Risk-based inspection system for organic forming certification: a bayesian networks approach

Journal title ECONOMIA AGRO-ALIMENTARE
Author/s Danilo Gambelli, Francesco Solfanelli, Raffaele Zanoli
Publishing Year 2012 Issue 2011/3 Language Italian
Pages 20 P. 37-56 File size 752 KB
DOI 10.3280/ECAG2011-003004
DOI is like a bar code for intellectual property: to have more infomation click here

Below, you can see the article first page

If you want to buy this article in PDF format, you can do it, following the instructions to buy download credits

Article preview

FrancoAngeli is member of Publishers International Linking Association, Inc (PILA), a not-for-profit association which run the CrossRef service enabling links to and from online scholarly content.

The existing method of certification in the organic agriculture system, which requires periodic inspection of all operators, is inefficient due to the high cost of these controls. A risk-based decision support system, which could assist the inspection body during the planning of the annual inspection visits, is advocated as being more cost-effective and efficient. The risk-based decision support system is constructed as a Bayesian network; the models incorporate the factors that influence risk of irregularity and analyse their effects by determining probability of noncompliance. Empirical findings, using a sample of Italian data regarding inspection of organic farms, support the idea that the current risk categories used by control bodies in Italy are reasonable, but could be recursively updated by using a Bayesian network model and incremental inspection evidence.

Keywords: Organic certification, risk-based inspections, risk modelling, discretechoice models, bayesian networks

Jel codes: C41

Danilo Gambelli, Francesco Solfanelli, Raffaele Zanoli, Un sistema di certificazione <i>risk-based</i> per i controlli in agricoltura biologica: un’applicazione tramite <i>Bayesian networks</i> in "ECONOMIA AGRO-ALIMENTARE" 3/2011, pp 37-56, DOI: 10.3280/ECAG2011-003004