Farm structure (ef)

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

Compiling agency: State Statistical Office of the Republic of Macedonia

Time Dimension: 2016-A0

Data Provider: MK1

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


1. Contact Top
1.1. Contact organisation
State Statistical Office of the Republic of Macedonia
1.2. Contact organisation unit
Department for Agricultural Statistics and Environmental Statistics
1.5. Contact mail address
Dame Gruev 4, Skopje

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

The SSO carried out an Agricultural Census in 2007 (AC 2007), for the first time after 40 years which was base for establishing a Statistical Farm Register and Farm Typology. In 2010, 2013 and 2016 SSO carried out FSS as a sample survey. From 10 to 30 June 2016, a sample based FSS was carried out in the field, covering individual agricultural holdings and agricultural enterprises with full coverage.

The family farms in urban areas and business entities were informed with the basic information on the FSS by letters while for rural areas, informative leaflets were prepared in order to inform the farmers about the purpose of the FSS and when the FSS will be carried out. All large individual agricultural holdings were surveyed full scale. In rural areas, every 4th statistical district was selected. In the frame of the selected statistical districts in rural areas, all households were surveyed door-to-door and changes were registered. In urban areas, every 4th statistical district was selected as well, where specifically selected addresses of farms were surveyed.

Data on individual agricultural holdings were collected with authorised interviewers, while the data on agricultural enterprises were collected by post. Some agricultural enterprises that did not respect the deadline were interviewed by phone.

An application for data entry was developed and tested. For the process of data entry, special logic-numeric control for micro data was prepared. Also, all validation rules given by Eurostat were checked before data transmittion.


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

Five-Year Statistical Programme, 2013-2017

- the obligations of the respondents with respect to the survey If the respondents are legal entities they are obliged to give complete and accurate data in the prescribed period, otherwise they are subject to fines, according to the Law on Statistics “Official Gazette of the Republic of Macedonia” No. 54/1997, 21/2007, 51/2011, 104/2013, 42/2014, 192/2015, 27/2016 and 83/2018.
- the identification, protection and obligations of survey enumerators The interviewers are obliged to identify themselves to the holdings with an authorization given by the State Statistical Office. The interviewers sign the Contract with the State Statistical office where their rights and obligations are written.
2.2. Classification system

[Not requested]

2.3. Coverage - sector

[Not requested]

2.4. Statistical concepts and definitions
List of abbreviations
 SSO - State Statistical Office
2.5. Statistical unit
The national definition of the agricultural holding
The national definition of the holding is according to the EU definition. Agricultural holding or holding means a single unit, both technically and economically, which has a single management and which undertakes agricultural activities based on the European Statistical Classification of Economic Activities (NACE Rev. 2) for crop and animal production, hunting and related service activities, either as its primary or secondary activity.

Also, the holdings with land maintained in good agricultural and environmental conditions, even if they do not have other agricultural activity are included in the survey.

2.6. Statistical population
1. The number of holdings forming the entire universe of agricultural holdings in the country
The threshold in the country is quite low. There are numerous households with very small areas of arable land and also some who have a very small number of animals.


2. The national survey coverage: the thresholds applied in the national survey and the geographical coverage
Individual agricultural holdings are covered if they meet the following requirements:
- use 1 000 m2 of agricultural area (A_3_1$ha) or more, or:
- use less than 1 000 m2 of agricultural area or none at all, but own a certain minimum number of livestock, poultry or beehives:
- 1 cow (sum (C_2_6$heads, C_2_99$heads)) and 1 calf (C_2_1$heads), or
- 1 cow (C_2_6$heads, C_2_99$heads)) and 1 heifer (sum(C_2_3$heads, C_2_5$heads)), or
- 1 cow (C_2_6$heads, C_2_99$heads)) and 2 adult heads of small livestock (sum(C_3_1$heads,C_3_2$heads, C_4$heads)), or
- 5 adult sheep (C_3_1$heads) or goats (C_3_2$heads), or
- 3 adult pigs (C_4$heads), or
- 4 adult sheep or goats and pigs together (sum(C_3_1$heads,C_3_2$heads, C_4$heads)), or
- 50 head of adult poultry (sum(C_5_2$heads, C_5_3$heads), or
- 20 beehives (C_7$hive).
As an exception, also covered are the households that have agricultural production, but do not meet the requirements to be individual agricultural holdings, if agricultural production is the only source of income for those households.


3. The number of holdings in the national survey coverage 
 The population in the statistical farm register, before the FSS was conducted, was around 190 000 agricultural holdings. The number of units in the extrapolated population in FSS 2016 is 178125.


4. The survey coverage of the records sent to Eurostat
The coverage of the records sent to Eurostat is equal with the national survey coverage.


5. The number of holdings in the population covered by the records transferred to Eurostat
 14990 (14710 individual farms and 280 legal units). The number of units in the extrapolated population in FSS 2016 is 178125.


6. Holdings with standard output equal to zero included in the records sent to Eurostat
There are records with standard output equal to 0, representing:
-  holdings with data only on fallow land kept in good agricultural and environmental conditions, which makes them eligible for being included in the survey;
- holdings with data only on kitchen gardens. As an exception, also covered are the households that have agricultural production, but do not meet the requirements to be individual agricultural holdings, if agricultural production is the only source of income for those households.


7. Proofs that the requirements stipulated in art. 3.2 the Regulation 1166/2008 are met in the data transmitted to Eurostat
MK uses a threshold of less than 1 ha UAA, so MK does not have to comply with art 3.2 of the Regulation. 


8. Proofs that the requirements stipulated in art. 3.3 the Regulation 1166/2008 are met in the data transmitted to Eurostat
All physical thresholds used are lower than those required by art 3.3 (Annex II) of the Regulation. 
2.7. Reference area
Location of the holding. The criteria used to determine the NUTS3 region of the holding
 The majority of the total area of the holding where the holding is located is used for determining the NUTS 3 region of the holding.
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)

Several questions were defined with a reference period (01 June 2015 - 31 May 2016); while for others reference day was used (31 May 2013).

- for land characteristics - reference year - 01 June 2015 - 31 May 2016

- for livestock characteristic - reference day - 31 May 2016

- for labour force characteristics - reference year - 01 June 2015 - 31 May 2016

2.9. Base period

[Not requested]

3. Statistical processing Top
1.Survey process and timetable
 1. definition of survey objective and requirements:
  1.1. formation of workgroups for survey organisation- September 2015
  1.2. consultation of users - March 2016
  1.3. set-up objectives, target population, statistical units, classifications, precision requirements etc. - September - November 2015
  1.4. survey promotion - May 2016

2. survey design:
  2.1. set-up organisation of the survey (e.g. detailed timetable, specification of resources, costs estimation) - 2015
  2.2. definition of the survey variables - November 2015
  2.3. design of the sampling frame and sampling procedures - March - April 2016
  2.4. design of data collection procedures (e.g. questionnaire design, selection of data collection modes etc.) - November 2015 - February 2016
  2.5. design of data processing procedures - April - May 2016
  2.6. pilot survey organisation and execution - May 2016

3. data collection:
  3.1. sampling frame construction and sample selection - April - May 2016
  3.2. recruitment of interviewers - May 2016
  3.3. training of interviewers - May 2016
  3.4. fieldwork - June 2016
  3.5. evaluation and assessment of fieldwork - July 2016

4. data processing and validation: entry and data coding - August - September 2016
  4.2. data validation (at record level) - October - December 2016
  4.3. data correction - October - December 2016

5. data compilation:
  5.1. weight calculation and estimation - March - April 2016
  5.2. calculation of derived variables - December 2016
  5.3. calculation of quality indicators (e.g. non-response rates, relative standard errors, coverage errors, bias etc.) - December 2016
  5.4. aggregation and tabulation - December 2016 - January 2017
  5.5. validation of aggregated data - January 2017

6. data analysis December 2016 - January 2017

7. data dissemination -  Press Release - January 2017

                                 - Publication - Structure of agricultural holdings and Farm Typology - December 2017

                                 - Main indicators delivery to Eurostat - May 2017

                                 - Data delivery to Eurostat - November 2017


2. The bodies involved and the share of responsibilities among bodies
The SSO and the eight regional departments were responsible for carrying out FSS in the field. Authorised interviewers and supervisors as well as experts from the central office were engaged for successful and timely completion of the fieldwork. Every field supervisor coordinated the work of the interviewers, and the supervisors were coordinated by the experts from the central office. The SSO equipped the field supervisors and interviewers with all the necessary materials (lists of farms, questionnaires, methodological guidelines, authorisation for work).


3. Serious deviations from the established timetable (if any)

Data for organic farming are not collected with the FSS sample survey. They are collected on regular annual base from administrative data sources but the problem is linking data from administrative and statistical data sources.

3.1. Source data
1. Source of data
The source of data is a combination between sample based survey for individual agricultural holdings and exhaustive coverage of legal units. Administrative data were not used.


2. (Sampling) frame

The full list of agricultural farms covered in the survey was created from the Statistical Business Register and the Statistical Farm Register. The list frame is used in the survey. The Farm Register is developed after the First Agricultural Census 2007. It can be updated exhaustively when an agricultural census is carried out. Between the agricultural census 2007 and 2016 the farm register was updated partially based on the information of  FSSs (2010, 2013) and regular annual sample surveys.


3. Sampling design
3.1 The sampling design

The sample of individual agricultural holdings is designed as one-stage stratified cluster sampling in rural areas and two-stage stratified sampling in urban areas.  

In both rural and urban areas in the Republic of North Macedonia, primary sampling units are statistical districts.  Statistical districts with less than 10 agricultural holdings are excluded from the sample frame.  The statistical districts was sorted by the number of agricultural holdings and each fourth statistical district in every stratum is selected systematically.

In rural areas, in the frame of the selected statistical districts all households are surveyed, i.e. the method of survey is door-to-door, while in the urban areas specifically determined households with agricultural activity (data from the Farm Register) are surveyed.

Exhaustive strata are:

- all big agricultural  holdings from the Statistical Farm Register

- all agricultural enterprises

3.2 The stratification variables
Strata are the eight regions (NUTS 3) in the Republic of North Macedonia and the type of the settlement, urban and rural.
3.3 The full coverage strata
All large individual agricultural holdings classified from seventh to fourteenth class in the Farm Typology were surveyed full scale. 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

FSS 2010 was conducted on the sample of 5000 agricultural holdings. After the analyses, it was decided that for the next FSS 2013 the sample has to be much bigger. According the financial analysis, the sample of 30000 farms was decided. In FSS 2016, according to the financial frame, 20000 farms were included in the sample survey. 512 statistical districts were selected with stratified systematic random sample, equal allocation. 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 on the NUTS3 level. The sample size was 21299 agricultural holdings.


3.5 The method for the allocation of the overall sample size
Optimal allocation considering different costs across strata. Agricultural holdings from the biggest size class were all included in the sample.
3.6 Sampling across time
New sample is drawn for each occasion.
3.7 The software tool used in the sample selection
3.8 Other relevant information, if any
Not available.


4. Use of administrative data sources
4.1 Name, time reference and updating
MK hasn't used administrative data sources in FSS 2016.
4.2 Organisational setting on the use of administrative sources
Not applicable.
4.3 The purpose of the use of administrative sources - link to the file
Not applicable.


4.4 Quality assessment of the administrative sources
  Method  Shortcoming detected Measure taken
- coherence of the reporting unit (holding) Not applicable. Not applicable. Not applicable.
- coherence of definitions of characteristics Not applicable. Not applicable. Not applicable.
- coverage: Not applicable. Not applicable. Not applicable.
  over-coverage Not applicable. Not applicable.  Not applicable.
  under-coverage Not applicable. Not applicable. Not applicable.
  misclassification Not applicable.  Not applicable.  Not applicable.
  multiple listings Not applicable.  Not applicable. Not applicable.
- missing data Not applicable. Not applicable. Not applicable.
- errors in data Not applicable. Not applicable. Not applicable.
- processing errors Not applicable. Not applicable. Not applicable.
- comparability Not applicable. Not applicable. Not applicable.
- other (if any) Not applicable. Not applicable. Not applicable. 


4.5 Management of metadata
Not applicable.
4.6 Reporting units and matching procedures
Not applicable.
4.7 Difficulties using additional administrative sources not currently used
The reason for not using administrative data sources is the differences in the definition of the holder. In other institutions (e.g. Ministry for agriculture, forestry and water economy) the holders are persons who apply for subsidies while in State Statistical office the holder is the person who manage the work of the individual agricultural holding, taking care of everything connected with the agricultural holding.
3.2. Frequency of data collection
Frequency of data collection
Survey was conducted in the field in a period of 21 days (10-30 June 2016).
3.3. Data collection
1. Data collection modes

Data on agricultural enterprises were collected by postal method (self-completed paper questionnaire).

Data on individual agricultural holdings were collected by authorised interviewers (face-to-face interviews with paper questionnaires).


2. Data entry modes
An application for data entry was developed and tested. Paper questionnaires were entered by persons in data entry programme.


3. Measures taken to increase response rates
Training staff in handling difficult respondents; call-back strategies, telephone reminders, contacting respondents who have only partly completed the questionnaires.


4. Monitoring of response and non-response

Number of holdings in the survey frame plus possible (new) holdings added afterwards

In case of a census 1=3+4+5


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

3 Number of ineligible holdings 3199

Number of ineligible holdings with ceased activities

This item is a subset of 3.


Number of holdings with unknown eligibility status


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

Number of eligible holdings



Number of eligible non-responding holdings


5.1.1 Number of eligible non-responding holdings – re-weighted  3110
5.1.2 Number of eligible non-responding holdings – imputed
5.2 Number of eligible responding holdings 14990

Number of the records in the dataset 




5. Questionnaire(s) - in annex
The data in the survey were collected with 2 types of questionnaires designed on the basis of the EC Regulation 1166/2008.
3.4. Data validation
Data validation

The phase of quality control of the data produced is essential given the complexity of this type of survey. Usually there are three different levels of control. The first is the level of interviewers, the second level is controllers in the Regional Offices and the third level is at the subject-matter department after data entry. The major part of checking and correction work is done at central level.

In the process of data entering, microdata were analysed for possible corrections. In the part of the questionnaire on the labour force there were several cross-checks. Before corrections were accepted and entered, the field supervisors or farmers had been contacted by telephone if necessary or administrative sources for comparison were used. Also, maximum values for each characteristic were checked. Data were validated in the process of data entering with incorporated logical and mathematical controls in the data editing software.

In the next phase, data were validated on the totals through comparisons with FSS 2013 data.

Before sending data to Eurostat, the Validation Rules were performed in the SSO.

3.5. Data compilation
Methodology for determination of weights (extrapolation factors)
1. Design weights
In the design phase of the survey, an initial weight (design weight) was given to each sampling unit (holding). This initial weight was estimated as the inverse of the probability of selection. In detail, for the holding i that belongs to the stratum h the initial weight is whi =1/Prob (selected unit i in the stratum h). As in each separate stratum h, the sampling units were selected with equal probabilities, the initial weights for all sampling units belonging to the stratum h are equal to wh=Nh/nh.
2. Adjustment of weights for non-response
For the non-response cases, the initial weights were corrected by a factor that takes into account the response rates in each separate stratum. The essence of this correction is to increase the initial weights of the respondents, so that they represent the non-respondents. More specifically, the initial weight win each stratum h is multiplied by the inverse of the response rate rh=mh/nh (mh : the number of respondents). As a result, the final weight in stratum h is: wh= wh* rh1 = Nh/nh* nh/ mh = Nh/ mh.
3. Adjustment of weights to external data sources
Not applied.
4. Any other applied adjustment of weights
Not applied.
3.6. Adjustment

No other adjustments were made.

4. Quality management Top
4.1. Quality assurance

[Not requested]

4.2. Quality management - assessment

[Not requested]

5. Relevance Top


5.1. Relevance - User Needs
Main groups of characteristics surveyed only for national purposes 
  • The usage of agricultural products for the needs of EAA
  • Identification number in administrative farm register (if exists)
5.2. Relevance - User Satisfaction

User satisfaction survey is conducted in the SSO, but it is not performed on survey level.

5.3. Completeness
Non-existent (NE) and non-significant (NS) characteristics - link to the file. Characteristics possibly not collected for other reasons
Please access the information on NE and NS characteristics in the file at the link (Eurostat will add the link to the file as soon as possible).
5.3.1. Data completeness - rate

[Not requested]

6. Accuracy and reliability Top


6.1. Accuracy - overall
Main sources of error
Various types of errors and warnings may affect the quality of the results obtained from the FSS. One such error that affects the overall quality of the survey is the observation (or collection) error, which should be corrected wherever possible. Other errors arise during input, encryption or data processing. Main sources of error are sampling errors, over-coverage, non-response and measurement errors.
6.2. Sampling error
Method used for estimation of relative standard errors (RSEs)
The estimations in the Farm Structure Survey 2016 are generally in the form of totals and ratios. The domain of estimating is the country level (Republic of North Macedonia) and the eight regions. In the estimation procedure data on individual agricultural holdings are weighted with design weights adjusted for non-response. The deviations of estimated data from the sample and hypothetically true data on the population are calculated as standard deviations and coefficients of variation (relative standard errors). All calculations are made with the SAS 9.1 statistical software package. SAS PROC SURVEYMEANS procedure was used for the calculation of standard errors and coefficients of variation. The sampling design was considered during the calculation of RSEs.
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
 Almost all precision requirements stipulated in Annex IV "Precision Requirements" of the Regulation 1166/2008 have been met except for bovine animals and sheep.

6.2.1-2. Relative standard error_FSS 2016
6.3. Non-sampling error


6.3.1. Coverage error
1. Under-coverage errors
During the FSS 2016 some changes in the farms were obtained. In most cases changes in the farms were related to the changes of the farm holder.  During the survey, 2323 were newly-established agricultural holdings. Such holdings were not recorded in the Farm Register, but they were surveyed as they belong to the target population. All new agricultural holdings were added into the Statistical Farm Register.


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). Between the agricultural censuses 2007 and 2016 the farm register was updated partially based on the information of  FSS (2010, 2013) and regular annual sample surveys. 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 no multiple units in the frame.


3. Misclassification errors
Urban, rural areas and regions as stratification variables are the same in the sample and in the data processing and there are no differences in the classifications and no impact on weights.


4. Contact errors

For individual agricultural holdings the interviewers had a list of holders (in the territory for which they were engaged). This list contained main contact data for the holder. As they interviewed door-to-door, they checked the contact data on the list, and recorded the changes (if any).

For legal entities, the contact data were checked with other sources in SSO and if it was needed they were additionally contacted by phone or e-mail. 


5. Other relevant information, if any
Not available. Over-coverage - rate
Over-coverage - rate
From the initial list of agricultural holdings  2902 of units  with ceased activity were surveyed as they belong to the target population and it turned out that they didn’t belong to the target population. The over-coverage rate was 12.6%. Some of the units have changed their owner, others have ceased their farming activities. For the aggregates, the coefficients of the other eligible holdings have not been changed following the ineligibility of these units. No weight correction due to over-coverage has been done. Common units - proportion

[Not requested]

6.3.2. Measurement error
Characteristics that caused high measurement errors

Many sources, which occurred in the period of data collection, had influence on measurement errors:

      1. questionnaire,

      2. interviewers,

      3. respondents,

      4. data collection.

1. Questionnaires

The data in the survey were collected with 2 types of questionnaires designed on the basis of the EC Regulation 1166/2008.

2. Interviewers

Having well-prepared interviewers is an essential part of collecting complete and reliable data. For this survey, the SSO engaged interviewers to carry out the FSS. These interviewers were trained and made familiar with the survey to ensure that the data are collected correctly.

The training was done by persons responsible for methodology from the SSO, for all responsible persons (supervisors) for the survey from each of the 8 regional statistical offices. After that, the responsible persons from the regional statistical offices trained the interviewers.

The training programme included methodology, questionnaires, classifications, method of interview.

3.  Respondents

Target group of the survey are agricultural holdings on the territory of the Republic of North Macedonia. Proxy interviews are also used in agricultural households because most of the household members during the season periods are working in the fields and they are not at their homes.

4. Data collection

The mode of data collection in FSS is via the PAPI method.

Data collection, coding and control of the collected data were organised in the eight regional offices. A number of enumeration districts were given to each interviewer. For each enumeration district, they got a list of a number of households.
6.3.3. Non response error
1. Unit non-response: reasons, analysis and treatment

For individual agricultural holdings, the interviewers were trained to give their maximum to obtain unit response. If it was not possible, the special set of reasons for unit non-response were given in the questionnaire. The main reasons for non-response were the following: holders consider themselves as “non-agricultural holdings” and general refusal because they didn't like to participate in the survey. They were adequately treated in weighting procedure. Weighting factors were calculated with the formula (responses+nonresponses)/responses - on the level of strata.

Legal entities with unit non-response were additionally contacted by phone.


2. Item non-response: characteristics, reasons and treatment

For individual agricultural holdings, the item non-response was negligible as during the face-to-face interviews the appropriate tables of the questionnaires have been completed by the enumerator.

For legal entities for some important item non-responses, the legal entities were additionally interviewed by phone. Unit non-response - rate
Unit non-response - rate

If the non-response rate is considered as the share of the non-respondents among all eligible agricultural farms then the unit non-response rate is 17.2%. Item non-response - rate
Item non-response - rate
 Item non-response was corrected during the data entering phase by the supervisors and their knowledge. In this case it was not possible to count them.
6.3.4. Processing error
1. Imputation methods
Not applied.


2. Other sources of processing errors

Data processing was carried out in two phases:

1/ Data-entry phase

A large number of edit checks between questions in questionnaires were implemented for ensuring data correctness and consistency. During the data entry phase, data entry operators were prevented from entering some outliers and some impossible modality answers without on-line warnings.

2/ Data processing phase

After the data-entry phase, further data checking and editing was performed by the subject-matter department. Initially, data were checked whether all questionnaires have been entered and completed. Data checking was done by the national checks (prepared by the subject-matter department) and Eurostat’s programme for validations with warnings and errors. All these validations were checked one by one and all errors were corrected. Also, all warnings were checked and if there were no mistakes they were approved.


3. Tools used and people/organisations authorised to make corrections
 Supervisor in SSO Imputation - rate
Imputation - rate

Not available. Item non-response was corrected during the data entering phase by the supervisors and their knowledge. In this case it was not possible to count them.

6.3.5. Model assumption error

[Not requested]

6.4. Seasonal adjustment

[Not requested]

6.5. Data revision - policy
Data revision - policy
6.6. Data revision - practice
Data revision - practice
Preliminary data for main indicators from Farm Structure Survey were published in News Release in January 2017. Publication with data from Farm Structure Survey and Farm Typology were published in December 2017.
6.6.1. Data revision - average size

[Not requested]

7. Timeliness and punctuality Top


7.1. Timeliness


7.1.1. Time lag - first result
Time lag - first result
June 2016 + 7 months, December 2016 + 1 month
7.1.2. Time lag - final result
Time lag - final result
June 2016 + 18 months, December 2016 + 12 months
7.2. Punctuality


7.2.1. Punctuality - delivery and publication
Punctuality - delivery and publication

8. Coherence and comparability Top


8.1. Comparability - geographical
1. National vs. EU definition of the agricultural holding
The definition of the holding is in line with the EU definition.


2.National survey coverage vs. coverage of the records sent to Eurostat
Data are internationally comparable as they are in line with EC Regulation 1166/2008.


3. National vs. EU characteristics

The definitions of characteristics for the FSS 2016 were in accordance with the EU requirements and in compliance with the Handbook FSS 2016.

The number of hours per year for a full-time employee used to calculate the Annual Work Unit is 1800.


4. Common land
4.1 Current methodology for collecting information on the common land
Data on common land were not collected
4.2 Possible problems encountered in relation to the collection of information on common land and possible solutions for future FSS surveys
Not available.
4.3 Total area of common land in the reference year
Not available.
4.4 Number of agricultural holdings making use of the common land or Number of (especially created) common land holdings in the reference year
Not available.


5. Differences across regions within the country
 No differences.


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
Data for organic farming were not collected in FSS. The data for organic farming as administrative data are regularly sent to Eurostat, but till now it is not possible to link the individual data from FSS and the administrative source.
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
Data are comparable over time (FSS 2013, FSS 2010 and AC 2007).


2. Possible changes in the coverage of holdings for which records are sent to Eurostat
For the individual agricultural holdings data are comparable for the years 2007, 2010, 2013 and 2016, while regarding business entities, in 2010 they were not surveyed.


3. Changes of definitions and/or reference time and/or measurements of characteristics
No changes.


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

Information not available.


5. Common land
5.1 Possible changes in the decision or in the methodology to collect common land
Not available.
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
Not available.


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  178125  170885  4.2  
Utilised agricultural area (ha)  320738  315863  1.5  
Arable land (ha) + Kitchen gardens (ha)  248269  237583  4.5 Kitchen gardens are included
Cereals (ha)  161370  158314  1.9  
Industrial plants (ha)  23284  24717  -5.8  
Plants harvested green (ha) 32929 23534 39.9  
Fallow land (ha)  7640  9581  -20.3  
Permanent grassland (ha)  32629  37905  -13.9  
Permanent crops (ha)  39840  40375  -1.3  
Livestock units (LSU)  381361  365868  4.2  
Cattle (heads)  252936  239362  5.7  
Sheep (heads)  717244  734472  -2.3  
Goats (heads)  121359  96281  26.0  
Pigs (heads)  175172  163770  7.0  
Poultry (heads)  1887239  2055837  -8.2  
Family labour force (persons) 437318 439839 -0.6  
Family labour force (AWU) 206909 223475 -7.4  
Non family labour force regularly employed (persons)  4481  3681  21.7  
Non family labour force regularly employed (AWU)  4286  3624  18.3  
8.2.1. Length of comparable time series

[Not requested]

8.3. Coherence - cross domain
1. Coherence at micro level with other data collections
 Some data were compared with the micro data from AC 2007, FSS 2010, FSS 2013 and other statistical surveys such as livestock surveys, to check some items. The results were appropriate.


2. Coherence at macro level with other data collections
Data were compared with the results of the AC 2007, FSS 2010, FSS 2013 and other statistical surveys such as crop and livestock surveys and institutional labour survey. The results met the expectations.
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

[Not requested]

9.2. Dissemination format - Publications
1. The nature of publications

First data containing the main variables on territorial level of the whole country were published through a News release as first results.  The data are available simultaneously to all interested parties on the day of release by issuing a news release (Farm Structure Survey, 2016). The news release is published on the website of the State Statistical Office.

Publication "Structure and Typology of Agricultural Holdings, 2016" contains definitive data. The definitive data became available for the users at the same time.


2. Date of issuing (actual or planned)

The publication dates are included in the Dissemination Plan of the SSO prepared annually.

  • Preliminary data publishing  – News Release   (main variables) - January 2017;
  • Data publishing - final results - December 2017.


3. References for on-line publications

9.3. Dissemination format - online database
Dissemination format - online database
The online database (MAKStat) is the main format for publishing data from statistical surveys where users can find detailed data by subject areas, supported with methodological and other information. The users are able to download data in a format of their choice.
9.3.1. Data tables - consultations
Data tables - consultations
The tables have not yet been loaded in MakStat database with the FSS 2016 data.
9.4. Dissemination format - microdata access
Dissemination format - microdata access

Eurofarm database containing microdata has been prepared and sent to Eurostat for further validation and calculation of the Farm Typology, but microdata are not disseminated.

The SSO, in its premises and in accordance with the rules and procedures for using anonymised data, allow users secure access to more detailed data from different areas for scientific and research purposes according to the microdata access policy.

9.5. Dissemination format - other

[Not requested]

9.6. Documentation on methodology
1. Available documentation on methodology
The publication on Structure of Agricultural Holdings in North Macedonia includes a short description of the methodology with the following topics: history of FSS; units of observation; definitions; sample design; sampling error of the estimates and coefficients of variation.


2. Main scientific references
Särndal, C. E, Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. New York: Springer

Estevao, V., Hidiroglou, M. A. and Särndal, C. E. (1995) Methodological principles for a generalized estimation system at Statistics Canada. J. Off. Stat. 11 181-204

9.7. Quality management - documentation
Quality management - documentation
Information not available.
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
Until now there is no coordination between surveys to decrease the burden of the respondents in agricultural statistics.

11. Confidentiality Top


11.1. Confidentiality - policy
Confidentiality - policy
In performing its functions, the State Statistical Office pays special attention to statistical data confidentiality. The data gathered from the reporting units represent confidential data, used for statistical purposes only and published as aggregated data (Law on Statistics, “Official Gazette of the Republic of North Macedonia” No. 54/1997, 21/2007, 51/2011, 104/2013, 42/2014, 192/2015, 27/2016 and 83/2018).
11.2. Confidentiality - data treatment
Confidentiality - data treatment
The data are disseminated on aggregated level i.e NUTS 1 and NUTS 3 level, so there is no need for additional data confidentiality treatment.

12. Comment Top
1. Possible improvements in the future
Not available.


2. Other annexes
Not available.

Related metadata Top

Annexes Top