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

Gambelli, Danilo

Solfanelli, Francesco

Zanoli, Raffaele

English

Organic Eprints

  Non	 ﾠcompliance	 ﾠin	 ﾠorganic	 ﾠcertification:	 ﾠdeterminants	 ﾠfor	 ﾠItaly Gambelli, D.1, Solfanelli, F 2, & Zanoli, R.3  Key  words:  organic  certification,  non  compliance,  count  data  models,  Bayesian networks, Italy Abstract 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.  Introduction  Certification is a distinctive feature of organic farming, and a concrete tool to assure that  organic  production  rules  are  fulfilled.  However,  organic  certification  costs represent an important competitive disadvantage for organic farming. An improvement in the efficiency of control procedures of organic control bodies may help in reducing this  transaction  cost,  providing  a  basis  for  a  general  increase  in  organic  farms competitiveness. Here we present the results of a study for risk based inspections aiming at facilitating the individuation of the main risk factors of non compliances for organic operators.  The study is part of the EU research project CERTCOST. The general aim is to individuate key factors that are more likely to be associated to non compliances,  using  both  parametric  and  non  parametric  approaches.  Only  non compliances that  generate sanctions are considered, and classified according to their severeness. A general description of the sanction distribution for 2008 is provided, and the main outcomes of the statistical analyses are discussed, also in terms of potential further research in this field. Materials and methods Data  are  taken  from  a  dataset  developed  in  the  CERTCOST  EU  Project,  and  are based on data from certification bodies from different Euroepan countries. Italian data are provided by ICEA, the main Italian certification body, and for 2008 consists of 9.351 farmers, of which 1219 also have a processing activity. The dataset contains information  on  structural-managerial  characteristics  (e.g.  farm  size,  crops  types,  livestock  types,  product  type),  control  data  (type  and  number  of  controls)  and                                                  1 Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Italy, E-Mail d.gambelli@univpm.it 2 As Above, E-Mail f.solfanelli@agrecon.univpm.it 3 As Above, E-Mail r.zanoli@univpm.it    sanctions  data  (type  and  number  of  sanctions).  Explanatory  variables  have  been discretised  or  dicothomised.  Since  no  information  is  available  about  the  type  and severity of non-compliances, we use sanctions data as a proxy. Following Accredia guidelines,  for  each  operator  we  have  deduced  the  type  of  non-compliance encountered from the resulting sanctions. Sanctions have been grouped and recoded into  two  general  categories: moderat e sanct i ons,  ref erri ng t o i rregul ari t i es,  i . e.  l ess severe non-compliances, and severe sanctions, referring to infringements, i.e. most severe non-compliances. See Tab. 1 for a description of sanctions type distribution. On average, nearly 11% of the Italian farms were sanctioned in 2008. Almost 75 % of the sanction imposed was less severe (slight and moderate sanctions), while the rest (25%) were enclosed in the most severe group (severe and extreme).  Tab.1: Distribution of sanctions by type, IT 2008. Number of operators   Nr. of sanctions  imposed on a farm  Moderate sanctions  Severe sanctions  Total 0  8.779  9.153  8.605 1  430  147  545 2  118  46  167 3  13  4  14 >4  11  1  20 Total sanctions  751  255  1.006 Source: CERTCOST database - ITALY 2008 Here we present results arising from two approaches: a parametric approach based on binary choice models, and a probabilistic approach based on Bayesian Networks (BN).  The  binary  choice  model  here  used  is  a  logit  model,  based  on  logistic  distribution, and allows to explain the presence of sanctions detected as function of a set  of  explanatory  variables  (Greene  2008).  The  logistic  distribution  has  been preferred to the standard normal distribution as it has shown a better management of the  extremely  sparse  data  on  sanctions  in  the  sample.  The  unrestricted  model consists of a wide explanatory variable set, with 46 variables referring to crop and livestock types (e.g. cereals, poultry, etc) , structural variables (e.g. utilisable arable area  (UAA),  livestock  units  activity,  etc)  and  specific  risk  factors  (occurrence  of sanctions in the previous year). A backward stepwise procedure has been followed, testing for statistical significance of the single coefficients (at least 5% significance required)  and  eliminating  those  that  proved  to  be  not  relevant.  Finally,  we  have performed a LR test to consider the validity of the restricted model. The BN approach (Horvitz et al., 1988) builds up a network of connections among variables, and the links  among  variables  are  measured  in  terms  of  conditional  probabilities.  More specifically BNs are used to determine the conditional probability of non-compliance given  a  set  of  “evidences”,  i.e.  the  actual  occurrence  of  the  event  that  a  certain variable assumes a given value. For instance, we can infer the probability of getting an infringement if a farmer cultivates a specific crop. The impact of evidences on the network  has  been  designed  using  the  PC  algorithm,  while  conditional  probabilities have been computed through the expecation maximiisation procedure of Hugin 7.0 software. For both models, variables have been discretised or dichotomised.   Results Results from logit regression and BN are summarised in Tab 2 and show the variables that  have  been  found  as  relevant  impact  on  the  risk  of  moderate  and  severe sanctions.  For  the  logit  model,  only  variables  resulting  from  the  restricted  model resulting from the stepwise estimation are listed (the final restricted model has passed the LR constraint test). For the BN model only variables showing a sensible impact on the probabilities of sanctions are considered. + and – signs are respectively meaning a positive or negative impact of the variable on the probability of a farmer to get at least one sanction. Labels in bold indicate variables that are relevant for both models.  Tab. 2: Variables affecting sanctions risk: results from logit regression, ICEA data 2008. Variables are dichotomised, unless differently specified    Moderate Sanctions   Severe Sanctions    logit  bbn  logit  bbn Crop and livestock types         Cereals      +     + Citrus      -     - Dried pulses      +     + Fresh vegetables         +    Fruit   -          Grapes   +     +    Grassland   +     +    Green fodder   +  +       Industrial crops   -     +    Olives            - Poultry   +     +    Structural factors             Conventional UAA  -     +  + GMO-risk crops (maize, soya)  -  +     + Livestock Units <10        +    Crop structure complexity*  +  +  +  + Nr of products (nr)     +       Processor  +     -    UAA (ha)     +     + Sanctions         Moderate Sanctions in 2007   +  +  +    Severe sanctions in 2007         +  + *a Shannon index has been used for the logit models as a proxy of crop structure complexity, while the number of crops has been used for BN models; Discussion Results from Tab 2 indicate that among crop and livestock type category, logit and BN individuate different group of variables as relevant, with the only exception of “Green fodder”, which is considered as a risk factor for moderate sanctions in both models. Also, while many risk factors are common for both moderate and severe sanctions, some variables are only relevant for one sanction type: “Fresh vegetables”, “Fruit”, “Olives”. For what concerns the structural risk factors, “Conventional UAA” and “Crop structure complexity” are significant in both models, with the second one found as relevant  for  both  the  moderate  and  severe  sanction  risk.  Controversial  results  are found for “GMO-risk crops” in the moderate sanction models. Finally, almost univocal   results are found for moderate and severe sanctions issued in 2007: it is interesting to note how the risk of  moderate sanctions is not affected by the occurrence of severe sanctions  in  2007,  while  some  evidence  from  the  logit  model  indicate  that  2007 moderate  sanctions  increase  the  risk  of  severe  sanctions.  The  occurrence  of sanctions in 2007 can be interpreted as a proxy of the farmers’ individual effect, like farmers’ attitude to fraud, managerial errors, geographical aspects, etc. Unfortunately the scientific literature on these aspects is extremely scarce. Gambelli and Solfanelli (2009)  have  performed  similar  analysis  for  moderate  sanctions  using  a  dataset  of Italian farm from another certification body, testing different farm types risk of non-compliances.  Similar results are found in particular for what concerns the key role of the sanctions issued in the previous years. Also, the negative effect on sanctions risk of citrus and olives and the positive one of livestock related crops (grassland, green fodder, mais) are confirmed in both studies. Conclusions The  approach  we  have  used  for  this  study  show  encouraging  results  and  can  be considered  as  complimentary  tools  for  understanding  risk  patterns  in  the  organic certification schemes. It is necessary to consider that results cannot be generalised and should be considered relevant only for the specific control body that provided the dataset.  Further  work  is  needed,  in  particular  for  what  concerns  the  analysis  of different combination of variables and the elaboration of more powerful econometric models. For what concerns the first aspect, it is reasonable to suppose that some variables  could  be  considered  at  risk  only  when  combined  with  other.  For  what concerns the second aspect, the incorporation of the time dimension and the use of count data models, which also explain the actual number of sanction detected, could be  considered  as  interesting  options  to  consider.  However,  the  general  approach focussing on a standardisation of sanction types and the use of probabilistic models can be considered a promising step towards the definition of a risk based inspection systems that could improve efficiency in the organic sector. Acknowledgments This  publication  was  generated  as  part  of  the  CERTCOST  project,  agreement  no. 207727 (http://www.certcost.org), with financial support from the European Community under  the  7th  Framework  Programme.  The  publication  reflects  the  views  of  the author(s) and not those of the European Community, who is not to be held liable for any use that may be made of the information contained References  ACCREDIA (2009): Directives for accreditation of Bodies,    http://www.accredia.it/extsearch_documentazione.jsp?area=7&ID_LINK=57&page=13&IDCTX=168&id_context=168 CERTCOST www.certcost.org Gambelli, D., Solfanelli, F. (2009), Una Rete Bayesiana per migliorare l’efficienza della fase di ispezione  del  processo  di  certificazione  biologica,  Cresimanno  M,,  Schifani  G.  (eds) Agricoltura  Biologica:  sistemi  produttivi  e  modelli  di  commercializzazione  e  consumo, proceedings of the IV GRABIT Workhop, Palermo, p 91-96.  Greene, W. H. (2008) Econometric Analysis, Prentice Hall, upper Saddle River - N.J., 1178 p. Horvitz, E.J., Breese, J.S. & Henrion, M., 1988. Decision theory in expert systems and artificial intelligence. International Journal of Approximate Reasoning 2, 247-302. 

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 approac

GAMBELLI, Danilo

SOLFANELLI, FRANCESCO

ZANOLI, RAFFAELE

IRIS UniversitÃ  Politecnica delle Marche

  Non	  compliance	  in	  organic	  certification:	  determinants	  for	  Italy Gambelli, D.1, Solfanelli, F 2, & Zanoli, R.3  Key words: organic certification, non compliance, count data models, Bayesian networks, Italy Abstract 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.  Introduction  Certification is a distinctive feature of organic farming, and a concrete tool to assure that organic production rules are fulfilled. However, organic certification costs represent an important competitive disadvantage for organic farming. An improvement in the efficiency of control procedures of organic control bodies may help in reducing this transaction cost, providing a basis for a general increase in organic farms competitiveness. Here we present the results of a study for risk based inspections aiming at facilitating the individuation of the main risk factors of non compliances for organic operators.  The study is part of the EU research project CERTCOST. The general aim is to individuate key factors that are more likely to be associated to non compliances, using both parametric and non parametric approaches. Only non compliances that  generate sanctions are considered, and classified according to their severeness. A general description of the sanction distribution for 2008 is provided, and the main outcomes of the statistical analyses are discussed, also in terms of potential further research in this field. Materials and methods Data are taken from a dataset developed in the CERTCOST EU Project, and are based on data from certification bodies from different Euroepan countries. Italian data are provided by ICEA, the main Italian certification body, and for 2008 consists of 9.351 farmers, of which 1219 also have a processing activity. The dataset contains information on structural-managerial characteristics (e.g. farm size, crops types,  livestock types, product type), control data (type and number of controls) and                                                 1 Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Italy, E-Mail d.gambelli@univpm.it 2 As Above, E-Mail f.solfanelli@agrecon.univpm.it 3 As Above, E-Mail r.zanoli@univpm.it    sanctions data (type and number of sanctions). Explanatory variables have been discretised or dicothomised. Since no information is available about the type and severity of non-compliances, we use sanctions data as a proxy. Following Accredia guidelines, for each operator we have deduced the type of non-compliance encountered from the resulting sanctions. Sanctions have been grouped and recoded into two general categories: moderate sanctions, referring to irregularities, i.e. less severe non-compliances, and severe sanctions, referring to infringements, i.e. most severe non-compliances. See Tab. 1 for a description of sanctions type distribution. On average, nearly 11% of the Italian farms were sanctioned in 2008. Almost 75 % of the sanction imposed was less severe (slight and moderate sanctions), while the rest (25%) were enclosed in the most severe group (severe and extreme).  Tab.1: Distribution of sanctions by type, IT 2008. Number of operators  Nr. of sanctions  imposed on a farm Moderate sanctions Severe sanctions Total 0 8.779 9.153 8.605 1 430 147 545 2 118 46 167 3 13 4 14 >4 11 1 20 Total sanctions 751 255 1.006 Source: CERTCOST database - ITALY 2008 Here we present results arising from two approaches: a parametric approach based on binary choice models, and a probabilistic approach based on Bayesian Networks (BN). The binary choice model here used is a logit model, based on logistic  distribution, and allows to explain the presence of sanctions detected as function of a set of explanatory variables (Greene 2008). The logistic distribution has been preferred to the standard normal distribution as it has shown a better management of the extremely sparse data on sanctions in the sample. The unrestricted model consists of a wide explanatory variable set, with 46 variables referring to crop and livestock types (e.g. cereals, poultry, etc) , structural variables (e.g. utilisable arable area (UAA), livestock units activity, etc) and specific risk factors (occurrence of sanctions in the previous year). A backward stepwise procedure has been followed, testing for statistical significance of the single coefficients (at least 5% significance required) and eliminating those that proved to be not relevant. Finally, we have performed a LR test to consider the validity of the restricted model. The BN approach (Horvitz et al., 1988) builds up a network of connections among variables, and the links among variables are measured in terms of conditional probabilities. More specifically BNs are used to determine the conditional probability of non-compliance given a set of “evidences”, i.e. the actual occurrence of the event that a certain variable assumes a given value. For instance, we can infer the probability of getting an infringement if a farmer cultivates a specific crop. The impact of evidences on the network has been designed using the PC algorithm, while conditional probabilities have been computed through the expecation maximiisation procedure of Hugin 7.0 software. For both models, variables have been discretised or dichotomised.   Results Results from logit regression and BN are summarised in Tab 2 and show the variables that have been found as relevant impact on the risk of moderate and severe sanctions. For the logit model, only variables resulting from the restricted model resulting from the stepwise estimation are listed (the final restricted model has passed the LR constraint test). For the BN model only variables showing a sensible impact on the probabilities of sanctions are considered. + and – signs are respectively meaning a positive or negative impact of the variable on the probability of a farmer to get at least one sanction. Labels in bold indicate variables that are relevant for both models.  Tab. 2: Variables affecting sanctions risk: results from logit regression, ICEA data 2008. Variables are dichotomised, unless differently specified   Moderate Sanctions  Severe Sanctions   logit bbn logit bbn Crop and livestock types     Cereals    +   + Citrus    -   - Dried pulses    +   + Fresh vegetables      +   Fruit  -       Grapes  +   +   Grassland  +   +   Green fodder  + +     Industrial crops  -   +   Olives        - Poultry  +   +   Structural factors         Conventional UAA -   + + GMO-risk crops (maize, soya) - +   + Livestock Units <10     +   Crop structure complexity* + + + + Nr of products (nr)   +     Processor +   -   UAA (ha)   +   + Sanctions     Moderate Sanctions in 2007  + + +   Severe sanctions in 2007      + + *a Shannon index has been used for the logit models as a proxy of crop structure complexity, while the number of crops has been used for BN models; Discussion Results from Tab 2 indicate that among crop and livestock type category, logit and BN individuate different group of variables as relevant, with the only exception of “Green fodder”, which is considered as a risk factor for moderate sanctions in both models. Also, while many risk factors are common for both moderate and severe sanctions, some variables are only relevant for one sanction type: “Fresh vegetables”, “Fruit”, “Olives”. For what concerns the structural risk factors, “Conventional UAA” and “Crop structure complexity” are significant in both models, with the second one found as relevant for both the moderate and severe sanction risk. Controversial results are found for “GMO-risk crops” in the moderate sanction models. Finally, almost univocal   results are found for moderate and severe sanctions issued in 2007: it is interesting to note how the risk of  moderate sanctions is not affected by the occurrence of severe sanctions in 2007, while some evidence from the logit model indicate that 2007 moderate sanctions increase the risk of severe sanctions. The occurrence of sanctions in 2007 can be interpreted as a proxy of the farmers’ individual effect, like farmers’ attitude to fraud, managerial errors, geographical aspects, etc. Unfortunately the scientific literature on these aspects is extremely scarce. Gambelli and Solfanelli (2009) have performed similar analysis for moderate sanctions using a dataset of Italian farm from another certification body, testing different farm types risk of non-compliances.  Similar results are found in particular for what concerns the key role of the sanctions issued in the previous years. Also, the negative effect on sanctions risk of citrus and olives and the positive one of livestock related crops (grassland, green fodder, mais) are confirmed in both studies. Conclusions The approach we have used for this study show encouraging results and can be considered as complimentary tools for understanding risk patterns in the organic certification schemes. It is necessary to consider that results cannot be generalised and should be considered relevant only for the specific control body that provided the dataset. Further work is needed, in particular for what concerns the analysis of different combination of variables and the elaboration of more powerful econometric models. For what concerns the first aspect, it is reasonable to suppose that some variables could be considered at risk only when combined with other. For what concerns the second aspect, the incorporation of the time dimension and the use of count data models, which also explain the actual number of sanction detected, could be considered as interesting options to consider. However, the general approach focussing on a standardisation of sanction types and the use of probabilistic models can be considered a promising step towards the definition of a risk based inspection systems that could improve efficiency in the organic sector. Acknowledgments This publication was generated as part of the CERTCOST project, agreement no. 207727 (http://www.certcost.org), with financial support from the European Community under the 7th Framework Programme. The publication reflects the views of the author(s) and not those of the European Community, who is not to be held liable for any use that may be made of the information contained References  ACCREDIA (2009): Directives for accreditation of Bodies,    http://www.accredia.it/extsearch_documentazione.jsp?area=7&ID_LINK=57&page=13&IDCTX=168&id_context=168 CERTCOST www.certcost.org Gambelli, D., Solfanelli, F. (2009), Una Rete Bayesiana per migliorare l’efficienza della fase di ispezione del processo di certificazione biologica, Cresimanno M,, Schifani G. (eds) Agricoltura Biologica: sistemi produttivi e modelli di commercializzazione e consumo, proceedings of the IV GRABIT Workhop, Palermo, p 91-96.  Greene, W. H. (2008) Econometric Analysis, Prentice Hall, upper Saddle River - N.J., 1178 p. Horvitz, E.J., Breese, J.S. & Henrion, M., 1988. Decision theory in expert systems and artificial intelligence. International Journal of Approximate Reasoning 2, 247-302. 

Non compliance in organic certification: determinants for Italy

Abstract

Similar works

Full text

Available Versions

Organic Eprints

IRIS UniversitÃ Politecnica delle Marche