Livestock and meat (apro_mt)

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

Compiling agency: DESTATIS (Statistisches Bundesamt)


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

DESTATIS (Statistisches Bundesamt)

1.2. Contact organisation unit

G 104

1.5. Contact mail address

Postfach 170377
53029 BONN


2. Statistical presentation Top
2.1. Data description

The livestock and meat statistics are collected under Regulation (EC) No 1165/2008 since 2009. They cover slaughtering in slaughterhouses (monthly) and other slaughtering (annual), GIP (gross indigenous production) forecast (semi-annual or quarterly data), and livestock statistics (once or twice a year), including regional statistics (annual).This template lists the questions constituting the quality report required in Article 17 of EU Regulation N°1165/2008 on livestock and meat statistics.
This quality report covers the year 2019 and all the quality indicators already reported for years 2010, 2013 and 2016.

2.2. Classification system

Not requested for reference year 2019.

2.3. Coverage - sector

Not requested for reference year 2019.

2.4. Statistical concepts and definitions

Not requested for reference year 2019.

2.5. Statistical unit

Not requested for reference year 2019.

2.6. Statistical population

Not requested for reference year 2019.

2.7. Reference area

Not requested for reference year 2019.

2.8. Coverage - Time

Not requested for reference year 2019.

2.9. Base period

Not applicable.


3. Statistical processing Top
3.1. Source data

Data sets on pigs and sheep are based on surveys - here stratified random samples with a maximum of 20000 reporting units for pigs and 5000 reporting units for sheep. Sample characteristics for pigs are the number of pigs and breeding sows. Sample characteristics for sheep are the number of sheep and milk ewes.

Data on goats are estimated based on the goat slaughterings in a given period.

Data sets on bovine animals are based on administrative data sources - here the HIT-database for cattle which was established as a system for the identification and registration of bovine animals according to Regulation (EC) No 1760/2000 of the European Parliament and of the Council of 17 July 2000.

For the slaughtering statistics, the reports of the official veterinarians about the examined slaughterings are evaluated monthly by the responsible veterinary offices. The data of the slaughter statistics are secondary statistics based on the compilations of the official veterinarians. But data set on poultry slaughtering are based on surveys.

3.2. Frequency of data collection

See item 3.2.1.

3.2.1. Reference date of the statistics

Reference date of the statistics

Livestock statistics Bovines animal Pigs Sheep Goats
November/December

11/03

11/03

11/03

12/31

November/ December - Regional

11/03

11/03

11/03

12/31

May/June

05/03

05/03

 
3.3. Data collection

See item 3.3.1.

3.3.1. Production of estimates

See items 3.3.1.1 and 3.3.1.2

3.3.1.1. Process for GIP forecast

Is the following information taken into account to produce the GIP forecast?

  Bovines Pigs Sheep and Goats
No forecast required for sheep and goats    
Extrapolation of known results on slaughtering
Expert assessment of the market
Expert assessment of the GIP by internal expert by internal expert
Models Recent changes in the stocks (livestock for fattening) for the forecast for the short term
Recent changes in stocks (breeding animals) for the forecast for the longer term
Production models (e.g. cycle of pig market)
Recent changes in the stocks (livestock for fattening) for the forecast for the short term
Recent changes in stocks (breeding animals) for the forecast for the longer term
Production models (e.g. cycle of pig market)
Other
Other slaughtering covered by the GIP forecast


Additional comments (on the GIP forecast)

3.3.1.2. Process for estimate of other slaughtering

What significant source do you use to estimate other slaughtering?

 

Bovines

Pigs

Sheep and Goats
Administrative information from veterinary service
Farm Survey
Consumer Survey
Coefficients based on ad hoc study
Year of calculation (Coefficients based on ad hoc study)
Discrepancy between slaughtering and estimated GIP (For instance, if overall GIP is directly estimated based on data at farm level)
Comprehensive study
Year of calculation (Comprehensive study)


Additional comments (on other slaughtering)

3.4. Data validation

Not requested for reference year 2019.

3.5. Data compilation

Not requested for reference year 2019.

3.6. Adjustment

Not requested for reference year 2019.


4. Quality management Top
4.1. Quality assurance

Not requested for reference year 2019.

4.2. Quality management - assessment

Not requested for reference year 2019.


5. Relevance Top
5.1. Relevance - User Needs

See items 5.1.1, 5.1.2 and 5.1.3.

5.1.1. Main national users of the statistics on livestock and meat

The data produced under Regulation (EC) No 1165/2008 are used by the following users, in addition to being delivered to Eurostat:

  National accounts (National Accounts, including European Accounts of Agriculture) Supply balance sheets Gross nutrient balance
Main national users Livestock - Bovines
Livestock - Pigs
Livestock - Sheep and goats
Slaughtering - Bovines
Slaughtering - Pigs
Slaughtering - Sheep and goats
Slaughtering - Poultry
Other slaughtering
Slaughtering - Bovines
Slaughtering - Pigs
Slaughtering - Sheep and goats
Slaughtering - Poultry
Other slaughtering
Livestock - Bovines
Livestock - Pigs
Livestock - Sheep and goats
5.1.2. Other Main national users of the statistics on livestock and meat

Other Main national users of the statistics on livestock and meat, please specify:

Livestock

Bovines

 Yes

Pigs

 Yes

Sheep and goats

 Yes

Slaughtering

Bovines

 Yes

Pigs

 Yes

Sheep and goats

 Yes

Poultry

 Yes

GIP forecast

Bovines

 

Pigs

 

Sheep and goats

 

Other slaughtering

 Yes

5.1.3. Main international users of the statistics on livestock and meat

Does the department in charge of livestock and meat statistics provide data to the following international organisations at their request?

DG Agriculture and Rural Development YES
Other EU institutions YES
FAO YES
Other international ‘governmental’ organisation Not known
5.2. Relevance - User Satisfaction

See item 5.2.1.

5.2.1. User satisfaction survey

User satisfaction survey

  Answer
Have you already carried out a survey on user satisfaction?
If yes, how long ago (months)?
If yes, are the results available to the public?
5.3. Completeness

Not requested for reference year 2019.

5.3.1. Data completeness - rate

Not requested for reference year 2019.


6. Accuracy and reliability Top
6.1. Accuracy - overall

See items 6.1.1. and 6.1.2.

6.1.1. Thresholds and legal derogation

Thresholds and legal derogation (Article 4 of regulation N°1165/2008)

Livestock statistics Total number of animals if under the legal threshold in December 2015 (Number of Head)
Bovine animals (under 1.5 million head)  
Pigs (under 3 million head)  
Sheep (under 500 000 head)  
Goats (under 500 000 head)

138.810

6.1.2. Quality control survey

Quality control survey

Quality control survey (livestock statistics)
Quality control survey (meat statistics)
6.2. Sampling error

See items 6.2.1.1. and 6.2.1.2.

6.2.1. Sampling error - indicators

See items 6.2.1.1 and 6.2.1.2

6.2.1.1. Coefficient of variation achieved for the main variables (only for sample survey)

Coefficient of variation achieved for the main variables (only for sample survey)

 Livestock statistics  Bovine animals  Pigs  Sheep  Goats
 Livestock - Nov./Dec. (in %)

0%

0,34%

0,39%

 
 Livestock - Nov./Dec. Regional (in %)

0%

0,55% - 0,96%

0,83% - 3,1%

 
 Livestock - May/June (in %)

0%

0,28%

  
 Assessment method

Register

Cv is calculated as shown under [1] (Guidelines)

Cv is calculated as shown under [1] (Guidelines)

Estimated value without calculating a cv

 Further comment  

CV regional: The figures reflect the range between the region with the lowest cv in % and the region with the highest cv in %.

CV regional: The figures reflect the range between the region with the lowest cv in % and the region with the highest cv in %.

 

 

 Slaughtering statistics  Bovine animals  Pigs  Sheep  Goats  Poultry
 Slaughtering statistics (in %)

0%

0%

0%

0%

0%

 Assessment method

census

census

census

census

census

 Further comment          
6.2.1.2. Sampling rate

Sampling rate

  Non-relevant * 1. Frame (Number of units) 2. Sample size (Number of units) Sampling rate ((2/1) x 100%)
Slaughterhouses
Cattle farms
Pig farms

24777

10633

42,91

Sheep farms

10962

4818

43,95

Goat farms
Animal farms **


* If the main information is drawn from a census, a register or a source other than a survey, the first column is ticked ** Animal farms: if the survey is designed for all livestock together

6.3. Non-sampling error

Errors in data collection can be reduced by correctly defining the population. The decisive factor for this is extensive knowledge of the units of the population. The farm register is used for pig/sheep surveys to form the population. This register is updated continuously by the statistical offices, e.g. with data from surveys or administrative data. In particular, the data of the HIT-register are compared annually and used to find new farms.

Reports that are only submitted to the statistical offices only after data processing has ended are considered as missing answers in the surveys. Due to a legally defined obligation of the units to give information, almost all reports can be collected. This currently results in a response rate of over 95% at the time of the preliminary results. At the time of the final results, there are almost no missing response.

Missing information on the characteristics of the survey will be filled in by inquiries from the regional statistical offices and thus kept as low as possible.

Other causes of non-sampling errors are incorrect information provided by reporting units. In most cases, such information is largely recognized and corrected by numerous plausibility checks.

6.3.1. Coverage error

See items 6.3.1.1, 6.3.1.2 and 6.3.1.3

6.3.1.1. Over-coverage - rate

None.

6.3.1.2. Common units - proportion

Not requested for reference year 2019.

6.3.1.3. Coverage error for each process

No coverage errors.

6.3.2. Measurement error

See item 6.3.2.1

6.3.2.1. Checklist on measurement errors

Whereas coherence refers to the data disseminated, the measurement errors refer to the data collection.

Slaughtering:

Young cattle and calves recorded separately
Goats actually recorded
Carcass weight recorded fully compliant (Compliant with Regulation (EC) No 1165/2008)
Even for poultry
Poultry slaughtering recorded in tonnes and head
6.3.3. Non response error

See items 6.3.3.1 and 6.3.3.2

6.3.3.1. Unit non-response - rate

Pigs and sheep: 5% unit non-response at the time of preliminary results for pigs, less than 1% unit non-response at the time of final results.

Bovine animals and slaughterings: No missing units because of use of administrative data for bovine animals and for slaughterings.

Poultry slaughterings: No unit non-responses.

6.3.3.2. Item non-response - rate

Due to inquiries missing information on characteristics of the survey can be added resulting in 0% item non-response.

6.3.4. Processing error

Other causes of non-sampling errors are incorrect information provided by reporting units. In most cases, such information is largely recognized and corrected by plausibility checks. Numerous error codes are used in the preparation and plausibility check program of the surveys.

6.3.4.1. Imputation - rate

No imputation for livestock statistics and slaughterings, little imputation (less than 1% imputation for poultry statistics).

6.3.5. Model assumption error

Not requested for reference year 2019.

6.4. Seasonal adjustment

Not requested for reference year 2019.

6.5. Data revision - policy

see revision calendar: https://www.destatis.de/DE/Themen/Wirtschaft/Aussenhandel/Methoden/Erlaeuterungen/uebersicht-monatliche-revisionen.html

6.6. Data revision - practice

Not requested for reference year 2019.

6.6.1. Data revision - average size

Not requested for reference year 2019.


7. Timeliness and punctuality Top
7.1. Timeliness

See items 7.1.1 and 7.1.2

7.1.1. Time lag - first result

Bovine animals: see item 7.1.1 in Metadata file ANI_MTN1EQ_DE_2019

Pigs: see item 7.1.1 in Metadata file ANI_MTN2EQ_DE_2019

Sheep: see item 7.1.1 in Metadata file ANI_MTN3EQ_DE_2019

Slaughterings: see item 7.1.1 in Metadata file ANI_MTN4EQ_DE_2019

Poultry slaughterings: see item 7.1.1 in Metadata file ANI_MTN5EQ_DE_2019

7.1.2. Time lag - final result

Bovine animals: see item 7.1.2 in Metadata file ANI_MTN1EQ_DE_2019

Pigs: see item 7.1.2 in Metadata file ANI_MTN2EQ_DE_2019

Sheep: see item 7.1.2 in Metadata file ANI_MTN3EQ_DE_2019

Slaughterings: see item 7.1.2 in Metadata file ANI_MTN4EQ_DE_2019

Poultry slaughterings: see item 7.1.2 in Metadata file ANI_MTN5EQ_DE_2019

7.2. Punctuality

See item 7.2.1.

7.2.1. Punctuality - delivery and publication

Statistics are punctual if the results are published on the previously planned and (possibly) announced date. The results of the statistics have been delivered on time.


8. Coherence and comparability Top
8.1. Comparability - geographical

See items 8.1.2 to 8.1.15.

8.1.1. Asymmetry for mirror flow statistics - coefficient

Not requested for reference year 2019.

8.1.2. Comparability – geographical Calves

Is there any difference between the above referred definitions based on the animals age and/or intended use with the ones used by the respondents in your Member State?


If yes, please describe briefly the difference in the definition


Geographical correction

Do you apply correction of your data in order to meet the EU definition? (Yes/No)

 

IF YES correction of your data is applied in order to meet the EU definition?

Value /Method of calculation

Briefly describe the value of the correction or of the coefficient?

 

Frequency of the revision of the values

Please specify the last time that this value was updated?

 

Main source used

Please define the data source used for the data

 

IF NO, correction of your data is not applied in order to meet the EU definition?

Is a standard operation procedure (SOP) envisaged to solve the above-mentioned differences?

Briefly describe if a correction will be implemented and if so when?

 

SOP to correct the differences is not envisaged

Please explain the reasons

 

Any further comments, please provide them here

 

8.1.3. Comparability – geographical Bulls

Is there any difference between the above referred definitions based on the castration status of the animals and the ones used by the respondents in your Member State?


If yes, please describe briefly the difference in the definition


Geographical correction

Do you apply correction of your data in order to meet the EU definition?(Yes/No)

 

IF YES correction of your data is applied in order to meet the EU definition?

Value /Method of calculation

Briefly describe the value of the correction or of the coefficient?

 

Frequency of the revision of the values

Please specify the last time that this value was updated?

 

Main source used

Please define the data source used for the data

 

IF NO, correction of your data is not applied in order to meet the EU definition?

Is a standard operation procedure (SOP) envisaged to solve the above-mentioned differences?

Briefly describe if a correction will be implemented and if so when?

 

SOP to correct the differences is not envisaged

Please explain the reasons

 

Any further comments, please provide them here

 

8.1.4. Comparability – geographical Buffaloes

Is there any difference between the accounting of buffaloes in the different age bovine animal categories and the above referred classes?


If yes, please describe briefly the difference in the definition


Geographical correction

Do you apply correction of your data in order to meet the EU definition? (Yes/No)

 

IF YES correction of your data is applied in order to meet the EU definition?

Value /Method of calculation

Briefly describe the value of the correction or of the coefficient?

 

Frequency of the revision of the values

Please specify the last time that this value was updated?

 

Main source used

Please define the data source used for the data

 

IF NO, correction of your data is not applied in order to meet the EU definition?

Is a standard operation procedure (SOP) envisaged to solve the above-mentioned differences?

Briefly describe if a correction will be implemented and if so when?

 

SOP to correct the differences is not envisaged

Please explain the reasons

 

Any further comments, please provide them here

 

8.1.5. Comparability – geographical Cows

Is there any difference between the above referred definitions based on the animals age and/or purpose and the ones used by the respondents in your Member State?


If yes, please describe briefly the difference in the definition


Geographical correction

Do you apply correction of your data in order to meet the EU definition? (Yes/No)

 

IF YES correction of your data is applied in order to meet the EU definition?

Value /Method of calculation

Briefly describe the value of the correction or of the coefficient?

 

Frequency of the revision of the values

Please specify the last time that this value was updated?

 

Main source used

Please define the data source used for the data

 

IF NO, correction of your data is not applied in order to meet the EU definition?

Is a standard operation procedure (SOP) envisaged to solve the above-mentioned differences?

Briefly describe if a correction will be implemented and if so when?

 

SOP to correct the differences is not envisaged

Please explain the reasons

 

Any further comments, please provide them here

 

8.1.6. Comparability – geographical Heifers

Is there any difference between the above referred definitions based on the age and/or the intended use and the ones used by the respondents in your Member State?


If yes, please describe briefly the difference in the definition


Geographical correction

Do you apply correction of your data in order to meet the EU definition? (Yes/No)

 

IF YES correction of your data is applied in order to meet the EU definition?

Value /Method of calculation

Briefly describe the value of the correction or of the coefficient?

 

Frequency of the revision of the values

Please specify the last time that this value was updated?

 

Main source used

Please define the data source used for the data

 

IF NO, correction of your data is not applied in order to meet the EU definition?

Is a standard operation procedure (SOP) envisaged to solve the above-mentioned differences?

Briefly describe if a correction will be implemented and if so when?

 

SOP to correct the differences is not envisaged

Please explain the reasons

 

Any further comments, please provide them here

 

8.1.7. Comparability – geographical Lambs

Is there any difference between the above referred definitions based on the age and/or the purpose and the ones used by the respondents in your Member State?


If yes, please describe briefly the difference in the definition


Geographical correction

Do you apply correction of your data in order to meet the EU definition? (Yes/No)

 

IF YES correction of your data is applied in order to meet the EU definition?

Value /Method of calculation

Briefly describe the value of the correction or of the coefficient?

 

Frequency of the revision of the values

Please specify the last time that this value was updated?

 

Main source used

Please define the data source used for the data

 

IF NO, correction of your data is not applied in order to meet the EU definition?

Is a standard operation procedure (SOP) envisaged to solve the above-mentioned differences?

Briefly describe if a correction will be implemented and if so when?

 

SOP to correct the differences is not envisaged

Please explain the reasons

 

Any further comments, please provide them here

 

8.1.8. Comparability – geographical Goats

Is there any difference between the above referred definitions based on the animals status and the ones used by the respondents in your Member State?


If yes, please describe briefly the difference in the definition

'Due to regulation 1165/2008, Article 4 germany is not obliged to collect data on goats. Data on goats are estimated except in years with a farm structure survey, where goats are surveyed in the above mentioned categories.


Geographical correction

Do you apply correction of your data in order to meet the EU definition? (Yes/No)

 

IF YES correction of your data is applied in order to meet the EU definition?

Value /Method of calculation

Briefly describe the value of the correction or of the coefficient?

 

Frequency of the revision of the values

Please specify the last time that this value was updated?

 

Main source used

Please define the data source used for the data

 

IF NO, correction of your data is not applied in order to meet the EU definition?

Is a standard operation procedure (SOP) envisaged to solve the above-mentioned differences?

Briefly describe if a correction will be implemented and if so when?

 

SOP to correct the differences is not envisaged

Please explain the reasons

 

Any further comments, please provide them here

 

8.1.9. Comparability – geographical Piglets

Is there any difference between the above referred definitions based on the weight and the one used by the respondents in your Member State?


If yes, please describe briefly the difference in the definition


Geographical correction

Do you apply correction of your data in order to meet the EU definition? (Yes/No)

 

IF YES correction of your data is applied in order to meet the EU definition?

Value /Method of calculation

Briefly describe the value of the correction or of the coefficient?

 

Frequency of the revision of the values

Please specify the last time that this value was updated?

 

Main source used

Please define the data source used for the data

 

IF NO, correction of your data is not applied in order to meet the EU definition?

Is a standard operation procedure (SOP) envisaged to solve the above-mentioned differences?

Briefly describe if a correction will be implemented and if so when?

 

SOP to correct the differences is not envisaged

Please explain the reasons

 

Any further comments, please provide them here

 

8.1.10. Comparability – geographical Sows

Is there any difference between the above referred definitions based on the covered status of the animals and the ones used by the respondents in your Member State?


If yes, please describe briefly the difference in the definition


Geographical correction

Do you apply correction of your data in order to meet the EU definition? (Yes/No)

 

IF YES correction of your data is applied in order to meet the EU definition?

Value /Method of calculation

Briefly describe the value of the correction or of the coefficient?

 

Frequency of the revision of the values

Please specify the last time that this value was updated?

 

Main source used

Please define the data source used for the data

 

IF NO, correction of your data is not applied in order to meet the EU definition?

Is a standard operation procedure (SOP) envisaged to solve the above-mentioned differences?

Briefly describe if a correction will be implemented and if so when?

 

SOP to correct the differences is not envisaged

Please explain the reasons

 

Any further comments, please provide them here

 

8.1.11. Comparability – geographical Slaughter units

Is there any difference between the above referred definitions based on the units provided by the respondents in your Member State?


If yes, please describe briefly the difference in the definition


Geographical correction

Do you apply correction of your data in order to meet the EU definition? (Yes/No)

 

IF YES correction of your data is applied in order to meet the EU definition?

Value /Method of calculation

Briefly describe the value of the correction or of the coefficient?

 

Frequency of the revision of the values

Please specify the last time that this value was updated?

 

Main source used

Please define the data source used for the data

 

IF NO, correction of your data is not applied in order to meet the EU definition?

Is a standard operation procedure (SOP) envisaged to solve the above-mentioned differences?

Briefly describe if a correction will be implemented and if so when?

 

SOP to correct the differences is not envisaged

Please explain the reasons

 

Any further comments, please provide them here

 

8.1.12. Comparability – geographical Carcasses

Is there any difference between the above referred definitions based on the animals carcass weight and the ones used by respondents in your Member State?


If yes, please describe briefly the difference in the definition


Geographical correction

Do you apply correction of your data in order to meet the EU definition? (Yes/No)

 

IF YES correction of your data is applied in order to meet the EU definition?

Value /Method of calculation

Briefly describe the value of the correction or of the coefficient?

 

Frequency of the revision of the values

Please specify the last time that this value was updated?

 

Main source used

Please define the data source used for the data

 

IF NO, correction of your data is not applied in order to meet the EU definition?

Is a standard operation procedure (SOP) envisaged to solve the above-mentioned differences?

Briefly describe if a correction will be implemented and if so when?

 

SOP to correct the differences is not envisaged

Please explain the reasons

 

Any further comments, please provide them here

 

8.1.13. Comparability – geographical Carcasses poultry

Is there any difference between the above referred definition and the one used by respondents in your Member State?


If yes, please describe briefly the difference in the definition


Geographical correction

Do you apply correction of your data in order to meet the EU definition? (Yes/No)

 

IF YES correction of your data is applied in order to meet the EU definition?

Value /Method of calculation

Briefly describe the value of the correction or of the coefficient?

 

Frequency of the revision of the values

Please specify the last time that this value was updated?

 

Main source used

Please define the data source used for the data

 

IF NO, correction of your data is not applied in order to meet the EU definition?

Is a standard operation procedure (SOP) envisaged to solve the above-mentioned differences?

Briefly describe if a correction will be implemented and if so when?

 

SOP to correct the differences is not envisaged

Please explain the reasons

 

Any further comments, please provide them here

 

8.1.14. Comparability – geographical Slaughterhouse

Is there any difference between the above referred definition and the one used by the respondents in your Member State?


If yes, please describe briefly the difference in the definition


Geographical correction

Do you apply correction of your data in order to meet the EU definition? (Yes/No)

 

IF YES correction of your data is applied in order to meet the EU definition?

Value /Method of calculation

Briefly describe the value of the correction or of the coefficient?

 

Frequency of the revision of the values

Please specify the last time that this value was updated?

 

Main source used

Please define the data source used for the data

 

IF NO, correction of your data is not applied in order to meet the EU definition?

Is a standard operation procedure (SOP) envisaged to solve the above-mentioned differences?

Briefly describe if a correction will be implemented and if so when?

 

SOP to correct the differences is not envisaged

Please explain the reasons

 

Any further comments, please provide them here

 

8.1.15. Comparability – geographical Gross indigenous production

Is there any difference between the above referred definition and the one used by the respondents in your Member State?


If yes, please describe briefly the difference in the definition


Geographical correction

Do you apply correction of your data in order to meet the EU definition? (Yes/No)

 

IF YES correction of your data is applied in order to meet the EU definition?

Value /Method of calculation

Briefly describe the value of the correction or of the coefficient?

 

Frequency of the revision of the values

Please specify the last time that this value was updated?

 

Main source used

Please define the data source used for the data

 

IF NO, correction of your data is not applied in order to meet the EU definition?

Is a standard operation procedure (SOP) envisaged to solve the above-mentioned differences?

Briefly describe if a correction will be implemented and if so when?

 

SOP to correct the differences is not envisaged

Please explain the reasons

 

Any further comments, please provide them here

 

8.2. Comparability - over time

See item 8.2.2.

8.2.1. Length of comparable time series

Not requested for reference year 2019. 

8.2.2. First year of availability of comparable data

First year when the statistics were produced with comparable figures for all, most or only the main variables (e.g. total numbers of animals):

 

All

Most

Main variables

Number of periods per year*

Livestock

 

 

Bovines

 2008

 2008

 2008

 2

Pigs

 2010

 2010

 2010

 2

Sheep and goats

 2010

 2010

 2010

 1

Slaughtering

 

 

 

Bovines

 2009  

 

 12

Pigs

 1990  

 

 12

Sheep and goats

 1990  

 

 12

Poultry

 2010

 

 

 12

* Number of periods per year: according to the frequency of statistics, i.e. 12 for monthly data, 4 for quarterly data, 1 for annual data, etc.

8.3. Coherence - cross domain

Data on bovine animals, pigs, sheep and goats are also recorded in the farm structure surveys in 2010, 2013 and 2016. However, the farm structure surveys and the surveys on bovine animals, pigs and sheep differ in reference dates and thresholds.

 

8.4. Coherence - sub annual and annual statistics

Not requested for reference year 2019.

8.5. Coherence - National Accounts

Not requested for reference year 2019.

8.6. Coherence - internal

Datasets are internally consistent.


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

Not requested for reference year 2019.

9.2. Dissemination format - Publications

Regular publication of tables, downloadable print products and tables in online database accessible via homepage of statistical office - https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/Tiere-Tierische-Erzeugung/_inhalt.html .

9.3. Dissemination format - online database

Link to online database: https://www-genesis.destatis.de/genesis/online?sequenz=statistikTabellen&selectionname=41311

9.3.1. Data tables - consultations

Number of consultations not known.

9.4. Dissemination format - microdata access

Microdata for this statistic are not accessible for users outside the statistical office.

9.5. Dissemination format - other

No other disseminations.

9.6. Documentation on methodology

Methodology is described in quality reports which are avaiable via homepage: https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/Tiere-Tierische-Erzeugung/_inhalt.html

9.7. Quality management - documentation

Quality reports which are avaiable via homepage: https://www.destatis.de/DE/Themen/Branchen-Unternehmen/Landwirtschaft-Forstwirtschaft-Fischerei/Tiere-Tierische-Erzeugung/_inhalt.html

9.7.1. Metadata completeness - rate

Not requested for reference year 2019.

9.7.2. Metadata - consultations

Number of consultations not known.


10. Cost and Burden Top

See item 10.1.

10.1. Burden on the respondents

Estimated burden on the respondents (in hours and minutes) to statistical surveys on livestock and meat (administrative sources are excluded)

Label statistical survey The number of respondents (average) The average time spent by the respondents to provide information (in minutes) The number of occurrences of the statistical survey over the reference year The overall yearly burden on the respondents - TOTAL (in minutes)

Livestock Pigs (May)

11.800

30

2

708000

Livestock Sheep (Nov)

5.000

15

1

75000

Livestock Bovine Animals

0 (Register)

0 (Register)

0 (Register)

0 (Register)

Slaughtering (Poultry)

229

35

12

96.180

Slaughtering (Bovine/Pigs/Sheep/Goats)

0 (Register)

0 (Register)

0 (Register)

0 (Register)

GIP forecast

0 (calculation)

0 (calculation)

0 (calculation)

0 (calculation)

         
         
         
         


11. Confidentiality Top
11.1. Confidentiality - policy

Data collected are generally kept secret in accordance with § 16 BStatG (national statistics law). Individual information may only be transmitted in exceptional cases expressly regulated by law. Under no circumstances will the names and addresses of the respondents be passed on to third parties. Further information are recorded in quality reports (see item 9.7).

11.2. Confidentiality - data treatment

In livestock statistics data are kept confidential with cell supression methods. For primary confidentiality p%-rule applies. Secondary confidentiality is placed manually for pigs, sheep and slaughtered poultry, and automatically via algorithms for bovine animals. There are no confidential data concerning slaughtering statistics (except poultry).


12. Comment Top

No comments.


Related metadata Top


Annexes Top
SP1 : Survey for May/November data collection on cattle (Bovine register)
SP2 : Survey for May/November data collection on Pigs
SP3 : Survey for November data collection on Sheep
SP4 : Slaughtering statistics (Pigs, Cattle, Sheep, Goats)
SP 5 : Slaughtering statistics (Poultry)