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Home » Part B: OrMaCode Code of Practice » B4: Statistical Output

B4: Statistical Output

Principle 11 (Relevance)

Only organic market data that meet the prioritised needs of key market operators, policy makers and other users should be collected.

Indicators

11.1 End users should be identified and consulted. Organisations aiming at developing, producing and disseminating organic market data should prioritise data collection that are relevant to end users. This indicator should be balanced with indicator 13.2.

11.2 The use of the statistics should be monitored - monitoring may be as simple as counting the number of downloads of a report/statistics table or could be more sophisticated.

11.3 Each organisation engaged in organic market data development, production and dissemination should ask to be referenced and informed when another institute makes use of their data in reports, papers, etc.

11.4 The OrganicDataNetwork promotes periodical surveys to monitor organic market data users’ satisfaction.

Principle 12 (Accuracy and Reliability)

To ensure accuracy and reliability, all organic market data should be validated by means of consistency checks and periodic reviews. As far as commercial confidentiality allows, all data should be reviewed by at least one independent individual who is not directly employed by the people or lead organisation collecting and processing the data.

Indicators

12.1 Multiple information sources should be used where at all possible and used to "triangulate" the data or as cross checks, in compliance with Indicator 6.2.

12.2 Standardised consistency checks should be carried out on the data (comparison with prior year, comparison with data for conventional farms, sense checks, supply balance equations, etc.). The OrganicDataNetwork publishes and maintains a list of these checks to be performed on the data.

12.3 Measurement or sampling errors should be identified, documented and published together with actual data. In case estimates are produced, these should be clearly indicated, and their reliability mentioned in the publication, in conformity to Indicator 6.3.

12.4 Periodically, a subset of organic market data should be extracted for further consistency checks made by and external independent statistical auditor - like a university or other research centre - using a predefined and standardized auditing methodology. Organisations engaged in organic market data development, production and dissemination should allocate some financial resources for such audits.

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Principle 13 (Timeliness and Punctuality)

Organisations engaged in organic market data dissemination should work to ensure easy and timely access to organic market data for all the relevant stakeholders, in order to optimise the transparency, efficiency and competitiveness of the organic market sector.

Indicators

13.1 Data production (collection and processing) should be carried out efficiently so as to minimise the time lag between the period to which the data relates and the release of the data. Data disseminated later than one year from the period to which the data relates are usually not considered timely.

13.2 Organisations aiming at developing, producing and disseminating organic market data should prioritise data collection for which they have the resources to systematically collect data for more than one period. Ceteris paribus, the usefulness of organic market data is directly proportionate to the length of their time series. This indicator should be balanced with Indicator 11.1.

13.3 This principle must be balanced with accuracy, as early release of inaccurate data is worst than no release.

13.4 Organic market data should be produced in time to be released for specific events which are important for the organic sector (e.g. Biofach). Consideration should be given to the pre-release of some aggregate level data (e.g. release cattle numbers before releasing the more detailed breakdown into dairy cows, dairy heifers, beef cows, beef heifers, youngstock, bulls).

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Principle 14 (Coherence and Comparability)

The OrganicDataNetwork aims at ensuring a high degree of internal consistency and comparability over regions, countries and time periods. 

Indicators

14.1 Organic market data should be checked for basic coherence and internal consistency by simple arithmetic checks (e.g. the area devoted to each organic crop in one country should sum up to the total organic area in the country, the sum of dairy cows in each EU country should sum up to the total number of dairy cows in the EU, etc.).

14.2 The use of standard, harmonised classifications, and statistical methods and procedures by the organisations engaged in organic market data development, production and dissemination should allow comparability of data collected in the various countries.

14.3 Reconciliations and comparisons between different data sources should be carried out to ensure data consistency and coherence.

14.4 Comparison between years should always be taken as a prerequisite (harmonization through time). Time series should be consistent in term of data collection procedure and classification, and changes to the procedure should be made sparingly and always allowing backward-comparability of data, in accordance with Indicator 10.1.

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Principle 15 (Accessibility and Clarity)

Organic market data and metadata should be clear and easily accessible.

Indicators

15.1 Information and Communication Technology should be used to improve accessibility. It is recommended that organic market data were published online, to allow faster and ampler access to data.

15.2 When organic market data are provided at a cost, the organisation could consider wether it is possible to allow open access of the data themselves (or at least some summary aggregated information) at a later stage (e.g. after 1-2 years) to allow monitoring of the development of the organic sector in time and backward-comparability with other data sources.

15.3 Organisations engaged in organic market data development, production and dissemination should be clear about their assumptions, units, nomenclature, classifications and statistical methods and procedures and these should be publicly available.