Weighted Bayesian network for the classification of unbalanced food safety data: Case study of risk-based monitoring of heavy metals.

Historical data on food safety monitoring often serve as an information source in designing monitoring plans. However, such data are often unbalanced: a small fraction of the dataset refers to food safety hazards that are present in high concentrations (representing commodity batches with a high risk of being contaminated, the positives) and a high fraction of the dataset refers to food safety hazards that are present in low concentrations (representing commodity batches with a low risk of being contaminated, the negatives). Such unbalanced datasets complicate modeling to predict the probability of contamination of commodity batches. This study proposes a weighted Bayesian network (WBN) classifier to improve the model prediction accuracy for the presence of food and feed safety hazards using unbalanced monitoring data, specifically for the presence of heavy metals in feed. Applying different weight values resulted in different classification accuracies for each involved class; the optimal weight value was defined as the value that yielded the most effective monitoring plan, that is, identifying the highest percentage of contaminated feed batches. Results showed that the Bayesian network classifier resulted in a large difference between the classification accuracy of positive samples (20%) and negative samples (99%). With the WBN approach, the classification accuracy of positive samples and negative samples were both around 80%, and the monitoring effectiveness increased from 31% to 80% for pre-set sample size of 3000. Results of this study can be used to improve the effectiveness of monitoring various food safety hazards in food and feed.

Designing a monitoring program for aflatoxin B1 in feed products using machine learning.

Agricultural commodities used for feed and food production are frequently contaminated with mycotoxins, such as Aflatoxin B1 (AFB1). In Europe, both the government and companies have monitoring programs in place for the presence of AFB1. With limited resources and following risk-based monitoring as prescribed in EU Regulation 2017/625, these monitoring programs focus on batches with the highest probability of being contaminated. This study explored the use of machine learning algorithms (ML) to design risk-based monitoring programs for AFB1 in feed products, considering both monitoring cost and model performance. Historical monitoring data for the presence of AFB1 in feed products (2005-2018; 5605 records in total) were used. Four different ML algorithms, including Decision tree, Logistic regression, Support vector machine and Extreme gradient boosting (XGB), were applied and compared to predict the high-risk feed batches to be considered for further AFB1 sampling and analysis. The monitoring cost included the cost of: sampling and analysis, disease burden, storage, and of recalling and destroying contaminated feed batches. The ML algorithms were able to predict the high-risk batches, with an AUC, recall, and accuracy higher than 0.8, 0.6, and 0.9, respectively. The XGB algorithm outperformed the other three investigated ML. Its incorporation would result into up to 96% reduction in monitoring cost in 2016-2018, as compared to the official monitoring program. The proposed approach for designing risk based monitoring programs can support authorities and industries to reduce the monitoring cost for other food safety hazards as well.

Designing optimal food safety monitoring schemes using Bayesian network and integer programming: The case of monitoring dioxins and DL-PCBs.

Efficient food safety monitoring should achieve optimal resource allocation. In this article, a methodology is presented to optimize the use of resources for food safety monitoring aimed at identifying noncompliant samples and estimating background level of hazards in food products. A Bayesian network (BN) model and an optimization model were combined in a single framework. The framework was applied to monitoring dioxins and dioxin-like polychlorinated biphenyls (DL-PCBs) in primary animal-derived food products in the Netherlands. The BN model was built using a national dataset with monitoring results of dioxins and DL-PCBs in animal-derived food products over a 10-year period (2008-2017). These data were used to estimate the probability of detecting suspect samples with dioxins and DL-PCBs levels above preset thresholds, given certain sample conditions. The results of the BN model were then inserted into the optimization model to compute an optimal monitoring scheme. Model estimates showed that the probability of dioxins and DL-PCBs exceeding threshold limits was higher in laying hen eggs and sheep meat than in other animal-derived food (except deer meat). Compared with the monitoring scheme used in the Netherlands in 2018, the optimal monitoring scheme would save around 10,000 EUR per year. This could be obtained by reallocating monitoring resources from products with lower probability of dioxin and DL-PCBs exceeding threshold limits (e.g., pig meat) to products with higher probability (e.g., bovine animal meat), and by shifting sample collection from the last quarter of the year toward the first three quarters of the year.

Financial losses for Dutch stakeholders during the 2013 aflatoxin incident in Maize in Europe.

Early 2013, high concentrations of aflatoxin M1 were found in the bulk milk of a few dairy farms in the Netherlands. These high concentrations were caused by aflatoxin B1 contaminated maize from Eastern Europe that was processed into compound feed, which was fed to dairy cows. Since the contamination was discovered in the downstream stages of the supply chain, multiple countries and parties were involved and recalls of the feed were necessary, resulting into financial losses. The aim of this study was to estimate the direct short-term financial losses related to the 2013 aflatoxin incident for the maize traders, the feed industry, and the dairy sector in the Netherlands. First, the sequence of events of the incident was retrieved. Then, a Monte Carlo simulation model was built to combine the scarce and uncertain data to estimate the direct financial losses for each stakeholder. The estimated total direct financial losses of this incident were estimated to be between 12 and 25 million euros. The largest share, about 60%, of the total losses was endured by the maize traders. About 39% of the total losses were for the feed industry, and less than 1% of the total losses were for the dairy sector. The financial losses estimated in this study should be interpreted cautiously due to limitations associated with the quality of the data used. Furthermore, this incident led to indirect long-term financial effects, identified but not estimated in this study.

Modeling cost-effective monitoring schemes for food safety contaminants: Case study for dioxins in the dairy supply chain.

Food safety monitoring is essential for hazard identification in food chain, but its application may be limited due to costly analytical methods and (inefficient) sampling procedures. The objective of this study was to design cost-effective monitoring schemes for food safety contaminants along the food production chain, given restricted monitoring budgets. As a case study, we focused on dioxins in the dairy supply chain with feed mills, dairy farms, dairy trucks and storage silos in dairy plants as possible control points. The cost-effectiveness of monitoring schemes was assessed using a model consisting of a simulation module and an optimization module. In the simulation module, the probability to collect at least one contaminated sample was computed for different sampling strategies (simple random sampling, stratified random sampling and systematic sampling) at each control point. The optimization module maximized the effectiveness of a monitoring scheme to identify the contaminated sample by determining the optimal sampling strategies, the optimal number of incremental samples collected, and the pooling rate (number of collected samples mixed into one aggregated sample) at each control point. The modelling approach was applied to two cases with different types of contamination. Results of these cases showed that, to identify the same contaminated sample, monitoring schemes with systematic sampling were more cost-effective at feed mills and dairy farms. The combination of simulation and optimization methods showed to be useful for developing cost-effective food safety monitoring schemes along the food supply chain.

The relation between technical farm performance and antimicrobial use of broiler farms.

The aim of the present study was to explore the relation between both farm performance and antimicrobial use (AMU) of broiler farms. Farm performance was expressed as technical efficiency, obtained by using a bootstrap data envelopment analysis. AMU was expressed as treatment incidence. Cluster analysis is used to obtain groups of farms with similar characteristics regarding technical farm performance and AMU. Results indicate that the farms within the different clusters combine different technical farm performance and different levels of AMU. Between the clusters, significant differences were found in technical farm performance, AMU, the resource intensity of the number of animals at set-up, the number of antimicrobial treatments, the number of antimicrobial treatments related to either gut health or combined problems, and the number of antimicrobial treatments with either yellow or orange active substances. Farmers who combine high levels of AMU with high technical farm performance are likely to overestimate the real economic value of AMU. Proper coordination between the farmer and the veterinarian can be crucial in that case for reducing AMU. Farms with low performance are likely to have poor farm conditions. Improving those farm conditions can help reducing the need for AMU on this kind of farms. The farm-specific conditions have to be considered in future policies aimed at reducing AMU in livestock production.

Optimization of the Aflatoxin Monitoring Costs along the Maize Supply Chain.

An optimization model was used to gain insight into cost-effective monitoring plans for aflatoxins along the maize supply chain. The model was based on a typical Dutch maize chain, with maize grown in the Black Sea region, and transported by ship to the Netherlands for use as an ingredient in compound feed for dairy cattle. Six different scenarios, with different aflatoxin concentrations at harvest and possible aflatoxin production during transport, were used. By minimizing the costs and using parameters such as the concentration, the variance of the sampling plan, and the monitoring and replacement costs, the model optimized the control points (CPs; e.g., after harvest, before or after transport by sea ship), the number of batches sampled at the CP, and the number of samples per batch. This optimization approach led to an end-of-chain aflatoxin concentration below the predetermined limit. The model showed that, when postharvest aflatoxin production was not possible, it was most cost-effective to collect samples from all batches and replace contaminated batches directly after the harvest, since the replacement costs were the lowest at the origin of the chain. When there was aflatoxin production during storage, it was most cost-effective to collect samples and replace contaminated batches after storage and transport to avoid the duplicate before and after monitoring and replacement costs. Further along the chain a contaminated batch is detected, the more stakeholders are involved, the more expensive the replacement costs and possible recall costs become.

Cost-Effective Sampling and Analysis for Mycotoxins in a Cereal Batch.

The presence of hazards (e.g., contaminants, pathogens) in food/feed, water, plants, or animals can lead to major economic losses related to human and animal health or the rejection of batches of food or feed. Monitoring these hazards is important but can lead to high costs. This study aimed to find the most cost-effective sampling and analysis (S&A) plan in the cases of the mycotoxins deoxynivalenol (DON) in a wheat batch and aflatoxins (AFB1 ) in a maize batch. An optimization model was constructed, maximizing the number of correct decisions for accepting/rejecting a batch of cereals, with a budget as major constraint. The decision variables were the choice of the analytical method: instrumental method (e.g., liquid chromatography combined with mass-spectrometry (LC-MS/MS)), enzyme-linked-immuno-assay (ELISA), or lateral flow devices (LFD), the number of incremental samples collected from the batch, and the number of aliquots analyzed. S&A plans using ELISA showed to be slightly more cost effective than S&A plans using the other two analytical methods. However, for DON in wheat, the difference between the optimal S&A plans using the three different analytical methods was minimal. For AFB1 in maize, the cost effectiveness of the S&A plan using instrumental methods or ELISA were comparable whereas the S&A plan considering onsite detection with LFDs was least cost effective. In case of nonofficial controls, which do not have to follow official regulations for sampling and analysis, onsite detection with ELISA for both AFB1 in maize and DON in wheat, or with LFDs for DON in wheat, could provide cost-effective alternatives.

Effects of incorporating environmental cost and risk aversion on economic values of pig breeding goal traits.

Economic values (EVs) of traits, accounting for environmental impacts and risk preferences of farmers, are required to design breeding goals that contribute to both economic and environmental sustainability. The objective of this study was to assess the effects of incorporating environmental costs and the risk preferences of farmers on the EVs of pig breeding goal traits. A breeding goal consisting of both sow efficiency and production traits was defined for a typical Brazilian farrow-to-finish pig farm with 1,500 productive sows. A mean-variance utility function was employed for deriving the EVs at finishing pig level assuming fixed slaughter weight. The inclusion of risk and risk aversion reduces the economic weights of sow efficiency traits (17%) while increasing the importance of production traits (7%). For a risk-neutral producer, inclusion of environmental cost reduces the economic importance of sow efficiency traits (3%) while increasing the importance of production traits (1%). Genetic changes of breeding goal traits by their genetic standard deviations reduce emissions of greenhouse gases, and excretions of nitrogen and phosphorus per finished pig by up to 6% while increasing farm profit. The estimated EVs could be used to improve selection criteria and thereby contribute to the sustainability of pig production systems.

Delaying investments in sensor technology: The rationality of dairy farmers' investment decisions illustrated within the framework of real options theory.

The adoption rate of sensors on dairy farms varies widely. Whereas some sensors are hardly adopted, others are adopted by many farmers. A potential rational explanation for the difference in adoption may be the expected future technological progress in the sensor technology and expected future improved decision support possibilities. For some sensors not much progress can be expected because the technology has already made enormous progress in recent years, whereas for sensors that have only recently been introduced on the market, much progress can be expected. The adoption of sensors may thus be partly explained by uncertainty about the investment decision, in which uncertainty lays in the future performance of the sensors and uncertainty about whether improved informed decision support will become available. The overall aim was to offer a plausible example of why a sensor may not be adopted now. To explain this, the role of uncertainty about technological progress in the investment decision was illustrated for highly adopted sensors (automated estrus detection) and hardly adopted sensors (automated body condition score). This theoretical illustration uses the real options theory, which accounts for the role of uncertainty in the timing of investment decisions. A discrete event model, simulating a farm of 100 dairy cows, was developed to estimate the net present value (NPV) of investing now and investing in 5 yr in both sensor systems. The results show that investing now in automated estrus detection resulted in a higher NPV than investing 5 yr from now, whereas for the automated body condition score postponing the investment resulted in a higher NPV compared with investing now. These results are in line with the observation that farmers postpone investments in sensors. Also, the current high adoption of automated estrus detection sensors can be explained because the NPV of investing now is higher than the NPV of investing in 5 yr. The results confirm that uncertainty about future sensor performance and uncertainty about whether improved decision support will become available play a role in investment decisions.

Systematic Review of Methods to Determine the Cost-Effectiveness of Monitoring Plans for Chemical and Biological Hazards in the Life Sciences.

This study reviews the methods used to determine the cost-effectiveness of monitoring plans for hazards in animals (diseases), plants (pests), soil, water, food, and animal feed, and assesses their applicability to food safety hazards. The review describes the strengths and weaknesses of each method, provides examples of different applications, and concludes with comments about their applicability to food safety. A systematic literature search identified publications assessing the cost-effectiveness of monitoring plans in the life sciences. Publications were classified into 4 groups depending on their subject: food safety, environmental hazards, animal diseases, or pests. Publications were reviewed according to the type of model and input data used, and the types of costs included. Three types of models were used: statistical models, simulation models, and optimization models. Input data were either experimental, historical, or simulated data. Publications differed according to the costs included. More than half the publications only included monitoring costs, whereas other publications included monitoring and management costs, or all costs and benefits. Only a few publications were found in the food safety category and all were relatively recent studies. This suggests that cost-effectiveness analysis of monitoring strategies in food safety is just starting and more research is needed to improve the cost-effectiveness of monitoring hazards in foods.

Using farmers' attitude and social pressures to design voluntary Bluetongue vaccination strategies.

Understanding the context and drivers of farmers' decision-making is critical to designing successful voluntary disease control interventions. This study uses a questionnaire based on the Reasoned Action Approach framework to assess the determinants of farmers' intention to participate in a hypothetical reactive vaccination scheme against Bluetongue. Results suggest that farmers' attitude and social pressures best explained intention. A mix of policy instruments can be used in a complementary way to motivate voluntary vaccination based on the finding that participation is influenced by both internal and external motivation. Next to informational and incentive-based instruments, social pressures, which stem from different type of perceived norms, can spur farmers' vaccination behaviour and serve as catalysts in voluntary vaccination schemes.

Economic Analysis of Classical Swine Fever Surveillance in the Netherlands.

Classical swine fever (CSF) is a highly contagious pig disease that causes economic losses and impaired animal welfare. Improving the surveillance system for CSF can help to ensure early detection of the virus, thereby providing a better initial situation for controlling the disease. Economic analysis is required to compare the benefits of improved surveillance with the costs of implementing a more intensive system. This study presents a comprehensive economic analysis of CSF surveillance in the Netherlands, taking into account the specialized structure of Dutch pig production, differences in virulence of CSF strains and a complete list of possible surveillance activities. The starting point of the analysis is the current Dutch surveillance system (i.e. the default surveillance-setup scenario), including the surveillance activities 'daily clinical observation by the farmer', 'veterinarian inspection after a call', 'routine veterinarian inspection', 'pathology in AHS', 'PCR on tonsil in AHS', 'PCR on grouped animals in CVI' and 'confirmatory PCR by NVWA'. Alternative surveillance-setup scenarios were proposed by adding 'routine serology in slaughterhouses', 'routine serology on sow farms' and 'PCR on rendered animals'. The costs and benefits for applying the alternative surveillance-setup scenarios were evaluated by comparing the annual mitigated economic losses because of intensified CSF surveillance with the annual additional surveillance costs. The results of the cost-effectiveness analysis show that the alternative surveillance-setup scenarios with 'PCR on rendered animals' are effective for the moderately virulent CSF strain, whereas the scenarios with 'routine serology in slaughterhouses' or 'routine serology on sow farms' are effective for the low virulent strain. Moreover, the current CSF surveillance system in the Netherlands is cost-effective for both moderately virulent and low virulent CSF strains. The results of the cost-benefit analysis for the moderately virulent CSF strain indicate that the current surveillance system in the Netherlands is adequate. From an economic perspective, there is little to be gained from intensifying surveillance.

Simulation of Cross-border Impacts Resulting from Classical Swine Fever Epidemics within the Netherlands and Germany.

The cross-border region of the Netherlands (NL) and the two German states of North Rhine Westphalia (NRW) and Lower Saxony (LS) is a large and highly integrated livestock production area. This region increasingly develops towards a single epidemiological area in which disease introduction is a shared veterinary and, consequently, economic risk. The objectives of this study were to examine classical swine fever (CSF) control strategies' veterinary and direct economic impacts for NL, NRW and LS given the current production structure and to analyse CSF's cross-border causes and impacts within the NL-NRW-LS region. The course of the epidemic was simulated by the use of InterSpread Plus, whereas economic analysis was restricted to calculating disease control costs and costs directly resulting from the control measures applied. Three veterinary control strategies were considered: a strategy based on the minimum EU requirements, a vaccination and a depopulation strategy based on NL and GER's contingency plans. Regardless of the veterinary control strategy, simulated outbreak sizes and durations for 2010 were much smaller than those simulated previously, using data from over 10 years ago. For example, worst-case outbreaks (50th percentile) in NL resulted in 30-40 infected farms and lasted for two to four and a half months; associated direct costs and direct consequential costs ranged from €24.7 to 28.6 million and €11.7 to 26.7 million, respectively. Both vaccination and depopulation strategies were efficient in controlling outbreaks, especially large outbreaks, whereas the EU minimum strategy was especially deficient in controlling worst-case outbreaks. Both vaccination and depopulation strategies resulted in low direct costs and direct consequential costs. The probability of cross-border disease spread was relatively low, and cross-border spread resulted in small, short outbreaks in neighbouring countries. Few opportunities for further cross-border harmonization and collaboration were identified, including the implementation of cross-border regions (free and diseased regions regardless of the border) in case of outbreaks within close proximity of the border, and more and quicker sharing of information across the border. It was expected, however, that collaboration to mitigate the market effects of an epidemic will create more opportunities to lower the impact of CSF outbreaks in a cross-border context.

A conceptual framework for economic optimization of an animal health surveillance portfolio.

Decision making on hazard surveillance in livestock product chains is a multi-hazard, multi-stakeholder, and multi-criteria process that includes a variety of decision alternatives. The multi-hazard aspect means that the allocation of the scarce resource for surveillance should be optimized from the point of view of a surveillance portfolio (SP) rather than a single hazard. In this paper, we present a novel conceptual approach for economic optimization of a SP to address the resource allocation problem for a surveillance organization from a theoretical perspective. This approach uses multi-criteria techniques to evaluate the performances of different settings of a SP, taking cost-benefit aspects of surveillance and stakeholders' preferences into account. The credibility of the approach has also been checked for conceptual validity, data needs and operational validity; the application potentials of the approach are also discussed.

Expected utility of voluntary vaccination in the middle of an emergent Bluetongue virus serotype 8 epidemic: a decision analysis parameterized for Dutch circumstances.

In order to put a halt to the Bluetongue virus serotype 8 (BTV-8) epidemic in 2008, the European Commission promoted vaccination at a transnational level as a new measure to combat BTV-8. Most European member states opted for a mandatory vaccination campaign, whereas the Netherlands, amongst others, opted for a voluntary campaign. For the latter to be effective, the farmer's willingness to vaccinate should be high enough to reach satisfactory vaccination coverage to stop the spread of the disease. This study looked at a farmer's expected utility of vaccination, which is expected to have a positive impact on the willingness to vaccinate. Decision analysis was used to structure the vaccination decision problem into decisions, events and payoffs, and to define the relationships among these elements. Two scenarios were formulated to distinguish farmers' mindsets, based on differences in dairy heifer management. For each of the scenarios, a decision tree was run for two years to study vaccination behaviour over time. The analysis was done based on the expected utility criterion. This allows to account for the effect of a farmer's risk preference on the vaccination decision. Probabilities were estimated by experts, payoffs were based on an earlier published study. According to the results of the simulation, the farmer decided initially to vaccinate against BTV-8 as the net expected utility of vaccination was positive. Re-vaccination was uncertain due to less expected costs of a continued outbreak. A risk averse farmer in this respect is more likely to re-vaccinate. When heifers were retained for export on the farm, the net expected utility of vaccination was found to be generally larger and thus was re-vaccination more likely to happen. For future animal health programmes that rely on a voluntary approach, results show that the provision of financial incentives can be adjusted to the farmers' willingness to vaccinate over time. Important in this respect are the decision moment and the characteristics of the disease. Farmers' perceptions of the disease risk and about the efficacy of available control options cannot be neglected.

A conceptual framework for economic optimization of single hazard surveillance in livestock production chains.

Economic analysis of hazard surveillance in livestock production chains is essential for surveillance organizations (such as food safety authorities) when making scientifically based decisions on optimization of resource allocation. To enable this, quantitative decision support tools are required at two levels of analysis: (1) single-hazard surveillance system and (2) surveillance portfolio. This paper addresses the first level by presenting a conceptual approach for the economic analysis of single-hazard surveillance systems. The concept includes objective and subjective aspects of single-hazard surveillance system analysis: (1) a simulation part to derive an efficient set of surveillance setups based on the technical surveillance performance parameters (TSPPs) and the corresponding surveillance costs, i.e., objective analysis, and (2) a multi-criteria decision making model to evaluate the impacts of the hazard surveillance, i.e., subjective analysis. The conceptual approach was checked for (1) conceptual validity and (2) data validity. Issues regarding the practical use of the approach, particularly the data requirement, were discussed. We concluded that the conceptual approach is scientifically credible for economic analysis of single-hazard surveillance systems and that the practicability of the approach depends on data availability.

Cross-border collaboration in the field of highly contagious livestock diseases: a general framework for policy support.

This paper analyses the potential gains and the main challenges for increased cross-border collaboration in the control of highly contagious livestock diseases in regions with cross-border reliance on production and consumption of livestock commodities. The aim of this intensification of cross-border collaboration is to retain the economic advantages of cross-border trade in livestock and livestock commodities while maintaining a low risk of highly contagious livestock diseases. From these two foci, possibilities for future policy making with respect to highly contagious livestock diseases are discussed: peacetime cross-border cooperation to improve the cost-effectiveness of routine veterinary measures and crisis time cross-border harmonization of current disease control strategies. A general disease management framework was used to describe the way in which these two fields are related to and affect the epidemiological system and, consequently, how they impact the stakeholders. In addition to this framework, the importance of a good understanding of influencing factors, that is, the production structure of livestock, was stressed because these factors are important determinants of the frequency and magnitude of highly contagious livestock diseases and their economic impact. The use of the suggested integrated approach was illustrated for the extended cross-border region of the Netherlands and Germany, that is, North Rhine Westphalia and Lower Saxony. For this region, current difficulties in cross-border trade in livestock and livestock commodities and possibilities for future cross-border collaboration were examined. The concepts and ideas presented in this paper should foster future development of cross-border collaboration in animal health control.

Optimizing bulk milk dioxin monitoring based on costs and effectiveness.

Dioxins are environmental pollutants, potentially present in milk products, which have negative consequences for human health and for the firms and farms involved in the dairy chain. Dioxin monitoring in feed and food has been implemented to detect their presence and estimate their levels in food chains. However, the costs and effectiveness of such programs have not been evaluated. In this study, the costs and effectiveness of bulk milk dioxin monitoring in milk trucks were estimated to optimize the sampling and pooling monitoring strategies aimed at detecting at least 1 contaminated dairy farm out of 20,000 at a target dioxin concentration level. Incidents of different proportions, in terms of the number of contaminated farms, and concentrations were simulated. A combined testing strategy, consisting of screening and confirmatory methods, was assumed as well as testing of pooled samples. Two optimization models were built using linear programming. The first model aimed to minimize monitoring costs subject to a minimum required effectiveness of finding an incident, whereas the second model aimed to maximize the effectiveness for a given monitoring budget. Our results show that a high level of effectiveness is possible, but at high costs. Given specific assumptions, monitoring with 95% effectiveness to detect an incident of 1 contaminated farm at a dioxin concentration of 2 pg of toxic equivalents/g of fat [European Commission's (EC) action level] costs €2.6 million per month. At the same level of effectiveness, a 73% cost reduction is possible when aiming to detect an incident where 2 farms are contaminated at a dioxin concentration of 3 pg of toxic equivalents/g of fat (EC maximum level). With a fixed budget of €40,000 per month, the probability of detecting an incident with a single contaminated farm at a dioxin concentration equal to the EC action level is 4.4%. This probability almost doubled (8.0%) when aiming to detect the same incident but with a dioxin concentration equal to the EC maximum level. This study shows that the effectiveness of finding an incident depends not only on the ratio at which, for testing, collected truck samples are mixed into a pooled sample (aiming at detecting certain concentration), but also the number of collected truck samples. In conclusion, the optimal cost-effective monitoring depends on the number of contaminated farms and the concentration aimed at detection. The models and study results offer quantitative support to risk managers of food industries and food safety authorities.