Incentives to Stimulate European Wheat Farmers to Adapt Their Fusarium Species Mycotoxin Management.

Fusarium species infection in wheat can lead to Fusarium Head Blight (FHB) and contamination with mycotoxins. To fully exploit more recent insights into FHB and mycotoxin management, farmers might need to adapt their agronomic management, which can be stimulated through incentives. This study aimed to identify incentives to stimulate European farmers to adapt their agronomic management to reduce FHB and related mycotoxins in wheat. A questionnaire was distributed among 224 wheat farmers from Italy, the Netherlands, Serbia, and the United Kingdom. Using the respondents' data, Bayesian Network modelling was applied to estimate the probability that farmers would adapt their current agronomic management under eight different incentives given the conditions set by their farm and farmer characteristics. Results show that most farmers would adapt their current agronomic management under the incentives "paid extra when wheat contains low levels of mycotoxins" and "wheat is tested for the presence of mycotoxins for free". The most effective incentive depended on farm and farmer characteristics, such as country, crop type, size of arable land, soil type, education, and mycotoxin knowledge. Insights into the farmer characteristics related to incentives can help stakeholders in the wheat supply chain, such as farmer cooperatives and the government, to design tailor-made incentive plans.

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment.

In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal- and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g., in vitro/in vivo data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO2, SiO2, Ag, CeO2, ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.

Big data in food safety: An overview.

Technology is now being developed that is able to handle vast amounts of structured and unstructured data from diverse sources and origins. These technologies are often referred to as big data, and open new areas of research and applications that will have an increasing impact in all sectors of our society. In this paper we assessed to which extent big data is being applied in the food safety domain and identified several promising trends. In several parts of the world, governments stimulate the publication on internet of all data generated in public funded research projects. This policy opens new opportunities for stakeholders dealing with food safety to address issues which were not possible before. Application of mobile phones as detection devices for food safety and the use of social media as early warning of food safety problems are a few examples of the new developments that are possible due to big data.

A holistic approach to food safety risks: Food fraud as an example.

Production of sufficient, safe and nutritious food is a global challenge faced by the actors operating in the food production chain. The performance of food-producing systems from farm to fork is directly and indirectly influenced by major changes in, for example, climate, demographics, and the economy. Many of these major trends will also drive the development of food safety risks and thus will have an effect on human health, local societies and economies. It is advocated that a holistic or system approach taking into account the influence of multiple "drivers" on food safety is followed to predict the increased likelihood of occurrence of safety incidents so as to be better prepared to prevent, mitigate and manage associated risks. The value of using a Bayesian Network (BN) modelling approach for this purpose is demonstrated in this paper using food fraud as an example. Possible links between food fraud cases retrieved from the RASFF (EU) and EMA (USA) databases and features of these cases provided by both the records themselves and additional data obtained from other sources are demonstrated. The BN model was developed from 1393 food fraud cases and 15 different data sources. With this model applied to these collected data on food fraud cases, the product categories that thus showed the highest probabilities of being fraudulent were "fish and seafood" (20.6%), "meat" (13.4%) and "fruits and vegetables" (10.4%). Features of the country of origin appeared to be important factors in identifying the possible hazards associated with a product. The model had a predictive accuracy of 91.5% for the fraud type and demonstrates how expert knowledge and data can be combined within a model to assist risk managers to better understand the factors and their interrelationships.