Association of infrastructure and operations with antibiotic resistance potential in the dairy environment

Association of infrastructure and operations with antibiotic resistance potential in the dairy environment | 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 Association of infrastructure and operations with antibiotic resistance potential in the dairy environment Harshita Singh, Kenyum Bagra, Sourabh Dixit, Awanish Kumar Singh, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3926998/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We investigated the link between infrastructure and operations and the levels of antibiotic resistance potential within the dairy farm environment in India, which is the highest producer and consumer of dairy products. We sampled sixteen dairy farms in the Dehradun district, India, that varied in their herd size, infrastructure, and operational features during winter, summer, and monsoon. We collected dung, manure, wastewater, manure-amended and control soil samples from these farms. We quantified six antibiotic resistance genes (ARGs) [1] ( sul 1, sul 2, par C, mcr 5, erm F, and tet W), an integron integrase gene cassette ( int I1), and 16S rRNA gene copies as an indicator for total bacterial count. We observed that with increased ventilation in the farm that exposed the animals to external weather, the levels of sul 2 (x͂=10 -1.63 ) and par C (x͂=10 -4.24 ) in manure increased. Farms with textured floor types like brick and cement floors had higher levels of erm F in dung (x͂=10 -4.36 ) and par C in manure (x͂=10 -4.18 ) than farms with rubber mat-lined floors. When farmers prescribed antibiotic therapy without contacting any veterinary professional the relative levels of int I1 (x͂=10 -2.36 ), sul 2 (x͂=10 -1.58 ) and tet W (x͂=10 -3.04 ) in manure were lower than the cases where professional advice was involved. Small-scale farms had lower relative ARG levels than medium- and large-scale farms, except for mcr 5 (x͂=10 -3.98 ) in wastewater. The relative ARG levels trended as: manure-amended soil (x͂=10 -2.34 ) and control soil (x͂=10 -2.24 )> wastewater (x͂=10 -2.90 )> manure (x͂=10 -3.39 )> dung (x͂=10 -2.54 ); and summer (x͂=10 -2.91 ) and monsoon (x͂=10 -2.75 ) > winter (x͂=10 -3.38 ). Significant positive correlations were observed between specific ARGs and the int I1: dung ( sul 1 (ρ=0.88); sul 2 (ρ=0.94)), manure ( sul 2 (ρ=0.87); erm F (ρ=0.53)), wastewater ( sul 1 (ρ=0.74); sul 2 (ρ=0.66); par C (ρ=0.37); erm F (ρ=0.52)), and manure-amended soil ( sul 1 (ρ=0.73); sul 2 (ρ=0.77); par C (ρ=0.32); erm F (ρ=0.46). dairy excrement manure infrastructure-operations antibiotic resistance environment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The dairy industry, comprising 50% of the global livestock units (Baker et al., 2022 ; FAO, 2020 ) is a significant source of environmental dissemination and proliferation of antibiotic resistance (Baker et al., 2022 ). The current regulatory approaches have been ineffective in curbing the misuse of antibiotics in dairy farms (Gelband & Delahoy, 2014 ; Klein et al., 2021 ; Tacconelli & Diletta Pezzani, 2019 ), particularly in resource-constrained settings of low- and middle-income countries (LMICs), indicating the need to shift towards a disease prevention-based approach to reduce the need for antibiotic therapy (Pinto Jimenez et al., 2023 ; H. Singh et al., 2023 ). Since the disease prevalence in dairy farms is linked with infrastructural factors (floor type and ventilation) (A. K. Singh et al., 2020 ; Witkowska & Ponieważ, 2022 ) and operational practices (hygiene and footbaths) (Jacobs et al., 2019 ; Lindahl et al., 2019 ) improving infrastructure and operations could be key to controlling antibiotic resistance in the dairy environment. However, knowledge on the kind of infrastructure and operations that affect antibiotic resistance potential in dairy environment is missing, especially in resource-constrained dairy farms that are distinct and peculiar to top dairy producing countries, India and Pakistan (OECD/FAO, 2022 ). Despite being the largest in the world (Global Livestock Populations, 2020; OECD/FAO, 2022 ) the Indian dairy sector, comprising primarily small homestead farms, has unique infrastructural features and relies mostly on manual labour, and is challenged by limited access to trained veterinary care, underdiagnosis of diseases, and easy access to antibiotics, which are frequently marketed directly to farmers (Chauhan et al., 2018 , 2019 ; Jani et al., 2021 ; Mutua et al., 2020a ; Tiseo et al., 2020 ; Van Boeckel et al., 2015 ). As a result, Indian dairy farms find themselves simultaneously susceptible to under treatment of sick animals (Chauhan et al., 2018 ; Sharma et al., 2020a ) and misuse of antibiotics, including third- and fourth-generation antibiotics (Jindal et al., 2057 ; Ranjalkar & Chandy, 2019 ). In 2020–2030, the dairy industry expects its highest growth in production in India and Pakistan (OECD/FAO, 2022 ). Concurrently, antibiotic consumption is also estimated to rise by 67%, with India emerging as the largest consumer of antibiotics by 2030 (Laxminarayan et al., 2020 ; Van Boeckel et al., 2015 ). Being among the largest dairy producers and antibiotic consumers, the Indian dairy industry could become a fertile ground for the emergence and selection of antibiotic resistance (Holmes et al., 2016 ; Laxminarayan et al., 2013 ). Up to 90% of the antibiotics used in dairy farms are excreted in whole or metabolized form (Wallace et al., 2018 ; Zhao et al., 2010 ). The co-selectors that are released from dairy farms can exert selection pressure on local bacteria (Peng et al., 2015 , 2017 ; Sivagami et al., 2020 ; Tasho & Cho, 2016 ; Wichmann et al., 2014 ; Wu et al., 2023 ; Zainab et al., 2020 ; Zhang et al., 2019 ). Subsequent enrichment of the environmental resistome could present a public health challenge (Berendonk et al., 2015a ) if the antibiotic resistance transfers to clinically relevant pathogens (Berendonk et al., 2015b ; Brinkac et al., 2017 ; Smith et al., 2002 ). Exposure to livestock is a known risk factor for acquiring antibiotic resistance and is a threat to public health.(Landers et al., n.d.). In LMICs, such as India, where the dairy farms are usually very close to the human dwellings (N. Kumar et al., 2021 ), the risk of zoonotic transmission of antibiotic resistant pathogens and resistome is high (Dafale et al., 2020 ; Garcia et al., 2019 ; J. Li et al., 2019 ; Swarthout et al., 2022 ) and presents an occupational hazard for the exposed dairy farm workers and agricultural farmers (Kraemer et al., 2019 ; Xiong et al., 2018 ). We investigated the effect of infrastructure type (herd size, housing system, ventilation level, type of floor, drain lining) and operations (dung and wastewater disposal and veterinary consultation) in dairy farms on the levels of ARGs in excrements and manure-amended soil. We collected dung, manure, wastewater, manure-amended soil, and control soil from sixteen dairy farms in Dehradun district, India during summer, monsoon, and winter. These sixteen dairy farms had varying herd sizes and distinct infrastructure and operations. We checked for the presence of twenty ARGs conferring resistance to sulfonamides ( sul 1, sul 2), fluoroquinolones ( par C, gyr A, qnr A), tetracyclines ( tet A, tet O, tet W, tet M), polymyxin ( mcr 1, mcr 2, mcr 3, mcr 4, mcr 5), macrolides ( erm F), glycopeptides ( van A), β-lactams ( bla OXA1, bla TEM), multi-drug efflux pump ( acr A, acr B) and one integron integrase gene cassette int I1, of which only eight ARGs ( sul 1, sul 2, par C, mcr 5, erm F, tet W, bla OXA1, blaTEM) and class 1 integrase-integron gene, int I1, were detected. The following were quantified using quantitative polymerase chain reaction (qPCR): mcr 5, tet W, erm F, par C, sul 1, sul 2, int I1, and 16S rRNA gene copies. Some ARGs were associated with infrastructure (herd size, floor type, and ventilation) and operational practices (dung management and veterinary consultation). The ARG levels were higher during monsoon and summer than in winter, with animal-associated matrices (dung, manure, wastewater, and manure-amended soil) having higher potential for horizontal gene transfer. Dairy farm workers had greater ARG exposure while handling dung than manure and manure-amended soil. 2. Materials and Methodology 2.1 Site description and sampling : Sixteen dairy farms in Dehradun, India, were sampled in December 2018, May 2019, and August 2019, pertaining to winter (15 ± 0.8 ˚C), summer (31.7 ± 1.2 ˚C), and monsoon (25.3 ± 1.2 ˚C), respectively (Fig. 1 ). The dairy farms were selected based on accessibility and the owners' consent to collect samples regularly. Infrastructure- and operation-related factors on the farm were identified via observation and a short interview with the farm owner. The detailed features of the selected dairy farms are summarized in Table S1 . Dung samples were collected using a flame-sterilized spatula from the composite stack of the dung collected by each dairy farm at the end of the day. Similarly, manure samples were collected from the compost pits or heaps at the site. Wastewater samples expected to contain disinfectants, excrement, and milk from the farm were collected from outlet drains (lined or unlined) in 50 mL sterile centrifuge tubes (Abdos Labtech Pvt. Ltd, India) and aseptically filtered through a sterile 0.2 µM cellulose acetate membrane filter (Axiva Membrane Filters®, Axiva Sichem Pvt. Ltd, New Delhi). The filter paper was stored on ice during the transport to the laboratory and then at -20°C until further analysis. Manure-amended soil was collected from the fields amended with manure and wastewater from the dairy using a soil sampler, sterilized with 70% ethanol, following a random grid sampling method. The field was virtually divided into six nearly equal sections, and a sample was randomly collected from each section. Care was taken not to sample from the path disturbed by the sampling team. Soil samples in the summer and winter were collected nearly one month after manure application, and in monsoon were collected almost four months after application. However, the manure-amended farms regularly received wastewater from dairy farms. Control soil samples were collected from an area with minimal or no past interaction with cattle excrement but were exposed to other anthropogenic activities. Winter samples were stored in 50% ethanol, transferred to the laboratory, and stored at -20°C until further analysis. 2.2 Molecular biology assay : DNA was extracted from the samples using the Qiagen DNeasy® Powersoil DNA extraction kit (Qiagen®, Hilden, Germany) on the Qiacube® (Qiagen®, Hilden, Germany) following the manufacturer's protocol with a Tissue Lyser (Qiagen®, Hilden, Germany) used at 25 Hz for 30 seconds in the cell lysis step. The DNA was stored at − 20°C until downstream analysis. Polymerase chain reaction using Prima-96™ Thermal Cycler (HiMedia Laboratories Pvt Limited, Mumbai, India) was done initially to screen the presence of 20 ARGs (Table S2) and int I1, of which eight ARGs and int I1 were detected by gel electrophoresis using Hi-Gel Run1014 (HiMedia Laboratories Pvt Limited, Mumbai, India) and were targeted with qPCR using Rotor-Gene-Q (Qiagen®, Hilden, Germany). Of the targeted genes, 16S rRNA gene, sul 1, sul 2, tet W, erm F, mcr 5, par C and int I1 were quantifiable via qPCR. The quality and concentration of the extracted DNA were determined by a Nanodrop One C spectrophotometer (Thermo Scientific™, Massachusetts, USA). For qPCR, the DNA of the samples was diluted to 1:50 (Dung), 1:10 (Manure), 1:10 (wastewater), and 1:50 (manure-amended and control soil) for removal of PCR inhibition; the dilution ratio determined by a dilution curve of 16S rRNA gene copies. The qPCR standards were prepared by cloning the target amplicons on TOP10 competent cells using the TOPO TA Cloning kit (Invitrogen, CA, USA). Each qPCR run included a standard curve covering eight orders of magnitude and a negative control that used molecular biology grade water as a DNA control. Melt curve analysis with a temperature gradient from 50°C to 95°C was done at the end of every qPCR run to validate the specificity of the amplified products. The cycling conditions for each qPCR run are provided in SI text S1. The relative gene levels were calculated by dividing absolute gene copies with copies of 16S rRNA per sample and unless otherwise mentioned, only the relative levels of ARGs and int I1 are reported in this study. 2.3 Infrastructure and operational parameters 2.3.1 Scale Dairy farms were classified as small-scale (≤ 10 animals), medium-scale (11–100 animals), and large-scale (> 100 animals) based on herd size. 2.3.2 Floor type The floors of the surveyed dairy farms were lined with either brick, cement, or mud. Sometimes, the farmers laid rubber mats on the brick and cement floors to provide friction and comfort the animals. 2.3.3 Ventilation Dairy farms were categorized into four categories based on ventilation levels in decreasing order: Level A had high natural ventilation with no or very low walls and occasionally a basic frame-supported roof; Level B featured solid walls on three sides and a fourth wall with large doors and windows; Level C included housing with few windows, exhaust fans, and small entrance doors; and Level D had limited ventilation, with small windows and doors. 2.3.4 Dung management The dung from farms was either sent to composting pits (pit composting) or piled up on nearby fallow land (heap composting). A detailed description of all the other parameters considered in the study (housing system, drain lining, source of feed, and wastewater disposal) is provided in SI (Text S2). 2.4 Exposure assessment of ARGs for farm workers An occupational exposure assessment for farmers working with dairy waste, dung and manure was estimated using the following equations (Y. Wang et al., 2019 ): $${ADD}_{dermal}=\frac{C\times SA\times {P}_{C}\times EF\times {ET}_{skin}\times 24}{AT\times BW}$$ 1 $$BSA=0.02350\times {HT}^{0.42246}\times {BW}^{0.51456}$$ 2 $$SA=P\times BSA$$ 3 where, \({ADD}_{dermal}\) : average exposure dose of skin contact (copies/d/kg); C: average median concentration of ARG in gene copies/g. SA: exposed surface area (hands and feet) (m 2 ). \({P}_{C}\) : skin permeability (m/h). EF: exposure frequency (d). \({ET}_{skin}\) : skin contact exposure duration (h/d). AT: average lifespan (d). BW: body weight (Kg). BSA: total body surface area (m 2 ). HT: height (cm). BW: body weight (Kg). P: percentage of area covered by hands and feet The total body surface area was calculated using modified DuBois and DuBois formula(USEPA, 2011 ) with the average height and body weight specific to Indian males and females adapted from the National Institute of Nutrition, India report (ICMR-NIN, 2020 ). Skin permeability coefficient and average life span were adapted from Wang et al. ( 2019 )(Y. Wang et al., 2019 ). The percentage of body surface area in hands and feet used in Eq. ( 3 ) was adapted Exposure Factors Handbook from USEPA (USEPA, 2011 ). The skin contact frequency and duration were derived from discussions with farmers and dairy farm workers in the study region. These parameters are summarized in Tables S3 and S4. The interviews with farmers and this study were approved by Institute Human Ethics Committee (IITR/IIC/22/04). 2.5 Statistical analysis : Statistical analyses were done on R version 3.6.3 (R Core Team, 2022 ) with RStudio (RStudio Team, 2023 ) 2023 .03.0 + 386 "Cherry Blossom" Release for Windows as its graphical user interface. The Shapiro-Wilk test was employed to assess the normality of the ARGs. As the data was not normally distributed, Wilcoxon rank sum exact test was utilized to identify significant differences in the relative ARG levels across various sample matrices. Spearman correlation coefficients between the relative levels of target ARGs in all sample matrices were calculated using corrplot (Taiyun Wei & Viliam Simko, 2021) and ggcorrplot (Alboukadel Kassambara, 2022 ) packages. All statistical tests were done at 5% level of significance. 3. Results 3.1 Effect of season and matrix type on the ARG levels The levels of targeted gene markers in different matrices are summarized in SI Text S2. Overall, the levels of the targeted ARGs were 1–4 orders of magnitude higher in summer and monsoon than in winter (Fig. 2 , Table S7) and trended thus across all the matrices: manure amended soil and control soil > wastewater > manure > dung (Fig. 2 , Table S6). 3.1.1 Dung The levels of sul 1 in winter were lower than in summer (~ 1.6 orders of magnitude, p-value = 9.59E-05) and monsoon (~ 2 orders of magnitude, p-value = 2.23E-05). Similarly, the levels of sul 2 in winter were lower than in summer (~ 1.4 orders of magnitude, p-value = 0.0001) and monsoon (~ 1.9 orders of magnitude, p-value = 4.33E-05). The par C levels in summer were higher than in winter (~ 1.4 orders of magnitude, p-value = 9.59E-05) and monsoon (~ 2.5 orders of magnitude, p-value = 0.003). The levels of erm F in monsoon were higher (~ 1.6 orders of magnitude, p-value = 0.03) than in winter. The levels of tet W in winter were lower than in summer (~ 1.1 orders of magnitude, p-value = 0.001) and monsoon (~ 0.7 orders of magnitude, p-value = 0.008). The levels of int I1 in monsoon were higher (~ 2.7 orders of magnitude, p-value = 3.12E-05) than in winter. The levels of mcr 5 were comparable across all seasons. The levels of int I1 correlated with sul 1 (ρ = 0.94, p-value = 1.86E-13) and sul 2 (ρ = 0.88, p-value = 2.23E-10) in dung samples (Fig. 3 A). 3.1.2 Manure The levels of all target genes except par C and mcr 5 were higher in monsoon and summer than in winter (Fig. 2 , Table S7). The levels of sul 1 in monsoon were higher than in summer (~ 0.8 orders of magnitude, p-value = 0.01) and winter (~ 1.3 orders of magnitude, p-value = 7.24E-06). The levels of sul 2 in monsoon were also higher than in summer (~ 0.6 orders of magnitude, p-value = 0.048) and winter (~ 0.9 orders of magnitude, p-value = 0.001). The levels of int I1 in monsoon were higher than in summer (~ 0.8 orders of magnitude, p-value = 0.04) and winter (~ 1.1 orders of magnitude, p-value = 0.0005). The levels of erm F in monsoon were higher than in winter (~ 1 order of magnitude, p-value = 0.001). The levels of tet W in monsoon were slightly higher than in summer (~ 0.8 orders of magnitude, p-value = 0.02), and the par C levels in winter were slightly higher (~ 0.8 orders of magnitude, p-value = 0.006) than in summer. The levels of int I1 positively correlated with sul 2 (ρ = 0.87, p-value = 2.38E-16) and erm F (ρ = 0.53, p-value = 0.0004) in manure samples (Fig. 3 B). 3.1.3 Wastewater The levels of mcr 5 and tet W were comparable in all the sampling events (Fig. 2 , Table S7). The levels of sul 1 in summer (~ 0.8 orders of magnitude, p-value = 0.03) and monsoon (~ 1.2 orders of magnitude, p-value = 0.0001) were higher than in winter. The levels of sul 2 in monsoon were higher than in summer (~ 0.7 orders of magnitude, p-value = 0.046) and winter (~ 1.3 orders of magnitude, p-value = 0.001). The levels of int I1 were higher in summer (~ 1 order of magnitude, p-value = 0.007) and monsoon (~ 1.2 orders of magnitude, p-value = 0.0001) than in winter. The levels of par C in summer were higher in monsoon (~ 1.2 orders of magnitude, p-value = 0.0003) and winter (~ 1 order of magnitude, p-value = 0.03). The levels of erm F in winter were lower than in summer (~ 1.1 orders of magnitude, p-value = 0.02) and monsoon (~ 1.04 orders of magnitude, p-value = 0.0002). The levels of class I integron integrase, int I1, positively correlated with sul 1 (ρ = 0.74, p-value = 1.41E-07), sul 2 (ρ = 0.66, p-value = 3.43E-05), par C (ρ = 0.37, p-value = 0.03) and erm F (ρ = 0.52, p-value = 0.02) in the wastewater samples (Fig. 3 C). 3.1.4 Manure-amended soil The levels of erm F and tet W were similar across seasons (Fig. 2 , Table S7). The levels of sul 1 in monsoon were nearly an order of magnitude higher than in summer (p-value = 0.02) and winter (p-value = 0.004). The levels of sul 2 in monsoon were higher than in summer (~ 1.8 orders of magnitude, p-value = 0.001) and winter (~ 0.9 orders of magnitude, p-value = 0.01). Interestingly, the levels of sul 2 in winter were higher than in summer (~ 0.8 orders of magnitude, p-value = 0.005). The levels of int I1 in monsoon were higher than in winter (~ 1.4 orders of magnitude, p-value = 0.0007) and summer (~ 1.5 orders of magnitude, p-value = 0.001). The levels of mcr 5 in summer were slightly higher than in monsoon (~ 0.6 orders of magnitude, p-value = 0.03). The levels of par C in monsoon were higher than in summer (~ 1.2 orders of magnitude, p-value = 0.001) and winter (~ 0.9 orders of magnitude, p-value = 0.004). The levels of int I1 positively correlated with sul 1 (ρ = 0.73, p-value = 0.006), sul 2 (ρ = 0.77, p-value = 6.29E-08), par C (ρ = 0.32, p-value = 0.03) and erm F (ρ = 0.46, p-value = 0.01) in manure-amended soil samples (Fig. 3 D). 3.1.5 Control soil The levels of erm F and par C were comparable for all the sampling events (Fig. 2 , Table S7). The levels of sul 1 in monsoon were slightly higher than in winter (~ 0.8 orders of magnitude, p-value = 0.02). The levels of sul 2 in monsoon were higher than in summer (~ 1.3 orders of magnitude, p-value = 0.047) and winter (~ 1 order of magnitude, p-value = 0.02). The levels of int I1 in monsoon were higher than in winter (~ 1.1 orders of magnitude, p-value = 0.03). The levels of mcr 5 in summer were higher than in monsoon (~ 1.6 orders of magnitude, p-value = 0.04). Interestingly, the levels of tet W were higher in winter than in monsoon (~ 4.6 orders of magnitude, p-value = 0.002). No correlation was found between the level of ARGs and int I1 in control soil that was not amended with manure (Fig. 3 E). 3.2 Effect of herd size on the levels of ARGs The ARG levels were generally lower in small-scale farms than medium- and large-scale farms, except mcr 5 in wastewater. In manure, the levels of tet W (~ 0.8 order of magnitude, p-value = 0.02) and sul 2 (~ 0.5 order of magnitude, p-value = 0.01) were higher in medium-scale farms than in small-scale farms. The levels of all other target ARGs and int I1 in manure were similar in medium- and large-scale farms (Fig. 4 A and 4 B, Table S7). In wastewater, the levels of mcr 5 from small-scale farms were higher than those in medium-scale farms (~ 1.5 orders of magnitude, p-value = 0.01) and large-scale farms (~ 1.8 orders of magnitude, p-value = 0.005). The levels of tet W in wastewater were slightly higher in medium-scale farms than in small-scale farms (p-value = 0.03) (Fig. 4 C and 4 D, Table S7). In dung, the levels of tet W in medium-scale farms were comparable to those in large-scale farms and slightly (~ 0.8 order of magnitude, p-value = 0.04) higher than those in small-scale farms. No significant difference was observed in the levels of all other targeted ARGs and int I1 in dung from farms of different scales (Fig. 4 E, Table S7). 3.3 Effect of infrastructure on the levels of ARGS. The levels of some ARGs in dung, manure and wastewater samples varied with the ventilation level and floor type in the dairy farms (Fig. 5 , Table S7): 3.3.1 Ventilation In manure samples, the levels of sul 2 in farms with ventilation level A were slightly higher (~ 0.6 orders of magnitude, p-value = 0.03) than in farms with ventilation level C. In wastewater, the levels of par C were higher in farms with ventilation level C than those with ventilation level D (~ 1 order of magnitude, p-value = 0.04). 3.3.2 Type of floor The levels of erm F in dung from farms with brick floors (n = 3) was higher (~ 2 orders of magnitude, p-value = 0.03) than in farms with rubber mat lined-cement floors (n = 8). The levels of par C in manure in farms with cement floors (n = 2) were slightly higher (~ 0.8 orders of magnitude, p-value = 0.04) than in farms with rubber mat-lined cement floors. 3.4 Effect of the dairy operational practices on the levels of ARGS. In the case of operational practices, the levels of some ARGs in different matrices varied with dung management and the choice of veterinary consultation on the farm. 3.4.1 Dung management Only the levels of mcr 5 in manure were slightly (~ 0.7 orders of magnitude, p-value = 0.003) higher in farms with heap composting than in farms with pit composting (Fig. 4 F, Table S7). 3.4.2 Reported choice of veterinary consultation In manure, the levels of int I1 (p-value = 0.02), sul 2 (p-value = 0.006) and tet W (p-value = 0.01) were an order of magnitude higher in farms that reported administering the medication on the advice of the para-veterinary worker and older prescriptions, hearsay and one’s experience compared to the farms that reported using treatment exclusively based on older prescriptions. Similarly, the levels of int I1 (p-value = 0.04) and sul 2 (p-value = 0.02) were nearly an order of magnitude higher for farms where animals were reportedly treated on veterinarians’ advice exclusively compared to farms that relied on themselves, hearsay, or older prescriptions for providing veterinary care. The levels of mcr 5 were slightly higher (p-value = 0.03) for farms that reported seeking advice from veterinarians and treatment based on one’s experience than farms that reported treatment exclusively on one’s knowledge or hearsay (Fig. 6 ). 3.5 Exposure assessment The average daily dermal exposure dose was calculated using median of absolute levels of total ARGs and Eq. ( 1 ) for exposure to dung, manure, and manure-amended soil samples. The ADD dermal for dairy farm workers from dung was 7.56 gene copies/kg/day for males and 7.66 gene copies/kg/day for females and from manure was 3.89 x 10 − 1 gene copies/kg/day for males and 3.94 x 10 − 1 gene copies/kg/day for females. For farmers exposed to the manure-amended soil, the ADD dermal was 3.84 gene copies/kg/day for males and 3.89 gene copies/kg/day for females (Fig. 3 F). 4. Discussion 4.1 Certain infrastructure and operational features are associated with higher levels of ARGs. The housing type determines the exposure to extreme temperatures and ventilation, while the floor type impacts the ease of maintaining hygiene, which affects the exposure of animals to wet excrements. Thus, these two infrastructure and operational features - ventilation level (determined by the housing type) and floor type - can contribute to the increased prevalence of infections in dairy farms. As observed in the current study, dairy farms characterized by challenging-to-clean floor types (brick and textured cement floors) and ventilation levels that expose animals to extremes in weather conditions were associated with higher levels of ARGs. In a prior study, we reported that the presence of brick, mud, and textured cement floors increased the likelihood of reporting diseases like mastitis and secondary infections related to foot and mouth disease (H. Singh et al., 2023 ). In the current study, the higher relative levels of ARGs in farms with brick and textured cement floors compared to the farms that used rubber mats as a barrier to the floor could be attributed to the direct contact with the retained dairy waste in pores and crevices (Jørgensen et al., 2016 ; Rapp et al., 2021 ) and to difficulty in cleaning (Calderón-amor & Gallo, 2020 ). The farm floor frequently receives antibiotics and other co-selectors from excrement, milk, and disinfectants (Li et al., 2016 ). It has been documented elsewhere that properly cleaning the floors significantly reduces the pathogen count for Streptococci , Escherichia coli , Klebsiella spp., and coliform counts (Lowe et al., 2015 ; Velazquez et al., 2019 ). Floors that are inherently hard to clean (brick and textured cement floors) may result in a higher count of these pathogens on the floor (DeVries et al., 2012 ). We note that a barrier between the animal and the floor (rubber mats) that is easy to clean was associated with lower levels of ARGs in the dairy environment and could be used as an intervention. Very high levels of ventilation associated with open housing may expose animals to adverse weather conditions, potentially resulting in poor immunity and increasing the risk of infections. Conversely, inadequate ventilation could prolong exposure to moisture and heighten the circulation of pathogens and ARGs in the air inside the farm environment, as reflected in the findings of this study (Gao et al., 2022 ; Gibbs et al., 2006 ). We note that adequate ventilation would be the middle ground between nearly open sheds and closed poorly ventilated housing for dairy animals. In the current study, 75% of the farms (n = 12) utilised heap composting for manure. This practice involves piling up dung on nearby fallow land, which sometimes receives dung from multiple farms. A higher relative level of gene conferring resistance to polymyxin E ( mcr 5) was observed in manure that was heap composted (piled-up dung) compared to manure that was aerobically composted in pits, implying that pit composting performed better than heap composting for preventing any selection of mcr 5 (Qian et al., 2018 ; M. Wang et al., 2018 ). The selection of polymyxin E/colistin resistance gene, mcr 5, in the heap could directly add to the increased resistance in the environment when applied to the fields as fertiliser or through surface runoff. Even though colistin is not used very frequently in dairy farms, being on the ‘reserve’ group of the AWaRe watchlist of WHO (Infographics, 2022 ), the significantly higher relative levels of gene conferring resistance to polymyxin E in the manure is concerning. 4.2 Manure amendment did not increase ARG levels in soil 1–4 months post-application. Previous studies have documented that the application of manure and dairy-impacted wastewater elevates the relative abundance of ARGs in soil (Dungan et al., 2018 ; Ruuskanen et al., 2016 ), which attenuates over time. Our study noted no significant difference in the relative levels of ARGs between soil that had been amended with manure one month after amendment in summer and winter and four months after application in monsoon compared to control soil. This suggests that the impact of animal excrement on antibiotic resistance potential in soil may not be long-term. Other studies (Chen et al., 2017 ; Muurinen et al., 2017 ) similarly noted that the initially elevated levels of ARGs in soil resulting from manure application tended to return to baseline over two to six weeks. The gap between manure application and sample collection in our study might explain the comparable relative levels of ARGs in the manure-amended control soil samples. Any trends in the increase in antibiotic resistance potential due to anthropogenic impacts are expected to be reflected in the levels of int I1 and sul 1 (Chaturvedi et al., 2021 ; Davis et al., 2020 ). However, in our study, the levels of these genes also remained comparable, regardless of whether manure was applied or not. At the same time, the levels of int I1 correlated with the levels of other targeted ARGs in the samples associated with dairy farms (dung, manure, wastewater, and manure-amended soil), but this was not the case for the control soil that was not amended with manure. Even though the levels of targeted ARGs were similar in both soil samples, the significant correlation between targeted ARGs and int I1 in manure-amended soil implies that the potential for horizontal gene transfer of ARGs may be higher in manure-amended soil. Despite no long-term increase in ARG levels in manure-amended soil, the link between ARGs and int I1 in dairy-impacted samples (including manure-amended soil) suggests that dairy waste and manure impact the soil resistome. 4.3 Exposure assessment The study reveals a hierarchy of exposure risks, showing that dairy farm workers handling dung were exposed to the highest ADD dermal levels followed by agricultural farm workers working with manure-impacted soil. The least exposed population were the workers handling manure, consistent with their comparatively lower frequency and duration of exposure. Notably, manual cleaning of dairy farms is typical for India and small-scale farms across LMICs. Manual cleaning increases direct exposure to dung, manure, and wastewater from the dairy farms for the dairy farmers who work in contrast to the farms that use mechanical scrapers to clean the floor. Even though the levels of ARGs detected in the environment of dairy farms in India that we targeted herein are comparable to the levels detected in high- and middle-income countries (Huang et al., 2023 ; Kyselková et al., 2015 ; M. M. Li et al., 2020 ; Munir & Xagoraraki, 2011 ; Sun et al., 2019 ; Tian et al., 2021 ; G. Wang et al., 2022 ; L. Wang et al., 2019 ), the amount of exposure to pathogens and antibiotic resistance potential expected in Indian dairy farms, who rely on manual cleaning, would be much higher. Most dairy farmers utilize family labour to clean the dairy farms manually, increasing the potential for community transmission. The use of mechanical scrapers is expected to reduce workers' direct exposure to dung and manure. Notably, samples for manure-amended soil in our study were collected 1–4 months after manure application. This temporal delay suggests that the ARG levels to which farmers were exposed during application were likely higher than those observed in this study. While the agricultural farmers have a high overall exposure to ARGs, their frequency of exposure is lower compared to the dairy farm workers. 4.4 Small-scale farms had lower levels of some ARGs and might be undertreating the animals. In the current study, we noted that despite the variety in how antibiotics were prescribed, the levels of ARGs in dairy farms were comparable in most cases. In LMICs like India, dairy farms are primarily small stead (Mutua et al., 2020b ), maintained by farmers for economic and nutritional resilience, rely mostly on severely constrained capital, and have limited profit margins (Chauhan et al., 2018 ). There is also a lack of awareness of diseases and prevention (V. Kumar & Gupta, 2018 ), antibiotic usage (Sharma et al., 2020a ), government policies, and access to trained veterinary healthcare in small-scale farms (Chauhan et al., 2018 ; Sharma et al., 2020b ). In our study, the participating farms had unequal access to para-veterinary workers, quacks, and veterinarians, and the choice of consultation and prescription was subject to ease of access and availability of funds. Only 25% (n = 4) of farms, which were all large and medium scale, exclusively approached a veterinarian for advice, while the rest reported mainly self-medicating through old prescriptions and only sought veterinary advice if it was affordable and readily available at the time of need. It has also been reported elsewhere that the use of antibiotics is highly influenced by factors such as affordability and availability of a drug, farmers’ expectations, and the potential of a follow-up on treatment (Chauhan et al., 2018 ; Klein et al., 2021 ; N. Kumar et al., 2021 ; Sulis et al., 2020 ; Vijay et al., 2021 ). As reported by other studies in India, the cost of treatment is a limiting factor in the choice of veterinary healthcare options for most small-scale farmers (Chauhan et al., 2018 ; Mutua et al., 2020b ). Due to the lack of affordability, small farmers sometimes do not opt for antibiotic therapy (N. Kumar et al., 2021 ). This could account for the lower ARG levels observed in the small-scale farms in our study. Even though the dairy industry has been accused of overconsumption of antibiotics, our study suggests that in small-scale farms, rather than misuse or abuse of antibiotics, there is probably undertreatment of animals due to the economic constraints of the farmer. In a mixed method study conducted in India, Kumar et al. ( 2021 ) found that only 10% of dairy farmers reported using antibiotics in the past year, confirmed by the positive detection of antibiotic residue in milk samples from only 8% of the total farms. Despite lack of awareness of antibiotic resistance and ease of availability of antibiotics, in small-scale farms the animals are more likely to be undertreated, and their contribution to the dissemination of resistance into the environment is probably overestimated (N. Kumar et al., 2021 ). Some farmers reported seeking care from parallel (frequently untrained) healthcare - quacks and para veterinarians, and, at times, medicating animals based on hearsay and personal experiences. A qualitative study by Chauhan et al. ( 2018 ) also reported the widespread practice of self-prescribing medical treatment due to a shortage of trained professionals and over-the-counter availability of antibiotics from pharmacies, often without formal prescriptions(Chauhan et al., 2018 ). The treatment from parallel healthcare workers can be effective (Sudhinaraset et al., 2013 ); however, it is susceptible to the misuse of antibiotics or inappropriate or inadequate doses, and failed treatment(Sharma et al., 2020a ; Sudhinaraset et al., 2013 ). When inappropriate antibiotic dose is used for less than the required duration, it can lead to incomplete recovery and development of antibiotic resistance(Lipsitch & Samore, 2002 ). In contrast, farms with larger herds are primarily commercial and had sufficient capital to invest in animal healthcare. Studies conducted elsewhere have documented a higher rate of consultation with a veterinarian and higher per day average consumption of antibiotics in large-scale farms (V. Kumar & Gupta, 2018 ; Santman-Berends et al., 2014 ). Interestingly, the relative levels of mcr 5 in wastewater from small-scale farms were higher than those from medium- and large-scale farms. One possible reason could be that most small-scale dairy farms participating in the study were also engaged in backyard poultry farming, thus adding to the released load of co-selectors into the shared environment. Elsewhere, it has been documented that the reported abundance of ARGs coding resistance to certain classes of antibiotics in the animal population that they were not exposed to in the first place suggests that antibiotic residue in the environment could promote the development and spread of resistance (Durso et al., 2011 ; Feng et al., 2020 ; Thames et al., 2012 ). 4.5 The ARG levels in dairy waste and manure-impacted soil were higher in warmer seasons. The bacterial infections in livestock exhibit seasonal variation, with higher prevalence during the summer and monsoon seasons (Islam et al., 2019 ; Klotz et al., 2019 ; Saminathan et al., 2016 ). Studies from India, Pakistan and Nepal have reported higher incidences of mastitis in hot and humid months (Ali et al., 2021 ; Joshi & Gokhale, 2006 ; Regmi et al., 2020 ; K. Singh et al., 2021 ). Many pathogens like Campylobacter and E. coli in fecal samples also tend to have higher abundance during summer months in dung (Hoque et al., 2021 ; Schneider et al., 2018 ; Stanford et al., 2016 ). In the region investigated in this study, antibiotics are predominantly used on dairy farms for therapeutic purposes. The higher relative levels of ARGs observed in dung, manure and wastewater during the warmer season may be attributed to an increased prevalence of diseases and antibiotic therapy during the warmer seasons and subsequent release of antibiotic residue into the environment (Gullberg et al., 2011 ; Xie et al., 2018 ). 5. Conclusion We investigated the link between the infrastructure and operations and the antibiotic resistance release potential within the environment of the dairy farms in resource-constrained settings of north India. The surveyed dairy farms had distinct infrastructural and operational characteristics, some associated with higher ARG levels in dung, manure, and wastewater. Notably, the application of manure and wastewater to soil did not significantly elevate ARG levels compared to non-amended control soil in the long term. The ARG levels demonstrated a seasonal variation, with higher concentrations observed during warmer seasons and followed the pattern: manure-amended and control soil > wastewater > manure > dung. Significantly, in dung, wastewater, manure, and manure-amended soil, the int I1 correlated positively with ARGs. Recognizing the interconnectedness of infrastructure, operational practices, and antibiotic resistance potential in dairy farms provides valuable insights for developing targeted interventions. Declarations Ethical Approval: The interviews with farmers and this study were approved by Institute Human Ethics Committee (IITR/IIC/22/04). Consent to participate: Due consent was taken from all the participating farmers for interviews and collection of samples from their dairy farms. Consent to publish: No consent to publish is required as the study contains original material. All the authors have agreed to the submission of this manuscript. Authors contribution: All authors contributed to the study conception and design. Author credits are as follows: Conceptualization: Gargi Singh and Awanish Kumar Singh; Methodology: Awanish Kumar Singh and Gargi Singh; Formal analysis and investigation: Harshita Singh, Kenyum Bagra, and Sourabh Dixit; Writing - original draft preparation: Harshita Singh; Writing - review and editing: Gargi Singh; Funding acquisition: Gargi Singh and Awanish Kumar Singh; Resources: Gargi Singh and Awanish Kumar Singh; Supervision: Gargi Singh and Awanish Kumar Singh Funding: This research was supported by Science and Engineering Research Board, India (Grant number: CRG/2020/005658) and Faculty Initiation Grant provided by IIT Roorkee (FIG/100762). We are also grateful to all the participating farmers, who consented to volunteer their time and share their experiences for this study. Competing interests: The authors have no competing financial or non-financial interests to disclose. References Alboukadel Kassambara. 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Long-term effects of manure and chemical fertilizers on soil antibiotic resistome. Soil Biology and Biochemistry , 122 , 111–119. https://doi.org/10.1016/j.soilbio.2018.04.009 Xiong, W., Sun, Y., & Zeng, Z. (2018). Antimicrobial use and antimicrobial resistance in food animals. In Environmental Science and Pollution Research (Vol. 25, Issue 19, pp. 18377–18384). Springer Verlag. https://doi.org/10.1007/s11356-018-1852-2 Zainab, S. M., Junaid, M., Xu, N., & Malik, R. N. (2020). Antibiotics and antibiotic resistant genes (ARGs) in groundwater: A global review on dissemination, sources, interactions, environmental and human health risks. In Water Research (Vol. 187). Elsevier Ltd. https://doi.org/10.1016/j.watres.2020.116455 Zhang, Y. J., Hu, H. W., Chen, Q. L., Singh, B. K., Yan, H., Chen, D., & He, J. Z. (2019). Transfer of antibiotic resistance from manure-amended soils to vegetable microbiomes. Environment International , 130 . https://doi.org/10.1016/j.envint.2019.104912 Zhao, L., Dong, Y. H., & Wang, H. (2010). Residues of veterinary antibiotics in manures from feedlot livestock in eight provinces of China. Science of the Total Environment , 408 (5), 1069–1075. https://doi.org/10.1016/j.scitotenv.2009.11.014 Footnotes Antibiotic resistant genes Deoxyribonucleic acid Quantitative polymerase chain reaction Supplementary Files DairypaperSI30112023.docx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3926998","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275505391,"identity":"9b2803e0-1033-47ac-ae0e-335d5c841b3e","order_by":0,"name":"Harshita Singh","email":"","orcid":"","institution":"Indian Institute of Technology Roorkee","correspondingAuthor":false,"prefix":"","firstName":"Harshita","middleName":"","lastName":"Singh","suffix":""},{"id":275505392,"identity":"06e82704-4a04-4a20-be1b-3adf18321707","order_by":1,"name":"Kenyum Bagra","email":"","orcid":"","institution":"Indian Institute of Technology Roorkee","correspondingAuthor":false,"prefix":"","firstName":"Kenyum","middleName":"","lastName":"Bagra","suffix":""},{"id":275505393,"identity":"fe55a13b-4168-4213-8f1e-bdcd4e2f0ae3","order_by":2,"name":"Sourabh Dixit","email":"","orcid":"","institution":"Indian Institute of Technology Roorkee","correspondingAuthor":false,"prefix":"","firstName":"Sourabh","middleName":"","lastName":"Dixit","suffix":""},{"id":275505394,"identity":"4ec9fb9b-3e53-455e-8563-73b4c1a26bc1","order_by":3,"name":"Awanish Kumar Singh","email":"","orcid":"","institution":"Govind Ballabh Pant University of Agriculture and Technology: Govind Ballabh Pant University of Agriculture \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Awanish","middleName":"Kumar","lastName":"Singh","suffix":""},{"id":275505395,"identity":"519e5ace-99f0-4e8a-8f2e-6938946072e1","order_by":4,"name":"Gargi Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYLCCBAYGHiBifABk87BBBXmI0cJsANbCRowWqBo2CTDNRkCdvPvZxy8eMNTJmPOvPVbxo4JBhk++9wHDjxoGGXMcWgzPpJtZJDAc5rGc8S7tZs8ZkMPYDRh7jjHwWDbg0NKQxmaQwHCAx+DGGbPbjG0gLUCX8TYw8BgcwKGl/xlISx1YSzHjP4gWxr94tMhLpDE/SGBg5jE432PGzNgA0cKMzxYDiWdsDAkGh4G28BhL9hyTAGpJYzgsA2TgtKU/jfnjj4o6e4PzZww//KixsZdvPsb48A2QgdOWAwzA6ADFoUQCiA+JmgMwBlZbGhiYP4BZ/DgMHQWjYBSMglEAAGHnSdfF+6MjAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-2433-5441","institution":"Indian Institute of Technology Roorkee","correspondingAuthor":true,"prefix":"","firstName":"Gargi","middleName":"","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2024-02-04 09:24:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3926998/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3926998/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51973369,"identity":"a7a58eeb-ae8e-4d46-af11-1db0197b9898","added_by":"auto","created_at":"2024-03-04 19:01:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":501079,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Map of India with study site, Dehradun district, marked with red dot. The green colour shows the state of Uttarakhand. (B) The locations of dairy farms sampled in this study (S1 to S16) are shown in blue encircled numbers \u003cstrong\u003e(1 to 16)\u003c/strong\u003e in a Google Map snapshot of the Dehradun district.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3926998/v1/14991707d7d281a263bcf1a0.png"},{"id":51973370,"identity":"b943161a-0377-45ac-b675-6604f71e45e8","added_by":"auto","created_at":"2024-03-04 19:01:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":149085,"visible":true,"origin":"","legend":"\u003cp\u003eThe y-axis represents levels of ARGs (\u003cem\u003esul\u003c/em\u003e2, \u003cem\u003esul\u003c/em\u003e1, \u003cem\u003etet\u003c/em\u003eW, \u003cem\u003eerm\u003c/em\u003eF, \u003cem\u003emcr\u003c/em\u003e5 and \u003cem\u003epar\u003c/em\u003eC) and \u003cem\u003eint\u003c/em\u003eI1 relative to 16S rRNA gene count in dung, manure, wastewater, manure-amended soil, and control soil samples on a log\u003csub\u003e10\u003c/sub\u003e scale. The x-axis represents the sampling seasons of \u003cstrong\u003esummer \u003c/strong\u003e(March), \u003cstrong\u003emonsoon \u003c/strong\u003e(July), and \u003cstrong\u003ewinter \u003c/strong\u003e(December).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3926998/v1/030b78897ffa67abcdafcb94.png"},{"id":51974363,"identity":"4c171a07-d746-476c-93c9-f245c0815505","added_by":"auto","created_at":"2024-03-04 19:09:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":177874,"visible":true,"origin":"","legend":"\u003cp\u003eA-E represent the Spearman correlation between ARGs with significance levels (*** 0.001, ** 0.01, * 0.05) in dung, manure, wastewater, manure-amended soil and control soil samples. Table F displays the average dairy dermal exposure dose ADDdermal) of total ARGs in (copies/d/Kg) for a male and female worker dealing with dung, manure and manure-amended farm soil.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3926998/v1/8b25d97f43db9e61bc1fae0a.png"},{"id":51974362,"identity":"38485e6d-99e8-4f88-bb02-00621a95f0b9","added_by":"auto","created_at":"2024-03-04 19:09:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":160762,"visible":true,"origin":"","legend":"\u003cp\u003eThe y-axis indicates levels of ARGs relative to the 16S rRNA gene count in the samples from dairy farms on a log\u003csub\u003e10\u003c/sub\u003e scale. In \u003cstrong\u003eA-E\u003c/strong\u003e, the x-axis represents the scale of dairy farms with ‘\u003cstrong\u003eSmall’\u003c/strong\u003e, ‘\u003cstrong\u003eMedium’\u003c/strong\u003e, and ‘\u003cstrong\u003eLarge’\u003c/strong\u003e referring to small-scale (animals \u0026lt;=10), medium-scale (animals 10 -100), and large-scale (animals \u0026gt;100) dairy farms, respectively. In \u003cstrong\u003eF\u003c/strong\u003e, the x-axis represents the method of dung management, where \u003cstrong\u003e‘Compost pits’\u003c/strong\u003e refers to the dairy farms that used pit composting and \u003cstrong\u003e‘Fallow land’\u003c/strong\u003e refers to the dairy farms that practice heap composting over fallow lands.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3926998/v1/02e103d93aaf40096d85bfe5.png"},{"id":51973374,"identity":"07e9897e-bba2-416e-8128-d3e3af018c67","added_by":"auto","created_at":"2024-03-04 19:01:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":100518,"visible":true,"origin":"","legend":"\u003cp\u003eThe y-axis indicates levels of ARGs relative to the 16S rRNA gene count in the samples from dairy farms on a log\u003csub\u003e10\u003c/sub\u003e scale. In \u003cstrong\u003eA\u003c/strong\u003e and \u003cstrong\u003eB\u003c/strong\u003e, the x-axis represents the level of ventilation in the farm in descending order: Level A (\u003cstrong\u003eA\u003c/strong\u003e), Level B (\u003cstrong\u003eB\u003c/strong\u003e), Level C (\u003cstrong\u003eC\u003c/strong\u003e) and Level D (\u003cstrong\u003eD\u003c/strong\u003e). In \u003cstrong\u003eC\u003c/strong\u003e and \u003cstrong\u003eD\u003c/strong\u003e, the x-axis represents the type of floor on the farm- Brick floors (\u003cstrong\u003eBrick\u003c/strong\u003e), Rubber mat-lined brick floors (\u003cstrong\u003eBrick +R\u003c/strong\u003e), Cement floors (\u003cstrong\u003eCement\u003c/strong\u003e), Rubber mat-lined cement floors (\u003cstrong\u003eCement+R\u003c/strong\u003e) and Mud floors (\u003cstrong\u003eMud\u003c/strong\u003e). (Factors as defined in section 2.3).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3926998/v1/60fcb8c3ae287cd10df4c957.png"},{"id":51973373,"identity":"4477fc4d-f335-466f-9308-ea243dd62073","added_by":"auto","created_at":"2024-03-04 19:01:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":158448,"visible":true,"origin":"","legend":"\u003cp\u003eIn \u003cstrong\u003eA-D\u003c/strong\u003e, the y-axis indicates levels of ARGs relative to the 16S rRNA gene count in the samples from dairy farms on a log\u003csub\u003e10\u003c/sub\u003e scale. The x-axis represents the source of veterinary advice on the farm. \u003cstrong\u003eQ+S\u003c/strong\u003e, \u003cstrong\u003eP+S\u003c/strong\u003e, and \u003cstrong\u003eV+S\u003c/strong\u003e refer to farms that relied on quacks, para-veterinary workers, and veterinarians, along with previous prescriptions/ personal experience in some instances. \u003cstrong\u003eV\u003c/strong\u003e and \u003cstrong\u003eS\u003c/strong\u003e refer to farms that reported relying exclusively on a veterinarian and personal experience, respectively.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3926998/v1/f73f8983883bc462d90d638c.png"},{"id":54823801,"identity":"c3203ccb-9a81-4822-87ba-23f4a405b83a","added_by":"auto","created_at":"2024-04-17 09:26:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1775714,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3926998/v1/81161141-26fd-4305-8b89-088ddf4e2957.pdf"},{"id":51973375,"identity":"90095f3e-f330-401b-9d2c-dbd56da25aab","added_by":"auto","created_at":"2024-03-04 19:01:03","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":69084,"visible":true,"origin":"","legend":"","description":"","filename":"DairypaperSI30112023.docx","url":"https://assets-eu.researchsquare.com/files/rs-3926998/v1/0057fbbb3ce975147292cf82.docx"}],"financialInterests":"","formattedTitle":"Association of infrastructure and operations with antibiotic resistance potential in the dairy environment","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe dairy industry, comprising 50% of the global livestock units (Baker et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; FAO, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) is a significant source of environmental dissemination and proliferation of antibiotic resistance (Baker et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The current regulatory approaches have been ineffective in curbing the misuse of antibiotics in dairy farms (Gelband \u0026amp; Delahoy, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Klein et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tacconelli \u0026amp; Diletta Pezzani, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), particularly in resource-constrained settings of low- and middle-income countries (LMICs), indicating the need to shift towards a disease prevention-based approach to reduce the need for antibiotic therapy (Pinto Jimenez et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; H. Singh et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Since the disease prevalence in dairy farms is linked with infrastructural factors (floor type and ventilation) (A. K. Singh et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Witkowska \u0026amp; Ponieważ, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and operational practices (hygiene and footbaths) (Jacobs et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lindahl et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) improving infrastructure and operations could be key to controlling antibiotic resistance in the dairy environment. However, knowledge on the kind of infrastructure and operations that affect antibiotic resistance potential in dairy environment is missing, especially in resource-constrained dairy farms that are distinct and peculiar to top dairy producing countries, India and Pakistan (OECD/FAO, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite being the largest in the world (Global Livestock Populations, 2020; OECD/FAO, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) the Indian dairy sector, comprising primarily small homestead farms, has unique infrastructural features and relies mostly on manual labour, and is challenged by limited access to trained veterinary care, underdiagnosis of diseases, and easy access to antibiotics, which are frequently marketed directly to farmers (Chauhan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jani et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mutua et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Tiseo et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Van Boeckel et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As a result, Indian dairy farms find themselves simultaneously susceptible to under treatment of sick animals (Chauhan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e) and misuse of antibiotics, including third- and fourth-generation antibiotics (Jindal et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2057\u003c/span\u003e; Ranjalkar \u0026amp; Chandy, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2020\u0026ndash;2030, the dairy industry expects its highest growth in production in India and Pakistan (OECD/FAO, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Concurrently, antibiotic consumption is also estimated to rise by 67%, with India emerging as the largest consumer of antibiotics by 2030 (Laxminarayan et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Van Boeckel et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Being among the largest dairy producers and antibiotic consumers, the Indian dairy industry could become a fertile ground for the emergence and selection of antibiotic resistance (Holmes et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Laxminarayan et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Up to 90% of the antibiotics used in dairy farms are excreted in whole or metabolized form (Wallace et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The co-selectors that are released from dairy farms can exert selection pressure on local bacteria (Peng et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sivagami et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tasho \u0026amp; Cho, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wichmann et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zainab et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Subsequent enrichment of the environmental resistome could present a public health challenge (Berendonk et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e) if the antibiotic resistance transfers to clinically relevant pathogens (Berendonk et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e; Brinkac et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Exposure to livestock is a known risk factor for acquiring antibiotic resistance and is a threat to public health.(Landers et al., n.d.). In LMICs, such as India, where the dairy farms are usually very close to the human dwellings (N. Kumar et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the risk of zoonotic transmission of antibiotic resistant pathogens and resistome is high (Dafale et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Garcia et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; J. Li et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Swarthout et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and presents an occupational hazard for the exposed dairy farm workers and agricultural farmers (Kraemer et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe investigated the effect of infrastructure type (herd size, housing system, ventilation level, type of floor, drain lining) and operations (dung and wastewater disposal and veterinary consultation) in dairy farms on the levels of ARGs in excrements and manure-amended soil. We collected dung, manure, wastewater, manure-amended soil, and control soil from sixteen dairy farms in Dehradun district, India during summer, monsoon, and winter. These sixteen dairy farms had varying herd sizes and distinct infrastructure and operations. We checked for the presence of twenty ARGs conferring resistance to sulfonamides (\u003cem\u003esul\u003c/em\u003e1, \u003cem\u003esul\u003c/em\u003e2), fluoroquinolones (\u003cem\u003epar\u003c/em\u003eC, \u003cem\u003egyr\u003c/em\u003eA, \u003cem\u003eqnr\u003c/em\u003eA), tetracyclines (\u003cem\u003etet\u003c/em\u003eA, \u003cem\u003etet\u003c/em\u003eO, \u003cem\u003etet\u003c/em\u003eW, \u003cem\u003etet\u003c/em\u003eM), polymyxin (\u003cem\u003emcr\u003c/em\u003e1, \u003cem\u003emcr\u003c/em\u003e2, \u003cem\u003emcr\u003c/em\u003e3, \u003cem\u003emcr\u003c/em\u003e4, \u003cem\u003emcr\u003c/em\u003e5), macrolides (\u003cem\u003eerm\u003c/em\u003eF), glycopeptides (\u003cem\u003evan\u003c/em\u003eA), β-lactams (\u003cem\u003ebla\u003c/em\u003eOXA1, \u003cem\u003ebla\u003c/em\u003eTEM), multi-drug efflux pump (\u003cem\u003eacr\u003c/em\u003eA, \u003cem\u003eacr\u003c/em\u003eB) and one integron integrase gene cassette \u003cem\u003eint\u003c/em\u003eI1, of which only eight ARGs (\u003cem\u003esul\u003c/em\u003e1, \u003cem\u003esul\u003c/em\u003e2, \u003cem\u003epar\u003c/em\u003eC, \u003cem\u003emcr\u003c/em\u003e5, \u003cem\u003eerm\u003c/em\u003eF, \u003cem\u003etet\u003c/em\u003eW, \u003cem\u003ebla\u003c/em\u003eOXA1, blaTEM) and class 1 integrase-integron gene, \u003cem\u003eint\u003c/em\u003eI1, were detected. The following were quantified using quantitative polymerase chain reaction (qPCR): \u003cem\u003emcr\u003c/em\u003e5, \u003cem\u003etet\u003c/em\u003eW, \u003cem\u003eerm\u003c/em\u003eF, \u003cem\u003epar\u003c/em\u003eC, \u003cem\u003esul\u003c/em\u003e1, \u003cem\u003esul\u003c/em\u003e2, \u003cem\u003eint\u003c/em\u003eI1, and 16S rRNA gene copies. Some ARGs were associated with infrastructure (herd size, floor type, and ventilation) and operational practices (dung management and veterinary consultation). The ARG levels were higher during monsoon and summer than in winter, with animal-associated matrices (dung, manure, wastewater, and manure-amended soil) having higher potential for horizontal gene transfer. Dairy farm workers had greater ARG exposure while handling dung than manure and manure-amended soil.\u003c/p\u003e"},{"header":"2. Materials and Methodology","content":"\u003cp\u003e \u003cb\u003e2.1 Site description and sampling\u003c/b\u003e: Sixteen dairy farms in Dehradun, India, were sampled in December 2018, May 2019, and August 2019, pertaining to winter (15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 ˚C), summer (31.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 ˚C), and monsoon (25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 ˚C), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The dairy farms were selected based on accessibility and the owners' consent to collect samples regularly. Infrastructure- and operation-related factors on the farm were identified via observation and a short interview with the farm owner. The detailed features of the selected dairy farms are summarized in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDung samples were collected using a flame-sterilized spatula from the composite stack of the dung collected by each dairy farm at the end of the day. Similarly, manure samples were collected from the compost pits or heaps at the site. Wastewater samples expected to contain disinfectants, excrement, and milk from the farm were collected from outlet drains (lined or unlined) in 50 mL sterile centrifuge tubes (Abdos Labtech Pvt. Ltd, India) and aseptically filtered through a sterile 0.2 \u0026micro;M cellulose acetate membrane filter (Axiva Membrane Filters\u0026reg;, Axiva Sichem Pvt. Ltd, New Delhi). The filter paper was stored on ice during the transport to the laboratory and then at -20\u0026deg;C until further analysis. Manure-amended soil was collected from the fields amended with manure and wastewater from the dairy using a soil sampler, sterilized with 70% ethanol, following a random grid sampling method. The field was virtually divided into six nearly equal sections, and a sample was randomly collected from each section. Care was taken not to sample from the path disturbed by the sampling team. Soil samples in the summer and winter were collected nearly one month after manure application, and in monsoon were collected almost four months after application. However, the manure-amended farms regularly received wastewater from dairy farms. Control soil samples were collected from an area with minimal or no past interaction with cattle excrement but were exposed to other anthropogenic activities. Winter samples were stored in 50% ethanol, transferred to the laboratory, and stored at -20\u0026deg;C until further analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2 Molecular biology assay\u003c/b\u003e: DNA\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e was extracted from the samples using the Qiagen DNeasy\u0026reg; Powersoil DNA extraction kit (Qiagen\u0026reg;, Hilden, Germany) on the Qiacube\u0026reg; (Qiagen\u0026reg;, Hilden, Germany) following the manufacturer's protocol with a Tissue Lyser (Qiagen\u0026reg;, Hilden, Germany) used at 25 Hz for 30 seconds in the cell lysis step. The DNA was stored at \u0026minus;\u0026thinsp;20\u0026deg;C until downstream analysis. Polymerase chain reaction using Prima-96\u0026trade; Thermal Cycler (HiMedia Laboratories Pvt Limited, Mumbai, India) was done initially to screen the presence of 20 ARGs (Table S2) and \u003cem\u003eint\u003c/em\u003eI1, of which eight ARGs and \u003cem\u003eint\u003c/em\u003eI1 were detected by gel electrophoresis using Hi-Gel Run1014 (HiMedia Laboratories Pvt Limited, Mumbai, India) and were targeted with qPCR\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e using Rotor-Gene-Q (Qiagen\u0026reg;, Hilden, Germany). Of the targeted genes, 16S rRNA gene, \u003cem\u003esul\u003c/em\u003e1, \u003cem\u003esul\u003c/em\u003e2, \u003cem\u003etet\u003c/em\u003eW, \u003cem\u003eerm\u003c/em\u003eF, \u003cem\u003emcr\u003c/em\u003e5, \u003cem\u003epar\u003c/em\u003eC and \u003cem\u003eint\u003c/em\u003eI1 were quantifiable via qPCR. The quality and concentration of the extracted DNA were determined by a Nanodrop One\u003csup\u003eC\u003c/sup\u003e spectrophotometer (Thermo Scientific\u0026trade;, Massachusetts, USA). For qPCR, the DNA of the samples was diluted to 1:50 (Dung), 1:10 (Manure), 1:10 (wastewater), and 1:50 (manure-amended and control soil) for removal of PCR inhibition; the dilution ratio determined by a dilution curve of 16S rRNA gene copies. The qPCR standards were prepared by cloning the target amplicons on TOP10 competent cells using the TOPO TA Cloning kit (Invitrogen, CA, USA). Each qPCR run included a standard curve covering eight orders of magnitude and a negative control that used molecular biology grade water as a DNA control. Melt curve analysis with a temperature gradient from 50\u0026deg;C to 95\u0026deg;C was done at the end of every qPCR run to validate the specificity of the amplified products. The cycling conditions for each qPCR run are provided in SI text S1. The relative gene levels were calculated by dividing absolute gene copies with copies of 16S rRNA per sample and unless otherwise mentioned, only the relative levels of ARGs and \u003cem\u003eint\u003c/em\u003eI1 are reported in this study.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Infrastructure and operational parameters\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Scale\u003c/h2\u003e \u003cp\u003eDairy farms were classified as small-scale (\u0026le;\u0026thinsp;10 animals), medium-scale (11\u0026ndash;100 animals), and large-scale (\u0026gt;\u0026thinsp;100 animals) based on herd size.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Floor type\u003c/h2\u003e \u003cp\u003eThe floors of the surveyed dairy farms were lined with either brick, cement, or mud. Sometimes, the farmers laid rubber mats on the brick and cement floors to provide friction and comfort the animals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Ventilation\u003c/h2\u003e \u003cp\u003eDairy farms were categorized into four categories based on ventilation levels in decreasing order: Level A had high natural ventilation with no or very low walls and occasionally a basic frame-supported roof; Level B featured solid walls on three sides and a fourth wall with large doors and windows; Level C included housing with few windows, exhaust fans, and small entrance doors; and Level D had limited ventilation, with small windows and doors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Dung management\u003c/h2\u003e \u003cp\u003eThe dung from farms was either sent to composting pits (pit composting) or piled up on nearby fallow land (heap composting).\u003c/p\u003e \u003cp\u003eA detailed description of all the other parameters considered in the study (housing system, drain lining, source of feed, and wastewater disposal) is provided in SI (Text S2).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Exposure assessment of ARGs for farm workers\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAn occupational exposure assessment for farmers working with dairy waste, dung and manure was estimated using the following equations (Y. Wang et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2019\u003c/span\u003e):\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${ADD}_{dermal}=\\frac{C\\times SA\\times {P}_{C}\\times EF\\times {ET}_{skin}\\times 24}{AT\\times BW}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$BSA=0.02350\\times {HT}^{0.42246}\\times {BW}^{0.51456}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$SA=P\\times BSA$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ewhere,\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({ADD}_{dermal}\\)\u003c/span\u003e \u003c/span\u003e : average exposure dose of skin contact (copies/d/kg);\u003c/p\u003e \u003cp\u003eC: average median concentration of ARG in gene copies/g.\u003c/p\u003e \u003cp\u003eSA: exposed surface area (hands and feet) (m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({P}_{C}\\)\u003c/span\u003e \u003c/span\u003e : skin permeability (m/h).\u003c/p\u003e \u003cp\u003eEF: exposure frequency (d).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({ET}_{skin}\\)\u003c/span\u003e \u003c/span\u003e : skin contact exposure duration (h/d).\u003c/p\u003e \u003cp\u003eAT: average lifespan (d).\u003c/p\u003e \u003cp\u003eBW: body weight (Kg).\u003c/p\u003e \u003cp\u003eBSA: total body surface area (m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eHT: height (cm).\u003c/p\u003e \u003cp\u003eBW: body weight (Kg).\u003c/p\u003e \u003cp\u003eP: percentage of area covered by hands and feet\u003c/p\u003e \u003cp\u003eThe total body surface area was calculated using modified DuBois and DuBois formula(USEPA, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) with the average height and body weight specific to Indian males and females adapted from the National Institute of Nutrition, India report (ICMR-NIN, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Skin permeability coefficient and average life span were adapted from Wang et al. (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)(Y. Wang et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The percentage of body surface area in hands and feet used in Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) was adapted Exposure Factors Handbook from USEPA (USEPA, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The skin contact frequency and duration were derived from discussions with farmers and dairy farm workers in the study region. These parameters are summarized in Tables S3 and S4. The interviews with farmers and this study were approved by Institute Human Ethics Committee (IITR/IIC/22/04).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2.5 Statistical analysis\u003c/b\u003e: Statistical analyses were done on R version 3.6.3 (R Core Team, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with RStudio (RStudio Team, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e.03.0\u0026thinsp;+\u0026thinsp;386 \"Cherry Blossom\" Release for Windows as its graphical user interface. The Shapiro-Wilk test was employed to assess the normality of the ARGs. As the data was not normally distributed, Wilcoxon rank sum exact test was utilized to identify significant differences in the relative ARG levels across various sample matrices. Spearman correlation coefficients between the relative levels of target ARGs in all sample matrices were calculated using corrplot (Taiyun Wei \u0026amp; Viliam Simko, 2021) and ggcorrplot (Alboukadel Kassambara, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) packages. All statistical tests were done at 5% level of significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Effect of season and matrix type on the ARG levels\u003c/h2\u003e \u003cp\u003eThe levels of targeted gene markers in different matrices are summarized in SI Text S2. Overall, the levels of the targeted ARGs were 1\u0026ndash;4 orders of magnitude higher in summer and monsoon than in winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S7) and trended thus across all the matrices: manure amended soil and control soil\u0026thinsp;\u0026gt;\u0026thinsp;wastewater\u0026thinsp;\u0026gt;\u0026thinsp;manure\u0026thinsp;\u0026gt;\u0026thinsp;dung (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Dung\u003c/h2\u003e \u003cp\u003eThe levels of \u003cem\u003esul\u003c/em\u003e1 in winter were lower than in summer (~\u0026thinsp;1.6 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;9.59E-05) and monsoon (~\u0026thinsp;2 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;2.23E-05). Similarly, the levels of \u003cem\u003esul\u003c/em\u003e2 in winter were lower than in summer (~\u0026thinsp;1.4 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.0001) and monsoon (~\u0026thinsp;1.9 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;4.33E-05). The \u003cem\u003epar\u003c/em\u003eC levels in summer were higher than in winter (~\u0026thinsp;1.4 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;9.59E-05) and monsoon (~\u0026thinsp;2.5 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.003). The levels of \u003cem\u003eerm\u003c/em\u003eF in monsoon were higher (~\u0026thinsp;1.6 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.03) than in winter. The levels of \u003cem\u003etet\u003c/em\u003eW in winter were lower than in summer (~\u0026thinsp;1.1 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.001) and monsoon (~\u0026thinsp;0.7 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.008). The levels of \u003cem\u003eint\u003c/em\u003eI1 in monsoon were higher (~\u0026thinsp;2.7 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;3.12E-05) than in winter. The levels of \u003cem\u003emcr\u003c/em\u003e5 were comparable across all seasons. The levels of \u003cem\u003eint\u003c/em\u003eI1 correlated with \u003cem\u003esul\u003c/em\u003e1 (ρ\u0026thinsp;=\u0026thinsp;0.94, p-value\u0026thinsp;=\u0026thinsp;1.86E-13) and \u003cem\u003esul\u003c/em\u003e2 (ρ\u0026thinsp;=\u0026thinsp;0.88, p-value\u0026thinsp;=\u0026thinsp;2.23E-10) in dung samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Manure\u003c/h2\u003e \u003cp\u003eThe levels of all target genes except \u003cem\u003epar\u003c/em\u003eC and \u003cem\u003emcr\u003c/em\u003e5 were higher in monsoon and summer than in winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S7). The levels of \u003cem\u003esul\u003c/em\u003e1 in monsoon were higher than in summer (~\u0026thinsp;0.8 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.01) and winter (~\u0026thinsp;1.3 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;7.24E-06). The levels of \u003cem\u003esul\u003c/em\u003e2 in monsoon were also higher than in summer (~\u0026thinsp;0.6 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.048) and winter (~\u0026thinsp;0.9 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.001). The levels of \u003cem\u003eint\u003c/em\u003eI1 in monsoon were higher than in summer (~\u0026thinsp;0.8 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.04) and winter (~\u0026thinsp;1.1 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.0005). The levels of \u003cem\u003eerm\u003c/em\u003eF in monsoon were higher than in winter (~\u0026thinsp;1 order of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.001). The levels of \u003cem\u003etet\u003c/em\u003eW in monsoon were slightly higher than in summer (~\u0026thinsp;0.8 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.02), and the \u003cem\u003epar\u003c/em\u003eC levels in winter were slightly higher (~\u0026thinsp;0.8 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.006) than in summer. The levels of \u003cem\u003eint\u003c/em\u003eI1 positively correlated with \u003cem\u003esul\u003c/em\u003e2 (ρ\u0026thinsp;=\u0026thinsp;0.87, p-value\u0026thinsp;=\u0026thinsp;2.38E-16) and \u003cem\u003eerm\u003c/em\u003eF (ρ\u0026thinsp;=\u0026thinsp;0.53, p-value\u0026thinsp;=\u0026thinsp;0.0004) in manure samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Wastewater\u003c/h2\u003e \u003cp\u003eThe levels of \u003cem\u003emcr\u003c/em\u003e5 and \u003cem\u003etet\u003c/em\u003eW were comparable in all the sampling events (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S7). The levels of \u003cem\u003esul\u003c/em\u003e1 in summer (~\u0026thinsp;0.8 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.03) and monsoon (~\u0026thinsp;1.2 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.0001) were higher than in winter. The levels of \u003cem\u003esul\u003c/em\u003e2 in monsoon were higher than in summer (~\u0026thinsp;0.7 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.046) and winter (~\u0026thinsp;1.3 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.001). The levels of \u003cem\u003eint\u003c/em\u003eI1 were higher in summer (~\u0026thinsp;1 order of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.007) and monsoon (~\u0026thinsp;1.2 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.0001) than in winter. The levels of \u003cem\u003epar\u003c/em\u003eC in summer were higher in monsoon (~\u0026thinsp;1.2 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.0003) and winter (~\u0026thinsp;1 order of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.03). The levels of \u003cem\u003eerm\u003c/em\u003eF in winter were lower than in summer (~\u0026thinsp;1.1 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.02) and monsoon (~\u0026thinsp;1.04 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.0002). The levels of class I integron integrase, \u003cem\u003eint\u003c/em\u003eI1, positively correlated with \u003cem\u003esul\u003c/em\u003e1 (ρ\u0026thinsp;=\u0026thinsp;0.74, p-value\u0026thinsp;=\u0026thinsp;1.41E-07), \u003cem\u003esul\u003c/em\u003e2 (ρ\u0026thinsp;=\u0026thinsp;0.66, p-value\u0026thinsp;=\u0026thinsp;3.43E-05), \u003cem\u003epar\u003c/em\u003eC (ρ\u0026thinsp;=\u0026thinsp;0.37, p-value\u0026thinsp;=\u0026thinsp;0.03) and \u003cem\u003eerm\u003c/em\u003eF (ρ\u0026thinsp;=\u0026thinsp;0.52, p-value\u0026thinsp;=\u0026thinsp;0.02) in the wastewater samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4 Manure-amended soil\u003c/h2\u003e \u003cp\u003eThe levels of \u003cem\u003eerm\u003c/em\u003eF and \u003cem\u003etet\u003c/em\u003eW were similar across seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S7). The levels of \u003cem\u003esul\u003c/em\u003e1 in monsoon were nearly an order of magnitude higher than in summer (p-value\u0026thinsp;=\u0026thinsp;0.02) and winter (p-value\u0026thinsp;=\u0026thinsp;0.004). The levels of \u003cem\u003esul\u003c/em\u003e2 in monsoon were higher than in summer (~\u0026thinsp;1.8 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.001) and winter (~\u0026thinsp;0.9 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.01). Interestingly, the levels of \u003cem\u003esul\u003c/em\u003e2 in winter were higher than in summer (~\u0026thinsp;0.8 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.005). The levels of \u003cem\u003eint\u003c/em\u003eI1 in monsoon were higher than in winter (~\u0026thinsp;1.4 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.0007) and summer (~\u0026thinsp;1.5 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.001). The levels of \u003cem\u003emcr\u003c/em\u003e5 in summer were slightly higher than in monsoon (~\u0026thinsp;0.6 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.03). The levels of \u003cem\u003epar\u003c/em\u003eC in monsoon were higher than in summer (~\u0026thinsp;1.2 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.001) and winter (~\u0026thinsp;0.9 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.004). The levels of \u003cem\u003eint\u003c/em\u003eI1 positively correlated with \u003cem\u003esul\u003c/em\u003e1 (ρ\u0026thinsp;=\u0026thinsp;0.73, p-value\u0026thinsp;=\u0026thinsp;0.006), \u003cem\u003esul\u003c/em\u003e2 (ρ\u0026thinsp;=\u0026thinsp;0.77, p-value\u0026thinsp;=\u0026thinsp;6.29E-08), \u003cem\u003epar\u003c/em\u003eC (ρ\u0026thinsp;=\u0026thinsp;0.32, p-value\u0026thinsp;=\u0026thinsp;0.03) and \u003cem\u003eerm\u003c/em\u003eF (ρ\u0026thinsp;=\u0026thinsp;0.46, p-value\u0026thinsp;=\u0026thinsp;0.01) in manure-amended soil samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.1.5 Control soil\u003c/h2\u003e \u003cp\u003eThe levels of \u003cem\u003eerm\u003c/em\u003eF and \u003cem\u003epar\u003c/em\u003eC were comparable for all the sampling events (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S7). The levels of \u003cem\u003esul\u003c/em\u003e1 in monsoon were slightly higher than in winter (~\u0026thinsp;0.8 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.02). The levels of \u003cem\u003esul\u003c/em\u003e2 in monsoon were higher than in summer (~\u0026thinsp;1.3 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.047) and winter (~\u0026thinsp;1 order of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.02). The levels of \u003cem\u003eint\u003c/em\u003eI1 in monsoon were higher than in winter (~\u0026thinsp;1.1 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.03). The levels of \u003cem\u003emcr\u003c/em\u003e5 in summer were higher than in monsoon (~\u0026thinsp;1.6 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.04). Interestingly, the levels of \u003cem\u003etet\u003c/em\u003eW were higher in winter than in monsoon (~\u0026thinsp;4.6 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.002). No correlation was found between the level of ARGs and \u003cem\u003eint\u003c/em\u003eI1 in control soil that was not amended with manure (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Effect of herd size on the levels of ARGs\u003c/h2\u003e \u003cp\u003eThe ARG levels were generally lower in small-scale farms than medium- and large-scale farms, except \u003cem\u003emcr\u003c/em\u003e5 in wastewater.\u003c/p\u003e \u003cp\u003eIn manure, the levels of \u003cem\u003etet\u003c/em\u003eW (~\u0026thinsp;0.8 order of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.02) and \u003cem\u003esul\u003c/em\u003e2 (~\u0026thinsp;0.5 order of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.01) were higher in medium-scale farms than in small-scale farms. The levels of all other target ARGs and \u003cem\u003eint\u003c/em\u003eI1 in manure were similar in medium- and large-scale farms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Table S7).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn wastewater, the levels of \u003cem\u003emcr\u003c/em\u003e5 from small-scale farms were higher than those in medium-scale farms (~\u0026thinsp;1.5 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.01) and large-scale farms (~\u0026thinsp;1.8 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.005). The levels of \u003cem\u003etet\u003c/em\u003eW in wastewater were slightly higher in medium-scale farms than in small-scale farms (p-value\u0026thinsp;=\u0026thinsp;0.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, Table S7).\u003c/p\u003e \u003cp\u003eIn dung, the levels of \u003cem\u003etet\u003c/em\u003eW in medium-scale farms were comparable to those in large-scale farms and slightly (~\u0026thinsp;0.8 order of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.04) higher than those in small-scale farms. No significant difference was observed in the levels of all other targeted ARGs and \u003cem\u003eint\u003c/em\u003eI1 in dung from farms of different scales (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, Table S7).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Effect of infrastructure on the levels of ARGS.\u003c/h2\u003e \u003cp\u003eThe levels of some ARGs in dung, manure and wastewater samples varied with the ventilation level and floor type in the dairy farms (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table S7):\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Ventilation\u003c/h2\u003e \u003cp\u003eIn manure samples, the levels of \u003cem\u003esul\u003c/em\u003e2 in farms with ventilation level A were slightly higher (~\u0026thinsp;0.6 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.03) than in farms with ventilation level C. In wastewater, the levels of \u003cem\u003epar\u003c/em\u003eC were higher in farms with ventilation level C than those with ventilation level D (~\u0026thinsp;1 order of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.04).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Type of floor\u003c/h2\u003e \u003cp\u003eThe levels of \u003cem\u003eerm\u003c/em\u003eF in dung from farms with brick floors (n\u0026thinsp;=\u0026thinsp;3) was higher (~\u0026thinsp;2 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.03) than in farms with rubber mat lined-cement floors (n\u0026thinsp;=\u0026thinsp;8). The levels of \u003cem\u003epar\u003c/em\u003eC in manure in farms with cement floors (n\u0026thinsp;=\u0026thinsp;2) were slightly higher (~\u0026thinsp;0.8 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.04) than in farms with rubber mat-lined cement floors.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Effect of the dairy operational practices on the levels of ARGS.\u003c/h2\u003e \u003cp\u003eIn the case of operational practices, the levels of some ARGs in different matrices varied with dung management and the choice of veterinary consultation on the farm.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Dung management\u003c/h2\u003e \u003cp\u003eOnly the levels of \u003cem\u003emcr\u003c/em\u003e5 in manure were slightly (~\u0026thinsp;0.7 orders of magnitude, p-value\u0026thinsp;=\u0026thinsp;0.003) higher in farms with heap composting than in farms with pit composting (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, Table S7).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Reported choice of veterinary consultation\u003c/h2\u003e \u003cp\u003eIn manure, the levels of \u003cem\u003eint\u003c/em\u003eI1 (p-value\u0026thinsp;=\u0026thinsp;0.02), \u003cem\u003esul\u003c/em\u003e2 (p-value\u0026thinsp;=\u0026thinsp;0.006) and \u003cem\u003etet\u003c/em\u003eW (p-value\u0026thinsp;=\u0026thinsp;0.01) were an order of magnitude higher in farms that reported administering the medication on the advice of the para-veterinary worker and older prescriptions, hearsay and one\u0026rsquo;s experience compared to the farms that reported using treatment exclusively based on older prescriptions. Similarly, the levels of \u003cem\u003eint\u003c/em\u003eI1 (p-value\u0026thinsp;=\u0026thinsp;0.04) and \u003cem\u003esul\u003c/em\u003e2 (p-value\u0026thinsp;=\u0026thinsp;0.02) were nearly an order of magnitude higher for farms where animals were reportedly treated on veterinarians\u0026rsquo; advice exclusively compared to farms that relied on themselves, hearsay, or older prescriptions for providing veterinary care. The levels of \u003cem\u003emcr\u003c/em\u003e5 were slightly higher (p-value\u0026thinsp;=\u0026thinsp;0.03) for farms that reported seeking advice from veterinarians and treatment based on one\u0026rsquo;s experience than farms that reported treatment exclusively on one\u0026rsquo;s knowledge or hearsay (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Exposure assessment\u003c/h2\u003e \u003cp\u003eThe average daily dermal exposure dose was calculated using median of absolute levels of total ARGs and Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) for exposure to dung, manure, and manure-amended soil samples. The ADD\u003csub\u003edermal\u003c/sub\u003e for dairy farm workers from dung was 7.56 gene copies/kg/day for males and 7.66 gene copies/kg/day for females and from manure was 3.89 x 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e gene copies/kg/day for males and 3.94 x 10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e gene copies/kg/day for females. For farmers exposed to the manure-amended soil, the ADD\u003csub\u003edermal\u003c/sub\u003e was 3.84 gene copies/kg/day for males and 3.89 gene copies/kg/day for females (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e4.1 Certain infrastructure and operational features are associated with higher levels of ARGs.\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe housing type determines the exposure to extreme temperatures and ventilation, while the floor type impacts the ease of maintaining hygiene, which affects the exposure of animals to wet excrements. Thus, these two infrastructure and operational features - ventilation level (determined by the housing type) and floor type - can contribute to the increased prevalence of infections in dairy farms. As observed in the current study, dairy farms characterized by challenging-to-clean floor types (brick and textured cement floors) and ventilation levels that expose animals to extremes in weather conditions were associated with higher levels of ARGs.\u003c/p\u003e \u003cp\u003eIn a prior study, we reported that the presence of brick, mud, and textured cement floors increased the likelihood of reporting diseases like mastitis and secondary infections related to foot and mouth disease (H. Singh et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the current study, the higher relative levels of ARGs in farms with brick and textured cement floors compared to the farms that used rubber mats as a barrier to the floor could be attributed to the direct contact with the retained dairy waste in pores and crevices (J\u0026oslash;rgensen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rapp et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and to difficulty in cleaning (Calder\u0026oacute;n-amor \u0026amp; Gallo, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe farm floor frequently receives antibiotics and other co-selectors from excrement, milk, and disinfectants (Li et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It has been documented elsewhere that properly cleaning the floors significantly reduces the pathogen count for \u003cem\u003eStreptococci\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e spp., and coliform counts (Lowe et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Velazquez et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Floors that are inherently hard to clean (brick and textured cement floors) may result in a higher count of these pathogens on the floor (DeVries et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We note that a barrier between the animal and the floor (rubber mats) that is easy to clean was associated with lower levels of ARGs in the dairy environment and could be used as an intervention.\u003c/p\u003e \u003cp\u003eVery high levels of ventilation associated with open housing may expose animals to adverse weather conditions, potentially resulting in poor immunity and increasing the risk of infections. Conversely, inadequate ventilation could prolong exposure to moisture and heighten the circulation of pathogens and ARGs in the air inside the farm environment, as reflected in the findings of this study (Gao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gibbs et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). We note that adequate ventilation would be the middle ground between nearly open sheds and closed poorly ventilated housing for dairy animals.\u003c/p\u003e \u003cp\u003eIn the current study, 75% of the farms (n\u0026thinsp;=\u0026thinsp;12) utilised heap composting for manure. This practice involves piling up dung on nearby fallow land, which sometimes receives dung from multiple farms. A higher relative level of gene conferring resistance to polymyxin E (\u003cem\u003emcr\u003c/em\u003e5) was observed in manure that was heap composted (piled-up dung) compared to manure that was aerobically composted in pits, implying that pit composting performed better than heap composting for preventing any selection of \u003cem\u003emcr\u003c/em\u003e5 (Qian et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; M. Wang et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The selection of polymyxin E/colistin resistance gene, \u003cem\u003emcr\u003c/em\u003e5, in the heap could directly add to the increased resistance in the environment when applied to the fields as fertiliser or through surface runoff. Even though colistin is not used very frequently in dairy farms, being on the \u0026lsquo;reserve\u0026rsquo; group of the AWaRe watchlist of WHO (Infographics, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the significantly higher relative levels of gene conferring resistance to polymyxin E in the manure is concerning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Manure amendment did not increase ARG levels in soil 1\u0026ndash;4 months post-application.\u003c/h2\u003e \u003cp\u003ePrevious studies have documented that the application of manure and dairy-impacted wastewater elevates the relative abundance of ARGs in soil (Dungan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ruuskanen et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which attenuates over time. Our study noted no significant difference in the relative levels of ARGs between soil that had been amended with manure one month after amendment in summer and winter and four months after application in monsoon compared to control soil. This suggests that the impact of animal excrement on antibiotic resistance potential in soil may not be long-term. Other studies (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Muurinen et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) similarly noted that the initially elevated levels of ARGs in soil resulting from manure application tended to return to baseline over two to six weeks. The gap between manure application and sample collection in our study might explain the comparable relative levels of ARGs in the manure-amended control soil samples. Any trends in the increase in antibiotic resistance potential due to anthropogenic impacts are expected to be reflected in the levels of \u003cem\u003eint\u003c/em\u003eI1 and \u003cem\u003esul\u003c/em\u003e1 (Chaturvedi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Davis et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, in our study, the levels of these genes also remained comparable, regardless of whether manure was applied or not. At the same time, the levels of \u003cem\u003eint\u003c/em\u003eI1 correlated with the levels of other targeted ARGs in the samples associated with dairy farms (dung, manure, wastewater, and manure-amended soil), but this was not the case for the control soil that was not amended with manure. Even though the levels of targeted ARGs were similar in both soil samples, the significant correlation between targeted ARGs and \u003cem\u003eint\u003c/em\u003eI1 in manure-amended soil implies that the potential for horizontal gene transfer of ARGs may be higher in manure-amended soil. Despite no long-term increase in ARG levels in manure-amended soil, the link between ARGs and \u003cem\u003eint\u003c/em\u003eI1 in dairy-impacted samples (including manure-amended soil) suggests that dairy waste and manure impact the soil resistome.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Exposure assessment\u003c/h2\u003e \u003cp\u003eThe study reveals a hierarchy of exposure risks, showing that dairy farm workers handling dung were exposed to the highest ADD\u003csub\u003edermal\u003c/sub\u003e levels followed by agricultural farm workers working with manure-impacted soil. The least exposed population were the workers handling manure, consistent with their comparatively lower frequency and duration of exposure. Notably, manual cleaning of dairy farms is typical for India and small-scale farms across LMICs. Manual cleaning increases direct exposure to dung, manure, and wastewater from the dairy farms for the dairy farmers who work in contrast to the farms that use mechanical scrapers to clean the floor. Even though the levels of ARGs detected in the environment of dairy farms in India that we targeted herein are comparable to the levels detected in high- and middle-income countries (Huang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kyselkov\u0026aacute; et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; M. M. Li et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Munir \u0026amp; Xagoraraki, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; G. Wang et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; L. Wang et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the amount of exposure to pathogens and antibiotic resistance potential expected in Indian dairy farms, who rely on manual cleaning, would be much higher. Most dairy farmers utilize family labour to clean the dairy farms manually, increasing the potential for community transmission. The use of mechanical scrapers is expected to reduce workers' direct exposure to dung and manure.\u003c/p\u003e \u003cp\u003eNotably, samples for manure-amended soil in our study were collected 1\u0026ndash;4 months after manure application. This temporal delay suggests that the ARG levels to which farmers were exposed during application were likely higher than those observed in this study. While the agricultural farmers have a high overall exposure to ARGs, their frequency of exposure is lower compared to the dairy farm workers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Small-scale farms had lower levels of some ARGs and might be undertreating the animals.\u003c/h2\u003e \u003cp\u003eIn the current study, we noted that despite the variety in how antibiotics were prescribed, the levels of ARGs in dairy farms were comparable in most cases. In LMICs like India, dairy farms are primarily small stead (Mutua et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e), maintained by farmers for economic and nutritional resilience, rely mostly on severely constrained capital, and have limited profit margins (Chauhan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). There is also a lack of awareness of diseases and prevention (V. Kumar \u0026amp; Gupta, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), antibiotic usage (Sharma et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e), government policies, and access to trained veterinary healthcare in small-scale farms (Chauhan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). In our study, the participating farms had unequal access to para-veterinary workers, quacks, and veterinarians, and the choice of consultation and prescription was subject to ease of access and availability of funds. Only 25% (n\u0026thinsp;=\u0026thinsp;4) of farms, which were all large and medium scale, exclusively approached a veterinarian for advice, while the rest reported mainly self-medicating through old prescriptions and only sought veterinary advice if it was affordable and readily available at the time of need. It has also been reported elsewhere that the use of antibiotics is highly influenced by factors such as affordability and availability of a drug, farmers\u0026rsquo; expectations, and the potential of a follow-up on treatment (Chauhan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Klein et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; N. Kumar et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sulis et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vijay et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As reported by other studies in India, the cost of treatment is a limiting factor in the choice of veterinary healthcare options for most small-scale farmers (Chauhan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mutua et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDue to the lack of affordability, small farmers sometimes do not opt for antibiotic therapy (N. Kumar et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This could account for the lower ARG levels observed in the small-scale farms in our study. Even though the dairy industry has been accused of overconsumption of antibiotics, our study suggests that in small-scale farms, rather than misuse or abuse of antibiotics, there is probably undertreatment of animals due to the economic constraints of the farmer. In a mixed method study conducted in India, Kumar et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that only 10% of dairy farmers reported using antibiotics in the past year, confirmed by the positive detection of antibiotic residue in milk samples from only 8% of the total farms. Despite lack of awareness of antibiotic resistance and ease of availability of antibiotics, in small-scale farms the animals are more likely to be undertreated, and their contribution to the dissemination of resistance into the environment is probably overestimated (N. Kumar et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome farmers reported seeking care from parallel (frequently untrained) healthcare - quacks and para veterinarians, and, at times, medicating animals based on hearsay and personal experiences. A qualitative study by Chauhan et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also reported the widespread practice of self-prescribing medical treatment due to a shortage of trained professionals and over-the-counter availability of antibiotics from pharmacies, often without formal prescriptions(Chauhan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The treatment from parallel healthcare workers can be effective (Sudhinaraset et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); however, it is susceptible to the misuse of antibiotics or inappropriate or inadequate doses, and failed treatment(Sharma et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Sudhinaraset et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). When inappropriate antibiotic dose is used for less than the required duration, it can lead to incomplete recovery and development of antibiotic resistance(Lipsitch \u0026amp; Samore, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, farms with larger herds are primarily commercial and had sufficient capital to invest in animal healthcare. Studies conducted elsewhere have documented a higher rate of consultation with a veterinarian and higher per day average consumption of antibiotics in large-scale farms (V. Kumar \u0026amp; Gupta, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Santman-Berends et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInterestingly, the relative levels of \u003cem\u003emcr\u003c/em\u003e5 in wastewater from small-scale farms were higher than those from medium- and large-scale farms. One possible reason could be that most small-scale dairy farms participating in the study were also engaged in backyard poultry farming, thus adding to the released load of co-selectors into the shared environment. Elsewhere, it has been documented that the reported abundance of ARGs coding resistance to certain classes of antibiotics in the animal population that they were not exposed to in the first place suggests that antibiotic residue in the environment could promote the development and spread of resistance (Durso et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Feng et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Thames et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.5 The ARG levels in dairy waste and manure-impacted soil were higher in warmer seasons.\u003c/h2\u003e \u003cp\u003eThe bacterial infections in livestock exhibit seasonal variation, with higher prevalence during the summer and monsoon seasons (Islam et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Klotz et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Saminathan et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Studies from India, Pakistan and Nepal have reported higher incidences of mastitis in hot and humid months (Ali et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Joshi \u0026amp; Gokhale, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Regmi et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; K. Singh et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Many pathogens like \u003cem\u003eCampylobacter\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e in fecal samples also tend to have higher abundance during summer months in dung (Hoque et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Schneider et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Stanford et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the region investigated in this study, antibiotics are predominantly used on dairy farms for therapeutic purposes. The higher relative levels of ARGs observed in dung, manure and wastewater during the warmer season may be attributed to an increased prevalence of diseases and antibiotic therapy during the warmer seasons and subsequent release of antibiotic residue into the environment (Gullberg et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eWe investigated the link between the infrastructure and operations and the antibiotic resistance release potential within the environment of the dairy farms in resource-constrained settings of north India. The surveyed dairy farms had distinct infrastructural and operational characteristics, some associated with higher ARG levels in dung, manure, and wastewater. Notably, the application of manure and wastewater to soil did not significantly elevate ARG levels compared to non-amended control soil in the long term. The ARG levels demonstrated a seasonal variation, with higher concentrations observed during warmer seasons and followed the pattern: manure-amended and control soil\u0026thinsp;\u0026gt;\u0026thinsp;wastewater\u0026thinsp;\u0026gt;\u0026thinsp;manure\u0026thinsp;\u0026gt;\u0026thinsp;dung. Significantly, in dung, wastewater, manure, and manure-amended soil, the \u003cem\u003eint\u003c/em\u003eI1 correlated positively with ARGs. Recognizing the interconnectedness of infrastructure, operational practices, and antibiotic resistance potential in dairy farms provides valuable insights for developing targeted interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical Approval:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe interviews with farmers and this study were approved by Institute Human Ethics Committee (IITR/IIC/22/04).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to participate:\u003c/em\u003e\u003c/strong\u003e Due consent was taken from all the participating farmers for interviews and collection of samples from their dairy farms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to publish:\u003c/em\u003e\u003c/strong\u003e No consent to publish is required as the study contains original material. All the authors have agreed to the submission of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors contribution:\u003c/em\u003e\u003c/strong\u003e All authors contributed to the study conception and design. Author credits are as follows: Conceptualization: Gargi Singh and Awanish Kumar Singh; Methodology: Awanish Kumar Singh and Gargi Singh; Formal analysis and investigation: Harshita Singh, Kenyum Bagra, and Sourabh Dixit; Writing - original draft preparation: Harshita Singh; Writing - review and editing: Gargi Singh; Funding acquisition: Gargi Singh and Awanish Kumar Singh; Resources: Gargi Singh and Awanish Kumar Singh; Supervision: \u0026nbsp;Gargi Singh and Awanish Kumar Singh\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding:\u003c/em\u003e\u003c/strong\u003e This research was supported by Science and Engineering Research Board, India (Grant number: CRG/2020/005658) and Faculty Initiation Grant provided by IIT Roorkee (FIG/100762). We are also grateful to all the participating farmers, who consented to volunteer their time and share their experiences for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests:\u003c/em\u003e\u003c/strong\u003e The authors have no competing financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlboukadel Kassambara. (2022). \u003cem\u003eggcorrplot: Visualization of a Correlation Matrix using \u0026ldquo;ggplot2\u0026rdquo;\u003c/em\u003e (R package version 0.1.4). https://CRAN.R-project.org/package=ggcorrplot\u003c/li\u003e\n \u003cli\u003eAli, T., Kamran, Raziq, A., Wazir, I., Ullah, R., Shah, P., Ali, M. I., Han, B., \u0026amp; Liu, G. (2021). 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W., Chen, Q. L., Singh, B. K., Yan, H., Chen, D., \u0026amp; He, J. Z. (2019). Transfer of antibiotic resistance from manure-amended soils to vegetable microbiomes. \u003cem\u003eEnvironment International\u003c/em\u003e, \u003cem\u003e130\u003c/em\u003e. https://doi.org/10.1016/j.envint.2019.104912\u003c/li\u003e\n \u003cli\u003eZhao, L., Dong, Y. H., \u0026amp; Wang, H. (2010). Residues of veterinary antibiotics in manures from feedlot livestock in eight provinces of China. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, \u003cem\u003e408\u003c/em\u003e(5), 1069\u0026ndash;1075. https://doi.org/10.1016/j.scitotenv.2009.11.014\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Antibiotic resistant genes\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Deoxyribonucleic acid\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Quantitative polymerase chain reaction\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"dairy, excrement, manure, infrastructure-operations, antibiotic resistance, environment","lastPublishedDoi":"10.21203/rs.3.rs-3926998/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3926998/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe investigated the link between infrastructure and operations and the levels of antibiotic resistance potential within the dairy farm environment in India, which is the highest producer and consumer of dairy products. We sampled sixteen dairy farms in the Dehradun district, India, that varied in their herd size, infrastructure, and operational features during winter, summer, and monsoon. We collected dung, manure, wastewater, manure-amended and control soil samples from these farms. We quantified six antibiotic resistance genes (ARGs)\u003ca href=\"#_ftn1\" title=\"\"\u003e[1]\u003c/a\u003e (\u003cem\u003esul\u003c/em\u003e1, \u003cem\u003esul\u003c/em\u003e2, \u003cem\u003epar\u003c/em\u003eC, \u003cem\u003emcr\u003c/em\u003e5, \u003cem\u003eerm\u003c/em\u003eF, and \u003cem\u003etet\u003c/em\u003eW), an integron integrase gene cassette (\u003cem\u003eint\u003c/em\u003eI1), and 16S rRNA gene copies as an indicator for total bacterial count. We observed that with increased ventilation in the farm that exposed the animals to external weather, the levels of \u003cem\u003esul\u003c/em\u003e2 (x͂=10\u003csup\u003e-1.63\u003c/sup\u003e) and \u003cem\u003epar\u003c/em\u003eC (x͂=10\u003csup\u003e-4.24\u003c/sup\u003e) in manure increased. Farms with textured floor types like brick and cement floors had higher levels of \u003cem\u003eerm\u003c/em\u003eF in dung (x͂=10\u003csup\u003e-4.36\u003c/sup\u003e) and \u003cem\u003epar\u003c/em\u003eC in manure (x͂=10\u003csup\u003e-4.18\u003c/sup\u003e) than farms with rubber mat-lined floors. When farmers prescribed antibiotic therapy without contacting any veterinary professional the relative levels of \u003cem\u003eint\u003c/em\u003eI1 (x͂=10\u003csup\u003e-2.36\u003c/sup\u003e), \u003cem\u003esul\u003c/em\u003e2 (x͂=10\u003csup\u003e-1.58\u003c/sup\u003e) and \u003cem\u003etet\u003c/em\u003eW (x͂=10\u003csup\u003e-3.04\u003c/sup\u003e) in manure were lower than the cases where professional advice was involved. Small-scale farms had lower relative ARG levels than medium- and large-scale farms, except for \u003cem\u003emcr\u003c/em\u003e5 (x͂=10\u003csup\u003e-3.98\u003c/sup\u003e) in wastewater. The relative ARG levels trended as: manure-amended soil (x͂=10\u003csup\u003e-2.34\u003c/sup\u003e) and control soil (x͂=10\u003csup\u003e-2.24\u003c/sup\u003e)\u0026gt; wastewater (x͂=10\u003csup\u003e-2.90\u003c/sup\u003e)\u0026gt; manure (x͂=10\u003csup\u003e-3.39\u003c/sup\u003e)\u0026gt; dung (x͂=10\u003csup\u003e-2.54\u003c/sup\u003e); and summer (x͂=10\u003csup\u003e-2.91\u003c/sup\u003e) and monsoon (x͂=10\u003csup\u003e-2.75\u003c/sup\u003e) \u0026gt; winter (x͂=10\u003csup\u003e-3.38\u003c/sup\u003e). Significant positive correlations were observed between specific ARGs and the \u003cem\u003eint\u003c/em\u003eI1: dung (\u003cem\u003esul\u003c/em\u003e1 (ρ=0.88);\u0026nbsp; \u003cem\u003esul\u003c/em\u003e2 (ρ=0.94)), manure (\u003cem\u003esul\u003c/em\u003e2 (ρ=0.87); \u003cem\u003eerm\u003c/em\u003eF (ρ=0.53)), wastewater (\u003cem\u003esul\u003c/em\u003e1 (ρ=0.74); \u003cem\u003esul\u003c/em\u003e2 (ρ=0.66); \u003cem\u003epar\u003c/em\u003eC (ρ=0.37); \u003cem\u003eerm\u003c/em\u003eF (ρ=0.52)), and manure-amended soil (\u003cem\u003esul\u003c/em\u003e1 (ρ=0.73); \u003cem\u003esul\u003c/em\u003e2 (ρ=0.77); \u003cem\u003epar\u003c/em\u003eC (ρ=0.32); \u003cem\u003eerm\u003c/em\u003eF (ρ=0.46).\u003c/p\u003e","manuscriptTitle":"Association of infrastructure and operations with antibiotic resistance potential in the dairy environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-04 19:00:58","doi":"10.21203/rs.3.rs-3926998/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cebaea40-98a9-4ef3-8a27-3836008c6b74","owner":[],"postedDate":"March 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-17T09:26:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-04 19:00:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3926998","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3926998","identity":"rs-3926998","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}