Next Article in Journal
Biofertilizers Enhance Soil Fertility and Crop Yields Through Microbial Community Modulation
Previous Article in Journal
Long-Term Conservation Tillage Increases Soil Organic Carbon Stability by Modulating Microbial Nutrient Limitations and Aggregate Protection
Previous Article in Special Issue
Rapid Identification of Rhizobia Nodulating Soybean by a High-Resolution Melting Analysis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 15
Issue 7
10.3390/agronomy15071573
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effect of Grassland Vegetation Units on Soil Biochemical Properties and the Abundance of Selected Microorganisms in the Obra River Valley

by
Justyna Mencel
1,*,
Anna Wojciechowska
2 and
Agnieszka Mocek-Płóciniak
1,*
1
Department of Soil Science and Microbiology, Poznań University of Life Sciences, Szydłowska 50, 60-656 Poznań, Poland
2
Department of Geobotany and Landscape Planning, Nicolaus Copernicus University in Toruń, Lwowska 1, 87-100 Toruń, Poland
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(7), 1573; https://doi.org/10.3390/agronomy15071573
Submission received: 27 May 2025 / Revised: 23 June 2025 / Accepted: 26 June 2025 / Published: 27 June 2025
(This article belongs to the Special Issue Plant Production and Microorganism Potential in Modern Agro-Ecosystems—2nd Edition)
Browse Figures
Versions Notes

Abstract

The study examined seasonal variability in soil enzymatic activity and microbial abundance across five grassland vegetation units: Molinietum caeruleae, Alopecuretum pratensis, Arrhenatheretum elatioris, Lolio–Cynosuretum, and com. Poa pratensis–Festuca rubra. Soils under Molinietum caeruleae showed higher fungal abundance and greater plant diversity, while Lolio–Cynosuretum was notable for elevated Azotobacter spp. populations. Actinobacteria preferred soils with more organic matter, whereas Azotobacter spp. favored higher pH. A negative correlation was observed between the Shannon diversity index (H′) and heterotrophic bacterial abundance in Arrhenatheretum elatioris and with fungal abundance in com. Poa pratensis–Festuca rubra. Acid and alkaline phosphatase and catalase activities were also negatively correlated with H′. Redundancy analysis showed these enzymes were related to total nitrogen content, and enzyme activity decreased with rising soil pH. In autumn 2022, high fungal abundance coincided with a reduction in other microorganisms. Seasonal trends were evident: catalase and urease activities peaked in autumn 2023, while other enzymes were more active in spring 2022. The results emphasize the significance of seasonal shifts in shaping microbial and enzymatic soil processes, which are vital for nutrient cycling, carbon sequestration, and climate regulation. Further research is essential to guide sustainable grassland soil management.

1. Introduction

It is widely recognized that semi-natural grasslands and pastures are key areas for biodiversity conservation, and maintaining high levels of biodiversity is essential for the proper functioning of numerous processes in ecosystems with multiple functions [1,2]. In areas undergoing intensive agricultural activity, a clear decline in both the area of semi-natural grasslands and the biodiversity associated with them is observed [3,4]. The grasslands of the Molinio–Arrhenatheretea class, which are described in this article, are a source of broadly understood biodiversity [4,5,6].
Grasslands are critical ecosystems that support biodiversity and ecosystem services, with soil microbes playing a central role in nutrient cycling and carbon sequestration. The abundance and activity of these microbial communities are influenced by a combination of factors, including soil composition, climate, agricultural practices, and plant–microbial interactions [1,7]. Soil is a dynamic environment in which microorganisms play a key role in maintaining its fertility and health. Bacteria, fungi, and other organisms participate in biogeochemical processes such as the decomposition of organic matter, the mineralization of nutrients, and the formation of humus [8,9,10]. Their enzymatic activity affects plant nutrient availability and the entire soil ecosystem [11].
Grassland soils have a rich and diverse microbiome. Bacteria account for 70–90% of the total biomass in the soil, making them the most widespread organisms in this environment. Fungi are in second place in terms of abundance. Both groups of microorganisms are involved in most of the biological and biochemical processes that directly affect the soil and therefore plants [12,13].
Plant growth-promoting rhizobacteria (PGPR) include Bacillus, Pseudomonas, Azospirillum, and Streptomyces and promote plant growth by producing phytohormones and organic acids or by fixing nitrogen and solubilizing phosphorus [14,15]. Other important microorganisms are the genera Azotobacter and Clostridium bacteria, which are also involved in nitrogen-fixing processes [16,17]. Soil fungi help improve soil structure and increase water retention capacity by producing organic acids and participating in the mineralization of organic matter [18,19,20]. About 90% of all plants form symbiotic relationships with mycorrhizal fungi using a network of filaments, a relationship called the mycorrhizal network. Through this relationship, plants obtain phosphates and other minerals, such as copper and zinc, while the fungus obtains nutrients like sugars. The mycorrhizal network allows fungi to transport nutrients over relatively long distances [21,22,23].
In soil ecosystems, enzymes come primarily from microorganisms, and in smaller amounts, they are produced by plants and other organisms found in the soil. Microorganisms secrete various enzymes into the environment, of which the key ones in the decomposition of plant residues are those directly involved in the degradation of lignin and cellulose as well as in the mineralization of organic forms of nitrogen, phosphorus, and sulfur, which promotes their reintroduction into circulation in the ecosystem [24,25,26]. Enzymes are a sensitive indicator of changes occurring in soil under the influence of both natural factors and human activity. The enzymatic activity of soil is considered a better indicator of its fertility and production potential than other biological indicators.
Enzymes such as dehydrogenases (DhA), urease (UA), acid phosphatase (AcP) and alkaline phosphatase (AlP), proteases (PA), and catalase (CAT) reflect the intensity of biological processes occurring in the soil. For example, DhA and CAT are related to the respiratory metabolism of microorganisms and can indicate the soil’s overall microbial activity [27,28,29,30,31,32]. DhA allows for various biochemical reactions [27,33,34], and CAT protects living cells from oxidative damage [27,34,35]. UA activity is sensitive to various xenobiotics and provides plant-available N [27,36,37,38], while phosphatases activity is related to phosphorus transformations [39,40,41,42]. PA participates in the initial breakdown of protein components containing organic nitrogen into simple amino acids [28,43,44]. Therefore, it can be stated that geochemical processes occurring in soil are closely related to its biological activity.
The abundance of microorganisms and the enzymatic activity of soil vary depending on its physical and chemical properties and agrotechnical treatments (cultivation methods, plant types, and fertilization methods) [29,45,46,47]. Soil water content is a critical driver of microbial activity in grasslands. Studies have shown that higher soil moisture levels can enhance microbial biomass and enzyme activity, particularly in water-limited ecosystems [48,49,50]. Soil organic matter (SOM) is another key factor. Labile fractions of SOM play a critical role in controlling microbial processes [10,50]. For example, in low-productivity grasslands, root litter decomposition is slower, which may be linked to lower microbial activity and slower carbon cycling rates [51]. Studies have shown that the organic matter content of soil is related to the size of the particles—the smaller the particles, the higher the level of organic matter. In addition, in such conditions, the structure of the microbial community is more complex, and biodiversity is greater [52]. Nutrient availability, particularly nitrogen (N) and phosphorus (P), also shapes microbial communities. For example, fertilization can indirectly influence microbial activity by altering plant diversity and soil carbon storage [53]. The pH value is an important indicator influencing the survival of microorganisms, their availability to other organisms, and the chemical form of the substrate in which they operate [10,54].
The main aim of this study was to evaluate the relationships between selected microbiological and biochemical properties in grassland soils under different syntaxonomic vegetation units. We address the following research hypothesis: The abundance of selected microorganisms and the enzymatic activity in grassland soils are determined by both biotic factors, such as vegetation units structure, and abiotic parameters, including soil pH, total organic carbon (TOC), total nitrogen (TN), and seasonal variation.

2. Materials and Methods

2.1. Study Area

The research area is located between the northern, middle, and southern branches of the Obra River in the Wielkopolska Lowland of central Poland. The grassland sites are situated in a fully humid, warm temperate climate zone characterized by warm summers [55]. The region experiences a mean annual temperature of 10.6 °C and an average yearly precipitation of 414.6 mm. Notably, the year 2022 was 1.2 °C warmer than the climate normal for 1991–2020, while total precipitation that year amounted to only 77% of the regional average [56]. The year 2023 was even warmer, being 1.4 °C warmer than the climate norm for 1991–2020, while total precipitation for that year was 132% of the regional average [57]. According to the Detailed Geological Map of Poland [58,59,60], the analyzed soils developed from shallow peat layers overlying alluvial materials—primarily sands, with occasional occurrences of silts. The current soil types were identified in the article by Mencel et al. [61]: postmurshic soils (1, 2, 4, 10, and 13), typical semimurshic soils (3, 5, 6, 9, 12, 14, 15, and 17), thin murshic soils (7, 8, 18, 19, and 20), and murshic gleysols (11 and 16). The groundwater depths observed in soil samples 1–18 ranged from 0.8 to 1.15 m, while in samples 19 and 20, shallower levels were recorded between 0.6 and 0.8 m. All locations were georeferenced in situ (Table 1).

2.2. Phytosociological Survey

Fieldwork was performed on semi-natural grasslands, where 76 phytosociological surveys were conducted following the Braun–Blanquet method [62] approach during the 2022–2023 study period. Phytosociological relevés were conducted at 20 designated sampling sites, each covering an area of 100 m2 and characterized by a homogeneous species composition. The collected data were entered into the TURBOVEG database [63], which is specifically designed for storing phytosociological relevés, and subsequently exported to the JUICE 7.1 software [64] for further analysis. Classification of vegetation types was performed according to the phytosociological framework proposed by Matuszkiewicz [5]. As a result, four plant associations and one plant community were identified: Molinietum caeruleae (12 relevés), the community Poa pratensis–Festuca rubra (16 relevés), Arrhenatheretum elatioris (16 relevés), Lolio–Cynosuretum (16 relevés), and Alopecuretum pratensis (16 relevés). These vegetation units were deliberately selected to cover a wide range of habitats with varying moisture content, soil fertility, degree of anthropogenic pressure, and floristic diversity, enabling a comprehensive analysis of the impact of environmental conditions on the microbiological and enzymatic properties of soils. Floristic diversity was assessed using species richness and the Shannon–Wiener diversity index (H′) [65].

2.3. Soil Survey and Sampling

Soil sampling associated with the phytosociological survey was conducted in May and September 2022 and 2023. The sampling sites were distributed across four municipalities: Kościan (sites 1–4), Wielichowo (sites 5–7, 9–10, 13–15, and 17), Przemęt (sites 8, 11–12, and 18–20), and Wolsztyn (site 16). At each site, soil samples for laboratory analysis were taken from the uppermost soil horizons (0–20 cm) in three subsections to account for spatial heterogeneity, according to ISO 10381-1:2002. Samples were taken using an Egner soil sampler (Egner’s cane) (Eijkelkamp, The Netherlands). The numbering of soil samples corresponded to specific phytosociological relevés and associated soil profiles. Collected soils were placed in plastic bags, stored in lightproof containers, and immediately transported under refrigeration to the laboratory for further analysis.

2.4. Chemical Analyses

In the laboratory, soil samples were air-dried, mechanically disaggregated, homogenized, and subsequently passed through a 2 mm mesh sieve to ensure uniformity. Chemical analyses included the determination of total organic carbon (TOC) and total nitrogen (TN) contents, which were measured using a Vario-Max CNS elemental analyzer (Elementar Analysensysteme GmbH, Germany). Soil pH was assessed potentiometrically in a 1:2.5 soil-to-distilled water suspension.

2.5. Abundance of Microorganisms In Vitro

The abundance of soil microorganisms was assessed using the plate culture method on appropriate selective media, with five replicates per sample. The analysis included quantification of colony-forming units (CFU) for heterotrophic bacteria, Actinobacteria, fungi, and Azotobacter spp. Heterotrophic bacteria were cultured on pre-prepared Merck standard nutrient agar and incubated at 28 °C for 5 days. Fungal populations were enumerated using Martin’s medium [66] following a 5-day incubation at 24 °C. Actinobacteria were cultured on Pochon’s selective medium [67] and incubated at 26 °C for 7 days. The abundance of Azotobacter spp. was determined using a selective medium as described by [68], with a 5-day incubation period at 24 °C. The results obtained show the number of four groups of microorganisms cultivated in specific conditions.

2.6. Enzymatic Activity of Soils

The activity of the following enzymes was determined: catalase (CAT), dehydrogenases (DhA), acid phosphatase (AcP), alkaline phosphatase (AlP), proteases (PA), and urease (UA). For easier recognition, enzymes were assigned an Enzyme Commission number (EC). CAT (EC 1.11.1.6) activity in soil samples was assessed using the titrimetric method described by Johnson and Temple [69]. The procedure involved the use of a 0.3% hydrogen peroxide (H2O2) solution and 1.5 M sulfuric acid (H2SO4), followed by titration of the residual H2O2 with 0.02 M potassium permanganate (KMnO4). The activity of CAT was expressed as milligrams of H2O2 decomposed per kilogram of dry soil per minute (mg H2O2 kg−1 d.m. min−1). DhA (EC 1.1) activity was determined following the method proposed by Thalmann [70], using a 1% solution of triphenyl tetrazolium chloride (TTC) as a substrate. DhA activity was expressed as milligrams of triphenyl formazan (TPF) produced per kilogram of dry soil per 24 h (mg TPF kg−1 d.m. 24 h−1). The activities of AcP (EC 3.1.3.2) and AlP (EC 3.1.3.1) were determined following the procedure described by Tabatabai and Bremner [71], utilizing a 0.8% solution of sodium p-nitrophenyl phosphate as the substrate. The assays were conducted at buffer pH values of 5.4 for AcP and 8.5 for AlP. Enzyme activities (AcP and AlP) were expressed as milligrams of p-nitrophenol (PNP) released per kilogram of dry soil per hour (mg PNP kg−1 d.m. h−1). PA (EC 3.4.4) activity was determined following the protocol established by Ladd and Butler [72], using sodium caseinate as the enzymatic substrate. PA activity was quantified and expressed as milligrams of tyrosine released per kilogram of dry soil per hour (mg tyrosine kg−1 d.m. h−1). UA (EC 3.5.1.5) activity was determined according to the method described by Hoffmann and Teichert [73], using a 2.5% urea solution as the substrate. The enzymatic activity was expressed as milligrams of ammonium nitrogen (N-NH4+) released per kilogram of dry soil per hour (mg N-NH4+ kg−1 d.m. h−1). All the measurements were made in three replicates.

2.7. Statistical Analysis

To establish the relationship between the occurrence of higher plants and the tested soil parameters, the Pearson correlation index was calculated, and its statistical significance was determined. The PAST 3.20 program was used [74].
The relationships between sets of variables (microbial abundance, enzymatic activity, and soil biochemical parameters) in the different study seasons for the study communities were analyzed using a direct ordination technique. Redundancy analyses were selected and, for all variants, performed using the Canoco 5.0 program [75]. Monte Carlo permutation and forward selection tests were performed during the analyses, which allowed the identification of variables that were significant for variation in the study communities.
For all the studied parameters, analyses of variance were performed to indicate significant differences between these parameters in the analyzed communities. ANOVA and Tuckey’s test as a post hoc test were performed in Statistica 9.0 [76].

3. Results

The research was conducted in spring and autumn, as these two seasons are characterized by the most dynamic biological and biochemical processes occurring in the soil. Spring is a period of intensive plant growth, increased microbial activity, and mobilization of nutrients after the winter dormancy period. Autumn, on the other hand, is the time when plants finish their growing season, and a significant amount of organic matter (e.g., fallen leaves and root residues) enters the soil, which can significantly affect the development of microbiota and enzymatic activity. The choice of these two seasons allowed us to capture the contrast between the phase of intensive biological development and the stage of organic matter decomposition. At the same time, we avoided the summer months, when droughts and high temperatures can limit the activity of microorganisms and enzymes, as well as winter, when most biological processes in the soil slow down significantly or stop altogether. This makes it possible to assess the seasonal impact of environmental factors on microbiological and enzymatic processes that are important for element cycling, soil fertility, and sustainable management of grassland habitats.

3.1. Characteristics of Selected Grasslands

The described vegetation syntaxonomic units belong to the class Molinio–Arrhenatheretea. Two orders are distinguished: (1) Arrhenatheretalia and within it Arrhenatheretum elatioris, Lolio–Cynosuretum, and com. Poa pratensis–Festuca rubra and (2) Molinietalia and within it Alopecuretum pratensis and Molinietum caeruleae. The biodiversity of selected grassland communities is shown in Table 2. The vegetation units with the highest number of species recorded were Molinietum caeruleae, while the poorest in species was Lolio–Cynosuretum.
The table is divided by season. In each vegetation unit analyzed, a higher number of species was noted in autumn. The values of the Shannon–Wiener H′ index were highest for Molinietum caeruleae and lowest for Lolio–Cynosuretum, both in autumn and spring. Consequently, a greater number of species were recorded during autumn phytosociological surveys, and higher values of the H′ index were recorded in autumn.

3.2. Chemical Soil Properties

The soils studied were characterized by varying organic matter content. The highest TOC and TN contents were recorded, both in spring and autumn, in Alopecuretum pratensis soils, which are the most productive grasslands. In contrast, the lowest TOC and TN content was found in Molinietum caeruleae soils, which are more valuable for nature than for production (Figure 1).
The highest mean C:N ratio in spring was recorded in Molinietum caeruleae soils and the lowest in com. Poa pratensis–Festuca rubra. In contrast, the highest C:N in autumn was observed in soils under Alopecuretum pratensis, while the lowest was recorded in soils associated with Arrhenatheretum elatioris. Conversely, the highest soil pH levels in spring and autumn were observed under Arrhenatheretum elatioris, likely due to liming practices. In contrast, the lowest pH values in these seasons were noted in soils beneath Alopecuretum pratensis.

3.3. Microbial Abundance and Enzymatic Activity

Heterotrophic bacteria were identified as the most abundant group of microorganisms in the studied soils (Figure 2). In the spring season, their highest numbers were recorded in soils associated with the Alopecuretum pratensis, whereas in autumn, peak abundance was observed in soils under the Poa pratensis–Festuca rubra community. The lowest bacterial counts were consistently found in Molinietum caeruleae soils during both sampling periods.
The subsequent group of microorganisms analyzed was Actinobacteria. Similar to heterotrophic bacteria, their highest abundance in spring was observed in soils associated with the Alopecuretum pratensis. In autumn, however, the differences in Actinobacteria abundance among the plant communities were less pronounced, with the highest levels recorded in Lolio–Cynosuretum soils. Bacteria of the genus Azotobacter reached their peak abundance in Lolio–Cynosuretum soils during both sampling periods. Regarding fungal populations, their highest abundance was recorded in spring in soils under the Poa pratensis–Festuca rubra community. In contrast, the greatest fungal abundance was observed in Arrhenatheretum elatioris soils in autumn.
Overall, higher activities of the analyzed enzymes were observed during the spring sampling period. The highest DhA activity was recorded in soils associated with Arrhenatheretum elatioris (Figure 3).
The highest activities of AcP and AlP were recorded in soils under the Alopecuretum pratensis in both sampling seasons. A similar pattern was observed for CAT and PA activities. UA was the only enzyme whose activity was consistently higher in autumn than in spring across all vegetation units. Moreover, UA activity exhibited relatively uniform levels among the analyzed soil types.

3.4. Relationship Between Biodiversity, Soil Parameters, and Seasons

According to analysis, soils from under Molinietum caeruleae and Lolio–Cynosuretum stand out significantly. Soils from under the first vegetation unit are characterized by more abundant fungi and a higher diversity of higher plants. Soils from under the second vegetation unit are characterized by the high abundance of Azotobacter spp. (Figure 4).
Pearson’s correlation coefficient analysis indicated a significant negative relationship between the diversity index H′ and the abundance of heterotrophic bacteria in the Arrhenatheretum elatioris soils (Table 3).
Similarly, a negative significant relationship applies to fungal abundance, which decreases with increasing H′ in com. Poa pratensis–Festuca rubra. In the same community, the chemical parameters (AcP, AlP, and CAT) are negatively and significantly correlated with the Shannon index (Table 4).
According to the analysis performed, we can conclude that Actinobacteria prefer soils with higher TN and TOC values, while Azotobacter spp. and fungi choose a habitat with higher pH (Figure 5). However, these correlations were not statistically confirmed. Thus, we discuss some trends found in the grassland soils studied.
Redundancy analysis indicated that the activities of AcP, AlP, and CAT were significantly related to levels of TN. Soil pH also emerged as an important factor influencing variability within the dataset, with higher pH values exerting a markedly inhibitory effect on the activity of the analyzed enzymes (Figure 6).
Among the relationships identified through redundancy analysis (RDA), the only statistically significant correlation was observed in autumn 2022, when a markedly high abundance of fungi coincided with a simultaneous decline in the populations of other soil microorganisms (Figure 7).
The activity of all analyzed enzymes exhibited significant seasonal variation. CAT and UA activities were markedly higher in autumn 2023, whereas the activities of the remaining enzymes showed significant associations with the spring 2022 sampling period (Figure 8).

4. Discussion

4.1. The Impact of Seasonality on Vegetation, the Abundance of Selected Microorganisms, and Enzyme Activity

Grasslands are dynamic ecosystems characterized by significant seasonal variations in species diversity, driven by climatic factors, resource availability, and management practices. Understanding these variations is crucial for biodiversity conservation and ecosystem management [77,78,79].
Spring is usually characterized by the highest levels of biodiversity and species richness in grasslands. This phenomenon is the result of the emergence of annual and perennial species that take advantage of favorable climatic conditions, such as moderate temperatures and increased rainfall [80,81]. During this period, the substrate shows less competitiveness, allowing subordinate species to develop before they are displaced by dominant species [79]. For example, grasslands in temperate climates are often dominated by spring-flowering species, which contributes to increased taxonomic and functional diversity [80]. The renewal of spring plant growth also supports a wide range of pollinators and herbivores, further enhancing ecosystem functionality [82]. However, the diversity and productivity of spring communities can be influenced by factors such as soil nutrient availability and management practices such as mowing and fertilization [78,79].
In contrast, autumn represents a transitional period in which species diversity and productivity decline as grasslands prepare for the winter dormancy period. The decline in diversity results from the aging of herbaceous plants and the disappearance of annual species [81,83]. The process of autumn senescence depends on factors such as soil moisture, temperature, and resource allocation below the soil surface, which can accelerate the end of the growing season [83]. Despite the overall decline in diversity, autumn still favors the existence of a specific set of species adapted to cooler and drier conditions. These species play an important role in maintaining ecosystem resilience during the transition to winter [84].
In the present study, a higher number of species and higher plant species diversity were recorded in the autumn season. This may be due to the fact that our study was performed in early autumn (September). In addition, the aforementioned climate change, which lengthens the growing season, may have an impact on repeat flowering. In addition, in autumn, all the plants were at the generative stage, making it easier to find and identify them. It is worth noting that the differences were not large.
In autumn, significant changes occur in the microbial community structure of soils. We noted a statistically significant correlation indicating that an increase in fungal abundance in autumn is associated with a concomitant decrease in the abundance of the other microorganisms analyzed. During this period, fungi—particularly saprotrophic species—play a key role in the decomposition of freshly fallen leaves, plant residues, and organic matter in general. Although bacteria are typically more abundant in soil, their activity may be lower in autumn compared to fungi, which are more effective at decomposing complex organic compounds such as lignin and cellulose [85,86,87,88,89]. The high abundance of fungi during this season, reported by previous studies, was also confirmed in our research.
We also noted that the activity of all analyzed enzymes showed significant seasonal variation. CAT and UA activities were significantly higher in autumn 2023. UA is the enzyme responsible for hydrolyzing urea to ammonia and carbon dioxide, playing a key role in the soil nitrogen cycle. Studies in various ecosystems have shown that UA activity is highest in spring and summer, which is associated with higher temperatures and greater availability of organic substrates. For example, UA activity was highest in June and September in a mountainous region in northern China and lowest in December [90]. Other studies indicate UA activity may be higher in spring and summer due to higher temperatures and greater availability of organic substrates. For example, a study conducted in Poland’s crop fields indicated UA activity was highest in May, lower in June, and lowest in March and August [91]. It is worth noting that soil UA activity can also be influenced by factors such as soil moisture, pH, organic matter content, and grassland management practices. Therefore, seasonal changes in the activity of this enzyme may vary depending on local environmental conditions and farming practices [32,92,93,94,95]. In the scientific literature, higher CAT activity is often observed in warmer months, such as spring and summer, which is associated with higher temperatures and greater activity of soil microorganisms. Therefore, the correlation of UA and CAT activity with the autumn of 2023 may be due to the significantly higher temperature in 2023, which we mentioned in the methodology. The growing season was longer and warmer.
In contrast, the activity of the other enzymes analyzed in our study was significant in spring 2022. Studies indicate that grasslands have the highest number of microorganisms and enzymatic activity, including DhA, phosphatases, and PA, in spring and summer. This phenomenon is attributed to the intensive growth of root systems of grassland plants during these seasons [32]. Similar observations were made in the subalpine meadows of Switzerland, where the activity of enzymes associated with the carbon cycle was higher in summer than in winter. This was due to greater water availability and higher microbial activity in the warmer months [96].

4.2. The Influence of Chemical Properties on Vegetation, the Abundance of Selected Microorganisms, and Enzyme Activity

Molinietum caeruleae was clearly distinguished from the other communities studied. These meadows exhibited the highest biodiversity index (H′ = 2.25 in May and H′ = 2.45 in September), which is confirmed in the literature. Molinia meadows are considered one of the most important communities in our region in terms of species richness and biological diversity, providing a habitat for rare and endangered plant species [5,97,98,99]. The soils of Molinietum caeruleae were also characterized by the lowest content of TOC and TN as well as the lowest enzymatic activity. Statistical analyses revealed that the soils of these grasslands exhibited a higher abundance of fungi, which significantly differentiated them from other syntaxonomic units. Similar relationships were noted in their studies by Zelnik and Čarni [100], Kozłowski et al. [101], and Swacha [102]. The authors described a low-nutrient status and low-organic-matter content in Molinia meadow soils.
The Alopecuretum pratensis soil was richest in TOC and TN, which confirms its high utility value, but this affects its lower floristic diversity. In addition, grass soils from this syntaxonomic unit were characterized by the highest AcP and AlP activity in both sampling seasons. This is directly related to the phosphorus content in the soil, as confirmed by Suder [103], who referred to Alopecurus pratensis as an indicator of phosphorus-rich soils. Mencel et al. [61] also confirmed a high phosphorus content in grassy soils that are part of Alopecuretum pratensis. This unit is characterized by the highest population of heterotrophic bacteria and Actinobacteria. Organic matter provides essential nutrients and energy, which promote the growth of microbial biomass and increase their enzymatic activity [104,105].
Soils under Lolio–Cynosuretum showed the highest abundance of Azotobacter spp., a group of nitrogen-fixing bacteria that enhance soil fertility by converting atmospheric nitrogen into plant-available forms [106]. Previous studies reported their presence in 43–52% of soils in Poland, with counts ranging from a few to nearly 10,000 cfu g−1 [107,108]. Their occurrence is typical in neutral to slightly alkaline soils and influenced by various environmental factors such as pH, organic matter, and moisture [109,110,111]. In our study, Azotobacter spp. abundance reached 34.8 cfu g−1 d.m. in spring and 32.6 cfu g−1 d.m. in autumn, with soil pH averaging 6.55 and 6.71, respectively—values confirming favorable conditions for their development.
Based on our analysis, it can be concluded that Azotobacter spp. choose habitats with higher pH. According to Jnawali et al. [106], these bacteria are sensitive to acidic pH. The results of studies on the presence of Azotobacter bacteria in soils indicate that their abundance depends on a number of physicochemical and microbiological factors. A study by Kizilkaya et al. [112] showed that Azotobacter spp. populations in soils are strongly related to organic matter content, pH, temperature, soil moisture, and depth of the soil profile. In addition, microbial interactions, such as competition with other microorganisms, also affect their abundance. These results may explain seasonal fluctuations, especially during periods of high nitrogen demand.
In Arrhenatheretum elatioris soil, we noticed a negative relationship between the H′ diversity index and the abundance of heterotrophic bacteria. In general, high floristic diversity does not necessarily directly cause a decrease in the abundance of bacteria in the soil, but it can affect the structure and dynamics of the microbial community indirectly. Greater plant diversity usually means more complex sources of organic matter (different types of roots, root secretions, and leaf fall), which promotes an increase in microbial diversity but does not necessarily increase total bacterial abundance. Instead of one dominant group of bacteria, many different, more specialized groups may develop. In addition, in soils with high plant diversity, a more diverse but less numerically dominant bacterial community is often observed—that is, there are more species of bacteria but fewer representatives of a given species [113,114,115,116]. Similarly, a negative significant relationship applies to fungal abundance, which decreases with increasing H′ in com. Poa pratensis–Festuca rubra soils. Current scientific research indicates that higher floristic diversity in grasslands tends to promote greater soil fungal diversity, not reduce it. For example, a study of 60 grassland sites showed that soil fungal diversity was positively correlated with plant diversity, even after accounting for environmental and geographic factors. However, this correlation was not observed for individual functional groups of fungi analyzed individually [117]. A study in Central Europe found that areas with high plant diversity had higher fungal diversity, including AMF, as well as different fungal community compositions and higher biomass of bacteria and AMF in the soil [45]. Current scientific evidence suggests that higher floristic diversity in grasslands promotes rather than reduces soil fungal diversity. However, this impact may vary depending on specific environmental conditions and ecosystem types. As with bacteria, lower abundance does not mean lower diversity.
According to the analysis performed, we can conclude that Actinobacteria prefer soils with higher TN and TOC values. In the study by Zhang et al. [118], the application of nitrogen fertilizers increased soil organic carbon (SOC) and nitrogen, which correlated with a higher abundance of Actinobacteria. Other studies [119] showed that TOC and TN are key factors determining the composition of bacterial communities, including Actinobacteria. Higher values of TOC and TN were associated with higher diversity and abundance of Actinobacteria.
In the Poa pratensis–Festuca rubra community, chemical parameters (AlP, AcP, and CAT) were negatively and significantly correlated with the Shannon index. Research from the Biodiversity and Climate (BAC) experiment in Minnesota [120] showed that higher plant diversity (up to 16 species) led to increased activity of enzymes associated with carbon, nitrogen, and phosphorus cycles, such as β-glucosidase, N-acetylglucosaminidase, phosphatase, and peroxidase. However, specific enzymatic activity relative to microbial biomass decreased with increasing plant diversity, suggesting changes in nutrient limitation or microbial community composition. The Jena experiment [121] found that the presence of legumes increased the activity of enzymes associated with nitrification and denitrification. In contrast, a higher proportion of grasses in the plant community decreased denitrification activity. These effects were more related to the functional composition of the plants than to the number of species themselves.
Activities of AlP, AcP, and CAT were significantly related to levels of TN. Research indicates that higher levels of TN in the soil promote increased activity of enzymes associated with the nitrogen and phosphorus cycles, which can improve soil quality and its ability to cycle nutrients. Analysis of data from different grassland types showed a significant correlation between enzyme activity (including AlP and CAT) and TN, organic matter, available potassium, and soil moisture [122]. In an experiment on the effect of plant diversity on enzyme activity in the soil, it was found that the activity of AcP was significantly correlated with the content of TN in the soil [120]. Other studies, including correlation and path analysis, showed a significant positive relationship between TN content and the activity of urease, invertase, alkaline phosphatase, and catalase in soil [123].
We noted that higher pH values had a markedly inhibitory effect on the activity of the analyzed enzymes. High soil pH values can significantly inhibit the activity of soil enzymes, which affects biogeochemical processes such as the decomposition of organic matter and the cycling of carbon, nitrogen, and phosphorus [29,124,125]. For example, studies in peatlands found that the activity of enzymes such as CAT, UA, and phosphatase was dependent on soil pH. Increasing pH led to a decrease in the activity of these enzymes, indicating their sensitivity to pH changes [126]. However, it is worth noting that there is research confirming that long-term changes in soil pH can lead microorganisms and enzymes to adapt to new conditions. Studies have shown that pH optima for enzymes can shift toward the pH of the source environment, suggesting the ability to adapt to changing pH conditions [127].

5. Conclusions

This study confirms that grassland soils exhibit complex seasonal dynamics in biological and chemical processes that are fundamental to ecosystem functioning. Considering their pivotal role in carbon sequestration, biodiversity maintenance, and the regulation of biogeochemical cycles, further investigation into their functioning is warranted. A comprehensive understanding of the interactions among soil biological activity, plant community composition, and soil chemical properties is essential for elucidating the underlying mechanisms governing these ecosystems. Such knowledge is critical for the development of evidence-based management strategies aimed at enhancing ecosystem resilience, mitigating climate change, and ensuring the long-term conservation of these ecologically valuable landscapes.

Author Contributions

Conceptualization, J.M. and A.M.-P.; methodology, J.M., A.W. and A.M.-P.; validation, J.M. and A.W.; investigation, J.M.; data curation, J.M. and A.W.; writing—original draft preparation, J.M.; writing—review and editing, J.M., A.W. and A.M.-P.; visualization, J.M. and A.W.; supervision, A.M.-P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AcPacid phosphatase
AlPalkaline phosphatase
AMFarbuscular mycorrhizae fungi
CATcatalase
CFUcolony-forming units
com.community
DhAdehydrogenases
PAproteases
PGPRplant growth-promoting rhizobacteria
RDAredundancy analysis
SOCsoil organic carbon
SOMsoil organic matter
TNtotal nitrogen
TOCtotal organic carbon
UAurease

References

  1. Van Oijen, M.; Bellocchi, G.; Höglind, M. Effects of Climate Change on Grassland Biodiversity and Productivity: The Need for a Diversity of Models. Agronomy 2018, 8, 14. [Google Scholar] [CrossRef]
  2. Gaujour, E.; Amiaud, B.; Mignolet, C.; Plantureux, S. Factors and Processes Affecting Plant Biodiversity in Permanent Grasslands. A Review. Agron. Sustain. Dev. 2012, 32, 133–160. [Google Scholar] [CrossRef]
  3. Batáry, P.; Báldi, A.; Erdős, S. Grassland versus Non-Grassland Bird Abundance and Diversity in Managed Grasslands: Local, Landscape and Regional Scale Effects. Biodivers. Conserv. 2007, 16, 871–881. [Google Scholar] [CrossRef]
  4. Sienkiewicz-Paderewska, D.; Paderewski, J.; Klarzyńska, A.; Wolański, P.; Rogut, K. Floristic Diversity versus Utilization Value of Selected Semi-Natural Central-European Grassland Communities: A Study from Poland. Ecol. Indic. 2021, 132, 108316. [Google Scholar] [CrossRef]
  5. Matuszkiewicz, W. Guide to the Identification of Plant Communities of Poland; PWN: Warsaw, Poland, 2023. [Google Scholar]
  6. Bonari, G.; Fajmon, K.; Malenovský, I.; Zelený, D.; Holuša, J.; Jongepierová, I.; Kočárek, P.; Konvička, O.; Uřičář, J.; Chytrý, M. Management of Semi-Natural Grasslands Benefiting Both Plant and Insect Diversity: The Importance of Heterogeneity and Tradition. Agric. Ecosyst. Environ. 2017, 246, 243–252. [Google Scholar] [CrossRef]
  7. Telo Da Gama, J. The Role of Soils in Sustainability, Climate Change, and Ecosystem Services: Challenges and Opportunities. Ecologies 2023, 4, 552–567. [Google Scholar] [CrossRef]
  8. Sun, W.; Li, Q.; Qiao, B.; Jia, K.; Li, C.; Zhao, C. Advances in Plant–Soil Feedback Driven by Root Exudates in Forest Ecosystems. Forests 2024, 15, 515. [Google Scholar] [CrossRef]
  9. Ma, Z.; Zhang, M.; Xiao, R.; Cui, Y.; Yu, F. Changes in Soil Microbial Biomass and Community Composition in Coastal Wetlands Affected by Restoration Projects in a Chinese Delta. Geoderma 2017, 289, 124–134. [Google Scholar] [CrossRef]
  10. Chen, Q.; Song, Y.; An, Y.; Lu, Y.; Zhong, G. Soil Microorganisms: Their Role in Enhancing Crop Nutrition and Health. Diversity 2024, 16, 734. [Google Scholar] [CrossRef]
  11. Bielinska, E.J.; Futa, B.; Mocek-Płóciniak, A. Soil Enzymes as Bioindicators of Soil Quality and Health; Libropolis Scientific 550 Publishers Society: Lublin, Poland, 2014; ISBN 978-83-63761-25-7. [Google Scholar]
  12. Ibáñez De Aldecoa, A.L.; Zafra, O.; González-Pastor, J.E. Mechanisms and Regulation of Extracellular DNA Release and Its Biological Roles in Microbial Communities. Front. Microbiol. 2017, 8, 1390. [Google Scholar] [CrossRef]
  13. Wang, X.; Chi, Y.; Song, S. Important Soil Microbiota’s Effects on Plants and Soils: A Comprehensive 30-Year Systematic Literature Review. Front. Microbiol. 2024, 15, 1347745. [Google Scholar] [CrossRef] [PubMed]
  14. Tabassum, B.; Khan, A.; Tariq, M.; Ramzan, M.; Iqbal Khan, M.S.; Shahid, N.; Aaliya, K. Bottlenecks in Commercialisation and Future Prospects of PGPR. Appl. Soil Ecol. 2017, 121, 102–117. [Google Scholar] [CrossRef]
  15. Grobelak, A.; Napora, A.; Kacprzak, M. Using Plant Growth-Promoting Rhizobacteria (PGPR) to Improve Plant Growth. Ecol. Eng. 2015, 84, 22–28. [Google Scholar] [CrossRef]
  16. Sumbul, A.; Ansari, R.A.; Rizvi, R.; Mahmood, I. Azotobacter: A Potential Bio-Fertilizer for Soil and Plant Health Management. Saudi J. Biol. Sci. 2020, 27, 3634–3640. [Google Scholar] [CrossRef]
  17. Al-Baldawy, M.S.M.; Matloob, A.A.A.H.; Almammory, M.K.N. The Importance of Nitrogen-Fixing Bacteria Azotobacter Chroococcum in Biological Control to Root Rot Pathogens (Review). IOP Conf. Ser. Earth Environ. Sci. 2023, 1259, 012110. [Google Scholar] [CrossRef]
  18. Prasad, R.; Bhola, D.; Akdi, K.; Cruz, C.; Kvss, S.; Tuteja, N.; Varma, A. Introduction to Mycorrhiza: Historical Development. In Mycorrhiza-Function, Diversity, State of the Art; Varma, A., Prasad, R., Tuteja, N., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 1–7. ISBN 978-3-319-53063-5. [Google Scholar]
  19. Verbruggen, E.; Van Der Heijden, M.G.A.; Weedon, J.T.; Kowalchuk, G.A.; Röling, W.F.M. Community Assembly, Species Richness and Nestedness of Arbuscular Mycorrhizal Fungi in Agricultural Soils. Mol. Ecol. 2012, 21, 2341–2353. [Google Scholar] [CrossRef]
  20. Fall, A.F.; Nakabonge, G.; Ssekandi, J.; Founoune-Mboup, H.; Apori, S.O.; Ndiaye, A.; Badji, A.; Ngom, K. Roles of Arbuscular Mycorrhizal Fungi on Soil Fertility: Contribution in the Improvement of Physical, Chemical, and Biological Properties of the Soil. Front. Fungal Biol. 2022, 3, 723892. [Google Scholar] [CrossRef] [PubMed]
  21. Yuvaraj, M.; Ramasamy, M. Role of Fungi in Agriculture. In Biostimulants in Plant Science; IntechOpen: London, UK, 2020; p. 12. [Google Scholar]
  22. Koorem, K.; Sepp, S.; Bueno, C.G.; Davison, J.; Liu, S.; Meng, Y.; Semchenko, M.; Vasar, M.; Zobel, M.; Moora, M. Plant Mycorrhizal Status Indicates Partner Selectivity in Arbuscular Mycorrhizal Interaction Networks. Funct. Ecol. 2024, 39, 1358–1368. [Google Scholar] [CrossRef]
  23. Simard, S.W.; Beiler, K.J.; Bingham, M.A.; Deslippe, J.R.; Philip, L.J.; Teste, F.P. Mycorrhizal Networks: Mechanisms, Ecology and Modelling. Fungal Biol. Rev. 2012, 26, 39–60. [Google Scholar] [CrossRef]
  24. Grgas, D.; Rukavina, M.; Bešlo, D.; Štefanac, T.; Crnek, V.; Šikić, T.; Habuda-Stanić, M.; Landeka Dragičević, T. The Bacterial Degradation of Lignin—A Review. Water 2023, 15, 1272. [Google Scholar] [CrossRef]
  25. Grzyb, A.; Wolna-Maruwka, A.; Niewiadomska, A. Environmental Factors Affecting the Mineralization of Crop Residues. Agronomy 2020, 10, 1951. [Google Scholar] [CrossRef]
  26. Kuhad, R.C.; Kuhar, S.; Sharma, K.K.; Shrivastava, B. Microorganisms and Enzymes Involved in Lignin Degradation Vis-à-Vis Production of Nutritionally Rich Animal Feed: An Overview. In Biotechnology for Environmental Management and Resource Recovery; Kuhad, R.C., Singh, A., Eds.; Springer: New Delhi, India, 2013; pp. 3–44. ISBN 978-81-322-0875-4. [Google Scholar] [CrossRef]
  27. Wang, L.; Hamel, C.; Lu, P.; Wang, J.; Sun, D.; Wang, Y.; Lee, S.-J.; Gan, G.Y. Using Enzyme Activities as an Indicator of Soil Fertility in Grassland—An Academic Dilemma. Front. Plant Sci. 2023, 14, 1175946. [Google Scholar] [CrossRef]
  28. Furtak, K.; Gałązka, A. Enzymatic Activity as a Popular Parameter Used to Determine the Quality of the Soil Environment. Pol. J. Agron. 2019, 37, 22–30. [Google Scholar] [CrossRef]
  29. Daunoras, J.; Kačergius, A.; Gudiukaitė, R. Role of Soil Microbiota Enzymes in Soil Health and Activity Changes Depending on Climate Change and the Type of Soil Ecosystem. Biology 2024, 13, 85. [Google Scholar] [CrossRef] [PubMed]
  30. Moeskops, B.; Sukristiyonubowo; Buchan, D.; Sleutel, S.; Herawaty, L.; Husen, E.; Saraswati, R.; Setyorini, D.; De Neve, S. Soil Microbial Communities and Activities under Intensive Organic and Conventional Vegetable Farming in West Java, Indonesia. Appl. Soil Ecol. 2010, 45, 112–120. [Google Scholar] [CrossRef]
  31. Borowik, A.; Wyszkowska, J.; Kucharski, J. Impact of Various Grass Species on Soil Bacteriobiome. Diversity 2020, 12, 212. [Google Scholar] [CrossRef]
  32. Mencel, J.; Mocek-Płóciniak, A.; Kryszak, A. Soil Microbial Community and Enzymatic Activity of Grasslands under Different Use Practices: A Review. Agronomy 2022, 12, 1136. [Google Scholar] [CrossRef]
  33. Marcos, M.S.; Carrera, A.L.; Bertiller, M.B.; Olivera, N.L. Grazing Enhanced Spatial Heterogeneity of Soil Dehydrogenase Activity in Arid Shrublands of Patagonia, Argentina. J. Soils Sediments 2020, 20, 883–888. [Google Scholar] [CrossRef]
  34. Hu, J.; Zhou, Q.; Cao, Q.; Hu, J. Effects of Ecological Restoration Measures on Vegetation and Soil Properties in Semi-Humid Sandy Land on the Southeast Qinghai-Tibetan Plateau, China. Glob. Ecol. Conserv. 2022, 33, e02000. [Google Scholar] [CrossRef]
  35. Li, M.; Wang, X.; Chen, J. Assessment of Grassland Ecosystem Services and Analysis on Its Driving Factors: A Case Study in Hulunbuir Grassland. Front. Ecol. Evol. 2022, 10, 841943. [Google Scholar] [CrossRef]
  36. Zhan, X.; Wu, W.; Zhou, L.; Liang, J.; Jiang, T. Interactive Effect of Dissolved Organic Matter and Phenanthrene on Soil Enzymatic Activities. J. Environ. Sci. 2010, 22, 607–614. [Google Scholar] [CrossRef]
  37. Wang, H.; Ma, S.; Shao, G.; Dittert, K. Use of Urease and Nitrification Inhibitors to Decrease Yield-Scaled N2O Emissions from Winter Wheat and Oilseed Rape Fields: A Two-Year Field Experiment. Agric. Ecosyst. Environ. 2021, 319, 107552. [Google Scholar] [CrossRef]
  38. Wang, C.; Lv, J.; Coulter, J.A.; Xie, J.; Yu, J.; Li, J.; Zhang, J.; Tang, C.; Niu, T.; Gan, Y. Slow-Release Fertilizer Improves the Growth, Quality, and Nutrient Utilization of Wintering Chinese Chives (Allium tuberosum Rottler Ex Spreng.). Agronomy 2020, 10, 381. [Google Scholar] [CrossRef]
  39. Wyszkowska, J.; Wyszkowski, M. Activity of Soil Dehydrogenases, Urease, and Acid and Alkaline Phosphatases in Soil Polluted with Petroleum. J. Toxicol. Environ. Health 2010, 73, 1202–1210. [Google Scholar] [CrossRef]
  40. Liao, P.; Ros, M.B.H.; Van Gestel, N.; Sun, Y.; Zhang, J.; Huang, S.; Zeng, Y.; Wu, Z.; Van Groenigen, K.J. Liming Reduces Soil Phosphorus Availability but Promotes Yield and P Uptake in a Double Rice Cropping System. J. Integr. Agric. 2020, 19, 2807–2814. [Google Scholar] [CrossRef]
  41. Wahdan, S.F.M.; Heintz-Buschart, A.; Sansupa, C.; Tanunchai, B.; Wu, Y.-T.; Schädler, M.; Noll, M.; Purahong, W.; Buscot, F. Targeting the Active Rhizosphere Microbiome of Trifolium Pratense in Grassland Evidences a Stronger-Than-Expected Belowground Biodiversity-Ecosystem Functioning Link. Front. Microbiol. 2021, 12, 629169. [Google Scholar] [CrossRef] [PubMed]
  42. Yang, X.; Li, Z.; Cheng, C. Effect of Conservation Tillage Practices on Soil Phosphorus Nutrition in an Apple Orchard. Hortic. Plant J. 2016, 2, 331–337. [Google Scholar] [CrossRef]
  43. Moreno, J.L.; García, C.; Hernández, T. Toxic Effect of Cadmium and Nickel on Soil Enzymes and the Influence of Adding Sewage Sludge. Eur. J. Soil Sci. 2003, 54, 377–386. [Google Scholar] [CrossRef]
  44. Naga Raju, M.; Golla, N.; Vengatampalli, R. Soil Protease. In Soil Enzymes; SpringerBriefs in Environmental Science; Springer International Publishing: Cham, Switzerland, 2017; pp. 19–24. ISBN 978-3-319-42654-9. [Google Scholar]
  45. Lepinay, C.; Větrovský, T.; Chytrý, M.; Dřevojan, P.; Fajmon, K.; Cajthaml, T.; Kohout, P.; Baldrian, P. Effect of Plant Communities on Bacterial and Fungal Communities in a Central European Grassland. Environ. Microbiome 2024, 19, 42. [Google Scholar] [CrossRef]
  46. Li, Y.; Zhao, L.; Hilal, M.G.; Guo, L.; Zhang, Y.; Ji, Y.; Jiang, X.; Hao, L.; Lin, K. Effects of Grassland Degradation on Soil Ecological Stoichiometry and Soil Microbial Community on the South of the Greater Khingan Mountains. Front. Microbiol. 2024, 15, 1438787. [Google Scholar] [CrossRef]
  47. Roux, X.L.; Recous, S.; Attard, E. Soil Microbial Diversity in Grasslands and Its Importance for Grassland Functioning and Services. In Grassland Productivity and Ecosystem Services; Lemaire, G., Hodgson, J., Chabbi, A., Eds.; CABI: Wallingford, UK, 2011; pp. 158–165. ISBN 978-1-84593-809-3. [Google Scholar]
  48. Gao, X.; Zheng, Z.; Diao, Z.; Zhang, Y.; Wang, Y.; Ma, L. The Effects of Litter Input and Increased Precipitation on Soil Microbial Communities in a Temperate Grassland. Front. Microbiol. 2024, 15, 1347016. [Google Scholar] [CrossRef] [PubMed]
  49. Bogati, K.; Walczak, M. The Impact of Drought Stress on Soil Microbial Community, Enzyme Activities and Plants. Agronomy 2022, 12, 189. [Google Scholar] [CrossRef]
  50. Siebert, J.; Sünnemann, M.; Hautier, Y.; Risch, A.C.; Bakker, J.D.; Biederman, L.; Blumenthal, D.M.; Borer, E.T.; Bugalho, M.N.; Broadbent, A.A.D.; et al. Drivers of Soil Microbial and Detritivore Activity across Global Grasslands. Commun. Biol. 2023, 6, 1220. [Google Scholar] [CrossRef]
  51. Breulmann, M.; Schulz, E.; Weißhuhn, K.; Buscot, F. Impact of the Plant Community Composition on Labile Soil Organic Carbon, Soil Microbial Activity and Community Structure in Semi-Natural Grassland Ecosystems of Different Productivity. Plant Soil 2012, 352, 253–265. [Google Scholar] [CrossRef]
  52. Lagomarsino, A.; Grego, S.; Kandeler, E. Soil Organic Carbon Distribution Drives Microbial Activity and Functional Diversity in Particle and Aggregate-Size Fractions. Pedobiologia 2012, 55, 101–110. [Google Scholar] [CrossRef]
  53. Wordell-Dietrich, P.; Don, A.; Helfrich, M. Controlling Factors for the Stability of Subsoil Carbon in a Dystric Cambisol. Geoderma 2017, 304, 40–48. [Google Scholar] [CrossRef]
  54. Hagh-Doust, N.; Mikryukov, V.; Anslan, S.; Bahram, M.; Puusepp, R.; Dulya, O.; Tedersoo, L. Effects of Nitrogen Deposition on Carbon and Nutrient Cycling along a Natural Soil Acidity Gradient as Revealed by Metagenomics. New Phytol. 2023, 238, 2607–2620. [Google Scholar] [CrossRef]
  55. Kottek, M.; Grieser, J.; Beck, C.; Rudolf, B.; Rubel, F. World Map of the Köppen-Geiger Climate Classification Updated. Meteorol. Z. 2006, 15, 259–263. [Google Scholar] [CrossRef]
  56. IMGW PIB. Meteorological Yearbook 2022; IMGW PIB: Warszawa, Poland, 2022. [Google Scholar]
  57. IMGW PIB. Meteorological Yearbook 2023; IMGW PIB: Warszawa, Poland, 2023. [Google Scholar]
  58. Szałajdewicz, J. Szczegółowa Mapa Geologiczna Polski (Detailed Geological Map of Poland), 1:50,000. Rakoniewice (541); Państwowy Instytut Geologiczny: Warsaw, Poland, 2004; ISBN 978-83-7372-649-9. [Google Scholar]
  59. Jodłowski, J. Szczegółowa Mapa Geologiczna Polski (Detailed Geological Map of Poland), 1:50,000. Wolsztyn (540); Państwowy Instytut Geologiczny: Warsaw, Poland, 2003; ISBN 978-83-7372-626-0. [Google Scholar]
  60. Krzysztofka, M. Szczegółowa Mapa Geologiczna Polski (Detailed Geological Map of Poland), 1:50,000. Kościan (542); Państwowy Instytut Geologiczny: Warsaw, Poland, 1993. [Google Scholar]
  61. Mencel, J.; Klarzyńska, A.; Piernik, A.; Mocek-Płóciniak, A. Differentiation of Grassland Vegetation in Relation to the Physicochemical Properties of Peat Soils in the Obra River Valley, Western Poland. Soil Sci. Annu. 2024, 75, 190113. [Google Scholar] [CrossRef]
  62. Braun-Blanquet, J. Pflanzensoziologie: Grundzüge Der Vegetationskunde; Springer: Vienna, Austria, 1964. [Google Scholar]
  63. Hennekens, S.M.; Schaminée, J.H.J. Turboveg, a Comprehensive Data Base Management System for Vegetation Data. J. Veg. Sci. 2001, 12, 589–591. [Google Scholar] [CrossRef]
  64. Tichý, L.; Holt, J.; Nejezchlebová, M. JUICE Program for Management, Analysis and Classification of Ecological Data, 2nd ed.; Vegetation Science Group: Masaryk University Brno, Czech Republic, 2011. [Google Scholar]
  65. Shannon, C.E.; Weaver, W. A Mathematical Theory of Communication; University of Illinois Press: Urbana, IL, USA, 1949. [Google Scholar]
  66. Martin, J.P. Use of Acid, Rose Bengal and Streptomycin in the Plate Method for Estimating Soil Fungi. Soil Sci. 1950, 69, 215–232. [Google Scholar] [CrossRef]
  67. Grabińska-Łoniewska, A. Laboratory Exercises in General Microbiology; Warsaw Technical University: Warsaw, Poland, 1999. [Google Scholar]
  68. Fenglerowa, W. Simple Method for Counting Azotobacter in Soil Samples. Acta Microbiol. Pol. 1970, 14, 203–206. [Google Scholar]
  69. Johnson, J.L.; Temple, K.L. Some Variables Affecting the Measurement of “Catalase Activity” in Soil. Soil Sci. Soc. Am. J. 1964, 28, 207–209. [Google Scholar] [CrossRef]
  70. Thalmann, A. Zur Methodik Der Bestimmung Der Dehydrogenaseaktivität Im Boden Mittels Triphenyltetrazoliumchlorid (TTC). Landwirtsch. Forsh 1968, 21, 249–258. [Google Scholar]
  71. Tabatabai, M.A.; Bremner, J.M. Use of P-Nitrophenyl Phosphate for Assay of Soil Phosphatase Activity. Soil Biol. Biochem. 1969, 1, 301–307. [Google Scholar] [CrossRef]
  72. Wyczółkowski, A.J.; Dabek-Szreniawska, M. Enzymes Taking Part in Organic Nitrogen Mineralization. Acta Agrophysica. Monogr. 2005, 3, 37–61. [Google Scholar]
  73. Hoffmann, G.; Teichert, K. Ein Kolorimetrisches Verfahren Zur Bestimmung Der Ureaseaktivität in Böden. Z. Für Pflanzenernährung Düngung Bodenkd. 1961, 91, 55–63. [Google Scholar] [CrossRef]
  74. Hammer, Ø.; Harper, D.A.T.; Ryan, P.D. PAST: Paleontological Statistics Software Package for Education and Data Analysis. Palaeontol. Electron. 2001, 4, 1–9. [Google Scholar]
  75. ter Braak, C.J.F.; Šmilauer, P. CANOCO Reference Manual and User’s Guide: Software Ordination (Version 5.0); Biometrics: Wageningen, České Budějowice, 2012. [Google Scholar]
  76. StatSoft, Inc. STATISTICA (Data Analysis Software System), Version 9.0. 2009. Available online: http://www.statsoft.com (accessed on 19 March 2025).
  77. Garbowski, M.; Boughton, E.; Ebeling, A.; Fay, P.; Hautier, Y.; Holz, H.; Jentsch, A.; Jurburg, S.; Ladouceur, E.; Martina, J.; et al. Temperature Seasonality and Nutrient Enrichment Drive Intra-Annual Community Turnover in Global Grasslands. bioRxiv 2022. [Google Scholar] [CrossRef]
  78. Pan, J.J.; Ammerman, D.; Mitchell, R.J. Nutrient Amendments in a Temperate Grassland Have Greater Negative Impacts on Early Season and Exotic Plant Species. Plant Ecol. 2011, 212, 853–864. [Google Scholar] [CrossRef]
  79. Doležal, J.; Lanta, V.; Mudrák, O.; Lepš, J. Seasonality Promotes Grassland Diversity: Interactions with Mowing, Fertilization and Removal of Dominant Species. J. Ecol. 2019, 107, 203–215. [Google Scholar] [CrossRef]
  80. Fischer, F.M.; Chytrý, K.; Chytrá, H.; Chytrý, M.; Těšitel, J. Seasonal Beta-Diversity of Dry Grassland Vegetation: Divergent Peaks of above-Ground Biomass and Species Richness. J. Veg. Sci. 2023, 34, e13182. [Google Scholar] [CrossRef]
  81. Yan, P.; Lu, X.; Li, W.; Zhang, J.; Li, P.; Li, Y.; Wang, K.; Ding, S. Seasonal Variations in Plant Species Diversity and Phylogenetic Diversity in Abandoned Farmland of China’s Huang–Huai Plain. Diversity 2023, 15, 922. [Google Scholar] [CrossRef]
  82. Wang, R.; Gamon, J.; Montgomery, R.; Townsend, P.; Zygielbaum, A.; Bitan, K.; Tilman, D.; Cavender-Bares, J. Seasonal Variation in the NDVI–Species Richness Relationship in a Prairie Grassland Experiment (Cedar Creek). Remote Sens. 2016, 8, 128. [Google Scholar] [CrossRef]
  83. Qiao, Y.; Cheng, H.; Zhu, H.; Zhu, Y.; Jia, Q.; Yang, Y.; Zhong, H.; Zohner, C.; Liu, J. Species Diversity Advances Autumn Senescence in Grasslands. Authorea 2024, preprint. [Google Scholar] [CrossRef]
  84. Fóti, S.; Bartha, S.; Balogh, J.; Pintér, K.; Koncz, P.; Biró, M.; Süle, G.; Petrás, D.; Luca, G.D.; Ladányi, M.; et al. Fluctuations and Trends in Spatio-Temporal Patterns of Plant Species and Diversity in a Sandy Pasture. J. Veg. Sci. 2023, 34, e13190. [Google Scholar] [CrossRef]
  85. Hsin, K.-T.; Lee, H.; Lin, Y.-C.J.; Chen, P.-Y. Lignocellulose Degradation in Bacteria and Fungi for Biomass Conversion. Front. Microbiol. 2024, 16, 1583746. [Google Scholar] [CrossRef]
  86. Shigyo, N.; Umeki, K.; Hirao, T. Seasonal Dynamics of Soil Fungal and Bacterial Communities in Cool-Temperate Montane Forests. Front. Microbiol. 2019, 10, 1944. [Google Scholar] [CrossRef]
  87. Schnecker, J.; Baldaszti, L.; Gündler, P.; Pleitner, M.; Sandén, T.; Simon, E.; Spiegel, F.; Spiegel, H.; Urbina Malo, C.; Zechmeister-Boltenstern, S.; et al. Seasonal Dynamics of Soil Microbial Growth, Respiration, Biomass, and Carbon Use Efficiency in Temperate Soils. Geoderma 2023, 440, 116693. [Google Scholar] [CrossRef]
  88. Li, Z.; Wang, F.; Wen, Y.; Ye, C.; Wang, P.; Bai, T.; Gu, X.; Guo, L.; Qiu, Y.; Zhang, Y.; et al. Temporal Dynamics of Climate Sensitivity of Litter Decomposition in a Semi-Arid Grassland. Geoderma 2025, 453, 117157. [Google Scholar] [CrossRef]
  89. Cereghetti, E.; Peller, T.; Kaeser, S.; Gounand, I.; Altermatt, F. Seasonal Dynamics of Detritus Flows and Decomposition across Ecosystem Boundaries. Curr. Biol. 2025, 35, p2139–p2145.e3. [Google Scholar] [CrossRef] [PubMed]
  90. Kong, Y.; Qu, A.; Feng, E.; Chen, R.; Yang, X.; Lai, Y. Seasonal Dynamics of Soil Enzymatic Activity under Different Land-Use Types in Rocky Mountainous Region of North China. Forests 2023, 14, 536. [Google Scholar] [CrossRef]
  91. Piotrowska-Długosz, A.; Wilczewski, E. Assessment of Soil Nitrogen and Related Enzymes as Influenced by the Incorporation Time of Field Pea Cultivated as a Catch Crop in Alfisol. Environ. Monit. Assess. 2014, 186, 8425–8441. [Google Scholar] [CrossRef]
  92. Alizadeh, H.; Kandula, D.R.W.; Hampton, J.G.; Stewart, A.; Leung, D.W.M.; Edwards, Y.; Smith, C. Urease Producing Microorganisms under Dairy Pasture Management in Soils across New Zealand. Geoderma Reg. 2017, 11, 78–85. [Google Scholar] [CrossRef]
  93. Song, W.; Han, F.; Bao, Z.; Chai, Y.; Wang, L.; Huang, C.; Cheng, H.; Chang, L. Mulching Practices Improve Soil Moisture and Enzyme Activity in Drylands, Increasing Potato Yield. Agronomy 2024, 14, 1077. [Google Scholar] [CrossRef]
  94. Motasim, A.M.; Samsuri, A.W.; Nabayi, A.; Akter, A.; Haque, M.A.; Abdul Sukor, A.S.; Adibah, A.M. Urea Application in Soil: Processes, Losses, and Alternatives—A Review. Discov. Agric. 2024, 2, 42. [Google Scholar] [CrossRef]
  95. Meena, A.; Rao, K.S. Assessment of Soil Microbial and Enzyme Activity in the Rhizosphere Zone under Different Land Use/Cover of a Semiarid Region, India. Ecol. Process. 2021, 10, 16. [Google Scholar] [CrossRef]
  96. Puissant, J.; Jassey, V.E.J.; Mills, R.T.E.; Robroek, B.J.M.; Gavazov, K.; De Danieli, S.; Spiegelberger, T.; Griffiths, R.; Buttler, A.; Brun, J.-J.; et al. Seasonality Alters Drivers of Soil Enzyme Activity in Subalpine Grassland Soil Undergoing Climate Change. Soil Biol. Biochem. 2018, 124, 266–274. [Google Scholar] [CrossRef]
  97. Chmolowska, D.; Nobis, M.; Rożej-Pabijan, E.; Grześ, I.M.; Radzikowski, P.; Okrutniak, M.; Celary, W.; Sternalski, J.; Shrubovych, J.; Wasak-Sęk, K. Matching the Puzzle Piece to a New Jigsaw: The Effect of Surrounding Environments on Plants and Invertebrates in the Translocated Wet Meadow. Sci. Total Environ. 2023, 904, 166637. [Google Scholar] [CrossRef]
  98. Wójcik, T.; Kostrakiewicz-Gierałt, K.; Makuch-Pietraś, I. The Effect of Accidental Burning on Habitat Conditions and Species Composition of Molinion Caeruleae Meadows. J. Nat. Conserv. 2022, 70, 126294. [Google Scholar] [CrossRef]
  99. Marciniuk, P.; Marciniuk, J.; Sychut-Czapla, E.; Oklejewicz, K. Meadows of the Molinietalia order as a refugium of rare plant species in the Nadhużański Landscape Park (F. Poland). Fragm. Florist. Geobot. Pol. 2016, 23, 73–81. [Google Scholar]
  100. Zelnik, I.; Čarni, A. Wet Meadows of the Alliance Molinion and Their Environmental Gradients in Slovenia. Biologia 2008, 63, 187–196. [Google Scholar] [CrossRef]
  101. Kozłowski, S.; Zielewicz, W.; Swędrzyński, A.; Olejarnik, Ł. Chemical properties of forest grasses. Grassl. Sci. Pol. 2012, 15, 109–118. [Google Scholar]
  102. Swacha, G.; Botta-Dukát, Z.; Kącki, Z.; Pruchniewicz, D.; Żołnierz, L. The Effect of Abandonment on Vegetation Composition and Soil Properties in Molinion Meadows (SW Poland). PLoS ONE 2018, 13, e0197363. [Google Scholar] [CrossRef]
  103. Suder, A. Vegetation of wet meadows (order Molinietalia caeruleae W. Koch 1926) in the eastern part of Silesia Upland. Grassl. Sci. Pol. 2007, 10, 159–172. [Google Scholar]
  104. Han, X.; Doménech-Pascual, A.; Pere Casas-Ruiz, J.; Donhauser, J.; Jordaan, K.; Ramond, J.-B.; Priemé, A.; Romaní, A.M.; Frossard, A. Soil Organic Matter Properties Drive Microbial Enzyme Activities and Greenhouse Gas Fluxes along an Elevational Gradient. Geoderma 2024, 449, 116993. [Google Scholar] [CrossRef]
  105. Xu, Q.; Li, L.; Guo, J.; Guo, H.; Liu, M.; Guo, S.; Kuzyakov, Y.; Ling, N.; Shen, Q. Active Microbial Population Dynamics and Life Strategies Drive the Enhanced Carbon Use Efficiency in High-Organic Matter Soils. mBio 2024, 15, e00177-24. [Google Scholar] [CrossRef]
  106. Jnawali, A.D.; Ojha, R.B.; Marahatta, S. Role of Azotobacter in Soil Fertility and Sustainability—A Review. Adv. Plants Agric. Res. 2015, 2, 250–253. [Google Scholar] [CrossRef]
  107. Martyniuk, S.; Martyniuk, M. Occurrence of Azotobacter Spp. in Some Polish Soils. Pol. J. Environ. Stud. 2002, 12, 371–374. [Google Scholar]
  108. Lenart, A. Occurrence, Characteristics, and Genetic Diversity of Azotobacter Chroococcum in Various Soils of Southern Poland. Pol. J. Environ. Stud. 2012, 21, 415–424. [Google Scholar]
  109. Mazinani, Z.; Asgharzadeh, A. Genetic Diversity of Azotobacter Strains Isolated from Soils by Amplified Ribosomal DNA Restriction Analysis. Cytol. Genet. 2014, 48, 293–301. [Google Scholar] [CrossRef]
  110. Kozieł, M.; Gałązka, A.; Martyniuk, S. Free-Living Atmospheric Nitrogen-Fixing Bacteria of the Azotobacter Genus—534 Occurrence, Abundance, and Significance. Stud. Rep. IUNG-PIB 2018, 56, 57–70. [Google Scholar] [CrossRef]
  111. Tejera, N.; Lluch, C.; Martìnez-Toledo, M.V.; Gonzàlez-López, J. Isolation and Characterization of Azotobacter and Azospirillum Strains from the Sugarcane Rhizosphere. Plant Soil 2005, 270, 223–232. [Google Scholar] [CrossRef]
  112. Kizilkaya, R. Nitrogen Fixation Capacity of Azotobacter Spp. Strains Isolated from Soils in Different Ecosystems and Relationship Between Them and the Microbiological Properties of Soils. J. Environ. Biol. 2009, 30, 73–82. [Google Scholar] [PubMed]
  113. Li, Z.; Liu, X.; Zhang, M.; Xing, F. Plant Diversity and Fungal Richness Regulate the Changes in Soil Multifunctionality in a Semi-Arid Grassland. Biology 2022, 11, 870. [Google Scholar] [CrossRef]
  114. Meliani, A.; Bensoltane, A.; Mederbel, K. Microbial Diversity and Abundance in Soil: Related to Plant and Soil Type. Am. J. Plant Nutr. Fertil. Technol. 2012, 2, 10–18. [Google Scholar] [CrossRef]
  115. Chen, Y.; Huang, X.; Lang, X.; Tang, R.; Zhang, R.; Li, S.; Su, J. Effects of Plant Diversity, Soil Microbial Diversity, and Network Complexity on Ecosystem Multifunctionality in a Tropical Rainforest. Front. Plant Sci. 2023, 14, 1238056. [Google Scholar] [CrossRef] [PubMed]
  116. Jayaramaiah, R.H.; Martins, C.S.C.; Egidi, E.; Macdonald, C.A.; Wang, J.-T.; Liu, H.; Reich, P.B.; Delgado-Baquerizo, M.; Singh, B.K. Soil Function-Microbial Diversity Relationship Is Impacted by Plant Functional Groups under Climate Change. Soil Biol. Biochem. 2025, 200, 109623. [Google Scholar] [CrossRef]
  117. Yang, T.; Adams, J.M.; Shi, Y.; He, J.; Jing, X.; Chen, L.; Tedersoo, L.; Chu, H. Soil Fungal Diversity in Natural Grasslands of the Tibetan Plateau: Associations with Plant Diversity and Productivity. New Phytol. 2017, 215, 756–765. [Google Scholar] [CrossRef]
  118. Zhang, W.; Wang, R.; Li, T.; Guo, M.; Guo, S.; Wang, R. Responses of Bacterial and Fungal Community to Long-Term Nitrogen Application in Loess Plateau. J. Plant Nutr. Fertil. 2024, 30, 232–241. [Google Scholar] [CrossRef]
  119. Kumar, A.; Rai, L.C. Organic Carbon and Nitrogen Availability Determine Bacterial Community Composition in Paddy Fields of the Indo-Gangetic Plain. 3 Biotech 2017, 7, 199. [Google Scholar] [CrossRef]
  120. Steinauer, K.; Tilman, D.; Wragg, P.D.; Cesarz, S.; Cowles, J.M.; Pritsch, K.; Reich, P.B.; Weisser, W.W.; Eisenhauer, N. Plant Diversity Effects on Soil Microbial Functions and Enzymes Are Stronger than Warming in a Grassland Experiment. Ecology 2015, 96, 99–112. [Google Scholar] [CrossRef] [PubMed]
  121. Le Roux, X.; Schmid, B.; Poly, F.; Barnard, R.L.; Niklaus, P.A.; Guillaumaud, N.; Habekost, M.; Oelmann, Y.; Philippot, L.; Salles, J.F.; et al. Soil Environmental Conditions and Microbial Build-Up Mediate the Effect of Plant Diversity on Soil Nitrifying and Denitrifying Enzyme Activities in Temperate Grasslands. PLoS ONE 2013, 8, e61069. [Google Scholar] [CrossRef]
  122. Xie, L.; Gong, L.; Zhu, M. Soil Enzyme Activities and Their Correlation with Physicochemical Factors in the Oasis of Southern Margin of Tarim Basin. Res. Environ. Sci. 2014, 27, 1306–1313. [Google Scholar]
  123. Liu, Q.; Wu, F.; Liu, H.; Zhang, Q.-F.; Zhang, J.; Li, H. Study on the Relationships Between Soil Enzyme Activities and Soil Fertility in Zhifanggou Watershed. J. Plant Nutr. Fertil. 2009, 15, 1100–1106. [Google Scholar] [CrossRef]
  124. Kotroczó, Z.; Veres, Z.; Fekete, I.; Krakomperger, Z.; Tóth, J.A.; Lajtha, K.; Tóthmérész, B. Soil Enzyme Activity in Response to Long-Term Organic Matter Manipulation. Soil Biol. Biochem. 2014, 70, 237–243. [Google Scholar] [CrossRef]
  125. Xu, R.; Li, J.; Li, X.; Zhang, J.; Song, W. Effect of Coal Mining Subsidence on Soil Enzyme Activity in Mining Areas with High Underground Water Levels. Water 2024, 16, 1704. [Google Scholar] [CrossRef]
  126. Ochmian, I.; Kozos, K.; Jaroszewska, A.; Malinowski, R. Chemical and Enzymatic Changes of Different Soils during Their Acidification to Adapt Them to the Cultivation of Highbush Blueberry. Agronomy 2020, 11, 44. [Google Scholar] [CrossRef]
  127. Puissant, J.; Jones, B.; Goodall, T.; Mang, D.; Blaud, A.; Gweon, H.S.; Malik, A.; Jones, D.L.; Clark, I.M.; Hirsch, P.R.; et al. The pH Optimum of Soil Exoenzymes Adapt to Long Term Changes in Soil pH. Soil Biol. Biochem. 2019, 138, 107601. [Google Scholar] [CrossRef]
Agronomy 15 01573 g001
Figure 1. Chemical soil properties (mean ± SD and SE). Values marked with the same letter do not differ statistically significantly (ANOVA, p < 0.05; Tukey’s test as post hoc).
Figure 1. Chemical soil properties (mean ± SD and SE). Values marked with the same letter do not differ statistically significantly (ANOVA, p < 0.05; Tukey’s test as post hoc).
Agronomy 15 01573 g001
Agronomy 15 01573 g002
Figure 2. Microbial abundance in soils (mean ± SD and SE). Values marked with the same letter do not differ statistically significantly (ANOVA, p < 0.05; Tukey’s test as post hoc).
Figure 2. Microbial abundance in soils (mean ± SD and SE). Values marked with the same letter do not differ statistically significantly (ANOVA, p < 0.05; Tukey’s test as post hoc).
Agronomy 15 01573 g002
Agronomy 15 01573 g003
Figure 3. Enzymatic activity of soils (mean ± SD and SE). Values marked with the same letter do not differ statistically significantly (ANOVA, p < 0.05; Tukey’s test as post hoc).
Figure 3. Enzymatic activity of soils (mean ± SD and SE). Values marked with the same letter do not differ statistically significantly (ANOVA, p < 0.05; Tukey’s test as post hoc).
Agronomy 15 01573 g003
Agronomy 15 01573 g004
Figure 4. Redundancy analysis RDA presents the diversity of vegetation units due to the plant diversity and abundance of selected soil microorganisms. Vectors marked in red represent communities significantly different from the others (Monte Carlo permutation test, p < 0.05). λ—eigenvalue of the axes, where axis one explains 17.7% of the variability, and axis two explains 12.2%.
Figure 4. Redundancy analysis RDA presents the diversity of vegetation units due to the plant diversity and abundance of selected soil microorganisms. Vectors marked in red represent communities significantly different from the others (Monte Carlo permutation test, p < 0.05). λ—eigenvalue of the axes, where axis one explains 17.7% of the variability, and axis two explains 12.2%.
Agronomy 15 01573 g004
Agronomy 15 01573 g005
Figure 5. Redundancy analysis RDA indicates the relationship between the number of soil microorganisms and the studied soil parameters. None of the studied soil variables is significant for the diversity of the data (Monte Carlo permutation test, p > 0.05). λ—eigenvalue of the axes, where axis one explains 13.4% of the variability, and axis two explains 5.9%.
Figure 5. Redundancy analysis RDA indicates the relationship between the number of soil microorganisms and the studied soil parameters. None of the studied soil variables is significant for the diversity of the data (Monte Carlo permutation test, p > 0.05). λ—eigenvalue of the axes, where axis one explains 13.4% of the variability, and axis two explains 5.9%.
Agronomy 15 01573 g005
Agronomy 15 01573 g006
Figure 6. Redundancy analysis RDA indicates the relationship between the enzymatic activity of soil and the tested soil parameters. Vectors marked in red represent variables significantly different from the others (Monte Carlo permutation test, p < 0.05). λ—eigenvalue of the axes, where axis one explains 41.9% of the variability, and axis two explains 10.5%.
Figure 6. Redundancy analysis RDA indicates the relationship between the enzymatic activity of soil and the tested soil parameters. Vectors marked in red represent variables significantly different from the others (Monte Carlo permutation test, p < 0.05). λ—eigenvalue of the axes, where axis one explains 41.9% of the variability, and axis two explains 10.5%.
Agronomy 15 01573 g006
Agronomy 15 01573 g007
Figure 7. Redundancy analysis RDA indicates the relationship between the abundance of soil microorganisms and the research season. Vectors marked in red represent variable significantly different from the others (Monte Carlo permutation test, p < 0.05). λ—eigenvalue of the axes, where axis one explains 6.8% of the variability, and axis two explains 2.4%.
Figure 7. Redundancy analysis RDA indicates the relationship between the abundance of soil microorganisms and the research season. Vectors marked in red represent variable significantly different from the others (Monte Carlo permutation test, p < 0.05). λ—eigenvalue of the axes, where axis one explains 6.8% of the variability, and axis two explains 2.4%.
Agronomy 15 01573 g007
Agronomy 15 01573 g008
Figure 8. Redundancy analysis RDA indicates the relationship between soil enzymatic activity and the research season. Vectors marked in red represent values of enzyme significantly different from the others (Monte Carlo permutation test, p < 0.05). λ—eigenvalue of the axes, where axis one explains 28.1% of the variability, and axis two explains 24.6%.
Figure 8. Redundancy analysis RDA indicates the relationship between soil enzymatic activity and the research season. Vectors marked in red represent values of enzyme significantly different from the others (Monte Carlo permutation test, p < 0.05). λ—eigenvalue of the axes, where axis one explains 28.1% of the variability, and axis two explains 24.6%.
Agronomy 15 01573 g008
Table 1. Location of sampling sites with phytosociological classification of grasslands.
Table 1. Location of sampling sites with phytosociological classification of grasslands.
Sampling SitesGrassland UnitsCoordinates WGS 84 (N/E)
1.Molinietum caeruleae52°05′42″ N 16°31′30″ E
2.Molinietum caeruleae52°05′42″ N 16°31′33″ E
3.Molinietum caeruleae52°05′43″ N 16°31′37″ E
4.Molinietum caeruleae52°05′43″ N 16°31′39″ E
5.com. Poa pratensis–Festuca rubra52°06′07″ N 16°22′12″ E
6.com. Poa pratensis–Festuca rubra52°06′07″ N 16°22′08″ E
7.com. Poa pratensis–Festuca rubra52°06′04″ N 16°22′03″ E
8.com. Poa pratensis–Festuca rubra52°01′27″ N 16°16′23″ E
9.Arrhenatheretum elatioris52°06′03″ N 16°22′07′′ E
10.Arrhenatheretum elatioris52°06′00″ N 16°21′56′′ E
11.Arrhenatheretum elatioris52°00′49″ N 16°16′52″ E
12.Arrhenatheretum elatioris52°01′01″ N 16°16′43″ E
13.Lolio–Cynosuretum52°06′02″ N 16°22′04″ E
14.Lolio–Cynosuretum52°05′59″ N 16°22′00″ E
15.Lolio–Cynosuretum52°06′00″ N 16°22′01″ E
16.Lolio–Cynosuretum52°04′23″ N 16°14′11″ E
17.Alopecuretum pratensis52°05′58″ N 16°21′57″ E
18.Alopecuretum pratensis52°00′47″ N 16°16′54″ E
19.Alopecuretum pratensis52°00′47″ N 16°16′57″ E
20.Alopecuretum pratensis52°01′02″ N 16°16′44″ E
Table 2. Biodiversity of selected grassland vegetation units.
Table 2. Biodiversity of selected grassland vegetation units.
Grassland UnitsTotal Number of SpeciesNumber of Species in the Reléve in Spring (Range and Mean)Number of Species in the Reléve in Autumn (Range and Mean)H′ SpringH′ Autumn
Molinietum caeruleae5717–201922–29262.252.45
Alopecuretum pratensis4410–221612–22171.721.79
Arrhenatheretum elatioris5416–201814–24192.002.06
Lolio–Cynosuretum368–12119–16131.491.48
com. Poa pratensis–Festuca rubra5012–221915–24192.172.19
Table 3. Pearson’s correlation index between the diversity index of a given vegetation units H′ and the number of soil microorganisms. Significance levels: * p < 0.05; ** p < 0.01; ns (not significant).
Table 3. Pearson’s correlation index between the diversity index of a given vegetation units H′ and the number of soil microorganisms. Significance levels: * p < 0.05; ** p < 0.01; ns (not significant).
Grassland UnitsHeterotrophic BacteriaActinobacteriaFungiAzotobacter spp.
Molinietum caeruleaensnsnsns
Alopecuretum pratensisnsnsnsns
Arrhenatheretum elatioris−0.53 *nsnsns
Lolio–Cynosuretumnsnsnsns
com. Poa pratensis–Festuca rubransns−0.63 **ns
Table 4. Pearson’s correlation index between the diversity index of a given vegetation units H′ and the soil enzymatic activity. Significance levels: * p < 0.05; ** p < 0.01; ns (not significant).
Table 4. Pearson’s correlation index between the diversity index of a given vegetation units H′ and the soil enzymatic activity. Significance levels: * p < 0.05; ** p < 0.01; ns (not significant).
Grassland UnitsDhAAcPAlPCATUAPA
Molinietum caeruleaensnsnsnsnsns
Alopecuretum pratensisns−0.33 *ns−0.33 **nsns
Arrhenatheretum elatiorisnsnsnsnsnsns
Lolio–Cynosuretumnsnsnsns−0.65 **ns
com. Poa pratensis–Festuca rubrans−0.63 **−0.51 *−0.62 **nsns
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Mencel, J.; Wojciechowska, A.; Mocek-Płóciniak, A. Effect of Grassland Vegetation Units on Soil Biochemical Properties and the Abundance of Selected Microorganisms in the Obra River Valley. Agronomy 2025, 15, 1573. https://doi.org/10.3390/agronomy15071573

AMA Style

Mencel J, Wojciechowska A, Mocek-Płóciniak A. Effect of Grassland Vegetation Units on Soil Biochemical Properties and the Abundance of Selected Microorganisms in the Obra River Valley. Agronomy. 2025; 15(7):1573. https://doi.org/10.3390/agronomy15071573

Chicago/Turabian Style

Mencel, Justyna, Anna Wojciechowska, and Agnieszka Mocek-Płóciniak. 2025. "Effect of Grassland Vegetation Units on Soil Biochemical Properties and the Abundance of Selected Microorganisms in the Obra River Valley" Agronomy 15, no. 7: 1573. https://doi.org/10.3390/agronomy15071573

APA Style

Mencel, J., Wojciechowska, A., & Mocek-Płóciniak, A. (2025). Effect of Grassland Vegetation Units on Soil Biochemical Properties and the Abundance of Selected Microorganisms in the Obra River Valley. Agronomy, 15(7), 1573. https://doi.org/10.3390/agronomy15071573

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop