- Introduction to Bayesian statistics. 1. Bayesian statistics and the agro-food production chain; T. Fearn. 2. Bayesian solutions for food-science problems? M.A.J.S. van Boekel.
- Methodology. 3. Bayesian statistics: principles and benefits; A. O'Hagan. 4. Calibration in a Bayesian modelling framework; M.J.W. Jansen, T.J. Hagenaars. 5. Bayesian methods for updating crop-model predictions, applications for predicting biomass and grain protein content; D. Makowski, M.-H. Jeuffroy, M. Guérif.
- Bayesian approaches to quality and safety in primary food production. 6. Applying prior knowledge to model batch keeping-quality of cucumber batches; R.E. Schouten, L.M.M. Tijskens, O. van Kooten, G. Jongbloed. 7. Risk-analysis of human pathogen spread in the vegetable industry: a comparison between organic and conventional production chains; E. Franz, A.H.C. van Bruggen, A.M. Semenov. 8. Are Bayesian approaches useful in plant pathology? J. Yuen, A. Mila.
- Bayesian approaches to quality and safety in food technology. 9. Bayesian networks and food security: an introduction; A. Stein. 10. Application of Bayesian belief network models to food-safety science; G.C. Barker.
- Bayesian approaches in the food chain, nutrition and epidemiology. 11. Bayesian statistics for infection experiments; L. Heres, B. Engel. 12. Quantitative modelling in design and operation of food supply systems; P. van Beek. 13. Some explorations into Bayesian modelling of risks due to pesticide intake from food; H. van der Voet, M. João Paulo. 14. General discussion and conclusion; M.A.J.S. van Boekel, A. Stein, A.H.C. van Bruggen.
- List of participants.
The food market is changing from a producer-controlled to a consumer-directed market. A main driving force is consumer concern about agricultural production methods and food safety. More than before, the consumer demands transparency of the production and processing chain.
A food chain can be quite complex and the use of models has become indispensable to handle this complexity. Modelling tools are becoming increasingly important to guide the decisions for production of high-quality and safe agricultural foods. With the aid of models it becomes possible to control and predict quality attributes, so that product innovation can be done more efficiently. However, quality is an elusive concept, and there is always an aspect of subjectivity and uncertainty.
A novel approach in the agro-food chain would be to tackle subjective elements and uncertainty in modelling by using Bayesian statistics and Bayesian Belief Networks. Bayesian approaches use prior probabilities (partly accounting for subjectivity) to estimate posterior probabilities, resulting in higher accuracy than is possible with classical statistical techniques. Thus, the variability and uncertainty in data and decisions, inherent in a complex food chain, can be dealt with.