Crop production (apro

Crop production (apro_cp)

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

Compiling agency: Statistics Denmark

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

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1. Contact

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1.1. Contact organisation

Statistics Denmark

1.2. Contact organisation unit

Food Industries.

1.5. Contact mail address

Sejrøgade 11

DK-2100 Copenhagen

Denmark

2. Statistical presentation

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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).

The harvest of cereals etc.

For each crop, questions are asked on arable land, total yield (hekto kilo), average yield (hekto kilo / hectare) and water percent if the production does not appear in dried weight. Water percentage is used to calculate standard moisture content (15 per cent for cereals and peas and 9 per cent for rape). Harvest of grain maize and corn cob mix is included in the statistics from 2011. Statistics on straw show the production and use of straw by the above-mentioned crops. Output is measured as a relation between yield (grain, rape and peas) and expected straw yield, while the use of straw is based on questionnaire information on the distribution of straw areas used for firing, for fodder, for other purposes. From 2006 the results are compiled for the new administrative structure in Denmark (regions). Regions are divided on specific agricultural land (sub-divisions of regions).

Harvest of roughage

Includes harvest of sugar beet, potatoes and roughage plus grain and corn harvested for silage or green fodder. Data collection: yield estimates from experts.

Forecast of areas planted with winter crops for harvest the following year.

Crops include: winter wheat, winter barley, rye, triticale and winter rape. The forecast is from 2016 prepared by DAKOFO and the Association of Danish plant breeders. DAKOFO is a trade association for grain and feed trade in Denmark.

2.2. Classification system

Hierarchical crop classification following the EU classifications in Eurostat Handbook for Annual Crop Statistics.

2.3. Coverage - sector

The agricultural 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 Denmark.

2.8. Coverage - Time

The statistics in their present form are generably available from 1990. However, see 'Comparability over time' for specification.

2.9. Base period

The statistics cover harvest in the calendar year.

3. Statistical processing

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Data for the harvest of grains, rape etc. is collected annually from farmers using questionnaires and every 3rd year for vegetables etc. The collected data is corrected based on consistency rules and yield limits. Corrected data are raised to the total population. Harvesting of roughage, Forecast for winter seed and Seed for seedling are collected annually from experts via questionnaires. The collected yields are raised by total areas (application for support).

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?	NO
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

Survey
Administrative data
Expert estimate

Harvest of Cereals etc.

IACS

DAKOFO (branch organization for the Danish grain and feed industry).

Final area under cultivation

Survey
Administrative data

Harvest of Cereals etc.

IACS

Production

Survey
Administrative data

Harvest of Cereals etc.

IACS

Yield

Survey
Expert estimate

Harvest of Cereals etc.

Root crops and green fodder: Various farming consultants

Non-existing and non-significant crops

Administrative data

IACS

Table 2: Vegetables, melons and strawberries

Early estimates for harvested areas

Census
Administrative data

Vegetables and strawberries in the open (census every 3. year)

Vegetables in greenhouses (census, every 3. year)

IACS

Final harvested area

Census
Administrative data

Vegetables and strawberries in the open (census every 3. year)

Vegetables in greenhouses (census, every 3. year)

IACS

Production

Census
Administrative data
Expert estimate

Vegetables and strawberries in the open (census every 3. year)

Vegetables in greenhouses (census, every 3. year)

IACS

Experts: Seges, Dansk Gartneri/Danish Horticulture (years without survey).

Non-existing and non-significant crops

Administrative data

IACS

Table 3: Permanent crops

Early estimates for production area

Census
Administrative data

Berries and stonefruit (census, every 3. year)

Apples and pears (census in orchard survey, every 5. year)

IACS

Final production area

Census
Administrative data

Berries and stonefruit (census, every 3. year)

Apples and pears (census in orchard survey, every 5. year)

IACS

Production

Census
Administrative data
Expert estimate

Berries and stonefruit (census, every 3. year)

Apples and pears (census in orchard survey, every 5. year)

IACS

Experts: Seges, Dansk Gartneri/Danish Horticulture

Non-existing and non-significant crops

Administrative data

IACS

Table 4: Agricultural land use

Main area

Census
Survey
Administrative data

Production of fruit and vegetables:

1. Vegetables and strawberries in the open (census every 3. year)
2. Vegetables in greenhouses (census, every 3. year)
3. Berries and stonefruit (census, every 3. year)
4. Apples and pears (census in orchard survey, every 5. year)

Harvest of Cereals etc., Forrest survey

IACS

Non-existing and non-significant crops

Administrative data

Total number of different data sources

Additional comments

The crop statistics are compiled using three different approaches:

Results on cereals, dried pulses and rape are based on a sample survey, where about 2800 farmers are selected (ca. 8 per cent of all farmers with such crops).
Results on root crops and green fodder plants are based on The accounts statistics for agriculture and expert estimates.
Results on vegetables and fruits, where census surveys with thresholds are conducted periodically for the single sectors (vegetables and strawberries in the open, vegetables in greenhouses, berries and stonefruit). Results for apples and pears are collected when surveys on permanent crops are conducted. In years without surveys, expert estimates are used.

All three approaches result in data on areas, yields and production. The areas used for estimating total production is From FSS and the IACS-support system for support to farmers.
The early estimates on main crops are based on previous years results, early figures from the IACS and information based on agricultural magazines etc.
Regarding the areas on winter crops, estimates are made based on the amount of certified seeds for cereals and rape.
Regarding land use statistics, information from crop production statistics, FSS, Forest surveys etc. are combined.

Data source for the humidity	Put x, if used
Surveyed: farmers report the humidity	x
Surveyed: farmers convert the production/yield into standard humidity
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
Other type
If other type, please explain
Additional information

Which method is used for calculating the yield for main arable crops?	Production divided by sown area yield is surveyed in the field and production volume is assessed on the basis of the yield
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	Harvest of Cereals etc. (annual)	Census for vegetables in the open (every 3 years).	Census for vegetables under glass (every 3 years).	Census for stone fruits and berries (every 3 years)	Census for apples and pears (Orchard survey, every 5 years).
Planning (month-month/year)	1-7/y	1/y+1 - 4/y+1	1/y+1 - 4/y+1	1/y+1 - 4/y+1	1/y+1 - 4/y+1
Preparation (month-month/year)	1-7/y	1/y+1 - 4/y+1	1/y+1 - 4/y+1	1/y+1 - 4/y+1	1/y+1 - 4/y+1
Data collection (month-month/year)	08/y - 02/y+1	5y+1 - 8y+1 Latest data collection: 2021 (y = production year).	5y+1 - 8y+1 Latest data collection: 2020 (y = production year).	5y+1 - 8y+1 Latest data collection: 2022 (y = production year).	5y+1 - 8y+1 Latest data collection: 2021 (y = production year).
Quality control (month-month/year)	08/y - 02/y+1	5/y+1 - 8/y+1	5/y+1 - 8/y+1	5/y+1 - 8/y+1	5/y+1 - 8/y+1
Data analysis (month-month/year)	10/y - 02/y+1	8/y+1	8/y+1	8/y+1	12/y+1
Dissemination (month-month/year)	11/y (preliminary results), 03/y+1	9/y+1	9/y+1	9/y+1	1/y+2
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?	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)?	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).	Surveyed areas are compared to FSS and IACS.
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)	Current updates.
Was a threshold applied?	YES
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 %)	Less than 5% - (survey population of total population)
Dried pulses and protein crops (in %)	Less than 5% - (survey population of total population)
Root crops (in %)	Less than 5% - (survey population of total population)
Oilseeds (in %)	Less than 5% - (survey population of total population)
Other industrial crops (included all industrial crops besides oilseeds) (in %)	Less than 5% - (survey population of total population)
Plants harvested green from arable land (in %)	Less than 5% - (survey population of total population)
Total vegetables, melons and strawberries (in %)	Less than 5% - (survey population of total population)
Cultivated mushrooms (in %)	Less than 5% - (survey population of total population)
Total permanent crops (in %)	Less than 5% - (survey population of total population)
Fruit trees (in %)	Less than 5% - (survey population of total population)
Berries (in %)	Less than 5% - (survey population of total population)
Nut trees (in %)	N.a. (insignificant)
Citrus fruit trees (in %)	N.a. (insignificant)
Vineyards (in %)	N.a. (insignificant)
Olive trees (in %)	N.a. (insignificant)

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	Harvest of Cereals etc.	Census for vegetables in the open (every 3 years).	Census for vegetables under glass (every 3 years).	Census for stone fruits and berries (every 3 years) .	Census for apples and pears (Orchard survey, every 5 years).
Which survey method was used?	On-line electronic questionnaire filled in by respondent Postal questionnaire filled in by respondent	On-line electronic questionnaire filled in by respondent Postal questionnaire filled in by respondent	On-line electronic questionnaire filled in by respondent Postal questionnaire filled in by respondent	On-line electronic questionnaire filled in by respondent Postal questionnaire filled in by respondent	On-line electronic questionnaire filled in by respondent Postal questionnaire filled in by respondent
If 'other', please specify			Postal questionnaire filled in by respondent was used by exceptions.	Postal questionnaire filled in by respondent was used by exceptions.	Postal questionnaire filled in by respondent was used by exceptions.
Please provide a link to the questionnaire	http://dst.dk/da/Indberet/oplysningssider/hoest	http://dst.dk/da/Indberet/oplysningssider/landbrug_gartneri	http://dst.dk/da/Indberet/oplysningssider/landbrug_gartneri	http://dst.dk/da/Indberet/oplysningssider/landbrug_gartneri	http://dst.dk/da/Indberet/oplysningssider/landbrug_gartneri
Data entry method, if paper questionnaires?	Manual	Manual	Manual	Manual	Manual

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 / GLR (Det generelle Landbrugsregister)
Description	National administration of EU subsidies Integrated Administration and Control System (IACS)
Data owner (organisation)	The Danish Agriculture Agency
Update frequency	Continuous
Reference date (month/year)	July/December (data to Stat.DK)
Legal basis	EU legislation
Reporting unit	Holdings
Identification variable (e.g. address, unique code, etc.)	Enterprise ID number
Percentage of mismatches (%)	Ca. 2% (missing ent.ID)
How were the mismatches handled?	If possible added to the Enterprise register. All units with mismatch are included in total area.
Degree of coverage (holdings, e.g. 80%)
Degree of completeness (variables, e.g. 60%)	Close to 100%. Less than 0,5% holdings are believed to produce crop with no EU-application.
If not complete, which other sources were used ?	None
Were the data used for sample frame?	Sample frame Validation Directly for estimates
Data used for other purposes, which?
Which variables were taken from administrative sources?	Crops (ha) applied for subsidies, ID-no. (enterprise or personal)
Were there any differences in the definition of the variables between the administrative source and those described in the Regulation?	YES
Please describe the differences	IACS is more detailed concerning crops.
What measures were taken to eliminate the differences?	Aggregation.
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?	Units (but no crops) are checked against Stat. DK's enterprise register (which is linked to tax authorities).
What were the possible limitations, drawbacks of using the data from administrative source(s)?	No particular.

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	Crop estimates, vegetables in the open, under glass and permanent crops (yield).	Early estimates for grain crops (yield)	Early estimates for vinter crops (area)
Data owner (organisation)	Dansk Gartneri/Danish Horticulture	SEGES	DAKOFO (branch organization for the Danish grain and feed industry.
Update frequency (e.g. 1 year or 6 months)	Yearly	Yearly	Yearly
Reference date (Month/Year e.g. 1/22 - 8/22)	1/y-12/y (2022 latest).	1/y-12/y (2022 latest).	12/y-1 (2022 latest)
Legal basis	None	None	None
Use purpose of the estimates?	Crop estimates, vegetables in the open, under glass and permanent crops (yield).	Early estimates for grain crops (yield)	Early estimates for vinter crops (area)
What kind of expertise the experts have?	Industry association.	Research institute.	Industry association.
What kind of estimation methods were used?	n.a.	n.a.	n.a.
Were there any differences in the definition of the variables between the experts' estimates and those described in the Regulation?	NO	NO	NO
If yes, please describe the differences	n.a.	n.a.	n.a.
What measures were taken to eliminate the differences?	n.a.	n.a.	n.a.
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?	Comparison to magazines, figures from int. organisations, data from crop surveys.	Comparison to magazines, figures from int. organisations, data from crop surveys.	Comparison to magazines, figures from int. organisations, data from crop surveys.
What were the possible limitations, drawbacks of using the data from expert estimate(s)?	n.a.	n.a.	n.a.
Additional comments

3.4. Data validation

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

3.5. Data compilation

Not Applicable.

3.6. Adjustment

Not applicable.

4. Quality management

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4.1. Quality assurance

	Completeness	Punctuality	Accuracy	Reliability	Overall quality
How would you describe the overall quality development since the previous quality report?	Stable	Stable	Stable	Stable	Stable
Is there a quality management process in place for crop statistics?	YES
If, yes, what are the components?	Documentation standards (update at each dissemination). Evaluation of standards for validation (software, rules) ca. every 2-3rd year. User feedback in questionnaires.
Is there a quality report available?	YES
If yes, please provide a link(s)	http://dst.dk/en/Statistik/dokumentation/documentationofstatistics/harvest-of-cereals-etc- https://dst.dk/en/Statistik/dokumentation/documentationofstatistics/agricultural-and-horticultural-survey
To which data source(s) is it linked?	The quality report on Harvest of Cereals etc. covers crops from arable land. The quality report on Agricultural and horticultural survey covers vegetables and permanent crops.
Has a peer-review been carried out for crop statistics?	YES
If, yes, which were the main conclusions?	Internal report on improvements of validation.
What quality improvement measures are planned for the next 3 years?	Systematic validation improvements
If, other, please specify	Improvement in automatic validation (data entry + other subsequently). The changes concern rules for validation and a new system to manage data sets and corrections.
Additional comments

4.2. Quality management - assessment

See the European level quality report.

5. Relevance

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5.1. Relevance - User Needs

Are there known unmet user needs?	5.1.1. Are there known unmet user needs?
Describe the unmet needs	5.1.2. Describe the unmet needs
Does the Regulation 543/2009 meet the national data needs?	5.1.3. Does the Regulation 543/2009 meet the national data needs?
Does the ESS agreement meet the national needs?	5.1.4. Does the ESS agreement meet the national needs?
If not, which additional data are collected?	5.1.5. If not, which additional data are collected?
Additional comments	5.1.6. Additional comments

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

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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	Harvest of Cereals etc.	Production of fruit and vegetables
Sampling basis?	Other	Other
If 'other', please specify	IACS Agricultural and Horticultural Survey	IACS Agricultural and Horticultural Survey
Sampling method?	Stratified
If stratified, number of strata?	40	n.a.
If stratified, stratification basis?	Location Size Specialisation
If 'other', please specify		Census/total count with minimum threshold.
Size of total population	43000	200-600 (varies by crop)
Size of sample	2800	200-600 (varies by crop)
Which methods were used to assess the sampling error?	Relative standard error	Other
If other, which?		Census/total count.
Which methods were used to derive the extrapolation factor?	Other
If other, which?	The farms with area less than 5 hectares were not part of the sampling frame. However, their contribution to the total Danish production are accounted for by means of the ratio estimator.
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas	The standard error is calculated by the CLAN software, which is an implementation of standard formulas.	n.a.
If the results were compared with other sources, please describe the results	Results are compared to previous years.	Results are compared to previous years.
Which were the main sources of errors?	No systematic measurement errors have been detected during many years of preparing the harvest survey. The random error is deemed to be the dominant (though unavoidable) part of the total error.	n.a.

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	Harvest of Cereals etc.	Production of fruit and vegetables
Cereals for the production of grain (in %)	0,3%	n.a.
Dried pulses and protein crops (in %)	n.a.	n.a.
Root crops (in %)	n.a.	n.a.
Oilseeds (in %)	n.a.	n.a.
Other industrial crops (included all industrial crops besides oilseeds) (in %)	n.a.	n.a.
Plants harvested green from arable land (in %)	n.a.	n.a.
Total vegetables, melons and strawberries (in %)	n.a.	n.a. (total count)
Cultivated mushrooms (in %)	n.a.	n.a. (total count)
Total permanent crops (in %)	n.a.	n.a. (total count)
Fruit trees (in %)	n.a.	n.a. (total count)
Berries (in %)	n.a.	n.a. (total count)
Nut trees (in %)	n.a. (insignifikant)	n.a. (insignifikant)
Citrus fruit trees (in %)	n.a. (insignifikant)	n.a. (insignifikant)
Vineyards (in %)	n.a. (insignifikant)	n.a. (insignifikant)
Olive trees (in %)	n.a. (insignifikant)	n.a. (insignifikant)
Additional comments	1. The harvest of grain is collected via a sample based survey. 2. Fruit and vegetables are collected as censuses (total count) with minimum thresholds. 3. Root and fodder plants are based on expert estimates.

6.3. Non-sampling error

6.3.1. Coverage error

Over-coverage - rate

not applicable

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	Administrative information on areas	Sample surveys	Horticultural surveys
Error type	Under-coverage	Under-coverage	Under-coverage
Degree of bias caused by coverage errors	Low	Low	Low
What were the reasons for coverage errors?	Some (very few) farmers do not apply for IACS subsidies and may not be covered.	Some (very few) farmers do not apply for IACS subsidies and may not be covered.	Some (very few) farmers do not apply for IACS subsidies and may not be covered.
Which actions were taken for reducing the error or to correct the statistics?	The undercovererage is believed to be negligible.	The undercovererage is believed to be negligible.	The undercovererage is believed to be negligible.
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	Harvest of Cereals etc.	Production of fruit and vegetables
Was the questionnaire based on usual concepts for respondents?	YES	YES
Number of surveys already performed with the current questionnaire (or a slightly amended version of it)?	7	>5
Preparatory (field) testing of the questionnaire?	NO	NO
Number of units participating in the tests?	n.a.	n.a.
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?	YES	YES
Percentage of pre-filled questions out of total number of questions	10%	15%
Were some actions taken for reducing the measurement error or to correct the statistics?	YES	YES
If yes, describe the actions and their impact	On-line validation, automatic and manual check with possible recontact/corrections	On-line validation, automatic and manual check with possible recontact/corrections

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	Harvest of Cereals etc.	Production of fruit and vegetables
Unit level non-response rate (in %)	3%	3%
Item level non-response rate (in %)
- Min% / item	n.a.	n.a.
- Max% / item	n.a.	n.a.
- Overall%	n.a.	n.a.
Was the non-response been treated ?	YES	YES
Which actions were taken to reduce the impact of non-response?	Reminders/Legal actions/Imputation/Weighting	Reminders/Legal actions/Imputation/Weighting
Which items had a high item-level non-response rate?	Use of straw, moisture-%	None in particular
Which methods were used for handling missing data? (several answers allowed)	Follow-up interviews Reminders Legal actions Imputations Weighting Other	Follow-up interviews Reminders Legal actions Imputations Weighting Other
In case of imputation which was the basis?	Imputation based on similar units	Imputation based on similar units
In case of imputation, which was the imputation rate (%)?	Not calculated	Not calculated
Estimated degree of bias caused by non-response?	Unknown	Unknown
Which tools were used for correcting the data?	Internally developed tools (Oracle/SAS/spreadsheet)	Internally developed tools (Oracle/SAS/spreadsheet)
Which organisation did the corrections?	Statistics Denmark	Statistics Denmark
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

Statistics Denmark revises published figures in accordance with the Revision Policy for Statistics Denmark. The common procedures and principles of the Revision Policy are for some statistics supplemented by a specific revision practice.

Annexes:
Revision policy in Statistics Denmark

6.6. Data revision - practice

Data are normally not revised, but follows the data revision policy.

6.6.1. Data revision - average size

Data are normally not revised.

7. Timeliness and punctuality

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The statistics are usually published without delay to the scheduled date. Preliminary data for the harvest of cereals, rape and pulses are published in late November. Final statement, including results for provinces and regions are published March of the following year, where the coarse fodder harvest also is published. End of reference Period: end of November. The forecast for the following year's winter land released in early December. End of reference Period: October 15.

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?

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

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

24/11/2022

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

24/03/2023

17/03/2023

24/03/2023

27/10/2023

Additional comments

Latest survey on production of apples and pears (2017) was dissminated 25 Jan. 2017.

7.2. Punctuality

The statistics are usually published without delay to the scheduled date. See also the European level Quality Report.

7.2.1. Punctuality - delivery and publication

See the European level Quality Report

8. Coherence and comparability

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8.1. Comparability - geographical

Similar statistics are produced among EU members and are available from the Eurostat's website. The statistics comply with EU standards.

8.1.1. Asymmetry for mirror flow statistics - coefficient

Not applicable.

8.2. Comparability - over time

Length of comparable time series

The final figures on crop yields of grain are, in principle, comparable as far back as 1900. Changes in methodology must be taken into account, but the present compilation method has been used since 1971. The results on crop yields at regional level are only comparable as from 2006 due to a new administrative structure of regions in Denmark. For 2006, results are compiled on the basis of the former counties as well as the present regions. Figures on coarse fodder are fully comparable as from 1982 and onwards.

	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?	FSS 2019
If no comparisons have been made, why not?	Mainly check of definitions, units and areas. The same source - National IACS-register - is used a base for the various populations. No comparison of statistic results across domains, though, as crop data only are collected from one source. The forecast for areas with winter seeds can be compared with the later results according to the FS Survey

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
Olive trees		Not applicable.
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

Data is internally consistent, deriving from the same source.

9. Accessibility and clarity

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9.1. Dissemination format - News release

Availability	Links
YES	https://dst.dk/en/Statistik/emner/erhvervslivets-sektorer/landbrug-gartneri-og-skovbrug

Annexes:
Subject page: AGRICULTURE, HORTICULTURE AND FORESTRY

9.2. Dissemination format - Publications

	Availability	Links
Publications	Electronic	https://www.dst.dk/en/Statistik/emner/erhvervsliv/landbrug-gartneri-og-skovbrug/vegetabilsk-produktion
Publications in English	Electronic	https://www.dst.dk/en/Statistik/emner/erhvervsliv/landbrug-gartneri-og-skovbrug/vegetabilsk-produktion

9.3. Dissemination format - online database

Data tables - consultations

not applicable

	Availability	Links
On-line database accessible to users	YES	http://www.statbank.dk/10474
Website	English	https://www.dst.dk/en/Statistik/emner/erhvervsliv/landbrug-gartneri-og-skovbrug/vegetabilsk-produktion

9.4. Dissemination format - microdata access

Restricted from publication

9.5. Dissemination format - other

Publications and statbank tables for free. Other tables by payment.

https://www.dst.dk/en/Statistik/emner/erhvervsliv/landbrug-gartneri-og-skovbrug/vegetabilsk-produktion

https://www.statbank.dk/20474

9.6. Documentation on methodology

	Availability	Links
Methodological report	None National language	http://dst.dk/en/Statistik/dokumentation/documentationofstatistics/harvest-of-cereals-etc- https://www.dst.dk/en/Statistik/dokumentation/documentationofstatistics/agricultural-and-horticultural-survey
Quality Report	National language English	http://dst.dk/en/Statistik/dokumentation/documentationofstatistics/harvest-of-cereals-etc- https://dst.dk/en/Statistik/dokumentation/documentationofstatistics/agricultural-and-horticultural-survey
Metadata	None
Additional comments

9.7. Quality management - documentation

See chapter 3.

9.7.1. Metadata completeness - rate

Not applicable.

9.7.2. Metadata - consultations

Not applicable.

10. Cost and Burden

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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

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11.1. Confidentiality - policy

Restricted from publication

11.2. Confidentiality - data treatment

Restricted from publication

12. Comment

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No further comments.

Related metadata

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Annexes

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