The first Agricultural Census in Finland was conducted in 1910, and the tenth in 2010. Since Finland joined the EU in 1995, the Information Centre of the Ministry of Agriculture and Forestry (Tike) was responsible for implementing Farm Structure Surveys until the year 2014. From the beginning of the year 2015 Tike's Statistical services were joined to the Luke (Natural Resources Institute Finland). Luke was responsible for implementing Farm Structure Survey 2016.

The Farm Structure Survey complies with current EU legislation. There is no separate national legislation governing Farm Structure Surveys. Lukes’s statistical production is based on the Act on the Natural Resources Institute of Finland (561/2014) and on the Act on Natural Resources Statistics (562/2014), which grants Tike extensive rights to collect data on commercial agriculture and horticulture that involves trade, product processing, and running a commercial rural enterprise.

Finland’s Statistics Act (280/2004) governs statistical production and disclosure obligation. According to this act, statistical authorities must attempt to produce their statistics using existing administrative material. Information that cannot be gathered from other sources may be collected from informants, as long as this has been agreed on in advance with either the informants or their benefits organisations. Changes to existing data collections must also be agreed in advance.

According to the Finland's Statistical Act respondents are required to answer to the statistical questionnaires. In practice there is no penalties in use. Farmes are very conscientious and the response rate of the surveys is high enough. 

The respondents were informed about the survey in advance by a letter. The telephone interviewers were obliged to keep the information collected as confidential. 

IACS: Integrated Administration and Control System

LUKE: Natural Resources Institute Finland

TIKE: Information Centre of the Ministry of Agriculture and Forestry

The agricultural holding is a single unit, both technically and economically, which has a single management and which undertakes the following agricultural activities within the economic territory of the European Union, either as its primary or secondary activity: growing of crops (non-perennial or perennial), plant propagation, animal production, mixed farming or support activities to agriculture and postharvest crop activities. An agricultural holding may maintain land in good agricultural and environmental conditions even if no other agricultural activities are covered.

The definition is in line with the EU definition (Regulation (EC) 1166/2008). 

Reference dates for the FSS 2016 are as follows:

Use of arable land: 30th of June 2015 - 1st of July 2016
Number of livestock: horses, pigs and poultry 1 April 2016; cattle, sheep and goats 1 May 2016

• Labour force: 1 September 2015–31 August 2016
• Energy production from renewable sources: calendar year 2016
• Irrigation: irrigated area 2016 (from late April to mid-October)
• Other gainful activities: calendar year 2016
• Land characteristics other than arable land: 1 May 2016
• Rural development measures: years 2014, 2015 and 2016

The project’s sub-areas were: information content, data collection, data processing, publication of the results.

Calendar (overview of work progress)

Luke was responsible for the implementation of the FSS 2016 Survey. Luke carried out its own data collection using data collection software, and also ran the telephone service for farmers that was used during electronic data collection. The telephone interviews were done by Kantar TNS Oy, which is an independent and unaffiliated Finnish market research company. Kantar TNS was used for the telephone interviews. Kantar TNS committed itself to complying with the information security and quality criteria set by Luke. The company uses both an ISO 9001:2008-certified quality system and the international market research industry standard ISO 20252. The company’s website address is http://www.tnsglobal.com/     

Quite a lot of the data (the geographical location of the farm, the area under different crops, the number of livestock, organic production, and questions and coordination data relating to rural development subsidies) were obtained from registers. The rural development data was especially compiled for the FSS and included only the farms of the survey sample.

The rest (labour force, other business activities on farms and some data on irrigation) was collected in a sample survey using either an online questionnaire or telephone interview.

The sample frame for the FSS 2016 included all farms recorded in the 2016 whose standard output was more than 2 000 €. The registers used to form the sample frame are updated annually. The sample frames were updated for the sampling 30.9.2016. As the Farm Register, Horticultural Enterprise Register and IACS use the same farm code, these registers could be successfully consolidated into a sample frame.

The sample frame is a list frame.

In 2016, no separate statistical survey was conducted for farms that had not applied for subsidies, as in connection with the farm survey these farms were asked to inform if they had stopped farming. In many cases this had happened. None of the farms that had not applied subsidies in the spring 2016, remained in the sample after the application of SO 2000 € threshold.

Farms that don't apply for subsidies are often small and their owners elderly. They want to cultivate a small area, mainly as a hobby. These farms often have only grassland and/or fallow land. It is sometimes difficult to decide whether such farms are active or not. 

Single-stage stratified random sample of holdings.  

About a half of the data for the FSS 2016 was obtained from statistical or administrative registers, and this information did not need to be obtained from farms during data collection. Sources for the FSS 2016:

IACS, Animal Register and Bovine Register 

Integrated Administration and Control System (IACS), which contains information provided by farmers in subsidy applications, was the source of basic farm details (farm code, location, etc.), arable land use, crop areas, and the number of horses and poultry. Pig numbers were initially taken from IACS or, if IACS contained no pig data for a specific farm, from the Animal Register. Cattle numbers were updated from the Bovine Register, and sheep and goat numbers from the Animal Register.

Data are copied from administrative registers for statistical use annually in October, when all subsidy applications have been recorded and no significant changes were made to administrative data.

IACS is maintained by the Agency for Rural Affairs (Mavi). Its data are obtained from farmers’ agricultural subsidy application forms, whose data are checked by municipal rural business authorities. About half of the farmers provide their information electronically using online forms. Pro Agria’s (an advisory organization) Agricultural Mtech Digital Solutions maintains the Bovine Register, while the Finnish Food Safety Authority (Evira) maintains the Animal Register. Farmers provide these organizations with data on livestock using online or printed forms, or over the telephone.

Areas of greenhouse crops are not in IACS. They are collected in an annual horticultural survey and obtained from the Agricultural and Horticultural Enterprise Register.

Organic Farming Register

Farms that engage in organic farming must be entered into the organic control system. Information on these monitored farms is collected in the Organic Farming Register, which is maintained by the Finnish Food Safety Authority (Evira).

Agricultural statistics have large rights of access to the administrative data. About a half of the FSS 2016 data was from administrative registers. Statistical Services of Luke is part of the working group which handles conceptual design. There are no big problems with co-operation.

However, the integration of administrative and statistical data definitions is not always completely problem-free. For example, crop area data are collected in subsidy application forms in much greater detail than in structure surveys. IACS included data on over 200 different variables (codes) for crops and land use. However, only about 50 different plant and land use variables are recorded in the FSS.

IACS: Not relevant, because IACS was used to define the population of active farms in FSS. 

Animal Register: With the register data of pigs, inconsistency in the classification is a problem. In the register, pigs are classified by age, whereas in FSS the required classification is by weight.  

Animal Register: A project on animal registers is starting to build a statistical model where administrative pig register data could be used reliably as the sole source of pig numbers for animal statistics and FSS. 

Animal Register: It sometimes takes a long time before changes in animal numbers are reported and entered into the register.

Animal Register: In the project on animal registers mentioned in item "misclassification", the process of estimating the number of animals from the register will be improved. 

However, with FSS there is a relatively long time between the reference date of animal numbers and the completion of the results, which mitigates the effect of delayed updating of the register on the animal numbers of the FSS.

Very few (some typing errors). 

Very few. 

The data was collected both electronically and via telephone interviews. About half of the data come from administrative registers. This means that those farms which did not answer to the questionnaire were not total non-response. They are partial non-response.

When data collection began, farmers were sent a printed letter containing a) a request to provide data electronically, and b) a user ID and password for the electronic data collection system. A reminder was sent to those farmers who had not responded after a set period. A printed data collection form was only sent with the second reminder. Farmers were, however, still able to respond electronically at that stage. Once electronic data collection had closed, telephone interviews were conducted with those farmers who hadn’t responded electronically. Farmers filled out data collection forms in advance, and then read this information to the telephone interviewer when prompted.

Telephone interviews were only conducted with those farms that had not responded electronically via the online form. Any farms that had ceased operation and notified Luke were removed from the list. The interviews went smoothly and to schedule. The table below shows a detailed breakdown of the non-response rate and the reasons for overcoverage in telephone interviews. However farms which did not answer to the questionnaire are not totally non response because a lot of data were from administrative registers.

Telephone interviews – non-responses and overcoverage

When using web questionnaire, respondent entered the data that was stored directly to Luke's database. Telephone interviewers used the same IT-system and also the intervieweres entered the data direct to the Luke's system. 

Half of the FSS data was taken from the administrative registers and half was collected with questionnaire. In practice there is no total non-response at all. Farms which didn't respond to the questionnaire are partial non-response.

Farmers were encouraged to respond electronically. One reminder was sent to those who had not answered by web by that time. The reminder also contained a printed data collection form. Text messages were also used to remind farmers to fill in questionnaire.

Kantar TNS won the contract for the computer-aided telephone interviews.  It was agreed on in advance that Kantar TNS would attempt to contact farmers nine times.

Farmers could call to Luke or send e-mail queries about the FSS .

Number of holdings in the survey frame plus possible (new) holdings added afterwards

In case of a census 1=3+4+5

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

Number of ineligible holdings with ceased activities

This item is a subset of 3.

Number of holdings with unknown eligibility status

4>4.1+4.2

Number of eligible holdings

5=5.1+5.2

Number of eligible non-responding holdings

5.1>=5.1.1+5.1.2

Number of the records in the dataset 

6=5.2+5.1.2+4.2

Thanks to both thorough guidelines and the numerous checks on electronically collected data, the information provided by farmers was largely reliable. The software rejected responses outside the value range and also ensured that information was recorded in every field. Checks resulted in either a warning or an error notification (=error). If an error was recorded, farmers were not able to submit the form until the error had been corrected.

Comparable checks were mostly used in the software used to record information given during telephone interviews.

The same checks were used in both the online forms and during the telephone interviews. There were two types of checks: a) errors that had to be corrected before the survey could continue, and b) warnings that could be skipped and did not prevent submission of the form.

Luke’s Statistics Group performed the same checks that had already been made by the data entry software and online form. The data were also subjected to several logical checks, the minimum and maximum values were ascertained, and checks were made for missing information. Due to the numerous checks and controls built into the interview software, Luke found very few deficiencies. Any errors or missing information were corrected by Luke. Efforts were made to use other register data in the place of missing information.

Data verification began during the collection period, as checks were carried out in online forms and by the software used to enter data during telephone interviews.

The checks done by Luke's Statistics Group were carried out with SAS software.

Although information was checked during collection, more thorough verification and processing were carried out once the data collection period had ended.

Only for national purposes were asked questions on the use of energy, development needs, the source of professional information, and the use of internet at farms. Use of energy was asked because we publish statistics about use of energy at farms. Use of internet is very topical because farmes should use it to fullfil their requirements. However internet is not working well enough in many places.

Altogether, the following data was collected for national statistical requirements: 

• Labour force on farms and horticultural enterprises: a by-person breakdown of the hours worked by permanent employees and farmers and their family members. Alongside agricultural and horticultural work, information was also collected on the time spent on forestry work and other business and income-generating activities.
• Foreign labour force: the number of employees and number of hours worked on agricultural and horticultural tasks.
• The most important sources of professional information for farms, and the sector of farm operations with the greatest need for development.
• Type, speed and reliability of Internet connection.
• The use of different sources of energy for farm activities.
• Information on other gainful activity (including line of business) was collected in greater detail than required by EU legislation.

Information on NS/NE characteristics of Finland can be found in the file at the link: (link available as soon as possible)

The results were estimated with SAS software. Variances of the characteristics collected on the sample survey were estimated using the CLAN software developed by Statistics Sweden (see annex, also available at http://www.amstat.org/meetings/ices/2000/proceedings/S09.pdf). 

Relative standard errors are in annex.

Even if we collected the main crop and livestock characteristics for each population unit, but carried out a sample survey for other characteristics, we reported at Eurostat's request the relative standard errors (RSEs) for the main crops and livestock characteristics, as if they were collected based on the sample, as well. The purpose is to illustrate the overall quality of the sample.

For the applicable cases, the RSEs are 0 as we conduct a census for crop and livestock characteristics.

However assuming a sample survey for crop and livestock characteristics, there are the following 'non-compliance' cases:
• breeding sows in NUTS2 region FI1C
• poultry in NUTS2 regions FI19 and FI1C.

The most important administrative source of data for farm structure statistics is Integrated Administration and Control System (IACS), where the date from farm subsidy applications is recorded. Farmers almost invariably fill in their subsidy applications meticulously, as they may otherwise face sanctions. Errors in land areas and livestock figures are usually minor and result from misunderstandings, lack of time, or inaccurate data entry. Information from other animal registers (bovine, pig, sheep and goat) is used as a source of animal number data. Farmers must inform the record keeper of any changes in their farm’s animal numbers by the due date. These registers are therefore largely comprehensive.

Farmers found questions concerning their labour force and the farm’s other business activities quite difficult. Calculating working hours retrospectively was a problem, as most farms do not keep an account of working hours. In these cases, calculating the annual number of hours spent on farm work was sometimes challenging. In Finland, agricultural workers – and livestock farmers in particular – work more than 1 800 hours per year, that is, more than one person-year. In previous surveys, forestry work may have been partially included in farm work. However, from 2005 onwards, the number of hours spent on forestry work has been a separate item in the questionnaire. Even now, the classification of certain tasks is open to various interpretations. In some cases, it is not always clear at what point farm or horticultural production becomes further processing, that is, other business activity.

Other questions for which farmers’ responses may contain measurement errors include irrigation, arable farming, horticulture, and livestock production. As this information may not be directly obtainable from registers, farmers may find it difficult to provide completely accurate information. This does not, however, have a significant effect on the final results.

There were two main sources of the FSS data. First source was administrative registers and the second source was questionnaires. Administrative registers include all the farms so there were actually not at all no responding to the variables which are from the registers. Instead there was some non-response in the questionnaires. About 1 600 farms had either refused to respond, or a response had not been received for some other reason (illness, farmer not reached, etc.).

Farms and horticultural enterprises that did not respond were left out and remaining farms were re-weighted. 

We have not run an analysis of non-response. However we suppose that there is no significant difference between non-response and respondents' characteristics.

Almost all respondents provided complete information. There were, however, rare exceptions when the farmer did not supply all the required information.

For example, some agricultural workers failed to disclose their year of birth and/or gender. In the case of farmers and their spouses, this information was, however, largely available in the Farm Register or IACS customer records. Information on other members of farmers’ families was obtained from the Population Register.

Some data on working hours was also missing. These gaps were filled in using a comparable person’s average working hours. For example, if the working hours for a milk-cattle farmer’s wife were missing, the average working hours of a milk-cattle farmers’ wife was used instead.

Missing information was so rare that it was dealt with on a case-by-case basis, and case-specific discretion was also used in individual cases. Discretion was used in, for example, information on a farm’s livestock numbers and the farmer’s employment outside the farm.

Missing information about other business activities was obtained using corresponding information from the 2013 and 2010 Farm Structure Survey.

The imputation method used varied depending on the amount of background information available for the variable in question. For example, IACS data on the farmer or farmer’s spouse could be used to fill in missing data about a farm’s labour force. The most common imputation method was to fill in a missing data item using an average obtained from similar farms, or to substitute information on a missing farm with data from a similar farm that had filled in the questionnaire. 

Due to numerous controls and checks, data processing errors are extremely unlikely. However, there was the potential for error when data from various registers were combined. As all the registers use the same farm identification code, combining register data was relatively trouble-free.

There is also a small chance of a processing error occurring when information is modified to fit the format specified by Eurostat. It is sometimes challenging to modify the data obtained from questionnaires so that it matches the variables used in the structure survey. Various errors can take place when information is reformatted. For example, labour force data were collected as working hours and then changed to person-years as required by Eurostat. However, Eurostat’s validation process is highly comprehensive and the potential for errors is minimal.

Corrections were the responsibility of the Luke's researcher in charge of the Farm Structure Survey. All corrections/changes were made by order of the researcher. Erroneous values were searched by looking at minimum and maximum values and outliers, and by cross-checks between different characteristics.

Once the electronic response period had closed, any missing information was collected via telephone interview.

The data collected during the FSS was delivered to Eurostat as a single file. The information was validated by Eurostat, which sent Luke a list of errors and items to be checked. Luke then carried out the necessary changes and corrections. If any errors are later detected or specified, a revised file will be sent to Eurostat.

Part of the data were published nationally. Once the data have passed Eurostat’s validation process, a final version will be published. Any corrections to published national data will be made according to the recommendations of the Official Statistics of Finland.

Telephone interwievs was finished on 10 March 2017. After that the data was checked and approved. The data collection was finished 31 of March 2017. The first preliminary data were published on 13 June 2017. The time between the end of data collection and publication of the first results was about three months. 

The time between the end of the reference period of labour characteristics (31.8.2016) and the publication of the first results was about nine and half months.

For other characteristics the reference period ended at the end of the year 2016, after which there were about six months before the publication of the first results. 

The estimation of the time between the end of data collection and completion of the final data is 12 months.

The estimation of the time between the end of the reference period of labour characteristics (31.8.2016) and the publication of the final results is about 20 months. The final results of labour force were published 19.4.2018.

For other characteristics the reference period ended at the end of the year 2016, the estimation of the time publication of the final results is about 18 months.

Results were published in five sets in the Luke's web page  http://stat.luke.fi/en/uusi-etusivu. There were no paper publications of the results.

The publication of the provisional data occurred as planned. First publications were done as preliminary because Eurostat has not yet accepted the data. The publication of the final data depens how long the validation process lasts. 

The data to be collected in the FSS is determined by EU legislation (European Parliament and Council Regulation (EEC) no. 1166/2008). The source used to obtain detailed definitions of this data was the Handbook on implementing the FSS and SAPM definitions – revision 10, CPSA/SB/652. Ver. 10.  The characteristics delivered to Eurostat are in line with the EU definitions.

AWU = 1800 working hours.

Weather conditions in Finland during the 2016 growing season

The summer was normal but in some parts of Finland the autumn was rainy. This affected the quality of the grain.

Number of farms has declined about 9% from 2013 to 2016. Family labour input of the same farm populations has declined by 10% in the same period. The number of farms in Finland has been decreasing for decades. The profitability of agricultural production has been very low long time. The economic situation is still bad.  Middle-age of Finnish farmers is quite high. Many farms close when farmer retires because there is no successor because of low profitability and hard work.

Information on the labour force and other business activities is comparable to previous Farm Structure Survey but is not fully comparable to the results of FSS 2010 because of the new threshold of farms. The new threshold affects the number of labour involved in agriculture more than the amount of labour input. However both the number of farms and people employed in agricultural occupations have declined steadily over the years. Questions concerning other business activities were first included in the 2000 Agricultural Census. The number of farms engaging in other business activities has also decreased over the years.

In Finland, arable land areas and livestock numbers are updated annually according to the data obtained from IACS and animal registers.

Permanent grassland is not very common in Finland. Animals can’t be outside through the year and number of grazing animals is not very high. It is possible that some of permanent grassland has become arable land. Some parts of this land may have become forest also. 

One way to describe the reliability of a sample is to compare the estimated data for farms included in the sample with the exhaustive data available for all farms. In the structure survey, this kind of comparison is possible for data such as livestock numbers and crop areas. These kinds of comparisons were also made during post-stratification. An attempt was made to adjust the stratification so that the values estimated from the sample were as close as possible to the ‘actual’ values calculated from the total data. Table "Structure survey differences between estimates and actual values in 2016" compares certain estimated data with exhaustive data. The estimated values for the most important crop areas and livestock numbers differ very little from the actual values, that is, usually less than 5 per cent.

Comparing agricultural labour force data with that collected by other organisations is more problematic. Statistics Finland collects labour force data in an annual survey, but differences in definitions mean that the results are not comparable. Statistics Finland’s labour force data are based on industry-specific information, while the FSS includes all those who engage in agricultural work on farms. 

The published FSS statistics use the same classification of regions and production sectors as other agricultural statistics.

Farm specific FSS data can be combined with other data using farm code, but this data is confidential.

First results were published as preliminary because the data was not yet accepted by Eurostat. The final data will be published more detailed.

Publications to date:

FSS 2016 - Internet connections and access to information of agricultural and horticultural enterprises, provisional data 13.6.2016, final data 5 of March 2018 http://stat.luke.fi/en/internet-connections-and-access-information-agricultural-and-horticultural-enterprises_en

FSS 2016 - Energy consumption, provisional data 8 June 2017, final data 25 of April 2018 http://stat.luke.fi/en/energy-consumption-of-agriculture-and-horticulture

FSS 2016 - Labour force, preliminary data 15 September 2017, final data 19 of April 2018 http://stat.luke.fi/en/labour-force

FSS 2016 - Other entrepreneurship, provisional data 12 October 2017, final data 16 of May 2018 http://stat.luke.fi/en/other-entrepreneurship-in-agriculture-and-horticulture

FSS 2016- Livestock Buildings and Manure Storages, provisional data 16 November 2017, final data 8 of June 2018 http://stat.luke.fi/en/livestock_buildings_and_manure_storages   

FSS 2016 - Farmland management and irrigation, preliminary data 14 December 2017, final data 20 of June 2018 http://stat.luke.fi/en/farmland-management-and-irrigation

There will be no printed publications about the results. All the results are published on Luke's web page http://stat.luke.fi/en/uusi-etusivu.

Microdata is not disseminated. Researchers can get microdata to the scientific research. This requires written application for the data. The researchers are not allowed to publish microdata.

Information for the FSS is collected for statistical use only. The format in which the results are published ensures that no information about individual farms can be deduced.

Farm-specific information is not surrendered to the authorities. Information can be provided to research institutions for research use, but only if the recipients and users adhere to the same confidentiality requirements as Luke.

The schedules of the FSS 2016 survey and other surveys were synchronised to avoid the situation where farmers must answer to several surveys simultaneously. 

Luke follows the data collection principle laid down in the Finnish Statistics Act: existing register data should be utilised where possible, and no information included in registers should be inquired upon again for statistical purposes. The majority of the data for the FSS 2016 was taken directly from statistical register.

For FSS 2016, we got farmers' education from education register and work done by farm worker from Farmers' Social Insurance Institution Mela. Crop rotation was also calculated using IACS data. We have studied how to reduce response burden in grant project. Report of this project in annex. 

Finland’s Statistics Act (280/2004) also governs confidentiality and data release for survey results. Statistical activities must adhere to the Act on Openness of Government Activities and the Personal Data Act. There is no separate national legislation governing the FSS or the Agricultural Census.