Soil microbial indicators and bacterial co-occurrence networks reveal early changes under no-tillage and cover crops in a tropical Oxisol

Abstract

Improving soil quality in tropical agroecosystems requires management strategies that enhance soil functioning and ecological processes. This study evaluated whether short-term adoption of no-tillage combined with cover crops induces early changes in soil microbial indicators and bacterial co-occurrence patterns in a tropical Oxisol under soybean cultivation in the Brazilian Cerrado. Conventional plowing was compared with no-tillage systems using maize, millet, Crotalaria spp., Urochloa spp., and intercropped combinations during the off-season. After three years, microbial biomass carbon, enzyme activities, potentially mineralizable nitrogen, and bacterial community structure (16S rRNA) were assessed. No-tillage with cover crops increased microbial biomass and enzymatic activities related to nutrient cycling. Crotalaria enhanced potentially mineralizable nitrogen and dehydrogenase activity, while intercropping increased β-glucosidase activity. Bacterial alpha diversity increased under no-tillage, whereas community composition showed limited variation. Network analysis revealed greater bacterial connectivity and interaction complexity under no-tillage, indicating that microbial interactions are sensitive early indicators of regenerative changes in tropical soils.

Key words
bacterial community; Brazilian Cerrado; soil biochemical and microbial indicators; soil health; tropical Oxisol

INTRODUCTION

Preserving soil habitat quality is among the greatest challenges of the 21st century, due to the crucial role that soils play in ensuring global food security and mitigating the effects of climate change (FAO & ITPS 2015, IPCC 2019, Lal 2020, Chowdhury et al. 2025). Intensive land use for agricultural purposes has led to the physical, chemical, and biological degradation of soils over time, resulting in a loss of productive capacity in agricultural ecosystems and a decline in soil ecosystem services (Govaerts et al. 2007, Zhang et al. 2024). In Brazil’s agricultural Cerrado, the conventional system, characterized by plowing and tilling the soil, is one of the primary soil preparation practices employed by farmers. However, this system leads to a progressive loss of SOM and negative impacts on soil physical properties (Silva et al. 2020, Freitas et al. 2024). Thus, the development of targeted conservation strategies is essential for enhancing soil ecosystem services and improving soil health (Calfapietra et al. 2025).

The no-tillage (NT) system is a conservation-oriented agricultural approach proposed to improve soil quality and health. NT positively affects the physical, chemical, and biological properties of the soil, contributing to reduced erosion, enhanced nutrient-use efficiency, and increased SOM (Silva et al. 2020, Zhang et al. 2024). Furthermore, improvements in physical, chemical, and biological properties are typically observed over the long term (>5 years) and are influenced by soil type, environmental conditions, and the quality and quantitative input of crop residues (Jacobs et al. 2022). Studies have shown that NT also enhances soil bacterial community diversity and alters the relative abundance of bacterial phyla (Li et al. 2020, Pratibha et al. 2023). In NT systems with legume crops, improvements in soil quality may take longer to become evident due to the rapid mineralization of organic residues and the reduced persistence of soil cover, particularly under tropical conditions. In Brazil, soybean production systems require complementary practices, such as crop succession and rotation with non-legume species (Silva et al. 2020, Mattiello et al. 2024).

The soil microbial community plays a fundamental role in ecosystem functioning, contributing to the transformation of organic matter, nutrient cycling, and nutrient use efficiency (Van Der Heijden et al. 2008, Fierer 2017, Du et al. 2025). However, the abundance, structure, richness, and diversity of soil microorganisms are influenced by several abiotic factors, including nutrient availability, carbon input, soil pH, soil moisture, and temperature (Zhang et al. 2024). All these factors are, in turn, affected by soil management practices, such as NT and the use of cover crops. In soil conservation systems, the soil habitat reorganizes, creating new conditions that can affect the composition and interactions within the microbial community (Chen et al. 2021, Pratibha et al. 2023). Recent studies on soil microbial ecology, including microbial networks, have revealed complex interactions among microorganisms within the community (Zhang et al. 2024, Hannula & Veen 2025, Saati-Santamaría et al. 2026). Unlike studies focused on alpha or beta diversity of microbial communities, network analyses provide more detailed insights into ecological interactions within the soil habitat, such as competition, cooperation, neutralism, and community coalescence (Guseva et al. 2022, Zhang et al. 2024). Network analysis has become an important ecological tool for investigating species relationships in ecosystems and linking these relationships to ecological processes in the soil (Lupatini et al. 2014, Qiao et al. 2024, Zhang et al. 2024).

Especially, the bacterial community in the soil has a direct influence on a wide range of ecological processes (Fierer et al. 2007, Fierer 2017, Du et al. 2025). The involvement of bacteria in biochemical processes such as biological nitrogen fixation, nitrification, denitrification, hormone production, and siderophore production has shown how much the bacterial community affects soil function (Fierer 2017, Muhammad et al. 2023, Zhang et al. 2024). This versatility of bacteria in the soil is mainly due to their high diversity, composition, and abundance in the soil habitat. Nevertheless, the organization and distribution of the bacterial community are affected by specific environmental changes or disturbances in the soil habitat. Thus, the interaction between taxa within the bacterial community can be a key point in ecological studies, as recently demonstrated in the literature (Muhammad et al. 2023, Zhang et al. 2024, Du et al. 2025), particularly for studies on soil management practices.

The restoration of degraded soils increasingly relies on management strategies capable of enhancing ecological processes and reinforcing soil resilience. Agricultural conservation practices, particularly no-tillage combined with diverse cover crops, have been recognized as promising nature-based solutions to restore soil functions by stimulating microbial activity, increasing organic matter inputs, and reorganizing the biological networks that sustain ecosystem stability (Calfapietra et al. 2025). In tropical soils, where degradation is accelerated by intensive land use and rapid organic matter turnover, microbial indicators and bacterial co-occurrence networks offer sensitive approaches for detecting early functional recovery. Such ecological indicators can capture subtle shifts in soil functioning before these become evident in chemical or physical attributes, thereby providing a robust framework for assessing resilience and soil rehabilitation under conservation management. Integrating cover crops into no-tillage systems has shown potential to improve soil quality, enhance microbial functional capacity, and strengthen the soil’s ability to maintain and recover key ecosystem processes (Chowdhury et al. 2025).

The Brazilian Cerrado is one of the main agricultural frontiers in the world, with the production of important commodities for the international market, such as soybeans, corn, cotton, and commercial forests. Around 40% of the Cerrado’s land is dedicated to agriculture and soybeans have become a dominant crop in this region, with an estimated grain production of 75–85 million tons (Mizobe 2019, CONAB 2025). However, these large-scale soybean production areas have led to a loss of soil quality due to the monoculture system usually adopted by farmers. The use of cover crops in succession or crop rotation may be a powerful alternative to not only improve soil quality but also increase soybean production. Here, we propose a field experimental design using cover crops in succession with soybean cultivation in the same year, including corn, millet, brachiaria, and crotalaria.

We hypothesized that the integration of cover crops into no-tillage systems enhances soil environmental quality by promoting shifts in bacterial network connectivity and biochemical functioning. Therefore, this study aimed to: (1) assess the effects of no-tillage and cover crop management on microbial and biochemical indicators of soil environmental quality; (2) characterize changes in the structure and connectivity of the soil bacterial community using network analysis; and (3) explore the relationships between bacterial network properties and soil quality indicators as early signals of ecosystem response to sustainable management in tropical systems.

MATERIALS AND METHODS

Study Site and Soil Characterization

The study was conducted in the city of Patos de Minas, Minas Gerais State, Brazil, located in the southeastern region of the country, at coordinates 18°40’31”S, 46°32’36”W, with an average altitude of 889 m. The region has a tropical climate with a dry winter period (Aw), according to the Köppen-Geiger classification (Alvares et al. 2014). The soil is classified as Latossolo Vermelho Distrófico according to the Brazilian Soil Classification System (Santos et al. 2018), corresponding to an Oxisol in the USDA classification (Soil Survey Staff 2014). It has a clayey texture, comprising 78.2% clay, 11.0% sand, and 10.8% silt (EMBRAPA 2006). A soil sample from the 0–0.2 m layer indicated the following properties: pH (H₂O) of 6.10; phosphorus (P) of 2.21 mg dm^-3; potassium (K) of 125 mg dm^-3; calcium (Ca²⁺) of 2.56 cmolc dm^-3; magnesium (Mg²⁺) of 1.42 cmolc dm^-3; and total organic carbon (TOC) of 16.7 g kg^-1.

Experimental Design and Management

The experiment was established in the winter of 2021 with cover crops in 10 × 10 m plots, following a randomized complete block design (RCBD) with three replications per treatment, and was conducted annually until 2024. The treatments were arranged as follows: T1 – Plowing (Pl); T2 – Fallow (Fw); T3 – Maize (Mz); T4 – Millet (Mt); T5 – Urochloa (Brachiaria) (Uro); T6 – Crotalaria (Crt); T7 – Maize/Urochloa (Mz/Uro); and T8 – Maize/Crotalaria (Mz/Crt). Summer planting was always carried out with soybean, using a row spacing of 0.5 m and a plant density of 240,000 plants per hectare. Fertilization was performed based on soil analysis and agronomic recommendations. Cover crops were grown in winter without nutrient additions. The plowing treatment (T1) was applied before soybean planting, while the other treatments followed a NT system. Cover crops were sown with a 0.5 m row spacing, except in treatments T7 and T8, where Uro and Crt were intercropped in the maize rows.

Soil sampling

Soil samples were collected in January 2024, while the area was cultivated with soybean (reproductive stage R2). Sampling was carried out in the crop row (25 cm row spacing) at a depth of 10 cm by removing a slice measuring 5 × 25 cm. Five samples were collected per plot, combined to form a composite sample, homogenized, and then sieved (2 mm). One portion of the soil samples (500 g) was packed in plastic containers and promptly transported to the laboratory for storage at 4–8°C for subsequent microbial, biochemical, and molecular analyses. A second portion was air-dried at room temperature in the laboratory for chemical analysis.

Soil Chemical Analysis

The physico-chemical analyses of the soil were conducted using air-dried soil. Soil pH was determined in water (1:2.5 soil/water). The nutrients potassium (K⁺), calcium (Ca²⁺), and magnesium (Mg²⁺) were extracted using an acid solution, following Tedesco et al. (1995). Labile phosphorus (P) was determined using the Mehlich method, as described by Tedesco et al. (1995). Total organic carbon (TOC) was measured in soil dried at 60 °C for 24 hours and sieved through a fine mesh (0.5 mm), followed by acid digestion with sulfuric acid containing potassium dichromate (Yeomans & Bremner 1988). Dissolved organic carbon (DOC) in soil was extracted using a 0.5 M K₂SO₄ solution, shaken for 30 minutes at 180 rpm, filtered, and determined using an acid solution with potassium dichromate (Yeomans & Bremner 1988).

Soil Microbial and Biochemical Analysis

Soil microbial respiration (SMR) was measured by CO₂ emissions from 100 g of field-moist soil in sealed 500 mL bottles using the standard method (Stotzky 1965) for 21 days at 25°C. Microbial biomass carbon (MBC) was determined by the extraction method using a potassium sulfate solution (0.5 mol L^-1), as described by Vance et al. (1987). In the same extract, nitrogen concentration was quantified to assess microbial biomass nitrogen (MBN) (Brookes et al. 1985). Potentially mineralizable nitrogen (PMN) was measured according to Clark et al. (2019) using a 40 mL flask with 5 g of soil, filled with water. The flasks were sealed and incubated under anaerobic conditions for 7 days at 40°C, after which ammonium concentration was measured (Mulvaney 1996).

Enzymatic activity assays of β-glucosidase (GLU), phosphatase (PHO), arylsulfatase (ARY), urease (URE), dehydrogenase (DHA) and fluorescein diacetate (FDA) were performed using field-moist soil samples with specific substrates for each enzyme (Sigma). All assay conditions are described in a previously published study by Vinhal-Freitas et al. (2017), except for anaerobic nitrogen, which was performed according to Clark et al. (2019).

Bacterial Community 16S rRNA Gene Sequencing

The soil bacterial community was evaluated using 16S rRNA gene sequencing. Genomic DNA was extracted from 24 fresh soil samples (0.25 g) using a DNeasy PowerSoil Pro Kit (Qiagen, Germany) according to the manufacturer’s instructions. The extracted DNA was quantified by a Qubit® 2.0 fluorometer using the dsDNA HS Working Solution (InvitrogenTM). The DNA integrity was also checked by electrophoresis in a 1.0% agarose gel with 1× TAE buffer. The V4 hypervariable region of the 16S rRNA gene was amplified using the 16S Barcoding Kit (SQK-16S024, Nanopore Technologies). The PCR protocol was performed in triplicate containing 5 μL of 10x High Fidelity PCR Buffer (Invitrogen), 1.5 μL of MgCl₂ (50 mM), 1 μL of dNTP mix (2.5 mM), 10 μL of each primer (10 μM 515F and 806R), 0.5 μL of PlatinumTM Taq DNA Polymerase (Invitrogen, Carlsbad, CA, USA), 10 μL DNA extract (10 ng total) and 22 μL of sterile ultrapure water to a final volume of 50 μL. The PCR conditions used were 95 °C for 1 min to denature the DNA, with amplification proceeding for 35 cycles at 95 °C for 20 s, 55 °C for 30 s, and 65 °C for 2 min; a final extension of 10 min at 65 °C was added to ensure complete amplification. The final concentration of the amplified DNA was estimated by using the Qubit Fluorometer Kit. The replicates of each treatment (10 µL) were mixed in a DNA LoBind Tube (1.5 mL) and cleaned up using Agencourt® AMPure® XP Reagent (1.8x sample volume) at room temperature for 10 min. Then, all washing steps were performed with 200 μL of freshly prepared 70% ethanol for 30 s. After washing, the amplified DNA was dried (15 min at room temperature) and separated from the beads in a 20 µL solution of Tris-EDTA buffer (pH 8.0). The final concentration of the amplified-purified DNA was estimated using the Qubit Fluorometer Kit (Invitrogen, Carlsbad, CA). The DNA library was prepared with final equimolar concentrations (100 ng of total DNA) of amplicons from all samples and loaded onto flow cell R9. Sequencing was carried out using the MinION Mk1C1 (Oxford Nanopore Technologies - ONT) for 48 hours, with high-accuracy basecalling and selection of fragments between 200 and 1000 bp.

Data Processing and Bioinformatic Analysis

Raw 16S rRNA gene sequencing data were uploaded to EPI2ME, the cloud-based platform developed by Oxford Nanopore Technologies (ONT) and processed using the FASTQ 16S workflow (v1.4.0), applying a minimum Q-Score of 10 and a 97% similarity threshold for taxonomic assignment with Kraken2 (v2.1.3). The resulting sequencing reads were further analyzed in the R environment (version 4.2.3) (R Development Core Team 2024) using RStudio (version 2023.12.0.369) (Posit Team 2023). Ecological analyses were performed with the phyloseq (McMurdie & Holmes 2013) and microeco (Liu et al. 2021) packages, whereas graphical outputs were generated using ggplot2 (Wickham 2011). Alpha diversity was evaluated using the Shannon and Simpson indices, and bacterial community structure (beta diversity) was assessed by principal coordinate analysis (PCoA) based on Bray–Curtis distance matrices. Differences in bacterial community composition among treatments were tested by permutational multivariate analysis of variance (PERMANOVA; 9,999 permutations) (Anderson 2001). Bacterial co-occurrence networks were constructed using the trans_network function implemented in the microeco package, with the parameters cor_method = “SparCC”, sparcc_method = “SpiecEasi”, and filter_thres = 0.0005 (Friedman & Alm 2012). Networks were generated using the cal_network function with a significance threshold of P < 0.001 and an absolute correlation coefficient (|r|) ≥ 0.8. The resulting networks were exported in GEXF format and visualized in Gephi v0.10 (Bastian et al. 2009) using the Fruchterman–Reingold layout. Node size was proportional to betweenness centrality, node colors represented bacterial phyla, and edges indicated significant positive (blue) or negative (red) correlations.

RESULTS

Change in Soil Chemical Properties

The soil pH values did not show significant differences between the no-tillage and cover crop treatments, but they were significantly lower in Pl compared to Fw and Mt (Table I). The concentrations of K⁺, Ca²⁺, Mg²⁺, and P did not differ significantly between the cover crops, with the lowest values found in Pl. There was no significant change in TOC content in the soil between the treatments, but a significant increase in DOC was observed in Pl compared to the other treatments, except for Mz and Crt (Table I). However, TOC content showed a positive correlation with the concentrations of Ca²⁺, Mg²⁺, and soil pH, while DOC correlated negatively (Figure 1).

Table I
Effect of no-tillage and cover crops on chemical attributes.

Treatment	pH	Nutrient1				TOC	DOC
	(water)	K	Ca	Mg	P
T1-Pluwing (Pl)	5.83c	179d	3.37b	1.29c	5.97b	19.5a	28.6a
T2-Fallow (Fw)	6.38ab	197cd	4.49ab	1.69abc	8.78ab	23.4a	13.6c
T3-Maize (Mz)	5.91bc	218bcd	3.52ab	1.33bc	10.41ab	21.1a	23.6ab
T4-Millet (Mt)	6.49a	203cd	4.58a	1.81a	9.75ab	22.6a	18.1bc
T5-Urochloa (Uro)	6.30abc	285a	4.11ab	1.62abc	12.07a	23.6a	15.6bc
T6-Crotalaria (Crt)	6.30abc	235bc	4.61a	1.77ab	10.68ab	26.1a	24.3ab
T7-Maize/ Urocho (Mz/Uro)	6.04bc	255ab	3.94ab	1.46abc	7.76ab	22.2a	16.3bc
T8-Maize/Crotalaria (Mz/Crt)	5.98bc	241abc	3.93ab	1.47abc	8.61ab	23.6a	19.1bc

• ¹Nutrient: potassium (K, mg dm^-3 soil), calcium (Ca, cmolc dm^-3 soil), magnesium (Mg, cmolc dm^-3 soil), phosphorus (P, mg dm^-3 soil), total organic carbon (TOC, g C kg^-1 soil), and dissolved organic carbon (DOC, g C kg^-1soil). Different letters between treatments indicate significant differences according to Tukey’s test at the 5% significance level.

Figure 1
Correlation analysis between soil chemical indicators in the experiment using cover crops under no-tillage system in a Brazilian Cerrado Oxisol. The figure shows the heatmap for each indicator and the significance levels.

Effect of No-tillage and Cover Crops on Microbial and Biochemical Indicators

The microbial and biochemical indicators were significantly altered in response to the adoption of NT and cover crops (Table II). The Pl treatment had the lowest averages for microbial and biochemical soil indicators compared to the other treatments, except for soil microbial respiration, which had the lowest average in the Mz treatment. The results show that PMN, PHO, and DHA indicators were significantly higher in Crt compared to Fw. The values of GLU activity in Mz/Uro and Mz/Crt significantly increased compared to Pl and Fw. However, PHO activity in Mz/Uro and Mz/Crt significantly decreased compared to the other treatments.

Table II
Microbial and biochemical indicators of soil under practical management in the Brazilian Cerrado. The values for each indicator represent the means of the repetitions (n=3).

Treatment	Microbial1				Biochemical2
	SMR	MBC	MBN	PMN	GLU	PHO	ARY	URE	DHA	FDA
T1-Pluwing (Pl)	35.1bc	191c	13.7e	55.5c	111c	182d	53b	65.5b	92.6bc	155c
T2-Fallow (Fw)	43.2abc	262b	36.5bcd	87.6bc	136b	265abc	108a	83.6ab	90.1bc	170bc
T3-Maize (Mz)	31.1c	291ab	40.2abcd	61.9abc	161ab	313a	108a	89.7a	89.6c	218ab
T4-Millet (Mt)	43.4ab	270b	55.3a	89.3ab	161ab	282ab	108a	86.8a	92.2bc	198abc
T5-Urochloa (Uro)	42.4abc	288ab	51.8ab	80.2abc	151ab	282ab	107a	98.4a	98.1abc	199abc
T6-Crotalaria (Crt)	51.4 a	281ab	47.1abc	100.5a	160ab	299a	114a	88.7a	109.4a	207abc
T7-Maize/ Urocho (Mz/Uro)	40.8bc	327a	25.7 de	82.6abc	163a	211cd	107a	89.2a	102.2ab	230a
T8-Maize/Crotalaria (Mz/Crt)	38.9bc	302ab	32.1cd	84.1abc	166a	231cd	100a	85.5ab	100.4ab	209abc

• ¹Microbial indicadors: SMR, soil microbial respiration (ugC-CO2 g^-1 soil); MBC, microbial biomass carbon (ug C g^-1 soil); MBN, microbial biomass nitrogen (ug N g^-1 soil); PMN, potential mineralizable nitrogen (ug N g^-1 soil).2Biochemical indicators: GLU, beta-glucosidase (ug pNP g^-1 soil h1); PHO, phosphatase (ug pNP g^-1 soil h^-1); ARY, arylsulphatase (ug pNP g^-1 soil h^-1); URE, urease (ug NH3+ g^-1 soil h^-1); DHA, dehidrogenase (ug TPF g^-1 soil h^-1); FDA, fluorescein diacetate (ug fluorescein g^-1 soil h^-1).

Change in Microbial Community Composition and Diversity

A total of 43,246 high-quality 16S rRNA gene reads were identified through high-throughput sequencing using nanopore technology. The bacterial community composition was predominantly made up of Pseudomonadota (39%), Acidobacteriota (22%), Bacillota (13%), Verrucomicrobiota (5%), Planctomycetota (4%), and Actinomycetota (4%). However, the composition of the bacterial community showed very little variation among the treatments (Figure 2).

Figure 2
Relative abundance of top 15 bacterial phyla in soil under no-tillage and cover crops. Treatments include Plowing (Pl); Fallow (Flw); Maize (Mz); Millet (Mt); Urochloa (Uro); Crotalaria (Crt); Maize/Urochloa (Mz/Uro); and Maize/Crotalaria (Mz/Crt).

Alpha diversity of the soil bacterial community was assessed using the richness and Shannon index (Figure 3). Results indicated that the richness index was significantly higher in NT treatments compared to the Pl treatment (Figure 3a). However, the Shannon index did not differ among treatments (Figure 3b). Beta diversity, determined using the Bray-Curtis index, showed no variation among treatments (p = 0.2155) (Figure 3c).

Figure 3
Effect of no-tillage and cover crops on bacterial diversity index. Changes of Alfa diversity were measured by Richness (a) a Shannon-Weiner index (H’) (b) and, beta diversity by abundance of phylum thought principal coordinate analysis (c). Mean values (n = 3) with different letters indicate Tukey’s HSD differences (p < 0.05).

Network Patterns of the Soil Bacterial Community

A microbial co-occurrence network for each treatment was constructed using taxa at the phylum level (Figure 4). The results show that nodes and edges varied significantly in the bacterial community co-occurrence under different treatments. The number of nodes ranged from 221 to 283, with the lowest values observed in Pl and the highest in Mz/Uro. The combination of cover crops (Mz/Uro and Mz/Crt) increased the number of nodes compared to Uro and Crt.

Figure 4
Changes of soil bacterial networks under no-tillage and cover crops. Networks were constructed based on significant correlations (|r| ≥ 0.7, p < 0.05) for each treatment. Each node represents a bacterial taxon (colored by phylum), and each edge represents a positive (blue) or negative (red) correlation. The number of nodes and edges, along with the proportion of positive and negative edges, is shown for each network. Larger node size indicates higher relative abundance.

The number of edges also showed significant variation (ranging from 316 to 550), indicating changes in the correlations between keystone species interactions (Figure 4). A notable increase in the number of edges was observed in the Fw and cover crop treatments compared to Pl (317 edges), except for Crt (316 edges). However, in Crt, positive interactions were more frequent than negative ones. The cover crop with Mz increased the number of edges by 22%, 26%, and 38% compared to Mt, Uro, and Crt, respectively. In contrast, the combinations Mz/Uro and Mz/Crt further increased the number of edges compared to Mz, Uro, and Crt. However, an increase in negative interactions was observed in the Mz/Uro and Mz/Crt treatments.

Interconnection of the Bacterial Community and Soil Quality Indexes

Microbial and biochemical indicators were used to evaluate correlations with soil microbial community parameters (Figure 5a). Among the microbial indicators, only MCB showed a significant correlation with network nodes and edges. Regarding the biochemical indicators, GLU and FDA were significantly and positively correlated with both node and edge indices, while ARY showed a significant correlation only with the edge index. In contrast, the Shannon, Simpson, and Richness diversity indexes did not exhibit significant correlations over the short-term period under NT and cover crop treatments.

Figure 5
Relationships between microbial network features, bacterial diversity, biochemical indicators, and soil chemical properties. (a) Pearson correlation matrix among bacterial diversity indices (Shannon, Simpson, Richness), network features (nodes, edges), microbial biomass (MBC), biochemical indicators (DHA, FDA, GLU, ARY, PHO, URE), and soil chemical attributes (TOC, DOC, SMR, MBN). Significant correlations are indicated (*p < 0.05, **p < 0.01, ***p < 0.001; ns = not significant). (b) Venn diagram showing the number of shared and unique bacterial amplicon sequence variants (ASVs) among the different treatments. The central number (319) represents the core bacterial ASVs shared among all treatments. (c) Heatmap of Pearson correlations between the relative abundance of dominant bacterial phyla and soil biochemical and chemical variables. Asterisks indicate statistically significant correlations (*p < 0.05, **p < 0.01).

A Venn diagram was constructed to illustrate the relationships in microbial community composition and differential speciation among treatments (Figure 5b). The data show that microbial taxa connectivity reached 319 connections, with speciation patterns depending on the treatment. NT under cover crop treatments enhanced microbial taxa speciation compared to Pl and Fw, with the exception of the Uro treatment.

A new Pearson correlation analysis was performed to explore the relationships between soil chemical, biochemical, microbial, and network indicators and the relative abundance of major bacterial phyla (Figure 5c). The analysis revealed positive associations between Bacteroidota and arylsulfatase (ARY), urease (URE), microbial respiration (SMR), and bacterial richness. Planctomycetota also showed positive correlations with ARY, URE, SMR, and richness. Bdellovibrionota was positively correlated with ARY, URE, and richness. Acidobacteriota displayed negative correlations with pH, calcium, and PMN, as also occurred with Verrucomicrobiota and ARY. Positive correlations also occurred between Cyanobacteriota and SMR, and between Nitrospirota and both ARY and URE.

DISCUSSION

In this study, we show that the short-term adoption of no-tillage combined with cover crops during the soybean off-season leads to measurable shifts in soil microbial indicators and bacterial community dynamics. These changes influenced soil functioning and altered bacterial co-occurrence patterns in a tropical Oxisol of the Brazilian Cerrado. The results indicate that the soil microbial habitat in this biome responds rapidly to modifications in conservation-based management, reflecting early transitions in soil functioning. This responsiveness is consistent with evidence from other soils and climatic regions, where improvements in microbial processes and network properties have been observed following the implementation of conservation or regenerative practices (Lupatini et al. 2014, Vinhal-Freitas et al. 2017, Bobuľská et al. 2021). Chemical indicators did not differ among treatments with cover crops; however, differences were observed between the plowing (Pl) treatment and those under NT with cover crops. The levels of Ca, Mg, and K were lower under Pl, possibly due to pH alterations and soil disturbance. TOC contents did not differ among treatments, which aligns with previous studies (Jacobs et al. 2022), likely due to the short duration of the experiment. Notably, DOC values varied among treatments, with higher levels in Pl and lower levels in the others. Elevated DOC in Pl may indicate that mineralization occurred; however, the resulting carbon may not have been effectively metabolized by microorganisms, leading to DOC accumulation in the soil. It is also important to note that TOC variation was positively correlated with microbial and biochemical indicators, suggesting that even small carbon inputs can influence soil habitat quality.

Microbial and biochemical soil indicators changed significantly in response to NT and cover crop practices compared to Pl, although the magnitude of the response varied depending on the specific indicator (Vinhal-Freitas et al. 2013, 2017, Bobuľská et al. 2021). The Pl treatment generally exhibited lower values for these indicators, confirming that NT practices positively influence soil functionality and quality. These results underscore the high sensitivity of microbial and biochemical indicators to soil management, particularly under conservation systems. Specifically, soils under Crotalaria showed significant effects on PMN and DHA compared to fallow (Fw), suggesting changes in nitrogen availability and organic compound oxidation in these systems. PMN is an indicator of ammoniacal nitrogen release in the soil due to heterotrophic microbial activity under anoxic conditions. DHA is a sensitive indicator of soil fertility and health, given its direct link to microbial metabolism (Brzezińska et al. 1998, Fernández-Ortega et al. 2025), and it reflects the microbial capacity to perform essential carbon transformation processes. These results suggest that cover crop systems may contribute additional carbon sources to the soil.

Treatments involving Mz/Crt and Mz/Uro led to significantly increased GLU activity in the soil compared to Pl and Fw, indicating that the intercropping system promotes changes in the mineralization of SOM. GLU is a critical soil indicator, as it is directly associated with carbon cycling processes. Its enzymatic activity releases glucose, serving as a carbon and energy source for heterotrophic soil microorganisms (Fernández-Ortega et al. 2025). Conversely, PHO activity decreased in these treatments relative to the single cover crop treatments and was similar to values observed under Fw. This trend contrasts with the response observed for GLU. Although no comparative studies are available on PHO activity under intercropping systems, these results may reflect specific conditions related to phosphorus use and microbial community structure. One possible explanation for the lower phosphatase activity is increased plant phosphorus uptake, which may reduce phosphorus availability in the rhizosphere. In addition, intercropping may alter root exudation patterns and influence microbial taxa involved in phosphorus mineralization.

Our findings show that the abundance and structure of the bacterial community did not undergo significant changes under NT, suggesting that, in the short term, the overall bacterial community composition remains relatively stable. Similarly, alpha and beta diversity analyses did not reveal significant differences between cover crops and the Pl or Fw treatments, likely because these metrics depend on broader taxonomic differences within the community. However, the species richness index did differ between Pl and NT treatments, although no significant variation was observed among the cover crop treatments. These findings suggest that NT under cover crops may gradually influence bacterial community composition, even in the short term, and that species richness is a more sensitive indicator of early microbial responses to conservation practices. Numerous studies have shown that land use affects microbial communities, with diversity often positively correlated with soil pH and organic matter (Bobuľská et al. 2021, Pratibha et al. 2023), which may help explain our results regarding soil bacterial diversity. In our study, soil pH and SOM were not significantly altered by the NT and cover crop treatments, possibly accounting for the lack of substantial shifts in bacterial diversity.

Co-occurrence network analysis has emerged as a valuable tool in microbial ecology (Barberán et al. 2012, Guseva et al. 2022). Although these networks use indirect information to infer co-occurrence or microbial association patterns, they provide an intuitive representation of taxa organization (nodes) and their associations (edges) (Guseva et al. 2022). To our knowledge, this is the first study to apply bacterial co-occurrence network analysis under NT and cover crop systems in tropical soils. In our study, bacterial networks revealed that sole cover crops, such as Crotalaria and Urochloa, promoted predominantly positive associations among taxa, with 61.39% and 53.97% of edges being positive, respectively. Similarly, Millet (Mt) and Maize (Mz) exhibited a higher proportion of positive correlations (55.42% and 55.35%, respectively). In contrast, intercropping maize with Urochloa or Crotalaria resulted in increased negative correlations, reaching 48.40% and 51.22%, respectively. In these networks, positive correlations are typically associated with cooperative interactions, such as mutualism or protocooperation, while negative correlations suggest competitive or antagonistic relationships, including amensalism (Barberán et al. 2012, Lupatini et al. 2014, Yuan et al. 2021, Guseva et al. 2022). These results indicate that intercropping systems, especially those involving maize, may intensify competition within the soil bacterial community, likely due to shifts in resource availability and niche overlap. Notably, co-occurrence network analysis proved to be a sensitive approach for detecting subtle changes in bacterial community structure induced by short-term NT and cover crop management.

Our results show that network co-occurrence has a positive correlation with the microbial and biochemical indicators analyzed, demonstrating that microbial interactions in the soil play a key role in soil functioning. These findings indicate that interactions not only reveal relationships among microbial populations but may also suggest a more efficient use of nutrients in no-tillage (NT) and cover crop systems in tropical regions. In addition, the results show that a differentiation of the soil bacterial community occurs in treatments with NT and cover crops compared to the conventional system. The data from this study provide essential insights for understanding the soil system under NT and cover crop management. However, long-term studies focusing on microbial interactions will be crucial to provide a more accurate understanding of soil habitat quality and health.

CONCLUSIONS

This study demonstrates that the integration of cover crops into no-tillage systems promotes early improvements in soil quality and strengthens microbial functioning in a tropical Oxisol. No-tillage combined with cover crops increased microbial biomass and enzymatic activities associated with carbon, nitrogen, and phosphorus cycling, indicating a rapid biochemical response to conservation management. Although bacterial community composition showed limited variation, co-occurrence network analysis revealed greater bacterial connectivity and interaction complexity under no-tillage with cover crops. These results suggest that microbial interaction networks are highly sensitive to management changes and can serve as early indicators of shifts in soil functioning. Crotalaria enhanced potentially mineralizable nitrogen and dehydrogenase activity, while intercropping with maize and Urochloa or Crotalaria increased β-glucosidase activity and microbial biomass, reflecting the functional complementarity of cover crops in supporting nutrient cycling. Overall, network properties provided deeper insight into the ecological processes underlying soil quality improvement than community composition alone. The combined use of network metrics and biochemical indicators represents a promising approach for assessing sustainability in tropical agroecosystems. Long-term monitoring is needed to determine whether these patterns persist under continued conservation management.

Acknowledgements

We thank the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG, project APQ-3024-17) for financial support of the research project.

• Data availability
  Data will be available upon request.

References

• ALVARES CA, STAPE JL, SENTELHAS PC, GONÇALVES JLM & SPAROVEK G. 2014. Köppen’s climate classification map for Brazil. Meteorol Z 22: 711-728. https://doi.org/10.1127/0941-2948/2013/0507.
  » https://doi.org/10.1127/0941-2948/2013/0507
• ANDERSON MJ. 2001. A new method for non-parametric multivariate analysis of variance. Austral Ecol 26: 32-46. https://doi.org/10.1046/j.1442-9993.2001.01070.x.
  » https://doi.org/10.1046/j.1442-9993.2001.01070.x
• BARBERÁN A, BATES ST, CASAMAYOR EO & FIERER N. 2012. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J 6: 343-351. https://doi.org/10.1038/ismej.2011.119.
  » https://doi.org/10.1038/ismej.2011.119
• BASTIAN M, HEYMANN S & JACOMY M. 2009. Gephi: An open-source software for exploring and manipulating networks. Proc Int AAAI Conf Web Soc Media 3: 361-362. https://doi.org/10.1609/icwsm.v3i1.13937.
  » https://doi.org/10.1609/icwsm.v3i1.13937
• BOBUL’SKÁ L, ESPÍNDOLA SP, COELHO MA & FERREIRA AS. 2021. Impact of land use on soil function and bacterial community in the Brazilian savanna. An Acad Bras Cienc 93: e20201906. DOI 10.1590/0001-3765202120201906.
  » https://doi.org/10.1590/0001-3765202120201906
• BROOKES PC, LANDMAN A, PRUDEN G & JENKINSON DS. 1985. Chloroform fumigation and the release of soil nitrogen: A rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol Biochem 17: 837-842. https://doi.org/10.1016/0038-0717(85)90144-0.
  » https://doi.org/10.1016/0038-0717(85)90144-0
• BRZEZIŃSKA M, STĘPNIEWSKA Z & STĘPNIEWSKI W. 1998. Soil oxygen status and dehydrogenase activity. Soil Biol Biochem 30: 1783-1790. https://doi.org/10.1016/S0038-0717(98)00043-1.
  » https://doi.org/10.1016/S0038-0717(98)00043-1
• CALFAPIETRA C, DI LONARDO S, PERUZZI E, DONI S & MASCIANDARO G. 2025. Perspectives from nature-based solutions to restore soil and ecosystems. Eur J Soil Sci 76: e70134. https://doi.org/10.1111/ejss.70134.
  » https://doi.org/10.1111/ejss.70134
• CHEN YP, TSAI CF, REKHA PD, GHATE SD, HUANG HY, HSU YH, LIAW LL & YOUNG CC. 2021. Agricultural management practices influence soil enzyme activity and bacterial community structure in tea plantations. Bot Stud 62: 8. https://doi.org/10.1186/s40529-021-00314-9.
  » https://doi.org/10.1186/s40529-021-00314-9
• CHOWDHURY S, PAUL C, HAMIDOV A, BAYER L, ARNDT M, MCPHERSON J & HELMING K. 2025. Developing a Generic DPSIR Framework for Land Use and Soil Management: A Systems Approach to Maximise Soil Health. Eur J Soil Sci 76: e70230. https://doi.org/10.1111/ejss.70230.
  » https://doi.org/10.1111/ejss.70230
• CLARK JD ET AL. 2019. Predicting economic optimal nitrogen rate with the anaerobic potentially mineralizable nitrogen test. Agron J 111: 3329-3338. https://doi.org/10.2134/agronj2019.03.0224.
  » https://doi.org/10.2134/agronj2019.03.0224
• CONAB. 2025. Acompanhamento da Safra Brasileira – Grãos. Companhia Nacional de Abastecimento.
• DU M, XUE P, MINASNY B, MCBRATNEY A & TANG Y. 2025. Patterns, processes, and predictions: Soil bacteria unique habitats along a megametre transect in eastern Australia. Soil Ecol Lett 7: 250313. https://doi.org/10.1007/s42832-025-0313-3.
  » https://doi.org/10.1007/s42832-025-0313-3
• EMBRAPA. 2006. Sistema Brasileiro de Classificação de Solos. 2a ed., Brasília: Embrapa.
• FAO & ITPS. 2015. Status of the World’s Soil Resources (SWSR). Rome: Food and Agriculture Organization.
• FERNÁNDEZ-ORTEGA J, FUENTES JA, DELGADO A, GARCÍA-LÓPEZ AM & CANTERO-MARTÍNEZ C. 2025. Assessing management strategies for carbon storage in Mediterranean soils. Soil Tillage Res 249: 106496. https://doi.org/10.1016/j.still.2025.106496.
  » https://doi.org/10.1016/j.still.2025.106496
• FIERER N. 2017. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol 15: 579-590. https://doi.org/10.1038/nrmicro.2017.87.
  » https://doi.org/10.1038/nrmicro.2017.87
• FIERER N, BRADFORD MA & JACKSON RB. 2007. Toward an ecological classification of soil bacteria. Ecology 88: 1354-1364. https://doi.org/10.1890/05-1839.
  » https://doi.org/10.1890/05-1839
• FREITAS VS, BABOS DV, GUEDES WN, SILVA FP, TOZO MLL, ANDRADE CA, VILLAS-BOAS PR, MILORI DMBP & MARTIN-NETO L. 2024. Soil organic matter dynamics and soil carbon stocks from tropical and sub-tropical areas under no-till: An on-farm research. Catena 247: 108491. https://doi.org/10.1016/j.catena.2024.108491.
  » https://doi.org/10.1016/j.catena.2024.108491
• FRIEDMAN J & ALM EJ. 2012. Inferring correlation networks from genomic survey data. PLoS Comput Biol 8: e1002687. https://doi.org/10.1371/journal.pcbi.1002687.
  » https://doi.org/10.1371/journal.pcbi.1002687
• GOVAERTS B, SAYRE KD & DECKERS J. 2007. A minimum data set for soil quality assessment of wheat and maize in the highlands of Mexico. Soil Tillage Res 94: 211-222. https://doi.org/10.1016/j.still.2006.07.005.
  » https://doi.org/10.1016/j.still.2006.07.005
• GUSEVA K, DARCY M, SIMONIN M, KORANDA M, RUPPENHOFER M, BASTIDA F, DELGADO-BAQUERIZO M & BANERJEE S. 2022. From diversity to complexity: microbial networks in soils. Soil Biol Biochem 169: 108663. https://doi.org/10.1016/j.soilbio.2022.108663.
  » https://doi.org/10.1016/j.soilbio.2022.108663
• HANNULA SE & VEEN GF. 2025. Drought has short-term effects on soil fungal communities leading to long-term effects on soil functions. Soil Biol Biochem 209: 109893. https://doi.org/10.1016/j.soilbio.2025.109893.
  » https://doi.org/10.1016/j.soilbio.2025.109893
• IPCC. 2019. Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. In: Shukla PR et al. (Eds).
• JACOBS A, FERREIRA AO, CAIRES EF & FRANCHINI JC. 2022. Cover crops and no-tillage reduce production costs and soil loss. Soil Tillage Res 218: 105310. https://doi.org/10.1016/j.still.2021.105310.
  » https://doi.org/10.1016/j.still.2021.105310
• LAL R. 2020. Regenerative agriculture for food and climate. J Soil Water Conserv 75: 123A-124A. https://doi.org/10.2489/jswc.2020.0620A.
  » https://doi.org/10.2489/jswc.2020.0620A
• LI Y, SONG D, LIANG S, DANG P, QIN X, LIAO Y & SIDDIQUE KHM. 2020. Effect of no-tillage on soil bacterial and fungal community diversity: a meta-analysis. Soil Tillage Res 204: 104721. https://doi.org/10.1016/j.still.2020.104721.
  » https://doi.org/10.1016/j.still.2020.104721
• LIU C, CUI Y, LI X & YAO M. 2021. microeco: an R package for data mining in microbial community ecology. FEMS Microbiol Ecol 97: fiaa255. https://doi.org/10.1093/femsec/fiaa255.
  » https://doi.org/10.1093/femsec/fiaa255
• LUPATINI M, SULEIMAN AKA, JACQUES RJS, ANTONIOLLI ZI, FERREIRA AS, KURAMAE EE & ROESCH LFW. 2014. Network topology reveals high connectance levels and few key microbial genera within soils. Front Environ Sci 2: 10. https://doi.org/10.3389/fenvs.2014.00010.
  » https://doi.org/10.3389/fenvs.2014.00010
• MATTIELLO EM, CASTRO GF, SILVA BA, SOUZA IF, ZANCANARO L, ONO FB, BERTOL F, FRANCISCO EAB, KAPPES C & CANTARUTTI RB. 2024. Trade-offs to manage sustainable systems of grain production in tropical soils. Soil Tillage Res 244: 106251. https://doi.org/10.1016/j.still.2024.106251.
  » https://doi.org/10.1016/j.still.2024.106251
• MCMURDIE PJ & HOLMES S. 2013. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8: e61217. https://doi.org/10.1371/journal.pone.0061217.
  » https://doi.org/10.1371/journal.pone.0061217
• MIZOBE CC. 2019. Cerrado: caracterização e importância para a produção de grãos. Documentos 316. Planaltina: Embrapa Cerrados.
• MUHAMMAD I, YANG L, AHMAD S, ZEESHAN M, FAROOQ S, ALI I, KHAN A & ZHOU XB. 2023. Irrigation and Nitrogen Fertilization Alter Soil Bacterial Communities, Soil Enzyme Activities, and Nutrient Availability in Maize Crop. Front Microbiol 14: 833758. https://doi.org/10.3389/fmicb.2022.833758.
  » https://doi.org/10.3389/fmicb.2022.833758
• MULVANEY RL. 1996. Total nitrogen. In: Sparks DL (Ed), Methods of soil analysis: Part 3. Chemical methods, SSSA Book Ser. 5. ASA and SSSA, Madison, WI, p. 1123-1184.
• POSIT TEAM. 2023. RStudio: Integrated Development Environment for R. Boston: Posit Software PBC.
• PRATIBHA G ET AL. 2023. Soil bacterial community structure and functioning in a long-term conservation agriculture experiment under semi-arid rainfed production system. Front Microbiol 14: 1102682. https://doi.org/10.3389/fmicb.2023.1102682.
  » https://doi.org/10.3389/fmicb.2023.1102682
• QIAO Y, WANG T, HUANG Q, GUO H, ZHANG H, XU Q, SHEN Q & LING N. 2024. Core species impact plant health by enhancing soil microbial cooperation and network complexity during community coalescence. Soil Biol Biochem 188: 109231. https://doi.org/10.1016/j.soilbio.2023.109231.
  » https://doi.org/10.1016/j.soilbio.2023.109231
• R DEVELOPMENT CORE TEAM. 2024. R: A Language and Environment for Statistical Computing Vienna: R Foundation for Statistical Computing.
• SAATI-SANTAMARÍA Z ET AL. 2026. Soil microbial diversity and network organization respond to land use and agricultural inputs worldwide. Glob Change Biol 32: e70984. https://doi.org/10.1111/gcb.70984.
  » https://doi.org/10.1111/gcb.70984
• SANTOS HG, JACOMINE PKT, ANJOS LHC, OLIVEIRA VÁ, LUMBRERAS JF, COELHO MR, ALMEIDA JA, ARAÚJO FILHO JC, OLIVEIRA JB & CUNHA TJF. 2018. Sistema Brasileiro de Classificação de Solos. 5a ed., Brasília: Embrapa.
• SILVA PCG, TIRITAN CS, ECHER FR, CORDEIRO CFS, REBONATTI MD & SANTOS CH. 2020. No-tillage and crop rotation increase crop yields and nitrogen stocks in sandy soils under agroclimatic risk. Field Crops Res 258: 107947. https://doi.org/10.1016/j.fcr.2020.107947.
  » https://doi.org/10.1016/j.fcr.2020.107947
• SOIL SURVEY STAFF. 2014. Keys to Soil Taxonomy. 12th ed., Washington, DC: USDA-Natural Resources Conservation Service.
• STOTZKY G. 1965. Microbial Respiration. In: Black CA (Ed), Methods of Soil Analysis. Part 2. Madison: American Society of Agronomy, p. 1550-1572.
• TEDESCO MJ, GIANELLO C, BISSANI CA, BOHNEN H & VOLKWEISS SJ. 1995. Análises de Solo, Plantas e Outros Materiais. 2a ed., Porto Alegre: Universidade Federal do Rio Grande do Sul. (Boletim Técnico, 5).
• VAN DER HEIJDEN MGA, BARDGETT RD & VAN STRAALEN NM. 2008. The unseen majority: soil microbes as drivers of plant diversity and productivity. Ecol Lett 11: 296-310. https://doi.org/10.1111/j.1461-0248.2007.01139.x.
  » https://doi.org/10.1111/j.1461-0248.2007.01139.x
• VANCE ED, BROOKES PC & JENKINSON DS. 1987. An extraction method for measuring soil microbial biomass C. Soil Biol Biochem 19: 703-707. https://doi.org/10.1016/0038-0717(87)90052-6.
  » https://doi.org/10.1016/0038-0717(87)90052-6
• VINHAL-FREITAS IC, CORRÊA GF, WENDLING B, BOBUĽSKÁ L & FERREIRA AS. 2017. Soil textural class plays a major role in evaluating the effects of land use on soil quality indicators. Ecol Indic 74: 182-190. https://doi.org/10.1016/j.ecolind.2016.11.020.
  » https://doi.org/10.1016/j.ecolind.2016.11.020
• VINHAL-FREITAS IC, FERREIRA AS, CORRÊA GF & WENDLING B. 2013. Land Use Impact on Microbial and Biochemical Indicators in Agroecosystems of the Brazilian Cerrado. Vadose Zone J 12(1): vzj2012.0027. https://doi.org/10.2136/vzj2012.0027.
  » https://doi.org/10.2136/vzj2012.0027
• WICKHAM H. 2011. ggplot2. Wiley Interdiscip Rev Comput Stat 3: 180-185. https://doi.org/10.1002/wics.147.
  » https://doi.org/10.1002/wics.147
• YEOMANS JC & BREMNER JM. 1988. A rapid and precise method for routine determination of organic carbon in soil. Commun Soil Sci Plant Anal 19(13): 1467-1476. https://doi.org/10.1080/00103628809368027.
  » https://doi.org/10.1080/00103628809368027
• YUAN MM, GUO X, WU L, ZHANG Y, XIAO N, NING D, SHI Z, ZHOU X, WU L, YANG Y, TIEDJE JM & ZHOU J. 2021. Climate warming enhances microbial network complexity and stability. Nat Clim Change 11: 343-348. https://doi.org/10.1038/s41558-021-00989-9.
  » https://doi.org/10.1038/s41558-021-00989-9
• ZHANG C, LEI S, WU H, LIAO L, WANG X, ZHANG L, LIU G, WANG G, FANG L & SONG Z. 2024. Simplified microbial network reduced microbial structure stability and soil functionality in alpine grassland along a natural aridity gradient. Soil Biol Biochem 191: 109366. https://doi.org/10.1016/j.soilbio.2024.109366.
  » https://doi.org/10.1016/j.soilbio.2024.109366

Edited by

• Handling editor
  Pablo Bolaños-Villegas

Data availability

Data will be available upon request.

Publication Dates

• Publication in this collection
  21 Aug 2026
• Date of issue
  2026

History

• Received
  15 Jan 2026
• Accepted
  06 Apr 2026