Soil nutrition, microbial composition and some selected associated P n S enzymes under charcoal production sites of derived Savanna, Nigeria

doi:10.21203/rs.3.rs-3970781/v1

Soil functions as the active force managing diverse biogeochemical processes in tropical forest ecosystem, which include the storage and recycling of nutrients, as well as the decomposition of organic matter. Anthropogenic activities, particularly deforestation with a focus on charcoal production, have substantially disrupted these processes, leading to notable changes in microbial activities, enzyme functions, and the availability and soil nutrient status of the derived savannah in southwestern Nigeria. While there is increasing recognition of charcoal’s impact on soil properties, there remains a noticeable research gap in understanding its specific effects on some associated soil microbial properties, soil enzymes and micronutrients in charcoal production site. Our investigation focuses on assessing soil nutrition, microbial composition and some selected associated P and S enzymes under charcoal production sites of derived Savanna, Nigeria. Soil samples were systematically collected at depths of 0–15 cm, 15–30 cm, and 30–45 cm in locations associated with charcoal production (CPS) and non-production sites (NPS). The objective was to assess the microbial biomass content in phosphorus, activity levels of microorganisms in soil, focusing on their production of phosphorus and sulfur enzymes, and to examine the overall nutrient release in these diverse environments. The findings revealed Biomass phosphorus (B_p), Phosphatase (Pho), Thiosulfate dehydrogenase (Tsd), Dimethyl sulfoxide reductase (Dsr), and micronutrients (Mg, Zn, Cu, Co, Fe) were significantly higher in CPS than in NPS. Phytase (Phy) followed a consistent trend at both sites with significant differences among means. Except for copper (Cu), the cobalt (Co), iron (Fe), manganese (Mn), and zinc (Zn) concentrations declined as the soil depth increased in the CPS and NPS across the three locations. This indicates that charcoal production sites in the derived savannah forest of southwestern Nigeria have a significant impact on soil properties and microbial activities. The higher levels of Bp, Pho, Tsd, and Dsr in CPS suggest increased microbial activity and nutrient availability compared to NPS. Additionally, the variation in micronutrient concentrations with soil depth indicates differences in nutrient distribution and availability between the two sites. These findings underscore the importance of further research to fully understand the effects of charcoal production on soil ecosystems and to develop sustainable management practices that mitigate these impacts.

Biological sciences/Biochemistry

Biological sciences/Ecology

Charcoal production

Deforestation

Microbial Composition

P and S Enzymes

Soil health

Soil nutrition

Agriculture, vital for human survival through extensive food production, has proven effective but has also led to widespread land degradation due to expanding farming areas. This large-scale change contributes to the depletion of forest ecosystems, crucial carbon sinks key in reducing CO₂ emissions and other greenhouse gases (GHGs) [1]. Deforestation, resulting from practices like burning or felling trees, annually clears a significant portion of the forest, exacerbating land degradation and compromising soil health, thereby restricting crop yields and posing challenges to global food security.

In an ecosystems prone to occasional wildfires, charcoal becomes a common component in soils, as highlighted by studies [2, 3, 4, 5]. Moreover, in regions where charcoal production is prevalent, its use has seen a faster growth rate compared to firewood consumption, emerging as a significantly larger component of overall wood energy consumption in Africa and South America [6]. Despite reported concerns about deforestation and forest degradation linked to charcoal production in many countries [7], detailed examinations of German soils revealed that charcoal accounted for approximately 45% of the organic carbon content [8]. Conventionally, charcoal generated from most brick kilns is produced in an oxygen deprived environment, which leads to the production of incomplete combustion waste products such as methane. As a result, the production of charcoal releases greenhouse gases such as such as carbon dioxide (CO₂) and methane (CH₄) which have an impact on global warming.

The burning process in charcoal production has been recognized as a potential factor causing the demise of microorganisms, ultimately leading to a reduction in microbial biomass [9, 10]. Investigations report a decline in microbial biomass in the lower layer following the burning process, initiating alterations in the microbiome composition, characterized by a decrease in Fungi and an elevation in Bacteria, specifically actinomycetes [11]. In addition, the microorganism plays a key role in an environment's micronutrient cycle and solubilizes or mobilizes precipitated nutrient forms to increase bioavailability of micronutrients [12]. Moreover, by releasing exudates and oxygen, plants alter the composition and diversity of microbial species in the rhizosphere, thus indirectly affecting enzyme activity. Earlier studies have shown that plants exert direct influence on soil enzyme activities by secreting external enzymes. Furthermore, plants indirectly modulate soil enzyme activities by controlling the amount of aboveground litter [13]. The activities of the microorganisms important for Carbon (C ), Nitrogen (N), Phosphorus (P) and Sulphur (S) cycling are also reflected in soil enzymes [14, 15].

In savanna regions, nutrient deficiencies, especially N, P and S are commonly observed [16], often attributed to reduce mineralization rates during the rainy season. Rapid nutrient cycling in grassland ecosystems contributes to further soil depletion. Additionally, specific regions may experience micronutrient deficiencies due to soil type or limitations in parent material. Recognizing the spatial and temporal variations in the physiochemical properties of savanna soil is crucial, influenced by factors such as rainfall patterns, slope gradients, vegetation composition, land use practices, and fire frequency [17]. Understanding this diversity is vital for assessing ecosystem functioning, implementing effective land management strategies, and promoting conservation agriculture practices in savanna areas. However, despite the increasing recognition of the substantial influence of charcoal on soil characteristics, there is a noticeable lack of research on its precise effects on microorganisms, microbial biomass phosphorus, soil P and S enzymes, as well as micronutrients. There are limited or no data on the impact of charcoal production on soil nutrient status, enzyme activity and microbial communities in this study area. Therefore, this study aims to investigate the influence of charcoal production on microbial composition, enzyme activity, nutrient status and availability as well as biomass phosphorus from a derived savannah area in Nigeria.

2.1 Experimental site

The study was conducted in three specific locations, as detailed by Adeyemo et al., [18], namely Ìrèle, Òkè-Àkò, and Ìpaò, situated in the Ikole local government area of Ekiti State, Southwestern Nigeria. These locations span from 7° 57' 22'' N to 5° 32' 52'' E of Greenwich Meridians and are positioned within the rainforest belt of the tropics. The local climate is characterized by a rainfall range of 120 to 170 mm and temperatures between 24.9 to 27.4°C. The prevailing soil texture in the study area is sandy clay loam alfisol. Ìrèle, Òkè-Àkò, and Ìpaò are well-known in the Ikole region of Ekiti for their active participation in charcoal production. The soil type is classified as Afisol, isohyperthermic, Oxic, paleustalf, and clayey skeletal, which are found in tropical environments in arid to humid regions beneath hardwood deciduous forest canopy.

2.2 Experimental design and Soil sample collection

Soil augers and core sampler were used to sample layers (0–15 cm, 15–30 cm, and 30–45 cm) at the three locations and at two sites: namely a charcoal production sites (CPS) and non-charcoal production sites (NPS). Each combination of CPS and NPS was situated at a distance of only 100 meters from each other using simple random sampling method. One hundred and eight (108) samples were collected from three different locations, totaling 12 sampling points using a manual soil auger equipped with a 20 cm core sampler with an internal diameter of 6 cm for efficient soil sample collection and pulverization. Subsequently, these samples were combined to create 54 treatment combinations, initially structured in 3 × 2 × 3 repetitions, which were later streamlined to three repetitions. Respective soil sample were carefully placed in a polythene bag and labeled with location descriptions for clear identification. Soil samples were air dried and protected from direct sunlight before being sieved through a 2 mm soil sieve and stored in a cool and ventilated place prior to laboratory analysis.

2.3 Analysis of soil microorganisms

Total bacteria counts and enumeration was done using the Standard Plate Count Method. Approximately 1 g of each of the samples was thoroughly mixed with 10ml of water. One milliliter (1ml) portion of the samples were pipette in test tube and serially diluted in another set of test tube containing 10ml of sterile distilled water to dilution factor 107`. They were plotted on Nutrient agar (NA) for bacterial culture and Potato dextrose agar (PDA) for fungal culture. NA plates were incubated at 37◦C for 24hrs while PDA plates were incubated at 25◦C for 48hrs. Extraction, purification and enumeration of nematode was done by careful mixing was necessary in order to avoid harm to the organisms during the extraction of Nematodes from newly harvested soils. Sieving of approximately 100 g soil was used to remove coarse materials, e.g. debris, stones and roots from the ground. Then the sieved soil was gently spread into a thin layer on the tissue placed in the sieve and placed on the table. In order to saturate the soil, 100 ml of water has been carefully introduced into a sieve. By allowing Nematodes to move freely without affecting the water, the tissue acted as a barrier to water contamination. Samples were kept under laboratory conditions for 48 hours, with continuous monitoring and additional water to be added if necessary. The sieve was removed after 48 hours, allowing water to drain from the soil into the container for about 15 seconds. The drained water was transferred to the test tube, along with additional washing. The Nematodes were placed in a test tube with approximately 5% chloroform and left to settle for an hour. The supernatant, comprising about 2/3 water, was discarded, and the remaining solution was carefully poured into a counting dish. They were examined under a stereomicroscope, counted, and the results were duly reported after allowing the Nematodes to settle for a few minutes. Nematodes were then isolated and identified using a guide by Jonathan [19] for the most common genera of plant-parasitic Nematodes.

2.4 Analysis of Microbial biomass phosphorus

2.4.1 Soil sample fumigation method

Ten gram of moist soil was put in a 50 ml beaker and the beaker was placed in a desiccator. In order to avoid desiccation of soil samples during fumigation, the desiccator has been lined with wet tissue paper. In the same desiccator, a further 50 ml beaker containing ethanol free chloroform and boiling chips was added. The desiccator was covered and evacuated by a vacuum pump as the chloroform boiled vigorously for 5 minutes. Evacuation was repeated 3 times in 15 minutes to allow the air to pass back into the filtration chamber and facilitate chloroform distribution throughout the soil. Evacuation of the desiccator was performed a fourth time, and 2 minutes after the chloroform began to boil very strongly. The valve of the desiccator had been closed and the desiccator had been placed in the dark for 5 days. Next, another 10 g (un-fumigated) soil was split into two parts, in which KH₂PO₄ of a spike of 250 µg was augmented into one part of the non-fumigated samples placed in a 50 ml beaker and placed in a separate desiccator. Before fumigation, this desiccator had been stored in a dark cupboard. After five days of fumigation, the chloroform and the tissue paper were removed and the desiccator was evacuated for 3 min for eight times allow air to pass into the desiccator after each evacuation to remove the chloroform. After 5 days of fumigation, the chloroform and tissue paper were removed and the desiccator was evacuated for 3 minutes 8 times, allowing air to pass through the desiccator after each evacuation to eliminate the chloroform.

2.4.2 Extraction Method

The Bray 1 Method [20] was employed to ascertain microbial biomass phosphorus in the soil. Duplicate samples of 10 grams each were measured and placed in centrifuge tubes. Subsequently, 50 ml of Bray 1 solution (0.03 M NH₄F + 0.025 M HCL) was introduced. The resulting solutions underwent a 5-minute shaking period on a mechanical shaker, followed by centrifugation at 2500 rpm for 5 minutes. The suspensions were then filtered through a No. 42 Whatman filter paper into 50 ml Erlenmeyer flasks. To determine the phosphorus concentration in the extracts, the Murphy and Riley Method was employed. For this, a 1 ml aliquot of the sample was pipetted into a 50 ml volumetric flask, and 8 ml of a solution containing concentrated sulfuric acid, ammonium molybdate, potassium antimony tartrate, and ascorbic acid was added. The solution was filled to the brim of the 50 ml volumetric flask with distilled water. Subsequently, the concentration was determined using a spectrophotometer at a wavelength of 882 nm.

2.5 Soil enzyme activities

2.5.1 Phosphorus enzymes

The assessment of phytase (Phy) activities were analyzed according to the ammonium molybdate method used by Sanni et al., [21], wherein the phosphorus rate was gauged through the elevation of absorbance at 700 nm. In the test tubes, 1 ml of the enzyme solution and 2 ml of the substrate solution were mixed, followed by an incubation at 37°C for 30 minutes using a controlled Gallenhamp water bath. To halt the reaction, 1 ml of 15% w/v trichloroacetic acid (TCA) was added, and color development ensued with the addition of 1 ml of the colour reagent. To determine soil phosphatase (Pho) activities. About two 2-gram portions was weighed from the soil sample and transfer them into screw-cap tubes labeled as "test" and "soil blank." Additionally, another screw-cap tube was designated as the "reagent blank." Following that, 5 ml of a 0.5 M CaCl₂ solution was pipetted into each of the three tubes, and thorough shaking ensued. For the tubes labeled "test" and "reagent blank," 1 ml of PNPP solution was pipetted, while 1 ml of phosphate buffer was introduced into the "soil blank" tube as a control. Subsequent to these steps, an incubation period of one hour at 37°C was instructed for all three tubes. The subsequent actions included transferring 4 ml of the liquid from each tube into labeled 16 × 100 mm test tubes, with caution given to avoid sediment transfer. These test tubes were then subjected to centrifugation for 5 minutes at 2500 rpm. Following centrifugation, 3 ml of the supernatant was transferred into clean test tubes, with a re-centrifugation step advised if the liquid displayed any cloudiness. Instructions were given to set the spectrophotometer wavelength to 440 nm, adjusting the absorbance to zero using the "soil blank" tube. The absorbance readings for the "test" and "reagent blank" tubes were then to be read and recorded. Additionally, the absorbance was to be set to zero using a blank tube containing 3 ml of CaCl₂, and the absorbance values for the prepared standards were to be read and recorded. The final step involved plotting the absorbance against concentration to create a standard curve.

2.5.2 Sulfur enzymes

The thiosulfate-oxidizing enzyme activities were analyzed using the method described by Trudinger [22]. The standard reaction mixture (1 mI) for measuring thiosulfate dehydrogenase (Tsd) activity contained 25 mM Tris-HC1 (pH 7.5), 1 mM K₃Fe (CN)₆, 1 mM Na₂S₂0₃, and enzyme. The measurements, starting with the addition of thiosulfate, were carried out at room temperature. The reduction of ferricyanide was gauged at 420 nm using a spectrophotometer (HP 8524A diode array spectrophotometer), with an extinction coefficient of 1.0 mM 1 cm^− 1. The unit of activity (U) was defined as 1 g/mol of ferricyanide reduced per minute. The unit of activity (U) was defined as 1 g/mol of ferricyanide reduced per minute. Tris-HCl buffer had been replaced by 50 mM phosphate buffer for the determination of enzyme activity at reduced pH values. The pH values were adjusted with 0.1 mg NaOH or 0.1 mg H₂SO₄, and both before and after enzyme activity was determined. Different amounts of thiosulfate have been introduced into the reaction mixture in kinetic studies. To determine Dimethyl sulfoxide reductase (Dsr) assay, the procedure involved the using PNPP (p-nitrophenyl phosphate) as a substrate to assess phosphatase activity, while DMSO reductase relied on DMSO (dimethyl sulfoxide) as its specific substrate. Hydrolysis of PNPP resulted in the release of p-nitrophenol, producing a yellow colour that is quantified at 440 nm. In contrast, the reduction of DMSO did not directly produce a coloured product. Phosphate buffer served as a control for phosphatase activity, while for DMSO reductase, various controls were utilized based on the chosen method, such as buffer only or heat-inactivated enzyme. The calculation of the increase in absorbance over time was used to determine PNPP activity. For DMSO reductase activity, measurement options included the direct reduction in DMSO absorbance at 243 nm or the employment of colorimetric or fluorometric detection in a coupled enzymatic reaction.

2.6 Micronutrients analysis

The soil samples were sieved after being repeatedly crushed with a mortar and pestle. Using a 2 mm soil sieve, the smaller particles were extracted. Two grams of soil samples were weighed and placed in a beaker, 20 cm³ of aqua-regia and 10 cm³ of 30% H₂O₂ were added. The addition of H₂O₂ was carried out gradually to prevent any potential overflow that would cause material loss. The beakers were covered with a watch glass, and heated for 2 hours as the temperature rose to 90°C. Filters were used to separate the insoluble solid from the supernatant liquid and the volume was increased to 100 cm³ using distilled water and the digested sample was analyzed for the presence of heavy metals with the aid of an AAS instrument.

2.7 Statistical analysis

The Minitab 17.0 edition's general linear model was used to analyze the variance in the acquired data. Data collected on the main and interactive effects of charcoal production sites, depth and location in measured and selected soil parameters were examined. The results were presented in tables and, to demonstrate the substantial differences of means obtained at 5% probability level of confidence, Tukey HSD was used as an experimental post-hoc test. Microsoft Excel 2016 was used to calculate the treatment means and standard errors.

3.1 Soil physical and chemical properties

The predominant soil type is Alfisol, comprising sand (60.74, 58.95, and 57.42 %), clay (28.26, 27.93, and 27.25 %), and silt (11.00, 13.11, and 15.33 %), with sand being the most abundant, followed by clay and silt. At Ìrèle and Ìpaò, the soil pH was strongly acidic, while it was moderate for Òkè-Àkò. Below the critical value of 10 mg/kg, Ìrèle exhibited the lowest available phosphorus level, whereas Òkè-Àkò and Ìpaò showed significant increases. Nitrogen and potassium (exchangeable) levels exceeded the critical levels of 1 mg/kg and 0.2 cmol/kg, respectively, although calcium and magnesium levels were lower, with the highest values recorded in Òkè-Àkò, significantly differing from the other two locations. The cation exchange capacity (CEC) was lower than the recommended range of between 10-20 cmol/kg, and exchangeable acidity (EA) was higher than the critical limit of 1.0 cmol/kg. At all locations, the base saturation level was slightly above the critical limit of 50 % and there were low levels of trace elements including copper, zinc, iron as well as manganese. Our previous work [18], further present the detailed physical and chemical properties of soil samples taken from three locations.

3.2 Precipitation and Temperature of the research area.

The presentation of precipitation and temperature patterns in the study area is outlined by [18]. The cumulative rainfall, measured in millimeters, during the period from April to August 2018 at the research site is detailed as follows: 120 mm in April, 152 mm in May, 168 mm in June, 170 mm in July, and 131 mm in August. Concurrently, the average temperatures, expressed in degrees Celsius, were recorded as 27.4 °C in April, 26.9 °C in May, 26.2 °C in June, 25.8 °C in July, and 24.9 °C in August.

3.3 The Interaction effect of sites and location on specific phosphorus and sulfur enzymes, and soil nutrient status.

Significant interaction effects between site and location were observed for the activities of phosphorus and sulfur cycling enzymes (Table 1). The recorded interaction effects were significant and varied both in size and direction of their response. Notably, phosphatase (Pho) levels were significantly higher at NPS in Òkè-Àkò and Ìpaò than sulfur enzymes. However, the peak value (2.94 mg/ml/min) was observed at NPS in Òkè-Àkò. Thiosulfate dehydrogenase (Tsd) exhibited significant site-by-location interaction effects, with the highest value recorded at NPS in Ìrèle and the lowest activity level at NPS in Òkè-Àkò, although the variation was insignificant. The recorded interaction effects were both significant and diverse, involving variations in magnitude and direction of response. Additionally, Dimethyl sulfoxide reductase (Dsr) was found to be significantly higher at CPS in Ìpaò, while low activity levels of (0.80 and 0.68 µg/ml/min) were observed at NPS in Òkè-Àkò and Ìpaò, respectively. Lastly, phytase (Phy) activity remained consistent across both sites and depths but showed significant variations among means.

Significant interaction effects between site and location were observed regarding the soil nutrient status, as indicated in Table 1. Copper and cobalt were found to be significantly highest at CPS in Òkè-Àkò, however similar trends occurred for Iron in Ìpaò. In contrast, significantly lowest values were recorded for cobalt, and zinc in Ìpaò at NPS.

3.4 Interaction effect of sites and depth on selected phosphorus and sulfur enzymes, and soil nutrient status.

With the exception of the 30-45 cm soil depth, which displayed lower values (1.78 & 0.84 µg/ml/min) at NPS for Tsd and Dsr, respectively, Table 2 highlights significant interaction effects between site and depth for the selected phosphorus and sulfur enzymes. Particularly noteworthy are the interaction effects observed, such as Tsd showing marginally higher values at the 0-15 and 30-45 cm soil depths at NPS and CPS, respectively, with the highest value recorded at the 0-15 cm soil depth at NPS. Additionally, Pho exhibited significant variation, with the highest activity at the 15-30 cm soil depth at CPS and the lowest activity at the 15-30 cm depth at NPS. Furthermore, at CPS, Cobalt, Iron, and Zinc exhibited their highest concentrations at the 15-30 cm soil depth, while the lowest amount of copper was observed at the 30-45 cm depth. Conversely, at NPS, zinc showed its significantly lowest concentration at the 30-45 cm soil depth. Interestingly, copper was found to be significantly highest at the 30-45 cm soil depth at CPS.

3.5 Interaction effects of locations and depth on selected P and S enzymes, and nutrient status

Significant interaction effects between location and soil depth were noticed in soil P and S enzymes (Table 3). Particularly, at the 0-15 cm soil depth, significantly higher activity levels (3.32 mg/ml/min & 9.41 µg/ml/min) were observed for Pho in Òkè-Àkò and Tsd in Ìrèle, respectively. A similar pattern was observed at the 30-45 cm soil depth, where Ìpaò recorded the highest activity levels for Dsr (1.78 µg/ml/min) and at Ìrèle for Tsd (12.51 µg/ml/min) at 15-30 cm depth. However, at the 30-45 cm depth, notably lower values were observed at Ìrèle for phosphatase and Òkè-Àkò for both Tsd and Dsr activity. Phytase activity remained consistent across the locations and depths but exhibited significant variations among means. In addition, location and depths showed significant interaction effect on the soil micronutrients. For instance, In Ìpaò, Iron and manganese were significantly highest at the 0-15 cm depth, whereas in Òkè-Àkò, both elements were found in lower amounts at the same depth. At the 15-30 cm depth, copper and cobalt were significantly highest, but they were located in different locations (Ìpaò & Ìrèle) respectively. Zinc exhibited the highest amount at the 0-15 cm soil depth in Ìrèle and the lowest amount at the 30-45 cm depth in both Ìrèle and Òkè-Àkò.

3.6 Interaction effects of charcoal production sites by location by soil depth on selected P and S enzymes, and soil nutrient status

At CPS, Pho, Tsd, and Dsr activity increased across all locations with increasing soil depth, except in Òkè-Àkò where it decreased (Table 4). Phy showed a consistent trend across all locations and depths at both CPS and NPS. For soil nutrients, Fe decreased across locations and depths. However, Co and Zn followed similar trends, except in Òkè-Àkò. Cu and Mg activity increased across locations with increasing soil depth, but Mn showed no significant difference. At NPS, Cu, Co, and Fe increased across two locations with increasing depth, with the exception in Ìpaò. Mn and Zn decreased down the soil profile at all locations, except in Òkè-Àkò. Pho, Tsd, and Dsr decreased with increasing soil depth, except Pho, which increased at Ìpaò across soil depth.

3.7 Interaction effects of charcoal production sites by location by soil depth on selected P and S enzymes, and soil nutrient status

Three-way analysis of variance presented in [Table 5], indicates the effect of charcoal production on some selected P and S enzymes at the CPS and NPS. Regardless of location and soil depth, there was no significant differences (P > 0.05) indicated in Phy and Tsd between CPS and NPS, but higher and significant differences (P < 0.05) were recorded in CPS with Pho and Dsr. The same table also showed the effect on some selected P and S enzymes at different locations. Irrespective of the production sites and soil depth, there were significant differences in soil P and S status amongst the three different locations. Phosphatase was significantly (P < 0.05) higher in Oke-Ako than in Irele and Ipao, higher significant differences were also recorded in Tsd and Dsr at Irele. Although no statistically detectable differences were indicated amongst the three locations. There were significant differences in soil P and S enzymes status as affected by soil depth (Table 4). Phytase and Dsr decreased in order of increasing soil depth with the lowest value recorded in 30 – 45 cm depth, however, Tsd and Pho with no significant differences recorded higher values in 0-15 and 15 – 30 cm depths respectively. There were significant interaction effects of site by location by soil depth recorded in P and S enzymes. The effect of charcoal production on some soil nutrient status at the CPS and NPS [Table 5]. Showed that Cu, Co, Fe, Mg and Zn were higher in CPS than NPS. In terms of location, Cu, Co, Mn was found to be higher in Ìpaò compared to the other two locations. However, Fe and Zn were found to be higher at Ìrèle with significant differences (P > 0.05). Furthermore, Cu, Co increased with increasing depth while Fe and Mn followed the opposite trend. Finally, Zn remained consistent across the soil profile showing significant difference (P > 0.05).

3.8 Correlation (r) among microbial biomass P, microorganisms, P and S enzyme, and micronutrients

The correlation analysis revealed significant associations among various parameters in the studied samples [Table 6]. The pairwise Pearson correlations, indicate the significance of these associations. Key observations include weak negative correlations between Mn and Zn, as well as Fe and Zn, indicating that fluctuations in Mn and Fe levels are not consistently linked to changes in Zn levels. Similarly, there is a weak positive correlation between Co and Zn, and Cu and Zn, suggesting that variations in Co and Cu levels are not significantly tied to changes in Zn levels. Furthermore, there are weak negative correlations between Nematode (10³) and Zn, and Biomass P and Zn, indicating that variations in Nematode (10³) and Biomass P levels do not consistently correspond to changes in Zn levels. Conversely, a strong positive correlation is detected between Dsr and Zn, and Tsd and Zn, both of which are statistically significant. This implies that as levels of Dsr and Tsd increase, Zn levels tend to increase.

Transitioning to correlations between different elements, a very weak positive correlation between Fe and Mn was observed, while weak negative correlations exist between Co and Mn, Cu and Mn, Nematode (10³) and Mn, Fungi (10⁷) and Mn, Bacteria (10⁸) and Mn, Biomass p and Mn, Dsr and Mn, Tsd and Mn, Phy and Mn, Pho and Mn. None of these correlations are statistically significant, indicating that variations in these elements are not reliably associated with changes in Mn levels. Also, a moderate positive correlation was identified between Co and Fe, and strong negative and moderate negative correlations are noted between Cu and Fe, and Nematode (10³) and Fe, respectively. These correlations are statistically significant, suggesting that as Co levels increase, Fe levels tend to increase, while an increase in Cu and a change in Nematode (10³) levels are linked to a decrease in Fe levels.

The correlations between Fe and Fungi (10⁷), Bacteria (10⁸), and Biomass P are very weak and not statistically significant. However, a moderate positive correlation is found between Dsr and Fe, and a strong negative correlation is observed between Pho and Fe, both of which are statistically significant. These results indicate that as Dsr levels increase, Fe levels tend to increase, while an increase in Phosphatase (Pho) levels is associated with a decrease in Fe levels. The analysis extends to correlations between elements such as Co, Cu, Nematode (10³), Fungi (10⁷), Bacteria (10⁸), and Biomass P, revealing various weak and very weak correlations, none of which are statistically significant. Shifting to Tsd and Fe, a moderate positive correlation is noted, indicating that as Tsd levels increase, Fe levels tend to increase. When exploring relationships with Cu, there is a moderate negative correlation with Co, a very weak negative correlation with Nematode (10³), Fungi (10⁷), and Biomass p, and a weak positive correlation with Bacteria (10⁸). While the correlation with Nematode (10³) is not statistically significant, correlations with Co and Fungi (10⁷) are, suggesting that as Cu levels increase, Co levels tend to decrease, and an increase in Cu levels is associated with a decrease in Fungi (10⁷) levels. Bacteria (10⁸) exhibits very weak correlations with Nematode (10³), Fungi (10⁷), and Biomass P, none of which are statistically significant. Moving on to Phy, there is a weak positive correlation with Co, a very weak positive correlation with Fungi (10⁷), and a very weak negative correlation with Nematode (10³). None of these correlations are statistically significant, suggesting that variations in Phy levels are not reliably associated with changes in Co, Fungi (10⁷), or Nematode (10³) levels. Phosphatase displays strong positive correlations with Cu, a strong negative correlation with Fe, and a weak positive correlation with Fungi (10⁷), all of which are statistically significant. These findings indicate that as Pho levels increase, Cu levels tend to increase, Fe levels tend to decrease, and there is a weak positive association with Fungi (10⁷) levels.

The analysis concludes with correlations involving Biomass P, showing weak positive correlations with Co and Tsd, and a marginal positive correlation with Phy. None of these correlations are statistically significant, suggesting that variations in Biomass P levels are not reliably associated with changes in Co, Thiosulfate dehydrogenase, or Phy levels. Finally, the correlations between various enzymes Dsr, Tsd, Phy, and Pho are explored, revealing weak and very weak correlations, with only the correlation between Tsd and Dsr being marginally statistically significant.

3.9 Selected soil microbial activities

The biomass phosphorus content in the designated areas exhibited its highest levels at CPS in the 0-15 cm soil depth in Ìrèle, Òkè-Àkò, and Ìpaò [Fig 1]. Subsequently, it gradually declined as the soil depth increased across the profile. Conversely, at NPS, the biomass phosphorus content demonstrated higher patterns at the 0-15 cm depth and reached its peak at the 15-30 cm soil depth in Òkè-Àkò. Then it further decreased with increasing depth across the soil profile. In the study area, the bacteria activity displayed a significant increase in the 0-15 cm soil depth, followed by a gradual decline across the three locations [Fig 2]. Specifically within the NPS, Òkè-Àkò and Ìpaò exhibited significantly higher bacteria activity at a depth of 15-30 cm. At the 30-45 cm soil depth, Òkè-Àkò registered the highest bacteria activity, while Ìpaò showed the lowest. Conversely, at CPS, bacteria were relatively abundant at the 0-15 cm depth in all three locations and at the 15-30 cm depth in Ìrèle; thereafter, it progressively decreased with an increase in depth. Fungal activity was found to be abundant at CPS across various soil depths and in all three locations [Fig 2]. Furthermore, it was observed to intensify with increasing soil depth at CPS. It was significantly higher at 0-15 cm in Ìpaò, at 30-45 cm in Ìrèle, and reached its peak at 0-15 cm soil depth in Òkè-Àkò. Conversely, at NPS, Fungi exhibited a different pattern, with a significant increase at the 15-30 cm soil depth across the three locations. Additionally, at the 30-45 cm depth, fungal activity was notably lower at Ìrèle and Ìpaò. 4Nematode abundance was found to be significantly higher at natural production sites (NPS) compared to charcoal production sites (CPS) in soil layers of 0-15 cm, 15-30 cm, and 30-45 cm across all three locations [Fig 2] . However, there was less variation in nematode abundance at the 0-15 cm soil depth, unlike Ìrèle, where the highest abundance was observed at the 15-30 cm soil depth at NPS. Similar patterns were observed in Òkè-Àkò and Ìpaò.

Soil nutrition is essential for sustaining plant growth and ecosystem functions. Microorganisms play a vital role in the recycling of organic matter (OM) and nutrients in soil. They act as repositories during the immobilization and as providers during the mineralization of labile nutrients, as highlighted by Stenström et al., [23]. Particularly in soils prone to leaching, immobilization serves as a significant mechanism for retaining nutrients. Phosphorus (P) is often scarce in terrestrial ecosystems, relying on efficient recycling mechanisms from biomass, particularly evident in mature ecosystems [24, 25, 26]. Despite the challenge of P availability, soil microorganisms significantly contribute to forest P nutrition by both mobilizing and immobilizing P [27], thereby influencing plant P nutrition through their biomass composition [28]. Similar to the patterns seen in phosphorus dynamics, the rise in soil nitrate-nitrogen levels in charcoal-enriched surface soils may be due to reduced leaching or improved biological cycling of nitrogen, as suggested by Cayuela et al., [29]. As a result, increased nitrogen and phosphorus levels could have a positive effect on the plant community in forests, similar to what has been observed in grassland systems [30, 31].

4.1 Microbial contribution to phosphorus in charcoal soil

Phosphate-solubilizing microorganisms, which rely on organic matter inputs, produce chelating organic acids to unlock phosphate bound to minerals [32]. As organic matter diminishes and microbial biomass decreases, phosphates and other nutrients are released into the soil. Indigenous microorganisms can transform insoluble phosphates into soluble forms under favorable conditions, underscoring their importance in P cycling. Acid phosphatase, which is actively released by both tree roots and microbial cells, plays a key role in phosphorus cycling and is affected by soil microclimate, as well as the presence of organic carbon and phosphorus, as noted by Saa et al, [33]. As stated in the result the bacteria activity displayed a significant increase in the 0–15 cm soil depth, followed by a gradual decline across the three locations. This suggests that the population of bacteria tends to rise after a fire due to the increased availability of carbon sources. McCormack [34] predicted a decrease in fungal abundance and an increase in bacterial abundance after the application of charcoal, which raises the pH. This increase in pH favored bacterial populations over fungal populations, as observed by Liiri et al., [35]. Fungal activity was found to be abundant at CPS across various soil depths and in all three locations. This result is supported from studies by Nishio [32] and Saito & Marumoto [36] which have demonstrated that the application of charcoal promotes the colonization of arbuscular mycorrhizal fungi in agricultural plants. Also, temperatures exceeding 50°C lead to the death of heat-sensitive microbes, with fungi being more susceptible than bacteria. Interestingly, nematode abundance was found to be significantly higher at non-charcoal production sites (NPS) compared to charcoal production sites (CPS) in soil layers. These findings suggest that the addition of charcoal could lead to changes in soil properties, resulting in reduced nematode abundances and changes in feeding type composition at kiln sites [37]. The distribution of nematode feeding types indicates that charcoal addition promotes fungi over bacteria within the litter microbial community, although bacteria still dominate. More studies have also shown that the addition of biochar can suppress infestations of plant-parasitic nematodes in soils with elevated levels of nitrogen and phosphorus [38]. The higher soil acidity and the introduction of magnesium, calcium, potassium, and manganese in charcoal soils may influence the nematode community.

4.2 Enzyme activities and nutrient dynamics in charcoal soils

Phosphorus enzyme activities, particularly phosphatase and phytase, were significantly higher in charcoal production sites (CPS) compared to non-charcoal production sites (NPS). Sulfur enzyme thiosulfate dehydrogenase also exhibited higher activity in CPS, indicating enhanced nutrient dynamics in charcoal-amended soils. In a study conducted by [39], it was noted that the utilization of biochar, a form of charcoal, led to heightened activity of thiosulfate reductase within soil samples. The researchers attributed this augmentation in activity to the presence of microbial communities within the biochar capable of generating sulfur enzymes. Similarly, in research conducted by [40], it was observed that the application of charcoal resulted in increased activity of dimethyl sulfoxide reductase within soils. The authors proposed that this enhancement in activity stemmed from the charcoal's ability to create a conducive environment for microbial communities producing sulfur enzymes.

Additionally, certain micronutrient elements such as Cu, Co, Mg, Zn, and Fe showed varied distribution patterns between CPS and NPS soils, highlighting the influence of charcoal on soil nutrient dynamics. The adsorption properties of charcoal holds these nutrients and prevent further leaching. Nonetheless, the influence of charcoal production on soil micronutrients is linked to the elevated temperatures characteristic of the process, which modify the chemical and physical properties of the soil. According to a study by Ghezzehei et al., [41], charcoal production alters the soil's pH, nutrient content, and water-holding capacity, affecting the soil micronutrients levels. Additionally, the process may lead to soil compaction, which further reduces the soil's ability to retain micronutrients. More so, Fagbenro et al., [42] stated that the volatilization of essential nutrients such as nitrogen, sulfur and potassium contributes to soil nutrient loss through burning wood for charcoal production. The study has also shown that soil pH levels are usually changed and this leads to acidity in the soil.

Micronutrients including iron, manganese, zinc, and copper all necessary for plant growth are less readily available in locations where charcoal is produced due to the high levels of acidity in the soil [43]. According to Matson [44], soil acidity is a major factor affecting micronutrient availability in soils in the tropics. Micronutrients are essential elements required in small quantities for plant growth and development. These include iron, zinc, copper, and manganese, among others. The availability and uptake of micronutrients in soil are influenced by various factors, which include soil texture, pH, and organic matter content. Furthermore, the impact of charcoal production on soil micronutrients includes the depletion of soil organic matter. Also, the burning of wood for charcoal decreases soil organic matter, leading to soil degradation and diminished soil nutrient retention capacity.

The study findings reveal significant differences in the abundance of micronutrients, P and S enzymes, and microbial activity between charcoal production sites and natural production sites. Specifically, charcoal production sites showed higher levels of micronutrients (Co, Cu, Fe, and Zn), enzymes Pho, Tsd, Dsr and Phy, and microbial activity compared to natural sites, indicating a strong influence of the production site on their distribution. Interestingly, Mn levels remained consistent across both types of sites, suggesting a minimal impact of the production site on its distribution. Copper and Fe exhibited variations based on location, while Co and Mn showed little variation by location, with the highest quantities found in Ìpaò. Zinc displayed a clear decreasing trend across the three locations, indicating significant differences among means. These findings underscore the role of local factors or unique production practices in shaping the distribution of these elements across different locations. Furthermore, soil microbial populations play a crucial role in regulating soil carbon storage, nutrient cycling, and overall soil health. Additionally, soil microbial communities can influence nitrogen cycling and fixation, leading to increased plant productivity. Given the large and diverse nature of microbial communities inhabiting the soil environment, microbial indices have been used earlier as a tool for observing soil quality. Microorganisms respond quickly to changes in soil environmental conditions, as such microbial indices can be used to monitor soil health and investigate the impact of environmental factors, nutrient addition, or some soil parameters on microbial communities. Therefore, soil nutrition, microbial composition, and enzyme activities play integral roles in sustaining ecosystem functions, particularly under charcoal production sites in derived savanna ecosystems. Understanding these dynamics is crucial for sustainable land management practices and ecosystem health.

Author Contribution

Credit Authors’ StatementAUTHORSHIP STATEMENT Manuscript title: Soil nutrition, microbial composition and some selected associated P n S enzymes under charcoal production sites of derived Savanna, Nigeria. All persons who meet authorship criteria are listed as authors, and all authors certify that they have participated sufficiently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript. Furthermore, each author certifies that this material or similar material has not been and will not be submitted to or published in any other publication before its appearance in the Scientific Africa. Authorship contributions are as follows: Category 1 (Conception and design of study): Adeyemo, Adebayo Jonathan., Oyun Mathew Banji, acquisition of data: Adeyemo Adebayo Jonathan1, Oluwagbemi Isreal A. Awodun Moses.Adeyemi, Ajiboye W.O; Akinnagbe, E.A, Akande, T. Y; Analysis and/or interpretation of data: Adeyemo Adebayo Jonathan, Oluwagbemi Isreal A. Awodun Moses.Adeyemi. Category 2 (Drafting the manuscript): Adeyemo Adebayo Jonathan1, ; revising the manuscript critically for important intellectual content: Adeyemo, Adebayo. Jonathan., Awodun Moses Adeyemi 1Category 3 (Approval of the version of the manuscript to be published): Adeyemo Adebayo Jonathan1*; Oyun Mathew Banji; Awodun Moses.Adeyem; Oyun Mathew Banji*Corresponding author: Adeyemo Adebayo Jonathan ([email protected])

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Table 1 Interaction effects of charcoal production sites and location on selected micronutrients, P and S enzymes.

Sites

Location

Copper Colbat Iron Manganese Zinc

.......................(mg/kg) .....................................

(Pho)

(mg/ml/min)

Phy Tsd Dsr

................(µg/ml/min) ................

(mg/ml/

CPS

Ìrèle

0.32^b

0.10^a

1.71^c

0.14^a

1.60^d

1.52^d

0.16^b

2.67^b

0.92^d

Òkè-Àkò

0.34^a

0.90^a

1.30^d

0.11^a

1.40^e

2.94^a

0.16^c

3.67^b

1.03^c

Ìpaò

0.31^b

0.10^a

2.11^a

0.11^a

1.80^a

1.92^c

0.16^d

12.93^a

1.96^a

NPS

Ìrèle

0.22^c

0.10^a

2.0^b

0.10^a

1.80^b

0.55^e

0.16^b

15.22^a

1.85^b

Òkè-Àkò

0.32^b

0.10^a

1.20^e

0.10^a

1.70^c

2.45^b

0.16^a

1.43^b

0.80^e

Ìpaò

0.31^b

0.07^b

1.30^d

0.20^a

1.02^f

2.35^b

0.16^d

3.81^b

0.68^f

According to Tukey's test, means that have the same letter in superscript on a column for the same parameter are not different from one another (P < 0.05).

Table 2 Interaction effects of charcoal production sites and soil depth on selected soil micronutrients, P and S enzymes

Sites

Depth

Copper Colbat Iron Manganese Zinc

.......................(mg/kg) .....................................

(Pho)

(mg/ml/min)

Phy Tsd Dsr

................(µg/ml/min) ................

(mg/ml/

CPS

0-15

0.31^b

0.09^b

1.84^b

0.10^a

1.50^c

2.07^ab

0.16^c

4.68^bc

1.13^d

15-30

0.30^b

0.11^a

2.0^a

0.11^a

1.81^a

2.31^a

0.16^c

4.86^bc

1.14^c

30-45

0.40^a

0.10^b

1.30^e

0.20^a

1.41^d

2.00^ab

0.16^d

9.73^a

1.64^a

NPS

0-15

0.30^b

0.10^b

1.52^c

0.20^a

1.64^d

1.82^b

0.16^a

10.51^a

1.35^b

15-30

0.30^b

0.10^b

1.52^c

0.11^a

1.49^c

1.75^b

0.16^a

8.17^ab

1.14^c

30-45

0.30^b

0.10^b

1.40^d

0.10^a

1.35^e

1.78^b

0.16^b

1.78^c

0.84^e

According to Tukey's test, means that have the same letter in superscript on a column for the same parameter are not different from one another (P < 0.05).

Table 3 :Interaction effects of charcoal production location and soil depth on selected micronutrients, P and S enzymes

Location

Depth

Copper Colbat Iron Manganese Zinc

.......................(mg/kg) .....................................

(Pho)

(mg/ml/min)

Phy Tsd Dsr

................(µg/ml/min) ................

(mg/ml/

Ìrèle

0-15

0.28^d

0.08^{c d}

1.93^c

0.10^a

2.0^b

0.59^f

0.16^a

9.41^ab

1.60^b

15-30

0.24^f

0.12^a

2.02^b

0.11^a

1.71^c

1.50^de

0.16^a

12.51^a

1.41^c

30-45

0.28^de

0.10^a-c

1.60^f

0.20^a

1.30^a

1.01^ef

o.16^a

4.93^bc

1.15^e

Òkè-Àkò

0-15

0.38^ab

0.06^d

0.94ⁱ

0.09^a

1.41^e

3.32^a

0.16^b

4.51^bc

1.11^f

15-30

0.26^ef

0.11^ab

1.70^d

0.13^a

1.71^c

2.55^b

0.16^b

1.92^c

0.85^h

30-45

0.35^c

0.11^a

1.10^h

0.11

1.30^g

2.21^bc

0.16^c

1.21^c

0.78ⁱ

Ìpaò

0-15

0.24^f

0.09^{b c}

2.17^a

0.30^a

1.33^f

1.92^cd

0.16^d

8.86^ab

1.00^g

15-30

0.40 ^a

0.08 ^{c d}

1.61 ^e

0.10^a

1.50 ^d

2.04^bc

0.16^d

5.12^bc

1.17^d

30-45

0.37^{b c}

0.08^{c d}

1.33^g

0.11^a

1.40^e

2.45^b

0.15^c

11.13^a

1.78^a

According to Tukey's test, means that have the same letter in superscript on a column for the same parameter are not different from one another (P < 0.05).

Table 4 Interaction effect of charcoal production sites by location by soil depth on selected micronutrients, P and S enzymes

	Copper Colbat Iron Manganese Zinc .......................(mg/kg) .................................					(Pho) (mg/ml/min)	Phy Tsd Dsr ................(µg/ml/min) ................
Site
CPS	0.32	0.10^a	1.70^a	0.13^a	1.57^a	2.13^a	0.16^a	6.82^a	1.30^a
NPS	0.29^b	0.09^b	1.48^b	0.12^a	1.50^b	1.78^b	0.16^a	6.42^a	1.11^b
Location
Ìrèle	0.27^b	0.10^a	1.84^a	0.12^a	1.66^a	1.03^c	0.16^a	8.95^a	1.39^a
Òkè-Àkò	0.33^a	0.10^a	1.23^c	0.11^a	1.53^b	2.69^a	0.16^a	2.55^b	1.32^b
Ìpaò	0.33^a	0.83^b	1.70^b	0.16^a	1.41^c	2.13^b	0.16^a	8.37^a	0.91^c
Soil depth (cm)
0-15	0.30^b	0.08^b	1.70^b	0.15^a	1.60^a	1.94^a	0.16^a	7.60^a	1.24^a
15-30	0.30^b	0.10^a	1.80^a	0.12^a	1.50^b	2.03^a	0.16^a	6.51^a	1.24^a
30-45	0.33^a	0.10 ^a	1.33^c	0.11^a	1.60^a	1.89^a	0.15^b	5.76^a	1.14^b
Three way ANOVA results
Site (S)	*	*	*	ns	*	*	ns	*	*
Location (L)	*	*	*	ns	*	*	ns	*	*
Soil depth (Sd)	*	*	*	ns	*	*	*	ns	*
S x L	*	*	*	ns	*	*	*	*	*
S x Sd	*	*	*	ns	*	*	*	*	*
L x Sd	*	*	*	ns	*	*	*	*	*
S x L x Sd	*	*	*	ns	*	*	*	*	*
Coefficient of Variation	2.2	2.8	34.6	28.3	32.2	46.74	1.82	118.43	54.52
Variance	0.12	0.25	0.23	0.34	0.56	0.91	0.00	7.84	0.66

According to Tukey's test, means that have the same letter in superscript on a column for the same parameter are not different from one another (P < 0.05).

Table 5: Correlation among variables

	Zn	Mn	Fe	Co	Cu	Dsr µg/ml/min	Tsd µg/ml/min	Phy µg/ml/min	Pho mg/ml/min	Nematode (10³)	Fungi (10⁵)		^Bacteria (10⁸)
Mn	-0.138
Fe	0.089	0.061
Co	0.212	-0.095	0.402
Cu	0.051	-0.161	-0.690	-0.374
Dsr	0.459	-0.071	0.306	-0.241	-0.238
Tsd	0.396	0.103	0.357	-0.173	-0.408	0.865
Phy	0.183	-0.099	-0.006	0.236	-0.359	-0.040	-0.045
Pho	-0.369	0.045	-0.531	-0.173	0.389	-0.268	-0.280	-0.278
Nematode	-0.056	-0.039	-0.278	-0.083	-0.099	-0.150	0.035	0.403	-0.065
Fungi	0.114	0.177	-0.088	-0.078	0.114	0.087	0.208	-0.152	0.282	0.105
Bacteria	0.026	-0.146	-0.008	0.229	0.086	-0.360	-0.446	0.260	0.196	-0.042	0.035
Biomass P	-0.060	-0.040	0.202	0.100	-0.063	-0.191	-0.219	0.087	0.192	-0.057	0.007	0.626

Pair(s) of variables with ( + ) correlation and P ˂0.05 increase together. For the pairs with (-) correlation and P ˂0.05, one variable decreases while the other increases. For pairs with P ˃0.05, no significant observation between the two variables.

No competing interests reported.

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Soil nutrition, microbial composition and some selected associated P n S enzymes under charcoal production sites of derived Savanna, Nigeria

Status:

Version 1

Abstract

Figures

1.0 Introduction

2.0 Materials and Methods

2.1 Experimental site

2.2 Experimental design and Soil sample collection

2.3 Analysis of soil microorganisms

2.4 Analysis of Microbial biomass phosphorus

2.4.1 Soil sample fumigation method

2.4.2 Extraction Method

2.5 Soil enzyme activities

2.5.1 Phosphorus enzymes

2.5.2 Sulfur enzymes

2.6 Micronutrients analysis

2.7 Statistical analysis

3.0 Results

4.0 Discussion

4.1 Microbial contribution to phosphorus in charcoal soil

4.2 Enzyme activities and nutrient dynamics in charcoal soils

5.0 Conclusion

Declarations

Author Contribution

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1