www.nature.com/scientificreports OPEN Traditional potato tillage systems in the Peruvian Andes impact bacterial diversity, evenness, community composition, and functions in soil microbiomes Aura L. García‑Serquén 1*, Lenin D. Chumbe‑Nolasco 1, Acacio Aparecido Navarrete 2, R. Carolina Girón‑Aguilar 1 & Dina L. Gutiérrez‑Reynoso 1 The soil microbiome, a crucial component of agricultural ecosystems, plays a pivotal role in crop production and ecosystem functioning. However, its response to traditional tillage systems in potato cultivation in the Peruvian highlands is still far from understood. Here, ecological and functional aspects of the bacterial community were analyzed based on soil samples from two traditional tillage systems: ’chiwa’ (minimal tillage) and ’barbecho’ (full tillage), in the Huanuco region of the Peruvian central Andes. Similar soil bacterial community composition was shown for minimal tillage system, but it was heterogeneous for full tillage system. This soil bacterial community composition under full tillage system may be attributed to stochastic, and a more dynamic environment within this tillage system. ’Chiwa’ and ’barbecho’ soils harbored distinct bacterial genera into their communities, indicating their potential as bioindicators of traditional tillage effects. Functional analysis revealed common metabolic pathways in both tillage systems, with differences in anaerobic pathways in ’chiwa’ and more diverse pathways in ’barbecho’. These findings open the possibilities to explore microbial bioindicators for minimal and full tillage systems, which are in relationship with healthy soil, and they can be used to propose adequate tillage systems for the sowing of potatoes in Peru. The soil microbiome plays an important role in food production and ecosystem functions, and recent studies have highlighted its relationship with human health1. In addition, soil microbiomes exhibit a great diversity of organisms that perform vital ecological functions, such as nitrogen and carbon cycles. These ecological roles of soil microbiomes maintain terrestrial e cosystems2 and plant productivity3, thereby mitigating the effects of climate change by increasing agroecosystem resilience4. Therefore, it is necessary to understand the composition and the functional profile of microbiome a groecosystems5. Peru is the center of origin of the potato Solanum spp.6, and its agricultural systems are recognized as a world patrimony7. Currently, Peruvian farmers continue to use traditional sowing systems8–11. Potato grows mainly between 3500 and 4300 masl, and for its sowing, Andean farmers developed three traditional tillage systems based on footplough: ‘chiwa’, ‘chacmeo’, and ‘barbecho’. ‘Chiwa’ is a minimal tillage system (MTS), in which ‘chakitaklla’ is commonly used (Fig. 1A). This pre-Inca instrument is used to posture the foot and is made of a 0.8–2.5 m long piece of wood with a 75–300 mm metal b ar9,10. In this MTS, the ‘chakitaklla’ has been used on the pasture to perforate a hole where the potato seed is deposited at a depth of 0.1–0.2 m and is covered with the same soil. After three–four weeks, the soil near the planting area is turned or flipped to form ridges on the seeded tubers10. In the highland regions of the Peruvian Andes, the utilization of this technique remains a common practice, particularly for cultivation on mountain slopes; additionally, this method serves to mitigate the effects of nocturnal frosts, which are more pronounced in the plain terrains10,11. The ‘chacmeo’ is another type of MTS, wherein two clods of soil are organized to create a row or furrow for planting potatoes in between the clods; the ‘chakitaklla’ serves also as an integral instrument to this a gricultural10. In turn, ‘barbecho’ is a full tillage system 1Laboratorio de Biología Molecular y Genómica, Dirección de Recursos Genéticos y Biotecnología, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina 1981, 15024 Lima, Peru. 2Graduate Program in Environmental Sciences, Brazil University (UB), Estrada Projetada F1, Fazenda Santa Rita, Fernandópolis, São Paulo 15613-899, Brazil. *email: agarcias@inia.gob.pe; auralizgarcia@gmail.com Scientific Reports | (2024) 14:3963 | https://doi.org/10.1038/s41598-024-54652-2 1 Vol.:(0123456789) www.nature.com/scientificreports/ A soil added B by hilling-up operations tilled soil undisturbed soil yunta chakitaklla Figure 1. Traditional tillage systems. (A) Minimal tillage system ‘chiwa’ in which we can see the use of the ‘chakitaklla’ and the form of tillage on the soil. (B) Full tillage ‘barbecho’ in which we can see the use of yunta and tractor and the form of tillage on the soil. Adapted from the "The complexity of simple tillage systems" by Oswald et al., 2009, The Journal of Agricultural Science, 147(4), 399–410. Copyright 2009 by Cambridge University Press. (FTS) that requires intensive soil preparation using the ‘yunta’ (for tilling the land with animal power support) or a tractor to turn, break and loosen the soil before sowing which facilitates the formation of furrows where the potato seeds are d eposited10 (Fig. 1B). Metagenomics has been used to understand the microbial communities, and is a high-throughput DNA sequencing technology used to study the genomic DNA contained in environmental samples, such as soil, sea environments, or freshwater habitats, to explore the taxonomic composition and potential uses of microorgan- isms that may benefit agriculture, bioremediation, and human health2. This sequencing technology has also been used to study intensive agriculture, land-use change, and agricultural soil in different countries, as well as to determine the effects of system tillage on soil microbial c ommunities12. However, few studies have used this approach to study the soil microbiome in Peru, and no reports have focused on the influence of traditional tillage systems on soil microbiomes. In this study, we utilized a shotgun metagenomic approach to study the bacterial community in Peruvian agricultural soil managed with traditional tillage systems such as ‘chiwa’ and ’barbecho’. We hypothesized that the use of these traditional tillage systems has an impact on the functional profile and taxonomic composition of soil microbiomes. Consequently, the objectives of this study were to characterize richness, diversity, evenness and taxonomic and functional composition of soil bacterial communities inhabiting two different Peruvian tra- ditional potato tillage systems. Additionally, we determined potential bioindicators of soil management effects in these potato traditional tillage systems. For this, we selected the department of Huanuco in the Peruvian Central Andes because it is one of the major areas of potato production in Peru where farmers still use traditional potato sowing systems. These outcomes are the first starting point to understand the microbial communities and their ecology of Peruvian agricultural soil under traditional tillage systems. Results Physicochemical properties of soil samples Soil physicochemical properties did not differ significantly between the traditional tillage systems (Table 1, p-value > 0.05). The soil samples were classified as sandy loam for MTS soils and sandy clay loam for FTS soils (Table S1). The PCA biplot of the physicochemical parameters revealed that soils under MTS exhibited a closer resemblance to soils under FTS (Fig. S1). Additionally, the PCA biplot indicated a positive relationship in soils under MTS with SOM and N, and soils under FTS exhibited a relationship with clay. Traditional potato tillage system impact on ecological metrics of soil bacterial community After quality filtering, we obtained an average of 9.3 million paired-end reads per soil sample (Table S2). Quality- filtered shotgun metagenomic reads were classified at rates of 5.9% and 6.8% for MTS and FTS, respectively, using Kraken2 (Table S2). These percentages encompass archaea, viruses, eukaryotes, and bacterial domains. Specifically, we focus solely on the bacteria domain, which constitutes an average of 3.3% and 4.4% for MTS and FTS, respectively. Taxonomic assignment recovered 4,488 bacterial species, representing 1,247 genera, 396 families, and 37 phyla (Table S3). Ecological metrics revealed no significant differences in richness for bacte- rial communities from MTS and FTS soils (Table S4, Fig. 2A). However, we observed significant differences in diversity and evenness indexes (Table S4, Fig. 2B,C). In beta diversity analysis, PCoA showed that MTS soils formed a clustered group, indicating similar composition, while FTS soil samples exhibited more heterogeneity (Fig. 2D); nevertheless, the application of a PERMANOVA revealed no evidence of differences between MTS and FTS, as indicated by the weighted UniFrac distances (R = 0.17792, p-value = 0.365) and the Bray–Curtis Scientific Reports | (2024) 14:3963 | https://doi.org/10.1038/s41598-024-54652-2 2 Vol:.(1234567890) www.nature.com/scientificreports/ Physicochemical properties Minimal tillage system (MTS) Full tillage system (FTS) p-value pH 4.6 ± 0.2 4.2 ± 0.3 0.667 SOM (%) 18 ± 3.4 4.2 ± 2.2 0.333 P (ppm) 4.9 ± 0.7 5 ± 1.5 1 K (ppm) 113 ± 7 97.5 ± 2.5 0.333 N (%) 0.9 ± 0.17 0.2 ± 0.11 0.333 Sand (%) 72.2 ± 5.4 66.1 ± 9.3 0.667 Clay (%) 11.2 ± 0 21.2 ± 2 0.221 Silt (%) 16.6 ± 7.6 12.7 ± 7.3 0.667 Table 1. Summary of physicochemical properties of soil samples from minimal tillage and full tillage systems. Values are presented as mean ± standard error (n = 2). Soil properties included soil pH (pH), soil organic matter content (SOM), available phosphorus (P), potassium (K), nitrogen (N), sand content (sand), clay content (clay), and silt content (silt). The p-values from the Wilcoxon rank-sum test are shown. Wilcoxon test was performed contrasting MTS vs. FTS samples across all physicochemical properties. A n. s. B * C *70 250 2100 60 200 50 1900 40 150 30 1700 20 100 Minimal Tillage Full Tillage Minimal Tillage Full Tillage Minimal Tillage Full Tillage System System System System System System D 0.1 E MTS1_2 FTS2_1 FTS2_2 FTS1_1 MTS1_1 FTS1_2 MTS2_2 0.0 MTS2_2 MTS2_1 MTS1_2 FTS2_2 MTS2_1 FTS2_1 −0.1 MTS1_1 FTS1_2 FTS1_1 −0.6 −0.3 0.0 0.3 0.6 0.75 0.50 0.25 0.00 PCo1 [74.7%] Bray-Curtis dissimilarity Figure 2. Potato soil bacterial microbiome alpha and beta diversity analyses. (A) Species richness. (B) Shannon effective numbers. (C) Simpson effective numbers. (D) Principal Coordinates Analysis using weighted UniFrac distances. (E) Dendrogram based on the Bray–Curtis dissimilarity * p-value < 0.05; n. s., not significant. dissimilarity (R = 0.27515, p-value = 0.129). Additionally, the FT2 bacterial community was more similar to the MTS microbiome than FT1 (Fig. 2E). Bacterial community composition of soil under traditional tillage system Pseudomonadota emerged as the predominant phylum across all the samples, accounting for 73.2% of the total bacterial composition. This phylum was followed by Actinomycetota (15.7%), Acidobacteriota (4.1%), Bacteroi- dota (2.1%), Planctomycetota (2.1%), and Bacillota (1.0%) (Table S5). In terms of class distribution, Alphaproteo- bacteria (34.6%), Betaproteobacteria (19.5%), Gammaproteobacteria (19.1%), Actinomycetes (14.8%), Terriglobia (4.0%), and Planctomycetia (2.0%) were the dominant taxonomical groups (Table S6). The prevailing families included Nitrobacteraceae (24.5%), Rhodanobacteraceae (13.0%), Burkholderiaceae (6.8%), Oxalobacteraceae Scientific Reports | (2024) 14:3963 | https://doi.org/10.1038/s41598-024-54652-2 3 Vol.:(0123456789) PCo2 [17.8%] Species richness Shannon effective numbers (Hill number of order 1) Simpson effective number (Hill number of order 2) www.nature.com/scientificreports/ (5.0%), Comamonadaceae (4.0%), and Acidobacteriaceae (3.7%) (Table S7). Among the genera, the most preva- lent were Bradyrhizobium (22.2%), Rhodanobacter (12.3%), Paraburkholderia (5.8%), Mesorhizobium (3.5%), Pseudomonas (3.5%), Collimonas (3.4%), and Arthrobacter (3.4%) (Table S8). A comparison between the two till- age systems revealed that the phyla Acidobacteriota, Actinomycetota, Bacteroidota, and Bacillota (the latter four showing statistical significance), and along with Planctomycetota were more abundant in MTS soils, whereas only Pseudomonadota was more abundant in FTS soils (Fig. 3A, Table S9). At the class level, Actinomycetes, Bacilli, A 100 Phylum 75 Others Verrucomicrobiota Myxococcota Bacillota 50 Planctomycetota Bacteroidota Acidobacteriota 25 Actinomycetota Pseudomonadota 0 MTS1_1 MTS1_2 MTS2_1 MTS2_2 FTS1_1 FTS1_2 FTS2_1 FTS2_2 B 100 Class Others 75 Thermoleophilia Flavobacteriia Bacilli Sphingobacteriia 50 Planctomycetia Terriglobia Actinomycetes 25 Gammaproteobacteria Betaproteobacteria Alphaproteobacteria 0 MTS1_1 MTS1_2 MTS2_1 MTS2_2 FTS1_1 FTS1_2 FTS2_1 FTS2_2 Genus C 100 Others Rhodoplanes Silvimonas 75 Nocardioides Catenulispora Nitrobacter Mycobacterium 50 Rhodoferax Arthrobacter Collimonas 25 Pseudomonas Mesorhizobium Paraburkholderia Rhodanobacter 0 MTS1_1 MTS1_2 MTS2_1 MTS2_2 FTS1_1 FTS1_2 FTS2_1 FTS2_2 Bradyrhizobium Figure 3. Bacterial taxonomic composition for traditional tillage systems (MTS: Minimal Tillage Systems, FTS: Full Tillage Systems) at the phylum (A), class (B), and genus (C) levels. Scientific Reports | (2024) 14:3963 | https://doi.org/10.1038/s41598-024-54652-2 4 Vol:.(1234567890) Relative abundance (%) Relative abundance (%) Relative abundance (%) www.nature.com/scientificreports/ Betaproteobacteria, Flavobacteriia, Planctomycetia, Terriglobia, and Thermoleophilia were more abundant in MTS soils, whereas Alphaproteobacteria, Gammaproteobacteria, and Sphingobacteriia were more abundant in the FTS soils (Fig. 3B, Table S10). Among these classes, only Bacilli and Betaproteobacteria showed significant differences. At the genus level, Mesorhizobium, Pseudomonas, Collimonas, Arthrobacter, Rhodoferax, Nitrobac- ter, Massilia, Nocardioides, and Silvimonas were more abundant in the MTS soils. In contrast, Bradyrhizobium, Rhodanobacter, Paraburkholderia, Mycobacterium, and Catenulispora were more abundant in the FT soils (Fig. 3C and Table S11). Among these genera, Pseudomonas, Rhodoferax, Catenulispora, Massilia, and Silvimonas showed significant differences. Potato core microbiome after harvest under traditional tillage system A total of 835 genera (Fig. 4A, Table S12) and 2632 species (Table S13) were shared between the two tillage systems. We decided to focus our analysis on the genus level, considering only those with a minimum relative abundance of 0.1%. This approach was chosen to explore the core microbiome and achieve a more comprehensive understanding of the bacterial community. Giving these details, our potato core microbiome consisted of 70 genera (Table S14). This core microbiome encompasses eight phyla, with the majority of shared genera belonging to Pseudomonadota (32 genera), Actinomycetota (17 genera), and Acidobacteriota (8 genera). On average, this core represents 85% of the bacterial microbiome. When comparing to the Potato Core Microbiome proposed by Pfeiffer13, we identified nine genera in common: which are Bradyrhizobium, Lysobacter, Massilia, Microbacterium, Nocardioides, Paenibacillus, Rhodoplanes, Streptomyces, and Variovorax (Fig. 4B). Potential microbial indicators of soil management effects in potato traditional tillage systems We identified specific microbial genera that should be better investigated as early warning of MTS and FTS management effects. A total of 115 genera were found to be significant after the LefSe analysis (p-value < 0.05, Table S15); however, we filtered these genera to include only those with a relative abundance greater than 1% (Fig. 5A,B). In MTS soils, we detected five distinct genera, with four falling within the taxonomic classification of the Pseudomonadota phylum and one in the Actinomycetota phylum. These genera are Pseudomonas, Rho- doferax, Silvimonas, Massilia, and Rhodococcus. Notably, Pseudomonas, the most diverse of the identified genera, comprises 241 species, with P. fluorescens emerging as the most dominant among them. Rhodoferax, however, consists of nine species, with R. sediminis exhibiting the highest abundance within this genus. Silvimonas was represented by a solitary species. Massilia presented higher diversity, with 16 species, among which M. putida emerged as the predominant member. Whitin Rhodococcus, there are 34 species, with R. globerulus being the dominant species. Conversely, in FTS, we identified a single indicator genus, Catenulispora, which belongs to the Actinomycetota phylum, with C. acidiphila being the sole representative. We also established correlations between indicators and physicochemical parameters. The MTS indicators showed positive correlations with SOM, N, and K content; however, only Rhodococcus showed a significant relationship with these parameters (p-value < 0.05). In contrast, the FTS indicators exhibited a positive correlation with sand and clay (Fig. 5C). Soil bacterial functions in potato traditional tillage systems We recovered 435 metabolic pathways from our potato traditional tillage systems (Table S16), which were grouped into 43 higher-order functional MetaCyc ‘superclass 2’ (Fig. 6B), and seven ‘superclasses 1’ (Fig. 6A). At the ‘superclass 1’ hierarchy, both tillage systems displayed similar functional patterns, with biosynthesis (68.8%) as the main class pathway, followed by metabolism (14.8%) and degradation/utilization/assimilation (14.5%) (Fig. 6A, Table S17). At the ‘superclass 2’ level, almost two-thirds of the pathways were grouped in the biosynthesis of macromolecules (Amino Acids 21.5%; Nucleoside and Nucleotide 15.3%; Cofactor, Carrier, and Vitamin Biosynthesis 8.3%; Fatty Acid and Lipid 7.5%; and Carbohydrate 7.0%) (Fig. 6B, Table S18). LEfSe analysis identified 31 MetaCyc functional pathways with significant differential abundance among our potato traditional tillage systems (Table S19). We filtered those pathways with an LDA score above 1 and removed two MetaCyc pathways attributed to non-bacterial organisms. Within MTS, two of the three pathways belonged to the degradation/utilization/assimilation pathway, whereas for FTS, we recovered six pathways that were more A Minimal Tillage Full Tillage B Core Microbiome Potato Core Microbiome System System (This study) (Pfeiffer et al. 2016) 222 865 190 61 9 25 Figure 4. (A) Venn diagram of exclusive and shared bacterial species in traditional soil tillage systems. (B) Venn diagram comparing the core microbiome of this study with the potato core microbiome proposed by Pfeiffer et al. (2016). Scientific Reports | (2024) 14:3963 | https://doi.org/10.1038/s41598-024-54652-2 5 Vol.:(0123456789) www.nature.com/scientificreports/ A B Pseudomonas Rhodoferax Silvimonas Massilia Rhodococcus Catenulispora 0% 2% 4% 6% −4 −2 0 2 4 Relative abundance (%) LDA score (log 10) C Silvimonas Pearson correlation Rhodoferax 1.0 Rhodococcus * * 0.5 Pseudomonas 0.0 - 0.5 Massilia - 1.0 Catenulispora P Silt OM N pH K and yS S Cl a Figure 5. Bioindicators for soil under traditional tillage systems. (A) Relative abundance of bacterial genera for proposed bioindicators. (B) Differential abundance of bacterial bioindicators (based on LEfSe Analysis, p-value < 0.05, a minimum relative abundance > 1%, LDA threshold = 3.5). (C) Correlation between bacterial genera indicator and soil physicochemical properties. * p-value < 0.05. diverse in their functionality (Fig. 6C). The pathway PWY-P42 ‘incomplete reductive TCA cycle’ was found to be the most remarkable function in MTS soils, which is connected to anaerobic conditions, and its main purpose is to fix carbon dioxide and produce organic molecules. The pathway PWY-5100 ’pyruvate fermentation to acetate and lactate II’ is also typically employed by anaerobic microorganisms to generate energy. The final significant pathway for MTS soils is PWY-5104 ’L-isoleucine biosynthesis IV ’, which is crucial for the growth and survival of organisms, as it enables protein synthesis and various metabolic processes. In FTS soils, the pathway PWY- 5695 ‘inosine 5’-phosphate degradation’ was the most significant function; it is involved in recycling nucleotide components. The second most important function of FTS soils is the ‘superpathway of glyoxylate bypass and TCA’ responsible for cellular energy metabolism and carbon utilization. The pathway ‘TCA cycle I (prokaryotic)’ is involved in aerobic respiration that generates energy and biosynthesis precursors. The pathway PWY0-166 ‘superpathway of pyrimidine deoxyribonucleotides de novo biosynthesis (E. coli)’ is involved in the synthesis of precursors for the synthesis of deoxyribonucleotides, which are essential for DNA replication and repair. The pathway PWY-6700 ‘queuosine biosynthesis I’ is important for the proper functioning of tRNAs, ensuring accurate translation during protein synthesis. The pathway PWY-8073 ‘lipid iva biosynthesis’ is responsible for the synthesis of lipopolysaccharides, which are essential components of the outer membrane in Gram-negative bacteria. Finally, PCoA of the metabolic pathways within each site revealed significant variability in their func- tional profiles (Fig. 6D). Although there was a degree of consistency in the metabolic pathways among replicates within the same site, these profiles exhibited substantial dissimilarity when comparing different sites. Notably, the metabolic pathways in MTS soils displayed a relatively close resemblance, whereas those in FTS soils exhibited a remarkable degree of heterogeneity. Scientific Reports | (2024) 14:3963 | https://doi.org/10.1038/s41598-024-54652-2 6 Vol:.(1234567890) www.nature.com/scientificreports/ Figure 6. Functional profile for traditional tillage systems inferred by HUMAnN3. (A) Metabolic pathways grouped on superclass1 hierarchy. (B) Metabolic pathways grouped on superclass2 hierarchy. (C) LEfSe analysis to determine the most differentially abundant MetaCyc pathways. (D) Principal Coordinates Analysis based on MetaCyc pathways. Discussion In our research, we documented that in the central highlands of Peru (Huanuco) traditional tillage systems such as ‘chiwa’ and ‘barbecho’ are still used. These traditional tillage systems have been documented since the pre-Inca p eriod8,9. It is important to document the continued use of these traditional tillage systems because they are part of the agricultural heritage shared between parents and children. In Peru, these systems have been developed for sowing crops, such as papa10, and mitigating the effects of climate instability, and improving seed stocks, and food reserves14–16. Minimal tillage is considered a key system in agricultural sustainability, because it reduces the negative effects of conventional agricultural systems and has beneficial effects on the physical, chemical, and biological properties of the soil17. This system also offers the advantage of lowering energy use and production costs18, impacting positively on carbon sequestration19, and improving soil texture and nutrient status20. Although we did not find any statistically significant differences in the physicochemical properties of potato soils under traditional tillage systems, the literature indicates these differences. This could be attributed to the low sampling effort in our study. The fact that soils under MTS had a higher SOM content than those under FTS suggests a high potential for soil carbon storage19. This is also because these soils had no crop rotation during the last two years, and SOM accumulated. SOM comprises 45–60% of its mass as soil organic carbon (SOC), which is a principal source of energy for soil microbial communities21,22 and thus plays an important role in soil f ertility5,6. Intensive tillage systems might affect SOC content in agricultural soil22. Therefore, promoting ’chiwa’ tillage system plays a crucial role in the global carbon cycle, allowing the conservation of agroecosystem functions in response to climate c hange16,23. Additionally, the nitrogen content was higher in soils under ‘chiwa’ soils due to the possibility that residue decomposition could contribute significant amounts of N to the soil20. Furthermore, we registered that both traditional tillage systems had acidic soils which may reflect an inadequate use of fertilizers in the potato c ultivation24, or the geological origins of these s oils25, or soil leaching as a result of irrigation or r ainfall26. On the other hand, the phosphorus content was similar between the systems; however, these soils can be classified as low-phosphorus soils, which is typical of acidic soils where the mineralization of phosphorus is l imited27. Potassium content was higher in MTS soils as reported by previous s tudies28,29. Clay, silt, and sand are part of soil aggregates, which are important in the function of soil and are produced by mechanical or human activities in cultivated s oil30. Some studies indicate that clay content is a predictor of SOM content31; however, other reports indicate that other physicochemical parameters are strongly related to SOM c ontent32. We found that soils under MTS had lower clay content. This is in contrast with other studies that indicated that the clay percentage was higher in no-tillage s ystems33. However, this low clay content is relatively common in the central Andean h ighlands10. In addition, traditional tillage system of ‘chiwa’ may be considered within Scientific Reports | (2024) 14:3963 | https://doi.org/10.1038/s41598-024-54652-2 7 Vol.:(0123456789) www.nature.com/scientificreports/ conservation agriculture, since soils undergo minimal mechanical alteration, leaving stumbles on the ground to protect the soil from sunlight and rainfall, enhance the biological activities of the soil, and reduce soil erosion17. Nevertheless, additional studies are needed to identify the factors that influence SOC, N, P, and K content and their relationship with traditional potato sowing systems in the central Andean highlands. We observed that traditional soil tillage systems had no discernible impact on soil bacterial richness. Our findings align with previous studies that also reported no significant change in bacterial richness when comparing different tillage s ystems28. However, some studies found lower bacterial richness under no-tillage s ystems34, and others suggested higher bacterial r ichness35 when compared to conventional tillage. This observed variation may be attributed to factors such as functional redundancy among different bacterial s pecies36. Additionally, bacte- rial richness could be influenced by factors such as the elevation of the sampling sites37, and soil acidity, which is a principal determinant of low bacterial r ichness38. Significantly greater diversity and evenness in bacteria communities were evident in MTS soils, likely resulting from the heterogeneity from higher SOM content and an undisturbed environment. Similar patterns have been observed in Chinese conventional tillage s ystems33, and in a global scale39. Regarding beta diversity, the Bray–Curtis dissimilarity index grouped the samples from MTS soils together, indicating a degree of similarity, while FTS soils displayed heterogeneity and did not cluster together. This divergence could be attributed to stochastic processes and a more dynamic environment within FTS soils. Furthermore, it is important to acknowledge that soil microbiomes can exhibit significant variation even over short distances40. The lack of clustering among similar tillage treatments in our samples may suggest a high level of heterogeneity Moreover, similar tillage treatments were not clustered together, which may indicate a high degree of heterogeneity in our samples, as observed within the agricultural soil m icrobiomes41. Pseudomonadota, Actinomycetota, Acidobacteriodota, Planctomycetota, Bacteriodota, and Bacillota were the most abundant phyla in all soil samples under traditional tillage systems, which is similar to other reports in agricultural s oils33,34. To understand the abundance of bacterial groups, we will use concepts based on the r and k strategist t heory42. We found that soil under ‘chiwa’ systems had 18% SOM, while soil under ‘barbecho’ systems had 4%, with this characteristic of SOM we could assume that soils under ‘chiwa’ and ‘barbecho’ systems could be a putative environment for copiotrophic and oligotrophic microorganisms respectively. In the soils under ‘chiwa’ systems, we found microorganisms reported as copiotrophic such as Actinomycetota, Bacteriodota, Bacillota, and Pseudomonadota, of which only the Bacillota taxa showed significant differences. Actinomycetota, Bacteriodota, and Pseudomonadota have been reported in the rhizosphere of potato p lants13,42; moreover, Pseudomonadota, and Bacteriodota have also been reported to have a positive relationship with residual potato y ield43. At the class level, Bacilli, Betaproteobacteria, and Flavobacteria were more abundant in soils under MTS, with the first two being significant. Additionally, Bacilli, which are mainly obligate a erobes44, and Betaproteobacteria respond well to labile carbon sources in the s oil42. These taxa are considered copiotrophic bacteria because of their fast growth, prosperity in nutrient-rich environments, lower carbon use e fficiency45, and ability to decompose labile soils carbon reservoir46. In the soils under ‘barbecho’ systems, the phylum Pseudomonadota and the classes Alphapro- teobacteria (copiotrophic) and Gammaproteobacteria (oligotrophic) were more abundant but lacked statistical significance. These findings indicate that although ’barbecho’ systems exhibit lower levels of SOM, this reduction does not hinder the proliferation of copiotrophic microorganisms such as Pseudomonata, indicating carbon availability. Nevertheless, a more targeted and comprehensive study is required to draw a definitive conclusion regarding the dynamics of copiotrophic and oligotrophic microorganisms in both ’chiwa’ and ’barbecho’ systems. A substantial number of genera and species were identified as shared components in both tillage systems. The core microbiome is intimately linked to networks that play a critical role in facilitating healthy plant microbiome i nteractions1. The higher number of shared genera and species observed in our study could be attributed to the evaluation of only one vegetation stage, in contrast to prior investigations on potatoes that considered different cultivars and encompassed four vegetation stages13. The potato rhizosphere microbiome is partially influenced by field site and climatic c onditions47,48. Moreover, the effects of soil tillage systems on bacterial communities, as discussed in the previous section, highlight the role of soil properties in this context. In smaller proportions, the microbiome may be the result of opportunistic bacteria13,41 and cultivar-dependent bacteria47. Applying the criterion of focusing on bacterial genera with a minimum relative abundance of 0.1%38 revealed a core microbiome consisting of 70 genera. Compared with the Potato Core Microbiome proposed by Pfeiffer13, our analysis identified nine common genera. Among the genera shared in our study, Bradyrhizobium was included in the Potato Stable Core Microbiome13. Bradyrhizobium is known for its association with nitrogen fixation symbiosis in l egumes49 and has been observed to be more abundant in potato fields with low disease incidence50. Another prominent member of our core microbiome is Sphingomonas, a genus also detected in fields with a low disease incidence50. Sphingomonas plays a dual role by promoting plant growth and enhancing plant resistance to p athogens51. Metagenomics has been used to identify potential bioindicators of agricultural management effects on soils under different tillage s ystems52,53. Pseudomonas, Rhodoferax, Silvimonas, Massilia, and Rhodococcus were detected as bioindicator genera in soils under MTS. The first four genera belong to the phylum Pseudomonadota, which is recognized as a copiotrophic taxon, characterized by its ability to efficiently utilize available carbon sources54 and exhibit a positive increase in bacterial biomass when plant residues are present55,56. Additionally, Pseudomonadota plays an essential role in carbon, nitrogen, and sulfur cycles12,57, and is considered a potential bioindicator of agricultural management effects in no-till s oils53. These characteristics could also explain the positive correlation with SOM, N, and K content of these four genera. Whereas Cautenulispora, a member of Actinomycetota phylum, was detected as a bioindicator of soils under FTS. Actinomycetota is considered an oligotrophic phylum and has been suggested as a microbial bioindicator of management effects in conventional tillage soils53. Catelunispora has been reported in acidic forest soils and paddy fi elds58; however, to our knowledge, this is the first report of these bacteria in soils associated with potato cultivation. Scientific Reports | (2024) 14:3963 | https://doi.org/10.1038/s41598-024-54652-2 8 Vol:.(1234567890) www.nature.com/scientificreports/ A comprehensive understanding of the metabolic pathways is essential for assessing the functional diversity inherent in traditional soil tillage s ystems36. Functionality in soil ecosystems is intricately linked to crop produc- tion and the overall health of these environments2,3. Both traditional tillage systems exhibit similar functional patterns at the ’superclass 1’ level, with biosynthesis emerging as the predominant class pathway. Biosynthesis plays a central role in the functional profile of microorganisms in agricultural s oils59. When comparing different functional pathways, MTS soils were associated with anaerobic conditions, with these pathways playing vital roles in processes such as carbon fixation and energy generation. These pathways are fundamental to primary metabolic processes, and their abundance can be attributed to SOM content, which is known to play a pivotal role in carbon metabolism52. In contrast, FTS soils exhibited pathways involved in nucleotide component recycling, along with key functions associated with energy metabolism, DNA replication, tRNA functionality, and lipid synthesis. Although there was some consistency among replicates within the same site, MTS soils demonstrated a relatively close resemblance, whereas FTS soils exhibited a remarkable degree of functional heterogeneity. Only 7% (31 out of 435) were differentially expressed between traditional tillage systems; this small number of features indicates functional r edundancy59. Conclusions Our findings revealed higher diversity and evenness for bacteria at the community level inhabiting soil under minimal tillage system in comparison with soil under full tillage system. However, traditional potato tillage systems had no discernible impact on soil bacterial richness. Similar soil bacterial community composition was shown for minimal tillage system, but it was heterogeneous for full tillage system, with copiotrophic and oligotrophic lifestyle microorganisms such as Pseudomonadota phylum, and Alphaproteobacteria and Gam- maproteobacteria classes. This soil bacterial community composition under full tillage system may be attributed to stochastic processes related to activities of biosynthesis and degradation and a more dynamic environment within this tillage system. Moreover, this research opened the possibilities to explore microbial bioindicators for minimal and full tillage systems, which are related to healthy of soil, and they can be used to propose adequate tillage systems for the sowing of potatoes in Peru. These outcomes represent the first advance in the understand- ing of the ecology of soils in the Peruvian highlands under traditional tillage systems, which are important for the production systems of potatoes used by Peruvian farmers. Methods Agricultural fields description and soil sampling The soil samples were collected from the Department of Huanuco in June 2017 (Fig. S2). Huanuco has an annual mean temperature ranging from 13.4 to 25.4 °C and an average precipitation of 38.1 mm per month. Peruvian farmers from two provinces of Huanuco used traditional tillage systems to sow potatoes: (1) ‘chiwa’ or MTS was identified in the province of Ambo, located between 3791 and 3881 masl, and this system was used for only sow- ing landraces potatoes; and (2) “barbecho’” or FTS was identified in the province of Yarowilca, located between 3648 and 3969 masl, and it was used for the sowing of improved potatoes (Fig. S2). Two agricultural fields were identified for each traditional tillage system. Soil samples were collected at harvest from a depth of 0–10 cm. For each field, nine random soil samples were collected. These samples were then mixed to obtain a composite soil sample, which was placed in a sterile Whirl–Pak bag for further analysis. Owing to the high level of similarity between the physicochemical parameters in the pre-analysis, we only examined one composite sample of soil per field for physicochemical characterization. For DNA sequencing, we considered two soil repetitions from each composite sample per field since our sequencing depth was required to be over 11 million reads and could have a good performance on species assignment; besides this depth is closer to the recommended sequencing d epth60. Soil sampling was approved by the Servicio Nacional Forestal y de Fauna Silvestre (SERFOR) (permit number:151-2017-122 SERFOR/DGGSPFFS). Physicochemical analysis of soil samples Physicochemical analysis was performed using the following parameters: pH, soil organic matter (SOM, %), nitrogen content (N, %), phosphorus content (P, ppm), potassium content (K, ppm), and texture (% of sand, clay, and silt). The pH determination was conducted using 10 g of soil. SOM was determined through the Walkey- Black method61. Nitrogen content was analyzed using the Kjeldahl method62. Phosphorus content was assessed using the Bray and Kurtz method63. Potassium content was determined using the spectrophotometric method. The soil texture was determined following the method outlined by B ouyoucos64. These analyses were carried out in the Laboratorio de Suelos of Estación Experimental Santa Ana of the Instituto Nacional de Innovación Agraria (INIA). DNA extraction and shotgun sequencing Before DNA extraction, 500 g of the soil was dried at room temperature and sieved sequentially using 850, 500, and 212 µm meshes. One gram of sieved soil sample was used for DNA extraction using the PowerMax® Soil DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA, USA), following the manufacturer’s protocol. The concentration and purity of the DNA were determined using an Epoch Biotek spectrophotometer, considering samples with absorbance values in the A260/280 and A260/230 ratio between 1.8 ~ 2.0 and 2.0 ~ 2.2, respectively. The integrity of the DNA was evaluated by electrophoresis on a 1% (w/v) agarose gel with 1X TBE buffer (Tris-HCl, boric acid, EDTA; pH 8.0) stained with ethidium bromide. For each well, 8 µL of loading buffer (bromophenol blue, xylene cyanol, orange G) was mixed with 2 µL of DNA and loaded into a horizontal electrophoretic chamber run in 1X TBE buffer at 100 V for 25 min. The results were recorded using a Bio-Rad transilluminator under UV light. Eight DNA sequencing libraries were prepared using Nextera XT DNA Sample Preparation Kit (Illumina, San Scientific Reports | (2024) 14:3963 | https://doi.org/10.1038/s41598-024-54652-2 9 Vol.:(0123456789) www.nature.com/scientificreports/ Diego, CA, USA) according to the manufacturer’s protocol using the Nextera index kit (Illumina, San Diego, CA, USA). Subsequent to indexing and clean-up processes, the libraries were pooled to an equimolar concentration of 1 nM and sequenced on the NextSeq 500 sequencing platform (Illumina, San Diego, CA, USA) to generate 2 × 150 bp paired-end reads. An average of 11.6 million raw paired-end reads were produced per soil sample; the minimum number of recorded reads for a single sample was 9.23 million, while the maximum reached 12.8 million paired-end reads (Table S2). Taxonomic assignation The metagenomic data files were quality-filtered using fastp v0.23.265 with the following parameters “-c -q 30 -n 0 -l 50”. Taxonomic assignment of the filtered reads was performed using Kraken2 v2.1.23466 using the following database “Standard plus protozoa, fungi & plant 20230605” with a 0.15 confidence t hreshold67. Abundances at taxonomic levels from species to phylum were reassessed using Bracken v2.868. Functional profiling assignment To recover the functional profile, the quality filtered, and concatenated shotgun metagenomic reads were classi- fied to UniProt Reference Clusters 50 (UniRef50) with the ‘uniref50_201901b’ database69 gene families, pathway abundances were normalized to relative abundances, by-passed the taxonomic classifier option, then mapped gene families to MetaCyc reactions and metabolic pathways with the ‘full_mapping_v201901b’ database using HUMAnN v3.6.170. Statistical analysis All statistical analyses were performed in R software v4.2.2 using factoextra v1.0.7, phyloseq v1.40.0, ggplot2 v3.4.0, microeco v0.14.2, hillR v0.5.2, file2meco v0.5.0, and microbiome v1.18.0 packages. For physicochemical analysis, the mean and ± standard error for each parameter and tillage system were calculated, as well as a Prin- cipal Component Analysis (PCA) to assess the relationship between the physicochemical parameters and the soil traditional tillage system. For alpha and diversity analysis, a phyloseq object was built with the taxonomic assignment matrix obtained from Kraken2, and then filtered by the bacterial domain for further analysis. Rich- ness, Shannon effective numbers, and Simpson effective numbers were calculated for each soil sample. For beta diversity, a weighted UniFrac distance matrix was employed in a principal coordinate analysis (PCoA), while a Bray–Curtis dissimilarity index was used to generate a dendrogram; subsequently, a permutational multivariate analysis of variance (PERMANOVA) was conducted using microeco, based on the adonis2 function from the vegan R package. The taxonomic composition at the phylum, class, and genus levels was assessed and plotted with relative abundance values. The core microbiome was defined by a minimum relative abundance threshold of 0.1% and an occurrence of 90% in all samples at the genus and species levels. The core microbiome was com- pared with the results of Pfeiffer et al.13 at the genus level and visualized using a Venn diagram. Soil tillage system bioindicators at the genus level were determined using linear discriminant analysis (LDA) effect size (LEfSe) analysis with a p-value of 0.05. The bacterial genera bioindicators were selected based on LDA for further analy- sis, and a correlation analysis between physicochemical parameters and bacterial bioindicators was performed. For functional analysis, HUMAnN3 metagenomic functional results were transformed into a microtable object. MetaCyc reactions were assessed at superclass1 and superclass2 categories of metabolic pathways, and then the functional different pathways were determined through a LEfSe analysis with a p-value of 0.05, and were filtered with an LDA score of 1. Data availability The sequencing data are available at the NCBI Sequence Read Archive (SRA) under the Project Num- ber PRJNA886760. Accession Numbers for MTS1 are SRR21793830, SRR21793833; MTS2, SRR21793827, SRR21793828; FTS1, SRR21793824, SRR21793825; and FTS2, SRR21793831, SRR21793832. Received: 9 November 2023; Accepted: 15 February 2024 References 1. Toju, H. et al. Core microbiomes for sustainable agroecosystems. Nat. Plants 4, 247–257 (2018). 2. Ray, P., Lakshmanan, V., Labbé, J. L. & Craven, K. D. Microbe to microbiome: A paradigm shift in the application of microorgan- isms for sustainable agriculture. Front. Microbiol. 11, 622926 (2020). 3. Chen, Q.-L., Ding, J., Zhu, Y.-G., He, J.-Z. & Hu, H.-W. Soil bacterial taxonomic diversity is critical to maintaining the plant pro- ductivity. Environ. Int. 140, 105766 (2020). 4. Griffiths, B. S. & Philippot, L. Insights into the resistance and resilience of the soil microbial community. FEMS Microbiol. Rev. 37, 112–129 (2013). 5. Xiong, C. & Lu, Y. Microbiomes in agroecosystem: Diversity, function and assembly mechanisms. Environ. Microbiol. Rep. 14, 833–849 (2022). 6. Hardigan, M. A. et al. Genome diversity of tuber-bearing Solanum uncovers complex evolutionary history and targets of domes- tication in the cultivated potato. Proc. Natl. Acad. Sci. U.S.A. 114, E9999–E10008 (2017). 7. FAO (Food and Agriculture Organization of the United Nations). Sistemas Importantes del Patrimonio Agrícola Mundial (SIPAM). https://w ww.f ao.o rg/g iahs/g iahsa round thewo rld/d esign ated-s ites/l atin-a meric a-a nd-t he-c aribb ean/a gricu ltura-a ndina/d etail ed- infor mation/ es/. 8. Gade, D. W. Landscape, system, and identity in the post-conquest andes. Ann. Assoc. Am. Geogr. 82, 460–477 (1992). 9. Horkheimer, H. Alimentación y obtención de alimentos en el Perú prehispánico (Instituto Nacional de Cultura, 2004). 10. Oswald, A., De Haan, S., Sanchez, J. & Ccanto, R. The complexity of simple tillage systems. J. Agric. Sci. 147, 399–410 (2009). Scientific Reports | (2024) 14:3963 | https://doi.org/10.1038/s41598-024-54652-2 10 Vol:.(1234567890) www.nature.com/scientificreports/ 11. Arce, et al. The spatial-temporal dynamics of potato agrobiodiversity in the highlands of central Peru: A case study of smallholder management across farming landscapes. Land 8, 169 (2019). 12. Souza, R. C., Cantão, M. E., Vasconcelos, A. T. R., Nogueira, M. A. & Hungria, M. Soil metagenomics reveals differences under conventional and no-tillage with crop rotation or succession. Appl. Soil Ecol. 72, 49–61 (2013). 13. Pfeiffer, S. et al. Rhizosphere microbiomes of potato cultivated in the High Andes show stable and dynamic core microbiomes with different responses to plant development. FEMS Microbiol. Ecol. 93, fiw242 (2017). 14. Perez, C. et al. Climate change in the high Andes: Implications and adaptation strategies for small-scale farmers. Int. J. Environ. Cult. Econ. Soc. Sustain. Annu. Rev. 6, 71–88 (2010). 15. De Haan, S. & Juárez, H. Land use and potato genetic resources in Huancavelica, central Peru. J. Land Use Sci. 5, 179–195 (2010). 16. Rolando, J. L. et al. Key ecosystem services and ecological intensification of agriculture in the tropical high-Andean Puna as affected by land-use and climate changes. Agric. Ecosyst. Environ. 236, 221–233 (2017). 17. Hobbs, P. R., Sayre, K. & Gupta, R. The role of conservation agriculture in sustainable agriculture. Philos. Trans. R. Soc. B Biol. Sci. 363, 543–555 (2008). 18. Lithourgidis, A. S., Damalas, C. A. & Eleftherohorinos, I. G. Conservation tillage: A promising perspective for sustainable agri- culture in Greece. J. Sustain. Agric. 33, 85–95 (2009). 19. Zhang, S., Li, Q., Lü, Y., Zhang, X. & Liang, W. Contributions of soil biota to C sequestration varied with aggregate fractions under different tillage systems. Soil Biol. Biochem. 62, 147–156 (2013). 20. Wang, Q. et al. Soil chemical properties and microbial biomass after 16 years of no-tillage farming on the Loess Plateau, China. Geoderma 144, 502–508 (2008). 21. Lal, R. Soil health and carbon management. Food Energy Secur. 5, 212–222 (2016). 22. Haddaway, N. R. et al. How does tillage intensity affect soil organic carbon? A systematic review. Environ. Evid. 6, 30 (2017). 23. Zhao, F. et al. Projected soil organic carbon loss in response to climate warming and soil water content in a loess watershed. Carbon Balance Manag. 16, 24 (2021). 24. Grados, D. & Schrevens, E. Multidimensional analysis of environmental impacts from potato agricultural production in the Peruvian Central Andes. Sci. Total Environ. 663, 927–934 (2019). 25. Pérez-Trujillo, E., Asado-Hurtado, A. M. & Vega-Jara, L. Relación del contenido de materia orgánica con el pH de los análisis de suelo en cinco provincias de Huánuco. Rev. Investig. Agrar. 4, 46–54 (2022). 26. Sandor, J. A. & Eash, N. S. Ancient agricultural soils in the Andes of Southern Peru. Soil Sci. Soc. Am. J. 59, 170–179 (1995). 27. Wilcke, W. et al. Temporal trends of phosphorus cycling in a tropical montane forest in Ecuador during 14 years. J. Geophys. Res. Biogeosci. 124, 1370–1386 (2019). 28. Kim, N., Zabaloy, M. C., Riggins, C. W., Rodríguez-Zas, S. & Villamil, M. B. Microbial shifts following five years of cover cropping and tillage practices in fertile agroecosystems. Microorganisms 8, 1773 (2020). 29. Haruna, S. I. & Nkongolo, N. V. Influence of cover crop, tillage, and crop rotation management on soil nutrients. Agriculture 10, 225 (2020). 30. Sweeney, M. R. Dust emission processes. In Treatise on Geomorphology 235–258 (Elsevier, 2022). https:// doi. org/ 10. 1016/ B978- 0-1 2- 818234-5 . 00015-8. 31. Fierer, N. & Schimel, J. P. Effects of drying–rewetting frequency on soil carbon and nitrogen transformations. Soil Biol. Biochem. 34, 777–787 (2002). 32. Rasmussen, C. et al. Beyond clay: Towards an improved set of variables for predicting soil organic matter content. Biogeochemistry 137, 297–306 (2018). 33. Wang, Z., Liu, L., Chen, Q., Wen, X. & Liao, Y. Conservation tillage increases soil bacterial diversity in the dryland of northern China. Agron. Sustain. Dev. 36, 28 (2016). 34. Navarro-Noya, Y. E. et al. Relative impacts of tillage, residue management and crop-rotation on soil bacterial communities in a semi-arid agroecosystem. Soil Biol. Biochem. 65, 86–95 (2013). 35. Legrand, F. et al. Effect of tillage and static abiotic soil properties on microbial diversity. Appl. Soil Ecol. 132, 135–145 (2018). 36. Souza, R. C. et al. Metagenomic analysis reveals microbial functional redundancies and specificities in a soil under different tillage and crop-management regimes. Appl. Soil Ecol. 86, 106–112 (2015). 37. Nottingham, A. T. et al. Microbes follow Humboldt: Temperature drives plant and soil microbial diversity patterns from the Amazon to the Andes. Ecology 99, 2455–2466 (2018). 38. Wolińska, A. Metagenomic achievements in microbial diversity determination in croplands. In Microbial Diversity in the Genomic Era 15–35 (Elsevier, 2019). https:// doi. org/ 10. 1016/ B978-0- 12- 814849- 5. 00002-2. 39. Li, Y. et al. Effect of no-tillage on soil bacterial and fungal community diversity: A meta-analysis. Soil Tillage Res. 204, 104721 (2020). 40. Walters, K. E. & Martiny, J. B. H. Alpha-, beta-, and gamma-diversity of bacteria varies across habitats. PLoS ONE 15, e0233872 (2020). 41. O’Brien, S. L. et al. Spatial scale drives patterns in soil bacterial diversity. Environ. Microbiol. 18, 2039–2051 (2016). 42. Fierer, N., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364 (2007). 43. Jeanne, T., Parent, S. -É. & Hogue, R. Using a soil bacterial species balance index to estimate potato crop productivity. PLoS ONE 14, e0214089 (2019). 44. Zhao, J. et al. Pyrosequencing reveals contrasting soil bacterial diversity and community structure of two main winter wheat crop- ping systems in China. Microb. Ecol. 67, 443–453 (2014). 45. Rodrigues, J. L. M. et al. Conversion of the Amazon rainforest to agriculture results in biotic homogenization of soil bacterial communities. Proc. Natl. Acad. Sci. U.S.A. 110, 988–993 (2013). 46. Yang, Y., Chen, Q., Zhou, Y., Yu, W. & Shi, Z. Soil bacterial community composition and function play roles in soil carbon balance in alpine timberline ecosystems. J. Soils Sediments https://d oi. org/1 0. 1007/s 11368-0 23-0 3627-3 (2023). 47. Weinert, N. et al. PhyloChip hybridization uncovered an enormous bacterial diversity in the rhizosphere of different potato cul- tivars: many common and few cultivar-dependent taxa. FEMS Microbiol. Ecol. 75, 497–506 (2011). 48. Cangioli, L. et al. Effect of site and phenological status on the potato bacterial rhizomicrobiota. Microorganisms 10, 1743 (2022). 49. Durán, D. et al. Biodiversity of slow-growing Rhizobia: The Genus Bradyrhizobium. In Beneficial Plant-microbial Interactions (ed. González-López, J.) 21–46 (CRC Press, 2013). https://d oi.o rg/1 0. 1201/ b15251-3. 50. Kurm, V., Mendes, O., Gros, J. & van der Wolf, J. Data for ‘Potato tuber origin and microbial composition determines resistance against soft rot Pectobacteriacea. Eur. J. Plant Pathol. https:// doi. org/1 0. 4121/1 92371 46. V1 (2022). 51. Mazoyon, C. et al. Sphingomonas sediminicola Dae20 is a highly promising beneficial bacteria for crop biostimulation due to its positive effects on plant growth and development. Microorganisms 11, 2061 (2023). 52. Carbonetto, B., Rascovan, N., Álvarez, R., Mentaberry, A. & Vázquez, M. P. Structure, composition and metagenomic profile of soil microbiomes associated to agricultural land use and tillage systems in Argentine Pampas. PLoS ONE 9, e99949 (2014). 53. Behnke, G. D. et al. Soil Microbial indicators within rotations and tillage systems. Microorganisms 9, 1244 (2021). 54. Jenkins, S. N. et al. Taxon-specific responses of soil bacteria to the addition of low level C inputs. Soil Biol. Biochem. 42, 1624–1631 (2010). 55. Fierer, N. et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Natl. Acad. Sci. U.S.A. 109, 21390–21395 (2012). Scientific Reports | (2024) 14:3963 | https://doi.org/10.1038/s41598-024-54652-2 11 Vol.:(0123456789) www.nature.com/scientificreports/ 56. Franchini, J., Crispino, C., Souza, R., Torres, E. & Hungria, M. Microbiological parameters as indicators of soil quality under vari- ous soil management and crop rotation systems in southern Brazil. Soil Tillage Res. 92, 18–29 (2007). 57. Mendes, L. W., Kuramae, E. E., Navarrete, A. A., van Veen, J. A. & Tsai, S. M. Taxonomical and functional microbial community selection in soybean rhizosphere. ISME J. 8, 1577–1587 (2014). 58. Tamura, T., Ishida, Y., Otoguro, M. & Suzuki, K. Catenulispora subtropica sp. Nov. and Catenulispora yoronensis sp. nov.. Int. J. Syst. Evol. Microbiol. 58, 1552–1555 (2008). 59. Navarro-Noya, Y. E. et al. Bacterial communities in the rhizosphere at different growth stages of maize cultivated in soil under conventional and conservation agricultural practices. Microbiol. Spectr. 10, e0183421 (2022). 60. Liu, J., Wang, X., Xie, H., Zhong, Q. & Xia, Y. Analysis and evaluation of different sequencing depths from 5 to 20 million reads in shotgun metagenomic sequencing, with optimal minimum depth being recommended. Genome 65, 491–504 (2022). 61. Walkley, A. & Black, I. A. An examination of the Degtjareff method for determining soil organic matter, and a proposed modifica- tion of the chromic acid titration method. Soil Sci. 37, 29 (1934). 62. Kjeldahl, J. Neue Methode zur Bestimmung des Stickstoffs in organischen Körpern. Fresenius. Zeitschrift f. Anal. Chemie 22, 366–382 (1883). 63. Bray, R. H. & Kurtz, L. T. Determination of total, organic and available forms of phosphorus in soils. Soil Sci. 59, 39 (1945). 64. Bouyoucos, G. J. Directions for making mechanical analyses of soils by the hydrometer method. Soil Sci. 42, 225 (1936). 65. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinforma. Oxf. Engl. 34, i884–i890 (2018). 66. Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019). 67. Wright, R. J., Comeau, A. M. & Langille, M. G. I. From defaults to databases: parameter and database choice dramatically impact the performance of metagenomic taxonomic classification tools. Microbial. Genomics https://d oi.o rg/1 0.1 099/m gen.0.0 00949 (2023). 68. Lu, J., Breitwieser, F. P., Thielen, P. & Salzberg, S. L. Bracken: Estimating species abundance in metagenomics data. PeerJ Comput. Sci. 3, e104 (2017). 69. Suzek, B. E. et al. UniRef clusters: A comprehensive and scalable alternative for improving sequence similarity searches. Bioinforma. Oxf. Engl. 31, 926–932 (2015). 70. Beghini, F. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. elife 10, e65088. https:// doi. org/ 10. 1101/ 2020. 11.1 9.3 88223 (2020). Acknowledgements We would like to thank to the Prof. Dr. Siu Mui Tsai of the Universidad de São Paulo. Also, to our colleagues at Laboratorio de Semillas and Laboratorio de Biología Molecular y Genómica—DRGB of INIA. Author contributions A.L.G.S. conceptualized the project, designed the methodology, executed the experiment, and project adminis- tration. L.C.D.N. performed the data analysis. A.L.G.S., L.D.C.N., and A.A.N. interpreted the results, wrote and edited the manuscript. A.L.G.S. and R.C.G.A. identified the agricultural fields and collected the soil samples. D.L.G.R. provided feedback on the manuscript. All authors approved the manuscript. Funding We would like to thank the Instituto Nacional de Innovación Agraria (INIA). Competing interests The authors declare no competing interests. Additional information Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1038/ s41598- 024- 54652-2. Correspondence and requests for materials should be addressed to A.L.G.-S. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons.o rg/ licen ses/b y/4. 0/. © The Author(s) 2024 Scientific Reports | (2024) 14:3963 | https://doi.org/10.1038/s41598-024-54652-2 12 Vol:.(1234567890)