Antimicrobial use, resistance awareness, and biosecurity practices among poultry and aquaculture farmers in Nigeria: a cross-sectional KAP survey

Abstract

Background

Antimicrobial resistance (AMR) poses a growing threat to food production systems. In Nigeria, poultry and aquaculture sectors face heightened vulnerability due to high disease burdens, frequent antimicrobial use, and variable biosecurity practices. This study assessed AMR-related knowledge, attitudes, and practices (KAP) among value-chain respondents across four Nigerian states.

Methods

A cross-sectional KAP survey was conducted in January 2025 among 183 respondents across Anambra, Akwa Ibom, Kano, and Kwara states using multi-stage sampling. Data were collected via structured face-to-face interviews and analysed using Chi-square tests, Spearman’s correlation, and binary logistic regression.

Results

Most respondents were aged 35–44 years (40.44%) with tertiary education having 80.33%. Disease incidence was high (84.15%), with bacterial infections most common (39.61%, n = 154). Antimicrobial use was high (92.90%), primarily for treatment (87.06% of users), with 22.35% self-prescribing. AMR awareness was reported by 71.58%, mainly from veterinary officers (87.02%) and social media (48.09%) among AMR-aware respondents (n = 131). Awareness was significantly associated with education level (χ²=57.16, df = 3, p < 0.001) and inversely correlated with use frequency (ρ=−0.279, p < 0.001). Multivariable logistic regression identified education as the strongest predictor; secondary-educated farmers were 98% less likely to be AMR-aware than tertiary-educated (OR = 0.02, 95% CI: 0.00-0.07, p < 0.001). Biosecurity awareness was high (72.68%) but training coverage was low (53.55%; χ²=13.46, df = 1, p < 0.001).

Conclusions

Widespread antimicrobial use, moderate AMR awareness, and inconsistent biosecurity practices were identified. Education was the strongest predictor of AMR awareness, underscoring the need for non-formal AMR communication strategies. Feed millers, sellers, and allied value-chain actors should be incorporated into national AMR stewardship programmes.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12917-026-05705-3.

Introduction

Antimicrobial resistance (AMR) is a major threat to global health, food security, and livelihoods, projected to cause 10 million deaths annually by 2050 if left unchecked [1, 2]. In food animal production, inappropriate antimicrobial use drives resistance emergence and dissemination across livestock, fish, humans, and the environment, a dynamic that is particularly concerning in LMICs where regulatory enforcement, veterinary oversight, and AMR surveillance remain limited [3–6]. The FAO and the World Organisation for Animal Health (WOAH) emphasise that food animal sectors represent a critical and underaddressed node in the global AMR response, with resistance genes transmissible to humans through the food chain, direct contact, and environmental pathways [7].

As the most populous country in Africa, Nigeria is particularly vulnerable to AMR, given its rapidly growing poultry and aquaculture sectors [8, 9]. These sectors are vital for food security, income generation, and employment across the country [10, 11], yet they are characterised by high disease burdens, suboptimal biosecurity, and heavy reliance on antimicrobials for both therapeutic and non-therapeutic purposes [12, 13]. Critically important antimicrobials including tetracyclines, fluoroquinolones, and macrolides are frequently used without veterinary oversight, often for growth promotion and prophylaxis, elevating the risk of resistant bacteria and antimicrobial residues in food products [14, 15]. Importantly, poultry and aquaculture value chains in Nigeria are deeply integrated: many smallholder operators are engaged in both enterprises simultaneously, and allied actors such as feed millers, drug vendors, and sellers operate across both sectors. This integration creates shared AMR risk pathways that sector-specific studies may miss, providing a strong rationale for a combined cross-sector KAP assessment.

Nigeria ranks among the top five countries projected to see the highest increase in antimicrobial use in food animals [16]. Previous research reported that 87.4% of poultry farms reported heavy antibiotic use, with tetracycline resistance reaching 58%, sulfonamide resistance 65%, and ciprofloxacin resistance 46%, correlating with farm-level antimicrobial usage [15, 17]. In aquaculture, a cross-sectional study in Northern Nigeria found that 78.1% of fish farmers used antibiotics in their ponds; 77.1% lacked understanding of antibiotic misuse and 73.3% did not recognise how misuse contributes to resistance [18, 19]. Despite this dual burden, no published KAP study has simultaneously surveyed poultry and aquaculture value-chain actors across multiple geopolitical zones in Nigeria, and the role of allied actors such as feed millers and sellers in AMR transmission remains unexamined. In cultured African catfish (Clarias gariepinus) from Awka, Anambra State, isolates showed 60.5% resistance to tetracycline, 23.7% to ciprofloxacin, and 13.2% to amoxicillin [19].

In response, the Nigerian government launched its first National Action Plan (NAP) for AMR (2017–2022), aligned with the Global Action Plan, and adopted a One-Health framework integrating human, animal, and environmental health [20, 21]. Despite this policy structure, implementation has been uneven, and gaps persist in farmer awareness, biosecurity uptake, and veterinary oversight [21]. The Fleming Fund’s investments have bolstered laboratory capacity for AMR surveillance [22], but farmer-level behavioural data remain particularly scarce in the aquaculture sector.

Knowledge, attitudes, and practices (KAP) surveys are a critical tool for identifying behavioural drivers of antimicrobial misuse and targeting awareness gaps that hinder AMR control [23]. Prior KAP studies in Nigeria have examined poultry and aquaculture producers separately [24, 25], missing the cross-sector dynamics that characterise Nigeria’s integrated smallholder value chains. The influence of sociodemographic factors including education level on AMR awareness and biosecurity adoption also remains poorly characterised across these sectors [26]. This study assessed AMR-related KAP among poultry and aquaculture value-chain respondents including primary producers, sellers, feed millers, and other allied actors across four geopolitically representative Nigerian states (Anambra, Akwa Ibom, Kano, and Kwara). The study examined patterns of antimicrobial use, AMR awareness and its predictors, and biosecurity practices, with findings intended to inform targeted One Health interventions and strengthen Nigeria’s AMR response in its food animal production sectors.

Materials and methods

Study area

The study was conducted in four Nigerian states (Anambra, Akwa Ibom, Kano, Kwara) purposively selected to capture regional diversity in poultry and aquaculture production (Fig. 1). These states differ in agro-ecological zones, farming systems, and market dynamics. Anambra State, located in Nigeria’s South-East, is highly urbanized with around 62% urban population and features numerous peri-urban, small-scale broiler poultry operations [27]. Kano State in the North-West is a major poultry production hub, and its expanding aquaculture sector is supported by large-scale irrigation systems like the Kano River Irrigation Scheme; most fish farms are small to medium-scale [28]. In the North-Central Kwara State, poultry remains predominantly smallholder-based, while the state’s extensive inland water bodies offer growing potential for aquaculture [29]. Akwa Ibom State, in the South-South region, is notable for its proximity to coastal waters, making aquaculture a key livelihood alongside poultry farming [30]. These states were chosen for their strategic roles in national poultry and aquaculture value chains, as well as their potential for representing the diversity of production and biosecurity practices in Nigeria.

Fig. 1.

A map of Nigeria showing the selected states (designed using QGIS, version 3.44.0)

Study design

A cross-sectional survey was conducted in January 2025 to assess the knowledge, attitudes, and practices (KAP) related to antimicrobial resistance (AMR) among poultry and aquaculture farmers using a pre-tested, structured questionnaire designed for this study (Suppl. 1). The design was chosen to enable the collection of data at a single point in time across multiple locations, providing a snapshot of farmer practices and perceptions.

Study population

The study population comprised poultry and aquaculture farmers actively engaged in production or related enterprises, including poultry sellers, fish sellers, feed millers, and other allied occupations. Of the 183 respondents, 149 (81.4%) were farm owners, 34 (18.6%) were farm managers and 12 (6.56) were other value-chain respondents. In all cases, the individual responsible for day-to-day enterprise management, antimicrobial procurement, and record-keeping completed the questionnaire, ensuring that responses reflected actual decision-making practices rather than secondhand accounts. Feed millers, poultry sellers, fish sellers, and other allied actors were deliberately included alongside primary producers because they constitute integral nodes in the poultry and aquaculture value chain with direct roles in antimicrobial distribution, recommendation, and use. Restricting the survey to primary farmers alone would have underrepresented the full scope of antimicrobial use behaviours within these sectors.

Inclusion and exclusion criteria

Inclusion criteria

• Adults aged 18 years and above.

• Individuals actively engaged in poultry or aquaculture production, sales, feed milling, or other related enterprises.

• Minimum of six months of continuous involvement in the poultry or aquaculture value chain before the survey.

Exclusion criteria

• Respondents unwilling to provide informed consent or participate in survey.

Sampling procedure

A total of 183 respondents were selected using a multi-stage sampling technique. In the first stage, four states (Anambra, Akwa Ibom, Kano, and Kwara) were purposively selected for their high levels of poultry and/or aquaculture activity and from 4 out of 6 geopolitical regions in Nigeria. In the second stage, specific local government areas (LGAs) within each state were purposively chosen to represent a range of production systems, including intensive/commercial, semi-intensive, and smallholder/traditional operations. In the third stage, proportional allocation was used to ensure that the number of respondents recruited from each enterprise category (poultry farmers, fish farmers, poultry sellers, fish sellers, feed millers, and other allied actors) reflected their relative representation within the local poultry and aquaculture value chains, based on state agricultural records and extension worker estimates. Within each LGA, respondents meeting the inclusion criteria were identified through local producer associations and extension service lists, and simple random sampling was applied to select participants within each category until the required quota was reached. However, 102 respondents (55.7%) were engaged in more than one enterprise type, including 50 (27.3%) who were involved in both fish and poultry farming simultaneously, reflecting the integrated nature of smallholder value-chain participation in these states. The target sample size was informed by the registers of producer associations and extension service lists within each selected LGA. Although each state had over 200 registered farmers, the final sample of 183 was determined by the number of eligible participants who were physically present and available during the data collection period in January 2025, consistent with availability sampling from a defined sampling frame as applied in similar KAP studies in sub-Saharan Africa [31, 32]. To minimise recall and social desirability bias inherent in self-reported data, interviewers underwent a two-day training workshop on standardised questionnaire administration, neutrality in probing, and avoidance of leading questions. Interviews were conducted in private settings to encourage candid responses, and where possible, farmers were asked to show relevant records (e.g., drug purchase logs) to verify self-reported practices.

Data collection

Data were collected using a structured, pre-tested questionnaire developed in line with AMR KAP survey guidelines from the World Health Organization (WHO) and the Food and Agriculture Organization (FAO). The questionnaire was designed to capture information on demographic and enterprise characteristics, disease incidence and management practices, patterns and frequency of antimicrobial use, awareness and understanding of AMR, biosecurity practices and training, record-keeping and withdrawal period observance. Sociodemographic variables were restricted to those directly relevant to AMR knowledge and practice, namely age, education, experience, and enterprise type. Marital status and income were not captured, as the study’s analytical focus was on enterprise-level KAP determinants rather than household socioeconomic profiling. Gender was not captured as a study variable because the survey was designed at the enterprise level, targeting the individual responsible for antimicrobial procurement and use decisions within each operation. The unit of analysis was the farming enterprise rather than the individual farmer, consistent with enterprise-level KAP survey approaches in the AMR literature. This is acknowledged as a limitation, as gender dynamics in antimicrobial stewardship represent an important area for future investigation. For the purposes of this study, ‘Tertiary’ education refers to post-secondary qualifications including university degrees, polytechnic diplomas, and other accredited higher education certificates. Disease occurrence was recorded based on farmer self-report, with respondents asked to identify the category of disease experienced (bacterial, viral, protozoan, or parasitic) drawing on their own knowledge or prior veterinary consultation; no laboratory confirmation of disease aetiology was performed as part of this study. The questionnaire was administered through face-to-face interviews conducted by trained data collectors fluent in English and relevant local languages. Pre-testing of the instrument was conducted in a farming community outside the study sites to ensure clarity, cultural appropriateness, and reliability. Content and face validity were established through expert review and the pre-testing exercise. Internal consistency (Cronbach’s alpha) was not computed as the instrument comprised categorical and binary items across distinct KAP domains rather than a unidimensional Likert-type scale, and such items do not satisfy the assumptions required for alpha estimation, consistent with standard practice in binary-item KAP surveys in the AMR literature [31, 32]. Necessary modifications were made before the main data collection exercise.

In addition, participatory data collection was undertaken in collaboration with experts from the Nigerian Veterinary Medical Association (NVMA), the Ministry of Livestock Development, and the Food and Agriculture Organization of the United Nations (FAO). These experts not only provided technical support during the survey process but also facilitated post-survey community engagements aimed at raising awareness on prudent antimicrobial use, AMR risks, and biosecurity practices among farmers. It is important to note that these post-survey community awareness engagements were conducted strictly after the completion of data collection in all sites, ensuring that respondents’ answers were not influenced by the awareness-raising content. This participatory approach ensured that data collection was coupled with immediate feedback and education for respondents, thereby reinforcing knowledge translation and stakeholder engagement.

Data analysis

Responses from questionnaires were checked for completeness, coded, and entered into Microsoft Excel. Descriptive statistics (frequencies and percentages) were used to summarise variables presented in tables and figures for clarity. Inferential statistics included Chi-square (χ²) test to assess associations between categorical variables and where the assumptions for Chi-square were not met due to small, expected cell counts, Fisher’s exact test was applied to ensure accuracy of results. Spearman’s rank-order correlation was used to evaluate relationships between AMR awareness and the frequency of antimicrobial use. Multiple-response items (questions where respondents could select more than one option) were converted into binary variables prior to analysis. For each possible response option, a new variable was created and coded as “1” if the option was selected and “0” if it was not selected. This allowed inclusion of these items in frequency summaries and cross-tabulations with other categorical variables. Degrees of freedom (df) and p values were reported, and statistical significance was set at p < 0.05. Binary logistic regression was performed to identify independent predictors of AMR awareness, with education level, state, age group, farming experience, disease incidence, antibiotic use, record-keeping, withdrawal period observance, biosecurity awareness, and biosecurity training included as covariates. Education categories with small cell sizes (No Formal and Primary) were collapsed into a single ‘Below Secondary’ category prior to modelling. Results are reported as odds ratios (OR) with 95% confidence intervals (CI). Clustering at the LGA and state levels was not formally accounted for in the statistical models, which is acknowledged as a limitation given the multi-stage sampling design. Given the exploratory nature of the study and the number of statistical tests performed, results are interpreted with appropriate caution and p values are reported without formal adjustment for multiple comparisons, consistent with the descriptive and hypothesis-generating intent of KAP surveys. Analyses were performed in IBM SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, USA).

Results

Demographic characteristics of respondents

Respondents were drawn from four states, with Anambra accounting for the largest proportion (28.42%), followed by Kano (26.23%), Kwara (23.50%), and Akwa Ibom (21.86%). The majority were aged between 35 and 44 years (40.44%), whereas only 2.19% were aged 18–24 years (Table 1). Educational attainment was generally high, with 80.33% having tertiary education, while just 1.09% reported no formal education. In terms of farming experience, 82.51% had more than 20 months of involvement in poultry or aquaculture-related enterprises. The majority of respondents were farm owners (81.42%), with the remaining 18.58% serving as farm managers. Reflecting the integrated nature of smallholder value-chain participation, 102 respondents (55.74%) were engaged in more than one enterprise type, including 50 (27.32%) who were involved in both fish and poultry farming simultaneously. Fish farming (58.47%) and poultry farming (53.55%) were the most common primary ventures, with smaller proportions engaged in poultry selling (15.85%), fish selling (12.57%), feed milling (8.74%), and other related occupations such as transportation and drug vendors (6.56%) (Table 1).

Table 1.

Demography of respondents

Variables	Frequency (%)
State of Residence
Anambra	52 (28.42)
Akwa Ibom	40 (21.86)
Kano	48 (26.23)
Kwara	43 (23.50)
Age (Years)
18–24 years	9 (4.92)
25–34	33 (18.03)
35–44	74 (40.44)
45–54	41 (22.40)
55 and above	31 (16.94)
Education Level
No Formal Education	2 (1.09)
Primary	7 (3.82)
Secondary	27 (14.75)
Tertiary	147 (80.32)
Experience in Poultry/Aquaculture Business (Months)
6–12	19 (10.38)
13–19	13 (7.10)
20 and above	151 (82.51)
Type of Poultry/Aquaculture Business
Fish Farmer	107 (58.47)
Poultry Farmer	98 (53.55)
Poultry Seller	29 (15.85)
Fish Seller	23 (12.57)
Feed Miller	16 (8.75)
Others (Transporter, Drug vendor)	12 (6.56)
Role of Respondent
Farm Owner	137 (74.86)
Farm Manager	34 (18.58)
Other value-chain	12 (6.56)
Enterprise Engagement
Single enterprise type	81 (44.26)
Multiple enterprise types	102 (55.74)
of which: Fish + Poultry farming	50 (27.32)

Key: Type of Business was a multiple-response item; respondents could select more than one enterprise type. Business type percentages are calculated among all respondents (n = 183). Enterprise Engagement figures are derived from multiple-response data. Role of Respondent and Enterprise Engagement percentages are calculated among all respondents (n = 183)

Disease incidence and management practices

A substantial proportion of respondents (84.15%) reported experiencing disease outbreaks in their livestock or aquaculture operations. Bacterial infections such as colibacillosis and salmonellosis in poultry and Aeromonas/Edwardsiella infections in fish were most frequently reported (39.61%), followed by viral diseases (32.47%), protozoan infections (20.13%), and parasitic infestations (7.79%). These were based on farmer-reported diagnoses, often informed by veterinary consultations, but not consistently confirmed through laboratory testing. When animals became sick, the most common first responses included purchasing drugs (35.52%) and calling extension workers (19.67%), followed by calling veterinary doctors (18.58%), while smaller proportions opted for traditional medicine (5.46%) or took no action (4.92%) (Table 2).

Table 2.

Disease incidence and management

Variables	Frequency (%)
Disease Incidence
Yes	154 (84.15)
No	29 (15.85)
Diseases Occurrence
Bacterial (Colibacillosis, Salmonellosis, Rotten fin, Aeromonas and Edwardsiella infection)	61 (39.61)
Viral (Newcastle, Fowl pox, Gumboro)	50 (32.47)
Protozoan (Coccidiosis)	31 (20.13)
Parasitic (Ectoparasites)	12 (6.56)
First Steps when Animals Become Sick
Buy Drugs	65 (35.52)
Call Veterinary Doctor	34 (18.58)
Try Traditional Medicine	10 (5.46)
Call Extension Worker	36 (19.67)
Do Nothing	9 (4.92)

Key: Disease occurrence percentages are calculated among respondents who reported disease incidence (n = 154). First Steps percentages are calculated among all respondents (n = 183). Both items were multiple-response; respondents could select more than one option

Antibiotics and antimicrobial use

Regarding sector differences, AMR awareness was similar between poultry-primary respondents (73.5%) and aquaculture-primary respondents (71.6%), as was antibiotic use prevalence (92.8% vs. 92.6%, respectively). Disease incidence was slightly higher among aquaculture-primary respondents (88.9%) compared to poultry-primary respondents (80.7%), while biosecurity awareness was broadly comparable across both groups (69.9% vs. 71.6%). These findings suggest broadly similar AMR-related KAP profiles across the two sectors within this survey population. Antimicrobial use was prevalent among respondents, with 92.90% confirming usage. The dominant practice was administration of drugs whenever animals were sick (65.29%), while others used them monthly (14.12%), weekly (8.82%), every two weeks (5.29%), or every two months (6.47%). The primary motivation for antimicrobial use was to treat sick animals (87.06%), followed by disease prevention (48.24%), growth promotion (12.35%), and increased egg production (6.47%). Veterinary doctors were the principal prescribers (25.88%), but a considerable number engaged in self-prescription (22.35%), sought advice from fellow farmers (20.59%), or obtained recommendations from drug vendors (18.24%). While 61.20% kept records of drug use, 74.86% observed the recommended withdrawal periods (Table 3).

Table 3.

Antibiotics/antimicrobials use

Variables	Frequency (%)
Use Antibiotics/Antimicrobials
Yes	170 (92.89)
No	13 (7.11)
Frequency of Antibiotics/Antimicrobials Use
Anytime animal is sick	111 (65.29)
Monthly	24 (14.12)
Weekly	15 (8.82)
Every 2 Weeks	9 (5.29)
Every 2 Months	11 (6.47)
Reason for using Antibiotics/Antimicrobials
Help Sick Animals	148 (87.06)
Prevent Sickness	82 (48.24)
Promote Growth	21 (12.35)
Increase Egg Production	11 (6.47)
Don’t Use	6 (3.28)
Who Prescribes Antibiotics/Antimicrobials
Veterinary Doctor	44 (25.88)
Self	38 (22.35)
Fellow Farmers	35 (19.13)
Drug Vendor	31 (16.94)
Animal Health Worker	22 (12.94)
Keep Records of Drug Use
Yes	112 (61.20)
No	71 (38.80)
Observe Withdrawal Period
Yes	137 (74.86)
No	46 (25.14)

Key: Frequency of use, Reason for use, and Prescriber percentages are calculated among antimicrobial users (n = 170). Keep Records and Withdrawal Period percentages are calculated among all respondents (n = 183). Reason for use and Prescriber were multiple-response items; respondents could select more than one option

Awareness of Antimicrobial Resistance (AMR)

Awareness of AMR was reported by 71.58% of respondents. Among these, the most common perception was that drugs were no longer effective (68.70%), followed by recognition of antibiotic resistance (15.27%) and awareness of misuse or abuse of drugs (8.40%). Additionally, 7.63% identified “other” drivers of AMR, which included poor infection prevention and control measures, improper disposal of animal waste, and cross-contamination between species. Veterinary officers were the main source of AMR information (87.02%), followed by social media (48.09%) and fellow farmers (32.06%). (Table 4). A significant association was observed between AMR awareness and education level (χ² = 57.16, df = 3, p < 0.001), with respondents holding tertiary education showing the highest awareness (84.4%) (Table 5). There was also a significant negative correlation between AMR awareness and frequency of antimicrobial use (Spearman’s rho = − 0.279, p < 0.001), indicating that higher awareness corresponded with reduced usage frequency (Table 6). Multivariable logistic regression identified education level and state of residence as the primary independent predictors of AMR awareness (Table 7). Compared to tertiary-educated farmers, those with secondary education were 98% less likely to be AMR-aware (OR = 0.02, 95% CI: 0.00-0.07, p < 0.001), and those with below secondary education were 97% less likely (OR = 0.03, 95% CI: 0.00-0.23, p < 0.001). Farmers in Kano State were significantly more likely to be AMR-aware compared to those in Anambra (OR = 4.22, 95% CI: 1.05–16.95, p = 0.042). Farmers who observed withdrawal periods were less likely to be AMR-aware compared to those who did not (OR = 0.27, 95% CI: 0.08–0.88, p = 0.029), a finding that warrants careful interpretation. All other covariates, including age, farming experience, disease incidence, antibiotic use, record-keeping, biosecurity awareness, and biosecurity training, were not significantly associated with AMR awareness in the multivariable model.

Table 4.

Awareness of AMR

Variables	Frequency (%)
Aware of AMR
Yes	131 (71.58)
No	52 (28.42)
Knowledge/drivers of AMR
Drugs are no Longer Effective	90 (68.70)
Resistance to Antibiotics	20 (15.27)
Misuse/Abuse of Drugs	11 (8.40)
Others (Poor infection prevention and control)	10 (7.63)
Sources of Information on AMR
Veterinary Officers	114 (87.02)
Social Media	63 (48.09)
Fellow Farmers	42 (32.06)
Television	22 (16.79)
Newspapers	10 (7.63)

Key: Knowledge/drivers of AMR and Sources of information percentages are calculated among AMR-aware respondents (n = 131). Both items were multiple-response; respondents could select more than one option

Table 5.

Contingency table for awareness of AMR and education level

			Awareness of AMR		Total	χ²	df	p value
			No	Yes
Education Level	No Formal Education	Count	2	0	2	57.16	3	< 0.001*
		% within Education Level	100.0%	0.0%	100.0%
	Primary	Count	4	3	7
		% within Education Level	57.1%	42.9%	100.0%
	Secondary	Count	23	4	27
		% within Education Level	85.2%	14.8%	100.0%
	Tertiary	Count	23	124	147
		% within Education Level	15.6%	84.4%	100.0%
Total		Count	52	131	183
		% within Education Level	28.4%	71.6%	100.0%

Key: χ² Chi-square test, df Degree of freedom, AMR Antimicrobial resistance, ^*Significantly different at p < 0.05

Table 6.

Correlations for awareness of AMR and frequency of antibiotics/antimicrobial use

			Awareness of AMR	Frequency of Antibiotics/Antimicrobial Use
Spearman’s rho	Awareness of AMR	Correlation Coefficient	1.000	− 0.279**
		Sig. (2-tailed)	.	< 0.001
		N	183	183
	Frequency of Antibiotics/Antimicrobial Use	Correlation Coefficient	− 0.279**	1.000
		Sig. (2-tailed)	< 0.001	.
		N	183	183

^**Correlation is significant at the 0.01 level (2-tailed), N Total respondents, - Not available, AMR Antimicrobial resistance

Table 7.

Multivariable logistic regression of factors associated with AMR awareness

Variable	OR	95% CI	p value
Education
Below Secondary vs. Tertiary (ref)	0.03	0.00-0.23	< 0.001
Secondary vs. Tertiary (ref)	0.02	0.00-0.07	< 0.001
Age group (years)
18–34 vs. 35–44 (ref)	0.95	0.31–2.92	0.934
45–54 vs. 35–44 (ref)	2.57	0.75–8.85	0.135
≥ 55 vs. 35–44 (ref)	1.33	0.34–5.18	0.684
State
Akwa Ibom vs. Anambra (ref)	1.05	0.30–3.65	0.940
Kano vs. Anambra (ref)	4.22	1.05–16.95	0.042*
Kwara vs. Anambra (ref)	0.83	0.26–2.69	0.761
Farming experience (months)
13–19 vs. ≥ 20 (ref)	0.67	0.13–3.45	0.628
6–12 vs. ≥ 20 (ref)	1.81	0.31–10.67	0.511
Disease incidence
Yes vs. No (ref)	0.68	0.19–2.52	0.568
Antibiotic use
Yes vs. No (ref)	0.33	0.05–2.20	0.252
Keeps drug records
Yes vs. No (ref)	2.42	0.96–6.07	0.060
Observes withdrawal period
Yes vs. No (ref)	0.27	0.08–0.88	0.029*
Biosecurity awareness
Yes vs. No (ref)	0.81	0.26–2.47	0.705
Biosecurity training received
Yes vs. No (ref)	0.61	0.24–1.55	0.294

Key: OR Odds ratio, CI Confidence interval, ref Reference category. Education categories ‘No Formal’ and ‘Primary’ were collapsed into ‘Below Secondary’ prior to modelling due to small cell sizes. ^*p < 0.05. Model fit: N = 183, McFadden Pseudo R²=0.356, overall model χ²=77.79 (df = 16), p < 0.001

Awareness and practice of biosecurity measures

Overall, 72.68% of respondents reported awareness of biosecurity, and among those aware (n = 133), the most common facilities in place included sanitizers (62.41%), foot dips (56.39%), and fences (54.14%), while isolation pens and personal protective equipment (PPE) (49.62% each) were less frequently reported. Slightly more than half (53.55%) had received biosecurity training (Table 8). A significant association was found between biosecurity awareness and disease incidence (χ² = 13.46, df = 1, p < 0.001), with those experiencing disease outbreaks more likely to be aware of biosecurity measures (77.9%) compared to those without outbreaks (44.8%) (Table 9).

Table 8.

Awareness of biosecurity

Variables	Frequency (%)
Aware of Biosecurity
Yes	133 (72.67)
No	50 (27.32)
Biosecurity Facilities
Sanitizers	83 (62.41)
Foot Dip	75 (56.39)
Fence	72 (54.14)
Isolation Pen	66 (49.62)
PPE	66 (49.62)
Received Training on Biosecurity
Yes	98 (53.55)
No	85 (46.45)

Key: PPE Personal Protective Equipment, Biosecurity facilities percentages are calculated among biosecurity-aware respondents (n = 133); Biosecurity facilities was a multiple-response item

Table 9.

Contingency table for awareness of biosecurity and disease incidence

			Aware of Biosecurity		Total	χ²	Df	p value
			No	Yes
Disease Incidence	No	Count	16	13	29	13.46	1	< 0.001*
		% within Disease Incidence	55.2%	44.8%	100.0%
	Yes	Count	34	120	154
		% within Disease Incidence	22.1%	77.9%	100.0%
Total		Count	50	133	183
		% within Disease Incidence	27.3%	72.7%	100.0%

Key: χ² Chi-square test, df Degree of freedom, AMR Antimicrobial resistance, ^*Significantly different at p < 0.05

Discussion

This study provides important insights into the knowledge, attitudes, and practices related to AMR among poultry and aquaculture farmers in four Nigerian states. The results highlight high disease incidence, widespread antimicrobial use, moderate but variable AMR awareness, and incomplete biosecurity adoption, all of which have significant implications for AMR control in Nigeria’s livestock and aquaculture sectors.

The respondents were dominated by adults aged 35–44 years and the highest frequency of education level was tertiary. This is consistent with earlier reports indicating that commercial poultry and aquaculture production in Nigeria tends to attract relatively educated, economically active individuals [33, 34]. The high level of formal education among respondents presents opportunities for effective AMR awareness campaigns, as education is positively associated with comprehension of public health messaging [35, 36]. However, the presence of less-educated farmers underscores the need for simplified, multilingual awareness materials, particularly in rural settings where literacy levels may be lower [37].

Fish and poultry farming in this study reflects the growing demand for animal protein in Nigeria and aligns with FAO reports that poultry and aquaculture are the fastest-growing livestock sectors in sub-Saharan Africa [38]. However, the rapid expansion of these sectors, especially under small- to medium-scale operations with limited veterinary oversight, increases the risk of AMR emergence [39]. The high disease incidence (84.15%) reported in this study is concerning and suggests substantial gaps in preventive health measures. The predominance of bacterial infections such as colibacillosis and salmonellosis mirrors findings from other Nigerian studies, where these pathogens are frequently isolated from poultry and aquaculture systems [13, 40]. Such infections are associated with heavy antimicrobial use, which, if unregulated, can accelerate resistance development [41]. The observed reliance on immediate drug purchase (35.52%) as a first response, coupled with self-medication and non-veterinary advice, mirrors patterns reported by Egbule [42], who noted that limited access to veterinary services often leads to unsupervised antibiotic administration. This practice increases the likelihood of inappropriate drug selection, under-dosing, or failure to complete treatment courses, all of which are well-established drivers of AMR [43].

The finding that 92.90% of respondents used antimicrobials, primarily for treatment (87.06%) but also for prophylaxis and growth promotion, is consistent with studies in Nigeria [13, 40] and Ghana [36]. The continued use of antimicrobials for non-therapeutic purposes contradicts WHO and WOAH recommendations, which call for the elimination of such practices in food animals [7]. Veterinary doctors were among the prescribers (25.88%), but a notable proportion engaged in self-prescription (22.35%). Similar trends have been documented in other LMICs, where antimicrobial access is often unregulated, and over-the-counter sales remain common [44]. The high proportion of farm owners (81.4%) among respondents, as opposed to hired managers, strengthens the validity of self-reported antimicrobial use and record-keeping data, as owners are more directly involved in procurement and treatment decisions and more likely to possess accurate knowledge of on-farm practices. The finding that most farmers reported observing withdrawal periods (74.86%) is encouraging, as this practice is critical for preventing antimicrobial residues in food products. However, adherence was self-reported and may not reflect actual compliance, as residue monitoring in Nigeria remains limited [40].

The AMR awareness level in this study is higher than the 53% reported in earlier Nigerian KAP surveys among poultry farmers [33], possibly reflecting the impact of recent AMR awareness campaigns supported by the Fleming Fund and the Nigeria Centre for Disease Control. Importantly, multivariable logistic regression confirmed education level as the dominant independent predictor of AMR awareness, with tertiary-educated farmers significantly more likely to be aware than those with secondary or lower education. This finding is consistent with KAP studies across sub-Saharan Africa, where formal education has consistently been identified as a key facilitator of health literacy and disease risk perception [31, 32]. The interaction between farming system type, education level, and biosecurity practice also warrants consideration. In this study, tertiary-educated respondents were more likely to be biosecurity-aware (74.8% vs. 33.3% among non-tertiary), consistent with the expectation that higher educational attainment correlates with better uptake of preventive health and farm management practices. Similarly, respondents aged 45–54 years showed numerically higher AMR awareness than younger age groups in the multivariable model (OR = 2.57), suggesting that farming experience may reinforce awareness even where formal educational pathways are absent, though this did not reach statistical significance. The substantial difference in awareness between tertiary and non-tertiary educated farmers underscores the urgent need for targeted, non-formal AMR education programmes that reach farmers with limited schooling, who represent a critical but underserved group in AMR communication strategies. The finding that farmers in Kano State were significantly more likely to be AMR-aware compared to Anambra may reflect differences in veterinary extension service activity, producer association engagement, or the influence of ongoing AMR awareness initiatives in the North-West region. However, farmers who observed withdrawal periods were less likely to be AMR-aware in the adjusted model; this counterintuitive finding likely reflects that withdrawal period compliance in this population is driven by habit, regulatory instruction, or market requirements rather than an understanding of AMR mechanisms per se. The negative correlation between AMR awareness and antimicrobial use frequency is consistent with findings from Kenya and Vietnam, where better AMR knowledge was linked to more judicious antimicrobial use [44, 45]. However, given the cross-sectional design, this association cannot be interpreted as causal; rather, it indicates a relationship that warrants further longitudinal investigation. Furthermore, understanding of AMR mechanisms was limited, with most defining it only as “drugs no longer being effective,” without recognising underlying drivers such as inappropriate dosing, poor infection prevention, or cross-species transmission of resistant pathogens. This gap aligns with previous findings [46] which emphasised that superficial AMR awareness does not necessarily translate into responsible antimicrobial practices.

The integrated nature of value-chain participation observed in this study, with over half of respondents engaged in multiple enterprise types and 27.3% operating in both fish and poultry simultaneously, has important implications for AMR transmission. Co-production of fish and poultry on or near the same premises increases the risk of cross-species antimicrobial use, shared water sources, and co-contamination of the farm environment. In aquaculture specifically, the nature of fish pond systems creates direct pathways for AMR dissemination into the aquatic environment, as antimicrobials administered to fish are largely excreted into pond water and sediment, from which resistant bacteria and resistance genes may spread to surrounding ecosystems, irrigation water, and ultimately human populations through food and water consumption [47]. This environmental dimension of AMR in aquaculture warrants explicit recognition in national AMR action plans and was not fully captured by the self-report design of the current study. Biosecurity awareness among farmers was relatively high and over half had received formal training. Nonetheless, implementation was often partial, with low-cost measures like sanitizers, foot dips, and fencing more commonly adopted than higher-investment practices such as isolation pens and full personal protective equipment (PPE). A study in Plateau and Oyo states found that 58% of farms were fenced, all had foot dips at the entrance, many changed disinfectant regularly, and most had dedicated clothing, yet access by customers remained a risk, highlighting how farmers prioritise accessible, lower-cost interventions over more comprehensive measures [33]. In Kano State, while some awareness of biosecurity exists, many farmers reported inadequate capital (93.4%) and high cost of biosafety practices (83.1%) as major barriers to adoption [48]. An investigation among smallholder poultry farmers in rural Ghana showed that even when farmers knew recommended practices, they frequently implemented them only partially due to economic constraints and cost-benefit calculations; for instance, preferring antibiotics as cheaper “quick fixes” over more resource-intensive biosecurity investments [49]. The significant association between biosecurity awareness and prior disease incidence is noteworthy. Farmers who have experienced disease outbreaks appear more likely to be aware of and improve upon biosecurity measures, suggesting a reactive learning pattern. This mirrors previous Nigerian findings that producers with disease experience were more likely to implement biosecurity actions [50], and broader analyses showing epidemic exposure promotes enhanced biosecurity behaviour across livestock systems [51].

While poultry results dominate both this and prior Nigerian AMR studies, the aquaculture findings here are equally important. High tetracycline use in fish farming, as observed in this study, aligns with reports from northern Nigeria [18] and raises environmental concerns, as fish ponds can act as reservoirs for resistant Aeromonas and Edwardsiella spp., which may transfer resistance genes to other aquatic bacteria. These resistant strains pose risks to farmed fish, wild aquatic species, downstream water users, and human consumers. Given aquaculture’s rapid expansion in Nigeria, underestimating its AMR contribution could undermine broader containment strategies.

The findings of this study carry important implications for Nigeria’s National Action Plan (NAP) on AMR and its alignment with the global One Health strategy. The persistence of non-therapeutic antimicrobial use, including prophylactic and growth-promotion practices, signals weak policy enforcement despite existing guidelines. Notably, 88.5% of small-scale commercial poultry farms reported frequent antimicrobial use without veterinary consultation, highlighting significant regulatory gaps in AMU control [52]. Moreover, the substantial influence of non-veterinary actors such as fellow farmers and drug vendors on antimicrobial-use decisions underscores the urgent need for tighter regulatory oversight of drug sales.

It should be noted that biosecurity practices in this study were assessed through self-report rather than direct observation using a structured checklist, which may overestimate actual implementation due to social desirability bias. Future studies should complement farmer-reported data with on-farm observational checklists to validate self-reported biosecurity compliance. Partial adoption of biosecurity measures suggests that financial incentives or subsidies could support more comprehensive implementation. The broadly similar KAP profiles observed between poultry-primary and aquaculture-primary respondents in this study suggest that AMR-related knowledge gaps and risky practices are not sector-specific but are systemic across the food animal value chain in Nigeria. This supports the rationale for integrated, cross-sector AMR interventions rather than siloed approaches targeting only one livestock subsector. From a One Health perspective, AMR risks in poultry and aquaculture extend beyond the immediate farm environment, posing threats to human populations through multiple transmission pathways such as food, occupational exposure, and environmental contamination. A One Health review of Nigeria highlights that resistant bacteria have been detected in food animals, potable water, natural and wastewater, and soil environments, illustrating intricate interconnections across sectors [21]. Complementing this, a study in Oyo State revealed a high prevalence of multidrug-resistant Salmonella (including fluoroquinolone resistance) and extended-spectrum β-lactamase (ESBL)-producing E. coli in poultry farms, signifying a direct public health risk via contaminated food products [53]. These findings underscore that AMR threats are not confined to production sites but can spill over into broader ecosystems, reinforcing the importance of integrated, intersectoral interventions. Furthermore, the participatory engagement of experts from the Nigerian Veterinary Medical Association, the Ministry of Livestock Development, and FAO during data collection not only enhanced the validity of farmer responses but also ensured that immediate awareness-raising and knowledge transfer occurred at the community level. This dual role of data gathering and education strengthens the translational value of the study and aligns with the One Health approach to AMR mitigation.

Conclusion

This multi-state cross-sectional study shows that poultry and aquaculture farmers in Nigeria have high levels of antimicrobial use, moderate awareness of antimicrobial resistance (AMR), and only partial adoption of biosecurity measures. While veterinary doctors remain the primary prescribers, self-prescription and informal advice from fellow farmers and drug vendors are common, creating pathways for misuse of critically important antimicrobials. Education level was strongly associated with AMR awareness, and higher awareness was linked to less frequent antimicrobial use, indicating that targeted educational interventions may encourage more prudent practices. The persistence of non-therapeutic antimicrobial use, incomplete biosecurity implementation, and the continued involvement of non-veterinary actors in prescribing decisions reveal gaps in Nigeria’s AMR containment framework. From a One Health perspective, these findings highlight the need for integrated interventions addressing human, animal, and environmental dimensions of AMR. Policy priorities should include restricting over-the-counter antimicrobial sales, integrating aquaculture-specific guidelines into the National Action Plan on AMR, and establishing robust residue monitoring systems for poultry and aquaculture products.

Farmer education programmes should be scaled up to promote preventive health strategies, responsible antimicrobial use, and awareness of resistance risks, while expanding access to veterinary services to reduce reliance on informal prescribers. Enhancing biosecurity through targeted training, infrastructure support, and incentives could further reduce disease incidence and the need for antimicrobials. These measures should be complemented by strengthened AMR surveillance across poultry and aquaculture, longitudinal research to assess intervention impacts, and environmental studies to trace resistance dissemination from farms to surrounding ecosystems. Furthermore, embedding participatory data collection with technical experts ensured that the research process itself contributed to awareness creation, farmer education, and capacity building, thereby enhancing both scientific and practical outcomes. By implementing such strategies, Nigeria can move toward sustainable livestock and aquaculture systems that minimise AMR risks, protect public health, and align with global One Health objectives.

Limitations

This study relied on self-reported practices, which may be subject to recall and social desirability bias. In addition, the cross-sectional design precludes establishing causality between AMR awareness and antimicrobial use behaviour. Nevertheless, the multi-state coverage and inclusion of both poultry and aquaculture farmers provide valuable insights into AMR-related behaviours in Nigeria’s food animal sector.

Authors’ contributions

CDO: Conceptualization, implementation, Supervision, Writing, Review and Editing, Project administration. DDI: implementation, visualization, Writing, Review and Editing. KOJ: Formal analysis, Data Curation, Visualization, Writing, Review and Editing. AG: Initial Data Analysis, Writing, Review and Editing. DU: Implementation, Writing, Review and Editing HE: Implementation, Writing, Review and Editing. NW: Implementation, Writing, Review and Editing. ZA: Implementation, Data Curation, Writing, Review and Editing. MRK: Implementation, Writing, Review and Editing. EA: Conceptualization, Writing. DK: Implementation, Review and Editing. CS: Implementation, Writing, Review and Editing IMM: Conceptualization, Project administration, Review and Editing. CV: Conceptualization, Project administration, Review and Editing. SA: Implementation, Review and Editing. BA: Conceptualization, Implementation, Project Administration, Review and Editing. MB: Conceptualization, Project administration, Review and Editing. CE: Implementation, Review and Editing.

Funding

This project was part of the implementation of the Fleming Fund Phase II Country Grant in Nigeria. The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data.

Data availability

Available on reasonable request.

Declarations

Ethics approval and consent to participate

Ethical approval for this study was granted by the FCTA Agriculture and Rural Development Secretariat Veterinary Ethics and Research Committee (VERC), Ref No: FCTA/ARDS/DVS/VERC/2025/008. Informed consent was obtained from all respondents prior to participation.

Consent for publication

Consent was obtained from the respondents.

Competing interests

The authors declare no competing interests.