Safe food and feed through an integrated toolbox for mycotoxin management: the MyToolBox approach

There is a pressing need to mobilise the wealth of knowledge from the international mycotoxin research conductedover the past 25-30 years, and to perform cutting-edge research where knowledge gaps still exist. This knowledgeneeds to be integrated into affordable and practical tools for farmers and food processors along the chain inorder to reduce the risk of mycotoxin contamination of crops, feed and food. This is the mission of MyToolBox – a four-year project which has received funding from the European Commission. It mobilises a multi-actorpartnership (academia, farmers, technology small and medium sized enterprises, food industry and policystakeholders) to develop novel interventions aimed at achieving a significant reduction in crop losses due tomycotoxin contamination. Besides a field-to-fork approach, MyToolBox also considers safe use options ofcontaminated batches, such as the efficient production of biofuels. Compared to previous efforts of mycotoxin reduction strategies, the distinguishing feature of MyToolBox is to provide the recommended measures to theend users along the food and feed chain in a web-based MyToolBox platform (e-toolbox). The project focuseson small grain cereals, maize, peanuts and dried figs, applicable to agricultural conditions in the EU and China. Crop losses using existing practices are being compared with crop losses after novel pre-harvest interventionsincluding investigation of genetic resistance to fungal infection, cultural control (e.g. minimum tillage or cropdebris treatment), the use of novel biopesticides suitable for organic farming, competitive biocontrol treatment and development of novel modelling approaches to predict mycotoxin contamination. Research into post-harvestmeasures includes real-time monitoring during storage, innovative sorting of crops using vision-technology, novelmilling technology and studying the effects of baking on mycotoxins at an industrial scale

Critical review of methods for risk ranking of food related hazards, based on risks for human health.

This study aimed to critically review methods for ranking risks related to food safety and dietary hazards on the basis of their anticipated human health impacts. A literature review was performed to identify and characterize methods for risk ranking from the fields of food, environmental science and socio-economic sciences. The review used a predefined search protocol, and covered the bibliographic databases Scopus, CAB Abstracts, Web of Sciences, and PubMed over the period 1993-2013. All references deemed relevant, on the basis of of predefined evaluation criteria, were included in the review, and the risk ranking method characterized. The methods were then clustered - based on their characteristics - into eleven method categories. These categories included: risk assessment, comparative risk assessment, risk ratio method, scoring method, cost of illness, health adjusted life years, multi-criteria decision analysis, risk matrix, flow charts/decision trees, stated preference techniques and expert synthesis. Method categories were described by their characteristics, weaknesses and strengths, data resources, and fields of applications. It was concluded there is no single best method for risk ranking. The method to be used should be selected on the basis of risk manager/assessor requirements, data availability, and the characteristics of the method. Recommendations for future use and application are provided

Dynamic geospatial modeling of mycotoxin contamination of corn in Illinois: unveiling critical factors and predictive insights with machine learning

Mycotoxin contamination of corn is a pervasive problem that negatively impacts human and animal health and causes economic losses to the agricultural industry worldwide. Historical aflatoxin (AFL) and fumonisin (FUM) mycotoxin contamination data of corn, daily weather data, satellite data, dynamic geospatial soil properties, and land usage parameters were modeled to identify factors significantly contributing to the outbreaks of mycotoxin contamination of corn grown in Illinois (IL), AFL >20 ppb, and FUM >5 ppm. Two methods were used: a gradient boosting machine (GBM) and a neural network (NN). Both the GBM and NN models were dynamic at a state-county geospatial level because they used GPS coordinates of the counties linked to soil properties. GBM identified temperature and precipitation prior to sowing as significant influential factors contributing to high AFL and FUM contamination. AFL-GBM showed that a higher aflatoxin risk index (ARI) in January, March, July, and November led to higher AFL contamination in the southern regions of IL. Higher values of corn-specific normalized difference vegetation index (NDVI) in July led to lower AFL contamination in Central and Southern IL, while higher wheat-specific NDVI values in February led to higher AFL. FUM-GBM showed that temperature in July and October, precipitation in February, and NDVI values in March are positively correlated with high contamination throughout IL. Furthermore, the dynamic geospatial models showed that soil characteristics were correlated with AFL and FUM contamination. Greater calcium carbonate content in soil was negatively correlated with AFL contamination, which was noticeable in Southern IL. Greater soil moisture and available water-holding capacity throughout Southern IL were positively correlated with high FUM contamination. The higher clay percentage in the northeastern areas of IL negatively correlated with FUM contamination. NN models showed high class-specific performance for 1-year predictive validation for AFL (73%) and FUM (85%), highlighting their accuracy for annual mycotoxin prediction. Our models revealed that soil, NDVI, year-specific weekly average precipitation, and temperature were the most important factors that correlated with mycotoxin contamination. These findings serve as reliable guidelines for future modeling efforts to identify novel data inputs for the prediction of AFL and FUM outbreaks and potential farm-level management practices

Association of herd BRSV and BHV-1 seroprevalence with respiratory disease and reproductive performance in adult dairy cattle

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to detect the associations between bovine herpesvirus 1 (BHV-1) status of a herd and respiratory disease (BRD) occurrence and reproductive performance in pregnant heifers and cows. The association between management-related factors and higher BRD occurrence was also estimated.</p> <p>Methods</p> <p>Serum samples, collected from cows and youngstock from 103 dairy cattle herds, were analyzed for antibodies against BHV-1, bovine respiratory syncytial virus (BRSV), bovine viral diarrhoea virus (BVDV), and <it>Mycoplasma bovis</it>. A questionnaire was used to collect data concerning herd management factors and reproductive performance, as well as the occurrence of clinical signs of respiratory disease in the last two years, as evaluated by the veterinarian or farm manager. Multiple correspondence analysis (MCA) and logistic regression analysis were performed to identify and quantify the risk factors.</p> <p>Results</p> <p>A low to moderate prevalence (1-49%) of BRSV antibodies among youngstock was associated with a high occurrence of respiratory disease (OR = 6.2, p = 0.010) in cows and in-calf heifers. Employees of the farm may participate in the spread of such disease. Larger herd size, loose-housing of cows, housing youngstock separately from cows until pregnancy, and purchasing new animals were factors possibly related to a high occurrence of respiratory disease symptoms in pregnant heifers and cows. The highest risk of abortions (> 1.3%) and increased insemination index (number of inseminations per pregnancy) (> 1.9) occurred in herds with a moderate prevalence of BHV-1 antibodies (1-49%) in cows.</p> <p>Conclusions</p> <p>BHV-1 was not associated with acute respiratory disease in adult dairy cattle, however was significantly related to reproductive performance. BRSV possesses the main role in respiratory disease complex in adult dairy cattle.</p

A framework for integrated environmental health impact assessment of systemic risks

Traditional methods of risk assessment have provided good service in support of policy, mainly in relation to standard setting and regulation of hazardous chemicals or practices. In recent years, however, it has become apparent that many of the risks facing society are systemic in nature – complex risks, set within wider social, economic and environmental contexts. Reflecting this, policy-making too has become more wide-ranging in scope, more collaborative and more precautionary in approach. In order to inform such policies, more integrated methods of assessment are needed. Based on work undertaken in two large EU-funded projects (INTARESE and HEIMTSA), this paper reviews the range of approaches to assessment now in used, proposes a framework for integrated environmental health impact assessment (both as a basis for bringing together and choosing between different methods of assessment, and extending these to more complex problems), and discusses some of the challenges involved in conducting integrated assessments to support policy