{"paper_id":"2413debf-324b-4809-9f92-c2b98fa50258","body_text":"A 16S rRNA gene-based analysis of microbial communities in compost-bedded pack barns 1 \nfrom dairy farms in Argentina. 2 \n 3 \nJuan L. Mongea, Cecilia Peraltab and Leopoldo Palmab* 4 \n 5 \naCentro de Investigaciones y Transferencia de Villa Mar ía (CIT-VM), Consejo Nacional de 6 \nInvestigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Villa María, 5900 7 \nVilla María, Argentina. 8 \nbLaboratorio de Control Biotecnológico de Plagas, Instituto BIOTECMED, Departamento de 9 \nGenética, Universitat de València, Burjassot, València, 46100, Spain. 10 \n 11 \n*Corresponding author: Leopoldo Palma (leopoldo.palma@uv.es) 12 \nAbstract 13 \nMicrobial communities play a central role in compost -bedded pack (CBP) systems by driving 14 \norganic matter decomposition and nutrient cycling. The objective of this study was to characterize 15 \nand compare the bacterial community structure of CBP from two dairy farms in Córdoba, 16 \nArgentina, using 16S rRNA gene sequencing.  Two CBP systems were evaluated: Martin Bono 17 \n(MB; 30 months in operation) and Angela Teresa (AT; 20 months). The MB system was 18 \nestablished on natural soil without bedding addition and included concrete feed alleys, whereas 19 \nAT was initiated with peanut shell bedding and lacked concrete alleys. In both systems, compost 20 \nwas tilled twice daily. Two samples per farm were collected at a depth of 30 cm during winter 21 \n2019. Raw Illumina reads were processed using the DADA2 pipeline, including quality filtering, 22 \nerror modeling, denoising, and chimera removal. A total of four samples yielded 2,503 amplicon 23 \nsequence variants (ASVs), with approximately 76% of reads retained after filtering and chimera 24 \nremoval, indicating high -quality sequencing data.  Taxonomic analysis revealed that bacterial 25 \ncommunities in both systems were dominated by phyla typically associated with compost 26 \nenvironments, including Actinobacteriota, Proteobacteria, and Firmicutes. Differences in relative 27 \nabundance between systems suggested shifts in community composition associated with 28 \nmanagement conditions. 29 \n 30 \nKey words: compost-bedded pack barns, 16S rRNA, microbiome, nitrifying bacteria, dairy 31 \nsystems 32 \n 33 \n 34 \n  35 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2026. ; https://doi.org/10.64898/2026.04.04.716490doi: bioRxiv preprint \n\nIntroduction 36 \nCompost-bedded pack (CBP) systems are increasingly adopted in dairy production due to 37 \ntheir benefits in animal welfare, cow comfort, and manure management efficiency (Biasato et al., 38 \n2019; Black et al., 2013). In these systems, microbial communities play a central role in organic 39 \nmatter decomposition, heat generation, and nutrient cycling, driving the biological processes that 40 \nsustain compost functionality (Insam and de Bertoldi, 2007 ; Sánchez-Monedero et al., 2001 ).  41 \nNitrogen transformations mediated by microorganisms are critical for maintaining compost 42 \nquality and environmental sustainability, as they regulate ammonia volatilization, nitrate 43 \nformation, and overall nitrogen balance (Kumar et al., 2025). 44 \nThe structure and function of microbial communities in CBP systems are strongly influenced 45 \nby management factors such as bedding material, aeration, moisture content, stocking density, 46 \nand system age (Black et al., 2013 ; Endres and Barberg, 2007 ). These variables shape 47 \nphysicochemical conditions within the pack, thereby affecting microbial activity, diversity, and 48 \nmetabolic pathways. Among microbial functional groups, nitrifying bacteria are of particular 49 \ninterest, as they mediate the oxidation of ammonia to nitrite and nitrate, playing a key role in 50 \nnitrogen cycling and influencing nitrogen losses through volatilization and leaching (Kowalchuk 51 \nand Stephen, 2001; Prosser et al., 2007). 52 \nPrevious studies have reported the presence of highly diverse microbial communities in 53 \ncomposting environments, including taxa associated with organic matter degradation and nutrient 54 \ntransformations (Kumar et al., 2025 ; Palaniveloo et al., 2020 ). However, limited information is 55 \navailable on how CBP management practices specifically influence the abundance and diversity 56 \nof nitrifying bacteria in dairy production systems. This represents an important knowledge gap, 57 \ngiven the relevance of nitrogen cycling for both compost efficiency and environmental impact. 58 \nAdvances in high-throughput sequencing technologies, particularly 16S rRNA gene amplicon 59 \nsequencing, have enabled detailed characterization of microbial communities and their potential 60 \nfunctional roles in complex environments such as compost -bedded packs (Callahan et al., 2016; 61 \nKnight et al., 2018 ). These approaches allow for the identification of key microbial taxa and 62 \nprovide insights into how management practices shape microbiome composition. 63 \nThe objective of this study was to characterize and compare the bacterial community structure 64 \nof CBP systems from two dairy farms in Córdoba, Argentina, with particular emphasis on 65 \nnitrifying bacteria, using 16S rRNA gene sequencing. 66 \nMaterials and Methods 67 \nStudy sites and sampling 68 \nTwo compost -bedded pack (CBP) dairy systems located in Córdoba, Argentina, were 69 \nevaluated: Martin Bono (MB; 30 months in operation) and Angela Teresa (AT; 20 months). The 70 \nMB system was established on natural soil without bedding addition and included concrete feed 71 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2026. ; https://doi.org/10.64898/2026.04.04.716490doi: bioRxiv preprint \n\nalleys, whereas AT was initiated with peanut shell bedding and lacked concrete alleys. In both 72 \nsystems, compost was tilled twice daily. 73 \nSampling was conducted during winter (July 2019). Two samples were collected from each 74 \nCBP system at a depth of approximately 30 cm, resulting in a total of four samples (AT1, AT2, 75 \nMB1, and MB2). 76 \nDNA extraction and sequencing 77 \nSoil samples were first lyophilized to facilitate handling and homogenization due to their 78 \nsticky consistency. Subsequently, dried samples were homogenized in a high-speed mixer (High-79 \nspeed Universal Disintegrator, Pro-Lab Diagnosis) and DNA extraction was performed using the 80 \nmicrobiome DNA purification kit PureLink™ (ThermoFisher) and subjected to 16S rRNA gene 81 \namplicon sequencing using Illumina technology  (INDEAR, Argentina) , following standard 82 \nprotocols for microbiome characterization (Caporaso et al., 2012). Paired-end reads were obtained 83 \nand merged prior to downstream analysis.  84 \nBioinformatic processing 85 \nRaw sequence data were initially processed in Geneious Prime (version 2025.1.3), where 86 \ntrimming and quality filtering were performed to remove low -quality reads and adapter 87 \nsequences. Subsequently, the filtered reads were analyzed using the DADA2 pipeline in R  88 \n(Callahan et al., 2016 ). This included error rate learning, sequence denoising, and inference of 89 \namplicon sequence variants (ASVs). Chimeric sequences were identified and removed using a 90 \nconsensus-based approach. 91 \nAfter processing, a total of 2,503 amplicon sequence variants ( ASVs) were obtained across 92 \nfour samples, with approximately 76% of reads retained after filtering and chimera removal. 93 \nTaxonomic classification of ASVs was performed using the SILVA reference database (v138.1)   94 \n(Quast et al., 2013), assigning taxonomy across multiple levels, including phylum and genus. 95 \nStatistical and ecological analysis 96 \nMicrobial community analyses were conducted using the (McMurdie and Holmes, 2013 ) . 97 \nRelative abundance of taxa was calculated and visualized at the phylum level. Alpha diversity 98 \nwas assessed using the Shannon index (Shannon, 1948). Beta diversity was evaluated using Bray–99 \nCurtis dissimilarity (Bray and Curtis, 1957), and principal coordinate analysis (PCoA) was used 100 \nto visualize differences in community structure between systems.  101 \nNitrifying bacteria were identified based on taxonomic assignment at the genus level, 102 \nfocusing on Nitrosomonas, Nitrosococcus, and Nitrobacter. Differences in abundance and 103 \ndiversity between systems were evaluated descriptively due to the limited number of replicates.  104 \nAdditionally, genera associated with mastitis -related pathogens, including Staphylococcus, 105 \nStreptococcus, Corynebacterium, and Escherichia, were specifically examined to assess potential 106 \nsanitary risks.   107 \nResults 108 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2026. ; https://doi.org/10.64898/2026.04.04.716490doi: bioRxiv preprint \n\nMicrobial diversity and community structure 109 \nAlpha diversity, estimated using the Shannon index, showed slightly higher values in MB 110 \ncompared to AT samples (Figure 1). However, these differences were not statistically significant 111 \n(Wilcoxon test, p = 0.33), likely due to the limited number of replicates. Despite this, MB samples 112 \nconsistently exhibited higher diversity values, suggesting a trend toward increased microbial 113 \ncomplexity. 114 \n 115 \nFigure 1. Shannon diversity index of bacterial communities in compost -bedded pack systems 116 \n(AT and MB). Boxplots represent alpha diversity values for each system. No significant 117 \ndifferences were observed between groups (Wilcoxon test, p = 0.33). 118 \nBeta diversity analysis based on Bray –Curtis dissimilarity revealed a clear separation 119 \nbetween AT and MB samples (Figure 2) , indicating distinct microbial community structures 120 \nbetween systems. Replicates clustered according to treatment, supporting the reproducibility of 121 \nthe observed patterns. 122 \n 123 \nFigure 2.  Principal coordinate analysis (PCoA) based on Bray –Curtis dissimilarity showing 124 \ndifferences in bacterial community structure between compost -bedded pack systems (AT and 125 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2026. ; https://doi.org/10.64898/2026.04.04.716490doi: bioRxiv preprint \n\nMB). Each point represents one sample, and colors indicate system type. The first principal 126 \ncoordinate explains 78.5% of the variation. 127 \nMastitis-associated genera 128 \nGenera associated with mastitis -related pathogens were detected in both systems, including 129 \nCorynebacterium, Pseudomonas, and Staphylococcus (Figure 3). 130 \nAT samples were dominated by Corynebacterium, with only minor contributions from other 131 \ngenera. In contrast, MB samples showed a more balanced composition, with increased relative 132 \nabundance of Pseudomonas and detectable levels of Staphylococcus. These results suggest 133 \ndifferences in the potential sanitary profile between systems. 134 \n 135 \nFigure 3.  Relative abundance of mastitis -associated genera ( Corynebacterium, Pseudomonas, 136 \nStaphylococcus) in AT and MB samples. 137 \nNitrifying bacteria 138 \nNitrifying bacteria were detected at the genus level, including Nitrosomonas and 139 \nNitrosococcus (Figure 4) . These taxa were present in both systems, although their relative 140 \nabundance appeared higher and more consistent in MB samples, suggesting enhanced nitrogen 141 \ncycling potential in this system. 142 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2026. ; https://doi.org/10.64898/2026.04.04.716490doi: bioRxiv preprint \n\n 143 \nFigure 4.  Relative abundance of nitrifying bacteria ( Nitrosomonas and Nitrosococcus) across 144 \nsamples from both CBP systems. 145 \nFunctional microbial groups 146 \nAnalysis of key functional genera associated with compost processes revealed marked 147 \ndifferences between systems  (Figure 5). AT samples were largely dominated by Pseudomonas, 148 \nindicating a simpler functional structure. In contrast, MB samples displayed a broader functional 149 \ndiversity, including the presence of Mycobacterium and Streptomyces, in addition to nitrifying 150 \nbacteria such as Nitrosomonas.  151 \nThe exclusive detection of Mycobacterium in MB samples further highlights differences in 152 \nenvironmental conditions and microbial niches between systems. Overall, these findings suggest 153 \na more complex and functionally diverse microbial community in MB. 154 \n 155 \n 156 \nFigure 5. Relative abundance of selected functional genera associated with compost processes 157 \n(Mycobacterium, Nitrosomonas, Pseudomonas, Streptomyces) in AT and MB systems. 158 \nCommunity abundance patterns 159 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2026. ; https://doi.org/10.64898/2026.04.04.716490doi: bioRxiv preprint \n\nHeatmap analysis of the most abundant genera revealed distinct community patterns between 160 \nsystems (Figure 6). AT samples exhibited a more homogeneous microbial profile, with higher 161 \nrelative abundance of Corynebacterium, whereas MB samples showed a more heterogeneous 162 \ndistribution of taxa, including increased representation of several genera associated with compost 163 \nprocesses. These patterns further support differences in microbial structure between CBP systems. 164 \n 165 \nFigure 6. Heatmap showing the relative abundance of the most abundant bacterial genera across 166 \nsamples. Color intensity represents relative abundance, highlighting differences in microbial 167 \ncomposition and heterogeneity between AT and MB systems. 168 \nDiscussion 169 \nThe present study provides a first 16S rRNA gene -based characterization of microbial 170 \ncommunities in compost -bedded pack (CBP) barns from dairy farms in Córdoba, Argentina. 171 \nAlthough based on a limited number of samples, the results show that bacterial community 172 \ncomposition differed clearly between the two evaluated systems, suggesting that management 173 \nconditions, bedding characteristics, and system configuration can strongly influence microbial 174 \nstructure in CBP environments. 175 \nBoth systems were dominated by phyla commonly associated with composting and organic 176 \nmatter degradation, including Actinobacteriota, Proteobacteria, and Firmicutes. This is consistent 177 \nwith the biological function of CBP systems, where microbial activity supports decomposition, 178 \nheat generation, and nutrient turnover  (Insam and de Bertoldi, 2007 ; Tiquia et al., 2002 ). The 179 \nseparation observed in the Bray –Curtis PCoA indicates that, despite the shared productive 180 \ncontext, each barn harbored a distinct bacterial assemblage. The close clustering of replicates 181 \nwithin each farm further supports the consistency of the microbial profiles detected. 182 \nThe MB system showed a tendency toward higher alpha diversity and a broader distribution 183 \nof taxa compared with AT. This pattern may reflect the combined influence of longer operational 184 \ntime, absence of initial bedding addition, and the presence of concrete feed alleys, all of which 185 \nmay affect moisture gradients, organic matter inputs, and aeration dynamics (Endres and Barberg, 186 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2026. ; https://doi.org/10.64898/2026.04.04.716490doi: bioRxiv preprint \n\n2007; Endres and Janni, 2008 ). In compost -based systems, greater microbial diversity is often 187 \nassociated with increased functional redundancy and process stability, which may be 188 \nadvantageous for maintaining compost performance under variable environmental conditions  189 \n(Allison and Martiny, 2008; Shade et al., 2012). However, because of the small sample size, these 190 \ndifferences should be interpreted cautiously. 191 \nParticular attention was given to nitrifying bacteria because of their relevance to nitrogen 192 \ntransformations in compost systems. Genera such as Nitrosomonas and Nitrosococcus were 193 \ndetected in both farms, but their abundance appeared greater and more consistent in MB. This 194 \nsuggests that microbial processes linked to ammonia oxidation may be favored under the 195 \nenvironmental and management conditions present in that system. Since nitrogen cycling is a 196 \ncentral component of compost quality and environmental performance, these differences may 197 \nhave practical implications for ammonia retention, nitrate formation, and overall nutrient 198 \ndynamics in CBP barns  (Kowalchuk and Stephen, 2001 ; Sánchez-Monedero et al., 2001 ). 199 \nNonetheless, functional activity was inferred only from taxonomic composition, and future 200 \nstudies should incorporate direct measurements of nitrogen transformation rates or functional 201 \ngenes involved in nitrification. 202 \nThe detection of genera associated with mastitis -related pathogens, including 203 \nStaphylococcus, Streptococcus, Corynebacterium, and Escherichia, also highlights the sanitary 204 \nrelevance of microbial monitoring in these systems. Although 16S rRNA sequencing does not 205 \nallow confirmation of pathogenic strains or viability, the presence and relative abundance of these 206 \ngenera suggest that CBP management may influence microbial groups with potential relevance 207 \nto udder health (Bradley, 2002; Smith et al., 1985). Differences between systems may therefore 208 \nreflect not only composting performance but also distinct sanitary profiles linked to bedding 209 \ncomposition, moisture, and aeration 210 \nThis study has some limitations that should be acknowledged. The analysis was based on 211 \nonly four samples collected at a single time point during winter, which limits statistical power 212 \nand prevents evaluation of seasonal dynamics. In addition, taxonomic inference from 16S rRNA 213 \namplicons provides limited functional resolution  (Janda and Abbott, 2007; Knight et al., 2018 ). 214 \nEven so, the results offer a useful initial description of microbial community patterns in Argentine 215 \nCBP systems and identify management -associated differences that merit deeper investigation. 216 \nFuture work should include a larger number of farms, repeated sampling over time, and 217 \nintegration of physicochemical variables such as temperature, moisture, pH, and nitrogen forms 218 \nto better explain microbial shifts and their functional consequences. 219 \nIn conclusion, the bacterial communities of the two CBP systems differed in both composition 220 \nand inferred functional potential. The MB system showed higher diversity and a greater 221 \nrepresentation of nitrifying bacteria, suggesting that barn design and management history may 222 \nshape key microbial processes in compost-bedded pack environments. These findings contribute 223 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2026. ; https://doi.org/10.64898/2026.04.04.716490doi: bioRxiv preprint \n\nto a better understanding of microbiological dynamics in dairy CBP systems and may support 224 \nfuture improvements in compost management, nutrient cycling, and animal health. 225 \n 226 \n 227 \n 228 \n 229 \n 230 \n 231 \n 232 \n 233 \n 234 \n 235 \n  236 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 4, 2026. ; https://doi.org/10.64898/2026.04.04.716490doi: bioRxiv preprint \n\nAuthor contributions 237 \nConceptualization, L.P., methodology,  J.M., C.P. and L.P.; formal analysis, J.M., C.P. and L.P.; 238 \ninvestigation, J.M.; C.P. and L.P.; resources, J.M. and L.P.; data curation, L.P.; writing—original 239 \ndraft preparation, L.P.; writing, review and editing, L.P.; visualization, L.P.; supervision, J.M. and 240 \nL.P.; project administration, J.M. and L.P.; funding acquisition, J.M. and L.P. All authors have 241 \nread and agreed to the published version of the manuscript. 242 \nConflicts of interest 243 \nThe authors declare there are no conflicts of interest 244 \nAcknowledgments 245 \nLeopoldo Palma gratefully acknowledges the Spanish Ministry of Science, Innovation, and  246 \nUniversities, the Spanish State Research Agency, and the European Union for funding his Ramón 247 \ny Cajal contract (grant ref. RYC2023-043507-I). 248 \nData availability 249 \nThe raw sequencing reads have been deposited in the NCBI Sequence Read Archive (SRA) under 250 \nBioProject accession PRJNA1449009, with associated BioSample accession numbers XXX. 251 \n  252 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 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