Farm structure (ef)

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

Compiling agency: Institut national des statistiques et des études économiques (STATEC), Luxembourg

Time Dimension: 2016-A0

Data Provider: LU1

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

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1. Contact Top
1.1. Contact organisation

Institut national des statistiques et des études économiques (STATEC), Luxembourg

1.2. Contact organisation unit
ENT3 “Statistiques structurelles d’entreprises”
1.5. Contact mail address
B.P. 304, L-2013 Luxembourg


2. Statistical presentation Top
2.1. Data description
1. Brief history of the national survey 
A yearly Farm Structure Survey has been organised in Luxembourg since 1953. This survey is also used to meet the requirements of the European FSS. Until 2009 included, this survey was organised as a census with an indirect collection mode (i.e. via municipalities). As from the reference year 2010, Luxembourg organised a direct data collection to satisfy the requirements of the 2008 FSS Regulation (EC) no 1166/2008.

The institutes involved in the data collection process are:

- STATEC, as coordinator and compiler for the micro-data;

- Service d'Economie Rurale (SER), as provider for both direct survey and administrative sources data.

 

2. Legal framework of the national survey 
- the national legal framework No specific framework applicable in 2016. As of 2016, the data collection process is entirely ensured by SER.
- the obligations of the respondents with respect to the survey No statistical obligation for respondents. Nevertheless, unit non-response is low.
- the identification, protection and obligations of survey enumerators Not applicable because the survey was organised by mail.
2.2. Classification system

[Not requested]

2.3. Coverage - sector

[Not requested]

2.4. Statistical concepts and definitions
List of abbreviations
FEADER - Fonds européen agricole pour le développement rural

FEAGA - Le Fonds européen agricole de garantie

IACS - Integrated Administration and Control System

LSU - Livestock unit

MAFEA - Database of the Ministry of agriculture on the payments made to farmers and other beneficiaries eligible for FEAGA or FEADER

SER - Service d'économie rurale

UAA - Utilised agricultural area

2.5. Statistical unit
The national definition of the agricultural holding
There are no differences between the national and the EU definition in terms of activities. Holdings maintaining land in good agricultural and environmental conditions and with no other agricultural activities are also covered.

Excerpts in French from the modified law as of April 18, 2008 on the revision of rural development support:

(1) Au sens de la présente loi, la notion d'exploitant agricole couvre l'ensemble des activités des agriculteurs, viticulteurs, éleveurs, arboriculteurs, horticulteurs, pépiniéristes, jardiniers, maraîchers, pisciculteurs, sylviculteurs et apiculteurs.

(2) Par exploitation agricole, on entend une unité technico-économique à caractère agricole gérée distinctement de toute autre, disposant d'un ensemble de moyens humains et matériels, et comprenant en propriété ou ayant à sa disposition permanente, notamment par voie de location, tous les moyens de production nécessaires permettant d'en assurer une gestion indépendante, dont notamment les bâtiments, les machines et les équipements et exploitant au minimum 3 hectares de terres agricoles ou 0,10 hectare de vignobles ou 0,50 hectare de pépinières ou 0,30 hectare de vergers ou 0,25 hectare de maraîchages.

(3) Par association d'exploitations agricoles, on entend la fusion de deux ou plusieurs exploitations agricoles. (...)

(4) Par entreprise, au sens de la présente loi, on entend un ensemble de moyens humains et matériels concourant, sous une direction économique, à la réalisation d'un objectif économique."

2.6. Statistical population
1. The number of holdings forming the entire universe of agricultural holdings in the country
The final list of agricultural holdings provided by SER and used by STATEC as a basis to determine the target population contained 2 122 registered agricultural holdings with a total utilised agricultural area (total UAA) of 130 723 hectares.

 

2. The national survey coverage: the thresholds applied in the national survey and the geographical coverage
The questionnaire was sent to all the agricultural holdings by SER in March 2016.

Not all agricultural holdings in that list are considered to be agricultural holdings as specified by the Regulation definition. Only those agricultural holdings which met any of the following criteria at the reference date were obliged to respond to the survey:

- at least 3 hectares of utilised agricultural area (A_3_1), i.e. arable land, permanent grassland, permanent crops, kitchen gardens, or

- at least 0.25 hectares of fresh vegetables, melons and strawberries (B_1_7), flowers and ornamental plants (B_1_8), or

- at least 0.30 hectares of fruit and berry plantations (B_4_1), or

- at least 0.50 hectares of nurseries (B_4_5), or

- at least 0.10 hectares of vineyards (B_4_4), or

- at least 10 horses/donkeys (subset of C_1) or 10 bovines (C_2) or 20 sheep (C_3_1) or 20 goats (C_3_2) or 50 pigs (C_4) or 10 breeding sows (C_4_2) or 1 000 poultry (C_5*) or 1 000 rabbits (C_6) or 50 beehives (C_7).

Holdings with less than 3 hectares of utilised agricultural area don’t have to answer if they hold woods or bushes or if they keep riding horses or fatten pigs for their own consumption or cultivate vegetables, strawberries and so forth for their own consumption, unless they exceed any of the above thresholds.

The survey is carried through at the headquarters of the holding. It is considered that the whole surface of the agricultural holding resides in the municipality where the headquarters are located, even if the surfaces are all in another municipality or outside the national borders. This is also true concerning geolocalisation.

Surfaces on lease are not indicated by the owner but by the tenant.

The above definition has not changed in 2016.

ad (*) Please note that even though the survey form specifies a 1 000 threshold for either laying hens and other poultry, the actual implementation is applied on administrative data with a single threshold of 1 000 poultry. Any missing survey data are dealt with appropriate imputation strategies.

 

3. The number of holdings in the national survey coverage 
The final target population comprised 1 965 agricultural holdings for the reference year 2016 defined in conformity with the Regulation.

 

4. The survey coverage of the records sent to Eurostat
No difference.

 

5. The number of holdings in the population covered by the records transferred to Eurostat
No difference.

 

6. Holdings with standard output equal to zero included in the records sent to Eurostat
No such records.

 

7. Proofs that the requirements stipulated in art. 3.2 the Regulation 1166/2008 are met in the data transmitted to Eurostat
The target population (1 965 agricultural holdings) as defined by national criteria represents 99.44% of total UAA and 99.85% of total LSU.

 

8. Proofs that the requirements stipulated in art. 3.3 the Regulation 1166/2008 are met in the data transmitted to Eurostat
Refer to point 7.
2.7. Reference area
Location of the holding. The criteria used to determine the NUTS3 region of the holding
Not applicable because of the single NUTS3 region for Luxembourg.

The survey is carried through at the headquarters of the holding. It is considered that the whole surface of the agricultural holding resides in the municipality where the headquarters are located, even if the surfaces are all in another municipality or outside the national borders. This is also true concerning geolocalisation.

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 reference date for the national characteristics was April 1, 2016.

Even though land is measured at the reference date April 1 2016, the measurement covers the crop year is the 12 months period covering the whole cultivation period (from seed to harvest) of the main arable crops cultivated in Luxembourg. The cultivation period lasts from autumn 2015 to autumn 2016. It lasts from  September 1, T to August 31, T+1.

Labour characteristics refer to the period of 12 months preceding the reference date and rural development characteristics refer to the 3 years period from 2014 until 2016 included.

The reference date is fully in line with the administrative source data collection.

2.9. Base period

[Not requested]


3. Statistical processing Top
1.Survey process and timetable
The organisation of the survey 2016 started at the end of November 2015 with a user consultation (coordinated by SER) in order to update the questionnaire. These discussions ended in January 2016.

The printed questionnaires were available in the end of March 2016. SER managed the whole data collection. Surface and animal figures (except bovine livestock) proceed from the application forms for subsidies, which were sent to all agricultural holdings. Bovine livestock figures proceed from the identification and registration system SANITEL. Data on organic farming exist in the Organic farming register and data on rural development are provided by the Ministry of Agriculture MAFEA database (payments supporting rural development). These data were merged and made available to STATEC as a single source to minimise the statistical burden on agricultural holdings. SER covered the characteristics related to other gainful activities, labour force and agricultural production methods through a direct survey.

In the mid of April 2016, the agricultural holders received the SER direct survey questionnaire (reference date April 1, 2016) at the same time as the SER/IVV application forms for subsidies (reference date April 1, 2016). The web forms (provided by STATEC's IT department) were ready to use before the paper questionnaires were sent. The survey questionnaires were sent back by the agricultural holdings to IVV (winegrowers) or SER (all other agricultural holdings) during the period covering April until September 2016.

As a transitory solution, data entry of survey questionnaires was done by SER staff as from July 2016 onwards using the web form.

In autumn 2016, the administrative sources obtained through SER were combined with the survey data using an official common identifier to form a combined micro data set. Micro data validation was performed at STATEC to check the accuracy and plausibility of both the survey and the administrative data. Analysis was also performed at a national aggregate level with historical data. Moreover, SER assisted the data validation by providing feedback on the aggregated data at national level for the purpose of the production of economic accounts of agriculture.

While data entry was achieved in the end of 2016, the data validation process was only completed by the end of the year 2017, this delay being mainly caused by both the partial overhaul and late availability of Eurostat's transmission format for quite a few characterstics (notably OGA variables).

The final target population comprised 1 965 agricultural holdings for the reference year 2016 defined in conformity with the Regulation and was obtained from the database of agricultural holdings managed by SER.

Item non-response was addressed using cold-deck imputation, hot-deck imputation and, to a minor extent, ratio imputation as well as deductive imputation – the type of imputation applied depending on the nature of the variables to be imputed. Imputation was also extended to unit non-response for regulatory reasons. Meanwhile, the transmission format, i.e. the conversion program of national variables into the characteristics defined in the Regulation was updated in spring and autumn 2017.

In December 2017, the first batch of micro-data established in conformity with the farm structure characteristics (including rural development) defined in the Regulation was transmitted to Eurostat via the Edamis platform.

 

2. The bodies involved and the share of responsibilities among bodies
The actors involved in the survey organisation are:

- STATEC as a coordinator to ensure the achievement of the data collection;

- SER as the data provider and the national producer of economic accounts of agriculture.

SER is responsible for all the variables. Nonetheless, the cooperation and coordination goes beyond the data collection. Both actors meet regularly every year.

 

3. Serious deviations from the established timetable (if any)
None to report.
3.1. Source data
1. Source of data
The survey design was based on a census data collection based on a survey conducted by SER for the characteristics related to other gainful activities, labour force, and agricultural production methods, complemented with administrative data for the rest of the characteristics.

 

2. (Sampling) frame
Agricultural holdings were based on the database of agricultural holdings managed by SER.

The type of frame was a list with administrative source data.

The initial frame referred to March 2016. The time frame of the administrative source data collection was April 2016. The final updated frame for FSS 2016 was in October 2017. 

 

3. Sampling design
3.1 The sampling design
Not applicable.
3.2 The stratification variables
Not applicable.
3.3 The full coverage strata
Not applicable.
3.4 The method for the determination of the overall sample size
Not applicable.
3.5 The method for the allocation of the overall sample size
Not applicable.
3.6 Sampling across time
Not applicable.
3.7 The software tool used in the sample selection
Not applicable. 
3.8 Other relevant information, if any
Not applicable.

 

4. Use of administrative data sources
4.1 Name, time reference and updating
The characteristics of the sections A_2, E, F, most of M, as well as characteristic A_3_3_2 of the FSS have been obtained directly from the holders whereas the characteristics of the remainder of section A and of the sections B, C and G were taken from administrative sources. The administrative data were gathered by the SER and provided to the STATEC.

The administrative data sources used are the following:

- Integrated administration and control system (IACS);

- Bovine register (SANITEL);

- Database of the Ministry of agriculture on the payments made to farmers and other beneficiaries eligible for FEAGA or FEADER (MAFEA);

- Organic farming register.

 

IACS:

EU-legislation: Regulation (EU) 1306/2013 of the European Parliament and the Council on the financing, management and monitoring of the common agricultural policy;

National legislation: Règlement grand-ducal du 30 juillet 2015 portant application au Grand-Duché de Luxembourg de règles communes relatives aux paiements directs [...] et au soutien au développement rural.

Time reference: crop year for crop productions, 1st April 2016 for livestock.

Please note that the crop year is the 12 months period covering the whole cultivation period (from seed to harvest) of the main arable crops cultivated in Luxembourg. It lasts from  September 1, T to August 31, T+1. As the crop year goes over 2 civil years, by convention the year under which the information is stored in IACS is the year of harvest;

Updating of the source: annually.

SANITEL:

EU-legislation: Regulation (EC) 820/97;

National legislation: Règlement grand-ducal du 22 avril 1999 portant mesures d’application du règlement (CE) 820/97.

SANITEL is a permanently updated database. The bovine livestock can be derived from SANITEL at any date.

MAFEA:

EU-legislation: Regulation (EU) 1306/2013 of the European Parliament and the Council on the financing, management and monitoring of the common agricultural policy;

National legislation: Règlement grand-ducal du 30 juillet 2015 portant application, au Grand-Duché de Luxembourg de règles communes relatives aux paiements directs [...] et au soutien au développement rural;

This database is fully integrated with the IACS database.

Time reference: an extraction of the payments made to farmers can be derived from MAFEA. For FSS, it is checked whether there was a payment or not for at least one of the 3 preceding harvest years;

Updating of the source: permanent.

Organic farming register:

EU-legislation: Regulation (EC) 834/2007.

This register is permanently updated and is fully compatible with IACS (same identification of the units). The information on the status of the holdings concerning organic farming at a reference day (1st of April) is uploaded in IACS yearly. The register of agricultural holdings certified or under certification in organic farming is managed by the Administration des Services Techniques de l’Agriculture (ASTA), an administration of the Ministry of agriculture.

4.2 Organisational setting on the use of administrative sources
According to article 13 of STATEC's law, all individuals or legal entities are obliged to provide the statistical information requested by STATEC within preset deadlines.

The statistical department of SER has the possibilities to specify its statistical needs with the SER team in charge of the administrative source.

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, Organic Farm Register and MAFEA: The definition of the unit (agricultural holding) corresponds to the definition of the statistical unit in FSS.

SANITEL: The reporting unit is the holder of the animals. A permanent mutual updating between IACS and SANITEL concerning the list of reporting units (holding/holder) exists.

none not applicable
- coherence of definitions of characteristics IACS and MAFEA:

There are no differences in the definition of the characteristics between administrative source and FSS as the statistical division of SER is involved in the preparation of the data collection process from the beginning on.

SANITEL:

The characteristics requested in FSS are calculated/deducted from the characteristics registered in SANITEL per individual animal (date of birth, sex, age of first calving, date of slaughtering, etc).

Organic farming register:

A list of organic farms is extracted from this register. For the units within the scope of FSS, all the characteristics from other administrative sources and survey results are considered as “organic”. This is possible because in Luxembourg, a holding, if it seeks cofinancing through subsidies, can only be 100% organic or not at all. Hence, there are no holdings with partial organic production.

 none  not applicable
- coverage:      
  over-coverage   IACS and MAFEA: Due to the application of the thresholds in FSS, there is over-coverage. 

Organic farming register: This register also covers farms that don’t belong to the scope of FSS, so there is over-coverage. 

SANITEL covers all units holding bovine animals.

All sources: The out-of-scope units are excluded.
  under-coverage IACS and MAFEA: The register of agricultural holdings of the Ministry of Agriculture is complete for all holdings applying for payments within CAP or which have to be officially registered at the Ministry of Agriculture following the EU and national legislation (for example bovine register, food safety, animal welfare, ….). IACS and MAFEA: Not all the units of the population to be covered by FSS apply for payments within CAP and thus no IACS or MAFEA data are available for these units. IACS and MAFEA: The units not covered are addressed with a special questionnaire containing the characteristics needed.
SANITEL: It is excluded that events concerning live bovine animals are not reported to SANITEL. At the end of life (slaughterhouse , rendering plant) or at the export of each animal the official documents (passport, ear tags) are subject to official controls and in case of missing documents, the different events in the life of the animal are retraced. Sanctions are applied, for instance through cross compliance applicable to the direct aids of the common agricultural policy, to the holder of the animal in case of missing official documents. SANITEL and Organic farming register: No under-coverage.  
  misclassification   No such issue.  
  multiple listings   Normally no such duplicates due to a common identifier.  
- missing data   None.  
- errors in data SER is contacted by STATEC to deal with the detected issues (if any).    
- processing errors   None detected.  
- comparability Only historical comparisons. No major issues.  
- other (if any)   No other drawbacks.  

 

4.5 Management of metadata
Performed by SER and agreed with STATEC to ensure a smooth sharing of data for statistical production purposes. Metadata for the sole purpose of FSS are managed in an agreed-upon table.
4.6 Reporting units and matching procedures
IACS: For the definition of the reporting unit, see article 4 of Regulation (EU) 1307/2013 of the European Parliament and of the Council establishing rules for direct payments to farmers under support schemes within the framework of the common agricultural policy.

SANITEL: The reporting unit is the holder of bovine animals. A link with the reporting unit in IACS (agricultural holding) exists. There are regular exchanges of information and permanent mutual updating between IACS and SANITEL concerning the list of reporting units (holding/holder).

MAFEA: This register is permanently updated and is fully compatible with IACS (same identification of the units). See article 4 of Regulation (EU) 1307/2013.

Organic farming register: This register is permanently updated and is fully compatible with IACS (same identification of the units). See article 4 of Regulation (EU) 1307/2013.

Common identifiers: the same across all sources.  Linking was performed using the common identifier.

4.7 Difficulties using additional administrative sources not currently used
No significant difficulties.
3.2. Frequency of data collection
Frequency of data collection
Annual
3.3. Data collection
1. Data collection modes
Mixed-mode: choice to respond via paper questionnaire (84%) or web questionnaire (16% of the target population).

 

2. Data entry modes
For the survey characteristics covered by SER, the web form was used for this task. Some application controls were configured. However, most data accuracy and plausibility tests were only performed and documented after data entry.

Electronic data for web questionnaires (16% of the target population).

 

3. Measures taken to increase response rates
The following measures were initially planned to increase response rates:

- The survey questionnaire was sent by SER at the same time as the subsidy application package. The plan was to avoid agricultural holdings having to delve twice into their reporting systems and thus to accelerate the response.

- Reminders were managed by SER for the reference year 2016. In the end, the unit non-response was only 5.24%.

No special priority was given to important agricultural holdings.

 

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

2 122
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

 not applicable
3 Number of ineligible holdings 157
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

4>4.1+4.2

 0
4.1 Number of holdings with unknown eligibility status – re-weighted not applicable
4.2 Number of holdings with unknown eligibility status – imputed
5 Number of eligible holdings

5=5.1+5.2

1 965
5.1 Number of eligible non-responding holdings

5.1>=5.1.1+5.1.2

 103
5.1.1 Number of eligible non-responding holdings – re-weighted   not applicable
5.1.2 Number of eligible non-responding holdings – imputed  103
5.2 Number of eligible responding holdings  1 862
6 Number of the records in the dataset 

6=5.2+5.1.2+4.2

1 965

 

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


Annexes:
3.3-5. FSS 2016 direct survey questionnaire FR
3.3-5. FSS 2016 direct survey questionnaire DE
3.4. Data validation
Data validation
The questionnaires are first checked manually to find the missing items and obvious errors in order to complete and correct the answers even before entering the data. The comparison with the prior year begins here. At this stage, the questionnaires are made ready for coding. Moreover, it is decided whether or not the questionnaire can be accepted as such. Sometimes, a high item non-response causes the survey manager to get back to the agricultural holding for further information.

As far as web forms are considered, tests were integrated in them to make impossible missing, inaccurate or implausible items as far as possible. The results of web forms were moved to an excel file and checked manually. In order not to render the 2016 coding too burdensome, these checks were performed ex-post, i.e. after the coding stage. Typically, the tests are written using statistics software syntax (e.g. SPSS).

As the survey FSS data did not include any surface and animal figures, arithmetic checks and ratio edits were not necessary when entering the data, but the administrative data (i.e. surfaces and animals) were checked for arithmetic, ratio and coherence issues at the integration with the survey data.

Same as above, the tests were written in statistical software.

The tools used for data validation were the Eurostat validation programme for micro-data implemented using SPSS.

Survey data and administrative data were handled by SER. The data integration as well as any data processing thereafter was performed by STATEC.

3.5. Data compilation
Methodology for determination of weights (extrapolation factors)
1. Design weights
Not applicable.
2. Adjustment of weights for non-response
Not applicable.
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 
To satisfy national needs, SER surveyed the characteristic “succession prospects” in addition to the FSS characteristics. Furthermore, other gainful activities related or not to the agricultural holding are asked for all regular personnel types. Finally, characteristics relating to agricultural production methods (including data on succession planning and milk yield) are collected in a more detailed manner to avoid lengthy definitions and to allow for better quality checks. SER is the main user of FSS.
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 main sources of error are:

- measurement errors, in particular agricultural production methods;

- non-response (5%) and the related unit imputation errors;

- item imputation errors;

- asynchronous administrative sources (minor impact).

6.2. Sampling error
Method used for estimation of relative standard errors (RSEs)
Not applicable. A census is conducted.
6.2.1. Sampling error - indicators

 

2. Reasons for possible cases where precision requirements are applicable and estimated RSEs are above the thresholds
Not applicable.
6.3. Non-sampling error

See below

6.3.1. Coverage error
1. Under-coverage errors
Under-coverage is unavoidable for demographic reasons regarding the agricultural holdings and is eliminated by reframing with the administrative data sources.

  

2. Over-coverage errors
Over-coverage is possible, however, the agricultural holding has the choice not to respond to the questionnaire under the condition that it is not in the scope of survey (cf. applicable thresholds in the survey questionnaire). Any records from responding holdings which should not be in the final target population are eliminated.
2.1 Multiple listings 
The administrative data of the SER were tested for multiple listing. Multiple listing is possible because surface data were collected on viticulture by the SER for holdings that had also other surfaces and by the Institut viti-vinicole (IVV) for holdings with only vineyards. The multiple listing issues have been solved on a case by case basis.

 

3. Misclassification errors
There were no misclassification issues.

 

4. Contact errors
Contact errors are dealt with by a research in the national register of physical and legal persons. Whenever possible, the units are recontacted with the new address. In the worst case, they are dealth with through imputation.

 

5. Other relevant information, if any
None.
6.3.1.1. Over-coverage - rate
Over-coverage - rate
The a priori probability of over-coverage (i.e. without any reframing) is approximately 5%. However, the reframing process pushes this probability practically down to 0%. A few minor cases due to asynchronous administrative sources remain but they are as already mentioned minor in terms of the key FSS characteristics.
6.3.1.2. Common units - proportion

[Not requested]

6.3.2. Measurement error
Characteristics that caused high measurement errors
The following measurement errors were detected:

- inconsistent data concerning OGA and working time;

- difficulty to measure agricultural production methods, as the data collection is separate from the official grant forms.

6.3.3. Non response error
1. Unit non-response: reasons, analysis and treatment
Had it been for reasons of bias, unit non-response would not have needed to be accounted for (unit non-response rate being equal to 5%). However, given that the Regulation requires a completely imputed micro data set in case of census data, i.e. including unit non-response, the imputation procedures had to be extended to impute the characteristics covered by the survey to entire agricultural holdings. This process was performed in 2017. The impact of this imputation was low, except for the work force characteristics.

Reasons for unit non-response

It appears that smaller agricultural holdings which receive less or no agro-environmental funding are more likely not to respond to the survey. Another explanatory factor are holdings who are classified as specialist horticulture and specialist permanent crops - these farm types respond relatively less often to the survey than other farm types. While agro-environmental payments were found to significantly explain non-response, taking into account holding size and farm type was also important to avoid any unwanted and uncontrolled side effects during imputation.

Treatment of unit non-response

First of all, unit non-response is minimised by 2 stage reminder policy, one early on after the launch of the survey (May / June of the reference year), another one upon reception of the first set of administrative data (somewhere in autumn of the reference year). In some minor cases, big agricultural holdings are contacted by telephone to encourage response. Once unit non-response remains confirmed, the fall-back strategy is imputation. Re-weighting is not an eligible strategy because of the census type survey.

 

2. Item non-response: characteristics, reasons and treatment
Identification of item non-response

The questionnaire design has been optimised since 2011 to minimise the item non-response and in the worst case to at least identify item non-response. For example, before asking about agricultural production methods, a yes/no question would capture whether or not there are any fields on which production methods could be applied. A non-response on this question or a yes-answer on this question without any subsequent answers would trigger item non-response analysis.

Reasons for item non-response

Item non-response by agricultural holdings is by far less common. Though agricultural production methods variables is typically burdened by such problems.

Another rare form of item non-response are responses invalidated by the statistical producer due to internal inconsistencies. Such responses would then be set to missing and imputed by the relevant strategy.

Treatment of item non-response

At the stage of data entry, we distinguished between two scenarios:

1) the questionnaire was insufficiently filled out for an agricultural holding in the target population: we contacted the agricultural holding either by phone or by mail to request the remaining information;

2) the questionnaire contained a few minor item non-responses. If the missing item was a structural characteristic (e.g. legal status, holding manager, etc.) and the prior year questionnaire was available, the information was directly manually imputed. This procedure was not performed for characteristics that are likely to change every year (e.g. labour force, other gainful activities, agricultural production methods, etc.).

After that, imputation procedures serve as fall-back strategy.

6.3.3.1. Unit non-response - rate
Unit non-response - rate
5.24%
6.3.3.2. Item non-response - rate
Item non-response - rate
For FSS 2016, item non-response which had to be dealt with by imputation was close to zero for the structural variables. On the other hand, agricultural production methods variables were subject to moderate non-response.
6.3.4. Processing error
1.Imputation methods
The non-response strata

The strata were defined so that they would best explain non-response. Various combinations of variables available in administrative sources, provided by SER and thus for all agricultural holdings in the target population, were tested against unit non-response using the logit classifier.

Consequently, based on our analysis conducted in 2010 and reconfirmed in 2016, the strata defined on the basis of the following ancillary information were found to sufficiently explain unit non-response:

- UAA size class: less than 10 ha, at least 10 ha ;

- 1st digit of the farm type code in reference to the typology defined in Commission Regulation (EC) No 1242/2008 ;

- agro-environmental payments (rural development support): yes, no.

The strata were used both for item non-response and then for unit non-response.

 

The imputation strategies for item non-response

Item non-response was addressed before unit not response.

A few characteristics required deductive imputation in case of item non-response. These were mainly the characteristics related to the manager in case of group holdings and legal persons but also some national characteristics.

Random hot-deck imputation was performed by predefined strata. The procedure is used for any characteristics for which there is no prior year data and no deductive imputation strategy.

 

The imputation strategies for unit non-response

At the stage of data production, we used automated rules to impute:

Cold-deck imputation was used for labour force and related characteristics. The cold-deck contained fresh 2015 survey data. Data available in 2015 were then directly imputed without any other adjustment for a given agricultural holding. This imputation procedure concerned 4% of the target population in terms of number of units;

A few characteristics required deductive imputation in case of item non-response. These were mainly the characteristics related to the manager in case of group holdings and legal persons but also some national characteristics.

Random hot-deck imputation was performed by predefined strata. The procedure was used for all characteristics other than those imputed by cold-deck or deductive imputation.

 

2. Other sources of processing errors
As for any survey of this kind, there are a multitude of processing error sources, such as e.g. data entry errors for paper questionnaires, double counting due simultaneous reception of paper questionnaire and web form data for a given holding, non-respect of the questionnaire routing or inconsistencies between the questionnaire sections. etc. Most of these sources are either addressed by application controls or ex-post validation rules. At several pre-defined spots, the production process contains a few working areas which allow to correct or adjust the data.

 

3. Tools used and people/organisations authorised to make corrections
Corrections on the sole survey data are performed by the statistical analysts at SER but included at STATEC level only via a working area. Corrections on the administrative sources are also dealt with at SER - in the worst case, the survey manager is the only person who is authorised to implement other corrections where deemed necessary. Whenever possible, imputations are dealt with automatically (using statistical software SPSS and Stata) and not manually.
6.3.4.1. Imputation - rate
Imputation - rate
For FSS 2016, item non-response which had to be dealt with by imputation was close to zero for all structural variables. For agricultural production methods variables, the non-response rate was moderate.
6.3.5. Model assumption error

[Not requested]

6.4. Seasonal adjustment

[Not requested]

6.5. Data revision - policy
Data revision - policy
Generally, STATEC performs revisions when receiving packages of administrative data from the SER or in case of major errors detected in the direct survey.
6.6. Data revision - practice
Data revision - practice
Revisions are a matter of definition. We produce at least 2 versions of data each year, one preliminary and one final. Only the final version is fully published, whereas the preliminary serves as input for preliminary economic agricultural accounts.
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
No publication of first results. However, t+10 months is feasible for use as input in preliminary economical agricultural accounts. t = end of reference year 2016
7.1.2. Time lag - final result
Time lag - final result
Generally, t+13 months for annual national data collections (t = end of reference year 2016). However, for "European years" (e.g. 2010, 2013, 2016), t+23 months given the many additional characteristics as well as the SO data.
7.2. Punctuality

See below

7.2.1. Punctuality - delivery and publication
Punctuality - delivery and publication
No official national schedule.

The first version of micro-data were transmitted to Eurostat in December 2017.


8. Coherence and comparability Top
8.1. Comparability - geographical
1. National vs. EU definition of the agricultural holding
As stated in section 2.5 Statistical unit, there are no differences in definition and in terms of the agricultural activities undertaken by the agricultural holdings.

 

2.National survey coverage vs. coverage of the records sent to Eurostat
No difference.

 

3. National vs. EU characteristics
The characteristics that have been surveyed and their definitions are in general those of the Regulation No 1166/2008 of the European Parliament and the Council of the 19th November 2008, as they are specified in the Handbook implementing the definitions of the FSS without distinction between the data obtained via SER from administrative sources or the data obtained directly from the survey.

For reasons of consistency with economic accounts of agriculture in Luxembourg, the definition of the Annual Work Unit (AWU) is, as in 2010, defined as follows: “a person is considered working full-time with an average of 8 hours a day during 275 days (2 200 hours a year). Persons with less than 15 years or with more than 80 years are excluded. Persons aged between 15 and 18 years as well as persons aged at least 65 years declared as full-time working in the survey have been transformed into part-time working using fixed coefficients, which is in line with national economic accounts of agriculture recommendations.

 

4. Common land
4.1 Current methodology for collecting information on the common land
Though existing in the Middle Ages, common land has disappeared in Luxembourg due to a specific evolution of agricultural laws.
4.2 Possible problems encountered in relation to the collection of information on common land and possible solutions for future FSS surveys
Not applicable.
4.3 Total area of common land in the reference year
Not applicable.
4.4 Number of agricultural holdings making use of the common land or Number of (especially created) common land holdings in the reference year
Not applicable.

 

5. Differences across regions within the country
No differences.

 

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
No differences.
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
No changes.

 

2. Possible changes in the coverage of holdings for which records are sent to Eurostat
No changes.

The 2013 NMR thresholds '1 000 laying hens or 1 000 other poultry' are replaced by '1000 poultry' in item 2.6-2 of the present 2016 NMR. Please note that even though the 2016 survey form specifies a 1 000 threshold for either laying hens and other poultry, the actual implementation is applied on administrative data with a single threshold of 1 000 poultry. Any missing survey data are dealt with appropriate imputation strategies.

The 2016 threshold of 10 breeding sows was not mentioned in the FSS 2013 questionnaire, however, during Eurostat FSS validation phase, it was added to the national implementation. Consequently, there is no change between the FSS microdata 2013 and the FSS microdata 2016 for this threshold.

 

3. Changes of definitions and/or reference time and/or measurements of characteristics
No changes.

 

4. Changes over time in the results as compared to previous FSS, which may be attributed to sampling variability
Not applicable.

 

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 1 965   2 077 -5.4%   
Utilised agricultural area (ha) (A_3_1) 130 651  131 043 -0.3%   
Arable land (ha) (B_1) 61 984   62 602 -1.0%   
Cereals (ha) (B_1_1) 27 857  29 072 -4.2%   
Industrial plants (ha) (B_1_6) 3 886  4 826 -19.5%  minor decrease in absolute terms, mainly due to the decrease of rape
Plants harvested green (ha) (B_1_9) 27 631  26 774 +3.2%   
Fallow land (ha) (B_1_12) 223  157 +42.0%  minor increase in absolute terms
Permanent grassland (ha) (B_3) 67 115  66 897 +0.3%   
Permanent crops (ha) (B_4) 1 541  1 532 +0.6%   
Livestock units (LSU) 173 596  165 398 +5.0%   
Cattle (heads) (C_2) 201 416  193 623 +4.0%   
Sheep (heads) (C_3_1) 8 951  8 582 +4.3%   
Goats (heads) (C_3_2) 5 130  4 456 +15.1%  minor increase in absolute terms
Pigs (heads) (C_4) 92 312  87 518 +5.5%   
Poultry (heads) (C_5) 115 263  112 504 +2.5%   
Family labour force (persons) (count 1 for E_1_1$WorkCodeH if the latter is neither '0' nor 'z' + E_1_3$pers) 3 609 3 792 -4.8% in line with the decrease of the number of holdings
Family labour force (AWU) (E_1_1$AWU + E_1_3$AWU) 2 265  2 408 -5.9% in line with the decrease of the number of holdings
Non family labour force regularly employed (persons) (E_1_4$pers) 1 167  1 160 +0.6%   
Non family labour force regularly employed (AWU) (E_1_4$AWU) 982  968 +1.4%   
8.2.1. Length of comparable time series

[Not requested]

8.3. Coherence - cross domain
1. Coherence at micro level with other data collections
There are no comparisons with other data at this level of agregation.

The data are reconcilable with other data sources. However, due to multiple identifier types existing in the various sources, the micro-data linking through such identifiers is not always possible or flawless.

However, perfect reconciliation is possible with administrative sources obtained through SER due to the use of a common identifier.

 

2. Coherence at macro level with other data collections
Coherence is checked with FSS prior survey data.
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
On-line tables and metadata are the main publication channel. Occasionally, a detailed written publication (including metadata) is published. Short publications (4 pages max) are currently in development.

 

2. Date of issuing (actual or planned)
Spring 2017 for certain characteristics (fields, animals). Other FSS characteristics are published upon completion of the Eurostat related production.

 

3. References for on-line publications
Tables:

http://www.statistiques.public.lu/stat/ReportFolders/ReportFolder.aspx?IF_Language=eng&MainTheme=4&FldrName=2&RFPath=7274

https://agriculture.public.lu/de/agrarstatistik/structures-agricoles.html

Metadata and publications:

http://www.statistiques.public.lu/en/methodology/methodes/enterprises/Agriculture/agriculture/index.html

https://agriculture.public.lu/de/agrarstatistik/structures-agricoles.html

9.3. Dissemination format - online database
Dissemination format - online database
http://www.statistiques.public.lu/stat/ReportFolders/ReportFolder.aspx?IF_Language=eng&MainTheme=4&FldrName=2&RFPath=7274

https://agriculture.public.lu/de/agrarstatistik/structures-agricoles.html

9.3.1. Data tables - consultations
Data tables - consultations
Approx. 2860 consultations during the 10 months period March 2017 to December 2017.
9.4. Dissemination format - microdata access
Dissemination format - microdata access
No microdata dissemination.
9.5. Dissemination format - other

[Not requested]

9.6. Documentation on methodology
1. Available documentation on methodology
http://www.statistiques.public.lu/en/methodology/methodes/enterprises/Agriculture/agriculture/index.html

 

2. Main scientific references
Not applicable.
9.7. Quality management - documentation
Quality management - documentation
http://www.statistiques.public.lu/en/methodology/methodes/enterprises/Agriculture/agriculture/index.html
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
The survey questionnaire was sent at the same time as the subsidy application package sent by the SER. The idea is to avoid agricultural holdings having to delve twice into their reporting systems and thus to accelerate the response. Moreover, the questionnaires are specialised such that double counting of characteristics is practically minimised.


11. Confidentiality Top
11.1. Confidentiality - policy
Confidentiality - policy
According to article 16 of STATEC's law, the dissemination of confidential data is forbidden and subject to criminal penalty. The definition of confidential data is in line with the Regulation (EC) 223/2009 on European Statistics.
11.2. Confidentiality - data treatment
Confidentiality - data treatment
Tabular data are protected using the following approaches:

- table design: very detailed tables are avoided (e.g. data published by municipality) unless the data is not confidential (e.g. number of agricultural holdings, total UAA, etc.). On a national level, almost any data (i.e. low occurrence variables) can be published. Problems arise with national data broken down by one or more spanning variables (farm type, size class, municipality, etc.). The table design usually results in a compromise between relevance and confidentiality;

- cell suppression: any cell that contains confidential data according to a sensitivity rule (n,k) or a minimum frequency rule are suppressed due to primary confidentiality. Any cell that is needed to protect one or more primary confidential cells are suppressed due to secondary confidentiality. In practice, suppression comes down to aggregation for one-dimensional tables and to flagged cells for multi-dimensional tables – linked tables are also accounted for. To guarantee the proper protection of tabular data, the exact parameters used to apply the above rules are kept confidential. Calculations are performed with tau-Argus and verified ex-post.


12. Comment Top
1. Possible improvements in the future
Not available.

 

2. Other annexes
Not available.


Related metadata Top


Annexes Top