Towards automated compliance checking based on a formal representation of agricultural production standards

Edward Nash, Jens Wiebensohn*, Raimo Nikkilä, Anna Vatsanidou, Spyros Fountas, Ralf Bill

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    14 Citations (Scopus)

    Abstract

    Production standards in the form of legal regulations or quality assurance labels are playing an increasingly important role in farming. Each farm must therefore gather information on all standards which apply, which may vary from field-to-field, and ensure that they are respected during operations. This information may be provided on paper or as electronic documents, by the standards publishers or by advisors. Together with the need to document compliance, the need to collect and process the requirements is becoming increasingly burdensome for farmers.

    In this paper, two questions are addressed: whether an automation of the compliance checking is possible, in order to assist the farmer by proactively warning against 'forbidden' operations, and how the definition of the production standard may be formally represented in order to clearly and unambiguously inform the farmer as to what is required. This formal representation also forms one of the prerequisites for any automated assessment.

    As an initial step, a general model of production standards was developed and applied to some common standards in European agriculture. Based on this model, separating standards into metadata and a list of individual rules (check points), a formal representation was developed and an assessment was made as to whether an automated compliance check was feasible. (C) 2011 Elsevier B.V. All rights reserved.

    Original languageEnglish
    Pages (from-to)28-37
    Number of pages10
    JournalComputers and Electronics in Agriculture
    Volume78
    Issue number1
    DOIs
    Publication statusPublished - Aug 2011
    MoE publication typeA1 Journal article-refereed

    Keywords

    • Metadata
    • Ontology language
    • Rules
    • Decision support
    • Knowledge management

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