Crop production (apro_cp)

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

Compiling agency: Federal Statistical Office of Germany


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 Top
1.1. Contact organisation

Federal Statistical Office of Germany
1.2. Contact organisation unit

Division G1: Agriculture and Forestry, Fisheries
1.5. Contact mail address

Graurheindorfer Straße 198, 53117 Bonn, Germany


2. Statistical presentation Top
2.1. Data description

Annual crop statistics provide statistics on the area under main arable crops, vegetables and permanent crops as well as 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 available mostly at national level but for some crops also regional data exist (NUTS 1/2).
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: Eurostat 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 according to the following thresholds (predominantly intended for the market):

1. Survey on agricultural land use
     a) The holding has a utilised agricultural area of at least five ha, or
     b) The holding has a utilised agricultural area of less than five ha, but meets at least one of the criteria below:
          • 10 cattle
          • 50 pigs
          • 10 breeding sows
          • 20 sheep
          • 20 goats
          • 1000 places for poultry keeping
          • 0,5 ha of hops
          • 0,5 ha of tobacco
          • 1,0 ha of permanent outdoor crops or 0.5 ha each of area under fruit trees, vines or tree nurseries
          • 0,5 ha of outdoor vegetable or strawberry cultivation
          • 0,3 ha of outdoor flower or ornamental plant cultivation
          • 0,1 ha of crops under glass or other accessible protective cover
          • 0,1 ha of mushrooms

2. Special harvest and quality survey
    Agricultural holdings with cereals, potatoes and winter rape according to the thresholds of the survey on agricultural land use.

3a. Survey on vineyards
     All proprietors and operators of vineyards that are recorded in the vineyard register.

3b. Expert estimation on the yield and quality of grape must. Experts reporting on agricultural holdings with wine production according to No 3a.

4. Surveys on vegetables and strawberries
    The thresholds for vegetables and strawberries are:
    • 0.5 ha area of vegetables (without herbs) and/or strawberries and their young plants cultivated on arable land
    • 0.1 ha area of vegetables (without herbs) and/or strawberries and their young plants cultivated under glass or high accessible cover.

5. Expert estimation on the yields of field crops
    Sample of farmers and expert estimation according to farmers of No 1 with field crops.

6. Survey on mushrooms: all areas of producers with production areas of at least 1 000 m².

7. Survey on bush berries: all areas of producers with production areas of bush berries of at least 0,5 ha on open field or 0,1 ha under glas or high accessible cover.

8. Expert estimation on fruit tree production
    Farmers and experts reporting on agricultural holdings with fruit tree production according to No 9.

9. Orchard survey 2017 and 2022: all areas of producers with production areas of at least 0,5 ha fruit or nut trees (apples, pears, cherries, all kinds of plums, walnuts (2022 also haselnuts)) in main use.

 

     

 
2.7. Reference area

The entire territory of the country according to the thresholds mentioned under 2.6.
2.8. Coverage - Time

Crop year 2022
2.9. Base period

Not applicable.


3. Statistical processing Top
3.1. Source data

  Source 1 Source 2 Source 3 Source 4
Have new data sources been introduced since the previous quality report?       
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 Survey
Administrative data
Expert estimate

Survey on agricultural land use

IACS

Expert estimation

 

  
Final area under cultivation Survey
Administrative data

Survey on agricultural land use

IACS
Production Survey
Expert estimate

Survey on agricultural land use

Expert estimations on the yields of field crops

Special harvest and quality survey
Yield Survey
Expert estimate

Special harvest and quality survey

Expert estimations on the yields of field crops

 
Non-existing and non-significant crops Other

NE: crops not grown due to climatic conditions e.g. rice, cotton

Climatic conditions
Table 2: Vegetables, melons and strawberries       
Early estimates for harvested areas Survey

Survey on vegetables (only asparagus and strawberries)
Final harvested area Census
Survey

Census/Survey on vegetables and strawberries

Census on mushrooms 
Production Survey
Expert estimate
Non-existing and non-significant crops Census
Other

NE: crops not grown due to climatic conditions e.g. Watermelons

NS: crops very rarely grown mainly due to climatic conditions e.g. Chicory for processing, Artichokes, Tomatoes on open field, Eggplants, Muskmelons, Garlic

Climatic conditions
Table 3: Permanent crops      
Early estimates for production area
Final production area Census
Administrative data
Production Census
Administrative data
Expert estimate

Expert estimation on fruit tree production (yearly with thresholds)

Surveys on bush berries (estimations by farmers / yearly with thresholds)

Surveys on vineyards

Expert estimation on the yield and quality of grape must

 
Non-existing and non-significant crops

NE/0: mostly because of climatic conditions,

NS: producer and marketing organisations
Table 4: Agricultural land use      
Main area Survey
Administrative data

Survey on agricultural land use (yearly with thresholds)

IACS
Non-existing and non-significant crops Census
Other

NE: crops not grown due to climatic conditions e.g. Citrus fruits, Olives

 

Climatic conditions
Total number of different data sources

10 (see item 2.6)

  
Additional comments
  Put x, if used
Surveyed: farmers report the humidy  
Surveyed: farmers convert the production/yield into standard humidity       x
Surveyed: whole sale purchasers report the humidy  
Surveyed: whole sale purchasers convert the production/yield into standard humidity       
Surveyed by experts (e.g. test areas harvested and measured)  x (Tab 1)
Estimated by experts  x
Other type  
If other type, please explain  
Additional information  
   


Which method is used for calculating the yield for main arable crops? another method
If another method, describe it.

Area is taken from IACS and surveyed from farms, yield is partially surveyed and estimated by farmers and experts and production volume is assessed on the basis of the yield and the area.




 
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

Survey on agricultural land use (primary survey and adm. data (IACS))

Special harvest and quality survey (primary survey)

Surveys on vineyards (register of vines) and expert estimation on the yield and quality of wine must

Survey on vegetables and strawberries (primary survey)

Expert estimation on the yields of field crops including area estimation

Census on mushrooms (primary census on areas incl. production estimation)

Census on bush berries (primary census on areas incl. production estimation)

Expert estimation on fruit tree production (estimation by farmers and experts )

Census on orchards 2022
Planning (month-month/year)

April 2021 - February 2022

January until May 2022

May until June 2022 (register of vines)

January until March 2022 (expert estimation) 

May-December 2022

September 2021 until March 2022

May-Juni 2022

January - March 2022

November 2021 - February 2022

January 2020 until December 2020
Preparation (month-month/year)

April 2021 - February 2022

May until June 2022

November and December 2022 (register of vines)

April until July 2022 (expert estimation)

January-October 2022

September 2021 until March 2022

July until November 2022

March until September 2022

February - May 2022

January 2021 until December 2021.
Data collection (month-month/year)

February until October 2022

August until September 2022

July 2022 and January 2023 (register of vines)

August until October 2022 (expert estimation)

June-December 2022

November 2021 until November 2022

December 2022 - February 2023

September - December 2022

June, July, August, November 2022

January 2022 until May 2022
Quality control (month-month/year)

February until November 2022

August until September 2022

August 2022 until January 2023 (register of vines)

August until October 2022 (expert estimation)

July 2019, Oktober 2022 -January 2023

November 2021 until November 2022

March 2023

October 2022 - January 2023

June, July/August, August/September, December 2022/ January 2023

June 2022 until August 2022
Data analysis (month-month/year)

from July 2022

from August 2022

December 2022 until February 2023 (register of vines)

September until November 2022 (expert estimation)

Dezember 2022 - Februar 2023

November 2021 until November 2022

March 2023

December 2022 until January 2023

June, July/August, August/September, December 2022/ January 2023

June 2022 until August 2022
Dissemination (month-month/year)

November 2022 (final data)

January 2023 (final data)

March until April 2023 (register of vines)

September until December 2022 (expert estimation)

Juli 2022

February 2023

January 2022 until January 2023 (final data)

March 2023

February 2023

July, August, September 2022, January 2023

September 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? YES

C1211 'Sorghum' is included in C1219 'Buckwheat, millet, canaryseed (other cereals)'; C1520 'Fibre flax' is included in 'C1510-Other fibre crops'.

 
Are there differences between the national methodology and the methodology described in the Handbook concerning e.g. the item and aggregate calculations? YES

Table 1: Sown area is used only as there is no additional survey in autumn. Only in case of maize the area is adjusted in those years where grain maize is used as green maize in a bigger amount because of bad weather conditions.

 
Are special estimation/calculation methods used for main crops from arable land? NO
Are special estimation/calculation methods used for vegetables or strawberries? YES

In years of census auxiliary information about the area is used for extrapolation of the production.
In one of the Länder the same is done in years of survey in form of a sub-sample.
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)

V4210 Onions include V4220 Shallots. There is no single item for shallots in Germany.

I1111 includes only winter rape (excluding winter turnip rape seeds)

I1112 includes spring rape as well as winter and spring turnip rape seeds

Regional data on crop production at NUTS 1 level:

C0000, C1000 exluding production of Sorghum and other cereals (from 2010 onwards)

P0000 excluding production of other dry pulses and protein crops ne.c. (2010-2014)

I1110-I1130 excluding production of Soya seed (2010-2014)
In case data are delivered for one of the items below, describe the crop species included in the item: V1900 - Other brassicas n.e.c.
V2900 - Other leafy or stalked vegetables n.e.c.
V3900 - Other vegetables cultivated for fruit n.e.c.
F3900 - Other berries n.e.c.

V1900 - Other brassicas n.e.c.:  Kohlrabi, Chinese cabbage, curly kale
V2900 - Other leafy or stalked vegetable: Eichblattsalat (curled lettuce, Lactuca sativa var. crispa), corn-salad (Valerianella locusta), lollo rossa/bionda (Lactuca sativa var. crispa), rhubarb (Rheum rhabarberum), rucola
V3900 - Other vegetables cultivated for fruit n.e.c.:  sweet corn (Zea mays var. saccharata)
F3900 - Other berries n.e.c: elderberry, chokeberries (Aronia), seabuckthorn, gooseberries, blackberries and other berries 
F1190 - Other pome fruits n.e.c. (from 2017 onwards): quinces

 


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).

The fulfilment of these conditions were proved after the last agricultural census (2020). The coverage of total UAA is 99,05%.
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)

permanently
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 %
Dried pulses and protein crops (in %)

less than 5 %
Root crops (in %)

less than 5 %
Oilseeds (in %)

less than 5 %
Other industrial crops (included all industrial crops besides oilseeds)  (in %)

less than 5 %
Plants harvested green from arable land (in %)

less than 5 %
Total vegetables, melons and strawberries (in %)

less than 5 %
Cultivated mushrooms (in %)

less than 5 %
Total permanent crops (in %)

less than 5 %
Fruit trees (in %)

less than 5 %
Berries (in %)

less than 5 %
Nut trees (in %)

less than 5 %
Citrus fruit trees (in %)

-
Vineyards (in %)

less than 5 %
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

Survey on agricultural land use

Special harvest and quality survey

Survey on vegetables and strawberries

Survey on mushrooms

Survey on bush berries

Orchard survey 2017
Which survey method was used? On-line electronic questionnaire filled in by respondent
Postal questionnaire filled in by respondent		 Other 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

For important crops (cereals, potatoes, and winter rape) the sampling method of the special harvest and quality Survey is applied: On fields selected at random, average crop yields and other variables determining the yield are ascertained for the most frequent cereals (winter wheat, rye, winter barley, spring barley and triticale), potatoes and winter rape. The number of yield measurement is tailored to the volume and regional distribution of areas under cultivation, so that average yields can be calculated with sufficient accuracy for the Federation and the Länder. For cereals, the statistical offices of the Länder can generally choose between the sample threshing procedure and the complete threshing procedure. For potatoes, the sample digging-up procedure is applied, while for rape it generally is the complete threshing procedure.
Please provide a link to the questionnaire

Survey on agricultural land use

Surveys on vegetables and strawberries

Survey on mushrooms

 

 Survey on bush berries

Orchard survey 2022
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

Register of vines (area under vines, quantity of grape must)
Description

IACS is the most important system for the management and control of payments to farmers made by the Member States in application of the Common Agricultural Policy.

The main objectives of the vineyard register are the monitoring and verification of the production potential. 
Data owner (organisation)

Agricultural administrations which are responsible for the payments made by the Member States in application of the Common Agricultural Policy.

Agricultural administrations which are in charge of the vineyard register in accordance to Commission Delegated Regulation (EU) 2018/273.
Update frequency Once per year or more often Once per year or more often
Reference date (month/year)

May 2022

July 2022 for area under vines and January 2023 for quantity of grape must
Legal basis

National agriculture statistic law

National agriculture statistic law
Reporting unit

applicant of the payments

Wine grower (natural or legal person) with an area planted with vines of at least 0,1 hectares
Identification variable (e.g. address, unique code, etc.)

Address and code

In the vineyard register: address and code
Percentage of mismatches (%)

7,7%

0%
How were the mismatches handled?

survey
Degree of coverage (holdings, e.g. 80%)

92,3%

100%
Degree of completeness (variables, e.g. 60%)

from 70% until 95%, depends on the federal Land

Exclusively administrative sources are used.
If not complete, which other sources were used ?

survey

complete
Were the data used for sample frame? Sample frame
Directly for estimates		 Directly for estimates
Data used for other purposes, which?
Which variables were taken from administrative sources?

Just the variables which fit exactly to those in the regulation 543/2009

area under vines and quantity of grape must
Were there any differences in the definition of the variables between the administrative source and those described in the Regulation? NO 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?

There is an ex-ante check in Germany for the variables in IACS. 
What were the possible limitations, drawbacks of using the data from administrative source(s)?

If there is a change in IACS, it automatically leads to changes in the survey.


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 estimations on the yields of field crops including area estimation

Expert estimation on fruit tree production

Expert estimation on the yield and quality of wine must
Data owner (organisation)

Federal Statistical Office of Germany

Federal Statistical Office of Germany

Federal Statistical Office of Germany
Update frequency (e.g. 1 year or 6 months) 6 times per year 4 times per year 3 times per year
Reference date (Month/Year  e.g. 1/22 - 8/22)

04/22, 06/22, 07/22, 08/22, 10/22, 11/22

depending on the kind of orchards: 06/22, 07/22, 08/22, 11/22

08/22, 09/22, 10/22
Legal basis

EU Regulation N°543/2009 and Delegated Regulation (EU) 2015/1557 on Crop statistics and national law (Agrarstatistikgesetz, AgrStatG)

EU Regulation N°543/2009 and Delegated Regulation (EU) 2015/1557 on Crop statistics and national law (Agrarstatistikgesetz, AgrStatG)

Commission Delegated Regulation (EU) 2018/273 and national law (Agrarstatistikgesetz)
Use purpose of the estimates?

fulfilling EU and national data requirements

fulfilling EU and national data requirements

Fulfilling EU and national data requirements
What kind of expertise the experts have?

experts are farmers or former farmers and have extensive experience in growing field crops 

experts are farmers or former farmers and have extensive experience in growing orchards

Experts are farmer or former farmers with extensive experience in growing wine grapes
What kind of estimation methods were used?

Advanced experienced data

Yield or production are estimated per farm and fruit (voluntary),
production then is aggregated and extrapolated and divided by proportional regional area to get the average yield per region
(if the yield was estimated, the yield is multiplied with the dedicated area before).

Advanced experienced data
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
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?

Comparisons year over year 

Comparisons year over year.

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

low number of experts for a certain field crop in a certain region

Low number of experts for certain fruits in several regions.

Low number of experts for certain grape varieties in several regions
Additional comments


 
3.4. Data validation

Which kind of data validation measures are in place? Automatic
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
Other dataset
If other, please describe

IFS, agricultural census among each other where appropriate






 
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 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?

completeness-check, plausibilty-check, confidentality-check, surveys based on a permanent actualised register

        
Is there a quality report available? YES        
If yes, please provide a link(s)

 

https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/Feldfruechte-Gruenland/_inhalt.html#sprg394882

https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/Wein/_inhalt.html#sprg394832

https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/Obst-Gemuese-Gartenbau/_inhalt.html

 

 

        
To which data source(s) is it linked?

1. Survey on agricultural land use

2. Special harvest and quality survey

3a. Surveys on vineyards

3b. Expert estimations on the yields and quality of grape must

4. Surveys on vegetables and strawberries

5. Expert estimations on the yields of field crops including area estimation

6. Survey on mushrooms

7. Survey on bush berries

8. Expert estimation on fruit tree production

9. Orchard survey 2022

 

 

        
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? Systematic validation improvements        
If, other, please specify

New data processing of the expert estimations on fruit tree production has been introduced.

        
Additional comments        






 
4.2. Quality management - assessment

See the European level Quality Report.


5. Relevance Top
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

The major needs are successfully met.
Some further specific vegetables and fruits (subitems) are included on national level.






 
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 Top
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

Survey on agricultural land use

Special harvest and quality survery

Surveys on vineyards

Survey on vegetables and strawberries
Sampling basis? Other Other Other Other
If 'other', please specify

Agricultural register which covers all agricultural holdings.

Agricultural register which covers all agricultural holdings.

No survery, use of administrative data, therefore no sampling error

Agricultural register which covers all agricultural holdings.
Sampling method? Stratified Stratified Stratified
If stratified, number of strata?

860

80

284
If stratified, stratification basis? Location
Size
Specialisation		 Location
Specialisation		 Location
Size
Specialisation
If 'other', please specify
Size of total population

262 800 agricultural holdings

7 Mio. Hectar

6920 holdings producing vegetables and/or strawberries according to the thresholds
Size of sample

78 100 agricultural holdings

10.000 test fields

4529 holdings producing vegetables and/or strawberries according to the thresholds
Which methods were used to assess the sampling error?  Relative standard error Relative standard error Relative standard error
If other, which?
Which methods were used to derive the extrapolation factor?  Basic weight
Non-response		 Basic weight Basic weight
Non-response
Other
If other, which?

In years of census (every 4 years) auxiliary information about the area is used for extrapolation of the production.
In one of the Länder the same is done in years of survey in form of a sub-sample.
If CV (co-efficient of variation) was calculated, please describe the calculation methods and formulas

The RSE is calculated according the formula for stratified sample surveys

The RSE is calculated according the formula for stratified sample surveys

The RSE is calculated according the formula for stratified sample surveys
If the results were compared with other sources, please describe the results
Which were the main sources of errors?

unit non response, especially some small farms couldn't estimate the harvest production


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

Survey on agricultural land use

Survey on vegetables and strawberries
Cereals for the production of grain (in %)

0,20
Dried pulses and protein crops (in %)

0,75
Root crops (in %)

1,04
Oilseeds (in %)

0,36
Other industrial crops (included all industrial crops besides oilseeds)  (in %)

0,35
Plants harvested green from arable land (in %)

0,31
Total vegetables, melons and strawberries (in %)

1,05

less than 2%
Cultivated mushrooms (in %)

0,01

0%
Total permanent crops (in %)

0,78
Fruit trees (in %)

1,20
Berries (in %)

2,76
Nut trees (in %)

11,64
Citrus fruit trees (in %)
Vineyards (in %)

0,55
Olive trees (in %)
Additional comments            




 
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

Survey on agricultural land use

Special harvest and quality survery

Surveys on vineyards

Survey on vegetables and strawberries

Expert estimations on the yields of field crops

Survey on mushrooms (census)

Survey on bush berries (census)

Expert estimation on fruit tree production (voluntary survey)

Orchard survey 2022 (census)
Error type Under-coverage Under-coverage No error Under-coverage Other No error No error Other Under-coverage
Degree of bias caused by coverage errors Unknown Unknown Unknown Low Low Unknown
What were the reasons for coverage errors?

A small number of unknown holdings, which are not included in the frame population

A small number of unknown holdings with the specified crops (cereals, potatoes and rape seed)

A small number of unknown holdings, which are not included in the frame population

low number of experts for certain field crops in certain regions

low number of experts for certain fruit crops in certain regions

A very small number of unknown holdings, which are not included in the frame population
Which actions were taken for reducing the error or to correct the statistics?

Permanent check of administrative data to find the unknown holdings

Permanent check of administrative data. Some statistic offices ask the agricultural holdings about the crops planning   

Permanent check of administrative data, to find the unknown holdings

recruitment of further experts

recruitment of further experts, payments, advertisement, online questionnaire is introduced.

Permanent check of administrative data, to find the unknown holdings.
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

Survey on agricultural land use

Special harvest and quality survey

Surveys on vineyards

Survey on vegetables and strawberries

Expert estimations on the yields of field crops

Survey on mushrooms (census)

Survey on bush berries (census)

Expert estimation on fruit tree production

Orchard survey 2022 (census)
Was the questionnaire based on usual concepts for respondents? YES YES NO YES YES YES YES YES YES
Number of surveys already performed with the current questionnaire (or a slightly amended version of it)?

6

29

11

9

11

11

several (different in the Länder)

2
Preparatory (field) testing of the questionnaire? NO NO NO NO YES YES NO NO
Number of units participating in the tests? 

10

8
Explanatory notes/handbook for surveyors/respondents?  YES YES YES YES YES YES YES YES
On-line FAQ or Hot-line support for surveyors/respondents? YES NO YES NO YES YES YES YES
Were pre-filled questionnaires used? NO NO NO YES NO NO YES NO
Percentage of pre-filled questions out of total number of questions

40%

25%
Were some actions taken for reducing the measurement error or to correct the statistics? YES YES YES NO YES YES YES YES
If yes, describe the actions and their impact

Plausibility checks were integrated in the questionnaire

Cross-check of results

Improvement of the stratified sample, revision of explanations

Plausibility checks are integrated in the questionnaire.

Plausibility are checks integrated in the questionnaire.

Plausibility checks are integrated in the new online-questionnaire, special data validation by the statistical offices of the Länder and phone recalls to the farmers or surveyors.

Plausibility checks are integrated in the questionnaire.






 
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

Survey on agricultural land use

Special harvest and quality survey

Surveys on vineyards

Survey on vegetables and strawberries

Orchard survey 2022 (census)

Survey on mushrooms and survey on bush berries (censuses).
Unit level non-response rate (in %)

0,6

0

0

0,8%

0,3%

Bushberries: 0,2%

Mushrooms: 5%
Item level non-response rate (in %)              
               - Min% / item

n.e.

unknown

0

unknown

unknown

unknown
               - Max% / item

n.e

unknown

0

unknown

unknown

unknown
               - Overall%

n.e

unknown

0

 

0,7%

 

0,3%

Bushberries: 0%

Mushrooms: 5%
Was the non-response been treated ? YES NO YES YES YES
Which actions were taken to reduce the impact of non-response?

Callbacks in the agricultural holdings. If it was a unit non response, the extrapolation factor was adapted

Other fields of the same crops and environment are selected

contact the responders

 

contact the responders

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

n.e.

items which concern to harvest

not yet been evaluated

items which concern to harvest
Which methods were used for handling missing data?
(several answers allowed)		 Follow-up interviews
Reminders		 Reminders Follow-up interviews
Reminders
Legal actions		 Follow-up interviews
Reminders
Legal actions		 Follow-up interviews
Reminders
Legal actions
In case of imputation which was the basis? Imputation based on the same unit in previous data None Imputation based on the same unit in previous data
Other		 None Imputation based on the same unit in previous data
Other
In case of imputation, which was the imputation rate (%)?

1%

Bushberries: 0,3%

Mushrooms: 2%
Estimated degree of bias caused by non-response? Unknown Unknown Unknown None
Which tools were used for correcting the data?

AGRA 2010, Plausibility checks during data entry

Plausibility checks during data entry

Plausibility checks during data preparation and data entry, comparison with previous and similar surveys.

Plausibility checks during data preparation and data entry, comparison with previous and similar surveys.

Plausibility checks during data preparation and data entry, comparison with previous and similar surveys.
Which organisation did the corrections?

The statistcal offices of the federal länder

Statistical Offices

Statistical Offices

 

Statistical Offices

Statistical Offices
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 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?

area: 2
production: 4

area: 2
production: 4

area: 2
production: 4

area: 2
production: 4

area: 2
production: 4

area: 2
production: 4

1 (2 only for asparagus)

2

1

5

1

1 (only area)

0

4

0
How many of them are forecasts (releases before the harvest)?

1

1

1

1

0

1

1 only for asparagus

1

0

3

0

0

3
When was the first  forecasting published for the crop year on which is reported? (day/month/year) 03/08/2022 26/08/2022 22/09/2022 03/08/2022 22/09/2022 21/07/2022 21/07/2022 29/06/2022 15/09/2022
When were the final results published for the crop year on which is reported? (day/month/year) 23/01/2023 23/01/2023 23/01/2023 23/01/2023 23/01/2023 23/01/2023 27/02/2023 27/02/2023 14/03/2023 09/01/2023 09/02/2023 16/09/2022 23/03/2023
Additional comments

final results area: 22/11/2022
final results production: 23.01.2023

final results area: 22/11/2022
final results production: 23.01.2023

final results area: 22/11/2022
final results production: 23.01.2023

final results area: 22/11/2022
final results production: 23.01.2023

final results area: 22/11/2022
final results production: 23.01.2023

final results area: 22/11/2022
final results production: 23.01.2023


final results area: 16/09/2022
final results production: 09/01/2023




 
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
8.1. Comparability - geographical

Comparability across the federal Länder is given.

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 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

not applicable
8.3. Coherence - cross domain

With which other data sources the crop statistics data have been compared?  Other
IACS
If others, which?

Orchard survey 2017

IACS

IFS
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

+ 8,2 %

 

 

Compared with Orchard survey (ACS) 2022 area in IFS 2020 is bigger because
- of different thresholds: threshold for both is 0.5 ha, but in IFS smaller orchard areas are included from farmers with less than 0.5 ha of orchards because they may be included because of thresholds for different crops or animals;
- in ACS only market production areas (threshold 0.5 ha) is included;
- planted areas not yet in production are excluded in ACS and
- fruit tree area is decreasing over time.

  
Berries

+ 5,0 %

  

 

 

Compared with survey on bush berries (ACS) 2022 area in IFS 2020 is bigger because of different thresholds. In IFS smaller berry areas are included from farmers with less than 0.5 ha of berries because they are included because of thresholds for different crops or animals. 

 
Nut trees

- 28,7 %

  

Compared with ACS data from 2022, nuts area was smaller in 2020 but is increasing since 2016. 
Citrus fruit trees

Not applicable.
Vineyards

- 3,2 %

+ 0,2 %

Other source: vineyard census 2022
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

Not applicable.


9. Accessibility and clarity Top
9.1. Dissemination format - News release

Availability Links
YES

https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/Feldfruechte-Gruenland/_inhalt.html#sprg394882

https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/Wein/_inhalt.html#sprg394832

https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/Obst-Gemuese-Gartenbau/_inhalt.html

 






 
9.2. Dissemination format - Publications

  Availability Links
Publications Electronic

https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/Feldfruechte-Gruenland/_inhalt.html#sprg239470  

https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/Wein/_inhalt.html#sprg239642

https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/Obst-Gemuese-Gartenbau/_inhalt.html

 
Publications in English Electronic

https://www.destatis.de/EN/Themes/Economic-Sectors-Enterprises/Agriculture-Forestry-Fisheries/Field-Crops-Grassland/_node.html#sprg265390

https://www.destatis.de/EN/Themes/Economic-Sectors-Enterprises/Agriculture-Forestry-Fisheries/Wine/_node.html#265446

https://www.destatis.de/EN/Themes/Economic-Sectors-Enterprises/Agriculture-Forestry-Fisheries/Fruit-Vegetables-Horticulture/_node.html

 






 
9.3. Dissemination format - online database

Data tables - consultations


  Availability Links
On-line database accessible to users YES

Genesis-online: https://www-genesis.destatis.de/genesis/online?operation=previous&levelindex=0&step=0&titel=&levelid=1599054040087&acceptscookies=false

 
Website National language
English

https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/_inhalt.html
https://www.destatis.de/EN/Themes/Economic-Sectors-Enterprises/Agriculture-Forestry-Fisheries/_node.html

 





 
9.4. Dissemination format - microdata access

Availability Links
YES

Special harvest and quality survery: https://www.forschungsdatenzentrum.de/de/agrar/bee






 
9.5. Dissemination format - other
9.6. Documentation on methodology

  Availability Links
Methodological report
Quality Report National language

Survey on agricultural land use

Special harvest and quality survey

Surveys on vineyards

Expert estimations on the yields and quality of grape must

Surveys on vegetables and strawberries

Expert estimations on the yields of field crops including area estimation

Survey on mushrooms

Survey on bush berries

Expert estimation on fruit tree production

Orchard survey 2022

 
Metadata
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 Top

Efficiency gains if compared to the previous quality report? Further automation
If other, which?

More efficient process of data acquisition
Burden reduction measures since the previous reference year  Less respondents
More user-friendly questionnaires
Easier data transmission
If other, which?






 


11. Confidentiality Top
11.1. Confidentiality - policy

Are confidential data transmitted to Eurostat? Restricted from publication
If yes, are they confidential in the sence of Regulation 223/2009? Restricted from publication
Describe the data confidentiality policy in place Restricted from publication






 
11.2. Confidentiality - data treatment

Describe the procedures for ensuring confidentiality during dissemination (incl. general description of the rules for defining confidential cells in output tables and procedures for detecting and preventing residual disclosure). Restricted from publication
Additional comments Restricted from publication






 


12. Comment Top


Related metadata Top


Annexes Top