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

Soil nutrition, microbial composition and some selected associated P n S enzymes under charcoal production sites of derived Savanna, Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Soil nutrition, microbial composition and some selected associated P n S enzymes under charcoal production sites of derived Savanna, Nigeria Adebayo Jonathan Adeyemo, Isreal A. Oluwagbemi, William O. Ajiboye, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3970781/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract 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 Figures Figure 1 Figure 2 1.0 Introduction 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 CO 2 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 (CO 2 ) and methane (CH 4 ) 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. 2.0 Materials and Methods 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 KH 2 PO 4 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 NH 4 F + 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 CaCl 2 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 CaCl 2 , 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 K 3 Fe (CN) 6 , 1 mM Na 2 S 2 0 3 , 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 H 2 SO 4 , 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 cm 3 of aqua-regia and 10 cm 3 of 30% H 2 O 2 were added. The addition of H 2 O 2 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 cm 3 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. 3.0 Results 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 (10 3 ) and Zn, and Biomass P and Zn, indicating that variations in Nematode (10 3 ) 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 (10 3 ) and Mn, Fungi (10 7 ) and Mn, Bacteria (10 8 ) 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 (10 3 ) 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 (10 3 ) levels are linked to a decrease in Fe levels. The correlations between Fe and Fungi (10 7 ), Bacteria (10 8 ), 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 (10 3 ), Fungi (10 7 ), Bacteria (10 8 ), 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 (10 3 ), Fungi (10 7 ), and Biomass p, and a weak positive correlation with Bacteria (10 8 ). While the correlation with Nematode (10 3 ) is not statistically significant, correlations with Co and Fungi (10 7 ) 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 (10 7 ) levels. Bacteria (10 8 ) exhibits very weak correlations with Nematode (10 3 ), Fungi (10 7 ), 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 (10 7 ), and a very weak negative correlation with Nematode (10 3 ). None of these correlations are statistically significant, suggesting that variations in Phy levels are not reliably associated with changes in Co, Fungi (10 7 ), or Nematode (10 3 ) levels. Phosphatase displays strong positive correlations with Cu, a strong negative correlation with Fe, and a weak positive correlation with Fungi (10 7 ), 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 (10 7 ) 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. 4.0 Discussion 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. 5.0 Conclusion 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. Declarations 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] ) References Kumar, R., Kumar, A., & Saikia, P. (2022). Deforestation and forests degradation impacts on the environment. In V. P. Singh, S. Yadav, K. K. Yadav, & R. N. Yadava (Eds.), Environmental Degradation: Challenges and Strategies for Mitigation (Water Science and Technology Library, Vol. 104, pp. 1–18). Springer. https://doi.org/10.1007/978-3-030-95542-7_2 . Zackrisson, O., M.C. Nilsson, and D.A. Wardle. 1996. Key ecological function of charcoal from wildfire in the boreal forest. Oikos 77, 10–19. Schmidt, M.W.I., and A.G. Noack. 2000. Black carbon in soils and sediments: Analysis, distribution, implications, and current challenges. Global Biogeochemical Cycles 14:777–794. Skjemstad, J.O., D.C. Reicosky, A.R. Wilts, and J.A. McGowan. 2002. Charcoal carbon in U.S. agricultural soils. Soil Science Society. Am. J. 66:1249–1255. Brown, K.J., J.S. Clark, E.C. Grimm, J.J. Donovan, P.G. Mueller, B.C.S. Hansen, and I. Stefanova. (2005). Fire cycles in North American interior grasslands and their relation to prairie drought. Proc. Natl. Acad. Sci. 102:8865–8870. Chidumayo, Emmanuel N., & Gumbo, Davison J. (2013). The environmental impacts of charcoal production in tropical ecosystems of the world: A synthesis. Energy for Sustainable Development, 17(2), 86–94. doi: 10.1016/j.esd.2012.07.004 . Hofstad, O., Kohlin, G., & Namaalwa, J. (2009). How can emissions from woodfuel be reduced? In A. Angelsen, M. Brockhaus, M. Kanninen, E. Sills, W. D. Sunderlin, & S. Wertz-Kanounnikoff (Eds.), Realising REDD+: National Strategy and Policy Options (pp. 237–249). Center for International Forestry Research . Schmidt, M.W.I., J.O. Skjemstad, E. Gehrt, & I. Kogel-Knabner. 1999. Charred organic carbon in German chernozemic soils. Eur. J. Soil Science. 50:351–365. Pietikäinen, J., & Fritze, H. (1993). Microbial biomass and activity in the humus layer following burning: short-term effect of two different fires. Canadian Journal of Forest Research, 23, 1275–1285. Prieto-Fernández, A., Acea, M. J., & Carballas, T. (1998). Soil microbial and extractable C and N after wildfire. Biology and Fertility of Soils, 27, 132–142. Bååth, E., Frostegård, Å., Pennanen, T., & Fritze, H. (1995). Microbial community structure and pH response in relation to soil organic matter quality in wood-ash fertilized, clear-cut or burned coniferous forest soils. Soil Biology and Biochemistry, 27, 229–240. Singh, D., Verma, A.K., Choudhary, M., Mahawar, H., Thapa, S., & Mehriya, M.L. (2023). Micronutrient Mobilizer Microorganisms: Significance in Crop Sustainability. In S. Singh, R. Prasanna, & K. Pranaw (eds), Bioinoculants: Biological Option for Mitigating global Climate Change. Springer, Singapore . https://doi.org/10.1007/978-981-99-2973-3_5 . Caravaca F, Alguacil MM, Hernández JA, Roldán A (2005) Involvement of antioxidant enzyme and nitrate reductase activities during water stress and recovery of mycorrhizal Myrtus communis and Phillyrea angustifolia plants. Plant Science 169, 191–197. Sinsabaugh, R. L., & Moorhead, D. L. (1994). Resource allocation to extracellular enzyme production: A model for nitrogen and phosphorus control of litter decomposition. Soil Biology and Biochemistry, 26(10), 1301–1311. doi: 10.1016/0038-0717(94)90211-9 . Boerner, R. E. J. (2006). Earth, wind, water, and fire: How the elements conspire in the forest context. In M. B. Dickinson (Ed.), Fire in Eastern Oak Forests: Delivering Science to Land Managers (pp. 104–122). Proceedings of a conference, 15–17 November 2005, Columbus, Ohio. General Technical Report NRS-P-1. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, USA. Sanchez P (1976) Properties and management of soils in the tropics. Wiley, New York. Brady, N. C., & Weil, R. R. (2008). The Nature and Properties of Soils (14th ed.). Pearson Prentice Hall. Adeyemo, A. J., Ayorinde, A. S., Awodun, M. A., & Oyun, M. B. (2023). Nutrients status and soil microbial biomass C and N in charcoal production sites of derived savannah forest of southern Nigeria. Science of the Total Environment, 2468 – 2276, https://doi.org/10.1016/j.sciaf.2023.e01684 . Jonathan, E. I. (2022). Nematology Fundamentals & Applications (2nd Revised & Enlarged Edition). New India Publishing Agency. Bray, R. H. and Kurtz, L. T. (1945). Determination of total, organic and available forms of phosphorus in soil. Soil Science 599: 39–45. Sanni, D. M., Jimoh, M. B., Fatoki, T. H., Ekundayo, F. O., & Omotoyinbo, O. V. (2017). Isolation of phytate degrading activity from Africa Giant Land Snail (Archachatina marginata). Journal of Agriculture and Food Technology, 7(5), 1–9. ISSN 2090–424X. Trudinger, P. A. (1961). Biochemical Journal, 78, 673. Stenström, J., Stenberg, B., & Johanson, M. (1998). Kinetics of substrate-induced respiration (SIR): theory. Ambio, 27(1), 35–39. Du, E., Terrer, C., Pellegrini, A. F. A., Ahlström, A., van Lissa, C. J., Zhao, X., Xia, N., & Jackson, R. B. (2020). Global patterns of terrestrial nitrogen and phosphorus limitation. Nature Geoscience, 13, 221–226. doi: 10.1038/s41561-019-0530-4 . Hou, E. Q., Luo, Y. Q., Kuang, Y. W., Chen, C. R., Lu, X. K., Jiang, L. F., Luo, X.Z., Wen, D. Z. (2020). Global meta-analysis shows pervasive phosphorus limitation of aboveground plant production in natural terrestrial ecosystems. Nature Communications, 11, 637. doi: 10.1038/s41467-020-14492-w . Sohrt, J., Lang, F., & Weiler, M. (2017). Quantifying components of the phosphorus cycle in temperate forests. WIREs Water, 4, e1243. doi: 10.1002/wat2.1243 . Oberson, A., & Joner, E. J. (2005). Microbial turnover of phosphorus in soil. In B. L. Turner, E. Frossard, & D. S. Baldwin (Eds.), Organic Phosphorus in the Environment (pp. 133–164). CABI. Makino, W., Cotner, J. B., Sterner, R. W., & Elser, J. J. (2003). Are bacteria more like plants or animals? Growth rate and resource dependence of bacterial C:N:P stoichiometry. Functional Ecology, 17, 121–130. Cayuela, M. L., van Zwieten, L., Singh, B. P., Jeffery, S., Roig, A., & Sánchez-Monedero, M. A. (2014). Biochar's role in mitigating soil nitrous oxide emissions: A review and meta-analysis. Agriculture, Ecosystems & Environment. http://dx.doi.org/10.1016/j.agee.2013.1010.1009 . Ceulemans, T., Merckx, R., Hens, M., & Honnay, O. (2011). A trait-based analysis of the role of phosphorus vs. nitrogen enrichment in plant species loss across North-west European grasslands. Journal of Applied Ecology, 48(5), 1155–1163. Harpole, W. S., & Tilman, D. (2007). Grassland species loss resulting from reduced niche dimension. Nature, 446(7137), 791–793. Nishio, M. (1996). Microbial fertilizers in Japan. FFTC Extension Bulletins, 1–12. National Institute of Agro-Environmental Science, Ibaraki, Japan. Saa, A., Trasar-Cepeda, M. C., Gil-Sotres, F., & Carballas, T. (1993). Changes in soil phosphorus and acid phosphatase activity immediately following forest fires. Soil Biology & Biochemistry, 25, 1223–1230. McCormack, S. A., Ostle, N., Bardgett, R. D., Hopkins, D. W., & Vanbergen, A. J. (2013). Biochar in bioenergy cropping systems: Impacts on soil faunal communities and linked ecosystem processes. GCB Bioenergy, 5, 81–95. doi: 10.1111/gcbb.12046 . Liiri, M., Setälä, H., Haimi, J., Pennanen, T., & Fritze, H. (2002). Soil processes are not influenced by the functional complexity of soil decomposer food webs under disturbance. Soil Biology & Biochemistry, 34, 1009–1020. doi: 10.1016/S0038-0717(02)00034-2 . Saito, M., & Marumoto, T. (2002). Inoculation with arbuscular mycorrhizal fungi: The status quo in Japan and the future prospects. In S. E. Smith & F. A. Smith (Eds.), Diversity and Integration in Mycorrhizas (Developments in Plant and Soil Sciences, Vol. 94). Springer . https://doi.org/10.1007/978-94-017-1284-2_27 . Borchard, N., Ladd, B., Eschemann, S., Hegenberg, D., Möseler, B. M., & Amelung, W. (2014). Black carbon and soil properties at historical charcoal production sites in Germany. Geoderma, 232–234, 236–242. doi: 10.1016/j.geoderma.2014.05.007 . Cao, Y., Gao, Y., Qi, Y., & Li, J. (2018). Biochar-enhanced composts reduce the potential leaching of nutrients and heavy metals and suppress plant-parasitic nematodes in excessively fertilized cucumber soils. Environmental Science and Pollution Research, 25, 7589–7599. doi: 10.1007/s11356-017-1061-4 . Zhang, L., Xu, M., Liu, Y., Zhang, F., Hodge, A., & Feng, G. (2016). Carbon and phosphorus exchange may enable cooperation between an arbuscular mycorrhizal fungus and a phosphate-solubilizing bacterium. New Phytologist, 210, 1022–1032. Zhou, X., Sun, H., Pumpanen, J., Sietiö, O.-M., Heinonsalo, J., Köster, K., & Berninger, F. (2018). The impact of wildfire on microbial C:N:P stoichiometry and the fungal-to-bacterial ratio in permafrost soil. Biogeochemistry, 142(1), 1–17. https://doi.org/10.1007/s10 . Bogie, N. A., Bayala, R., Diedhiou, I., Dick, R. P., & Ghezzehei, T. A. (2018). Alteration of soil physical properties and processes after ten years of intercropping with native shrubs in the Sahel. Soil and tillage research, 182 , 153–163. Fagbenro, J. A., Oshunsanya, S. O., Oyeleye, B., & Aduayi, E. A. (2018). Effect of two biochar types and inorganic fertilizer on soil chemical properties and growth of maize (Zea mays L.). International Educational Scientific Research Journal, 2(4), 43–50. Lal, R. (2006). Enhancing crop yields in the developing countries through restoration of soil organic carbon pool in agricultural lands. Land Degradation & Development, 17, 197–209. Vitousek, P. M., & Matson, P. A. (2012). Nutrient cycling and biogeochemistry. In D. E. Schimel & J. H. Chadwick (Eds.), The Princeton Guide to Ecology (pp. 330–339). Princeton University Press . Tables 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 0.94 i 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 0.78 i 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 (10 3 ) Fungi (10 5 ) Bacteria (10 8 ) 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. Additional Declarations No competing interests reported. Supplementary Files suppliplimetarylist.docx Cite Share Download PDF Status: Published Journal Publication published 01 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Jun, 2024 Reviews received at journal 03 Jun, 2024 Reviewers agreed at journal 22 May, 2024 Reviews received at journal 11 May, 2024 Reviews received at journal 06 May, 2024 Reviewers agreed at journal 24 Apr, 2024 Reviewers agreed at journal 23 Apr, 2024 Reviewers agreed at journal 19 Apr, 2024 Reviewers invited by journal 19 Apr, 2024 Editor assigned by journal 16 Apr, 2024 Editor invited by journal 20 Mar, 2024 Submission checks completed at journal 20 Mar, 2024 First submitted to journal 19 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3970781","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":283098278,"identity":"0af9b05a-4b86-44b0-8ad7-98dfa525602d","order_by":0,"name":"Adebayo Jonathan Adeyemo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYFCCAwwSYJq98QFMgFgtPIcNIKoJa2GAapFIJlILf+Phhzc+ttlE80s+ZpP+UMMgx3cjge3hF3w2HDhmbDmzLS135uxkNiCHwVjyRgK7sQxerxwwk+ZtO5y74Xb+MYkDbAyJG4C2SEvg0SF/4Pg36b9t/3M33DwMtOUfQz1BLQYHzphJM7YdyN1wg5lN4mAbQ4IBUIvkBzxaDA+cKbbsOZecO7MnmdnibJ+E4cwzD9uk8XlF7sbxjTd+lNnl9rMfZrxR8c1Gnu948jHJH/j0SBxgYGBkQ3CBmLGBmQefFv4GIPEHTZARry2jYBSMglEw0gAAOzFXnCXGW7EAAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Technology, Akure, Nigeria","correspondingAuthor":true,"prefix":"","firstName":"Adebayo","middleName":"Jonathan","lastName":"Adeyemo","suffix":""},{"id":283098279,"identity":"44d1c40a-eca8-4322-be0e-715c8c080c50","order_by":1,"name":"Isreal A. Oluwagbemi","email":"","orcid":"","institution":"Federal University of Technology, Akure, Nigeria","correspondingAuthor":false,"prefix":"","firstName":"Isreal","middleName":"A.","lastName":"Oluwagbemi","suffix":""},{"id":283098280,"identity":"b72b02d6-3471-4c2f-ab8d-c324eef30141","order_by":2,"name":"William O. Ajiboye","email":"","orcid":"","institution":"Federal University of Technology, Akure, Nigeria","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"O.","lastName":"Ajiboye","suffix":""},{"id":283098281,"identity":"838220ed-c60e-4d73-ad32-1e0932ce0585","order_by":3,"name":"Evelyn Atinuke Akinnagbe","email":"","orcid":"","institution":"Federal Polytechnic Ile-Oluji","correspondingAuthor":false,"prefix":"","firstName":"Evelyn","middleName":"Atinuke","lastName":"Akinnagbe","suffix":""},{"id":283098282,"identity":"4d01492d-4ace-4f1f-904f-acf91e262aa9","order_by":4,"name":"Tolulope Yetunde Akande","email":"","orcid":"","institution":"Federal University Oye Ekiti","correspondingAuthor":false,"prefix":"","firstName":"Tolulope","middleName":"Yetunde","lastName":"Akande","suffix":""},{"id":283098283,"identity":"6f00cca2-3969-4b5e-ad33-93179a4ae183","order_by":5,"name":"Mathhew Banji Oyun","email":"","orcid":"","institution":"Federal University of Technology, Akure, Nigeria","correspondingAuthor":false,"prefix":"","firstName":"Mathhew","middleName":"Banji","lastName":"Oyun","suffix":""},{"id":283098285,"identity":"fcfa0469-bd8c-4c98-8120-682c2e7409e0","order_by":6,"name":"Moses Adeyeye Awodun","email":"","orcid":"","institution":"Federal University of Technology, Akure, Nigeria","correspondingAuthor":false,"prefix":"","firstName":"Moses","middleName":"Adeyeye","lastName":"Awodun","suffix":""}],"badges":[],"createdAt":"2024-02-19 19:47:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3970781/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3970781/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-90938-9","type":"published","date":"2025-09-01T15:58:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53361800,"identity":"4ec4c683-4753-4472-8196-24b4fa157580","added_by":"auto","created_at":"2024-03-25 05:08:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30483,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial biomass phosphorus in the soils of CPS and NPS in different locations and depths in Ikole, Ekiti, Southwestern Nigeria. Vertical bars indicate standard errors of the means (n = 5). Bars with same alphabet (s) within a soil layer for the same parameter are not significantly different (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3970781/v1/44bfc93c1a88711ca2a91a8d.png"},{"id":53362615,"identity":"00a1fb01-7035-41bb-832d-fcac6c70ee44","added_by":"auto","created_at":"2024-03-25 05:16:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64823,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial, Fungi and Nematodes counts in the soils of CPS and NPS in different locations and depths in Ikole, Ekiti, Southwestern Nigeria. Vertical bars indicate standard errors of the means (n = 5). Bars with same alphabet (s) within a soil layer for the same parameter are not significantly different (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3970781/v1/130ca4cd75d82cdaee549d2f.png"},{"id":90827987,"identity":"c556832f-e5e1-4b36-a0b9-79801b6ec471","added_by":"auto","created_at":"2025-09-08 16:04:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1848855,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3970781/v1/3230d3f4-9c82-4200-a789-d0ac80c19bbf.pdf"},{"id":53361802,"identity":"6f7b3050-db2a-435e-a5fa-420bb94055c0","added_by":"auto","created_at":"2024-03-25 05:08:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18556,"visible":true,"origin":"","legend":"","description":"","filename":"suppliplimetarylist.docx","url":"https://assets-eu.researchsquare.com/files/rs-3970781/v1/e2beed1a87d13203cc1f08c8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Soil nutrition, microbial composition and some selected associated P n S enzymes under charcoal production sites of derived Savanna, Nigeria","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eAgriculture, 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 CO\u003csub\u003e2\u003c/sub\u003e emissions and other greenhouse gases (GHGs) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eIn an ecosystems prone to occasional wildfires, charcoal becomes a common component in soils, as highlighted by studies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite reported concerns about deforestation and forest degradation linked to charcoal production in many countries [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], detailed examinations of German soils revealed that charcoal accounted for approximately 45% of the organic carbon content [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. 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 (CO\u003csub\u003e2\u003c/sub\u003e) and methane (CH\u003csub\u003e4\u003c/sub\u003e) which have an impact on global warming.\u003c/p\u003e \u003cp\u003eThe 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 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The activities of the microorganisms important for Carbon (C ), Nitrogen (N), Phosphorus (P) and Sulphur (S) cycling are also reflected in soil enzymes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn savanna regions, nutrient deficiencies, especially N, P and S are commonly observed [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], 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 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. 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.\u003c/p\u003e "},{"header":"2.0 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental site\u003c/h2\u003e \u003cp\u003eThe study was conducted in three specific locations, as detailed by Adeyemo \u003cem\u003eet al.\u003c/em\u003e, [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], namely \u0026Igrave;r\u0026egrave;le, \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;, and \u0026Igrave;pa\u0026ograve;, situated in the Ikole local government area of Ekiti State, Southwestern Nigeria. These locations span from 7\u0026deg; 57' 22'' N to 5\u0026deg; 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\u0026deg;C. The prevailing soil texture in the study area is sandy clay loam alfisol. \u0026Igrave;r\u0026egrave;le, \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;, and \u0026Igrave;pa\u0026ograve; 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Experimental design and Soil sample collection\u003c/h2\u003e \u003cp\u003eSoil augers and core sampler were used to sample layers (0\u0026ndash;15 cm, 15\u0026ndash;30 cm, and 30\u0026ndash;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 \u0026times; 2 \u0026times; 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Analysis of soil microorganisms\u003c/h2\u003e \u003cp\u003eTotal 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 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] for the most common genera of plant-parasitic Nematodes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analysis of Microbial biomass phosphorus\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Soil sample fumigation method\u003c/h2\u003e \u003cp\u003eTen 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 KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e of a spike of 250 \u0026micro;g 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Extraction Method\u003c/h2\u003e \u003cp\u003eThe Bray 1 Method [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] 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 NH\u003csub\u003e4\u003c/sub\u003eF\u0026thinsp;+\u0026thinsp;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.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Soil enzyme activities\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Phosphorus enzymes\u003c/h2\u003e \u003cp\u003eThe assessment of phytase (Phy) activities were analyzed according to the ammonium molybdate method used by Sanni \u003cem\u003eet al.\u003c/em\u003e, [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], 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\u0026deg;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 CaCl\u003csub\u003e2\u003c/sub\u003e 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\u0026deg;C was instructed for all three tubes. The subsequent actions included transferring 4 ml of the liquid from each tube into labeled 16 \u0026times; 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 CaCl\u003csub\u003e2\u003c/sub\u003e, 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Sulfur enzymes\u003c/h2\u003e \u003cp\u003eThe thiosulfate-oxidizing enzyme activities were analyzed using the method described by Trudinger [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The standard reaction mixture (1 mI) for measuring thiosulfate dehydrogenase (Tsd) activity contained 25 mM Tris-HC1 (pH 7.5), 1 mM K\u003csub\u003e3\u003c/sub\u003eFe (CN)\u003csub\u003e6\u003c/sub\u003e, 1 mM Na\u003csub\u003e2\u003c/sub\u003eS\u003csub\u003e2\u003c/sub\u003e0\u003csub\u003e3\u003c/sub\u003e, 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\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. 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 H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, 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.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Micronutrients analysis\u003c/h2\u003e \u003cp\u003eThe 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 cm\u003csup\u003e3\u003c/sup\u003e of aqua-regia and 10 cm\u003csup\u003e3\u003c/sup\u003e of 30% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e were added. The addition of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e 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\u0026deg;C. Filters were used to separate the insoluble solid from the supernatant liquid and the volume was increased to 100 cm\u003csup\u003e3\u003c/sup\u003e using distilled water and the digested sample was analyzed for the presence of heavy metals with the aid of an AAS instrument.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe 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.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1 \u0026nbsp; \u0026nbsp; \u0026nbsp; Soil physical and chemical properties\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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 \u0026Igrave;r\u0026egrave;le and \u0026Igrave;pa\u0026ograve;, the soil pH was strongly acidic, while it was moderate for \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;. Below the critical value of 10 mg/kg, \u0026Igrave;r\u0026egrave;le exhibited the lowest available phosphorus level, whereas \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve; and \u0026Igrave;pa\u0026ograve; 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 \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;, 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ePrecipitation and Temperature of the research area.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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 \u0026deg;C in April, 26.9 \u0026deg;C in May, 26.2 \u0026deg;C in June, 25.8 \u0026deg;C in July, and 24.9 \u0026deg;C in August.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.3 \u0026nbsp; \u0026nbsp; \u0026nbsp; The Interaction effect of sites and location on specific phosphorus and sulfur enzymes, and soil nutrient status.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant 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 \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve; and \u0026Igrave;pa\u0026ograve; than sulfur enzymes. However, the peak value (2.94\u0026nbsp;mg/ml/min) was observed at NPS in \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;. Thiosulfate dehydrogenase (Tsd) exhibited significant site-by-location interaction effects, with the highest value recorded at NPS in \u0026Igrave;r\u0026egrave;le and the lowest activity level at NPS in \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;, 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 \u0026Igrave;pa\u0026ograve;, while low activity levels of (0.80 and 0.68\u0026nbsp;\u0026micro;g/ml/min)\u0026nbsp;were observed at NPS in \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve; and \u0026Igrave;pa\u0026ograve;, respectively. Lastly, phytase (Phy) activity remained consistent across both sites and depths but showed significant variations among means.\u003c/p\u003e\n\u003cp\u003eSignificant 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 \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;, however similar trends occurred for Iron in \u0026Igrave;pa\u0026ograve;. In contrast, significantly lowest values were recorded for cobalt, and zinc in \u0026Igrave;pa\u0026ograve; at NPS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.4 \u0026nbsp; \u0026nbsp; \u0026nbsp; Interaction effect of sites and depth on selected phosphorus and sulfur enzymes, and soil nutrient status.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith the exception of the 30-45 cm soil depth, which displayed lower values (1.78 \u0026amp; 0.84 \u0026micro;g/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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.5 \u0026nbsp; \u0026nbsp; \u0026nbsp; Interaction effects of locations and depth on selected P and S enzymes, and nutrient status\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant 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 \u0026amp; 9.41 \u0026micro;g/ml/min) were observed for Pho in \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve; and Tsd in \u0026Igrave;r\u0026egrave;le, respectively. A similar pattern was observed at the 30-45 cm soil depth, where \u0026Igrave;pa\u0026ograve; recorded the highest activity levels for Dsr (1.78 \u0026micro;g/ml/min) and at \u0026Igrave;r\u0026egrave;le for Tsd (12.51 \u0026micro;g/ml/min) at 15-30 cm depth. However, at the 30-45 cm depth, notably lower values were observed at \u0026Igrave;r\u0026egrave;le for phosphatase and \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve; 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 \u0026Igrave;pa\u0026ograve;, Iron and manganese were significantly highest at the 0-15 cm depth, whereas in \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;, 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 (\u0026Igrave;pa\u0026ograve; \u0026amp; \u0026Igrave;r\u0026egrave;le) respectively. Zinc exhibited the highest amount at the 0-15 cm soil depth in \u0026Igrave;r\u0026egrave;le and the lowest amount at the 30-45 cm depth in both \u0026Igrave;r\u0026egrave;le and \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.6 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Interaction effects of charcoal production sites by location by soil depth\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eon selected P and S enzymes, and soil nutrient status\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt CPS, Pho, Tsd, and Dsr activity increased across all locations with increasing soil depth, except in \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve; 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 \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;. 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 \u0026Igrave;pa\u0026ograve;. Mn and Zn decreased down the soil profile at all locations, except in \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;. Pho, Tsd, and Dsr decreased with increasing soil depth, except Pho, which increased at \u0026Igrave;pa\u0026ograve; across soil depth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.7 \u0026nbsp; \u0026nbsp; \u0026nbsp; Interaction effects of charcoal production sites by location by soil depth\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eon selected P and S enzymes, and soil nutrient status\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree-way analysis of variance presented in [Table 5], indicates\u0026nbsp;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 (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt;\u0026nbsp;0.05) indicated in Phy and Tsd between CPS and NPS, but higher and significant differences (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;\u0026nbsp;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 (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 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 \u0026ndash; 45 cm depth, however, Tsd and Pho with no significant differences recorded higher values in 0-15 and 15 \u0026ndash; 30 cm depths respectively. \u0026nbsp;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 \u0026Igrave;pa\u0026ograve; compared to the other two locations. However, Fe and Zn were found to be higher at \u0026Igrave;r\u0026egrave;le with significant differences (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt; 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 (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.8 \u0026nbsp; \u0026nbsp; \u0026nbsp; Correlation (r) among microbial biomass P, microorganisms, P and S enzyme, and micronutrients\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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 (10\u003csup\u003e3\u003c/sup\u003e) and Zn, and Biomass P and Zn, indicating that variations in Nematode (10\u003csup\u003e3\u003c/sup\u003e) 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.\u003c/p\u003e\n\u003cp\u003eTransitioning 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 (10\u003csup\u003e3\u003c/sup\u003e) and Mn, Fungi (10\u003csup\u003e7\u003c/sup\u003e) and Mn, Bacteria (10\u003csup\u003e8\u003c/sup\u003e) 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 (10\u003csup\u003e3\u003c/sup\u003e) 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 (10\u003csup\u003e3\u003c/sup\u003e) levels are linked to a decrease in Fe levels.\u003c/p\u003e\n\u003cp\u003eThe correlations between Fe and Fungi (10\u003csup\u003e7\u003c/sup\u003e), Bacteria (10\u003csup\u003e8\u003c/sup\u003e), 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 (10\u003csup\u003e3\u003c/sup\u003e), Fungi (10\u003csup\u003e7\u003c/sup\u003e), Bacteria (10\u003csup\u003e8\u003c/sup\u003e), 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 (10\u003csup\u003e3\u003c/sup\u003e), Fungi (10\u003csup\u003e7\u003c/sup\u003e), and Biomass p, and a weak positive correlation with Bacteria (10\u003csup\u003e8\u003c/sup\u003e). While the correlation with Nematode (10\u003csup\u003e3\u003c/sup\u003e) is not statistically significant, correlations with Co and Fungi (10\u003csup\u003e7\u003c/sup\u003e) 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 (10\u003csup\u003e7\u003c/sup\u003e) levels. Bacteria (10\u003csup\u003e8\u003c/sup\u003e) exhibits very weak correlations with Nematode (10\u003csup\u003e3\u003c/sup\u003e), Fungi (10\u003csup\u003e7\u003c/sup\u003e), 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 (10\u003csup\u003e7\u003c/sup\u003e), and a very weak negative correlation with Nematode (10\u003csup\u003e3\u003c/sup\u003e). None of these correlations are statistically significant, suggesting that variations in Phy levels are not reliably associated with changes in Co, Fungi (10\u003csup\u003e7\u003c/sup\u003e), or Nematode (10\u003csup\u003e3\u003c/sup\u003e) levels. Phosphatase displays strong positive correlations with Cu, a strong negative correlation with Fe, and a weak positive correlation with Fungi (10\u003csup\u003e7\u003c/sup\u003e), 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 (10\u003csup\u003e7\u003c/sup\u003e) levels.\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.9 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Selected soil microbial activities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe biomass phosphorus content in the designated areas exhibited its highest levels at CPS in the 0-15 cm soil depth in \u0026Igrave;r\u0026egrave;le, \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;, and \u0026Igrave;pa\u0026ograve; [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 \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;. 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, \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve; and \u0026Igrave;pa\u0026ograve; exhibited significantly higher bacteria activity at a depth of 15-30 cm. At the 30-45 cm soil depth, \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve; registered the highest bacteria activity, while \u0026Igrave;pa\u0026ograve; 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 \u0026Igrave;r\u0026egrave;le; 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 \u0026Igrave;pa\u0026ograve;, at 30-45 cm in \u0026Igrave;r\u0026egrave;le, and reached its peak at 0-15 cm soil depth in \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;. 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 \u0026Igrave;r\u0026egrave;le and \u0026Igrave;pa\u0026ograve;. 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 \u0026Igrave;r\u0026egrave;le, where the highest abundance was observed at the 15-30 cm soil depth at NPS. Similar patterns were observed in \u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve; and \u0026Igrave;pa\u0026ograve;.\u0026nbsp;\u003c/p\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eSoil 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 Stenstr\u0026ouml;m \u003cem\u003eet al.\u003c/em\u003e, [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Despite the challenge of P availability, soil microorganisms significantly contribute to forest P nutrition by both mobilizing and immobilizing P [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], thereby influencing plant P nutrition through their biomass composition [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. 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 \u003cem\u003eet al.\u003c/em\u003e, [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Microbial contribution to phosphorus in charcoal soil\u003c/h2\u003e \u003cp\u003ePhosphate-solubilizing microorganisms, which rely on organic matter inputs, produce chelating organic acids to unlock phosphate bound to minerals [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. 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 \u003cem\u003eet al\u003c/em\u003e, [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. As stated in the result the bacteria activity displayed a significant increase in the 0\u0026ndash;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 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] 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 \u003cem\u003eet al.\u003c/em\u003e, [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and Saito \u0026amp; Marumoto [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] which have demonstrated that the application of charcoal promotes the colonization of arbuscular mycorrhizal fungi in agricultural plants. Also, temperatures exceeding 50\u0026deg;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 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The higher soil acidity and the introduction of magnesium, calcium, potassium, and manganese in charcoal soils may influence the nematode community.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Enzyme activities and nutrient dynamics in charcoal soils\u003c/h2\u003e \u003cp\u003ePhosphorus 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 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], 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 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], 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.\u003c/p\u003e \u003cp\u003eAdditionally, 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 \u003cem\u003eet al.\u003c/em\u003e, [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], 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 \u003cem\u003eet al.\u003c/em\u003e, [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] 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.\u003c/p\u003e \u003cp\u003eMicronutrients 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 [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. According to Matson [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], 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.\u003c/p\u003e \u003c/div\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eThe 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 \u0026Igrave;pa\u0026ograve;. 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCredit Authors\u0026rsquo; 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])\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKumar, R., Kumar, A., \u0026amp; Saikia, P. (2022). Deforestation and forests degradation impacts on the environment. In V. P. Singh, S. Yadav, K. K. Yadav, \u0026amp; R. N. Yadava (Eds.), Environmental Degradation: Challenges and Strategies for Mitigation (Water Science and Technology Library, Vol. 104, pp. 1\u0026ndash;18). Springer. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-95542-7_2\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-95542-7_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZackrisson, O., M.C. Nilsson, and D.A. Wardle. 1996. Key ecological function of charcoal from wildfire in the boreal forest. Oikos 77, 10\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt, M.W.I., and A.G. Noack. 2000. Black carbon in soils and sediments: Analysis, distribution, implications, and current challenges. Global Biogeochemical Cycles 14:777\u0026ndash;794.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkjemstad, J.O., D.C. Reicosky, A.R. Wilts, and J.A. McGowan. 2002. Charcoal carbon in U.S. agricultural soils. Soil Science Society. Am. J. 66:1249\u0026ndash;1255.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown, K.J., J.S. Clark, E.C. Grimm, J.J. Donovan, P.G. Mueller, B.C.S. Hansen, and I. Stefanova. (2005). Fire cycles in North American interior grasslands and their relation to prairie drought. Proc. Natl. Acad. Sci. 102:8865\u0026ndash;8870.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChidumayo, Emmanuel N., \u0026amp; Gumbo, Davison J. (2013). The environmental impacts of charcoal production in tropical ecosystems of the world: A synthesis. Energy for Sustainable Development, 17(2), 86\u0026ndash;94. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.esd.2012.07.004\u003c/span\u003e\u003cspan address=\"10.1016/j.esd.2012.07.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHofstad, O., Kohlin, G., \u0026amp; Namaalwa, J. (2009). How can emissions from woodfuel be reduced? In A. Angelsen, M. Brockhaus, M. Kanninen, E. Sills, W. D. Sunderlin, \u0026amp; S. Wertz-Kanounnikoff (Eds.), Realising REDD+: National Strategy and Policy Options (pp. 237\u0026ndash;249). \u003cem\u003eCenter for International Forestry Research\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt, M.W.I., J.O. Skjemstad, E. Gehrt, \u0026amp; I. Kogel-Knabner. 1999. Charred organic carbon in German chernozemic soils. Eur. J. Soil Science. 50:351\u0026ndash;365.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePietik\u0026auml;inen, J., \u0026amp; Fritze, H. (1993). Microbial biomass and activity in the humus layer following burning: short-term effect of two different fires. Canadian Journal of Forest Research, 23, 1275\u0026ndash;1285.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrieto-Fern\u0026aacute;ndez, A., Acea, M. J., \u0026amp; Carballas, T. (1998). Soil microbial and extractable C and N after wildfire. Biology and Fertility of Soils, 27, 132\u0026ndash;142.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026aring;\u0026aring;th, E., Frosteg\u0026aring;rd, \u0026Aring;., Pennanen, T., \u0026amp; Fritze, H. (1995). Microbial community structure and pH response in relation to soil organic matter quality in wood-ash fertilized, clear-cut or burned coniferous forest soils. Soil Biology and Biochemistry, 27, 229\u0026ndash;240.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh, D., Verma, A.K., Choudhary, M., Mahawar, H., Thapa, S., \u0026amp; Mehriya, M.L. (2023). Micronutrient Mobilizer Microorganisms: Significance in Crop Sustainability. In S. Singh, R. Prasanna, \u0026amp; K. Pranaw (eds), Bioinoculants: Biological Option for Mitigating global Climate Change. \u003cem\u003eSpringer, Singapore\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-981-99-2973-3_5\u003c/span\u003e\u003cspan address=\"10.1007/978-981-99-2973-3_5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaravaca F, Alguacil MM, Hern\u0026aacute;ndez JA, Rold\u0026aacute;n A (2005) Involvement of antioxidant enzyme and nitrate reductase activities during water stress and recovery of mycorrhizal \u003cem\u003eMyrtus communis\u003c/em\u003e and \u003cem\u003ePhillyrea angustifolia\u003c/em\u003e plants. Plant Science 169, 191\u0026ndash;197.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinsabaugh, R. L., \u0026amp; Moorhead, D. L. (1994). Resource allocation to extracellular enzyme production: A model for nitrogen and phosphorus control of litter decomposition. Soil Biology and Biochemistry, 26(10), 1301\u0026ndash;1311. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0038-0717(94)90211-9\u003c/span\u003e\u003cspan address=\"10.1016/0038-0717(94)90211-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoerner, R. E. J. (2006). Earth, wind, water, and fire: How the elements conspire in the forest context. In M. B. Dickinson (Ed.), Fire in Eastern Oak Forests: Delivering Science to Land Managers (pp. 104\u0026ndash;122). Proceedings of a conference, 15\u0026ndash;17 November 2005, Columbus, Ohio. General Technical Report NRS-P-1. USDA Forest Service, Northern Research Station, Newtown Square, Pennsylvania, USA.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanchez P (1976) Properties and management of soils in the tropics. Wiley, New York.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrady, N. C., \u0026amp; Weil, R. R. (2008). The Nature and Properties of Soils (14th ed.). Pearson Prentice Hall.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdeyemo, A. J., Ayorinde, A. S., Awodun, M. A., \u0026amp; Oyun, M. B. (2023). Nutrients status and soil microbial biomass C and N in charcoal production sites of derived savannah forest of southern Nigeria. Science of the Total Environment, 2468\u0026thinsp;\u0026ndash;\u0026thinsp;2276, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.sciaf.2023.e01684\u003c/span\u003e\u003cspan address=\"10.1016/j.sciaf.2023.e01684\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJonathan, E. I. (2022). Nematology Fundamentals \u0026amp; Applications (2nd Revised \u0026amp; Enlarged Edition). New India Publishing Agency.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBray, R. H. and Kurtz, L. T. (1945). Determination of total, organic and available forms of phosphorus in soil. Soil Science 599: 39\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanni, D. M., Jimoh, M. B., Fatoki, T. H., Ekundayo, F. O., \u0026amp; Omotoyinbo, O. V. (2017). Isolation of phytate degrading activity from Africa Giant Land Snail (Archachatina marginata). Journal of Agriculture and Food Technology, 7(5), 1\u0026ndash;9. ISSN 2090\u0026ndash;424X.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrudinger, P. A. (1961). Biochemical Journal, 78, 673.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStenstr\u0026ouml;m, J., Stenberg, B., \u0026amp; Johanson, M. (1998). Kinetics of substrate-induced respiration (SIR): theory. Ambio, 27(1), 35\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu, E., Terrer, C., Pellegrini, A. F. A., Ahlstr\u0026ouml;m, A., van Lissa, C. J., Zhao, X., Xia, N., \u0026amp; Jackson, R. B. (2020). Global patterns of terrestrial nitrogen and phosphorus limitation. Nature Geoscience, 13, 221\u0026ndash;226. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41561-019-0530-4\u003c/span\u003e\u003cspan address=\"10.1038/s41561-019-0530-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou, E. Q., Luo, Y. Q., Kuang, Y. W., Chen, C. R., Lu, X. K., Jiang, L. F., Luo, X.Z., Wen, D. Z. (2020). Global meta-analysis shows pervasive phosphorus limitation of aboveground plant production in natural terrestrial ecosystems. Nature Communications, 11, 637. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-020-14492-w\u003c/span\u003e\u003cspan address=\"10.1038/s41467-020-14492-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSohrt, J., Lang, F., \u0026amp; Weiler, M. (2017). Quantifying components of the phosphorus cycle in temperate forests. WIREs Water, 4, e1243. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/wat2.1243\u003c/span\u003e\u003cspan address=\"10.1002/wat2.1243\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOberson, A., \u0026amp; Joner, E. J. (2005). Microbial turnover of phosphorus in soil. In B. L. Turner, E. Frossard, \u0026amp; D. S. Baldwin (Eds.), \u003cem\u003eOrganic Phosphorus in the Environment\u003c/em\u003e (pp. 133\u0026ndash;164). CABI.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMakino, W., Cotner, J. B., Sterner, R. W., \u0026amp; Elser, J. J. (2003). Are bacteria more like plants or animals? Growth rate and resource dependence of bacterial C:N:P stoichiometry. Functional Ecology, 17, 121\u0026ndash;130.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCayuela, M. L., van Zwieten, L., Singh, B. P., Jeffery, S., Roig, A., \u0026amp; S\u0026aacute;nchez-Monedero, M. A. (2014). Biochar's role in mitigating soil nitrous oxide emissions: A review and meta-analysis. Agriculture, Ecosystems \u0026amp; Environment. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.agee.2013.1010.1009\u003c/span\u003e\u003cspan address=\"10.1016/j.agee.2013.1010.1009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCeulemans, T., Merckx, R., Hens, M., \u0026amp; Honnay, O. (2011). A trait-based analysis of the role of phosphorus vs. nitrogen enrichment in plant species loss across North-west European grasslands. Journal of Applied Ecology, 48(5), 1155\u0026ndash;1163.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarpole, W. S., \u0026amp; Tilman, D. (2007). Grassland species loss resulting from reduced niche dimension. Nature, 446(7137), 791\u0026ndash;793.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishio, M. (1996). Microbial fertilizers in Japan. FFTC Extension Bulletins, 1\u0026ndash;12. National Institute of Agro-Environmental Science, Ibaraki, Japan.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaa, A., Trasar-Cepeda, M. C., Gil-Sotres, F., \u0026amp; Carballas, T. (1993). Changes in soil phosphorus and acid phosphatase activity immediately following forest fires. Soil Biology \u0026amp; Biochemistry, 25, 1223\u0026ndash;1230.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCormack, S. A., Ostle, N., Bardgett, R. D., Hopkins, D. W., \u0026amp; Vanbergen, A. J. (2013). Biochar in bioenergy cropping systems: Impacts on soil faunal communities and linked ecosystem processes. GCB Bioenergy, 5, 81\u0026ndash;95. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/gcbb.12046\u003c/span\u003e\u003cspan address=\"10.1111/gcbb.12046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiiri, M., Set\u0026auml;l\u0026auml;, H., Haimi, J., Pennanen, T., \u0026amp; Fritze, H. (2002). Soil processes are not influenced by the functional complexity of soil decomposer food webs under disturbance. Soil Biology \u0026amp; Biochemistry, 34, 1009\u0026ndash;1020. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0038-0717(02)00034-2\u003c/span\u003e\u003cspan address=\"10.1016/S0038-0717(02)00034-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaito, M., \u0026amp; Marumoto, T. (2002). Inoculation with arbuscular mycorrhizal fungi: The status quo in Japan and the future prospects. In S. E. Smith \u0026amp; F. A. Smith (Eds.), Diversity and Integration in Mycorrhizas (Developments in Plant and Soil Sciences, Vol. 94). \u003cem\u003eSpringer\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-94-017-1284-2_27\u003c/span\u003e\u003cspan address=\"10.1007/978-94-017-1284-2_27\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorchard, N., Ladd, B., Eschemann, S., Hegenberg, D., M\u0026ouml;seler, B. M., \u0026amp; Amelung, W. (2014). Black carbon and soil properties at historical charcoal production sites in Germany. Geoderma, 232\u0026ndash;234, 236\u0026ndash;242. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.geoderma.2014.05.007\u003c/span\u003e\u003cspan address=\"10.1016/j.geoderma.2014.05.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao, Y., Gao, Y., Qi, Y., \u0026amp; Li, J. (2018). Biochar-enhanced composts reduce the potential leaching of nutrients and heavy metals and suppress plant-parasitic nematodes in excessively fertilized cucumber soils. Environmental Science and Pollution Research, 25, 7589\u0026ndash;7599. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11356-017-1061-4\u003c/span\u003e\u003cspan address=\"10.1007/s11356-017-1061-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, L., Xu, M., Liu, Y., Zhang, F., Hodge, A., \u0026amp; Feng, G. (2016). Carbon and phosphorus exchange may enable cooperation between an arbuscular mycorrhizal fungus and a phosphate-solubilizing bacterium. New Phytologist, 210, 1022\u0026ndash;1032.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, X., Sun, H., Pumpanen, J., Sieti\u0026ouml;, O.-M., Heinonsalo, J., K\u0026ouml;ster, K., \u0026amp; Berninger, F. (2018). The impact of wildfire on microbial C:N:P stoichiometry and the fungal-to-bacterial ratio in permafrost soil. Biogeochemistry, 142(1), 1\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10\u003c/span\u003e\u003cspan address=\"10.1007/s10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBogie, N. A., Bayala, R., Diedhiou, I., Dick, R. P., \u0026amp; Ghezzehei, T. A. (2018). Alteration of soil physical properties and processes after ten years of intercropping with native shrubs in the Sahel. Soil and tillage research, \u003cem\u003e182\u003c/em\u003e, 153\u0026ndash;163.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFagbenro, J. A., Oshunsanya, S. O., Oyeleye, B., \u0026amp; Aduayi, E. A. (2018). Effect of two biochar types and inorganic fertilizer on soil chemical properties and growth of maize (Zea mays L.). International Educational Scientific Research Journal, 2(4), 43\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLal, R. (2006). Enhancing crop yields in the developing countries through restoration of soil organic carbon pool in agricultural lands. Land Degradation \u0026amp; Development, 17, 197\u0026ndash;209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVitousek, P. M., \u0026amp; Matson, P. A. (2012). Nutrient cycling and biogeochemistry. In D. E. Schimel \u0026amp; J. H. Chadwick (Eds.), The Princeton Guide to Ecology (pp. 330\u0026ndash;339). \u003cem\u003ePrinceton University Press\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eInteraction effects of charcoal production sites and location on selected micronutrients, P and S enzymes.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"731\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\" valign=\"top\"\u003e\n \u003cp\u003eSites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\" valign=\"top\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.902872777017784%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eCopper \u0026nbsp; \u0026nbsp;Colbat \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Iron \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Manganese \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Zinc \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;.......................\u003c/strong\u003e(mg/kg) .....................................\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e(Pho)\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(mg/ml/min) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.326949384404926%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003ePhy \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Tsd \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Dsr\u003c/p\u003e\n \u003cp\u003e................(\u0026micro;g/ml/min) \u0026nbsp;................ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.397260273972606%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.52054794520548%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.191780821917808%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.191780821917808%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.191780821917808%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(mg/ml/\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.506849315068493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\" valign=\"top\"\u003e\n \u003cp\u003eCPS\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026Igrave;r\u0026egrave;le\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.934336525307797%\" valign=\"top\"\u003e\n \u003cp\u003e0.32\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.566347469220246%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.71\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\" valign=\"top\"\u003e\n \u003cp\u003e0.14\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e1.60\u003csup\u003e\u0026nbsp;d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e1.52\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.448700410396716%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.67\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e0.92\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.934336525307797%\" valign=\"top\"\u003e\n \u003cp\u003e0.34\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e0.90\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.566347469220246%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.30\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e1.40\u003csup\u003e\u0026nbsp;e\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e2.94\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.448700410396716%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3.67\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e1.03\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026Igrave;pa\u0026ograve;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.934336525307797%\" valign=\"top\"\u003e\n \u003cp\u003e0.31\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.566347469220246%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.11\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e1.80\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e1.92\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.448700410396716%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e12.93\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e1.96\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\" valign=\"top\"\u003e\n \u003cp\u003eNPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026Igrave;r\u0026egrave;le\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.934336525307797%\" valign=\"top\"\u003e\n \u003cp\u003e0.22\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.566347469220246%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.0\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e1.80\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e0.55\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.448700410396716%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e15.22\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e1.85\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.934336525307797%\" valign=\"top\"\u003e\n \u003cp\u003e0.32\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.566347469220246%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.20\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e1.70\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e2.45\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.448700410396716%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.43\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e0.80\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026Igrave;pa\u0026ograve;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.934336525307797%\" valign=\"top\"\u003e\n \u003cp\u003e0.31\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.566347469220246%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.30\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\" valign=\"top\"\u003e\n \u003cp\u003e0.20\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e1.02\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e2.35\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.448700410396716%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e3.81\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e0.68\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.934336525307797%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.745554035567715%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0.8207934336525308%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.283173734610123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.188782489740082%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.915184678522572%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0.27359781121751026%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.175102599179207%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAccording to Tukey\u0026apos;s test, means that have the same letter in superscript on a column for the same parameter are not different from one another (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2 \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eInteraction effects of charcoal production sites and soil depth on selected soil micronutrients,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eP and S enzymes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"731\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\" valign=\"top\"\u003e\n \u003cp\u003eSites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\" valign=\"top\"\u003e\n \u003cp\u003eDepth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.902872777017784%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eCopper \u0026nbsp; \u0026nbsp;Colbat \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Iron \u0026nbsp; \u0026nbsp; Manganese \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Zinc \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;.......................\u003c/strong\u003e(mg/kg) .....................................\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e(Pho)\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(mg/ml/min) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.326949384404926%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003ePhy \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Tsd \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Dsr\u003c/p\u003e\n \u003cp\u003e................(\u0026micro;g/ml/min) \u0026nbsp;................ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.397260273972606%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.52054794520548%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.191780821917808%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.191780821917808%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.191780821917808%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(mg/ml/\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.506849315068493%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\" valign=\"top\"\u003e\n \u003cp\u003eCPS\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\" valign=\"top\"\u003e\n \u003cp\u003e0-15\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.934336525307797%\" valign=\"top\"\u003e\n \u003cp\u003e0.31\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e0.09\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.566347469220246%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.84\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e1.50\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e2.07\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.448700410396716%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4.68\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e1.13\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\" valign=\"top\"\u003e\n \u003cp\u003e15-30\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.934336525307797%\" valign=\"top\"\u003e\n \u003cp\u003e0.30\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.566347469220246%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.0\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e1.81\u003csup\u003ea\u003cbr\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e2.31\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.448700410396716%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4.86\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e1.14\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\" valign=\"top\"\u003e\n \u003cp\u003e30-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.934336525307797%\" valign=\"top\"\u003e\n \u003cp\u003e0.40\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.566347469220246%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.30\u003csup\u003e\u0026nbsp;e\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\" valign=\"top\"\u003e\n \u003cp\u003e0.20\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e1.41\u003csup\u003e\u0026nbsp;d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e2.00\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.448700410396716%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e9.73\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e1.64\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\" valign=\"top\"\u003e\n \u003cp\u003eNPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\" valign=\"top\"\u003e\n \u003cp\u003e0-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.934336525307797%\" valign=\"top\"\u003e\n \u003cp\u003e0.30\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.566347469220246%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.52\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\" valign=\"top\"\u003e\n \u003cp\u003e0.20\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e1.64\u003csup\u003e\u0026nbsp;d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e1.82\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.448700410396716%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e10.51\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e1.35\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\" valign=\"top\"\u003e\n \u003cp\u003e15-30\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.934336525307797%\" valign=\"top\"\u003e\n \u003cp\u003e0.30\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.566347469220246%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.52\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e1.49\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e1.75\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.448700410396716%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e8.17\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e1.14\u003csup\u003ec\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\" valign=\"top\"\u003e\n \u003cp\u003e30-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.934336525307797%\" valign=\"top\"\u003e\n \u003cp\u003e0.30\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.566347469220246%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.40\u003csup\u003e\u0026nbsp;d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" valign=\"top\"\u003e\n \u003cp\u003e1.35\u003csup\u003e\u0026nbsp;e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e1.78\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.448700410396716%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.78\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\" valign=\"top\"\u003e\n \u003cp\u003e0.84\u003csup\u003ee\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.566347469220246%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.712722298221614%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.934336525307797%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.745554035567715%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0.8207934336525308%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.627906976744185%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.387140902872777%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.283173734610123%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.188782489740082%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.915184678522572%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0.27359781121751026%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.175102599179207%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.49110807113543%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAccording to Tukey\u0026apos;s test, means that have the same letter in superscript on a column for the same parameter are not different from one another (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3 :Interaction effects of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003echarcoal production location and soil depth on selected micronutrients,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eP and S enzymes\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"749\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.811748998664887%\" valign=\"top\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.479305740987984%\" valign=\"top\"\u003e\n \u003cp\u003eDepth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.91989319092123%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eCopper \u0026nbsp; \u0026nbsp;Colbat \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Iron \u0026nbsp; \u0026nbsp; Manganese \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Zinc \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;.......................\u003c/strong\u003e(mg/kg) .....................................\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e(Pho)\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(mg/ml/min) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.574098798397863%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003ePhy \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Tsd \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Dsr\u003c/p\u003e\n \u003cp\u003e................(\u0026micro;g/ml/min) \u0026nbsp;................ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.903743315508024%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.68983957219251%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1390374331550803%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1390374331550803%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.898395721925134%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(mg/ml/\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.229946524064172%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.811748998664887%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026Igrave;r\u0026egrave;le\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.479305740987984%\" valign=\"top\"\u003e\n \u003cp\u003e0-15\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.74365821094793%\" valign=\"top\"\u003e\n \u003cp\u003e0.28\u003csup\u003e\u0026nbsp;d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u003csup\u003e\u0026nbsp;c d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.408544726301735%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.93\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.348464619492656%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e2.0\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e0.59\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e9.41\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e1.60\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.811748998664887%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.479305740987984%\" valign=\"top\"\u003e\n \u003cp\u003e15-30\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.74365821094793%\" valign=\"top\"\u003e\n \u003cp\u003e0.24\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e0.12\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.408544726301735%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.02\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.348464619492656%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e1.71\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e1.50\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e12.51\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e1.41\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.811748998664887%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.479305740987984%\" valign=\"top\"\u003e\n \u003cp\u003e30-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.74365821094793%\" valign=\"top\"\u003e\n \u003cp\u003e0.28\u003csup\u003e\u0026nbsp;de\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;a-c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.408544726301735%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.60\u003csup\u003e\u0026nbsp;f\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.348464619492656%\" valign=\"top\"\u003e\n \u003cp\u003e0.20\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e1.30\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e1.01\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eo.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4.93\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e1.15\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.811748998664887%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.479305740987984%\" valign=\"top\"\u003e\n \u003cp\u003e0-15\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.74365821094793%\" valign=\"top\"\u003e\n \u003cp\u003e0.38\u003csup\u003e\u0026nbsp;ab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e0.06\u003csup\u003e\u0026nbsp;d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.408544726301735%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.94\u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.348464619492656%\" valign=\"top\"\u003e\n \u003cp\u003e0.09\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e1.41\u003csup\u003e\u0026nbsp;e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e3.32\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e4.51\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e1.11\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.811748998664887%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.479305740987984%\" valign=\"top\"\u003e\n \u003cp\u003e15-30\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.74365821094793%\" valign=\"top\"\u003e\n \u003cp\u003e0.26\u003csup\u003e\u0026nbsp;ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003csup\u003e\u0026nbsp;ab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.408544726301735%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.70\u003csup\u003e\u0026nbsp;d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.348464619492656%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e1.71\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e2.55\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.92\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e0.85\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.811748998664887%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.479305740987984%\" valign=\"top\"\u003e\n \u003cp\u003e30-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.74365821094793%\" valign=\"top\"\u003e\n \u003cp\u003e0.35\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.408544726301735%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.10\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.348464619492656%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e1.30\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e2.21\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.21\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e0.78\u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.811748998664887%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026Igrave;pa\u0026ograve;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.479305740987984%\" valign=\"top\"\u003e\n \u003cp\u003e0-15\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.74365821094793%\" valign=\"top\"\u003e\n \u003cp\u003e0.24\u003csup\u003e\u0026nbsp;f\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e0.09\u003csup\u003e\u0026nbsp;b c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.408544726301735%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.17\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.348464619492656%\" valign=\"top\"\u003e\n \u003cp\u003e0.30\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e1.33\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e1.92\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e8.86\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.811748998664887%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.479305740987984%\" valign=\"top\"\u003e\n \u003cp\u003e15-30\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.74365821094793%\" valign=\"top\"\u003e\n \u003cp\u003e0.40 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e0.08 \u003csup\u003ec d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.408544726301735%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.61 \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.348464619492656%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e1.50 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e2.04\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e5.12\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e1.17\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.811748998664887%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.479305740987984%\" valign=\"top\"\u003e\n \u003cp\u003e30-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.74365821094793%\" valign=\"top\"\u003e\n \u003cp\u003e0.37\u003csup\u003e\u0026nbsp;b c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u003csup\u003e\u0026nbsp;c d\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.408544726301735%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.33\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.348464619492656%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" valign=\"top\"\u003e\n \u003cp\u003e1.40\u003csup\u003e\u0026nbsp;e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e2.45\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e0.15\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.149532710280374%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e11.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\" valign=\"top\"\u003e\n \u003cp\u003e1.78\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.811748998664887%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.479305740987984%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.74365821094793%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.607476635514018%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0.8010680907877169%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.348464619492656%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.209612817089453%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"3.2042723631508676%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.1361815754339117%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.8691588785046729%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0.26702269692923897%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.882510013351135%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.214953271028037%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAccording to Tukey\u0026apos;s test, means that have the same letter in superscript on a column for the same parameter are not different from one another (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eInteraction effect of charcoal production sites by location by soil depth\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eon selected micronutrients, P and S enzymes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.775510204081634%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eCopper \u0026nbsp; \u0026nbsp; \u0026nbsp; Colbat \u0026nbsp; \u0026nbsp; \u0026nbsp;Iron \u0026nbsp; \u0026nbsp; Manganese \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Zinc \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;.......................\u003c/strong\u003e(mg/kg) .................................\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e(Pho)\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(mg/ml/min) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ePhy \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Tsd \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Dsr\u003c/p\u003e\n \u003cp\u003e................(\u0026micro;g/ml/min) \u0026nbsp;................ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.775510204081634%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003eCPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.70\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.57\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e2.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e6.82\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e1.30\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003eNPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.29\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.09\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.48\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.12\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.50\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e1.78\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e6.42\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e1.11\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026Igrave;r\u0026egrave;le\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.27\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.84\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.12\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.66\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e1.03\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e8.95\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e1.39\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026Ograve;k\u0026egrave;-\u0026Agrave;k\u0026ograve;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.23\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.53\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e2.69\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e2.55\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e1.32\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026Igrave;pa\u0026ograve;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.83\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.70\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.41\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e2.13\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e8.37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e0.91\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil depth (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.30\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.70\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.15\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.60\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e1.94\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e7.60\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e1.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e15-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.30\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.80\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.12\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.50\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e2.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e6.51\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e1.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e30-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.10 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.33\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e1.60\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e1.89\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e0.15\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e5.76\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e1.14\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003eThree way ANOVA results\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003eSite (S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003eLocation (L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003eSoil depth (Sd)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003eS x L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003eS x Sd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003eL x Sd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003eS x L x Sd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003eCoefficient of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e34.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e32.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e46.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e118.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e54.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.46808510638298%\" valign=\"top\"\u003e\n \u003cp\u003eVariance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\" valign=\"top\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAccording to Tukey\u0026apos;s test, means that have the same letter in superscript on a column for the same parameter are not different from one another (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: \u0026nbsp; Correlation among variables\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"939\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.12700106723586%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.336179295624333%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Zn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Mn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Fe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Co\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Cu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.110992529348986%\" valign=\"top\"\u003e\n \u003cp\u003eDsr\u003cbr\u003e\u0026nbsp;\u0026micro;g/ml/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.245464247598719%\" valign=\"top\"\u003e\n \u003cp\u003eTsd\u003cbr\u003e\u0026nbsp;\u0026micro;g/ml/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Phy\u003cbr\u003e\u0026nbsp;\u0026micro;g/ml/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.391675560298825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Pho\u003cbr\u003e\u0026nbsp;mg/ml/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4706510138740665%\" valign=\"top\"\u003e\n \u003cp\u003eNematode\u003cbr\u003e(10\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFungi (10\u003csup\u003e5\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4034151547492%\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003eBacteria\u003c/sup\u003e\u003cbr\u003e(10\u003csup\u003e8\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.3479188900747063%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.12700106723586%\" valign=\"top\"\u003e\n \u003cp\u003eMn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.336179295624333%\" valign=\"top\"\u003e\n \u003cp\u003e-0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.110992529348986%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.245464247598719%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.391675560298825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4706510138740665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.08751334044824%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.3479188900747063%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.12700106723586%\" valign=\"top\"\u003e\n \u003cp\u003eFe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.336179295624333%\" valign=\"top\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.110992529348986%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.245464247598719%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.391675560298825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4706510138740665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.08751334044824%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.3479188900747063%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.12700106723586%\" valign=\"top\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.336179295624333%\" valign=\"top\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.110992529348986%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.245464247598719%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.391675560298825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4706510138740665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.08751334044824%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.3479188900747063%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.12700106723586%\" valign=\"top\"\u003e\n \u003cp\u003eCu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.336179295624333%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.110992529348986%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.245464247598719%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.391675560298825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4706510138740665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.08751334044824%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.3479188900747063%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.12700106723586%\" valign=\"top\"\u003e\n \u003cp\u003eDsr\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.336179295624333%\" valign=\"top\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.110992529348986%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.245464247598719%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.391675560298825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4706510138740665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.08751334044824%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.3479188900747063%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.12700106723586%\" valign=\"top\"\u003e\n \u003cp\u003eTsd\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.336179295624333%\" valign=\"top\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.110992529348986%\" valign=\"top\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.245464247598719%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.391675560298825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4706510138740665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.08751334044824%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.3479188900747063%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.12700106723586%\" valign=\"top\"\u003e\n \u003cp\u003ePhy \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.336179295624333%\" valign=\"top\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.110992529348986%\" valign=\"top\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.245464247598719%\" valign=\"top\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.391675560298825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4706510138740665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.08751334044824%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.3479188900747063%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.12700106723586%\" valign=\"top\"\u003e\n \u003cp\u003ePho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.336179295624333%\" valign=\"top\"\u003e\n \u003cp\u003e-0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.110992529348986%\" valign=\"top\"\u003e\n \u003cp\u003e-0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.245464247598719%\" valign=\"top\"\u003e\n \u003cp\u003e-0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\" valign=\"top\"\u003e\n \u003cp\u003e-0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.391675560298825%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4706510138740665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.4034151547492%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.3479188900747063%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.12700106723586%\" valign=\"top\"\u003e\n \u003cp\u003eNematode\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.336179295624333%\" valign=\"top\"\u003e\n \u003cp\u003e-0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.110992529348986%\" valign=\"top\"\u003e\n \u003cp\u003e-0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.245464247598719%\" valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\" valign=\"top\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.391675560298825%\" valign=\"top\"\u003e\n \u003cp\u003e-0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4706510138740665%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.08751334044824%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.3479188900747063%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.12700106723586%\" valign=\"top\"\u003e\n \u003cp\u003eFungi\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.336179295624333%\" valign=\"top\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.110992529348986%\" valign=\"top\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.245464247598719%\" valign=\"top\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\" valign=\"top\"\u003e\n \u003cp\u003e-0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.391675560298825%\" valign=\"top\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4706510138740665%\" valign=\"top\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.08751334044824%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.3479188900747063%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.12700106723586%\" valign=\"top\"\u003e\n \u003cp\u003eBacteria\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.336179295624333%\" valign=\"top\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.110992529348986%\" valign=\"top\"\u003e\n \u003cp\u003e-0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.245464247598719%\" valign=\"top\"\u003e\n \u003cp\u003e-0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\" valign=\"top\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.391675560298825%\" valign=\"top\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4706510138740665%\" valign=\"top\"\u003e\n \u003cp\u003e-0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.04375667022412%\" valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.04375667022412%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.3479188900747063%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.12700106723586%\" valign=\"top\"\u003e\n \u003cp\u003eBiomass P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.336179295624333%\" valign=\"top\"\u003e\n \u003cp\u003e-0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\" valign=\"top\"\u003e\n \u003cp\u003e-0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.110992529348986%\" valign=\"top\"\u003e\n \u003cp\u003e-0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.245464247598719%\" valign=\"top\"\u003e\n \u003cp\u003e-0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\" valign=\"top\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.391675560298825%\" valign=\"top\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4706510138740665%\" valign=\"top\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.04375667022412%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.04375667022412%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.3479188900747063%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.12700106723586%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.336179295624333%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.549626467449306%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.110992529348986%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.245464247598719%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.68409818569904%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.391675560298825%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.4706510138740665%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.04375667022412%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0.6403415154749199%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.4034151547492%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.3479188900747063%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePair(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.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Charcoal production, Deforestation, Microbial Composition, P and S Enzymes, Soil health, Soil nutrition","lastPublishedDoi":"10.21203/rs.3.rs-3970781/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3970781/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil 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\u003csub\u003ep\u003c/sub\u003e), 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.\u003c/p\u003e","manuscriptTitle":"Soil nutrition, microbial composition and some selected associated P n S enzymes under charcoal production sites of derived Savanna, Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-25 05:08:33","doi":"10.21203/rs.3.rs-3970781/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-03T08:12:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-03T04:58:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20606388030092514826669559231667804378","date":"2024-05-22T21:32:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-11T23:34:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-06T11:22:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81c18f45-0b08-4580-b019-8fa39b2e5ece","date":"2024-04-24T13:05:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a0e94960-ac37-40f9-8669-503b3f023487","date":"2024-04-23T06:11:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59b14e85-b029-450d-b7c8-8589a9fff18a","date":"2024-04-19T16:11:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-19T14:40:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-16T06:29:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-20T20:58:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-20T19:55:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-02-19T19:44:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"14721a48-9d91-4181-bd0a-b71c46c12b94","owner":[],"postedDate":"March 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29806606,"name":"Biological sciences/Biochemistry"},{"id":29806607,"name":"Biological sciences/Ecology"}],"tags":[],"updatedAt":"2025-09-08T16:01:40+00:00","versionOfRecord":{"articleIdentity":"rs-3970781","link":"https://doi.org/10.1038/s41598-025-90938-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-09-01 15:58:03","publishedOnDateReadable":"September 1st, 2025"},"versionCreatedAt":"2024-03-25 05:08:33","video":"","vorDoi":"10.1038/s41598-025-90938-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-90938-9","workflowStages":[]},"version":"v1","identity":"rs-3970781","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3970781","identity":"rs-3970781","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}