The aim of this paper is to provide a DSS to improve the effectiveness of the inspection procedures in organic certification. The analysis is based on data from the archives of the largest Italian organic control body. Bayesian Networks are used to develop a DSS combining available data and expert information, with the aim to support inspectors in the design of inspection schemes that could lead to a high likelihood of non-compliance detection. Results show that there is scope to increase the effectiveness of inspections, and in particular by organizing the sampling at key times during the year.
A DSS to improve inspection procedures in organic certification. Evidence from an Italian case study / Gambelli, Danilo; Solfanelli, Francesco; Zanoli, Raffaele. - STAMPA. - 261:(2014), pp. 441-446. [10.3233/978-1-61499-399-5-441]
A DSS to improve inspection procedures in organic certification. Evidence from an Italian case study
GAMBELLI, Danilo;SOLFANELLI, FRANCESCO;ZANOLI, RAFFAELE
2014-01-01
Abstract
The aim of this paper is to provide a DSS to improve the effectiveness of the inspection procedures in organic certification. The analysis is based on data from the archives of the largest Italian organic control body. Bayesian Networks are used to develop a DSS combining available data and expert information, with the aim to support inspectors in the design of inspection schemes that could lead to a high likelihood of non-compliance detection. Results show that there is scope to increase the effectiveness of inspections, and in particular by organizing the sampling at key times during the year.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
