Organic farming significantly improves microbial community structure, network complexity, and functional diversity in the Gannan navel orange orchard

Organic farming significantly improves microbial community structure, network complexity, and functional diversity in the Gannan navel orange orchard | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Organic farming significantly improves microbial community structure, network complexity, and functional diversity in the Gannan navel orange orchard Lianlian Liu, Muhammad Atif Muneer, Yanting Zhong, Boyi He, Xuexian Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4933005/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Aug, 2025 Read the published version in BMC Microbiology → Version 1 posted 4 You are reading this latest preprint version Abstract In recent years, improper agricultural management practices have led to the loss of biodiversity and poor fruit quality in orchards. Converting conventional farming to organic farming is an environmentally responsible approach to improving sustainable fruit production. However, questions remain regarding how the microbial community responds to different farming practices in citrus trees. Here, we explored and compared the microbial community structure and functional diversity of the Gannan navel orange orchard under organic and conventional farming using 16S rRNA gene sequencing and Biolog Eco-Plate analysis. The results showed that the microbial diversity (α-diversity index) under organic farming was higher than that under conventional farming, especially in the soil and fruit. The predominant bacteria found in the soil, root, leaf, and fruit were Proteobacteria, Actinobacteria, Bacteroidetes, Acidobacteria, and Firmicutes. However, distinct abundance patterns were observed under different farming practices. Actinobacteria, Bacteroidetes, and Firmicutes were more abundant in root and fruit compartments under organic farming, indicating that organic farming promotes the enrichment of copiotrophic bacteria (r-strategists). Furthermore, organic farming resulted in a considerable increase in the relative abundance of Burkholderia and Streptomyces in root tissues (the genus level), indicating that organic farming probably favors the proliferation of beneficial microorganisms and antagonists of pathogenic species. Interestingly, organic farming exhibited a more complex microbial network. Biolog analysis further revealed higher functional diversity of the soil microbial community under organic farming when compared with that under conventional farming. These findings provide evidence that organic farming improves the microbial community structure and promotes its functional diversity in the citrus orchards, contributing to the overall health and production of the citrus crop. Gannan navel orange Conventional farming Organic farming Microbial community Functional diversity Carbon source utilization 1. Introduction Conventional agricultural practices, mostly reliant on chemical inputs, have made significant progress in agricultural production to ensure global food security in recent decades (Pretty, 2007 ; Cui et al., 2018 ; Renard and Tilman, 2019 ; Wang et al., 2021 ). Nevertheless, the heavy reliance on synthetic chemical fertilizers and pesticides has resulted in substantial negative externalities, such as biodiversity loss (Vitousek et al., 1997 ), soil erosion (Pimentel et al., 1995 ) or acidification (Guo et al., 2010 ), and air pollution (Liu et al., 2013 ; Raza et al., 2020 ). Additionally, there are detrimental effects on human health (Pimentel, 2005a; Rivera et al., 2017 ). Hence, it is necessary to explore alternative approaches to reducing dependency on agrochemicals, improving agricultural output, and achieving the equilibrium between productivity and sustainability (Khoiri et al., 2021 ). Agricultural management practices need to change to meet agriculture and food system development goals (Eyhorn et al., 2019 ). Organic farming, although not a silver bullet, seems to be a potential strategy for maintaining biodiversity and increasing crop production sustainability (Gonthier et al., 2014 ). It prevents negative environmental impacts of chemical inputs to improve overall sustainable production and agroecosystem health. Compared to conventional farming, organic farming applies organic fertilizers (e.g., manure, biochar, animal waste), biological control for pest management, and green manuring or grass mowing for weed control (Castaneda et al., 2018 ). Moreover, organic farming incorporates various techniques, such as intercropping and stubble-mulch, to improve soil fertility and maintain food productivity by nurturing a beneficial soil microbiome (Maeder et al., 2002 ; Pimentel et al., 2005b ; Xiang et al., 2023 ). Chemical pesticides or fertilizers are not allowed in organic farming; therefore, soil microorganisms perform a key role in nutrient mineralization, utilization, and cycling (Friedel et al., 2001 ). Hence, it is particular important to explore the potential of soil microbiomes in promoting agricultural sustainability. Organic farming management practices promote the abundance and diversity of most microorganisms (Lori et al., 2017 ). After conventional to organic farming conversion, soil microbial communities adapt to new conditions and function accordingly, such as improving soil microbial biomass (Santos et al., 2012 ), changing soil organic matter in labile fractions (Fließbach and Mäder, 2000 ), enhancing plant associations with beneficial microorganisms (Gosling et al., 2006 ), and proving positive regulations (Bei et al., 2023 ). Organic farming increases functional diversity of belowground microorganisms (i.e., soil and root niches) (Lupatini et al., 2016 ; Hartman et al., 2018 ) as well as aboveground microbial communities (leaves and fruits) (Leff and Fierer, 2013 ; Khoiri et al., 2021 ) as compared to conventional farming. Low-input farming systems are featured by the higher complexity and biodiversity of microbial networks compared to conventional farming systems (Hartmann et al., 2015 ; Banerjee et al., 2019 ). Changes in soil microorganisms and keystone taxa driven by organic fertilization also enhance the resistance and resilience against environmental disturbances through the diversified bacterial communities and copiotrophic bacterial assemblages (Luo et al., 2023 ). In this regard, manipulating the microbial community to enrich beneficial bacteria and reduce harmful bacteria might provide a basis for improving plant growth and agricultural sustainability (Hartmann et al., 2015 ). Although impacts of agricultural management on soil (Lupatini et al., 2016 ; Luo et al., 2023 ), rhizosphere (Hartman et al., 2018 ; Blundell et al., 2020 ), or phyllosphere microbiomes (Ottesen et al., 2009 ; Karlsson et al., 2017 ; Khoiri et al., 2021 ) have been studied, the overall microbial structure and functionality along the soil-plant continuum has been less well-studied. Citrus, one of the top three fruit crops worldwide (Wang, 2019 ), serves as an ideal model plant for microbial taxonomic, genomic, and functional studies (Xu et al., 2018 ). The Gannan navel orange is among the most prestigious fruits in China, with high internal quality in terms of sweetness, juice percentage, and high levels of vitamin C (Liu et al., 2010 ). However, the quality of orchard soils has recently declined due to improper agricultural management and adverse environmental factors. Deterioration of the eco-environment and disease challenges also negatively affect global citrus production (Zhou et al., 2021 ). In particular, soil acidification, which is worsened by intensive application of chemical nitrogen fertilizers, has emerged as a significant problem for citrus production in China (Chang et al., 2016). To date, most studies focus on harnessing citrus-microbiome interactions to address biotic and abiotic pressures (Zhang et al., 2017; Xu et al., 2018 ; Zhou et al., 2021 ). However, limited research has been conducted on microbial changes along the soil-plant continuum in citrus orchards under different agricultural management practices. In this study, we combined microbiological sequencing and Biolog-Eco microplate analysis to investigate (a) how different agricultural management practices affect the diversity and composition of microbial communities, (b) the characteristics of the core microbial community and its association with different farming practices, and (c) the effects of different management practices on the capacity of microbial communities to utilize carbon. 2. Materials and methods 2.1. Experimental site Orange orchards selected for conventional (C) and organic farming (O) study were located in Tanshi Village (26°07'58.9" N, 115°34'42.6" E), Yudu County, Jiangxi Province, China. The citrus variety was New Holland navel orange planted in 2013. This region has a humid subtropical climate with 1507 mm of annual precipitation and 1622 h of sunshine. The average annual temperature is 19.7℃, with the lowest in January (8.2℃) and the highest in July (29.7℃). Nine plots (3 trees/plot) were selected from each farming system as experimental replicates. For each tree, the soil sample was collected at 0–15 cm depth from the 4 ordinate directions along the dripline and then pooled as a single sample. Before fertilization, five pooled soil samples were taken from each farming system to measure their physicochemical characteristics (Table 1 ). Under conventional farming, compound fertilizers (2–3 kg per plant per year) and pesticides were applied, and the synthetic herbicide glufosinate-ammonium was used to control weeds. Organic fertilizers (compost of maize flour, rape cake, and brown sugar at a mass ratio of 25:75:1; 8–10 kg per plant per year) and plant extracts (azadirachtin, matrine, and d-limonene) and microbial biopesticides (spinosad) were applied for organic farming. Different from conventional farming, organic farming relied on cover crops and manual and mechanical weed management. Table 1 Effects of different agricultural management on the soil properties of citrus orchards (means ± standard deviation, n = 4). Soil organic matter (SOM), total nitrogen (TN), mineral nitrogen (N min ), available phosphorus (AP), available potassium (AK) Treatment pH (H 2 O) SOM (g kg − 1 ) TN (g kg − 1 ) N min (g kg − 1 ) AP (g kg − 1 ) AK (g kg − 1 ) Conventional farming 3.90a 14.10a 0.29a 83.98a 96.01a 149.03a Organic farming 5.04b 42.25b 0.77b 54.65a 123.88a 72.21b 2.2. Soil, root, leaf, and fruit sampling Citrus trees without symptoms of pests, diseases, and nutrient deficiencies were sampled at the fruit expansion stage in July 2022. We sampled nine orange trees as biological replications for each farming practice. The rhizosphere soil and root samples were taken at the depth of 10–15 cm. Briefly, the root was carefully shaken and gently brushed to remove loosely adherent soil. Roots were washed in the sterile phosphate-buffered saline (PBS) solution and gently agitated with sterile forceps to remove the soil from the root surface. The resulting soil was briefly centrifuged to collect and stored as rhizosphere soil (RS) at 4°C. Then, root samples were washed with sterilized water three times and wiped with paper towels. We sampled the non-rooted soil from the same plot as bulk soil (BS). The pooled soil or root samples from each tree were stored at -20°C for further analysis. Nine root and five bulk soil samples were used as independent biological replicates. Fruits of similar size (5–6 cm in diameter) and mature leaves (free of pests and diseases) closest to the fruit were selected from the external middle canopy in four directions (east, south, west, and north). Eight leaves and four fruits from each tree were harvested as a pooled leaf or fruit sample, with nine trees as independent biological replicates for each farming system. Samples were instantly placed in liquid nitrogen and then kept at − 80°C. 2.3. Functional diversity of the soil microbial community The Biolog Eco-Plate (Biolog Inc., CA, USA) was used to examine the functional diversity of rhizosphere and bulk soil microorganisms (Bei et al., 2018 ). Each plate contained 96 wells with three replicates of water control and 31 different carbon sources which were classified into six major categories: polymers, carbohydrates, phenolic acids, carboxylic acids, amino acids, and amines (Sala et al., 2010 ). Fresh rhizosphere (RS) or bulk (BS) soil (~ 10 g) was weighed in a 250 ml flask containing 90 ml of sterilized 0.85% NaCl solution, shaken at 200 rpm for 30 min, suspended, and diluted to 10 − 3 with 0.85% NaCl solution. Applied 150 µl of the diluted sample to the individual well of the 96-well microplate for 9-day incubation at 25°C and recorded the absorbance value every 24 h at 590 nm. The average well color development (AWCD) was calculated to assess the utilization abilities of carbon sources by the soil microbial community (Garland, 1996 ; Zhang et al., 2019 ). The 72-hour absorbance was used to analyze the Shannon-Weiner (H) (Garland and Mills, 1991 ; Hu et al., 2016 ), Simpson (D) (Zhang et al., 2019 ), Pielou evenness (J) (Hu et al., 2016 ), and Richness index (E) (Gryta et al., 2014 ) using the following equations: Shannon-Weiner index (H): H = −ΣP i ln(P i ) (1) Simpson index (D): D = 1-Σ(Pi) 2 (2) Pielou Evenness index (J): J = H/lnS (3) Richness index (E): E = H/lnSR (4) where P i is the ratio of the relative absorbance of well i to the total absorbance of all wells on a plate. S is the number of wells with color change. 2.4. DNA extraction, sequencing and data analyses The genomic DNA was isolated following the CTAB/SDS method (Saghai-Maroof et al., 1984 ). DNA concentration and purity were monitored using 1% agarose gels, and then diluted to 1 µg µL − 1 using aqua sterilisata. Bacterial 16S rRNA genes were amplified using the following primer set: 799F (5′-AACMGGATTAGATACCCKG-3′) and 1193R (5′-ACGTCATCCCCACCTTCC-3′). DNA libraries were generated using the TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) according to the manufacturer’s instructions and sequenced on the Illumina NovaSeq platform to generate 250-bp paired-end reads. Raw data were filtered by QIIME 2 (Quantitative Insights Into Microbial Ecology 2, version 2019.7) and its plug-ins (Bolyen et al., 2019). The operational taxonomic unit (OTU) was subjected to removal of low-quality reads and noises (Callahan et al., 2016 ), and taxonomically classified by comparing with the Silva database using the q2-feature-classifier (Bokulich et al., 2018 ). Specific packages in R version 3.6.3 were used for statistical analyses based on the feature or OTU table. 2.5. Statistical analysis The AWCD values, microbial abundance, and α-diversity data were all subject to the analysis of variance (ANOVA). Fisher's Least Significant Difference (LSD) was tested at the 5% level (SPSS version 26.0). Principal component analysis of the Biolog data (AWCD) was carried out using Origin 2019b. Effects of different farming practices on bacterial communities and carbon utilization were analyzed using the ComplexHeatmap package in R (version 4.3.1). The α-diversity indices (Chao, Shannon, Simpson, and Pielou index) were derived from the OTU data by using Vegan packages in R version 4.3.1, with the β-diversity generated using the Vegan package in R. Principal co-ordinate analysis was based on the Bray-Curtis distance (Hamady et al., 2010 ). Bacterial OTUs with an average relative abundance of less than 0.01% were removed from the OTU table. The computation of pairwise similarity matrix was based on Spearman correlation with P -value adjusted using the BH method. The igraph package was used to calculate topological features of co-occurrence networks (Csardi and Nepusz, 2006 ), including the number of edges and nodes, positive and negative edges, network density, average degree and path length, network diameter, clustering coefficients, betweenness centralization, and modularity, which were illustrated using the Gephi software (0.10.1). 3. Results 3.1. Bacterial richness, diversity, and community composition under different agricultural management practices in citrus orchards The rarefaction curve gradually leveled off and the number of OTUs reached its peak level with the increase of sequencing depth, together with the gradually stabilized Shannon index curve (Fig. 1), indicating reliable sampling and reasonable sample size to represent the bacterial communities in this study. Aboveground (fruit and leaf) bacterial communities had lower levels of diversity compared to belowground (root and soil) communities (Fig. 1A). The Chao1 and Ace indices represent the richness of the bacterial community, Shannon and Simpson's indices indicate the bacterial diversity, and the Pielou's Evenness index measures bacterial community evenness (Sun et al., 2015 ; Zhu et al., 2020 ). The α-diversity indices of bacterial communities significantly differed ( P < 0.05) under conventional and organic farming. The soil bacterial community under organic farming (O_Soil) exhibited higher richness, diversity, and evenness compared to other microbial communities. Specifically, the Shannon index of C_Fruit, O_Fruit, C_Leaf, O_Leaf, C_Root, O_Root, and C_Soil was 77.55%, 89.42%, 93.61%, 90.33%, 41.79%, 45.99%, and 23.54% lower than O_Soil, respectively (Table 2 ). The α-diversity was significantly different between soil (or fruit) samples under conventional and organic farming, while not between root (or leaf) samples. Notably, there were significant changes in microbial diversity across ecological niches under conventional farming, while not between leaf and fruit microbial diversity under organic farming. Principal coordinate analysis (PCoA) indicated that microbial communities were well separated by agricultural management practices or ecological niches when comparing all samples (Fig. 2). The microbial community in leaf samples was more similar to that in fruit samples, therefore most samples clustered together. Root microbiomes were also different depending on agricultural management practices. Table 2 Bacterial community richness and diversity indices under different agricultural management practices and ecological niches. Conventional farming (C), Organic farming (O). Treatment Chao1 Shannon Simpson Pielou C_Fruit 197.67 ± 56.81d 1.23 ± 0.22d 0.39 ± 0.08c 0.16 ± 0.03d O_Fruit 114.11 ± 31.71de 0.58 ± 0.27e 0.19 ± 0.11d 0.08 ± 0.03e C_Leaf 55.33 ± 46.63e 0.35 ± 0.21e 0.12 ± 0.06d 0.06 ± 0.02e O_Leaf 63.22 ± 40.01e 0.53 ± 0.28e 0.19 ± 0.10d 0.09 ± 0.04e C_Root 381.33 ± 68.98c 3.19 ± 0.73c 0.82 ± 0.13b 0.37 ± 0.08c O_Root 430 ± 99.56c 2.96 ± 0.61c 0.74 ± 0.05b 0.34 ± 0.06c C_Soil 537.2 ± 152.65b 4.19 ± 0.73b 0.9 ± 0.05ab 0.46 ± 0.06b O_Soil 856.8 ± 136.94a 5.48 ± 0.33a 0.98 ± 0.01a 0.56 ± 0.02a 3.2. Variations in the bacterial community structure under different agricultural management practices The bacterial community composition in ecological niches in citrus orchards was analyzed under different agricultural management practices (Fig. 3 and Supplementary Table S1 ). The dominant phylum included Proteobacteria, Actinobacteria, Bacteroidetes, Acidobacteria, and Fusobacteria. The relative abundance of Proteobacteria was significantly lower under organic farming compared to conventional farming, while Bacteroidetes were more abundant under organic farming. Organic farming led to abundant Proteobacteria (relative abundance = 27.17%) in the leaf, and Proteobacteria even showed higher levels of abundance in other ecological niches ( P < 0.05). The relative abundance of Bacteroidetes (34.36%) and Fusobacteria (22.53%) was also significantly higher in the leaf under organic farming as compared to the other samples. In the soil under organic farming, there were significantly more abundant Acidobacteria (13.33%), Gemmatimonadetes (2.63%), and Verrucomicrobia (1.34%), as compared to the endosphere compartments. However, the relative abundance of Acidobacteria and Chloroflexi was significantly higher in soil and root samples under conventional farming compared to the other samples (Fig. 3A). At the genus level, the community composition of fruit, leaf, root, and soil microbiomes under organic farming was significantly different from that under conventional farming considering the top 10 bacteria (Fig. 3B). The relative abundance of Pseudomonas was higher in the fruit under conventional farming. Prevotella, Staphylococcus , and Corynebacterium showed higher relative abundance in the leaf under conventional farming. Cetobacterium and Parabacteroides had higher relative abundance in leaf tissues under organic farming. Burkholderia , Streptomyces , and Mycobacterium had higher relative abundance in root tissues under organic farming. For soil samples, the major genera were Acidobacterium and Mycobacterium under conventional farming, with Pseudomonas as the staple genus under organic farming. 3.3. Core microbial community characteristics The taxonomic characteristics of bacterial communities in the conventional and organic orchard were investigated using co-occurrence analysis (Fig. 4). Bacterial communities had significant associations regardless of farming practices; however, they showed very different phylum abundance and structural variations. High-abundance nodes in the co-occurrence network were mostly related to eight distinct phyla. Proteobacteria (46.11%), Actinobacteria (26.42%), Acidobacteria (13.99%), Firmicutes (3.63%), Bacteroidetes (3.63%), Chloroflexi (2.07%), TM6 (1.55%), and Armatimonadetes (1.04%) represented the most dominant bacterial community under conventional farming (Fig. 4A). While, for organic farming, Proteobacteria (47.96%), Actinobacteria (22.17%), Acidobacteria (12.22%), Bacteroidetes (5.88%), Firmicutes (4.52%), Verrucomicrobia (1.36%), Chloroflexi (1.36%), and TM6 (1.36%) were the most dominant bacterial phyla (Fig. 4B). Meanwhile, topological characteristics were computed to quantify the between-node correlation (Supplementary Table S2), indicating higher complexity and connectivity under organic farming compared to conventional farming. The organic OTU association network consisted of 221 nodes and 4443 edges in comparison with 193 nodes and 2067 edges for conventional farming. The network of organic farming showed more connections per node (average degree = 40.21) as compared to that of conventional farming (21.42), indicating high levels of interconnectivity within the microbial community under organic farming (Supplementary Table S2). The positive edges of the conventional and organic farming networks accounted for 93.37% and 96.89%, respectively, suggesting that symbiotic relationships predominated among the indicator microbial networks under different farming practices. Overall, the bacterial network showed higher levels of complexity and interconnection under organic farming compared to that under conventional farming. As shown in the Venn diagram (Fig. 4C-D), for the common OTUs shared by all ecological niches, 104 were from organic farming and 96 from conventional farming. O_Soil, O_Root, and O_Leaf had 703, 380, and 32 more OTUs, respectively, compared to C_Soil, C_Root and C_Leaf although C_Fruit had 321 more OTUs than O_Fruit, suggesting generally more OTUs under organic farming. 3.4. Characterization of the functional diversity of soil microbiomes The carbon utilization abilities of soil microbiomes gradually increased in all treatments over the 9-day culture period, and the overall trend followed the S-curve of bacterial growth (Fig. 5). The capacity of soil microbiomes to utilize carbon sources was lower in C_BS compared to other treatments. The AWCD value of O_BS was significantly higher than all other treatments until the 6th day and appeared lower than C_RS from the 7th day (Fig. 5). After 3-day culture, utilization of six types of carbon sources by the bulk or rhizosphere soil microbiomes from long-term conventional or organic citrus farming was quantified (Table. 3). The highest utilization abilities were observed in the O_BS treatment and carboxylic acid was the mostly utilized carbon source in the bulk soil treatment under two different farming practices. The rhizosphere soil microbiome utilized the carbon source differently, with amino acid and amine being efficiently metabolized in the O_RS and C_RS treatment, respectively (Table 3 ). The AWCD data on day 3 during the log period were selected to analyze functional diversity of the soil microbiomes (Table 4 ). The functional diversity indices (Shannon-Weiner, Simpson, and Richness index) of the O_BS microbiome were significantly higher than the others, except for the Pielou Evenness index. The diversity indices of bulk or rhizosphere soil microbiomes from organic farming were higher than those from conventional farming, except for the Richness or Simpson index of the rhizosphere soil microbiome. For the same farming practice, significant differences were found between bulk and rhizosphere soil microbiomes in organic farming, except for the Pielou Evenness index. No significant differences were found between bulk and rhizosphere soil samples under conventional farming, except for the richness index. Table 3 The utilization of six types of carbon sources by microorganisms (based on the third day data from Biolog data) under different long-term farming management. Conventional farming (C), Organic farming (O), rhizosphere soil (RS), bulk soil (BS). Treatment Polymer Carbohydrates Phenolic acids Carboxylic acids Amino acids Amine O_RS 0.87 ± 0.13b 0.86 ± 0.02c 0.51 ± 0.10c 0.79 ± 0.15c 0.9 ± 0.20b 0.53 ± 0.22b C_RS 0.92 ± 0.07b 1.23 ± 0.05b 0.99 ± 0.05b 1.05 ± 0.06b 0.88 ± 0.03b 1.36 ± 0.03a O_BS 1.80 ± 0.13a 1.78 ± 0.14a 1.85 ± 0.14a 1.95 ± 0.18a 1.76 ± 0.11a 1.53 ± 0.20a C_BS 0.8 ± 0.09b 0.65 ± 0.06d 0.62 ± 0.11bc 1.00 ± 0.07bc 0.75 ± 0.16b 0.66 ± 0.04b Table 4 Functional diversity indices of the microbial community (based on the third day data from Biolog analysis) under different long-term farming management. Conventional farming (C), Organic farming (O), rhizosphere soil (RS), bulk soil (BS). Treatment Richness Index Simpson Index Shannon-Weiner Index Pielou Evenness Index O_RS 23 ± 1.15d 0.95 ± 0.01b 4.4 ± 0.09b 1.41 ± 0.01a C_RS 26 ± 1.00c 0.95 ± 0.01b 4.43 ± 0.02b 1.36 ± 0.01b O_BS 31 ± 0.58a 0.96 ± 0.01a 4.8 ± 0.04a 1.4 ± 0.00a C_BS 28 ± 0.58b 0.95 ± 0.01b 4.54 ± 0.05b 1.37 ± 0.01b Principal component analysis (PCA) revealed obvious differences in the patterns of carbon source utilization by bulk or rhizosphere soil microbiomes under different farming practices (Fig. 6). PC1 and PC2 contributed to 69.44% and 21.23% of the total variation, respectively. Along the PC1 axis, treatments were categorized into two clusters according to microbial utilization of carbon sources, with O_BS as one cluster and O_RS, C_RS, and C_BS forming the other cluster (Fig. 6). 3.5. Metabolic capacity of soil microbiomes in carbon source utilization Based on the 3rd day AWCD values after incubation, hierarchical clustering analysis was performed to compare utilization capacities of 31 carbon sources by the bulk and rhizosphere soil microbiomes (Fig. 7) and showed obvious cross-treatment differences. For vertical clustering, the heatmap displayed two distinct clusters: cluster 1 with O_BS and cluster 2 with C_RS, O_RS, and C_BS. Cluster 2 could be further divided into subgroup 1 with C_RS and subgroup 2 with O_RS and C_BS. As to horizontal clustering, most carbon sources were more efficiently utilized in the O_BS treatment, while four carbon sources (N-Acetyl-D-Glucosamine, Tween 40, Putrescine, and β-Methyl-D-Glucoside) were efficiently utilized in the C_RS treatment. Both O_RS and C_BS had lower carbon utilization capacities compared to O_BS and C_RS, except for efficient utilization of D-Galactonic Acid γ-Lactone by O_RS and active metabolism of D-Malic Acid and L-Threonine by C_BS. 4. Discussion 4.1. Effects of different agricultural management practices on the diversity and composition of microbial communities Agricultural management practices play a pivotal role in conditioning microbiome assembly in different ecological niches (Zhu et al., 2022 ). Our study suggests that microbial assembly is largely influenced by both farming practices and ecological niches. Consistent with grape, citrus, and maize-wheat/barley rotation system studies (Wu et al., 2020 ; Miliordos et al., 2021 ; Xiong et al., 2021 ; Zhou et al., 2021 ), our results showed higher microbial diversity in belowground (root and soil) samples than in aboveground (leaf and fruit) samples (Fig. 1 and Table 2 ). This disparity can be attributed to the dynamic and heterogeneous aboveground environment, highly selective oligotrophic conditions, and high exposure to various biotic (e.g., pollinator and pathogen infection) and abiotic (e.g., sunshine, precipitation) stimuli, as well as anthropogenic pressures (e.g., farming management) imposed on aboveground parts of the plant (Liu et al., 2020 ). The study on source tracing has shown that crop microbiomes mainly come from the soil and are progressively enriched and filtered in different plant ecological niches (Xiong et al., 2021 ). Diversity analyses revealed that farming management and ecological niches significantly affect the diversity and composition of the microbiota (Fig. 2 and Table 2 ). Organic farming resulted in slightly higher diversity (α-diversity index) than conventional farming, especially in the leaf and soil (Table 2 ), consistent with previous reports (Esperschutz et al., 2007 ; Khoiri et al., 2021 ). Moreover, conventional farming showed significant changes in microbial diversity across different ecological niches. In contrast, no significant differences between leaf and fruit microbial diversity were observed under organic farming (Table 2 ). In this regard, the higher diversity and lower dispersion in organic farming may suggest that the soil and leaf microbiota under organic farming are more resilient to environmental stresses, benefiting from the complementary interactions among different taxa (Hartmann et al., 2015 ). Mineral fertilization enhances nutrient absorption by fruits, however, excessive nitrogen application can increase the disease severity, coupled with fungicide application in agricultural production (Wei et al., 2018 ). High nutrient levels and disease pressures result in greater microbial diversity in fruit under conventional farming (Leff and Fierer, 2013 ). However, more studies are needed to solidify this argument. Furthermore, farming practices had more pronounced effects on bacterial diversity and community structure in the soil compared to other plant niches (Table 2 ). These results indicate that microbial communities in the soil more sensitive to farming management, whereas microbiota in the leaf and root remain relatively stable, possibly due to the strong selection pressure from the plant host (Xiong et al., 2021 ). In agreement with previous results (Xu et al., 2018 ), we found that Proteobacteria, Actinobacteria, Bacteroidetes, Acidobacteria, and Firmicutes were the most abundant bacterial phyla in the citrus orchard (Fig. 3). In the rhizosphere, root exudates serve as signaling molecules and nutrient sources, selectively recruiting microbes from bulk soil and strongly affecting the composition of surrounding soil microbiomes (Zhong et al., 2022 ). We observed the enrichment of the bacterial phyla Proteobacteria and Actinobacteria in the root compared to bulk soils, whereas Bacteroidetes and Acidobacteria were depleted (Fig. 3A). This contrasts with other findings (Xu et al., 2018 ; Trivedi et al., 2020 ), probably due to crop developmental progression, seasonal variations, and agriculture management practices. Moreover, under organic farming, a substantial decrease in Acidobacteria and Chlorofexi abundance was observed in the leaf and fruit, whereas Actinobacteria, Bacteroidetes, and Firmicutes were enriched in fruit and root compartments. Bacteroidetes were also enriched in the citrus leaf under organic farming (Fig. 3A and Table S1 ), consistent with previous studies (Blaustein et al., 2017 ; Passera et al., 2018 ; Bai et al., 2019 ). Such compositional diversity most likely results from the decreasing abundance of slow-growing and oligotrophic bacteria (K-strategists), such as Acidobacteria and Chlorofexi, and the increasing dominance of fast-growing and copiotrophic bacteria (r-strategists), such as Bacteroidetes, Firmicutes, and Actinobacteria (Fierer et al., 2007 ). Consistent with other studies (Bei et al., 2018 ; Luo et al., 2023 ), our results suggest that organic farming leads to the depletion of K-strategists and the enrichment of fast-growing microorganisms. Organic farming increases the abundance of beneficial microbes while suppressing pathogen growth (Granado et al., 2008 ; Karlsson et al., 2017 ; Banerjee et al., 2019 ), well supporting our findings in this study. For instance, Proteobacteria, Firmicutes, and Actinobacteria (Fig. 3A) are the most dominant phyla in the citrus microbiome (Bai et al., 2019 ; Passera et al., 2018 ; Trivedi et al., 2012 ; Zhang et al., 2021 ). The core genera from these phyla, including Burkholderia and Streptomyces , were more abundant under organic farming (Fig. 3B), and some are potentially beneficial microbes (Bei et al., 2018 ; Xu et al., 2018 ). Pseudomonas , Burkholderia , Sphingomonas , and Bacillus have been identified as agents capable of inhibiting plant diseases in different contexts (Trivedi et al., 2012 ; Riera et al., 2017 ; Zhang et al., 2017; Tang et al., 2018 ; Li et al., 2022 ). Notably, Pseudomonas was enriched in the fruit microbiome under conventional farming (Fig. 3), possibly as an adaption strategy to cope with the disease stressed condition under conventional farming. 4.2. Organic farming leads to higher microbial network stability Microorganisms form intricate networks through multi-level associations which are beneficial for sustaining plant health and soil fertility, providing novel strategies for sustainable agricultural management (Agler et al., 2016 ; van der Heijden and Hartmann, 2016 ; Morrien et al., 2017 ; de Vries et al., 2018 ). Microbial co-occurrence networks of both farming practices exhibited distinct patterns of microbe-microbe connectivity and structure (Fig. 4A–B), indicating the significance of particular microbial nodes in organic and conventional farming systems (van der Heijden and Hartmann, 2016 ). Our study highlights the significant impact of agricultural management on microbiota network structures, with a significantly more complex network under organic farming compared to conventional farming. According to network topology analysis (Table S2), organic networks had more edges, nodes, degrees, and connectivity, consistent with other studies based on different agricultural systems (Hartmann et al., 2015 ; Banerjee et al., 2019 ; Khoiri et al., 2021 ; Zheng et al., 2022 ). Complex networks with higher stability are more resistant to environmental stresses or invasion by exogenous microorganisms than simple networks with relatively low connectivity (Montoya et al., 2006 ; Santolini and Barabasi, 2018 ). In these co-occurrence networks, certain highly related taxa are considered as plant-associated microorganisms, and they could indeed play an important role in the plant microbiota (van der Heijden and Hartmann, 2016 ). Contrary to conventional farming, higher complexity and connectivity in the organic network are associated with more hub taxa identified (Khoiri et al., 2021 ). Similar to the study in wheat (Banerjee et al., 2019 ), the high complexity within the organic network (Fig. 4A and B) suggests that the microbiome under organic farming may be more resilient to environmental perturbations due to complementary interactions among the different taxa. However, further investigation is necessary to better understand underlying mechanisms, and the long-term outcomes remain to be further explored. The core microbiome, often defined as an essential set of microbes common in different microbial consortia from similar habitats, is crucial for understanding the composition, functions, and stability of complex microbial assemblages (Shade and Handelsman, 2012 ; Busby et al., 2017 ). The predominant taxa of the global rhizosphere microbiome in citrus orchards include Proteobacteria, Actinobacteria, Acidobacteria, and Bacteroidetes (Xu et al., 2018 ). Some of these core microorganisms are more abundant under organic farming (Fig. 3 and Fig. 4A-B) and function redundantly for nitrogen fixation; such functional redundancy is important for the recovery and stability of microbial communities (Shade and Handelsman, 2012 ). 4.3. Impacts of farming practices on carbon source utilization by microbial communities The Biolog Eco-Plate method is an efficient way to assess the physiological properties of microbial communities, especially in bulk and rhizosphere soils (Grayston et al., 1998 ; Zhao et al., 2016 ). The AWCD value, which represents soil microbial ability to utilize different carbon sources, serves as a vital index of microbial functional diversity (Feigl et al., 2017 ). Our results revealed significant effects of farming practices on the carbon utilization patterns of the bulk and rhizosphere soil microbial communities (Figs. 6 and 7, Table 3 ). Moreover, soil microorganisms were able to proliferate with high biodiversity under organic farming (Table 4 ). The results were supported by our 16s DNA sequencing showing that the organic farming system has more abundant and diverse soil microorganisms than the conventional farming counterpart. Similar trends have been observed in apple (Zhu et al., 2020 ), banana (Chou et al., 2017 ), grape (Katayama et al., 2019 ), and wheat (Karlsson et al., 2017 ) systems under diverse agricultural management practices. Microbial communities utilized six types of carbon sources in a distinct manner within and between farming practices (Table 3 ), probably attributed to the presence of different functional groups (Kumar et al., 2017 ). Efficient utilization of carbohydrates, amino acids, and carboxylic acids in O_BS suggests that organic farming may have provided more simple sugars compared to conventional farming. According to previous studies (Deleij et al., 1994 ), rapidly multiplied microorganisms are selected under uncrowded environmental conditions with easily accessible and rich nutrients (r-strategists). This might contribute to more diverse fast-growing heterotrophs in organic farming soils (Chou et al., 2017 ). Notably, the distribution of microorganisms in bulk soils appears more homogeneous under organic farming (Bei et al., 2023 ), unlike the functional diversity of the rhizosphere microbiome. The rhizosphere soil under organic farming exhibits lower microbial richness and higher microbial evenness compared to that under conventional farming (Table 4 ), possibly due to competitive interactions between root and soil microbes under organic farming. Plants tend to release certain volatiles that may restrict proliferation of microbial populations under organic farming (Chen et al., 2017 ; Just et al., 2023 ; Semenov et al., 2022 ). Biolog analysis showed separation of the O_RS microbiome from other communities (Fig. 7), suggesting greater influence of organic farming on microbial functionality than conventional farming. Changes in the diversity, composition, and functions of soil bacterial communities may be partially attributed to higher soil organic matter content and reasonable pH (Sun et al., 2015 ; Xun et al., 2015 ; Zhou et al., 2016 ; Muneer et al., 2022 ). 5. Conclusion In this study, we focused on impacts of organic farming on composition, diversity, and functions of microbial communities along the plant-soil continuum with the conventional citrus orchard as the control. The organic farming orchard accommodated a more diverse microbial community than the conventional orchard, especially in soil and fruit compartments. The predominant bacterial phyla, including Proteobacteria, Actinobacteria, Bacteroidetes, Acidobacteria, and Firmicutes, showed distinct abundance under different farming practices. The relative abundance of Acidobacteria and Chlorofexi decreased significantly in the soil and fruit samples of the organic farming system. However, Actinobacteria, Bacteroidetes, and Firmicutes were enriched in fruit and root compartments, indicating depletion of slow-growing bacteria (K-strategists) and enrichment of copiotrophic bacteria (r-strategists) under organic farming. Meanwhile, relative abundance of Burkholderia and Streptomyces significantly increased under organic farming, indicating preferential colonization of potentially beneficial microbes. Moreover, the co-occurrence network analysis with topology assessments indicated greater complexity and connectivity of the organic network, implying that organic farming not only affects the abundance of specific taxa but also shapes interactions within the microbial community. Importantly, the current study highlighted the potential of organic farming to enhance soil microbial functional diversity and increase simple sugar availability. Overall, organic farming favorably alters microbial communities and the functional diversity in the citrus orchard. Our study provides a foundational framework for future research to elucidate mechanisms of the response of citrus microbiota to specific farming management. These valuable insights hold significant potential for enhancing citrus productivity and overall ecosystem health. Application of preconditioned beneficial soil and plant microbiota could potentially convert agrochemical-dependent agricultural systems into healthier and more environment-friendly systems. Declarations Author Contribution Lianlian Liu: Conceptualization, Validation, Investigation, Formal analysis, Writing - Original Draft, Writing - Review & Editing, Visualization. Muhammad Atif Muneer: Writing – review & editing. 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Zhu, Z., Bai, Y., Lv, M., Tian, G., Zhang, X., Li, L., Jiang, Y., Ge, S., 2020. Soil Fertility, Microbial Biomass, and Microbial Functional Diversity Responses to Four Years Fertilization in an Apple Orchard in North China. Hortic. Plant J. 6 (4), 223–230. https://doi.org/10.1016/j.hpj.2020.06.003. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 29 Aug, 2025 Read the published version in BMC Microbiology → Version 1 posted Editorial decision: Revision requested 30 Aug, 2024 Editor assigned by journal 30 Aug, 2024 Submission checks completed at journal 30 Aug, 2024 First submitted to journal 18 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Introduction","content":"\u003cp\u003eConventional agricultural practices, mostly reliant on chemical inputs, have made significant progress in agricultural production to ensure global food security in recent decades (Pretty, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Cui et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Renard and Tilman, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, the heavy reliance on synthetic chemical fertilizers and pesticides has resulted in substantial negative externalities, such as biodiversity loss (Vitousek et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), soil erosion (Pimentel et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) or acidification (Guo et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and air pollution (Liu et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Raza et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, there are detrimental effects on human health (Pimentel, 2005a; Rivera et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Hence, it is necessary to explore alternative approaches to reducing dependency on agrochemicals, improving agricultural output, and achieving the equilibrium between productivity and sustainability (Khoiri et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgricultural management practices need to change to meet agriculture and food system development goals (Eyhorn et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Organic farming, although not a silver bullet, seems to be a potential strategy for maintaining biodiversity and increasing crop production sustainability (Gonthier et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). It prevents negative environmental impacts of chemical inputs to improve overall sustainable production and agroecosystem health. Compared to conventional farming, organic farming applies organic fertilizers (e.g., manure, biochar, animal waste), biological control for pest management, and green manuring or grass mowing for weed control (Castaneda et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, organic farming incorporates various techniques, such as intercropping and stubble-mulch, to improve soil fertility and maintain food productivity by nurturing a beneficial soil microbiome (Maeder et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Pimentel et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2005b\u003c/span\u003e; Xiang et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChemical pesticides or fertilizers are not allowed in organic farming; therefore, soil microorganisms perform a key role in nutrient mineralization, utilization, and cycling (Friedel et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Hence, it is particular important to explore the potential of soil microbiomes in promoting agricultural sustainability. Organic farming management practices promote the abundance and diversity of most microorganisms (Lori et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). After conventional to organic farming conversion, soil microbial communities adapt to new conditions and function accordingly, such as improving soil microbial biomass (Santos et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), changing soil organic matter in labile fractions (Flie\u0026szlig;bach and M\u0026auml;der, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), enhancing plant associations with beneficial microorganisms (Gosling et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and proving positive regulations (Bei et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Organic farming increases functional diversity of belowground microorganisms (i.e., soil and root niches) (Lupatini et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hartman et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) as well as aboveground microbial communities (leaves and fruits) (Leff and Fierer, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Khoiri et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) as compared to conventional farming. Low-input farming systems are featured by the higher complexity and biodiversity of microbial networks compared to conventional farming systems (Hartmann et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Banerjee et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Changes in soil microorganisms and keystone taxa driven by organic fertilization also enhance the resistance and resilience against environmental disturbances through the diversified bacterial communities and copiotrophic bacterial assemblages (Luo et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this regard, manipulating the microbial community to enrich beneficial bacteria and reduce harmful bacteria might provide a basis for improving plant growth and agricultural sustainability (Hartmann et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Although impacts of agricultural management on soil (Lupatini et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), rhizosphere (Hartman et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Blundell et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), or phyllosphere microbiomes (Ottesen et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Karlsson et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Khoiri et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have been studied, the overall microbial structure and functionality along the soil-plant continuum has been less well-studied.\u003c/p\u003e \u003cp\u003eCitrus, one of the top three fruit crops worldwide (Wang, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), serves as an ideal model plant for microbial taxonomic, genomic, and functional studies (Xu et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The Gannan navel orange is among the most prestigious fruits in China, with high internal quality in terms of sweetness, juice percentage, and high levels of vitamin C (Liu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, the quality of orchard soils has recently declined due to improper agricultural management and adverse environmental factors. Deterioration of the eco-environment and disease challenges also negatively affect global citrus production (Zhou et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In particular, soil acidification, which is worsened by intensive application of chemical nitrogen fertilizers, has emerged as a significant problem for citrus production in China (Chang et al., 2016).\u003c/p\u003e \u003cp\u003eTo date, most studies focus on harnessing citrus-microbiome interactions to address biotic and abiotic pressures (Zhang et al., 2017; Xu et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, limited research has been conducted on microbial changes along the soil-plant continuum in citrus orchards under different agricultural management practices. In this study, we combined microbiological sequencing and Biolog-Eco microplate analysis to investigate (a) how different agricultural management practices affect the diversity and composition of microbial communities, (b) the characteristics of the core microbial community and its association with different farming practices, and (c) the effects of different management practices on the capacity of microbial communities to utilize carbon.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Experimental site\u003c/h2\u003e \u003cp\u003eOrange orchards selected for conventional (C) and organic farming (O) study were located in Tanshi Village (26\u0026deg;07'58.9\" N, 115\u0026deg;34'42.6\" E), Yudu County, Jiangxi Province, China. The citrus variety was New Holland navel orange planted in 2013. This region has a humid subtropical climate with 1507 mm of annual precipitation and 1622 h of sunshine. The average annual temperature is 19.7℃, with the lowest in January (8.2℃) and the highest in July (29.7℃). Nine plots (3 trees/plot) were selected from each farming system as experimental replicates. For each tree, the soil sample was collected at 0\u0026ndash;15 cm depth from the 4 ordinate directions along the dripline and then pooled as a single sample. Before fertilization, five pooled soil samples were taken from each farming system to measure their physicochemical characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Under conventional farming, compound fertilizers (2\u0026ndash;3 kg per plant per year) and pesticides were applied, and the synthetic herbicide glufosinate-ammonium was used to control weeds. Organic fertilizers (compost of maize flour, rape cake, and brown sugar at a mass ratio of 25:75:1; 8\u0026ndash;10 kg per plant per year) and plant extracts (azadirachtin, matrine, and d-limonene) and microbial biopesticides (spinosad) were applied for organic farming. Different from conventional farming, organic farming relied on cover crops and manual and mechanical weed management.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffects of different agricultural management on the soil properties of citrus orchards (means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, n\u0026thinsp;=\u0026thinsp;4). Soil organic matter (SOM), total nitrogen (TN), mineral nitrogen (N\u003csub\u003emin\u003c/sub\u003e), available phosphorus (AP), available potassium (AK)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH (H\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOM (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTN (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003csub\u003emin\u003c/sub\u003e (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAP (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAK (g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConventional farming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.90a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.10a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.98a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.01a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e149.03a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganic farming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.04b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.25b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.65a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e123.88a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72.21b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Soil, root, leaf, and fruit sampling\u003c/h2\u003e \u003cp\u003eCitrus trees without symptoms of pests, diseases, and nutrient deficiencies were sampled at the fruit expansion stage in July 2022. We sampled nine orange trees as biological replications for each farming practice.\u003c/p\u003e \u003cp\u003eThe rhizosphere soil and root samples were taken at the depth of 10\u0026ndash;15 cm. Briefly, the root was carefully shaken and gently brushed to remove loosely adherent soil. Roots were washed in the sterile phosphate-buffered saline (PBS) solution and gently agitated with sterile forceps to remove the soil from the root surface. The resulting soil was briefly centrifuged to collect and stored as rhizosphere soil (RS) at 4\u0026deg;C. Then, root samples were washed with sterilized water three times and wiped with paper towels. We sampled the non-rooted soil from the same plot as bulk soil (BS). The pooled soil or root samples from each tree were stored at -20\u0026deg;C for further analysis. Nine root and five bulk soil samples were used as independent biological replicates.\u003c/p\u003e \u003cp\u003eFruits of similar size (5\u0026ndash;6 cm in diameter) and mature leaves (free of pests and diseases) closest to the fruit were selected from the external middle canopy in four directions (east, south, west, and north). Eight leaves and four fruits from each tree were harvested as a pooled leaf or fruit sample, with nine trees as independent biological replicates for each farming system. Samples were instantly placed in liquid nitrogen and then kept at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Functional diversity of the soil microbial community\u003c/h2\u003e \u003cp\u003eThe Biolog Eco-Plate (Biolog Inc., CA, USA) was used to examine the functional diversity of rhizosphere and bulk soil microorganisms (Bei et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Each plate contained 96 wells with three replicates of water control and 31 different carbon sources which were classified into six major categories: polymers, carbohydrates, phenolic acids, carboxylic acids, amino acids, and amines (Sala et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Fresh rhizosphere (RS) or bulk (BS) soil (~\u0026thinsp;10 g) was weighed in a 250 ml flask containing 90 ml of sterilized 0.85% NaCl solution, shaken at 200 rpm for 30 min, suspended, and diluted to 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e with 0.85% NaCl solution. Applied 150 \u0026micro;l of the diluted sample to the individual well of the 96-well microplate for 9-day incubation at 25\u0026deg;C and recorded the absorbance value every 24 h at 590 nm. The average well color development (AWCD) was calculated to assess the utilization abilities of carbon sources by the soil microbial community (Garland, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The 72-hour absorbance was used to analyze the Shannon-Weiner (H) (Garland and Mills, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Simpson (D) (Zhang et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Pielou evenness (J) (Hu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and Richness index (E) (Gryta et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) using the following equations:\u003c/p\u003e \u003cp\u003eShannon-Weiner index (H): H = \u0026minus;ΣP\u003csub\u003ei\u003c/sub\u003e ln(P\u003csub\u003ei\u003c/sub\u003e) (1)\u003c/p\u003e \u003cp\u003eSimpson index (D): D\u0026thinsp;=\u0026thinsp;1-Σ(Pi)\u003csup\u003e2\u003c/sup\u003e (2)\u003c/p\u003e \u003cp\u003ePielou Evenness index (J): J\u0026thinsp;=\u0026thinsp;H/lnS (3)\u003c/p\u003e \u003cp\u003eRichness index (E): E\u0026thinsp;=\u0026thinsp;H/lnSR (4)\u003c/p\u003e \u003cp\u003ewhere P\u003csub\u003ei\u003c/sub\u003e is the ratio of the relative absorbance of well i to the total absorbance of all wells on a plate. S is the number of wells with color change.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. DNA extraction, sequencing and data analyses\u003c/h2\u003e \u003cp\u003eThe genomic DNA was isolated following the CTAB/SDS method (Saghai-Maroof et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). DNA concentration and purity were monitored using 1% agarose gels, and then diluted to 1 \u0026micro;g \u0026micro;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e using aqua sterilisata. Bacterial 16S rRNA genes were amplified using the following primer set: 799F (5\u0026prime;-AACMGGATTAGATACCCKG-3\u0026prime;) and 1193R (5\u0026prime;-ACGTCATCCCCACCTTCC-3\u0026prime;). DNA libraries were generated using the TruSeq\u0026reg; DNA PCR-Free Sample Preparation Kit (Illumina, USA) according to the manufacturer\u0026rsquo;s instructions and sequenced on the Illumina NovaSeq platform to generate 250-bp paired-end reads.\u003c/p\u003e \u003cp\u003eRaw data were filtered by QIIME 2 (Quantitative Insights Into Microbial Ecology 2, version 2019.7) and its plug-ins (Bolyen et al., 2019). The operational taxonomic unit (OTU) was subjected to removal of low-quality reads and noises (Callahan et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and taxonomically classified by comparing with the Silva database using the q2-feature-classifier (Bokulich et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Specific packages in R version 3.6.3 were used for statistical analyses based on the feature or OTU table.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe AWCD values, microbial abundance, and α-diversity data were all subject to the analysis of variance (ANOVA). Fisher's Least Significant Difference (LSD) was tested at the 5% level (SPSS version 26.0). Principal component analysis of the Biolog data (AWCD) was carried out using Origin 2019b. Effects of different farming practices on bacterial communities and carbon utilization were analyzed using the ComplexHeatmap package in R (version 4.3.1).\u003c/p\u003e \u003cp\u003eThe α-diversity indices (Chao, Shannon, Simpson, and Pielou index) were derived from the OTU data by using Vegan packages in R version 4.3.1, with the β-diversity generated using the Vegan package in R. Principal co-ordinate analysis was based on the Bray-Curtis distance (Hamady et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBacterial OTUs with an average relative abundance of less than 0.01% were removed from the OTU table. The computation of pairwise similarity matrix was based on Spearman correlation with \u003cem\u003eP\u003c/em\u003e-value adjusted using the BH method. The igraph package was used to calculate topological features of co-occurrence networks (Csardi and Nepusz, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), including the number of edges and nodes, positive and negative edges, network density, average degree and path length, network diameter, clustering coefficients, betweenness centralization, and modularity, which were illustrated using the Gephi software (0.10.1).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Bacterial richness, diversity, and community composition under different agricultural management practices in citrus orchards\u003c/h2\u003e \u003cp\u003eThe rarefaction curve gradually leveled off and the number of OTUs reached its peak level with the increase of sequencing depth, together with the gradually stabilized Shannon index curve (Fig.\u0026nbsp;1), indicating reliable sampling and reasonable sample size to represent the bacterial communities in this study. Aboveground (fruit and leaf) bacterial communities had lower levels of diversity compared to belowground (root and soil) communities (Fig.\u0026nbsp;1A).\u003c/p\u003e \u003cp\u003eThe Chao1 and Ace indices represent the richness of the bacterial community, Shannon and Simpson's indices indicate the bacterial diversity, and the Pielou's Evenness index measures bacterial community evenness (Sun et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The α-diversity indices of bacterial communities significantly differed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) under conventional and organic farming. The soil bacterial community under organic farming (O_Soil) exhibited higher richness, diversity, and evenness compared to other microbial communities. Specifically, the Shannon index of C_Fruit, O_Fruit, C_Leaf, O_Leaf, C_Root, O_Root, and C_Soil was 77.55%, 89.42%, 93.61%, 90.33%, 41.79%, 45.99%, and 23.54% lower than O_Soil, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The α-diversity was significantly different between soil (or fruit) samples under conventional and organic farming, while not between root (or leaf) samples. Notably, there were significant changes in microbial diversity across ecological niches under conventional farming, while not between leaf and fruit microbial diversity under organic farming. Principal coordinate analysis (PCoA) indicated that microbial communities were well separated by agricultural management practices or ecological niches when comparing all samples (Fig.\u0026nbsp;2). The microbial community in leaf samples was more similar to that in fruit samples, therefore most samples clustered together. Root microbiomes were also different depending on agricultural management practices.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBacterial community richness and diversity indices under different agricultural management practices and ecological niches. Conventional farming (C), Organic farming (O).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChao1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimpson\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePielou\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC_Fruit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197.67\u0026thinsp;\u0026plusmn;\u0026thinsp;56.81d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO_Fruit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.11\u0026thinsp;\u0026plusmn;\u0026thinsp;31.71de\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC_Leaf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.33\u0026thinsp;\u0026plusmn;\u0026thinsp;46.63e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO_Leaf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.22\u0026thinsp;\u0026plusmn;\u0026thinsp;40.01e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC_Root\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e381.33\u0026thinsp;\u0026plusmn;\u0026thinsp;68.98c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO_Root\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e430\u0026thinsp;\u0026plusmn;\u0026thinsp;99.56c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC_Soil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e537.2\u0026thinsp;\u0026plusmn;\u0026thinsp;152.65b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO_Soil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e856.8\u0026thinsp;\u0026plusmn;\u0026thinsp;136.94a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Variations in the bacterial community structure under different agricultural management practices\u003c/h2\u003e \u003cp\u003eThe bacterial community composition in ecological niches in citrus orchards was analyzed under different agricultural management practices (Fig.\u0026nbsp;3 and Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The dominant phylum included Proteobacteria, Actinobacteria, Bacteroidetes, Acidobacteria, and Fusobacteria. The relative abundance of Proteobacteria was significantly lower under organic farming compared to conventional farming, while Bacteroidetes were more abundant under organic farming. Organic farming led to abundant Proteobacteria (relative abundance\u0026thinsp;=\u0026thinsp;27.17%) in the leaf, and Proteobacteria even showed higher levels of abundance in other ecological niches (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe relative abundance of Bacteroidetes (34.36%) and Fusobacteria (22.53%) was also significantly higher in the leaf under organic farming as compared to the other samples. In the soil under organic farming, there were significantly more abundant Acidobacteria (13.33%), Gemmatimonadetes (2.63%), and Verrucomicrobia (1.34%), as compared to the endosphere compartments. However, the relative abundance of Acidobacteria and Chloroflexi was significantly higher in soil and root samples under conventional farming compared to the other samples (Fig.\u0026nbsp;3A).\u003c/p\u003e \u003cp\u003eAt the genus level, the community composition of fruit, leaf, root, and soil microbiomes under organic farming was significantly different from that under conventional farming considering the top 10 bacteria (Fig.\u0026nbsp;3B). The relative abundance of \u003cem\u003ePseudomonas\u003c/em\u003e was higher in the fruit under conventional farming. \u003cem\u003ePrevotella, Staphylococcus\u003c/em\u003e, and \u003cem\u003eCorynebacterium\u003c/em\u003e showed higher relative abundance in the leaf under conventional farming. \u003cem\u003eCetobacterium\u003c/em\u003e and \u003cem\u003eParabacteroides\u003c/em\u003e had higher relative abundance in leaf tissues under organic farming. \u003cem\u003eBurkholderia\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e, and \u003cem\u003eMycobacterium\u003c/em\u003e had higher relative abundance in root tissues under organic farming. For soil samples, the major genera were \u003cem\u003eAcidobacterium\u003c/em\u003e and \u003cem\u003eMycobacterium\u003c/em\u003e under conventional farming, with \u003cem\u003ePseudomonas\u003c/em\u003e as the staple genus under organic farming.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Core microbial community characteristics\u003c/h2\u003e \u003cp\u003eThe taxonomic characteristics of bacterial communities in the conventional and organic orchard were investigated using co-occurrence analysis (Fig.\u0026nbsp;4). Bacterial communities had significant associations regardless of farming practices; however, they showed very different phylum abundance and structural variations. High-abundance nodes in the co-occurrence network were mostly related to eight distinct phyla. Proteobacteria (46.11%), Actinobacteria (26.42%), Acidobacteria (13.99%), Firmicutes (3.63%), Bacteroidetes (3.63%), Chloroflexi (2.07%), TM6 (1.55%), and Armatimonadetes (1.04%) represented the most dominant bacterial community under conventional farming (Fig.\u0026nbsp;4A). While, for organic farming, Proteobacteria (47.96%), Actinobacteria (22.17%), Acidobacteria (12.22%), Bacteroidetes (5.88%), Firmicutes (4.52%), Verrucomicrobia (1.36%), Chloroflexi (1.36%), and TM6 (1.36%) were the most dominant bacterial phyla (Fig.\u0026nbsp;4B). Meanwhile, topological characteristics were computed to quantify the between-node correlation (Supplementary Table S2), indicating higher complexity and connectivity under organic farming compared to conventional farming. The organic OTU association network consisted of 221 nodes and 4443 edges in comparison with 193 nodes and 2067 edges for conventional farming. The network of organic farming showed more connections per node (average degree\u0026thinsp;=\u0026thinsp;40.21) as compared to that of conventional farming (21.42), indicating high levels of interconnectivity within the microbial community under organic farming (Supplementary Table S2). The positive edges of the conventional and organic farming networks accounted for 93.37% and 96.89%, respectively, suggesting that symbiotic relationships predominated among the indicator microbial networks under different farming practices. Overall, the bacterial network showed higher levels of complexity and interconnection under organic farming compared to that under conventional farming.\u003c/p\u003e \u003cp\u003eAs shown in the Venn diagram (Fig.\u0026nbsp;4C-D), for the common OTUs shared by all ecological niches, 104 were from organic farming and 96 from conventional farming. O_Soil, O_Root, and O_Leaf had 703, 380, and 32 more OTUs, respectively, compared to C_Soil, C_Root and C_Leaf although C_Fruit had 321 more OTUs than O_Fruit, suggesting generally more OTUs under organic farming.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Characterization of the functional diversity of soil microbiomes\u003c/h2\u003e \u003cp\u003eThe carbon utilization abilities of soil microbiomes gradually increased in all treatments over the 9-day culture period, and the overall trend followed the S-curve of bacterial growth (Fig.\u0026nbsp;5). The capacity of soil microbiomes to utilize carbon sources was lower in C_BS compared to other treatments. The AWCD value of O_BS was significantly higher than all other treatments until the 6th day and appeared lower than C_RS from the 7th day (Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eAfter 3-day culture, utilization of six types of carbon sources by the bulk or rhizosphere soil microbiomes from long-term conventional or organic citrus farming was quantified (Table. 3). The highest utilization abilities were observed in the O_BS treatment and carboxylic acid was the mostly utilized carbon source in the bulk soil treatment under two different farming practices. The rhizosphere soil microbiome utilized the carbon source differently, with amino acid and amine being efficiently metabolized in the O_RS and C_RS treatment, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The AWCD data on day 3 during the log period were selected to analyze functional diversity of the soil microbiomes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The functional diversity indices (Shannon-Weiner, Simpson, and Richness index) of the O_BS microbiome were significantly higher than the others, except for the Pielou Evenness index. The diversity indices of bulk or rhizosphere soil microbiomes from organic farming were higher than those from conventional farming, except for the Richness or Simpson index of the rhizosphere soil microbiome. For the same farming practice, significant differences were found between bulk and rhizosphere soil microbiomes in organic farming, except for the Pielou Evenness index. No significant differences were found between bulk and rhizosphere soil samples under conventional farming, except for the richness index.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe utilization of six types of carbon sources by microorganisms (based on the third day data from Biolog data) under different long-term farming management. Conventional farming (C), Organic farming (O), rhizosphere soil (RS), bulk soil (BS).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolymer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCarbohydrates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhenolic acids\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCarboxylic acids\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAmino acids\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAmine\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO_RS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC_RS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO_BS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC_BS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunctional diversity indices of the microbial community (based on the third day data from Biolog analysis) under different long-term farming management. Conventional farming (C), Organic farming (O), rhizosphere soil (RS), bulk soil (BS).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichness Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSimpson Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShannon-Weiner Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePielou Evenness Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO_RS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC_RS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO_BS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC_BS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) revealed obvious differences in the patterns of carbon source utilization by bulk or rhizosphere soil microbiomes under different farming practices (Fig.\u0026nbsp;6). PC1 and PC2 contributed to 69.44% and 21.23% of the total variation, respectively. Along the PC1 axis, treatments were categorized into two clusters according to microbial utilization of carbon sources, with O_BS as one cluster and O_RS, C_RS, and C_BS forming the other cluster (Fig.\u0026nbsp;6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Metabolic capacity of soil microbiomes in carbon source utilization\u003c/h2\u003e \u003cp\u003eBased on the 3rd day AWCD values after incubation, hierarchical clustering analysis was performed to compare utilization capacities of 31 carbon sources by the bulk and rhizosphere soil microbiomes (Fig.\u0026nbsp;7) and showed obvious cross-treatment differences. For vertical clustering, the heatmap displayed two distinct clusters: cluster 1 with O_BS and cluster 2 with C_RS, O_RS, and C_BS. Cluster 2 could be further divided into subgroup 1 with C_RS and subgroup 2 with O_RS and C_BS. As to horizontal clustering, most carbon sources were more efficiently utilized in the O_BS treatment, while four carbon sources (N-Acetyl-D-Glucosamine, Tween 40, Putrescine, and β-Methyl-D-Glucoside) were efficiently utilized in the C_RS treatment. Both O_RS and C_BS had lower carbon utilization capacities compared to O_BS and C_RS, except for efficient utilization of D-Galactonic Acid γ-Lactone by O_RS and active metabolism of D-Malic Acid and L-Threonine by C_BS.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Effects of different agricultural management practices on the diversity and composition of microbial communities\u003c/h2\u003e \u003cp\u003eAgricultural management practices play a pivotal role in conditioning microbiome assembly in different ecological niches (Zhu et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our study suggests that microbial assembly is largely influenced by both farming practices and ecological niches. Consistent with grape, citrus, and maize-wheat/barley rotation system studies (Wu et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Miliordos et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), our results showed higher microbial diversity in belowground (root and soil) samples than in aboveground (leaf and fruit) samples (Fig.\u0026nbsp;1 and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This disparity can be attributed to the dynamic and heterogeneous aboveground environment, highly selective oligotrophic conditions, and high exposure to various biotic (e.g., pollinator and pathogen infection) and abiotic (e.g., sunshine, precipitation) stimuli, as well as anthropogenic pressures (e.g., farming management) imposed on aboveground parts of the plant (Liu et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The study on source tracing has shown that crop microbiomes mainly come from the soil and are progressively enriched and filtered in different plant ecological niches (Xiong et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Diversity analyses revealed that farming management and ecological niches significantly affect the diversity and composition of the microbiota (Fig.\u0026nbsp;2 and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Organic farming resulted in slightly higher diversity (α-diversity index) than conventional farming, especially in the leaf and soil (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), consistent with previous reports (Esperschutz et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Khoiri et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, conventional farming showed significant changes in microbial diversity across different ecological niches. In contrast, no significant differences between leaf and fruit microbial diversity were observed under organic farming (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In this regard, the higher diversity and lower dispersion in organic farming may suggest that the soil and leaf microbiota under organic farming are more resilient to environmental stresses, benefiting from the complementary interactions among different taxa (Hartmann et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Mineral fertilization enhances nutrient absorption by fruits, however, excessive nitrogen application can increase the disease severity, coupled with fungicide application in agricultural production (Wei et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). High nutrient levels and disease pressures result in greater microbial diversity in fruit under conventional farming (Leff and Fierer, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, more studies are needed to solidify this argument. Furthermore, farming practices had more pronounced effects on bacterial diversity and community structure in the soil compared to other plant niches (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results indicate that microbial communities in the soil more sensitive to farming management, whereas microbiota in the leaf and root remain relatively stable, possibly due to the strong selection pressure from the plant host (Xiong et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn agreement with previous results (Xu et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), we found that Proteobacteria, Actinobacteria, Bacteroidetes, Acidobacteria, and Firmicutes were the most abundant bacterial phyla in the citrus orchard (Fig.\u0026nbsp;3). In the rhizosphere, root exudates serve as signaling molecules and nutrient sources, selectively recruiting microbes from bulk soil and strongly affecting the composition of surrounding soil microbiomes (Zhong et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We observed the enrichment of the bacterial phyla Proteobacteria and Actinobacteria in the root compared to bulk soils, whereas Bacteroidetes and Acidobacteria were depleted (Fig.\u0026nbsp;3A). This contrasts with other findings (Xu et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Trivedi et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), probably due to crop developmental progression, seasonal variations, and agriculture management practices. Moreover, under organic farming, a substantial decrease in Acidobacteria and Chlorofexi abundance was observed in the leaf and fruit, whereas Actinobacteria, Bacteroidetes, and Firmicutes were enriched in fruit and root compartments. Bacteroidetes were also enriched in the citrus leaf under organic farming (Fig.\u0026nbsp;3A and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), consistent with previous studies (Blaustein et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Passera et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bai et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Such compositional diversity most likely results from the decreasing abundance of slow-growing and oligotrophic bacteria (K-strategists), such as Acidobacteria and Chlorofexi, and the increasing dominance of fast-growing and copiotrophic bacteria (r-strategists), such as Bacteroidetes, Firmicutes, and Actinobacteria (Fierer et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Consistent with other studies (Bei et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), our results suggest that organic farming leads to the depletion of K-strategists and the enrichment of fast-growing microorganisms.\u003c/p\u003e \u003cp\u003eOrganic farming increases the abundance of beneficial microbes while suppressing pathogen growth (Granado et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Karlsson et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Banerjee et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), well supporting our findings in this study. For instance, Proteobacteria, Firmicutes, and Actinobacteria (Fig.\u0026nbsp;3A) are the most dominant phyla in the citrus microbiome (Bai et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Passera et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Trivedi et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The core genera from these phyla, including \u003cem\u003eBurkholderia\u003c/em\u003e and \u003cem\u003eStreptomyces\u003c/em\u003e, were more abundant under organic farming (Fig.\u0026nbsp;3B), and some are potentially beneficial microbes (Bei et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eBurkholderia\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, and \u003cem\u003eBacillus\u003c/em\u003e have been identified as agents capable of inhibiting plant diseases in different contexts (Trivedi et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Riera et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang et al., 2017; Tang et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notably, \u003cem\u003ePseudomonas\u003c/em\u003e was enriched in the fruit microbiome under conventional farming (Fig.\u0026nbsp;3), possibly as an adaption strategy to cope with the disease stressed condition under conventional farming.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Organic farming leads to higher microbial network stability\u003c/h2\u003e \u003cp\u003eMicroorganisms form intricate networks through multi-level associations which are beneficial for sustaining plant health and soil fertility, providing novel strategies for sustainable agricultural management (Agler et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; van der Heijden and Hartmann, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Morrien et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; de Vries et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Microbial co-occurrence networks of both farming practices exhibited distinct patterns of microbe-microbe connectivity and structure (Fig.\u0026nbsp;4A\u0026ndash;B), indicating the significance of particular microbial nodes in organic and conventional farming systems (van der Heijden and Hartmann, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our study highlights the significant impact of agricultural management on microbiota network structures, with a significantly more complex network under organic farming compared to conventional farming. According to network topology analysis (Table S2), organic networks had more edges, nodes, degrees, and connectivity, consistent with other studies based on different agricultural systems (Hartmann et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Banerjee et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Khoiri et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Complex networks with higher stability are more resistant to environmental stresses or invasion by exogenous microorganisms than simple networks with relatively low connectivity (Montoya et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Santolini and Barabasi, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In these co-occurrence networks, certain highly related taxa are considered as plant-associated microorganisms, and they could indeed play an important role in the plant microbiota (van der Heijden and Hartmann, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Contrary to conventional farming, higher complexity and connectivity in the organic network are associated with more hub taxa identified (Khoiri et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similar to the study in wheat (Banerjee et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the high complexity within the organic network (Fig.\u0026nbsp;4A and B) suggests that the microbiome under organic farming may be more resilient to environmental perturbations due to complementary interactions among the different taxa. However, further investigation is necessary to better understand underlying mechanisms, and the long-term outcomes remain to be further explored.\u003c/p\u003e \u003cp\u003eThe core microbiome, often defined as an essential set of microbes common in different microbial consortia from similar habitats, is crucial for understanding the composition, functions, and stability of complex microbial assemblages (Shade and Handelsman, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Busby et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The predominant taxa of the global rhizosphere microbiome in citrus orchards include Proteobacteria, Actinobacteria, Acidobacteria, and Bacteroidetes (Xu et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Some of these core microorganisms are more abundant under organic farming (Fig.\u0026nbsp;3 and Fig.\u0026nbsp;4A-B) and function redundantly for nitrogen fixation; such functional redundancy is important for the recovery and stability of microbial communities (Shade and Handelsman, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Impacts of farming practices on carbon source utilization by microbial communities\u003c/h2\u003e \u003cp\u003eThe Biolog Eco-Plate method is an efficient way to assess the physiological properties of microbial communities, especially in bulk and rhizosphere soils (Grayston et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The AWCD value, which represents soil microbial ability to utilize different carbon sources, serves as a vital index of microbial functional diversity (Feigl et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our results revealed significant effects of farming practices on the carbon utilization patterns of the bulk and rhizosphere soil microbial communities (Figs.\u0026nbsp;6 and 7, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, soil microorganisms were able to proliferate with high biodiversity under organic farming (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results were supported by our 16s DNA sequencing showing that the organic farming system has more abundant and diverse soil microorganisms than the conventional farming counterpart. Similar trends have been observed in apple (Zhu et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), banana (Chou et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), grape (Katayama et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and wheat (Karlsson et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) systems under diverse agricultural management practices.\u003c/p\u003e \u003cp\u003eMicrobial communities utilized six types of carbon sources in a distinct manner within and between farming practices (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), probably attributed to the presence of different functional groups (Kumar et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Efficient utilization of carbohydrates, amino acids, and carboxylic acids in O_BS suggests that organic farming may have provided more simple sugars compared to conventional farming. According to previous studies (Deleij et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), rapidly multiplied microorganisms are selected under uncrowded environmental conditions with easily accessible and rich nutrients (r-strategists). This might contribute to more diverse fast-growing heterotrophs in organic farming soils (Chou et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNotably, the distribution of microorganisms in bulk soils appears more homogeneous under organic farming (Bei et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), unlike the functional diversity of the rhizosphere microbiome. The rhizosphere soil under organic farming exhibits lower microbial richness and higher microbial evenness compared to that under conventional farming (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), possibly due to competitive interactions between root and soil microbes under organic farming. Plants tend to release certain volatiles that may restrict proliferation of microbial populations under organic farming (Chen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Just et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Semenov et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Biolog analysis showed separation of the O_RS microbiome from other communities (Fig.\u0026nbsp;7), suggesting greater influence of organic farming on microbial functionality than conventional farming. Changes in the diversity, composition, and functions of soil bacterial communities may be partially attributed to higher soil organic matter content and reasonable pH (Sun et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Xun et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Muneer et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, we focused on impacts of organic farming on composition, diversity, and functions of microbial communities along the plant-soil continuum with the conventional citrus orchard as the control. The organic farming orchard accommodated a more diverse microbial community than the conventional orchard, especially in soil and fruit compartments. The predominant bacterial phyla, including Proteobacteria, Actinobacteria, Bacteroidetes, Acidobacteria, and Firmicutes, showed distinct abundance under different farming practices. The relative abundance of Acidobacteria and Chlorofexi decreased significantly in the soil and fruit samples of the organic farming system. However, Actinobacteria, Bacteroidetes, and Firmicutes were enriched in fruit and root compartments, indicating depletion of slow-growing bacteria (K-strategists) and enrichment of copiotrophic bacteria (r-strategists) under organic farming. Meanwhile, relative abundance of \u003cem\u003eBurkholderia\u003c/em\u003e and \u003cem\u003eStreptomyces\u003c/em\u003e significantly increased under organic farming, indicating preferential colonization of potentially beneficial microbes. Moreover, the co-occurrence network analysis with topology assessments indicated greater complexity and connectivity of the organic network, implying that organic farming not only affects the abundance of specific taxa but also shapes interactions within the microbial community. Importantly, the current study highlighted the potential of organic farming to enhance soil microbial functional diversity and increase simple sugar availability. Overall, organic farming favorably alters microbial communities and the functional diversity in the citrus orchard. Our study provides a foundational framework for future research to elucidate mechanisms of the response of citrus microbiota to specific farming management. These valuable insights hold significant potential for enhancing citrus productivity and overall ecosystem health. Application of preconditioned beneficial soil and plant microbiota could potentially convert agrochemical-dependent agricultural systems into healthier and more environment-friendly systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLianlian Liu: Conceptualization, Validation, Investigation, Formal analysis, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization. Muhammad Atif Muneer: Writing \u0026ndash; review \u0026amp; editing. Yanting Zhong: Validation, Investigation, Supervision. Boyi He: Validation. Xuexian Li: Conceptualization, Writing - Review \u0026amp; Editing, Supervision, Project administration, Funding acquisition.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Research and Development Program of China\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(2021YFD1901100).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgler, M.T., Ruhe, J., Kroll, S., Morhenn, C., Kim, S.T., Weigel, D., Kemen, E.M., 2016. Microbial Hub Taxa Link Host and Abiotic Factors to Plant Microbiome Variation. PLoS Biol. 14 (1), e1002352 https://doi.org/10.1371/journal.pbio.1002352.\u003c/li\u003e\n\u003cli\u003eBai, Y., Wang, J., Jin, L., Zhan, Z., Guan, L., Zheng, G., Qiu, D., Qiu, X., 2019. Deciphering bacterial community variation during soil and leaf treatments with biologicals and biofertilizers to control huanglongbing in citrus trees. J. 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Plant J. 6 (4), 223\u0026ndash;230. https://doi.org/10.1016/j.hpj.2020.06.003.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gannan navel orange, Conventional farming, Organic farming, Microbial community, Functional diversity, Carbon source utilization","lastPublishedDoi":"10.21203/rs.3.rs-4933005/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4933005/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, improper agricultural management practices have led to the loss of biodiversity and poor fruit quality in orchards. Converting conventional farming to organic farming is an environmentally responsible approach to improving sustainable fruit production. However, questions remain regarding how the microbial community responds to different farming practices in citrus trees. Here, we explored and compared the microbial community structure and functional diversity of the Gannan navel orange orchard under organic and conventional farming using 16S rRNA gene sequencing and Biolog Eco-Plate analysis. The results showed that the microbial diversity (α-diversity index) under organic farming was higher than that under conventional farming, especially in the soil and fruit. The predominant bacteria found in the soil, root, leaf, and fruit were Proteobacteria, Actinobacteria, Bacteroidetes, Acidobacteria, and Firmicutes. However, distinct abundance patterns were observed under different farming practices. Actinobacteria, Bacteroidetes, and Firmicutes were more abundant in root and fruit compartments under organic farming, indicating that organic farming promotes the enrichment of copiotrophic bacteria (r-strategists). Furthermore, organic farming resulted in a considerable increase in the relative abundance of \u003cem\u003eBurkholderia\u003c/em\u003e and \u003cem\u003eStreptomyces\u003c/em\u003e in root tissues (the genus level), indicating that organic farming probably favors the proliferation of beneficial microorganisms and antagonists of pathogenic species. Interestingly, organic farming exhibited a more complex microbial network. Biolog analysis further revealed higher functional diversity of the soil microbial community under organic farming when compared with that under conventional farming. These findings provide evidence that organic farming improves the microbial community structure and promotes its functional diversity in the citrus orchards, contributing to the overall health and production of the citrus crop.\u003c/p\u003e","manuscriptTitle":"Organic farming significantly improves microbial community structure, network complexity, and functional diversity in the Gannan navel orange orchard","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-30 09:14:22","doi":"10.21203/rs.3.rs-4933005/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-30T11:46:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-30T04:24:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-30T04:23:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2024-08-18T10:50:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d5445a5-1707-4cc5-9afa-4006e66054cd","owner":[],"postedDate":"September 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T16:04:37+00:00","versionOfRecord":{"articleIdentity":"rs-4933005","link":"https://doi.org/10.1186/s12866-025-04271-2","journal":{"identity":"bmc-microbiology","isVorOnly":false,"title":"BMC Microbiology"},"publishedOn":"2025-08-29 15:57:28","publishedOnDateReadable":"August 29th, 2025"},"versionCreatedAt":"2024-09-30 09:14:22","video":"","vorDoi":"10.1186/s12866-025-04271-2","vorDoiUrl":"https://doi.org/10.1186/s12866-025-04271-2","workflowStages":[]},"version":"v1","identity":"rs-4933005","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4933005","identity":"rs-4933005","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}