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

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

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

The State of Organic Seed in Europe

This booklet will present the information collected through research of the LIVESEED project in order to shed light on the actual situation of organic seed use in Europe. In particular, it will focus on the following questions: 1. How does the farmer know what varieties are available as organic seed? How do organic seed databases work in different EU countries and how could they be improved? 2. How much non-organic seed or planting material is used in EU organic farming? How many derogations are granted in different EU Member States and Switzerland? 3. Which are the factors encouraging or discouraging farmers to use organic seed? Which farm and farmer characteristics influence adoption of organic seed? How can organic plant breeding contribute to 100% organic seed use? 4. How has the organic seed market developed in the last years? According to seed suppliers` perspective, what factors hamper the further development of the organic seed sector? To answer these questions, researchers in the LIVESEED project applied integrated research tools and methodologies: a comparative review of the different databases on organic seed in 28 EU countries; an integrated analysis of national derogation reports to measure the current use of nonorganic seed in Europe; a survey among farmers, to understand their perspective on the use of organic seed; and finally a survey among seed suppliers to evaluate trends in the offer of organic seed on the market. A quantitative model was used to estimate the potential demand for organic seed in Europe on the basis of the data collected

IMPROVING FOOD QUALITY FOR THE ORGANIC POULTRY MEAT SECTOR: A QUALITY FUNCTION DEPLOYMENT APPROACH

The paper presents results from an Italian study on the development of the food quality for poultry meat into the organic sector, using the Quality Function Deployment (QFD) technique. Results show that among the visual characteristics of poultry meat consumers assign greater importance to attributes strictly related to the animal welfare issue. Price and product appearance (colour and fat presence) come as second. To meet these needs, producers can effectively operate along the supply chain by acting on: housing type, genotype lines and stocking density (animal concentration/mq). Information about these issues (labelling) should also be provided to the consumers

Quality function deployment in the organic animal food sector: application to poultry meat

none3This study presents the results of an Italian investigation into the development of food quality for poultry meat in the organic sector, using the quality function deployment technique. The results show that among the characteristics of poultry, meat consumers assign greater importance to those that are strictly related to animal welfare issues. Price and product appearance (i.e., colour, presence of fat) come in second. To meet these needs, producers can effectively operate along the supply chain by acting on poultry housing type, genotype lines, and stocking density. Information about these issues should then be shared with the consumers (i.e., clear product labelling).Naspetti, Simona; Alberti, Francesca; Solfanelli, FrancescoNaspetti, Simona; Alberti, Francesca; Solfanelli, Francesc

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

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

Assessing the risk of non-compliance in UK organic agriculture: An econometric analysis

The purpose is to provide an analysis of the risk factors influencing non-compliance in UK organic farming. The paper uses a formal econometric model of risk analysis to provide empirical evidence on the determinants of non-compliance in organic farming. A panel of data from the archives of the largest control body in the UK for 2007-2009 is used, and specific analyses are performed for two types of non-compliances. A zero inflated count data model is used for the estimation, taking into account the fact that the occurrences of non-compliance are very sparse. Results show the existence of strong co-dependence of non-compliant behaviours (i.e. the occurrence of major and critical non-compliance increases the probability of occurrence of the minor one; similarly the probability of occurrence of major non-compliance increases when minor non-compliance occur). Besides, livestock production and farm size are relevant risk factors. Albeit highly representative, the findings are based on Soil Association data only and not on all UK organic farms. The paper provides practical indications for control bodies, concerning aspects that could be strengthened for more efficient risk-based inspections. The paper advocates the use of financial information like turnover or capital stock, and of data concerning the characteristics of the farmers, that could substantially improve the probability of detecting the most severe non-compliances. Certification is essential for organic farming, and an improvement of inspection procedures through a risk-based approach could add efficiency and effectiveness to the whole organic food system, with obvious advantages for consumers and the society as a whole. This paper provides for the first time empirical evidence concerning the implementation of the organic certification system in the UK

The EU organic seed sector – statistics on organic seed supply and demand

The EU organic seed sector – statistics on organic seed supply and deman