Farm structure (ef)

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

Compiling agency: REPUBLIC OF CROATIA - CROATIAN BUREAU OF STATISTICS 

Time Dimension: 2016-A0

Data Provider: HR1

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

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1. Contact Top
1.1. Contact organisation

REPUBLIC OF CROATIA - CROATIAN BUREAU OF STATISTICS 

1.2. Contact organisation unit

AGRICULTURE, FORESTRY, FISHERIES AND ENVIRONMENT DIRECTORATE/Agriculture, Forestry and Fisheries Production Statistics Department/Crop Production Statistics and Register of Agricultural Holdings Unit

1.5. Contact mail address

REPUBLIC OF CROATIA - CROATIAN BUREAU OF STATISTICS

Ilica 3, 10000 Zagreb

Republic of Croatia

www.dzs.hr

 

AGRICULTURE, FORESTRY, FISHERIES AND ENVIRONMENT DIRECTORATE/Agriculture, Forestry and Fisheries Production Statistics Department/Crop Production Statistics and Register of Agricultural Holdings Unit,

Branimirova 19, 10 000 Zagreb


2. Statistical presentation Top
2.1. Data description
1. Brief history of the national survey 
In Croatia only one independent census of agricultural holdings, or farm structure surveys, was conducted before the year 2000 and it was in 1960. In 1969 a sample census of agricultural holdings was conducted, and in 1971, 1981 and 1991 data about agriculture were collected within population censuses. However, due to a limited number of questions related to agriculture, these data do not provide complete and comparable information on the structure of agricultural holdings in Croatia. In the year 2003, the first EU comparable Agricultural Census (hereinafter AC) was carried out. A survey on the structure of agricultural holdings was conducted on the sample basis for the first time in 2005. The survey “conducted in 2005, was in fact a study of the structure of agricultural holdings. The 2005 survey included all sown areas, main categories of land use, labour force, supplementary activities, agricultural machinery, production for own use, or for sale. Since the said 2005 survey was the first ever conducted of the kind, the set of questions pertaining to the number of livestock was not included. In 2007, a survey on the structure of agricultural holdings was conducted on a new sample. Questions pertaining to livestock were included for the first time. The sample was also stratified for the first time. In 2010 the FSS and the Survey on agricultural production methods are conducted. In 2013 and 2016, as a Member State, Croatia launched a farm structure survey in accordance with the requirements of the European Union. Anonymous individual data from the sample surveys carried out in  2013 and 2016 have been transmitted to Eurostat together with their respective methodological reports.

 

2. Legal framework of the national survey 
- the national legal framework National Statistics Act (OJ HR No. 103/03, 75/09. and 59/12.) 
- the obligations of the respondents with respect to the survey Additional burden on the respondents were call backs in order to clarify the questionable data. The main measure taken to decrease the costs and burden was the use of the Register data where was it possible. Additional measure taken was legal actions such as sending warning letters to business entities. 
- the identification, protection and obligations of survey enumerators The interviewers and supervisors had to sign a statement that they will return all the material connected with the FSS and that they will not copy, transcribe or otherwise misuse the data from the questionnaires on family farms. 
2.2. Classification system

[Not requested]

2.3. Coverage - sector

[Not requested]

2.4. Statistical concepts and definitions
List of abbreviations
CAPI - Computer Assisted Personal Interview

CAWI – Computer Assisted Web Interview 

CBS - Croatian Bureau of statistics

EAA - Economic accounts of agriculture

2.5. Statistical unit
The national definition of the agricultural holding
An agricultural holding is defined by the following criteria:

- a single unit both technically and economically; in general this is indicated by a common use of labour and means of production (machinery, buildings or land, etc.);

- single management; there can be single management even though this is carried out by two or more persons acting jointly.

Units where agricultural products are not produced but only land is maintained in good agricultural and environmental condition, even if they do not have other agricultural activity, are included. 

Agricultural production includes:

• growing annual crop,

• growing perennial crops,

• growing seeding material and ornamental plants,

• breeding livestock, poultry and other animals,

• mixed agricultural production (growing crops and breeding livestock and other animals together),

• auxiliary activities in agriculture and activities which follow harvest (land preparation, planting, crop attendance, harvest/gathering, cleaning, peeling, seed processing). Activities connected to breeding animals, such as feeding or cleaning facilities are also included.

 

Agricultural production does not include:

• processing of agricultural products,

• forestry (growing and exploitation of forests),

• fishery (fish farming and fishing).

2.6. Statistical population
1. The number of holdings forming the entire universe of agricultural holdings in the country
There were altogether 161 912 private family farms and legal entities included into the sampling frame (before application thresholds).

 

2. The national survey coverage: the thresholds applied in the national survey and the geographical coverage
From 2010 onwards, the FSS covers at least 98% of the total utilised agricultural area excluding common land and 98% of the total number of farm livestock units.

The sampling frame comprised agricultural holdings with:

     • more than 0.40 hectares of utilised agricultural area (A_3_1) or

     • more than 0.5 livestock size unit or

     • at least 0.1 hectares of vineyards (B_4_4), olive groves (B_4_3) and/or orchards (B_4_1 +B_4_2), or

     • any areas of nurseries (B_4_5), or

     • are market producers of vegetables, herbs, melons and strawberries (B_1_7), flowers or ornamental plants (B_1_8) or

     • any number of beehives (C_7).

The geographical coverage refers to Republic of Croatia and NUTS classification level.

 

3. The number of holdings in the national survey coverage 
The population size after application of thresholds is 134 459 agricultural holdings. 

 

4. The survey coverage of the records sent to Eurostat
The same coverage as in item 2. above.

 

5. The number of holdings in the population covered by the records transferred to Eurostat
The same number as in item 3. above.

 

6. Holdings with standard output equal to zero included in the records sent to Eurostat
There are 53 holdings in the sample with SO=0,  with only fallow land and/or permanent grassland no longer in production purposes and eligible for subsidies. All of these holdings have land in good agricultural and environmental conditions. 

 

7. Proofs that the requirements stipulated in art. 3.2 the Regulation 1166/2008 are met in the data transmitted to Eurostat
Croatia uses the threshold of 1 hectare of utilised agrocultural area, thus art 3.2 is not applicable for Croatia. Anyway, using our thresholds, 98% of utilised agricultural land and 98% of the LSU is covered.

 

8. Proofs that the requirements stipulated in art. 3.3 the Regulation 1166/2008 are met in the data transmitted to Eurostat
Croatia uses lower thresholds than the ones provided by the Regulation.  
There were no thresholds for market producers of vegetables, herbs, strawberries, mushrooms, flowers or ornamental plants.That means that we covered all area under vegetables, strawberries, herbs, flowers and mushrooms even if they have only these crops (despite having threshold for agricultural land).Vegetables and flowers in our questionnaire are divided into four sub-categories (on the open field, market gardening, under glass or other protective cover and vegetable in kitchen gardens). All vegetables that are not grown in the kitchen garden are considered to be produced for the market.
2.7. Reference area
Location of the holding. The criteria used to determine the NUTS3 region of the holding
We used all criteria depending of type of holding to determine the NUTS3 region. In most cases for private family farms, the residence of farmer was taken (if it is not further than 5 km from the farm). In some cases for legal units we chose one of the following criteria:

- the most important parcel or majority of the total area of the holding where the holding is located,

- the building (administrative, for livestock or other production),

- the majority of the total area of the holding where the holding is located.

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)
- land characteristics (1 st June 2015 to 31 May 2016);

- livestock characteristics (1 st June 2016);

- labour force characteristics (1 June 2015 to 31 May 2016);

- rural development (reference period 2014-2016).

2.9. Base period

[Not requested]


3. Statistical processing Top
1.Survey process and timetable
Before the FSS, the CBS organised training for supervisors and interviewers as well. Training was carried out by the CBS. Twenty training sessions, one day long, were held throughout the country, in each regional statistical office and in the City of Zagreb, during 13-30 May 2016.

The trainings were held at the headquarters of the 19 regional statistical offices and at CBS headquarters for City of Zagreb and Zagrebacka Zupanija.

At the training we provided all necessary documents to the interviewers:

• questionnaires with pre-printed addresses,

• separate list of agricultural holdings that interviewer has to visit,

• methodological explanations and definitions of characteristics surveyed,

• all the necessary stationery.

 

Tasks Dates
QUESTIONNAIRE AND METHODOLOGY  
Preparation of questionnaire and methodology October 2015 – May 2016
Harmonisation of list of characteristics with main users January – May 2016
Questionnaires (CAPI, CAWI, paper) January – May 2016
Methodological explanations for interviewers January – May 2016
Other material January – May 2016
Updating of list of farms January – May 2016
Determination of criteria for data sampling January – April 2016
SAMPLE  
Selecting of sample April 2016
Distributions of agricultural holdings among interviewers May 2016
Selection of interviewers, preparation of contracts May 2016
Preparation of material for interviewers May 2016
Preparation of payment for interviewers May 2016
Training of supervisors and interviewers; preparation and realization May 2016
Advance letter to agricultural holdings May 2016
Field work (1 - 30 June 2016) June 2016
Recording of incoming questionnaires and sorting (for PAPI mode) June – July 2016
Preparation of program for data entry July – August 2016
Data entry and checking of data (SQL) July – December 2016
Obtaining of data September 2016
Administrative data June 2016 –  November 2017
Data analysis November 2016 – November 2017
Calculation of sampling weights June –  November 2016
Calculation of coefficients of variation November 2017–  December 2017
Preparation of rules for data checking and imputations Mar – December 2017
Publishing of preliminary results (land use, livestock) November 2016
Preparation of a report on discrepancies between FSS questionnaire and Eurofarm requirement June 2017
Preparation of a transition table from national FSS Database record to Eurofarm record December 2016 – December 2017
Calculation of SO coefficients September 2016 – December 2016
Calculation of other characteristics (e.g. LSU, AWU, type of farming, socio-economic type) January 2017 – December 2017
Delivery of SO coefficients  and methodological report on its calculation  December 2016
Adjustment of the FSS database and preparation of a module to transfer data in to acceptable format for Eurofarm  June 2017 – December 2017
Delivery of data to Eurostat in Eurofarm format via eDamis December 2017
Correcting the data until it passes Eurostat data validation control December 2017
Preparation of tables - final results (land use, livestock) December 2017
Preparation of methodological report September 2017 – December 2017
DATA PUBLISHING December 2017 first release (April 2018 in database)

 

2. The bodies involved and the share of responsibilities among bodies
The Croatian Bureau of Statistics is the sole responsible body for this survey. 

 

3. Serious deviations from the established timetable (if any)
No deviations.
3.1. Source data
1. Source of data
The FSS survey was carried out on a sample of family farms and on a complete enumeration of agricultural enterprises. All characteristics were collected from the surveyed holdings.

Concerning data obtained from administrative data sources we used only the register of organic producers, but only to take a list of producers. All characteristics concerning organic farming were not taken directly from this source but were asked from holdings.

 

2. (Sampling) frame
The sampling frame was a list of all active family farms from the Statistical Register of Agricultural Holdings (which consists of Census of Agriculture Holdings regularly updated with surveys conducted by CBS and with the Register of Paying Agency for Agriculture, Fisheries and Rural Development).The list of agricultural enterprises was obtained from the Statistical Register of Agricultural Holdings which is updated from regular surveys.

Both sampling frames are list frames.

The regularly procedure for updating the data on agricultural holdings is set up, primarily by using available administrative sources and current survey data.
Updating of list of farms: January – May 2016. 

 

3. Sampling design
3.1 The sampling design
The sampling design for family farms is one-stage stratified random sampling of holdings based on sampling experts’ choice and history experiences of Agriculture Department. There was no subsampling for any characteristics.
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 are done on county level (NUTS3) on the 4 size classes. 

In size class 5 are all those units who did not meet the requirements to strata 1 to 4.

 

Stratum UAA vineyards orchards cattle pigs sheep goats poultry  horses olive groves
1 >=2500 >=200 >=300 >39 >47 >80 >50 >1000 >20  
2 >=700 >=50 >=50 >14 >19 >50 >30 >500 >10  
3 >=200 >=20 >=20 >4 >5 >27 >20 >200 >5  
4 >=40 >=10 >=10 >=1 >=2 >=5 >=5 >100 >=1 >=10
5 <40 <10 <10 <1 <2 <5 <5 <=100 <1 <10
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 24432.
3.5 The method for the allocation of the overall sample size
We used Neymann allocation and we computed standard deviation for eight key variables: UAA,vineyards,orchards,cattle, pigs, sheep, goats and poultry. From eight 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 four size classes. 
3.6 Sampling across time
A new sample is drawn for each FSS. The holdings assigned to a full coverage stratum are probably assigned to a full coverage stratum in the following survey. 
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
Organic Farming Register  

• Legal base: Council Regulation (EC) 834/2007; Regulation Agricultural law (Narodne novine, official gazette of the Republic of Croatia, nb. 30/15); Ordinance on organic production (Narodne novine, official gazette of the Republic of Croatia, nb. 86/13) ;

• Time reference: 2016; 

• Updating of the source: continuously.

 

Integrated Administrative and Control System (IACS) 

• Legal base:  Council Regulation (EEC) 1782/2003; 

• Time reference: 2016; 

• Updating of the source: continuously. 

 

Agricultural Land Agency (Register of common pastures)

• Legal base: Ordinance on the method of keeping the register of common pastureland owned by the Republic of Croatia (Narodne novine, official gazette of the Republic of Croatia, nb. 18/14); Regulation Agricultural law (Narodne novine, official gazette of the Republic of Croatia, nb. 30/15);

• Time reference: 2016; 

• Updating of the source: continuously.

4.2 Organisational setting on the use of administrative sources
The Official Statistics Act, lays down in its Article 41 (OG, No 103/03; 75/09, 59/12, and 12/13 - consolidated version) the right of the CBS to use all administrative data sources for the purpose of conducting official statistics tasks.

Furthermore, Article 41a determines the obligations of the holders of the administrative data to allow an assessment of the content and potential possibilities in the data sources by the special request of the CBS and rights of CBS to provide methodological support for development of the administrative sources of data for requirements of official statistics.

According to Article 41b the holders of administrative data sources have to familiarise CBS in a timely manner with the intention of collecting administrative data and changes in data content contained in existing administrative sources and subsequently submit the metadata for administrative data which is used for statistical purposes as well as to notify CBS in writing about introduction, amending or expansion of administrative data sources before such matters.

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) Organic Farming Register  and IACS: The reporting unit is the agricultural holding with the same definition as in the FSS.

The reporting unit in Register of common pastures is the production and technological unit of agricultural land owned by the Republic of Croatia which has a pasture, meadows, hayfields and barren plot of land for grazing of livestock and poultry. 

   
- coherence of definitions of characteristics   IACS:  Some variables do not correspond to the list of characteristics for FSS e.g. definition of dried pulses is not same.  
- coverage:      
  over-coverage All registers: Only agricultural holdings that are under thresholds and are in the registers could be treated in the scope of over-coverage.     
  under-coverage     When information is not gathered from the administrative source, data is collected on the field. 
  misclassification   Not detected.  
  multiple listings   There are no multiple listings errors.   
- missing data   Not detected.  
- errors in data   Not detected.   
- processing errors Not measured.    
- comparability Not measured.    
- other (if any)   Not detected.   

 

4.5 Management of metadata
Administrative metadata provided are stored and maintained in dedicated databases.
4.6 Reporting units and matching procedures
For the Organic Farming Register  and IACS, the reporting unit is the agricultural holding with the same definition as in the FSS.
The reporting unit in Register of common pastures is the production and technological unit of agricultural land owned by the Republic of Croatia which has a pasture, meadows, hayfields and barren plot of land for grazing of livestock and poultry. 
4.7 Difficulties using additional administrative sources not currently used
Not applicable.
3.2. Frequency of data collection
Frequency of data collection
The frequency of data collection is triennially. 
3.3. Data collection
1. Data collection modes
The data collection modes which we used are as follows:

- face to face interview by CAPI mode (for private family farms);

- self-completed internet questionnaire by CAWI mode (for business entities);

- self-completed paper questionnaire by PAPI mode (for business entities);

- mix mode (in cases where the respondents were not reached by face to face interview or they missed to send data by post).

 

2. Data entry modes
The data entry modes were as follows:
  • electronic data capture during CAPI
  • entering the data by the holder  - the authorisation for the CAWI questionnaire application was downloaded from the CSO web site and installed on the respondent's computer. Once the form has been completed and the holder has saved the data, the data were automatically encrypted and sent to the central server.
  • entering the data that was collected with PAPI into a web application for those businesses who did not want to fill out the questionnaire via CAWI

 

3. Measures taken to increase response rates
The measures taken to increase response rates were:

• call-back strategies,

• written / telephone reminders,

• contacting,

• legal actions taken on non-response for business entities.

 

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

144 588 holdings in the sampling frame and 134 459 holdings in the extrapolated population

 
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

24 432
3 Number of ineligible holdings 748
3.1 Number of ineligible holdings with ceased activities

This item is a subset of 3.

446
4 Number of holdings with unknown eligibility status

4>4.1+4.2

504
4.1 Number of holdings with unknown eligibility status – re-weighted 504
4.2 Number of holdings with unknown eligibility status – imputed
5 Number of eligible holdings

5=5.1+5.2

23 180
5.1 Number of eligible non-responding holdings

5.1>=5.1.1+5.1.2

589
5.1.1 Number of eligible non-responding holdings – re-weighted 589
5.1.2 Number of eligible non-responding holdings – imputed
5.2 Number of eligible responding holdings 22 591
6 Number of the records in the dataset 

6=5.2+5.1.2+4.2

22 591

 

5. Questionnaire(s) - in annex
See annex.


Annexes:
3.3-5. FARM STRUCTURE SURVEY -1st JUNE 2016_questionnaire
3.4. Data validation
Data validation
The following rules were done at micro level: data format checks, completeness of data, routing checks (soft errors), relational checks (relations among certain characteristics).

All controls were in SQL server and programme for editing was in Visual Basic.

After the data entry, we also used special logic-numeric control for micro data. These controls were calculation controls and logical controls. Before corrections were accepted and entered, the field supervisors or farmers had been contacted by telephone if necessary.

3.5. Data compilation
Methodology for determination of weights (extrapolation factors)
1. Design weights
As estimation method we have used Horvitz-Thomson estimator (regular design weight), but we have also multiplied this estimator with response weights which we have calculated according to provided statuses of respondents. So, final weight was product of these two, and we have used them during estimation procedure. 
2. Adjustment of weights for non-response
Unit non response was treated by adjusting the weights for the non response. The non response rates were calculated at the level of counties (NUTS3) for each stratum (each stratum belong to one county). In case that during the survey a farm changed the stratum in respect to area size, the farm kept the initial weight and was adjusted for non response in the stratum from which it was selected in the sample. The weight related to non response was calculated in the following way:

NONRESPONSE WEIGHT = (X1 + X3 + X4) / (X1 + X4)

Where:

X1 represents number of respondents,

X3 represents number of farms which didn’t want to participate or weren’t reachable,

X4 represents number of respondents whose data of the owner or the address wasn’t correct.

The adjustment for non response was done for the sample survey of family producers. For agricultural enterprises, the weights were calculated at the level of counties. Each enterprise obtained the weight 1 adjusted for non response. Also, on the base of auxiliary information, for some important crops a calibration is done. For all organic producers, data are controlled on the basis of administrative source of data and when it was necessary imputation is done.

3. Adjustment of weights to external data sources
We did calibration according to administrative source for permanent grassland (rough grazing’s) for those holdings which have only rough grazing’s. Due the calibration some holdings have sampling weights under 1. 
4. Any other applied adjustment of weights
No. 
3.6. Adjustment

[Not requested]


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

• holder's name and surname,

• areas under triticale (included in other cereals),

• areas under secondary crops,

• address of the holder,

• number of trees in extensive orchards and olive groves and number of vines in vineyards – needed for calculation of production,

• all spices of vegetables are added in open fields, in glasshouses and in kitchen gardens.

 

The characteristics surveyed only for national purposes are used in EAA, for updating farm register and for calculating standard output.

5.2. Relevance - User Satisfaction

[Not requested]

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 in the file at the link: (link available 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
The results of sample surveys are extrapolated with one factor for a wide range of characteristics. Therefore the accuracy of specific variables is usually lower than in special surveys (crop surveys, vineyard surveys, orchard surveys, livestock surveys, and labour force surveys).

Since post-stratification is not done for this survey, misclassification was not assessed.

Statistics corrects possible errors of measurement using the logic-numeric control. We are trying to avoid the measurement error by training of interviewers and supervisor, control data and process validation.

After data entry, extreme values of variables are checked and corrected if necessary.

The probability of undercoverage in the FSS is very low since there are not many new agricultural holdings.

The unit non-response was one of the main sources of error.

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 CBS 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 sampling design meets the precision requirements of the Regulation 1166/2008. The a posteriori calculation of the RSEs gives the following deviations:

- for the area of plants harvested green in HR04: The main reason is great dispersion in population because stratification did not get the homogeneity of the population by strata.

- for the area of pasture excluding rough grazings in HR03: The main reason is scattered population due to changes since the last update framework for sampling

- for the area of pasture excluding rough grazings in HR04: The main reason is scattered population due to changes since the last update framework for sampling

- for the number of dairy cows in HR03: The main reason is scattered population due to changes since the last update framework for sampling.

- for the number of dairy cows in HR04: The main reason is scattered population due to changes since the last update framework for sampling.

- for the number of other bovine animals in HR04: The main reason is scattered population due to changes since the last update framework for sampling.



Annexes:
6.2.1-1. Relative standard errors FSS2016
6.3. Non-sampling error

See below

6.3.1. Coverage error
1. Under-coverage errors
The probability of undercoverage in the FSS is very low since there are not many new agricultural holdings. We consider that the number of agricultural households decreases, and that the number of newly established farms is not in balance, i.e., that those who go out of farming are more numerous. All important new farms are included in administrative registers and were consequently included into the list.

  

2. Over-coverage errors
With the aid of questions in the questionnaire we also recorded the reasons for the non-eligibility. This helps us for updating the Statistical Register of Agricultural Holdings (exclusion of ineligible family farms from the frame).We assume that the next Agricultural Census will give us the real degree of overcoverage when the entire frame will be updated again.  

Weighting factors were calculated on the basis of eligibility status of agricultural holdings, with the formula (number of eligible respondents + number of eligible non-respondents + number of units with unknown eligibility)/ number of eligible respondents - on the level of strata. 

2.1 Multiple listings 
Altogether 52 family farms were listed twice. They were treated as ineligible. 

 

3. Misclassification errors
Since post-stratification is not done in Croatia, misclassification was not assessed. However, results of the FSS prove that there were no problems with misclassification. 

 

4. Contact errors
There were altogether 2,1% of total sample not contacted farms (the holder could not be reached - there was nobody at the address, the person was not known at the address, the address of the holding was incomplete or the telephone number of these family farms did not exist). The extrapolation factor is adjusted for these farms. 

 

5. Other relevant information, if any
Not available.
6.3.1.1. Over-coverage - rate
Over-coverage - rate

Over-coverage rate was calculated as follows: 

Over-coverage rate = out-of-scope units / (eligible units + number of units with unknown eligibility + out-of-scope units) 

The share of units that were included in the frame and it turned out that they didn’t belong to the target population was 3,06%. 
6.3.1.2. Common units - proportion

[Not requested]

6.3.2. Measurement error
Characteristics that caused high measurement errors
Statistics corrects possible errors of measurement using the logic-numeric control. We are trying to avoid the measurement error by training of interviewers and supervisors, control data and process validation. Characteristics that are complicated for both respondents and interviewers are related to labour force, more than 50% of production self-consumed by the holder and importance of other gainful activities directly related to the holding.

After data entry, extreme values of variables are checked and corrected if necessary.

6.3.3. Non response error
1. Unit non-response: reasons, analysis and treatment
Unit non-response was treated with re-weighting. 

The main reasons for non-response were refusals because of the following reasons:

• dissatisfaction with the current agricultural policy in Croatia, 

• problems with unsolved ownership (official procedures regarding succession can be very long),

• general refusal because of other reasons.

The survey results are weighted in order to adjust for the sampling design and for unit non-response to produce valid results for the target population. Unit non-response is accounted by re-weighting.This will automatically adjust the sample weights of the respondents to compensate for unit non-response. So, CBS experts have used the basic method for adjusting for the sampling design and for unit non-response and they calculated weights only by using module SAS-base.

The bias risks associated with non-response are low because of the very low non-response rate (4,6%). 

 

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

Unit non response rate was calculated as follows:

Unit non response rate = (1-(number of eligible respondents / (number of eligible respondents + number of eligible non-respondents + number of units with unknown eligibility)))*100

Unit non-response rate amounts to 4.6 %. 

6.3.3.2. Item non-response - rate
Item non-response - rate

Not available. All known cases of non-response items were solved by re-interviewing or by imputation. 

6.3.4. Processing error
1. Imputation methods
Imputation of missing data has been done for some organic characteristics. The Cold-deck imputation method was used to "fill-in" the missing item. 

 

2. Other sources of processing errors
Data on the number of corrections were not collected during data processing.
Follow-up interviews were used for corrections.

 

3. Tools used and people/organisations authorised to make corrections
Data entry and checking of data (logic-numeric control incorporated in questionnare, SQL). All the corrections are done in Agriculture, forestry, fishery and environment Directorate/Crop production statistics and Register of Agricultural Holdings Unit. 
6.3.4.1. Imputation - rate
Imputation - rate

2 % regarding organic farming characteristics. 

6.3.5. Model assumption error

[Not requested]

6.4. Seasonal adjustment

[Not requested]

6.5. Data revision - policy
Data revision - policy
Revision Policy of the Croatian Bureau of Statistics is based on the principles of the European Statistics Code of Practice.

Revision policy of the Croatian Bureau of Statistics distinguishes three types of revisions: regular revisions, major revisions and unscheduled revisions. 

Unplanned revision of the FSS 2016 may be carried out. In any case it is necessary to clarify the reasons for a revision (mistake in data sources or calculations or due to the unexpected changes in the methodology or data sources).

6.6. Data revision - practice
Data revision - practice
Data revision is not planned so far.
6.6.1. Data revision - average size

[Not requested]


7. Timeliness and punctuality Top
7.1. Timeliness

See below

7.1.1. Time lag - first result
Time lag - first result
Reference period 1 June 2016
Published 4 November 2016
Difference number of months 5
7.1.2. Time lag - final result
Time lag - final result
Reference period 1 June 2016
Published 29 December 2017
Difference number of months 19

 

Last day of the reference period 31 December 2016
Day of publication of final results 29 December 2017
Difference number of months 12
7.2. Punctuality

See below

7.2.1. Punctuality - delivery and publication
Punctuality - delivery and publication
All publications were planned and all publications were published on time. First results and final results -0 days (we do not expect delays in dissemination).
Reference period 1 June 2016; 31 May 2015 to 1 June 2016
Date scheduled for delivery 29 December 2017
Announcement for delivery 29 December 2017
Difference number of days 0


8. Coherence and comparability Top
8.1. Comparability - geographical
1. National vs. EU definition of the agricultural holding
There are no deviations from the EU definitions.

The agricultural activities specified in Annex I of the Regulation 1166/2008 are used to define the agricultural holding.

Also, we included into the scope of survey all of non-profit institutions (schools, hospitals, prisons, churches, etc.) that perform agricultural activity as additional or supplementary activity since their production can be of significant volume.

 

2.National survey coverage vs. coverage of the records sent to Eurostat
There are some differences between the population covered in national survey and the population covered by the records sent to Eurostat. The difference is in number of agricultural holdings which are below the national thresholds (area of crops or number of livestock), but it is not significant number of holdings. The national estimates exclude the holdings below all thresholds and there is no difference from the population sent to Eurostat. 

 

3. National vs. EU characteristics
We used the Handbook implementing the FSS2016.

The number of hours per year for a full-time employee is 1800 hours.

There are no differences between national and EU definitions and all definitions and classifications are harmonized with set of characteristics (1166/2008) and definitions (1200/2009).

 

4. Common land
4.1 Current methodology for collecting information on the common land
In FSS 2016, HR used three methods to record common land:

1)        directly asking the farmers: there are 1302 agricultural holdings using a total of 13 711 ha common land recorded under the heading 'common land', but in most cases they have more  types of tenure and  they have all types of land (mostly permanent grassland).

2)        using a model: The estimated area obtained by modelling is about 60 000  ha. Actualy we used calibration of permanent grassland on the basis of LSU as auxiliary variable. So, type of land is permanent grassland and mixed type of tenure as farmer declared. The problem is that in the model HR could not uniquely determine that it was common land and the area is allocated according to the existing type of tenure.

3)        common land units. There are 14 common land units (at NUTS 3 level) with 278 891 ha total area =278891 ha UAA =278 891 ha permanent grassland and meadow = 278 891 ha rough grazing’s. They can be identified using FSS category A_2=6 (Holding is a common land unit). All area is recorded under "common land" legal type.  All the HR common land units are tenure classified as common land.

A separate questionnaire for common land was not used in FSS questionnaire.

4.2 Possible problems encountered in relation to the collection of information on common land and possible solutions for future FSS surveys
Farmers have problems to estimate the share of common land they are actually using. 
4.3 Total area of common land in the reference year
The area under common land is 352 602 hectares. Se under point 8.1-4.1.
4.4 Number of agricultural holdings making use of the common land or Number of (especially created) common land holdings in the reference year
The 14 common land units at NUTS3 level. 

 

5. Differences across regions within the country
Not applicable. 

 

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.  Thresholds are the same in 2013 and 2016. Wording of thresholds is correct in the 2016 NMR. Mushrooms is a non-significant characteristic.

 

3. Changes of definitions and/or reference time and/or measurements of characteristics
There have been some changes but not enough to warrant the designation of a break in series. Separate data on common land was collected for the first time from farmers.

 

4. Changes over time in the results as compared to previous FSS, which may be attributed to sampling variability
Separate data on common land was collected for the first time from farmers.

 

5. Common land
5.1 Possible changes in the decision or in the methodology to collect common land
Concerning common land in 2016 we used combination of three methods: direct question on the farm level, modelling and data from the administrative sources. See also 8.3 Comparability - cross domain. 
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
The total common land decreased from 438 891 to 352 602 ha (by 19.7%). The difference between 2016 and 2013 occurred because a part of these areas is purchased or rented  out and is no longer common land.

In each 2013 and 2016, there are 14 common land units defined at NUTS3 level.

 

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 134 459  157 441  -14.6%  The number of economically active holdings of Croatia decreased due to the fact that small holdings of natural persons ceased their agricultural activities.
Utilised agricultural area (ha) 1 562 983 1 571 202 -0,5%   
Arable land (ha) 881 616 878 429 +0,4%   
Cereals (ha) 533 085 590 938 -9,8%   
Industrial plants (ha) 173 968 120 620  +44,2%  The increase in area results from  continuing upward trend in soya beans and rapeseed which are more cost-effective than growing of cereals.
Plants harvested green (ha) 116 459 118 895  -2,0%   
Fallow land (ha) 18 256 5 443  +235,4%  The increase in area of fallow land is due to the introduction of subsidies to the areas of protein crops as well as the implementation of the greening policy.
Permanent grassland (ha) 607 555 618 073  -1,7%   
Permanent crops (ha) 71 965 72 936 -1,3%   
Livestock units (LSU) 754 706 864 015 -12,7%  The number of livestock units has decreased due to the crises in the milk sector (which has caused the decrease in the number of dairy cows) and to high import of pigs (which has caused the decrease in the number of pigs).
Cattle (heads) 418 443 453 199 -7,7%  
Sheep (heads) 778 211 802 315 -3,0%   
Goats (heads) 99 623  86 141 +15,6%  The number of goats is increased due to additional payments for growing breeding goats. The data was checked with administrative data. Also, the Animal statistics survey confirm an increase in the number of goats.
Pigs (heads) 944 877 1 186 456 -20,4%  The number of pigs is decreasing due to high imports of pigs.
Poultry (heads) 10 388 542 13 630 038  -23,8%  The number of poultry is rather stable during 2013-2016 and this difference is due to fact that the number of poultry is very much related to the reference date of the survey.
Family labour force (persons) 300 157  374 912  -19,9%  The number of holdings has decreased by 14,6% and many small holdings of natural persons have disappeared, so the family labour force has decreased accordingly.
Family labour force (AWU) 141 451 163 143 -13,3%  As above.
Non family labour force regularly employed (persons) 17 887 13 455 +32,9%  The number of legal persons has increased by 37,1 % and non family labour force has increased accordingly.
Non family labour force regularly employed (AWU) 14 072 10 104 +39,3%  As above.
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 comparisons were done at micro level with administrative sources (organic producers data, IACS) and adjustments are made mainly for land and labour force characteristics. 

 

2. Coherence at macro level with other data collections
The results of the FSS 2016 were checked and compared with all the available administrative data, previous surveys and other surveys conducted by CBS. There were no significant differences.

Procedures for checking the quality of administrative data depend on specific administrative source and statistics derived from it. Usually, agricultural surveys contain unique identifiers which enable linking survey data with available administrative sources. Generally, quality of administrative data is being checked upon using analytical methods (scope, coding, double counting, consistency, year to year changes, etc.). CBS cooperates with owners of administrative data sources to define validation rules already within administrative source in order to raise quality of data. Results of quality analysis are communicated to the owner of the administrative data source for resolving the inconsistencies.

The FSS statistics are reconcilable with other statistical domains (economic, environment etc.).

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
Preliminary results were published in paper version on 4 November 2016.

Final results were published in December 2017. The database with FSS 2016 data is available on the web site of CBS

https://www.dzs.hr/App/PXWeb/PXWebEng/Menu.aspx?px_language=en&px_type=PX&px_db=Agriculture%2c+Hunting%2c+Forestry+and+Fishing&rxid=04654694-2080-4769-8ff9-1140ef84ab42

 

2. Date of issuing (actual or planned)
December 2017 (final); November 2016 (preliminary)

 

3. References for on-line publications
Results are published and are available in CBS database (www.dzs.hr).

https://www.dzs.hr/App/PXWeb/PXWebEng/Menu.aspx?px_language=en&px_type=PX&px_db=Agriculture%2c+Hunting%2c+Forestry+and+Fishing&rxid=04654694-2080-4769-8ff9-1140ef84ab42

9.3. Dissemination format - online database
Dissemination format - online database
Database with FSS2016 data is available on the website of CBS (http://www.dzs.hr/App/PXWeb/PXWebEng/Menu.aspx?px_language=en&px_type=PX&px_db=Agriculture%2c+Hunting%2c+Forestry+and+Fishing&rxid=04654694-2080-4769-8ff9-1140ef84ab42). 
9.3.1. Data tables - consultations
Data tables - consultations
Not available.
9.4. Dissemination format - microdata access
Dissemination format - microdata access
The information is provided in the Ordinance on Conditions and Terms of Using Confidential Data for Scientific Purposes (Official Gazette, No. 137/13) which defines in detail conditions, modalities and measures for protecting confidential information (research proposal submitted by independent researchers or research entities referred to in Article 2 of the Ordinance; access to confidential data on the basis of research proposals submitted and approved; confidential declaration has to be signed by any individual researcher using confidential data; special contract has to be concluded inter CBS and independent researcher or research entity; access to confidential data may be granted only for the period of the duration of the research project, max 5 years; obligations for taking all legal, administrative, technical and organisational safeguards of the confidential data for scientific purpose which have been granted; confidential data must be destroyed when the research project is finished; after expiry of the research project, the researchers or research entity are obliged to provide CBS with references to all reports that have been produced using the data; termination access to data etc.).

Each usage of confidential information is regulated through a specific contract with CBS, which strictly regulates this issue.

9.5. Dissemination format - other

[Not requested]

9.6. Documentation on methodology
1. Available documentation on methodology
The short methodological notes are available within the press release on structure of agricultural farms, but more extensive methodological explanations have been published in December 2017 on the following link: https://www.dzs.hr/Eng/DBHomepages/Agriculture/Farm%20structure%20survey/methodology.htm. Manual for interviewers, CBS, Agriculture, Forestry and Fisheries Production Statistics Department.

 

2. Main scientific references
  1. Lavallee P., Hidiroglou M. (1988),  On the Stratification of Skewed Populations, Survey Methodology, 14, pp. 3 – 43
  2. Paul S. Levy, Stanley Lemeshow, Sampling of Populations - Methods and Applications 
  3. Statistics Canada, Survey methods and practices
  4. Food and Agriculture Organisation of the United Nations (1989), Sampling methods for agricultural surveys
  5. Statistical Reporting Service U.S. Department of Agriculture Washington D.C.  (1986), Survey Design and Estimation for Agricultural Sample Surveys
9.7. Quality management - documentation
Quality management - documentation
CBS accepted TQM approach as the general model for quality management, quality assessment and quality improvement. To support implementation of this model the basic strategic document is developed where the following main cornerstones of the TQM model are explained and described:

•            High quality statistical processes and products

•            User satisfaction

•            Professional orientation of the employees

•            Efficiency of the processes

•            Reduction of the response burden

For each of these general aims, concrete actions are foreseen and plans for their implementation described.

https://www.dzs.hr/Eng/international/Quality_Report/Quality_Report_Documents/Quality_Report_Statistical_TQM.pdf

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
Within the framework of the FSS 2016, the regular annual Survey on Areas Sown was carried out. With this kind of organization we carried out only one survey and reduced the response burden on farmers. On the other hand, we have to provide results for the Survey on Areas Sown much earlier than for the FSS, which means more burdens for the CBS.

The biggest burden is on biggest units for which we have full coverage in the sample and for all cycles of surveys while for the smaller units the Classifications, Sampling, Statistical Methods and Analyses Department controlled that the same unit is not included in the sample in consecutive number of times. 


11. Confidentiality Top
11.1. Confidentiality - policy
Confidentiality - policy
The statistical confidentiality of NSI is regulated in Chapter IX Confidentiality and protection of statistical data, Articles 59-66 of Official Statistics Act (OG, No 103/03; 75/09, 59/12, and 12/13 - consolidated version). In accordance to these articles, statistical data on legal and natural persons, if these data can directly or indirectly be related to legal or natural persons, shall be considered statistically confidential, may only be used for statistical purposes, and shall be expressed in aggregate form.

Statistical confidentiality is further strengthened through the following documents:

- Ordinance on access to confidential statistical data, Notification No. 4/2013

- Ordinance on Conditions and Terms of Using Confidential Data for Scientific Purposes, OG, No. 137/13

Statistical data collected in this survey, according to the Law on official statistics (NN, br. 103/03., 75/09. i 59/12.) is confidential and its purpose is restricted exclusively to statistical usage. Dissemination of micro-data to external users for research purposes respects statistical confidentiality.

The information is published on CBS website - Official Statistics Act (OG, No 103/03; 75/09, 59/12, and 12/13 - consolidated version) and on CBS website, under theme “About us”.

11.2. Confidentiality - data treatment
Confidentiality - data treatment
In the ongoing CBS restructuring, it is foreseen to place the focal point for ensuring confidentiality, including provision of guidance, recommending appropriate methodologies and periodical examination of methods used for data protection, within the Statistical Business Register, Classifications, Sampling, Statistical Methods and Analyses Department. A filter is applied during the table compilation using the following processes:

• dominance treatment: if any holdings account for at least 85% of the value, this value is put to zero;

• small number of units: if a value is calculated from less than 3 holdings, this value is put to zero;

• rounding: the values are rounded to the closer multiple of 10.


12. Comment Top
1. Possible improvements in the future
In order to modernize the methods of data collection, for businesses we planned to improve and supplement already developed modes of data collection.  Also, we planned to introduce CAWI method for private family farms and ensure the helpdesk during completing the questionnaire.

 

2. Other annexes
Not available.


Related metadata Top


Annexes Top