Biological dynamics of no-tillage soils in the western region of Paraná

Dinâmica biológica de solos sob plantio direto na região oeste do Paraná

Abstract

This study investigates soil dynamics on farms in the western region of Paraná, Brazil, highlighting the importance of biological parameters in agriculture. In particular, focusing on the interaction of management practices with soil biodiversity and biological functions, the aim is to understand and promote sustainable and efficient agricultural practices. To do this, we collected soil samples from 15 farms close to Toledo, Paraná, Brazil. These samples were then analyzed to determine biological and physicochemical parameters using techniques such as carbon and nitrogen microbial biomass, metabolic coefficient, basal respiration, bacterial and fungal biomass, and length of the hyphae. The most contrasting soils were evaluated for physicochemical composition and metagenomic analyses. The results showed significant differences in biological parameters between 2020 and 2021, including fungal biomass, hyphae length, and soil basal respiration. Statistical analyzes revealed strong relationships between biological variables, notably the correlation between fungal hyphae and total nitrogen. Climate changes and management practices appear to influence the microbial composition and biological functions of the soil over the years. Soil P9 stood out with superior biological activity and richer microbial diversity, contrasting with soil P13. These differences reflect the influence of management and climatic conditions on soil composition and biological functions. The microbial comparison of the soils emphasized the need for continuous and careful agricultural management, highlighting the importance of biodiversity and ecological functionality of the soil for agricultural sustainability. So, the study underscores the relevance of considering soil biological parameters, in addition to physicochemical aspects, to optimize soil health and productivity.

Keywords:
biological and physicochemical analyses; soil biodiversity; sustainable agriculture; management practices; metagenomics

Resumo

Este estudo investiga a dinâmica dos solos em propriedades rurais da região oeste do Paraná, Brasil, ressaltando a importância dos parâmetros biológicos na agricultura. Ao focar na interação das práticas de manejo com a biodiversidade do solo e suas funções biológicas, busca-se compreender e promover práticas agrícolas sustentáveis e eficientes. Para isso, coletaram-se amostras de solo de 15 propriedades próximas a Toledo (PR). As amostras foram analisadas para determinar parâmetros biológicos e físico-químicos mediante técnicas como biomassa microbiana de carbono e nitrogênio, coeficiente metabólico, respiração basal, biomassa bacteriana e fúngica, e comprimento de hifas. Os solos mais contrastantes foram avaliados quanto à composição físico-química e submetidos a análises metagenômicas. Os resultados indicaram diferenças significativas nos parâmetros biológicos entre 2020 e 2021, incluindo biomassa fúngica, extensão de hifas e respiração basal do solo. Análises estatísticas revelaram fortes relações entre variáveis biológicas, destacando-se a correlação entre hifas fúngicas e nitrogênio total. Mudanças climáticas e práticas de manejo parecem influenciar a composição microbiana e as funções biológicas do solo ao longo dos anos. O solo P9 destacou-se pela elevada atividade biológica e maior diversidade microbiana, em contraste com o solo P13. Essas diferenças refletem a influência do manejo e das condições climáticas na composição e nas funções biológicas do solo. A comparação microbiana dos solos ressalta a necessidade de um manejo agrícola contínuo e criterioso, evidenciando a importância da biodiversidade e da funcionalidade ecológica do solo para a sustentabilidade agrícola. Assim, o estudo reforça a relevância de considerar parâmetros biológicos, além dos aspectos físico-químicos, para otimizar a saúde e a produtividade do solo.

Palavras-chave:
análises biológicas e físico-químicas; biodiversidade do solo; agricultura sustentável; práticas de manejo; metagenômica

1. Introduction

As widely recognized in the literature, soil health and productivity rely on physicochemical and biological characteristics (Sharma et al., 2023; Steinberger et al., 2022). Despite this, many farmers—even those adopting precision agriculture technologies—still prioritize physical and chemical analyses (e.g., nitrogen, organic carbon, phosphorus, pH, water content, and electrical conductivity), often overlooking biological indicators, despite the growing use of bioinputs to enhance soil biodiversity (Wilhelm et al., 2023). This practice results in a partial diagnosis of soil conditions, limiting the understanding of its functionality and resilience, particularly under climatic stress.

A more comprehensive understanding of soil health requires attention to biological indicators such as basal respiration, bacterial and fungal biomass, carbon and nitrogen microbial biomass, enzymatic activity, hyphal length, and the metabolic quotient (Qiu et al., 2023). In recent years, DNA and RNA-based sequencing methods have advanced the understanding of microbial communities and their functional roles in soil ecosystems (Bharti and Grimm, 2019; Jagadesh et al., 2024). These tools allow the characterization of soil microbiomes beyond traditional culturable methods, providing insights into diversity, functionality, and ecological interactions (Nam et al., 2023). Thus, integrating physicochemical and biological parameters offers a more holistic view of soil health and its response to management practices and environmental changes. Within this context, the western region of Paraná stands out as one of Brazil’s most productive agricultural zones, contributing approximately 4% of national soybean and maize production (Brasil, 2021). Since the widespread adoption of no-tillage practices in the 1980s—a major shift toward sustainable soil use—the region has experienced ongoing challenges related to climate variability, emerging pests, and changes in crop dynamics (Nguru and Mwongera, 2023). Despite a large body of work on no-tillage systems in Brazil, few studies in this region have integrated biological, physicochemical, and metagenomic analyses to assess microbial responses to climate and management variability.

Given this scenario, we ask the following question: How do agricultural management practices and interannual climatic variations affect the biological and microbial composition of no-tillage soils in the western region of Paraná? We hypothesize that biological soil parameters are sensitive to such variations and can provide complementary information not captured by physicochemical analyses alone.

Specifically, we aim to:

• 1.Evaluate the temporal variation of soil biological indicators (e.g., microbial biomass, hyphal length, respiration) over two consecutive years in no-tillage systems;

• 2.Compare the microbial diversity and co-occurrence network complexity of soils under contrasting management practices;

• 3.Investigate correlations among biological, physicochemical, and metagenomic variables to identify key drivers of soil microbial structure and function.

While our findings are directly applicable to the regional context of western Paraná, they may also inform broader discussions on microbial-based soil management in other subtropical, high-input agricultural systems with similar constraints and opportunities.

2. Materials and Methods

In this section, we detail the methodology employed in our study, starting with an overview of the farm sites and the specific procedures for soil sample collection and preparation. We then delve into the meteorological conditions that were monitored to contextualize our findings. Following this, we outline the laboratory analysis procedures applied to the collected soil samples, ensuring a thorough examination of their physicochemical and biological properties. Finally, we describe the statistical methods utilized to analyze the data, aiming to draw meaningful insights and conclusions from our research work.

2.1. Collection and preparation of samples

Soil samples were collected in the years 2020 and 2021, immediately before soybean planting (i.e., during the off-season period in August), from 15 farms located near the city of Toledo, in the western region of Paraná, Brazil (latitude -24.7199, 24°43’12’’ S; longitude -53.7433, 53°44’36’’ W). The region is known for its fertile soils, classified in the Brazilian Soil Classification System (SiBCS) as Latossolo Vermelho. According to international classification systems, these correspond to Red Ferralsols under the World Reference Base for Soil Resources (IUSS, 2022), and to Typic Haplorthox, within the Oxisol order, under the USDA Soil Taxonomy (IUSS, 2022; Staff, 2014). All farms have a proven history of no-tillage, including correction and fertilization based on physicochemical tests. This ensures that the soil is in ideal conditions for cultivation, mainly of soybeans and corn, the region’s predominant crops. Note that a brief history of the properties’ management is summarized in Table 1.

Table 1
Soil management history of the farms considered (1 year).

Sample	Crop Rotation	Management
P1	Maize/soybean	Chemical
P2	Maize/soybean	Chemical
P3	Wheat/soybean	Chemical
P4	Maize/soybean	Chemical
P5	Maize/soybean	Chemical
P6	Maize/soybean	Chemical/Organomineral
P7	Maize/soybean	Chemical
P8	Maize/soybean	Chemical/Organomineral
P9	Maize/soybean	Chemical/Biological/Swine manure
P10	Maize/soybean	Chemical/Organomineral
P11	Maize/soybean	Chemical
P12	Maize/soybean	Chemical
P13	Maize/soybean	Poultry litter/Chemical
P14	Maize/soybean	Chemical
P15	Maize/soybean	Chemical

• Due to the limited number of samples under certain management types (e.g., swine manure and poultry litter), no statistical comparisons among management categories were performed. These cases are treated qualitatively in the discussion.

All sampled farms are located in the rural area surrounding the city of Toledo, Paraná. The maximum distance between any two farms was approximately 20 km, with an average distance of 5–10 km between sampling sites. This geographical proximity ensured comparable climatic conditions and minimized edaphic variability across samples. The soil collection process was done using a manual auger to obtain subsamples up to 10 cm deep. In each of the selected farms, a specific area of 5 hectares was demarcated for this purpose. Within this area, ten subsamples were collected and combined to form a single composite sample, representative of each property. Each of these composite samples weighed approximately 500 g. After collection, the samples were identified, packaged, and taken to the laboratory, where they were sieved and stored at a temperature of 4 °C to maintain the quality of the samples until the analyses, which occurred within a maximum of 15 days after collection.

2.2. On the meteorological conditions

Regarding the meteorological conditions (SIMEPAR, 2024), it is important to mention that in the months leading up to the collection (i.e., June, July, and August) an average solar radiation of 309.97 W m^–2 in 2020 and 312.03 W m^–2 in 2021 was observed, indicating a slight year-on-year increase. The relative humidity of the air, in turn, showed an average of 71.25% in 2020, decreasing to 69.43% in 2021. As for temperatures, the average in 2020 was 19.16 °C, with highs of 26.60 °C and lows of 13.45 °C, while in 2021, the average temperature was lower, at 16.72 °C, with highs of 24.65 °C and lows of 11.40 °C. Note that, in August 2020, the average solar radiation was higher, at 374.12 W m^–2, and the relative humidity of the air was 58.26%. In contrast, August 2021 had an average solar radiation of 346.81 W m^–2 and higher relative humidity of the air, at 61.19%. So, these aspects can help to understand the differences in soil biological characteristics.

2.3. Analysis protocols

Now, protocols employed in analyzing the soil samples are briefly revisited, encompassing a broad spectrum of tests designed to assess the soil’s physical, chemical, and biological characteristics. Such tests include the determination of soil water content, soil basal respiration, carbon and nitrogen in microbial biomass, metabolic quotient, bacterial, fungal biomass, and hyphae length, total nitrogen and total organic carbon, electrical conductivity, soil pH, and phosphorus, and metagenomic analysis (as detailed ahead). For such, the samples were initially destoned, i.e., freed from clods and larger aggregates, and then passed through a 2.0 mm mesh sieve. This process allowed the removal of roots, visible plant residues, and small soil organisms, ensuring that the samples were free of contaminants and ready for more precise analyses. These analyses were conducted through a partnership between the Soil Microbiology and Molecular Biology Laboratories of the Paranaense University (UNIPAR), in Umuarama, Paraná, Brazil; the LARA–Agro-industrial Waste Laboratory of the Western Paraná University (UNIOESTE), in Cascavel, Paraná, Brazil; and the LAGBio–Genomic Analyses and Biotechnology, in Toledo, Paraná, Brazil.

2.3.1. Soil water content

To quantify soil water content, 20 g of each sample was weighed and dried at 105 °C for 24 h. After cooling in a desiccator, dry mass was recorded. Moisture percentage was calculated from the mass difference before and after drying, providing an estimate of soil water content, essential for evaluating microbial activity and plant development (Gardner, 1986).

2.3.2. Soil basal respiration

Soil basal respiration (BSR) was determined by quantifying the CO₂ released from soil samples over 168 h under aerobic conditions (Alef, 1995). Samples were incubated in sealed containers with NaOH solution to trap the evolved CO₂, which was subsequently titrated with HCl. Analyses were performed in triplicate, including controls without soil to establish baselines (Stotzky, 1965). Results, expressed as mg CO₂ kg^–1 h^–1, indicate microbial activity and reflect soil biological health and fertility.

2.3.3. Carbon and nitrogen in microbial biomass

Microbial biomass carbon (CBM) and nitrogen (NBM) were quantified using the chloroform fumigation-extraction method (Vance et al., 1987). Soil samples were fumigated with ethanol-free chloroform and extracted with potassium sulfate. Carbon and nitrogen concentrations in the extracts were determined, with non-fumigated controls used for baseline correction. Conversion factors of 0.30 for carbon and 0.45 for nitrogen were applied to estimate microbial biomass (Feigl et al., 1995). Results were expressed as mg C g^–1 h^–1 for CBM and mg N g^–1 soil for NBM, reflecting microbial biomass size and soil fertility.

2.3.4. Metabolic quotient

The metabolic quotient (qCO₂) was calculated as the ratio between soil basal respiration (mg CO₂ kg^–1 h^–1) and microbial biomass carbon (mg C g^–1 h^–1) (Anderson and Domsch, 1985). This index reflects the efficiency of microbial communities in carbon utilization for growth versus respiration, serving as an indicator of the metabolic status and biological health of the soil.

2.3.5. Bacterial and fungal biomass, and hyphae length

Bacterial and fungal biomass, as well as hyphal length, were determined following the protocol by Bloem and Vos (2004). A 6 g soil subsample was suspended in distilled water, agitated, and fixed with formaldehyde for microscopic analysis (triplicate). Bacteria were stained using the fluorescent dye DTAF, while fungi were visualized using fluorescent brightener 28. Slides were incubated in the dark, washed, and dried before analysis. Bacterial biomass was quantified via epifluorescence microscopy at 1000× magnification. Hyphal length was estimated using the intersection grid method across 100 random fields. Final biomass and hyphal length calculations followed the methodology of Bloem and Vos (2004), ensuring accurate microbial quantification.

2.3.6. Total nitrogen

Total nitrogen content was determined using the Kjeldahl method (Instituto Adolfo Lutz, 2008), by comparing fumigated and non-fumigated soil extracts. Samples (in triplicate) were digested with sulfuric acid and distilled with NaOH at 450 °C.

The distillate was collected in boric acid and titrated with HCl. Nitrogen content was calculated using:

$$N=\frac{(V_a\text{-}{V}_b)\times M\times c\times 20.014}{V_1\times V_2\times Ps}$$ (1)

where 𝑉_𝑎 and2 𝑉_𝑏 are HCl volumes for sample and blank titration, 𝑀 is HCl molarity, 𝑐 is a correction factor, 20.014 is the milliequivalent of N, 𝑉₁ is the extract volume, 𝑉₂ is the aliquot volume, and 𝑃_𝑠 is the dry soil mass. This method provides an important measure of soil nitrogen status and nutrient cycling.

2.3.7. Total organic carbon

Total organic carbon (TOC) was determined by measuring compostable and compost-resistant organic matter, along with the chemical oxygen demand (COD). Organic matter was oxidized using a sulfochromic solution, and residual potassium dichromate was titrated with ferrous sulfate. TOC was calculated from the difference in reagent volumes. COD was also assessed to estimate the oxidative potential of organic matter (Walkley and Black, 1934). TOC, alongside total nitrogen, serves as a key indicator of soil nutrient cycling, microbial activity, and fertility.

2.3.8. Electrical conductivity

Soil electrical conductivity was determined following the methodology described by Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA, 2017). A 0.01 mol L^–1 KCl standard solution (1.4 mS cm^–1) was prepared for calibrating the conductivity meter. After calibration, the soil saturation extract was analyzed. The conductivity cell was thoroughly cleaned and immersed in the extract for direct measurement (mS cm^–1). This parameter provides key information on soil salinity, which directly affects plant growth and nutrient availability.

2.3.9. pH

Soil pH was determined according to the method described by Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA, 2017). Solutions of KCl and CaCl₂ were prepared and their conductivity verified. Soil samples were mixed with distilled water or saline solution, stirred, and left to settle. The pH was measured using calibrated electrodes and standard buffer solutions. This analysis provides essential information on soil acidity or alkalinity, which, together with salinity and phosphorus data, informs the soil’s suitability for plant growth and microbial activity.

2.3.10. Phosphorus

Soil phosphorus was determined according to the methodology described by Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA, 2017), using the Mehlich-1 extraction method. After extraction and decantation, an aliquot was reacted with ammonium molybdate and ascorbic acid in acidic medium. The resulting blue coloration was measured via spectrophotometry, and phosphorus concentration was calculated by comparison with a standard curve. Phosphorus availability is a key factor influencing both plant development and microbial activity in the soil.

2.3.11. Metagenomic

Metagenomic analysis among the most contrasting samples from 2020 and 2021 was performed. For this analysis, total soil DNA was extracted using the DNEASY PowerSoil Pro kit from Qiagen. The concentration and quality of the extracted DNA were assessed using the dsDNA HS Assay Kit Qubit^® from Life Technologies. After extraction, genomic libraries were constructed using 1 𝜇g of DNA per sample. The Nextera XT kit from Illumina, based in San Diego, CA, USA, was used for this purpose. The subsequent sequencing was carried out on the Illumina MiSeq equipment. The raw reads obtained underwent the standardized MGnify analysis pipeline (version 5), from the European Molecular Biology Laboratory of the European Bioinformatics Institute (EMBL–EBI) (Richardson et al., 2022). All data generated are publicly available at the European Nucleotide Archive (ENA), under the project number PRJEB64357, including sequences, metadata, and information associated with taxonomic and functional annotations for further reference. Note that this analysis delves into the soil microbiome, revealing microbial communities’ diversity and functional roles.

2.4. Statistical methods

To analyze temporal variations in biological parameters (carbon and nitrogen biomass, qCO2, hyphae, fungi, bacteria, basal soil respiration), data from 2020 and 2021 were compared using ANOVA and Student’s t-tests for independent samples. Tests of normality and homogeneity of variances ensured t-test assumptions were met. Results were expressed as t statistics and p-values, with p-values transformed to the -log10 scale for better visualization, where smaller p-values appeared as taller bars in the graph Post-ANOVA.

Tukey’s test was conducted to compare soil genotypes and identify significant differences in biological variables across years. Pairwise mean differences, confidence intervals, and adjusted p-values were reported, with significance set at p < 0.05. Pearson correlation analyses were conducted to assess linear relationships between variables, categorized as strong (|𝑟 | > 0.7), moderate (0.5 ≤ |𝑟 | ≤ 0.69), or weak (|𝑟 | < 0.5). A co-occurrence network of phyla was constructed for soils P09 and P13, where nodes represented phyla and edges denoted correlations. The Average Clustering Coefficient (ACC) was used to quantify network clustering. Microbial diversity was evaluated using the Shannon diversity index (H’). Still, Spearman correlation analyses linked biological data with functional profiles from microbiome analyses cataloged in the Gene Ontology (GO) were perfomed .

3. Results

In this section, we detail the outcomes of our research, starting with a temporal analysis to track changes over time in the collected data. We then explore the relationships among biological parameters through Pearson Correlation, followed by the findings from Tukey’s Test. Subsequently, we delve into the results of physicochemical analyses to understand the soil’s fundamental characteristics. Finally, we present insights from metagenomic analysis and examine the abundance of various genera within the soil ecosystem.

3.1. Temporal analysis

Figure 1 presents the temporal analysis of the biological data collected from the 15 farms, between 2020 and 2021, revealing significant differences in terms of hyphae, fungi, and BSR parameters. (The taller bars in the graph for these parameters indicate that the differences are statistically significant, with smaller p-values suggesting greater significance.) This result implies that there was a noticeable change in the biomass of fungi and hyphae, as well as in the metabolic activity of the soil, measured by BSR, over the period considered.

Figure 1
Temporal variations in soil biological characteristics from 15 farms in the western region of Paraná, between 2020 and 2021, in which * indicates significant differences (< 0.001).

3.2. Pearson correlation

Figure 2 shows Pearson correlation analysis for the years 2020 (Figure 2a) and 2021 (Figure 2b), highlighting relationships between biological parameters. Figure 2a shows a perfect correlation between fungi and hyphae, as expected. Other notable correlations include NBM with N, presenting a correlation coefficient of 0.85, and NBM with CBM, with a correlation coefficient of 0.72. Figure 2b exhibits similar significant correlations for fungi and hyphae (0.96), NBM and N (0.85), and NBM and CBM (0.73). Nevertheless, some changes stand out when comparing results from 2020 (Figure 2a) with those observed in 2021 (Figure 2b). Note in particular that the correlation coefficients between BSR and CBM (-0.82), BSR and NBM (-0.73), BSR and bacteria (-0.7), and BSR and N (-0.9) became strongly negative.

Figure 2
Pearson correlation matrices for the years (a) 2020 and (b) 2021, including various soil biological variables such as carbon biomass (CBM), nitrogen biomass (NBM), qCO₂, hyphae, fungi, bacteria, basal soil respiration (BSR), and total nitrogen.

3.3. Tukey’s test

Figure 3 presents comparisons of biological parameters of the soil samples over the years of 2020 and 2021. In particular, Figure 3a depicts the results of Tukey’s test for CBM while Figure 3b for NBM, in which we observe significant trends among different soils. Although most soils showed lower values of CBM and NBM compared to the reference soil (i.e., P1), soil P9 consistently stood out from the others by exhibiting significantly higher values of CBM and NBM (even more pronounced in 2021). On the other hand, soil P13 exhibits lower levels of CBM and NBM. Figure 3c exhibits comparisons in terms of qCO₂ with soils P9 and P13 showing distinct characteristics; specifically, soil P9 achieved in 2020 a higher level of qCO₂ production compared to the reference soil (i.e., P1), standing out as the soil with the highest respiratory activity among the samples considered, while soil P13 showed a moderate increase relative to P1, indicating lower respiratory activity compared to P9. The trend remained similar in 2021. Figure 3d illustrates the outcomes of Tukey’s test for hyphae across soil samples. This analysis revealed minimal differences among soil samples in 2020; conversely, considerable disparities in hyphae quantities were observed in 2021 between reference soil samples P1 and P7, P9, and P10. These findings underscore the greater diversity in fungal activity within the soils, marking a more pronounced diversity in 2021. Figure 3e presents the results of the samples when assessed for fungi, highlighting only a few small and insignificant statistical differences for either 2020 or 2021. Figure 3f details the variation in bacterial counts across soil samples, revealing consistent and persistent bacterial community diversity between reference soil P1 and soils P9, P10, P11, P13, P14, and P15. Notice the gap between P1 and P15 especially in 2021, indicating evolving microbiological dynamics within the soils over the two years. Figure 3g presents the BSR levels of the soil samples considered. Note that BSR levels between reference soil P1 and soil P9 were similar in 2020; however, a drastic fall in BSR was observed in 2021, indicating changes in soil conditions or microbial activity (caused by management practices or environmental factors) affecting basal respiration. The remaining soil samples exhibited almost the same BSR as the reference soil P1. Lastly, Figure 3h shows the nitrogen (N) content of the soil samples. Notice that soil P9 has a higher N concentration while soil P13 showed lower levels relative to P1. Observe the stable pattern of N content in these soils over time.

Figure 3
Comparison of biological parameters in different soil samples over the years 2020 (dark-gray bars) and 2021 (light-gray bars). (a) CBM (mg C g⁻¹ h⁻¹); (b) NBM (mg g⁻¹ of soil); (c) qCO₂ (mg C g⁻¹ h⁻¹); (d) Hyphae length (mg g⁻¹ of soil); (e) Fungi (mg g⁻¹ of soil); (f) Bacteria (mg g⁻¹ of soil); (g) BSR (𝜇mol CO₂ g⁻¹ soil h⁻¹); (h) Total nitrogen (%). Standard deviations are displayed on the top of each bar.

Therefore, after carefully analyzing the results from Tukey’s test, two soils with markedly contrasting biological characteristics were identified, namely: P9 and P13. Soil P9 stood out for exhibiting the highest biological activity among all evaluated soils, with significantly superior indicators in various parameters. On the other hand, P13 was selected for showing one of the lowest biological activities among the evaluated soils, indicating a soil with characteristics opposite to P9 and possibly with limitations in fundamental aspects for the health and productivity of the ecosystem. So, based on these contrasting soil samples, we proceeded with detailed physicochemical and metagenomic analyses. These additional analyses allowed us not only to explore the composition and functionality of the present microorganisms but also to understand the physicochemical conditions of the soils, providing a more comprehensive view of the interaction between soil biology, its hysicochemical quality, and its overall health.

3.4. Physicochemical analysis

Table 2 summarizes the physicochemical parameters of the soil samples P9 and P13 over the years 2020 and 2021, with each parameter measured in triplicate to ensure the reliability of the results. Notably, regarding organic matter and carbon, the results indicated that there are no significant relevant differences between the soil samples over the years considered. In turn, electrical conductivity showed variation across the years; especially, for the soil sample P13. Concerning soil pH, significant shifts were observed for P13 across the years, highlighted by differences between P13 and P9 in 2020 and substantial year-over-year variations for P9. Lastly, phosphorus levels exhibited significant fluctuations, with noticeable year-to-year changes for P13, pronounced differences between P13 and P9 in 2020, and marked variations in P9 across 2020 and 2021.

Table 2
Summary of the soil physicochemical parameters, considering samples P9 and P13 over the years 2020 and 2021.

	P9		P13
	2020	2021	2020	2021
Organic matter	14.73 ± 3.38a	12.60 ± 1.23a	11.67 ± 0.60a	9.70 ± 1.65ab
Carbon	8.19 ± 1.88a	6.99 ± 0.69a	6.49 ± 0.32a	5.38 ± 0.91ab
Electrical conductivity	211.53 ± 23.21c	258.80 ± 19.15ab	274.07 ± 8.75a	132.53 ± 3.59d
pH	6.97 ± 0.23a	6.11 ± 0.11c	5.65 ± 0.03d	6.40 ± 0.03b
Phosphorus	25.45 ± 1.99d	35.82 ± 0.78c	37.07 ± 3.54b	45.62 ± 0.60a

• Results are presented as “mean ± standard deviation” (i.e., 𝜇 ± 𝜎).

3.5. Metagenomic analysis

Figures 4 and 5 illustrate microbial co-occurrence networks for soil samples P9 and P13, respectively, over the years 2020 and 2021, offering insights into the complexity and interactions within the microbial communities of each soil sample. The co-occurrence network for P9, encompassing both years, demonstrated a more complex network structure with 69 nodes and 2346 edges; in contrast, P13 presented a simpler network with 53 nodes and 1378 edges. This difference emphasizes the higher microbial diversity and more intricate ecological interactions in P9 when compared to P13. Both networks exhibited an Average Clustering Coefficient (ACC) equal to 1, highlighting the intense interconnectedness among the microbial species within each soil type. These results are corroborated by the Shannon diversity index, where the soil sample P9 exhibited a greater diversity index (i.e., 5.58 in 2020 and 5.17 in 2021), indicating a rich microbial diversity. In contrast, the soil sample P13 showed a lower diversity index (i.e., 3.30 in 2020 and 3.33 in 2021), suggesting a less diverse microbial community. So, these results highlight significant differences in the composition, complexity, and interactions of microbial communities between the two soil samples over the period considered.

Figure 4
Microbial co-occurrence network in soil sample P9 over the years 2020 and 2021, highlighting the interactions and complexity of microbial communities at the phylum level.

Figure 5
Microbial co-occurrence network in soil sample P13 over the years 2020 and 2021, highlighting the interactions and complexity of microbial communities at the phylum level.

3.6. Genus abundance

Figure 6 depicts the abundance and diversity of bacterial genera in the soil samples P09 and P13 over the years 2020 and 2021. Specifically, Figures 6a and 6b illustrate the genus abundance for soil sample P09 in 2020 and 2021, respectively, while Figures 6c and 6d show the obtained results regarding soil sample P13 in 2020 and 2021, respectively. Notice that the genera Streptomyces and Bradyrhizobium dominate in terms of abundance across soil samples as well as over the years considered. Additionally, in all four soil samples, of the 12 most abundant genera, three consistently repeat: Streptomyces, Micromonospora, and Bradyrhizobium, underscoring their prevalence and possible resilience across distinct soil environments. Also, one observes in soil sample P09 that the third most predominant genus is Sphingomonas in 2020 and Nocardia in 2021 (compare Figures 6a and 6b); in turn, for soil sample P13, Mycobacterium appears as the third most predominant genus for both 2020 and 2021. Furthermore, there is a greater similarity in bacterial genera composition between P09 and P13 for the year 2020 than between the same soil sample across different years, suggesting that climate-specific variations may play a significant role in shaping microbial communities year to year. Still, one verifies that the composition of lesser abundant genera (with 2% or less in terms of reads) significantly varies, likely reflecting influences from environmental conditions, agricultural practices, or inherent microbial dynamics.

Figure 6
Distribution of the 12 most abundant genera (relative to the total generated reads) in soil samples P9 and P13 over the years 2020 (left) and 2021 (right). (a) and (b) Soil samples P09. (c) and (d) Soil samples P13.

To assess the relationship between Gene Ontology (GO)/gene functions derived from metagenomic analyses and soil biological parameters, a Spearman correlation analysis was performed, followed by hierarchical clustering. This approach enabled the identification of associations between specific gene functions and soil biological indicators, providing insights into the functional dynamics within each soil sample (Figure 7). A strong positive correlation was observed between functions involved in the nitrogen cycle and most of the biological indicators evaluated, contrasting with a significant negative correlation with BSR. Notably, CBM exhibited a positive correlation with phosphorus cycle functions, cellular maintenance, and organic matter decomposition. Gene Ontology (GO) functions related to detoxification and protection displayed positive correlations with fungal presence, hyphal length, and soil moisture. Additionally, the phosphorus cycle correlated positively with total nitrogen and bacterial abundance. Conversely, BSR presented a negative correlation with functions associated with detoxification and protection, nitrogen cycling, and metabolism. Similarly, CBM showed negative correlations with functions involved in the carbon and phosphorus cycles.

Figure 7
Spearman correlation between metagenomic GO functions and soil biological analyses, highlighting the relationships between observed genomic functions (GO) from metagenomic analyses and various soil biological characteristics. Included GO functions are Sulfur Cycle (S cycle), Transport and Distribution, Organic Matter, Cellular Maintenance, Detoxification and Protection, Nitrogen Cycle, Metabolism, Phosphorus Cycle (P cycle), and Carbon Cycle (C cycle). These are correlated with biological indicators such as Basal Soil Respiration (BSR), Soil Microbial Biomass Carbon (SMBC), Total Nitrogen (Total N), Bacteria/Fungi Ratio, Hyphal Length, Moisture, Metabolic Quotient (qCO2), and Soil Microbial Biomass Nitrogen (SMBN).

4. Discussion

This study examined soil dynamics on farms in the Toledo region, Paraná, highlighting the importance of microbial interactions and ecological soil functions in agricultural systems. The findings provide valuable contributions to scientific knowledge in soil microbiology while offering practical insights for sustainable soil management practices. (Torres et al., 2024).

The biological analyses revealed significant temporal variations in key parameters over the years 2020 and 2021. Statistical analyses indicated a marked reduction in BSR in 2021, accompanied by an increase in fungal abundance and hyphal length. These findings align with existing scientific literature, with multiple studies, including those by Dacal et al. (2022) and Zhang et al. (2023), demonstrating that BSR typically rises in response to moderate or gradual temperature increases. However, the stability of other evaluated parameters suggests that the input of organic matter from corn/wheat cultivation in no-till soil may have provided an energy reserve, sustaining microbial activity over time. No-tillage practices, widely adopted in the region and throughout Brazil (Torres et al., 2024), have proven to be a sustainable approach, promoting water retention, reducing erosion, and enhancing soil quality. Despite observed climatic variations—with 2020 experiencing higher temperatures prior to sample collection and 2021 characterized by increased solar radiation and relative humidity—the management practices employed in the region appear to have mitigated more severe impacts on the soil microbiota.

In addition to management practices and their influence on soil microbiota, the Pearson correlation analysis of soil biological variables across 2020 and 2021 reveals significant trends in soil dynamics, underscoring interactions between biological components and their responses to environmental fluctuations. The strong and consistent correlation observed between fungal biomass and hyphal length in both years reinforces the established understanding that mycelial development is intrinsically linked to fungal proliferation (Lehmann et al., 2019), reflecting their vitality and capacity to drive processes such as organic matter decomposition.

Furthermore, the analysis of negative correlations between basal soil respiration (BSR) and other biological variables in 2021 reveals complex soil dynamics potentially driven by shifts in microbial community composition. This observation aligns with findings by Zhou et al. (2012), who suggest that microorganisms play a key role in regulating soil carbon dynamics, affecting both the thermal sensitivity of BSR and the selective degradation of labile carbon. Additionally, the hypothesis proposed by Conant et al. (2011) supports that the resistance of soil organic matter to decomposition is influenced by its composition and structural protection. These insights suggest that variations in BSR may represent an adaptive response to climatic factors such as temperature and humidity fluctuations, which, in turn, modulate metabolic activity and biomass composition within the soil. This adaptive response likely impacts overall soil decomposition dynamics and microbial activity.

In the evaluation of Tukey’s Test results for the biological indicators of soils P9 and P13 for the years 2020 and 2021, distinct patterns emerged, underscoring the influence of the applied management techniques. It is pertinent to note that the inclusion of these soils in the study was determined solely by the willingness and cooperation of the participating farmers, rather than by pre-selection criteria. The choice of soil P9, recognized for its history of biological management practices, was incidental and reflects the local producers’ openness to scientific collaboration. Although it is known that the farmer managing soil P9 has long employed microbial inoculants for nitrogen fixation and applies swine residue as an amendment, specific details regarding these biological practices were not accessible for this study.

Soil P9 distinguished itself by exhibiting significantly higher biological activity compared to other soils, including those that have recently begun incorporating organomineral products and poultry litter, as reported by the farmers. This superiority was evident across several biological parameters, such as Soil Microbial Biomass Carbon (CBM), Soil Microbial Biomass Nitrogen (NBM), Basal Soil Respiration (BSR), metabolic quotient (qCO2), total nitrogen, and bacterial abundance, suggesting a nutrient-rich soil with a highly active microbial community. Conversely, soil P13, despite the recent addition of poultry litter, and similarly to other soils utilizing organomineral inputs, demonstrated lower biological activity relative to P9. This observation suggests that while poultry litter and organominerals contribute organic matter and nutrients, the process of soil restoration and enhancement does not occur instantaneously and requires consistent application over time. Additional factors may include nutrient imbalances, suboptimal physical conditions, or a history of unfavorable management practices, which are not fully mitigated by the short-term use of these amendments (Ranjan et al., 2023).

In this study, we observed that, with respect to phosphorus levels, soil P9 exhibited lower concentrations in 2020 compared to soil P13. This discrepancy may reflect a focus on chemical phosphorus correction or the recent incorporation of poultry litter, which serves as a significant source of phosphorus. By 2021, soil P13 displayed even higher phosphorus levels, likely as a response to cumulative management practices, both chemical and organic, implemented over time. These findings underscore the complexity of interactions among soil management practices, physicochemical parameters, and biological activity. This intricate relationship parallels observations in previous studies, such as that of Santi et al. (2016) in Rio Grande do Sul, Brazil, which demonstrated that soils with elevated phosphorus concentrations do not necessarily yield the highest productivity. However, contrasting studies, such as Rosas et al. (2024), indicate that phosphorus correction practices have significantly increased available P stocks in Brazilian soils, positively impacting soybean production. Nonetheless, caution is warranted, as excessive P inputs can reduce the activity of phosphorus- solubilizing microorganisms (Silva et al., 2023). Such results emphasize the need for an integrated approach that considers not only chemical amendments but also microbiological management and the nuanced interactions among soil components to optimize agricultural productivity.

Due to the nature of the experimental design and the high cost of metagenomic sequencing, only one representative sample per soil (P9 and P13) was sequenced in each year (2020 and 2021). Given that microbial co-occurrence network analyses require multiple replicates to compute meaningful correlation-based interactions between taxa, it was not statistically feasible to construct separate networks for each year. As a methodological alternative, we combined the sequencing data from both years per soil (P9 and P13) to generate more robust networks and enhance the detection of microbial associations. While this approach does not isolate year-specific dynamics, it still allowed for a comparative evaluation of microbial complexity and connectivity between soils with contrasting biological activity, providing valuable insights into the influence of long-term management. Metagenomic analyses revealed a more interconnected and diverse microbial community in soil P9, as indicated by the high Shannon diversity index, which suggests a robust and resilient ecosystem, potentially attributed to biological management practices. In contrast, while soil P13 exhibits a less dense network and lower microbial diversity, the observed Average Clustering Coefficient of 1 across both soils indicates a high degree of interconnectivity, possibly reflecting system stability despite reduced diversity. This interpretation aligns with findings by Kajihara and Hynson (2024), demonstrating that microbiome complexity and stability are not necessarily contingent on initial diversity levels but rather on the strength of microbial interactions. Consequently, while P9 presents higher network density, the network structure of P13 may also denote a stable ecosystem. These results are consistent with literature associating microbial network architecture with ecosystem stability and soil functionality (Montesinos-Navarro et al., 2017), emphasizing the critical role of management practices in sustaining soil health. Additionally, it is noteworthy that these observations correlate with carbon content and organic matter levels, which showed no significant differences between soils P9 and P13 over the two years analyzed. This supports the perspective that soil stability and health may rely more on microbial community structure and interactions than on absolute organic matter content.

It is important to note that this study focused exclusively on consolidated no-tillage areas, primarily due to historical factors and the predominance of this system in the western region of Paraná. While the relevance of comparative analyses with conventional tillage systems is well-recognized in the scientific literature, such comparisons were beyond the scope of the present work. Nevertheless, the data generated herein offer a valuable baseline for future investigations aimed at contrasting different soil management systems, contributing to a more comprehensive understanding of their long-term impacts on soil health and microbial dynamics.

Furthermore, although detailed information on the crop varieties used at each site was not available, all farms were known to cultivate commercial soybean and maize hybrids commonly adopted in the region. According to farmer reports, these varieties are typically sourced from the same regional suppliers, suggesting minimal variability in genetic background. The analysis of bacterial genera in soils P09 and P13 across 2020 and 2021 reveals a complex microbial dynamic responsive to environmental factors. The consistent predominance of Streptomyces in both soils and years, alongside variations in genera such as Bradyrhizobium, Sphingomonas, Nocardia, Mycobacterium, and Nocardioides, suggests an adaptive response of the soil microbiota to environmental fluctuations, as discussed by Mukhtar et al. (2023) and Qiqige et al. (2023). These studies emphasize the significant impact of climatic changes, including shifts in temperature and precipitation, on microbial community structure and nutrient cycling, affecting both biodiversity and functional stability within soil ecosystems. Additionally, soils P09 and P13 shared a greater number of genera in common in the year 2020 compared to the same soil in different years, further supporting the notion that climate exerts a considerable influence on microbial community composition. The observed shifts in the predominance of certain genera between years may reflect microbial adaptation to varying climatic and management conditions, underscoring the importance of temporality and environmental interactions in soil microbiological analyses. Further supporting this perspective, Hartmann and Six (2023) emphasizes that the soil microbiome, influenced by soil structure, fulfills essential functions in agroecosystems, contributing to soil fertility, crop productivity, and resilience against stressors. This understanding reinforces the need to incorporate soil structure and management practices into strategies aimed at fostering a balanced and sustainable soil ecosystem.

The Spearman analysis and clustering reveal a strong relationship between genetic functions and soil biological parameters. The positive correlation between nitrogen cycling functions and most biological indicators suggests that nitrogen cycling processes are closely connected to soil health and biological activity. This observation aligns with findings from Scarlett et al. (2021), who demonstrated that ammonia-oxidizing bacteria (AOB), influenced by soil pH, play a significant role in plant health, particularly in tree ecosystems. Additionally, the observed negative correlation with basal soil respiration (BSR) suggests that increased microbial activity associated with nitrogen cycling may correspond to lower BSR levels, potentially due to shifts in microbial energy allocation or metabolic processes. These results highlight the importance of maintaining a balanced carbon-to-nitrogen (C:N) ratio in soil, as this balance influences the abundance of AOB and other nitrogen-transforming microorganisms, ultimately impacting ecosystem stability and health.

In our study, we observed a positive correlation between microbial biomass carbon (CBM) and genetic functions related to the phosphorus cycle, cellular maintenance, and organic matter decomposition. This correlation suggests that higher microbial biomass is associated with increased activity in essential soil processes, such as organic matter decomposition and phosphorus cycling, which are critical to maintaining a healthy soil ecosystem. The study by Luo et al. (2020) supports these findings by highlighting the importance of soil stoichiometric balance (C:N:P) in regulating microbial communities and their responses to environmental disturbances. Specifically, they identified the soil C:P ratio as a key predictor of genetic resilience in nitrogen cycling functions. These observations underscore that maintaining nutrient balance within the soil is vital for sustaining functional ecosystem capacities, especially amid global environmental changes, thereby reinforcing the significance of stoichiometric balance in our study.

This study underscores the critical interaction between soil management practices, environmental conditions, and soil microbiota, highlighting their collective impact on sustainable agricultural systems. As a pilot initiative conducted in one of the most agriculturally productive regions of Paraná, Brazil, this work provides foundational insights into the relationships among soil biological activity, management strategies, and microbial diversity. Although certain limitations exist—such as the restricted number of metagenomic replicates and the limited detail on long-term management histories—this research marks a significant step forward in integrating microbiological and molecular tools into applied soil science. Encouragingly, the positive response and collaboration of local farmers reveal a growing openness to science-based decision-making in the field. Building on this momentum, we are currently developing a more comprehensive and large-scale research initiative which will expand the number of sampled areas and include temporal monitoring across multiple years, diverse management systems, and a broader integration of molecular, biochemical, and spatial analyses. This ongoing project aims to produce high-resolution, predictive insights into soil health, ultimately supporting the co-development of sustainable agricultural solutions through farmer–scientist partnerships.

5. Conclusion

The research reveals notable differences in biological parameters between 2020 and 2021, including fungal biomass, hyphal length, and basal soil respiration, suggesting shifts in metabolic activity and biomass composition. Pearson correlation analysis underscores strong linear relationships between key biological variables, particularly between fungal abundance and hyphal length, as well as between total nitrogen, microbial biomass nitrogen (NBM), and microbial biomass carbon (CBM). Moderate negative correlations observed in 2020, such as those involving basal soil respiration and other variables, intensified in 2021, indicating possible changes in microbial metabolic balance over time.

The microbial comparison of soils P9 and P13 demonstrates that soil P9 harbors greater microbial diversity and a more complex network of interactions, which may confer increased stability. The findings further suggest that management practices and climatic conditions exert significant influence on microbial composition and soil biological functions across years. This highlights the importance of sustained, well-informed agricultural management practices, emphasizing the critical role of soil biodiversity and ecological functionality in promoting agricultural sustainability.

Data Availability Statement

Data will be made available on request.

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Edited by

• Editor:
  Takako Matsumura Tundisi

Publication Dates

• Publication in this collection
  27 Oct 2025
• Date of issue
  2025

History

• Received
  08 July 2025
• Accepted
  19 Aug 2025