Farm structure (ef)

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

Compiling agency: Danmarks Statistik 

Time Dimension: 2016-A0

Data Provider: DK1

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
Danmarks Statistik 
1.2. Contact organisation unit
Division of agricultural statistics 
1.5. Contact mail address
Sejrøgade 11
København Ø


2. Statistical presentation Top
2.1. Data description
1. Brief history of the national survey 
The farm structure survey (FSS) survey goes back to 1977 where the separate surveys for agriculture and horticulture were integrated in one survey.

Since then the survey has taken place every year as a questionnaire-based survey where the farmer has received a questionnaire in a letter with an obligation to complete it. Nowadays, however, the contact with Statistics Denmark is digital.

Total censuses held in: 1977-83, 1985, 1987, 1989, 1999 and 2010.

Sample surveys held in: 1984, 1986, 1988, 1990-98, 2000-09 and 2011-17.

Generally the samples have been quite big, 20-50 percent of all farms.

The surveys have always had a threshold so that small holdings are excluded:

1977-82: FSS included farms with at least 0.5 hectares or at least a production with a value corresponding to 0.5 hectares with barley.

1983-1994: FSS included farms with at least 5.0 hectares or at least standard gross margin of 3 000 euros at 1985 prices.

1995-2009: FSS included farms with at least 5.0 hectares or at least a standard gross margin of 4 000 euros at 1990 or 1995 prices.

From 2010 Eurostat introduced a harmonization of the thresholds in FSS.


2. Legal framework of the national survey 
- the national legal framework There is no specific law on farm structure statistics. The Danish FSS is held with reference to Law on Statistics Denmark. 
- the obligations of the respondents with respect to the survey Law on Statistics Denmark puts an obligation on all business units, including agricultural farms, to complete questionnaires. Likewise all government institutions must send their administrative data to Statistics Denmark on request. 
- the identification, protection and obligations of survey enumerators The Danish FSS is held as a postal survey. It means that there are no survey enumerators. Colleagues working on FSS have to treat individual farm information strictly confidential. 
2.2. Classification system

[Not requested]

2.3. Coverage - sector

The Danish FSS covers all business units active in agriculture, also if the NACE code is different from agriculture, for example forestry.

2.4. Statistical concepts and definitions
List of abbreviations
IACS - Integrated Administration and Control System
2.5. Statistical unit
The national definition of the agricultural holding
The Danish definition of a holding is in accordance with the definition in Regulation (EC) No 1166/2008. However, Denmark also includes farms with fur animals, unlike most other EU countries.

The agricultural activities include:

  • All agricultural crops, also when only land maintained in good agricultural and environmental conditions.
  • Equidae (horses)
  • Bovine animals (cattle)
  • Sheep and goats
  • Pigs
  • Poultry
  • Rabbits, breeding females
  • Bees
  • Livestock not mentioned elsewhere (fur animals, excluding rabbits) 
2.6. Statistical population
1. The number of holdings forming the entire universe of agricultural holdings in the country
Understood literally it would be extremely difficult to count the total number of “farms” in the sense of all units producing agricultural products even at a small scale. We have no knowledge of private families growing for instance fruits, berries or vegetables in their gardens or summer houses.

The closest we could come to “the total number of farms in Denmark” would the number of applicants crop subsidies, which was 40.087 in 2016 according to IACS.      


2. The national survey coverage: the thresholds applied in the national survey and the geographical coverage
The Danish FSS has since 2010 covered all agricultural units on the Danish territory which fulfil at least one of the following criteria:
  1. An agricultural area of at least 5 hectares (A_3_1$ha)
  2. A standard output of at least 7.500 euros
  3. Fruits, berries and nursery area of at least 0,5 hectares (B_4_1$ha+B_4_5$ha)
  4. Vegetables and strawberries of at least 0,5 hectares (B_1_7$ha)
  5. Greenhouse and mushrooms of at least 1 000 m (0.1ha) (B_1_7_2$ha +B_1_8_2$ha +B_4_7$ha)
  6. At least 10 cattle (C_2$heads)
  7. At least 50 pigs (C_4$heads)
  8. At least 10 sows (C_4_2$heads)
  9. At least 20 sheep (C_3_1$heads)
  10. At least 20 goats (C_3_2$heads)
  11. At least 1 000 poultries (C_5$heads)
  12. At least 40 fur animals

The source is most often the most recent FSS for each farm (FSS 2010-15) but could also be information from IACS or the livestock register.


3. The number of holdings in the national survey coverage 
The population of the Danish FSS 2016 consisted of 37.161 farms. These farms fulfilled at least one of the criteria mentioned in item 2. above according the most recent FSS or for some farms IACS or the livestock register. 

According to the final national Danish publication, Denmark has 35.669 farms where 622 of them have fur animals as their only agricultural activity. They will be removed in the Eurostat publication, which thereby have 35.047 Danish farms. 


4. The survey coverage of the records sent to Eurostat
13.087 farms constitute the base for the national Danish publication. Of these Eurostat removes 398 farms with fur animals as the only agricultural activity and has thereby 12.689 Danish farms as base for their publication.  


5. The number of holdings in the population covered by the records transferred to Eurostat
12.689 corresponding to 35.047 farms when extrapolated. 


6. Holdings with standard output equal to zero included in the records sent to Eurostat
The Danish FSS sample consists of 398 farms which have fur animals other than rabbits as their only agricultural activity. It means that they have no other animals and no crops, neither on free land nor in area green house. Extrapolated to a total level these farms amount to 622.  Fur animals play an important role in Danish agriculture and it is necessary to include farms with fur animals in the FSS. These holdings are removed from the data disseminated by Eurostat.


There are 20 farms in the sample sent to Eurostat with no livestock and where the agricultural land consists of only fallow land and permanent grassland no longer used for production. This is not a very common case but perfectly possible. There are additionally 3 farms fulfilling the same conditions except that they do have fur animals other than rabbits.
These farms have applied for single payment in the 2016 season. In order to obtain this support they have an obligation to keep the land in good agricultural and environmental condition.


19 farms sent to Eurostat have no agricultural land and no livestock at all. It would normally mean closed down farms which should not be included in the sample sent to Eurostat. However, for these farms something special applies: They are farms with poultries but they happen to have an empty animal house at the reference day.
So instead of reporting the number of chickens (or whatever) on the reference day they have instead reported a production of poultries for slaughtering in the recent year thereby indicating that they are still active farms.
We have chosen to include such farms in our own national publication and also send them to Eurostat. It should be noticed that these farms have complete and valid labour force information. 


7. Proofs that the requirements stipulated in art. 3.2 the Regulation 1166/2008 are met in the data transmitted to Eurostat
The Danish IACS has in 2013 44 866 of which 36 568 are big. They have an utilised agricultural area (UAA)  of at least 5.0 hectares or an area with fruits, berries and vegetables of at least 0.5 hectares. The remaining 8 298 are small farms. So the small farms amount to about 18 percent of all the farms but they have only about 1.0 percent of the UAA among farms included in IACS. It should be noticed that the UAA in IACS and in FSS are close to identical, namely around 2.6 mio. hectares. 

Some of these “small farms” could, however, be big by virtue of their livestock (e.g. at least 20 sheep) or green house area. But this information on livestock and green house area is not included in IACS.

It is not possible to make a similar study for livestock using the livestock register for two reasons:

1) The exercise mentioned above for IACS defines as small farms the farms with both an area smaller than 5.0 hectares and an area with fruits, berries and vegetables smaller than 0.5 hectares. Here we suppose that these farms do not have livestock to any considerable content. This is normally true since many animals normally mean a big agricultural area. However, this rule does not work the other way round. It is quite normally to have some animals - but very few – in combination with a big area. So a farm with, say 5 sheep, cannot be classified as a small farm since it is extremely likely that it has more than 5.0 hectares of agricultural land.

It would in principle be possible to merge IACS and the livestock register whereby small farms could be identified as all farms below both the area and livestock thresholds stipulated in the regulation. Such an exercise is not as easy as it might sound and would involve certain match problems. Statistics Denmark has not yet found resources for such a study.  

2) Even though the livestock register has information on number of animals this information is much more uncertain than the similar area information in IACS. For all other livestock than cattle the number of animals is a mix of stock information and production figures and the figures are also often up to one year old.  

But it could be mentioned that Statistics Denmark in 2006 carried through a project with grants called “Danish TAPAS project on small units”. The study showed that Danish farms below the threshold have about 0.6 percent of the total standard gross margin of the whole Danish agriculture. The work can be found on CircaBC, see page 10. It makes it extremely unlikely that farms below the Danish threshold should possess more than 2.0 percent of the livestock units.


8. Proofs that the requirements stipulated in art. 3.3 the Regulation 1166/2008 are met in the data transmitted to Eurostat
The thresholds mentioned in item 2 above are lower than the thresholds of Annex II of Regulation 1166/2008.
2.7. Reference area
Location of the holding. The criteria used to determine the NUTS3 region of the holding
The location is the head quarter of the farm where the farm activity takes place. The information is contained in the Danish business register as a municipality code. From the municipality code the NUTS 3 region can be derived.

However, the preferred solution is to derive the municipality code from the property code belonging to the farm. It is because the business register information code could refer to the place where the farmer lives rather than where the farm activity takes place.

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)
The survey day of the FSS 2016 was Friday May 13. This survey date concerns all characteristics which meaningfully can be assigned to one specific day. This is first and foremost the case for livestock. For some other characteristics other principles apply:
  • Crops are collected from IACS where farmers should apply for crop subsidies no later than April 21 2016. The information on crops concerns the season 2015/2016 from the point of time where winter crops are sown – normally September or October 2015 - till the crops are harvested – normally August 2016. Evidently the time for sowing and the time for harvesting might differ depending on weather conditions. For green house crops and farms not applying for subsidies the survey day applies.
  • Cattle are collected from the livestock register dated June 1 2016, pretty close to May 13.
  • Labour force characteristics, including other gainful activities concern a period of one year prior to the survey date rather than the survey day itself.
  • Rural development characteristics concern the calendar year 2014-2016. According to art 8.(d) of Regulation 1166/2008 this reference period should be 3 years.
  • Education in the recent year concerns necessarily a period of one year prior to the survey day.
  • Irrigated area in the recent year concerns necessarily a period of one year prior to the survey day.
2.9. Base period

[Not requested]

3. Statistical processing Top
1.Survey process and timetable
Steps in the Danish FSS 2016  
  • The questionnaire: The work on designing the Danish questionnaire began in September 2015. The online version of the questionnaire was prepared by our division of data collection - based on the paper version of the questionnaire - and was ready in the beginning of May 2016.
  • Sample selection: The sample was selected by our methodological unit in April 2016.
  • Sending the questionnaires: May 6 2016.
  • Survey day: May 13 2016.
  • Reminders:

    First reminder: Beginning of June 2016, about 7.500 farms
    Second reminder: End of June 2016, about 4.500 farms
    Third reminder: Middle of July 2016, about 3.000 farms
    Fourth reminder: From beginning of August 2016, about 1.000 farms
    About 50 farmers were reported to the police regarding FSS 2016. However, a non-respondent is not reported systematically to the police, but only after the second violation within a period of one year.  So still a certain non-response had to be tolerated.

  • Validation of questionnaires: This work took place from May 2016.
  • Integration with administrative data: January 2017.
  • Calculation of extrapolation factors: May 2017.
  • Publication: May 22 2017, results were published in a small online newsletter with detailed results.
  • Data to Eurostat: July 2017, with certain later corrections.


2. The bodies involved and the share of responsibilities among bodies
The work on FSS 2016 has been divided by three different units:

Division of agricultural statistics: This division has the main responsibility for the survey and has performed these tasks: 

- Designing of the questionnaire
- Determining the sample size
- Validation work
- Contact with the farmers
- Receiving and treating administrative data
- Register work
- Contact with Eurostat, meetings in Luxembourg, e-mail correspondence
- Creation of the dataset to Eurostat
- Publication of results

Division of data collection: This division is responsible for collecting answers from the farmers: 

- Preparing the online questionnaire
- Letter to the farmers
- Sending questionnaires and reminders
- Telephone reminder

Methodological unit:

- Selection of the sample.
- Calculation of extrapolation factors.

All in all, about 15 colleagues at Statistics Denmark took part in the FSS work, however for some of them just very few hours. No bodies outside Statistics Denmark have been involved in the survey work but of course informal conversations with interested users have taken place.


3. Serious deviations from the established timetable (if any)
Nothing to mention.
3.1. Source data
1. Source of data
The Danish FSS 2016 covers all characteristics in one survey, both crops, livestock, labour force, other gainful activities and machinery. So there are no sub samples.

Information on crops, cattle, organic farming characteristics and rural development characteristics are collected from administrative register but connected to farms in the sample. It means that in the end the FSS survey register will contain complete information, also on crops, cattle etc. exactly as if all questions had been on the questionnaire in the traditional way.  


2. (Sampling) frame
The Danish sample frame consists of all agricultural units in the business register above the survey threshold mentioned in concept 2.6. Statistical population - item 2. The number of farms in this population was 37.161. prior to FSS 2016.

Since the total FSS census in 2010 - where the number of farms was 42.099 – 10.473 have stopped their farm activities or have fallen below the threshold. 5.535 farms have since then been added to the population.

The Danish population must be defined as a list frame consisting of all farms on the Danish territory above the threshold. These farms are the survey units. 

The Danish population of farms has since 2010 been a part of the general business register. Before 2010 Statistics Denmark had separate registers for agricultural and forestry units.

Any business unit could, irrespective of the NACE code, be active in agriculture or forestry or both. For this reason a variable in the Danish business register “Active in agriculture” has been created. It can assume four different values:

0= Not active in agriculture or forestry
1=Active in agriculture, but not forestry
2=Active in forestry, but not in agriculture
3= Active in both agriculture and forestry

All units with value 1 or 3 are furthermore marked as small or big where big means above the thresholds mentioned in concept 2.6. Statistical population - item 2.big enough to be included in an agricultural survey.

The register is continuously updated with information from IACS and the livestock register where these routines are established:

- Farms in IACS and the livestock register should be updated with the active value 1 or 3 in the business register where this is not already the case.

- Big farms in IACS – at least 5.0 hectares of agricultural land or at least 0.5 hectares with fruit, berries or vegetables – should be updated as big farms in the business register if they have status as small farms.

Routines which could delete units from the list of active farms are not developed successfully yet. As it is now farms are mainly deleted in connection with agricultural surveys if a farmer when receiving the questionnaire tells that he has stopped all farm activities.  

Small farms below the threshold are still in the business register but marked as small so that they cannot be selected to a survey until information from either IACS or the livestock register indicates that they should be updated as big farms.


3. Sampling design
3.1 The sampling design
The Danish FSS sample is a single-stage stratified random sample which aims at minimising the sample error for selected variables. 
Farms are selected randomly within each stratum.
There are no sub-samples in the Danish FSS.
3.2 The stratification variables
The stratification has three dimensions:

1) Region

Denmark has five different NUTS 2 regions and these regions are a part of the FSS sample stratification:

Region code Name of the region Number of farms in the sample
81 Region Nordjylland 2.927
82 Region Midtjylland 4.342
83 Region Syddanmark 4.429
84 Region Hovedstaden 939
85 Region Sjælland 2363


2) Typology


Typology code Description Number of farms in the sample
1500 Cereals, group 1.5 2.370
1600 Other field crops, group 1.6 1.432
2000 All horticultural farms, group 2.1, 2.2, 2.3, 3.6 and 3.8 and other farms not belonging to any of these groups but having   at least 50 percent of standard output from horticultural crops. 1.545
4500 Dairy cows, group 4.5 2.424
4650 Other cattle farms, group 4.6 and 4.7 1.078
4800 Other grazing animals, group 4.8 796
5100 Pig farms, group 5.1 2.180
5200 Poultry farms, group 5.2 231
5400 Fur animals, group 5.4 852
6100 Mixed field crops, group 6.1 91
7000 Mixed livestock, group 5.3, 7.3 and 7.4 157
8300 Field crops and grazing livestock, group 8.3 936
8400 Other farms, group 8.4 410
9000 Farms where typology and standard output are unknown 498


The standard output and the typology of the farms are calculated based on information from the most recent FSS, 2010-15 . For farms in the population with no survey information two rules are applied to describe the farms:

A) If the farm is in IACS and not in the livestock register the standard output and the typology are calculated based on crop information from IACS. The farm is assumed not to have any livestock.

B) If the farm is:

1) not in IACS but in the livestock register or

2) in both registers or

3) in none of the registers (could for instance be newly established horticultural farms)  it belongs to typology group 9000, which of course is not any real typology but merely means “typology and standard output unknown”.
All farms from this group are selected to the sample (unless they have as status as have a status in the business register as bankruptecy or they do not have a valid business number) and no size groups are created.


3) Size of standard output. Different size groups are applied for different typology groups.

3.3 The full coverage strata
The Danish sample has 842 different strata. In 296 of these strata all farms are selected. It is mainly big farms with more than 500 000 euros in standard output. However, horticultural farms (typology groups 2 and 3) and poultry farms (typology group 5.2) are also selected totally. Furthermore farms with no survey information and where typology and SO cannot be calculated from IACS information are also selected totally, see item 3.2 above. In practice, however, even in "100 percent strata" a few farms might be sorted out if they have a status in the business register as bankruptecy or they do not have a valid business number. The 296 strata are the cases where it was possible to select all farms.   
3.4 The method for the determination of the overall sample size
The size of the sample was determined from experience from the sample survey in 2011. An analysis made by our methodological unit showed that a sample of less than 15 000 farms – which was the size of the 2011 sample – could ensure a sufficient quality. However, to make up for an expected non response of maybe 10-15 percent it was decided to keep a sample size of 15 000 farms. The non-response became in fact much smaller, only about 5 percent in 2011. In 2016 the non response was bigger, namely 7,5 pct.  
3.5 The method for the allocation of the overall sample size
The sample is selected optimally to minimise the expected standard error of these selected variables:
  • Agricultural area
  • Number of cattle
  • Number of pigs
  • Number of fur animals
  • Standard output
3.6 Sampling across time
For each survey a new sample is selected independently of previous surveys. 
3.7 The software tool used in the sample selection
SAS programs are used to select the sample. 
3.8 Other relevant information, if any
The population of farms is prepared by the division of agriculture but the sample selection is a task of the methodological unit of Statistics Denmark. 


4. Use of administrative data sources
4.1 Name, time reference and updating
IACS 2016 is used as the source for collecting information on crops. IACS is created once a year. Its legal base is Regulation 1307/2013

Information on cattle is collected from the livestock register. The legal base is Danish legislation ("lovbekendtgørelse nr. 511 af 23. april 2015"). Unlike IACS, there is no final version of the livestock register during the year, since the register is continuously updated. Statistics Denmark receives a copy of the register four times a year and we use the June version as the source for FSS.

Information on Rural development support is collected from the Ministry of Agriculture. This register is created once a year.

Information on organic farming is collected from the register of organic farms 2016 kept by the Ministry of Agriculture. This register is updated once a year.

The last two registers have no legal base as such but are used by the respective authorities when administrating the concerned arrangements.
4.2 Organisational setting on the use of administrative sources
Statistics Denmark has a right to receive all kinds of administrative registers from other government institutions according to Law on Statistics Denmark.
It is strictly necessary to get precise agreements with the colleagues in the administrative bodies. Statistics Denmark has not recently had problems with actually getting data delivered. It is a good idea to involve both bosses and “people working on the floor” in the agreement. Otherwise the colleague in the administrative body who actually has to create the data to the statistical office might be tempted to believe that this task can wait.

All IACS, the livestock register, the organic farm register and the rural development register live up to the statistical requirements so it has not been necessary for Statistics Denmark to influence the content. If any such need arises it is difficult to ascertain if it would be possible to change the content of an administrative register in order to meet statistical needs. That would depend on circumstances. An administrative body has no obligation to adopt its registers to meet statistical needs. We have, however, one positive experience going back to 1995 where the Ministry of agriculture agreed to divide beets into sugar beets and fodder beets.

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) Organic farming: The organic farm register has the same unit as IACS.    
  IACS: An applicant and a farm are not by definition identical with each other: there are very few cases – less than 10 in Denmark – where a unit might own land in different parts of Denmark and thus cannot meet the requirements of the farm definition. IACS: If one farm does not correspond to one subsidy application this farm has to report the crops to Statistics Denmark when completing the FSS questionnaire.
  Livestock register: One farm can have more than one number in the livestock register. Livestock register: All farmers in the survey are asked if they have cattle and if yes they must specify one or more numbers in the livestock register in order to create a match.
- coherence of definitions of characteristics  IACS contains a huge number of crops where the standard is changed slightly from year to year. The 2016 standard had 290 different crops; in particular there are many categories of fruits, berries, vegetables and seeds for sowing. The list is available in Danish only.

IACS reflects the crop year 2016, which means winter crops sown in the autumn 2015 and spring crops sown in the spring 2016. As such IACS is assumed to meet the FSS standard and thus no adjustment procedure is necessary. 

The farmers report their land use in April for the given crop year. If changes are made the farmers are obliged to report the changes. The crops might not have a survey day as such but does rather concern the season. In 2016 the farmers had a deadline of April 21 2016. It means that IACS 2016 had information on winter crops sown in the autumn 2015 and spring crops recently sown.

Livestock register: The livestock register is delivered to Statistics Denmark with these 12 categories of cattle:
  • Bull-calves and steer-calves,- under 1/2 year, ca_1
  • Bull-calves and steer-calves, 1/2-1 year, ca_2
  • Bulls and bullocks, 1-2 years, ca_3
  • Bulls and bullocks, 2 years and over, ca_4
  • Heifer-calves, under 1/2 year, ca_5
  • Heifer-calves, 1/2-1 year, ca_6
  • Heifers, 1-2 years, in calf, ca_7
  • Heifers, 1-2 years, not in calf, ca_8
  • Heifers, 2 years and over, in calf, ca_9
  • Heifers, 2 years and over, not in calf, ca_10
  • Dairy cows, ca_11
  • Cows kept for suckling, ca_12

The FSS categories required by Eurostat can easily be created from these 12 categories:

FSS, Eurostat Danish livestock register
C_2_1: Bovine under one year old - total ca1_+ca2+ca_5+ca_6
C_2_2: Bovine under 2 years - males ca_3
C_2_3: Bovine under 2 years - females ca_7+ca_8
C_2_4: Bovine under 2 years and older - males ca_4
C_2_5: Heifers, 2 years and older ca_9_+ca_10
C_2_6: Diary cows ca_11
C_2_99: Other cows ca_12
- coverage:      
  over-coverage   Administrative registers contain also units not in the scope of the survey. For all administrative registers the information is used only for farms in the survey. So over coverage cannot play a role. 
  under-coverage  Livestock register:
As far as cattle are concerned the livestock register contains information on the single animal. Each single animal has a unique number. This number is information-bearing with information on birthday, gender and use. Use could for instance be milk production or meat production.

The livestock register is updated weekly or continuously by the cattle farmer regarding acquisition (either new born calves or purchased animals) and disposal of animals (either died of natural causes or sold). This update is also with information on suppliers and buyers of cattle. Furthermore the deliverance of cattle for slaughtering is verified by the slaughter houses and the animals died of natural causes by the carcase disposal plants.

Due to this tight system of control and validation unreported events must be assumed to be negligible, maybe even non-existent.

IACS contains only information on farms applying for subsidies.

Approximately 5 percent of the Danish farms in the FSS do not apply for single payment. Generally these farms are:

  • Horticultural farms with green house area but no crops on free land.
  • Livestock farms with no agricultural land.
  • Small farms which have not find it worthwhile to apply for a small area. But these small farms are normally not included in the Danish FSS.
IACS:  Farms not applying for subsidies and in the scope of the FSS are obliged to specify their crops on the statistical questionnaire.
  misclassification   No such problems are known to exist in the administrative registers used in the Danish FSS.    
  multiple listings   No such problems are known to exist in the administrative registers used in the Danish FSS.    
- missing data   No such problems are known to exist in the administrative registers used in the Danish FSS.   
- errors in data   No such problems are known to exist in the administrative registers used in the Danish FSS.   
- processing errors   No such problems are known to exist in the administrative registers used in the Danish FSS.   
- comparability   Nothing to remark.   
- other (if any)   Nothing to remark.   


4.5 Management of metadata
We have no thorough documentation of the data files. Years back we received old-fashioned record-layouts. Today the data are self-explaining SAS or csv-files.

IACS and the organic farm are delivered as SAS-data sets and  the rural development register and the livestock register as csv-files.

The choice of file type and data format is up to the data provider.

4.6 Reporting units and matching procedures
IACS: The unit in IACS is an applicant for crop subsidies. The applicant must be either a person or a legal business unit. This unit is almost always identical with the farm unit. It means more precisely that the land which the farmer cultivates within one farm unit also is the land for which he applies for subsidies. Normally a match between the farm register and IACS can be obtained using the business number as the match key.  Since an applicant and a farm are not by definition identical with each other there are very few cases – less than 10 in Denmark – where a unit might own land in different parts of Denmark and thus cannot meet the requirements of the farm definition. If one farm does not correspond to one subsidy application this farm has to report the crops to Statistics Denmark when completing the FSS questionnaire.     

IACS is the source for collecting crops for farms having applied for single payment. On the questionnaire the farmer is asked if he has applied for subsidies this year, and if yes he also indicates his number in the subsidy system. It is a unique number which has no use anywhere else. This makes the match with IACS easy. In cases where a farmer forgets to indicate the number it is most often available from the survey the previous year or the number can be found in other ways, for instance using match criteria like business number or personal codes. If a farmer answers “no, I do not apply for subsidies” he has to give a full specification of all crops.

Livestock register: In the livestock register the reporting unit is a physical place where the animals are located. This place is in most cases an agricultural property. This place has a unique number. The livestock register contains information on all Danish farms with cattle. It is strictly illegal to keep cattle without being registered. All farmers in the survey are asked if they have cattle and if yes they must specify one or more numbers in the livestock register in order to create a match.

Rural development support: The register on rural development contains information on all farms receiving rural development support. In the register on rural development the unit is a business unit with a business number. For small farms with no business number the personal code is the identifier. The link to this register is created by match with the business code or the personal code.

Organic farming: All organic farms have to be registered in the Ministry of Agriculture. The organic farm register has the same unit as IACS. The link to this register is created by match with the business number.  If an organic farm does not apply for subsidies (a rare case) the unit is the business unit with a business number. An organic farm is obliged to have a business number.  

IACS and Livestock register: The procedure is that the farmer who applies for subsidies must write his number in IACS on the FSS questionnaire. Likewise a farmer with cattle must write his number in livestock register on the FSS questionnaire. These numbers are in the following called “register identification number”.  If all farmers have indicated the correct identification number the match is easy and painless.  

False matches are eliminated by using the following procedures, and they apply to both IACS and the livestock register:
a) Two or more farms have indicated on the FSS questionnaire the same register identification number.
b) A farm has indicated a non-existing identification number, probably a simple writing mistake.
c) A farmer has not indicated any number.
d) A farm has indicated an existing identification number, but neither the business number nor the personal civil registration code are the same in respectively the statistical register and IACS/livestock register. The farmer has most likely made a simple writing mistake and has accidentally chosen an existing number of a farm not included in the survey.

All these mistakes must be eliminated before the match can take place.

4.7 Difficulties using additional administrative sources not currently used
Not relevant.
3.2. Frequency of data collection
Frequency of data collection
The farm structure survey is held yearly, also in years not required by Regulation 1166/2008. 
3.3. Data collection
1. Data collection modes
The preferred method of completing the questionnaire is the online solution where a farmer uses his digital signature.

However, farmers who have difficulties with the web questionnaire may request a paper questionnaire or simply give the information on phone. 


2. Data entry modes
FSS 2016
  • Internet: 12 446
  • By post or telephone interviews: 1 524

These 13 970 answers include 13 087  farms sent to Eurostat, as well as farms below the threshold and closed down farms. Of the 13 087 farms sent to Eurostat, 398 are farms with only fur animals other than rabbits as the only agricultural activity.  

Questionnaires not completed online are all registered manually. Since the FSS in 2014 we do not use scanning as a tool for registering questionnaires.


3. Measures taken to increase response rates
In principle Statistics Denmark insists on getting answers from all farms being selected to the survey. However, due to lack of resources we had to accept a certain non-response in FSS 2016. This non response was 1030 farms (7.5 percent).

Three reminders are sent to the farmers before they are contacted by phone, and only the biggest of the farms are contacted in this way.

It means that it is impossible to completely avoid non-response.

Farmers can find information on our web site regarding the survey. They can also find a phone number and an e-mail address if they wish to get into contact with Statistics Denmark.

Farmers having troubles with the online solution may request a paper questionnaire.

Farmers who wish to complete the questionnaire by phone are never denied this option but they must contact Statistics Denmark themselves. The reason is that the online solution is seen as the preferred solution. Other solutions should be reduced as much as possible.


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

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


(including holdings with fur animals other than rabbits) 

3 Number of ineligible holdings 1.281 
3.1 Number of ineligible holdings with ceased activities

This item is a subset of 3.

4 Number of holdings with unknown eligibility status


4.1 Number of holdings with unknown eligibility status – re-weighted
4.2 Number of holdings with unknown eligibility status – imputed
5 Number of eligible holdings


5.1 Number of eligible non-responding holdings


5.1.1 Number of eligible non-responding holdings – re-weighted 1.030 
5.1.2 Number of eligible non-responding holdings – imputed
5.2 Number of eligible responding holdings 12.689
6 Number of the records in the dataset 



(excluding holdings with only fur animals other than rabbits)


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

3.3-5. Danish questionnaire FSS 2016
3.3-5. Instructions to the farmers
3.4. Data validation
Data validation
All farms are checked whether the questionnaire is completed online, scanned or registered manually. The validation can be divided into two groups:
  • Warning checks to detect possible mistakes, for instance more than 30 horses. It is not necessarily a mistake but most likely it is.
  • Logical mistakes. It could be no working time indicated by the farmer or that the irrigated area is bigger than the irrigable area.

The questionnaires are registered and also edited in an Oracle database. There are about 150 validation rules where some are meant as warning rules and some are meant as “serious” mistakes meaning the concerned problems have to be solved before the questionnaire can be accepted. In addition to these rules some ad-hoc controls might be carried through.

The tools used in the validation process are Oracle, SAS and Excel.

All the validation work is made at Statistics Denmark. The final survey register is stored at the PC network of Statistics Denmark. It is also the case for the special version of the survey register which Eurostat receives.

3.5. Data compilation
Methodology for determination of weights (extrapolation factors)
1. Design weights
Design weights are calculated using the usual approach as noted (the inverse of the inclusion probabilities). 
2. Adjustment of weights for non-response
Adjustment for non-response was done combined with calibration to known totals as described in item 3 below. No response homogeneity groups were formed. 
3. Adjustment of weights to external data sources
When calculating the extrapolation factors three different external sources have been used:


1) IACS: The target values concern farms applying for subsidies only.

Target values for extrapolation of the Danish FSS, farms based on IACS

Farms applying for subsidies
Size of UAA Number of farms UAA, ha
0,1-4,9 ha * 194 542
5,0-9,9 7 340 53 438
10,0-19,9 6 262 90 339
20,0 - 29,9 ha 3 484 86 082
30,0 - 49,9 ha 3 872 151 202
50,0 - 99,9 ha 4 720 339 299
At least 100,0 ha 7 693 1 898 327

* But with at least 0.5 hectares with fruits, berries and vegetables.


2) Pig surveys:

The quarterly pig surveys are specially designed to cover farms with pigs and it means that the sample error for pigs is lower than in FSS. The extrapolation has thus had number of pigs in the pig surveys in April and July as a weighted average of these two surveys as the target value:

Target value for extrapolation of the Danish FSS, based on the pig surveys

   Number of pigs Number of sows
Pig survey, April   2016, weight 2/3 12 418 000 1 000 000
Pig survey, July 2016,   weight 1/3 12 313 000 998 000
Target value for FSS 12 383 000 1 183 330

The target value for sows exclude young sows which the farmer have planned to use for breeding but which have not yet been inseminated. 

The April survey has been given a higher weight since it is a bit closer to FSS in May and also because the farmers in FSS who took part in the pig survey in April did not have to report pigs also to FSS.  


3) Fur animals from The Danish Fur animal Farmers Association:

All farmers with fur animals are assumed to be members of the association, which collects number of minks and other fur animals from the members. The association sends every year a register with individual farm information to Statistics Denmark.

Target values for extrapolation of the Danish FSS, based on information from the fur Farmers Association 

Target value, number of fur animals 3 268 948
Target value, farms with fur animals 1 438

It should be noticed that the calibration method used may lead to extrapolation factors smaller than one.

4. Any other applied adjustment of weights
Nothing to remark 
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 
There are no sections on the two questionnaires with questions collected exclusively for national reasons but in certain cases some details exist not required by the regulation. In the following certain examples are given:
  • Crops: The Danish survey collects information on winter crops and spring crops for wheat, barley and rape.
  • Pigs: There are 10 categories of pigs where the regulation demands 3 categories only.
  • Cattle: There are 12 categories of cattle where the regulation only requires 7.
  • Poultry: There 8 categories on the Danish questionnaire where the regulation requires only 3.
  • Fur animals: The Danish questionnaire has from 2010 onwards had a question on fur animals where the regulation only requires a yes/no question on “other animals".
  • Other animals: For sheep, goats, horses, rabbits and bee hives the Danish questionnaire is identical with the regulation requirements.
  • The survey in 2016 had questions on production and areas with vegetables and strawberries.

The users are in particular EU, the ministries, farmer’s organisations, but also students and interested people in general. EU uses the statistics as a tool in the planning of the common agricultural policy.

The farm structure survey is discussed at meetings in user board on agricultural statistics. The members may put forward suggestions to new questions on the questionnaire as well new statistical tables for publication. This led for instance to that we in FSS 2017 had some simple questions on farms with use of precision technology.

5.2. Relevance - User Satisfaction

[Not requested]

Statistics Denmark has no survey regarding the user’s satisfaction with the agricultural statistics. The main impression when talking with the users is a high degree of satisfaction.

Some users often request figures for municipalities. Also some users ask for figures regarding agro environmental subjects.

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 most important source of errors is the sample error. It is rather small for the most important crops and animals at the national level but bigger for animals and crops of minor importance and for regional figures.

Together with normal routines of data validation the quality of the FSS statistics could be assumed to be rather good.

6.2. Sampling error
Method used for estimation of relative standard errors (RSEs)
The standard errors for the estimates are calculated using standard theory for finite population sampling. The estimation is model assisted and utilizes known marginal totals of farmland area and number of units. The actual calculations are done using CLAN software (courtesy of Statistics Sweden).
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
There are no cases where precision requirements are applicable and RSEs exceed thresholds.

6.2.1-1. Sample errors, Danish FSS 2016
6.3. Non-sampling error

See below

6.3.1. Coverage error
1. Under-coverage errors
The procedures for register updating applied prior to the survey and described in concept 2.6 Statistical population - item 2. are assumed to secure an almost perfect coverage. It is hardly likely that there could be any farm of importance:
a) Not included in IACS as an applicant of farm subsidies.
b) Not included in the livestock register with number of animals over the survey thresholds.
c) Not included in the business register with an agricultural NACE code. 


2. Over-coverage errors
398 farms were included in the Danish sample with no other agricultural activities than fur animals other than rabbits.  

352 farms were small meaning that their crops and livestock are lower than the thresholds mentioned in concept 2.6 Statistical population - item 2. They have completed the questionnaire on equal foot with all other farms but they are not a part of publication nor are they sent to Eurostat. Likewise no extrapolation is calculated for these survey units. Of these small farms:

  • 88 have cattle
  • 12 have pigs
  • 51 have poultries
  • 43 have sheep
  • 16 have goats
  • 76 have horses
  • 147 have no livestock but only a small agricultural area
2.1 Multiple listings 
No such problem exists in the Danish FSS.


3. Misclassification errors
No such problem exists in the Danish FSS. 


4. Contact errors
Farmers having completed the questionnaire may be contacted by phone to clarify questionable cases. Approximately 3 percent of the farmers are contacted again. 


5. Other relevant information, if any
Not available. Over-coverage - rate
Over-coverage - rate
The population prior to the farm structure survey 2016 was 37.161 farms which based on available information were active in agriculture in April 2016.

But when completing the farm structure survey the figure for the number of farms was 35.669 (including the holdings with only fur animals other than rabbits).

Thus the over coverage could be estimated to 37.161-35.669=1.492 farms or 4.0 percent. Common units - proportion

[Not requested]

6.3.2. Measurement error
Characteristics that caused high measurement errors
There is no information on measurement errors regarding the farm structure survey 2016.

The most difficult questions might concern the labour force section of the questionnaire, more specific work time of the farmer and his wife. It is of course not so difficult for those working full time on big farms. Neither is it so difficult for owners of very small farms working only a few hours per week to indicate "less than 1/4 of full time". The problem concerns farmers working less than full time but on the other hand more than just a few hours per week and maybe with varying work time over the season. For them it can be difficult to choose the correct of the three "in between working times".

6.3.3. Non response error
1. Unit non-response: reasons, analysis and treatment
The reason for non-response is that it is rather time consuming to contact all farmers over and over again until all or at least almost all farmers have completed the questionnaire. Unlike previous surveys Statistics Denmark has no longer the necessary resources and thus we need to accept a certain amount of non-response.  

The farm structure survey 2016 had a non-response of about 1.000 farms or approximately 7 percent of the sample. A non-response farm is in principle treated as if it never had been selected whereby the extrapolation should be increased in the concerned stratum.
The extrapolation has certain targets which should be obtained based on IACS, pig surveys and information from the fur animals farmer’s organisation.

No analysis of non-response farms has been made.


2. Item non-response: characteristics, reasons and treatment
Statistics Denmark chose to conduct a sample survey on SAPM characteristics in 2011 after the total FSS in 2010.  

In 2011 all in all 7 552 or a bit more than 50 percent did not respond to at least one SAPM question but completed the questionnaire otherwise. The missing information was imputed by means of imputation. 

No imputation work was done like this in the Danish FSS 2016. It might happen that a farmer does not complete the questionnaire in all details. Most often this is rather harmless:

  • A farmer does not answer the section on irrigation but he lives in a region where almost no farmer irrigates so obviously the answer is simply “no irrigation”. 
  • A farmer does not answer the section on greenhouses. But since he does not use to have greenhouse crops and since most farmers do not have greenhouse crops the answer is obviously no. 
  • Also incomplete labour force information is often rather easy to correct by means of this manual imputation. For instance it is quite obvious that the holder of a big farm works full time and cannot have a work outside the farm. Likewise a young holder of a small farm most likely has the lowest work time and a work outside the farm.  

Some cases are more serious:

  • A farmer known to have pigs does not indicate number of pigs. 
  • A farmer known to have poultries forgets to answer the section on poultries. 
  • A farmer known to have greenhouses forgets to indicate his greenhouse crops. 

When it comes to pigs we can often take the figures from the closest pig survey. But otherwise it is necessary to call the farmer.

It is not possible to identify any specific reason for item non-response other than normal carelessness. Unit non-response - rate
Unit non-response - rate
The unit non-response rate is about 7% in FSS 2016.

A re-weighting method is used in the Danish FSS. See item 3.5-2 for more information. Item non-response - rate
Item non-response - rate
We have no exact figures but a minor part of the questionnaires is subject to manual imputation (see above concept 6.3.3. Non response error - item 2 and item 6.3.4-1) and about 3% of the farmers are contacted on the phone. 
6.3.4. Processing error
1. Imputation methods
Manual imputation is made for approximately 10% of the farms. 

When it comes to pigs we can often take the figures from the closest pig survey. But otherwise it is necessary to call the farmer.


2. Other sources of processing errors
There is no certain information on to what extent processing errors are caused by the different types of data collection. We do not bookkeep errors found and corrections made so when a survey is finalised we cannot know if one specific farm was perfectly in order right from the beginning or if it during the survey work was subject to one or more corrections.

However, some qualitative assessments could be made:

1) About 90 pct. of all farmers complete the questionnaire online and here chances of a mistake caused by free completion are good since the farmers have to answer all questions. For instance he has to answer yes/no to whether he has sheep or not, and if he answers yes he has to indicate at least one sheep. The same rule applies to the other categories of livestock. Likewise he has to report his work time on the farm and also his wife’s work time (or answer that he has no wife or that his wife does not take part in the farm work). It means all in all that the completion of the questionnaire at least is consistent from a logical point of view but of course there is no guarantee that farmers could not by mistake write for instance 54 sheep instead of 45 sheep.               

The remaining 10 of the farms complete the questionnaire in other ways:

2) Some might call by phone asking for help to complete the questionnaire and we then almost always fill in the questionnaire immediately by interviewing the farmer. Also here, chances of a mistake caused by free completion are good since the interviewer knows the questionnaire and can secure that no questions are forgotten.

3) The remaining farms have asked for a paper version of the questionnaire to fill in the traditional way. Here risks of forgetting answering some questions evidently exist.

We have no certain information on the distribution of farms between group 2 or 3). A qualified guess is fifty fifty.  


Since the questionnaire did not contain many new items compared to the questionnaire in previous years we did not face any major problems, like for instance a huge number of farmers who misunderstood one or more questions. So only well-known measures were taken:

- Recontact to approximately 3 percent of the farmers by means of telephone calls.

- Manual imputation of about 10 percent of the questionnaires. (Qualified guess)

- The non-response was about 7 percent compared to 5 percent in 2013. 


3. Tools used and people/organisations authorised to make corrections
All colleagues who work on FSS have an authority to correct survey information, for instance after having been in telephone contact with a farmer - all in all 5 colleagues. Imputation - rate
Imputation - rate
A minor part of the farms have been subject to a certain degreee of manual imputation.

Manual imputation is made for approximately 10% of the farms. 

6.3.5. Model assumption error

[Not requested]

6.4. Seasonal adjustment

[Not requested]

6.5. Data revision - policy
Data revision - policy
Not relevant for FSS 2016 since there is no provisional version of the survey. If major mistakes are found after first publication they will of course be corrected and the correction will be announced on our homepage. No such thing happened to the Danish FSS 2016. 

Statistics Denmark has no special revision policy and practice differs from statistics to statistics.  

6.6. Data revision - practice
Data revision - practice
There was only one publication of FSS 2016 and thereby no revisions. 
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
There is no provisional publication. 
7.1.2. Time lag - final result
Time lag - final result
The survey was published, May 22 2017, about 12 months after the reference day and 5 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 Danish FSS 2016 was published May 22 2017, a little bit more than one year after the reference day May 13 2016. This publication was as scheduled. 

8. Coherence and comparability Top
8.1. Comparability - geographical
1. National vs. EU definition of the agricultural holding
Denmark includes farms with fur animals – unlike most other EU countries. Denmark has about 1.400 farms with fur. Of these about 600 have no other agricultural activities, neither other livestock nor crops. Minks are of big economic importance in Denmark. About 6 % of the Danish agricultural output comes from minks in 2015. This activity is far more important than for example poultry, sheep and goats. 


2.National survey coverage vs. coverage of the records sent to Eurostat
The individual farms sent to Eurostat are exactly the same as the farms used for the national publication. However, Eurostat deducts 398 farms with fur animals as the only agricultural activity from the Danish sample. These 398 farms corresponds to 622 when extrapolated.


3. National vs. EU characteristics
When designing the questionnaire we have used the handbook on definitions, rev. 10.

The Danish questionnaire on work time has these five categories:


Work time category Text on Danish questionnaire One person with this work time   corresponds to:
Full time At least 37 hours/week 1.000 AWU
¾- < 1 full time 27-36 hours/week   0,875 AWU
½ - < ¾ full time 19-26 hours/week 0,625 AWU
¼ < ½ full time 9-18 hours/week 0,375 AWU
< ¼ full time 1-8 hours/week 0.125 AWU


So hours per year play no role when calculating AWU but it could be assumed that a person working exactly on full time has 7.4*225=1 665 hours per year.


4. Common land
4.1 Current methodology for collecting information on the common land
In Denmark common land is assumed not to exist. All land is owned by somebody. In a few cases an area of agricultural land could be owned by for instance a municipality and if so the person who takes care of the land is assumed to be the manager. Common land has never been covered by a farm structure survey in Denmark. 
4.2 Possible problems encountered in relation to the collection of information on common land and possible solutions for future FSS surveys
No common land in Denmark. 
4.3 Total area of common land in the reference year
No common land in Denmark. 
4.4 Number of agricultural holdings making use of the common land or Number of (especially created) common land holdings in the reference year
No common land in Denmark. 


5. Differences across regions within the country
Not applicable.


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
The Danish legislation on organic farming is in perfect accordance with Regulation 834/2007. 
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
Over time the definition of a farm is unchanged as a technical and economic unit producing agricultural products. However, see item 2. below on the new thresholds in 2010 and the inclusion of farms with fur animals in the Danish FSS.


2. Possible changes in the coverage of holdings for which records are sent to Eurostat
In 2010 Statistics Denmark introduced the new thresholds of Regulation (EC) No 1166/2008. At the same time we included farms with fur animals other than rabbits in FSS. It means that the number of farms in 2010 is 1.200 higher than otherwise would have been where 800 farms could be attributed to farms with fur animals other than rabbits and 400 to small farms (but now just big enough to be included). The impact on figures for crops and livestock is as good as negligible.

It means that Denmarks finds itself in situation  “There have been some changes but not enough to warrant the designation of a break in series”.

Otherwise the users can find figures from 1982-2016 at a comparable level.

In the Eurostat publication farms with fur animals other than rabbits as the only agricultural activity are not included.


3. Changes of definitions and/or reference time and/or measurements of characteristics
No such changes have taken place. 


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


5. Common land
5.1 Possible changes in the decision or in the methodology to collect common land
Not applicable.
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 35 047 38 284  -8.5   
Utilised agricultural area (ha) 2 614 597  2 619 338  -0.2   
Arable land (ha) 2 361 196  2 397 225  -1.5   
Cereals (ha) 1 466 687  1 434 781  2.2   
Industrial plants (ha) 165 427  175 702  -5.8   
Plants harvested green (ha) 510 907  565 725  -9.7   
Fallow land (ha) 33 936  26 239  29.3  It is almost exclusively due to an increase in fallow land without subsidies of about 10.000 hectares where fallow land with subsidies has remained more stable in the years after the abolition of the arrangement with compulsory set aside in 2008.
Permanent grassland (ha) 225 620  195 484   15.4  It is difficult to give any explanation for this development. It seems to be in line with the normal choice of the farmer's crop plan. Probably there has always been some uncertainty when it comes to distinguishing between permanent grass and grass in rotation.
Permanent crops (ha) 27 781  26 629  4.3   
Livestock units (LSU) 4 128 341  4 133 389  -0.1  
Cattle (heads) 1 568 288  1 614 644  -2.9   
Sheep (heads) 147 209  151 300  -2.7   
Goats (heads) 13 023  12 090  7.7   
Pigs (heads) 12 383 000  12 075 750  2.5   
Poultry (heads) 18 507 220  19 431 441  -4.8   
Family labour force (persons) 47 534  53 633  -11.4  It reflects a tendency towards bigger and more professional farms; when the number of farms decreases some of the disappeared farms will also be farms where the farmer had assistance from their wives.  
Family labour force (AWU) 24 739 28 019 -11.7 
Non family labour force regularly employed (persons) 25 783  25 952  -0.7   
Non family labour force regularly employed (AWU) 22 838 23 074 -1.0   


8.2.1. Length of comparable time series

[Not requested]

8.3. Coherence - cross domain
1. Coherence at micro level with other data collections
No checks with other sources are made at micro level. The reason is that if such a source existed - highly comparable with certain FSS questions - we would delete these questions from the questionnaire as we would now have another source of data collection for these questions. That is what we do for crops (IACS) and cattle (livestock register).  


2. Coherence at macro level with other data collections
It is not possible to conduct a check with other sources. A real check requires that one or more independent sources exist which are highly comparable with FSS. This would be the case if we had had crops and cattle on the questionnaire in the traditional way. If so we could compare FSS with IACS and the livestock register. But as already described we have long time ago removed crops and cattle questions from the questionnaire and implemented IACS and the livestock register as sources for collecting figures for crops and cattle. It means that when comparing FSS with these two administrative registers it merely serves as a check to secure that categories are not confused – wheat has become barley and vice versa. We do in fact make such a check but evidently it says nothing about data reliability.

If categories in fact are not confused the FSS compared with IACS/the livestock register must be expected to be very close to each other but not necessarily identical for these reasons:

1) FSS is a sample survey and any result is subject to sample errors, also characteristics collected from an administrative register.

2) The thresholds used in FSS do not apply to IACS. It works in the direction of making IACS/livestock register results a little bit bigger than FSS results since IACS/livestock register include some small farms not covered by FSS. In 2016 IACS had 40.087 applicants for single payments. FSS had 35.669 farms published nationally and including farms with only fur animals other then rabbits. 

3) It is perfectly possible for a farmer not to apply for subsidies but still be covered by FSS. However, for farms growing cereals and other normal agricultural crops it is extremely rare not to apply for subsidies.       

The closest we come at a check is when comparing FSS with a recent pig survey:


  FSS 2016, May 13 Pig survey, July 2016  Difference, percent
Pigs, total 12 383 000 12 252 000 1.1
Piglets (C_4_1) 4 369 411 4 277 000 2.1
Sows for breeding (C_4_2) 1 183 331 1 188 000 -0.4
Other pigs (C_4_99) 6 830 257 6 787 000 0.6

Both surveys are sample surveys and are thus subject to normal sample errors. The difference is biggest for piglets but still no more than 2.1 percent.  

Also the Danish FSS is comparable with the FADN statistics as far as the farm concept is the same. BUT FADN has a higher threshold, namely 10.0 hectares of agricultural land or a standard output of 15 000 euros and excludes about 6.500 farms from FSS.

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
Our homepage is the most important channel of publication. It is available free of charge for everybody having access to the Internet, and the user can choose between Danish and English text.

The principle is that as soon as a survey is ready for publication Statistics Denmark will publish a small newsletter of two pages with a few main results, and with focus on a particularly interesting development. The Danish FSS 2016 was published May 22 2017. At the same day detailed figures were also published on the Internet with for instance regional figures. The newsletter focused on farmer by type of ownership.

Figures from FSS 2016 were also published in Statistical Yearbook 2017 (published April 2017; the English version is only published online: and Statistical Ten years Review 2017 (published August 2017).


2. Date of issuing (actual or planned)
The Danish FSS 2016 was published May 22 2017. At the same day detailed figures were also published on the Internet with for instance regional figures.

Statistical Yearbook 2017 (published April 2017)

Statistical Ten years Review 2017 (published August 2017)


3. References for on-line publications
Analysis on types of ownership, only available in Danish:

Statistical Yearbook 2017:

9.3. Dissemination format - online database
Dissemination format - online database
Our homepage is the most important channel of publication.

Here are a few examples of the statistical tables on FSS:

- [Farms](
- [Labour](
- [Crops](
- [Livestock](

9.3.1. Data tables - consultations
Data tables - consultations
Not relevant for the farm structure survey. 
9.4. Dissemination format - microdata access
Dissemination format - microdata access
Researchers can obtain access to the surveys but only as anonymous information. If a researcher publishes statistical tables based on the individual information it must take place in agreement with Statistics Denmark. It is an extremely rare case that a researcher requests micro data from FSS. 
9.5. Dissemination format - other

[Not requested]

9.6. Documentation on methodology
1. Available documentation on methodology
Online documentation of the Danish FSS:


2. Main scientific references
Not available.
9.7. Quality management - documentation
Quality management - documentation
The quality of the Danish FSS is described in our national system of quality reports with text in both Danish and English:

[Agricultural and horticultural survey](

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
Farmers having taken part in the pig survey in April do not need to report pigs to the FSS only one month later. But they have to complete all other parts of the FSS questionnaire. 

11. Confidentiality Top
11.1. Confidentiality - policy
Confidentiality - policy
Individual information from surveys is treated strictly confidential. This is also the case for FSS.

In practice it means that only a few colleagues at Statistics Denmark have the right to access the FSS survey registers.

Such a practice is in line with principle 5 in European Statistics Code of Practice on statistical confidentiality.

11.2. Confidentiality - data treatment
Confidentiality - data treatment
All data sets regarding FSS are stored at a special library on Statistics Denmark’s computer network as SAS data sets going back to 1982. Only authorised colleagues can access the individual farm information. The surveys are delivered to the Danish National Archive, which keeps the information as strictly confidential for 80 years.

When designing statistical tables the aim is to secure that no table cells contain very few farms. There are no exact rules regarding "very few farms".

12. Comment Top
1. Possible improvements in the future
When it comes to question on use of water it is absolutely necessary to accept “round figure” answers. Many farmers easily understand this but others do not feel comfortable about giving “incorrect” information. So the task of the interviewer and designer of the questionnaire with instructions is to encourage the farmers to give as good information as possible for questions where it is not realistic to demand strictly exact information. 


2. Other annexes
Not available.

Related metadata Top

Annexes Top