Crop production (apro_cp)

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

Compiling agency: Federal Statistical Office of Germany

Time Dimension: 2016-A0

Data Provider: DE1

Data Flow: CROPROD_ESQRSCP_A


Eurostat metadata
Reference metadata
1. Contact
2. Statistical presentation
3. Statistical processing
4. Quality management
5. Relevance
6. Accuracy and reliability
7. Timeliness and punctuality
8. Coherence and comparability
9. Accessibility and clarity
10. Cost and Burden
11. Confidentiality
12. Comment
Related Metadata
Annexes (including footnotes)
 



For any question on data and metadata, please contact: EUROPEAN STATISTICAL DATA SUPPORT

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1. Contact Top
1.1. Contact organisation

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 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 (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: 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:

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 poultry
          • 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.

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

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 estimations on the yields of field crops
    Sample 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. Sample expert estimation according to No 9.

9. Orchard survey 2012 (partially adjusted): all areas of producers with production areas of at least 0,5 ha fruit trees (apples, pears, cherries, all kinds of plums) 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 2016.

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


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

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

Survey on agricultural land use

IACS

 

Final area under cultivation Survey
Administrative data

Survey on agricultural land use

IACS

Production Survey
Expert estimate
Other

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

Special harvest and quality survey

Non-existing and non-significant crops
Table 2: Vegetables, melons and strawberries       
Early estimates for harvested areas Survey

Survey on vegetables (only asparagus and strawberries)

Final harvested area Census
Survey

Survey on vegetables and strawberries

Census on mushrooms (with thresholds)

Production Census
Survey

Survey on vegetables and strawberries

Census on mushrooms (with thresholds)

Non-existing and non-significant crops
Table 3: Permanent crops      
Early estimates for production area
Final production area Census
Administrative data
Expert estimate

Orchard survey (census) every 5 years

Surveys on bush berries

Surveys on vineyards 

Production Census
Administrative data
Expert estimate

Expert estimation on fruit tree production

Surveys on bush berries (estimations by farmers)

Surveys on vineyards

 

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

IACS

Non-existing and non-significant crops
Total number of different data sources

2

3

2

2

1

   
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? 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

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

 Special harvest and quality survey (primary survey)

Surveys on vineyards (Adm. data (Register of vines))

Survey on vegetables and strawberries (primary survey)

Expert estimation on the yields of field crops (Expert estimation)

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

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

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

Census on orchards 2012

Planning (month-month/year)

January 2014 until May 2015

January 2016 until May 2016

May until June 2016  

November-December 2015

Jan until Mar 2016

October-December 2016

March-May 2016

March-May 2016

January 2010 until December 2010

Preparation (month-month/year)

May 2015 until February 2016

May until June 2016

November and December 2016

April-June 2016

Jan until Mar 2016

December 2016

June-August 2016

June-December 2016

January 2011 until December 2011.

Data collection (month-month/year)

February until October 2016

August, September 2016

July 2016 and January 2017

June-December 2016

Apr until Nov 2016

January-February 2017

September-December 2016

June, July, August, November 2016

January 2012 until June 2012

Quality control (month-month/year)

March 2016 until December 2016

August, September 2016

August 2016 until January 2017

July 2016, Oktober-January 2017

Apr until Nov 2016

March 2017

Oktober-December 2016

June, July,August, November 2016

July 2012 until August 2012

Data analysis (month-month/year)

from January 2017

from August 2016

December 2016 until February 2017

Feb-17

Apr until Nov 2016

March 2017

January 2016

June, July, August, November 2016

September 2012 until December 2012

Dissemination (month-month/year)

May 2017

May 2017

March until April 2017

Feb-17

May 2016 until May 2017

March 2017

February 2016

July, August, September 2016, January 2017

December 2012

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.

The Handbook says for V2000 Leafy and stalked vegetables (excluding brassicas): “This class excludes rucola (Eruca sativa L.)”. But V2000 includes V2900 Other leafy or stalked vegetables where it says: “This class includes other leafy and stalkedvegetables not elsewhere classified:… , rucola (Eruca sativa L.), …”.
For the Handbook 2017 this will be corrected, V2000 includes rucola (in V2900).

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?
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

F1200 Stone fruits do only include F1240 Cherries and F1250 Plums

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.
V9000 - Other fresh vegetables 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)
V9000 - Other fresh vegetables n.e.c.: rucola is included in V2900 as it is defined in the handbook. There was a first version of data delivered to Eurostat where rucola was included in V9000. But the data has been corrected in the last version delivered to Eurostat
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 between the last two total surveys.

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)

permanent

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 %)

0.47 %

Dried pulses and protein crops (in %)

0.24 %

Root crops (in %)

0.28 %

Oilseeds (in %)

0.14 %

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

0.25 %

Plants harvested green from arable land (in %)

0.41 %

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 %)

1.46 %

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 bush berries

Survey on vegetables and strawberries

Expert estimations on the yields of field crops

Survey on mushrooms

Orchard survey 2012

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
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

agrarstrukturerhebung2016

Strauchbeerenerhebung

Speisepilzerhebung

Baumobstanbauerhebung 2012

Data entry method, if paper questionnaires? Optical 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

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 Regulation (EC) No 436/2009.

Update frequency Once per year or more often Once per year or more often
Reference date (month/year)

May-16

July 2016 for area under vines and January 2017 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

In the vineyard register: address and code

Percentage of mismatches (%)

e.g. 20%

0%

How were the mismatches handled?

survey

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

e.g. 80%

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

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

Just these 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 

Expert estimation on fruit tree production

Data owner (organisation)

Federal Statistical Office of Germany

Federal Statistical Office of Germany

Update frequency (e.g. 1 year or 6 months) 2 times per year 3 times per year
Reference date (Month/Year  e.g. 1/16 - 8/16)

depending on the kind of field crop: 08/16, 10/16, 05/17

depending on the kind of orchards: 06/16, 07/16, 08/16, 11/16

Legal basis

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

EU: Regulation (EC) 543/2009 and Delegated Regulation (EU) 2015/1557

National: Agrarstatistikgesetz

Use purpose of the estimates?

fulfilling EU and national data requirements

fulfilling EU and national data requirements

What kind of expertise the experts have?

experts are owner or tenant farmer of agricultural holdings and posses extensive experience in growing field crops 

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

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).

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

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.

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
If other, please describe

agricultural census

3.5. Data compilation

Not Applicable.

3.6. Adjustment

Not applicable.


4. Quality management Top
4.1. Quality assurance

  Completeness Punctuality Accuracy Reliability Overall quality
How would you describe the overall quality development since 2014? 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)

Bodennutzungshaupterhebung

Besondere Ernte- und
Qualitätsermittlung (BEE)

Weinstatistik: Erhebung der Weinernte und Erhebung der Weinerzeugung

Grunderhebung der Rebflächen und
Rebflächenerhebung

Gemüseerhebung

Ernte- und Betriebsberichterstattung
(EBE): Feldfrüchte und Grünland

Speisepilzerhebung

Strauchbeerenerhebung

Baumobstanbauerhebung 2012

Ernte- und Betriebsberichterstattung (EBE): Baumobst

       
To which data source(s) is it linked?

Survey on agricultural land use

Special harvest and quality survey

Surveys on vineyards

Surveys on vegetables

Expert estimations on the yields of field crops

Survey on mushrooms

Survey on bush berries

Expert estimation on fruit tree production

Orchard survey 2012

       
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

       
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.

5.2. Relevance - User Satisfaction

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

The actuality of the data should be improved in case of expert estimations.

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

Expert estimations on the yields of field crops

Sampling basis? Other 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.

No sample survey, no sampling error

Sampling method? Stratified Stratified Stratified
If stratified, number of strata?

1109

81

228

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

293927

184100

7670

Size of sample

78058

10000

5340

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
Non-response
Basic weight
Non-response
Other
If other, which?

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.

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 %)

0,23

Dried pulses and protein crops (in %)

0,74

Root crops (in %)

0,99

Oilseeds (in %)

0,32

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

2,98

Plants harvested green from arable land (in %)

0,39

Total vegetables, melons and strawberries (in %)

1,36

less than 2%

Cultivated mushrooms (in %)

2,88

0%

Total permanent crops (in %)

0,95

Fruit trees (in %)

1,06

Berries (in %)

3,44

Nut trees (in %)

8,91

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

0,75

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 2012 (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 a certain field crop in a certain region

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

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 under development.

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

Additional comments




 

6.3.1.1. Over-coverage - rate

not applicable

6.3.1.2. Common units - proportion

not applicable

6.3.2. Measurement error

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

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 2012 (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)?

3

26

5

6

5

5

several (different in the Länder)

0

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

0

10

8

0

0

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 NO YES
Were pre-filled questionnaires used? NO NO NO YES NO NO NO NO
Percentage of pre-filled questions out of total number of questions

0

0

40%

0

0

0

0

Were some actions taken for reducing the measurement error or to correct the statistics? YES YES YES NO YES YES NO 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 explainations

Plausibility checks were integrated in the questionnaire.

Plausibility checks were integrated in the questionnaire.

Plausibility checks were integrated in the data preparation.

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

Expert estimations on the yields of field crops 

Survey on mushrooms and survey on bush berries (censuses).

Orchard survey 2012 (census)

Unit level non-response rate (in %)

0,52

0

0

2%

0

0

0

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

n.e.

unknown

0

4%/peas

0

0

               - Max% / item

n.e

unknown

0

25%/radish

0

0

               - Overall%

n.e

unknown

0

areas: 0%

yields 20%

0

0

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

not yet been evaluated

Which methods were used for handling missing data?
(several answers allowed)
Follow-up interviews
Reminders
Reminders Follow-up interviews
Reminders
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 None
In case of imputation, which was the imputation rate (%)?

not yet been evaluated

Estimated degree of bias caused by non-response? Unknown Unknown Unknown None 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 entry

Plausibility checks during data entry

Plausibility checks during data preparation.

Which organisation did the corrections?

The statistcal offices of the federal länder

Statistical Offices

Statistical Offices

 

Statistical Offices

Statistical Offices

Additional comments
6.3.3.1. Unit non-response - rate

not applicable

6.3.3.2. Item non-response - rate

not applicable

6.3.4. Processing error

Not applicable.

6.3.4.1. Imputation - rate

Not applicable.

6.3.5. Model assumption error

Not applicable.

6.4. Seasonal adjustment

Not applicable.

6.5. Data revision - policy

Not applicable.

6.6. Data revision - practice

Not applicable.

6.6.1. Data revision - average size

Not applicable.


7. Timeliness and punctuality Top
7.1. Timeliness

Time lag - first result


Time lag - final result


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

2

2

2

2

2

2

2

2

1

4

1

1 (only area)

0

4

0

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

1

1

1

1

1

1

1 (only for asparagus)

1

0

3

0

3

When was  the first  forecasting published for crop year 2016? (day/month/year) 08/08/2016 26/08/2016 22/09/2016 08/08/2016 22/09/2016 19/07/2016 19/07/2016 06/07/2016 13/09/2016
When were the final results published for crop year 2016? (day/month/year) 24/05/2017 24/05/2017 24/05/2017 24/05/2017 24/05/2017 24/05/2017 03/03/2017 03/03/2017 15/03/2017 04/01/2017 13/02/2017 24/05/2017 05/04/2017
Additional comments
7.1.1. Time lag - first result
7.1.2. Time lag - final result
7.2. Punctuality

See the European level Quality Report

7.2.1. Punctuality - delivery and publication

See the European level Quality Report


8. Coherence and comparability Top
8.1. Comparability - geographical

Not applicable.

8.1.1. Asymmetry for mirror flow statistics - coefficient

Not applicable.

8.2. Comparability - over time

Length of comparable time series

not applicable


  Crops from arable land
(Table 1)
Vegetables, melons and strawberries (Table 2) Permanent crops
(Table 3)
Agricultural land use
(Table 4)
Have there been major breaks in the time series since 2013? NO NO NO NO
If yes, to which were they related?
If other, which?
Which items were affected?
Year of break (number)
Impact on comparability None
Additional comments
8.2.1. Length of comparable time series

not applicable

8.3. Coherence - cross domain

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

Orchard survey

Employer's liability insurance coverage

If no comparisons have been made, why not?


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

6 325 023

 
Dried pulses and protein crops

187 096

 
Root crops

582 554

 
Oilseeds

1 351 723

 
Other industrial crops (than oilseeds)

41 374

 
Plants harvested green

2 790 499

 
Total vegetables, melons and strawberries

130 161

 

140 076

Survey on vegetables and strawberries (harvested areas instead of main areas in FSS)

Vegetables and melons  
Strawberries  
Cultivated mushrooms

32,2

 
Total permanent crops

199 735

 
Fruit trees

54 191

 

45 146  

 

Orchard survey 2012: included are only market production areas and planted areas not yet in production are excluded, while in FSS all fruit tree areas are surveyed and the data is from 2016. 

 

Berries

9 193

 

8 459

 

Survey on bush berries (same situation as with fruit trees except for the different years).

 

Nut trees

694

 
Citrus fruit trees  
Vineyards

99173

102 543

102 493

survey of vineyards 2016

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

Not applicable.

8.5. Coherence - National Accounts

Not applicable.

8.6. Coherence - internal

Not applicable.


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

Availability Links
YES

Pressemitteilungen

9.2. Dissemination format - Publications

  Availability Links
Publications Electronic

https://www.destatis.de/DE/Publikationen/Thematisch/LandForstwirtschaft/ThemaLandForstwirtschaft.html

Publications in English None
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/link/statistiken/41*

Website

Website: https://www.destatis.de/DE/ZahlenFakten/Wirtschaftsbereiche/LandForstwirtschaftFischerei/LandForstwirtschaft.html;jsessionid=20C93D062D92EFE4A1C4A6CF4935AEC2.cae4

9.3.1. Data tables - consultations
9.4. Dissemination format - microdata access

Availability Links
9.5. Dissemination format - other

Free/against payment access policy

https://www.destatis.de/DE/Publikationen/Thematisch/LandForstwirtschaft/ThemaLandForstwirtschaft.html

9.6. Documentation on methodology

  Availability Links
Methodological report
Quality Report
Metadata English

no additional metadata files

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 reference year (2013) Further automation
If other, which?

More efficient process of data acquisition

Burden reduction measures since the previous reference year  Less frequent surveys
Less respondents
More user-friendly questionnaires
Easier data transmission
If other, which?


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


12. Comment Top


Related metadata Top


Annexes Top