initial soil moisture 

content. This is connected to the texture of the soils and the level of compaction of the soils in 

the study areas. The rate at which water enters the soil also increases with soil dryness (Musa 

and Adeoye, 2010). In other circumstances, the cumulative infiltration volume decreases as the 

initial soil moisture content rises, according to Qing et al. (2020). The high moisture-holding 

ability of clay, which was discovered to be expected at this soil layer, is thought to be the cause 

of the detected discrepancy. As indicated by Umaru et al. (2021), the highest value of the soil’s 

average moisture content may be beneficial for crops that constantly require water, which may 

thrive in waterlogged conditions. The results contradict those of Musa et al. (2019), who 

investigated the effect of water stress on the yield of selected vegetable crops in Nigeria’s 

Southern Guinea Savannah Ecological Zone. This can be attributed to the study area’s seasonal 

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variation, soil structure, texture, and climate change (Umaru et al., 2021). However, the locations 

are contiguous within the same study environment. According to Musa and Adeoye (2010), initial 

movement and the overall amount of infiltration were thought to be influenced by the initial soil 

moisture content at any given time, both of which decreased as the soil moisture content 

increased. According to several studies, sandy soil has a higher infiltration rate than clay soil under 

the same circumstances (Runbin, 2011; Igbadun et al., 2016). Musa et al. (2017) assert that all 

water will enter if the water at the soil surface exceeds the infiltration capacity. This is related to 

plot F’s low soil moisture content reading. 

Bulk density is a sign of the health and compaction rate of the soil. This is also believed to affect 

the depth or limitations of plant roots, soil water holding capacity, plant nutrition availability, soil 

porosity, soil microbe activity, and nutrient availability, all of which have an impact on soil 

productivity (Arévalo-Gardini et al., 2015). It was observed that plot N had the highest value of 

the soil bulk density, ranging from 1.40 to 1.80 g/cm3. The bulk density values observed in this 

study are similar to those discovered by Igboekwe and Adindu (2014) in an entirely separate 

region, with this finding ranging from 1.40 to 1.80 g/cm3. A rise in infiltration capacity has been 

seen to be closely related to a fall in bulk density. The high percentage of clay content, the 

potential proximity of underlying rocks to the soil surface, and mechanical impedance may all 

have contributed to plot N’s high bulk density. 

The difference in bulk density measurements may be related to prevailing variables such as the 

soil’s degree of compaction in the study area, the volume of the core ring, and the depth at which 

the soil samples were taken. Bulk density rises with the degree of compaction, which may be 

caused by the impact of farming techniques and rainfall events on the topsoil, according to Musa 

and Adeoye (2010). Thus, soil compaction has been linked to flooding, as Grzesiak et al. (2016) 

and Ferreira et al. (2018) documented.  

This study showed that sandy soil was most predominant, as this demonstrates that the saturated 

hydraulic conductivity of a particular soil is significantly determined by the actual amount of sand 

in that soil sample (Musa et al., 2020, 2021). Plot D had the highest percentage of sand compared 

to other study locations. Each soil sample from plots A to O holds a different amount of sand, 

ranging from 11.50% at 0 to 30 cm depth to 83.10% at 30 to 60 cm depth. Similarly, the silt 

content of plot L is higher than that of the other samples, while the clay content of plots K and 

B is higher than that of the other samples at both depths. Plots J, N, and 0 at a depth level of 30-

60 cm and plot K at a depth level of 0-30 cm, respectively, showed the highest values of sandy 

clay loam, which is easily detached but difficult to transport (Bonilla and Johnson, 2012; Mirzaee 

et al., 2017) while the lowest concentrations of silt loam and silt clay were found at plots A and 

L, each at a depth of 30 to 60 cm, and plot K, also at a depth of 30 to 60 cm respectively. The 

study area reveals some degree of variation in the percentages of sand, silt, and clay in the various 

soil samples that were collected, which is comparable to the works of Okon et al. (2017) and 

Deb et al. (2019) for the differences in physicochemical properties of soils under oil palm 

plantations of different ages in Ohaji/Egbema, Imo State, and Variability of soil physicochemical 

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properties at different agroecological zones of Himalayan region: Sikkim, India respectively. At a 

depth of 0 to 30 cm, the highest sand value was 83.10%. According to Girei et al. (2016), higher 

soil resistance to continuous cultivation may be the cause of the increased sand fraction in plot 

D.  The data collected in the field showed that there are low silt/clay soil ratios, which may 

suggest that the study region has not been effectively used for agriculture, resulting in a low level 

of erosion activities. The findings of the study conducted by Afolabi et al. (2014) on the Evaluation 

of Some Soils of Minna Southern Guinea Savanna of Nigeria for Arable Crop Production within 

the Permanent Site Irrigation Farm of the Federal University of Technology, Minna is invalidated 

by the current findings. This can be ascribed to the spatial heterogeneity of the research region 

and additional physical characteristics of the soil, such as the soil structure, porosity, and moisture 

content, which may also impact the infiltration process (Yan et al., 2013).  

Infiltration tests were conducted on-site to address the regional heterogeneity of the infiltration 

rate. As a result, the study area’s initial and final infiltration rates vary from 1080 to 1176 cm/h. 

Results from Figures 1 (a to o) show that the infiltration data for Plots K and B reached field 

capacity more quickly and had the lowest infiltration readings. This can be attributed to the plots’ 

high initial soil moisture content values and high bulk densities from soil compaction, as expressed 

in Table 1. Low values of the infiltration characteristics point to potentially excessive runoff 

(erosion) on such topo-sequences or slopes (Oku and Aiyelari, 2011). Another explanation for 

the poor infiltration rate in the study could be the spatial diversity of soil parameters within the 

field (Bui et al., 2019). It was observed that Plots F, D, C, and E took longer to be saturated than 

other plots due to the high bulk density noted in the plots. Low bulk density, however, may cause 

a high infiltration rate, as seen in the C, D, E, J, M, and O plots. In addition, the soil particles at 

plots F, G, I, H, L, and N were wetter than those at the other spots, which caused them to 

permeate water more slowly than the other points. Therefore, the soil aggregates’ shape, size, 

and stability may impact the infiltration rate. 

Contrary to the findings of (Ieke et al., 2013), coarse-grained sandy soils are known to contain 

vast gaps between each grain, which allows water to penetrate quickly. Until a steady state is 

reached, soil infiltration rates gradually decrease. Several variables affect infiltration, such as soil 

texture, structure, initial soil water content, pore size, soil metric potential, and vegetation (Al-

Janobi et al., 2010). This tendency is also evident in the cumulative infiltration presented in Figure 

1.  

The rainfall-runoff mechanism must be understood using data on water infiltration for various 

soil types. Table 2 describes the infiltration equation’s parameters, and Figure 1 (a to o) displays 

graphs of the calculated infiltration rate. The values of the empirical constant ‘K’ and the 

infiltration decay constants’ a’ for Kostikov’s infiltration model were calculated to be in the range 

of 0.4099 to 2.9866 and 0.1888 to 0.9763, respectively. The values of the infiltration decay 

constant ‘a’ were consistent with the infiltration hypothesis, which stated that the values should 

always be positive and less than unity. According to reports from Ogbe et al. (2011) and Farid et 

al. (2019), most of these parameters’ values fall between 0.2 and 0.9. In addition, the sorptivity 

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(S) values were found in the range of 0.1087 to 0.8264 cm/min, and the conductivity constant (A) 

values ranged between 0.0038 and 0.0889 cm/min for all fifteen plots of Philip’s infiltration model. 

The S-estimated values and those provided by Vand et al. (2018) agreed with most of the findings 

in the study area. Similar to this, the values of the parameters of the Modified Kostiakov’s model, 

such as k, were determined to range between 0.0069 and 0.4400, and the infiltration decay 

constants a and f0 were determined to range between 0.0099 and 0.6671. Finally, the values for 

the SCS model’s infiltration parameters, such as a, were obtained in the range of 0.9353 to 3.1025 

and those for b, between 0.1825 and 0.9777. Although the values of the model’s infiltration 

parameters have no physical significance, they reflect the influence of the soil’s physical 

characteristics on infiltration, the state of the surface, and the soil’s moisture content (Ogbe et 

al., 2011). Farid et al. (2019) reported using the Coefficient of Determination (R2) as a metric to 

compare the infiltration models. In particular, the Absolute Mean Difference (AMD) and R2 were 

employed to compare Kostikov’s and Philip’s models. Haghighi et al. (2010) used the R2 and root 

mean square error (RMSE) metrics to choose the optimal model for infiltration data. Igbadun and 

Idris (2007) employed the efficiency coefficient to assess the infiltration model’s applicability. The 

estimated average Coefficient of Correlation (CC) values were 0.992, 0.999, 0.996, 0.991; the 

estimated average R2 values were 0.9849, 0.9977, 0.9911, 0.9821 (Table 3); and the estimated 

average Root Mean Square Error (RMSE) values were 0.6124, 0.2116, 0.5262, and 0.9815 cm/min. 

The infiltration rate highly depends on soil texture (Haghighi et al., 2010), and the result is 

consistent with the findings of Thomas et al. (2020). They evaluated six infiltration equations on 

homogeneous coarse-textured soils. They also found that Philip’s model provided an excellent 

representation of the infiltration. At the same time, Kostiakov, modified Kostiakov, Green Apmt, 

and Holtan-Overton performed in that order, as Igbadun et al. (2016) suggested. Oku and Aiyelari 

(2011) likewise anticipated cumulative infiltration beneath the Inceptisols in the humid forest 

zones. The outcome demonstrated that Philip’s model was more appropriate than the Kostiakov 

model. However, the findings of this study are in contrast to those of Igbadun et al. (2007), who 

reported that the Kostiakov model related closely to the measured data than Philip’s model for 

a hydromorphic soil at Samura, Nigeria, and the findings of Musa and Adeoye, (2010) who studied 

the adaptability of infiltration equations to soils of the permanent site farm of the Federal 

university of technology, Minna, in the guinea savannah zone of Nigeria and found that the 

Kostiakov model fit the data better than the Horton and Philip models. This can be attributed to 

several elements, including spatial variability, rainfall patterns, vegetation types, soil water content, 

soil characteristics of the study area, and the fact that the previous study was carried out in an 

undeveloped area with a rural setting. In contrast, the current study was carried out in an urban 

setting.  

Table 4 shows that the F-F-statistic for all models is less than the P-value and f-critical. If the F-

statistic is less than the p-value and the f-critical value in a model, it suggests no significant 

difference between the means of the groups being compared. This conclusion can be drawn 

because the F-statistic tests whether the variability between groups is more significant than within 

groups. A low F-value indicates that the variability between groups is not much larger than the 

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variability within groups, which suggests that there is no significant difference between the means 

of the groups being compared. Therefore, when the F-statistic is less than the f-critical value, and 

the p-value is more significant than the alpha level (typically 0.05), it is appropriate to fail to reject 

the null hypothesis and conclude that there is not statistically significant difference between the 

group means. 

5.0 Conclusion 

Most of the soils in the study area were sandy loam, which may be the cause of the relatively low 

infiltration rate seen in the area. Thus, it can be deduced that bulk density and soil texture have 

a more significant impact on infiltration rate than moisture content, which caused the infiltration 

of the study area to be low; hence, water logging at some points.  

This study concluded that Philip’s model was substantially closer to the observed data when the 

field and predicted infiltration rates were compared. Philip’s model outperformed the other four 

infiltration models to estimate infiltration rate in specific land use and soil parameter models. The 

model offered the lowest values, which led to the conclusion that it accurately captured the 

infiltration rate based on the mean values of RMSE. Therefore, it is concluded that the Philip 

model can be utilised more effectively to simulate cumulative infiltration to generate infiltration 

data artificially where there is no actual infiltration data for the studied area. 

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