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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 



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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. 



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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)



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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.

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