Farm structure (ef)

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

Compiling agency: Statistics Portugal

Time Dimension: 2016-A0

Data Provider: PT1

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
Statistics Portugal
1.2. Contact organisation unit
Economic Statistics Department / Agriculture and Environment Statistics Unit
1.5. Contact mail address
Av. António José de Almeida
1000-043 LISBOA

2. Statistical presentation Top
2.1. Data description
1. Brief history of the national survey 
In Portugal the series of agricultural and livestock surveys dates back to the first half of the last century. In fact, the first exhaustive, systematic and organised listing of statistical data on the Portuguese agriculture occurred in 1934, with the “General inventory of livestock and poultry”, and was later repeated in 1940 and 1972. Mainland farm surveys were held in 1952-1954, 1968 and 1979, and in 1965 a Census was conducted on agricultural holdings in adjacent islands (Azores and Madeira).
The first “General census on agriculture” harmonised with the European Union (EEC at the time) was held in 1989, exhaustively and simultaneously across all regions in the country. Subsequently, farm structure surveys were held in 1992, 1995 and 1997, followed by the 1999 General Agricultural Census, the 2003, 2005 and 2007 sample iterations, the 2009 Agricultural Census and the 2013 farm structure survey (sample iteration).
In 2016 Portugal held the Farm Structure Survey, a mandatory statistical operation as laid down in Regulation (EC) No 1166/2008 of the European Parliament and of the Council, with amends on Annex III by the Commission Regulation (EU) No 715/2014, as regards the list of characteristics to be collected in this farm structure survey. This survey was also structured to obtain a series of data of national/regional interest that, although not mandatory in Community terms, were considered to be pertinent and timely.
Data obtained will also make it possible to update the farm register and the agricultural sample base (Portuguese acronym: BAA).


2. Legal framework of the national survey 
- the national legal framework The attached Law No 22/2008 of 13 May laying down the principles, rules and structure of the National Statistical System (NSS) and the Decree-Law No 136/2012 of 2 July, approving the organisation of Statistics Portugal.
- the obligations of the respondents with respect to the survey The national legislation provides for the obligation of respondents, individuals and enterprises, to provide the information deemed necessary to produce official statistics (Article 4 (1) of the Law No 22/2008 and Article 4 of the Decree-Law 136/2012).
- the identification, protection and obligations of survey enumerators Principle of Statistical Confidentiality is strictly observed by all employees, agents and contractors who have access to individual statistical data on individual or collective, in the exercise of their activity, constituting Confidentiality, even after the functions.

All these people sign a declaration that they undertake to comply with the rules relating to the application of the Principle of Statistical Confidentiality.

Face to face interviews:

  • The statistical units are pre-informed about all aspects related to the survey, in particular about the confidentiality of the information provided.
  • For security reasons, interviewers always display the respective identification / credentials to the respondent and then inform them about the Principle of Statistical Confidentiality and inform them that the data collection is intended solely for statistical purposes.

2.1-2. Decree-Law 136/2012
2.1-2. Law No 22_2008 of 13 May
2.2. Classification system

[Not requested]

2.3. Coverage - sector

[Not requested]

2.4. Statistical concepts and definitions
List of abbreviations
BAA - Farm register;

CN - National coordinating body;

CR - Regional coordinating body;

TL - Local technical staff member.

2.5. Statistical unit
The national definition of the agricultural holding
Agricultural holdings are techno-economic units where there is common use of inputs (labour, machinery, buildings, land, etc.) and that cumulatively:
• produce agricultural products or maintain in good agricultural and environmental conditions land which is no longer used for production purposes;
• reach or exceed a specific size (minimum limit);
• are in a specific identifiable location, even when their area covers more than one commune or even more than one municipality;
• are operated under the single management of an agricultural holder, who assumes substantive decision-making.

Holdings that undertake only activities other than the ones listed in Annex I of Regulation (EC) No 1166/2008 (e.g. forests, fur animals other than rabbits, fish ponds, reeds) where not included in the national definition

2.6. Statistical population
1. The number of holdings forming the entire universe of agricultural holdings in the country
Number of holdings: 303 116


2. The national survey coverage: the thresholds applied in the national survey and the geographical coverage
The population includes the agricultural holdings existing in Portugal. The FSS was held on the Mainland and in the Autonomous Regions of the Azores and Madeira.

The size conditions that agricultural holdings must meet are, in the following order:

• to have, in the Mainland, at least 1 hectare (ha) of Utilised Agricultural Area – UAA (arable land + kitchen garden + permanent crops + permanent grassland). In the Autonomous Regions limits are lower, the minimum UAA being 0.1 ha;

• minimum area (or production) limits for at least one of the following crops:


Crops Threshold
76 B_1_8

Flowers and ornamental plants (excluding nurseries)



75 B_1_7_2 + B_1_8_2

Fresh vegetables, melons, strawberries and flowers - under glass



109 B_4_5




69 B_1_6_12

Aromatic, medicinal and culinary plants



74 B_1_7_1_2

Market gardening fresh vegetables


1 000

85 B_1_10

Arable land seed and seedlings


1 000

57 B_1_6 (excludes 69 B_1_6_12)

Industrial crops (excluding aromatic, medicinal and culinary plants)


2 000

94 B_4_1 + B_4_2

Fruit, berry and citrus plantations - total


2 000

104 B_4_4



2 000

101 B_4_3

Olive plantation


5 000

54 B_1_3

Potatoes (do not include potatoes from market gardening and kitchen gardening)


5 000

73 B_1_7_1_1

Open field fresh vegetables


5 000



• existence, on September 1st 2016, or production in the crop year 2015/2016, of at least:


Livestock Threshold 
Existence Production

125 C_2_4

Breeding bulls



126 C_2_6 + 128 C_2_99

Cows (exclude work animals)



125 C_2_4 + 126 C_2_5 + 126 C_2_6 + 128 C_2_99

Bovine animals with 2 years old and over (exclude work animals)



138 C_4_99

Fattening pigs



137 C_4_2

Breeding sows



129 C_3_1




132 C_3_2




149 C_6

Breeding female rabbits



139 C_5

Laying poultry and breeding poultry (chickens, turkeys, ducks, geese, guinea fowls, etc.)



150 C_7

Inhabited hives and traditional cork hives



no code

Breeding ostriches



no code

Breeding quails



121 C_2

Bovine animals



135 C_4




145 C_5_3_3




143 C_5_3_1




No code

Guinea fowls



140 C_5_1




144 C_5_3_2




146 C_5_3_4




No code



10 000


As the survey progressed, a number of agricultural holdings in the list of producers might be considered as non-existent or in no enquiry conditions. This included, in particular, those that were incorporated into other holdings, whose agricultural area or animal houses went on to have another type of (non-agricultural) utilisation, those that, despite maintaining some sort of agricultural activity, lost the enquiry limits, or those that were double-counted in the list of producers.


3. The number of holdings in the national survey coverage 
Number of holdings: 258 982


4. The survey coverage of the records sent to Eurostat
The same survey coverage as in the national survey.


5. The number of holdings in the population covered by the records transferred to Eurostat
Number of holdings: 302 783


6. Holdings with standard output equal to zero included in the records sent to Eurostat
Data for 193 holdings, corresponding to 1 228 population holdings with standard output equal to zero (0.5% of the population), were recorded. Most of holdings recorded were related to the fact that their activity was temporarily suspended. These holdings are considered in the results because they represent a reality that cannot be neglected. These units, although having their activities temporarily suspended (mainly for sanitary break reasons, but also to change their production systems, install greenhouses or new orchards) are still holdings and have to remain in the frame list of the survey.

The situations behind are the following:

  1. 171 holdings have fallow land /permanent grassland no longer in production purposes, but have the land in good agricultural and environmental conditions;
  2. 13 holdings with only kitchen gardens. These holdings were sampled because they had  some SO characteristics;
  3. 2 holdings with any agricultural activities (corresponding to 20 population holdings).


7. Proofs that the requirements stipulated in art. 3.2 the Regulation 1166/2008 are met in the data transmitted to Eurostat
Since the PT holdings threshold is one hectare of UAA, the art. 3.2 is not applicable.


8. Proofs that the requirements stipulated in art. 3.3 the Regulation 1166/2008 are met in the data transmitted to Eurostat
PT complies with art. 3.3 of the Regulation 1166/2008. See 2.6-2.
2.7. Reference area
Location of the holding. The criteria used to determine the NUTS3 region of the holding
The criterion used to determine the NUTS3 region of the holding is the majority of the total area of the holding where the holding is located.
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)

There are various reference periods, depending on the variable to be collected:


Reference date/period

Related to area and labour force

Crop year 2015/2016, starting on 1 November 2015 and ending on 31 October 2016


September 1st 2016

Some characteristics related to the sole holder's household

The day of the interview

Rural development measures

A period of three years ending in December 2016

2.9. Base period

[Not requested]

3. Statistical processing Top
1.Survey process and timetable
Preparation work
FSS preparatory work took place from the first semester of 2015 onwards and extended until the third quarter of 2016. The main tasks of the teams responsible for organising the FSS were the following: Consultation of users, definition, design and composition of the questionnaire and respective instruction manual, update of the list of producers, recruitment and training of the interviewers. It was also necessary to prepare the webization of the data collection. This data collection mode was used on a sub-sample, and implied a set of new tasks of preparation work, namely the adaptation of the questionnaire and the technical definitions, development and testing the web registration application.
For the face-to-face collection mode, there wasn’t any questionnaire test due to the fact that the questionnaire remained mostly unchanged since 2013.

Chronogram of the main operation activities
The main activities of this statistical operation, from the preparation to the dissemination of results, are recorded in the chronogram shown in 3-1. Calendar (overview of work progress) (in annex).

The FSS data collection was split in two periods: September-October 2016 for the web data collection; from November 2016 to May 2017 for the face-to-face data collection. Data collection also included critical appraisal, recording, validation and analysis.
Collected data was subject to a critical appraisal according to the guidelines defined in the control manual (document provided to field chain and containing, for each question, the procedures to be adopted for a preliminary control of data collection, especially identifying compulsory questions, relationships among variables, etc.). In turn, the consistency of collected data should be analysed in line with the provisions of the Instruction Manual and its alignment with local circumstances.


2. The bodies involved and the share of responsibilities among bodies
Statistics Portugal (INE) was the entity responsible for conducting the FSS, in cooperation with the Regional Statistical Office of the Azores and the Regional Directorate of Statistics of Madeira. The Agriculture and Environment Statistics Unit of the Economic Statistics Department and the Data Collection Department were the two units responsible for the operation at national level, having been in charge of organising and conducting all tasks from data collection to data validation and data dissemination.

This statistical operation involved over 200 staff across the whole country (Mainland and Islands), and was based on compliance with pre-defined data collection procedures aimed at organising, managing, monitoring and controlling data collection.

The FSS face-to-face data collection model focused on the collection services, with the coordination and technical support structure and coordination upstream, and field teams downstream. 3-2. FSS – Organisational structure (in annex) shows the organisational structure of the statistical operation.

The responsibilities are allocated as follows:

Interviewer: data recording, validation, critical appraisal, analysis and confirmation/correction in computer-readable format.

Local technical staff member (Portuguese acronym: TL)
- guiding and monitoring data collection, recording, validation and analysis,
- ensuring the organisational logistics and administrative management at their section level,
- ensuring information sessions to interviewers,
- ensuring,  in cooperation with the regional coordinating body, the allocation of the different intervening parties to the SAGR chain and the distribution of work to the interviewers,
- managing the questionnaire transfer, notably by reallocating questionnaires at the level of the collection section and between this section and other sections of the region or of other regions,
- contributing to overcome the difficulties shown by the interviewers and assessing the quality of the information provided by them, being able to hand them back certified questionnaires,
- preparing meetings and drafting periodical monitoring reports on the work.

Regional coordinating body (Portuguese acronym: CR)

- recruiting, selecting and training human resources, effectively establishing shared intervention in terms of operational management,
- coordinating the operation in each region, being responsible for compliance with the respective budget (upon final validation of collection structure expenses),
- guiding and monitoring data collection, recording, validation and analysis at regional level,
- ensuring  information sessions for the interviewers, and overcoming difficulties these considered to be insurmountable,
- preparing periodical monitoring reports on the work at regional level, as well as a regional report on the operation.

National coordinating body (Portuguese acronym: CN) - composed of representatives of the Economic Statistics Department and the Data Collection Department – organisational units of Statistics Portugal:

- defining the organisational and logistical structure of the statistical operation,
- monitoring works, thereby gauging the need to intervene in order to solve critical situations.
It also assumed responsibility for the project’s budget control.


Structure of the field chain

The collection structure was initially sized based on the number of agricultural holdings forming the farm register, the size of the geographical area of intervention of teams, and, at the upper levels of the chain of collection, the profile and availability of human resources.


Geographical distribution/organisation

The field structure was composed of 7 services distributed across the country, 32 staff supervising a team incorporating 216 interviewers.

As the collection stage of the operation progressed, the national coordinating body sent to the different regional coordinating bodies the current status of the collection on a weekly basis. This included, per collection service, information on the number of questionnaires collected, still to collect, their status and certification.


3. Serious deviations from the established timetable (if any)
There was not any deviation from the established calendar.

3-1. Calendar (overview of work progress)
3-2. FSS organisational structure
3.1. Source data
1. Source of data
The FSS is a sample survey.


2. (Sampling) frame
The source of the frame was BAA (farm register). To update the list of holders, and with reference to the agricultural farm register/agriculture sample base – an agricultural holdings base to support agricultural surveys – ad hoc cross-checks were made with statistical files (statistical units file and specific surveys), with data from administrative sources, namely Financing Institute for Agriculture and Fisheries (Portuguese acronym: IFAP)

The statistical files are:

SOURCE: Agricultural surveys (vegetable, orchard, floriculture, etc.)

Responsibility: Statistics Portugal

Coverage: All agricultural holders with specific productions

Geographical scope: Mainland, Autonomous Region of the Azores, Autonomous Region of Madeira

Reference point: 2009 to 2015

SOURCE: Statistical units file

Responsibility: Statistics Portugal

Coverage: All companies and self-employed people

Geographical scope: Mainland, Autonomous Region of the Azores, Autonomous Region of Madeira 

Reference point: online (2016)


For administrative sources, see item 4.1 below

In addition, use was made of other sources:
- of files with specific information from the Autonomous Region of Madeira and,
- on an ad hoc basis, of information scattered in files from other statistical surveys of the Economic Statistics Department, notably the inquiry populations of statistical operations targeted at poultry farms and nurseries.


The type of frame is a list frame.

The list of producers became available at April 2016.

In the process of updating and rendering the different available sources compatible, Statistics Portugal used a tool developed in QualityStage, a data quality management application. This application supported the implementation of processes within the scope of the standardisation and consolidation of names and addresses.
Based on the use of the QualityStage tool, a sequential process was established to compare sources, two by two. After the definition of survival criteria, this gave rise to provisional lists of producers.
The provisional result is compared with a new source, until a final consolidated list of producers is obtained.  

It was deemed necessary to define survival rules for the “selection” of the producer who subsists given a group of “potentially” equal producers; essentially, the rules are related to the quality/timeliness associated with each source.
In the 2016 process the list was updated with the latest data obtained from the Financing Institute for Agriculture and Fisheries, the Autonomous Region of Madeira, and also available in the Economic Statistics Department.
Following implementation of the QualityStage software and definition of survival rules for producers, a provisional list was obtained, which was subject to various types of analyses, as a result of the enhancement, improvement and update of information:
(a) Spelling correction of names, addresses and cities, undetectable in the standardisation made by the software application;
(b) Construction of Access queries to detect and eliminate possible double counting that may persist: same names/District/Municipality/Commune (DT/MN/FREG)/Tax identification number; names/telephone/ DT/MN/FREG/address; etc.
(c) Comparison with data from the statistical units file to complement registers with missing information, in particular names, incomplete or unknown addresses, DT/MN/FREG/4 and 7-digit postal_code;
(d) Update of the DT/MN/FREG code of the holding and the producer with current territorial referencing (Portuguese acronym: REFTER) codes, ensuring completion of all fields;
(e) Assignment of cities of the Portuguese Mail Services (CTT) when there is a common 7-digit DT/MN/FREG/postal_code key between the two bases of comparison;
(f) Completion of not valid postal codes through the DT/MN/FREG link with the CTT table for which there is a direct match;
(g) Replacement of legal person identification numbers started with 8xx by new numbers, started with 1xx or 2xx;
(h) Filtering of telephone characters and elimination of telephones without 9 digits;
(i) Elimination of the completion of the address field, whenever the address and city fields were exactly alike.


3. Sampling design
3.1 The sampling design
Single-stage stratified random sampling of holdings.

The sample was selected independently in each stratum by sequential simple random selection without replacement. That is, within each stratum holdings were sorted by the random number associated with them and were selected for the sample the first agricultural holdings.

3.2 The stratification variables
The sample was stratified by agrarian region, geographical region (NUTS level II), groups of general farm type, and a variable (ST) which characterizes the holding by effective livestock or by size classes of UAA.

In order to obtain good results for some variables, with significative importance at national level, but concentrated in a relatively small number of holdings, it was adopted a stratification “in cascade”. Some strata were built that contained all the holdings of the region with a non-zero value or above a certain value for those variables. It’s called stratification “in cascade”, because the holdings with values of the concerning variables above certain limits were progressively isolated.

All the remaining holdings, not belonging to these special strata, were stratified by size classes of UAA.

The stratification and the variables used can be found in annex: 3.1-3.2. The stratification variables and the sampling stage.

3.3 The full coverage strata
Strata with less than 10 holdings were fully covered.
3.4 The method for the determination of the overall sample size
The size of the sample was calculated by NUTS II in order to meet the accuracy requirements defined in the EU Regulation.

The total sample size was 27575 holdings, corresponding to a sampling fraction of approximately 9,1%. This sample size was considered adequate in order to get, for each region (NUTS II), a sufficient precision for the most important variables. 

Later adjustments to sample size were made to improve the accuracy of some indicators considered relevant.

3.5 The method for the allocation of the overall sample size
For the non-exhaustive stratum, Neyman allocation was used to calculate the optimal sample size for each stratum, based on the number of holdings.

See annex: 3.1-3.5 Neyman allocation.

3.6 Sampling across time
Not applicable.
3.7 The software tool used in the sample selection
For the study and selection of the sample the package SAS was used, with programs made for the occasion.
3.8 Other relevant information, if any
Nothing relevant.


4. Use of administrative data sources
4.1 Name, time reference and updating
The farm register was updated from crossing the agricultural sample base (based on the 2009 General Agricultural Census and updated on the basis of agricultural surveys and other sources) with data from the following administrative sources:

SOURCE: Payments under PAC policy

Responsibility: Financing Institute for Agriculture and Fisheries (IFAP) - IACS

Coverage: Agricultural holders that actually received aid in the reference year

Geographical scope: Mainland, Autonomous Region of the Azores, Autonomous Region of Madeira

Reference point: Crop year 2015/2016

Legal Basis: Regulation (EU) No 1307/2013 of the European Parliament and of the Council of 17 December 2013 establishing rules for direct payments to farmers under support schemes within the framework of the common agricultural policy and repealing Council Regulation (EC) No 637/2008 and Council Regulation (EC) No 73/2009

Updating frequency: yearly


SOURCE: SNIRA (Animal register)

Responsibility: Financing Institute for Agriculture and Fisheries (IFAP) - IACS

Coverage: livestock keepers at national level

Geographical scope: Mainland, Autonomous Region of the Azores, Autonomous Region of Madeira

Reference point: September 2016

Legal Basis: Council Directive 92/102/EEC of 27 November 1992 on the identification and registration of animals; Commission Regulation (EC) No 1678/98 of 29 July 1998 amending Regulation (EEC) No 3887/92 laying down detailed rules for applying the integrated administration and control system for certain Community aid schemes; Council Regulation (EC) No 21/2004 of 17 December 2003 establishing  a  system  for  the  identification  and  registration  of  ovine  and  caprine  animals  and amending  Regulation  (EC)  No  1782/2003  and  Directives  92/102/EEC  and  64/432/EEC; Commission Regulation (EC) No. 759/2009 amending the Annex to Council Regulation (EC) No. 21/2004 establishing a system for the identification and registration of ovine and caprine animals; Commission Decision 2010/280/EU amending   Decision   2006/968/EC   implementing   Council   Regulation   (EC)   No   21/2004   as   regards guidelines  and  procedures  for  the  electronic  identification  of  ovine  and  caprine  animals.

Updating frequency: on-line (cattle); 3-times a year (pigs); yearly (sheep and goats)

4.2 Organisational setting on the use of administrative sources
The national legislation provides for access to administrative records (Article 4 (2)) which in the case of the FSS are used to validate the information provided by respondents. Statistics Portugal does not participate in the conceptual design and subsequent related revisions of the administrative sources.
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

Payments under PAC policy


Method  Shortcoming detected Measure taken
- coherence of the reporting unit (holding)   There are no significant differences between the definitions of holding. However, it is possible that several beneficiaries apply for different payments in the same holding. To improve the comparability between the FSS data and this source, it was included one question (see in the item 3.3-5 the annex Questionnaire FSS 2016 Mainland, question number 22) about the beneficiaries associated with the holding.
- coherence of definitions of characteristics Most characteristics have the same definition in FSS and this source, after an effort to harmonize them.    
- coverage:      
  under-coverage   Not all units of the farm register are present in this source, since it only includes the ones that actually received aid in the reference year. Taking in account this shortcoming, it is only possible to use this source value for validating microdata (individual data) or to consider it as the minimum allowable value for a given aggregate characteristic.
  multiple listings      
- missing data      
- errors in data   It is not uncommon the incorrect classification of the oat harvested green as oat for the production of grain in this source as compared to FSS.  
- processing errors      
- comparability   See item coverage: under-coverage See item coverage: under-coverage
- other (if any)      


SNIRA (Animal register)


Method  Shortcoming detected Measure taken
- coherence of the reporting unit (holding)   There are differences between the definitions of holding in SNIRA and FSS. It’s not uncommon that one holding in FSS corresponds to two or more holdings of SNIRA. To improve the comparability between the FSS data and this source, it was included one question (see in the item 3.3-5 the annex Questionnaire FSS 2016 Mainland, question number 22) about the beneficiaries associated with the holding.
- coherence of definitions of characteristics  All characteristics have the same definition in FSS and this source.    
- coverage:      
  under-coverage   Not all units of the farm register are present in this source. The herd database (namely for sheep, goats and pigs) is not yet exhaustive, since there is an ongoing task for the registration of all the holders in the database. The missing units are necessarily small holders, namely those under thresholds of the aid scheme. Taking in account this shortcoming, it is only possible to use this source value for validating microdata (individual data) or to consider it as the minimum allowable value for a given aggregate characteristic.
  multiple listings      
- missing data      
- errors in data      
- processing errors      
- comparability    See item coverage: under-coverage  See item coverage: under-coverage
- other (if any)      


4.5 Management of metadata
 Both sources are managed by IFAP (IACS). The metadata describing both administrative sources are systematically stored and maintained over time by Statistics Portugal in dedicated databases.
4.6 Reporting units and matching procedures

External source


Correspondence between the INE’s definition of holding and the one from the external source

Financing Institute for Agriculture and Fisheries (IFAP) – IACS Holders that received payments under the common agricultural policy from IFAP in 2016 Theoretically there are no significant differences between the concepts of INE and IFAP. However, often the beneficiaries of IFAP and holders don’t have a perfect match (e.g.: one holding may correspond to two or more beneficiaries of IFAP, when/if different household members apply for aid).
SNIRA (Animal Register) – livestock keepers at national level There are differences between the "holding" of SNIRA and "holding" of INE (e.g., one holding may correspond to two or more holdings of SNIRA).
4.7 Difficulties using additional administrative sources not currently used
Organic farming:
The data produced in Portugal under the Reg. 834/2007 presents problems of quality and timeliness. Also with regard to the concepts there are differences, particularly because in animal production insects can be included while in crop production, wild plant products. Moreover certification companies often do not report to the Ministry of Agriculture individual data but only aggregate information by control body.

3.1-3.5. Neyman allocation
3.1-3.2. The stratification variables and the sampling stage
3.2. Frequency of data collection
Frequency of data collection
 Since 1989, data collection on FSS was made in the following years:
  • 1989 - Census
  • 1993
  • 1995
  • 1997
  • 1999 - Census
  • 2003
  • 2005
  • 2007
  • 2009 - Census
  • 2013
  • 2016
3.3. Data collection
1. Data collection modes
The survey was conducted using two different data collection modes: i) Internet, using questionnaires which were completed through internet component (WEBINQ); ii) face-to-face interviews, with the collection based on paper questionnaires. The WebInq questionnaire was available to a sub-sample during September and October 2016. After that period, those units which didn't answer through this mode were transfered to face-to-face data collection mode, used also in the remaining units of the sample. In this late mode, interviewers were also responsible for the recording of data on the laptops. The type of data recording may be characterised as “heads up”, given that the tailor-made software application to support the agricultural survey system of Statistics Portugal (SAGR) supplied instantaneous feedback to the staff member using a laptop to record data electronically regarding the information that was being recorded.


2. Data entry modes
Internet data collection mode:

The data capture and editing on the internet data collection mode was supported by the component WEBINQ, a part of the SIGINQ, the Statistics Portugal integrated approach to survey management systems. In this component (WEBINQ, questionnaires on the web) respondents submit their answers online. Acknowledging the relevance of data capture and editing on the final data quality, WEBINQ follows the new concept "Data Version Stack", which has four main rules: i) there is a single response for each tuple (survey, data reference period, statistical unit); ii) data editing performs a new response version; iii) all versions of the responses are saved; iv) last version goes to data warehouse. Data Version Stack allows the survey manager to track changes and ensure data quality. For more detailed information on this issue, check annex 3.3-2 Data collection - Statistics Portugal Survey Management System Architecture (also available at


Face-to-face data collection mode:

A generic management and recording application was developed, standardisable by survey (set of validation items and rules), to support the agricultural direct collection statistical operations.
The application is composed of the following modules:
• Management of the survey. Import of validation items and rules;
• Management of agricultural holdings. Import and consultation of the sample. Formulation of monitoring lists;
• Management of the chain of collection. Assignment of user profiles and allocation of agricultural holdings to interviewers;
• Management of questionnaires. Includes the recording module;
• Payments. Introduction of generic and specific variables to prepare payment slips;
• Data analysis. Totalisers, ad-hoc selections and comparison with external sources;
• Maps;
• Synchronisation (between the interviewers’ laptops and the central database).

A web application was developed with a central environment targeted at survey management and analysis, and a local environment on laptops, targeted at questionnaire recording and validation by interviewers.
Hardware used:
• 1 web server/application server (virtual machine, 4 CPU, 4 Gb ram memory)
• 1 database server (16 Gb ram memory)

Software used:
• Java
• Oracle 10 and Oracle Express

Software architecture: see 3.3-2.Physical model of the application in annex.

Strengths of the collection and recording application
• Solution that may be used in other statistical operations;
• Recording by interviewers. Correction of errors by interviewers;
• Validation rules editor. Time saving in the programming and testing of rules;
• Selection editor (ad-hoc queries). Research by users with no need for programming;
• Update of the local online application;
• Advantages inherent in a web application (broadly-based access, central application update, centralised database, online output).


3. Measures taken to increase response rates

Internet data collection mode:

Prior to the beginning of the collection via Internet, agricultural holders from the sub-sample selected to this collection mode were contacted through circular letters, sent to inform them on the statistical operation, its purposes and the importance of their cooperation. It was also mentioned in the letter that they had been selected to answer using Internet and the necessary procedures to answer using Statistics Portugal’s WEBINQ. After that, agricultural holders were contacted by phone and questioned about their intention to answer via Internet (a valid email was then collected to allow the login to the service and further contacts). During the collection period, agricultural holders that didn’t answer the questionnaire were contacted by email, remembering the need to do it and reinforcing that, if not, they would be later contacted by an interviewer that would visit the holding to get the required data.


Face-to-face data collection mode:

Promoting and advertising the statistical operation
Before the interviews, letters (circular letters) were sent to agricultural holders informing them on the statistical operation, its purposes, the importance of their cooperation, and the date of the interviews.

Priority of data collection
• Holdings with a location other than the address of the holder – prior to the interviews, interviewers identified the agricultural holdings that had been allocated to them and which were located in a commune other than that of the holder's address. Priority was given to these interviews, especially those located in different agricultural areas. It was thus ensured that the questionnaire would be transferred accordingly and the interview made in due time. In addition, this procedure also permitted to avoid the possibility of the holder returning to the agricultural holding, leading to a new transfer of the questionnaire. Priority contact with these holders could also in a first instance avoid the transfer of the respective questionnaires.
• Large holdings and/or holdings with significant activity in their location area – the evaluation of the holdings' size, as well as the importance of their activity in the respective geographical area, were two crucial factors for interview priority.

Prior scheduling of the interview
With a view to enhancing the success of the interview, in particular the required availability of the agricultural holder to respond to the interview on a single occasion, where possible, the interviewer made an appointment with the agricultural holder. It was thus possible to avoid, for instance, incomplete collection of data, additional visits, and unnecessary further availability for concluding the collection/interview.

“I have been here” message
Reminders were adopted insisting on the need for making the interview and obtaining the necessary information. Therefore, in those cases where interviewers visited the address of the holder or agricultural holding, but could not get in touch with the holder, they left the message “I have been here”, i.e. the indication that they had visited the holding/address of the holder, and informing of a date for a new contact. This was intended to speed up the process, permitting the interviewer to establish a future contact with the holder in order to obtain response.

Interview techniques
With a view to raising the awareness of interviewees, leading them to cooperate and supply the required information, during the interviews interviewees were always informed about the purpose of the survey. They were persuaded, motivated and clarified regarding the importance of their cooperation. Where necessary, interviewers always sought to provide the required explanations, showing dependability and availability. In order to ensure the confidentiality and reliability of data, no third persons were allowed during the interviews, except where that was required by the person responsible for the information supplied.

Reminder that a response must be given
Whenever an interview could not be made, irrespective of the reason (impossibility to locate the holding or to contact the holder, absence of the interviewee, refusal by the holder to answer the interview), every effort was made to reverse the situation. The interviewer could resort to the local technical staff member. The interview would be considered non-achieved only after such conclusion had been drawn by higher hierarchical members in the chain of collection, and the decision communicated to the local technical staff member.

Obtaining alternative contacts
In addition, obtaining alternative contacts whenever it was impossible to contact the holder proved to be quite an important asset in terms of recovering missing interviews.

Payment of non-achieved interviews
The actual payment of non-achieved interviews was a further incentive for the interviewer to take all necessary steps to obtain the interview.

Treatment of refusals – Reminders until interview was considered non-achieved
In those cases where the reason for not making the interview (non-achieved interviews) was refusal, the interviewer tried to reverse the situation, insisting, in person and accompanied by the local technical staff member, on the need for the agricultural holder, or the responsible person, to supply the information required.
When it was not possible to reverse the situation at this level, the section manager was informed and indicated the subsequent step. If the situation remained unchanged (non-achieved interview), the section manager would follow the procedures in force, and request guidance to the regional coordinating body. This body would be responsible for any decision on the impossibility of conducting the interview. This decision alone made it possible to record a questionnaire to an agricultural holding as a non-achieved interview.

Treatment of refusals – Circular letter
In the cases of refusal confirmed by the regional coordinating body, a circular letter was sent to the holder/person responsible for supplying the information, informing them of the mandatory nature of the response and the fines to which they were subject in case of non-compliance with the legal obligation (according to Article 4 (1) of the Law No 22/2008 and Article 4 of the Decree-Law 136/2012). This made it possible to reverse a number of refusals. The final number of non-achieved interviews due to refusal was rather low at national level (see item 6.3.3 Non response error - item 1).


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

3 Number of ineligible holdings 2925
3.1 Number of ineligible holdings with ceased activities

This item is a subset of 3.

This information wasn't collected
4 Number of holdings with unknown eligibility status


4.1 Number of holdings with unknown eligibility status – re-weighted 550
4.2 Number of holdings with unknown eligibility status – imputed 0
5 Number of eligible holdings



5.1 Number of eligible non-responding holdings


This information wasn't collected
5.1.1 Number of eligible non-responding holdings – re-weighted
5.1.2 Number of eligible non-responding holdings – imputed
5.2 Number of eligible responding holdings 24528
6 Number of the records in the dataset 




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

3.3-2. Physical model of the application
3.3-5. Azores questionnaire 2016 (Portuguese only)
3.3-5. Mainland questionnaire 2016 (Portuguese only)
3.3-5. Madeira questionnaire 2016 (Portuguese only)
3.3-5. Web questionnaire (Portuguese only)
3.3-2. Statistics Portugal Survey Management System
3.4. Data validation
Data validation
Internet data collection mode:

Data validation on the electronic questionnaire available on WebInq was primarily executed by the 659 on-line validation rules, the large majority (653) classified has fatal errors, which disable the conclusion and submission of the questionnaire. These errors were from different types, such as completeness checks (non-compliance with the compulsory filling-in of a certain field; e.g. total area of the holding not filled in), relational/consistency checks (if a certain field is/is not recorded, another field must be/does not have to be filled in; e.g. existence of irrigated area, where the field irrigated land had not been filled in) and range checks (data must be included within a certain range, or cannot attain a certain value; e.g. rice fields in Entre Douro e Minho). Correction of these errors was mandatory.

There were also the so called warning errors, which basically warns the respondent that the situation he is describing is not usual. These errors are mostly relational/consistency checks (e.g. the holding has an irrigation system and does not record irrigated land, or the only labour force of the elder is the manager).


Face-to-face data collection mode:

Interviewer/staff member using a laptop to record data electronically
The interviewer's functions include data analysis, especially as regards consistency and alignment with local circumstances. Moreover, interviewers/staff using laptops also record, validate and review data in computer-readable format. In order to assist interviewers/staff in this function, 1 910 validation rules were created for recorded data, by resorting to the validation rules editor of SAGR. This editor makes it possible to centrally create strings of rules. A number of fine-tuning interventions and updates were made to the original rules in the course of the operation.
Validation rules triggered errors, which can be broken down into three large groups:

  • Intrinsic errors (8) – those usually associated with the introduction of characters that are not accepted in specific recording fields, especially those related to the identification of the holder (e.g. characters not accepted in names, addresses, etc.). Any one of these errors prevents the questionnaire from being recorded;
  • Fatal errors (1 298) – this type of error enables the questionnaire to be recorded; however, its validation will undoubtedly result in the questionnaire being labelled as incorrect, which prevents its conclusion. These errors can be completeness checks (non-compliance with the compulsory filling-in of a certain field; e.g. legal personality not filled in), relational/consistency checks (if a certain field is/is not recorded, another field must be/does not have to be filled in; e.g. existence of irrigated area, where the field irrigated land had not been filled in), arithmetic checks (wrong totals; e.g. wrong total cereal), range checks (data must be included within a certain range, or cannot attain a certain value; e.g. rice fields in Entre Douro e Minho), or sequential checks (non-sequential filling-in of certain fields; e.g. non-sequential filling-in of members of the holder's household). Correction of these errors is mandatory;
  • Warning errors (604) – this type of error enables the questionnaire to be recorded and concluded. These errors may also be of the following types: range checks (e.g. rice field in the Algarve or rice exceeding 50 acres in Beira Litoral) and relational/consistency checks (e.g. the holding has an irrigation system and does not record irrigated land), completeness checks (e.g. the telephone was not filled in). These errors basically play a warning role, and the interviewer/staff member using a laptop will analyse the data triggering errors and confirm or correct them.


Validation may be broadly based, covering the whole national territory (443), or be restricted to the Mainland (413), Madeira (459), the Azores (144) or specific agricultural regions (451). For the Mainland questionnaire, specific validation rules were implemented for each region, so as to identify and validate certain characteristics.
Errors in SAGR were automatically triggered during the data recording procedure, enabling the staff member using a laptop to immediately correct/analyse data.
See 3.4 Example of the list of errors triggered during the data recording procedure (in annex, only available in Portuguese).
After the correction of fatal errors and the analysis of warning errors (reflected in confirmations and/or corrections of recorded data), interviewers, where they considered their work to have been concluded regarding the questionnaire/holding in question, would label it as “Concluded” in SAGR (afterwards, they would inform local technical staff members that the recorded information was ready to be analysed).

Local technical staff member
Local technical staff members analysed the information to detect possible inconsistencies in data collected and recorded, as well as incorrectly implemented concepts or misalignments with local/regional circumstances. An analysis was made of the information contained in the questionnaires concluded by the interviewers (those that they deemed ready to be analysed by the local technical staff member). For the purpose, the local technical staff member could/should resort to the Error Report, the Validate function, the Selection module and/or the Comparison with other Sources module. The local technical staff member could introduce corrections/changes, return it to the interviewer (Return to lower level). After a critical appraisal and analysis of the information, the local technical staff member would certify the questionnaire (thereby signalling the regional coordinating body that their work had been concluded and the questionnaire was ready to be analysed).

Regional coordinating body
After analysis, the regional coordinating body could return the questionnaire to the local technical staff member, who could resort to the interviewer, only if necessary for contacting the person responsible for the information supplied.

National coordinating body
In the course of the operation, the national coordinating body, similarly to the other elements in the data collection chain structure of the FSS, prepared regular analyses of data collected and, in order to complement and support regional analyses, submitted the output to be validated at the different levels in the chain of collection. As a result, the analyses implied the justification or correction of data collected. For different geographic levels, it usually covered information regarding:

  • Comparison with other sources;
  • Frequency of errors;
  • Maximum permissible errors;
  • Selections;
  • Totalisers.

In addition to validating the information registered through warning and fatal errors, aggregate data and microdata in the FSS were also analysed. Information was analysed through the SAGR software application, by using features specifically developed for the purpose, in particular: totalisers, selections of holdings and comparison with external sources (microdata and aggregate data).


Both data collection modes:

The analysis of totalisers, i.e. aggregate information per geographic level, is essential to evaluate the consistency of collected data vis-à-vis local circumstances. Totalisers of the different geographic levels were analysed according to profiles in the chain of collection, thereby ensuring that an analysis would be carried out for all geographic levels.


Selections refer to the search for holdings according to selected conditions, with a view to detecting incorrections in data collection. The critical appraisal of aggregate data made it possible to obtain elements for the analysis of microdata, particularly in the identification of overvalued variables and high variable values – maximum permissible errors. Selections were frequently based on the existence of a given warning error (so as to identify potential systematic errors made by interviewers) and were especially adjusted to local agricultural specificities, i.e. a dynamic process.

The comparison of recorded data with other sources
was instrumental for validating the information. For further information, see item 8.3 Coherence - cross domain.

3.4. Example of the error report (only available in Portuguese)
3.4. Example of the list of errors triggered during the data recording procedure (only available in Portuguese)
3.5. Data compilation
Methodology for determination of weights (extrapolation factors)
1. Design weights
See annex:  3.5-1. Design weights
2. Adjustment of weights for non-response
See annex: 3.5-2. Adjustment of weights for non-response
3. Adjustment of weights to external data sources
Not applicable.
4. Any other applied adjustment of weights
Not applicable.

3.5-1. Design weights
3.5-2. Adjustment of weights for non-response
3.6. Adjustment

[Not requested]

4. Quality management Top
4.1. Quality assurance

[Not requested]

4.2. Quality management - assessment

[Not requested]

5. Relevance Top
5.1. Relevance - User Needs
Main groups of characteristics surveyed only for national purposes 
The FSS was structured to make it possible to provide information on the characteristics defined for the farm structure survey (general characteristics, crop areas, livestock, type of machinery and equipment, agricultural buildings and structures, agricultural population and labour force, other non-agricultural activities and measures to support rural development).
Other characteristics (variables), or greater detail in mandatory characteristics pursuant to Community legislation, were also surveyed as a response to specific national requirements (see 5.1 Table 1 and 5.1 Table 2 in annex).
The determination of the final version of the national characteristics to be included and the formulation of mandatory questions pursuant to Community legislation resulted from contacts with a number of entities, which contributed to defining the variables that would provide relevant statistical data with no excessive statistical burden on respondents. See 5.1 Contacts in annex.

5.1. Table 1 - National characteristics as a result of the breakdown_individualization of the characteristics defined in Community legislation
5.1. Table 2 - National characteristics without relation to the characteristics defined in Community legislation
5.1. Contacts
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 find the information on NE and NS characteristics 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
Only sampling errors were estimated.

For budgetary reasons, the FSS did not include the planned quality survey, therefore it was not possible to estimate coverage errors and content/measurement errors.

6.2. Sampling error
Method used for estimation of relative standard errors (RSEs)
Please see the annex  6.2. Method used for estimation of RSEs.

6.2. Method used for estimation of RSEs
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
Some cases were found. You can find attached those cases (6.2.1-2. Precision requirements above thresholds). These differences are related with structural changes that occurred in some holdings since the agricultural census, namely based in size and/or type changes.

6.2.1-1. Relative standard errors
6.2.1-2. Precision requirements above thresholds
6.3. Non-sampling error

See below

6.3.1. Coverage error
1. Under-coverage errors
As the FSS was a sample operation, its purpose was to survey agricultural holdings representative of the population (all Portuguese agricultural holdings). In view of the possibility that the farm register (BAA) might not be exhaustive, the necessary steps were taken to identify holdings not included therein – new agricultural holdings – thereby ensuring an exhaustive coverage of data collection. For this purpose, during an interview with a holder in their list of agricultural holdings, or through other contacts, interviewers would ask about land transactions. Subsequently, they would check whether such holders were included in BAA, and, if not, they would collect information enabling them to enter into contact with the holders in question. A more exhaustive coverage of data collection was hence ensured.


2. Over-coverage errors
There was an adjustment of weights taking into account the units that do not belong to the target population, excluding them from the universe.
2.1 Multiple listings 
The duplicated units were eliminated from the population and the sampling, and the coefficients associated with them recalculated (re-weighting). There were 183 identified duplicated cases (0.7% of the sampling units).


3. Misclassification errors
No method was applied. We assumed that if exist, this kind of errors are residual.


4. Contact errors
Wrong details in contact data were investigated and corrected during the operation.


5. Other relevant information, if any
Not available. Over-coverage - rate
Over-coverage - rate
Over-coverage rate=10.4% (computed by dividing the number of ineligible units in the sample to the gross sample). Common units - proportion

[Not requested]

6.3.2. Measurement error
Characteristics that caused high measurement errors
The methodology used to avoid/minimise incorrect and/or incomplete data included:
• Interview techniques (interpretation of the questions) – questions would be posed to the interviewee in a way to avoid personal interpretations;
• Outline of the agricultural holding – on the occasion of the interview, the interviewer would always prepare an outline of the agricultural holding characterising it correctly, to be used as an auxiliary tool in subsequent analyses. The outline would be duly identified and attached to the questionnaire;
• Entry of “Observations” – the “Observations” field of the questionnaire should include all information deemed relevant by the interviewer, which would help to validate and analyse collected data after the interview. This prevented questionnaires from being returned and/or avoided subsequent contacts with the interviewee to confirm/justify the information.
6.3.3. Non response error
1. Unit non-response: reasons, analysis and treatment
The main reasons for non-response were: i) absence of the holder - 176 units ; ii) refuse to answer - 58 units.

The non-response units and the unknown eligibility status units were eliminated from the sample, which was re-weighted.

There were also 316 units not found by the interviewer, which were considered with unknown eligibility status.

There is no pattern for the non-respondent holdings. This occurred throughout all the holdings' categories (whether we consider the size or the type of holding), which means that the non-response bias risks are not significant.


2. Item non-response: characteristics, reasons and treatment
PT is not aware of any higher non-response rate for a specific characteristic. Unit non-response - rate
Unit non-response - rate
Eligible units: 24528; Non-responding holdings: 550; Rate: 2.2%. Item non-response - rate
Item non-response - rate
Not available. All known cases of non-response items were solved by re-interviewing the unit.
6.3.4. Processing error
1. Imputation methods
Imputation was not used.


2. Other sources of processing errors
The following procedures where taken during the data collection period. No errors where detected/corrected after that period.

Questionnaire returned by the chain of collection
In addition to the interviewer, the questionnaire was analysed by different profiles in the FSS chain of collection (see section 3.4 Data validation), implying its return in case of error and/or information misaligned with local circumstances. This led to a more thorough analysis/validation. Returning the questionnaire to the interviewer and identifying the reasons avoided the perpetuation of possible errors and erroneous interpretations of the concepts.

Regular meetings involving the collection structure
Meetings carried out at the different levels of the FSS chain of collection facilitated information flows among them. Discussing the main issues/problems arising from the work developed made it easier to standardise the criteria to solve similar situations.

Procedures to confirm/correct microdata
The illustration in annex outlines the procedures to be followed when incorrections are detected or when questions raise doubts. The solution may involve confirming the situation after analysis of the “Observations” field, in a simpler case, or a new contact with the holder, in the most complex case. See annex.


3. Tools used and people/organisations authorised to make corrections
The corrections were made by the interviewers and the diferent profiles in the FSS chain (local and regional staff and national staff) on the custom-made software application that supports the agricultural survey system of Statistics Portugal (SAGR).

6.3.4-2. Procedures to confirm/correct microdata, conducted by data collection chain Imputation - rate
Imputation - rate
This method was not applied in FSS.
6.3.5. Model assumption error

[Not requested]

6.4. Seasonal adjustment

[Not requested]

6.5. Data revision - policy
Data revision - policy
As a rule, data in FSS are not subject to revisions.
6.6. Data revision - practice
Data revision - practice
Not relevant.
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
Time lag – first results: 11 months.
7.1.2. Time lag - final result
Time lag - final result
Time lag – final results: 11 months.
7.2. Punctuality

See below

7.2.1. Punctuality - delivery and publication
Punctuality - delivery and publication

Punctuality of the publication of the results


Scheduled date of publication

Actual date of publication

Time lag

First results

28 November 2017

28 November 2017


Final results

28 November 2017

28 November 2017


8. Coherence and comparability Top
8.1. Comparability - geographical
1. National vs. EU definition of the agricultural holding
The national definition of the agricultural holding is the same as the definition from Regulation 1166/2008 art.2.a. 


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


3. National vs. EU characteristics
The latest version of the Handbook implementing the FSS and SAPM definitions used during the preparation/organisation of the FSS was the CPSA/SB/652 ver.10.

The number of hours per year for a full-time employee, used to calculate the Annual Work Unit, was 1 800h.


4. Common land
4.1 Current methodology for collecting information on the common land
In Portugal there is land owned and managed by local communities, the so-called common land. Common land are common adjacent grounds for agricultural, forestry, sylvo-pastoral or apicultural uses, notably cattle grazing, cultivation, harvesting of wood and scrub, etc. For the purpose of representation, planning, management and auditing, they have an assembly of counterparts, a governing board and an auditing commission. In general, the governing board is composed of a group of inhabitants of the commune where the common land is located, and may also be managed by the commune office. The right to use the common land is restricted to counterparts according to customs. As a rule, it has no permanent workers or livestock.
Common land should be recorded using one of the following three methods (by priority according to the following order):
1. In proportion to its use by each holding. In this method, common land used by a holding would be included in its UAA (taking care to avoid double counting);
2. As “common land agricultural holding”, as long as the techno-economic unit fulfils the agricultural holding definition criteria;
3. Indication, for the most relevant geographic level (e.g. NUTS 3), of the total area of common permanent grassland.
Following the method already used in previous farm structure surveys, Portugal adopted method 2 and considered common lands as agricultural holdings.
The questionnaire used to survey common land was that used for any other holding. The common land option was included in the legal personality of the holding, and these units were recorded as "common land agricultural holdings" in the EU characteristics on the legal personality of the holding. The rule is to treat the common land, in terms of tenure, as agricultural area utilised for farming by owner.
See Question on the legal personality of the holding (Mainland FSS questionnaire) (in Annex) for the question on the legal personality of the holding.

Finally, it is important to stress that the entire UAA of common land holdings is common land.

4.2 Possible problems encountered in relation to the collection of information on common land and possible solutions for future FSS surveys
The main concern was to avoid double-counted areas in the common land and in the holdings of holders using common land. The focus was on training, interviewers having been alerted to these situations. Data analysis, throughout the collection, allowed for the control, detection and correction of those cases where there was double counting.
4.3 Total area of common land in the reference year
Total area of common land is 215 168 ha (113 348 ha of UAA). Approximately 99% of the UAA of common land is permanent grassland.
4.4 Number of agricultural holdings making use of the common land or Number of (especially created) common land holdings in the reference year
The FSS computed 633 common land holdings.


5. Differences across regions within the country
The thresholds applied in the Mainland (1 ha of UAA) are different from the ones on Autonomous Regions of Azores and Madeira (0.1 ha of UAA).


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 data produced in Portugal under the Reg. 834/2007 presents problems of quality and timeliness.
Also with regard to the concepts there are differences, mostly due to the inclusion of insect production (in animal production) and wild plant production (in crop production). Moreover certification companies often do not report to the Ministry of Agriculture individual data but only aggregate information by control body.

8.1-4.1. Question on the legal personality of the holding (Mainland FSS questionnaire)
8.1.1. Asymmetry for mirror flow statistics - coefficient

[Not requested]

8.2. Comparability - over time
1. Possible changes of the definition of the agricultural holding
There were no changes in the definition of agricultural holding, therefore data comparability with previous operations is perfectly possible.


2. Possible changes in the coverage of holdings for which records are sent to Eurostat
There were no changes in the coverage of holdings for which records are sent to Eurostat, therefore data comparability with previous operations is perfectly possible.


3. Changes of definitions and/or reference time and/or measurements of characteristics
No changes were introduced to the definitions or measurement of characteristics. The reference date for livestock was the day of the interview, now has changed to a fixed date i.e. 1 September. This change is not likely to jeopardize the comparability with previous statistical operations.


4. Changes over time in the results as compared to previous FSS, which may be attributed to sampling variability
Anomalies in the evolution related with high sampling errors can be found in the following characteristics:
  • All variables concerning organic farming;
  • A_3_1_4 - Area of common land;
  • A_2 - Legal personality of the holding, namely those identified as common land;
  • B_1_8 - Flowers and ornamental plants (excluding nurseries);
  • C_6 - Rabbits, breeding females


5.Common land
5.1 Possible changes in the decision or in the methodology to collect common land
There were no changes in the decision or in the methodology to collect common land, therefore data comparability with previous operations is perfectly possible.
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
With regard to the 2013 farm structure survey there is an increase in the number of holdings (+158%), and in the UAA (+11%).


Common Land

FSS 13

FSS 16

Variation %

Holdings (No.)




UAA (ha)





These changes may be chiefly accounted for by two reasons:
• The type of survey held in 2013 and 2016 (sample surveys). The common land (as the other legal person) wasn´t considered in the sampling design (in Annex IV of Regulation (EC) No 1166/2008 there is no precision requests concerning the legal personality of the holding);
• The difficulty in quantifying precisely the areas of these holdings. Common land extends throughout large open hilly mountainous areas, with poor soils and pastures or forest land, and it is often very difficult for the common land governing board itself to indicate the area in a precise manner.


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 258,983 264,419 -2   
Utilised agricultural area (ha) 3,641,691 3,641,592  
Arable land (ha) 1,043,298 1,100,861 -5   
Cereals (ha) 251,844 301,607 -16   a)
Industrial plants (ha) 20,003 19,277  
Plants harvested green (ha) 437,627 377,674 +16  b)
Fallow land (ha)  251,759 333,072 -24   c)
Permanent grassland (ha)  1,876,943 1,816,585  
Permanent crops (ha) 705,120 708,765 -1   
Livestock units (LSU) 2,223,717 2,035,511  
Cattle (heads)  1,566,643 1,407,269 11   d)
Sheep (heads)  2,199,660 2,067,234  
Goats (heads) 390,458 383,030  
Pigs (heads) 1,875,110 1,844,950  
Poultry (heads) 36,052,165 28,614,815 26   e)
Family labour force (persons)  527,470 565,830 -7   
Family labour force (AWU)  229,952 250,058 -8   
Non family labour force regularly employed (persons)  76,247 60,561 26  f)
Non family labour force regularly employed (AWU)  56,780 48,493 17  f)

Comments on changes exceeding 10% vis-à-vis 2013:

a) the decrease on the area of cereals followed the tendency registered on the annual crop statistics, and it is particularly notable on common wheat and grain maize. The main reasons for these changes are the crop diversification (related with the ecological part rules of the direct payments of the first pilar of the CAP) and the steep decrease of maize price on international markets;

b) the increase on the area of plants harvested green was mainly due to the increase on temporary grassland;

c) the compliance of the ecological part rules of the direct payments can be done by ensuring an ecological focus area (which includes fallow land), but also by crop diversification or by maintenance of permanent grassland, which has led to a diversion from fallow land to these latter options;

d) this increase followed the increasing tendency 2013-2016 of livestock statistics for cattle (administrative source: bovine register);

e) this increase followed the increasing tendency 2013-2016 of day chicks for meat production (+9%) and of broiler meat production (+10%);

f) these trends are related to the decrease of family agriculture (mostly based in family labour force), replaced by an enterprise/professional agriculture (mostly based in non-family labour force). Also, stress should be drawn to the fact that some of these holdings that are now registered as enterprises continue to be managed on a family-based way, and have only changed their legal personality due to tax issues. The labour force of these holdings is collected as non family labour force.

8.2.1. Length of comparable time series

[Not requested]

8.3. Coherence - cross domain
1. Coherence at micro level with other data collections
8.3-1. Data sources for comparisons (in annex) shows the information sources, as well as the respective comparison level and items. See Microdata in column Comparison level.


2. Coherence at macro level with other data collections
8.3-1. Data sources for comparisons (in annex) shows the information sources, as well as the respective comparison level and items. See Aggregated data in column Comparison level.
8.3-2. Main output of the comparison with IFAP _ areas of land declared
(in annex) presents the main output of the comparison with the IFAP (IACS) source with respect to Areas of land declared. The comparison of collected data with IFAP data – Areas of land declared allowed for effective monitoring during collection. By comparison, rather satisfactory data were obtained for the main monitored crops. As the coverage of FSS is larger than the IFAP, it is only natural that data collected exceed data taken from the IFAP source (areas of land declared by the beneficiaries receiving aid in 2016). In the case of oat, it is possible that some of this cereal has been incorrectly classified in the source as oat harvested green, thus justifying the large difference between FSS and IFAP. As regards the total area, much other land contributing to the total holdings' area is neglected in the information reported to the IFAP.
8.3-2. Main output of the comparison with IFAP – SNIRA (Animal register) (in annex) presents the main output of the comparison with the IFAP source – SNIRA (Animal register) with respect to dairy cows, other cows, cattle, pigs, sheeps and goats.

The random differences in animal categories between this administrative source and the Farm structure survey results can mainly be justified by the basic methodological differences of these two operations (sample survey versus administrative exhaustive source).

FSS statistics are, obviously, crossable with other statistics/domains, namely crop, animal and national account statistics.

8.3-1. Data sources for comparison 2016
8.3-1. Main output with the comparison with SNIRA (animal register) 2016
8.3-2. Main output with the comparison with IFAP (IACS) areas of land declared 2016
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

A press release with the main results is scheduled for the end of November.

9.2. Dissemination format - Publications
1. The nature of publications
A publication with the main results and comparisons with previous editions is scheduled for the end of November.


2. Date of issuing (actual or planned)
28 November 2017


3. References for on-line publications
Statistics Portugal published the publication and press release in its official statistics website
9.3. Dissemination format - online database
Dissemination format - online database
The on-line database releasing the output can be found on the Statistics Portugal website -> Statistical data -> Database (Theme: Agriculture, forestry and fishing; Sub-theme: Agricultural census).  Every indicator is linked to the associated metadata, by just following the link close to the name of the indicator. 
See attached 9.3 Example of the statistical data available in the online database.

9.3. Example of the statistical data available in the online database
9.3.1. Data tables - consultations
Data tables - consultations
See the indicative number of consultations in annex.

9.3.1. Indicative number of consultations 2012
9.4. Dissemination format - microdata access
Dissemination format - microdata access
The academic community has special requirements as regards statistical data, especially in terms of the development of research and preparation of Masters and PhD theses.
Against this background, Statistics Portugal established a Protocol with the Ministry of Science, Technology and Higher Education, with a view to facilitating access by researchers to the statistical data required for their activity. See attachment.
For this purpose, the interested researchers must be approved by the Office of Planning, Strategy, Assessment and International Relations, where they may obtain all the necessary information.

9.4. Protocol INE_MCTES (only PT)
9.5. Dissemination format - other

[Not requested]

9.6. Documentation on methodology
1. Available documentation on methodology
The methodology on FSS is available for consulting in official statistics website (just in Portuguese).


2. Main scientific references
Levy, P., Lemeshow  S. - Sampling of Populations- Methods and Applications;

Statistics Canada, Survey methods and practices.

9.7. Quality management - documentation
Quality management - documentation
Within the statistical production process of Statistics Portugal, any statistical operation should be certified through ​​a methodological dossier validated by all the organizational structure of Statistics Portugal ensuring compliance with the European Statistics Code of Practice. The document attached, is only available in Portuguese and can be consulted for any stakeholder through the INE website

9.7. FSS methodological report PT
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 co-ordination is made by the field chain (interviewers) since field work is distributed previously taking into account the location of the farm. Since 95% of the holdings have holders which are natural persons, there are few cases where the respondents have to answer multiple questionnaires with the same kind of questions.

11. Confidentiality Top
11.1. Confidentiality - policy
Confidentiality - policy
The national legislation provides for the confidentiality of data collected both as regards data on enterprises and on individuals. The principle of statistical confidentiality is thus applied, i.e. individual statistical data cannot be disclosed (Article 6 of Law No 22/2008 of 13 of May). The violation of statistical confidentiality considered as a breach of the obligation of professional secrecy is punishable (Article 32 of Law No 22/2008 of 13 of May).
All those involved in the FSS were bound by contracts or protocols listing their responsibilities with regard to the FSS. These responsibilities were notably technical, or within the scope of statistical confidentiality and professional secrecy, in accordance with the law (Articles 6 and 32 of Law No 22/2008 of 13 of May).
11.2. Confidentiality - data treatment
Confidentiality - data treatment
The FSS promotes the most extensive use possible of information, while ensuring compliance with the NSS Law.

Output dissemination at NUTS II level

The analysis made to the variables collected in FSS and their mostly physical nature prevents the respective agricultural holders from being in any way identified. Moreover, there are variables such as crop area (in the case of temporary crops, as the name indicates, they vary every year depending on market and weather conditions during the crop year, and, in the case of permanent crops, they vary depending on the options taken by farmers at a given moment, with new planting or grubbing-up, etc.), which, due to its variability, do not allow for the identification of any holder. Information on livestock is under the same conditions: due to seasonality throughout the crop year, arising either from the productive cycle or from demand peaks on feast days (Christmas, Easter, etc.), it reveals significant changes in total livestock over the year. Also, agricultural labour is not subject to secrecy, given that it is collected and made available in groups, according to the legislation in force (Article 6 (4) (b) of the NSS Law).
Therefore, and also given the vast geographical area covered, no situations are envisaged in which the information released leads to direct or indirect identification of a certain agricultural holder, therefore there will be no statistical confidentiality treatment.

Dissemination of economic data, by type
Any issues related to the typology of holdings and economic data associated with physical data that are measurable in euro may be released, provided that they are based on aggregates. This information is currently already released as such, therefore this situation is also covered by the legislation in force.

12. Comment Top
1. Possible improvements in the future
The structure of the quality report was an adaptation to the structure defined for the FSS 2009/2010. Statistics Portugal considers that this version is necessarily provisional and should be improved (simplified in terms of content and improved in terms of structure). For example, item 3 is redundant with the information reported in many of the previous points.


2. Other annexes
Not available.

Related metadata Top

Annexes Top