Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain: Proceedings of the Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain, held in Wageningen, The Natherlands, 1-14 May 2003 (Wagening

M.A.J.S. van Boekel, A. Stein, A.H.C. van Bruggen

ISBN 10: 1402019165 / ISBN 13: 9781402019166

Published by Kluwer, 2004

About this Item

165 pp., Hardcover, NEW. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

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Bibliographic Details

• Title: Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain: Proceedings of the Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain, held in Wageningen, The Natherlands, 1-14 May 2003 (Wagening
• Publisher: Kluwer
• Publication Date: 2004
• Language: English
• ISBN 10: 1402019165
• ISBN 13: 9781402019166
• Binding: Hardcover
• Condition: Fine

About this title

Synopsis

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.