Farm structure (ef)

National Reference Metadata in ESS Standard for Quality Reports Structure (ESQRS)

Compiling agency: Statistical Office of the Republic of Slovenia

Time Dimension: 2016-A0

Data Provider: SI1

Data Flow: FSS_ESQRS_A


Eurostat metadata
Reference metadata
1. Contact
2. Statistical presentation
3. Statistical processing
4. Quality management
5. Relevance
6. Accuracy and reliability
7. Timeliness and punctuality
8. Coherence and comparability
9. Accessibility and clarity
10. Cost and Burden
11. Confidentiality
12. Comment
Related Metadata
Annexes (including footnotes)
 



For any question on data and metadata, please contact: EUROPEAN STATISTICAL DATA SUPPORT

Download


1. Contact Top
1.1. Contact organisation
Statistical Office of the Republic of Slovenia
1.2. Contact organisation unit
Department for Agriculture, Forestry, Fishery and Hunting
1.5. Contact mail address

Litostrojska 54

1000 Ljubljana

Slovenia


2. Statistical presentation Top
2.1. Data description
1. Brief history of the national survey 

In Slovenia only two independent censuses of agricultural holdings, or farm structure surveys, were conducted before the year 2000 – the first one in 1930 and the second one in 1960. However, due to political and economic changes in this period, it is difficult to compare them. In 1969 a sample census of agricultural holdings was conducted, and in 1971, 1981 and 1991 censuses of agricultural holdings were conducted within population censuses. In 1997 the first Farm Structure Survey, harmonized with EU legislation, was carried out. 

After the Agricultural Census 2000 we followed the EC program of Farm Structure Surveys (FSS) regarding the list of characteristics as well as the timetable. We conducted sample FSS surveys in 2003, 2005 and 2007. In 2010 second agricultural census was conducted in Slovenia.

Based on the AC data from 2000, the Statistical Farm Register (SFR) was established in Slovenia in 2004. The SFR was later on updated with every statistical survey that was conducted in Slovenia and with all reliable administrative sources of data.

 

2. Legal framework of the national survey 
- the national legal framework

The legal bases for conducting the FSS are two acts:

  • National Statistics Act (OJ RS No. 45/95 and No. 9/01);
  • National Programme of Statistical Surveys (OJ RS No 89/15 and 32/16), which deals with all statistical surveys and work to be done in 2016.

 The National Statistics act defines the following fundamental principles:

  • Professional and institutional independence;
  • Statistical confidentiality;
  • Availability, accessibility and clarity of information;
  • International comparability;
  • Transparency of methodology;
  • Rational use of resources;
  • Access to administrative data sources.
- the obligations of the respondents with respect to the survey Legal entities are obliged to give response. Family farms are not legaly obliged to give response.
- the identification, protection and obligations of survey enumerators In 2016 we conducted computer assisted telephone interview; all operators signed the confidentiality statement.
2.2. Classification system

The following groups of questions were included in the questionnaire for FSS 2016:

  • Chapter A: Address of the holding – questions enable us to update the address of the agricultural holding in the Statistical Register of Agricultural Holdings;
  • Chapter B: Number of livestock;
  • Chapter C: Land section, irrigation, manure treatment;
  • Chapter D: Labour force on family farms, supplementary activities;
  • Chapter E: Labour force in agricultural enterprises;
  • Chapter H: Destination of the holdings production and economical importance of other gainful activities.
2.3. Coverage - sector

Agricultural sector.

2.4. Statistical concepts and definitions
List of abbreviations
  • SURS: Statistical Office of the Republic of Slovenia
  • FSS: Farm Structure Survey
  • LSU: Livestock unit
  • MAFF: Ministry of Agriculture, Forestry and Food
  • SFR: Statistical Farm Register
2.5. Statistical unit
The national definition of the agricultural holding

National definition of the holding is as according to the EU definition.
An agricultural holding is a single unit, both technically and economically, which has a single management and which undertakes agricultural activities within the economic territory of the European Union, either as its primary or secondary activity.

Also agricultural holdings, maintaining land in good agricultural and environmental conditions are included.

 Agricultural production includes:

- crop production:

  • production of cereals, other arable crops and grassland,
  • production of vegetables, ornamental plants, seeds and seedlings,
  • wine and fruit growing,
  • mushroom production;
- livestock breeding:
  • cattle,
  • pigs,
  • poultry,
  • sheep,
  • horses,
  • beekeeping,
  • breeding of other animals for human consumption.

 Agricultural production does not include:

  • processing of agricultural products produced on agricultural holdings or agricultural products bought,
  • agriculture services,
  • forestry,
  • fish farming and fishery,
  • raising horses for recreation, if all fodder is bought.
2.6. Statistical population
1. The number of holdings forming the entire universe of agricultural holdings in the country
The threshold in Slovenia is quite low, in order to meet the legislation criteria. It is hard to assess the total number of agricultural holdings, since there are numerous households with very small areas of "arable land" - "kitchen gardens", and also some who have a very small number of animals (poultry, rabbits).

 

2. The national survey coverage: the thresholds applied in the national survey and the geographical coverage
The survey covers holdings with:
  • at least one hectare of utilised agricultural area (A_3_1), or
  • less than one hectare of utilised agricultural area (A_3_1), but:
  • at least 0.1 hectare of utilised agricultural area (A_3_1) and 0.9 hectare of forest (B_5_2), or
  • at least 0.3 hectares of vineyards (B_4_4) and/or orchards (B_4_1), or
  • 2 or more livestock units (LSU), or
  • 0.15 to 0.3 hectare of vineyards (B_4_4) and/or orchards (B_4_1) and 1 or 2 LSU, or
  • more than 50 beehives (C_7), or
  • are market producers of vegetables, herbs, strawberries, mushrooms, flowers or ornamental plants.

 All the statistics of agriculture correspond to these thresholds and they are consistent with Articles 2 and 3 of Regulation (EC) 1166/2008.

 

3. The number of holdings in the national survey coverage 
The population in the statistical farm register, before the FSS was conducted, was around 76 000 agricultural holdings. The number of units in the extrapolated population in FSS 2016 is 69 902.

 

4. The survey coverage of the records sent to Eurostat
The survey coverage of the records sent to Eurostat is the same as the national survey coverage.

 

5. The number of holdings in the population covered by the records transferred to Eurostat
The number of the holdings sent to Eurostat is the same as the number of records that were gathered on the field (9 891). The number of units in the extrapolated population in FSS is 69 902.

 

6. Holdings with standard output equal to zero included in the records sent to Eurostat

In 2016 there were three agricultural holdings with only fallow land and/or only kitchen gardens and/or only crops and animals for which standard output coefficients are not defined.

Those agricultural holdings are in the target population, since we consider them to have agricultural production and are likely to change crops in the future (to have production for sale).

 

7. Proofs that the requirements stipulated in art. 3.2 the Regulation 1166/2008 are met in the data transmitted to Eurostat
As the survey uses a threshold of UAA of 1 ha,  art 3.2 is not applicable.

 

8. Proofs that the requirements stipulated in art. 3.3 the Regulation 1166/2008 are met in the data transmitted to Eurostat

National thresholds are lower than the EU threshold - so we meet the criteria.

In some specific cases, Slovenian threshold criteria would not meet EU criteria. The case would be when holding would have nothing else but:

      - 50 piglets - no such holdings

      - 0.5 ha of hops - no such holdings

2.7. Reference area
Location of the holding. The criteria used to determine the NUTS3 region of the holding

The NUTS3 region was primarly determined as where the building for livestock or other production is situated.

If there was no agricultural building for livestock, or other available address, then the residence of the farmer (if it is not further than 5 km from the farm) was taken.

2.8. Coverage - Time
Reference periods/dates of all main groups of characteristics (both included in the EU Regulation 1166/2008 and surveyed only for national purposes)

The reference day of the Farm structure survey was 1 June 2016.

For livestock characteristics the reference date was 1 June 2016.

For land characteristics, a period of 12 months ending on the reference day (1 June 2016) was taken.

For data on labor force characteristics, a period of 12 months ending on the reference day (1 June 2015 – 31 May 2016) was taken.

For data on rural development measures, a period of three years ending on the reference day (1 June 2013 – 31 May 2016) was taken.

2.9. Base period

[Not requested]


3. Statistical processing Top
1.Survey process and timetable
An approximate work plan done for FSS is in the attached file. Preparations for the FSS 2016 started in first half of 2015 and stopped with the end of grant agreement (30.6.2018). The 2016 farm structure survey met its purpose; farmers were mostly prepared for the survey. 

The purpose of the FSS was to monitor and to show structural changes in agriculture in the last 3 years, i.e. since the previous FSS was in 2013.

 

2. The bodies involved and the share of responsibilities among bodies
SURS was the responsible body for conducting the FSS 2016.

SURS was also responsible for promotion of the FSS 2016 and is responsible for dissemination of the results.

We had CATI survey (computer assisted telephone interview) in combination with administrative data sources. Due to procedural restrictions, an external contractor was hired just to provide and organise the surveyors for phone interview.

Telephone interview started on 1st of June 2016 and ended at the end of July. 

For sampling, data verification, imputations and estimation of sampling errors, SAS program was used.

 

3. Serious deviations from the established timetable (if any)
No such deviations.


Annexes:
3-1. Timetable FSS 2016
3.1. Source data
1. Source of data
The FSS 2016 was a stratified random sample survey, combined with administrative sources.

 

2. (Sampling) frame
The list of agricultural holdings was fully obtained from the Statistical Farm Register (SFR).

The Statistical Farm Register was established after the AC 2000 in order to have a stable sampling frame for all agricultural surveys. It has been operational since 2004.

After the 2010 census we managed to get a very clear list of agricultural holdings in Slovenia. Most of the duplicates were removed, and the connection with administrative data is now almost full (approximately 95% data can be directly linked with administrative sources). Because of the good connection with administrative sources, updating with national surveys and continuous checking of the summands, we believe the SFR is in very good shape for conducting samples.

The register is updated twice a year (February/September), which enables us to have an updated sampling frame for the surveys in June and December. Results of statistical surveys as well as IACS data are used for updating the register.

All the addresses of the holdings were updated using the Register of Territorial Units.

SURS put a lot of effort into using all available statistical and administrative sources for updating the SFR. We minimise errors for agricultural holdings applying for supports by using data from the IACS. All new farms from administrative sources are added just before any survey starts, so we are up to date.

The sampling frame is a list frame.

 

3. Sampling design
3.1 The sampling design

Single-stage stratified systematic random sample of holdings, which is probability sample.

Stratification variables:

- NUTS 2 regions;

- size classes defined in relation to certain values of a set of preselected variables.

 

Exhaustive strata are:

- all agricultural enterprises;

- all holdings from the first stratum i.e. which have at least one of a set of preselected variables greater than certain value.

3.2 The stratification variables
All agricultural enterprises are included in the sample survey, so they are in separate strata where weights are set as 1.

Stratification criteria is done on NUTS2 level on the 4 size classes.

Stratification variables Stratum 1 Stratum 2 Stratum 3 Stratum 4
Utilised agricultural area (ares) 3000 750 400 0
Arable land (ares) 1300 300 150 0
Cereals (ares) 850 200 100 0
Oil seed (ares) 200 70 40 0
Extensive orchards (number of trees) 250 100 40 0
Grassland (ares) 1900 800 350 0
Cattle (number) 50 20 7 1
Pigs (number) 65 30 15 1
Breeding sheep (number) 50 20 10 1
Breeding goats (number) 40 20 10 1
Poultry (number) 500 100 60 1
Equidae (number) 20 10 5 1
Deer (number) 35 10 5 1
Beehives (number) 70 30 15 1
Intensive poultry breeder  'Yes'      
Market Gardner 'Yes'      
Market area (m2) 10000 1000 100 0

If at least one of the value of the variable meets the criteria, then the agricultural holding is placed into the suitable stratum.

The size classes limits are taken as "greater or equal". Example: if Utilised agricultural area (ares)= 30 ha then Stratum=1.

3.3 The full coverage strata
First stratum was covered with complete enumeration.

All agricultural enterprises are included in the sample survey, so they are in separate strata where weights are set as 1.

3.4 The method for the determination of the overall sample size
The sample size was decided regarding the precision table as set down in Annex IV of the Regulation (EC) No 1166/2008 and on the basis of Statistical farm register data. We used optimal allocation, as average of few key variables, on the NUTS2 * size class level. The sample size was 15 592.
3.5 The method for the allocation of the overall sample size
For the Neymann allocation we computed standard deviation and optimal allocation for some key variables: (Utilised agricultural area, Arable land, Cereals, Oil seed, Grassland, Cattle, Pigs, Sheep, Goats, Poultry, Equidae). From those different optimal allocations we calculated average optimal sample size per strata. Agricultural holdings from the biggest size class were all included in the sample; optimal allocation was used only in the remaining three size classes.
3.6 Sampling across time
A new sample was drawn.
3.7 The software tool used in the sample selection
SAS procedure PROC SURVEYSELECT.
3.8 Other relevant information, if any
Not available.

 

4. Use of administrative data sources
4.1 Name, time reference and updating
We used administrative data sources in the Farm Structure Survey according to Article 4 of Regulation (EC) No. 1166/2008 of the European Parliament and of the Council of 19 November 2008 on farm structure surveys and the survey on agricultural production methods and repealing Council Regulation (EEC) No. 571/88.

1. System for the Identification and Registration of Bovine Animals (Register of Bovine Animals)

  • It is regulated with Regulation “OJ. RS, No. 16/2003”, legislation “OJ. RS, No. 45/2008” and “OJ. RS, No. 18/2002”.
  • The register is updated all the time, when changes are reported.

2. Organic farming register

  • It is regulated with Regulation Agricultural law (OJ RS, nb. 54/2000 , 16/2004, 45/2004-ZdZPKG, 20/2006, 51/2006-UPB1, 45/2008-ZKme-1).
  • Rules on organic production and processing of agricultural products and food stuffs are set in Official Gazette of RS no. 8/2014.
  • The register is updated when the holding is visited by the control organization.

3. Rural Development Measures

Regulation (EU) No 1310/2013 of the European Parliament and of the Council of 17 December 2013 laying down certain transitional provisions on support for rural development by the European Agricultural Fund for Rural Development (EAFRD), amending Regulation (EU) No 1305/2013 of the European Parliament and of the Council as regards resources and their distribution in respect of the year 2014 and amending Council Regulation (EC) No 73/2009 and Regulations (EU) No 1307/2013, (EU) No 1306/2013 and (EU) No 1308/2013 of the European Parliament and of the Council as regards their application in the year 2014
  • The register included agricultural holdings that got the approved support for rural development.
  • Administrative data were used instead of the survey and were put directly into the database.

4. Information from the Integrated Administration and Control System (IACS)

  • It is regulated with Regulation “(EC) No 1782/2003” and legislation “OJ. RS, No. 45/2008”.
  • For the purpose of control of subsidies applications (and registers), Slovenia had to introduce graphical control of subsidies applications for areas. Since the Land Cadaster is not updated, Slovenia introduced in 2005 a new system of land use called GERK (graphical units of land use) – GERK refers to so called “farm’s block” in IACS legislation. All areas in registers are based on the GERK system.

5. Register of beehives

  • It is regulated with the Regulation “OJ. RS, No. 117/2008”, legislation “OJ. RS, No. 45/2008” and “OJ. RS, No. 18/2002”.
  • Definition: Number of hives occupied by bees (Apis mellifera) kept for the production of honey.
  • In charge of the register is the Ministry of Agriculture and Environment.
  • The data in register is filled twice a year, on 30 October and on 15 April.
  • SURS already used data from register of beehives for the Farm structure survey in 2007, agricultural census 2010 and Farm structure survey 2013.

6. Production record of agricultural seeds and propagating material

  • It is regulated with the regulations:  Zakon o zdravstvenem varstvu rastlin (“OJ. RS, No. 40/2014”) and Zakon o semenskem materialu kmetijskih rastlin (“OJ. RS, No. 90/2012”).
  • In charge of the register is the Ministry of Agriculture and Environment.
  • The data is reported for the seeds and propagating material planted on agricultural holding.
  • The register is used only to check the data gathered from the field and perhaps for possible imputations.

7. Central Population Register

  • It is regulated with the regulatios: Zakon o centralnem registru prebivalstva (“OJ. RS, No. 72/2006”) and Zakon o prijavi prebivališča (“OJ. RS, No. 9/2001”).
  • In charge of the register is the Ministry of the Interior.
  • The data in register is filled on daily basis.
  • In register there are data on each person, living on the holding (number of persons on the holding, age and sex).
  • The register is used only to check the data gathered from the field and perhaps for possible imputations.
  • SURS already used data from central population register for Population Census 2011.

8. Register on equidae

  • It is regulated with regulations: COMMISSION REGULATION (EC) No 504/2008 of 6 June 2008 implementing Council Directives 90/426/EEC and 90/427/EEC as regards methods for the identification of equidae.
  • In charge of the register is the Ministry of Agriculture and Environment.
  • The register is used to check the data gathered from the field and perhaps for possible imputations.
  • The data in register is filled when a change occurs in the field.
4.2 Organisational setting on the use of administrative sources
SURS has a signed agreement with administrative data providers to access the data when needed.

In most cases SURS is notified before changes in administrative data occur and has the possibility to comment the revisions.

4.3 The purpose of the use of administrative sources - link to the file
Please find the information in the file at the link: (link available as soon as possible)

 

4.4 Quality assessment of the administrative sources
  Method  Shortcoming detected Measure taken
- coherence of the reporting unit (holding) For the registers, the reporting unit is the agricultural holding with the same definition as in the FSS.  /  /
- coherence of definitions of characteristics

Characteristics have the same definition.

 /  /
- coverage:      
  over-coverage All registers have agricultural holdings that are not included in sample frame of FSS, because the FSS designed threshold is used. For all administrative registers the information is used only for farms in the survey. So over coverage cannot
play a role.
 /
  under-coverage All registers with exception of "Register of Equidae" and informations from IACS are complete. Register on equidae

The data in register is probably still incomplete since it exists only for a few years; it will take some time to become the only source for the number of equidae.

IACS

There are still some agricultural holdings, that do not apply for any subsidies, or have some other purpose to register.

 /
  misclassification  Not detected.  /  /
  multiple listings  Not possible.  /  /
- missing data  Some data are missing.  Bovine register: the division of cows on dairy cows and other cows is not included in the register.

IACS:

the division of vegetables on open field and market gardening is missing.

the division of Permanent grassland and meadow on"pasture and meadow" and "rough grazing" is missing.

There is no information on wooded area and other land.

The data not available in register are collected with CATI.
- errors in data  Not detected.  /  /
- processing errors  Not detected.  /  /
- comparability Before the administrative data were fully integrated in FSS, they were checked with field survey (2005, 2007). The data was comparable with field work survey.  /
- other (if any) Production record of agricultural seeds and propagating material and Register on equidae

Since the data serves to check the collected data and only possible imputations, we will be able to estimate accuracy in the next survey.

 /  /

 

4.5 Management of metadata
The description of the variables in administrative source can be found in Rules of each administrative source (see point 3.1.-4.1.) The metadata of administrative sources are not systematically stored and maintained over time in SURS.
4.6 Reporting units and matching procedures
For the registers, the reporting unit is the agricultural holding with the same definition as in the FSS.

Key for data linkage was ID of agricultural holding established by the Ministry of Agriculture, Forestry and Food (MAFF). Each agricultural holding in the Statistical Register of Agricultural Holdings has also ID number of the MAFF.

For all registers, there were a few mismatching cases - those under the thresholds or those that were established in the period from updating the statistical farm register till survey date (cca. one month time).In addition, for Rural Development Measures, additional mismatches come from the fact that some holdings probably changed the ID number in the period of three years or they are not eligible any more.

Register of beehives

Records are matched by a unique identification number, used by “Ministry of Agriculture and the Environment”, which is also included in Statistical Register of agricultural holdings.

Production record of agricultural seeds and propagating material

Records are matched by a unique farm number, which is used also in administrative register of farms (is linked with Statistical Register of agricultural holdings).

Central Population Register

Records are matched by a unique identification number (household number), which is used also in administrative Register of farms (is linked with Statistical Register of agricultural holdings).

Register on equidae

Records are matched by a unique farm number, which is used also in administrative Register of farms (is linked with Statistical Register of agricultural holdings).

4.7 Difficulties using additional administrative sources not currently used
Not detected.
3.2. Frequency of data collection
Frequency of data collection
The frequeny of data collection is determined with the Regulation (EC) 1166/2008 of the European Parliament and of the Council on farm structure surveys and the survey on agricultural production methods and repealing Council Regulation (EEC) 571/88.
3.3. Data collection
1. Data collection modes
The data collection is carried out through the telephone interviews, supported by the CATI technology. Agricultural enterprises were asked to give response through postal survey (292 units).

 

2. Data entry modes
Data was entered directly to computer database with the help of data entry program.

 

3. Measures taken to increase response rates
Informing the farmers

We took care that the list of farmers (names, etc.) was updated. Key units (large holdings) were called back more times than "normal size farms".

The farmers were informed about the FSS by a letter of notification sent to all family farms and agricultural enterprises with the basic information on the FSS:

  • what is the AC and what is the purpose,
  • when it will be carried out,
  • who is responsible,
  • which data will be collected,
  • information about the protection of collected data and
  • information about the legislation on which FSS is based.

Training staff in handling difficult respondents

Training of interviewers was carried out by SURS. Trainings pointed out methodological issues as well as good practices concerning handling difficult respondents and data protection.

SURS controlled the work done by interviewers

Since the data was inserted directly into database, several controls were made on daily bases, to disclose possible "bad interviewers".

 

4. Monitoring of response and non-response
1 Number of holdings in the survey frame plus possible (new) holdings added afterwards

In case of a census 1=3+4+5

76606
2 Number of holdings in the gross sample plus possible (new) holdings added to the sample

Only for sample survey, in which case 2=3+4+5

  15592
3 Number of ineligible holdings   423
3.1 Number of ineligible holdings with ceased activities

This item is a subset of 3.

  400
4 Number of holdings with unknown eligibility status

4>4.1+4.2

 0
4.1 Number of holdings with unknown eligibility status – re-weighted 0
4.2 Number of holdings with unknown eligibility status – imputed 0
5 Number of eligible holdings

5=5.1+5.2

15169
5.1 Number of eligible non-responding holdings

5.1>=5.1.1+5.1.2

5336
5.1.1 Number of eligible non-responding holdings – re-weighted 5278
5.1.2 Number of eligible non-responding holdings – imputed 58
5.2 Number of eligible responding holdings 9833
6 Number of the records in the dataset 

6=5.2+5.1.2+4.2

9891

 

5. Questionnaire(s) - in annex
The following groups of questions were included in the questionnaire:
  • Chapter A: Address of the holding – questions enable us to update the address of the agricultural holding in the Statistical Register of Agricultural Holdings;
  • Chapter B: Number of livestock;
  • Chapter C: Land section, irrigation, manure treatment;
  • Chapter D: Labour force on family farms, supplementary activities;
  • Chapter E: Labour force in agricultural enterprises;
  • Chapter H: Destination of the holdings production and economical importance of other gainful activities.
The list of characteristics follows the EC program of Farm Structure Surveys as well as national needs.


Annexes:
3.3-5.Questionnaire for family farms
3.3-5.Questionnaire for agricultural enterprises
3.4. Data validation
Data validation
Since this FSS was conducted as computer assisted telephone interviewing (CATI) in combination with administrative data sources, we had a chance to put the validation rules directly into the data entry program. When data was inserted, and something would be written wrong, the program would alert the interviewer to check the consistency again.

For controlling of the data, we used numerous administrative data and the previous FSS.

The data was checked on many ways: completeness checks, skip checks, outlier checks, relational checks, etc.

The data were checked also with Eurostat’s validation rules.

Some controls were added in the data entry program and also SAS program was used for data validation.

Data validation is done on the level of individual unit as well as on the level of the interviewer.

3.5. Data compilation
Methodology for determination of weights (extrapolation factors)
1. Design weights
Design weights are defined as the inverse of the units’ selection probabilities.
2. Adjustment of weights for non-response
We consider sampling strata as most homogeneous groups and we used them also by calculating non-response weights, which we calculate as: (response + nonresponse) / response .

Final weights are product of selection weight and non-response weight at the level of strata (2 NUTS2 regions combined with 4 size classes).

3. Adjustment of weights to external data sources
At the end of weighting process data was adjusted with calibration to the number of cattle from administrative source, using four size classes, on the level of NUTS2 regions.
4. Any other applied adjustment of weights
No.
3.6. Adjustment

[Not requested]


4. Quality management Top
4.1. Quality assurance

In SURS we use a special program, when editing the data (SOP - Statistical data processing). The processes are efficient, repeatable, and the processes assure traceability (metadata is stored in database).

4.2. Quality management - assessment

The data are in line with article 5 of the Regulation (EC) No 1166/2008.


5. Relevance Top
5.1. Relevance - User Needs
Main groups of characteristics surveyed only for national purposes 
National needs are discussed with main users represented in the Agricultural, Forestry and Fishery Statistics Committee, which is an advisory body of SURS.

Some of the characteristics were added to the questionnaire for national purposes only:

  • some categories of livestock and crops are more detailed than needed in Eurofarm dataset;
  • number of vines in vineyards – needed for calculation of production;
  • cutting timber on family farms was implemented in FSS due to national needs.
5.2. Relevance - User Satisfaction

Researchers can gain access to micro-data in "Eurofarm dataset" (without precise location of individual agricultural holding). All items, collected in FSS are discussed and agreed with the consultation of main users (ministry, research institutions, advisory institutions for agriculture, etc.)

5.3. Completeness
Non-existent (NE) and non-significant (NS) characteristics - link to the file. Characteristics possibly not collected for other reasons
Please find the information on NE and NS characteristics in the file at the link: (link available as soon as possible)

Hence the definition of Energy crops: “the production area of energy crops benefiting from the following support schemes under Council Regulation (EC) No 1782/2003” and since the CAP health check (Council Regulation (EC) No 73/2009) the area payment supports have been dropped from 2010, Energy crops were voluntary to collect in each country. We decided that data on “2.06.03 Energy crops” and “2.06.03.01 Energy crops on set-aside area” will not be collected. The characteristics are filled with zeros ("0"), because by the definition there were no such areas that benefited from schemes under Council Regulation (EC) No 1782/2003.

5.3.1. Data completeness - rate

There were 91% of all variables delivered to Eurostat. Not delivered were only the "NE - Non-existent" and "NS2 - Non-significant".


6. Accuracy and reliability Top
6.1. Accuracy - overall
Main sources of error
Main sources of error are over-coverage, non-response and measurement errors.
6.2. Sampling error
Method used for estimation of relative standard errors (RSEs)
The method for estimation of RSEs was SAS PROC SURVEYMEANS procedure. We calculated standard errors and coefficients of variation, by using general SAS programs that are used in most of the SURS surveys.
6.2.1. Sampling error - indicators

1. Relative standard errors (RSEs) - in annex

  

2. Reasons for possible cases where precision requirements are applicable and estimated RSEs are above the thresholds
The coefficients of variation are provided in annex.

We meet all precision requirenments stipulated in Annex IV "Precision Requirements" of the Regulation 1166/2008.



Annexes:
6.2.1-1.Sampling error
6.3. Non-sampling error

See below

6.3.1. Coverage error
1. Under-coverage errors
The probability of under-coverage in the FSS is very low since there are not many new agricultural holdings. All important new farms are included in administrative registers and were consequently included into the list. All new agricultural holdings from administrative sources were added just before the survey started.

  

2. Over-coverage errors
We updated the Statistical Register of Agricultural Holdings (we excluded ineligible family farms from the frame, which turned out that they didn’t belong to the target population). Overcoverage is adjusted with the help of the extrapolator factor.

Weighting factors were calculated on the basis of eligibility status of agricultural holdings, with the formula (responses + nonresponses)/responses - on the level of strata.

2.1 Multiple listings 
There were less than five agricultural holdings listed twice in the FSS 2016. They were treated as ineligible.

 

3. Misclassification errors
There are no significant misclassification errors. Only major misclassifications were modified before extrapolation factor was calculated.  In the responses, we found some units obviously belonging to different (bigger) size class and for them we updated the strata assignment and adapted weighting factors.

 

4. Contact errors
The initial list of agricultural holdings in FSS 2016 was 15 592 agricultural holdings. There were 1,2% of all agricultural holdings without telephone number.

There were altogether 14% of not contacted farms (the holder could not be reached - there was no telephone number or no answer). The non-contacting of the person which could give response has been taken into account when calculating extrapolation factor.

 

5. Other relevant information, if any
Not available.
6.3.1.1. Over-coverage - rate
Over-coverage - rate
The initial list of agricultural holdings in FSS 2016 was 15 592 agricultural holdings. The share of units that were included in the frame and it turned out that they didn’t belong to the target population was 2.7%.
6.3.1.2. Common units - proportion

If the data was available in administrative register, then the data was not collected from agricultural holdings.

6.3.2. Measurement error
Characteristics that caused high measurement errors

We are aware of measurement errors and we try to avoid this kind of errors by training interviewers, supervisors, by data checking and validation process. Where inconsistency or extreme values were discovered, the data were checked with possible administrative data or there was also a “call-back” to the farmers, and the data were checked again. So extreme values of variables were checked and corrected if necessary. Since the data was inserted directly into the data entry program (controls were included), there was likely to have less mistakes caused by interviewer.

Eurofarm variable Variable describtion Difficulties
A_3_3_1 More than 50% of production self-consumed by the holder Very difficult to assess for farmers - subjective estimation.
A_3_3_2 More than 50% of sales are direct sales Very difficult to assess for farmers - subjective estimation.
B_5_3 Other land Respondents' inability to provide accurate answers.
E_1_x Farm work for each of the persons (AWU) Sensitivity of the characteristic and subjective estimation.
F_2_1 Importance of other gainful activities directly related to the holding Sensitivity of the characteristic. Also quite difficult to assess for farmers - subjective estimation.
M_6_5_1 Broadcast application of manure with no incorporation New variable, introduced in 2016 and since it is not based on the area of crop (reported in IACS), it is quite difficult to assess for farmers.
M_6_5_2 Broadcast application of manure with  incorporation within 4 hours New variable, introduced in 2016 and since it is not based on the area of crop (reported in IACS), it is quite difficult to assess for farmers.
M_6_5_3 Broadcast application of manure with  incorporation after 4 hours New variable, introduced in 2016 and since it is not based on the area of crop (reported in IACS), it is quite difficult to assess for farmers.
M_6_6_1 Bandspread application of manure with trailing hose New variable, introduced in 2016 and since it is not based on the area of crop (reported in IACS), it is quite difficult to assess for farmers.
M_6_6_2 Bandspread application of manure with trailing shoe New variable, introduced in 2016 and since it is not based on the area of crop (reported in IACS), it is quite difficult to assess for farmers.
M_6_7_1 Injection of manure on a shallow or open slot New variable, introduced in 2016 and since it is not based on the area of crop (reported in IACS), it is quite difficult to assess for farmers.
M_6_7_2 Injection of manure on a deep or closed slot New variable, introduced in 2016 and since it is not based on the area of crop (reported in IACS), it is quite difficult to assess for farmers.
M_1_1 Tillage: conventional New variable, introduced in 2016 and since it is not based on the area of crop (reported in IACS), it is quite difficult to assess for farmers.
M_1_2 Tillage: conservation New variable, introduced in 2016 and since it is not based on the area of crop (reported in IACS), it is quite difficult to assess for farmers.
M_1_3 Tillage: zero New variable, introduced in 2016 and since it is not based on the area of crop (reported in IACS), it is quite difficult to assess for farmers.
M_2_1_1 Soil cover: normal winter crop New variable, introduced in 2016 and since it is not based on the area of crop (reported in IACS), it is quite difficult to assess for farmers.
M_2_1_2 Soil cover: cover or intermediate crop New variable, introduced in 2016 and since it is not based on the area of crop (reported in IACS), it is quite difficult to assess for farmers.
M_2_1_3 Soil cover: plant residues New variable, introduced in 2016 and since it is not based on the area of crop (reported in IACS), it is quite difficult to assess for farmers.
M_2_1_4 Soil cover: bare soil New variable, introduced in 2016 and since it is not based on the area of crop (reported in IACS), it is quite difficult to assess for farmers.
M_2_1_5 Outdoor arable land areas which are covered by multi-annual plants New variable, introduced in 2016 and since it is not based on the area of crop (reported in IACS), it is quite difficult to assess for farmers.
6.3.3. Non response error
1. Unit non-response: reasons, analysis and treatment
Unit non-response was treated with re-weighting and imputation.

The main reasons for non-response were the following:

  • holders consider themselves as “non-agricultural holding”,
  • dissatisfaction with the current agricultural policy in Slovenia,
  • problems with unsolved ownership (official procedures regarding succession can be very long),
  • general refusal because of low economic conditions of living.

Agricultural enterprises: According to the National Programme of Statistical Surveys, reporting of data is obligatory for the enterprises (and voluntary for family farms).

Weighting factors were calculated with the formula (responses+nonresponses)/responses - on the level of strata.

The distribution of non-response across holdings' categories was checked and no significant discrepancies were found.

 

2. Item non-response: characteristics, reasons and treatment
In the process of data validation, we considered national rules as well as validation rules for EUROFARM. There were no specific units discovered which had not responded to a particular item.
6.3.3.1. Unit non-response - rate
Unit non-response - rate
If the response rate is considered as the share of response among all eligible family farms, then the response rate is 65%. The non-response rate is thus 35%.
6.3.3.2. Item non-response - rate
Item non-response - rate
In the process of data validation, we considered national rules as well as validation rules for EUROFARM. There were no specific units discovered which had not responded to a particular item. Since we had a computer assisted telephone interview, all questions had to be answered, otherwise the application did not allow to go further.
6.3.4. Processing error
1. Imputation methods
  • Method of logical imputations (if some values were inconsistent with other values (we discovered there was clearly a typing error), we imputed the values with the “Method of logical imputations”).
  • Hot deck method (if we had only some data from administrative registers and no data for some variables (from logical point of view), then we used the “Hot deck method” to get the data from similar farms (same UAA, same region, etc.)).
  • Structural hot deck method (if we had data from administrative data only for totals, then we used the “Structural hot deck method” to get all the subcategories. The proportions were taken from similar farms (same UAA, same region, etc.)).
  • Method of cut average (if the data were missing (from logical point of view), there was a possibility to input the mean value within a given variable (e.g. intra-regional or intra-county), whereby a certain percentage of the maximum and minimum values are removed from the average computation).

 

2. Other sources of processing errors
FSS was conducted as computer assisted telephone interview. The application had some controls already implemented in the data entry application.  After the telephone interview we made corrections and imputations based on administrative sources and logical controls. Final extrapolation factor/weight is a product of sampling weight, non-response weight and calibration weight.

The descriptions of imputations were written (established) by methodologists in the Department for Agriculture, Forestry, Fishery and Hunting (SURS). They were based on national rules, validation rules in Eurofarm and different calculations.

The actual imputation was also made in SURS, in the Department for General Methodology and Standards.

 

3. Tools used and people/organisations authorised to make corrections
In the process of data validation, we considered national rules as well as validation rules for EUROFARM. Validations and imputations were done by SAS.
6.3.4.1. Imputation - rate
Imputation - rate

The data set relating to labour force and gainful activities on agricultural holdings is methodologically complex. We therefore believe that for an adequate level of data quality it is not enough to put direct questions prescribed by Regulation into the questionnaire. For this reason we included more detailed and explicit questions into the questionnaire in order to obtain high-quality basic information on which further calculations of Eurofarm variables are based. It would therefore be incorrect for this set of variables to calculate imputed value of the shares of the Eurofarm variables in the same manner as for other variables which are collected directly from the data sources (primary or administrative). The range of imputed shares is from 0% to 30%, depending on the single variable.

Values taken from administrative data are not counted as imputed values.

58 agricultural holdings were imputed.

DESCRIPTION OF THE CODE OR SECTION RATIO OF THE CORRECTED VALUE (in %) COMMENT
Legal personality of the holding 0  
Data on organic farming 0 Data is taken directly from administrative source.
More than 50% of production self-consumed by the holder 22 Very difficult to assess for farmers - subjective estimation.
More than 50% of sales are direct sales 8 Very difficult to assess for farmers - subjective estimation.
Production animals 0 The number of livestock was gathered from the agricultural holdings or from administrative sources (it is not counted as imputed value). Variables on livestock sent to Eurostat were imputed (corrected) in less than 1%, except the distribution of cows (dairy cows and other cows).  The total of cows is gathered from the administrative source, but the distribution into dairy cows and other cow was imputed (ratio of cca. 10%).
Land section 0 The area of land was gathered directly from the agricultural holdings or from administrative sources (it is not counted as imputed value). Most variables on land section sent to Eurostat were not imputed (or were imputed in less than 1% of the total), except the ones listed bellow.
Potatoes less than 1%  
Peas, field beans and sweet lupines less than 1%  
Fodder roots and brassicas 1%  
Fresh vegetables, melons, strawberries less than 1%  
Flowers less than 1%  
Total irrigable area less than 1%  
Farm work non-family members non-regularly employed less than 1%  
Data on support for rural development 0 Data is taken directly from administrative source.
Turnover from other gainful activity 20 Very difficult to assess for farmers - subjective estimation.
6.3.5. Model assumption error

[Not requested]

6.4. Seasonal adjustment

[Not requested]

6.5. Data revision - policy
Data revision - policy
SURS has an internal revision policy. It is available on: http://www.stat.si/dokument/5151/Navodilo_revizije.pdf

All publications and revisions, due to provisional data, are planned.

Data for FSS are firstly published as “provisional data” due to users' needs for timely information. The data became final 15 months after the publication of provisional data.

6.6. Data revision - practice
Data revision - practice
Provisional data for FSS were published 29.9.2016 (2 months and a half after the survey was conducted).

Final data (without typology and economic size) was published on 29.6.2017.

Final data on typology and economic size was published on 20.12.2017.

6.6.1. Data revision - average size

The differences in the key statistics was less than 1%.


7. Timeliness and punctuality Top
7.1. Timeliness

See below

7.1.1. Time lag - first result
Time lag - first result
The reference month is June-July 2016.

Provisional data for FSS were published 29.9.2016 (4 months after the reference day).

Time lag first results: 4 months after the survey reference day.

7.1.2. Time lag - final result
Time lag - final result
Final data (without typology and economic size) was published on 29.6.2017 (6 months ).

Final data on typology and economic size was published on 20.12.2017 (12 months ).

7.2. Punctuality

See below

7.2.1. Punctuality - delivery and publication
Punctuality - delivery and publication
All publications and revisions were planned and all publications were published on time.


8. Coherence and comparability Top
8.1. Comparability - geographical
1. National vs. EU definition of the agricultural holding
There is no difference.

 

2.National survey coverage vs. coverage of the records sent to Eurostat
The population covered in the national survey is the same as the population which was sent to Eurostat.

 

3. National vs. EU characteristics
We implemented the Handbook: "Draft proposal for the FSS 2016  variables definitions for: (i)  Commission Regulation (EU)  N° 1391/2015 (ii) Handbook on FSS definitions for  FSS 2016". There are no differences between EU and national concepts.

There are also no important changes in definitions of characteristics or reference time or measurement which would affect the comparability with previous census/FSS data.

When calculating Annual Work Unit, 1800 hours were taken for a full-time employee.

Organic kitchen garden area is included in the Total organic area (A_3_2_3_HA) even though it is not included in any sub-category under organic farming.

 

4. Common land
4.1 Current methodology for collecting information on the common land
As agreed during the FSS Working Group meeting on 21-22 September 2009, the common land area could be recorded in three ways. We decided to use the first method:

common land is included in the land use data of the agricultural holdings making use of the common land - “In proportion to the use by each holding".

The area of common land was not double counted, because the data on common land were gathered from administrative data, and divided in proportion to each holding (on the basis of the LSU). Holders reported land use without common land.

The area of common land consists only of pastures (rough grazing).

Until 2010 no common land was included in UAA that was sent to Eurostat. In the national publications there was always a comment about the area of common land in the country. It is very difficult to provide the data on common land on each agricultural holding when conducting sample surveys. That is why only the data at national level were published.

 The total common land in different FSS years (until it was counted in the FSS):

2000 2003 2005 2007
22 786 ha 22 786 ha 22 786 ha 9 062 ha
4.2 Possible problems encountered in relation to the collection of information on common land and possible solutions for future FSS surveys
It is difficult to assess the area when having an administrative data and we are conducting a sample survey.
4.3 Total area of common land in the reference year
In FSS 2016 there were 8 812 ha area of common land.
4.4 Number of agricultural holdings making use of the common land or Number of (especially created) common land holdings in the reference year
In FSS 2016 there were 1 646 agricultural holdings indicating that are using common land.

 

5. Differences across regions within the country
SURS has been disseminating the data as of 1 January 2015 on according to the changed cohesion and statistical regions. These changes are in line with the NUTS Regulation (EC) No. 1319/2013 and are explained in a Special Release published in December 2013.

 

6. Organic farming. Possible differences between national standards and rules for certification of organic products and the ones set out in Council Regulation No.834/2007
There are no differences detected.
8.1.1. Asymmetry for mirror flow statistics - coefficient

[Not requested]

8.2. Comparability - over time
1. Possible changes of the definition of the agricultural holding
There have been no changes.

 

2. Possible changes in the coverage of holdings for which records are sent to Eurostat
There have been no changes.

 

3. Changes of definitions and/or reference time and/or measurements of characteristics
In the variable "A_3_2_3_99_HA - Organic farming - other crops including rough grazings", the area of rough grazings is included in 2016.

 

4. Changes over time in the results as compared to previous FSS, which may be attributed to sampling variability
See item 6. below

 

5. Common land
5.1 Possible changes in the decision or in the methodology to collect common land
No changes.
5.2 Change of the total area of common land and of the number of agricultural holdings making use of the common land / number of common land holdings
An average agricultural holding who had common land in 2010 (census data 8 221 ha) was using 5.0 ha.

An average agricultural holding who had common land in 2013 (sample survey 8 733 ha) was using 4.8 ha.

An average agricultural holding who had common land in 2016 (sample survey 8 812 ha) was using 5.4 ha.

 

6. Major trends on the main characteristics compared with the previous FSS survey

Main characteristic Current FSS survey Previous FSS survey Difference in % Comments
Number of holdings 69 902 72 377 -3%  
Utilised agricultural area (ha) 488 401 485 756 1%  
Arable land (ha) 175 124 172 690 1%  
Cereals (ha) 98 378 99 231 -1%  
Industrial plants (ha) 14 126 12 205 16% Data for most area come from IACS, which, being submitted to quality checks,  is a reliable source for the increase.
Plants harvested green (ha) 52 656 53 323 -1%  
Fallow land (ha) 1 072 409 162% The area is very small and a slight change has a big influence on the relative change. Data for most area come from IACS, which, being submitted to quality checks,  is a reliable source for the increase.
Permanent grassland (ha) 285 056 284 780 0%  
Permanent crops (ha) 26 826 27 278 -2%  
Livestock units (LSU) 512 107 487 962 5%  
Cattle (heads) 486 014 462 066 5%  
Sheep (heads) 134 929 130 657 3%  
Goats (heads) 38 564 34 542 12% The data was checked with administrative data. Also in other agricultural surveys (Animal statistics), the increase was detected.
Pigs (heads) 273 359 287 498 -5%  
Poultry (heads) 6 222 661 4 858 025 28% The data was checked with administrative data. Also in other agricultural surveys (Animal statistics), the increase of Poultry was detected through the last three year period.
Family labour force (persons) 193 163 197 998 -2%  
Family labour force (AWU) 73 581 77 286 -5%  
Non family labour force regularly employed (persons) 2 872 2 633 9%  
Non family labour force regularly employed (AWU) 2 443 2 188 12%  
8.2.1. Length of comparable time series

[Not requested]

8.3. Coherence - cross domain
1. Coherence at micro level with other data collections
The results of the FSS 2016 were checked and compared with all the available administrative data. Actually a lot of administrative data was used directly from administrative sources.

 

2. Coherence at macro level with other data collections
The results of the FSS 206 were checked and compared with all the available administrative data, previous surveys and other surveys conducted by SURS. A comparison was made with other sources at macro-data level. The data is comparable and all minor differences can be explained.

All surveys in agricultural statistics department can be combined and compared between themselves.

Comparison to other statistical domains can be done on macro level.

8.4. Coherence - sub annual and annual statistics

[Not requested]

8.5. Coherence - National Accounts

[Not requested]

8.6. Coherence - internal

[Not requested]


9. Accessibility and clarity Top
9.1. Dissemination format - News release

Provisional data for FSS was published 29.9.2016: http://www.stat.si/StatWeb/en/News/Index/6208

Final data (without typology and economic size) was published on 29.6.2017: http://www.stat.si/StatWeb/en/News/Index/6742

Final data on typology, economic size, tillage methods and soil cover was published on 20.12.2017: http://www.stat.si/StatWeb/en/News/Index/7121

9.2. Dissemination format - Publications
1. The nature of publications
Provisional data for FSS was published 29.9.2016 (2 months and a half after the survey was conducted) - web publication.

Final data (without typology and economic size) was published on 29.6.2017 - web publication + database.

Final data on typology and economic size was published on 20.12.2017 - web publication + database.

 

2. Date of issuing (actual or planned)
29.9.2016

29.6.2017

20.12.2017

 

3. References for on-line publications
Results are published and are available in SURS’s SI-STAT database (www.stat.si).

http://pxweb.stat.si/pxweb/Database/Environment/Environment.asp

9.3. Dissemination format - online database
Dissemination format - online database
http://pxweb.stat.si/pxweb/Database/Environment/Environment.asp
9.3.1. Data tables - consultations
Data tables - consultations
Not available.
9.4. Dissemination format - microdata access
Dissemination format - microdata access
In the Statistical Office of the Republic of Slovenia, dissemination of statistically protected micro-data and sensitive tables (from the point of view of statistical confidentiality) to researchers is organized through the function of the Data Protection Committee, the advisory body of the Director General, in compliance with the system of rules and procedures related to the dissemination of statistically protected micro-data to researchers, and the use of software for the statistical protection of data.

Micro-data are not disseminated.

The micro-data are available according to special conditions to researchers for research purposes.  Basic instructions concerning the access and the use of statistically protected micro-data are available on the web page: http://www.stat.si/StatWeb/en/StaticPages/Index/For-Researchers

SURS decided that researchers could gain access to micro-data in "Eurofarm data-set" (without precise location of individual agricultural holding). There will also be a possibility to gain other data on individual holding that are not in the "Eurofarm data-set", but each request will be dealt with individually. Researchers must sign the contract with SURS, where confidentiality rules are included. Results intended for the export are later on reviewed by SURS concerning the statistical confidentiality.

9.5. Dissemination format - other

[Not requested]

9.6. Documentation on methodology
1. Available documentation on methodology
Methodological explanations on farm structure survey: http://www.stat.si/StatWeb/File/DocSysFile/8048

 

2. Main scientific references
Not available.
9.7. Quality management - documentation
Quality management - documentation
Document on quality report is published on the web page: http://www.stat.si/statweb/en/Methods/QuestionnairesMethodologicalExplanationsQualityReports
9.7.1. Metadata completeness - rate

[Not requested]

9.7.2. Metadata - consultations

[Not requested]


10. Cost and Burden Top
Co-ordination with other surveys: burden on respondents
All agricultural statistics are produced in the same department and there is a good coordination of surveys to avoid situation that some farms have to answer multiple questionnaires with the same kind of questions (ex. a small part of data for annual crop statistics is retained from FSS).


11. Confidentiality Top
11.1. Confidentiality - policy
Confidentiality - policy
The National Statistics respect statistical confidentiality.

In the National Statistics Act (Official Gazette of the Republic of Slovenia, No. 45/95 and 9/01), article 50 ,is described that statistics published by SURS, are in a form and in a manner that does not allow identification of the reporting unit to which the data relates.

11.2. Confidentiality - data treatment
Confidentiality - data treatment
The confidentiality issue was determined by the methodologists on protection in SURS and methodologists for farm structure survey.

Regarding protection of final output tables, two confidentiality rules were applied:

  • "Threshold rule" - the individual cell in the table is protected if there are fewer than "t" reporting units.
  • "Dominancy, (n,k) rule" - if the "n" reporting units contribute more than "k"% of the whole value, then the individual cell is protected.

Researchers must sign the contract with SURS, where confidentiality rules are included. Results intended for the export are later on reviewed by SURS concerning the statistical confidentiality.


12. Comment Top
1. Possible improvements in the future

Not available.

 

2. Other annexes
Not available.


Related metadata Top


Annexes Top