Farm structure (ef)

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

Compiling agency: Federal Statistical Office of the Federal Republic of Germany 

Time Dimension: 2016-A0

Data Provider: DE1

Data Flow: FSS_ESQRS_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


1. Contact Top
1.1. Contact organisation
Federal Statistical Office of the Federal Republic of Germany 
1.2. Contact organisation unit
Division G1 – Agriculture and Forestry, Fisheries 
1.5. Contact mail address
Bonn Branch Office
Postfach 17 03 77
53029 Bonn 

2. Statistical presentation Top
2.1. Data description
1. Brief history of the national survey 
Farm Structure Surveys (FSS) were conducted at two-year intervals from 1975 until 2007, and some of the variables were requested in a complete count during every second survey (that is every four years). The FSS was an element of the Census of Agriculture in years that it was conducted (1979, 1991, 1999 and 2010). Beginning with 2010, the frequency of the Farm Structure Surveys was changed from two-year to three-year intervals and the frequency of the complete surveys (every four years between 1979 and 2007) was reduced. On principle, complete Farm Structure Surveys are now conducted roughly every ten years as part of the Census of Agriculture. The most recent Census of Agriculture was conducted in 2010 and the FSS was the main element of the complete census survey. In years between the censuses, the FSS is conducted as a sample survey in 2013 and 2016. According to national law, selected variable complexes are surveyed in their entirety in the 2016 FSS.

Holdings that reach specific minimum requirements in the size of utilised agricultural area (UAA) or for livestock or the crop areas for specialised crops are obligated to respond to the FSS. Due to structural changes in agriculture, the coverage thresholds have been adjusted in various ways over time, most recently prior to the 2010 Census of Agriculture. The survey program of the FSS, which provides an overview of the structure of agricultural holdings in Germany, was also adjusted over time to the respective prevailing data requirements for agricultural policy. Effects on chronological comparability of the data are mentioned in the respective methodology reports.


2. Legal framework of the national survey 
- the national legal framework
  • Federal Statistics Law (Bundesstatistikgesetz - BStatG) of 22 January 1987 (Federal Law Gazette I p. 462, 565) as amended
  • Law on Agricultural Statistics (Agrarstatistikgesetz - AgrStatG) of 17 December 2009 (Federal Law Gazette I p. 3886) as amended
  • Act on Equal Status for Set-Aside and Agriculturally Used Areas (Gesetz zur Gleichstellung stillgelegter und landwirtschaftlich genutzter areas) of 10 July 1995 (Federal Law Gazette I p. 910) as amended
- the obligations of the respondents with respect to the survey There is an obligation to respond to the FSS. Pursuant to Article 93 (2) No 1 of the Law on Agricultural Statistics in conjunction with Article 15 of the Federal Statistics Law, the owners or managers of the agricultural holdings are obligated to respond. 
- the identification, protection and obligations of survey enumerators In one Land survey offices offered assistance in completing the questionnaire for the respondents.
2.2. Classification system

[Not requested]

2.3. Coverage - sector

[Not requested]

2.4. Statistical concepts and definitions
List of abbreviations 

AGRA2010 =  processing and data editing program for agricultural statistics

AgrStatG  =  Law on Agricultural Statistics

approx.  = approximately

BMEL =  Bundesministerium für Ernährung und Landwirtschaft (Federal Ministry for Food and Agriculture )

BStatG  =  Bundesstatistikgesetz (Federal Statistics Law)

cf.  =  confer (compare)

EAFRD  =  European Agricultural Fund for Rural Development

EC =  European Community

e.g. =  exempli gratia (for example)

ESU = European Size Units

etc.  =  et cetera (and so forth)

EU =  European Union

EUROFARM  =  EC information system for farm structure statistics

Eurostat  = Statistical Office of the European Union

FADN  = Farms Accountancy Data Network

FSS  = Farm Structure Survey

GbR = Gesellschaft bürgerlichen Rechts (civil law company)

GENESIS =  Gemeinsames neues statistisches Informationssystem (Common new statistical information system)

ha  =  hectares

HIT =  Herkunftssicherungs- und Informationssystem für Tiere (central database on animal identification and registration (Bovine Register))

IACS =  Integrated Administration and Control System

IDEV  = Internet-Datenerhebung im Verbund (internet-based data collection of the statistical offices of the Federation and the Länder)

i.e.  =  id est (that is)

NE = not existing

No  = number

NS  =  not significant

NUTS  =  Nomenclature des unités territoriales statistiques (Nomenclature of Territorial Units for Statistics)

OJ  =  Official Journal

p. =  page

SAPM  = Survey of Agricultural Production Methods

StLÄ  =  statistical offices of the Länder

UAA  =  utilised agricultural area

2.5. Statistical unit
The national definition of the agricultural holding
An agricultural holding is defined pursuant to Article 2 of Regulation (EC) 1166/2008. An agricultural holding is defined as a unit, both technically and economically, which has a single management and which undertakes agricultural activities listed in Annex I of Regulation (EC) 1166/2008 either as its primary or secondary activity.
In Germany, the threshold for poultry is 1.000 places (see 2.6-2). As a result, holdings that currently have no poultry (and no other animals or areas) but more than 1.000 places for poultry are included in the survey.
2.6. Statistical population
1. The number of holdings forming the entire universe of agricultural holdings in the country
The exact number of holdings disregarding the coverage thresholds is not known. Under national law, an agricultural holding is only considered as such if it exceeds the prescribed thresholds (cf. item 2. below). The law does not foresee cataloguing the holdings below the threshold in surveys or in the registers. 


2. The national survey coverage: the thresholds applied in the national survey and the geographical coverage
The following coverage thresholds for agricultural holdings are defined in § 91 (1a) of the Law on Agricultural Statistics:
  • 5 ha of utilised agricultural area (A_3_1)
  • or 10 bovine animals (C_2)
  • or 50 pigs (C_4)
  • or 10 breeding sows (C_4_2)
  • or 20 sheep (C_3_1)
  • or 20 goats (C_3_2)
  • or 1 000 places for poultry (C_5)
  • or 0.5 ha of hops (B_1_6_2)
  • or 0.5 ha of tobacco(B_1_6_1)
  • or 1.0 ha of permanent outdoor crops (B_4_1 + B_4_2 + B_4_3 + B_4_4 + B_4_5)
  • or 0.5 ha each of area under vines (B_4_4) , tree nurseries (B_4_5) or fruit trees (B_4_1)
  • or 0.5 ha of outdoor vegetables or strawberries (B_1_7_1)
  • or 0.3 ha of outdoor flowers or ornamental plants (B_1_8_1)
  • or 0.1 ha of crops under glass or other accessible protective cover (B_1_7_2 + B_1_8_2)
  • or 0.1 ha of mushrooms (B_6_1).

Stricter thresholds exceeding the requirements of Regulation (EC) No 1166/2008 were set for some special crops (vineyards, nurseries or fruit-growing areas, flowers and ornamental plants outdoors, crops under high accessible protective cover including under glass and production areas for mushrooms). The additional limits for these crops prevent the survey coverage for these crops dropping too low (cf. BR document No 694/081). Furthermore, the previous threshold "1,000 heads of poultry" has been replaced by the more stable threshold "1,000 places for poultry".

The threshold for cotton production area in Annex II of Regulation (EC) 1166/2008 was not relevant for Germany, because cotton cannot be grown profitably for climatic reasons and therefore is not applied as a threshold.

The specified coverage thresholds for Germany therefore fulfil the conditions of Annex II and Article 3 of Regulation (EC) 1166/2008. Article 3 indicates that member states shall fix the thresholds at a level that excludes only the smallest agricultural holdings. This issue was already described in detail in the National Methodological Report for FSS 2010 2.


Geographic coverage:

As in the 2013 FSS, the sampling procedure for the 2016 FSS included the entire national area with one exception: alpine pasture cooperatives in Bavaria (common land units, cf. Section 8.1. Comparability - geographical - item 8.1), thus these units or the alpine pastures and meadows managed by them in the Bavarian Alps, were not included in the FSS 2016. 

1 Bundesrat document No 694/08, p. 59



3. The number of holdings in the national survey coverage 
The surveyed sample holdings form a target population of 276 121 holdings (on the basis of the Farm Register).


4. The survey coverage of the records sent to Eurostat
There is no difference between the national survey coverage and the survey coverage of the records sent to Eurostat. 


5. The number of holdings in the population covered by the records transferred to Eurostat
Cf. item 3. above


6. Holdings with standard output equal to zero included in the records sent to Eurostat
Individual records with standard output equal to zero can be explained by holdings with only fallow land and permanent grassland and meadow with no use for production. The units have the land in good agricultural and environmental conditions and therefore are eligible for subsidies. There is also a small number of holdings with exclusively pullets and an even smaller number of holdings only with places for poultry (but no poultry or other animals or areas); these also have no standard output.


7. Proofs that the requirements stipulated in art. 3.2 the Regulation 1166/2008 are met in the data transmitted to Eurostat
The last time that the coverage thresholds were raised (from 2 to 5 ha UAA) was for the Census of Agriculture in 2010. Under Article 3 (2) of Regulation (EC) No 1166/2008 increasing the coverage thresholds above the limit of one hectare of UAA is only permissible if the Member State can prove that exceeding the one hectare threshold excludes only the smallest agricultural holdings from the survey, which together contribute 2 percent or less to the total utilised agricultural area and 2 percent or less to the total number of farm livestock units. The pilot calculations carried out with the data material from the 2007 FSS showed that although raising the coverage thresholds to the above minimum areas and minimum livestock units excluded 14.9 percent of the holdings from the group of respondents, nonetheless 99.1 percent of the total UAA and 99.4 percent of the total livestock units would be covered by the Census of Agriculture. Since the significance of these holdings is so minor with regard to area management and livestock keeping, use was made of the opportunity offered by Article 3 (2) of Regulation (EC) No 1166/2008 and the coverage thresholds were raised to those shown above. 


8. Proofs that the requirements stipulated in art. 3.3 the Regulation 1166/2008 are met in the data transmitted to Eurostat
The physical thresholds specified in Article 3 (3) of Regulation (EC) No 1166/2008 were met. Tighter national thresholds than required apply for individual crops (cf. item 2. above). 
2.7. Reference area
Location of the holding. The criteria used to determine the NUTS3 region of the holding
The NUTS3 key was derived for each holding from the address of the holding location or from the associated official municipality codes.
The location of the holding was, in most cases, the parcel on which the farm building was located.
If farm buildings were located on multiple parcels the holding location was the parcel on which the most important farm building or buildings were located.
If the holding had no farm buildings, the parcel from which the holding is managed was the location of the holding. 
2.8. Coverage - Time
Reference periods/dates of all main groups of characteristics (both included in the EU Regulation 1166/2008 and surveyed only for national purposes)
All of the survey variables were surveyed simultaneously in the first half of 2016 pursuant to Regulation (EC) No 1166/2008. However, pursuant to Article 8 of Regulation (EC) No 1166/2008 and Article 27 of the Law on Agricultural Statistics, different reporting times or periods were set for the individual variables. A table of the variable complexes and relevant reference periods and dates is contained in annex 2.8. Reporting periods

2.8. Reporting periods FSS 2016
2.9. Base period

[Not requested]

3. Statistical processing Top
1.Survey process and timetable

The most important work steps of the 2016 Farm Structure Survey as well as the bodies responsible for each of the steps are illustrated below in Table 2.


Table 2: Survey organisation and calendar

Steps Time lag Body (FSO – Federal Statistical Office. LSO – Land Statistical Office/Offices)
1. definition of survey objective and requirements:    
  1.1. formation of workgroups for survey organisation; - -
  1.2. consultation of users; - -
  1.3. set-up objectives, target population, statistical units, classifications, precision requirements etc.; Dec 2014 FSO/LSO
  1.4. survey promotion. Individually LSO
2. survey design:    
  2.1. set-up organisation of the survey (e.g. detailed timetable, specification of resources, costs estimation); Oct 2014 FSO/LSO
  2.2. definition of the survey variables; Apr 2014 FSO/LSO
  2.3. design of the sampling frame and sampling procedures; Sep 2014 - Nov 2015 FSO
  2.4. design of data collection procedures (paper questionnaire); Jan 2015 - Dec 2015 FSO
  2.5. design of data processing procedures (online questionnaire); May 2015 - Jan 2016 FSO/LSO
  2.6. pilot survey organisation and execution. - -
  Development of the instruction manual for conducting FSS 2016 Jan 2015 - Dec 2015 FSO
  Check and transformation of the possible administrative data Feb 2015 - Nov 2015 FSO/LSO
3. data collection:    
  3.1. sampling frame construction and sample selection; Jul 2015 - Jan 2016 LSO
  3.2. recruitment of interviewers; Individually LSO
  3.3. training of interviewers; Individually LSO
  3.4. fieldwork; Feb 2016 - May 2016 LSO
  3.5. evaluation and assessment of fieldwork. Jun 2016 - Jul 2017 FSO/LSO
4. data processing and validation:    
  Check on returns and on completeness, special and visual inspection Mar 2016 - Jan 2017 LSO
  4.1. data entry and data coding: Mar 2016 - Jan 2017 LSO
  4.2. data validation (at record level); Mar 2016 - Jan 2017 LSO
  4.3. data correction and imputation. Mar 2016 - Jan 2017 LSO
  Loading the various administrative data Apr 2016 - Apr 2017 LSO
  Adaptation of the data processing program AGRA2010, incl. the adaptation of plausibility checks and compilation rules May 2015 - Aug 2016 FSO
5. data compilation:    
  5.1. weight calculation and estimation; Apr 2017 - Dec 2017 LSO
  5.2. calculation of derived variables; Apr 2017 - Dec 2017 LSO
  5.3. calculation of quality indicators (e.g. non-response rates, relative standard errors, coverage errors, bias etc.); Apr 2017 - Dec 2017 FSO/LSO
  5.4. aggregation and tabulation; May 2017 - Oct 2017 FSO/LSO
  Confidentiality treatment Mar 2017 - Oct 2017 FSO/LSO
  5.5. validation of aggregated data. May 2017 - Dec 2017 FSO/LSO
6. data analysis Feb 2017 - Oct 2017 FSO/LSO
7. data dissemination May 2017 - Oct 2017 FSO/LSO


2. The bodies involved and the share of responsibilities among bodies
The responsible bodies are contained in Table 2 above. 


3. Serious deviations from the established timetable (if any)
All work steps in Table 2 above were carried out on time. 
3.1. Source data
1. Source of data
The 2016 FSS consists of a total survey, which was only for national purposes, and a representative survey that mainly covered the data requirements by Regulation (EC) No 1166/2008, with a sample size of a maximum of 80,000 holdings. Some of the data were not directly requested of the respondents, but taken from various administrative sources. These administrative sources are described in more detail in item 4.1 below. 


2. (Sampling) frame
The population for the 2016 FSS was established on the basis of the Farm Register which was regulary updated by results from various agricultural statistical surveys and by information from administrative sources such as the Herkunfts- und Informationssystem für Tiere (HIT, Bovine Register). The adds and outs (e.g. newly established or abandoned holdings) ascertained in intermediate years in the Farm Register, which result from the regular updates of survey units using administrative sources and various, were taken into consideration. With a population of approximately 275,000 holdings, the sampling fraction is approx. 0.29 (n/N)[1]

The sampling frame for 2016 FSS is a list frame (list of names and addresses that provide direct access to 'individuals').
The Federal Statistical Office provided the statistical offices of the Länder with a concept for register-based establishment of the population for the 2016 FSS in July 2015 and in November 2015 with various corresponding programs to compile the various data (cf. Section 12.1.c.1). The steps required to establish the population (e.g. combining various survey and administrative sources, adding newly established holdings from the Farm Register, and giving a signature to the holdings contained in the population in the Farm Register) were carried out by the statistical offices of the Länder between March 2015 and January 2016. The information from the Farm Register used for this is not subject to any fixed updating schedules, the contents of the Farm Register are constantly (but at least once a year) updated by the statistical offices of the Länder.

[1] Ratio of the size of a sample (n) to the population size (N)


3. Sampling design
3.1 The sampling design
The 2016 FSS is a single-stage stratified random sample of holdings. No sub-samples are drawn, neither for individual survey variables.
3.2 The stratification variables
The following stratification variables are used for the stratification procedure: NUTS2 regions, the size classes of the utilised agricultural area, the relevant crop and livestock variables (e.g. cereals for the production of grain, bovine animals) for meeting the precision requirements of Regulation (EC) 1166/2008, the farming methods of the holdings (organic/conventional) and the field of specialization of holdings at NUTS2 level. The latter encompass holdings that stand out from the farm population through fields of specialization (e.g. large amounts of livestock, special crops, horticulture) or through the special importance of this production. There is also an additional stratum for the new holdings. 
3.3 The full coverage strata
The strata plan for the 2016 FSS included various full coverage strata, e.g. holdings with large numbers of livestock and organic farming. This report does not contain a list of the full coverage strata for the NUTS2 regions since these are classified nationally as confidential. 
3.4 The method for the determination of the overall sample size
According to the sampling fraction, the sample size for the 2016 Farm Structure Survey was 77 370 holdings. Since a sample of maximum 80 000 holdings is permitted by law for the 2016 FSS, only approximately 77 500 holdings were selected during the sampling procedure for the FSS in order to take all new holdings into consideration. 
3.5 The method for the allocation of the overall sample size
In a first step the sample size (77 370 holdings) is allocated to the NUTS1 regions proportionally to the square root of the number of agricultural holdings.
  • If a NUTS1 region has associated NUTS2 regions with at least 10 000 holdings each, the sample size of this NUTS1 region is allocated to its NUTS2 regions by the method mentioned above.
  • If a NUTS1 region consists of at least two NUTS2 regions with fewer than 10 000 holdings each, the NUTS2 regions are treated as one region. The sample size of the NUTS1 region is allocated to this combined region and its NUTS2 regions with at least 10 000 holdings each by the method mentioned above.
  • If a NUTS1 region has several NUTS2 regions with at least 10 000 holdings and only one NUTS2 region with less than 10 000 holdings then the last NUTS2 region is treated like the other NUTS2 regions (only one case: DE12). The sample size of the NUTS1 region is allocated to its NUTS2 region by the method mentioned above.

In a second step the sample size of a NUTS2 region is allocated to the region-specific strata by a combination of the optimal allocation (Neyman) with the Standard Output as target variable and an allocation proportional to the square root of the number of agricultural holdings. The resulting sample size of each stratum is the weighted mean of the two methods, where the optimal allocation is weighted three times stronger than the proportional allocation.

3.6 Sampling across time
A new sample is drawn for each FSS. It is highly probable that holdings assigned to a full coverage stratum – if their production bases have not changed – are again assigned to a full coverage stratum in the following survey. 
3.7 The software tool used in the sample selection
The preliminary assessments for the sample design were conducted using the statistical software SAS. The method of “controlled sampling” was used by the statistical offices of the Länder for the random selection of the sample holdings. Using the national STIA sampling program it was possible to draw any number of independent samples for this. For each of these samples an extrapolation of the stratification variables was carried out. The extrapolated results were then compared with the corresponding totals of the sampling frame and the sample with the least deviations compared with the corresponding total values of the control variables was chosen. 
3.8 Other relevant information, if any
Not available


4. Use of administrative data sources
4.1 Name, time reference and updating
For the FSS, information on land use was taken from the Integrated Administration and Control System (IACS) and all data on bovine livestock from the HIT (Bovine Register). In addition, subsidy data was taken from administrative sources about rural development measures and the data from the Official Building Coordinates were used to ascertain the geographic coordinates of the holdings.

Furthermore, data from agricultural liability insurance, from organic farming registers (organic farming yes/no), from animal disease fund as well as the Register of Establishments Keeping Laying Hens were used to update the population in the Farm Register.

An overview of administrative sources is contained in the following Table:


Table: Administrative sources – Name, legal base, time reference and updating


Legal base

Time reference

Updating of the source


Article 4 Regulation (EC) No 1782/2003[1]



Bovine Register

Regulation (EC) No 1760/2000[2]



Rural development measures

Regulation (EU) No 1305/2013 in conjunction with national law



Official Building Coordinates

Legislative bases of the national cadastral land register

(The Official Building Coordinates are taken from the cadastral land register)

1 Apr

Once a year

Organic Farming Register

Regulation (EC) No 834/2007[3]

Länder specific

Länder specific

Data from agricultural liability insurance

National law


Once a year

Register of establishments keeping laying hens

Commission Directive 2002/04/EC[4]



Animal disease fund

National law

Länder specific

Länder specific


[1] Council Regulation (EC) No 1782/2003 of 29 September 2003 establishing common rules for direct support schemes under the common agricultural policy and establishing certain support schemes for farmers (OJ L 270, 21.10.2003, p. 1–69).

[2] Regulation (EC) No 1760/2000 of the European Parliament and of the Council of 17 July 2000 establishing a system for the identification and registration of bovine animals and regarding the labelling of beef and beef products (OJ L 204, 11.8.2000, p. 1–10).

[3] Council Regulation (EC) No 834/2007 of 28 June 2007 on organic production and labelling of organic products and repealing Regulation (EEC) No 2092/91 (OJ L 189, 20.7.2007, p. 1).

[4] Commission Directive 2002/4/EC of 30 January 2002 on the registration of establishments keeping laying hens, covered by Council Directive 1999/74/EC

4.2 Organisational setting on the use of administrative sources
Pursuant to Article 93 (5) of the Law on Agricultural Statistics, the statistical offices of the Länder are permitted to use administrative sources for statistical purposes as long as the information matches the variables of the Farm Structure Survey and refers to the same reporting times and periods. Much use is made of this possibility for the 2016 FSS. The possibility of influencing the concepts is not given. 
4.3 The purpose of the use of administrative sources - link to the file
Please access the information in the file at the link: (link available as soon as possible)


4.4 Quality assessment of the administrative sources
  Method  Shortcoming detected Measure taken
- coherence of the reporting unit (holding) IACS and Bovine Register: In the questionnaire, the respondents were asked for the administrative code(s) for IACS and the Bovine Register. These numbers were used to assign most of the holdings to relevant administrative sources. The administrative sources were then stored for each holding in the AGRA2010 processing program.    IACS and Bovine Register: When using the Bovine Register in particular, assigning unpaired Bovine Register units to the holdings was very time consuming.  
- coherence of definitions of characteristics   IACS is different in each of the Länder, which is reflected in different variable lists. Due to the different variable lists, the extent of use of IACS data was not uniform among the Länder

Another problem with the use of IACS data in some Länder is that each variable item might not contain the entire area, but only the funded part of the area. 

IACS: Variables that were not contained in IACS had to be requested from the respondents and therefore resulted in time consuming Land-specific design of the questionnaire
- coverage: Bovine Register: Because of validation checks prior to use of the Bovine Register data, doubles, over and/or under-coverage are excluded.       
 over-coverage    IACS and Bovine Register: The respective administrative source contains information on units that may not belong to the surveyed population. IACS and Bovine Register: These are eliminated in the FSS by the application of coverage thresholds, so that no over-coverage exists in the dataset used for the survey.  
  Rural development measures: In some Länder there are combined rural development support programs, consisting, for example, of payments for agricultural areas in the scope of NATURA 2000 and payments related to the Water Framework Directive. Rural development measures:
In these cases, participation in both schemes was cited for the respective holding.
 under-coverage    IACS: This administrative source contains only agricultural applicants who received IACS support funds and therefore not all holdings surveyed by the FSS.  IACS:
Companies that do not apply for IACS support are surveyed.
  Rural development measures: The approved measures could only be identified for co-funded payments (i.e. when EU funds were used). Non-co-funded payments (i.e. if only federal or state funds were used) were usually recorded as sums by the agricultural administrations.

Rural development measures:
In such cases, the measures paid were cited and not the approved measures.

 misclassification   Bovine Register: There were uncertainties in assigning the categories “dairy cows” and “other cows” due to missing or dated information on the farms’ types of production. Bovine Register:
If necessary, additional information is requested from the holdings in some Länder in order to stabilize the database.
 multiple listings      
- missing data      
- errors in data      
- processing errors      
- comparability      
- other (if any)      


4.5 Management of metadata
The administrative metadata, as well as the administrative data are maintained and systematically stored in electronic form by respective responsible authorities. The processing of these data for FSS is done in the statistical offices of the Länder.
4.6 Reporting units and matching procedures
Name Definition of the reporting unit
IACS Funding unit

(agricultural enterprises and holdings that applied for IACS)

Bovine Register Units under animal disease control law are local units defined by the veterinary authorities to monitor livestock and to prevent the spread of diseases. These are matched to agricultural holdings in the meaning of the Law on 
Agricultural Statistics
Rural development measures Funding unit

(“an operator, body or firm, whether public or private, responsible for implementing operations or receiving support,” i.e. farms or enterprises who were approved to receive rural development support in the period of 01.01.2011 until 31.12.2013)

Official Building Coordinates Building  
Organic Farming Register Organic farms
Data from agricultural liability insurance Agricultural enterprises

(e.g. enterprises in agriculture and forestry including horticulture and viticulture, fisheries, fish farming, lake, stream and river fishery (freshwater fishery), beekeeping as well as landscape management serving the objectives of nature conservation and environmental protection, enterprises keeping productive and breeding livestock for the purposes of breeding, fattening and to obtain animal products without cultivation of soil, etc.)

Register of establishments keeping laying hens Holding

A local, economic and hygienic unit consisting of one or more coops for the production of eggs.

Animal disease fund Animal health unit
4.7 Difficulties using additional administrative sources not currently used
Nothing to report.
3.2. Frequency of data collection
Frequency of data collection
The Farm Structure Survey has been conducted every three years since 2007. 
3.3. Data collection
1. Data collection modes
For the 2016 FSS, the obligation to report via online questionnaire according to Federal Statistics Law (§11a (2) BStatG) was applied for the first time. This meant that the respondents mainly submitted their information to the statistical offices of the Länder via the online questionnaire. In justified exceptions, e.g. in case of holdings without internet access, the respondents were allowed to hand in paper questionnaires. However, the implementation of the obligation was handled with varying degrees of severity by the statistical offices of the Länder.

In one Land the survey was not conducted directly by the statistical offices of the Länder, but in survey offices set up in municipalities. Survey offices are organisational units separated from regular administrative performance that carry out statistical tasks. The organisational separation ensures data protection. The work of a survey office primarily includes the distribution of questionnaires, controlling completeness and the complete counts and, if needed, supplementation of missing data by means of follow-ups.


2. Data entry modes
The collected data were entered in the processing and data editing program AGRA2010 at the StLÄ either by means of recording of the online questionnaires or data capture in dialog mode (e. g. during telephone call-backs). The questionnaires undergo an initial incoming inspection of numbers and completeness at the statistical offices of the Länder.


3. Measures taken to increase response rates
During survey conception, great store was set by the questionnaire’s user friendliness. This was achieved by means of simplified controls (e.g. by the use of routing questions) and as comprehensible as possible formulated questions. This reduced the time needed to complete the questionnaire and therefore the burden on respondents. If erroneous and incomplete questionnaires were returned in spite of this, use was made of extensive follow-ups with the respondents, of some administrative sources, data from previous surveys, and individual data from other comparable holdings.

Prior to the survey, the respondents were informed by the statistical offices via articles in agricultural journals, information events and an internet site about the upcoming survey. In addition, the farmers' associations have participated in the public relations work.

In order to increase response willingness, multiple reminder calls were made in some Länder. In most cases, however, a number of reminder and dunning letters were sent before the last resort was taken of citations, fine or penalty notices. Overall, the number of refusals could thus be minimised to 700 (or approx. 0.9% of all respondents).


4. Monitoring of response and non-response
1 Number of holdings in the survey frame plus possible (new) holdings added afterwards

In case of a census 1=3+4+5

293 927
2 Number of holdings in the gross sample plus possible (new) holdings added to the sample

Only for sample survey, in which case 2=3+4+5

83 086
3 Number of ineligible holdings 5 022 (Number of units under the applied threshold)
3.1 Number of ineligible holdings with ceased activities

This item is a subset of 3.

Not available. 
4 Number of holdings with unknown eligibility status


Not available.
4.1 Number of holdings with unknown eligibility status – re-weighted Not available.
4.2 Number of holdings with unknown eligibility status – imputed Not available.
5 Number of eligible holdings


78 064

Number of eligible non-responding holdings



Of which 642 refused to respond (holdings that were surveyed but refused to respond) with the adapted weight and 58 new holdings (which were neither reweighted nor were they imputed because there was no information about these units). 

5.1.1 Number of eligible non-responding holdings – re-weighted 642

(refusals from the stratum for new holdings do not alter the weight) 

5.1.2 Number of eligible non-responding holdings – imputed Not available
5.2 Number of eligible responding holdings 77 364
6 Number of the records in the dataset 


77 364


5. Questionnaire(s) - in annex
See the annex.

3.3-5. Questionnaire_FSS_2016
3.4. Data validation
Data validation
In part, data validation began at the respondent level if they used the online reporting option since parts of the plausibility controls were contained in the online questionnaire.

The returned questionnaires (online and paper questionnaires) first underwent an initial incoming inspection of numbers and completeness at the statistical offices of the Länder (with the exception of one Land where this was done by the survey offices). If survey offices were used, these were responsible for checking the numbers of questionnaires.

After the data was input in AGRA2010 by the statistical offices of the Länder, it underwent automatic plausibility checks as well as corrections. This procedure is described in detail in Section 6.3.4. Processing error - item 2. The data from each farm were checked for format retention, completeness, logical correlations and probabilities (e.g. previous survey comparisons, control of upper and lower thresholds as well as ratio values).

After the transmission of the edited Länder datasets to the Federal Statistical Office, these individual datasets were combined in one EUROFARM data record (data record for the data transmission to EUROSTAT). The extensive plausibility checks of the EUROFARM data record covered most of the checks prescribed in Annex VI of the Data Supplier Manual.

3.5. Data compilation
Methodology for determination of weights (extrapolation factors)
1. Design weights
Since the 2016 Farm Structure Survey is a random sample, the data compiled must be extrapolated. The results of the sample were extrapolated using the Horvitz–Thompson estimator. The weight is the inverse value of the sampling fraction, i.e. per stratum N/n whereby N = stratum size and n = sample size per stratum. The smaller the sample size in each stratum, the greater the extrapolation factor. Holdings from a full coverage stratum, e.g. new holdings or holdings with large amounts of livestock and organic farming are given the weight 1. 
2. Adjustment of weights for non-response
The extrapolation factor for sample holdings is adjusted for “true” non-responses (cf. Section 6.3.3. Non response error - item 1.). For this, a correction factor was included in the extrapolation method in the sample survey. Under the assumption that the “true” nonresponses possess the same structure as the units that responded, the mathematical adjustment was made so that only the observed values of the effective sample size were used to identify the extrapolation factor, i.e. nstrata minus the number of “true” non-responses within strata. 
3. Adjustment of weights to external data sources
Not applicable.
4. Any other applied adjustment of weights
Not applicable.
3.6. Adjustment

[Not requested]

4. Quality management Top
4.1. Quality assurance

[Not requested]

4.2. Quality management - assessment

[Not requested]

5. Relevance Top
5.1. Relevance - User Needs
Main groups of characteristics surveyed only for national purposes 
The national legislators supplement the programme of variables for the 2016 FSS by the Agricultural Statistics Act by:
  • Coordinates of the holding location
    The location of the holding is surveyed for the Farm Structure Survey using the official building coordinates in the form of geographical coordinates or Gauss-Krüger coordinates from administrative sources of the Land survey offices.
  • Rented areas and rents
    The leased UAA and the relevant annual rents were broken down by the types of use arable land, permanent grassland and other UAA. In addition, first leases in the last two were listed as a separate item on the questionnaire. The rented UAA within an entire rented farm was identified separately.
  • Catch crops
    Cultivation of summer catch crop in 2015 and winter catch crops from winter 2015 until May 2016.
  • Market gardening module
    Special module for horticultural holdings with questions on high accessible protective covers/green houses (types (such as greenhouse constantly with lower or high temperature), kind of coverage and used energy sources/amounts) and on sources of revenue. Moreover horticultural training of the manager is surveyed.
  • Keeping places
    This section encompasses the number of keeping places on the farm used in the last 12 months according to the respective types of poultry.
  • Types and amount of manure applications
    This section requested additional variables on the amount and application techniques for farm manure.
  • Annual net income in sole holder holdings
    Comparison of the holdings net income with the external net income of the holder/spouse
  • Determination of profit and turnover taxation
    As in the previous Census 2010, the type of determination of profit and of turnover taxation was indicated here.

In addition, individual characteristics (coordinates of the holding location, legal personality, land use and crop production, catch crops, market gardening module, livestock and keeping places, determination of profit and turnover taxation and organic farming) were collected in a complete count in order to obtain regional results.

5.2. Relevance - User Satisfaction

[Not requested]

5.3. Completeness
Non-existent (NE) and non-significant (NS) characteristics - link to the file. Characteristics possibly not collected for other reasons

Please access the information in the file at the link: (link available as soon as possible)

5.3.1. Data completeness - rate

[Not requested]

6. Accuracy and reliability Top
6.1. Accuracy - overall
Main sources of error
The results for a population of units (in this case holdings) ascertained with a sample are usually flawed with random sampling errors, even if the sample is conducted with the greatest thoroughness. The sample-based errors arise because not all units of the relevant population are surveyed and the results of the randomised sample holdings may deviate from the “true value” of the whole.

In addition to sample-based errors, there are also non-sample-based, systematic errors. Non-sample-based errors can result from shortcomings in the survey technique, in the selection of the population of holdings or in the processing technique. Non-responses can, in turn, lead to systematic errors when the surveyed units provide no or incomplete information.

6.2. Sampling error
Method used for estimation of relative standard errors (RSEs)
To assess the quality of results gained from a sample, they must be statistically evaluated by means of error calculation. Therefore, the national results of the FSS are produced using a processing program in which a calculation of the simple relative standard errors is integrated on the basis of the individual values for representative results. The simple relative standard error is used as the measure for the size of the random error and calculated in a stratified random sample using the following formula:



h = stratum
N= number of holdings in stratum h
n= number of sample holdings in stratum h
xhi = variable value i (i=1,2,…..,nh) in stratum h
= mean value of the variables in stratum h


For reasons of clarity, the standard errors (in %) were not published as error calculation results. Instead, a corresponding alphabetic character is placed after the representative resultant values, which stands for the respective error class of the simple relative standard error.

Allocation of the error classes of the simple relative standard errors:
A:                           to under                                ± 2 percent
B:            ± 2          to under                                ± 5 percent
C:            ± 5          to under                                ± 10 percent
D:            ± 10        to under                                ± 15 percent
E:            ± 15 percent and more

Results that fell under error class E were replaced in the national publication tables by the character “/” because the estimation error is then too great and the numerical value thus not reliable enough. In these cases, the sample size is too small for the assertion made. This can occur among variables that are too infrequent. The error marking is intended to enable users to sufficiently estimate the reliability for their purposes.

Several variable groups, such as, land use, livestock, organic farming, were surveyed in total in the 2016 FSS for national purpose, therefore in the national publication there were no RSEs shown for those variable groups. In national publications RSEs were only calculated for sample surveyed characteristics.

6.2.1. Sampling error - indicators

1. Relative standard errors (RSEs) - in annex


2. Reasons for possible cases where precision requirements are applicable and estimated RSEs are above the thresholds

Attached RSEs were calculated on the basis of the sample survey, which are delivered to Eurostat. There are no cases where precision requirements are applicable and estimated RSE's are above the thresholds.

6.2.1-1. Relative standard errors (RSE)
6.3. Non-sampling error

See below

6.3.1. Coverage error
1. Under-coverage errors
Errors can basically occur when determining the sample population regardless of the method used. Under-coverage can occur when holdings that are agricultural holdings in the legal sense are not identified as such and are therefore not surveyed.

The population for the 2016 Farm Structure Survey was defined with great care. The Farm register, which serves as the basis for determining the population, is regularly managed and updated by the statistical offices of the Länder. Primarily various administrative sources as well as information from past surveys are used to update the register (cf. Section 3.1. Source data - item 4).


2. Over-coverage errors
Over-coverage occurs when holdings that do not or no longer belong to the target population and are therefore not (or no longer) obligated to respond are surveyed. These holdings are so-called “false non-responses” (cf. Section 6.3.3. Non response error - item 1). In order to prevent this, holdings that are identified as below the threshold or that have abandoned agricultural production are labelled accordingly in the Farm Register and no longer considered when drawing the sampling frame. The Farm Register is regularly updated by the statistical offices of the Länder.

Moreover the questionnaire contains a question whether the holding reaches the coverage thresholds. Holdings which do not fulfill the thresholds are marked during data processing and excluded from further data processing. In general false non-responses only have an effect on the sampling error and can deteriorate the relative standard error.

These "false" non-responses did not change the extrapolation factor. How we dealt with non-responses and their extrapolation is explained in more detail in section 3.5. Data compilation - item 2.

2.1 Multiple listings 
In order to prevent multiple listings (particularly when adding new respondents), a duplicate search is conducted in the Farm Register. Additionally, the holdings get distinct identification numbers. The integrated duplicate search (carried out using the names and locations of the holdings) and constant comparisons with various administrative sources practically exclude multiple listings from the same unit. If a number of holdings are listed under one address – not necessarily an error – this situation is checked immediately (e.g. by telephone). In case of doubt they were surveyed as new respondents. 


3. Misclassification errors
The variables used for the classification are surveyed and checked in the FSS. So misclassification errors cannot occur in this survey. 


4. Contact errors
The respondents can enter changes of address or correct errors in the address in the questionnaire. The address changes provided are checked for postal correctness, then transferred to the Farm Register and promptly displayed during the processing procedure.

Contact data are not always changed entirely in the questionnaire. Obvious incomplete or erroneous information (e.g. post code) or survey documents that cannot be delivered by post are corrected using public registries (telephone books, Internet), in part also using administrative sources, through enquiries with the register of residents, municipalities, trade or regulatory agencies as well as through queries among respondents.


5. Other relevant information, if any
Not available. Over-coverage - rate
Over-coverage - rate

5 022 holdings of the frame did not fulfill the survey thresholds and were excluded from further data processing. The proportion of out-of scope units in the gross sample was 6.0 percent. Common units - proportion

[Not requested]

6.3.2. Measurement error
Characteristics that caused high measurement errors
The primary reasons for missing or erroneous information in the 2016 Farm Structure Survey are the size of the questionnaire and different reference periods between variables. Furthermore, some questionnaire variables are considered sensitive by respondents (e.g. ownership and tenancy including rents (national purpose), and the manure management), which lessens response willingness. In addition, and in despite of the great care that was taken in preparing the questionnaire comprehension difficulties frequently occurred in the questionnaire sections soil cover, tillage methods, crop rotation and questions about other gainful activities, as the relatively large number of follow-up enquiries by farmers showed. In one Land there were technical difficulties of matching länder specific rural development measures and EU rural development measures.

All measurement errors were corrected – if recognised as such, for example through distinct deviations from previous year or experienced values – during data editing. Moreover a pretest was conducted with voluntary farmers to improve the questionnaire. In the context of the pretest, the performance and the usability (understanding / user-friendliness) of the online-questionnaire were tested.

6.3.3. Non response error
1. Unit non-response: reasons, analysis and treatment
Unit non-responses are non-sampling errors. We differentiate between “true” and “false” non-responses. “True” non-responses are holdings that existed at the survey time and should have been surveyed but for which no responses are available. This also includes holdings that were newly created in the meantime, either as new establishments or through farm division, or that were not recorded due to gaps in the population. The same applies to holdings that were surveyed but refused to respond. The “false” non-responses are holdings that no longer existed at the time of the survey or no longer belonged to the group of respondents.

While the extrapolation factor was adapted when possible for sample holdings that were “true” non-responses (in this case refusals), the “false”non-responses did not change the extrapolation factor. How we dealt with non-responses and their extrapolation is explained in more detail in section 3.5. Data compilation - item 2.

A non-response analysis was not conducted.


2. Item non-response: characteristics, reasons and treatment
Item non-responses were primarily supplemented by means of telephone follow-ups with the farmers. If they refused to provide information even on follow-ups, missing values were supplemented using imputation methods (cf. Section 6.3.4. Processing error - item 1.). There were problems with response willingness mainly with variables considered as sensitive such as ownership and type of tenure (including rents), the breakdown of the number of workers and work hours, soil management and farm manure which required a comparatively large amount of follow-ups with the respondents. Unit non-response - rate
Unit non-response - rate

A total of 700 of 78 064 agricultural holdings refused to respond to the 2016 FSS, equaling a non-response rate of approx. 0.9%.[1] 

[1] This includes only “true” non-responses (cf. Section 6.3.3. Non response error - item 1.). Item non-response - rate
Item non-response - rate

Not available. 

6.3.4. Processing error
1. Imputation methods
As described above, missing and inconsistent values were – wherever possible – completed by means of follow-ups with the respondents and only in exceptional cases through comparisons with previous surveys or administrative sources (cold deck imputation) or individual data from similar holdings (hot deck imputation). The majority of the statistical offices of the Länder employ cold deck imputation; hot deck imputation is used in five offices of the Länder


2. Other sources of processing errors
Processing errors occur during processing of statistics, for example during signing, data capture or corrections made during data editing. To prevent processing errors, the programs used were tested extensively. To prevent signature errors or data capture errors, corresponding signature and value range checks were recorded in the data editing program. The tests and the plausibility checks minimised possible processing errors.

Most missing or erroneous information should be identified by the extensive data editing program. Where implausible or missing information occurred in the data material, they were completed or corrected by means of telephone follow-ups with the farmers, comparison with individual data of other holdings, comparison with previous surveys or comparison with administrative sources.

The AGRA2010 processing program was the chief instrument for completeness and plausibility checks. This program stores 330 obligatory error tests, 207 facultative error tests and 15 automated corrections. In the following, we explain the differentiation of these error messages.

  • Obligatory errors must be adjusted in all cases (e.g. missing age for an individual) and are obvious, unacceptable erroneous information or inconsistencies in correlations of data.
  • Facultative errors occur when information or correlations of information are possible, but either are improbable or rare, taking into consideration the operating and economic circumstances in agriculture, or originate from chronologically different individual surveys and therefore need not necessarily match (e.g. maximum controls). In such cases, we checked whether and, if so, in what way correction of the relevant information is necessary through individual and targeted follow-ups with the holding or, from case to case, drawing on other information.
  • Automatically adjusted errors are errors that can be corrected without a doubt and unequivocally based on the available information without follow-up interviews or data matching (e.g. by inserting missing total values).

The extensive plausibility checks cover the majority of the rules described in Annex 7 of the data supplier manual[1]. Due to a different approach in data editing, however, we cannot guarantee one-to-one implementation of the data editing rules. The transferred Eurofarm data material must consequently be verified for plausibility once again by Eurostat according to the rules in Annex VI of the data supplier manual. If the data editing program showed errors following the checks, these erroneous data had to be processed or corrected. Following these checks and the associated corrections no more missing or erroneous data should exist in the data material of the Farm Structure Survey.

[1] Eurofarm manual for data suppliers – Farm Structure Survey 2016, rev. 7, 20 December 2016


3. Tools used and people/organisations authorised to make corrections
All subsequent work on the individual material (follow-ups, corrections, input of data from administrative sources, etc.) was done by the staff members of the statistical offices of the Länder using the AGRA2010 processing and data editing program for agricultural statistics. Imputation - rate
Imputation - rate

The imputation rates of item non-response are not available.

6.3.5. Model assumption error

[Not requested]

6.4. Seasonal adjustment

[Not requested]

6.5. Data revision - policy
Data revision - policy
We define a data revision as all subsequent modifications to data that have already been released to the public. This is the case when at first preliminary statistical results are published and final results at a later time. On principle, preliminary data are always identified as such in all publications.

Errors in publications can also be the reason for data revisions. The treatment of errors in publications is prescribed at the Federal Statistical Office in a special guideline (Richtlinie zum Umgang mit Veröffentlichungsfehlern). Should publication errors occur they are allocated to error categories – depending on the severity of the error – and treated depending on the error category. Corrected data are then identified in the national publications by a special signature. The statistical offices of the Länder have comparable guidelines for handling publication errors or use a comparable procedure for revisions.

6.6. Data revision - practice
Data revision - practice
Preliminary results were published online for the integrated Survey of Land Use as part of the 2016 FSS and in a press release, so there were two planned data revisions for the 2016 FSS (publication dates cf. Section 7.1.1).

An additional data revision has been necessary due to errors in a national online publication on manure management (Series 2.2.2: Farm manure). The incorrect data were as quickly as possible corrected. The corrected data were flagged with a special signature. Additionally, errors, their reasons and treatments were described in the updated version of the publication.

6.6.1. Data revision - average size

[Not requested]

7. Timeliness and punctuality Top
7.1. Timeliness

See below

7.1.1. Time lag - first result
Time lag - first result
The preliminary national results from the Main Survey of Land Use, which encompassed topics only referring to agricultural land use, were published approx. 5 months after the survey's reference day.

The period between the beginning of the survey and publication of the first results from the entire feature set of the 2016 FSS (key figures in the January 2017 press release) was approx. 11 months.

7.1.2. Time lag - final result
Time lag - final result
The publication of the final results (Fachserie or subject matter series) of the Farm Structure Survey was divided up into 13 parts (series) (cf. Section 9.2. Dissemination format - Publications -item 1.) and carried out in a number of steps. The first set of Fachserien was published approx. 15 months and the last approx. 20 months after the survey's reference day. This means, the first set of Fachserien was published approx. 5 months and the last approx. 10 months after the end of the reference year.
7.2. Punctuality

See below

7.2.1. Punctuality - delivery and publication
Punctuality - delivery and publication
The results of the Farm Structure Survey were delivered to Eurostat on time on October, 18th 2017. The national publication of the national results was also on time with the first press release on January, 20th 2017 and the last, final results were published in October 2017. 

8. Coherence and comparability Top
8.1. Comparability - geographical
1. National vs. EU definition of the agricultural holding
The national definition of an agricultural holding in the Law on Agricultural Statistics (§ 25 and § 91 (1a) AgrStatG) in principle corresponds to that in Article 2 of Regulation (EC) 1166/2008. Thus an agricultural holding is defined as a unit, both technically and economically, which has a single management and which undertakes agricultural activities listed in Annex I of Regulation (EC) 1166/2008 either as its primary or secondary activity. From these activities only the cultivation of crops that cannot be grown profitably for climatic reasons (like (sub-) tropical fruits) and the cultivation of crops respectively the keeping of animal species that are not very common in Germany (like camels or ostriches) are excluded.

However, according to the Law on Agricultural Statistics a holding must exhibit specific minimum requirements in the size of utilised agricultural area (UAA) or for livestock or the crop areas for specialised crops, i.e. it must reach specific coverage thresholds. In Germany, the threshold for poultry is 1.000 places (see 2.6-2).As a result, holdings that currently have no poultry (and no other animals or areas) but more than 1.000 places for poultry are included in the survey. 


2.National survey coverage vs. coverage of the records sent to Eurostat
There are no differences between the population covered in the national survey and the population covered by the records sent to Eurostat. 


3. National vs. EU characteristics
Version of the definition handbook used

The catalogue of variables for the 2013 FSS was created largely according to the definitions in the corresponding Eurostat handbook (Handbook on implementing the FSS and SAPM definitions – 12 October 2015).

Differences between national and EU definitions:

    • Farm work: Under the national definition, work in other gainful activities is part of the time spent for farm work on the holding if the work is directly connected to the holding. This definition was used before 2010 (introduction of a separate question on hours worked in
      other gainful activities) and a change would cause a methodological break in the labour force time series.
    • Other gainful activities: The variable “Processing of farm products” was worded in the questionnaire as “Processing and direct sales of farm products” because in Germany the processing of products is usually combined with subsequent direct sales of the products.
    • Support schemes: The information on the receipt of rural development payments and on the ecological focus area were taken from administrative sources. The responsible administrative offices were therefore asked to transmit the authorised support schemes. However, the approved measures could only be identified for co-funded payments (i.e. when EU funds were used). Non-co-funded payments (i.e. if only federal or state funds were used) were usually recorded as sums by the agricultural administrations. In such cases, the measures paid were cited and not the approved measures. This can result in under-coverage.

      In some Länder there are combined rural development support programs, consisting, for example, of payments for agricultural areas in the scope of NATURA 2000 and payments related to the Water Framework Directive. In these cases, participation in both schemes was cited for the respective holding. Any resulting over-coverage is considered less problematic than under-coverage of measures.

    • Ecological focus areas: Due to the Länder specific nature of the IACS support, the ecological focus areas also include areas outside the arable land in some Länder. This variable therefore can include areas of permanent crops and permanent grassland.


4. Common land
4.1 Current methodology for collecting information on the common land
For the FSS 2016, we carried out a new query on the common land among the Länder. As a result, there is only common land in Bavaria and  it is not significant for Bavaria and certainly not for Germany.  Common land consists mainly of permanent grassland.

In 2015 only Bavaria reported the existence of common land units, namely 218 common land units with approximately 18,000 hectares of UAA. These are 0.23 percent of the holdings and 0.57 percent of the UAA of Bavaria. Most of these units are in the NUTS region DE27; but even here these units account only for 1.03 percent of the holdings in this NUTS region (157 holdings with 13,219 ha) and have only 2.61 percent of the whole UAA in this NUTS region. In the other regions of Bavaria, the proportion of holdings and UAA tends to zero or is even zero. The common land units from Bavaria are not included in the dataset.

In the other Länder, the share of the common land is even lower and if at all included in the data of the agricultural holdings under 'tenant farming' or 'share farming or other modes' and cannot be identified.

For the FSS 2016 survey, common land is a new characteristic and so we set it up as 'non-significant, and not collected and delivered to Eurostat under its own heading' but possibly under other codes.

4.2 Possible problems encountered in relation to the collection of information on common land and possible solutions for future FSS surveys
4.3 Total area of common land in the reference year
In 2015 only Bavaria reported the existence of common land units, with approximately 18 000 hectares of UAA (common land).  This common land area is not included in the dataset.
4.4 Number of agricultural holdings making use of the common land or Number of (especially created) common land holdings in the reference year
In 2015 only Bavaria reported the existence of 218 common land units. These common land units are not included in the dataset.


5. Differences across regions within the country
No differences across regions within the country.


6. Organic farming. Possible differences between national standards and rules for certification of organic products and the ones set out in Council Regulation No.834/2007
All holdings that farm organically according to Regulation (EC) No 834/2007[1] were classified here. The definitions are the same as those in the EU guidelines. 

[1] Council Regulation (EC) No 834/2007 of 28 June 2007 on organic production and labelling of organic products

8.1.1. Asymmetry for mirror flow statistics - coefficient

[Not requested]

8.2. Comparability - over time
1. Possible changes of the definition of the agricultural holding
There are no changes to report, except that the survey newly covers holdings with more than 1000 places for poultry, even if they have no agricultural activities in the reference period including poultry.


2. Possible changes in the coverage of holdings for which records are sent to Eurostat
There are no changes to report, except that the survey newly covers holdings with more than 1000 places for poultry, even if they have no agricultural activities in the reference period including poultry.


3. Changes of definitions and/or reference time and/or measurements of characteristics
There have been some adjustments to the definitions in the Eurostat definitions handbook but not enough to warrant the designation of a break in series. The most significant adaptation was done for the explanation of characteristic “plant residue” in the questionnaire section “soil management” where the minimum of remaining residue was raised from 10% to 30%.


4. Changes over time in the results as compared to previous FSS, which may be attributed to sampling variability
No changes


5. Common land
5.1 Possible changes in the decision or in the methodology to collect common land
Variable is not significant for Germany.
5.2 Change of the total area of common land and of the number of agricultural holdings making use of the common land / number of common land holdings
Not applicable.


6. Major trends on the main characteristics compared with the previous FSS survey
Main characteristic Current FSS survey Previous FSS survey Difference in % Comments
Number of holdings 276 121  285 033  -3%   
Utilised agricultural area (ha) 16 715 323  16 699 581  0%   
Arable land (ha) 11 819 330  11 875 889  -1%   
Cereals (ha) 6 351 808  6 533 706  -3%   
Industrial plants (ha) 1 414 749  1 535 534  -8%   
Plants harvested green (ha) 2 810 477  2 760 341  2%   
Fallow land (ha) 311 964  198 852 57%  Trend results from current provisions for the ecological focus area. 
Permanent grassland (ha) 4 692 004  4 620 975 2%   
Permanent crops (ha) 202 140  199 825 1%   
Livestock units* (LSU) 13 001 762  13 088 776  -1%   
Cattle (heads) 12 354 875  12 370 675  0%   
Sheep (heads) 1 856 015  1 893 273  -2%   
Goats (heads) 138 086  130 188  6%   
Pigs (heads) 28 652 961  28 697 432  0%   
Poultry (heads) 169 720 913  177 333 070  -4%   
Family labour force (persons) 475 115  529 287  -10%  Trend results from structural change
Family labour force (AWU) 292 190  322 916  -10%  Trend results from structural change
Non family labour force regularly employed (persons) 178 643  176 970  1%   
Non family labour force regularly employed (AWU) 145 725  143 914  1%   

* Calculated with national livestock unit coefficients.

8.2.1. Length of comparable time series

[Not requested]

8.3. Coherence - cross domain
1. Coherence at micro level with other data collections
Comparisons with other data sources (previous land use, livestock surveys, FSS or administrative sources) were done for specific variables for data editing purposes. Striking deviations were then clarified by means of follow-ups with the respondents.


2. Coherence at macro level with other data collections
There are notable differences to comparable statistics with regard to numbers of employees, the BMEL’s Farms Accountancy Data Network (FADN) and livestock.

The numbers of employees in agriculture in the Farm Structure Survey and the national employment accounts are not comparable as they are based on different methodologies. While the national employment accounts record all gainfully employed persons only once with their main employment, FSS covers all employees, regardless whether it is their main or secondary occupation. If the main and secondary employments are not in the same economic classification, deviations occur in the economic sector-related portrayal of results. This affects the many family workers having side-line jobs on farms in particular.

In addition, the results of the Farm Structure Survey differ from the information in the FADN in their allocation of agricultural holdings to socio-economic farm types: full and part-time farms. While in the FSS this classification is made exclusively for agricultural sole holder holdings, but not for group holdings and legal persons, the FADN includes group holdings, but do not typify legal persons either. Furthermore, the FSS and the FADN use different bases for classifying full and part-time farms. In the FSS questionnaire, all owners of agricultural sole holder holdings provide information on whether the income from the agricultural holding or from non-farm sources was higher. If the farm income was higher, the sole holder holding is considered a full-time farm; if the income from non-farm sources was higher, the holding is classified as a part-time farm. By contrast, for the FADN, enterprises with the legal forms of sole holder holdings and group holdings with 16 and more European Size Units (ESU) and at least one full-time worker are classified as full-time farms. All enterprises with the legal forms of sole proprietorships and partnerships with 8 to less than 16 ESU or less than one full-time worker are considered small and secondary occupation holdings. The test farm network (FADN) comprises only holdings from 8 ESU, whereby 1 ESU corresponds to a total standard coverage of 1,200 euros.

Furthermore, the Farm Structure Survey provides information that is partly comparable with variables from self-contained individual surveys (e.g. the survey on livestock for bovines, pigs and sheep). There are crucial differences in the respective survey methods with regard to the population, the thresholds and the reference dates. In the FSS, bovine livestock is reported according to the associated holdings while in the survey of bovine livestock the observed units are different and cattle is reported in total regardless to any thresholds. This can lead to differences with regard to the number of holdings or farms as well as the livestock between the surveys. In general, the Farm Structure Survey provides a structural overview of the agricultural variables while individual specialised statistics provide specific and more detailed characteristic information. More information is contained in the relevant quality reports at

8.4. Coherence - sub annual and annual statistics

[Not requested]

8.5. Coherence - National Accounts

[Not requested]

8.6. Coherence - internal

[Not requested]

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

[Not requested]

9.2. Dissemination format - Publications
1. The nature of publications
The results of the 2016 Farm Structure Survey were reported separately by the Federal Statistical Office according to national region and Länder. The statistical offices of the Länder published results at the NUTS2 level. The Federal Statistical Office publishes the Fachserie (subject-matter series) 3 “Land- und Forstwirtschaft, Fischerei” online as part of its information activities. Within Fachserie 3, the results are arranged as follows:
  • Series 2.1.1: Holdings with forest areas
  • Series 2.1.2: Land use of the holdings (Structure)
  • Series 2.1.3: Livestock of the holdings
  • Series 2.1.4: Business focuses and standard output
  • Series 2.1.5: Legal personality and socio-economic holding types
  • Series 2.1.6: Ownership and tenancy
  • Series 2.1.7: Agricultural holdings with other gainful activities
  • Series 2.1.8: Labour force
  • Series 2.2.1: Holdings with organic farming
  • Series 2.2.2: Farm manure
  • Series 2.2.3: Holdings with viticulture
  • Series 2.2.4: Horticulture in agricultural holdings
  • Series 3.1.2: Land use of the holdings (UAAs)

The Fachserie contains tables as well as an explanatory section.


2. Date of issuing (actual or planned)
The Fachserie on agricultural statistics was issued successively between May and October 2017. 


3. References for on-line publications
Publications in German about agriculture, forestry and fisheries can be downloaded for free as PDF or Excel files at > Publikationen > Thematische Publikationen > Land- & Forstwirtschaft, Fischerei. 
9.3. Dissemination format - online database
Dissemination format - online database
Detailed results of the Farm Structure Survey in various file formats (.xls, .html and .csv) can be obtained directly via the database system GENESIS-Online (after registering as “gast” using the password “gast”) by navigating to > Themes > 4 Wirtschaftsbereiche > 41 Land- und Forstwirtschaft, Fischerei > 411 Struktur der land- und forstwirtschaftl. Betriebe. The database also contains metadata from each of the surveys. 
9.3.1. Data tables - consultations
Data tables - consultations

Not available. 

9.4. Dissemination format - microdata access
Dissemination format - microdata access
The individual data collected are generally kept confidential in compliance with Article 16 of the Federal Statistics Law. Only in exceptional cases explicitly regulated by law can individual data be transmitted. The names and addresses of the respondents are never passed on to third parties.

The transmission of information collected to the responsible supreme federal or Länder authorities in the form of tables with statistical results is allowed according to Article 98 (1) of the Law on Agricultural Statistics in conjunction with Article 16 (4) of the Federal Statistics Law. This applies even if individual table cells only identify a single case. Under Article 16 (6) of the Federal Statistics Law, it is possible to provide individual data (microdata) to universities or other institutions tasked with independent scientific research for scientific projects if individual data can only be assigned to the respondents or parties concerned with a disproportionately large investment of time, cost and labour. Research institutions may, in addition, apply to the research data centres of the federal and Länder statistical offices where they have various ways of obtaining access to data, such as the on-site use (via safe centres or remote execution) or off-site use (scientific use files, public use files).

The research data centres are obliged to ensure statistical confidentiality when offering access to microdata for scientific use. They may not release results that allow conclusions on individual cases.

For this reason, the research data centres check every result that is produced within an on-site use of microdata for statistical confidentiality. This happens according to set rules. If  results allow conclusions on individual cases, these results will to be blocked. [1].

[1] Cf.

9.5. Dissemination format - other

[Not requested]

9.6. Documentation on methodology
1. Available documentation on methodology
Explanations of the content, the methodology and procedures of the 2016 Farm Structure Survey were published in Fachserie 3, series 2.S.5 and are available to all users online at > Startseite > Publikationen > Thematische Publikationen > Land- & Forstwirtschaft, Fischerei > Methodische Grundlagen der Agrarstrukturerhebung. 


2. Main scientific references
Not available. 
9.7. Quality management - documentation
Quality management - documentation

A quality report with explanations about the metadata and quality characteristics will be publicly available at > Startseite > Publikationen > Qualitätsberichte > Land- & Forstwirtschaft, Fischerei > Strukturerhebungen. 

9.7.1. Metadata completeness - rate

[Not requested]

9.7.2. Metadata - consultations

[Not requested]

10. Cost and Burden Top
Co-ordination with other surveys: burden on respondents
To lower the burden of the respondents and the statistical offices of the Länder and lessen costs, in FSS years the Survey of Land Use is conducted as an integrated element of the FSS. Additionally, for the 2016 FSS online reporting was mandatory. This obligation also lowered the survey costs.

Reporting burden for the holdings was also reduced by using as many administrative data sources as possible. However, this procedure not always reduced the cost of data handling in the statistical offices too.

11. Confidentiality Top
11.1. Confidentiality - policy
Confidentiality - policy
The individual data collected are generally kept confidential in compliance with Article 16 of the Federal Statistics Law. Only in exceptional cases explicitly regulated by law individual data can be transferred (e.g. the provision of anoymised individual data to the research data centres of the Federal and Land statistical offices for universities and other independent, academic institutions), without citing names or addresses (factually anonymised individual data).
11.2. Confidentiality - data treatment
Confidentiality - data treatment
A number of steps were taken in order to ensure compliance with this legal requirement. To prevent particulars about holdings from being disclosed in the nationally published tables, the results were subject to harmonised, nationwide confidentiality. It was taken into account how many cases are behind each individual table element and to what extent individual cases contribute to the values in the table elements (primary confidentiality). For the production of published tables, automatic primary confidentiality was carried out based on the p-percent rule1.

Primary confidentiality is applied for total (T) and representative (R) tables. Results of the representative survey were rounded published (thousands with one or two position after decimal point). The “natural confidentiality effect” of rounding was taken into account by means of a relevant adjustment of the p-percent rule. Results of total survey were published in complete length.

To prevent the disclosure of the primarily suppressed table elements by forming sums or differences in the tables, so-called secondary suppressions were carried out (secondary confidentiality) in addition to the primary suppressions. The secondary confidentiality was carried out manually by the Federal and Land statistical offices. The suppressed table elements based on primary or secondary confidentiality are marked with a dot in the publication tables.

The FSS results were tabulated and made confidential using the AMT and GHMAN Java applications developed especially for this purpose. While AMT supports the entire process of results tabulation including primary confidentiality, GHMAN enables manual entry of secondary confidentiality in the table cells.

1 Cf. Gießing, Sarah (1999): “Methoden zur Sicherung der statistischen Geheimhaltung”; Volume 31 of the series Forum der Bundesstatistik, published by the Federal Statistical Office, pp. 6-26.

12. Comment Top
1. Possible improvements in the future

Acceptance of the questionnaire and the questions it contains could be improved by shortening the survey length and making the questions more understandable. The relevance of the questions needs to be directly recognisable to the respondents. 


2. Other annexes

Not available.

Related metadata Top

Annexes Top