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.
Eurostat, the statistical office of the European Union
1.2. Contact organisation unit
E1: Agriculture and fisheries
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
2920 Luxembourg LUXEMBOURG
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
30 September 2024
2.2. Metadata last posted
30 September 2024
2.3. Metadata last update
30 September 2024
3.1. Data description
Labour productivity indicators by country, type of farm, size of farm and sex of manager
Land productivity indicators by country, type of farm, size of farm and sex of manager
3.2. Classification system
Productivity indicators are disseminated by using the following classifications:
Type of farm. The farm typology means a uniform classification of the holdings based on their type of farming and their economic size. Both are determined on the basis of the standard output (SO) which is calculated for each crop and animal. The farm type is determined by the relative contribution of the different productions to the standard output of the holding. Detailed description of the classification is available at: Glossary:Farm typology - Statistics Explained (europa.eu)
For the productivity indicators the highest level of classification is used, where the farms are classified in three different groups:
LS – LIVESTOCK -livestock specialist (FT4 + FT5 + FT7)
OTH – OTHER – mixed-farming holding (FT8 + FT9)
Size of farm. Farms are classified into two classes:
SMALL - Small farms. Include small-scale food producers, according to the definition proposed by FAO, using a combination of two criteria, namely the physical size of the food producer, as expressed by the amount of operated land and number of livestock heads in production, and the economic size of the food producer, as expressed by its revenues. In practice, the threshold, which defines the small-scale producers, is determined through the following steps:
Sort the micro dataset inside the country according to the Utilised Agriculture Area (UAA) and calculate weighted cumulative value of variable UAA (CUM_UAA) for each unit.
Sort the micro dataset inside the country according to the Livestock Unit (LSU) and calculate weighted cumulative value of variable LSU (CUM_ LSU) for each unit.
Sort the micro dataset inside the country according to the Standard Output (SO) and calculate weighted cumulative value of variable SO (CUM_ SO) for each unit.
Calculate country totals for variables UAA, LSU, SO and denote them as UAA_TOT, LSU_TOT and SO_TOT respectively.
Calculate SO_XR_PPP= SO *XR/PPP, where XR is exchange rate and PPP is purchase power parity for a specific country.
Farm is considered as small-scale producer if one of the following criteria is fulfilled:
Data on Utilised Agriculture Area fall in the first two quintiles of the country distribution (CUM_UAA/UAA_TOT<=0.4)
Data on Livestock Unit fall in the first two quintiles of the country distribution (CUM_LSU/LSU_TOT<=0.4)
Data on Standard Output fall in the first two quintiles of the country distribution (CUM_SO/SO_TOT<=0.4)
An additional absolute cap is applied, to exclude producers earning a revenue higher than 34 387 Purchasing Power Parity Dollars per year (SO_PPP <=34387)
LARGE - Large farms. Agriculture holdings which are not classified to the group of small-scale producers are considered as large farms.
Sex of manager. Determined on the basis of the reported variable.
Male
Female
3.3. Coverage - sector
Indicators cover agricultural holdings undertaking agricultural activities as listed in item 3.5 below and meeting the minimum coverage requirements (thresholds) as listed in item 3.6 below.
3.4. Statistical concepts and definitions
As described in the METHODOLOGY FOR COMPUTING AND MONITORING THE SUSTAINABLE DEVELOPMENT GOAL INDICATORS 2.3.1 AND 2.3.2, productivity measures the amount of output produced by an economic unit (country, industry, sector, farm or other economic operators) given a set of resources and inputs. Productivity can be measured for a single economic entity, such as the farm or commodity, a group of farms, at any geographical scale depending on the purpose of the inquiry.
For 2020, only “main frame data” are used for the calculation of the indicators. Main frame data consist of agricultural holdings that meet at least one of the physical thresholds set in Annex II of Regulation (EU) 2018/1091 with regard to the size of agricultural land or the number of livestock units.
For years before 2020, the physical thresholds from Regulation (EU) 2018/1091 were applied to FSS survey data in order to define the statistical population.
Common land agricultural units were excluded from the statistical population for calculation of productivity indicators.
3.7. Reference area
Data are presented for all EU Member States.
3.8. Coverage - Time
Productivity indicators were calculated from year 2010 on, so they cover the reference years, for which FSS/IFS surveys were carried out: 2010, 2013, 2016, 2020.
3.9. Base period
Not applicable.
Index
The reference periods for the variables that are included in the calculation of the productivity indicators are presented below.
For 2020 the reference periods are:
Land variables: The use of land refers to the reference year 2019/2020. In the case of successive crops from the same piece of land, the land use refers to a crop that is harvested during the reference year, regardless of when the crop in question is sown.
Livestock variables: A reference day within the reference year 2019/2020. That day varies across countries.
Variables on labour force: The 12-month period ending on a day within the reference year 2019/2020. That period varies across countries.
Productivity indicators are ratios of two sums. In the statistical disclosure control procedure, sums in numerator and denominator are treated separately first.
To see how the confidentiality of sums (totals) is treated in Farm structure surveys, see general Eurostat metadata for Farm structure: Farm structure (ef) (europa.eu).
Indicator is considered confidential if the sum in the numerator or the sum in the denominator is determined to be confidential.
A confidential value is suppressed and replaced with ":c".
8.1. Release calendar
As for the other IFS and FSS statistics, there is no fixed release deadline. Only transmission deadlines are set in legislation.
In line with the Community legal framework and the European Statistics Code of Practice, Eurostat disseminates European statistics on Eurostat's website (see principle 10 - 'Accessibility and clarity') respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. The detailed arrangements are governed by the Eurostat protocol on impartial access to Eurostat data for users
The indicators are being disseminated for the first time and cover results for the years 2010, 2013, 2016, and 2020. In the future, the indicators will be disseminated together with other tables for the future implementations of IFS survey.
10.1. Dissemination format - News release
Not applicable.
10.2. Dissemination format - Publications
Only disseminated in online database (see 10.3).
10.3. Dissemination format - online database
Productivity indicators are disseminated at website of Eurostat. They are published under nodes: “Agriculture, forestry and fisheries” →”Agriculture (agr)” → "Farm structure(ef)" → “Main farm indicators by NUTS2 regions (ef_mainfarm)” → “Land and Labour productivity of agriculture holdings by sex of farm manager and type and size of the farm (ef_m_pry).
10.3.1. Data tables - consultations
The ranking and trends of the most consulted tables from 2018 to 2022 are available in this website (select domain "FSS").
Quality of the indicators is assured by systematic data validation and statistical process control, which is carried out first on national level (see the National quality reports) and also at Eurostat (see section 18.4).
11.2. Quality management - assessment
Data, which are used for calculation of productivity indicators), are collected from reliable sources applying high standards regarding the methodology and ensuring a high degree of comparability.
The national methodological reports (quality reports), sent by the countries for each survey, comprise information on each of the quality aspects defined by Eurostat.
12.1. Relevance - User Needs
Originally requested by FAO, with the publication in the online database, these data are available to the general public.
12.2. Relevance - User Satisfaction
There is no information on user satisfaction for this specific data.
12.3. Completeness
Productivity indicators are disseminated for EU member states. The indicators are not disseminated for Switzerland, Iceland, and Norway because labor force data for these countries are not available for all the disseminated years.
12.3.1. Data completeness - rate
Not applicable.
13.1. Accuracy - overall
Based on the estimated standard errors, the accuracy of the indicators is generally very high. There are some table cells, mostly those with a small number of contributing units, where, according to the standard error, the estimated indicator is either unreliable (the value is suppressed) or less reliable (the value is flagged with a 'w' warning).
13.2. Sampling error
13.2.1. Sampling error - indicators
Standard errors for all indicators are disseminated together with the indicators themselves.
13.3. Non-sampling error
See the sub-categories below.
13.3.1. Coverage error
The national quality reports provide reasons and treatments on coverage errors in IFS/FSS surveys.
13.3.1.1. Over-coverage - rate
The national quality reports provide over-coverage rates in IFS/FSS surveys.
13.3.1.2. Common units - proportion
Not applicable.
13.3.2. Measurement error
The national quality reports provide reasons and treatments on measurement errors in IFS/FSS surveys.
13.3.3. Non response error
The national quality reports provide reasons and treatments on non-response errors in IFS/FSS surveys.
13.3.3.1. Unit non-response - rate
The national quality reports provide unit non-response rates in IFS/FSS surveys.
13.3.3.2. Item non-response - rate
The national quality reports provide information on item non-response rates. In most of the cases, countries reported that the item non-response doesn’t exist or is very low. Concerning items, which are included in the calculation of productivity indicators, only Sweden reported that in the farm labour force section, about 72 % of the respondents had values that were imputed. However, only about 13 % had missing values for all characteristics in the labour force section.
13.3.4. Processing error
The national quality reports provide reasons and treatments on processing errors in IFS/FSS surveys.
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
See the sub-categories below.
14.1.1. Time lag - first result
3 years and 9 months from the end of the reference year 2020.
14.1.2. Time lag - final result
First results are also final results.
14.2. Punctuality
Not applicable.
14.2.1. Punctuality - delivery and publication
Not applicable.
15.1. Comparability - geographical
By limiting the calculation of the indicators only on main frame data, geographical comparability is conceptually ensured. Issues with geographical comparability can now arise only from differences in the implementation of surveys in different countries.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
See the sub-categories below.
15.2.1. Length of comparable time series
Indicators are disseminated for years 2010, 2013, 2016 and 2020.
15.3. Coherence - cross domain
Not available.
15.3.1. Coherence - sub annual and annual statistics
According to the IFS data collection organization, data from a core data set and labour force module are used for the calculation of the indicators. Additionally, the following external sources are used:
Exchange rates in euro were taken from this website (STATINFO = Average UNIT = National currency)
Working days per year, collected from the national quality reports
18.2. Frequency of data collection
The agricultural census is conducted every 10 years. The decennial agricultural census is complemented by sample or census-based data collections organised every 3-4 years in-between.
18.3. Data collection
The methods used to collect the data vary in countries depending on national practices.
18.4. Data validation
For the totals in enumerator and denominator we checked:
internal consistency between fields (validation rules)
consistency with aggregates in other EF tables
applicability of precision requirements
data distribution, with the focus on outlying values
For the ratio (index) we checked consistency of time series.
18.5. Data compilation
The aggregation was carried out by taking into account formulas, provided in section 3.4. It has to be noted that for in the case of Labour productivity, different weights were used for calculation of sums in numerator and denominator. In numerator the weight from core module (EXT_CORE) was used, while in denominator weight from labour force module (EXT_LAFO).
18.5.1. Imputation - rate
Information on the imputation procedures and imputation rate on country level is available in the national quality reports.
Besides the imputations, carried out at national level, Eurostat carried out imputation of one of the components of the “Annual Working Unit (AWU) variable” in 2016 dataset. Namely, variable E_1_6_AWU (AWU of farm work during the 12 months preceding the day of the survey, not included under previous categories, undertaken on the holding by persons not employed directly by the holding, e.g. contractors' employees) was not collected in 2016. Since it is included in the variable TOTAL_AWU, this caused comparability problems for the labour productivity indicator. To assure better comparability through time, Eurostat imputed this variable in its own processing. Imputed values were estimated as follows: In year 2013, we estimated the share of E_1_6_AWU in TOTAL_AWU for each cell determined by REGIONS, FARMTYPE, FARMSIZE. Then we took these shares for respective cells, joined them to 2016 data and estimated the E_1_6_AWU for each record respectively. The main assumption was that the share of E_1_6_AWU at the level of REGIONS*FARMTYPE*FARMSIZE doesn’t change significantly from 2013 to 2016.
Share of imputed values in the whole AWU variable by countries is presented in the below table:
Labour productivity indicators by country, type of farm, size of farm and sex of manager
Land productivity indicators by country, type of farm, size of farm and sex of manager
30 September 2024
As described in the METHODOLOGY FOR COMPUTING AND MONITORING THE SUSTAINABLE DEVELOPMENT GOAL INDICATORS 2.3.1 AND 2.3.2, productivity measures the amount of output produced by an economic unit (country, industry, sector, farm or other economic operators) given a set of resources and inputs. Productivity can be measured for a single economic entity, such as the farm or commodity, a group of farms, at any geographical scale depending on the purpose of the inquiry.
For 2020, only “main frame data” are used for the calculation of the indicators. Main frame data consist of agricultural holdings that meet at least one of the physical thresholds set in Annex II of Regulation (EU) 2018/1091 with regard to the size of agricultural land or the number of livestock units.
For years before 2020, the physical thresholds from Regulation (EU) 2018/1091 were applied to FSS survey data in order to define the statistical population.
Common land agricultural units were excluded from the statistical population for calculation of productivity indicators.
Data are presented for all EU Member States.
The reference periods for the variables that are included in the calculation of the productivity indicators are presented below.
For 2020 the reference periods are:
Land variables: The use of land refers to the reference year 2019/2020. In the case of successive crops from the same piece of land, the land use refers to a crop that is harvested during the reference year, regardless of when the crop in question is sown.
Livestock variables: A reference day within the reference year 2019/2020. That day varies across countries.
Variables on labour force: The 12-month period ending on a day within the reference year 2019/2020. That period varies across countries.
Based on the estimated standard errors, the accuracy of the indicators is generally very high. There are some table cells, mostly those with a small number of contributing units, where, according to the standard error, the estimated indicator is either unreliable (the value is suppressed) or less reliable (the value is flagged with a 'w' warning).
Index
The aggregation was carried out by taking into account formulas, provided in section 3.4. It has to be noted that for in the case of Labour productivity, different weights were used for calculation of sums in numerator and denominator. In numerator the weight from core module (EXT_CORE) was used, while in denominator weight from labour force module (EXT_LAFO).
According to the IFS data collection organization, data from a core data set and labour force module are used for the calculation of the indicators. Additionally, the following external sources are used:
Exchange rates in euro were taken from this website (STATINFO = Average UNIT = National currency)
Working days per year, collected from the national quality reports
The indicators are being disseminated for the first time and cover results for the years 2010, 2013, 2016, and 2020. In the future, the indicators will be disseminated together with other tables for the future implementations of IFS survey.
See the sub-categories below.
By limiting the calculation of the indicators only on main frame data, geographical comparability is conceptually ensured. Issues with geographical comparability can now arise only from differences in the implementation of surveys in different countries.
