Connection Between the Microbial Community and the Management Zones Used in Precision Agriculture Cultivation

Abstract

In precision agriculture, the delineation of Management Zones (MZs) is essential for optimizing input use efficiency and site-specific nutrient management. MZs are established based on spatial variability derived from remote sensing data—such as Normalized Difference Vegetation Index (NDVI) from satellite or UAV-based imagery—and yield maps collected during harvest. However, the microbial community composition of the soil is often overlooked in MZ delineation. To address this gap, we investigated the soil bacterial community structure across different MZs in an arable field. The zones were delineated using NDVI data, soil profiles were described, and bulk soil samples were collected. Soil physicochemical parameters were analyzed in parallel with 16S rRNA gene amplicon sequencing to characterize bacterial community composition and diversity. The results demonstrated that soil texture and soil organic matter content were the primary drivers influencing bacterial community structure across the field. Moreover, patterns in microbial composition aligned closely with MZ delineations, indicating that microbial profiles could aid in better understanding and supporting the nutrient management practices. Our findings suggest that soil microbiological data can enhance the stability and biological relevance of MZ definitions, thereby improving resource allocation, soil health management, and overall sustainability in precision farming systems.

1. Introduction

Tillage is a conventional agronomic practice employed to improve crop growth conditions, integrate crop residues into the soil, facilitate weed control, promote nutrient mineralization, and support nutrient uptake [1]. However, while agricultural intensification has contributed to higher yields to meet global food demands, intensive practices may also drive soil degradation. Common consequences include depletion of soil organic matter (SOM), compaction, loss of soil structure, enhanced erosion by water and wind, increased CO₂ emissions, and declines in soil biodiversity and ecological functioning. Heavy reliance on synthetic fertilizers and pesticides further undermines soil biotic integrity and ecological resilience [2,3].

Historically, SOM was measured infrequently, but after decades of decline, it has regained attention as a critical indicator of soil health [4]. Numerous studies have documented continuing SOM losses in conventional agricultural landscapes [5,6,7,8,9,10], including in fertile Chernozem regions [10].

After 50–60 years of intensive cultivation, many arable soils—especially in developed countries—exhibit severely diminished biological activity, including reduced microbial function. This decline adversely impacts plant nutrient uptake, because microbial communities mediate nutrient transformation and availability. Though nutrients may remain bound to soil particles (e.g., clay, SOM), the microbial or genetic “tools” required to mobilize them for plant use may be absent or impaired. Bacteria play vital roles in soil health and crop productivity through activities such as organic matter decomposition, nutrient mineralization, nitrification, and dissimilatory nitrate reduction to ammonium [11,12].

To address these challenges, Precision Agriculture (PA) offers a promising framework. In PA, fields are subdivided into MZs—subfield units that are relatively homogeneous in physical and chemical attributes. These zones are delineated using geospatial data (e.g., digital elevation models, remote sensing indices such as NDVI, yield maps) under the assumption that they will respond similarly to inputs such as fertilizer or irrigation [13]. Yet typical soil sampling methods yield limited insight into spatial variability and often omit microbiological information entirely. Because soil biodiversity and microbiome parameters are important soil ecological functions and critically influence soil health, nutrient cycling, and disease suppression, they are increasingly proposed as soil health indicators [14]. Soil microorganisms are central to nutrient availability, fertility regulation, and plant–pathogen interactions [15,16,17], converting organic residues into bioavailable forms of nitrogen, phosphorus, potassium, and amino acids [18].

Despite this, many PA approaches disregard microbial heterogeneity, which can result in mismatches between nutrient supply and plant demand [19]. Maintaining an optimal soil bacterial ecology is essential for sustainable agriculture, as it reduces environmental impact and improves nutrient use efficiency [20]. As PA methods proliferate, there is growing interest in integrating microbiological data into zone delineation. In addition to soil physicochemical measures, microbial profiles can refine MZ boundaries and guide precision farming decisions.

In this study, we aimed to characterize bacterial community composition within PA-derived MZs and evaluate how those microbial patterns correspond to soil physical and chemical properties. Specifically, we tested whether MZs delineated via UAV orthophotography and Sentinel-2 NDVI (with supporting digital elevation data) also display distinct bacterial community structures, and we quantified the influence of soil parameters on microbial community differentiation.

2. Materials and Methods

2.1. Study Area

The investigated area was a 20-hectare field of arable land situated close to Boldog village, Pest County, Hungary (Figure 1). The climate of the sampling site was typical European continental/Pannonian with warm, dry summers. The annual temperature was 9.8–10.2 °C, while annual precipitation ranged between 510 and 550 mm, with average max temperature of 34–35 °C and a min of −16 °C. The land was ploughed to a depth of 20–30 cm, and 120 kg ha⁻¹ NPK (18:7:7) fertilizer was applied to the winter wheat crop.

2.2. Delineation of Management Zones

To delineate MZs, we employed a multi-source remote sensing approach integrating UAV and satellite imagery. A DJI Phantom 4 quadcopter (DJI Technology Co., Shenzhen, China) equipped with an RGB camera was used to capture high-resolution aerial photographs of the 20-hectare study area during clear sky conditions. These images were processed to generate an orthophoto mosaic and a digital elevation model (DEM), providing detailed spatial information on surface topography and field structure.

In addition to UAV data, Sentinel-2 satellite imagery was used to acquire Normalized Difference Vegetation Index (NDVI) data for the vegetation periods of 2018 and 2019, capturing seasonal variations in crop growth. The NDVI layers were analyzed to assess spatial variability in vegetation vigor across seasons.

Using QGIS, orthophoto, DEM, and multi-year NDVI layers were visually analyzed and interpreted. Based on this combined dataset, 13 MZs were manually delineated to represent relatively homogeneous areas in terms of topography and vegetative performance. These zones served as the basis for subsequent soil sampling and microbiological assessments (Figure 1).

2.3. Field Sampling and Sample Preparation

In every zone, a 1 m deep georeferenced soil profile was augered, described and sampled from each master/genetic horizon (FAO Guidelines for Soil Description 2006 [21]). The following parameters were recorded in the field: soil color—Munsell soil color chart, soil texture—finger test, CaCO₃ content—10% HCl, coatings and hydromorphic features, i.e., capillary fringe mottling, etc. Furthermore, composite samples from 20 subsamples were collected by hand auger equipment from the upper 30 cm bulk soil layers in all MZs, in February of 2020. Collected bulk soil samples were placed in plastic bags. Samples for microbial analysis were transported to the laboratory in a cooling box, stored at +5 °C for 24 h, for DNA extraction. For chemical analysis, soils were air-dried and sieved under 2 mm and stored at room temperature (22 °C).

2.4. Laboratory Analyses

From the genetic/master horizons, the following parameters were determined: soil pH (in soil–water suspension (1:2.5), soil organic matter content, CaCO₃ content, total water soluble salt content, and soil texture was determined according Arany, (K_A) the upper limit of soil plasticity (the results can be found in Supplementary Materials, and the abbreviations of the profiles are B01–B13), while from the 0–30 cm composite samples, soil pH (in soil–1NKCl suspension (1:2.5), soil organic matter content, CaCO₃ content, total water soluble salt content, and soil texture were determined according Arany, the upper limit of soil plasticity, NO₂ + NO₃-N, plant available P₂O₅, K₂O, Na, Zn, Cu, Mn, Mg, S. All the parameters were determined according to the Procedures for soil analysis 4th edition 1995, Edited by L.P. van Reeuwijk 1995 Technical Paper 9 [22]. ISRIC, FAO and performed in Szolnok, Soil Conservation Laboratory Ltd., Szolnok, Hungary.

2.5. Soil Classification

Based on the soil survey and the results of the laboratory analyses, all the diagnostic horizons, materials, and properties determined with the depths and soil profiles were ‘simply’ classified, applying the most pronounced features according to the IUSS Working Group WRB 2014 [23].

2.6. Illumina 16S rRNA Gene Amplicon Sequencing and Bioinformatics Analysis

Illumina 16S rRNA gene amplicon sequencing was performed to precisely assess the bacterial community composition of the soil samples obtained from the MZs. For this, community DNA was extracted from the composite soil samples (eight subsamples) using the NucleoSpin Soil Mini Kit (Macherey-Nagel Co., Düren, Germany), according to the instructions of the manufacturer. Subsequently, for paired-end 16S rDNA amplicon sequencing, the variable V3 and V4 regions of the 16S rRNA gene were amplified using forward (5′-TCGT CGGCAGCGTCAGATGTG TATAAGAGACAGCCTA CGGGNGGCWGCAG-3′) and reverse (5′-GTCT CGTGGGCT CGGAGATGTGTATAAGAGAC AGGACTACHVGGGTATCTAATCC-3′) primers with Illumina adapter overhangs [24]. PCR mixtures contained 12.5 ng of DNA, 0.2 μM of each primer and 12.5 μL of 2X KAPA HiFi HotStart Ready Mix (KAPA Biosystems, London, UK) supplemented with MQ water up to 25 μL final volume. The temperature profile was the following: initial denaturation for 5 min at 95 °C, 25 cycles of amplification (30 s at 95 °C, 30 s at 55 °C, 30 at 72 °C). The last step was a final extension for 5 min at 72 °C. All amplifications were carried out in a ProFlex PCR System (Life Technologies, Carlsbad, CA, USA). Amplicons were analyzed by agarose gel electrophoresis. Paired-end fragment reads were generated on an Illumina MiSeq sequencer (Illumina Inc. San Diego, CA, USA) using MiSeq Reagent Kit v3 (600-cycle). Primary data analysis (base-calling) was carried out with Bbcl2fastq^ software (v2.17.1.14, Illumina). Reads were quality- and length-trimmed in CLC Genomics Workbench Tool 9.5.1 using an error probability of 0.05 (Q13) and a minimum length of 50 nucleotides as the threshold. Trimmed sequences were processed using mothur v 1.41.1. as recommended by the MiSeq SOP page (https://www.mothur.org/wiki/MiSeq_SOP, accessed on 14 January 2021). Paired-end sequence (contig) numbers ranged between 45,323 and 49,853. The sequence assortment was based on the alignment with the SILVA 132 SSURef NR99 database. Chimera detection was performed with mothur’s uchime command. The ‘split.abund’ command was used to remove singleton reads. The standard 97% similarity threshold was used to determine operational taxonomic units (OTUs), as suggested for prokaryotic species delineation [25]. Rarefaction curves were also generated and showed high sequencing coverage in all samples. Raw sequence reads were deposited in NCBI SRA under BioProject ID PRJNA1266106. The 50 most abundant OTUs were identified using the EzBioCloud 16S rDNA database.

2.7. Statistical Analysis

The chemical and physical properties of all composite samples were applied to calculate the Principal Component Analyses (PCA) using PAST 4.05 software. One-way ANOSIM was used to determine the differences among the sites.

To analyze the molecular biological background of the samples, Principal Coordinate Analysis (PCoA) based on Bray–Curtis dissimilarity was performed on OTU abundance data to assess differences in bacterial community composition. Canonical Correspondence Analysis (CCA) was used to examine the relationship between the top 50 OTUs and 14 environmental variables. Hierarchical cluster analysis using UPGMA linkage and Bray–Curtis distances was applied to explore compositional similarities among samples based on both OTU profiles and soil parameters. Analyses were conducted in R software (version 4.3.2, R Core Team, 2023) using the vegan, dendextend, cluster, ggplot2, and dplyr packages. A significance level of p < 0.05 was used throughout.

The comparison of the enzymatic pathways according to the amplicon result was evaluated by KEGG Orthology (Kyoto Encyclopedia of Genes and Genomes) by the following method. Raw sequence data were processed into Amplicon Sequence Variants (ASVs) using the dada2 pipeline with the following parameters: trimLeft = c(10, 10), trimRight = c(10, 10). Forward and reverse reads were then merged and filtered for chimeras. The resulting ASV table was subsequently analyzed using the full PICRUSt2 pipeline on the Galaxy Europe server. This analysis utilized the EPA-ng placing tool for reference tree construction, the SILVA database for sequence alignment, a minimum alignment length of 0.8, a transition cost weight of 0.5, minimum read and ASV per sample limits of 1, and an NSTI index of 2.0. The resulting KO (KEGG Orthology) table was then analyzed using the ggpicrust2 R package (version 2.5.2), employing the ALDEx2 method for differential abundance analysis with FDR p-value adjustment. Samples were grouped into three distinct categories for this analysis [26].

3. Results

3.1. Soil Properties of the Investigated Area

Thirteen soil profiles were augered, described, and classified following the FAO Guidelines for Soil Description (2006) [21] and the World Reference Base for Soil Resources (WRB, IUSS Working Group, 2014) [23] to characterize the pedological conditions of the study area. The detailed soil descriptions are provided in the Supplementary Materials, with a summary of key characteristics presented in

Table 1. Basic soil and land size information of the MZs.

Name of the Zone	Elevation (m)	Simplified RSG	Size [ha]
B01	113	Gleyic Kastanozem (Loamic, Aric) “Steppe Soil”	2.32
B02	111	Calcic Pellic Vertisol (Aric, Mollic, Gleyic)	1.85
B03	114	Eutric Arenosol (Aric, Humic)	2.7
B04	112	Pellic Vertisol (Aric, Mollic, Gleyic)	1.34
B05	112	Calcic Pellic Vertisol (Aric, Mollic, Gleyic)	1.17
B06	112	Calcic Pellic Vertisol (Aric, Mollic, Gleyic)	1.2
B07	110	Haplic Vertisol (Aric, Mollic, Gleyic)	1.2
B08	110	Calcic, Pellic Vertisol (Aric, Mollic, Gleyic)	1.84
B09	115	Eutric Arenosol (Aric)	3.65
B10	113	Gleyic Eutric Arenosol (Aric)	0.33
B11	110	Calcic Pellic Vertisol (Aric, Mollic, Gleyic)	1.92
B12	117	Eutric Arenosol (Aric)	0.59
B13	114	Calcaric Phaeozem (Arenic, Aric, Novic) “Steppe Soil”	0.17

.

Based on the field survey and subsequent laboratory analyses, three major Reference Soil Groups (RSGs) were identified:

• Arenosols: Represented in four MZs—B03, B09, B10, and B12—characterized by sandy texture and poor profile development.
• Vertisols: Occurring in seven MZs—B02, B04, B06, B11, B05, B07, and B08—distinguished by high clay content, characterized with shrinking and swelling properties and vertic horizons.
• Steppe soils: Two MZs, B01K and B13K, were classified as Kastanozem and Phaeozem, respectively, based on the presence and depth of mollic horizons referring to the deep and organic carbon-rich surface horizons and secondary calcium carbonate accumulation (calcic horizon).

This soil classification provides the pedological foundation for further analysis of soil physicochemical properties and microbial community structure across the delineated MZs.

Topographical variation across the study area strongly influenced the soil physical and chemical properties as well as the soil type distributions. The lower lying areas were predominantly characterized with heavy clayey soils (Vertisols), and gleyic conditions indicated that the ground water table was relatively close to the soil surface (B07, B08, B11, B02, B04, B05, B06), with elevation between 110 and 112 m. Higher elevation (113 m) Kastanozem and Arenosol (B01, B10) were described: both were sandy and gleyic, but the Kastanozem was characterized with higher and deeper organic matter-rich horizons, indicating more pronounced humifaction processes, compared to the Arenosol.

Arenosol and Phaeozem (B03, B13) were found on 114 m; both are sandy, without ground water table influence within 1 m from the soil surface; in the case of Phaeozem, deeper organic matter-rich horizons were described. MZs with the highest topography positions were characterized as Arensols (sandy soils) (B09, B12), with weak or degraded profile developments.

The summarized results of the soil investigations from the upper 30 cm can be seen in

Table 2. The results of soil investigation from the upper 30 cm.

Sample	Texture (KA)	SOM % m/m	pH KCl	Total Salt % m/m	CaCO3% m/m	NO2 + NO3-N	Mg	S	P2O5	K2O	Na	Zn	Cu	Mn
						mg kg−1
B01K	sandy loam	1.71	6.63	0.04	0	5.6	564	2.5	71.6	196	21	1.16	3.62	310
B02K	heavy clay	2.76	6.78	0.09	1.6	9.35	926	2.5	122	321	40.4	1.07	4.07	135
B03K	sand	1.56	5.44	0.02	0	4.25	200	2.5	324	215	12.8	3.17	2.19	233
B04K	clay	2.1	6.7	0.07	0.6	10.4	575	2.5	93.5	230	27.9	1.53	4.24	301
B05K	loamy clay	1.89	6.52	0.08	0	7.76	819	2.5	70.5	250	24.3	1.17	5.98	319
B06K	clay	2.14	6.87	0.07	2.3	6.47	738	2.5	172	286	35.2	0.8	2.52	61.1
B07K	heavy clay	2.79	6.14	0.11	0	11.7	1295	2.5	124	464	53.6	2.26	7.94	180
B08K	heavy clay	2.65	6.62	0.11	0	10.1	1260	3.59	88.4	354	77.4	1.72	7.16	217
B09K	sand	0.85	5.06	0.02	0	1.54	117	2.5	160	210	12.6	1.98	3.13	136
B10K	sand	0.84	4.81	0.02	0	3.77	68.7	2.5	179	122	17	0.99	2.32	116
B11K	clay	2.2	6.81	0.07	1	7.51	651	2.5	103	265	35.6	0.88	3.61	258
B12K	sand	0.69	5.35	0.02	0	2.67	96.2	2.5	183	184	8.26	1.6	10.3	131
B13K	sand	2.28	6.37	0.02	0	3.88	191	2.5	290	393	14.4	2.32	5.27	206

.

The texture of the low-lying areas was dominantly clay (Vertisols B07, B08, B11, B02, B04, B05, B06), with high soil organic matter content and containing the highest amount of water-soluble salt, sodium and magnesium. These MZs were characterized by the highest amounts of nitrogen content.

The Steppe soils (Kastanozem, Phaeozem B01, B13) had relatively high organic matter content, with a sandy loam and sandy texture reflecting intermediate characteristics between sandy and clayey soils. In the case of Arenosols (sandy soils) (B10, B03, B09, B12), the lowest organic matter content was measured, reflecting weak or degraded profile developments.

CaCO₃ was reported only in Vertisols, representing four MZs (B02, B04, B06, B11).

The lowest pH values were measured in the Arenosols (B03, B09, B10 and B12), in the range of 4.81 to 5.44, while in the other cases, pH values were above 6, with the highest results measured in clayey textured soils.

The plant available phosphorus content varied between 71.6 and 324 mg kg⁻¹, while the plant available potassium content ranged from 122 to 464 mg kg⁻¹.

The zinc and copper concentrations varied between 0.8 and 3.17 and 2.19 and 10.3 mg kg⁻¹, respectively.

In the case of sulfur content, one outlier result was measured: 3.59 mg kg⁻¹ in B08.

To explore the underlying patterns in soil variability, a Principal Component Analysis (PCA) was performed based on the measured soil properties from the upper 30 cm of the MZs. The first two principal components accounted for a combined 89.7% of the total variance in the dataset. Specifically, Principal Component 1 (PC1) explained 74.9%, while Principal Component 2 (PC2) accounted for 14.8% of the variance.

The PCA ordination revealed the formation of three distinct soil groups along PC1, primarily reflecting differences in soil texture and soil organic matter (SOM) content:

• Vertisols, characterized by high clay content and elevated SOM,
• Steppe soils (Kastanozem and Phaeozem), with intermediate texture and SOM,
• Arenosols, with sandy texture and low SOM content.

Separation along PC2 was primarily driven by calcium carbonate (CaCO₃) content. Within the Vertisol group, soils containing secondary CaCO₃ accumulation were clearly distinguished from those without carbonate accumulation.

These patterns are shown in Figure 2, where the spatial grouping of MZs aligns with both textural classification and carbonate content, supporting the pedological classification and underlying drivers of microbial differentiation.

Overall, a clear relationship was observed between soil texture and SOM content: finer-textured soils retained significantly more organic matter than coarse-textured (sandy) soils. Additionally, clay content was positively associated with total salt content. The heavy clay soils without CaCO₃ exhibited salt concentrations approximately five times higher than those of the sandy soils. However, this salinity gradient did not appear to influence the structure of the bacterial communities in this study.

Interestingly, among sandy soils, those with higher organic matter also showed higher pH values compared to sandy zones with lower SOM content.

3.2. Bacterial Community Structure of the Soil Samples Taken from the MZs

The bacterial community composition of soil samples collected from each MZ was assessed through amplicon sequencing, followed by statistical analyses to explore potential correlations with soil physicochemical parameters.

The results are summarized in Figure 3, which presents the relative abundance of the top 20 bacterial genera identified across all samples. These dominant taxa represent the core microbiome of the investigated field and provide insight into the spatial distribution of key microbial groups in relation to soil properties and MZs.

Overall, the soil bacterial communities across the investigated MZs were relatively homogeneous, as indicated by alpha-diversity indices. However, compositional differences were observed, particularly when comparing soils with different textures.

In this study, the analysis focused on the 25 most abundant Operational Taxonomic Units (OTUs), each representing at least 1% relative abundance in at least one sample. Among these, several genera were consistently dominant across all MZs:

• Gaiella (2.5–7% relative abundance),
• Pseudolabrys (2.3–3.7%),
• PAC001932_g genus belonging to Chthoniobacteraceae family (1.7–3.8%).

The PAC001874_g genus belonging to the family Vicinamibacteraceae was also frequently detected, with relative abundances ranging from 2.7% to 4.8%; however, this genus was absent in samples from MZs B09, B10, and B12 (Arenosol group with sandy texture). All other genera included in the top 25 OTUs were generally present at or just above the 1% threshold.

Textural differentiation was evident in the microbial profiles. Specifically, the following genera were absent from the sandy soil samples (MZs B03, B09, B10, B12): the EF125410_g genus belonging to the Geminicoccaceae and Microvirga. The Thermomicrobia and PAC001874_g were missing from B09, B10, and B12 (except in MZ B03).

Conversely, the genus HM748754_g belonging to the Acidobacteriaceae and genus Dictyobacter were not detected in clay-rich and steppe soils (MZs B01, B02, B04, B05, B06, B07, B08, B11, and B13).

These findings indicate that while the overall bacterial community structure was relatively stable, specific taxa displayed clear habitat preferences associated with soil texture and physicochemical conditions, particularly distinguishing between sandy Arenosols and heavier clay or steppe soils.

3.3. Correlations Between Bacterial Groups and Soil Chemical Properties of the MZs

3.3.1. Cluster Analysis of Soil Microbial Communities Based on UPGMA Dendrogram

The hierarchical clustering of microbial community profiles, conducted using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA), revealed two primary groups corresponding to soil texture classes. The first distinct cluster comprised the sandy soils (Arenosols), forming a well-separated group, while the second major cluster included samples from clay-rich Vertisols and Steppe soils (Figure 4).

Within this Vertisol–Transition cluster, three sub-clusters emerged, consistent with groupings previously identified through physicochemical soil analyses. These subgroups were as follows:

• Steppe soils (e.g., Kastanozem and Phaeozem),
• Vertisols with CaCO₃ accumulation, and
• Vertisols without CaCO₃ accumulation.

This alignment of microbial clustering with soil classification and measured parameters (e.g., texture, SOM, and CaCO₃ content) supports the strong influence of edaphic factors on shaping bacterial community structure. The observed consistency across independent data analyses (physicochemical and microbial) further validates the grouping of MZs for precision agriculture purposes.

Canonical Correspondence Analysis (CCA) was performed to investigate the relationships between bacterial community composition and soil physicochemical properties across the delineated MZs. The ordination revealed a clear separation of the samples into three distinct groups, primarily driven by differences in soil texture, soil organic matter (SOM), and CaCO₃ content.

The first two canonical axes explained a total of 78.3% of the variation in the dataset, with Axis 1 accounting for 59.4% and Axis 2 for 18.9% (Figure 5). Along Axis 1, three main clusters emerged, corresponding to the following:

• Vertisols with CaCO₃ accumulation,
• Vertisols without CaCO₃, and
• Arenosols.

This grouping aligned with gradients in soil texture, pH, SOM, magnesium (Mg), potassium (K), and CaCO₃ content. Axis 2 further differentiated samples based on the concentrations of CaCO₃ and zinc (Zn).

Taxonomic distribution along the ordination axes also reflected these soil property gradients. Notably, OTU11 (HM748754_g from Acidobacteriaceae) and OTU17 (Dictyobacter) were positively associated with sandy-textured soils, specifically in B03, B09, B10, and B12, which were all classified as Arenosols. These OTUs were absent or minimally represented in clay-rich Vertisol samples.

In contrast, OTU26 (EF125410_g from Geminicoccaceae) and OTU32 (Desertimonas) were consistently present in Vertisol and Steppe soil samples but absent from the sandy Arenosol zones (B03, B09, B10, B12). This distribution pattern suggests that these genera may be associated with higher clay content and potentially more stable moisture or nutrient regimes, typical of finer-textured soils.

3.3.2. Comparison of the Enzymatic Pathways According to the Amplicon Results Evaluated by KEGG Orthology

For functional inference, microbial community data derived from amplicon sequencing were grouped into three categories based on soil characteristics, including texture, soil organic matter (SOM) content, and presence of calcium carbonate (CaCO₃):

• Vertisol group (MZs B05, B07, B08),
• Vertisol with CaCO₃ group (MZs B02, B04, B06, B11),
• Arenosol group (MZs B01, B03, B09, B10, B12, B13).

Functional predictions were conducted using KEGG Orthology (KO) annotation, allowing for the evaluation of microbial metabolic potential. Figure 6 presents the aggregated functional profiles, highlighting the relative abundance of KEGG metabolic pathways across all 13 samples.

Among the predicted functions, the most prevalent categories included

• Amino acid biosynthesis,
• Cofactor biosynthesis,
• Nucleotide biosynthesis, and
• Carbohydrate metabolism.

These findings indicate that, although soil properties vary among samples, the core microbial functional potential consistently centers on primary metabolic pathways fundamental to microbial growth and nutrient cycling.

The KEGG Orthology (KO)-based functional profiling reflects the relative metabolic potential of the microbial communities across the sampled soils, with functions ranked by descending abundance. Amino acid biosynthesis emerged as the most dominant functional category, indicating a high microbial capacity for de novo synthesis of both essential and non-essential amino acids.

Cofactor biosynthesis and nucleotide biosynthesis pathways were also highly represented, highlighting their essential roles in enzymatic function, DNA replication, and RNA synthesis. The prominence of carbohydrate biosynthesis and energy metabolism pathways further suggests metabolic versatility and the capacity for ATP generation via multiple bioenergetic routes.

Moderately abundant categories included lipid metabolism, nucleotide metabolism, and carbohydrate degradation, which are crucial for membrane integrity, nucleotide turnover, and the breakdown of organic substrates.

The presence of pathways involved in fermentation, cell envelope biosynthesis, and aromatic compound degradation points was predicted.

Pathways such as the methionine salvage cycle, carbon fixation, photosynthesis, and various stress response mechanisms were detected at lower relative abundances, suggesting these functions are either less active or under reduced selective pressure in the investigated soil environment.

Similarly, sulfur and nitrogen metabolism, osmolyte biosynthesis, and pigment production pathways were represented at minimal levels, indicating a limited microbial investment in these specialized processes under the current edaphic and climatic conditions of the studied field.

Figure 7 presents a comparative analysis of the predicted metabolic pathway abundances in the microbial communities of Arenosol samples. The data reveal a strong representation of pathways associated with amino acid biosynthesis, carbohydrate biosynthesis, nucleotide metabolism, and energy metabolism.

The predominance of core biosynthetic pathways reflects the microbial community’s strategy to support cellular homeostasis, growth, and survival under low organic matter availability. In particular, the enrichment of amino acid, nucleotide, and cofactor biosynthesis pathways indicates an investment in fundamental cellular functions, enabling sustained microbial activity despite limited nutrient inputs.

These findings underscore the selective pressure in Arenosol environments that favors microbial taxa with versatile and robust metabolic capacities, oriented towards de novo synthesis and efficient energy utilization, which are essential for maintaining ecosystem function in sandy, low-fertility soils.

Figure 8 shows that the metabolic profile of microbial communities associated with Vertisol soils closely parallels that observed in Arenosol samples. This similarity indicates a highly integrated metabolic network in which carbohydrate metabolism acts as a pivotal driver supporting amino acid biosynthesis, nucleotide synthesis, and energy metabolism.

The simultaneous presence of both aerobic and anaerobic metabolic pathways, alongside cofactor and lipid biosynthesis, suggests that the Vertisol microbial community is well adapted to the pronounced physicochemical heterogeneity characteristic of these soils. Such metabolic versatility enables the microbial consortia to effectively balance cellular growth, maintenance, and responses to environmental stresses, thereby sustaining soil functionality under variable edaphic conditions.

Figure 9 illustrates that the VertisolCa group exhibits the most pronounced differences in metabolic pathway abundances compared to the Arenosol and Vertisol groups. Overall, pathway abundances in VertisolCa are lower, ranging between 50,000 and 150,000. Notably, the abundances of amino acid biosynthesis pathways—specifically those involved in the synthesis of methionine, isoleucine, valine, lysine, and arginine—are reduced. These pathways represent the biosynthetic capacity of the microbial community, reflecting its nutrient assimilation and growth potential within the VertisolCa environment.

In contrast to the other groups, the biosynthesis of cofactors in VertisolCa encompasses two distinct pathways, while nucleotide biosynthesis includes four pathways compared to three observed in the other samples. This indicates a functionally diverse and biosynthetically active microbial community, characterized by robust capacities for amino acid, nucleotide, and carbohydrate biosynthesis.

The observed balance between energy-generating and stress-responsive metabolic pathways suggests that the microbial community in VertisolCa soils is well-adapted to the mineral-rich conditions and high cation-exchange capacity characteristic of this soil type. These findings reinforce the hypothesis that soil type and chemical properties significantly shape the functional potential of soil microbial communities.

4. Discussion

The objective of this study was to investigate the relationships among MZs —delineated using orthophoto-based NDVI satellite imagery—soil parameters, and the bacterial communities associated with each zone. The results indicate a correlation between soil physicochemical properties and soil microbial community composition, suggesting that these relationships can help clarify and support the delineation of management zones (MZs) for more effective implementation of precision agriculture.

Soil texture, SOM, and CaCO₃ are primary drivers in the grouping of MZs and can explain variations in bacterial community composition across the field. While salinity levels were markedly different among soil types, they did not appear to be a major determinant of microbial structure in this case [27,28,29].

Among the measured environmental variables, soil texture emerged as the primary factor influencing bacterial community composition. This finding is consistent with previous research [30,31,32,33], where soil texture was identified as a major determinant of microbial community structure. In our analysis, distinct genera were associated with specific soil textural groups:

• Sandy soils (B03, B09, B10, B12) hosted unique genera such as HM748754_g belonging to Acidobacteria (OTU11), Dictyobacter (OTU17), Solibacter (OTU23), Sphingomonas (OTU41), and Gaiella (OTU43). This is supported by the study of Baćmaga et al., 2021 [34], where Arenosols were colonized in the highest numbers by Acidobacteriaceae. These taxa were either absent or occurred at <1% relative abundance in clay and steppe soils.
• Interestingly, different Gaiella-linked OTUs displayed divergent patterns: OTU36 and OTU46 were exclusive to clay soils, whereas OTU5 was detected across all MZs, regardless of soil texture.
• Clay soils (B04, B05, B06, B07, B08, B11) were characterized by the abundance of taxa such as Actinomycetia, Skermanella, Rubrobacter, Terrimicrobium, Thermomicrobia, PAC001874_g belonging to Vicinamibacteraceae, Microvirga, EF125410_g belonging to Geminicoccaceae, Desertimonas, and PAC001932_g belonging to Chitinophagaceae. These genera were largely absent in sandy soils but were present in steppe MZs (B01 and B13), which had intermediate textures and organic matter content.

The Chitinophagaceae family, abundant in clay and steppe soils, includes phosphate- and zinc-solubilizing rhizobacteria that are important for nutrient mobilization and plant health [35]. Members of this group also produce antibiotic compounds in the rhizosphere, contributing to disease suppression [36]. Additionally, Microvirga zambiensis (OTU25), a nitrogen-fixing bacterium involved in organic matter decomposition, was identified in these zones [37].

A moderate correlation was observed between CaCO₃ content and the abundance of two genera—EF125410_g (OTU26) and Desertimonas (OTU32)—which were particularly abundant in MZs B02, B04, B06, and B11, all of which had measurable carbonate content. However, CaCO₃ was not a primary driver of overall microbial diversity.

Soil pH is widely recognized as a key determinant of microbial community composition in terrestrial ecosystems. Numerous studies have demonstrated that bacterial diversity and phylum-level abundances are often strongly correlated with soil pH, with higher diversity typically observed near neutral conditions and significant shifts in community structure occurring under acidic conditions [38]. Moreover, soil acidity has been linked to reduced carbon availability for microbes [39], and may function as an environmental filter by selectively favoring acid-tolerant taxa while stressing others [40].

However, in our study, the soil pH appeared to play a minimal role in shaping bacterial community structure across the 13 delineated MZs. The measured pH values ranged from 4.8 to 6.8, with the majority of zones (eight MZs) classified as slightly acidic (pH 6.0–6.8), primarily associated with clay-rich soils. Two zones, both characterized by sandy textures, were moderately acidic (pH < 5.5).

Despite this pH gradient, no clear differences in bacterial community abundance or composition were observed between acidic and neutral MZs. In particular, members of the Acidobacteria phylum—commonly reported as more abundant in acidic soils [41] (Lauber et al., 2009)—were present at comparable relative abundances across all MZs, regardless of soil pH or texture. This suggests that, within the pH range observed in this field, acidity was not the primary driver of microbial community variation.

An exception was the Vicinamibacteraceae family, a member of the Acidobacteria phylum, which was detected only in clay soils with neutral pH. While this could indicate some degree of niche specificity, the pattern was not consistent enough to suggest a strong pH-driven effect on broader community structure.

Importantly, further analysis revealed that soil organic matter (SOM) content exhibited a more significant relationship with microbial community composition than pH. Among the six MZs with acidic or slightly acidic soils (pH 4.5–5.3), SOM content varied widely: three zones had less than 1% SOM, two zones had SOM above 2.5%, and one had approximately 1.5%. This variability suggests that SOM, rather than pH, played a more critical role in determining microbial abundance and distribution in this study.

In summary, although soil pH is often considered a dominant factor influencing bacterial communities, our findings indicate that in this particular field, characterized by a narrow pH range and variable SOM content, organic matter availability had a greater influence on microbial community structure. This highlights the need to consider multiple interacting soil properties when evaluating microbial ecology in precision agriculture contexts.

The bacterial community structure across the 20-hectare cultivated field showed a relatively homogeneous profile in terms of diversity, as indicated by DNA-based diversity indices. Despite the spatial division into 13 MZs, there were no significant differences in overall bacterial diversity metrics. Notably, the presence and relative abundance of key plant growth-promoting rhizobacteria (PGPR)—including Bacillus, Pseudomonas, Nitrobacter, and Streptomyces—were consistent across all MZs, suggesting a generally healthy microbial status throughout the field.

This observation aligns with findings by Wang et al. (2017) [42], who reported that healthy agricultural soils typically exhibit a high abundance of beneficial bacterial genera such as Bacillus, Actinomycetia, and Rhizobium, which are known to suppress soil-borne pathogens and enhance crop resilience, particularly against bacterial wilt. In our study, Bacillus were detected in all MZs, although their relative abundance was notably higher (approximately two-fold) in clay-rich soils compared to sandy soils. Members of the Bacillus genus are recognized for their antibiotic-producing capabilities, including activity against Ralstonia solanacearum, the causative agent of bacterial wilt [43,44,45].

Functional profiling via KEGG Orthology (KO) analysis did not reveal significant differences in predicted metabolic pathways among sample groups stratified by soil texture, SOM, pH, or CaCO₃. All major biochemical pathways were detected across MZs, suggesting a highly stress-adapted and functionally stable microbial community. These findings highlight the ongoing challenge of reliably predicting actual community function discussed by Breitkreuz et al. (2021) [46]. Only minor variations were noted in three categories—amino acid biosynthesis, cofactor biosynthesis, and nucleotide biosynthesis—each differing by one pathway between groups. The literature specifically advocates for the need to validate functional predictions by relating them back to the ecosystem’s physicochemical properties [47]. While taxonomic structure can be highly variable, functional potential often remains stable or redundant across different sites. Our findings demonstrate that selection acts upon specific functions rather than taxonomic composition. As a result, relevant gene sets remain conserved or enriched independently of their taxonomic distribution. The correlation thus explains why the observed functional potential is enriched, grounding the metagenomic findings in ecological context.

In summary, our results indicate that soil texture (i.e., sand and clay content) was the most influential factor shaping microbial community composition across the field. This was followed by soil organic matter (SOM) content, with CaCO₃ playing a minor but detectable role. The strong association between bacterial taxa and soil texture underscores the value of integrating microbiological data into MZ delineation for precision agriculture. Beyond nutrient management, soil microbial profiles offer insights into soil health, fertility, and biological resilience, with potential implications for disease suppression and sustainable crop production.

5. Conclusions

The aim of this study was to investigate the relationship between MZs—areas defined by precision agriculture (PA) methods for nutrient and cultivation management—and the underlying soil parameters and bacterial community composition. The primary objective was to identify correlations between soil microbial data and MZ alignment, with the goal of optimizing input use (such as fertilizers, seeding density, and agrotechnical practices) on arable land.

In the first step, orthophoto-Sentinel-2 NDVI data were used to classify a 20-hectare arable field into 13 different MZs, based on vegetative index patterns. Soil samples were collected from each of these zones, and laboratory analyses were conducted to determine key soil properties. Based on physical soil texture, soil organic matter (SOM) content, and calcium carbonate concentration, the original 13 MZs could be consolidated into four distinct groups.

This four-zone classification was further supported by bacterial community composition data. Taxonomic analysis showed that microbial communities aligned with the soil-based groupings, reinforcing the relevance of soil characteristics in MZ delineation. Comparisons of soil parameters with microbial data indicated that the simplified four-zone model provides a more robust and manageable framework for precision agriculture operations.

Overall, the findings suggest that soil texture and SOM content are the main factors influencing bacterial community structure. Furthermore, microbial data can play a supporting role in precision agriculture by informing decisions related to nutrient management, pest control, and overall soil health monitoring.