Reference metadata describe statistical concepts and methodologies used for the collection and generation of data. They provide information on data quality and, since they are strongly content-oriented, assist users in interpreting the data. Reference metadata, unlike structural metadata, can be decoupled from the data.
For more information, you can consult the Eurostat glossary page on the standard output.
3.2. Classification system
Data collected include information on the number of harvests, value, quantity and unit price corresponding to the standard output coefficients of a list of products, broken down by FADN region for each country (reference area). Such values are expressed in euro (and in national currency for countries out of euro area).
The list of products is based on the classifications of crop and livestock variables available in Annex III of Regulation (EU) 2018/1091.
The FADN regions refer to the Farm Accountancy Data Network divisions that are territories of a Member State, or any part thereof, delimited with a view to the selection of returning holdings. See Council Regulation (EC) No 1217/2009 for more information.
The standard output coefficients are used to calculate the standard output of agricultural holdings and to classify agricultural holdings by type of farming and by economic size.The farm type is determined by the relative contribution of the different productions to the total standard output of the holding.
The standard output coefficient of an agricultural product (crop or livestock), abbreviated as SOC, is the average monetary value of the agricultural output at farm-gate price, in euro per hectare or per head of livestock.
Other concepts and definitions are presented in the Typology handbook (RI/CC 1500 rev 5) prepared by the Committee for the Farm Accountancy Data Network.
Standard Output Coefficients (SOC) are collected under the Farm Accountancy Data Network (FADN), by FADN regions in each country. FADN regions are mapped with NUTS regions. For more information on NUTS regions, see the NUTS classification.
3.8. Coverage - Time
The SO 2004 was calculated using the average of 2003, 2004 and 2005 prices. It is applied in 2007 Farm structure survey data and has been applied to 2005 Farm structure survey to allow comparability over the time periods.
The SO 2007 was calculated using the average of 2005, 2006, 2007, 2008 and 2009 prices. It is applied in the 2010 Farm structure survey data.
The SO 2010 was calculated using the average of 2008, 2009, 2010, 2011 and 2012 prices. It is applied in the 2013 Farm structure survey data.
The SO 2013 was calculated using the average of 2011, 2012, 2013, 2014 and 2015 prices. It is applied in the 2016 Farm structure survey data.
The SO 2017 was calculated using the average of 2015, 2016, 2017, 2018 and 2019 prices. It is applied in the 2020 Farm structure survey data.
The SO 2020 was calculated using the average of 2018, 2019, 2020, 2021 and 2022 prices. It is applied in the 2023 Farm structure survey data.
3.9. Base period
do not apply
Units of measure utilised in the SOC data set are listed in the data transmission file to Eurostat by product. In general terms the used ones are EUR/100HEAD, EUR/100M2, EUR/HA, EUR/HEAD, EUR/HIVE.
Article 4 of Commission Delegated Regulation (EU) No 1198/2014 states on the reference period for the standard output: “For the purposes of calculating standard outputs for the Union farm structure survey for year N, as referred to in Article 5b(2) of Regulation (EC) No 1217/2009, the reference period consists of the five successive years from year N-5 to year N-1. The standard outputs shall be determined using average basic data calculated over the reference period laid down in the first paragraph and commonly referred to as ‘N-3 standard outputs’. These N-3 standard outputs shall be updated to take account of economic trends at least each time a Union farm structure survey is carried out.”
For IFS 2020 this means that the Standard Output Coefficients required will be those calculated on the period of 5 years that spans from 2015 to 2019 (SOC2017).
The SO 2020 was calculated using the average of 2018, 2019, 2020, 2021 and 2022 prices. It is applied in the 2023 Farm structure survey data.
6.1. Institutional Mandate - legal acts and other agreements
EU level
Regulation (EC) No 543/2009 of the European Parliament and of the Council of 18 June 2009 concerning crop statistics
Council Regulation (EC) No 1217/2009 of 30 November 2009 setting up the Farm Sustainability Data Network
COMMISSION DELEGATED REGULATION (EU) No 1198/2014 of 1 August 2014 supplementing Council Regulation (EC) No 1217/2009
COMMISSION IMPLEMENTING REGULATION (EU) 2015/220 of 3 February 2015 laying down rules for the application of Council Regulation (EC) No 1217/2009
COMMISSION DELEGATED REGULATION (EU) 2017/2278 of 4 September 2017 amending Annex I to Council Regulation (EC) No 1217/2009
Regulation (EU) 2018/1091 of the European Parliament and of the Council of 18 July 2018 on integrated farm statistics
Regulation (EU) 2023/2674 of the European Parliament and of the Council of 22 November 2023 amending Council Regulation (EC) No 1217/2009 as regards conversion of the Farm Accountancy Data Network into a Farm Sustainability Data Network
Spain level
Ley 12/1989, de 9 de mayo, de la Función Estadística Pública
Real Decreto 1110/2020, de 15 de diciembre, por el que se aprueba el Plan Estadístico Nacional 2021-2024
Real Decreto 51/2024, de 16 de enero, por el que se aprueba el Programa anual 2024 del Plan Estadístico Nacional 2021-2024.
Real Decreto 427/2022, de 7 de junio, por el que se establecen las normas de funcionamiento de la Red Contable Agraria Nacional, y se determinan las funciones, la composición y las normas de funcionamiento de su Comité Nacional
6.2. Institutional Mandate - data sharing
Not requested for this reference year.
7.1. Confidentiality - policy
For specific use of data in FSDN based on the IFS Regulation (Regulation No 2018/1091) and on the FADN/FSDN Regulation No 1217/2009, as last amended by Regulation 2023/2674
The entities collecting the FADN/FSDN data are National Statistical offices or “other national authorities” (ONAs) under Regulation No 223/2009.
7.2. Confidentiality - data treatment
In addition to the provisions of the aforementioned General Data Protection Regulation, with regard to data protection in statistical operations in Spain, we currently apply the provisions described in the following regulations and the same will be done with the FSDN.:
When developing the FADN statistical operation in Spain, we consider in all its phases what is described in Regulation (EC) No 223/2009 of the European Parliament and of the Council of 11 March 2009 on European Statistics, which specifically includes in its article 20 the rules relating to the protection of confidential data.
At the national level, the basic regulations on data collection, compilation and dissemination of statistics by public administrations are set out in Law 12/1989, of 9 May, on the Public Statistical Function, specifically in Chapter III of Title I, which details all the rules that deal with statistical confidentiality and in Title V, which sets out the infringements and penalties in the event of non-compliance, establishing as a very severe infringement the breach of the duty of statistical confidentiality, in which case fines of between 3,000 and 30,000 euros may be imposed.
Royal Decree 1110/2020, of 15 December, approving the National Statistical Plan 2021-2024, by which most of the European standards on statistics are adapted to our legal system, such as, for example, Regulation (EU) 2015/759 of the European Parliament and of the Council, of 29 April, on European statistics which establishes a series of requirements for NSIs and other national statistical authorities, Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard to the processing of personal data or the European Statistics Code of Practice (ESPC). Specifically, point 6 contains the issues relating to statistical confidentiality and data secrecy.
Royal Decree approving the Annual Programme 2024, which establishes the specific conditions for the calendar year 2024 for all statistical operations carried out in Spain, including the FADN. Again, point 2 establishes the legislative framework considered in terms of data protection with which all statistical operations carried out during the calendar year 2024 must comply.Art. 16b par 2 of the same act requires MS to determine, according to General Data Protection Regulation (EU 2016/679), the national controller and, where relevant, the processor.
The current reporting of FADN data complies with Council Regulation (EC) No 1217/2009 of 30 November 2009 setting up a network for the collection of accountancy data on the incomes and business operation of agricultural holdings in the European Union, as well as with Commission Delegated Regulation (EU) No 1998/2014 of 1 August 2014 and the corresponding Implementing Regulations.
This is materialised through a system of information collection, data processing and file exchange with the Accounting Offices by means of our own domain hosting and content on a secure server. The security and privacy of the data transmitted is guaranteed. This privacy is achieved by using a protocol that provides security in the transmission of information, the packets are encrypted before being sent by e-mail to the Liaison Agency (LA), i.e. the Ministry of Agriculture (MAPA).
At MAPA, the encrypted files with the information are stored on private servers contracted through an external service where their security and privacy are guaranteed. Likewise, the computer application we work with as LA to generate the XML files is developed and maintained by the MAPA's internal computer services.
Subsequently, MAPA sends the databases with the FADN information to the Commission by uploading the corresponding XML files on the RICA1 website. For the sending of the Bulk corrections related to the Data Verification File, e-mail is used with the Commission.
In relation to data sharing for the FSDN, the different possibilities for transmitting information on interventions and beneficiaries are currently being studied in the GREX. In Spain, as communicated to the Commission in the survey on data sharing, we believe that the most practical and efficient approach to carry this out for the future FSDN is through option 1 of the two options proposed to us, i.e. to send the Commission the unique identifier of the beneficiary related to the return holding, thus linking the FSDN number of the accounting holding with data from the intervention and beneficiary files.
Finally, the Ministry also has processes in place to ensure compliance with Regulation (EU) 2016/679 and Regulation (EU) 2018/1725, regarding the choice of technical and organisational measures to manage and protect farmers' data. In addition, these procedures prohibit persons participating in the FSDN from disclosing individual data.
8.1. Release calendar
For FADN there is public available timetable on a year basis for the dissemination of statistical operations at the Ministry of Agriculture, Fisheries and Food official website.
By virtue of the provisions of Law 12/1989 of 9 May 1989 on the Public Statistical Function, in Regulation (EC) No 223/2009 of the European Parliament and of the Council on European statistics, with regard to access to confidential data for scientific purposes, our regulations provide as a general principle the confidentiality of data used in statistical operations. However, it allows for certain exceptional cases in which this principle can be modulated, so as to allow specific access to confidential data for scientific purposes.
In turn, Commission Regulation (EU) 557/2013 of 17 June 2013 implementing Regulation (EC) 223/2009 as regards access to confidential data for scientific purposes states in its recitals that "Regulation (EC) 223/2009 establishes a legal framework for developing, compiling and disseminating European statistics, and includes general provisions on the protection of and access to confidential data", however "researchers should be able to enjoy broad access to confidential data used to develop, produce and disseminate European statistics, in order to analyse them for the benefit of scientific progress, without compromising the high level of protection required by confidential statistical data". For this reason, it urges the Member States and the Commission to take "appropriate measures to prevent and sanction any breach of statistical secrecy", bearing in mind that the "real and virtual protection of confidential data must be ensured by regulatory, administrative, technical and organisational measures. These measures should not go beyond what is necessary to limit the use of the data for scientific research purposes."
More specifically, Article 23 of Regulation (EC) 223/2009 provides that the Commission (Eurostat) or the NSCIs or other national authorities, such as the Sub-Directorate-General for Analysis, Coordination and Statistics, in their respective areas of competence, may grant access to confidential data that only allow the indirect identification of statistical units to researchers who carry out statistical analyses for scientific purposes.
In line with the previous European framework, article 15.1 of Law 12/1989, of 9 May, states that the communication for statistical purposes between the Administrations and public bodies of confidential data protected by statistical secrecy will only be possible if the following requirements are met, which must be verified by the service or body that holds them in custody:
a) That the services receiving the data perform mainly statistical functions and have been regulated as such before the data are transferred.
(b) That the purpose of the data is precisely the production of statistics for State purposes entrusted to those services.
(c) That the services receiving the information have the necessary means to preserve statistical secrecy.
Notwithstanding , Article 15.3 of Law 12/1989 authorises statistical services to grant research, studies or analysis institutions access to confidential data that only allow the indirect identification of statistical units for the purpose of carrying out statistical analyses for scientific purposes of public interest. provided that the confidentiality of the data and statistical secrecy are respected.
SOC data are published on Eurobase under the “Additional data” page on Agriculture statistics.
The publication of the data normally takes place within N+1 year after the deadline for the data transmission.
Nevertheless, updates of the data can occur, triggered by DG Agri and Eurostat revision and post-validation checks.
10.1. Dissemination format - News release
Statistics news releases with the latest publications of FADN data are made on the following official website of the Ministry of Agriculture, Fisheries and Food.
There is currently no online public accessible database available.
10.3.1. Data tables - consultations
Not applicalbe
10.4. Dissemination format - microdata access
In order to access microdata, inviduals must submit a request through a specific consultation procedure at the Electronic Office of the Ministry of Agriculture, Fisheries and Food, completing the required documentation.
Statistics Yearbook: the result of the compilation of data from various sources, internal and external to the Ministry, with the aim of integrating in a single publication the most relevant information for the different sectors that make up the Department.
Monthly Statistics Bulletin: which presents a compilation of the main economic indicators, as well as a wide range of indicators related to the agricultural, livestock, fisheries and rural development sectors, among others.
Weekly Situation Report: provides information on national average prices and prices in representative markets of the European Union for certain agricultural and livestock products.
The representativeness of the Selection Plan has been carried out on the basis of the methodological description contained in the attached document ‘2 - Análisis de Representatividad Muestra RECAN 2017 con EEEA2016’ with the subsequent revision of the same in the attached document ‘2- Criterios aplicados en la Distribución por estratos de la muestrav2’.
FADN:
Data quality control is performed through the various data depuration processes that are carried out throughout the accounting year. This is described in more detail in the attached document ‘Informe de situación’.
SOC:
Described in the methodological document that can be downloaded from the MAPA website.
Regarding quality assurance procedures specifically applied to the statistical process for which the report is being prepared, the Ministry of Agriculture (and the training services attached to it) and the National Institute of Statistics provide various training courses on statistical confidentiality, open data and its reuse, etc.
the main data quality verification system is the one described in the previous section.
12.1. Relevance - User Needs
On the one hand, the data are provided to requesters with a research profile, belonging to different public or private entities such as universities or research centres. On the other hand, data is also provided to other statistical units, both within and outside of the Ministry of Agriculture, for the development of statistical work or operations in their corresponding areas.
In particular, each of these users has different needs depending on the destination and usefulness of the information they need, but in any case, and in general, this statistical operation provides knowledge of the activity of the Spanish agricultural sector through economic indicators.
12.2. Relevance - User Satisfaction
At the moment, do not exist a user satisfaction measure system.
12.3. Completeness
12.3.1. Data completeness - rate
do not aplies
13.1. Accuracy - overall
Errors in the data that occur throughout the survey, collection, introduction or depuration process are mainly due to specific random elements that are not systematized, such as an error in the information in the farmer survey, an error in the collection of the information or the introduction of it into the computer application by the interviewer or an error in the process of correction or depuration of the data that gives rise to inconsistencies in the data between the different tables.
As described in the methodology, there are automated systems for detecting and depuration of errors in accounting applications both in the Accounting Offices (in charge of collecting the information) and in the Ministry of Agriculture. These applications include basic error validation systems as well as the implementation of the coherence tests that are included in the European Commission's application RICA1, including maximum and minimum values for the detection of outliers. These coherence test are annexed in the "coherence test definition" file. The basic error validation system works on the basis of the definition and formulas for the variables calculation annexed file.
Likewise, for the detection and evaluation of these errors, a system of analysis of the main results and variables obtained from the statistical operation in each of the NUTS2 regions is carried out, with the objective of detecting which statistical units (agricultural holding) is producing statistical noise and then proceed to study it to determine whether it is an error or an outlier that must be justified, assuming in the second case the withdrawal of the holding from the sample if necessary.
As described in the methodological files attached in point 13.2
13.3. Non-sampling error
Coverage: The representativeness of the sample with respect to the total population is over 95%
Measurement error: main source of error identified due to human errors because of lack of experience of new field agents/interviewers. New TICs solutions like ad-hoc applications are being implement to avoid measurement errors.
Non-response error: arise from difficulty of knowledge, high number of variables requested and refusal of some farmers to collaborate. Some efforts to reduce non-response error could be clearer questionnaires with practical examples and complete and FADN awareness-raising campaigns.
Processing error: arise from interpreting manual free text observationes, incorrect manual coding and automated coding systems. Corrections performed as soon as they are detected during the processing flow.
Model assumption errors: based on a Quota Sampling model to minimize the variance of the estimator of a population's characteristic.
13.3.1. Coverage error
There are population strata that are not represented in the sample, but always less than 5% of the population.
Action: Study of sample redistribution and contacts with farmers.
13.3.1.1. Over-coverage - rate
The proportion of units accessible via the frame that do not belong to the target population based on last Agricultural Census 2020 is 47,7%, corresponding to those farms with < 8.000 € of Standard Ouput.
13.3.1.2. Common units - proportion
does not apply
13.3.2. Measurement error
Errors in the data that occur throughout the survey, collection, introduction or depuration process are mainly due to specific random elements that are not systematized, such as an error in the information in the farmer survey, an error in the collection of the information or the introduction of it into the computer application by the interviewer or an error in the process of correction or depuration of the data that gives rise to inconsistencies in the data between the different tables.
As described in the methodology, there are automated systems for detecting and depuration of errors in accounting applications both in the Accounting Offices (in charge of collecting the information) and in the Ministry of Agriculture. These applications include basic error validation systems as well as the implementation of the coherence tests that are included in the European Commission's application RICA1, including maximum and minimum values for the detection of outliers. These coherence test are annexed in the "coherence test definition" file. The basic error validation system works on the basis of the definition and formulas for the variables calculation annexed file.
Likewise, for the detection and evaluation of these errors, a system of analysis of the main results and variables obtained from the statistical operation in each of the NUTS2 regions is carried out, with the objective of detecting which statistical units (agricultural holding) is producing statistical noise and then proceed to study it to determine whether it is an error or an outlier that must be justified, assuming in the second case the withdrawal of the holding from the sample if necessary.
Data collection instruments: it is carried out by means of a questionnaire in the field, which is sometimes difficult to fill in for certain variables, due to the difficulty of knowledge, accompanied by the high number of variables.
Reworking the questionnaire with practical examples to make it easier to understand and complete.
Lack of response: refusal of some farmers to collaborate.
FADN dissemination campaigns and highlight the importance of collaboration.
13.3.3.1. Unit non-response - rate
Due to the fact that the surveys are carried out ny an external agent, we do not have such information related to the ratio of the number of units with no information or not usable information to the total number of in-scope (eligible) units.
13.3.3.2. Item non-response - rate
It is currently not quantified at Liaison Agency level, as the accounting offices that collect the information from voluntary farms send the standardised form with the required information filled in.
13.3.4. Processing error
Most of the interviews are performed physically at farm level through printed or digital standardized questionnaires, although other interviews are done by telephone computer-assisted where the main part of data registration occurs at the same time as the data collection.
For observations where matching cannot be carried out directly during the interview, manual free text format is recorded in the questionare by the field agent. These answers have to be interpreted in practice and on the knowledge/skills of the coders and/or quality of the automated coding systems back at the Accounting/Regional Offices.
When coding errors give rise to incorrect classifications, these give rise to errors in the statistics. Some groups may be slightly overestimated, for example with respect to the number of persons or anual time employed.
Currently such processing errors are evaluated and corrected internally in the shortest possible time as soon as they are detected during the processing flow. However, there is currently no detailed evaluation or analysis of them.
13.3.5. Model assumption error
In FADN our proposed methodology is based on the technique known as Quota Sampling. This technique consists of defining homogeneous strata of population units and assigning to each stratum a number of units to be sampled. Strata are formed using variables or characteristics of population units, e.g., technical‐economic orientation (OTE), economic dimension (DE), region.
The allocation of quotas is carried out using statistical criteria to minimize the variance of the estimator of a population's characteristic.
Once the sample size of the resulting strata has been determined, as it is a quota sampling, no probabilistic selection is made of the agricultural holdings to be investigated in each Autonomous Community and stratum, but the procedure consists of the recruitment of agricultural holdings that, voluntarily, participate in the survey.
The sample design is conveniently explained in the methodology and in the Selection Plan.
14.1. Timeliness
Length of time between data availability and the event or phenomenon they describe can sometimes vary:
Time lags are usually arise from the main sources of error identified (13.3.2. Measurement error), therefore these errors should first be assessed and corrected to the required level (e.g. farm level, by verifying it with the respondent again or in one of the applications used to store and process the data)
Efforts to reduce time lag in future: identify any posible systematic errors and try implement automatic controls, provide advice and training to the different agents involved in the data capture and processing chain.
14.1.1. Time lag - first result
The FADN cycle is 2 years (N+2)
The accounting data of the holdings for year N (accounting year) are collected in year N+1 and must be previously processed and submitted to the European Commission before the end of that year (delivery date).
For this reason, the average time lag between the delivery date of data and the publication date of the preliminary results (TP1 first release) is along the first quarter of year N+2, approximately 3-4 months.
14.1.2. Time lag - final result
The FADN cycle is 2 years (N+2)
The accounting data of the holdings for year N (accounting year) are collected in year N+1 and must be previously processed and submitted to the European Commission before the end of that year (delivery date).
For this reason, the average time lag between the delivery date of data and the publication date of the definitive results (TP2 final release) is along the second quarter of year N+2, approximately 6 months (around July), although these results can sometimes be revised later on if needed due to EU requirements.
14.2. Punctuality
14.2.1. Punctuality - delivery and publication
The Commission's timetables are respected, and can be seen on the MAPA website through the link provided for this purpose.
Currently, the publication of the data by the MAPA is not disclosed on the date announced in the publication calendar visible on the MAPA website, but it is planned to adapt the publication to that date.
15.1. Comparability - geographical
With regard to international comparability, this is possible since the FADN methodology follows EU requirements, with the same accounting principles as in the rest of the countries of the EU.
The data is integrated into the FADN
15.1.1. Asymmetry for mirror flow statistics - coefficient
No inbound and outbound flows statistcs are currently performed between regions.
FADN statistics focuses in microeconomic data based on harmonised bookkeeping of representative agricultural holdings as statistical units.
15.2. Comparability - over time
The data are comparable in time since 1987.
15.2.1. Length of comparable time series
The data are comparable in time since 1987.
In general, the results are prepared from the current data, in order to be able to construct the series from the beginning of the collection of the information.
15.3. Coherence - cross domain
The use of the same national classification of economic activities makes it possible to compare the information with other economic statistics that study the production and employment of other domains related to the agricultural sector or to compare the data of certain variables of the agricultural sector with data at the regional level from other studies.
15.3.1. Coherence - sub annual and annual statistics
Not requested for this reference year.
15.3.2. Coherence - National Accounts
Not requested for this reference year.
15.4. Coherence - internal
The internal coherence of the statistics is a consequence of the application of the same methodological criteria and an analysis of the possible inconsistencies between its different variables.
Cost
Annual operational costs of the process: The estimate of the budgetary appropriation necessary to finance this statistical operation is 3.041.488,20 €
Recent efforts to improve efficiency:application called "GesRECAN Virtual" was created for the management of accounting notebooks of the FADN. Designed to be used by all personnel involved in the data collection, data processing and process supervision (field agents, supervisors), on both Windows & Android OS for PCs or tablets.
Extent to which TICs are used: every information and communication technology is used throughout the whole operation, including direct telephone calls, emails, official stamped letters, videoconferences or adhoc software such as "gesRecan" (a technical-accounting application adapted to the collaborating farms and to the RICA methodology with implicit internal consistency to prevent from certain errors).
Burden
Estimated burden imposed by the process for the respondent: The workload per farm (including farm capture, information collection, validation, obtaining and sending results) estimated and measured is 15 hours.
Compensation payments to farms amount to €41.32 per farm. In the new RECAN contract for the years 2023-2024, which amounts to the sum of €70 per farm (which represents an increase of 69.4% compared to the previous contract).
Means taken to minimise burden: certain surveys are conducted during periods of lower field workload for respondents, or conducted over the phone or through their agent representatives.
17.1. Data revision - policy
Provisional data are published and then corrected with definitive data.
17.2. Data revision - practice
It is not foreseen in the timetable when the final review is made.
17.2.1. Data revision - average size
Please refer to 17.2
18.1. Source data
Farm Accountancy Data Network (FADN) is a national survey which aims to capture and monitor farms' income and business activities, targeting agricultural holdings which, due to their size, can be considered commercial. The methodology applied aims to provide representative data (sample) according to 3 categories: region, economic size and type of farming.
In Spain, FADN currently collects and analyses annual data from around 9,200 farms (sample population), representing 465,000 farms (52% of the total). This is done through 9 Accounting Offices + 3 Autonomous Communities, then combined at Liaison Agency level and final dataset submitted to DG AGRI after applying several data validations based on RICA Coherence Tests.
18.2. Frequency of data collection
The FADN cycle is 2 years.
The accounting data of the holdings for year N (accounting year) are collected in year N+1 and must be submitted to the European Commission before the end of that year. For this reason, farms are progressively surveyed each week cumulatively according to their productive activity.
Finally, the provisional and final aggregate data are published in year N+2.
18.3. Data collection
Under the umbrella of EU methodology, the Liaison Agency sets up 4 documents for the collection of data at farm level:
Two explanatory documents:
Accounting Information Summary Sheets: farm return data definitions, based on the Commission’s RICC 1680 document, but translated into Spanish with examples to clarify how to collect data and with information about the design of every table.
Accounting Notebook Manual: explains how to proceed with the workbooks.
Two workbooks in PDF so the field agent can choose which one to use to collect data:
Accounting Notebooks (printed & digital): printed and filled on paper or directly filled in without needing to print it.
Respondents fill in the questionnaires with the advice and supervision of the Accounting Offices (AO), who then record the information in a harmonized accounting system program, common to all farms, called "Management Application for the Agricultural Accounting Network (“GesRECAN”)
AOs carry out an initial check of the information through a "Testing Program" of Coherence" which partly includes the "error tests" provided by the EU accountant Network Committee and some specific national accountants.
A further validation is made by Eurostat and DGAgri.
18.5. Data compilation
The Liaison Agency carries out a second fact-check of the information through RICA‐1 application, which is made available by the EU Accounting Network Committee to Member States.
Errors are typified (there is a specific document for this purpose, which is reviewed and updated every year, where appropriate), and can be:
severe (they cannot be justified and must be corrected)
critical (they must also be corrected but do not invalidate the use of the exploitation)
warnings (their origin must be sought; if it is an error, it is corrected, if not, it is justified;
anomalies (they have a lower degree of severity; the same criterion applies as in the previous cases). These errors or alarms are corrected or justified in RICA‐1
18.5.1. Imputation - rate
Currently not recorded
18.6. Adjustment
No statistical procedures are used to adjust the data.
For more information, you can consult the Eurostat glossary page on the standard output.
30 April 2024
The standard output coefficient of an agricultural product (crop or livestock), abbreviated as SOC, is the average monetary value of the agricultural output at farm-gate price, in euro per hectare or per head of livestock.
Other concepts and definitions are presented in the Typology handbook (RI/CC 1500 rev 5) prepared by the Committee for the Farm Accountancy Data Network.
Standard Output Coefficients (SOC) are collected under the Farm Accountancy Data Network (FADN), by FADN regions in each country. FADN regions are mapped with NUTS regions. For more information on NUTS regions, see the NUTS classification.
Article 4 of Commission Delegated Regulation (EU) No 1198/2014 states on the reference period for the standard output: “For the purposes of calculating standard outputs for the Union farm structure survey for year N, as referred to in Article 5b(2) of Regulation (EC) No 1217/2009, the reference period consists of the five successive years from year N-5 to year N-1. The standard outputs shall be determined using average basic data calculated over the reference period laid down in the first paragraph and commonly referred to as ‘N-3 standard outputs’. These N-3 standard outputs shall be updated to take account of economic trends at least each time a Union farm structure survey is carried out.”
For IFS 2020 this means that the Standard Output Coefficients required will be those calculated on the period of 5 years that spans from 2015 to 2019 (SOC2017).
The SO 2020 was calculated using the average of 2018, 2019, 2020, 2021 and 2022 prices. It is applied in the 2023 Farm structure survey data.
Errors in the data that occur throughout the survey, collection, introduction or depuration process are mainly due to specific random elements that are not systematized, such as an error in the information in the farmer survey, an error in the collection of the information or the introduction of it into the computer application by the interviewer or an error in the process of correction or depuration of the data that gives rise to inconsistencies in the data between the different tables.
As described in the methodology, there are automated systems for detecting and depuration of errors in accounting applications both in the Accounting Offices (in charge of collecting the information) and in the Ministry of Agriculture. These applications include basic error validation systems as well as the implementation of the coherence tests that are included in the European Commission's application RICA1, including maximum and minimum values for the detection of outliers. These coherence test are annexed in the "coherence test definition" file. The basic error validation system works on the basis of the definition and formulas for the variables calculation annexed file.
Likewise, for the detection and evaluation of these errors, a system of analysis of the main results and variables obtained from the statistical operation in each of the NUTS2 regions is carried out, with the objective of detecting which statistical units (agricultural holding) is producing statistical noise and then proceed to study it to determine whether it is an error or an outlier that must be justified, assuming in the second case the withdrawal of the holding from the sample if necessary.
Units of measure utilised in the SOC data set are listed in the data transmission file to Eurostat by product. In general terms the used ones are EUR/100HEAD, EUR/100M2, EUR/HA, EUR/HEAD, EUR/HIVE.
The Liaison Agency carries out a second fact-check of the information through RICA‐1 application, which is made available by the EU Accounting Network Committee to Member States.
Errors are typified (there is a specific document for this purpose, which is reviewed and updated every year, where appropriate), and can be:
severe (they cannot be justified and must be corrected)
critical (they must also be corrected but do not invalidate the use of the exploitation)
warnings (their origin must be sought; if it is an error, it is corrected, if not, it is justified;
anomalies (they have a lower degree of severity; the same criterion applies as in the previous cases). These errors or alarms are corrected or justified in RICA‐1
Farm Accountancy Data Network (FADN) is a national survey which aims to capture and monitor farms' income and business activities, targeting agricultural holdings which, due to their size, can be considered commercial. The methodology applied aims to provide representative data (sample) according to 3 categories: region, economic size and type of farming.
In Spain, FADN currently collects and analyses annual data from around 9,200 farms (sample population), representing 465,000 farms (52% of the total). This is done through 9 Accounting Offices + 3 Autonomous Communities, then combined at Liaison Agency level and final dataset submitted to DG AGRI after applying several data validations based on RICA Coherence Tests.
SOC data are published on Eurobase under the “Additional data” page on Agriculture statistics.
The publication of the data normally takes place within N+1 year after the deadline for the data transmission.
Nevertheless, updates of the data can occur, triggered by DG Agri and Eurostat revision and post-validation checks.
Length of time between data availability and the event or phenomenon they describe can sometimes vary:
Time lags are usually arise from the main sources of error identified (13.3.2. Measurement error), therefore these errors should first be assessed and corrected to the required level (e.g. farm level, by verifying it with the respondent again or in one of the applications used to store and process the data)
Efforts to reduce time lag in future: identify any posible systematic errors and try implement automatic controls, provide advice and training to the different agents involved in the data capture and processing chain.
With regard to international comparability, this is possible since the FADN methodology follows EU requirements, with the same accounting principles as in the rest of the countries of the EU.
