Effectiveness of organic certification: a study on an italian organic certificator's data.

The aim of this paper is to implemnt risk-based models for the inspection procedures in the organic certification. particularly, the aim is to analyse the the relationship between the type of sanction a farm receives, and the farm's structure and productions, aiming at the definition of potential risk factors

Non compliance in organic certification: determinants for Italy

Organic certification is based on controls on operators, and verify if they are compliant with respect to organic regulations. Control procedures are a transaction cost that affect organic farming relative competiveness. Here we propose an analysis aiming at increasing the efficiency in the individuation of key risk factors in the organic certification process. The study refers to Italian organic farmers and represents an attempt to implement a risk based inspection scheme based on a statistical approach

Organic Farming in Europe by 2010: Scenarios for the future

How will organic farming in Europe evolve by the year 2010? The answer provides a basis for the development of different policy options and for anticipating the future relative competitiveness of organic and conventional farming. The authors tackle the question using an innovative approach based on scenario analysis, offering the reader a range of scenarios that encompass the main possible evolutions of the organic farming sector. This book constitutes an innovative and reliable decision-supporting tool for policy makers, farmers and the private sector. Researchers and students operating in the field of agricultural economics will also benefit from the methodological approach adopted for the scenario analysis

Output und Public Expenditure Implications of the Development of Organic Farming in Europe

Organic Farming is becoming a key issue in the context of the present EU agro-environmental policy lines. However, its specific impact on agricultural output and on public expenditure is still scarcely investigated. In this book, Raffaele Zanoli and Danilo Gambelli provide a detailed analysis of the consequences that organic farming has produced insofar on these aspects for EU countries and three European non-EU countries - NO, CH, CZ - drawing on the latest studies in this field. The emphasis throughout is on factual information and empirical data provision, making this book a valueable resource to understand the success, challenges and prospects of the policy support to organic farming in Europe. This book is aimed at policy makers, the private sector, researchers and students in the field of economics and politics of organic farming

Exploiting Qualitative Information for Decision Support in Scenario Analysis

The development of scenario analysis (SA) to assist decision makers and stakeholders has been growing over the last few years through mainly exploiting qualitative information provided by experts. In this study, we present SA based on the use of qualitative data for strategy planning. We discuss the potential of SA as a decision-support tool, and provide a structured approach for the interpretation of SA data, and an empirical validation of expert evaluations that can help to measure the consistency of the analysis. An application to a specific case study is provided, with reference to the European organic farming business

Scenario of the organic food market in Europe

Scenario analysis is a qualitative tool for strategic policy analysis that enables researchers and policymakers to support decision making, and a systemic analysis of the main determinants of a business or sector. In this study, a scenario analysis is developed regarding the future development of the market of organic food products in Europe. The scenario follows a participatory approach, exploiting potential interactions among the relevant driving forces, as selected by experts. Network analysis is used to identify the roles of driving forces in the different scenarios, and the results are discussed in comparison with the main findings from existing scenarios on the future development of the organic sector

Can the inspection procedures in organic certification be improved? Evidence from a case study in Italy

The aim of this paper is to analyze if the effectiveness of the inspection procedures in the organic certification is conditioned by measurable structural and managerial factors under control of organic control bodies (CBs), and if there is scope for possible improvements. The analysis is based on data from the archives of the largest Italian organic CB, containing information on operators’ characteristics, including a qualitative discrete risk score defined by the CB, inspectors’ characteristics, type of inspection and the outcome of the inspection, in terms of noncompliance detected and sanctions imposed. The aim is to analyze factors that could make an inspection more effective. Our measure of effectiveness is the number of detected noncompliance per inspection visit. No specific literature on this issue is available, therefore on the basis of available information we develop a set of hypothesis concerning measurable factors that might have an effect on the effectiveness of the inspections. Discrete choice models are used to estimate the likelihood of noncompliance conditional to a set of covariates concerning risk assessment of the operators, inspectors characteristics, and modalities of the inspections. Different models and their distributional assumptions are discussed and tested. Results show that there is scope for an increase of effectiveness of inspections, and the particular relevance of two factors: samples taken during the inspection and timing of the visit are confirmed as significant factors increasing the likelihood of both slight and severe noncompliance

Una Rete Bayesiana per migliorare l’efficienza della fase di ispezione del processo di certificazione biologica

Organic certification costs represent an important competitive disadvantage for organic farming. A proportion of the higher costs of organic products may result from the costs of certification along the entire supply chain. A reduction of such transaction costs provides a basis for a general increase in organic farms competitiveness. In this paper we present a model based on Bayesian Networks (BN) for the support of certification bodies in the phase of inspection planning. BNs are probabilistic models with a graphical interface, representing a network of a set of interconnected random variables, and provide a basis for influence diagrams based on conditional probabilities computations. The model implements a BN approach using risk factors – such as crop rotation, farm size, etc. – that are expected to influence risk of infraction. The model analyses the joint effects of different factors on the farmers’ probability of non-compliance. The study exploits a dataset based on data from Istituto Mediterraneo di Certificazione (IMC) for 2007. The main results concern the development of the network showing the main factors influencing the probability of non compliance, and a simulation run on different structural factors aiming to discriminate between farm types different risks of non compliance

AN ECONOMETRIC ANALYSIS FOR THE EVALUATION OF RISK OF NON-COMPLIANCES IN TURKEY

In this paper we analyse the data from inspections of two European control bodies in Turkish farms to analyse risk pattern of non-compliance with the EU organic regulation. A logit model is used to identify the types of crops the are more likely related to non-compliant operators

Modelling risk-based inspections in EU organic certification: data requirements and analysis tools

A Risk Based Inspection (RBI) scheme is a planning tool used to develop the optimum plan for the execution of inspection activities. Organic certification system could benefit from the implementation of RBIs in terms of higher effectiveness, i.e. trustability, and lower transaction costs for organic operators. Data from certification bodies provide basic information about non-compliances and structural aspects of organic operators. Here we propose a methodological approach to risk analysis modelling, based on discrete choice models and Bayesian networks, both aiming at the identification of key risk factor in the organic certification process in the European Union