the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Microbiome structure, function, and drivers across different soil groups in an agricultural region of Serbia
Abstract. Soil is the largest reservoir of biodiversity, with distinct physical, chemical, and biological properties. Microorganisms play essential roles in soil formation and fertility. This study aimed to analyze the microbiomes of three selected soil groups in an important agricultural region of Vojvodina (Serbia) by 16S rRNA gene metabarcoding and explore their association with soil properties. Soil samples from a total of 26 field plots (in 5 replicates) were analyzed using Illumina MiSeq paired-end sequencing and processed through the QIIME2 pipeline. The obtained results indicate that the analyzed soils generally exhibit physicochemical properties typical for the respective soil groups. Alpha diversity indices revealed the highest microbiome diversity in Chernozem, consistent with its favourable physicochemical characteristics. Based on beta diversity, clear separation of soil groups according to their properties was determined. Proteobacteria, Acidobacteriota, and Actinobacteriota dominate the microbial community composition at the phylum level. Redundancy analysis revealed that soil properties account for 53.8 % of the variation in community composition, with pH value, iron availability, and CaCO3 content having the strongest influence, pH being particularly significant. The functional potential of microbial communities showed dominance of functions related to metabolism, with significant representation of functions belonging to the following groups: genetic information processing, environmental information processing, and cellular processes. The analysis of genes involved in nitrogen cycling using Kruskal-Wallis test showed no statistically significant differences in their abundances across different soil groups (p-value > 0.05). This study provides the first detailed analysis of soil microbial communities across Serbia and highlights factors shaping them. These findings underscore the importance of microbial diversity for ecosystem functioning and offer a framework for soil health monitoring, while providing insights relevant for sustainable agriculture.
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Status: open (until 31 Jul 2026)
- RC1: 'Comment on egusphere-2026-2321', Anonymous Referee #1, 06 Jul 2026 reply
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RC2: 'Comment on egusphere-2026-2321', Anonymous Referee #2, 28 Jul 2026
reply
Overview:
In this effort, the authors work to characterize Serbian soil microbiome structure with a focus on agriculture soils and contextualize how different agriculture management practices and soil types influence the microbiome structure and function. The study was thorough in sequencing analyses performed and produced interesting results. Some comments below for consideration with ultimate consideration that more information is needed around Ag management practices etc to strengthen paper.
Comments:
Major:
- If possible, in 2.1.2, a table might be good to outline the distinct agricultural practices and crop rotations for each location (maybe progressing back a few growing seasons or just general overview of standard practice in each area). This is quite intriguing on how it might vary between the soil types and location and thus leading to some of the outcomes—I read the cited paper and appreciated that detail but would appreciate the extra information here as well. Also, gives opportunity to expand a bit more based on the last sentence in this method.
- Figure 3/5, would consider combining figure 3 and 5 or move figure 5 up in front of figure 4 as it is referenced first and helps to contextualize figure 3. Figure 4 does help bring clarity but could come last. Also, if possible, would consider trying to lock in color patterns for the major phyla in Figure 3 and holding constant in the different subplots specifically maybe revamping subpanel A to match B and C more. Results are quite interesting and found the Verrucomicrobiota shift intriguing
- Figure 6/7 are quite insightful and interesting results
- Table 4 and Figure 10 are interesting—going back to the first major point—a table highlighting Ag management strategies/crop rotations/fertilizer strategies could help tie into this more and add some context given lines 420-425. Also, this could help give some ideas about the plot variability
Minor:
- Line 187—guessing this is paired 300 bp?
- In lines 216-217, were the reverse reads completely unusable or just lower quality? How much lower quality?
- Lines 267-271/Table2, bringing the pH forward from the previous citation would help with doing comparisons—maybe as supplemental or addition to Table2. Agreed with the thoughts in this area though.
- Figure 1 and throughout, Chao1 richness is not readily compatible with ASVs generated from DADA2—would consider removing completely and just using observed richness as that is sufficient
- Figure 2, Jaccard and Bray-Curtis are similar but in unweighted and weighted forms and only the weighted UniFrac is shown, was unweighted UniFrac calculated?
- Line 369, P2O5 missing subscripts
- Line 478-479 reinforce the major comments made—interesting discussion point but having more context about the Ag management in each plot and soil type would strengthen these points
- Lines 551-560 area thoughts are sensible under stable fertilizer regimes where micronutrients and other factors could become the major drivers—yet need to know more about this management practices to solidify these thoughts
- Discussion on nif and nir genes follow the same ideas as comment immediately above and in conclusion. The more that can be known about Ag management and contextualization information, the stronger the conclusions become.
Citation: https://doi.org/10.5194/egusphere-2026-2321-RC2
Data sets
Environmental DNA: Biomarker of Soil Quality in Vojvodina Bioproject PRJNA1116093 Ana Kuzmanović, Dragana Tamindžija, Jordana Ninkov, Jovica Vasin, Mihajla Djan, Stanko Milić, and Dragan Radnović http://www.ncbi.nlm.nih.gov/bioproject/1116093
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- 1
L10. Indicate the soil groups (Chernozem, Solonchak, and Vertisol)
L11. Indicate which soil properties have been measured or almost, the number of soil health indicators.
L11. This sentence for me it's not clear. According to this, 26 field Plots were collected. However, according to material and methods, 15 fields (5*3) were collected. Is it right?
L26-113. The introduction section is a bit longer. I suggest to reduce. Try to reduce the soil health section or rewritte them,
L136. I suggest adding information about practices here, as a table or as SUPPLEMENTARY data.
L168. "extracted using hot water" Please, indicate the method, water temperature, and extraction time.
L171. Operation conditions can be moved to SUPPLEMENTARY data, although they are ok in this version.
L246. Results previously published should be indicated here. This will help readers to understand your work. It's not easy to refer to previous work and see the info there. To avoid issues with auto-plagiarism, you can indicate a footnote that XXX were published in XX. In fact, the authors wrote about them, but they don't indicate them.