ISSN 1794-6190 e-ISSN 2339-3459 https://doi.org/10.15446/esrj.v28n1.109054 EARTH SCIENCES RESEARCH JOURNAL Earth Sci. Res. J. Vol. 28, No. 1 (March, 2024): 79 - 91 SO IL S ABSTRACT The demand for economical means of evaluating soil nutrients’ unpredictability triggered the use of physical factors against the costlier, laborious, and time-consuming chemical approach. This drive led to resolving its capability in eva- luating intricate soil properties as a productivity checker. This study aimed at assessing the efficiency of apparent elec- trical conductivity as a useful alternative to the conventional chemical examination of available nutrients. A petrogra- phic examination was conducted on four rock samples for their classification as the source of soil formation. Apparent Electrical Conductivity (ECa) measurements were seasonally executed in the wet (912-station) and dry (906-station); the ArcGIS 10.2 programme was used in generating the ECa maps for classifying the ECa into low, moderate, and high sections. Ten cored (in-situ) soil samples were subjected to permeability test to ascertain water infiltration rate and retention. Twenty soil samples were examined for pH, Electrical Conductivity (EC), available phosphorus, acidity, Na, Mg, K, and Ca using standard soil science procedures. The mineralogical composition of six samples (two samples per ECa region) was determined with X-ray diffraction. The soils ECa were 10–344 µS/cm, categorized as low (1–49 µS/cm), moderate (50–99 µS/cm), and high (>100 µS/cm). The ECa distribution varied from moderate (61%) to high (64%), suggesting a heterogeneous pattern of soil attributes. The infiltration rate was slow in high ECa (5.56x10-5-1.67x10-4 cm/s), signifying good retention capability, whereas the low and moderate ECa (moderate-moderately rapid) sections promote nutrient leaching. ‘Positive correlation was observed between CEC and ECa in the low ECa (2.99 cmol/kg), the same was observed in the moderate and high ECa (3.30-4.85 cmol/kg) segments’. The base cation saturation varied from high (81.38%), moderate (73.34%) and low (71.89%), characterizing the high ECa as the higher fertility status. Soils with moderate and high ECa have a greater ability to adsorb cations onto their surfaces than soils with low ECa; similarly, the deduction from base saturation showed that more cations are available in soils with high ECa. The high ECa region had low quartz (41.3%) and microcline (15.7%), but high kaolinite (31.1%) had an affinity to adsorb more cations compared to other ECa regions in terms of quantity. The rock is biotite granite gneiss, containing plagioclase (22%), microcline (24%), orthoclase (4%), quartz (25%), biotite (7%), and others (18%). ECa variability is practicable in predicting the spatial distribution of soil properties and delineating the management zones. Thereby reducing the cost that would have been expended on geochemical sampling of the entire farm. Geo-spatial sensing of apparent electrical conductivity: a leeway to agricultural soil assessment Abayomi Adesola Olaojo1*, Michael Adeyinka Oladunjoye2 1. Department of Earth Sciences, Ajayi Crowther University, Oyo, Nigeria. 2. Department of Geology, University of Ibadan, Ibadan, Nigeria. *Corresponding author: aa.olaojo@acu.edu.ng Record Manuscript received: 22/05/2023 Accepted for publication: 27/03/2024 How to cite this article: Olaojo, A. A., & Oladunjoye, M. A. (2024). Geo-spatial sensing of apparent electrical conductivity: a leeway to agricultural soil assessment. Earth Sciences Research Journal, 28(1), 79 - 91. https://doi.org/10.15446/esrj. v28n1.109054 Keywords: electrical conductivity, soil composition, mineral assemblages, nutrient variability, agricultural soil assessment, cation exchange capacity, spatial distribution Detección geoespacial de la conductivad eléctrica aparente: una posibilidad para la evaluación de suelos agrícolas Palabras clave: Conductividad eléctrica; composición del suelo; conjuntos minerales; variabilidad de nutrientes; evaluación del suelo agrícola; capacidad de intercambio de cationes; distribución espacial. RESUMEN La demanda de medios económicos para evaluar la imprevisibilidad de los nutrientes del suelo ha llevado a que el uso de factores físicos prevalezca sobre los factores químicos, más costosos, laboriosos y demandantes de tiempo. Este enfoque condujo a resolver su capacidad de evaluar propiedades complejas del suelo como verificador de productividad. Este es- tudio se enfoca en medir la eficiencia de la conductividad eléctrica aparente como una alternativa útil a la examinación química convencional de los nutrientes disponibles. Para esto se realizó un análisis petrográfico en cuatro muestras rocosas para su clasificación como fuente de formación del suelo. Las medidas de Conductividad Eléctrica Aparente (ECa) se lleva- ron a cabo durante la temporada de húmedad (912 estaciones) y la temporada seca (906 estaciones); el programa ArcGIS 10.2 se utilizó para la generación de mapas de clasificación de la ECa que permitiera su clasificación en secciones baja, moderada y alta. Diez muestras de suelo (in-situ) fueron sometidas a pruebas de permeabilidad para determinar el índice de infiltración de agua y de retención. A veinte muestras de suelo se les examinó el potencial de hidrógeno (pH), la Con- ductividad Eléctrica, el fósforo disponible, la acidez, el sodio, el magnesio, el potasio y el calcio a través de procedimientos estándares de las ciencias del suelo. La composición mineralógica de seis muestras (dos muestras por cada región ECa) se determinó con difracción por rayos X. El rango ECa de los suelos osciló en 10–344 µS/cm, categorizados como bajos (1–49 µS/cm), moderados (50–99 µS/cm) y altos (>100 µS/cm). La distribución ECa varía de moderada (61 %) a alta (64 %), lo que sugiere un patrón heterogéneo de atributos del suelo. El grado de infiltración fue bajo en la región alta de ECa (5.56x10-5-1.67x10-4 cm/s), lo que significa una buena capacidad de retención, mientras que las regiones baja y moderada de ECa (moderado-moderadamente rápido) promueven la filtración de nutrientes. Se observó una correlación entre la Capacidad de Intercambio de Cationes (CTC) con la ECa; la CTC baja (2.99 cmol/kg) ocurre en la región baja de la ECa, al igual que la moderada (3.30-4.85 cmol/kg) ocurre en la región moderada de la ECa y la alta ocurre en la región alta de la ECa. La saturación base del cation varía entre alto (81.38 %), moderado (73,34 %) y bajo (71,89 %), lo que caracteriza la región alta de ECa como la de mayor estatus de fertilidad. Los suelos de las regiones moderada y alta de ECa tiene una mayor capacidad de absorber los cationes en sus superficies que los suelos de la región baja ECa; igualmente, la deducción de la saturación base muestra que más cationes están disponibles en suelos de la región alta de la ECa. La región alta de la ECa tiene un bajo porcentaje de cuarzo (41,3 %) y microclina (15,7 %), pero la alta caolinita (31,1 %) tiene una afinidad de absorber más cationes en comparación con otras regiones de la ECa en términos de cantidad. La roca es un gneis granítico de biotita que contiene plagioclasa (22 %), microclina (24 %), ortoclasa (4 %), cuarzo (25 %), biotita (7 %), y otros (18 %). La variabilidad de la ECa es práctica para predecir la distribución espacial de las propiedades del suelo y delinear las zonas de manejo. Además sirve para reducir el costo que se gastaría en la realización de un muestreo del área completa de trabajo. https://doi.org/10.15446/esrj.v28n1.109054 mailto:aa.olaojo@acu.edu.ng https://doi.org/10.15446/esrj.v28n1.109054 https://doi.org/10.15446/esrj.v28n1.109054 80 Abayomi Adesola Olaojo, Michael Adeyinka Oladunjoye 1. Introduction Soil is developed from the parent rock material which is influenced by the climatic condition, topography, and living organisms acting on it; and tends to exhibit varying textural classes based on the mineral composition of the parent rocks (McCauley et al., 2005; Botta, 2015; Jaja, 2016). The soil horizon is developed from the continuous weathering of the parent rock leading to the development of a vertical section of soil known as the soil profile (Gregory et al., 2009). The chemical content of soil and its formation depend on the nature of the bedrock as derived from weathering processes, biological components acting on it, and the introduction of external materials from other sources. Oftentimes, the nutrient content of soil is dependent on the bedrock, and its variability depends on the conditions in which the parent rock is in the soil’s formation. This in turn affects the weathering rates, resulting in different soil horizons with varying physical, chemical, and mineralogical compositions (Demir et al., 2022). Demir et al. (2022) suggested that soil nutrient evaluation should be determined from its physico-chemical and mineralogical properties in order to reveal its composition. Evaluating soil nutrients’ capacity has been done through a geochemical approach but it’s laborious, and time-consuming and the cost of analysis is expensive; although detailed information would be available at the end of the exercise. Considering the number of samples that would be required for a detailed assessment, often time farmers embarked on a smaller number of sampling points to get around the problem of cost, but this would not allow the production of a detailed and accurate map of soil nutrients’ variability. The quality of soil type is determined by the proportions of the clay, silt, and sand combination; this invariably dictates the porosity/permeability, nutrient catchment capacity, water retention, and electrical conductivity which are essential for plants’ performance. Soil nutrient is derived from the chemistry of the parent rock. The petrographic evaluation of rock serves as a way to examine the optical properties of minerals and possibly determines the expected product when it weathers. Minerals in rock exhibit specific texture and colour, which can be identified from their optical properties. The rock’s mineralogy dictates the resultant soil mineral contents and colour, this in turn influences the soil’s productive capacity. Apparent electrical conductivity (ECa) serves as an alternative for dense sampling and offers a way to reduce the cost, coupled with a good correlation with soil variables (Costa et al. 2014). Soil electrical conductivity is useful in assessing soil mineralogy, soil texture, nutrients, temperature, field holding capacity, chemical properties, and soil moisture; these variables play a vital role in plant development. The flow of electrical charges through a material enables the conduction of charged particles; soil also displays electrical properties based on its physical and chemical properties such as texture, and water content (Samouëlian et al., 2005). Field measurement of soil apparent electrical conductivity (ECa) is a simple and inexpensive method and can be correlated with the other soil properties such as soil texture, cation exchange capacity, drainage conditions, organic matter level, salinity, water holding capacity, temperature and elevation (Grisso et al., 2009; Siqueira et. al., 2014; Mertzanides et al., 2020; Fagbemigun et al., 2021). Swileam et al. (2019) studied the spatial inhomogeneity of soil properties using electrical resistivity approach, the variabilities observed in resistivity study were correlated with moisture and bulk density; soil of low resistivity was noted with high soil moisture and bulk density, it was expressed with curvilinear models. They opined that the study could be used for soil irrigation and salinity management. Negative relationship occurred between moisture content and electrical resistivity, and aids in delimiting the spatial variability of the moisture content being extrapolated from the resistivity distribution (Mertzanides et al., 2020). Electrical resistivity has been found useful in monitoring of herbicide contamination in soil as a bioremediation device capable of evaluating its varying concentration (Balwant et al. 2021). Mohammed et al. (2022) carried out a study on a natural agricultural oasis of Al-Ahsa region in Saudi Arabia using electrical resistivity tomography and electromagnetic induction. The techniques classified the hydrogeological conditions being influenced by soil textural and moisture content variations. Areas with soil degradation requiring attentions were delineated and this affirms the use of electrical method as a viable tool in agricultural soil administration. Electrical resistivity technique (VES and 2-D) is effective in characterizing the soil horizons and depicting structural inhomogeneity, the findings of Ganiyu et al. (2019) revealed the soil horizon and suitable crop that could be grown on soil as delineated by the physical parameters. Thereby, enhancing its soil management capability in agricultural discipline. Electrical conductivity (EC) has been reported by USDA (2011) to be a functional tool in assessing soil productivity, such that high EC value has been linked with high concentrations of P, K, Ca, Mg, Mn, Zn, and Cu and nitrate. Africa’s rainfall pattern is torrential which is responsible for the leaching of soil nutrients beyond the root zone and blanket use of fertilizer may be injurious to plants (Adewole and Adeoye, 2014). The clay minerals play a vital role in their ability to adsorb cations onto their surfaces and determine the quantity of cation exchange capacity sites. Exchangeable cations are electrostatically bound or attached to the clay surface, although few anions are in a diffuse layer due to electrostatic repulsion (Al-Ani and Sarapää, 2008). The inception of this study was prompted by meticulous field observations conducted on cacao trees cultivated concurrently within the same parcel of land in year 2000. Evident disparities in cocoa pod development rates among these trees spurred a quest for deeper insights into the underlying factors driving such variations in soil productivity. This observational imperative underscores the need for an in-depth exploration of the physical determinants shaping these differences. In this pursuit, we draw inspiration from the burgeoning field of agrogeophysics, which serves as a conduit for interconnecting diverse interdisciplinary tenets aimed at tackling challenges inherent to agricultural soil dynamics. Recognizing the potential of physical properties, we have integrated them into comprehensive agroecosystem assessments, perceiving them as enduring tools for assessing the mutable nature of soil nutrient content. The crux of our study revolves around the pivotal role of spatial apparent electrical conductivity as a predictive gauge for soil properties. This foundational insight empowers us to venture into new realms, where the relationship between electrical conductivity and soil permeability is elucidated, unraveling how the latter inherently impacts productivity dynamics. Furthermore, we embarking on an effort to identify rock types, deciphering soil mineralogy, and conducting chemical assessments. This holistic undertaking is designed to affirm the credibility and utility of physical properties as a robust alternative avenue for soil analysis. As we delve into the depths of this investigation, our ultimate goal is to leverage the conclusions drawn from our study to guide future practices. The implications extend to actionable recommendations that can shape agricultural approaches, enhance productivity, and inform decision-making processes. These include adopting spatial apparent electrical conductivity as a strategic tool in predictive soil assessment, optimizing the correlation between electrical conductivity and permeability for enhanced productivity outcomes, and validating the reliability of physical property assessments through comprehensive chemical and mineralogical analyses. 2. Material and Methods 2.1 General Features of the Field Study The area spans 99 meters by 78 meters and falls within the geographical coordinates of Latitudes 7°13ʹ15.9ʺN to 7°13ʹ19.6ʺN and Longitudes 3°51ʹ40.1ʺE to 3°51ʹ43ʺE. The study area’s location rests upon a migmatite complex within Ibadan, encompassing the Cocoa Research Institute of Nigeria (Fig. 1). The rock samples were obtained from outcrops around the farm and they were situated between Latitudes 7˚13ʹ10.2ʺN and 7˚14ʹ04.6ʺN, and Longitudes 3˚51ʹ34.8ʺE and 3˚51ʹ55.6ʺE. 2.2 Rock Analysis The thin sectioning of the rock samples into slides was carried out in the Department of Earth Sciences at Ajayi Crowther University Oyo, using Hillquist thin section machine, and the petrographic studies were conducted via Brunel petrographic microscope. The rock samples were prepared into thin section via the approach stated below: 1. The rock samples obtained were prepared into small sizes of about 8 mm thick. 2. One of its surfaces was polished using 400-grade carborundum on the glass plate. 3. The polished surface mounted on the glass slide using Araldite epoxy resin. 81Geo-spatial sensing of apparent electrical conductivity: a leeway to agricultural soil assessment 4. The mounted slide set for 30 minutes and the chip cut into a size of 90 microns. 5. The sample surface was ground using four grades (90, 400, 600, and 800) of carborundum until it reached 30-micron size. 6. Excess Araldite removed and the slide was covered using Araldite epoxy resin on the hot plate. 7. The mineral properties through the cross and plane polarised light were examined; and modal analysis for the relative mineral composition. Figure 1. Geological map of the research vicinity (NGSA, 2009). 2.3 Electrical Resistivity Procedure The basic principle of electrical resistivity involves the measurement of the resistance (R) of a conductor which opposes the flow of electric current when a voltage is applied across the two ends and its unit is Ohm (Ω). Resistance is the ratio of the potential difference (V) to the resulting current flow (I) as defined by Ohm’s Law (Equation 1): V = IR (1) The apparent electrical resistivity (ρa) is the multiplication of the resistance (R) with the geometric factor (k). The ‘K’ for the Wenner array is 2Пa, where ‘a’ is the spacing between adjacent electrodes. The apparent electrical resistivity (ρa) was converted into apparent electrical conductivity (σa) using the inverse relationship between them as noted in Equation 2. σa = 1ρa (2) The apparent electrical conductivity was used in generating the conductivity maps of the farm. The study was carried out during the wet (August 2016) and dry (March 2017) seasons; the electrodes were fixed on a wooden frame to facilitate the same penetration depth and inter-electrode spacing was 40 cm (Fig. 2). The effective depth of investigation is approximately 31 cm (Edwards, 1977) which is situated within the root zone. The resistivity measurements were taken at every 3 m along a profile (99 m long) and inter-profile spacing of 3 m was adopted for 27 profiles; 912 and 906 resistivity stations were investigated for soil variability during the wet and dry seasons, respectively. The ECa data set which were regularly spaced on the field were input as CSV file (comma delimited). Interpolation of the data enables the prediction for all the points in the study area. The ECa data were related to one another via kriging and the correlation function was used for interpolating the data as provided by the ArcGIS 10.2 program. The approaches have been reported and found useful in generating the apparent electrical conductivity maps (Oladunjoye, 2022; Olaojo and Oladunjoye, 2023). This revealed the spatial heterogeneity in the soil EC contrast on which further inferences could be determined. Figure 2. Fixed electrodes on a wooden frame 2.4 Procedure for the Modified Constant Head Permeability Techniques Ten undisturbed soil samples were taken at the root zone (0.3 m) with the aid of a cylindrical metallic core barrel (diameter 7 cm and height 7 cm). They were saturated in water for 24 hours to ensure water rose through the capillary fringes. This analysis was conducted at the Department of Agronomy, the University of Ibadan using a modified Wageningen constant head permeameter. The pressure head difference was established by clamping an empty metallic core cutter tightly on top of the core with a soil sample and then filled with water. The water flows through the soil sample and is collected at the down end through outlet tubing onto a funnel seated on a beaker. The volume of water was quantified using a measuring cylinder while the time taken for the water to pass was measured with a stopwatch. Darcy’s law was used in ascertaining the hydraulic conductivity, parameters measured include; the volume of the water (Q) that flows through the soil column (cm3), the cross-sectional area (A) of flow through the soil column (cm2); time interval (t), length of soil (L) column (cm) and the hydraulic head difference (∆H) in cm. Saturated hydraulic conductivity was determined using Equation 3 (Hillel 2004). Ksat = QL/At∆H (3) 2.5 Geochemical and Mineralogical Assessments of the Soils 2.5.1 Determination of Soil Chemistry The field apparent electrical conductivity aided in demarcating the farm into different ECa zones from which soil-sampling stations were selected. Geochemical analyses were conducted on twenty soil samples (low ECa -9, moderate ECa-5, and high ECa-6 segments). The electrical conductivity and pH of soil samples were determined using the Hanna EC meter by weighing 10 grams of ˂ 75 µm soil particles into an extraction cup mixed with distilled water (10 ml). The spectrophotometry analysis was engaged for the determination of available phosphorus, the acidity in the soil was evaluated using titration method, the potassium and sodium were determined with a flame photometer (Jenway FP640) while calcium and magnesium via Atomic Absorption Spectrophotometer AAS (Buck scientific 210/211 model). The Cation Exchange Capacity (CEC) was computed from the addition of cations (Ca+Mg+Na+K) plus the acidity. All the analyses were executed at the Department of Agronomy, University of Ibadan. 2.5.2 Determination of Primary Mineral X-Ray Diffraction (XRD) analysis was carried out on six soil samples via the Malvern PaNalytical empyrean XRD system at the College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Saudi Arabia. Two soil samples were chosen from each of the ECa regions ((low ECa -2, moderate ECa-2, and high ECa-2 segments) to ascertain the mineralogical composition. A fine fraction (< 45 µm) of soil was packed into a hollow-cavity sample mount and the quantitative XRD analysis is achieved employing a whole-pattern fitting method utilizing a measured and calculated XRD scan. Minerals identification was carried out by comparing calculated d-spacing with a library of standard d-spacing. 82 Abayomi Adesola Olaojo, Michael Adeyinka Oladunjoye 3. Results and Discussion 3.1 Geological Mapping of Basement Rock The gneiss exhibited pronounced foliation, discernible through the alternating or banded arrangement of mafic (dark) and felsic (light) mineral constituents (Fig. 3a). Concordant with the host rock, the quartzo-feldspathic segregations exhibited striking patterns between 3590 and 60 degrees, with a maximum length of 4 meters (Fig. 3b). The distribution of these segregations encompassed varying areas, spanning from 20 m² to 300 m². Additionally, these outcrops were characterized by a medium-grained texture. Primary mineral constituents comprised quartz, feldspar, and mica, with biotite-rich mica and muscovite predominating. Within this mineral composition, mafic components exhibited prominence over their felsic counterparts, rendering a gneissic appearance. An understanding of the rock composition is vital in determining soil mineral constituents that would result from the weathering of the parent rock. This would serve as an indirect approach in evaluating the soil nutrient that would be available for plant uptake and ensure proper management of agricultural soil. Figure 3a. Banding of mafic and felsic mineral components Figure 3b. Outcrop intruded with quartzo-felspathic vein 3.2 Petrographic Investigations of Rocks In the examination of thin-sectioned rock samples, a complex interlocking arrangement of minerals was revealed, encompassing feldspars (plagioclase, microcline, and orthoclase), mica (biotite and phlogopite), quartz, and the accessory mineral zircon. Cross and plane polarised light microscopy unveiled distinctive features, notably elongated quartz crystals exhibiting a preferred alignment alongside present biotite minerals (Figs. 4 and 5). A notable feature emerged in the form of wavy extinction within the quartz crystals. Furthermore, the feldspars (plagioclase, microcline, and orthoclase), biotite, and quartz all demonstrated elongation along the axis of orientation. Intriguingly, certain feldspar grains appeared as porphyroclasts, embedded within the groundmass of deformed mineral grains at specific locations (Fig. 4). A sample from location 1 unveiled the presence of zircon as an accessory mineral within the groundmass (Fig. 6). FLD FLD BIOBIO Figure 4. Photomicrograph showing the porphyroclast of feldspar grains in the matrix of biotite quart and medium grain feldspar minerals (cross polar) at location 1. Mag. x100. Figure 5. Photomicrograph showing the biotite (Bio), porphyroclast of feldspar (FLD) grains in the matrix of others minerals (plane polar) at location 1. Mag.x100. Figure 6. Photomicrograph showing zircon as an accessory mineral, biotite, quart and feldspar under cross polar at location 1. Mag. x100 FLD Bio 83Geo-spatial sensing of apparent electrical conductivity: a leeway to agricultural soil assessment Quantitative modal analysis provided insight into the mineral composition distribution at each location (Table 1). Through comparison with Egesi (2019), Ibrahim et al. (2015), and Parsons and Zwanzig (2003), the rock formations observed in the study location were classified as biotite granite gneisses. These rock constituents are destined to weather into soil under the influence of temperature and pressure differentials from their formation conditions. The subsequent transformation of rock constituents into soil components was discussed, particularly the alteration of plagioclase, microcline, orthoclase, and biotite into kaolinite due to weathering. The varying susceptibilities to weathering were highlighted; plagioclase and biotite undergo more rapid weathering than microcline and orthoclase, with quartz demonstrating higher resistance (White et al., 2001; Wilson, 2004). The mineralogical characteristics of the rock have profound implications for the potential clay content of the resultant soil after weathering. This composition significantly influences soil productivity, particularly in terms of adsorption and nutrient holding capacity. Kaolinite’s distinctive attribute, its relatively lower Cation Exchange Capacity (CEC) compared to illite and montmorillonite, underscores its role (Sonon et al., 2014). 3.2 Electrical conductivity of Soil The apparent electrical conductivity (ECa) data of the soil were subjected to statistical analysis, as presented in Table 2. The coefficient of variation (CV) was utilized to determine the variability of the ECa values. During both rainy and dry periods, moderate (61%) and high (64%) variability in ECa were observed, respectively, as highlighted by Warrick and Nielsen (1980). Molin and Faulin (2013) emphasized the significance of CV in identifying spatial variability, serving as an initial indicator. Consequently, ECa can be considered a valuable tool for evaluating soil quality and assessing the suitability of subsequent study sites. The measured ECa values were found to fall within the non-saline class according to USDA (2011), suggesting that crops would not face water stress. Spatial distribution analysis of apparent electrical conductivity of soil was conducted using ArcGIS 10.2 software. The data points were interpolated using a correlation function known as kriging. The agricultural area was classified into distinct ECa zones: low ECa zone ranging from 0 to 49 µS/cm, moderate ECa zone ranging from 50 to 99 µS/cm and high ECa zone with a conductivity of 100 µS/cm and above. The most conductive regions (highlighted in red) were primarily concentrated in three areas: north/northeast, southwestern, and central portions of the map (Fig. 7). Moderate ECa zones (depicted in light brown) extended from high ECa areas and gradually transitioned into low ECa areas, notably in the southeastern, central, and northern parts of the map. Low ECa terrain (shown in blue) was prominently observed in the southeast, southern section, central, and northwest/north-northwest (NNW) regions of the map. Similar distribution patterns of electrical conductivity were observed during the dry season as well (Fig. 8). Table 1. Modal analysis of analysed rock samples in the study area Rock samples from the study area Previous works used for comparison Mineral (%) L1 L2 L3 L4 70 1 3l 12 .8 ll 30 5 1l 55 .6 ll 70 1 3l 10 .2 ll 30 5 1l 53 .3 ll 70 1 3l 54 .5 ll 30 5 1l 48 .5 ll 70 1 4l 04 .6 ll 30 5 1l 34 .8 ll Range Av er ag e Parsons and Zwanzig (2003) Composition of Granite Gneiss Egesi (2019) Average Composition of Granite Gneiss Ibrahim et al. 2015 Hornblende Granite Gneiss Yusof & Zabidi (2016) Granitic Compositional Range Plagioclase 25.2 23.6 18.0 19.3 18.0-25.2 21.5 35 20 35 } 40.0-70.0Microcline 28.1 27.0 21.3 20.8 20.8-28.1 24.3 } 21 } 30 - Orthoclase 3.4 4.0 - - 3.4-4.0 3.7 - Quartz 23.3 23.7 26.0 27.5 23.3-27.5 25.1 24 27 30 5.0-30.0 Biotite 14.0 14.2 33.2 31.4 14.0-33.2 23.2 7 7 5 } 20.0-40.0 Zircon 5.3 6.5 - - 5.3-6.5 5.9 - - - Hornblende - - - - - - } 0-24 6 15 Cummingtonite Muscovite - - - - - - - 5 - Opaque Mineral - - - - - - - 2 10 Total 99.3 99.0 98.5 99.0 L1-Location Table 2. Statistical assessment of soil apparent electrical conductivity (ECa) of cacao field Season Number of points Range (µS/cm) Mean (µS/cm) Standard Deviation Coefficient of Variation (%) Wet 912 13-344 68 41 61 Dry 906 10-267 45 29 64 84 Abayomi Adesola Olaojo, Michael Adeyinka Oladunjoye Figure 7. Apparent electrical conductivity distributions within the cacao field during wet season Figure 8. Apparent electrical conductivity distributions within the cacao field during dry season However, numerical values were reduced in segments corresponding to high ECa (> 60 µS/cm), moderate ECa (40-59 µS/cm), and low conductive sections (< 40 µS/cm) compared to the wet season. The noticeable difference between the two seasons was the distinct presence of low ECa segments during the dry season. It can be inferred that zones with high ECa usually have more fine soil fractions than those with low ECa (Mzuku et al., 2005; Rodríguez-Pérez et al., 2011; Gholizadeh et al., 2012; Fagbemigun et al., 2021; Mohammed et al., 2022), regions with low ECa loses water faster than other regions resulting in variability of water content (Costa et al., 2014; Swileam et al., 2019) as the water drains through it. 3.3 Constant Head Permeability Assessment of Soils in the Cacao Farm The constant head permeability test (Table 3) revealed varying permeability; low permeability (5.56x10-5-1.67x10-4 cm/sec) in high ECa segment, moderate in the moderate ECa (0.00128 cm/sec) whereas high permeability (6.67x10-4-2.86x10-3 cm/sec) in the low ECa section. Soils of high ECa have low relative permeability classified as silty sand; sand/fine sand (medium relative permeability) in the moderate ECa segment and low ECa is sand/fine sand to silty sand with a low to medium range relative permeability (Terzaghi and Peck, 1967). Soil Infiltration Rate in the Cacao Plot Scherer et al. (2013) established the infiltration rate in the soil as the amount of water absorbed by the soil over a given time. The rate at which water moves through soil units in high ECa ranged from moderately slow to slow whereas moderate infiltration in the region of moderate ECa. Soil materials in the low ECa segment indicate a moderate to moderately rapid infiltration rate (Table 4). Table 3. Permeability (k) coefficients of some selected soil samples from cacao farm S/N Coordinates ECa of sample selection point ksat (cm/sec) Wet Season Dry Season Northing Easting ECa (µS/ cm) ECa (µS/ cm) 1 7013ʹ18.5ʺN 3051ʹ40.6ʺE High 8.33x10-5 133 112 2 7013ʹ16.8ʺN 3051ʹ40.6ʺE High 8.33x10-5 159 89 3 7013ʹ16.2ʺN 3051ʹ41.1ʺE High 5.56x10-5 118 81 4 7013ʹ17.7ʺN 3051ʹ41.6ʺE Low 6.67x10-4 23 20 5 7013ʹ19.3ʺN 3051ʹ41.4ʺE Low 1.61x10-3 40 15 6 7013ʹ16.4ʺN 3051ʹ41.9ʺE Low 2.86x10-3 49 30 7 7013ʹ16.6ʺN 3051ʹ42.9ʺE Medium 1.28x10-3 51 36 8 7013ʹ17.8ʺN 3051ʹ42.3ʺE High 1.11x10-4 114 85 9 7013ʹ17.9ʺN 3051ʹ41.9ʺE Low 7.50x10-4 48 26 10 7013ʹ19.0ʺN 3051ʹ43.0ʺE High 1.67x10-4 136 103 Table 4. Classification of soil moisture infiltration rate (Modified after Scherer et al., 2013) S/N Classification Infiltration Rate (inches/hour) Infiltration Rate (cm/s) 1 Very slow less than 0.06 ˂ 4.233x10-5 2 Slow 0.06 to 0.2 4.233x10-5 to 1.411x10-4 3 Moderately slow 0.2 to 0.6 1.411x10-4 to 4.233x10-4 4 Moderate 0.6 to 2.0 4.233x10-4 to 1.411x10-3 5 Moderately rapid 2.0 to 6.0 1.411x10-3 to 4.233x10-3 6 Rapid 6.0 to 20.0 4.233x10-3 to 1.411x10-2 7 Very rapid greater than 20.0 > 1.411x10-2 3.3.1 Correlation Analysis between Permeability (k) Coefficients and ECa of the Soils The plot of permeability coefficients with the ECa (Fig. 9) showed that its determination coefficient is 0.460 in which 46.0% of the variables were related linearly and also exhibits a strong negative correlation coefficient (0.68) in the wet season. The degree of interaction revealed that 50.1 % of these variables have linear dependence as suggested by the determination coefficient while a negatively strong correlation (0.708) in the dry period (Fig. 10). Positive correlation exists between electrical resistivity and hydraulic conductivity (Fagbenro and Woma, 2013); this deduction also validates the negative correlation obtained from the plot as a result of an inverse relationship between electrical resistivity and conductivity. Kayode et al. (2022) engaged electrical resistivity in assessing soil inhomogeneity and the technique was reported to be efficient in characterizing the soil moisture and void distribution, thus useful in agricultural management programmes. Mary et al. (2020) monitored the root water uptake using electrical method via a controlled infiltration process. The permeability evaluations agreed clearly that soils of high ECa are characterized by low permeability at both seasons and vice versa. It can be inferred that there is the presence of more fine particles in the high ECa section than in the moderate and low ECa segments. This promotes the adsorption of essential nutrients onto its surface due to its large surface area. 85Geo-spatial sensing of apparent electrical conductivity: a leeway to agricultural soil assessment Figure 9. Plot of permeability (CHP) versus ECa in the cacao farm during the wet season Figure 10. Plot of permeability (CHP) versus ECa in the cacao farm during the dry season 3.4 Soil Chemical Assessment 3.4.1 Assessment of Physical and Chemical Properties of Soil in the Farms Soil pH: Overall pH values range from 6.1 to 7.1 with a mean concentration of 6.71, the average pH in the low ECa (6.1-6.8) horizon is 6.62; the moderate ECa section (pH: 6.6-6.8) has a mean value of 6.70 while the high ECa segment has the mean pH level (6.4-7.1) of 6.80. All the mean pH recorded falls in the neutral (6.6-7.3) category (Horneck et al., 2011). The measured pH at all the zones is within the required concentration (6.0–7.5) which favours the availability of soil nutrients for most crops (Moral and Rebollo, 2017; Khadka et al., 2018). The soil nutrients would be available for cacao consumption since pH values are close to either side of neutrality, not likely subjected to Al3+ toxicity which can limit root growth, restraining access to soil water and nutrient since the pH is considerably above 5.5 (FAO, 2008; Ribeiro et al., 2013; Botta, 2015). Soil EC: The mean soil electrical conductivity value (30-180 µS/cm) in the farm is 62 µSc/m. The low ECa section (30-70 µS/cm) has an average conductivity of 48 µS/cm, and those in the moderate class (30-80 µS/cm) have an average value of 54 µS/cm. The most conductive section (50-180 µS/cm) has the highest conductivity (90 µS/cm). There is the presence of more conductive ions in the high ECa region than in the other sections. EC values measured in soil samples were regarded as non-saline (Botta, 2015). The measured EC values were in a low category (˂1.0 dS/cm) which is suitable for optimum plant growth, in essence, the cacao trees can uptake the soil solution without resulting in water stress (Horneck et al. 2011). Available phosphorus: The average concentration of available phosphorus across all the investigated sections (6.0-12.71 mg/kg) is 8.89 mg/kg. Region of low ECa has the highest mean concentration of available P (9.52 mg/kg) and its concentration is between 8.32 mg/kg and 12.71 mg/kg. Its mean proportion in the moderate ECa areas (7.32-10.51 mg/kg) is 8.32 mg/kg. The least mean concentration (8.23 mg/kg) was noticed in the high ECa region (6.0-10.51 mg/kg). The mean concentration of available P (8.89 mg/kg) across the whole farm was below the critical limit (12 mg/kg) needed to maximize crop productivity (Horneck et al., 2011; van Vliet et al., 2015), and classified as low category (<20). Acidity of soil: The average acidic cation across the farm (0.64-1.20 cmol/kg) is 0.82 cmol/kg. Zone of low ECa (0.64-0.96 cmol/kg) had a mean concentration of 0.78 cmol/kg, moderate ECa segment (0.64-1.04 cmol/kg) had a common concentration of 0.85 cmol/kg. Its concentration in high ECa portion stretched from 0.64 to 1.20 cmol/kg with an equal strength of 0.87 cmol/kg. Soil acidification in the high ECa was ranked highest due to the preferential consumption of base cations by cacao trees and organic acid being derived from the decomposition of litter (Watanabe et al., 2015). It is not a useful nutrient for plant survival but it could be toxic to plants if the pH is below 5.5 (Botta, 2015). The pH values across the soils were within the neutral level (Horneck et al., 2011), therefore acidic cation content is not harmful to the growth of the cacao plant. Soil Calcium Content: The average concentration of Ca (0.46- 3.72 cmol/kg) across the entire field was 1.29 cmol/kg. Its mean distribution in the low ECa (0.46-1.28 cmol/kg) was 0.93 cmol/kg, the moderate ECa zone (0.77-1.34 cmol/kg) had 1.02 cmol/kg while its specific proportion in the high ECa segment (1.27-3.72 cmol/kg) was 2.05 cmol/kg. There is no significant difference in the concentration of Ca in the low ECa (0.93 cmol/kg) and moderate ECa (1.02 cmol/kg) sections but twice their values were noted in the high ECa segment (2.05 cmol/kg). These mean values fall within the low category (˂5.0) class (Proffitt, 2014). Botta (2015) and White and Broadley (2003) reported that the deficiency of Ca is not a common phenomenon but its excessive can restrict adequate growth. Its deficiency is noted in soils having low base saturation and high acidic content (White and Broadley, 2003; Horneck et al., 2011). Magnesium Content: Magnesium concentration on the average was 0.76 cmol/kg in the low ECa (0.25-1.97 cmol/kg), mean distribution in the moderate ECa zone (0.37-1.38 cmol/kg) was 0.94 cmol/kg while the most electrically conductive zone (0.58-2.65 cmol/kg.) has its mean concentration to be 1.37 cmol/kg. The average concentration computed from the entire cacao soils (0.25-2.65 cmol/kg) was 0.99 cmol/kg. The mean proportion of Mg in low ECa and moderate ECa regions was within the low class (˂1.0 cmol/kg); the moderate category (1-5 cmol/kg) was noted in the high ECa (Proffitt, 2014) which is approximately twice and one-half the contents in the low ECa and the moderate ECa sections. Thus, the quantity of Mg content in the high ECa region plays a greater role in photosynthesis because it was an essential element for chlorophyll pigment (Botta, 2015). Potassium Content: Potassium content in all soil samples ranged from 0.14 cmol/kg to 0.44 cmol/kg indicating that its distribution was categorized between low (˂0.4 cmol/kg) and medium (0.4-0.6 cmol/kg). Its mean value is 0.27 cmol/kg was classified to be low by Horneck et al. (2011). Region of low ECa has its specific potassium concentration (0.14-0.44 cmol/kg) to be 0.28 cmol/kg in the low category. Similar mean concentrations were observed in the moderate and high ECa sections to be 0.24 cmol/kg and 0.30 cmol/kg respectively classified as low. The quantity of potassium available in high ECa for plant consumption was greater than those from other regions. Thus aiding a better regulation of nutrient uptake, water, flowering, and seed-bearing, and ensuring resistance to stress (Wodaje and Abebaw, 2014; Botta, 2015) invariably contributing to the productivity in that section. Sodium Content: The mean proportion of exchangeable sodium was 0.24 cmol/kg in the low ECa segment (0.2-0.27 cmol/kg). In soils of moderate ECa, its mean concentration (0.21-0.34 cmol/kg) was 0.25 cmol/kg. The mean fraction in the highly conductive segment (0.23-0.30 cmol/kg) was 0.27 cmol/ kg. These mean concentrations were classified as low (<0.3 cmol/kg) at all the ECa segments (Proffitt, 2014). The mean concentration computed in the entire farm was 0.25 cmol/kg. Sodium is not considered an essential soil nutrient for plants (Horneck et al., 2011; Botta, 2015). The high ECa section has the highest sodium content contributing to the cacao productivity than the other two segments. Cation Exchangeable Capacity (CEC) of soils: The ability of the soil to adsorb cations onto its surface is regarded as CEC. The mean cation exchangeable capacity (CEC) in the low ECa (1.92-4.53 cmol/kg) was 2.99 cmol/kg. In the moderate ECa region, it varied from 2.45 to 4.13 cmol/kg with an average capacity of 3.30 cmol/kg and in the high ECa section (2.94- 8.28 cmol/kg) has a representative value of 4.85 cmol/kg. The CEC in the low 86 Abayomi Adesola Olaojo, Michael Adeyinka Oladunjoye ECa segment was classified as low (˂3.0 cmol/kg) while the mean capacity in the moderate and high ECa sections was considered as moderate (3.0-10.0 cmol/kg) by Proffitt (2014). The clay type deduced from the mean CEC falls within the kaolinite (3.0-15.0 cmol/kg) class (Sonon et al., 2014). The high ECa region has a presence of more cations being held by the soil against leaching and retains more soil nutrients indicating higher fertility status than low CEC soils (Arévalo-Gardini et al., 2015). Percentage Base Saturation: This is the percentage proportion of basic cations in overall CEC. An increase in base saturation suggests that the soil cations are becoming more available in soil. The percentage concentration of base cation in the soils of low ECa ranged from 57.52 to 82.34% with a mean saturation of 71.80%. The section of moderate ECa (64.08-82.04%) has a mean saturation percent of 73.34% while the high ECa (78.23-85.51%) was ranked with the highest mean saturation (81.38%). High percentage base saturation signifies more fertile soil with little or no acidic cation that will hinder crop growth, buffered against acidic cation and a greater amount of basic cation for plant consumption (Sonon et al., 2014). Ca:Mg Dispersion: When the concentration of Mg is relatively high compared to the Ca, it would result in sealing the soil surface, thereby creating water run-off instead of infiltration. The ratio of Ca:Mg in the low ECa region stretched from 0.6 to 2.6 with an average ratio of 1.5. Its mean ratio was 1.2 in the moderate ECa zone (0.9 to 2.1), whereas the mean dispersion ratio in the high ECa section (0.8 to 2.7) was 1.7. Soils in this farm have Ca:Mg ratios to be less than 2 indicating that the soils are not well structured (Botta, 2015). The closest ratio to this value was 1.7 in the high ECa soil suggesting that it is fairly structured than other segments. High Mg concentration can have a negative effect on soil physical properties thereby sealing the soil surface leading to infiltration decrease, and promoting run-off leading to erosion during rainfall (Dontsova and Norton, 2001). Mg has greater hydration energy than Ca, resulting in a larger hydration radius/shell which in turn causes a higher distance of separation between clay layers with less surface attraction making it flocculate (Dontsova and Norton, 2001). Mg-dominated soils have a greater proportion of exchangeable Na accumulating on their surface than Ca- dominated soils (He et al., 2013). Exchangeable Sodium Percentage (ESP): Soils in the region of low ECa (4.99-11.17%) have a mean ESP to be 8.36%. Zone of moderate ECa has ESP in soils varying from 5.57% to 8.60% with an equitable percentage of 7.71%. The ESP in the soils of high ECa ranged from 3.26% to 7.82% and its average percentage was 5.99%. The low ECa and moderate ECa areas were classified as marginally sodic (>6%) and their aggregate is susceptible to dispersion when wet (Proffitt, 2014; Botta, 2015), therefore they tend to have poor drainage, aeration, and susceptible to erosion. Soils in the high ECa portion are categorized as non-sodic (˂6%), they are generally stable, have good aeration, and drainage and are not vulnerable to erosion. 3.4.2 Soil Nutrients Influencing the Measured Field Electrical Conductivity in the Soils A positive correlation (R) exists between the field ECa data and laboratory- determined EC (Figs. 11 and 12), having coefficients of 0.8 and 0.7 in the wet and dry seasons respectively. The coefficient of determination (R2) shows that 60.5% and 42.4% of the data were involved in the correlation during the wet and dry seasons respectively. Thus, it validates the effectiveness of the field ECa data as a useful proxy for assessing soil productivity. Figure 11. Relationship between field ECa and laboratory EC in the soils of cacao farm during the wet season Figure 12. Relationship between field ECa and laboratory EC in the soils of cacao farm during the dry season The field ECa was influenced strongly by Mg, Ca; weakly influenced by acidic cation; very weak influenced by Na, K, and an inverse relationship with available P (Figs. 13 and 14). Figure 13. Chemical constituents influencing the measured field ECa during wet season Figure 14. Chemical constituents influencing the measured field ECa during dry season A negative correlation was observed from the interaction of available phosphorus with ECa, indicating that is not contributing to the measured ECa, though moderate coefficients were generated in the wet (-0.4) and dry (-0.5) seasons (Figs. 13 and 14). Kim et al. (2007) observed that the concentration of phosphorus increases under anaerobic conditions and for a direct relationship to occur between phosphorus concentration and EC, denitrification must take place in advance before the release of phosphorus. Positive interaction was observed between the acidic cation and ECa with moderate coefficients of 0.4 and 0.6 in the wet and dry periods respectively (Figs. 13 and 14). The presence of acidic cations aids the conductivity of the media but the percentage of the cation involved in the interaction is between 87Geo-spatial sensing of apparent electrical conductivity: a leeway to agricultural soil assessment 15.9% and 30.4%. Soil acidity tends to build up hydrogen and aluminium cations in soil when the base cations are leached and replaced by aluminium or hydrogen ions (FAO & ITPS, 2015) but pH in the farm is within the tolerance range for plant growth. A strong positive relationship was noticed between calcium and ECa in the wet and dry seasons with the coefficient of 0.9 and 0.7 respectively (Figs. 13 and 14). 71.4% and 55.1% of the data were perfectly engaged in the correlation in which there was a great chunk of the ions participating in the fluid conductivity (Peralta and Costa, 2013; Heil and Schmidhalter, 2017; Medeiros et al., 2018). This suggests that Ca is one of the dominant divalent ions in soil solution as a result of its large hydrated radius responsible for its easy dislodge from soil charges-CEC (Gransee and Führs, 2013). Positive coefficients (0.6-0.7) were generated from the interaction (Figs. 13 and 14) between Mg and ECa (Korsaeth, 2005; Rodríguez-Pérez et al., 2011). The group II metals are characterised by S-P hybridisation in which the S and P electron shells overlap, thus availing the metal access to the unfilled P-subshell and finally aiding its electrical conductivity (Garcia and Damask, 1991). Magnesium has a smaller ionic radius compared to Ca, K and Na, and its hydrated radius is larger, this made magnesium to be less strongly bounded to soil charges which were responsible for higher magnesium concentration in soil solution with increasing mobility and the mobility leads to electrical conductivity (Gransee and Führs, 2013). A weak positive correlation was established from the interaction of potassium ions with the ECa, resultant coefficients of 0.3 were generated from the interaction at both seasons (Figs. 13 and 14). This indicates that it has less influence on the conductivity of the medium as deduced from the percentage (6.2-8.0%) of data involved in the interaction. Its strong adsorption onto clay surface was due to a decrease in hydrated radius compared to other cations (˂Na+˂Ca2+˂Mg2+˂Al3+), thereby reducing its mobility (Gransee and Führs, 2013; Olson-Rutz and Jones, 2018). The contribution of Na+ ions to the conductivity of soil unit was regarded as weak; its coefficient was 0.3 in both seasons (Figs. 13 and 14). 6.6% to 9.5% of the data fit perfectly, thus its influence on the conductivity of soil solution is less. UNSW (2007) reported that cations with small hydrated radii are strongly adsorbed onto clay surfaces because adsorption strength increases with decreasing the hydrated radius of the cation. Correlating the CEC with the ECa (Figs 13 and 14), a strong positive relationship was deduced with resultant coefficients of 0.8 at both seasons (Korsaeth 2005; Peralta and Costa 2013). Data interaction showed a good matched (57.4%-61.1%), suggesting a greater influence on the measured ECa. 3.5 Soil X-Ray Diffraction (XRD) Assessment XRD analysis reveals the prevailing clay mineral to be kaolinite and the occurrence of montmorillonite and nontronite as a trace. Non-clay minerals include quartz, microcline, albite, muscovite, biotite, oligoclase, corderite, and coquimbite. 3.5.1 Mineralogical Composition of Fine Fraction in the Cacao Soils Kaolinite (4.7-11.3%) is the dominant clay mineral in the low ECa section (Figs 15 and 16) with an equitable 8.0% occurring as subordinate whereas montmorillonite (0-0.1%) occurred as trace (Okunlola and Owoyemi, 2015). The mean quantity of quartz (61.1-67.2%) was 64.2% (dominant). Microcline content (14.2-24.6%) has a mean of 19.4% (subordinate). Other minerals include muscovite (2.9%-5.7%), corderite (0.4%-1.2%) and oligoclase (1.3%-5.4%). Region of moderate ECa (Figs 17 and 18) has 21.7% as the prevailing kaolinite content (14.9-28.4%) occurring as subordinate whereas nontronite (0-7.1%) occurred as the trace clay mineral. Quartz (49.2-51.2%) is the dominant mineral with a mean of 50.2%, while microcline fraction (15.3-16.1%) has an average of 15.7% (subordinate). Other minerals include corderite (0-9.3%), albite (0.4%-1.5%) and biotite (1.0%-4.0%). The high ECa section (Figs 19 and 20) has kaolinite (23.9-38.3%) as the abundant clay mineral with an average quantity of 31.1%, and montmorillonite (0-4.5%) occurred as a trace. Quartz (30.5-52.7%) has a mean percentage of 41.3% signifying an abundant fraction. Microcline (9.8-20.3%) has a common percentage of 15.1 (subordinate). Corderite and muscovite proportions varied from 0.5% to 6.4% and 0% to 10.1% respectively. Percentage contributions from oligoclase (0.4%), albite (1.7%) and biotite (1.0%) are regarded as trace in quantity. Analysis of average mineral distributions showed that the quantity of quartz decreases from the region of low ECa (64.2%) through the moderate ECa (50.2%) section down to the high ECa segment (41.3%). The quantity of microcline is highest (19.4%) in the low ECa section, intermediate (15.7%) in the moderate ECa region, and low (15.1%) in the high ECa. The quantity of kaolinite increases from the low ECa area to the high ECa segment; the kaolinite was 8% (low ECa segment), 21.7% (moderate ECa) and both contents are in the subordinate class (5-25%) whereas highest percentage of kaolinite (31.1%) was noted to be in abundant quantity (25-50%) in the high ECa zone. The ratio of kaolinite across the moderate ECa and high ECa sections was approximately three and four times respectively compared to the low ECa segment. The average quartz content in the low ECa region approximately doubles the fraction in the high ECa area. Grisso et al. (2009) reported that the EC of sand is low and sand constitutes 85% of quartz. The quartz particles are good insulator of electric current (negligible conductivity of 58 nS/m by Manoucheri, 2002) and it dominates sand and silt fractions whereas clay fraction transmits current because of their clay mineral and organic matter (Allred et al., 2008). In furtherance to this, the EC of silt was regarded as a medium while that of the clay was high (Grisso et al., 2009); kaolinite has its electrical conductivity to be 0.2 S/m (Kibria and Hossain, 2019). The conclusion reached by Revil and Glover (1998) was that the surface conductance of clay is about four higher than that of quartz; clay has a large surface area with porous pores than the sandier material and soil having high clay content has higher EC reading (Hawkins et al., 2017). The fraction with the greatest surface area controls the behaviour and performance (properties) of soil. Soil characterised with a high proportion of quartz will hold fewer soil nutrients compared to that with much more clay content. This accounted for the variation across the three segments, even when the segments were supplied with the same amount of water, the concentration of dissolved nutrients will vary, thereby impacting different plant productivity rates. Figure 15. X-ray diffraction result of soil-fine fraction in the low ECa region of cacao plot (Sample A2) 88 Abayomi Adesola Olaojo, Michael Adeyinka Oladunjoye Figure 16. X-ray diffraction result of soil-fine fraction in the low ECa region of cacao plot (Sample A3) Figure 17. X-ray diffraction result of soil-fine fraction in the moderate ECa region of cacao plot (Sample AA7) Figure 18. X-ray diffraction result of soil-fine fraction in the moderate ECa region of cacao plot (Sample AA9) Figure 19. X-ray diffraction result of soil-fine fraction in the high ECa region of cacao plot (Sample A1) 89Geo-spatial sensing of apparent electrical conductivity: a leeway to agricultural soil assessment Figure 20. X-ray diffraction result of soil-fine fraction in the high ECa region of cacao plot (Sample AA8) Figure 21. Relationship between ECa and mineral components during wet season Figure 22. Relationship between ECa and mineral components during dry season The relationship between apparent electrical conductivity and the dominant minerals (Figs. 21 and 22) showed that there is strong positive interaction with the kaolinite (0.765-0.800) whereas negative interactions with the quartz (0.878-0.884) and microcline (0.474-0.665) during wet and dry seasons. This suggests that the ECa is being influenced by the kaolinite; high ECa values denote a large quantity of kaolinite and better nutrient retention. Thus, it is possible to infer that soil derived from biotite granite gneiss with ECa of 100 µS/cm and above is adequate for effective growth of cacao plant. Conclusion The petrographic analysis unveiled the mineral constituents present in the rock, including the clay mineral that would form upon weathering, such as kaolinite. This observation aligns with the outcomes derived from the XRD analysis. The composition of minerals within the rock significantly influences the subsequent soil formation resulting from the disintegration of the crystalline rock. This connection can be utilized for evaluating soil productivity. The ECa map (Figs. 10 and 11) has proven instrumental in characterizing field conditions during both wet and dry seasons. ECa serves as an indirect method for assessing soil nutrients and water content. Notably, the soils were determined to be non-saline. Regions marked by high ECa values exhibit elevated levels of dissolved solutes, a notable concentration of clay, enhanced water retention, and valuable insights into soil permeability variation. The ECa map facilitates the grouping of areas characterized by similar soil properties, which is valuable for guiding further soil sampling and examination efforts. The mapping of soil ECa provides valuable information about changes in soil conditions and highlights areas warranting heightened attention. Soil pH significantly impacts the availability of essential nutrients for plant uptake. Notably, regions with high ECa values show elevated concentrations of Ca, Mg, K, Na, and CEC values. Additionally, available P distribution is relatively uniform across the three regions. Regions with high ECa values indicate a higher level of base saturation and non-sodic soil conditions. This favors nutrient retention, effective aeration, and drainage. On the other hand, low and moderate ECa regions tend to be sodic, leading to nutrient leaching and heightened erosion susceptibility. A notable agreement exists between the clay type determined via CEC analysis and the clay mineralogy revealed by XRD analysis (kaolinite). Soil enrichment is intricately linked to its mineral composition. Dominant minerals such as kaolinite, microcline, and quartz significantly influence soil characteristics. 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