Knowledge, attitude, and practices of dairy farmers on veterinary drug residues and antimicrobial resistance in Tigray region of Ethiopia: Implications for food safety and public health

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Kahsay, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9349578/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background The rapid growth of dairy production in Ethiopia is driven by increasing demand for milk and dairy products. While this transition supports food security and livelihoods, it also increases the risk of infectious diseases and inappropriate use of antimicrobials which can lead to drug residues in milk and contribute to antimicrobial resistance (AMR), posing significant threats to food safety and public health. This study assessed the knowledge, attitudes and practices (KAP) of dairy farming stakeholders regarding veterinary drug residues and AMR in dairy production systems along the Mekelle-Adigrat milk shed, in the Tigray region of Ethiopia. Methods A cross-sectional study was conducted from November 2025 to February 2026 among 528 participants. Data were collected using a structured close-ended questionnaire and analyzed using multivariate logistic regression to identify factors associated with KAP outcomes. Results Overall, 67.2% of respondents had adequate knowledge, 68.2% demonstrated desirable attitudes, and 60.2% reported good practices regarding veterinary drug residues and AMR. Participants’ location, education level, profession, and occupation were significantly associated with KAP scores, while farm size was significantly associated with good practices. Location showed the strongest association with knowledge outcomes. Respondents from Mekelle City had approximately eighteen (AOR:18.4; 95% CI: 10.9–30.7), twenty (AOR:19.5; 95% CI: 11.5–32.9) and five (AOR:4.5; 95% CI: 3.1–6.7) times adequate knowledge, desirable attitude and good practice scores respectively compared with respondents from the Eastern Zone. Conclusion Although most participants demonstrated adequate knowledge and favorable attitudes toward veterinary drug residues and AMR, the relatively lower level of good practices indicates a gap between knowledge and implementation. Strengthening farmer training, veterinary extension services, and regulatory oversight on antimicrobial use is essential to improve responsible antimicrobial use and reduce drug residue risks. These findings provide important evidence to inform targeted One Health-oriented policies and interventions to improve dairy product safety and mitigating AMR in Ethiopia’s rapidly expanding dairy sector. Knowledge Attitude practice veterinary drug residue antimicrobials resistance dairy farmers Tigray Figures Figure 1 Figure 2 Figure 3 Background Globally, the demand for animal source foods, particularly milk and milk products, has increased substantially due to population growth, urbanization, and raising protein and income demands. Hence, livestock production systems have increasingly shifted toward intensified production models aimed at improving productivity and efficiency. While this transition has enhanced milk production, it has also increased the risk of infectious diseases and the reliance on antimicrobial use for disease treatment and prevention, helping to minimize production losses [ 1 , 2 ]. The scale of antimicrobial use in food-producing animals is substantial. In 2017, antimicrobials used in animals accounted for approximately 73% of the total antimicrobials consumed throughout the world [ 3 ]. More recently, global antimicrobial consumption in livestock was estimated at 99,502 tonnes in 2020 and is projected to increase by about 8.0% to reach 107,472 tonnes by 2030 if the current trends continue [ 4 ]. Such widespread and often inappropriate use can lead to the presence of antimicrobial residues in milk (references) and raises concerns about the emergence and dissemination of antimicrobial resistant pathogens and genes across the food production chain as well as the environment through animal secretions such as milk and excreta. Contaminated environmental sources can therefore act as reservoirs and transmission pathways for antimicrobial-resistant pathogens and resistance genes, facilitating their circulation among humans, animals, and the environment results in increasing challenges to food safety and public health systems [ 5 , 6 ]. This complex transmission dynamic highlights the need of addressing AMR through an integrated One Health approach, considering the environmental dimensions of AMR. Moreover, the true burden of drug-resistant infections originating from animal production remains difficult to quantify due to limited surveillance and monitoring systems [ 7 – 9 ]. These challenges are particularly pronounced in low- and middle-income countries, where limited access to professional veterinary services and weak regulatory frameworks often allow unrestricted access to antimicrobial drugs, increasing the likelihood of non-prudent use [ 6 ]. In Ethiopia, the dairy sector has been expanding rapidly in response to growing urban demand for milk and milk products. Despite its economic and nutritional importance, the sector is largely characterized by fragmented production systems, limited veterinary oversight, and weak regulatory enforcement. As a result, infectious diseases remain common in dairy herds [ 10 ], and antimicrobial drugs are widely available without effective control mechanisms [ 11 ]. These conditions may promote indiscriminate antimicrobial use, increasing the risk of veterinary drug residues in milk and contributing to the development of AMR due to weak enforcement of regulatory system, inadequate knowledge, unfavorable attitude and bad practices among animal, human and environmental health experts, and antimicrobial end users (e.g. dairy farms) [ 12 ]. Improving awareness and promoting responsible antimicrobial use among dairy producers and other stakeholders are therefore critical steps towards addressing these challenges [ 13 ]. In recognition of the global AMR threat, international movements such as the World Health Organization’s (WHO) “World Antibiotic Awareness Week” celebrated annually have been initiated to strengthen public awareness and encourage prudent antimicrobial use across human and animal health sectors [ 14 ]. Understanding the knowledge, attitudes, and practices (KAPs) of antimicrobial users is essential for designing effective interventions and promoting responsible antimicrobial stewardship [ 15 ]. In Ethiopia, national policy frameworks – including the National Action Plan of Antimicrobial Resistance (NAP-AMR 2021–2025) and the National One Health Strategic Plan – emphasize strengthening surveillance, raising awareness, and generating evidence to guide policies aimed at reducing antimicrobial misuse and preventing drug residues along the food production chain (Ethiopian National Action Plan of AMR, 2021). However, the successful implementation of these policies requires reliable baseline data on antimicrobial use behaviors and awareness among key stakeholders in livestock production systems. Although several studies conducted in regions of Ethiopia have reported non-judicious antimicrobial use among livestock producers [ 17 – 22 ], evidence on the knowledge, attitudes, and behavioral drivers influencing antimicrobial use in dairy production remains limited. Moreover, antimicrobial use practices are often shaped by local socio-economic conditions, disease burden, access to veterinary services, and policy implementation capacity, which may vary across different geographic contexts. A study elsewhere has shown that factors such as herd health status, economic considerations, veterinarian consultation, producer experience, peer influence, and management practices can influence antimicrobial use decisions in livestock production systems [ 23 ]. In the Tigray region of Ethiopia, particularly along the Mekelle-Adigrat milk shed, the dairy sector is expanding but empirical data on veterinary drug residues, antimicrobial use practices, and AMR awareness among dairy producers remain scarce. The absence of such evidence limits the ability of policy makers and public health authorities to design targeted interventions and enforce effective food safety regulations. Therefore, this study aimed to assess the KAP of stakeholders involved in dairy production regarding veterinary drug residues and AMR along the Mekelle-Adigrat milk shed in the Tigray region of Ethiopia. Materials and methods Study area The study was conducted in Mekelle-Adigrat milk shed located in the Tigray Regional State of northern Ethiopia. The milk shed encompasses Mekelle City and selected districts of the Eastern Zone of Tigray. Mekelle, the regional capital, lies approximately 783 km north of Addis Ababa at an elevation of about 2,084 meters above sea level. Administratively, the city is divided into seven sub-cities: Hawelti, Hadnet, Kedamay Weyane, Ayder, Semien, Quiha, and Adi Haki. Mekelle experiences a semi-arid climate with an average annual rainfall ranging from 500 to 700 mm, with most precipitation occurring during the main rainy season between June to September. The mean annual minimum and maximum temperatures are approximately 12°C and 28°C, respectively, while relative humidity generally vary between 40% and 60% [ 24 , 25 ]. The eastern zone is one of the seven administrative zones of Tigray Regional State and consists of 18 districts, including 11 rural districts and seven town administrations. The zone has an altitude ranging from 2000 to 3000m above sea level, with an average annual rainfall of about 552mm and a mean temperature of 16°C [ 26 ]. The specific study districts include four sub-cities from Mekelle City (Ayder, Hadnet, Hawelti, and Semien) and three districts from the eastern zone (Adigrat, Tsrae Wenberta, and Wukro) (Fig. 1 ). Study population The study population comprised of dairy farmers aged 18 years and above who own dairy cattle and resided within the Mekelle-Adigrat milk shed during the study period. Operational definitions Milk shed (Dairy cluster) : Milk sheds are government-supported small and medium dairy production clusters established based on specific policy criteria. In Ethiopia, 14 milk sheds have been identified in areas with high dairy production potential using 24 indicators across six major categories: feed availability, environmental suitability for dairy cattle, current production status, access to inputs and services, market access for dairy outputs, and potential for production expansion [ 27 ]. Four of these milk sheds are located in Tigray Regional State, including the Mekelle-Adigrat milk shed where this study was conducted. Profession Certification in a formal discipline (diploma, degree, or higher) such as animal science/animal production, animal Health, human health, environmental science, crop science, plant science, economics, social science, or engineering. Sub-city An administrative unit within an urban municipality that functions similarly to a district in rural administrative structures. Zone An administrative division within regional state in Ethiopia, larger than districts but smaller than regional State. Eligibility criteria Dairy farmers were eligible for inclusion if they owned at least one dairy cow during the study period, were aged 18 years or older, resided within the Mekelle-Adigrat milk shed, and provided verbally informed consent to participate in the study. Study design and period A questionnaire-based cross-sectional study was conducted from November 2025 to February 2026 to assess KAPs of dairy farmers regarding drug residues in milk and AMR associated with dairy production. Sample size and sampling technique The sample size was determined using Yamane’s formula [ 28 ]: n= \(\:\frac{N}{{1+Ne}^{2}}\) where n = sample size; N= population; and e = the level of precision (margin of error) Assuming a precision level of 5%, a 10% non-response rate, and a design effect of 1.5, the calculated sample size was 528 dairy farmers. Three questionnaires were excluded due to incomplete responses. A multi-stage sampling technique combining purposive and random methods was employed. In the first stage, study zones and districts were purposively selected based on the concentration of dairy farms and logistical feasibility (transport accessibility and financial consideration). These were treated as first and second sampling units. Milk shed and dairy owners constituted the third and fourth sampling units, respectively. All milk sheds within the selected districts and sub-cities were included in the study. In total, 26 milk-sheds were sampled: 18 from Mekelle City (three from Hadnet, and five each from Ayder, Hawelti and Semien sub-cities) and eight from eastern zone (three each from Adigrat and Wukro, and two from Tsrae Wenberta districts). The sample size was proportionally allocated to each zone, district, and milk-shed based on the number of dairy farmers in each location. The detail list of dairy farms/farmers used as sampling frames for Ayder, Hadnet and Semien sub-cities were obtained from the respective agricultural offices, and for Adigrat, Wukro and Tsrae Wenberta districts as well as Hawelti sub-city were from the SNV BRIDGE Plus project. This project is funded by the Government of Netherlands and initiated to support the Ethiopian dairy sector transformation. Finally, dairy farmers owning at least one dairy cow were selected using systematic random sampling from the sampling frame of registered dairy farmers in each milk shed. Questionnaire Design and Data Collection Procedures Data were collected using a structured, close-ended questionnaire administered through Census and Survey Project System (CESPRO) mobile application. The questionnaire consisted of 51 questions and statements, and organized into four sections (Supplementary file). The first section included nine questions on socio-demographic characteristics of the respondents, such as gender, age, education, occupation, farm size, and profession. The second section comprised 17 items assessing respondents’ knowledge of veterinary drug residues in milk and AMR in dairy production. Responses were recorded using categorical options, “Yes”, “No” and “I don’t know”. The third section had 13 statements evaluating respondents’ attitudes toward veterinary drug residues and development of AMR. Attitudes responses were harvested using a five-point Likert scale (strongly agree, agree, uncertain, disagree and strongly disagree). The fourth section consisted of 12 questions addressing respondents’ practices related to drug residue and AMR in dairy production. Practice-related responses were collected using options such as “Yes, always”, “Yes, sometimes” and “No”. The questionnaire was initially prepared in English and translated into Tigrigna, the local language spoken in the study area. To ensure accuracy and consistency, the translated version was then back translated into English. The questionnaire was initially prepared by the first author and subsequently reviewed and approved by all co-authors. Data collectors were university graduates who received training on the questionnaire content, the use of the CESPRO mobile application, and standardized data collection procedures. Prior to the main survey, the questionnaire was pre-tested among dairy farmers outside the study sample to evaluate clarity, relevance, and reliability. Necessary adjustments were made based on the pretest results. Data were collected through face-to-face interviews with dairy farmers who consented to participate in the study. The collected data by each data enumerator via CESPRO mobile application were submitted to a local server in Tigray statistics and vital events registration agency. Data Management and Statistical Analysis Data collected using CESPRO mobile application were exported to Microsoft Excel for data cleaning, coding, and preliminary organization. The cleaned dataset was imported into STATA statistical software (Version 17.0, Stata Corp, College Station, Texas, USA) for statistical analysis. Descriptive statistics were used to summarize the socio-demographic characteristics of respondents and their KAPs regarding veterinary drug residues in milk and AMR in dairy production. Frequency distribution and percentages were calculated for categorical variables. Knowledge related questions were initially recorded using three response questions (“Yes”, “No” and “I don’t know”), while attitude items were measured using a five-point Likert scale (strongly agree, agree, uncertain, disagree and strongly disagree). Practice-related questions were recorded using response options of “Yes, always”, “Yes, sometimes” and “No”. For further statistical analysis, responses were reclassified into binary categories. Knowledge and practices responses were grouped into “Yes” and “No”, while attitude responses were categorized into “Agree” (strongly agree/agree) and “Disagree” (uncertain/disagree/strongly disagree). A scoring system was applied to quantify KAP levels. Each correct response was assigned a score of 1, whereas incorrect response was assigned a score of 0. Composite scores of knowledge, attitude, and practice were calculated by summing the scores of individual items for each respondent. The mean score was used as the cut-off point to classify respondents KAP levels. Respondents scoring above the mean were categorized as having adequate knowledge, desirable attitudes, and good practices, whereas those scoring below the mean were categorized as having inadequate knowledge, undesirable attitude, and bad practices, respectively. The internal consistency of the KAP scales was assessed using Cronbach’s alpha reliability analysis. A Cronbach’s alpha value of ≥ 0.70 was considered indicative of acceptable internal consistency of the measurement scales. To explore factors associated with KAP outcomes, inferential statistics were conducted. Initially, univariable binary logistic regression was performed to examine the association between each independent variable and the outcome variables (adequate knowledge, desirable attitude, and good practice). Prior to multivariable analysis, multicollinearity among independent variables was assessed using the Variance Inflation Factor (VIF). Variables with VIF values greater than 10 were considered to indicate significant multicollinearity and were excluded from the final models. Subsequently, multivariable binary logistic regression was conducted to identify independent predictors of adequate knowledge, desirable attitudes, and good practices. Adjusted odds ratios (AORs) with corresponding 95% confidence intervals (CIs) were calculated to determine the strength and direction of associations. The Hosmer–Lemeshow goodness-of-fit test was used to evaluate the adequacy of the logistic regression models. A p-value greater than 0.05 indicated an acceptable model fit. All statistical tests were two-sided, p-values less than 0.05 were considered statistically significant. Results Socio-demographic characteristics of the study participants A total of 528 dairy farmers participated in this study. Of these, 54% were from Mekelle city, and 46% were from districts in eastern zones. The majority (71.6%) of the dairy farmers were male. The average (mean) age of dairy farmers was 48.3 (95% CI:47.2–49.3) ranging from 21 to 88 years. Most farmers were within the 46–60 (40.7%) followed by 31–45(39.6%), and above 60 (13.3%) years. Regarding educational status, 22.5% of respondents had no formal education, 43.2% had primary school (grades 1–8), 23.7% had secondary education (grades 9–12), and only 10.6% had diploma or higher. Majority of the respondents (89.4%) had no professional training related to agriculture, veterinary sciences, or other technical fields. Most respondents (81.6%) relied exclusively on dairy farming as their primary occupation, whereas 18.4% combined dairy farming with other livelihood activities. The dairy production system was predominantly small-scale, with 96.8% of farms owning 1 to 10 cows and only 3.2% owning 11 or more cows. About 81% (427/528) of the farmers possessed five or fewer dairy cows. Detailed sociodemographic characteristics are presented in Table 1 . Table 1 Socio-demographic characteristics of the participants Characteristic n = 528 Number % Zone Mekelle 285 53.98 Eastern 243 46.02 Gender Male 378 71.59 Female 150 28.41 Age group 18–30 34 6.4 31–45 209 39.6 46–60 215 40.7 ˃60 70 13.3 Education No formal education 119 22.5 Grade 1–8 228 43.2 Grade 9–12 125 23.7 Diploma and above 56 10.6 Occupation Exclusively dairy cattle farming 431 81.6 Dairy farming and others 97 18.4 Year establishment 1974 to 2007 307 58.1 2008 to 2018 221 41.9 Farm size 1–10 cows 511 96.8 ≥ 11 cows 17 3.2 Profession Professional education 56 10.6 Non-professionals 472 89.4 Dairy farmers’ KAPs on veterinary drug residues and AMR Knowledge of dairy farmers Approximately 51% of the respondents reported having previously heard of AMR or drug-resistant bacteria. However, most respondents correctly recognized key drivers of AMR. Specifically, 64% reported misuse of veterinary drugs contributed to AMR, while 67.4% acknowledged that incomplete antimicrobial treatment courses could promote the emergence of resistant bacteria. With respect to food safety risks, 71.8% of respondents reported that veterinary drug residues in milk could directly affect human health, while 66.1% recognized that drug residues may contribute to the development of antimicrobial resistant bacteria in humans. Similarly, 71.4% of the respondents reported that antimicrobial-resistant bacteria could spread from animal to human. Additionally, 78.2% of respondents reported that drug resistant bacteria could be transmitted to humans through raw or undercooked milk and dairy products. Knowledge regarding environmental transmission routes was also relatively high. Approximately 72% of respondents recognized that humans may acquire antimicrobial resistant bacteria from dairy environments, and 72.5% indicated that water contaminated with dairy waste could facilitate the spread of drug-resistant bacteria. Importantly, most farmers demonstrated appropriate knowledge regarding antimicrobial stewardship. For example, 86.6% correctly stated that milk from cows undergoing veterinary drug treatment should not be consumed, and 80.5% indicated that veterinary drugs should not be administered without veterinary prescription. Despite these encouraging findings, notable knowledge gaps remained. Only 33.7% of respondents recognized that infections caused by resistant bacteria are more difficult to treat, suggesting limited understanding of the clinical implications of AMR. Detail responses to knowledge statements are presented in Table 2 . Table 2 Knowledge of the dairy farmers on drug residues and AMR in dairy production (n = 528) Knowledge statement Response, Number (%) Yes No I don’t know Have you ever heard about AMR or drug-resistant bacteria? 269 (51) 259 (49) - Misuse of veterinary drugs in dairy cattle can contribute to AMR. 338 (64) 76 (14.4) 114 (21.6) An incomplete full course of antimicrobial treatment contributes to AMR. 356 (67.4) 75 (14.2) 97 (18.4) If bacteria are resistant to antimicrobials, it can be very difficult to treat the infections they caused. 178 (33.7) 214 (40.5) 136 (25.8) Antimicrobial-resistant bacteria can spread from animal to animal. 377 (71.4) 27 (51.1) 124 (23.5) Antimicrobial-resistant bacteria can pass from dairy cattle to humans through raw or undercooked milk and milk products. 413 (78.2) 69 (13.1) 46 (8.7) Humans can acquire antimicrobial-resistant bacteria from dairy cattle environment (e.g. feces, soil, equipment). 380 (72) 54 (10.2) 94 (17.8) Humans can acquire antimicrobial-resistant bacteria from water contaminated with dairy cattle waste. 383 (72.5) 82 (15.5) 63 (12) Veterinary drug residues consumed in cattle milk can pose health risks to humans by itself 379 (71.8) 72 (13.6) 77 (14.6) Veterinary drug residues consumed in cattle milk can create antimicrobial resistant bacteria in human. 349 (66.1) 77 (14.6) 102 (19.3) Treatment regimen can be reduced or suspended as soon as symptoms disappear in your dairy cattle. 66 (12.5) 396 (75) 66 (12.5) Improper disposal of leftover or expired veterinary drugs can cause drug residues in nature/the environment. 304 (57.6) 78 (14.8) 146 (27.6) Improper disposal of leftover or expired veterinary drugs can be a source of AMR development in the environment. 306 (58) 89 (16.9) 133 (25.1) Milk from cows under treatment with veterinary drugs, can be used for human consumption. 43 (8.1) 457 (86.6) 28 (5.3) Veterinary drugs can be bought and given for milking cows without veterinarian’s prescription. 40 (7.6) 425 (80.5) 63 (12) Using leftover antimicrobials from other cows is acceptable if symptoms look similar. 25 (4.7) 427 (81) 76 (14.3) Television or radio and social media were the least used as sources of information for AMR. Most dairy farmers heard about AMR from multiple sources, mainly private- and government-employed experts (Fig. 2 ). Attitudes of dairy farmers Approximately 62% of respondents agreed that irrational use of veterinary drugs contributed to the development of AMR. Similarly, 75.5% agreed that antimicrobial resistant bacteria could be transmitted to humans through raw or undercooked milk and dairy products, and 71.2% believed that veterinary drug residues in milk may cause health problems in humans. However, attitudes toward milk safety during antimicrobial treatment were less consistent. Notably, 75.6% of the respondents disagreed with the statement that “milk from cows under treatment should never be used for human consumption”. Encouragingly, 58% of the respondents agreed that observing withdrawal periods is essential for milk safety, although a substantial proportion remained uncertain or disagreed. Detailed responses to attitude statements are presented in Table 3 . Table 3 Attitude of the dairy farmers towards drug residue and AMR in dairy production (n = 528) Attitude statement Response, number (%) Strongly agree Agree Uncertain Disagree Strongly disagree Irrational use of veterinary drugs in dairy cattle contributes to AMR. 46 (8.7) 325 (61.6) 147 (27.8) 10 (1.9) - Antimicrobial-resistant bacteria can spread to humans through consumption of raw or undercooked milk and milk products. 42 (10) 346 (65.5) 123 (23.3) 17 (3.2) - Infections caused by antimicrobial resistant bacteria are difficult to treat 22 (4.2) 155 (29.4) 101 (19.1) 229 (43.4) 21 (4) Humans can acquire resistant bacteria from dairy environments. 29 (5.5) 344 (65.2) 104 (19.7) 49 (9.3) 2 (0.4) Veterinary drug residues in milk can contribute to AMR in humans. 29 (5.5) 332 (62.9) 128 (24.2) 38 (7.2) 1 (0.2) Veterinary drug residues in milk can cause health problems in humans. 27 (5.1) 349 (66.1) 109 (20.6) 43 (8.1) - It is acceptable to use leftover drugs for cows with similar symptoms. 12 (2.3) 54 (10.2) 142 (26.9) 320 (60.6) - Improper disposal of expired drugs can contribute to AMR. 27 (5.1) 266 (50.4) 197 (37.3) 38 (7.2) - Milk from cows under treatment should never be used for human consumption. 7 (1.3) 34 (6.4) 54 (10.2) 399 (75.6) 34 (6.4) Observing withdrawal time is essential for milk safety. 62 (11.7) 306 (58) 105 (20) 53 (10) 2 (0.4) Poor record-keeping contributes to drug residues in milk. 28 (5.3) 313 (59.3) 133 (25.2) 54 (10.2) - Failure to follow instructions of the manufacturer and veterinarian leads to drug residues. 41 (7.8) 308 (58.3) 170 (32.2) 9 (1.7) - Contaminated water with dairy waste can transmit antimicrobial resistant foodborne pathogens. 22 (4.2) 359 (68) 101 (19.1) 46 (8.7) - Practices of dairy farmers In this study, 76.3% of respondents reported that their dairy cattle have received veterinary treatment within the previous 12 months. Encouragingly, 96% of the farmers reported that they did not ever consume or sold milk from a cow under treatment. However, reported practices regarding withdrawal period adherence were suboptimal. Only 13.3% of the respondents reported consistently (always) observing drug withdrawal periods, while 28% reported doing so occasionally (sometimes), 58.9% indicated that they did not adhere to the drug withdrawal recommendations. Moreover, 21% of the farmers reported occasionally purchasing veterinary drugs without professional consultation, while 20.8% reported obtaining drugs from human pharmacies for use in cattle. Additionally, 11% of respondents reported that they sometimes stored leftover drugs for future use, and 12.5% reported discontinuing antimicrobial treatment when clinical signs disappeared. Practice-related responses are summarized in Table 4 . Table 4 Practice of dairy farmers Addressing antimicrobial use in the dairy production (n = 528) Practice question Response, number (%) Yes, always Yes, sometimes No Have you ever bought veterinary drugs without prescription or professional consultation? 5 (1) 112 (21.2) 411 (77.8) Have you ever bought drugs from human pharmacies for your cattle? 3 (0.6) 110 (20.8) 415 (78.6) Have you ever administered orally Veterinary drugs to your dairy cattle without professional consultation? 1 (0.2) 84 (15.9) 443 (83.9) Have you ever applied topically Veterinary drugs to your dairy cattle without professional consultation? 1 (0.2) 51 (9.7) 476 (90.2) Have you ever injected your dairy cattle yourself? 1 (0.2) 20 (3.8) 507 (96) Have you ever stored leftover drugs for use of future illness episodes? 1 (0.2) 58 (11) 469 (88.8) Have you ever reduced or suspended treatment regimen as soon as symptoms disappear in your dairy cattle? 1 (0.2) 66 (12.5) 461 (87.3) Have you ever disposed of any leftover or expired drugs? 1 (0.2) 45 (8.5) 482 (91.3) Have you ever consumed or sold milk of a cow under treatment with veterinary drugs? - 21 (4) 507 (96) Have you ever practiced drug withdrawal time for your milking cow appropriately? 70 (13.3) 148 (28) 310 (58.7) The dairy farmers were asked to mention the drugs commonly used to treat their dairy cattle. More than three-fourth (76.5%, 404/528) of the dairy farmers did not know the drugs given to their animals while 23.5% of them mentioned either correctly specific drugs such as albendazole and oxytetracycline, or general names like acaricide, or agents used for supportive treatment, e.g., multivitamin. Albendazole was the highest reported drug used for treatment by dairy farmers (Fig. 3 ). Dairy farmers’ KAP scores on veterinary drug residue and AMR Knowledge scores of the dairy farmers The mean knowledge score among respondents was 10.86 ± 5.1 (standard error, SD). Based on this cut-off, 67.2% (355/528) of respondents demonstrated adequate knowledge, while 32.8% (173/528) had inadequate knowledge regarding veterinary drug residues and AMR. Even though the majority (71.6%) of the participants were male, adequate knowledge score of both male and female respondents were almost similar ( ≈ 67%). Substantial geographic variation was observed where farmers from Mekelle City exhibited higher knowledge levels (91%) compared with those from eastern zone (60.5%). Statistically significant associations were identified between knowledge levels and location, education, occupation, farm size, and profession p < 0.05). Detailed results are presented in Table 5 . Table 5 Adequate and inadequate knowledge scores of the dairy farmers on drug residue and AMR Demographic characteristics Knowledge χ 2 (P-value) Adequate Inadequate Zone Mekelle 259 (91%) 26 (9%) 157 (0.000) Eastern 147 (60.5%) 96 (39.5%) Gender Male 254 (67.2%) 124 (32.8%) 0.0 (0.976) Female 101 (67.3%) 49 (32.7%) Age (years) 18–30 28 (82.4%) 6 (17.6%) 3.9 (0.262) 31–45 137 (65.5%) 72 (34.5%) 46–60 142 (66%) 73 (34%) > 60 48 (68.6%) 22 (31.4%) Education No formal education 81 (68%) 38 (32%) 12.6 (0.006) Grade 1–8 168 (73.7%) 60 (26.3%) Grade 9–12 69 (55.2%) 56 (44.8%) Diploma and above 37 (66%) 19 (34%) Occupation Exclusively dairy cattle farming 278 (64.5%) 153 (35.5%) 7.9 (0.005) Dairy farming and others 77 (79.4%) 20 (20.6%) Year establishment 1974 to 2007 206 (67%) 101 (33%) 0.006 (0.938) 2008 to 2018 149 (67.4%) 72 (32.6%) Farm size 1–10 338 (66%) 173 (34%) 8.6 (0.003) ≥ 11 17 (100%) 0 Profession Professional education 25 (44.6%) 31 (55.4%) 14.5 (0.000) Non-professionals 330 (70%) 142 (30%) Overall score 355 (67.2%) 173 (32.8%) Attitude scores of the dairy farmers The mean attitude score was 7.8 ± 4.4 (SD). Overall, 68.2% of the respondents demonstrated a desirable attitude towards antimicrobial stewardship and drug residue control. Marked difference was again observed by location. Respondents from Mekelle City exhibited considerably higher desirable attitude scores (92%) compared with those from the eastern zone (40.3%). Location, education, occupation, farm size, and profession were statistically significant (; p < 0.05) (Table 6 ). Table 6 Desirable and undesirable attitude scores of the dairy farmers towards drug residue and AMR Demographic characteristics Attitude χ 2 (P-value) Desirable Undesirable Zone Mekelle 262 (92%) 23 (23%) 161 (0.000) Eastern 98 (40.3) 145 (59.7%) Gender Male 258 (68.2) 120 (31.8%) 0.00 (0.955) Female 102 (68%) 48 (32%) Age 18–30 28 (82.3%) 6 (17.7%) 3.75(0.289) 31–45 138 (66%) 71 (34%) 46–60 145(67.4%) 70 (32.6%) > 60 49 (70%) 21 (30%) Education No formal education 87 (73.1%) 32 (26.9%) 8.9 (0.030) Grade 1–8 163 (71.5%) 65 (28.5%) Grade 9–12 72 (57.6%) 53 (42.4%) Diploma and above 68 (67.9%) 18 (32.1%) Occupation Exclusively dairy cattle farming 280 (65%) 151 (35%) 11.2 (0.001) Dairy farming and others 80 (82.5%) 17 (17.5%) Year establishment 1974 to 2007 217 (70.7%) 90 (29.3%) 2.1 (0.146) 2008 to 2018 143 (64.7%) 78 (35.3%) Farm size 1–10 343 (67.1%) 168 (32.9%) 8.2 (0.004) ≥ 11 17 (100%) 0 Profession Professional education 27 (48.2%) 29 (51.8%) 11.5 (0.001) Non-professionals 333 (70.5%) 139 (29.5%) Overall score 360 (68.2%) 168 (31.8%) Practice scores of the dairy farmers The mean practice score was 7.1 ± 1.7 (SD). Based on this threshold, 60.2% of respondents demonstrated good antimicrobial use practice while 39.8% exhibited poor practices. Practice scores varied considerably by location: Farmers from Mekelle City had substantially higher good practice scores (76.1%) compared with 41.6% among farmers from eastern zone. Socio-demographic factors significantly associated with good practices included location, education, occupation, farm size, and profession (Table 7 ). Table 7 Good and bad practice scores of the dairy farmers addressing drug residue and AMR Demographic characteristics Practice χ 2 (P-value) Good Bad Zone Mekelle 217 (76.1%) 68 (23.9%) 65.5 (0.000) Eastern 101 (41.6%) 142 (58.4%) Gender Male 223 (59%) 155 (41%) 0.8 (0.358) Female 95 (63.3%) 55 (36.7%) Age 18–30 25 (73.5%) 9 (26.5%) 3.1 (0.371) 31–45 122 (58.4%) 87 (41.6%) 46–60 127 (59.1%) 88 (40.9%) > 60 44 (62.9%) 26 (37.1%) Education No formal education 78 (65.5%) 41 (34.5%) 1.2 (0.017) Grade 1–8 148 (64.9%) 80 (35.1%) Grade 9–12 63 (50.4%) 62 (49.6%) Diploma and above 29 (51.8%) 27 (48.2%) Occupation Exclusively dairy cattle farming 248 (57.5%) 183 (42.5%) 7.1 (0.008) Dairy farming and others 70 (72.2%) 27 (27.8%) Year establishment 1974 to 2007 193 (62.9%) 114 (37.1%) 2.1 (0.144) 2008 to 2018 125 (56.6%) 96 (43.4%) Farm size 1–10 303 (59.3%) 208 (40.7%) 5.8 (0.016) ≥ 11 15 (88.2%) 2 (11.8%) Profession Professional education 21 (37.5%) 35 (62.5%) 13.5 (0.000) Non-professionals 297 (62.9%) 175 (37.1%) Overall score 318 (60.2%) 210 (39.8%) Multivariant logistic regression of predictors associated with KAP scores After adjustment for potential confounders, location, education, occupation, and profession remained statistically associated with KAP outcomes. Farmers from Mekelle City were significantly more likely to demonstrate adequate KAP scores compared with those from eastern zone. Specifically, respondents from Mekelle city had approximately eighteen (AOR:18.4; 95% CI: 10.9–30.7), twenty (AOR:19.5; 95% CI: 11.5–32.9) and five (AOR:4.5; 95% CI: 3.1–6.7) times higher of adequate knowledge, desirable attitude and good practice respectively than respondents from eastern zone. Similarly, farmers who combined dairy farming with other occupations were more likely to demonstrated adequate knowledge (AOR = 3.4; 95% CI: 1.8–6.6), desirable attitudes (AOR = 4.5; 95% CI: 2.3–8.8), and good practices (AOR = 2.5; 95% CI: 1.5–4.5) compared with those exclusively engaged in dairy farming (Table 8 ). Table 8 Associations between predictor variables and KAP scores (using Adjusted odds ratio, AOR) Demographic variables Adequate knowledge Desirable attitude Good practice AOR (95% CI) P-value AOR (95% CI) P-value AOR (95% CI) P-value Zone Mekelle 18.4 (10.9–30.7) 0.000 19.5 (11.5–32.9) 0.000 4.5 (3.1–6.7) 0.000 Eastern Ref Ref Ref Education No formal education 1.3 (0.6–2.5) 0.441 1.7 (0.9–3.4) 0.134 1.6 (0.9–2.9) 0.087 Grade 1–8 2.7 (1.5–4.7) 0.001 2.0 (1.1–3.5) 0.021 1.8 (1.1–2.9) 0.019 Grade 9–12 Ref Ref Ref Diploma and above 2.7 (1.0–7.0) 0.044 2.2 (0.9–5.9) 0.101 1.2 (0.5–2.5) 0.706 Occupation Exclusively dairy cattle farming Ref Ref Ref Dairy farming and others 3.4 (1.8–6.6) 0.000 4.5 (2.3–8.8) 0.000 2.5 (1.5–4.5) 0.001 Farm size 1–10 Ref ≥ 11 Omitted Omitted 4.8 (1.0- 22.5) 0.044 Profession Professional education Ref Ref Ref Non-professionals 3.9 (1.7–9.1) 0.001 3.3 (1.4–7.6) 0.005 2.5 (1.2–4.9) 0.011 Discussion Nowadays, antimicrobial resistance is recognized as one of the most pressing global health challenges, posing substantial threat to human, animal, and environmental health. The WHO has identified AMR among the top ten global public health threats and introduced the Global Action Plan on AMR (GAP-AMR) to guide coordinated international responses [ 29 ]. In alignment with this initiative, Ethiopia adopted the NAP-AMR to address the drivers of AMR across human, animal, and environmental sectors, thereby to strategize its effective prevention methods. Within the livestock production system, dairy farmers play a critical role in antimicrobial use practices and consequently in the emergence and dissemination of antimicrobial-resistant pathogens and veterinary drug residues in milk [ 16 , 29 ]. Therefore, assessing KAPs of the dairy farmers regarding antimicrobial use and drug residues is essential for designing effective mitigation strategies. The present study investigated KAPs among dairy farmers in the Mekelle-Adigrat milk shed and identified key socio-demographic determinants influencing KAP outcomes. As to our knowledge, the KAP scores of dairy farmers towards veterinary drug residue and AMR in the dairy production along the Mekelle-Adigrat milk shed and predictor variables that statistically influenced KAP scores were for the first time reported. Knowledge of the dairy farmers on veterinary drug residue and AMR The study underscores that almost two-third of the dairy farmers (67.2%) had adequate knowledge while about one-third (32.8%) had inadequate knowledge on veterinary drug residues in milk and AMR in dairy production. In agreement with the present findings, comparable adequate knowledge scores of about 58, 61, 63 and 72% on antimicrobial use and resistance were reported from Ethiopia [ 20 ], Malaysia [ 30 ], Bangladesh [ 15 ] and Algeria [ 31 ], respectively. However, the current result of adequate knowledge score on drug residues and AMR in dairy farmers was discordant with the findings documented in central and western Ethiopia (6%) [ 21 ], Turkey (10%) [ 32 ], and Indonesia (19.8%) [ 33 ]. These variations may be attributed to differences in farmers’ access to veterinary services, educational exposure, extension programs, and awareness campaigns regarding antimicrobial stewardship. Multivariable logistic regression analysis showed that location, educational level, occupation, and profession were significant predictors of adequate knowledge scores. Farmers living in Mekelle were significantly more likely to possess adequate knowledge scores compared with those residing in eastern zone (AOR:18.4; 95% CI: 10.9–30.7). This might indicate local interventions designed to address knowledge on veterinary drug residues in milk and AMR should differ from location to location. Similar associations between geographic location and knowledge levels were reported in studies conducted in Turkey [ 32 ], Vietnam [ 34 ], and Bangladesh [ 35 ], suggesting that proximity to veterinary services and urban infrastructure can substantially influence awareness of antimicrobial stewardship. Findings from studies conducted in other regions of Ethiopia are inconsistent with the present study, as they reported no statistically significant association between study setting and participants’ adequate knowledge score of the antimicrobial use, residues and resistance [ 20 , 21 ]. In the present study, education was also found significantly associated with adequate knowledge score of the dairy farmers on veterinary drug residue and AMR. Similar findings were also reported from previous considerable studies conducted in different countries [ 15 , 18 , 20 – 22 , 34 ]. However, the direction of the association was diverse in different studies. In our study, dairy farmers who attended primary school education (AOR: 2.7; 95% CI: 1.5–4.7) and completed diploma or above (AOR: 2.7; 95% CI: 1.0–7.0) had statistically significant association with adequate knowledge score of the dairy farmers on drug residues and AMR. Other studies[ 15 , 20 , 21 , 34 ] reported that educational level of animal farmers was positively associated with their adequate knowledge score point on antimicrobial use and resistance; in other words, adequate knowledge score of animal farmers was improved with increment of educational level. However, enhanced adequate knowledge score was also negatively associated with educational level, particularly primary school education was significantly associated with adequate knowledge score of the livestock owners [ 22 ], which was consistent with present study. There was also a study[ 33 ] inconsistent with our findings that education was insignificantly associated with adequate knowledge score of livestock farmers on antimicrobial use and resistance. This could be due to differences in farm experience, disease occurrence, and veterinarian consultation that could help to increase farmers’ awareness [ 21 , 34 , 35 ] Interestingly, dairy farmers who engaged in additional occupations beyond dairy farming were more likely to possess adequate knowledge (AOR: 3.4; 95% CI: 1.8–6.6). Diversified occupational engagement may increase exposure to information networks, training opportunities, and social interactions that enhance awareness of livestock management practices. In contrast, other studies have demonstrated no significant association between occupation and animal farmers’ knowledge on antimicrobial use and resistance [ 34 , 35 ]. Considering profession, non-professionally trained farmers exhibited higher knowledge scores than professionally trained respondents (AOR: 3.9; 95% CI: 1.7–9.1). Although counterintuitive, this may reflect the possibility that non-professional farmers rely more heavily on practical experience, peer learning, and veterinary consultations, whereas professionally trained individuals may practice greater autonomy in decision-making regarding antimicrobial use. Attitudes of the dairy farmers towards veterinary drug residues and AMR The present study demonstrated that 68.2% of the respondents exhibited desirable attitudes towards veterinary drug residues and AMR. This proportion was considerably higher than findings reported in other Ethiopian settings, including Addis Ababa (47.7%) [ 20 ] and Oromia region (14.7%) [ 18 ], as well as poultry farms in Vietnam (20.4%) [ 35 ]. These differences may reflect variations in awareness programs, regulatory enforcement, and local livestock management practices. Consistent with the knowledge results, predictor variables i.e., location, education, occupation, and profession were significantly associated with dairy farmers’ desirable attitude scores. In contrast to the present study, findings from other regions of Ethiopia have shown that study setting was not significantly associated with desirable attitudes of animal farmers toward antimicrobial use, residues, and resistance [ 18 , 20 , 21 ]. However, studies conducted in Vietnam [ 35 ] Bangladesh [ 34 ], and Indonesia [ 33 ] have reported a significant association between study setting and farmers’ desirable attitudes towards antimicrobial use and resistance. Education was also associated with desirable attitude scores. However, only farmers with primary school education were more likely to express desirable attitude scores towards antimicrobial stewardship compared with respondents who attended high school education. But other considerable studies[ 15 , 20 , 21 , 34 , 35 ] documented that desirable attitude score was significantly associated with increment of educational level. In the studies reported by Dewi et al.[ 33 ] and Woldemichael et al. [ 22 ], logistic regression showed that education was not significantly associated with desirable attitude score of animal producers on antimicrobial use and resistance. But Woldemichael et al.[ 22 ] indicated that educational level (primary, secondary, and tertiary) was negatively related with desirable attitude score of the livestock producers towards antimicrobial use and resistance, with slightly higher desirable attitude score in respondents who attended primary school education, which agreed with the present study. In the present study, majority (81.6%) of the respondents were exclusively engaged in dairy cattle farming, the remaining (18.4%) were engaged in other activities in addition to dairy farming. However, the desirable attitude score of the respondents (82.5%) who engaged in dairy cattle farming and other activities was higher than respondents (65%) who engaged exclusively in dairy cattle farming. Occupation was found statistically significant association with desirable attitude score; and hence, dairy farmers who engaged in dairy cattle farming and other activities were about five (AOR: 4.5; 95% CI: 2.3–8.8) times more likely to have desirable attitude score than dairy farmers who engaged exclusively in dairy cattle farming towards antimicrobial residue and resistance. In other studies, occupation was not found to be a significant determinant of animal producers’ attitudes toward antimicrobial use and resistance [ 34 , 35 ]. Additionally, non-professional dairy farmers showed more significantly higher desirable attitudes (70.5%) than professionally trained farmers (48.2%) (AOR: 3.3; 95% CI: 1.4–7.6), suggesting that practical farming experience and reliance on veterinary guidance may shape perceptions toward responsible antimicrobial use. Practice of the dairy farmers addressing veterinary drug residue and AMR In this study, despite adequate knowledge (67.2%) and desirable attitudes (68.2%) were relatively favorable, only 60.2% of the dairy farmers demonstrated good practices related to antimicrobial use and prevention of drug residues in milk. This finding highlights a persistent knowledge-practice gap, which has been widely documented in antimicrobial stewardship research. Comparable practice levels have been reported among dairy farmers from Addis Ababa (53%)[ 20 ] and the Amhara region (47%)[ 19 ] of Ethiopia. However, both higher[ 21 ] and lower[ 17 , 18 ] practice levels have been documented in other regions of Ethiopia, reflecting heterogeneity in livestock management systems and regulatory oversight. The analysis revealed location, education, Occupation, profession, and farm size were significantly associated with good practice scores. In contrast to studies conducted in other settings of Ethiopia [ 18 , 20 , 21 ], the result of the present study showed that setting was significantly associated with good practice score of the dairy farmers on antimicrobial residue and resistance. Dairy Farmers from Mekelle were more likely to adopt appropriate practices compared with those from eastern zone (AOR: 4.5; 95% CI: 3.1–6.7). However, other studies conducted in different countries[ 15 , 33 – 35 ] were found concordant with the present study, as setting was significantly associated with good practice score of antibiotic use. Azim et al.[ 15 ] indicated that urban residents had good practice score addressing antibiotic use and resistance. These differences could be due to the fact that exposure of the dairy farmers to farm biosecurity systems or influences arising from the customers and/or experts/policy makers to realize farm biosecurity systems. In this study, education also influenced practice outcomes. Farmers with primary school education demonstrated better antimicrobial use practices than those with secondary education (AOR: 1.8; 95% CI: 1.1–2.9). Similar observations have been reported in other studies [ 15 , 18 , 20 , 21 , 34 ], suggesting that more highly educated farmers may sometimes rely on self-administration of veterinary drugs due to increased confidence in disease management, which could inadvertently lead to inappropriate antimicrobial use. Similarly, Tufa et al. (2023) indicated that high level of education was associated with bad practice score of animal farmers towards antimicrobial use, residue and resistance as educated farmers may have a better knowledge of animal diseases and practice self-administration of antimicrobials to their sick animals. In other ways, non-professional dairy farmers could be comparably risk averter who avoid self-prescription and-administration of drugs as compared to professionally trained individuals who could be risk takers. This is also supported by the present study that about 63% of non-professional dairy farmers had good practice score and were 2.5 times likely to have good practice scores than professionally educated dairy farmers towards antimicrobial residue and resistance. Moreover, occupation and livelihood diversification was associated with improved practices. Farmers who combined dairy farming with other economic activities were more likely to demonstrate good antimicrobial use practices (AOR: 2.5; 95% CI: 1.5–4.5). Diversified income sources may enable farmers to access veterinary services, depended on professional consultations, purchase quality veterinary drugs, and adopt improved biosecurity measures. Farm size was also another important determinant that predicted the practice score of the dairy farmers towards the veterinary drug residues and AMR. Farmers owning larger dairy herds (≥ 11 animals) were significantly 4.8 times more likely to demonstrate good practices than those who keep small number of dairy animals (1 to 10 dairy cattle). This could be due to the fact that larger dairy operation often requires structured herd management systems, regular veterinary supervision, and adherence to milk quality standards, all of which may encourage responsible antimicrobial use. Conclusion The study demonstrated that majority of the dairy farmers in the Mekelle-Adigrat milk shed possessed adequate knowledge, and desirable attitude scores toward veterinary drug residues and AMR. However, the proportion of farmers demonstrating good practice score of antimicrobial use was comparatively lower, indicating a disconnect between awareness and behavioral change in implementing rational use of drugs. Socio-demographic variables, including location, education, occupation, and profession significantly influenced KAPs outcomes. Farm size was also significantly associated with good practice. These findings highlight the need for targeted antimicrobial stewardship interventions, particularly in dairy production systems found in less urbanized locations. Expanding veterinary education services and promoting responsible antimicrobial use practices are essential to, reduce veterinary drug residues in milk and, mitigate the emergence of AMR in the dairy sector. Hence, the findings of this study provided baseline data, and called, for policy makers to conduct local interventions. Future research should incorporate qualitative approaches, laboratory analysis of antimicrobial residues, and antimicrobial susceptibility testing to generate more comprehensive evidence on antimicrobial use practices and resistance patterns within dairy production systems. Limitations of the study Several limitations should be considered when interpreting the findings of this study. First, the study was conducted within limited geographic areas of northern Ethiopia, which may limit generalizability of the findings to their regions with different socio-economic and production systems. Second, the cross-sectional design restricts causal inference regarding the relationship between sociodemographic factors and KAP outcomes. Third, the use of self-reported questionnaire data may introduce recall and social desirability biases, potentially leading to under-or over-reporting of the dairy farmers KAPs towards the activities that influence antimicrobial residue and resistance. Additionally, the questionnaire relied on categorical response options, which may have constrained the depth of information captured from respondents. Future research should incorporate qualitative approaches, laboratory analysis of antimicrobial residues, and antimicrobial susceptibility testing to generate more comprehensive evidence on antimicrobial use practices and resistance patterns within dairy production systems. Despite these limitations, the present study provides valuable baseline information to inform antimicrobial stewardship policies and interventions in the Ethiopia’s dairy sector. Declarations Ethics approval and consent to participate This study was conducted in accordance with the national and international ethical standards. Ethical approval was obtained from Institutional Review Board (IRB) of Mekelle University, College of Health Sciences, with reference number MU-IRB 2689/2025. Moreover, the study complied with the BELMONT REPORT [ 36 ] and declaration of Helsinki [ 37 ] which involve human participants. Participants were informed regarding the objectives and procedures of the study using the local language, Tigrigna. The study participants were assured that their privacy of research participation and confidentiality of personal information was protected including of anonymized data generated publications. Oral informed consent from all interviewees was obtained and recorded. Consent for publication : Not applicable Competing interests The authors declare that they have no competing interests. Funding The study was financially supported by MU-HU-NMBU ICP V with registration number project RADO/External/MS/0046/2025. Author Contribution Conceptualization: GR; Data curation and Methodology: GR GG, YTA, KA & AGK; Formal analysis: GR & GG; Data collection: GR and YTA; Project administration: GR; Writing– original draft: GR; Writing– review, editing & approval: GR GG, YTA, KA & AGK Acknowledgement The authors are indebted to the dairy farmers who consented to participate in this study. Moreover, all the study districts/sub-cities (Adigrat, Ayder, Hadnet, Hawelti, Semen, Tsireawenberta and Wukro) as well as Grace vet activities & services and Mekelle-Tigray SNV BRIDGE Plus project which give the sampling frame and cooperating the data collection to us were acknowledged. Acknowledgments also extended to Mr Abrha Kahsay who managed the data collection CESPRO mobile application and server as well as to Mr Abha Asefa who mapped the study area. 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Ethical Principles and Guidelines for the Protection of Human Subjects of Research: The National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, The BELMONT REPORT,April 18. Published online 1979. World Medical Association (WMA). Declaration of Helsinki – ethical principles for medical research involving human participants. Published online 2024. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor invited by journal 10 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Submission checks completed at journal 09 Apr, 2026 First submitted to journal 07 Apr, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9349578","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625678483,"identity":"dea4e3ad-98b1-4ff7-8946-5be8ba58573f","order_by":0,"name":"Gebremedhin Romha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYJACZgYGCTl+/uYDQLaEDLFaLIwlZxxLAGnhIVZLReKGAzkGIA5hLfLRh49uLqiRSJzZcObzqxs1FjwM7IePbsCnxfBcWtrtGcckjPuZe7dZ5xwDOownLe0GXi09PGa3edgkZGc2nN1mnMMG1CLBY0aEln8SjEC/PDPO+UeEFnkeoBbeNglFoBbmx7ltRGgx4GFLu83bJwEKZDPm3D4JHjZCfpHvYT52m+dbHSgqH3/OATHYDx/Db8sBBJtNAkziUw62pQHBZv5ASPUoGAWjYBSMTAAAWFFHOvvhLQ8AAAAASUVORK5CYII=","orcid":"","institution":"Mekelle University","correspondingAuthor":true,"prefix":"","firstName":"Gebremedhin","middleName":"","lastName":"Romha","suffix":""},{"id":625678484,"identity":"cd2ebb9e-ee79-4471-b6c4-b1ce1cb0ead9","order_by":1,"name":"Gebremedhin Gebrezgabiher","email":"","orcid":"","institution":"Samara University","correspondingAuthor":false,"prefix":"","firstName":"Gebremedhin","middleName":"","lastName":"Gebrezgabiher","suffix":""},{"id":625678485,"identity":"3f9c85e2-b6b9-415a-aad7-138844988dd4","order_by":2,"name":"Kiros Abebe","email":"","orcid":"","institution":"Mekelle University","correspondingAuthor":false,"prefix":"","firstName":"Kiros","middleName":"","lastName":"Abebe","suffix":""},{"id":625678486,"identity":"0670bb7c-d6d2-49a2-b8a6-3fb4dbf87bff","order_by":3,"name":"Abraha G. Kahsay","email":"","orcid":"","institution":"Umeå University","correspondingAuthor":false,"prefix":"","firstName":"Abraha","middleName":"G.","lastName":"Kahsay","suffix":""},{"id":625678487,"identity":"de0bb5b7-aae0-4a5e-a4b0-c6016c7f2cce","order_by":4,"name":"Yohannes Tekle Asfaw","email":"","orcid":"","institution":"Mekelle University","correspondingAuthor":false,"prefix":"","firstName":"Yohannes","middleName":"Tekle","lastName":"Asfaw","suffix":""}],"badges":[],"createdAt":"2026-04-07 22:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9349578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9349578/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107448064,"identity":"cb82fb58-17b5-4ac5-8241-0fcd79ebaccf","added_by":"auto","created_at":"2026-04-21 14:56:32","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":923473,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9349578/v1/6c3a44ac62bb6f906c398cbb.jpeg"},{"id":107448015,"identity":"57b26068-f05e-4e31-9beb-9b9c2da82156","added_by":"auto","created_at":"2026-04-21 14:56:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":204045,"visible":true,"origin":"","legend":"\u003cp\u003eDairy Farmers' sources of information about AMR\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9349578/v1/872995c8dc44aea2ace32953.jpeg"},{"id":107448060,"identity":"d4699e6b-0240-4de1-8186-584175d4750e","added_by":"auto","created_at":"2026-04-21 14:56:31","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":255461,"visible":true,"origin":"","legend":"\u003cp\u003eCommonly used drugs as reported by the dairy farmers\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9349578/v1/32dafcf426e54bf91d57753a.jpeg"},{"id":107488943,"identity":"b3fd541b-9d04-46ec-a194-c7e62b5c67f6","added_by":"auto","created_at":"2026-04-22 02:46:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2400734,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9349578/v1/8b8e987f-49f2-4bbd-a17c-7ccd112beef6.pdf"},{"id":107448067,"identity":"c1bd54c2-a59e-460a-9f62-aca103786f61","added_by":"auto","created_at":"2026-04-21 14:56:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":32271,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-9349578/v1/3414756937425150f162cea8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Knowledge, attitude, and practices of dairy farmers on veterinary drug residues and antimicrobial resistance in Tigray region of Ethiopia: Implications for food safety and public health","fulltext":[{"header":"Background","content":"\u003cp\u003eGlobally, the demand for animal source foods, particularly milk and milk products, has increased substantially due to population growth, urbanization, and raising protein and income demands. Hence, livestock production systems have increasingly shifted toward intensified production models aimed at improving productivity and efficiency. While this transition has enhanced milk production, it has also increased the risk of infectious diseases and the reliance on antimicrobial use for disease treatment and prevention, helping to minimize production losses [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The scale of antimicrobial use in food-producing animals is substantial. In 2017, antimicrobials used in animals accounted for approximately 73% of the total antimicrobials consumed throughout the world [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. More recently, global antimicrobial consumption in livestock was estimated at 99,502 tonnes in 2020 and is projected to increase by about 8.0% to reach 107,472 tonnes by 2030 if the current trends continue [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Such widespread and often inappropriate use can lead to the presence of antimicrobial residues in milk (references) and raises concerns about the emergence and dissemination of antimicrobial resistant pathogens and genes across the food production chain as well as the environment through animal secretions such as milk and excreta. Contaminated environmental sources can therefore act as reservoirs and transmission pathways for antimicrobial-resistant pathogens and resistance genes, facilitating their circulation among humans, animals, and the environment results in increasing challenges to food safety and public health systems [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This complex transmission dynamic highlights the need of addressing AMR through an integrated One Health approach, considering the environmental dimensions of AMR.\u003c/p\u003e \u003cp\u003eMoreover, the true burden of drug-resistant infections originating from animal production remains difficult to quantify due to limited surveillance and monitoring systems [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These challenges are particularly pronounced in low- and middle-income countries, where limited access to professional veterinary services and weak regulatory frameworks often allow unrestricted access to antimicrobial drugs, increasing the likelihood of non-prudent use [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Ethiopia, the dairy sector has been expanding rapidly in response to growing urban demand for milk and milk products. Despite its economic and nutritional importance, the sector is largely characterized by fragmented production systems, limited veterinary oversight, and weak regulatory enforcement. As a result, infectious diseases remain common in dairy herds [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and antimicrobial drugs are widely available without effective control mechanisms [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These conditions may promote indiscriminate antimicrobial use, increasing the risk of veterinary drug residues in milk and contributing to the development of AMR due to weak enforcement of regulatory system, inadequate knowledge, unfavorable attitude and bad practices among animal, human and environmental health experts, and antimicrobial end users (e.g. dairy farms) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Improving awareness and promoting responsible antimicrobial use among dairy producers and other stakeholders are therefore critical steps towards addressing these challenges [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In recognition of the global AMR threat, international movements such as the World Health Organization\u0026rsquo;s (WHO) \u0026ldquo;World Antibiotic Awareness Week\u0026rdquo; celebrated annually have been initiated to strengthen public awareness and encourage prudent antimicrobial use across human and animal health sectors [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnderstanding the knowledge, attitudes, and practices (KAPs) of antimicrobial users is essential for designing effective interventions and promoting responsible antimicrobial stewardship [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In Ethiopia, national policy frameworks \u0026ndash; including the National Action Plan of Antimicrobial Resistance (NAP-AMR 2021\u0026ndash;2025) and the National One Health Strategic Plan \u0026ndash; emphasize strengthening surveillance, raising awareness, and generating evidence to guide policies aimed at reducing antimicrobial misuse and preventing drug residues along the food production chain (Ethiopian National Action Plan of AMR, 2021). However, the successful implementation of these policies requires reliable baseline data on antimicrobial use behaviors and awareness among key stakeholders in livestock production systems.\u003c/p\u003e \u003cp\u003eAlthough several studies conducted in regions of Ethiopia have reported non-judicious antimicrobial use among livestock producers [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], evidence on the knowledge, attitudes, and behavioral drivers influencing antimicrobial use in dairy production remains limited. Moreover, antimicrobial use practices are often shaped by local socio-economic conditions, disease burden, access to veterinary services, and policy implementation capacity, which may vary across different geographic contexts. A study elsewhere has shown that factors such as herd health status, economic considerations, veterinarian consultation, producer experience, peer influence, and management practices can influence antimicrobial use decisions in livestock production systems [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the Tigray region of Ethiopia, particularly along the Mekelle-Adigrat milk shed, the dairy sector is expanding but empirical data on veterinary drug residues, antimicrobial use practices, and AMR awareness among dairy producers remain scarce. The absence of such evidence limits the ability of policy makers and public health authorities to design targeted interventions and enforce effective food safety regulations. Therefore, this study aimed to assess the KAP of stakeholders involved in dairy production regarding veterinary drug residues and AMR along the Mekelle-Adigrat milk shed in the Tigray region of Ethiopia.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe study was conducted in Mekelle-Adigrat milk shed located in the Tigray Regional State of northern Ethiopia. The milk shed encompasses Mekelle City and selected districts of the Eastern Zone of Tigray. Mekelle, the regional capital, lies approximately 783 km north of Addis Ababa at an elevation of about 2,084 meters above sea level. Administratively, the city is divided into seven sub-cities: Hawelti, Hadnet, Kedamay Weyane, Ayder, Semien, Quiha, and Adi Haki. Mekelle experiences a semi-arid climate with an average annual rainfall ranging from 500 to 700 mm, with most precipitation occurring during the main rainy season between June to September. The mean annual minimum and maximum temperatures are approximately 12\u0026deg;C and 28\u0026deg;C, respectively, while relative humidity generally vary between 40% and 60% [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe eastern zone is one of the seven administrative zones of Tigray Regional State and consists of 18 districts, including 11 rural districts and seven town administrations. The zone has an altitude ranging from 2000 to 3000m above sea level, with an average annual rainfall of about 552mm and a mean temperature of 16\u0026deg;C [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The specific study districts include four sub-cities from Mekelle City (Ayder, Hadnet, Hawelti, and Semien) and three districts from the eastern zone (Adigrat, Tsrae Wenberta, and Wukro) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe study population comprised of dairy farmers aged 18 years and above who own dairy cattle and resided within the Mekelle-Adigrat milk shed during the study period.\u003c/p\u003e\n\u003ch3\u003eOperational definitions\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eMilk shed (Dairy cluster)\u003c/b\u003e: Milk sheds are government-supported small and medium dairy production clusters established based on specific policy criteria. In Ethiopia, 14 milk sheds have been identified in areas with high dairy production potential using 24 indicators across six major categories: feed availability, environmental suitability for dairy cattle, current production status, access to inputs and services, market access for dairy outputs, and potential for production expansion [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Four of these milk sheds are located in Tigray Regional State, including the Mekelle-Adigrat milk shed where this study was conducted.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProfession\u003c/strong\u003e \u003cp\u003eCertification in a formal discipline (diploma, degree, or higher) such as animal science/animal production, animal Health, human health, environmental science, crop science, plant science, economics, social science, or engineering.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSub-city\u003c/strong\u003e \u003cp\u003eAn administrative unit within an urban municipality that functions similarly to a district in rural administrative structures.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eZone\u003c/strong\u003e \u003cp\u003eAn administrative division within regional state in Ethiopia, larger than districts but smaller than regional State.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eEligibility criteria\u003c/h3\u003e\n\u003cp\u003eDairy farmers were eligible for inclusion if they owned at least one dairy cow during the study period, were aged 18 years or older, resided within the Mekelle-Adigrat milk shed, and provided verbally informed consent to participate in the study.\u003c/p\u003e\n\u003ch3\u003eStudy design and period\u003c/h3\u003e\n\u003cp\u003eA questionnaire-based cross-sectional study was conducted from November 2025 to February 2026 to assess KAPs of dairy farmers regarding drug residues in milk and AMR associated with dairy production.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSample size and sampling technique\u003c/h2\u003e \u003cp\u003eThe sample size was determined using Yamane\u0026rsquo;s formula [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003en=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{N}{{1+Ne}^{2}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003ewhere n\u0026thinsp;=\u0026thinsp;sample size; N= population; and e\u0026thinsp;=\u0026thinsp;the level of precision (margin of error)\u003c/p\u003e \u003cp\u003eAssuming a precision level of 5%, a 10% non-response rate, and a design effect of 1.5, the calculated sample size was 528 dairy farmers. Three questionnaires were excluded due to incomplete responses.\u003c/p\u003e \u003cp\u003eA multi-stage sampling technique combining purposive and random methods was employed. In the first stage, study zones and districts were purposively selected based on the concentration of dairy farms and logistical feasibility (transport accessibility and financial consideration). These were treated as first and second sampling units. Milk shed and dairy owners constituted the third and fourth sampling units, respectively. All milk sheds within the selected districts and sub-cities were included in the study. In total, 26 milk-sheds were sampled: 18 from Mekelle City (three from Hadnet, and five each from Ayder, Hawelti and Semien sub-cities) and eight from eastern zone (three each from Adigrat and Wukro, and two from Tsrae Wenberta districts).\u003c/p\u003e \u003cp\u003eThe sample size was proportionally allocated to each zone, district, and milk-shed based on the number of dairy farmers in each location. The detail list of dairy farms/farmers used as sampling frames for Ayder, Hadnet and Semien sub-cities were obtained from the respective agricultural offices, and for Adigrat, Wukro and Tsrae Wenberta districts as well as Hawelti sub-city were from the SNV BRIDGE Plus project. This project is funded by the Government of Netherlands and initiated to support the Ethiopian dairy sector transformation. Finally, dairy farmers owning at least one dairy cow were selected using systematic random sampling from the sampling frame of registered dairy farmers in each milk shed.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuestionnaire Design and Data Collection Procedures\u003c/h3\u003e\n\u003cp\u003eData were collected using a structured, close-ended questionnaire administered through Census and Survey Project System (CESPRO) mobile application. The questionnaire consisted of 51 questions and statements, and organized into four sections (Supplementary file). The first section included nine questions on socio-demographic characteristics of the respondents, such as gender, age, education, occupation, farm size, and profession. The second section comprised 17 items assessing respondents\u0026rsquo; knowledge of veterinary drug residues in milk and AMR in dairy production. Responses were recorded using categorical options, \u0026ldquo;Yes\u0026rdquo;, \u0026ldquo;No\u0026rdquo; and \u0026ldquo;I don\u0026rsquo;t know\u0026rdquo;. The third section had 13 statements evaluating respondents\u0026rsquo; attitudes toward veterinary drug residues and development of AMR. Attitudes responses were harvested using a five-point Likert scale (strongly agree,\u0026emsp;agree,\u0026emsp;uncertain,\u0026emsp;disagree\u0026emsp;and strongly disagree). The fourth section consisted of 12 questions addressing respondents\u0026rsquo; practices related to drug residue and AMR in dairy production. Practice-related responses were collected using options such as \u0026ldquo;Yes, always\u0026rdquo;, \u0026ldquo;Yes, sometimes\u0026rdquo; and \u0026ldquo;No\u0026rdquo;.\u003c/p\u003e \u003cp\u003eThe questionnaire was initially prepared in English and translated into Tigrigna, the local language spoken in the study area. To ensure accuracy and consistency, the translated version was then back translated into English. The questionnaire was initially prepared by the first author and subsequently reviewed and approved by all co-authors. Data collectors were university graduates who received training on the questionnaire content, the use of the CESPRO mobile application, and standardized data collection procedures. Prior to the main survey, the questionnaire was pre-tested among dairy farmers outside the study sample to evaluate clarity, relevance, and reliability. Necessary adjustments were made based on the pretest results. Data were collected through face-to-face interviews with dairy farmers who consented to participate in the study. The collected data by each data enumerator via CESPRO mobile application were submitted to a local server in Tigray statistics and vital events registration agency.\u003c/p\u003e\n\u003ch3\u003eData Management and Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eData collected using CESPRO mobile application were exported to Microsoft Excel for data cleaning, coding, and preliminary organization. The cleaned dataset was imported into STATA statistical software (Version 17.0, Stata Corp, College Station, Texas, USA) for statistical analysis. Descriptive statistics were used to summarize the socio-demographic characteristics of respondents and their KAPs regarding veterinary drug residues in milk and AMR in dairy production. Frequency distribution and percentages were calculated for categorical variables. Knowledge related questions were initially recorded using three response questions (\u0026ldquo;Yes\u0026rdquo;, \u0026ldquo;No\u0026rdquo; and \u0026ldquo;I don\u0026rsquo;t know\u0026rdquo;), while attitude items were measured using a five-point Likert scale (strongly agree,\u0026emsp;agree,\u0026emsp;uncertain, disagree\u0026emsp;and strongly disagree). Practice-related questions were recorded using response options of \u0026ldquo;Yes, always\u0026rdquo;, \u0026ldquo;Yes, sometimes\u0026rdquo; and \u0026ldquo;No\u0026rdquo;. For further statistical analysis, responses were reclassified into binary categories. Knowledge and practices responses were grouped into \u0026ldquo;Yes\u0026rdquo; and \u0026ldquo;No\u0026rdquo;, while attitude responses were categorized into \u0026ldquo;Agree\u0026rdquo; (strongly agree/agree) and \u0026ldquo;Disagree\u0026rdquo; (uncertain/disagree/strongly disagree). A scoring system was applied to quantify KAP levels. Each correct response was assigned a score of 1, whereas incorrect response was assigned a score of 0. Composite scores of knowledge, attitude, and practice were calculated by summing the scores of individual items for each respondent. The mean score was used as the cut-off point to classify respondents KAP levels. Respondents scoring above the mean were categorized as having adequate knowledge, desirable attitudes, and good practices, whereas those scoring below the mean were categorized as having inadequate knowledge, undesirable attitude, and bad practices, respectively. The internal consistency of the KAP scales was assessed using Cronbach\u0026rsquo;s alpha reliability analysis. A Cronbach\u0026rsquo;s alpha value of \u0026ge;\u0026thinsp;0.70 was considered indicative of acceptable internal consistency of the measurement scales. To explore factors associated with KAP outcomes, inferential statistics were conducted. Initially, univariable binary logistic regression was performed to examine the association between each independent variable and the outcome variables (adequate knowledge, desirable attitude, and good practice). Prior to multivariable analysis, multicollinearity among independent variables was assessed using the Variance Inflation Factor (VIF). Variables with VIF values greater than 10 were considered to indicate significant multicollinearity and were excluded from the final models. Subsequently, multivariable binary logistic regression was conducted to identify independent predictors of adequate knowledge, desirable attitudes, and good practices. Adjusted odds ratios (AORs) with corresponding 95% confidence intervals (CIs) were calculated to determine the strength and direction of associations. The Hosmer\u0026ndash;Lemeshow goodness-of-fit test was used to evaluate the adequacy of the logistic regression models. A p-value greater than 0.05 indicated an acceptable model fit. All statistical tests were two-sided, p-values less than 0.05 were considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic characteristics of the study participants\u003c/h2\u003e \u003cp\u003eA total of 528 dairy farmers participated in this study. Of these, 54% were from Mekelle city, and 46% were from districts in eastern zones. The majority (71.6%) of the dairy farmers were male. The average (mean) age of dairy farmers was 48.3 (95% CI:47.2\u0026ndash;49.3) ranging from 21 to 88 years. Most farmers were within the 46\u0026ndash;60 (40.7%) followed by 31\u0026ndash;45(39.6%), and above 60 (13.3%) years. Regarding educational status, 22.5% of respondents had no formal education, 43.2% had primary school (grades 1\u0026ndash;8), 23.7% had secondary education (grades 9\u0026ndash;12), and only 10.6% had diploma or higher. Majority of the respondents (89.4%) had no professional training related to agriculture, veterinary sciences, or other technical fields. Most respondents (81.6%) relied exclusively on dairy farming as their primary occupation, whereas 18.4% combined dairy farming with other livelihood activities. The dairy production system was predominantly small-scale, with 96.8% of farms owning 1 to 10 cows and only 3.2% owning 11 or more cows. About 81% (427/528) of the farmers possessed five or fewer dairy cows. Detailed sociodemographic characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of the participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;528\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMekelle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e˃60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 1\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExclusively dairy cattle farming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDairy farming and others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear establishment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1974 to 2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2008 to 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFarm size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;10 cows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;11 cows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProfession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessional education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDairy farmers\u0026rsquo; KAPs on veterinary drug residues and AMR\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eKnowledge of dairy farmers\u003c/h2\u003e \u003cp\u003eApproximately 51% of the respondents reported having previously heard of AMR or drug-resistant bacteria. However, most respondents correctly recognized key drivers of AMR. Specifically, 64% reported misuse of veterinary drugs contributed to AMR, while 67.4% acknowledged that incomplete antimicrobial treatment courses could promote the emergence of resistant bacteria. With respect to food safety risks, 71.8% of respondents reported that veterinary drug residues in milk could directly affect human health, while 66.1% recognized that drug residues may contribute to the development of antimicrobial resistant bacteria in humans. Similarly, 71.4% of the respondents reported that antimicrobial-resistant bacteria could spread from animal to human. Additionally, 78.2% of respondents reported that drug resistant bacteria could be transmitted to humans through raw or undercooked milk and dairy products. Knowledge regarding environmental transmission routes was also relatively high. Approximately 72% of respondents recognized that humans may acquire antimicrobial resistant bacteria from dairy environments, and 72.5% indicated that water contaminated with dairy waste could facilitate the spread of drug-resistant bacteria. Importantly, most farmers demonstrated appropriate knowledge regarding antimicrobial stewardship. For example, 86.6% correctly stated that milk from cows undergoing veterinary drug treatment should not be consumed, and 80.5% indicated that veterinary drugs should not be administered without veterinary prescription. Despite these encouraging findings, notable knowledge gaps remained. Only 33.7% of respondents recognized that infections caused by resistant bacteria are more difficult to treat, suggesting limited understanding of the clinical implications of AMR. Detail responses to knowledge statements are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKnowledge of the dairy farmers on drug residues and AMR in dairy production (n\u0026thinsp;=\u0026thinsp;528)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKnowledge statement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eResponse, Number (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI don\u0026rsquo;t know\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave you ever heard about AMR or drug-resistant bacteria?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMisuse of veterinary drugs in dairy cattle can contribute to AMR.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e338 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114 (21.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAn incomplete full course of antimicrobial treatment contributes to AMR.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e356 (67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (18.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIf bacteria are resistant to antimicrobials, it can be very difficult to treat the infections they caused.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (25.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntimicrobial-resistant bacteria can spread from animal to animal.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e377 (71.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (23.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntimicrobial-resistant bacteria can pass from dairy cattle to humans through raw or undercooked milk and milk products.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e413 (78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (8.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHumans can acquire antimicrobial-resistant bacteria from dairy cattle environment (e.g. feces, soil, equipment).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e380 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (17.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHumans can acquire antimicrobial-resistant bacteria from water contaminated with dairy cattle waste.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e383 (72.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVeterinary drug residues consumed in cattle milk can pose health risks to humans by itself\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e379 (71.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77 (14.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVeterinary drug residues consumed in cattle milk can create antimicrobial resistant bacteria in human.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e349 (66.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102 (19.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment regimen can be reduced or suspended as soon as symptoms disappear in your dairy cattle.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e396 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (12.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproper disposal of leftover or expired veterinary drugs can cause drug residues in nature/the environment.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e304 (57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146 (27.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproper disposal of leftover or expired veterinary drugs can be a source of AMR development in the environment.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e306 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133 (25.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk from cows under treatment with veterinary drugs, can be used for human consumption.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e457 (86.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVeterinary drugs can be bought and given for milking cows without veterinarian\u0026rsquo;s prescription.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e425 (80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsing leftover antimicrobials from other cows is acceptable if symptoms look similar.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e427 (81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 (14.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTelevision or radio and social media were the least used as sources of information for AMR. Most dairy farmers heard about AMR from multiple sources, mainly private- and government-employed experts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAttitudes of dairy farmers\u003c/h2\u003e \u003cp\u003eApproximately 62% of respondents agreed that irrational use of veterinary drugs contributed to the development of AMR. Similarly, 75.5% agreed that antimicrobial resistant bacteria could be transmitted to humans through raw or undercooked milk and dairy products, and 71.2% believed that veterinary drug residues in milk may cause health problems in humans. However, attitudes toward milk safety during antimicrobial treatment were less consistent. Notably, 75.6% of the respondents disagreed with the statement that \u0026ldquo;milk from cows under treatment should never be used for human consumption\u0026rdquo;. Encouragingly, 58% of the respondents agreed that observing withdrawal periods is essential for milk safety, although a substantial proportion remained uncertain or disagreed. Detailed responses to attitude statements are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAttitude of the dairy farmers towards drug residue and AMR in dairy production (n\u0026thinsp;=\u0026thinsp;528)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAttitude statement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eResponse, number (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrongly agree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUncertain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStrongly disagree\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrrational use of veterinary drugs in dairy cattle contributes to AMR.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e325 (61.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntimicrobial-resistant bacteria can spread to humans through consumption of raw or undercooked milk and milk products.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e346 (65.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfections caused by antimicrobial resistant bacteria are difficult to treat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e229 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHumans can acquire resistant bacteria from dairy environments.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e344 (65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVeterinary drug residues in milk can contribute to AMR in humans.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e332 (62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVeterinary drug residues in milk can cause health problems in humans.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e349 (66.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIt is acceptable to use leftover drugs for cows with similar symptoms.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e320 (60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproper disposal of expired drugs can contribute to AMR.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266 (50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk from cows under treatment should never be used for human consumption.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e399 (75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34 (6.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObserving withdrawal time is essential for milk safety.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e306 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor record-keeping contributes to drug residues in milk.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e313 (59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFailure to follow instructions of the manufacturer and veterinarian leads to drug residues.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e308 (58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContaminated water with dairy waste can transmit antimicrobial resistant foodborne pathogens.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e359 (68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePractices of dairy farmers\u003c/h2\u003e \u003cp\u003eIn this study, 76.3% of respondents reported that their dairy cattle have received veterinary treatment within the previous 12 months. Encouragingly, 96% of the farmers reported that they did not ever consume or sold milk from a cow under treatment. However, reported practices regarding withdrawal period adherence were suboptimal. Only 13.3% of the respondents reported consistently (always) observing drug withdrawal periods, while 28% reported doing so occasionally (sometimes), 58.9% indicated that they did not adhere to the drug withdrawal recommendations. Moreover, 21% of the farmers reported occasionally purchasing veterinary drugs without professional consultation, while 20.8% reported obtaining drugs from human pharmacies for use in cattle. Additionally, 11% of respondents reported that they sometimes stored leftover drugs for future use, and 12.5% reported discontinuing antimicrobial treatment when clinical signs disappeared. Practice-related responses are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePractice of dairy farmers Addressing antimicrobial use in the dairy production (n\u0026thinsp;=\u0026thinsp;528)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePractice question\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eResponse, number (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, always\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes, sometimes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave you ever bought veterinary drugs without prescription or professional consultation?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e411 (77.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave you ever bought drugs from human pharmacies for your cattle?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e415 (78.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave you ever administered orally Veterinary drugs to your dairy cattle without professional consultation?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e443 (83.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave you ever applied topically Veterinary drugs to your dairy cattle without professional consultation?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e476 (90.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave you ever injected your dairy cattle yourself?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e507 (96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave you ever stored leftover drugs for use of future illness episodes?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e469 (88.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave you ever reduced or suspended treatment regimen as soon as symptoms disappear in your dairy cattle?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e461 (87.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave you ever disposed of any leftover or expired drugs?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e482 (91.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave you ever consumed or sold milk of a cow under treatment with veterinary drugs?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e507 (96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave you ever practiced drug withdrawal time for your milking cow appropriately?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e310 (58.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe dairy farmers were asked to mention the drugs commonly used to treat their dairy cattle. More than three-fourth (76.5%, 404/528) of the dairy farmers did not know the drugs given to their animals while 23.5% of them mentioned either correctly specific drugs such as albendazole and oxytetracycline, or general names like acaricide, or agents used for supportive treatment, e.g., multivitamin. Albendazole was the highest reported drug used for treatment by dairy farmers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDairy farmers\u0026rsquo; KAP scores on veterinary drug residue and AMR\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003eKnowledge scores of the dairy farmers\u003c/h2\u003e \u003cp\u003eThe mean knowledge score among respondents was 10.86\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;5.1 (standard error, SD). Based on this cut-off, 67.2% (355/528) of respondents demonstrated adequate knowledge, while 32.8% (173/528) had inadequate knowledge regarding veterinary drug residues and AMR. Even though the majority (71.6%) of the participants were male, adequate knowledge score of both male and female respondents were almost similar (\u003cb\u003e\u0026asymp;\u003c/b\u003e\u0026thinsp;67%). Substantial geographic variation was observed where farmers from Mekelle City exhibited higher knowledge levels (91%) compared with those from eastern zone (60.5%). Statistically significant associations were identified between knowledge levels and location, education, occupation, farm size, and profession p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdequate and inadequate knowledge scores of the dairy farmers on drug residue and AMR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eDemographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e (P-value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdequate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInadequate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMekelle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259 (91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e157 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147 (60.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254 (67.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0 (0.976)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (67.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (32.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (82.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e3.9 (0.262)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (34%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (68.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (31.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e12.6 (0.006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 1\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (73.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (55.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (44.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (34%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExclusively dairy cattle farming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278 (64.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.9 (0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDairy farming and others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (79.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear establishment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1974 to 2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.006 (0.938)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2008 to 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149 (67.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (32.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFarm size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e338 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e8.6 (0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProfession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessional education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (44.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (55.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e14.5 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e330 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e355 (67.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAttitude scores of the dairy farmers\u003c/h2\u003e \u003cp\u003eThe mean attitude score was 7.8\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;4.4 (SD). Overall, 68.2% of the respondents demonstrated a desirable attitude towards antimicrobial stewardship and drug residue control. Marked difference was again observed by location. Respondents from Mekelle City exhibited considerably higher desirable attitude scores (92%) compared with those from the eastern zone (40.3%). Location, education, occupation, farm size, and profession were statistically significant (; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDesirable and undesirable attitude scores of the dairy farmers towards drug residue and AMR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eDemographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAttitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e (P-value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDesirable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUndesirable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMekelle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262 (92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e161 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145 (59.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258 (68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 (31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.00 (0.955)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (32%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (82.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e3.75(0.289)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (34%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145(67.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (32.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (73.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e8.9 (0.030)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 1\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163 (71.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (28.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (57.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (67.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExclusively dairy cattle farming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e280 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.2 (0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDairy farming and others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (82.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear establishment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1974 to 2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e217 (70.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.1 (0.146)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2008 to 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143 (64.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFarm size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e343 (67.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e8.2 (0.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProfession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessional education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (48.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (51.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e11.5 (0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333 (70.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360 (68.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePractice scores of the dairy farmers\u003c/h2\u003e \u003cp\u003eThe mean practice score was 7.1\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;1.7 (SD). Based on this threshold, 60.2% of respondents demonstrated good antimicrobial use practice while 39.8% exhibited poor practices. Practice scores varied considerably by location: Farmers from Mekelle City had substantially higher good practice scores (76.1%) compared with 41.6% among farmers from eastern zone. Socio-demographic factors significantly associated with good practices included location, education, occupation, farm size, and profession (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGood and bad practice scores of the dairy farmers addressing drug residue and AMR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eDemographic characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePractice\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e (P-value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBad\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMekelle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e217 (76.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e65.5 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (41.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (58.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.8 (0.358)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (63.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (73.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e3.1 (0.371)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122 (58.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87 (41.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (59.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88 (40.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (62.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1.2 (0.017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 1\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148 (64.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (49.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (51.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (48.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExclusively dairy cattle farming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e248 (57.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183 (42.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7.1 (0.008)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDairy farming and others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (72.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (27.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear establishment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1974 to 2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193 (62.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.1 (0.144)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2008 to 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125 (56.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (43.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFarm size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e303 (59.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e208 (40.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.8 (0.016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (88.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProfession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessional education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e13.5 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297 (62.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e318 (60.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e210 (39.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMultivariant logistic regression of predictors associated with KAP scores\u003c/h2\u003e \u003cp\u003eAfter adjustment for potential confounders, location, education, occupation, and profession remained statistically associated with KAP outcomes. Farmers from Mekelle City were significantly more likely to demonstrate adequate KAP scores compared with those from eastern zone. Specifically, respondents from Mekelle city had approximately eighteen (AOR:18.4; 95% CI: 10.9\u0026ndash;30.7), twenty (AOR:19.5; 95% CI: 11.5\u0026ndash;32.9) and five (AOR:4.5; 95% CI: 3.1\u0026ndash;6.7) times higher of adequate knowledge, desirable attitude and good practice respectively than respondents from eastern zone. Similarly, farmers who combined dairy farming with other occupations were more likely to demonstrated adequate knowledge (AOR\u0026thinsp;=\u0026thinsp;3.4; 95% CI: 1.8\u0026ndash;6.6), desirable attitudes (AOR\u0026thinsp;=\u0026thinsp;4.5; 95% CI: 2.3\u0026ndash;8.8), and good practices (AOR\u0026thinsp;=\u0026thinsp;2.5; 95% CI: 1.5\u0026ndash;4.5) compared with those exclusively engaged in dairy farming (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between predictor variables and KAP scores (using Adjusted odds ratio, AOR)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eDemographic variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAdequate knowledge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eDesirable attitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eGood practice\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMekelle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4 (10.9\u0026ndash;30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.5 (11.5\u0026ndash;32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.5 (3.1\u0026ndash;6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (0.6\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7 (0.9\u0026ndash;3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.6 (0.9\u0026ndash;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 1\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7 (1.5\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0 (1.1\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.8 (1.1\u0026ndash;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7 (1.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2 (0.9\u0026ndash;5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.2 (0.5\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExclusively dairy cattle farming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDairy farming and others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4 (1.8\u0026ndash;6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.5 (2.3\u0026ndash;8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5 (1.5\u0026ndash;4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOmitted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOmitted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.8 (1.0- 22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProfession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessional education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9 (1.7\u0026ndash;9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.3 (1.4\u0026ndash;7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5 (1.2\u0026ndash;4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eNowadays, antimicrobial resistance is recognized as one of the most pressing global health challenges, posing substantial threat to human, animal, and environmental health. The WHO has identified AMR among the top ten global public health threats and introduced the Global Action Plan on AMR (GAP-AMR) to guide coordinated international responses [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In alignment with this initiative, Ethiopia adopted the NAP-AMR to address the drivers of AMR across human, animal, and environmental sectors, thereby to strategize its effective prevention methods. Within the livestock production system, dairy farmers play a critical role in antimicrobial use practices and consequently in the emergence and dissemination of antimicrobial-resistant pathogens and veterinary drug residues in milk [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, assessing KAPs of the dairy farmers regarding antimicrobial use and drug residues is essential for designing effective mitigation strategies. The present study investigated KAPs among dairy farmers in the Mekelle-Adigrat milk shed and identified key socio-demographic determinants influencing KAP outcomes. As to our knowledge, the KAP scores of dairy farmers towards veterinary drug residue and AMR in the dairy production along the Mekelle-Adigrat milk shed and predictor variables that statistically influenced KAP scores were for the first time reported.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eKnowledge of the dairy farmers on veterinary drug residue and AMR\u003c/h2\u003e \u003cp\u003eThe study underscores that almost two-third of the dairy farmers (67.2%) had adequate knowledge while about one-third (32.8%) had inadequate knowledge on veterinary drug residues in milk and AMR in dairy production. In agreement with the present findings, comparable adequate knowledge scores of about 58, 61, 63 and 72% on antimicrobial use and resistance were reported from Ethiopia [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], Malaysia [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], Bangladesh [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and Algeria [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], respectively. However, the current result of adequate knowledge score on drug residues and AMR in dairy farmers was discordant with the findings documented in central and western Ethiopia (6%) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], Turkey (10%) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and Indonesia (19.8%) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These variations may be attributed to differences in farmers\u0026rsquo; access to veterinary services, educational exposure, extension programs, and awareness campaigns regarding antimicrobial stewardship.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression analysis showed that location, educational level, occupation, and profession were significant predictors of adequate knowledge scores. Farmers living in Mekelle were significantly more likely to possess adequate knowledge scores compared with those residing in eastern zone (AOR:18.4; 95% CI: 10.9\u0026ndash;30.7). This might indicate local interventions designed to address knowledge on veterinary drug residues in milk and AMR should differ from location to location. Similar associations between geographic location and knowledge levels were reported in studies conducted in Turkey [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], Vietnam [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and Bangladesh [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], suggesting that proximity to veterinary services and urban infrastructure can substantially influence awareness of antimicrobial stewardship. Findings from studies conducted in other regions of Ethiopia are inconsistent with the present study, as they reported no statistically significant association between study setting and participants\u0026rsquo; adequate knowledge score of the antimicrobial use, residues and resistance [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, education was also found significantly associated with adequate knowledge score of the dairy farmers on veterinary drug residue and AMR. Similar findings were also reported from previous considerable studies conducted in different countries [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, the direction of the association was diverse in different studies. In our study, dairy farmers who attended primary school education (AOR: 2.7; 95% CI: 1.5\u0026ndash;4.7) and completed diploma or above (AOR: 2.7; 95% CI: 1.0\u0026ndash;7.0) had statistically significant association with adequate knowledge score of the dairy farmers on drug residues and AMR. Other studies[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] reported that educational level of animal farmers was positively associated with their adequate knowledge score point on antimicrobial use and resistance; in other words, adequate knowledge score of animal farmers was improved with increment of educational level. However, enhanced adequate knowledge score was also negatively associated with educational level, particularly primary school education was significantly associated with adequate knowledge score of the livestock owners [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which was consistent with present study. There was also a study[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] inconsistent with our findings that education was insignificantly associated with adequate knowledge score of livestock farmers on antimicrobial use and resistance. This could be due to differences in farm experience, disease occurrence, and veterinarian consultation that could help to increase farmers\u0026rsquo; awareness [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eInterestingly, dairy farmers who engaged in additional occupations beyond dairy farming were more likely to possess adequate knowledge (AOR: 3.4; 95% CI: 1.8\u0026ndash;6.6). Diversified occupational engagement may increase exposure to information networks, training opportunities, and social interactions that enhance awareness of livestock management practices. In contrast, other studies have demonstrated no significant association between occupation and animal farmers\u0026rsquo; knowledge on antimicrobial use and resistance [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Considering profession, non-professionally trained farmers exhibited higher knowledge scores than professionally trained respondents (AOR: 3.9; 95% CI: 1.7\u0026ndash;9.1). Although counterintuitive, this may reflect the possibility that non-professional farmers rely more heavily on practical experience, peer learning, and veterinary consultations, whereas professionally trained individuals may practice greater autonomy in decision-making regarding antimicrobial use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eAttitudes of the dairy farmers towards veterinary drug residues and AMR\u003c/h2\u003e \u003cp\u003eThe present study demonstrated that 68.2% of the respondents exhibited desirable attitudes towards veterinary drug residues and AMR. This proportion was considerably higher than findings reported in other Ethiopian settings, including Addis Ababa (47.7%) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and Oromia region (14.7%) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], as well as poultry farms in Vietnam (20.4%) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These differences may reflect variations in awareness programs, regulatory enforcement, and local livestock management practices. Consistent with the knowledge results, predictor variables i.e., location, education, occupation, and profession were significantly associated with dairy farmers\u0026rsquo; desirable attitude scores. In contrast to the present study, findings from other regions of Ethiopia have shown that study setting was not significantly associated with desirable attitudes of animal farmers toward antimicrobial use, residues, and resistance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, studies conducted in Vietnam [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] Bangladesh [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and Indonesia [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] have reported a significant association between study setting and farmers\u0026rsquo; desirable attitudes towards antimicrobial use and resistance.\u003c/p\u003e \u003cp\u003eEducation was also associated with desirable attitude scores. However, only farmers with primary school education were more likely to express desirable attitude scores towards antimicrobial stewardship compared with respondents who attended high school education. But other considerable studies[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] documented that desirable attitude score was significantly associated with increment of educational level. In the studies reported by Dewi et al.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and Woldemichael et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], logistic regression showed that education was not significantly associated with desirable attitude score of animal producers on antimicrobial use and resistance. But Woldemichael et al.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] indicated that educational level (primary, secondary, and tertiary) was negatively related with desirable attitude score of the livestock producers towards antimicrobial use and resistance, with slightly higher desirable attitude score in respondents who attended primary school education, which agreed with the present study.\u003c/p\u003e \u003cp\u003eIn the present study, majority (81.6%) of the respondents were exclusively engaged in dairy cattle farming, the remaining (18.4%) were engaged in other activities in addition to dairy farming. However, the desirable attitude score of the respondents (82.5%) who engaged in dairy cattle farming and other activities was higher than respondents (65%) who engaged exclusively in dairy cattle farming. Occupation was found statistically significant association with desirable attitude score; and hence, dairy farmers who engaged in dairy cattle farming and other activities were about five (AOR: 4.5; 95% CI: 2.3\u0026ndash;8.8) times more likely to have desirable attitude score than dairy farmers who engaged exclusively in dairy cattle farming towards antimicrobial residue and resistance. In other studies, occupation was not found to be a significant determinant of animal producers\u0026rsquo; attitudes toward antimicrobial use and resistance [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally, non-professional dairy farmers showed more significantly higher desirable attitudes (70.5%) than professionally trained farmers (48.2%) (AOR: 3.3; 95% CI: 1.4\u0026ndash;7.6), suggesting that practical farming experience and reliance on veterinary guidance may shape perceptions toward responsible antimicrobial use.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003ePractice of the dairy farmers addressing veterinary drug residue and AMR\u003c/h2\u003e \u003cp\u003eIn this study, despite adequate knowledge (67.2%) and desirable attitudes (68.2%) were relatively favorable, only 60.2% of the dairy farmers demonstrated good practices related to antimicrobial use and prevention of drug residues in milk. This finding highlights a persistent knowledge-practice gap, which has been widely documented in antimicrobial stewardship research. Comparable practice levels have been reported among dairy farmers from Addis Ababa (53%)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and the Amhara region (47%)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] of Ethiopia. However, both higher[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and lower[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] practice levels have been documented in other regions of Ethiopia, reflecting heterogeneity in livestock management systems and regulatory oversight.\u003c/p\u003e \u003cp\u003eThe analysis revealed location, education, Occupation, profession, and farm size were significantly associated with good practice scores. In contrast to studies conducted in other settings of Ethiopia [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the result of the present study showed that setting was significantly associated with good practice score of the dairy farmers on antimicrobial residue and resistance. Dairy Farmers from Mekelle were more likely to adopt appropriate practices compared with those from eastern zone (AOR: 4.5; 95% CI: 3.1\u0026ndash;6.7). However, other studies conducted in different countries[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] were found concordant with the present study, as setting was significantly associated with good practice score of antibiotic use. Azim et al.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] indicated that urban residents had good practice score addressing antibiotic use and resistance. These differences could be due to the fact that exposure of the dairy farmers to farm biosecurity systems or influences arising from the customers and/or experts/policy makers to realize farm biosecurity systems.\u003c/p\u003e \u003cp\u003eIn this study, education also influenced practice outcomes. Farmers with primary school education demonstrated better antimicrobial use practices than those with secondary education (AOR: 1.8; 95% CI: 1.1\u0026ndash;2.9). Similar observations have been reported in other studies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], suggesting that more highly educated farmers may sometimes rely on self-administration of veterinary drugs due to increased confidence in disease management, which could inadvertently lead to inappropriate antimicrobial use. Similarly, Tufa et al. (2023) indicated that high level of education was associated with bad practice score of animal farmers towards antimicrobial use, residue and resistance as educated farmers may have a better knowledge of animal diseases and practice self-administration of antimicrobials to their sick animals. In other ways, non-professional dairy farmers could be comparably risk averter who avoid self-prescription and-administration of drugs as compared to professionally trained individuals who could be risk takers. This is also supported by the present study that about 63% of non-professional dairy farmers had good practice score and were 2.5 times likely to have good practice scores than professionally educated dairy farmers towards antimicrobial residue and resistance.\u003c/p\u003e \u003cp\u003eMoreover, occupation and livelihood diversification was associated with improved practices. Farmers who combined dairy farming with other economic activities were more likely to demonstrate good antimicrobial use practices (AOR: 2.5; 95% CI: 1.5\u0026ndash;4.5). Diversified income sources may enable farmers to access veterinary services, depended on professional consultations, purchase quality veterinary drugs, and adopt improved biosecurity measures. Farm size was also another important determinant that predicted the practice score of the dairy farmers towards the veterinary drug residues and AMR. Farmers owning larger dairy herds (\u0026ge;\u0026thinsp;11 animals) were significantly 4.8 times more likely to demonstrate good practices than those who keep small number of dairy animals (1 to 10 dairy cattle). This could be due to the fact that larger dairy operation often requires structured herd management systems, regular veterinary supervision, and adherence to milk quality standards, all of which may encourage responsible antimicrobial use.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study demonstrated that majority of the dairy farmers in the Mekelle-Adigrat milk shed possessed adequate knowledge, and desirable attitude scores toward veterinary drug residues and AMR. However, the proportion of farmers demonstrating good practice score of antimicrobial use was comparatively lower, indicating a disconnect between awareness and behavioral change in implementing rational use of drugs. Socio-demographic variables, including location, education, occupation, and profession significantly influenced KAPs outcomes. Farm size was also significantly associated with good practice. These findings highlight the need for targeted antimicrobial stewardship interventions, particularly in dairy production systems found in less urbanized locations. Expanding veterinary education services and promoting responsible antimicrobial use practices are essential to, reduce veterinary drug residues in milk and, mitigate the emergence of AMR in the dairy sector. Hence, the findings of this study provided baseline data, and called, for policy makers to conduct local interventions. Future research should incorporate qualitative approaches, laboratory analysis of antimicrobial residues, and antimicrobial susceptibility testing to generate more comprehensive evidence on antimicrobial use practices and resistance patterns within dairy production systems.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eLimitations of the study\u003c/h2\u003e \u003cp\u003eSeveral limitations should be considered when interpreting the findings of this study. First, the study was conducted within limited geographic areas of northern Ethiopia, which may limit generalizability of the findings to their regions with different socio-economic and production systems. Second, the cross-sectional design restricts causal inference regarding the relationship between sociodemographic factors and KAP outcomes. Third, the use of self-reported questionnaire data may introduce recall and social desirability biases, potentially leading to under-or over-reporting of the dairy farmers KAPs towards the activities that influence antimicrobial residue and resistance. Additionally, the questionnaire relied on categorical response options, which may have constrained the depth of information captured from respondents. Future research should incorporate qualitative approaches, laboratory analysis of antimicrobial residues, and antimicrobial susceptibility testing to generate more comprehensive evidence on antimicrobial use practices and resistance patterns within dairy production systems. Despite these limitations, the present study provides valuable baseline information to inform antimicrobial stewardship policies and interventions in the Ethiopia\u0026rsquo;s dairy sector.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was conducted in accordance with the national and international ethical standards. Ethical approval was obtained from Institutional Review Board (IRB) of Mekelle University, College of Health Sciences, with reference number MU-IRB 2689/2025. Moreover, the study complied with the BELMONT REPORT [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and declaration of Helsinki [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] which involve human participants. Participants were informed regarding the objectives and procedures of the study using the local language, Tigrigna. The study participants were assured that their privacy of research participation and confidentiality of personal information was protected including of anonymized data generated publications. Oral informed consent from all interviewees was obtained and recorded.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e \u003cb\u003eConsent for publication\u003c/b\u003e:\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was financially supported by MU-HU-NMBU ICP V with registration number project RADO/External/MS/0046/2025.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: GR; Data curation and Methodology: GR GG, YTA, KA \u0026amp; AGK; Formal analysis: GR \u0026amp; GG; Data collection: GR and YTA; Project administration: GR; Writing\u0026ndash; original draft: GR; Writing\u0026ndash; review, editing \u0026amp; approval: GR GG, YTA, KA \u0026amp; AGK\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors are indebted to the dairy farmers who consented to participate in this study. Moreover, all the study districts/sub-cities (Adigrat, Ayder, Hadnet, Hawelti, Semen, Tsireawenberta and Wukro) as well as Grace vet activities \u0026amp; services and Mekelle-Tigray SNV BRIDGE Plus project which give the sampling frame and cooperating the data collection to us were acknowledged. Acknowledgments also extended to Mr Abrha Kahsay who managed the data collection CESPRO mobile application and server as well as to Mr Abha Asefa who mapped the study area.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eResearch materials will be made available to other researchers at the reasonable request of the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVan Boeckel TP, Brower C, Gilbert M, et al. Global trends in antimicrobial use in food animals. 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Published online 2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Knowledge, Attitude, practice, veterinary drug residue, antimicrobials resistance, dairy farmers, Tigray","lastPublishedDoi":"10.21203/rs.3.rs-9349578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9349578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe rapid growth of dairy production in Ethiopia is driven by increasing demand for milk and dairy products. While this transition supports food security and livelihoods, it also increases the risk of infectious diseases and inappropriate use of antimicrobials which can lead to drug residues in milk and contribute to antimicrobial resistance (AMR), posing significant threats to food safety and public health. This study assessed the knowledge, attitudes and practices (KAP) of dairy farming stakeholders regarding veterinary drug residues and AMR in dairy production systems along the Mekelle-Adigrat milk shed, in the Tigray region of Ethiopia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted from November 2025 to February 2026 among 528 participants. Data were collected using a structured close-ended questionnaire and analyzed using multivariate logistic regression to identify factors associated with KAP outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall, 67.2% of respondents had adequate knowledge, 68.2% demonstrated desirable attitudes, and 60.2% reported good practices regarding veterinary drug residues and AMR. Participants\u0026rsquo; location, education level, profession, and occupation were significantly associated with KAP scores, while farm size was significantly associated with good practices. Location showed the strongest association with knowledge outcomes. Respondents from Mekelle City had approximately eighteen (AOR:18.4; 95% CI: 10.9\u0026ndash;30.7), twenty (AOR:19.5; 95% CI: 11.5\u0026ndash;32.9) and five (AOR:4.5; 95% CI: 3.1\u0026ndash;6.7) times adequate knowledge, desirable attitude and good practice scores respectively compared with respondents from the Eastern Zone.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003e Although most participants demonstrated adequate knowledge and favorable attitudes toward veterinary drug residues and AMR, the relatively lower level of good practices indicates a gap between knowledge and implementation. Strengthening farmer training, veterinary extension services, and regulatory oversight on antimicrobial use is essential to improve responsible antimicrobial use and reduce drug residue risks. These findings provide important evidence to inform targeted One Health-oriented policies and interventions to improve dairy product safety and mitigating AMR in Ethiopia\u0026rsquo;s rapidly expanding dairy sector.\u003c/p\u003e","manuscriptTitle":"Knowledge, attitude, and practices of dairy farmers on veterinary drug residues and antimicrobial resistance in Tigray region of Ethiopia: Implications for food safety and public health","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 14:55:02","doi":"10.21203/rs.3.rs-9349578/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"227279528001260460829646650115234313115","date":"2026-04-15T17:16:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T06:09:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-10T08:31:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-09T06:09:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-09T06:08:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-04-07T22:10:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7cd241a2-849c-4f6f-b569-a177a6441bc8","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T14:55:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 14:55:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9349578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9349578","identity":"rs-9349578","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}