Crop production (apro_cp)

National Reference Metadata in ESS Standard for CROPS Reports Structure (ESQRSCP)

Compiling agency: Hungarian Central Statistical Office

Time Dimension: 2016-A0

Data Provider: HU1

Data Flow: CROPROD_ESQRSCP_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

Hungarian Central Statistical Office

1.2. Contact organisation unit

Agriculture and Environment Statistics Department

1.5. Contact mail address

H-1525 Budapest, P.O.B. 51.
Hungary


2. Statistical presentation Top
2.1. Data description

Annual crop statistics provide statistics on the area under main arable crops, vegetables and permanent crops and production and yield levels.  The statistics are collected from  a wide variety of sources: surveys, administrative sources, experts and other data providers. The data collection covers early estimates (before the harvest) and the final data. Data are collected mostly at national level but for some crops also regional data exist (NUTS1/2).

2.2. Classification system

Hierarchical crop classification system

2.3. Coverage - sector

Growing of non-perennial crops, perennial crops and plant propagation (NACE A01.1-01.3)

2.4. Statistical concepts and definitions

See: Annual crop statistics Handbook

2.5. Statistical unit

Utilised agricultural area cultivated by an agricultural holding.

2.6. Statistical population

All agricultural holdings growing crops.

2.7. Reference area

The entire territory of the country.

2.8. Coverage - Time

Crop year.

2.9. Base period

Not applicable.


3. Statistical processing Top

See points 3.1, 3.2, 3.3 and 3.4

3.1. Source data

  Source 1 Source 2 Source 3 Source 4
Have new data sources been introduced since the previous Quality Report (2014)?  YES      
If yes, which new data sources have been introduced since the previous quality report (2014)?

IACS

Type of source? Administrative data
To which Table (Reg 543/2009) do they contribute? Table4
Have some data sources been dropped since the previous Quality Report (2014)?      
Which data sources have been dropped since the previous quality report (2014)?
Type of source?
Why have they been dropped?
Additional comments

Currently we use the IACS data for control of the sown area we collected in our questionnaire.


Data sources: Please indicate the data sources which were used for the reference year 2016

  Type Name(s) of the sources If other type, which kind of data source?
Table 1: crops from arable land      
Early estimates for areas Survey
Expert estimate

Land area and sown area - FSS 2016 (Nr. 2242, 2243 and 2374)

Production of ear cereals (Nr. 1084)

Production data of main crops (Nr. 1085)

Expert estimation (early estimates)

Expert estimation - preliminary data

Final area under cultivation Survey

Annual data of agriculture (Nr. 1092, 1094, 2219 and 2375)

Expert estimation - final data

Production Survey

Annual data of agriculture (Nr. 1092, 1094, 2219 and 2375)

Expert estimation - final data

Yield Survey

Annual data of agriculture (Nr. 1092, 1094 and 2219)

)

Non-existing and non-significant crops Survey

Annual data of agriculture (Nr. 1092, 1094 and 2219)

)

Table 2: Vegetables, melons and strawberries       
Early estimates for harvested areas Survey
Expert estimate

Land area and sown area - FSS 2016 (Nr. 2242, 2243 and 2374)

Production data of main crops (Nr. 1085) 

Expert estimation (early estimates)

Expert estimation - preliminary data

Final harvested area Survey

Annual data of agriculture (Nr. 1092, 1094 and 2219)

)

Production Survey

Annual data of agriculture (Nr. 1092, 1094 and 2219)

)

Non-existing and non-significant crops Survey

Annual data of agriculture (Nr. 1092, 1094 and 2219)

)

Table 3: Permanent crops      
Early estimates for production area Survey
Expert estimate

Land area and sown area - FSS 2016 (Nr. 2242, 2243 and 2374)

Production data of main crops (Nr. 1085) 

Expert estimation (early estimates)

Expert estimation - preliminary data

Final production area Survey

Annual data of agriculture (Nr. 1092, 1094 and 2219)

)

Production Survey

Annual data of agriculture (Nr. 1092, 1094 and 2219)

)

Non-existing and non-significant crops Survey

Annual data of agriculture (Nr. 1092, 1094 and 2219)

)

Table 4: Agricultural land use      
Main area Survey
Administrative data
Expert estimate

Land area and sown area - FSS 2016 (Nr. 2242, 2243 and 2374)

IACS
 
Expert estimation - final data
Non-existing and non-significant crops Survey

Land area and sown area - FSS 2016 (Nr. 2242, 2243 and 2374)

Expert estimation - final data

Total number of different data sources

7

   
Additional comments

Crop statistics contain data of four periods of time annually. The first data collections are Land area and sown area (Nr. 2242 and 2243) in June. In the case of agricultural entreprises full scope observations, while in the case of private holdings sample surveys are carried out (FSS 2016). We use the IACS data for control of the sown area we collected in our questionnaire. The second data collection is the Production of ear cereals (Nr. 1084) in August. Observation contains data of the agricultural enterprises only (full scope), while data of the private holdings are estimated by experts. Data collection contains information on harvested area and production. The third data collection is the Production data of main crops (Nr. 1085) in November. Observation contains data of the agricultural enterprises only (full scope), while data of the private holdings are estimated by experts. Data collection contains information on harvested area and production, as well. Finally, the fourth data collections take place at the end of the given year. These are the Annual data of agriculture (Nr. 1092, 1094, 2219). In the case of agricultural entreprises full scope observations, while in the case of private holdings sample surveys are carried out. Data of these three collections serve as the basis of the Crops Balance Sheets and EAA. Data on crop production are finalised during the compilation of commodity balances. We use expert estimation to supplement our data collections. 

Data source for the humidity

Put x, if used

Surveyed: farmers report the humidity

 

Surveyed: farmers convert the production/yield into standard humidity     

 X

Surveyed: whole sale purchasers report the humidity

 

Surveyed: whole sale purchasers convert the production/yield into standard humidity     

 

Surveyed by experts (e.g. test areas harvested and measured)

 

Estimated by experts

 X

Other type

 

If other type, please explain

 

Additional information

We collect the the production data in national standard humidity.

   


Which method is used for calculating the yield for main arable crops? production divided by harvested area
If another method, describe it.
3.2. Frequency of data collection

  Source 1 Source 2 Source 3 Source 4 Source 5 Source 6 Source 7 Source 8 Source 9
Name of data source

Land area and sown area - FSS 2016 (Nr. 2242, 2243 and 2374)

Production of ear cereals (Nr. 1084)

Production data of main crops (Nr. 1085)

Annual data of agricultural (Nr. 1092, 1094 and 2219, 2375)

IACS

Planning (month-month/year)

06/2015-04/2016

 

02/2016

04/2016

05/2016

Preparation (month-month/year)

06/2015-04/2016

03-07/2016

05-06/2016

06-10/2016

Data collection (month-month/year)

05-07/2016

08/2016

11/2016

01-03/2017

Quality control (month-month/year)

05-11/2016

08-09/2016

12/2016-01/2017

03-06/2017

Data analysis (month-month/year)

06/2016-07/2017

09/2016

01/2017

06-08/2017

Dissemination (month-month/year)

11/2016-11/2017

09/2016

01/2017

08-10/2017)

If there were delays, what were the reasons?
3.3. Data collection

Definitions

  Question In case yes, how do they differ?
Do national definitions differ from the definitions in Article 2 of Regulation (EC) No 543/2009? YES

Main area (tab 4) corresponds to the sown area as at 31 May.

The total production includes the production of kitchen gardens. The production from kitchen gardens is still significant in Hungary.

Are there differences between the national methodology and the methodology described in the Handbook concerning e.g. the item and aggregate calculations? NO
Are special estimation/calculation methods used for main crops from arable land? NO
Are special estimation/calculation methods used for vegetables or strawberries? NO
Are special estimation/calculation methods used for permanent crops for human consumption? NO
Are special estimation/calculation methods used for main land use? NO
Do national crop item definitions differ from the definitions in the Handbook  (D-flagged data)? YES  
In case yes, how do they differ? ( list all items and explanations) I1190 - Other oilseed crops n.e.c.
I5000 - Aromatic, medicinal and culinary plants

• I5000: Aromatic, medicinal and culinary plants: The total
production of Hungarian red pepper accounted as industrial
crops. Poppy is excluded
• I1190: Other oilseed crops n.e.c.: the total production of
poppy accounted here

In case data are delivered for one of the items below, describe the crop species included in the item: C1900 - Other cereals n.e.c. (buckwheat, millet, etc.)
P9000 - Other dry pulses and protein crops n.e.c.
R9000 - Other root crops n.e.c.
I1190 -Other oilseed crops n.e.c
G2900 - Other leguminous plants harvested green n.e.c.
G9900 - Other plants harvested green from arable land n.e.c.
V1900 - Other brassicas n.e.c.
V2900 - Other leafy or stalked vegetables n.e.c.
V4900 - Other root, tuber and bulb vegetables n.e.c.
V9000 - Other fresh vegetables n.e.c.
F1190 - Other pome fruits n.e.c.
F3900 - Other berries n.e.c.
PECR9 - Other permanent crops

C1900: Other cereals n.e.c. includes buckwheat, millet, canary seed, Indian rice, other (unknown) cereals n.e.c.
P9000 Other dry pulses and protein crops n.e.c. includes lentils, chickling vetch, cow pea and other (unknown) dry pulses n.e.c.
R9000 Other root crops n.e.c. includes fodder beet, wild carrot, turnip rape (Brassica rapa ssp. Rapa !), Savoy cabbage for fodder, feeding cale, pumpkin feed, oil radish (green), tyfon
I1190 Other oilseed crops n.e.c. includes peanuts, mustard, sesame, poppy seed, ricin, saffron, oil radish, hempseed, oil squash seed, with skin, oil squash seed, without skin, niger-seed (Guisotia abessinia)
G2900 Other leguminous plants harvested green n.e.c. includes red clover, sainfoin, bird’s-foot trefoil, grass clover mixture, other (unknown) leguminous plants n.e.c.
G9900 Other plants harvested green from arable land n.e.c. includes sorghum for feed, millet for feed, soya for feed, winter fodder mix, spring fodder mix, crimson clover (Trifolium incarnatum), sweet lupine, white sweet clover, foxtail millet, Sudan grass, other (unknown) plants harvested green n.e.c.
V1900: Other brassicas n.e.c. includes Chinese cabbage and kohlrabi
V2900: Other leafy or stalked vegetables n.e.c. includes garden sorrel and chives
V4900: Other root, tuber and bulb vegetables n.e.c. includes parsley, horse-radish and pearl onion
V9000: Other fresh vegetables n.e.c. includes sweet corn and other (unknown) fresh vegetables
F1190: Other pome fruits n.e.c. includes quinces and medlar
F3900: Other berries n.e.c.: includes gooseberry, blackberry, josta, elderberry, sea buckthorn, raspberry X blackberry, black chokeberry (Aronia melanocarpa), other (unknown) berries n.e.c.

>PECR9: Other permanent crops: Christmas trees, willow/osier


Population

Which measures were taken in order to make sure that the requirement stipulated in Art. 3.2 are met?
(Statistics shall be representative of at least 95 % of the areas of each table in the Regulation).

Aggregated enterprise data and extrapolated private holding data are compared to the data of previous years, and to the data of all available statistical  data collection and administrative data sources (data of Ministry of Agriculture and Rural Development, Agricultural Economics Research Institute, National Council of Wine Communities and Product boards). Expert estimation is used complementarily.

Is the data collection based on holdings? YES
If yes, how the holdings were identified? Unique statistical farm identifier
If not, on which unit the data collection is based on?
When was last update of the holding register? (month/year)

June/2016

Was a threshold applied? NO
If yes, size of the excluded area Area excluded on the basis of the threshold in % of the total area for that crop
Cereals for the production of grain (in %)
Dried pulses and protein crops (in %)
Root crops (in %)
Oilseeds (in %)
Other industrial crops (included all industrial crops besides oilseeds)  (in %)
Plants harvested green from arable land (in %)
Total vegetables, melons and strawberries (in %)
Cultivated mushrooms (in %)
Total permanent crops (in %)
Fruit trees (in %)
Berries (in %)
Nut trees (in %)
Citrus fruit trees (in %)
Vineyards (in %)
Olive trees (in %)


Survey method (only for census and surveys)

  Survey 1  Survey 2 Survey 3  Survey 4 Survey 5  Survey 6 Survey 7
Name of the survey

Land area and sown area - FSS 2016 (Nr. 2242, 2243 and 2374)

Production of ear cereals (Nr. 1084)

Production data of main crops (Nr. 1084)

Annual data of agricultural (Nr. 1092, 1094, 2219 and 2375)

Which survey method was used? On-line electronic questionnaire filled in by respondent
Face-to-face interview
On-line electronic questionnaire filled in by respondent On-line electronic questionnaire filled in by respondent On-line electronic questionnaire filled in by respondent
Face-to-face interview
If 'other', please specify
Please provide a link to the questionnaire

Annex

Annex

Annex

Annex

Data entry method, if paper questionnaires?


Administrative data (This question block is only for administrative data)

  Admin source 1 Admin source 2 Admin source 3 Admin source 4 Admin source 5 Admin source 6
Name of the register

IACS

Description

Data of agricultural support

Data owner (organisation)

Hungarian State Treasury

Update frequency Continuous
Reference date (month/year)

2016

Legal basis
Reporting unit
Identification variable (e.g. address, unique code, etc.)
Percentage of mismatches (%)
How were the mismatches handled?
Degree of coverage (holdings, e.g. 80%)
Degree of completeness (variables, e.g. 60%)
If not complete, which other sources were used ?
How were the data used?
Validation
Data used for other purposes, which?
Which variables were taken from administrative sources?
Were there any differences in the definition of the variables between the administrative source and those described in the Regulation?
Please describe the differences
What measures were taken to eliminate the differences?
How was the reliability, accuracy and coherence (comparison to other available data) of the data originated from administrative data source (ante- and/or ex-post) checked?
What were the possible limitations, drawbacks of using the data from administrative source(s)?


Expert estimations (This question block is only for expert estimates)

  Expert estimate 1 Expert estimate 2 Expert estimate 3 Expert estimate 4 Expert estimate 5 Expert estimate 6
Name of the estimation

Expert estimation - final data

Expert estimation - preliminary data

Data owner (organisation)
Update frequency (e.g. 1 year or 6 months) 2 times per year 2 times per year
Reference date (Month/Year  e.g. 1/16 - 8/16)

6/16; 5/16

8/16; 12/16

Legal basis
Use purpose of the estimates?

Used  for estimating sown area, harvested area, harvested production and yield at county level.

Estimation includes data of:

- households below the threshold, 

- unavailable holdings, holdings refusing response, holdings with no Hungarian address,

- special land users (e.g.: National Parks, Hungarian National Railway, Water Conservancy Directorates, Hungarian Army, congregations and other areas.)

Used  for estimating harvested area, harvested production and yield of ear cereals and main crops at county level.

Estimation includes data of:

- private holdings,

- households below the threshold, 

- special land users (e.g.: National Parks, Hungarian National Railway, Water Conservancy Directorates, Hungarian Army, congregations and other areas.)

What kind of expertise the experts have?

agricultural

agricultural

What kind of estimation methods were used?

Experts of the Ministry of Agriculture, research institutes, National Council of Wine Communities, Product Boards, main producers and processors etc. cooperate with the experts of HCSO. 

Draft estimates for private holdings are based on their sown area and the reported yields of agricultural enterprises. Experts of the Ministry of Agriculture, research institutes, National Council of Wine Communities, Product Boards, main producers and processors etc. cooperate with the experts of HCSO. 

Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation? NO NO
If yes, please describe the differences
What measures were taken to eliminate the differences?
How were the reliability, accuracy and coherence (comparison to other available data) of the data originated from experts' estimates (ante- and/or ex-post)checked?

We use the expert estimation only to supplement our surveys.

We use the expert estimation only to supplement our surveys.

What were the possible limitations, drawbacks of using the data from expert estimate(s)?

In case of minor crops provide less reliable information

In case of minor crops provide less reliable information

Additional comments

We use the expert estimation only to supplement our surveys. We make these expert estimations four times per year: June (for sown area of crops); August (for ear cereals); December (for other main crops) and May (for all crops).

3.4. Data validation

Which kind of data validation measures are in place? Automatic and Manual
What do they target? Completeness
Outliers
Aggregate calculations
Is the data cross-validated against an other dataset? YES
If yes, which kind of dataset? Previous results
Farm Structure Survey
Other dataset
If other, please describe

IACS

3.5. Data compilation

Not Applicable.

3.6. Adjustment

Not applicable.


4. Quality management Top
4.1. Quality assurance

  Completeness Punctuality Accuracy Reliability Overall quality
How would you describe the overall quality development since 2014? Improvement Improvement Stable Stable Improvement
Is there a quality management process in place for crop statistics? YES        
If, yes, what are the components?

Automatic controlling operates in the data recording and controlling programs during data processing. Relational checks are carried out within and between the tables, and data are compared to the data of the previous surveys. This is completed by another check afterwards (apart from the controlling program) in order to analyse the unique or rare correlations.

       
Is there a Quality Report available? YES        
If yes, please provide a link(s)

http://www.ksh.hu/apps/meta.objektum?p_lang=EN&p_menu_id=110&p_ot_id=100&p_obj_id=OMN&p_session_id=77979071

       
To which data source(s) is it linked?

Name of the data source: Crop production statistics

       
Has a peer-review been carried out for crop statistics? NO        
If, yes, which were the main conclusions?        
What quality improvement measures are planned for the next 3 years?        
If, other, please specify        
Additional comments

Using time series analysis, regional series analysis and composition change analyis data are compared to the data of previous years, and to the data of all available statistical  data collection and administrative data sources (data of Ministry of Agriculture and Rural Development, Agricultural Economics Research Institute, National Council of Wine Communities and Product boards). Data on crop production are finalised during the compilition of commodity balances. Balances are compiled at country level for the following variables: sown area, harvested area, total harvested production.

       
4.2. Quality management - assessment

See the European level Quality Report.


5. Relevance Top

See points 5.1 and 5.2

5.1. Relevance - User Needs

Are there known unmet user needs? NO
Describe the unmet needs
Does the Regulation 543/2009 meet the national data needs? YES
Does the ESS agreement meet the national needs? YES
If not, which additional data are collected?
Additional comments

Because of national needs, Hungarian observation of crops is of wider range than Regulation 543/2009.  

5.2. Relevance - User Satisfaction

Have any user satisfaction surveys been done? NO
If yes, how satisfied the users were?
Additional comments
5.3. Completeness

See the European level Quality Report

5.3.1. Data completeness - rate

See the European level Quality Report


6. Accuracy and reliability Top

See points 6.2, 6.3.1, 6.3.2 and 6.3.3

6.1. Accuracy - overall

See the European level Quality Report.

6.2. Sampling error

Sampling method and sampling error

  Survey 1 Survey 2 Survey 3 Survey 4 Survey 5 Survey 6 Survey 7
Name

Annual data of agricultural (Nr. 1092, 1094 and 2219, 2375)

Sampling basis? Multiple frame
If 'other', please specify
Sampling method? Clustered
Stratified
Multi-stage
If stratified, number of strata?

3

If stratified, stratification basis? Location
Size
Legal status
If 'other', please specify

Agricultural Enterprises (Nr. 1092, 1094) and Key Private Farms (Nr. 2375):

Full Scope, no sampling

 

(Small) private holdings (apart from key private holdings) (Nr. 2219)

Stratified two-stage cluster sampling (probability design), where the stratification means geographical stratification (counties) and the clusters are enumeration districts.
Districts were selected with simple random sampling within counties and within each district all the farms are included (including new farms as well).
The selection rate of districts was different county by county. Each projection factors were calculated at county level.

First level – counties (each were selected),
Second level – enumeration districts

Size of total population

16364 survey districts which contain

564 557 small private holdings in Agricultural Census 2010
418 910 small private holdings – estimation based on FSS2016

 

Size of sample

648 survey districts which contain

30 998 small private holdings in Agricultural Census 2010
24 273 small private holdings in FSS2016

Which methods were used to assess the sampling error?  Relative standard error
If other, which?
Which methods were used to derive the extrapolation factor?  Basic weight
If other, which?
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas

The agricultural enterprises and key private farms were considered as being observed exhaustively, therefore no SE occurred here

For small private farms:
The square of the standard errors relating to the estimated total (Xi) of the characteristics by strata is calculated as follows:

 

 where:

 

 

 

i: county
l: survey district

nil: number of the holdings observed in survey district ‘l” in sample

ni:  number of the holdings observed in the county ‘i” in sample

nil0:  number of the holdings observed in survey district ‘l” in AC 2010

ni0:  number of the holdings observed in the county ‘i” in AC 2010

mi:  number of the survey districts selected in sample

Mi: total number of the survey districts within the counties

To calculate the square of the standard errors relating to unions of strata (i = 1,2,…, 20) squares of the standard error relating to the various strata are summed up.

The coefficient of variation (CV) is computed by dividing the standard error with the appropriate number of totals.

If the results were compared with other sources, please describe the results
Which were the main sources of errors?


Sampling error - indicators

not applicable


Coefficient of variation (CV) for the area (on the MS level)

  Survey 1  Survey 2 Survey 3  Survey 4 Survey 5  Survey 6 Survey 7
Name of the survey

Annual data of agricultural (Nr. 1092, 1094 and 2219, 2375)

Cereals for the production of grain (in %)

2.3 %

Dried pulses and protein crops (in %)

5.9 %

Root crops (in %)

7.7 %

Oilseeds (in %)

2.5 %

Other industrial crops (included all industrial crops besides oilseeds)  (in %)

2.4 %

Plants harvested green from arable land (in %)

3.1 %

Total vegetables, melons and strawberries (in %)

5.0 %

Cultivated mushrooms (in %)

2.2 %

Total permanent crops (in %)

4.6 %

Fruit trees (in %)

6.5 %

Berries (in %)

13.4 %

Nut trees (in %)

11.4 %

Citrus fruit trees (in %)
Vineyards (in %)

6.5 %

Olive trees (in %)
Additional comments

Sampling errors of FSS 2016 will be included in the quality report of FSS 2016.

           
6.3. Non-sampling error

See 6.3.1. - 6.6.1.

6.3.1. Coverage error

Over-coverage - rate

Units which do not belong to the target population were not measured, so no over-coverage error occured.


Common units - proportion

not applicable


  Data source 1 Data source 2 Data source 3 Data source 4 Data source 5 Data source 6 Data source 7 Data source 8 Data source 9
Name of the data source

Annual data of agricultural (Nr. 1092, 1094 and 2219, 2375)

Error type
Degree of bias caused by coverage errors None
What were the reasons for coverage errors?
Which actions were taken for reducing the error or to correct the statistics?
Additional comments




 

6.3.1.1. Over-coverage - rate

Units which do not belong to the target population were not measured, so no over-coverage error occured.

6.3.1.2. Common units - proportion

not applicable

6.3.2. Measurement error

  Data source 1 Data source 2 Data source 3 Data source 4 Data source 5 Data source 6 Data source 7 Data source 8 Data source 9
Name of the data source

Land area and sown area - FSS 2016 (Nr. 2242, 2243 and 2374)

Annual data of agricultural (Nr. 1092, 1094 and 2219, 2375)

Was the questionnaire based on usual concepts for respondents? NO YES
Number of surveys already performed with the current questionnaire (or a slightly amended version of it)?

0

1

Preparatory (field) testing of the questionnaire? NO NO
Number of units participating in the tests? 
Explanatory notes/handbook for surveyors/respondents?  YES YES
On-line FAQ or Hot-line support for surveyors/respondents? YES YES
Were pre-filled questionnaires used? NO YES
Percentage of pre-filled questions out of total number of questions

10

Were some actions taken for reducing the measurement error or to correct the statistics? YES YES
If yes, describe the actions and their impact

Supervisors at the regional departments controlled the activities of enumerators and checked the questionnaires.

Supervisors at the regional departments controlled the activities of enumerators and checked the questionnaires.

6.3.3. Non response error

Unit non-response - rate

not applicable


Item non-response - rate

not applicable


  Survey 1 Survey 2 Survey 3 Survey 4 Survey 5 Survey 6 Survey 7
Name

Land area and sown area - FSS 2016 (Nr. 2242, 2243 and 2374)

Annual data of agricultural (Nr. 1092, 1094 and 2219, 2375)

Unit level non-response rate (in %)

 3.3 %

2.0 %

Item level non-response rate (in %)              
               - Min% / item
               - Max% / item
               - Overall%

 

 

Was the non-response been treated ? YES YES
Which actions were taken to reduce the impact of non-response?

On the one hand supervisors, on the other hand colleagues of the regional departments collected information of missing data during data entry.

On the one hand supervisors, on the other hand colleagues of the regional departments collected information of missing data during data entry.

Which items had a high item-level non-response rate? 

Land registration number

Stocks from the previous year

Which methods were used for handling missing data?
(several answers allowed)
Follow-up interviews
Imputations
Follow-up interviews
In case of imputation which was the basis? Imputation based on similar units
In case of imputation, which was the imputation rate (%)?

1.1 %

Estimated degree of bias caused by non-response? Insignificant Insignificant
Which tools were used for correcting the data?

Corrections are completed with the aid of the following tools: field work check list and data entry application.

Corrections are completed with the aid of the following tools: field work check list and data entry application.

Which organisation did the corrections?

Regional departments and Agriculture and Environment Statistics Department of HCSO

Regional departments and Agriculture and Environment Statistics Department of HCSO

Additional comments
6.3.3.1. Unit non-response - rate

not applicable

6.3.3.2. Item non-response - rate

not applicable

6.3.4. Processing error

Not applicable.

6.3.4.1. Imputation - rate

Not applicable.

6.3.5. Model assumption error

Not applicable.

6.4. Seasonal adjustment

Not applicable.

6.5. Data revision - policy

Not applicable.

6.6. Data revision - practice

Not applicable.

6.6.1. Data revision - average size

Not applicable.


7. Timeliness and punctuality Top
7.1. Timeliness

Time lag - first result


Time lag - final result


  Cereals Dried pulses and protein crops Root crops Oilseeds Other industrial crops Plants harvested green Vegetables and melons Strawberries Cultivated mushrooms Fruit trees Berries Nut trees Citrus fruit trees Vineyards Olive trees
How many main data releases there are yearly in the national crop statistics for the following types of crops?

10

9

9

9

9

9

9

9

8

8

8

8

0

9

0

How many of them are forecasts (releases before the harvest)?

5

4

4

4

4

4

4

4

3

3

3

3

0

4

0

When was  the first  forecasting published for crop year 2016? (day/month/year) 31/07/2016 31/07/2016 31/07/2016 31/07/2016 31/07/2016 31/07/2016 31/07/2016 31/07/2016 31/07/2016 22/01/2017 22/01/2017 22/01/2017 31/07/2016
When were the final results published for crop year 2016? (day/month/year) 15/08/2017 15/08/2017 15/08/2017 15/08/2017 15/08/2017 15/08/2017 15/08/2017 15/08/2017 15/08/2017 15/08/2017 15/08/2017 15/08/2017 15/08/2017
Additional comments

For all the abovementioned groups of crops in 2016 the first preliminary data were published on 31 of July: Land area and sown area.

Final data of 2016 were published on 15 August in 2017.

7.1.1. Time lag - first result
7.1.2. Time lag - final result
7.2. Punctuality

See the European level Quality Report

7.2.1. Punctuality - delivery and publication

See the European level Quality Report


8. Coherence and comparability Top

See points 8.2 and 8.3

8.1. Comparability - geographical

Not applicable.

8.1.1. Asymmetry for mirror flow statistics - coefficient

Not applicable.

8.2. Comparability - over time

Length of comparable time series

not applicable


  Crops from arable land
(Table 1)
Vegetables, melons and strawberries (Table 2) Permanent crops
(Table 3)
Agricultural land use
(Table 4)
Have there been major breaks in the time series since 2013? NO NO NO NO
If yes, to which were they related?
If other, which?
Which items were affected?
Year of break (number)
Impact on comparability
Additional comments
8.2.1. Length of comparable time series

not applicable

8.3. Coherence - cross domain

  Data source
With which other data sources the crop statistics data have been compared?  Farm structure survey 2016
IACS
If others, which?

Price statistics

National accounts
Orchard survey
If no comparisons have been made, why not?


Results of comparisons FSS 2016 (if available) Vineyard survey 2015 IACS Other source(s)  In case of other sources, which?
Cereals
89,3 %

 

 

93,9 %

 

 

Dried pulses and protein crops

100,0 %

 

78,1 %

 

Root crops

82,5 %

 

72,3 %

 

Oilseeds

91 %

 

98,6 %

 

Other industrial crops (than oilseeds)  

 

Plants harvested green

97,5 %

 

 

Total vegetables, melons and strawberries

75,9 %

 

 

Vegetables and melons  

 

Strawberries  

 

Cultivated mushrooms

83,7 %

 
Total permanent crops

99,6 %

 

 

Fruit trees

92,5 %

 
Berries

96 %

 
Nut trees  
Citrus fruit trees  
Vineyards

93,1 %

 

Olive trees  
If there were considerable differences, which factors explain them?  
8.4. Coherence - sub annual and annual statistics

Not applicable.

8.5. Coherence - National Accounts

Not applicable.

8.6. Coherence - internal

Not applicable.


9. Accessibility and clarity Top

See points 9.1, 9.2, 9.3, 9.5 and 9.6

9.1. Dissemination format - News release

9.2. Dissemination format - Publications

9.3. Dissemination format - online database

Data tables - consultations

not applicable


  Availability Links
On-line database accessible to users YES

http://statinfo.ksh.hu/Statinfo/themeSelector.jsp?page=1&theme=OM&lang=en

Website National language
English

http://www.ksh.hu/engstadat?lang=en

https://www.ksh.hu/stadat?lang=hu

 

 

9.3.1. Data tables - consultations

not applicable

9.4. Dissemination format - microdata access

Availability Links
NO
9.5. Dissemination format - other

Not applicable.

9.6. Documentation on methodology

9.7. Quality management - documentation

Not applicable.

9.7.1. Metadata completeness - rate

Not applicable.

9.7.2. Metadata - consultations

Not applicable.


10. Cost and Burden Top

Efficiency gains if compared to the previous reference year (2013) Further automation
Increased use of administrative data
Staff further training
Other
If other, which?

Electronic data collection, no data entry

Burden reduction measures since the previous reference year  More user-friendly questionnaires
Other
If other, which?

Electronic data collection, no data entry


11. Confidentiality Top
Restricted from publication
11.1. Confidentiality - policy
Restricted from publication
11.2. Confidentiality - data treatment
Restricted from publication


12. Comment Top

Please find attached the questionnaires for Crop Production statistics.


Related metadata Top


Annexes Top
Questionnaire Nr. 1084
Questionnaire Nr. 1085
Questionnaire Nr. 1092
Questionnaire Nr. 1094
Questionnaire Nr. 2375
Questionnaire Nr. 2242
Questionnaire Nr. 2243
List of the data sources