Risk Based Inspections in organic farming

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Risk Based Inspections in organic farming
Improving the Organic Certification
SystemWorkshop in Brussels, October 14, 2011

• Raffaele Zanoli
• Università Politecnica delle Marche, IT

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A working definition of a RBI

• The goal of Risk Based Inspections (RBIs) is to
  develop a cost-effective inspection and
  maintenance program that provides assurance of
  acceptable integrity and reliability of a control
  system
• A risk based approach to inspection planning is
  used to
• Ensure risk is reduced as low as reasonably
  practicable
• Optimize the inspection schedule
• Focus inspection effort onto the most critical
  areas
• Identify and use the most appropriate methods of
  inspection

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Modelling RBI systems Objectives

1. Assessment and description of the current
   inspection practices in terms of risk and
   efficiency
2. Define a probabilistic model to increase the
   efficiency of the system based on probability
   theory
3. Optimisation of enforcement measures to reduce
   the occurrence of objectionable organic production

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Modelling RBI systems Data required

• In order to predict the risk of non-compliance
• At farmer/operator level
• Depending on crop type, farm type, geographic
  location, operators characteristics, etc.
• We need data on
• detected non-compliances
• structural, financial and managerial information
  at operator level

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Modelling RBI systems Data available

• Collected during CERTCOST EU project
• Data from 6 different European CBs (from CH, CZ,
  DE, DK, IT, UK)
• Three years covered (2007-2009)
• We used standard data that is routinely recorded
  by inspection bodies

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Available data do not match the requirements

• Databases mainly contain structural data
• CBs collect NC data with non-homogenised textual
  descriptions hard to rank NC severity
• Sanction data are more standardised, but
• they are only a proxy of NC
• no common definition of sanctions across CBs /
  countries
• no clear relationship between NC and sanctions
  (with some exceptions)
• no information available about why an operator
  receives a sanction (e.g. use of pesticides in
  wheat production, use of unauthorised feed for
  livestock, etc.)

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Homogenisation of sanctions across CBs and
countries

• IT, CZ (and UK) CBs use a similar 4 sanction
  category (UK NC)classification
• Further aggregation in terms of slight and severe
  sanction categories
• IT, CZ, UK straightforward interpretation DE,
  DK, CH input from CBs to correctly classify
  sanctions

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Distribution of farms, by sanction category,
country, and year
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Modelling RBI systems Analytical tools
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Potential risk factors

• 46 hypothesis concerning factors affecting the
  probability for an operator to get a sanction has
  been generated with collaboration from partners
• The hypothesis refers to the following aspects
• general risk,
• structural / managerial for farms,
• structural/managerial for processors,
• specific crop, livestock and product variables,
• control related issues
• Some of the hypothesis cannot be tested for all
  countries/years due to missing data (eg processor
  turnover, risk class)

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Factors increasing/decreasing risk
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Factors increasing/decreasing risk
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Factors increasing/decreasing risk

• Few risk factors found relevant for all
  countries Past behaviour, Farm Size, Bovine
  livestock
• History dependence operators who are not
  compliant tend to continue to be so
• if one operator has been non compliant the
  previous year is more likely to be non compliant
  in the next year
• If one operator has committed minor
  irregularities is more likely to be found to have
  committed major infringements
• No overall risk pattern for crop types, though
  country specific risks
• For livestock, bovines and pigs entail higher
  risk
• In countries where (slight) non compliances are
  more numerous (DK, UK, partly CH) there might be
  a higher farms homogeneity, hence lower
  discrimination effects of explanatory variables
• Personal, farmer-specific variables are probably
  crucial in explaining risk but we have VERY
  limited data on these

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General conclusions

• We can say with some confidence which factors
  contribute to risk, but we cannot rule out those
  who dont
• As a consequence, we cannot define low risk
  operator types
• To implement more efficient Risk Based Inspection
  procedures CBs would need better or different
  datasets
• RBI based on past experience can limit
  predictable risk, but cannot avoid potential
  catastrophic events
• uncertainty is an essential factor that should
  inform inspection procedures (black swans) think
  what can impact (the sector, the consumer, the
  CB, etc.) most, even if the risk (probability) of
  occurrence is low (but maybe the cost of
  detection is also low)

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Some statements to open discussion

• Harmonised RBI is fundamental to guarantee
  integrity, improve efficiency and reduce the cost
  of inspection a growing body of small organic
  farmers and growers are refusing certification
  and inspection schemes and selling on alternative
  short supply-chains this creates further
  confusion among consumers
• Without clear and uniform criteria for
  classifying non-compliances as irregularities or
  infringement AND without better data and better
  information systems, no RBI system can work on a
  global scale
• Without global trust on certification and
  inspection procedures no global organic trade can
  survive

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Grazie!Thank you!

• zanoli_at_agrecon.univpm.it

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Limitations of the study

• Data issues
• Data suffer from censoring (i.e. missing data)
  we only have information on NCs that were
  detected by the CBs, but we have no idea how many
  and what kind of NCs have NOT been detected
• Inspection data contain varying quality/quantity
  of management structural data, but little/no
  personal information on operators
• All operators should be inspected at least once
  per year (legal requirement), but the share of
  subsequent inspections (either unannounced or
  follow ups) varies across countries and CBs
• Data are little/no harmonised both within a
  country and across various countries

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Limitations of the study (2)

• Epistemological/methodological issues
• What is the data generating process (DGP)? Since
  CBs are actually using some form of internal RBI
  protocol to inform timing of compulsory announced
  inspections as well as follow-ups and unannounced
  inspections, the risk factors that we have
  observed may simply depend on their inspection
  planning and NOT actual risk (confirmation bias)
• Due to limited amount of severe NCs and related
  sanctions in the database, the reliability of the
  analysis of factors influencing severe risks is
  limited by statistical reasons