Crop production (apro_cp)

Crop production (apro_cp)

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

Compiling agency: Turkish Statistical Institute

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: Eurostat user support

1. Contact

1.1. Contact organisation

Turkish Statistical Institute

1.2. Contact organisation unit

Agricultural Statistics Department

1.5. Contact mail address

2. Statistical presentation

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

3.1. Source data

	Source 1	Source 2	Source 3	Source 4
Have new data sources been introduced since the previous quality report?	NO
If yes, which new data sources have been introduced since the previous quality report?
Type of source?
To which Table (Reg 543/2009) do they contribute?
Have some data sources been dropped since the previous quality report?
Which data sources have been dropped since the previous quality report?
Type of source?
Why have they been dropped?
Additional comments

Data sources: Please indicate the data sources which were used for the reference year on which is reported

Type

Name(s) of the sources

If other type, which kind of data source?

Table 1: Crops from arable land

Early estimates for areas

Administrative data

Statistical Data Network (SDN) ( see Chapter 3.3.75)

Final area under cultivation

Administrative data

Statistical Data Network (SDN) ( see Chapter 3.3.75)

Production

Administrative data

Statistical Data Network (SDN) ( see Chapter 3.3.75)

Yield

Administrative data

Statistical Data Network (SDN) ( see Chapter 3.3.75)

Non-existing and non-significant crops

Administrative data

Statistical Data Network (SDN) ( see Chapter 3.3.75)

Table 2: Vegetables, melons and strawberries

Early estimates for harvested areas

Administrative data

Statistical Data Network (SDN) ( see Chapter 3.3.75)

Final harvested area

Administrative data

Statistical Data Network (SDN) ( see Chapter 3.3.75)

Production

Administrative data

Statistical Data Network (SDN) ( see Chapter 3.3.75)

Non-existing and non-significant crops

Administrative data

Statistical Data Network (SDN) ( see Chapter 3.3.75)

Table 3: Permanent crops

Early estimates for production area

Administrative data

Statistical Data Network (SDN) ( see Chapter 3.3.75)

Final production area

Administrative data

Statistical Data Network (SDN) ( see Chapter 3.3.75)

Production

Administrative data

Statistical Data Network (SDN) ( see Chapter 3.3.75)

Non-existing and non-significant crops

Administrative data

Statistical Data Network (SDN) ( see Chapter 3.3.75)

Table 4: Agricultural land use

Main area

Administrative data

Statistical Data Network (SDN) ( see Chapter 3.3.75)

Non-existing and non-significant crops

Administrative data

Statistical Data Network (SDN) ( see Chapter 3.3.75)

Total number of different data sources

5

Additional comments

Data source for the humidity	Put x, if used
Surveyed: farmers report the humidity
Surveyed: farmers convert the production/yield into standard humidity
Surveyed: whole sale purchasers report the humidity	x
Surveyed: whole sale purchasers convert the production/yield into standard humidity
Surveyed by experts (e.g. test areas harvested and measured)
Estimated by experts
Other type
If other type, please explain
Additional information	Agricultural trade stock market records are used.

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	Statistical Data Network (SDN) ( see Chapter 3.3.75)
Planning (month-month/year)	January-February/2022
Preparation (month-month/year)	March-April/2022
Data collection (month-month/year)	May-December/2022
Quality control (month-month/year)	May-December/2022
Data analysis (month-month/year)	May-December/2022
Dissemination (month-month/year)	December 2022
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?	NO
Are there differences between the national methodology and the methodology described in the Handbook concerning e.g. the item and aggregate calculations?
Are special estimation/calculation methods used for main crops from arable land?
Are special estimation/calculation methods used for vegetables or strawberries?
Are special estimation/calculation methods used for permanent crops for human consumption?
Are special estimation/calculation methods used for main land use?
Do national crop item definitions differ from the definitions in the Handbook (D-flagged data)?	NO
In case yes, how do they differ? ( list all items and explanations)
In case data are delivered for one of the items below, describe the crop species included in the item:

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).	Threshold is not applied for the estimaiton of area and production of crops.(full cover)
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)	2020
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
Which survey method was used?
If 'other', please specify
Please provide a link to the questionnaire
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	Statistical Data Network (SDN)
Description	Register record a members of the agricultural budget, record the number of farms, production structure and capacity, and give the opportunity to define a strategy of agricultural development and lead a successful agricultural policy by Ministry of agriculture.
Data owner (organisation)	Ministry of Agriculture and Forestry (MAF). Crop production data have been compiled in magnetic form on web by MAF. It is called Statistical Data Network (SDN). The data are filled by agricultural agriculture engineers, veterinaries or technicians. Reference period is current crop production period( 1 October- 30 September).
Update frequency	Once per year or more often
Reference date (month/year)
Legal basis	Law on agriculture and rural development
Reporting unit	District
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%)	100%
If not complete, which other sources were used ?
Were the data used for sample frame?
Data used for other purposes, which?
Which variables were taken from administrative sources?	Identification variables and area of main crops registered (cereals, root crops, oil seed crops, other industrial crops, plants harvested green, vegetables and permanent crops)
Were there any differences in the definition of the variables between the administrative source and those described in the Regulation?	NO
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
Data owner (organisation)
Update frequency (e.g. 1 year or 6 months)
Reference date (Month/Year e.g. 1/22 - 8/22)
Legal basis
Use purpose of the estimates?
What kind of expertise the experts have?
What kind of estimation methods were used?
Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation?
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?
What were the possible limitations, drawbacks of using the data from expert estimate(s)?
Additional comments

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

3.5. Data compilation

Not Applicable.

3.6. Adjustment

Not applicable.

4. Quality management

4.1. Quality assurance

	Completeness	Punctuality	Accuracy	Reliability	Overall quality
How would you describe the overall quality development since the previous quality report?	Improvement
Is there a quality management process in place for crop statistics?	YES
If, yes, what are the components?	quality elements: relevance, accuracy, timeliness, punctuality, accessibility, comparability and coherence.
Is there a quality report available?	YES
If yes, please provide a link(s)	https://data.tuik.gov.tr/Bulten/Index?p=Crop-Production-Statistics-2023-49535
To which data source(s) is it linked?
Has a peer-review been carried out for crop statistics?
If, yes, which were the main conclusions?
What quality improvement measures are planned for the next 3 years?	Increase of resources Systematic validation improvements Quality report
If, other, please specify	Special survey for annual crop products, fruit and vineyard.
Additional comments

4.2. Quality management - assessment

See the European level Quality Report.

5. Relevance

5.1. Relevance - User Needs

Are there known unmet user needs?	YES
Describe the unmet needs	Detail information by species.
Does the Regulation 543/2009 meet the national data needs?	YES
Does the ESS agreement meet the national needs?
If not, which additional data are collected?
Additional comments

5.2. Relevance - User Satisfaction

Have any user satisfaction surveys been done?	YES
If yes, how satisfied the users were?	Satisfied
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

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
Sampling basis?
If 'other', please specify
Sampling method?
If stratified, number of strata?
If stratified, stratification basis?
If 'other', please specify
Size of total population
Size of sample
Which methods were used to assess the sampling error?
If other, which?
Which methods were used to derive the extrapolation factor?
If other, which?
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas
If the results were compared with other sources, please describe the results
Which were the main sources of errors?

Sampling error - indicators

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
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 %)
Additional comments

6.3. Non-sampling error

6.3.1. Coverage error

Over-coverage - rate

Common units - proportion

	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
Error type
Degree of bias caused by coverage errors
What were the reasons for coverage errors?
Which actions were taken for reducing the error or to correct the statistics?
Additional comments

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
Was the questionnaire based on usual concepts for respondents?
Number of surveys already performed with the current questionnaire (or a slightly amended version of it)?
Preparatory (field) testing of the questionnaire?
Number of units participating in the tests?
Explanatory notes/handbook for surveyors/respondents?
On-line FAQ or Hot-line support for surveyors/respondents?
Were pre-filled questionnaires used?
Percentage of pre-filled questions out of total number of questions
Were some actions taken for reducing the measurement error or to correct the statistics?
If yes, describe the actions and their impact

6.3.3. Non response error

Unit non-response - rate

Item non-response - rate

	Survey 1	Survey 2	Survey 3	Survey 4	Survey 5	Survey 6	Survey 7
Name
Unit level non-response rate (in %)
Item level non-response rate (in %)
- Min% / item
- Max% / item
- Overall%
Was the non-response been treated ?
Which actions were taken to reduce the impact of non-response?
Which items had a high item-level non-response rate?
Which methods were used for handling missing data? (several answers allowed)
In case of imputation which was the basis?
In case of imputation, which was the imputation rate (%)?
Estimated degree of bias caused by non-response?
Which tools were used for correcting the data?
Which organisation did the corrections?
Additional comments

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

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?

3

3

3

3

3

3

3

3

3

3

3

3

3

3

3

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

2

2

2

2

2

2

2

2

2

2

2

2

2

2

2

When was the first forecasting published for the crop year on which is reported? (day/month/year)

30/05/2022

30/05/2022

30/05/2022

30/05/2022

30/05/2022

30/05/2022

30/05/2022

30/05/2022

30/05/2022

30/05/2022

30/05/2022

30/05/2022

30/05/2022

30/05/2022

30/05/2022

When were the final results published for the crop year on which is reported? (day/month/year)

30/12/2022

30/12/2022

30/12/2022

30/12/2022

30/12/2022

30/12/2022

30/12/2022

30/12/2022

30/12/2022

30/12/2022

30/12/2022

30/12/2022

30/12/2022

30/12/2022

30/12/2022

Additional comments

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

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

	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 in the previous 5 years?	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

8.3. Coherence - cross domain

With which other data sources the crop statistics data have been compared?	IACS
If others, which?
If no comparisons have been made, why not?

Differences between ACS and other data sources (%)

Results of comparisons	IFS 2020	Vineyard survey 2020	IACS	Other source(s)	In case of other sources, which?
Cereals
Dried pulses and protein crops
Root crops
Oilseeds
Other industrial crops (than oilseeds)
Plants harvested green
Total vegetables, melons and strawberries
Vegetables and melons
Strawberries
Cultivated mushrooms
Total permanent crops
Fruit trees		Not applicable.
Berries
Nut trees
Citrus fruit trees		Not applicable.
Vineyards		not applicable
Olive trees		Not applicable.
If there were considerable differences, which factors explain them?			Register of agricultural holdings on behalf of Ministry of Agriculture is register for subsidies applying. Also, the Ministry of Agriculture doesn’t perform subsidies for all grown crops, so the crop registration by holdings is restricted.

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

9.1. Dissemination format - News release

Availability	Links
YES	https://data.tuik.gov.tr/Bulten/Index?p=Crop-Production-2020-33737

9.2. Dissemination format - Publications

	Availability	Links
Publications	Electronic	https://data.tuik.gov.tr/Kategori/GetKategori?p=tarim-111&dil=2
Publications in English	Electronic	https://data.tuik.gov.tr/Kategori/GetKategori?p=tarim-111&dil=2

9.3. Dissemination format - online database

Data tables - consultations

	Availability	Links
On-line database accessible to users	YES
Website		https://biruni.tuik.gov.tr/medas/?kn=92&locale=en

9.4. Dissemination format - microdata access

Availability	Links

9.5. Dissemination format - other

Not applicable.

9.6. Documentation on methodology

	Availability	Links
Methodological report	None National language	http://www.turkstat.gov.tr/PreTablo.do?alt_id=1001
Quality Report		https://data.tuik.gov.tr/Kategori/GetKategori?p=Agriculture-111#data6
Metadata	None National language	http://www.turkstat.gov.tr/PreTablo.do?alt_id=1001
Additional comments

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

Efficiency gains if compared to the previous quality report?
If other, which?
Burden reduction measures since the previous reference year
If other, which?

11. Confidentiality

11.1. Confidentiality - policy

Restricted from publication

11.2. Confidentiality - data treatment

Restricted from publication

12. Comment

The Community Regulations concerning the crop production statistics have been translated into Turkish. In addition, with traditional data collection method, Turkish Statistical Institute (TurkStat) collects the crop production information annually by means of Ministry of Agriculture and Forestry (MAF). TurkStat compiles land use (area) statistics for crops, vegetables, fruits, orchards by means of MAF Provincial and District Offices in every year. In addition survey, the method based on administrative records (MAF Offices administrative data) is going to be continued coming next years. Crop surveys on agricultural holdings are planned to be conducted when a strong address frame is present yearly.

Annual crop production data has been send to Eurostat by eDamıs. In addition, Data collection process of harvested products humidity(Table 1) was completed and send to Eurostat.

Also, The quality report of crop statistics is available now. In addition, IPA Multi-beneficiary Programme 2017 project is continue for improving the quality of crop statistics. Also, TurkStat has taken part in the national project of improving data collection of agricultural statistics and enhancing evaluation capacity carried out by MAF in order to improve administrative agricultural data.

Related metadata

Annexes