# Spatial Distribution and Evolution of pH as a Function of Cation Exchange Capacity, Sum of Exchangeable Bases, and Organic Matter and Aluminum in the Soils of Foumban

Authors: Vivien Piercy Lotse Tedontsah; Michel Bertrand Mbog; Lionelle Bitom-Mamdem; Gilbert François Ngon Ngon; Robert Christel Edzoa; Bernard Tassongwa; Dieudonné Bitom; Jacques Etame
Journal: Applied and Environmental Soil Science
 (2023)

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## Abstract

Foumban, located in the Noun valley in western Cameroon, is a prime location for food production. However, this locality is experiencing a significant decline in productivity due to the acidity of its soil. This acidity is linked to the behaviour of certain soil properties essential for plant growth. The present work aims to study the behaviour of pH as a function of certain chemical parameters such as CEC, organic matter, exchangeable bases, and exchangeable aluminum and to map the spatial structure of the pH parameter by kriging. To achieve this objective, ninety soil samples were taken in the locality of Foumban and sent to the soil laboratory where physicochemical analyses were carried out. The results obtained were processed by statistical and geostatistical software. This made it possible to establish relationships between pH and other soil properties. The obtainedR2 results show that pH-exchangeable aluminum and pH sum of bases are strongly correlated while pH-CEC and pH-organic matter are weakly correlated. For the spatial distribution of pH, the Gaussian model was applied to fit the experimental semivariogram. The low values of the semivariogram of the pH-water contents obtained overall reflect a strong correlation of the values. The spatial structure obtained after kriging defines two groups of soils in the study area according to their acidity: acidic soils which cover about 82.4% of the study area and moderately acidic soils which occupy about 17.6% of the study area. Finally, it appears that acidity remains a real problem for the soils of Foumban. The risks of aluminum toxicity should not be overlooked. The rate of use of ammonia fertiliser in the study area should be reduced, especially in soils with a pH below 5.4. The use of strong base inputs such as calcium carbonate (CaCO3) or calcium hydroxide (Ca (OH)2) at normal doses is strongly recommended in acid soils.


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## Body

## 1. Introduction

Soil has physical, chemical, and biological properties that enable it to provide nutrients in quantity and quality in a balance appropriate for plant growth [1–5], especially for sub-Saharan African countries [6]. It is the backbone on which all high-input production systems can be built. Cultivable land is a vital resource for humanity. Declining soil fertility is often put forward as an explanation for differences in the productivity of an agricultural sector in relation to regional development goals or the income level of rural populations. Limiting acidification is a necessary condition for maintaining the balance of the major physical, chemical and biological functions of soils [7]. Fertilisation and amendment practices can contribute to profound changes in soil properties [8]. The practice of long fallow is tending to disappear in favour of short fallow and sedentary agriculture [9, 10]. In most sub-Saharan African countries, soils have low fertility and exported nutrients are not adequately replaced. As a result, yields are relatively low and land productivity declines accordingly [9, 11]. Poor land management and cropping systems are one of the causes of the decrease in soil fertility. This is mainly due to the variation in soil pH, although the contribution of bedrock to soil acidity should not be overlooked. pH is a key element of soil chemistry and determines the availability of nutrients for plants and soil micro-organisms [12, 13]. It is also a good indicator for the uptake of mineral elements by the plant [14]. One of the essential characteristics of cultivated land is its heterogeneity [15]. The distribution of a mineral element in the soil can vary greatly within a decametric distance. Such heterogeneity is a limiting factor for the proper matching of fertiliser and pesticide doses to soil characteristics. Numerous studies have shown that a uniform application of fertilisers or pesticides to a heterogeneous plot causes excesses and shortages over large areas [16, 17]. Some authors, such as Arrouays et al. [18], have coupled geostatistical methods and intercellular mapping of soil characteristics to overcome the problem of heterogeneity of chemical elements in the soil, which is difficult to manage during fertiliser adjustment phases. In Cameroon, the cultivated area was estimated at about 3.7 million hectares in 2010, which is about 10% of the cultivable area [19]. According to RADEC-MINEPAT/Ouest [20], agriculture is the main economic activity and source of livelihood for the people of Foumban. This leads to high population growth, consequently an increase in food demand, intensification of cultivation techniques, and finally the extension of crops on the territory. However, the agricultural problems linked to the acidity of Cameroonian soils are also felt in this locality. Low agricultural yields, particularly in Foumban, are a hindrance to its economic development. According to Lotse Tedontsah et al. [21], this low agricultural yield is linked to the acidity of the soil in this locality. This high acidity can influence the behaviour of other nutrients in the soil. This means that a study on the pH and means of adjusting the acidity could have a positive influence on the behaviour of these nutrients. This study will obviously take into account the contribution of the parent rock on the acidity from the different soil groups listed in the study area and then the coupling of the geostatistical and mapping method of pH essentially. The main objectives here are to study the behaviour of pH as a function of certain chemical parameters such as CEC, organic matter, exchangeable bases, and exchangeable aluminum and then to map the spatial structure of the pH parameter by kriging in order to facilitate the adjustment of calcium carbonate (CaCO3) or calcium hydroxide ((Ca(OH)2) doses in soils.


## 2. Location

The study area, which extends between the northern parallels 5°39′ and 5°52′ and the eastern meridians 10°42′ to 11°00′, covers an area of 793.27 km2 (Figure 1) and is located in the central part of the Noun valley. The study area has a humid tropical climate at high altitude. The annual rainfall is 1994.2 mm with a temperature between 22°C and 24°C. It is drained by two large catchment areas: the Mfu basin and the Nchi basin. Both show a subparallel hydrographic network. The morphology is composed of two main domains: the lower domain with altitudes between 750 m and 1250 m, consisting of small rounded peaks with fairly wide U-shaped talwegs; the upper domain with altitudes between 1250 m and 1850 m, dominated by the Nkogam massif.Figure 1
Study area. (a) Map of Cameroon, (b) western region, (c) Noun department, and (d) location and morphology of the study area.The geology of the study area consists of volcanic, plutonic and metamorphic rocks [22, 23]. The volcanic rocks are represented by basalts and trachytes, while the metamorphic rocks are made up of migmatites and gneiss, and the plutonic rocks are essentially granites (Figure 2).Figure 2
Geological map extracted from Nzolang [24].


## 3. Methods of Study

Soil samples were taken from the A horizons of the soils, which were differentiated in the field into soil units. These soil units are differentiated according to the parent material (x) on which the soil develops, the altitude (y) and the type of soil profile (z) identified using the FAO soil description guide [25]. The cross-referencing of these three entities (x, y, z) resulted in a map of the soil units from which the samples were taken. In total, ninety (90) soil samples were taken at random from the surface horizons of all soil units (Figure 3). In this study, the random sampling model according to Fiers and Coll [26] is applied because of the different constraints encountered in the field (very rugged landscape and others), making some areas of the study area practically inaccessible. The sampling device was designed to meet geostatistical concerns. The number of samples taken also takes into account the surface area of each soil unit. The geographical coordinates of each sampling point were taken using a Garmin GPS (global positioning system) to facilitate digital mapping. The number of soil samples taken also took into account the area of each soil unit. These are(i)
The soil units developed on gneiss: thirteen (13) soil samples in soil unit 1 (SU1) with an area of approximately 139.06 km2 located at altitudes between 1150 m and 1260 m and twenty (20) samples in soil unit 2 (SU2) with an area of approximately 160.23 km2 located at altitudes between 750 m to 1150 m and 1260 m to 1300 m;(ii)
The soil units developed on migmatites: seven (07) samples in soil unit 3 (SU3) with an area of about 93.03 km2 located at altitudes between 1000 m to 1150 m, three (03) samples in soil unit 4 (SU4) with an area of about 49.53 km2 located at altitudes from 1150 m to 1300 m, and four (04) samples in soil unit 5 (SU5) with an area of about 63.29 km2 at altitudes from 800 m to 1000 m;(iii)
The soil units developed on basalts: seventeen (17) samples in soil unit 6 (SU6) with an area of about 123, 42 km2 located at altitudes between 1000 m to 1200 m, fourteen (14) samples in soil unit 7 (SU7) with an area of about 97.60 km2 located at altitudes between 1200 m and 1400 m, and two (02) samples in soil unit 8 (SU8) with an area of about 22.37 km2 located at altitudes above 1400 m;(iv)
The soil units developed on trachytes: four (04) samples in soil unit 9 (SU9) with an area of about 21.10 km2 located at altitudes above 1500 m and three (03) samples in soil unit 10 (SU10) with an area of about 11.42 km2 located at altitudes of between 1200 m and 1300 m;(v)
The soil units developed on granites: three (03) samples on soil unit 11 (SU11) with an area of about 13.22 km2 located at altitudes between 1000 m and 1300 m.Figure 3
Sampling map with distribution of soil units.Once the samples were taken, they were brought to the laboratory where the physicochemical analyses were performed. The exchangeable bases were extracted from the soil with a solution of ammonium acetate (C2H3O2NH4) at pH 7. The concentrations will be done by atomic absorption spectrometry (magnesium) and by flame emission (calcium, potassium, and sodium) [27]. The CEC is obtained from a solution of ammonium acetate at PH 7 and consists of three phases: (1) Saturation of the absorbent complex with NH4+ ions and extraction of exchangeable bases; (2) washing of the soil with alcohol in order to eliminate excess NH4+ ions; (3) dosing of NH4+ by distillation after desorption from a KCL solution [28]. The pH measurement was carried out on a distilled soil-water solution for the pH-water and a soil-KCL solution for the pH in a ratio of 1/2.5 using a pH meter fitted with a glass electrode. The pH meter was previously calibrated using the standard solutions [29]. Organic carbon is determined by the Walkley and Black [30] method, which is an oxidation with potassium bicarbonate (K2Cr2O7) in an acid medium (H2SO4). The determination is done by calorimetry. The organic matter content is obtained by multiplying the organic carbon content by the Sprengel factor [31], which is 1.724 for cultivated soils and 2 for uncultivated ones.The fertility parameters referred to: sum of exchangeable cations (S) (S < 2 meq/100 g) indicates very low; 2 < S (meq/100 g) < 5 indicates low; 5 < S (meq/100 g) < 10 indicates medium; 10 < S (meq/100 g) < 15 indicates medium;S > 15 meq/100 g indicates very high [32]; cationic exchange capacity (CEC) (CEC < 5 meq/100 g) indicates very low content; 5 < CEC (meq/100 g) < 10 indicates low content; 10 < CEC (meq/100 g) < 25 indicates medium content; 25 < CEC (meq/100 g) < 40 indicates high content; CEC > 40 meq/100 g indicates very high content [32]. S is obtained by adding the exchangeable cations which are Ca, Mg, K, and Na.For the statistics, the data obtained after analysis of the samples in the laboratory were processed using Excel 2016 and IBM SPSS Statistic version 20 software. A descriptive statistical analysis was carried out on 8 original soil variables, allowing an overall comparison between the means and standard deviations for each group of soils in the study area, and the regression lines carried out using Excel version 2016 software made it possible to establish the different linear correlations between the variables in relation to pH.The spatial distribution of elements in the soil was made possible by the geostatistical method of variogram analysis and kriging using ArcGIS software version 10.1. The main tool for this analysis was the semivariogram, which described the evolution of the semivariance as a function of the distance between measurements and thus made it possible to study the spatial relationship between the data. The ordinary kriging method has been adapted in this work.


## 4. Results and Discussion

### 4.1. Classical Statistics

Table1 presents the statistics of eight (08) soil variables obtained on different soil units. Overall, all variables show a positive skewness ranging from 0.14 to 5.36, which means that the mean is higher than the median and the mode, except the sands, which show a negative skewness ranging to −0.32. When a variable has a skewness less than −1 or greater than 1, it means that the right tail of the distribution is longer than the left for a positive skewness and the left tail is longer for a negative skewness [33]. Kurtosis is also highly variable, with some values greater than 1; this deviation of skewness and kurtosis from zero means that most of these variables have a slightly abnormal distribution. The range is higher (1079.02) in the (Ca + Mg)/K variable, which reflects a very large gap between the maximum and minimum values, in contrast to the pH variable, which has a practically low range. The high value of the standard deviation from the mean in the (Ca + Mg)/K ratio shows that the values are more dispersed around the mean, in contrast to the standard deviation of the pH variable, which is lower, thus reflecting a good concentration of values around the mean. On the whole, all the variables present a rather low coefficient of variation, i.e., close to 0, except for the variable (Ca + Mg)/K whose coefficient of variation is 2.75. The low values of the coefficient of variation show that the dispersion of the values around the mean is low.Table 1
Summary statistics of the original Foumban soil variables.
StatsNumber of valuesMinimumMaximumRangeMeanStandard deviationCoefficient of variationSkewKurtosis%S9023.5075.0051.5052.9911.830.22−0.32−0.43%L905.5037.5732.0718.506.380.341.020.57%A909.5061.5052.0028.6411.050.390.660.40pH903.806.402.605.010.580.120.14−0.51OM (%)900.6211.0910.465.212.170.420.220.11SBE (méq/100 g)900.5731.8731.3010.655.890.551.281.86CEC (méq/100 g)902.4639.6837.2220.456.840.330.281.16(Ca + Mg)/K900.371079.391079.0257.91159.512.755.3629.31%S: percentage of sands; %A: percentage of clays; %L: percentage of silts; SBE: sum of exchangeable bases. CEC: cation exchange capacity; Ca: calcium; Mg: magnesium; K: potassium; OM: organic matter.


### 4.2. Variation of Some Chemical Parameters and pH in the Soil Units of the Study Area

Table2 shows that all the soils in the study area have a pH value between 4.8 and 5.12. The pH value below 5.5 (4.9 ± 0.69 meq/100 g) indicates the presence of free Al3+ ions in these soils. Therefore, an exponential increase in exchangeable Al content and Al3+ can become the most abundant cation in the exchangeable complex. According to Beernaert and Bitondo [32], all the soil units in the study area are practically acidic, although they are developed on different bedrocks. This can be justified by the work of Laplanche and Balanchier [34], which shows that the soils of the West Cameroon region, more precisely the Bamoum plateau, are clearly acidic. These are zonal soils, i.e., their characteristics are not essentially linked to the parent rock but also to environmental factors. The CEC is moderately high in all the soil units of the study area except in soil unit 7 (SU7) developed on basalts, which shows a significant difference with the other soil units with a high CEC (29.76 ± 3.68 meq/100 g) [32]. This is believed to be due to the high organic matter content in this soil unit [35, 36]. It is a relative indicator of the fertility capacity of a soil [37]. Soils with a high CEC can retain more cations and have a higher capacity to exchange them than soils with a low CEC [37]. In the case of the study area, the CEC cannot be linked to the rock because according to the work of Laplanche and Balanchier [34], the basaltic rocks of the Noun plain are very old and their soils have a rather low exchange capacity; therefore, the moderately high CEC contents would come from the high organic matter as presented in Table 2. Exchangeable bases are high in soil units 4 developed on migmatites and 7 developed on basalts. This reflects the high content of exchangeable cations in these soils [32]. Soil units 4 and 7 are located at almost similar altitude intervals (between 1200 m and 1400 m). This could favour the formation of materials with similar characteristics, as is the case with ferralitic soils that are formed on gentle slopes [38]. The (Ca + Mg)/K ratio is globally above 40 meq/100 g. This reflects a good balance between Ca, Mg, and K. Values below 30 meq/100 g reflect an acceptable but unbalanced ratio of Ca, Mg, and K. This is caused by a strong deficiency of magnesium in relation to calcium and potassium. This shows that there is an antagonism between Ca and K in the medium despite the dominance of Ca [39] and too high a Ca level can cause Ca/Mg imbalances detrimental to the soil [40]. The high levels of exchangeable bases in these two soil units show a similar character. The level of organic matter is significantly higher in soil unit 7 than that in the other soil units. This reflects the very high organic matter content of this soil unit [32]. This rate, which is higher than the norm of 2 to 3% [41], can be explained in some soils such as andosols by double protection between the latter and free aluminum [42]. In other cases, it may be due to the effect of the high altitude where the soil unit is located [43] or to the very pronounced human activity at low altitude. The low organic matter content of soils predisposes them to rapid acidification and degradation [44]. This is not the case for the soil units in the study area, as the organic matter content is above 3% overall.Table 2
Statistical table of chemical parameters and pH of different soil units.
Rock substratumSampleNumber of valuespH H20CEC (meq/100 g)SBE (meq/100 g)MO (%)(Ca + Mg)/KMeanSDMeanSDMeanSDMeanSDMeanSDGneissSU1135.050.5920.535.712.325.514.282.3348.0541.73SU2205.120.6120.326.36116.645.672.1358.23141.3MigmatitesSU374.80.3720.825.0710.784.784.721.7319.6514.01SU434.80.6124.339.4115.199.294.890.3122.4124.93SU544.90.6322.45.410.874.635.992.54130.56327.56BasaltsSU6174.80.5718.256.998.665.585.381.7143.1629.46SU7145.40.829.763.6817.14.417.63.427.9823.57SU825.10.217.6714.655.915.24.420.3622.826.13TrachytesSU945.10.5216.228.56.94.064.190.7819.0512.88SU1034.90.78162.817.953.093.390.7137.1231.01GranitesSU1134.90.5316.354.876.865.13.030.3234.2830.02SU: soil unit; %S: percentage of sands; %A: percentage of clays; %L: percentage of silts; SEB: sum of exchangeable bases; CEC: cation exchange capacity; Ca: calcium; Mg: magnesium; K: potassium. SD: standard deviation.


### 4.3. Linear Correlation between pH-Water and Other Soil Properties (CEC, OM, SEB, and Al3+)

The strongest correlations are observed in Figures4(b) and 4(d), i.e., pH-water-sum of exchangeable bases (R2 = 0.108) and pH-water-exchangeable aluminum (R2 = 0.137). Variations in the concentration of exchangeable bases and exchangeable aluminum are related to pH [8]. In soils, exchangeable cations come from the alteration of minerals in the parent material and from the organic matter present in the environment. Aluminum is a highly reactive element that is only found in nature in association with other elements. Its origin is essentially related to the parent material. Very low levels of aluminum are essential for the plant and medium to high levels inhibit the root growth [45]. The absorption of aluminum by the plant is initially a passive phenomenon. Then, above a certain concentration, the absorption becomes proportional to the quantity of aluminum present [46]. The addition of phosphate fertilisers could precipitate aluminum in the study area and reduce the risk of toxicity [38, 47] and medium to high levels inhibit root growth [45]. According to the work of Van Oort et al. [48], the increase of Al3+ in the soil is due to the weathering of low-strength aluminosilicates. In almost the entire study area, the soils have an almost invariant (acidic) pH-water, i.e., the average value is below 6, which indicates the presence of free Al3+ ions in these soils. An increase in Al3+ ions in soils increases the content of exchangeable aluminum, which can become the most abundant cation in the exchangeable complex [8]. According to the work of [49], aluminum acts as an antagonist of calcium, thus increasing the risk of aluminum toxicity in the soil. Acidic soils have a strong buffering capacity. According to the work of Julien et al. [50], during acidification, the H+ ions produced protonate the basic forms of aluminum ions. The H+ ions break the Al-O (aluminum-oxygen) bonds and aluminum gradually replaces the exchangeable Ca2+. This explains the fact that these two Figures 4(b) and 4(c) show regression lines that are almost oblique but in opposite directions. This means that the increase in exchangeable aluminum would lead to a decrease in the calcium content or even a decrease in the exchangeable base content in the soils of the study area.Figure 4
Regression lines of pH-water versus CEC, SBE, OM, and Al3+; (a) pH-water-CEC correlation; (b) pH-water-SBE correlation; (c) pH-water-OM correlation; (d) pH-water-Al3+ correlation.
(a)(b)(c)(d)Figures4(a) and 4(c) show weak correlations between pH-water-cation exchange capacity (R2 = 0.037) and pH-water-organic matter (R2 = 0.012). This means that in the study area, the pH evolution is not directly influenced by the organic matter content and the CEC of the soil. In the narrow range of pH 7.5 to pH 6, exchangeable calcium is gradually removed from organic and mineral constituents as the cation exchange capacity (CEC) decreases. Thus, a high pH positively affects CEC [8]. According to the work of Alexandre et al. [51] and Koull and Halilat [52], CEC is closely related to the organic matter content and clay content of the soil. However, the link between pH and CEC is weak as shown by the graphical coefficient of determination (R2), which reflects a low number of negative charges in the soil [53]. Indeed, among the mineral fractions, kaolinite, iron, and aluminum oxides and hydroxides have a large part of their CEC dependent on pH. As for organic matter, all its loads are variable according to the soil pH [54]. Indeed, at low pH values (pH < 5), organic matter tends to develop more positive charges and the H+ ions resulting from deprotonation cause the solubilisation of the aluminum hydroxides present in the medium and thus the toxic forms [Al(OH)2+, Al(OH)+, and Al3+] appear. In contrast, under medium acidity conditions (pH ≥ 5), the net organic colloid load in the soil remains negative [55]. In the case of the soils in the study area, most pH values are above 5, so that CEC is influenced by the organic matter content and the clay content. An improvement in CEC levels can only be achieved through regular organic matter inputs as the clay content cannot be changed [56].


### 4.4. Spatial Distribution of pH in Foumban Soils

Figure5 shows the experimental semivariogram of the pH-water parameter in the Foumban area. Its horizontally stretched ¨S¨ shape gives it a Gaussian model variogram [57]. The normalized semivariogram of the pH content shows a plateau of the order of 0.4. This shows the existence of finite variance and dimensions of the study area sufficient to describe any spatial variability of the pH-water parameter [18]. The nugget effect is 0.166 with a range of 0.58. This value of the nugget effect reflects a relatively high variability of this parameter at a scale lower than that of the sampling and that of the range shows that beyond 0.58 m, the observations are no longer similar on average. The low values of the semivariogram of the pH-water content obtained overall reflect a strong correlation of values [18].Figure 5
Experimental semivariogram of the pH parameter.The spatial structure of the pH-water parameter was obtained by the ordinary kriging method [58]. Figure 6 shows the isovalues of this parameter, which could be taken into account for the modulation of the doses of certain inputs (good adjustment of CaO) in order to respect the regional prescriptions and the thresholds not to be exceeded [59]. Two groups of soils were defined in the study area according to their acidity (Figure 6). Acidic soils (pH < 5.4) are widely represented and distributed over most of the area. They cover 82.4% of the surface of the study area, i.e., approximately 653.65 km2. Moderately acidic soils (5.4 < pH < 6), which are not very well represented, cover 17.6% of the study area for a surface area of 139.61 km2. They appear in a wide band to the east of the study area and some pockets remain to the west, south and north of the area. The variation in pH is linked to the soil management and cropping systems [60]. The acidification of these soils would be due to organic acids resulting from biological activity in the soil, which releases hydrogen ions, or to certain ammoniacal fertilisers used by farmers. The water pH of the soils studied, which is moderately acidic, is a favourable factor for market gardening [61]. Moderately acidic soils have a low to medium buffer capacity. During acidification, the H+ ions produced protonate basic negative sites [50]. Figure 6 shows that only 17.6% of the soils in the study area have a pH-water that is favourable to the good assimilation of mineral elements by the plant, and therefore do not require lime. On the other hand, 82.4% of the soils in the study area require an adjustment in lime Ca(OH)2 or limestone (CaCO3) in order to reach a pH of about 6 as desired by Laveau and Juste [62]. Because unlike the gypsum (CaSO4, 2H2O) inputs, OH− is a strong base and CO32− is a weaker base capable of tearing off the H+ ions energetically linked by covalent bonds to reactive sites [53]. Thus, instead of H+, negative charges with basic properties appear in the soil and bind the H+. These results obtained by kriging confirm the work of Lotse Tedontsah et al. [21], which shows that the low agricultural productivity in the Foumban locality is linked to the acidity of the soil. The acidity of the soils in the study area is not directly related to the rock substratum, as it does not vary significantly from one rock substratum to another but is rather related to environmental factors, which may be due to poor land management. The coupling of the geostatistical method and digital mapping proves to be better in terms of a clear appreciation of the spatial distribution of pH in the soils of this region in order to better adjust the different grades.Figure 6
Spatial variation of pHwater in the soils of the study area.


## 4.1. Classical Statistics

Table1 presents the statistics of eight (08) soil variables obtained on different soil units. Overall, all variables show a positive skewness ranging from 0.14 to 5.36, which means that the mean is higher than the median and the mode, except the sands, which show a negative skewness ranging to −0.32. When a variable has a skewness less than −1 or greater than 1, it means that the right tail of the distribution is longer than the left for a positive skewness and the left tail is longer for a negative skewness [33]. Kurtosis is also highly variable, with some values greater than 1; this deviation of skewness and kurtosis from zero means that most of these variables have a slightly abnormal distribution. The range is higher (1079.02) in the (Ca + Mg)/K variable, which reflects a very large gap between the maximum and minimum values, in contrast to the pH variable, which has a practically low range. The high value of the standard deviation from the mean in the (Ca + Mg)/K ratio shows that the values are more dispersed around the mean, in contrast to the standard deviation of the pH variable, which is lower, thus reflecting a good concentration of values around the mean. On the whole, all the variables present a rather low coefficient of variation, i.e., close to 0, except for the variable (Ca + Mg)/K whose coefficient of variation is 2.75. The low values of the coefficient of variation show that the dispersion of the values around the mean is low.Table 1
Summary statistics of the original Foumban soil variables.
StatsNumber of valuesMinimumMaximumRangeMeanStandard deviationCoefficient of variationSkewKurtosis%S9023.5075.0051.5052.9911.830.22−0.32−0.43%L905.5037.5732.0718.506.380.341.020.57%A909.5061.5052.0028.6411.050.390.660.40pH903.806.402.605.010.580.120.14−0.51OM (%)900.6211.0910.465.212.170.420.220.11SBE (méq/100 g)900.5731.8731.3010.655.890.551.281.86CEC (méq/100 g)902.4639.6837.2220.456.840.330.281.16(Ca + Mg)/K900.371079.391079.0257.91159.512.755.3629.31%S: percentage of sands; %A: percentage of clays; %L: percentage of silts; SBE: sum of exchangeable bases. CEC: cation exchange capacity; Ca: calcium; Mg: magnesium; K: potassium; OM: organic matter.


## 4.2. Variation of Some Chemical Parameters and pH in the Soil Units of the Study Area

Table2 shows that all the soils in the study area have a pH value between 4.8 and 5.12. The pH value below 5.5 (4.9 ± 0.69 meq/100 g) indicates the presence of free Al3+ ions in these soils. Therefore, an exponential increase in exchangeable Al content and Al3+ can become the most abundant cation in the exchangeable complex. According to Beernaert and Bitondo [32], all the soil units in the study area are practically acidic, although they are developed on different bedrocks. This can be justified by the work of Laplanche and Balanchier [34], which shows that the soils of the West Cameroon region, more precisely the Bamoum plateau, are clearly acidic. These are zonal soils, i.e., their characteristics are not essentially linked to the parent rock but also to environmental factors. The CEC is moderately high in all the soil units of the study area except in soil unit 7 (SU7) developed on basalts, which shows a significant difference with the other soil units with a high CEC (29.76 ± 3.68 meq/100 g) [32]. This is believed to be due to the high organic matter content in this soil unit [35, 36]. It is a relative indicator of the fertility capacity of a soil [37]. Soils with a high CEC can retain more cations and have a higher capacity to exchange them than soils with a low CEC [37]. In the case of the study area, the CEC cannot be linked to the rock because according to the work of Laplanche and Balanchier [34], the basaltic rocks of the Noun plain are very old and their soils have a rather low exchange capacity; therefore, the moderately high CEC contents would come from the high organic matter as presented in Table 2. Exchangeable bases are high in soil units 4 developed on migmatites and 7 developed on basalts. This reflects the high content of exchangeable cations in these soils [32]. Soil units 4 and 7 are located at almost similar altitude intervals (between 1200 m and 1400 m). This could favour the formation of materials with similar characteristics, as is the case with ferralitic soils that are formed on gentle slopes [38]. The (Ca + Mg)/K ratio is globally above 40 meq/100 g. This reflects a good balance between Ca, Mg, and K. Values below 30 meq/100 g reflect an acceptable but unbalanced ratio of Ca, Mg, and K. This is caused by a strong deficiency of magnesium in relation to calcium and potassium. This shows that there is an antagonism between Ca and K in the medium despite the dominance of Ca [39] and too high a Ca level can cause Ca/Mg imbalances detrimental to the soil [40]. The high levels of exchangeable bases in these two soil units show a similar character. The level of organic matter is significantly higher in soil unit 7 than that in the other soil units. This reflects the very high organic matter content of this soil unit [32]. This rate, which is higher than the norm of 2 to 3% [41], can be explained in some soils such as andosols by double protection between the latter and free aluminum [42]. In other cases, it may be due to the effect of the high altitude where the soil unit is located [43] or to the very pronounced human activity at low altitude. The low organic matter content of soils predisposes them to rapid acidification and degradation [44]. This is not the case for the soil units in the study area, as the organic matter content is above 3% overall.Table 2
Statistical table of chemical parameters and pH of different soil units.
Rock substratumSampleNumber of valuespH H20CEC (meq/100 g)SBE (meq/100 g)MO (%)(Ca + Mg)/KMeanSDMeanSDMeanSDMeanSDMeanSDGneissSU1135.050.5920.535.712.325.514.282.3348.0541.73SU2205.120.6120.326.36116.645.672.1358.23141.3MigmatitesSU374.80.3720.825.0710.784.784.721.7319.6514.01SU434.80.6124.339.4115.199.294.890.3122.4124.93SU544.90.6322.45.410.874.635.992.54130.56327.56BasaltsSU6174.80.5718.256.998.665.585.381.7143.1629.46SU7145.40.829.763.6817.14.417.63.427.9823.57SU825.10.217.6714.655.915.24.420.3622.826.13TrachytesSU945.10.5216.228.56.94.064.190.7819.0512.88SU1034.90.78162.817.953.093.390.7137.1231.01GranitesSU1134.90.5316.354.876.865.13.030.3234.2830.02SU: soil unit; %S: percentage of sands; %A: percentage of clays; %L: percentage of silts; SEB: sum of exchangeable bases; CEC: cation exchange capacity; Ca: calcium; Mg: magnesium; K: potassium. SD: standard deviation.


## 4.3. Linear Correlation between pH-Water and Other Soil Properties (CEC, OM, SEB, and Al3+)

The strongest correlations are observed in Figures4(b) and 4(d), i.e., pH-water-sum of exchangeable bases (R2 = 0.108) and pH-water-exchangeable aluminum (R2 = 0.137). Variations in the concentration of exchangeable bases and exchangeable aluminum are related to pH [8]. In soils, exchangeable cations come from the alteration of minerals in the parent material and from the organic matter present in the environment. Aluminum is a highly reactive element that is only found in nature in association with other elements. Its origin is essentially related to the parent material. Very low levels of aluminum are essential for the plant and medium to high levels inhibit the root growth [45]. The absorption of aluminum by the plant is initially a passive phenomenon. Then, above a certain concentration, the absorption becomes proportional to the quantity of aluminum present [46]. The addition of phosphate fertilisers could precipitate aluminum in the study area and reduce the risk of toxicity [38, 47] and medium to high levels inhibit root growth [45]. According to the work of Van Oort et al. [48], the increase of Al3+ in the soil is due to the weathering of low-strength aluminosilicates. In almost the entire study area, the soils have an almost invariant (acidic) pH-water, i.e., the average value is below 6, which indicates the presence of free Al3+ ions in these soils. An increase in Al3+ ions in soils increases the content of exchangeable aluminum, which can become the most abundant cation in the exchangeable complex [8]. According to the work of [49], aluminum acts as an antagonist of calcium, thus increasing the risk of aluminum toxicity in the soil. Acidic soils have a strong buffering capacity. According to the work of Julien et al. [50], during acidification, the H+ ions produced protonate the basic forms of aluminum ions. The H+ ions break the Al-O (aluminum-oxygen) bonds and aluminum gradually replaces the exchangeable Ca2+. This explains the fact that these two Figures 4(b) and 4(c) show regression lines that are almost oblique but in opposite directions. This means that the increase in exchangeable aluminum would lead to a decrease in the calcium content or even a decrease in the exchangeable base content in the soils of the study area.Figure 4
Regression lines of pH-water versus CEC, SBE, OM, and Al3+; (a) pH-water-CEC correlation; (b) pH-water-SBE correlation; (c) pH-water-OM correlation; (d) pH-water-Al3+ correlation.
(a)(b)(c)(d)Figures4(a) and 4(c) show weak correlations between pH-water-cation exchange capacity (R2 = 0.037) and pH-water-organic matter (R2 = 0.012). This means that in the study area, the pH evolution is not directly influenced by the organic matter content and the CEC of the soil. In the narrow range of pH 7.5 to pH 6, exchangeable calcium is gradually removed from organic and mineral constituents as the cation exchange capacity (CEC) decreases. Thus, a high pH positively affects CEC [8]. According to the work of Alexandre et al. [51] and Koull and Halilat [52], CEC is closely related to the organic matter content and clay content of the soil. However, the link between pH and CEC is weak as shown by the graphical coefficient of determination (R2), which reflects a low number of negative charges in the soil [53]. Indeed, among the mineral fractions, kaolinite, iron, and aluminum oxides and hydroxides have a large part of their CEC dependent on pH. As for organic matter, all its loads are variable according to the soil pH [54]. Indeed, at low pH values (pH < 5), organic matter tends to develop more positive charges and the H+ ions resulting from deprotonation cause the solubilisation of the aluminum hydroxides present in the medium and thus the toxic forms [Al(OH)2+, Al(OH)+, and Al3+] appear. In contrast, under medium acidity conditions (pH ≥ 5), the net organic colloid load in the soil remains negative [55]. In the case of the soils in the study area, most pH values are above 5, so that CEC is influenced by the organic matter content and the clay content. An improvement in CEC levels can only be achieved through regular organic matter inputs as the clay content cannot be changed [56].


## 4.4. Spatial Distribution of pH in Foumban Soils

Figure5 shows the experimental semivariogram of the pH-water parameter in the Foumban area. Its horizontally stretched ¨S¨ shape gives it a Gaussian model variogram [57]. The normalized semivariogram of the pH content shows a plateau of the order of 0.4. This shows the existence of finite variance and dimensions of the study area sufficient to describe any spatial variability of the pH-water parameter [18]. The nugget effect is 0.166 with a range of 0.58. This value of the nugget effect reflects a relatively high variability of this parameter at a scale lower than that of the sampling and that of the range shows that beyond 0.58 m, the observations are no longer similar on average. The low values of the semivariogram of the pH-water content obtained overall reflect a strong correlation of values [18].Figure 5
Experimental semivariogram of the pH parameter.The spatial structure of the pH-water parameter was obtained by the ordinary kriging method [58]. Figure 6 shows the isovalues of this parameter, which could be taken into account for the modulation of the doses of certain inputs (good adjustment of CaO) in order to respect the regional prescriptions and the thresholds not to be exceeded [59]. Two groups of soils were defined in the study area according to their acidity (Figure 6). Acidic soils (pH < 5.4) are widely represented and distributed over most of the area. They cover 82.4% of the surface of the study area, i.e., approximately 653.65 km2. Moderately acidic soils (5.4 < pH < 6), which are not very well represented, cover 17.6% of the study area for a surface area of 139.61 km2. They appear in a wide band to the east of the study area and some pockets remain to the west, south and north of the area. The variation in pH is linked to the soil management and cropping systems [60]. The acidification of these soils would be due to organic acids resulting from biological activity in the soil, which releases hydrogen ions, or to certain ammoniacal fertilisers used by farmers. The water pH of the soils studied, which is moderately acidic, is a favourable factor for market gardening [61]. Moderately acidic soils have a low to medium buffer capacity. During acidification, the H+ ions produced protonate basic negative sites [50]. Figure 6 shows that only 17.6% of the soils in the study area have a pH-water that is favourable to the good assimilation of mineral elements by the plant, and therefore do not require lime. On the other hand, 82.4% of the soils in the study area require an adjustment in lime Ca(OH)2 or limestone (CaCO3) in order to reach a pH of about 6 as desired by Laveau and Juste [62]. Because unlike the gypsum (CaSO4, 2H2O) inputs, OH− is a strong base and CO32− is a weaker base capable of tearing off the H+ ions energetically linked by covalent bonds to reactive sites [53]. Thus, instead of H+, negative charges with basic properties appear in the soil and bind the H+. These results obtained by kriging confirm the work of Lotse Tedontsah et al. [21], which shows that the low agricultural productivity in the Foumban locality is linked to the acidity of the soil. The acidity of the soils in the study area is not directly related to the rock substratum, as it does not vary significantly from one rock substratum to another but is rather related to environmental factors, which may be due to poor land management. The coupling of the geostatistical method and digital mapping proves to be better in terms of a clear appreciation of the spatial distribution of pH in the soils of this region in order to better adjust the different grades.Figure 6
Spatial variation of pHwater in the soils of the study area.


## 5. Conclusion

The main objective of this work was to study the evolution of pH-water as a function of organic matter, CEC, sum of exchangeable bases and exchangeable aluminum. The data obtained in the laboratory and processed by statistical methods showed that there is a strong correlation between pH-water-exchangeable aluminum and pH-water-sum of bases. An increase in exchangeable aluminum would cause a decrease in calcium content, which in turn would cause a decrease in exchangeable bases. The pH-water was not strongly influenced by the organic matter and the CEC, but the organic matter correlates positively with the CEC of the soil. The geostatistics showed that the Gaussian model was applied to fit the semivariogram. The characteristics of the semivariogram (range, nugget effect, and sill) showed a strong correlation between the values, and the dimensions of the study area are sufficient to describe any spatial variability of the pH-water parameter. The spatial structure of pH obtained by the ordinary kriging method showed that 82.4% of the soils in Foumban are acidic (pH < 5.4) and cover an area of about 653.65 km2. The moderately acidic soils (5.4 < pH < 6) cover 17.6% of the study area for an area of 139.61 km2. The present study allows us to understand that 82.4% of Foumban’s soils have high acidity and therefore high toxicity risk. It is strongly recommended to use Ca(OH)2 lime or limestone (CaCO3) to bring the pH into the range favourable to plants and microorganisms. It is necessary for a future work in the study area to carry out a mineralogical study on soil samples in order to have a precise idea of the quality of its soils and to facilitate a significant improvement in fertility and then to map each nutrient present in the soil in order to promote precision agriculture.



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*Source: 5172804-2023-02-13.xml*