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Article

Assessment of Soil Organic Matter and Its Microbial Role in Selected Locations in the South Bohemia Region (Czech Republic)

by
David Kabelka
,
Petr Konvalina
,
Marek Kopecký
*,
Eva Klenotová
and
Jaroslav Šíma
Department of Agroecosystems, Faculty of Agriculture and Technology, University of South Bohemia in České Budějovice, 370 05 České Budějovice, Czech Republic
*
Author to whom correspondence should be addressed.
Land 2025, 14(1), 183; https://doi.org/10.3390/land14010183
Submission received: 18 November 2024 / Revised: 9 January 2025 / Accepted: 15 January 2025 / Published: 17 January 2025
(This article belongs to the Special Issue Soil Ecological Risk Assessment Based on LULC)
Browse Figures
Versions Notes

Abstract

:
Organic matter has a very important function in soil, without which, soil formation processes cannot take place properly. It can be divided and classified based on several aspects; the most general division is between the living and non-living parts of organic matter. The results presented in this paper specifically refer to the living microbial part of organic matter. This research was carried out in the years 2021–2024 in the South Bohemia region located in the Czech Republic. Two types of land use (four permanent grassland areas, two forest areas) were evaluated. Based on laboratory soil analyses, some significant dependencies were found. For example, in grasslands with statistically identical pH, there was a dependence (p-value 0.05) between soil organic carbon (SOC), carbon of microbial biomass (MBC) and microbial basal respiration (MBR). Additionally, coniferous forest experimental locations had a lower pH, which, in turn, slowed the activity of microorganisms and promoted the accumulation of SOC in the soil. The results from this experiment support the current knowledge of organic matter and are important for a better understanding of the soil organic matter cycle.

1. Introduction

Organic matter, only being a small part of soil, plays a key role because the decomposition, mineralisation and subsequent humification of organic material are among the most important soil processes affecting soil fertility and quality [1,2]. It is an important material derived from plant and animal sources [3] and has an effect on the following aspects of soil: (1) improving structure: it helps aggregate soil particles, which improves the stability of soil aggregates and increases soil porosity [4]; (2) nutrient source: organic matter contains nutrients that it releases when it decomposes [5]; (3) increasing soil retention: it gives soil a better ability to retain water [6]; (4) reduction of erosion and surface runoff [7,8]; and (5) providing a source of energy for microorganisms: it is the main source of feed and nutrients for soil microorganisms [9].
The total amount of organic matter in soil can be influenced in the long term by management practices [10]. The use of organic fertilisers, intercrops, deep-rooted crops and ploughing puts crop residues back into the soil and contributes to enriching soil with organic matter [7,10,11]. Conversely, a lack of organic matter input into soil and the loss of organic matter are behind some serious degradation processes [12].
The basic general division of organic matter is into its non-living and living parts. The non-living part of soil organic matter is divided according to the degree of decomposition [13] and the presence of non-humified soil organic matter comprising undecomposed organic matter. This includes the remains of plants (leaves, roots) and animals whose original structure is retained [9,14]. Another part consists of partially decomposed organic matter that has already been altered by soil organisms [15]. The next part is fully decomposed organic matter, which, in later stages, is referred to as humus [16]. This decomposed organic matter no longer shows signs of its original structure [17].
The living part of soil organic matter can be divided as follows: microflora [18], microfauna, mesofauna, macrofauna and megafauna [19]. This classification includes a range from smallest to largest, i.e., from micrometres to centimetres and, in some cases, up to metres [20]. Microfauna and microflora inhabit water-filled pores and water films around soil particles [21]. Mesofauna occur in existing air-filled pore spaces and are largely confined by these spaces. Macrofauna have the ability to create their own spaces through their activities and, like megafauna, can significantly influence the coarse structure of soils [22,23]. The biodiversity in soils is impressively large [24,25] and, therefore, species with similar biology and morphology are often grouped together for classification purposes [26]. The most abundant species are among the microbial component of soil [19]. When assessing the living microbial part of organic matter, the basic parameters evaluated include microbial basal respiration (MBR) and microbial biomass carbon (MBC). These parameters have been monitored by a number of authors [27,28,29] and are, therefore, also evaluated in this paper.
Of course, many variables enter into the whole process of microorganism activity [30,31]. For example, temperature [32,33], soil moisture [34,35], pH [36], nutrient availability [37,38], redox potential [39] and carbon/nitrogen ratio [40] are known to be the main factors that contribute to microbial activity.
Differences can also be expected for different types of land use [41,42]. Microbial activity is particularly important for land use types where soil is the basis for management, i.e., mainly agricultural land and forests, possibly also in other ecological systems [43]. The concept of agricultural land is general and includes different types of land cover. In soil environments where disturbance is frequent (typically conventional arable land management), it is more difficult to draw general conclusions, as a number of different agricultural operations can be used during cultivation that have a significant impact on soil parameters [44]. For this reason, grasslands and forests are the subject of the current assessment as they have a relatively stable soil environment and there is an assumption of less inter-annual variation.
The aim of this study was to verify the following hypotheses: (1) soil microbial activity will vary in permanent grassland and forest locations; (2) microbial activity assessed by MBR and MBC is dependent on the amount of soil organic carbon (SOC); and (3) the amount of SOC in soil is influenced by the pH level.

2. Materials and Methods

Six soil samples from South Bohemia (49°05′00.0″ N, 14°40′00.0″ E) in the Czech Republic were tested every year in 2021–2024 (Figure 1). Four soil samples were from agricultural land (grassland) and two were from coniferous forest soil. Soil texture at each location listed in Table 1 was determined using the hydrometric method. This method is based on the principle of measuring the sedimentation rate of soil particles in a water environment. A hydrometer (an instrument for measuring the density of the suspension) was used to record the concentration of particles in the water at various time intervals. This makes it possible to measure the proportion of each fraction and thus determine the soil texture. The subsequent classification was performed using the texture triangle [45], which is also currently used in the Czech Republic.
One composited soil sample was taken from each locality with a spade and placed in paper bags. The composited sample consisted of a total of 5 samples from a 10 × 10 m area. The depth of sampling was 0–15 cm and the amount of soil sampled from each location was approximately 3 kg. It should be mentioned that forest locations usually have an organic horizon, which has a strong influence on the amount of SOC in the soil. When taking soil samples, the emphasis was always on doing everything the same way every year. First, the organic horizon was carefully removed and then the topsoil was taken. Nevertheless, it was difficult to ensure the uniformity of the sampling. This problem can be clearly seen in Table 2, where the standard deviation is higher for forest locations than for grassland. In the case of grassland, the top layer containing live plants was removed. Subsequently, soil sampling was carried out using a spade.
After sampling, material from the composite soil samples was taken to the laboratory and used for subsequent analyses. Samples were air-dried so that they could be sieved and then particles larger than 2 mm were removed. The soil intended for pH and SOC analysis was dried at 60 °C to a constant weight before analysis. Part of the remaining soil was homogenised and sieved through a 0.25 mm sieve. Soil sieving was conducted because some analyses were performed on soil textures less than <2 mm (pH, MBR, MBC), while SOC was analysed on a fraction < 0.25 mm.
Individual analyses of soil samples were carried out using the following procedures:
  • Soil pH (ISO 10390:2005 [46])— was determined using a 1 M KCl solution. In 250 mL plastic bottles, 10 g of soil was weighed and 50 mL of KCl was added. Subsequently, the samples were shaken for 60 min on a shaker. The pH was then measured using the SI Analytics Lab 875P (Xylem Analytics, Weilhem, Germany). The measurements were carried out in two repetitions each time.
  • SOC—the basis of the analysis was the method of Tyurin [47], where the process of determination was as follows: 0.1 g of soil (<0.25 mm) was weighed in triplicate for each location. Subsequently, 0.3 M K2Cr2O7 and concentrated H2SO4 were mixed to form a chromosulfur mixture. The titration cups with the weighed soil were added to 12.5 mL of the chromo sulphur mixture and then the samples were burned in an oven at 135° for 30 min. Then, titration was performed using 0.2 M Mohr’s salt ((NH4)2Fe(SO4)2·6H2O + H2SO4) on the Mettler Toledo DL55 titrator (Mettler Toledo, Schwerzenbach, Switzerland).
  • MBR—the analysis was based on the ISO standard 16072:2002 [48], but some modifications were made in the determination (soil moistening). The procedure was as follows: 150 g of soil moistened to 50% retention capacity was pre-incubated for 3 days and then 25 g of soil was weighed into breathable bags (in three replications). The breathable bags were hung in a 750 mL sealed glass bottle with 20 mL of 0.05 M NaOH at the bottom for 3 days. Before titration, 2 mL of 0.5 M BaCl2 and phenolphthalein were added. Subsequently, titration was performed on the Mettler Toledo DL55 titrator (Mettler Toledo, Schwerzenbach, Switzerland) using 0.1 M HCl.
  • MBC—the analysis was performed according to the ISO standard 14240-2:1997 [49] with some modifications (the weighing and moistening of the soil): 250 g of soil moistened to 50% retention capacity was pre-incubated for 3 days and then 25 g of soil was weighed (in six replications). Three samples were fumigated in a desiccator using boiling chloroform and left in the dark for 24 h. To the remaining samples, 200 mL of 0.5 M K2SO4 was added, and the samples were shaken for 30 min. The same process was carried out for the fumigated samples after 24 h. MBC was determined by dichromate oxidation. All samples were filtered and 8 mL was taken. Subsequently, 2 mL 0.07 M K2Cr2O7 and 15 mL acid mixture (H2SO4, H3PO4) were added. After refluxing for 30 min, titration was carried out using 0.04 M Mohr’s salt ((NH4)2Fe(SO4)2·6H2O + H2SO4) on the Mettler Toledo DL55 titrator (Mettler Toledo, Schwerzenbach, Switzerland).
  • Microbial metabolic quotient (MMQ)—this was the ratio between the MBR and the MBC, where the MBR was divided by the MBC.
A basic statistical evaluation was performed on the results to obtain significant correlations using one-way ANOVA. The Pearson correlation coefficient was also determined for the processed data. This correlation coefficient was evaluated if there were at least two distinct locations in the statistical evaluation.

3. Results

3.1. Soil Reaction (pH) in Experimental Locations

The basic analysis performed was the determination of pH. The results of the measurements are shown in Figure 2 and Table 2. Statistically significantly higher values were measured at the grassland locations compared to the forest locations (Table 3). The highest pH across the grasslands was measured at G1 (4.90). The lowest value was at G3 (4.64). Locations G2 and G2 fell within this range in their values. Statistical evaluation of all grasslands showed no significant difference. They were therefore identical in terms of pH. Statistical agreement was also valid for the forest locations, where the mean pH for F1 was 3.22 and that for F2 was 3.32 (Table 3). The values and standard deviations (Table 2) across research years could be considered relatively stable in the locations. The highest standard deviation was measured at G2 (0.28), but this was not a significant fluctuation. Lower values were measured at the other locations.
The results from this analysis were very important for the subsequent evaluation because it was possible to divide all assessed locations into two groups with statistically identical pH. Forest locations represented one group and grasslands represented the second group. This made it possible to evaluate the effect of different pH on the other assessed parameters (SOC, MBC, MBR, MMQ). As shown in the following Section 3.2 and Section 3.3, this fact proved to be very important for some parameters.

3.2. Relationship Between pH and SOC

SOC, like pH, is an essential parameter in the assessment of soil parameters. The highest amount of SOC was found at the forest locations, where the average value was 7.37% at F1 and 7.28% at F2. In this context, it should be added that the method of soil sampling mattered because as the amount of organic horizon (for forest locations) in the soil sample increased, so did the amount of SOC. It was problematic to ensure the same amount of organic horizon in the soil samples. This can be clearly seen in the standard deviations (Table 2), which were higher for forest locations than for grasslands. These facts need to be taken into account when comparing the results with other studies. In the statistical evaluation, the values from forest locations (F1 and F2) were identical (Table 3). When comparing them with grasslands, they were significantly different (p-value < 0.05). The individual grassland locations, however, did not have statistically the same SOC content, confirming that the whole issue was much more complex. The highest SOC value was measured at G1 (3.93%). This location was statistically identical to G4 (3.44%) but significantly different from G2 (2.52%) and G3 (2.36%). Locations G2, G3 and G4 could be considered identical (Table 3).
Correlations were determined at all locations but also separately for grasslands and forests (Figure 3). In the case of the forest locations (F1, F2), which were statistically identical, correlations were not assessed further. When all locations were assessed, the strongest correlation was obtained between pH and SOC. Specifically, a strong inverse correlation (−0.913) was found, meaning that the lower the pH, the higher the SOC content of the soil. For this statement, the depth of sampling needs to be considered (see Discussion chapter).

3.3. Microbial Activity (MBR, MBC) as a Function of SOC

Soil microbial activity was measured using two basic indicators (MBR, MBC). The lowest MBR values were determined at both forest locations, which were also identical according to the statistical evaluation. The MBR values were higher for the grasslands. For MBC, the situation was similar to that of MBR. Lower MBC values were measured for forest locations compared to grasslands.
Based on the results (MBR, MBC), it was confirmed that microbial activity in soil differed between grasslands and forests (hypothesis 1), but statistical differences were also found for individual grassland locations (see, Table 3). In the case of MBR, only G2 and G3 were statistically identical. For MBC, no statistical concordance was confirmed and, therefore, each grassland location could be considered different.
The dependencies between MBR, MBC and SOC were assessed using the correlation coefficient and trend lines. When MBR and MBC were evaluated, there was a strong relationship (R2 = 0.9461) between these two parameters. The dependence was confirmed when evaluating all locations, but only at grasslands (Figure 3). This points to the fact that different soil pH did not significantly affect this dependence. Thus, at the locations evaluated, it was valid that MBC increased with increasing MBR.
The above statement did not apply to the relationship between SOC and MBC. In assessing the correlation across all locations (Figure 3), an inverse correlation (−0.557) was found, but, when looking for a definite trend between SOC and MBC (Figure 4), it is clear that no trend (R2 = 0.2914) was observed. The situation was completely different if the forest locations were omitted and only grassland locations with statistically equally high pH were evaluated. Then, a strong direct correlation (0.920) between SOC and MBC was evident and a significant trend (R2 = 0.907) could be established (Figure 4).
A similar situation, slightly less marked, existed for the relationship between SOC and MBR. When forest locations were included, an inverse correlation (−0.546) was found and the resulting trend (R2 = 0.2152) was significantly negatively affected. When assessing only grassland locations, a strong direct correlation (0.750) was found and a significant trend (R2 = 0.8703) was also established.
From the results, the conclusion was that hypothesis 2 (microbial activity assessed by MBR and MBC is SOC-dependent) was only valid for locations with the same pH. When different locations (grasslands and forests) were evaluated, the hypothesis was not confirmed.
In this paper, the efficiency of the microbial environment expressed in terms of MMQ was also calculated. The highest values were determined for forest locations, which were statistically significantly different from grasslands. It was also confirmed that all grasslands were statistically identical, as were forest locations (Table 3). Figure 3 shows that the highest correlation value (−0.898) was achieved between MMQ and pH when all locations were evaluated. Thus, the results indicate that the MMQ value increased with decreasing pH. An assessment of only grasslands or forests was not carried out for statistical concordance.

4. Discussion

Soil pH is one of the basic indicators that is monitored in a comprehensive soil assessment [50,51,52]. Its value influences a number of soil properties: physical, chemical and biological [53,54]. The level of pH is determined by the composition of the soil but is also influenced by the plants themselves, as the organic matter from each species can have significantly different compositions [55,56,57]. This can be clearly seen in Figure 2, where the pH at the two forest locations differed significantly from the pH at the grassland locations. The results confirm the generally accepted conclusion that coniferous forest locations need and have a lower pH than agricultural land in the upper soil layer (0–15 cm). This conclusion is in agreement with [58,59,60], but is only valid for topsoil, because at deeper soil levels, the differences in pH for individual land uses tend to decrease, as previously reported [61,62].
When assessing the pH of agricultural land, it must be taken into account that changes may occur as a result of agronomic operations. These may affect the pH. Some agrotechnical operations are directly carried out to adjust the pH to the optimum value for the crop [63]. The classic case is the liming of soils, which increases the pH [64]. However, the pH can also be lowered by, for example, applying certain types of mineral or organic fertilisers [65]. The way the soil is cultivated also has an influence. All these agrotechnical operations that are carried out affect not only pH but also other soil parameters including SOC, MBR and MBC [66,67]. The greatest changes in soil parameters can be expected on arable land, where the soil is generally subjected to more frequent cultivation than grasslands and forests. In this context, it is important to note that no agrotechnical operations (liming, organic fertilisation) were carried out on the experimental locations during the research that would have significantly altered the pH. This is apparent from Table 2 (standard deviation) and Figure 2 (box plots).
Similar to pH, the amount of SOC is influenced by a number of parameters. Some of the basic ones include the type of farming [68], soil texture [69] or species composition [70]. In evaluating the relationship of SOC with other parameters, certain dependencies were found. A very strong correlation was determined between pH and SOC, but it must be added that the results were valid for the upper layer in which the sampling took place (0–15 cm). For deeper soil levels, this statement may not be valid. This was also confirmed by some studies in which sampling was carried out in lower layers [43]. The same relationship (ph–SOC) in upper layer was found by [71] in a tropical forest soil environment, [72] in grasslands and forests, and [59] in steppe and cropland locations. Based on the results (Table 2), it can be confirmed with some simplification that, in this case, higher SOC was measured in forest locations due to a more acidic soil environment. This conclusion is also clearly visible in Figure 3, which shows the correlation coefficient between the evaluated parameters. For higher pH (above 8), this may no longer be applicable, as [73] reported a hump-back model between soil organic carbon and pH in grasslands. On arable land, there may even be reverse dependence due to soil cultivation, as noted by [74], i.e., a decrease in organic carbon as pH decreases. This means that hypothesis 3 involving the dependence of SOC on pH was only partially confirmed because in significantly different soil environments than those in this study, the relationship may not always be valid. This observed pH–SOC relationship can be significantly disturbed by agrotechnical operations. It is therefore essential to know the management practices that have been applied to the land.
There were also some findings for SOC and microbial activity (MBR, MBC). In the case of MBC, the lowest values were found at forest locations (F1, F2). The values in this study are in agreement with many authors [75,76,77] who determined the MBC for forest soils. However, values at forest locations can vary considerably. The main reason is due to the complicated soil sampling. It is necessary to separate soil from forest floor during sampling because [78,79] reported that MBC can vary significantly between forest floor and forest soil. For grassland, some authors [80,81] gave similar values to those in this paper, but, in some studies [82,83], there were higher values.
The situation was similar for MBR, but not the same. Almost all locations were statistically different, but several individual MBR values from forest locations were comparable to those of some grassland locations (Figure 4). This finding is in agreement with [84], who also compared grasslands and forests. When evaluating the relationship between MBR and MBC, it can be concluded that a higher MBR in soil indicates a higher MBC (Figure 5), as confirmed by other authors [85,86].
From the results of the SOC–MBC and SOC–MBR relationships, it can be further assumed that the more SOC in the soil, the more microorganisms. This is in line with other authors [87,88,89], but only applicable when the pH is the same. When forest locations are included, this statement cannot be confirmed. The same conclusion may apply to the MBR. A strong correlation between SOC and MBR at grassland locations was reported by [90,91] and also by [92], who assessed only forest locations.
MMQ is considered an important indicator of soil health [93] and the highest correlation was found for pH. Similar findings were reported in a study by [94], who analysed 24 studies and concluded that MMQ often declines with increasing pH in a stable environment. The results indicate that organic matter is more efficiently used by microorganisms in grassland locations, where MMQ is lower compared to forest locations. Higher ratios may reflect that the microbial community is exposed to stressful conditions such as inappropriate pH (Table 2). This means that microorganisms must expend more energy to maintain basic life functions and use less for growth [95]. Soil pH is not the only parameter that affects MMQ, but there are other soil parameters such as clay content, amount of MBC [94] or soil moisture [96].
The conclusion regarding the relationship between SOC and MBC, SOC and MBR or pH and MMQ highlights the issue of overgeneralisation. In some cases, it is not possible to make general conclusions that apply across locations. This is also the case for microbial activity, which is influenced by a number of factors. These factors may vary due to different land cover types and it is therefore always important to take into account basic parameters (e.g., pH) that may differ between land use types.

5. Conclusions

This paper summarised the knowledge concerning organic matter and its microbial role. The results show that some significant trends and dependencies can be found for the parameters evaluated (SOC, MBR, MBC). When assessing microbial activity, both parameters MBR and MBC appear to be suitable indicators in relation to SOC. However, it is also important to assess other parameters such as the pH in the soil environment. Different pH levels can significantly affect the resulting trends, which was evident when forest locations were included. The findings support the current knowledge of soil organic matter and are useful for other authors in evaluating and interpreting results related to microbial activity. Information on microbial activity is also important from the point of view of soil quality. This can be influenced (both negatively and positively) in the long term by the way it is managed. Therefore, management practices that contribute to the enrichment of the diversity of microflora and microfauna in the ecosystem should be promoted in order to increase landscape stability and improve soil health, crop health and agricultural production. From this perspective, it would be interesting to better clarify the dynamics of pH, SOC, MBR and MBC in other ecological systems and land use types. Microbial activity through the decomposition of organic matter influences nutrient cycles, and a deeper understanding of this could provide more support for sustainable management practices and development.

Author Contributions

Conceptualisation, D.K.; methodology, D.K. and M.K.; validation, P.K. and M.K.; formal analysis, D.K. and M.K.; investigation, D.K. and P.K.; resources, D.K, J.Š. and E.K.; data curation D.K. and E.K.; writing—original draft preparation, D.K. and E.K.; writing—review and editing, D.K., M.K. and P.K.; visualisation, J.Š.; supervision, D.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financially supported by the Grant Agency of the University of South Bohemia in České Budějovice, Czech Republic (no. GA JU 122/2025/Z).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Land 14 00183 g001
Figure 1. Map of the Czech Republic and the district of South Bohemia.
Figure 1. Map of the Czech Republic and the district of South Bohemia.
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Figure 2. Soil reaction in experimental locations (box plots).
Figure 2. Soil reaction in experimental locations (box plots).
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Figure 3. Correlation heat map for the evaluated parameters. Fields that are light grey were not evaluated due to statistical equality at all locations (p-value 0.05). Soil organic carbon (SOC), carbon of microbial biomass (MBC), microbial basal respiration (MBR), microbial metabolic quotient (MMQ).
Figure 3. Correlation heat map for the evaluated parameters. Fields that are light grey were not evaluated due to statistical equality at all locations (p-value 0.05). Soil organic carbon (SOC), carbon of microbial biomass (MBC), microbial basal respiration (MBR), microbial metabolic quotient (MMQ).
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Figure 4. Relationship between SOC and microbial parameters. Soil organic carbon (SOC), carbon of microbial biomass (MBC), microbial basal respiration (MBR).
Figure 4. Relationship between SOC and microbial parameters. Soil organic carbon (SOC), carbon of microbial biomass (MBC), microbial basal respiration (MBR).
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Figure 5. Dependence between basic parameters related to microbial activity (MBR, MBC). Carbon of microbial biomass (MBC), microbial basal respiration (MBR).
Figure 5. Dependence between basic parameters related to microbial activity (MBR, MBC). Carbon of microbial biomass (MBC), microbial basal respiration (MBR).
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Table 1. Experimental locations.
Table 1. Experimental locations.
LocationsType of LocationSoil Texture (USDA *)
G1GrasslandSandy Loam
G2GrasslandSandy Loam
G3GrasslandSandy Loam
G4GrasslandLoam
F2Forest (coniferous)Sandy Loam
F1Forest (coniferous)Sandy Loam
* USDA—U.S. Department of Agriculture: texture triangle.
Table 2. Results from the experimental plots (2021–2024).
Table 2. Results from the experimental plots (2021–2024).
LocationspHSOCMBRMBCMMQ—qCO3
(KCl)SD(%)SD(µg CO2·g−1·h−1)SD(µg·g−1)SD(×10−4 MBR:MBC)SD
G14.900.193.930.180.30340.0203756654.10.6
G24.770.282.520.250.16210.0112354384.60.6
G34.660.122.360.230.15890.0160301235.30.5
G44.700.093.440.350.22710.0243577374.00.7
F13.220.057.371.100.10210.0284141187.42.2
F23.320.097.281.740.13330.0143181427.92.7
Standard deviation (SD), soil organic carbon (SOC), carbon of microbial biomass (MBC), microbial basal respiration (MBR), microbial metabolic quotient (MMQ).
Table 3. Statistical evaluation of selected parameters (one-way ANOVA, p-value <0.05).
Table 3. Statistical evaluation of selected parameters (one-way ANOVA, p-value <0.05).
ParametersLocationsG1G2G3G4F1F2
pHG1x6.08 × 10−17.14 × 10−21.64 × 10−11.27 × 10−111.27 × 10−11
G2xx8.14 × 10−19.56 × 10−11.27 × 10−111.27 × 10−11
G3xxx9.99 × 10−11.27 × 10−111.27 × 10−11
G4xxxx1.27 × 10−111.27 × 10−11
F1xxxxx8.19 × 10−1
F2xxxxxx
SOCG1x3.75 × 10−39.14 × 10−47.58 × 10−12.50 × 10−112.81 × 10−11
G2xx9.98 × 10−11.44 × 10−12.29 × 10−112.29 × 10−11
G3xxx5.33 × 10−22.29 × 10−112.29 × 10−11
G4xxxx2.30 × 10−112.30 × 10−11
F1xxxxx1.00 × 10−0
F2xxxxxx
MBRG1x3.22 × 10−113.22 × 10−115.85 × 10−113.22 × 10−113.22 × 10−11
G2xx9.99 × 10−12.56 × 10−92.71 × 10−81.55 × 10−2
G3xxx5.70 × 10−101.25 × 10−74.37 × 10−2
G4xxxx3.22 × 10−113.23 × 10−11
F1xxxxx7.31 × 10−3
F2xxxxxx
MBCG1x2.29 × 10−112.29 × 10−112.30 × 10−112.29 × 10−112.29 × 10−11
G2xx3.26 × 10−22.29 × 10−112.29 × 10−112.31 × 10−11
G3xxx2.29 × 10−112.45 × 10−112.16 × 10−8
G4xxxx2.29 × 10−112.29 × 10−11
F1xxxxx1.96 × 10−1
F2xxxxxx
MMQG1x9.45 × 10−13.90 × 10−11.00 × 10−02.83 × 10−59.86 × 10−7
G2xx9.05 × 10−19.00 × 10−17.48 × 10−43.29 × 10−5
G3xxx3.11 × 10−11.96 × 10−21.34 × 10−3
G4xxxx1.64 × 10−55.55 × 10−7
F1xxxxx9.54 × 10−1
F2xxxxxx
Locations that were statistically equal are highlighted. Soil organic carbon (SOC), carbon of microbial biomass (MBC), microbial basal respiration (MBR), microbial metabolic quotient (MMQ). The light grey colour in the table indicates statistically identical locations.
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Kabelka, D.; Konvalina, P.; Kopecký, M.; Klenotová, E.; Šíma, J. Assessment of Soil Organic Matter and Its Microbial Role in Selected Locations in the South Bohemia Region (Czech Republic). Land 2025, 14, 183. https://doi.org/10.3390/land14010183

AMA Style

Kabelka D, Konvalina P, Kopecký M, Klenotová E, Šíma J. Assessment of Soil Organic Matter and Its Microbial Role in Selected Locations in the South Bohemia Region (Czech Republic). Land. 2025; 14(1):183. https://doi.org/10.3390/land14010183

Chicago/Turabian Style

Kabelka, David, Petr Konvalina, Marek Kopecký, Eva Klenotová, and Jaroslav Šíma. 2025. "Assessment of Soil Organic Matter and Its Microbial Role in Selected Locations in the South Bohemia Region (Czech Republic)" Land 14, no. 1: 183. https://doi.org/10.3390/land14010183

APA Style

Kabelka, D., Konvalina, P., Kopecký, M., Klenotová, E., & Šíma, J. (2025). Assessment of Soil Organic Matter and Its Microbial Role in Selected Locations in the South Bohemia Region (Czech Republic). Land, 14(1), 183. https://doi.org/10.3390/land14010183

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