Pa ge 1 Pa ge 69 American Journal of Environment and Climate (AJEC) Redefining Farm Risk: A Biosecurity and Proximity-Based Evaluation Matrix Hamid Ragab Orban1* Volume 4 Issue 3, Year 2025 ISSN: 2832-403X (Online) DOI: https://doi.org/10.54536/ajec.v4i3.5735 https://journals.e-palli.com/home/index.php/ajec Article Information ABSTRACT Received: July 12, 2025 Accepted: August 16, 2025 Published: October 08, 2025 Biosecurity, in current farm evaluation procedures, is typically assessed through general inspection checklists that often presume compliance with the stipulated minimum distance requirements. However, such assumptions may overlook the nuanced role of biosecurity in mitigating disease risks. Additionally, farm assessments conducted for import certification or domestic recognition frequently lack standardized criteria, resulting in inconsistent and potentially inequitable evaluations. This article introduces a structured, risk-based farm evaluation matrix comprising eight criteria, categorized into severity and probability dimensions. Each criterion is rated at three levels, low, medium, and high, with corresponding numerical weights (5, 10, and 15) to enable consistent scoring. The Risk-Based Farm Score (RBFS) is calculated by multiplying the average scores of severity-related and probability- related criteria. To evaluate the matrix’s applicability, all attributes were held constant except for regulatory compliance and proximity to neighboring farms, each tested at three levels. Results demonstrate a clear inverse relationship between regulatory compliance and (RBFS), with higher regulatory compliance associated with lower risk scores. In contrast, proximity to other farms correlates with higher (RBFS), though its influence is less significant than that of regulatory compliance. Standard deviation analysis reveals greater variability in (RBFS) across compliance levels, underscoring the critical role of regulatory adherence in determining farm risk. Adoption of the developed farm evaluation matrix globally could enhance transparency, reduce trade barriers, and promote safer agricultural trade. Keywords Agricultural Trade, Biosecurity, Farm Evaluation Matrix, Livestock Farming, Regulatory Compliance 1 Abu Dhabi Agriculture and Food Safety Authority, Abu Dhabi, United Arab Emirates * Corresponding author’s e-mail: hamidorban@outlook.com INTRODUCTION Livestock and poultry production significantly impacts the environment, affecting water quality, air pollution, and soil health (Scanes, 2018). The impact of the animals is proportional to their numbers. Furthermore , the World Trade Organization set a legal framework that organizes trade between countries ,the objective of the framework is to eliminate trade barriers and ease trade without compromising countries’ rights to protect their domestic trade . Among the negative impacts of animals on the environment, the risks of transboundary pests and diseases is receiving more attention by countries and international organizations , this is intensified after the COVID-19 pandemic. Organizations like the World Organization for Animal Health (WOAH) and the Food and Agriculture Organization of the United Nations (FAO), together with the World Health Organization (WHO), set the health and sanitary measures to minimize the spread of transboundary diseases . Individual countries set their domestic standards and legislations to protect themselves from these diseases. The set standards and legislations in most of the cases include minimum distances between the different plant and animal activities . Furthermore , almost all countries assure the safety and quality of the food and feed consumed by the inhabitants and animals regardless of their origin, that is imported or domestically produced. In case of imported food, most of the countries conduct visits to evaluate the control procedures in the exporting countries, this evaluation is based on the importing country’s criteria . The use of the importing country’s criteria might encore extra costs on the exporting country resembling a burden and a possible trade barrier. Currently there are hundreds of criteria and attributes which are used and can be used by countries to evaluate agriculture farms and livestock farming , however there is no common matrix that uses predefined and agreed evaluation criteria that can be used by different countries to minimize the variation in the evaluation outcomes while considering the discretion of each country. The current article argues that the development of a unified global farm evaluation matrix, featuring clear, standardized, and easily measurable attributes, has the potential to eliminate trade barriers between countries. By reducing discrepancies in biosecurity and quality standards between importing and exporting nations, such a matrix would facilitate smoother agricultural trade and promote the global exchange of agricultural commodities while ensuring the stipulation of strict biosecurity measures that minimize the negative impact of animal farming on the environment. LITERATURE REVIEW Biosecurity is defined by the World Organization for Animal Health (WOAH) as a set of management and physical measures designed to reduce the risk of introduction, establishment and spread of animal diseases, infections or infestations to, from and within Pa ge 70 https://journals.e-palli.com/home/index.php/ajec Am. J. Environ. Clim. 4(3) 69-73, 2025 an animal population (World Organization for Animal Health, 2023). Several approaches for assessing on-farm biosecurity exist varying in their purpose, implementation, and outputs (Alarcón et al., 2021; Fathelrahman et al, 2020; Gelaude et al., 2014; Martínez-Guijosa et al., 2021; Sasaki et al., 2020; Tilli et al., 2022; Zaabar et al., 2025). Maye and Chan (2020) reported that biosecurity is a complex, multi-layered and increasingly problematic term that manifests variously around agriculture and animal disease as forms of discourse, socio-material practice and risk politics. Furthermore, Huber et al. (2022) defined biosecurity measure (BSM) as the implementation of a segregation, hygiene, or management procedure, excluding medically effective feed additives and preventive/curative treatment of animals, that specifically aims at reducing the probability of the introduction, establishment, survival, or spread of any potential pathogen to, within, or from a farm, operation or geographical area. Zanon et al. (2024) reported that despite the suboptimal adoption of biosecurity measures, which they attributed to structural limitations in mountain farms and farmers awareness gaps, the economic significance of biosecurity adoption is evident. Moreover, Fadel et al. (2012) recommended that serious decisions should be taken by local authorities to remove poultry farms from urban areas to protect investments, society and the environment. However, Fathelrahman et al. (2020) reported that in the United Arab Emirates, poultry farms address biosecurity preparedness differently based on the scale, that is, large or small , and on the purpose of production , that is, broilers or layers. The authors findings indicated the low influence of the distance to the nearest farm on the vaccination use. MATERIALS AND METHODS Current procedures for assessing livestock farms’ compliance with the regulations often assume the adherence of the farms to the distance requirements, where such requirements exist. Additionally, biosecurity compliance is typically evaluated through its inclusion in the general inspection checklists. This tends to obscure the critical importance of biosecurity, regardless of the weight assigned to its components within the checklist. It is common for official authorities to evaluate and qualify farms in other countries for import-related purposes. Similarly, domestic farm competitions and awards are frequently held, however, the evaluation methods used for these purposes vary widely, often lacking standardized or shared assessment attributes. Farm Evaluation Matrix In the current article, eight criteria were defined and utilized as components of the severity and probability dimensions within the farm evaluation matrix. Each criterion was categorized into three evaluation levels: low, medium, and high. Table 1 presents the eight evaluation criteria along with their definitions. Table 1: Farm Evaluation Matrix Evaluation Dimension Criteria Description Severity Distance D Compliance to the distance requirement as per the pertaining regulations in the country. Farm Age Fage Considers the age of the farm buildings, in general the older the building the higher the biosecurity risks. Number of farms within 10 KM Radius Fn For most of the diseases , emergency responses require testing and restricting movement within a set radius. 10 kilometers is the commonest of the radii for most of the animal diseases. The more the neighboring farms within the radius , the higher the risks. Number of Activities An It is not uncommon that a farm practices more than an activity , which are mostly complementary to the main farm activity. The more the activities in a farm the higher the risks. Activity Risk Level RL Each activity has a risk which varies according to the risk leveling methodology adopted by the authority. Activities with high risks will impact the Farm Evaluation Result FER Probability Production Size Psize The production of the farm is related to the farm size and/or the production intensity. The larger the farm production, the higher the risks. Regulatory Compliance RC Results of the compliance of the farm to the pertaining regulations in the country and or to the international standards reflect its compliance to biosecurity. It will not be uncommon if a separate checklist for the biosecurity compliance is developed and used for the evaluation of the FER. Pa ge 71 https://journals.e-palli.com/home/index.php/ajec Am. J. Environ. Clim. 4(3) 69-73, 2025 Disease Outbreaks Dout Generally, the stricter the implementation of biosecurity measures in a farm, the lower the probability of disease outbreaks. The number of disease outbreaks reported during the data collection period can serve as an indicator of the farm’s compliance with biosecurity protocols. Risk-Based Farm Score (RBFS) The farm evaluation formula uses the evaluation result of the severity and probability criteria as shown in the following formula: RBFS = x ̄(D, Fage, Fn, An, Rl ) * x ̄(Psize, RC, Dout) Where, RBFS : farm evaluation result D : distance from nearest farm Fage : age of the farm constructions Fn : number of farms within 10 kilometers radius An : number of activities in the farm Rl : risk level of the farm activity Psize : production size RC : regulatory compliance Dout : number of disease outbreaks Criteria Range (Width) The range, or width, of each criterion level should be determined through a simple calculation based on the distribution of available data. Once established, this range is divided into three distinct categories: low, medium, and high. To facilitate quantitative evaluation, numerical weights are assigned to each category, 5 for low, 10 for medium, and 15 for high. This structured approach enables consistent scoring and comparison across farms, enhancing the reliability of the evaluation matrix. Risk-Based Farm Score (RBFS) The performance of a farm against the criteria listed in Table 1, excluding the distance from the nearest farm, can typically be assessed using data from the inspection database. However, when the evaluation is conducted by an importing country, it is advisable to perform an on-site biosecurity inspection during the visit. This inspection should follow a standardized checklist that has been shared in advance with the relevant authority in the exporting country to ensure consistency and transparency. The Risk-Based Farm Score (RBFS) is calculated by multiplying the average score of the severity-related criteria by the average score of the probability-related criteria, as outlined in the proposed formula. A lower Final Evaluation Result FER indicates better overall farm performance in terms of biosecurity and risk management. RESULTS AND DISCUSSION The current article defines the attributes of a risk-based farm evaluation matrix designed to assess the risk level of farms. To evaluate its applicability, all risk attributes listed in Table 1 were held constant, except for distance from the nearest farm and regulatory compliance. Each of these two variables was tested at three levels, low, medium, and high, and the corresponding Risk-Based Farm Score (RBFS) was calculated. Figure 1 illustrates the results, showing a consistent decline in the Risk-Based Farm Score (RBFS) as the level of regulatory compliance increases from low to high. This decline coincides with the increase in the level of compliance to the distance from neighboring farms. The lowest and highest Risk- Based Farm Score (RBFS), 58.3 and 105.0, were observed at the combination of the high and low levels of the two attributes respectively. This finding implies that strict compliance with regulations is associated with lower farm risk under the current scoring framework, potentially due to increased operational constraints or reporting rigor. The distance from the nearest farm shows a reverse relationship with Risk-Based Farm Score (RBFS), that is the closer a farm is to its neighbors, the higher its Risk- Based Farm Score (RBFS). Figure 1: Effect of Distance and Regulatory Compliance on Risk-Based Farm Score (RBFS) Pa ge 72 https://journals.e-palli.com/home/index.php/ajec Am. J. Environ. Clim. 4(3) 69-73, 2025 Upon visualization of the standard deviations SD of the Risk-Based Farm Score (RBFS) results in Table 2, the small standard deviation value of the mean of the Risk-Based Farm Score (RBFS) at the high distance score indicates the homogeneity of the results Moreover, further insights can be drawn from the standard deviation values of the Risk-Based Farm Score (RBFS) across the low, medium, and high levels of the evaluation matrix, as presented in Table 2. These values reveal greater variability in the (RBFS) results, which could primarily be attributed to differences in the regulatory compliance among farms. In practical terms, this suggests that regulatory compliance has a more pronounced impact on (RBFS) than the distance from neighboring farms. Table 2: Risk-Based Farm Score (RBFS) Regulatory Compliance Score Distance Score Low Medium High Mean ± SD High 81.7 70.0 58.3 70.00 ± 9.55 Medium 93.3 80.0 66.7 80.00 ± 10.86 Low 105.0 90.0 75.0 90.00 ± 12.25 Remarkably, although farms located in close proximity to others tend to exhibit higher Risk-Based Farm Score (RBFS) values, this spatial factor appears to exert a relatively minor influence on the farm risk score compared to regulatory compliance. These findings align with Fathelrahman et al. (2020), who observed that proximity to neighboring farms has limited impact on the adoption of vaccination as a biosecurity measure. Similarly, the current study supports the conclusions of Zanon et al. (2024), emphasizing the critical role of biosecurity measures in minimizing the risk score of the farm. However, the results of the developed Risk-Based Farm Score (RBFS) matrix do not support the recommendations of Fadel et al. (2012), which advocated for the relocation of poultry farms away from urban areas. The current findings suggest that such recommendations lack empirical support, as spatial proximity alone does not significantly influence the farm risk under the proposed evaluation matrix. Moreover, in countries where farm distance regulations were developed after the establishment of some existing farms, the Risk-Based Farm Score (RBFS) results from this study indicate that stipulating the regulations retrospectively will have a minimal impact on the overall farm risk. This finding highlights the limited effectiveness of distance-based regulations when not integrated into initial planning frameworks. More importantly, it underscores the need to contextualize risk attributes, particularly regulatory compliance, as central to effective biosecurity management. Prioritizing evidence-based regulatory compliance measures over spatial requirements offers a more accurate and practical approach to assessing and mitigating biosecurity risks in agricultural systems. CONCLUSION The current study presents a novel approach to evaluating farm risk by applying risk analysis principles within a structured matrix of eight attributes, five related to risk severity and three to risk probability. Each attribute is clearly defined, categorized into three levels, and assigned numerical weights, enabling a consistent and quantifiable assessment of farm performance. The case study findings reveal that regulatory compliance has a significantly greater influence on the Risk-Based Farm Score (RBFS) than proximity to neighboring farms, underscoring the critical role of biosecurity in protecting agricultural systems. In the context of international trade, the findings of this study support the adoption of the proposed matrix to harmonize farms assessment both within and across countries. Such a standardized framework would enhance transparency, build trust among trading partners, and facilitate the safe and efficient global exchange of agricultural commodities. inconsistencies and potential trade barriers. REFERENCES Alarcón, L. V., Allepuz, A., & Mateu, E. (2021). Biosecurity in pig farms: a review. Porcine Health Management, 7(1). https://doi.org/10.1186/s40813-020-00181-z Benavides, B., Casal, J., Diéguez, J. F., Yus, E., Moya, S. J., Armengol, R., & Allepuz, A. (2020). Development of a quantitative risk assessment of bovine viral diarrhea virus and bovine herpesvirus-1 introduction in dairy cattle herds to improve biosecurity. Journal of Dairy Science, 103(7), 6454–6472. https://doi.org/10.3168/ jds.2019-17827 Duarte, F., Tamminen, L.-M., Kjosevski, M., Ciaravino, G., Delpont, M., Correia-Gomes, C., van den Borne, B.H. P., Chantziaras, I., ... & Allepuz, A. (2025). Methods to assess on-farm biosecurity in Europe and beyond. Preventive Veterinary Medicine, 239, 106486. https://doi.org/10.1016/j.prevetmed.2025.106486 Fadel, M. A., Hussein, A. S., and Maamari, M. A. (2012). Mapping poultry farms in the UAE utilizing GIS technology [Review of Mapping poultry farms in the UAE utilizing GIS technology]. Journal of Food, Agriculture & Environment , 10(13 &14), 567–571. Gelaude, P., Schlepers, M., Verlinden, M., Laanen, M., & Dewulf, J. (2014). Biocheck.UGent: A quantitative tool to measure biosecurity at broiler farms and Pa ge 73 https://journals.e-palli.com/home/index.php/ajec Am. J. Environ. Clim. 4(3) 69-73, 2025 the relationship with technical performances and antimicrobial use. Poultry Science, 93(11), 2740–2751. https://doi.org/10.3382/ps.2014-04002 Huber, N., Andraud, M., Sassu, E. L., Prigge, C., Zoche- Golob, V., Käsbohrer, A., D’Angelantonio, D., Viltrop, A., Żmudzki, J., Jones, H., Smith, R. P., Tobias, T., & Burow, E. (2022). What is a biosecurity measure? A definition proposal for animal production and linked processing operations. One Health, 15(0), 100433. https://doi.org/10.1016/j.onehlt.2022.100433 Martínez-Guijosa, J., Lima-Barbero, J. F., Acevedo, P., Cano-Terriza, D., Jiménez-Ruiz, S., Barasona, J. Á., Boadella, M., García-Bocanegra, I., Gortázar, C., & Vicente, J. (2021). Description and implementation of an On-farm Wildlife Risk Mitigation Protocol at the wildlife-livestock interface: Tuberculosis in Mediterranean environments. Preventive Veterinary Medicine, 191, 105346. https://doi.org/10.1016/j. prevetmed.2021.105346 Maye, D., & Chan, K. W. (Ray). (2020). On-farm biosecurity in livestock production: farmer behaviour, cultural identities and practices of care. Emerging Topics in Life Sciences, 4(5). https://doi.org/10.1042/ etls20200063 Scanes, C. G. (2018). Impact of Agricultural Animals on the Environment. Animals and Human Society, 427– 449. https://doi.org/10.1016/b978-0-12-805247- 1.00025-3 Sasaki, Y., Furutani, A., Furuichi, T., Hayakawa, Y., Ishizeki, S., Kano, R., Koike, F., Miyashita, M., Mizukami, Y., Watanabe, Y., & Otake, S. (2020). Development of a biosecurity assessment tool and the assessment of biosecurity levels by this tool on Japanese commercial swine farms. Preventive Veterinary Medicine, 175, 104848. https://doi.org/10.1016/j. prevetmed.2019.104848 Tilli, G., Laconi, A., Galuppo, F., Mughini-Gras, L., & Piccirillo, A. (2022). Assessing Biosecurity Compliance in Poultry Farms: A Survey in a Densely Populated Poultry Area in North East Italy. Animals, 12(11), 1409. https://doi.org/10.3390/ani12111409 WOAH. (2023). Terrestrial Code Online Access. WOAH - World Organisation for Animal Health. h t tp s :// www.woah.org/en/what-we-do/standards/codes- and-manuals/terrestrial-code-online-access/ Zanon, T., Alrhmoun, M. & Gauly, M. (2024). Assessing the impact of biosecurity practices and animal welfare in small-scale mountain dairy farming. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024- 63841-y