DEFINITION OF DATA COLLECTION
NEEDS FOR AQUACULTURE
Reference No. FISH/2006/15 - Lot 6
FINAL REPORT
April 2009
Part 2.
FEASIBILITY ASSESSMENT OF
AN ON-GOING DATA COLLECTION SCHEME
FOR AQUACULTURE
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This study has been prepared by
FRAMIAN BV (coordinator)
Achterburg 9
2641 LA Pijnacker
The Netherlands
Contact person:
Pavel Salz
Tel : +31 1536 98145
Fax : +31 1536 98152
E-mail : [email protected]
in co-operation with
Institute of Agricultural Economics and Information (VÚZEI), Czech Republic
Institute of Food and Resource Economics (FOI), Denmark
Finnish Game and Fisheries Research Institute (FGFRI), Finland
French Research Institute for Exploitation of the Sea (IFREMER), France
COFAD Consultants, Germany
Hellenic Centre for Marine Research (HCMR), Greece
Agricultural Economics Research Institute (AKI), Hungary
Irish Sea Fisheries Board (BIM), Ireland
Economic Res. Institute for Fisheries and Aquaculture (IREPA), Italy
Lithuanian Institute of Agricultural Economics (LIAE), Lithuania
Agricultural Economics Research Institute (LEI), Netherlands
Inland Fisheries Institute (INFISH), Poland
Eurico de Brito Consult (EBC), Portugal
University of Vigo, Spain
Swedish Fishery Board (Fiskeriverket), Sweden
Poseidon Aquatic Resource Management Ltd. (PARM), United Kingdom
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TABLE OF CONTENTS
1. CONCLUSIONS AND RECOMMENDATIONS ...................................................... 6 1.1. 1.2. 1.3. 1.4. 1.5. 1.6. 1.7. 1.8. GENERAL CONCLUSION ................................................................................................................... 6 SUITABLE ORGANIZATION ............................................................................................................. 6 METHOD OF DATA COLLECTION ................................................................................................ 7 SIZE OF THE SURVEY.......................................................................................................................... 7 ESTIMATION OF COSTS ..................................................................................................................... 7 AVAILABILITY OF FUNDING .......................................................................................................... 8 PROBLEMS AND SOLUTIONS .......................................................................................................... 8 RECOMMENDATIONS .......................................................................................................................10
2. CZECH REPUBLIC ................................................................................................... 11 2.1. 2.2. 2.3. 2.4. 2.5. 2.6. SUITABLE ORGANIZATION ...........................................................................................................11 METHOD OF DATA COLLECTION ..............................................................................................11 SIZE OF PRESENT AND FUTURE SURVEY...............................................................................12 ESTIMATION OF COSTS ...................................................................................................................13 AVAILABILITY OF FUNDING ........................................................................................................14 PROBLEMS AND SOLUTIONS ........................................................................................................14
3. DENMARK ................................................................................................................. 16 3.1. 3.2. 3.3. 3.4. 3.5. 3.6. SUITABLE ORGANIZATION ...........................................................................................................16 METHOD OF DATA COLLECTION ..............................................................................................16 SIZE OF PRESENT AND FUTURE SURVEY...............................................................................17 ESTIMATION OF COSTS ...................................................................................................................18 AVAILABILITY OF FUNDING ........................................................................................................18 PROBLEMS AND SOLUTIONS ........................................................................................................19
4. FINLAND ................................................................................................................... 24 4.1. 4.2. 4.3. 4.4. 4.5. 4.6. SUITABLE ORGANIZATION ...........................................................................................................24 METHOD OF DATA COLLECTION ..............................................................................................24 SIZE OF PRESENT AND FUTURE SURVEY...............................................................................24 ESTIMATION OF COSTS ...................................................................................................................25 AVAILABILITY OF FUNDING ........................................................................................................26 PROBLEMS AND SOLUTIONS ........................................................................................................26
5. FRANCE...................................................................................................................... 30 5.1. 5.2. 5.3. 5.4. 5.5. 5.6. SUITABLE ORGANIZATION ...........................................................................................................30 METHOD OF DATA COLLECTION ..............................................................................................30 SIZE OF PRESENT AND FUTURE SURVEY...............................................................................31 ESTIMATION OF COSTS ...................................................................................................................32 AVAILABILITY OF FUNDING ........................................................................................................33 PROBLEMS AND SOLUTIONS ........................................................................................................33
6. GERMANY .................................................................................................................. 40 6.1. 6.2. 6.3. 6.4. 6.5. 6.6. SUITABLE ORGANIZATION ...........................................................................................................40 METHOD OF DATA COLLECTION ..............................................................................................41 SIZE OF PRESENT AND FUTURE SURVEY...............................................................................46 ESTIMATION OF COSTS ...................................................................................................................47 AVAILABILITY OF FUNDING ........................................................................................................48 PROBLEMS AND SOLUTIONS ........................................................................................................48
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7. GREECE ..................................................................................................................... 53 7.1. 7.2. 7.3. 7.4. 7.5. 7.6. SUITABLE ORGANIZATION ...........................................................................................................53 METHOD OF DATA COLLECTION ..............................................................................................53 SIZE OF PRESENT AND FUTURE SURVEY...............................................................................53 ESTIMATION OF COSTS ...................................................................................................................55 AVAILABILITY OF FUNDING ........................................................................................................55 PROBLEMS AND SOLUTIONS ........................................................................................................56
8. HUNGARY .................................................................................................................. 59 8.1. 8.2. 8.3. 8.4. 8.5. 8.6. SUITABLE ORGANIZATION ...........................................................................................................59 METHOD OF DATA COLLECTION ..............................................................................................59 SIZE OF SURVEY ..................................................................................................................................59 ESTIMATION OF COSTS ...................................................................................................................60 AVAILABILITY OF FUNDING ........................................................................................................60 PROBLEMS AND SOLUTIONS ........................................................................................................60
9. IRELAND.................................................................................................................... 63 9.1. 9.2. 9.3. 9.4. 9.5. 9.6. SUITABLE ORGANIZATION ...........................................................................................................63 METHOD OF DATA COLLECTION ..............................................................................................63 SIZE OF PRESENT AND FUTURE SURVEY...............................................................................64 ESTIMATION OF COSTS ...................................................................................................................64 AVAILABILITY OF FUNDING ........................................................................................................65 PROBLEMS AND SOLUTIONS ........................................................................................................65
10. ITALY .......................................................................................................................... 69 10.1. 10.2. 10.3. 10.4. 10.5. 10.6. SUITABLE ORGANIZATION ...........................................................................................................69 METHOD OF DATA COLLECTION ..............................................................................................69 SIZE OF THE PRESENT AND FUTURE SURVEY ....................................................................70 ESTIMATION OF COSTS ...................................................................................................................71 AVAILABILITY OF FUNDING ........................................................................................................72 PROBLEMS AND SOLUTIONS ........................................................................................................72
11. LITHUANIA ............................................................................................................... 78 11.1. 11.2. 11.3. 11.4. 11.5. 11.6. SUITABLE ORGANIZATION ...........................................................................................................78 METHOD OF DATA COLLECTION ..............................................................................................78 SIZE OF PRESENT AND FUTURE SURVEY...............................................................................78 ESTIMATION OF COSTS ...................................................................................................................79 AVAILABILITY OF FUNDING ........................................................................................................79 PROBLEMS AND SOLUTIONS ........................................................................................................80
12. NETHERLANDS ....................................................................................................... 83 12.1. 12.2. 12.3. 12.4. 12.5. 12.6. SUITABLE ORGANIZATION ...........................................................................................................83 METHOD OF DATA COLLECTION ..............................................................................................83 SIZE OF PRESENT AND FUTURE SURVEY...............................................................................83 ESTIMATION OF COSTS ...................................................................................................................84 AVAILABILITY OF FUNDING ........................................................................................................84 PROBLEMS AND SOLUTIONS ........................................................................................................85
13. POLAND ..................................................................................................................... 89 13.1. 13.2. 13.3. 13.4. 13.5. 13.6. SUITABLE ORGANIZATION ...........................................................................................................89 METHOD OF DATA COLLECTION ..............................................................................................89 SIZE OF PRESENT AND FUTURE SURVEY...............................................................................90 ESTIMATION OF COSTS ...................................................................................................................91 AVAILABILITY OF FUNDING ........................................................................................................91 PROBLEMS AND SOLUTIONS ........................................................................................................92 4
14. PORTUGAL ................................................................................................................ 95 14.1. 14.2. 14.3. 14.4. 14.5. 14.6. SUITABLE ORGANIZATION ...........................................................................................................95 METHOD OF DATA COLLECTION ..............................................................................................95 SIZE OF PRESENT AND FUTURE SURVEY...............................................................................95 ESTIMATION OF COSTS ...................................................................................................................96 AVAILABILITY OF FUNDING ........................................................................................................96 PROBLEMS AND SOLUTIONS ........................................................................................................96
15. SPAIN ......................................................................................................................... 101 15.1. 15.2. 15.3. 15.4. 15.5. 15.6. SUITABLE ORGANIZATION ........................................................................................................ 101 METHOD OF DATA COLLECTION ........................................................................................... 101 SIZE OF PRESENT AND FUTURE SURVEY............................................................................ 101 ESTIMATION OF COSTS ................................................................................................................ 102 AVAILABILITY OF FUNDING ..................................................................................................... 102 PROBLEMS AND SOLUTIONS ..................................................................................................... 103
16. SWEDEN................................................................................................................... 108 16.1. 16.2. 16.3. 16.4. 16.5. 16.6. SUITABLE ORGANIZATION ........................................................................................................ 108 METHOD OF DATA COLLECTION ........................................................................................... 108 SIZE OF PRESENT AND FUTURE SURVEY............................................................................ 109 ESTIMATION OF COSTS ................................................................................................................ 110 AVAILABILITY OF FUNDING ..................................................................................................... 111 PROBLEMS AND SOLUTIONS ..................................................................................................... 111
17. UNITED KINGDOM ................................................................................................118 17.1. 17.2. 17.3. 17.4. 17.5. 17.6. SUITABLE ORGANIZATION ........................................................................................................ 118 METHOD OF DATA COLLECTION ........................................................................................... 118 SIZE OF PRESENT AND FUTURE SURVEY............................................................................ 121 ESTIMATION OF COSTS ................................................................................................................ 122 AVAILABILITY OF FUNDING ..................................................................................................... 123 PROBLEMS AND SOLUTIONS ..................................................................................................... 123 5
1. CONCLUSIONS AND RECOMMENDATIONS
This report presents the results of the feasibility assessment of the collections of costs and earnings data
for aquaculture in the participating countries.
1.1.
GENERAL CONCLUSION
The survey has demonstrated that in principle it is feasible to collect detailed costs and earnings data
aquaculture in the EU. Definitions of Structural Business Statistics have proven to offer a suitable
framework for this purpose.
The survey has also demonstrated that usual problems arise when data is collected in a new area, with only
limited knowledge of the statistical characteristics of the population. For the design of the future on-going
data collection programme it will have to be determined what information is relevant (preferably
prioritising various indicators) and which level of precision and confidence should be achieved. The ongoing data collection programme will than have to be further developed to meet these requirements.
The EU aquaculture is a relatively small sector in terms of number of firms per Member State. Detailed
stratification by species and on-growing techniques may lead to strata with only low number of firms. In
order to generate data from homogenous groups it would have to be collected from a relatively large share
of the firms in each stratum not to compromise confidentiality. Such detailed approach is likely to
substantially increase the costs of the programme. The estimation of costs of data collection budgeted in
this study are based on experiences in other areas (e.g. DCR and FADN) and implicitly assume delivery of
a similar quality of data.
1.2.
SUITABLE ORGANIZATION
Aquaculture is a relatively small activity, which means that collection of statistical information is more
complicated than when collecting data on large populations. Therefore it is essential that the data be
collected by organizations which are already experienced in data collection in other sectors. In this way
also the costs of overhead, ICT and personnel will be reduced. Therefore in most countries it is proposed
that organization already involved either in FADN or in DCR should be also selected for the collection of
data on aquaculture.
It may be also considered to involve the national statistical offices. The obligation of the national statistical
offices to collect specific data is determined by EU legislation. The ability and willingness of the NSOs to
collect data on such a small sector differs per country. In most MS the NSOs are focussing increasingly on
collection of data on relatively large sectors as defined by NACE, leaving compilation of data at a lower
level of disaggregation to other specialized institutes.
The suitability of an organization depends also on the subsequent use of the data. NSOs are most suited
to collect and publish data which is used by a broad group of users. On the other hand, specialised
statistics (like those on agriculture and fisheries and possible aquaculture) may be better compiled by
organizations which are also involved in the empirical analysis of these sectors as understanding the
quality of the data is often important for interpretations and analysis.
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1.3.
METHOD OF DATA COLLECTION
There is a general agreement, supported by the experiences from the surveys, that reliable data can be
collected only with intensive contacts between the collectors and the firms. It is essential to build good
personal relations and use well trained staff. Surveys carried out by impersonal means (hard copy or digital
forms) are not likely to produce satisfactory results, and certainly not in the initial stage of the data
collection.
The following conditions must be met in order to collect reliable costs and earnings data:
• proper legislation must be in place;
• trained data collectors;
• software for data collection and verification at the firm level;
• central data processing;
• sufficient resources must be available.
One of the complicating factors is that many firms are small and are not obliged to maintain well
organized accounts, from which the various cost components can be quickly distinguished. Collection of
data from such firms will be very labour intensive.
1.4.
SIZE OF THE SURVEY
Size of the future survey depends on the objectives to be achieved, i.e. what do we wish to know and at
which level of precision. This will determine the size of the population (field of observation) to be
surveyed and consequently how large the sample will have to be. In this way it will be possible to optimize
the data collection effort and minimize the costs.
Data from various countries show that the samples (and consequently also the populations) often consist
of small and large firms. Therefore it will be necessary to carry first a general survey of the whole
population on the basis of which a proper stratification and the definition of the field of observation will
be developed. Only after this first stage will it be possible to define a cost effective strategy for the data
collection.
1.5.
ESTIMATION OF COSTS
The costs of data collection depend on the availability of accounts and the definition of the field of
observation (i.e. selection of a threshold). While application of a threshold is a common practice in
agricultural statistics under FADN, the data collection of fisheries (catching sector) does not allow use of a
threshold and requires sampling to achieve coverage of the full population. These two considerations have
evident implications (in most countries) on the level costs of the data collection programme on
aquaculture. Table 1.1 summarizes the national cost estimates. The total annual costs of the programme
can be at this stage estimated at 2.6 million Euro. Initial investment costs have been estimated at about 1
million Euro.
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Table 1.1
Summary of expected operational and investment costs (1000 Euro)
Operational costs
Without threshold
With threshold
Country
Estimated
Accounts
Accounts
Accounts
Accounts
average
not
not
available
available
available
available
Czech Republic
174
174
Denmark
249
236
243
Finland
175
113
144
France
364
412
339
379
374
Germany
707
707
Greece
105
135
85
110
109
Hungary
131
150
106
123
128
Ireland
220
220
145
145
183
Italy
137
189
122
168
154
Lithuania
20
23
22
Netherlands
75
75
Poland
15
21
18
Portugal
6
6
6
6
6
Spain
97
182
66
153
125
Sweden
81
81
United Kingdom
67
25
67
53
Total costs
2,593
Investment
costs
55
157
19
158
25
15
76
37
44
15
1
250
47
120
18
1,037
Source: national chapters
The differences of operational and investment costs between the various MS reflect the very different
conditions in terms of:
• Nature and size of the aquaculture sector
• Existence of infrastructure for data collection
• Level of costs, in particular labour
• Assessment of the complexity of the data collection scheme in general.
1.6.
AVAILABILITY OF FUNDING
There is a general consensus that the institutions suitable for the collection of the data on aquaculture do
not have own resources to execute it. Additional funding will be required to cover all costs connected to
this new activity.
1.7.
PROBLEMS AND SOLUTIONS
1.7.1.
Extrapolation from sample to population
Extrapolation from sample to population has been carried out by different partners. The descriptions are
presented in the national chapters.
1.7.2.
Evaluation of individual indicators
This section presents average values of the sample together with two statistical indicators:
• Relative standard deviation (also called coefficient of variation)
• Relative standard error.
Standard deviation is an indicator of the variability of the values of the firms in the sample. Relative
standard deviation expresses the standard deviation as a percentage of the sample mean. High relative
8
standard deviation means that the sample (and consequently also the population) is heterogeneous (e.g.
consists of small and large companies). In that case the sample average may not reflect any of the firms
well. This is particularly the case when the population is not normally distributed.
Relative standard error is a measures of the variability of the sample means, when various samples would
be drawn from the given population. The value of (relative) standard error may be reduced by increasing
the size of the sample. The relative standard error indicates how likely it is that a different sample mean
would be obtained if a different sample would have been drawn.
If the population would be normally distributed and the sample representative (random selected) than
68% of the population would be within +/- one standard deviation. Similarly, in 68% of the drawn
samples the sample mean would be within +/- one standard error.
It must be stressed that in most countries it is not known whether the population is normally distributed
or not. In fact there are indications that this is not the case. Furthermore, the surveys depended on the
willingness of the individual firms to cooperate and were too small to allow for incorporation of routines
to deal with non-response. Therefore it is not certain to which extent the requirement of ad random
selection has been met.
The presented indicators are based on Eurostat definitions for Structural Business Statistics. Although the
national surveys have been carried out in different ways (which is not unusual for data collection in the
EU), use of common definitions assures that the data are comparable across MS.
In conclusion, the presented data are valuable, as this is for the first time that this kind of information has
been collected. At the same time, care must be taken not to interpret the data in a deterministic way.
1.7.3.
Cross-check with other sources
Production volumes from aquaculture are presently collected or estimated by national statistical offices,
ministries of agriculture and some external firms. The same national data is than transmitted to Eurostat
and FAO. Furthermore FAO 1 estimates the values of production using average prices per species,
although it was not possible to obtain the precise methodology for this calculation.
Data from the samples have been extrapolated to the whole national populations. The results for volumes
and values have been compared to the already existing information at Eurostat and FAO. Level and
background of (in)consistencies are presented in the national chapters.
1.7.4.
General problems
The most important problem encountered was the poor willingness to cooperate on the part of the
farmers. They see no direct benefit for themselves and on the contrary face additional costs, having to
devote their own time to facilitate the survey. In view of the general policy to reduce administrative costs
for businesses it is essential to determine how the aquaculture industry will directly or indirectly profit
from the availability of the data in order to increase the level of cooperation.
It must be pointed out that the methodologies used for the collection, provision and validation of data for FAO are
not known. Harmonization of concepts and definition and the resulting ‘handbook’ is expected to be completed in
2010.
1
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1.8.
RECOMMENDATIONS
1. Maximum efficiency and effectiveness of an on-going data collection scheme can be only achieved if
the future intended data use is well defined, which will also allow a precise formulation of the
objectives of the scheme as well as prioritization of the indicators to be collected or estimated.
2. The pilot survey has demonstrated that a significant level of heterogeneity still exists within the
defined segments of aquaculture firms (based on species and on-growing technology). This
heterogeneity is caused by differences in size of the firms, but also by the level of vertical integration,
e.g. own production or acquisition of juveniles. Therefore it is recommended to define the ‘field of
observation’, including suitable thresholds (an approach also applied in FADN), and focus the ongoing data collection on it. The field of observation should be first of all defined selecting firms for
which aquaculture is the principle activity (see part 3, annex 4 for Eurostat definitions). Additional
criteria could be also applied, e.g. with focus on species or size. Data on segments which fall outside
the field of observation can be collected in ad hoc surveys to be carried out according to specific
needs once in several years. Average segment data should be based on at least five firms, none of
which should represent more than a specified percentage of the total production value.
3. In addition to the definition of the field of observation it is recommended to prioritize the indicators
to be collected. Data on high priority indicators (turnover, personnel costs, total operational costs,
employment) should be annually collected. Data on lower priority indicators (details on composition
to operational costs and capital costs) could be collected only once in several years in ad hoc surveys,
whilst estimation procedures should be developed to generate this data information whenever needed.
4. Co-operation of the aquaculture industry is indispensible for several reasons: a/ to obtain access to
the data, b/ to justify the additional administrative costs which the data collection will imply for the
surveyed firms and c/ to promote the legitimacy of analysis based on that data, so that the results are
not disputed or discredited as being based on biased information. Therefore the objective of the data
collection scheme as well as certain details of the implementation (prioritization of indicators) should
be developed in dialogue with the industry, e.g. within ACFA.
5. As the number of firms in new areas of aquaculture in individual countries is very low, it is
recommended to pool the data of the anonymised individual companies from several Member States
to calculate averages at EU level. This approach is likely to produce a lower relative standard error and
data confidentiality will be easier to guarantee. Submission of individual farm data is also practiced
under FADN.
6. Collection of the aquaculture data should be executed by organizations already involved in
compilation of statistical data scientific analysis in comparable areas, such as agriculture or fishing.
This approach will have several important advantages: a/ proximity of data collection and analysis
allows a better interpretation of the quantitative results due to precise knowledge of strengths and
weaknesses of the data, b/ the link between analysis and data collection will be beneficial for
prioritization and implementation of ad hoc studies on specific new aquaculture activities and/or
detailed indicators as proposed above, including various estimation procedures.
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2. CZECH REPUBLIC
2.1.
SUITABLE ORGANIZATION
The FADN Liaison Agency at the Institute of Agricultural Economics and Information could implement
a data collection system in aquaculture, provided that additional resources for budget and staff become
available. Experience with data collection in agriculture would be an advantage. Costs could also be
optimized, as some resources for ICT, staff and overhead, consumed by the FADN unit, could be utilized
for this system as well.
Cooperation with the Ministry of Agriculture and the Czech Fish Farmers Association on the organization
of a data collection system is assumed.
2.2.
METHOD OF DATA COLLECTION
A survey, concerning the economic performance of firms, is quite complicated with regard to the
definition of variables, possibilities of errors and low reliability of collected figures. The best way to carry
out such a survey is to use well-trained data collectors for data collection. It will be the responsibility of
interviewers to collect data corresponding to the data definitions, input data into electronic form and
execute primary tests of data at the farm level. In the case of big companies, these data collectors will
cooperate with the accountants of these companies. In small units without accounts, these data collectors
will have to ensure the required format and content of figures.
An efficient utilization of ICT for data collection and processing is essential. Interviewers collecting
figures at the farm level should have software available for data collection which involves a data test, in
order to do basic data checks and corrections at the farm level during data collection. Data transfer and
conversion from individual interviewers to the central database must be simple and fast. Database
management at the central level must allow further checking of data quality and the processing of outputs
from the database. The whole software solution has to be flexible, and most importantly it has to allow
simple and fast software modifications regarding survey content and changes in tests.
The survey carried out within the project was of a pilot character and was realized in a simple way. A
paper form questionnaire was used for data collection. It was sent to fish farmers either by mail or by
delivery from an employee of the Czech Fish Farmers Association. Data from questionnaires was
processed and checked at the Liaison Agency FADN CZ and was stored in a database. Problems in data
quality were discussed directly with fish farmers, generally by telephone.
It was not possible to cooperate with properly trained data collectors because of limited financial
resources and the short time period given for the survey. Software for data collection, including basic data
tests which would be done directly at a farm, was also not available. This led to anticipated negative
impacts on the quality of the data obtained. The questionnaire was filled in roughly and in different ways
by many of the respondents. Obtaining data from respondents without accounting was a big
methodological problem. Another problem, apparently specific to the Czech Republic, resulted from the
fact that fish farmers carry on other significant business activities, e.g. forestry production, agricultural
production, etc. It was difficult to allocate some of the questionnaire items (e.g. wages paid) to particular
activities. Solving these problems at the Liaison Agency FADN CZ, as was done within the project, was
very complicated and time consuming.
Any future survey has to be ensured by legislation, trained data collectors, software for data collection and
verification at the farm level, and by central data processing. These factors depend on sufficient financial
resources for the implementation of this information system. If these requirements are fulfilled, the survey
will correspond to FADN standards, as they are mentioned in this chapter opening, and it can be assumed
that this system would guarantee the required quality of data.
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2.3.
SIZE OF PRESENT AND FUTURE SURVEY
It is assumed that a sample survey will be applied as a basic approach. The sample size depends on the
farm population (or field of observation) and on the requirement that results of the survey be
representative. The objective is to minimize the sample size and survey costs while guaranteeing the
reliability of the survey results.
Specific farm populations exist in the CR. Most producers have a similar line of production, with carp
production prevailing, very often combined with minor production of salmonid and other freshwater fish
species. The basic technology is fish breeding in ponds. A majority of such farms run their own nurseries
and hatcheries.
A small number of big companies of this type represent a great percentage of the total national
production. But there exists a group of quite numerous small farmers with a similar farm orientation, and
the same growing techniques.
The population of farms specialized in other species of fish or specialized in nurseries and hatcheries is
very limited. It would be complicated to cover these firms as a separate segment in a sample, due to the
limited number of farms in the population.
One problem is to identify farms whose main activity is aquaculture. Due to its specific type of production
and low profitability of this production, fish breeding is very often combined with agricultural activities
and forestry production. Even very big fish breeders may produce fish as a secondary activity. It would be
a big problem for the representative nature of a survey sample to exclude these firms from the field of
observation. Our solution is to involve these big producers into the sample but use a specific survey
procedure, wherein the figures collected will be allocated only onto fish production. This procedure is also
applied in agricultural FADN surveys since Czech farms have many non-agricultural activities.
As regards applying a threshold for determining the field of observation, we don’t recommend using one.
The population of market-oriented fish producers is so small in the CR that use of a threshold for
reducing the number of farms in the survey is not needed. On the other hand, we do assume a different
selection rate will be applied in the sample for big producers and for small producers. The market-oriented
producers which create the field of observation cover more than 90% of total production.
Results of the pilot survey have proven that quite a small sample can provide quite reliable figures, which
enable us to make conclusions about the production and economy of the whole sector. On the basis of
this survey and observation of the firm population, we propose the following survey sample approach.
We assume that the main segment of the survey will be firms specialized in carp production with optional
minor production of other species. There is no significant difference in technology.
The survey sample is stratified with respect to farm size. Due to the small number of firms specialized in
other fish species and specialized in nurseries and hatcheries, we propose not distinguishing these firms as
a separate segment of the survey. However, data concerning the production and economy of these lines of
production will also be registered in the basic group of farms.
We proposed introducing two tiers in the sample:
• Farms with an annual production over 10 tons of fish. This group of farms is well-identified. As each
firm in this group (about 70 firms) is quite specific with respect to size and economic characteristics,
we propose that the selection rate in a sample be 100%.
• In the second tier there are farms with an annual production below 10 tons of fish. The selection rate
into a sample for this group of farms is assumed to be about 20%. This means a preliminary estimate
of around 100 units in a sample.
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Table 2.1
Czech Rep., Main segments of the national aquaculture sector (number of firms)
Segment
(Species / technology)
Carp/ponds;
large-scale producers
Carp/ponds;
small-scale producers
2.4.
Population
Without
With
threshold
threshold
Approximately
Not
70
recommended
Approximately
Not
500
recommended
Present survey
Number in
sample
43
11
Recommended future survey
Without
With
threshold
threshold
Approximately
Not
70
recommended
Approximately
Not
100
recommended
ESTIMATION OF COSTS
The pilot survey has proven that the main principles of efficient data collection described in chapter 2
have to be followed, and must be reflected by sufficient funds for this information system.
This means, first of all, sources for a sufficient number of people, including data collectors. Some
production entities - small units - might be without accounts. A special type of questionnaire and special
procedure for data collection, with great assistance from well-trained data collectors, will have to be
applied to this sample. On the other hand, big companies with accounts can have quite complicated
accounting, and the collection of figures required in the survey, especially allocation of figures on fish
production and other activities, will be quite complicated and time-consuming as well. So it is planned that
data collection within this group of firms will also be executed by trained data collectors. With awareness
of some simplification, the amount of data collection costs for units with and without accounts is
calculated at the same level for both types of farms.
The second basic factor reflected in cost calculations is the high performance IC technology used in this
system, including special software for data collection, tests and processing.
Costs calculations are also based on the assumption that the current FADN office will be in charge of
implementing and running an aquaculture data collection system. This means that some hardware and
software resources could be utilized from the current FADN system.
Table 2.2
Czech Rep., Estimation of costs
Item description
Investment costs
• Staff (training, etc.)
• Hardware (computers, office equipment, etc.)
• Software (data compilation and processing)
Total investment costs
Without threshold
Accounts
Accounts
available
not
available
With threshold
Accounts Accounts
available
not
available
10,000
15,000
30,000
55,000
Annual operational costs
68,000
• Data collection (labour)
• Date collection (other expenses)
96,000
• Data processing (labour)
10,000
• Data processing (other expenses)
Total annual operational costs
174,000
a) Building of a team and training of 4 persons on the central processing unit, 20 data collectors for data collection.
b) HW and office equipment of the central office, investment costs on data collectors are covered in operational
costs on data collection.
c) Development of software for data collection, tests and data processing on the central level.
d) Calculation is based on a payment to data collectors. It covers labour, use of own hardware, travel and other costs.
Amount of a payment to data collector depends on number of completed questionnaires. Payment is estimated 400
EUR for l questionnaire. Labour of the central office during data collection is calculated within item “Data
processing”.
13
e) Labour of staff in the central office on data collection and data processing. It is calculated 24,000 Euro per person
and a year. It covers wages, annual consumption of fixed costs, material, travel costs and overheads.
f) Annual costs on external services, updating of software, other ICT services, data publication and dissemination.
2.5.
AVAILABILITY OF FUNDING
The implementation of a data collection system has to be supported from national and EU resources. At
present, there is no budget planned for this issue at the national level.
2.6.
PROBLEMS AND SOLUTIONS
2.6.1.
Extrapolation of the sample to total population
The weighting system applied in the FADN survey could not be used to extrapolate data to the whole
sector, because of insufficient information about the total population. Therefore results of the survey have
been extrapolated to the population using a simplified method. To estimate results for the total
population, average indicators per hectare of utilized water area were calculated for the sample. These
average indicator values were multiplied by the total utilized water area used in aquaculture in the Czech
Republic. The estimates were compared with the official aquaculture data (i.e. value and volume of food
fish production) presented by the Czech Statistical Office. Calculated estimates proved to be reliable.
Extrapolated production volume amounted to 96% of the total production volume, and extrapolated
production value amounted to 99.9% of the total food fish production value. The share of the sample in
the value and volume of production of the total segment was calculated exclusive of the value and volume
of the juvenile fish production. Data on the total juvenile fish production volume is not available in the
Czech Republic, and an estimate of the juvenile fish production value seems to be inapplicable. Juvenile
fish production for the stocking of fishing grounds intended for recreational fishing contributes
significantly to total juvenile production value by 5 million Euro a year, on average. Juvenile fish for
stocking of fisheries are produced mainly by special hatcheries of the Czech Anglers Union.
The applied method was unsuitable for extrapolating an indicator of the total number of companies
(including single holders and legal entities) from the sample. Big producers were sufficiently represented,
however, the representation of a large group of small-scale producers was low in the sample. An exact
total number of producers is not available. The indicator was estimated.
Table 2.3
Czech Rep., Share of sample in value and volume of production of the total segment
On-growing technique
Ponds
Species
Carp combined with other species
Population (no. firms)
App 570
Sample (no. firms)
54
Share of sample in total value of the segment a) (%)
81%
Share of sample in total volume of the segment a) (%)
78%
a) Exclusive of value and volume of the juvenile fish production, data is not available.
2.6.2.
Evaluation of individual indicators
The basic problem is the willingness of producers to provide the required data. In general, firms are not
willing to provide such figures, and the motivation of producers will be a very complicated issue. For
future implementation of this system, supporting legislation will be helpful.
A sufficient budget is the second issue. All costs needed for efficient data collection have to be covered at
a sufficient level.
14
The main problems with data quality have already been mentioned, and were caused by the low budget
and short period for data collection and tests.
The homogeneity of figures is an issue. The statistical indicators noted below show high-variability data in
the surveyed sample. The survey has covered firms with significantly different scales of fish production.
Some stratification of producers according to the scale of production would provide more homogeneous
results.
Any future survey should involve more data items to enable more sophisticated analyses of the economic
performance of fish producers. Additional variables will be needed to identify other activities of firms for
the allocation of figures on fish and other businesses.
Some problems could arise from different accounting legislation in different countries. It will not be easy
to get uniform figures for some variables if there are differences in national accounting. This could occur,
for example, in calculating the value of fixed assets, depreciation, etc.
Table 2.4
Czech Rep., Statistical indicators, carp in ponds, large-scale and small-scale
producers
Sample mean
(1000 Euro or %)
Relative standard
deviation (%)
Absolute values
778
149
179
157
506
147
272
174
90
274
2,491
344
20
174
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
92
11
Total capital costs
8
136
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
20
73
Unpaid labour
10
190
Energy costs
7
100
Live raw material costs
7
142
Feed raw material costs
18
65
Repair and maintenance
11
141
Other operational costs
27
65
Total turnover
Personnel costs
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
2.6.3.
Relative standard
error (%)
20
21
20
23
37
46
23
2
18
10
26
13
19
9
19
9
Cross check with other sources
The extrapolated indicators (i.e. value and volume of the food fish production) were compared with
national data presented by the Czech Statistical Office (CZSO), because the 2007 aquaculture data
provided by other sources (FAO and Eurostat) 2 was not available at the time the national report was
finalized. As regards volume of production, all institutions (FAO/Eurostat/CZSO) presented identical
data up to 2006. Different methods caused an approximately 5% disparity in the CZSO and the
FAO/Eurostat estimates of the production value in recent years. As noted previously, extrapolated
production indicators amounted to 96% of the total production volume, and 99.9% of the total
production value. In the future, results of the survey might represent more exact data concerning
production value, compared with present official estimates.
2
FAO and Eurotat provide the same data concerning the value and volume of Czech aquaculture production.
15
3. DENMARK
3.1.
SUITABLE ORGANIZATION
Data for the Danish aquaculture sector is collected by the Institute of Food and Resource Economics
(FOI) in Copenhagen, Denmark. FOI produces the official Danish account statistics for agriculture,
horticulture, and fisheries as well as separate statistics for organic farming. As a pilot project, FOI has also
collected data for the aquaculture sector for the past three years.
FOI prepares separate profit and loss account statistics for the various branches of agricultural enterprise
as well as price statistics for agriculture and horticulture.
The statistics are currently available at FOI’s Danish website. The statistics and basic data also provide
important input to the research, advisory, and educational activities at FOI. Upon request, Statistics
Division offers customer tailored data operations.
FOI is continuously involved in different projects in Denmark and abroad. Recently, FOI was involved in
an EU project concerning the implementation of the FADN system in the Czech Republic, and Romania,
and FOI is currently managing a similar project in Turkey. FOI is also responsible for delivering
agricultural account data for the FADN statistics.
3.2.
METHOD OF DATA COLLECTION
The Danish Directorate of Fisheries provides a complete list of the population of aquaculture producers
in Denmark. Every year the aquaculture producers have to submit a questionnaire concerning the
production in volume and value, kind of species produced, and type of farm(s).
The information obtained from the population of aquaculture producers and the data produced by the
Danish Directorate of Fisheries are used for the segmentation of the aquaculture sector and for the
collection of account data by FOI. The segmentation is shown in Table 1.1.
The Danish Account Statistics for Aquaculture collects economic data for cost and earnings and balance
sheet. Data is collected on a voluntary basis from the owner’s chartered accountant. The accountant’s task
is to report the accounts of his aquaculture clients companies to FOI using a special harmonised balance
sheet form where the account information is further harmonised for statistical use. The accountant gets
paid for each account that is reported and approved by FOI. The FOI data is validated in a special
designed data system for quality control.
The Danish Commerce and Companies Agency (DCCA) also collect account data from companies but
not from single holders. For those companies that are not willing to participate in the FOI survey, the
accounts from DCCA are used instead even if they are not as detailed as the accounts reported to FOI by
the single chartered accountants. There are, however, some difficulties using accounts from the Danish
Commerce and Companies Agency due to the fact that they are not harmonised, and often both cost and
earnings are not specified as two separate posts but subtracted from each other into only one cost item. It
is also difficult to separate aquaculture income and income from other kinds of business.
In Denmark data is collected at farm level, which makes it easier to determine the companies’ core
business regarding the segmentation on species and technology, but also to separate other kinds of
production from the primary aquaculture production. If a company produces more than one species, it is
allocated to the segment generating the highest gross revenue. Some companies own different kinds of
farms. In Denmark these activities are split up because the farms are used as data collection units. When
farms are aggregated into companies, the company is allocated to the segment, where the highest gross
revenue is generated.
16
There are some differences between the volume and value collected by the Danish Directorate of
Fisheries and FOI, respectively. The main difference is that data collected by FOI are account data, and
the accounts do not usually follow the calendar year. For account statistics, accounts finalized within the
year are used.
After receiving the accounts from the chartered accountant, FOI tests and evaluates the accounts before
processing their data for the final aquaculture account statistics.
Data for the aquaculture sector is published once a year on aggregated level for each segment both at farm
and company levels. The aquaculture statistics are published on FOI’s Danish website approximately 12
months after the end of the reference year.
3.3.
SIZE OF PRESENT AND FUTURE SURVEY
The population in the present and future survey is based on the complete list of aquaculture producers in
Denmark provided by the Danish Directorate of Fisheries. The Danish aquaculture production is highly
specialised, and most companies are involved in only aquaculture production. The complete list from the
Danish Directorate of Fisheries has only a few companies more than the official business register
collected by Statistics Denmark, where the companies are listed by their main activity (NACE 5.02).
In Denmark a license is required to produce fish for consumption. It is quite expensive to start up a
production of fish for human consumption, because you have to pay for health and environmental control
of the farm. Because of these constrains there are very few small companies involved in aquaculture
production in Denmark. Introducing a threshold would not exclude many companies from the survey.
Table 3.1
Denmark, Main segments of the aquaculture sector, 2006
Population
Segment
Without
With
(Species/ technology)
threshold
threshold
Rainbow trout, Cages
6
6
Rainbow trout, Tanks and raceways
142
120
Rainbow trout, Recirculation system
20
20
European eel, Recirculation system
9
9
Blue mussels, Off bottom
10
10
Other species, Recirculation system
6
6
Total
193
170
Source: Institute of Food and Research Economics (FOI).
Present
survey
Number in
sample
6
63
12
7
5
3
96
Recommended future
survey
Without
With
threshold
threshold
6
6
70
50
15
15
9
9
10
10
6
6
115
95
The above threshold is used for commercial as well as non-commercial firms. The non-commercial firms
are identified as programs for restocking and small-scale innovative production programs funded by the
government. The production from non-commercial firms represents only 1% of the total value and
volume of production. Compilations of data from both freshwater and saltwater fish farms are included in
the calculation of costs.
For small segments with less than 10 companies, a complete sample is needed. In larger segments the aim
is to cover approximately 50% of the segment (see Table 1.1). To get the best possible sample, the gross
value collected for each farm by the Directorate of Fisheries is used for size class segmentation. The
population is divided into 5 size classes. The largest sample is taken in the class with the highest per firm
gross value, because there is a higher dispersion than in the smaller size classes. To guarantee
confidentiality there must be at least 3 companies in each segment, and no company must have a share of
more than 80% of the total production in a segment. It is preferred that at least 10 farms and 5 companies
are sampled in each segment.
17
3.4.
ESTIMATION OF COSTS
When starting up data collection from a new sector there will be some initial costs during the first 3 years.
The cost of establishing a database for the collected data and a data evaluations system for validating will
be some of the larger expenses in the beginning.
Cost for personnel will also be higher at the beginning because it takes time to get acquainted with a new
sector, it takes time to train the accountants, and furthermore, it takes time to establish good working
relations and networks within the sector.
Table 3.2
Denmark, Budget for data collection of aquaculture data (Euro)
Item description
Investment costs 3
• Staff (training, etc.)
• Hardware (computers, office equipment, etc.)
• Software (data compilation and processing)
Total investment costs
Annual operational costs 4
• Data collection (labour)
• Data collection (other expenses)
• Data processing (labour)
• Data processing (other expenses)
Total annual operational costs
Source: Institute of Food and Resource Economics.
Without threshold
Accounts
Accounts
available
not
available
With threshold
Accounts Accounts
available
not
available
77,000
20,000
60,000
157,000
77,000
20,000
60,000
157,000
122,000
72,000
35,000
20,000
249,000
120,000
64,000
33,000
19,000
236,000
The calculation of costs of data collection of aquaculture data at FOI is based on three years of experience
in collecting data for the Danish Aquaculture Account Statistics. The annual cost is also based on
experience in collecting data for the annual Agriculture Account Statistics under FADN and Account
statistics for Fishery under DCR.
In Denmark the most cost efficient way to collect aquaculture account data is to collect data from the
owner’s charted accountant in combination with the full list of aquaculture companies provided by the
Danish Directorate of Fisheries. The experience gained from collecting data for FADN on agriculture and
DCR for fisheries is that this method provides highly qualified data and larger sample sizes, compared to
other data collecting methods, for example interviews.
3.5.
AVAILABILITY OF FUNDING
For the time being the pilot project of data collection in the Danish aquaculture sector is funded by the
Financial Instrument for Fisheries Guidance (FIFG) and The Directorate for Food, Fisheries and Agri
Business. The pilot project is terminating in 2008 when data for 2007 are collected. There is no funding of
an aquaculture account statistic after 2008.
Include 1 fulltime employee, new hardware, software and a quality assurance system for the new data collection.
Data collection and processing include 1½ fulltime employee. Expenses include payment of accountant, working
group meetings, and travel costs. Other expenses in data processing are support and maintenance of the database
system and publication of the statistics.
3
4
18
3.6.
PROBLEMS AND SOLUTIONS
The account statistic for 2006 is based on a sample of 96 companies out of a population of 193 companies
collected from accountants and the Danish Commerce and Companies Agency. The sample covers 60
percent of the total population of farms, and 84 percent of the total gross output in terms of value. In
order to present results for homogeneous segments the farms are divided into segments according to ongrowing technique, species, and economic size class for trout farms exclusively.
3.6.1.
Extrapolation of the sample to the total population
The extrapolation of the sample to the total population is done in two steps.
First step is to control all accounts reported by the owner’s chartered accountant and the Danish
Commerce and Companies Agency. All results are entered into a database containing information on all
existing aquaculture producers in Denmark. From the reported accounts an average is calculated for all
indicators in each segment and for each size class.
Second step is to fill in the accounts for the rest of the population. From the information gathered by the
Danish Directorate of Fisheries, every company/farm can be placed in a segment and size class because of
the available information concerning on-growing technique, produced species and total gross value and
volume. From the average calculated in the first step, and the gross value and volume provided by the
Danish Directorate of Fisheries for each company/farm, the costs, earnings and balance sheet values for
all accounts of the remaining population are estimated.
The underlying assumptions for this calculation are that the production function for each company/farm
is identical within each segment and size class. When the production function is identical, the costs and
earnings can be distributed from the gross value and volume in each account.
Calculation of value of unpaid workers is identical with the calculation of unpaid workers in the Danish
Agricultural Account Statistics which follows the FADN regulation. The payment for one hour is
approximately 21 Euro. A full-time employee in Denmark is estimated to work 1,665 hours per year. A
yearly salary for a full-time employee is then approximately 34,400 Euro. The estimation of salary for
unpaid work in each segment is based on the number of single holders and their estimated working hours.
Table 3.3
Denmark, Share or sample in value and volume of production of the total segment
On-growing technique
Species
Population (no. firms)
Sample (no. firms)
Share of sample in total value of the segment (%)
Share of sample in total volume of the segment (%)
Source: Institute of Food and Resource Economics.
3.6.2.
Cages
Rainbow
trout
6
6
100
100
Tanks and
raceways
Rainbow
trout
142
63
72
75
Recirculati
on system
Rainbow
trout
20
12
81
81
Recirculati
on system
European eel
9
7
99
99
Off
bottom
Blue
mussels
10
5
54
63
Evaluation of individual indicators
It has proven very difficult to obtain reliable information about employment in the aquaculture sector,
both concerning the number of employees and number of working hours. In this report the numbers of
employees is based on three sources: the Danish Directorate of Fisheries, Statistics Denmark and the
employment numbers collected for the Aquaculture Account Statistic. The employment numbers in FAO
and EUROSTAT are provided by the Danish Directorate of Fisheries.
It is not possible to provide separate data on hatcheries and nurseries. For most companies the production
from egg to fish is an integrated part of the production process.
19
For some companies it is difficult to separate the primary aquaculture production from other activities
within the companies. In Denmark there are companies with an integrated production of slaughtering fish,
wholesale of fish and agriculture. Using the farm level instead of the company level makes it easier to
separate different kinds of production.
There is no clear definition of what a recirculation system is. This is a problem because of the big
difference between farms with high and low degrees of recirculation. In the segment there are companies
with a very intensive production, where 90% of the water is recirculated, and very extensive companies,
where only 5 to 10% is recirculated. A solution could be to divide the segment into an intensive and an
extensive segment of recirculation companies; however, this could lead to problems concerning
confidentiality because of the small size of the segment.
It can be very difficult to obtain data from small segments because some companies do not want to
participate because of competition between the companies, or other reasons. There have been problems
concerning the support from some of the small segments because they did not find it worthwhile to
participate in the statistics. But for the time being there is a good working relation between the producers,
the producer organisation and FOI, which means that it is possible to present data for all 5 segments. To
secure a good working relationship, a working group composed of aquaculture producers, aquaculture
producer organisation, accountants from the aquaculture sector, the Danish Directorate of Fisheries and
FOI was established.
In Denmark the production of local salmon species for restocking rivers is considered to be noncommercial because the product from these special farms is not sold on the market. Another thing is that
the labour used for this kind of production is mostly voluntary and it is therefore difficult to compare
these farms with the commercial farms in economic terms. Other non-commercial farms in Denmark are
farms used for education and innovation because for the most part, these farms do not compete on the
commercial market and are they heavily subsidised. A solution to this problem could be to introduce a
threshold excluding non-commercial farms like in the FADN Statistics.
The tables on statistical quality of the parameters, tables 3.1-3.5, show the sample mean in thousand Euro,
relative standard deviation, and relative standard error of mean. In segments with both large and small
companies, the relative standard deviation is high. Because most of the segments are very small, the
conclusions based on the numbers presented in tables 3.1-3.5 should be interpreted with caution. A single
company can have a large influence on both the sample mean and the standard deviation of the segment.
The quality of the Danish Aquaculture Account Statistics is very high. First of all, data for the statistics
purposes is provided by highly qualified accountants, and data is controlled and tested in a special design
data system, which is also used to test the Agriculture Statistic under FADN.
Secondly, FOI has three years of experience collecting and processing data from the aquaculture sector.
This knowledge and experience concerning the aquaculture sector has helped FOI to establish an
exhaustive database of all aquaculture producers in Denmark, and this has been the foundation of the
selection of homogeneous segments based on species, on-growing technique, and size classes.
Thirdly the sample covers more than 70% of the value and volume in almost all segments, except for the
mussel segment. Participation in the statistic is voluntary. The good results are due to a good working
relationship with owners, accountants, authorities and FOI, established over the past four years.
3.6.3.
Cross check with other sources
The data showed in the national chapter is identical with the production volume and value from both
FAO and EUROSTAT based on data provided by The Danish Directorate of Fisheries.
The data showed in the national chapter, tables 3.3 and 3.4, stem from the Aquaculture Account Statistics,
and both the volume and value are higher than the FAO and EUROSTAT numbers. The main difference
is that data collected by FOI are account data, and usually the accounting year does not follow the
20
calendar year. Account statistics use accounts finalized within the year. The large difference between
production values in sea cages is caused by the production of roe (trout eggs). The value from roe is not
collected by the Danish Directorate of Fisheries, because they only collect data for the volume and value
of whole fish. There will always be a difference between information from production, which in most
cases are estimates, and the value and volume presented in an account used for fiscal purposes.
Table 3.4
Denmark, Statistical indicators, Trout/Cages/saltwater
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Absolute values
Total turnover (incl. other income)
6.092
98
Personnel costs (excl. unpaid labour)
405
99
Operational costs (excl. labour)
4.921
102
Gross value added
1.287
91
Gross cash flow
766
97
Total assets
4.917
95
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
95
99
Total capital costs
5
103
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
8
99
Unpaid labour
Energy costs
0
120
Live raw material costs
38
128
Feed raw material costs
25
83
Repair and maintenance
6
94
Other operational costs
23
100
Source: Institute of Food and Resource Economics
Table 3.5
Relative
standard
error
(%)
40
40
42
37
39
39
40
42
40
49
52
34
38
41
Denmark, Statistical indicators, Trout/ Tanks and raceways /freshwater
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Absolute values
Total turnover (incl. other income)
759
140
Personnel costs (excl. unpaid labour)
108
159
Operational costs (excl. labour)
553
138
Gross value added
231
175
Gross cash flow
82
281
Total assets
1.060
168
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
92
136
Total capital costs
8
175
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
16
159
Unpaid labour
2
109
Energy costs
4
139
Live raw material costs
19
219
Feed raw material costs
35
126
Repair and maintenance
7
137
Other operational costs
17
179
Source: Institute of Food and Resource Economics
Relative
standard
error
(%)
18
20
17
22
35
21
17
22
20
14
17
28
16
17
23
21
Table 3.6
Denmark, Statistical indicators, Trout/ Recirculation system /freshwater
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Absolute values
Total turnover (incl. other income)
990
108
Personnel costs (excl. unpaid labour)
118
102
Operational costs (excl. labour)
636
92
Gross value added
408
130
Gross cash flow
233
161
Total assets
1.651
120
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
87
93
Total capital costs
13
118
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
16
102
Unpaid labour
0
346
Energy costs
7
86
Live raw material costs
16
100
Feed raw material costs
42
108
Repair and maintenance
7
111
Other operational costs
11
54
Source: Institute of Food and Resource Economics
Table 3.7
Relative
standard
error
(%)
31
29
27
38
46
35
27
34
29
100
25
29
31
32
15
Denmark, Statistical indicators, European eel/ Recirculation system /freshwater
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Absolute values
Total turnover (incl. other income)
2.400
100
Personnel costs (excl. unpaid labour)
263
93
Operational costs (excl. labour)
1.496
82
Gross value added
957
130
Gross cash flow
631
153
Total assets
2.858
97
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
93
82
Total capital costs
7
97
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
15
93
Unpaid labour
1
171
Energy costs
13
147
Live raw material costs
30
72
Feed raw material costs
16
32
Repair and maintenance
10
153
Other operational costs
15
109
Source: Institute of Food and Resource Economics
Relative
standard
error
(%)
38
35
31
49
58
37
31
37
35
65
56
27
12
58
41
22
Table 3.8
Denmark, Statistical indicators, Blue mussels/ Off bottom /saltwater
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Absolute values
Total turnover (incl. other income)
65
115
Personnel costs (excl. unpaid labour)
48
127
Operational costs (excl. labour)
42
94
Gross value added
33
184
Gross cash flow
-32
-261
Total assets
148
87
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
66
99
Total capital costs
34
91
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
50
127
Unpaid labour
7
224
Energy costs
Live raw material costs
Feed raw material costs
Repair and maintenance
32
105
Other operational costs
11
70
Source: Institute of Food and Resource Economics
Relative
standard
error
(%)
51
57
42
82
-117
39
44
41
57
100
47
31
23
4. FINLAND
4.1.
SUITABLE ORGANIZATION
The Finnish Game and Fisheries Research Institute produces statistics on aquaculture production. The obligation to
compile statistics on aquaculture production is stipulated by the European Union (EC 788/96). Producing statistics
is one of the continuous basic duties laid down by law for the FGFRI. This obligation is funded by the national
budget.
As the FGFRI is the national sector research institute that is obligated to produce statistics on the fishing and
fisheries industries, it is natural that it should also collect the statistics on aquaculture.
4.2.
METHOD OF DATA COLLECTION
The FGFRI has collected production statistics on aquaculture by direct surveys from fish farmers. The
survey was conducted by exhaustive sampling. The survey included questions on economic issues; what
are the main and subsidiary lines of business, what is the value of production and employment.
The economic information (cost and earnings) was collected by a register survey. The data on aquaculture
collected by the FGFRI was linked to the business register in Statistic Finland. This data includes the
financial data of firms with aquaculture as the main business. However, the information in financial
statements does not specify the detailed cost items that are specified in the TOR of this project. To
estimate the cost structure, a stratified survey was carried out as presented in table 1. Fish farmers were
interviewed about detailed cost structure by telephone.
The production data was collected by a postal survey and the response rate was good. Response rates
were:
• food fish farms 95%
• hatcheries and nurseries 99%
• natural food ponds 90%
The answers were reviewed and the results were estimated for the whole population based on the survey
design. The economic data in Statistic Finland has a good coverage for the firms that have fish farming as
their main line of business (Table 1.1). These cover most firms for food fish and hatcheries but natural
food ponds are only partly covered. The response rate for the telephone survey of detailed cost items was
not very good. Only seven food fish firms and seven natural food pond firms answered the questions
properly. Consequently, these results (cost items) should be interpreted with caution.
4.3.
SIZE OF PRESENT AND FUTURE SURVEY
The target population of the Finnish aquaculture production statistics and the survey carried out in this
project is all aquaculture production plants. It also includes production units that are part of a firm that
has a main line of business other than aquaculture. The objective of aquaculture production statistics is to
collect data on the total amount of fish being cultured. However, the production is highly concentrated.
Large producers and those firms that have aquaculture as their main line of business produce most of the
production and value added of fish farming.
Small producers that are not classified as aquaculture firms by the Standard industrial classification (SIC)
are numerous. Despite the small input to total production, this segment is challenging for data collection
and the estimation of production statistics. For economic data collection, they are even more challenging
because they do not have separate accounting (cost and earnings recordings) for fish farming. This factor,
together with the large number of firms that would need to be sampled, makes it difficult to collect
reliable economic data for all firms. It would be extremely expensive and even with the voluntary co24
operation, the results could not be guaranteed. This especially applies to natural food pond production
units.
To follow the economic performance of the sector, we have to define the target population that is
justifiable, precise and applicable. Therefore it is clear that some sort of definition has to be made. The
approach taken in the DCR that follows the Standard industrial classification (SIC) of aquaculture is clear
and precise. However, there are some major producers that have other lines of business also, but due to
the significance of their production, should be considered in the statistics.
The data collection of all production units would be an excessive burden. Taken their large number and
the fact that they do not have accounting available, it would demand an extensive workload to make audits
of the production sites and assess the costs of production. Compared to the survey on accounting, this
would require a manifold effort in labour and costs. We have estimated the costs of surveying all
production units to estimate their costs and earnings. Our estimate presents the costs of auditing small
production units for estimates of cost and earnings.
In this project, the data was collected according to the multi-stage hierarchical sampling design. The
sample sizes are presented in Table 1.
Table 4.1
Finland, Main segments of the national aquaculture sector (number of firms)
Segment
(Species / technology)
Population
Without
threshold
With
threshold*
Present
survey
1/2/3
Number in
sample
Saltwater fish in cages: Rainbow
65
65
65/52/20
trout, European whitefish
Freshwater fish: Cages and raceways:
40
40
40/28/10
Rainbow trout, European whitefish
Various: tanks
41
41
41/31/10
Various: ponds
197
30
197/27/10
* Threshold excludes firms with main activity other than aquaculture.
1) Production survey (including economic part)
2) Financial statistics survey (SF)
3) Account survey
Recommended future survey
1/2/3
Without
threshold
With
threshold*
65/52/20
65/52/20
40/28/10
40/28/10
41/31/10
197/27/50
41/31/10
30/27/10
An exhaustive sampling of the target population of producers will be carried out in future. Additional
questions on structure and the lines of business of the firms and on employment will be included in the
survey. Data on financial statements is available in the SF for firms that have fish farming as their main
line of business. Financial statements do not follow the detailed cost disaggregation laid down in
regulation. This information will be collected by a separate survey on accounting. The data on accounting
will be collected directly from the accountants.
4.4.
ESTIMATION OF COSTS
The estimation of the costs of data collection is based on existing data collection in aquaculture and on
knowledge of other economic data collection. The data collection is planned to be carried out as
multistage survey. There are two scenarios: with or without a threshold. A threshold criterion is whether
or not a firm is classified as an aquaculture firm in the business register. Both scenarios include exhaustive
data collection on aquaculture production. Financial statement data in SF is also utilized in both scenarios
and an additional survey to acquire the cost structure will be carried out.
In the threshold scenario, the account survey would include only the firms classified as aquaculture in the
business register. The scenario without a threshold would include a survey on accounting like the other
scenario but it would also endeavour to acquire reliable information on small scale natural food pond
production. Assuming high variation in this small scale production, and that separate accounting is not
25
available, sampling would have to be intensified in this stratum in order to provide precise information on
this segment. It would also require a separate system for auditing the costs and earnings data and this
would be expensive. In the table, this shows as a higher expense on establishing the system (staff training,
developing data processing routines). Annual data collection and processing would also require an
extensive effort.
Table 4.2
Finland, Estimation of costs
Item description
Without
threshold
With
threshold
Investment costs
•
Staff (training, contracting for account provision)
•
Hardware (computers, office equipment, etc.)
•
Software (data compilation and processing)
Total investment costs
15,000
5,000
10,000
30,000
6,000
2,000
8,000
Annual operational costs
•
Data collection (labour)
•
Date collection (other expenses)
•
Data processing (labour)
•
Data processing (other expenses)
Total annual operational costs
80,000
25,000
35,000
5,000
175,000
55,000
15,000
30,000
5,000
113,000
4.5.
AVAILABILITY OF FUNDING
FGFRI is funded by public funds. The increase of the prevailing budget share is not expected, even with
the obligation to collect economic data on the aquaculture sector. On the contrary, there are ongoing
programs for decreasing funding and for limiting staff in the public sector. Therefore even an increase in
an external source of funding does not guarantee increased resources for the data collection.
4.6.
PROBLEMS AND SOLUTIONS
4.6.1.
Extrapolation of the sample to the whole population
Segment totals were estimated by design-based, model-assisted estimation. Production value was asked for
firm by firm in the survey. The survey was exhaustive and the response rate was outstanding. The
response loss was estimated using additional information by strata. The total revenue in the financial
statement was regressed with the production value. Similarly, the total costs were estimated by regression
of the total revenue. Capital costs were also estimated by regression of the total revenue. Detailed cost
items from the accounting survey were estimated as ratio estimates from the total costs. The design-based,
model-assisted estimation is very precise for the cost structure as a whole (tables 4.1-4.4).
Table 4.3
Finland, Share or sample in value and volume of production of the total segment
On-growing technique / environment
Saltwater
fish
Cages
Species
Rainbow
trout,
Eur.
whitefish
65
52
Population (no. firms)
Sample (no. firms)
Share of sample in total value of the segment (%)
Share of sample in total volume of the segment (%)
Freshwater
fish
Cages,
raceways
Rainbow
trout,
Eur.
whitefish
40
28
Hatcheries
Tanks
Natural
food ponds
Various
species
Various
41
31
197
27
26
4.6.2.
Evaluation of individual indicators
We know that the coverage of the financial data is extensive for segments other than the natural food
ponds. For food fish production and hatcheries, the profitability information is good but the detailed cost
item data rests on the response rate of the survey. The success of the survey on economic data depends
completely on the cooperation of the fishing firms. The pilot survey on accounting showed that cooperation is weak. In future, it is the intention to contract the farmers and pay provision for the
accountants to cover the costs of providing the data. We will use quota for different segments and strata
to be able to have enough data to provide information by segments.
The cost and earnings survey of natural food pond production without a threshold will not produce
reliable estimates of the whole population. Based on the advance information, we only know that it is a
small scale, secondary line of business and very heterogeneous in nature. The biggest firms are included in
the business register in SF. These represent the largest part of the segment. Therefore, to collect reliable
economic information on the large number of small firms (whose main line of business is not aquaculture)
is challenging if not impossible and, in any case, expensive. This requires tailored in situ data collection
which requires an extensive amount of effort.
4.6.3.
Tables on statistical quality of the parameters
The economics of business statistics rely on the assumption of a homogenous cost structure of similar
production. Therefore we have estimated the cost structure using regression of production to costs and
earnings. Using design-based, model-assisted estimation we obtain a better fit and eventually more
accurate estimates. Assuming a simple random sampling, the variance estimator for the regression
estimator is obtained
2
n ⎞⎛ 1 ⎞ n
⎛
vˆ(tˆreg ) = N 2 ⎜1 − ⎟ ⎜ ⎟ ∑ ⎡( yk − y ) − bˆ( zk − Z ) ⎤ / ( n − 2 )
⎦
N ⎠ ⎝ n ⎠ k =1 ⎣
⎝
where
N = population total (number )
n = sample (number )
yk = observed value of observation k
y = average of observed var iables
bˆ = estimated regressor
zk = value of exp lanatory var iable of k
Z = average of population z
Variances should be summed up when using stratified sampling. Squaring the above gives us the standard
error of a regression estimator. As we are interested in the variation in the population, we have calculated
the relative standard error in tables 4.1-4.4 and these were used for estimating total cost (profit). The
results show a good precision that arises from good coverage and the fact that the assumption of
homogeneity in the cost structure is valid.
27
Table 4.4
Finland, Statistical indicators, Rainbow trout, European whitefish / Marine cages
Population
Relative
total
standard
(1000 Euro
deviation
or %)
(%)
Absolute values
Total turnover (incl. other income)
34684
Personnel costs (excl. unpaid labour)
-4385
Operational costs (excl. labour)
-24631
Gross value added
10020
Gross cash flow
4805
Total assets
23771
Engaged persons
??
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
86 %
Total capital costs
-4 %
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
-15 %
Unpaid labour
-3 %
Energy costs
-6 %
Live raw material costs
-13 %
Feed raw material costs
-45 %
Repair and maintenance
-4 %
Other operational costs
-15 %
Table 4.5
Relative
standard
error
(%)
2%
5%
4%
Finland, Statistical indicators, Rainbow trout, European whitefish / Inland cages
and raceways
Population
total
(1000 Euro
or %)
Absolute values
10248
-2024
-6439
3765
1385
9823
Relative
standard
deviation
(%)
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
86 %
Total capital costs
-6 %
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
-23 %
Unpaid labour
-5 %
Energy costs
-5 %
Live raw material costs
-11 %
Feed raw material costs
-40 %
Repair and maintenance
-3 %
Other operational costs
-13 %
Relative
standard
error
(%)
6%
12 %
6%
28
Table 4.6
Finland, Statistical indicators, Various species in hatcheries
Population
total
(1000 Euro
or %)
Absolute values
22100
-5174
-13863
8204
2894
33836
Relative
standard
deviation
(%)
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
87 %
Total capital costs
-4 %
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
-27 %
Unpaid labour
-1 %
Energy costs
-5 %
Live raw material costs
-11 %
Feed raw material costs
-39 %
Repair and maintenance
-3 %
Other operational costs
-13 %
Table 4.7
Relative
standard
error
(%)
4%
17 %
17 %
Finland, Statistical indicators, Various species in ponds
Population
total
(1000 Euro
or %)
Relative
standard
deviation
(%)
Absolute values
Total turnover (incl. other income)
4509
Personnel costs (excl. unpaid labour)
-772
Operational costs (excl. labour)
-2222
Gross value added
1664
Gross cash flow
1332
Total assets
14949
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
70 %
Total capital costs
-14 %
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
-24 %
Unpaid labour
-6 %
Energy costs
-3 %
Live raw material costs
-26 %
Feed raw material costs
0%
Repair and maintenance
-8 %
Other operational costs
-34 %
4.6.4.
Relative
standard
error
(%)
17 %
45 %
32 %
Cross check with other sources
The data presented here is based on the data collection that produces the production statistics that are
provided to the FAO and Eurostat. Therefore there should not be any inconsistencies between these
statistics. The only differences would arise from different practices in treating missing data. There are also
some minor differences in the determination of the target population.
29
5. FRANCE
5.1.
SUITABLE ORGANIZATION
Preliminary remark
The proposition of a suitable organization for economic data collection in aquaculture should inevitably
consider the setting up of a company database which is the preliminary task to be achieved. This task is
currently entrusted to the DPMA on a regular basis, but will be fine tuned, in particular to comply with
the new DCR which requires the regular updating of the database. Obvious synergies are expected with
the collection of statistics on aquaculture, the new regulation of which will be implemented from the
beginning of 2009.
The SCEES (Service central des enquêtes et etudes statistiques) is the national statistics institute which has
experience with large scale data collection in agriculture and food sector in general, and is also the French
institute involved in the European Farm Accountancy Data Network (FADN). As far as the aquaculture
sector is concerned, the SCEES experience relies on one-off operations of census (1991, 1998 and 2008
for fish farming, 2002 for shellfish farming). Its potential contribution to the future organization of the
DCR in aquaculture could be considered.
The DCR in aquaculture could be organised along the same lines as in agriculture, i.e. cooperation with
accountants. In France, some accounting offices operating for shellfish farmers have acquired good
expertise and knowledge, especially through the specialised accounting network NAUTIL, which deals
with the accountancy of about half of the French shellfish farming companies who use accountants 5 .
Other accountants have been identified in order to extend the coverage for shellfish farmers and also to
target fish farming. In the case of dissemination of accounts for fish farming companies, the option of
postal surveys should be also considered, as an alternative approach.
5.2.
METHOD OF DATA COLLECTION
Method used for the project
For the test study, the method used was based on surveys fulfilled in cooperation with accountants.
Required economic indicators were collected with two accountants (Nautil and CGO). The survey was
focused on two major segments of the population of bivalve mollusc farmers:
• Oyster farmers, inter-tidal cultivation techniques, located in the Channel & Atlantic Coastline,
representing 62% of the whole population of companies (76% if excluding the Mediterranean)
• Mussel farmers, inter-tidal cultivation techniques, located in the Channel & Atlantic Coastline,
representing 6% of the whole population of companies (8% if excluding the Mediterranean)
The sample totalled 169 companies, corresponding to 5% of the whole population of shellfish farmers.
For the two segments surveyed, the rate of sampling is worth 7% corresponding to 144 companies
specialised in oyster farming and 15 companies specialised in mussel farming.
In a first approach, segments based on other cultivation techniques were not investigated, such as tables
used in Mediterranean lagoons for oyster and for mussel farming (12% of the companies at national level),
long lines for mussels (around 2% of the companies) and deep water cultivation for oyster (2% of the
companies). Segments characterised by mixed farming (both oyster and mussel farmers) were neither
taken into account at this stage (14% of the population). In fact, the lack of coverage of the Mediterranean
companies in the survey (corresponding approximately to the non-coverage of the raft technique), was
According to census data, the shellfish farming companies without any accountancy system in 2001 prevailed in the
Mediterranean region (56% of the companies), while they were minority in the other regions. Companies without
accounting are globally smaller than other companies (1.4 FTE on average versus 3 FTE).
5
30
imposed by the method chosen for the test survey, which is based on the cooperation with accountants.
Actually, the selected accountants do not have a good coverage in the Mediterranean, and more generally,
only a small number of Mediterranean firms have accounts. But this coverage is expected to increase in
the future.
Method proposed for future data collection
In the future, the sample should be extended to improve statistical coverage and to represent all the
segments. The setting up of alternative methods (direct or postal surveys) for the saltwater fish farming
sector could be also envisaged, bearing in mind that these companies are few and far between, and do not
subscribe to common accountants.
In terms of segmentation, it goes without saying that it is essential to create segments for non specialised
farming companies, namely mixed oyster and mussel farmers, in order to limit the variability of economic
indicators for specialised segments.
5.3.
SIZE OF PRESENT AND FUTURE SURVEY
The estimation of the size of the survey is still theoretical at this stage since the segmentation basis is not
definitive, and a large statistical analysis is missing to assess the required rate of sampling. In a first
approach, we have applied different rates of sampling to the 3 sub-sectors of aquaculture: 20% of the total
number of companies for shellfish farming, 30% for trout farming and 50% for saltwater fish farming
(corresponding approximately to the share of the segment of seabass and seabream farming in cages,
which is the only one comprising more than 10 companies). In this way, the population to be surveyed
would top at around 750 companies for both shellfish and fish farming.
Table 5.1
France, Main segments of the national aquaculture sector (number of firms)
Segment
(Species / technology)
Population
Without
threshold
With
threshold
Present
survey
Sample
number
Recommended future survey
Without
threshold
With
threshold
Bivalve farming (all segments)
~ 3200
~ 2950
169
640
590
Trout farming (all segments)
~ 300
0
90
Saltwater fish farming (all segments)
~ 40
0
20
(1) The threshold is applied to companies employing less than one full time equivalent (about 8% of shellfish
farming companies)
The size differential between the 3 sub-sectors will entail different sampling rates and stratification
methods in the future survey. As concerns shellfish farming, the breakdown of the companies according
to the main types of farming (species, cultivation techniques) for the present survey was not so obvious
considering the state of the current company database (information about on-growing technique is not yet
collected by the DPMA), which obliged us to extrapolate from the last census data. The improvement of
the DPMA survey in order to meet the new regulation requirements is expected to fill in this gap. But the
outcome of the classification also points to the fact that the types of farming are not a sufficient basis for
segmentation, as the main part of the population of shellfish farmers in France belongs to the category of
oyster farmers in the Channel and the Atlantic coastlines, using the same main on-growing technique
(2,000-2,100 companies in 2006). How to further stratify the segment of oyster farmers was therefore one
of the key issues for this study to solve.
Concerning the segmentation of the population of freshwater fish farmers, the latest detailed data from census are
out of date and do not enable the classification of the current population. It seems sensible to wait for the next
census data. However, it appears likely that the future segmentation of the trout farming population will rely on the
size and the commercial status of the companies (in order to identify and perhaps exclude from the future economic
data collection non commercial companies). As for the saltwater fish sub-sector, both the modest size and the
31
heterogeneity of the population would lead to highlighting the main activity of seabass and seabream farming, but to
survey this population nearly exhaustively.
5.4.
ESTIMATION OF COSTS
Initially, the estimation of costs was based on the budget of the current French collection of economic
data for fisheries. According to information provided by the DPMA, the coordinator of this operation, the
whole budget dedicated to the latest data collection (2006 data) was 753,700 Euro, corresponding to the
economic monitoring of 1,641 fishing companies. The resulting average cost per surveyed company was
worth around 460 Euro, whatever the method used (direct surveys or purchase of accountings). This
global figure covers all kinds of operational costs: staff, data collection (including sub-contracting),
equipment and consumables, travelling…with staff representing a significant share of the expenses (47%)
in that they contribute to a major part of operations (organisation and coordination, preparation of the
survey, data processing …). On the other hand, this estimation does not include the investment costs
required to implement new data collection, such as computer cost, and training.
Additional information about costs was provided by the SCEES, the French statistical institute which is
involved in the economic data collection for the agriculture sector (FADN). For this purpose, the SCEES
works in cooperation with accountants, either in purchasing ready-made accounts for subscribing
companies or in funding the accountancy for non-subscribers. In the first case the cost is estimated to
reach around 400 Euro per surveyed company, in the second case around 1,200 Euro. These estimations
cover the purchase of accounts, but do not include staff and other costs for the different tasks assigned to
SCEES (data compilation, processing, validation…and other organisation tasks). When considering these
additional costs, it appears that the information provided by the SCEES is relatively close to that delivered
by the DPMA.
In a first approach to the budget to be allocated to the aquaculture data collection, we have considered
one and only one unit value per company surveyed (500 Euro), and extrapolated it to the aquaculture
company sample (about 750 companies). The resulting estimation for the budget amounts to 339,000
Euro (with threshold) or 364,000 Euro (without threshold). In a second approach, we have also taken into
account additional costs for surveying companies without any accountancy system. This concerns
particularly the segment of bivalve farmers in the Mediterranean (raft technique), for which only a small
number of companies has accounts. The second estimation of the budget reaches 412,000 Euro (without
threshold).
Table 5.2
France, Estimation of costs (Euro)
(derived from DCR in fisheries and FADN in agriculture)
Item description
Without threshold
Accounts Accounts not
available
available
With threshold
Accounts Accounts not
available
available
Annual operational costs
•
•
•
•
•
Data collection (labour)
Data collection (other expenses)
Data processing (labour)
Data processing (other expenses)
Sub-contracted work (purchase of accounts)
44,000
44,000
44,000
44,000
64,000
64,000
59,000
59,000
256,000
304,000
236,000
276,000
Total annual operational costs
364,000
412,000
339,000
379,000
The threshold is applied to companies employing less than one full time equivalent (around 8% of the shellfish
farming companies)
The table shows that the main part of the budget relies on subcontracting costs (purchase of economic
data to accountants). It also highlights that the application of a threshold in terms of employment would
not fundamentally reduce this budget.
32
5.5.
AVAILABILITY OF FUNDING
No specific funding is available at this time in order to collect economic data in aquaculture.
5.6.
PROBLEMS AND SOLUTIONS
5.6.1.
Extrapolation of the sample to total population
The samples of oyster and mussel farmers represent around 7% of their respective segments in number of
companies. Before assessing the share of the samples in terms of volume, it is at first necessary to estimate
the output of their respective segment in so far as no statistics are available per on-growing technique
(except for the oysters cultivated under tables which could be imputed to the Mediterranean production
only). The first assumption at this stage is to consider that the oyster sales per company in the Atlantic &
Channel do not differ from “bottom” to “other” techniques. The second hypothesis is to consider that
the sales from mixed farmers are split evenly between oysters and mussels. At the end of these two
successive approximations, the sales in volume of firms belonging to the “bottom oyster” segment are
assessed at 90% of the oyster sales (excluding Mediterranean production), and the resulting share of the
sample in the total segment is estimated at around 7%.
For mussel farming, the same method of calculation is applied but the assumption related to mussel &
oyster farming companies is a little higher (they represent a greater share of the population of companies
involved in mussel farming). The resulting share of the sample in the total volume of the segment is 8%.
Table 5.3
France, Share of sample in value and volume of sales 6 of the total segment
On-growing technique
Species
Population (no. firms)
Sample (no. firms)
Share of sample in total value of the segment (%)
Share of sample in total volume of the segment (%)
5.6.2.
“bottom”
oyster
2076
144
“bottom”
mussel
209
15
7%
8%
T3
S3
T4
S4
Evaluation of individual indicators
Specific issues raised by the collection of variables
Data were collected through accountants, who were in a position to supply the totality of the required
economic or non economic indicators. The data were provided in different ways:
• Nautil accountant : individual data for the list of DCR indicators plus a number of additional
variables, and a synthesis analysis of accounting and balance variables for the 6 groups of shellfish
farming companies surveyed (3 in Normandy and 3 in South Brittany)
• CGO accountant: individual data for the list of DCR indicators, plus the detailed balance sheet and
additional external variables for 4 groups of shellfish farming companies in Charente-Maritime.
The access to the detailed balance sheet with one accountant enabled us to scrutinize the data and to
identify different practices between accountants.
A first comment at this stage is related to the fiscal year in use for the shellfish farming sector. Most of the
time, the period covered by the accounts does not correspond to the calendar year, but to a “campaign” in
between two years. As concerns Nautil, the data selected for the campaign 2006-2007 were based on
accounts with the end of the fiscal year ranging from 06/12/31 to 07/11/30, considering that a majority
of accounts corresponds to the period 06/07/01 - 07/06/30. For CGO, the accounts were also delivered
for the 2006-2007 campaign, with the latest end of fiscal year at 07/09/30. Notwithstanding this small
6
For more elements about the distinction between sales and production indicators, refer to 6.3
33
difference, all the accounts used in the test survey (either from CGO or Nautil) cover at least up to the
end of the year 2006, which represents a determining share of the yearly activity for oyster farmers.
Other remarks are related to the collection of economic indicators, with the emphasis put on the
following variables:
Turnover
• The turnover of the shellfish farming activity covers all the sales of animals, including the value of
livestock variations which is assessed by the accountants
• The sales of oysters, mussels or other bivalves not only rely on the own production of the company
but also on its trading operations
• Other incomes relate to other activities than bivalve farming and trading.
The concrete implication of the integration of bivalve trading operations in the turnover of the activity is
that the sales in volume and value are not a reliable indicator of the level of production of the companies,
except for “strict farmers”. As far as “farmer-traders” are concerned, the gap between sales and
production could range from marginal (the company mainly sells its own production to final
consumption) to very significant (the company has become specialized in trading and the majority of its
final sales is produced by other farmers). Pure traders also exist, but are very few, and are normally
excluded from the population of bivalve farmers. At a national level, the transferred volumes of adult
bivalves from “strict farmers” to “farmer traders” were estimated at 56,000 tons of oysters and 18,000
tons of mussels in 2006 (DPMA survey), which represented respectively a half and a quarter of the whole
production.
Another element to take into account is the oyster refining process, which is considered as a farming
stage, and actually brings added-value but without, or very little, weight growth. Refining operations
explain a significant share of the transfer of adult oysters from Normandy, Brittany and other Atlantic
regions up to Charente-Maritime.
Raw material costs
In return, the purchases of live bivalves are comprised of both raw materials for on-growing (spat, halfgrown oysters…), for refining or simply for selling to final consumption. The distinction between
intermediate and final products is therefore difficult to establish as concerns the purchases of adult
bivalves.
It is apparent that the share of raw material costs in the total costs is strongly dependant on the level of
specialisation in farming. Thanks to CGO data on production level, and the derived ratio (volume of
production/volume of sales), it has been possible to test the correlation between the two variables for the
sample of 71 companies of Charente-Maritime.
The resulting equation is:
Ratio (raw material costs/total costs) = 0.62 -0.52*(Ratio volume of production/volume of sales), with a
regression coefficient of 87%.
Value of unpaid labour
Particular attention has been paid to harmonize the calculation of this item between the two accountants.
In this respect, the same yearly value has been allocated to each FTE, namely 19,000 Euro which is the
value used by Nautil at national level. In addition, the same treatment has been applied as concerns the
item “remuneration of associates” given that in the case of Nautil these remunerations were removed
from the EBIT calculation, which was not the case for CGO. Therefore we recalculated the EBIT without
the associate remuneration for CGO accounts, and then imputed a value for all the family labour,
including associates.
The method of calculation used by accountants to determine the number of FTE can differ according to
the type of job: on a weekly basis for part-time jobs, on an hourly basis for seasonal jobs, on an annual
basis for family jobs…. Family jobs are reported to be mainly full-time.
34
Other operational costs
This item covers other expenses, such as packaging, sanitary taxes, insurances, subcontracting, advertising,
transport… A specific point is worth mentioning concerning the transport costs which in some occasions
are charged to farmer-traders by customers (ex. big retailers), leading to a little overestimation of the
average price indicator.
Total assets
The main assets of the surveyed companies are, by decreasing order: fixed assets, current assets (bivalve
stocks, receivables) and intangibles.
The value of bivalve stocks represents a significant share of the total assets for both mussel and oyster
farmers (around 30%). There is potentially a need for harmonizing the methods of stock assessment
between accountants, due to the fiscal stake of such an evaluation. Another remark is related to the date
of closing accounts which is likely to influence the value of stocks in the total assets.
As concerns the land factor, it is worth mentioning that the value of “concessions” is very imperfectly and
heterogeneously registered in assets. The main reason is related to the specific status of the “concessions”
which are located in the maritime public domain, and temporarily delivered by the Administration to
shellfish farmers (for a period which cannot exceed 35 years). The transferability of concessions to other
farmers in exchange for an allowance, which was ratified by the 1987 decree, in fact formalized the
“market” of concessions. As regards the current national regulation, the value of the allowance to be paid
for a transfer of a concession is determined by the aggregation of three values: the value of bivalve stocks,
the value of equipments and the value for the improvement in production potential. But this definition,
especially the third part, is unable to reflect the real value of concessions, which implicitly comprises a useright value. Concretely, as regards the balance sheet, the inventory (bivalve stocks) and equipments are
registered respectively as current and tangible assets. On the other hand, the remaining share of the
concession value is not often registered as intangible assets (it is then registered in personal accounts).
Anyway, the accounting practices on this issue are far from being formalised.
Statistical quality of the parameters
Preliminary analysis of the relative standard deviation of the oyster farming sample (Table 4.2) shows that
globally, the dispersion is high, especially concerning the total turnover (137%), and cost items such as
personal costs (142%) and operational costs (excl. labour, 185%). Comparatively, relative standard
deviations are lower for mussel farming indicators, with a 55% value for turnover, 77% for personal costs
and 62% for operational costs (Table 4.1). The conclusions differ as regards the relative standard errors,
with higher statistical indicators for mussel farming, but above all these results reflect the small size of the
mussel farming sample. Moreover, the analysis of relative standard errors should be conducted with
caution, in so far as the assumption of random sampling is not verified in the test survey, in particular for
the oyster farming segment which has been stratified according to different criteria.
After subdivision of the oyster farming sample into two size classes, relative standard deviations of the
indicators are significantly decreased for the companies under 3 FTE (Table 4.2.1). They are also
decreased, but to a lesser extent for the companies over 3 FTE (Table 4.2.2). In terms of relative standard
error, the improvement of statistical indicators is observed in the first case, not for the second one. Again,
we must bear in mind that the interpretation of relative standard error values is tricky as regards the
assumption of random sampling. Notwithstanding, the comparison of statistical indicators for the two
sub-samples, suggests that the category of companies over 3 FTE would require further stratification, or a
higher sampling rate than smallest companies in order to reduce the dispersion of the economic results.
Final stratification of the sample of small companies according to commercial status provides different
types of information. On the one hand, the analysis of sample means highlights the differences existing
between “strict farmers” and “farmer-traders” in terms of turnover, operational costs and relative
composition of costs. But, on the other hand, it is also apparent that the dispersion of economic
indicators has not been reduced by the stratification. A recommendation that may be drawn at the end of
35
the test survey is to define the commercial status criteria more precisely, in order to distinguish the
companies according to their level of specialisation in farming 7 . This recommendation indeed implies that
the production data would be available for all the bivalve farming companies.
5.6.3.
Cross check with other sources
As previously indicated, the comparison of the results of the test survey extrapolated to the total
population with the official production data is not directly possible, due to i) the double counting of
production resulting from the activity of trading of a lot of bivalve farming companies, ii) the lack of
Mediterranean bivalve farmers in the sample (raft technique) and iii) the lack of oyster & mussel farmers
in the sample.
On the other hand, the comparison of the extrapolated sales of the test survey with the corresponding
data of the 2006 DPMA survey (namely the aggregation of “strict farmer sales to farmer-traders” and of
“farmer-traders sales to final consumption”) provides a more suitable method. If considering that the sales
of the 2006-2007 campaign are representative of the 2006 yearly sales, and that they are representative of
national sales excluding Mediterranean, extrapolated sales from the test survey data turn out to be close to
aggregated sales measured by the DPMA survey.
Table 5.4
France, Comparison between the extrapolated sales from sample data and the sales
from 2006 DPMA survey
Volume (tonnes)
Oyster sales to farmer-traders
Oyster sales to final consumption (1)
Total oyster sales
Mussel sales to farmer-traders
Mussel sales to final consumption (1)
Total mussel sales
Test survey data
(2006-2007)
1,326
8,168
9,493
0
3,098
3,098
Extrapolated data
survey (excl.
Mediterranean) (2)
151,533
78,585
2006 DPMA
survey (excl.
Mediterranean)
51,173
103,806
154,979
13,864
59,903
73,767
(1) Sales to final consumption provide the indicator of production in the DPMA survey
(2) The extrapolation of data from the survey to the whole population has been calculated by using the total
number of specialised oyster farming companies (or mussel farming companies) plus half the number of mixed
farming companies.
The CGO accountant, for instance, splits the farmer-traders into two groups on the basis of their ratio
production/sales: under or above 30%
7
36
Table 5.5
France, Statistical indicators, Mussels / "bottom" techniques
Sample mean
(1000 Euro)
Total Turnover
Personnel costs (excl. Unpaid labour)
Operational costs (excl. Labour)
Gross value-added
Gross cash flow
Total assets
Engaged persons (FTE)
Absolute values
302
50
79
223
144
397
3.5
Relative standard
deviation %
Relative standard
error %
55%
77%
62%
56%
67%
61%
45%
14%
20%
16%
14%
17%
16%
12%
Relative costs composition
Level 1 - Aggregated costs as % of total costs
Total operational costs
75%
8%
Total capital costs
25%
25%
Level 2 - Details of operational costs as % of total operational cost
Personnel costs
Energy costs
Live raw material costs
Feed raw material costs
Repair & Maintenance
Other operational costs
Table 5.6
52%
4%
15%
8%
21%
19%
36%
40%
34%
24%
2%
7%
5%
9%
10%
9%
6%
France, Statistical indicators, Oyster farmers / "bottom" techniques
Total Turnover
Personnel costs (excl. Unpaid labour)
Operational costs (excl. Labour)
Gross value-added
Gross cash flow
Total assets
Engaged persons (FTE)
Sample mean
(1000 Euro)
Absolute values
204
32
108
94
38
245
2.73
Relative standard
deviation %
Relative standard
error %
137%
142%
185%
110%
172%
86%
86%
11%
12%
15%
9%
14%
7%
7%
Relative costs composition
Level 1 - Aggregated costs as % of total costs
Total operational costs
86%
8%
Total capital costs
14%
49%
Level 2 - Details of operational costs as % of total operational costs
Personnel costs
47%
35%
Energy costs
5%
64%
Live raw material costs
27%
72%
Feed raw material costs
Repair & Maintenance
4%
70%
Other operational costs
17%
43%
1%
4%
3%
5%
6%
6%
4%
37
Table 5.7
France, Statistical indicators, Oyster farmers <3FTE
Total Turnover
Personnel costs (excl. Unpaid labour)
Operational costs (excl. Labour)
Gross value-added
Gross cash flow
Total assets
Engaged persons (FTE)
Sample mean
(1000 Euro)
Absolute values
105
13
50
54
18
162
1.7
Relative standard
deviation %
Relative standard
error %
56%
83%
81%
60%
156%
51%
33%
5%
8%
8%
6%
15%
5%
3%
Relative costs composition
Level 1 - Aggregated costs as % of total costs
Total operational costs
85%
8%
Total capital costs
15%
45%
Level 2 - Details of operational costs as % of total operational costs
Personnel costs
49%
32%
Energy costs
6%
57%
Live raw material costs
23%
77%
Feed raw material costs
Repair & Maintenance
5%
65%
Other operational costs
17%
45%
Table 5.8
1%
4%
3%
5%
7%
6%
4%
France, Statistical indicators, Oyster farmers >3 FTE
Total Turnover
Personnel costs (excl. Unpaid labour)
Operational costs (excl. Labour)
Gross value-added
Gross cash flow
Total assets
Engaged persons (FTE)
Sample mean
(1000 Euro)
Absolute values
537
93
304
228
107
525
6.0
Relative standard
deviation %
Relative standard
error %
82%
64%
115%
61%
94%
51%
49%
14%
11%
20%
11%
16%
9%
9%
Relative costs composition
Level 1 - Aggregated costs as % of total costs
Total operational costs
88%
8%
Total capital costs
12%
61%
Level 2 - Details of operational costs as % of total operational costs
Personnel costs
40%
43%
Energy costs
3%
54%
Live raw material costs
40%
47%
Feed raw material costs
Repair & Maintenance
3%
67%
Other operational costs
15%
28%
1%
11%
7%
9%
8%
12%
5%
38
Table 5.9
France, Statistical indicators, Oyster farmers <3FTE, strict farmers
Total Turnover
Personnel costs (excl. Unpaid labour)
Operational costs (excl. Labour)
Gross value-added
Gross cash flow
Total assets
Engaged persons (FTE)
Sample mean
(1000 Euro)
Absolute values
87
14
28
78
21
185
1.6
Relative standard
deviation %
Relative standard
error %
62%
83%
74%
46%
147%
51%
34%
10%
14%
12%
8%
25%
9%
6%
Relative costs composition
Level 1 - Aggregated costs as % of total costs
Total operational costs
83%
9%
Total capital costs
17%
46%
Level 2 - Details of operational costs as % of total operational costs
Personnel costs
60%
24%
Energy costs
6%
70%
Live raw material costs
12%
105%
Feed raw material costs
Repair & Maintenance
6%
69%
Other operational costs
17%
58%
2%
8%
4%
12%
18%
12%
10%
Table 5.10 France, Statistical indicators, Oyster farmers <3FTE, farmer-traders
Total Turnover
Personnel costs (excl. Unpaid labour)
Operational costs (excl. Labour)
Gross value-added
Gross cash flow
Total assets
Engaged persons (FTE)
Sample mean
(1000 Euro)
Absolute values
114
13
60
53
16
152
1,80
Relative standard
deviation %
Relative standard
error %
53%
83%
73%
57%
161%
50%
32%
6%
9%
8%
7%
18%
6%
4%
Relative costs composition
Level 1 - Aggregated costs as % of total costs
Total operational costs
86%
7%
Total capital costs
14%
43%
Level 2 - Details of operational costs as % of total operational costs
Personnel costs
44%
31%
Energy costs
6%
51%
Live raw material costs
29%
62%
Feed raw material costs
Repair & Maintenance
5%
62%
Other operational costs
17%
39%
1%
5%
4%
6%
7%
7%
4%
39
6. GERMANY
6.1.
SUITABLE ORGANIZATION
6.1.1.
Organizations proposed
Bundesministerium für Ernährung, Landwirtschaft und Verbraucherschutz (BMELV)
Responsible for data collection under TBN/FADN: Department (Referat) 426
Contact at TBN: Mr. Josef Hauser
Rochusstr. 1
D-53123 BONN
www.bmelv.de
Statistisches Bundesamt
Responsible for fisheries survey: Gruppe Land- und Forstwirtschaft, Fischerei
D-53117 BONN
[email protected]
6.1.2.
Rationale
Aquaculture in Germany comes under the sovereignty and jurisdiction of the federal states. Administrative
responsibility at their upper level rests, except for the city states, with the fisheries offices of the state
ministries for agriculture and/or environment, food, forestry and consumer protection.
Fisheries and aquaculture statistics and reporting fall (as in other fields of agriculture) under the
responsibility of
• the agricultural administration (ministries on federal and Länder level, specific research institutes) as
far as production volumes and values and economic performance is concerned, in fisheries also
concerning fleet structure and capacity
• the statistical offices at federal and Länder level concerning a regular survey on the structures of
inland fisheries and aquaculture.
The contributions of the individual institutions are the following:
• The Länder fisheries offices submit aquaculture production data that are compiled and aggregated in
an “Annual Report on German Inland Fisheries” (Jahresbericht zur deutschen Binnenfischerei). The
aggregated Länder data are also forwarded to the Commission in compliance with Council Regulation
(EC) No 788/96 and to FAO – apparently in some cases with minor modifications.
• The Federal Institute for Food, Agriculture and Consumer Protection, BLE, has been charged by
BMELV with market monitoring and reporting. It draws up, among others, a “Monthly report on
fisheries and the market situation for fisheries products” and the annual “Carp report”. The carp
report contained information on national aquaculture output (volume, value) and on prime
production costs as well as on the market for freshwater fish. Compilation of this report discontinued
after 2005.
• The Johann Heinrich von Thünen-Institut, VTI (and formerly the Federal Institute for Agricultural
Research, FAL), through its Institute for Micro-economy, carries out economic analyses and research
on agriculture and marine fisheries for projects under BMELV-portfolio research, and undertakes
analysis of the development of production systems in the international context.
• The Federal Statistical Office provides data on the structure of aquaculture which are collected in the
frame of the Inland Fisheries Surveys by the Länder statistical offices.
• The farm accountancy data network, FADN (Testbetriebsbnetz, TBN), which is directly implemented
by the federal ministry of agriculture, BMELV, is the most important source of micro-economic data
on agriculture and marine fisheries in Germany . It covers the entire agriculture sector (except
aquaculture) and involves the line ministries of all Länder. The TBN forms part of the European
FADN with BMELV acting as national liaison agency
40
Considering the present institutional set-up of data collection, processing and flow and the fact that the
ongoing aquaculture data collection scheme under discussion would likewise have to be organised through
the line ministries and statistical offices on both, federal and Länder levels, BMELV appears most suitable
to act as the leading implementing organization and principle agent vis-à-vis the Commission. It is furthermore assumed, especially with a view to the European context, that the TBN offers a suitable framework
for generating statistical data on the economy of aquaculture.
Other advantages of placing the aquaculture data collection scheme under the German “Testbetriebsnetz”
(TBN) are:
• the TBN has a legal basis
• it is institutionalised, well-tried (routinely operated since about 50 years) and well experienced in the
handling of large amounts of data
• it has the capacities to also cover aquaculture
• it puts no strain on the target population and keeps the involvement of the Länders’ fisheries offices
at a minimum.
The TBN was established under § 2 Agriculture Law (Landwirtschaftsgesetz LwG) of September 5, 1955
with the overall objective to gain economic information that represents the current situation of the
agricultural holdings. To this end the law stipulates to cover all forms of agriculture and to reflect the
entire heterogeneity of farming and production. As the TBN already covers the marine fisheries sector,
which in Germany comes under agriculture, it would appear reasonable to also include aquaculture.
6.2.
METHOD OF DATA COLLECTION
6.2.1.
Annual aquaculture statistics
The annual aquaculture data in the past were published in the Jahresbericht über die Deutsche Fischwirtschaft and
the Karpfenbericht, but, as many other statistical reports in the field of agriculture, these reports are
discontinued in 2007. Starting that year, the inland fisheries and aquaculture related part of the Jahresbericht
über die Deutsche Fischwirtschaft is published separately as Jahresbericht zur deutschen Binnenfischerei (Internet
publication; no printed version). The data presented there are estimates compiled from information
communicated by the fisheries offices of the Länder and are based on individual assessments of farmers
and their associations rather than on state-of-the-art data collection. The reporting has altogether much
improved in the recent past.
6.2.2.
Inland fishery survey
The inland fishery survey comes about every tenth year as a special structural survey under the regular
agricultural census and has been conducted by the Federal and the Länder Statistical Offices in 1962,
1972, 1982, 1994, and in 2004. The survey is supposed to cover all aquaculture entities as far as these are
commercially operated and exceed a threshold size of 100 m² production area (water surface) in the case
of trout farming, 5,000 m² in the case of carp farming or, as to technical aquaculture a yearly production
output of over 1 ton. The survey gathers data on the structure of the inland fisheries companies and their
production facilities and production. The survey results are expected to offer a scientific basis for political
decisions on Länder-, federal-, and EU-level. The data are being collected by way of questionnaires and
their quality is considered to be fairly good. Due to a change of the threshold-criteria, the 2004 figures are
not fully comparable with those of the previous survey of 1994.
The inland fisheries survey of 2004 identified 682 aquaculture units operated as a main activity (with no or
less income of the owner and his/her spouse generated from non-aquaculture activities) and 2,643 parttime commercial companies, much less than the corresponding figures of 1,121 full-time and 23,183 parttime commercial companies that were published by the annual report at that time; a fact, that has been
much debated but not fully resolved. It appears that incomplete farm registers and false or failed
responses by fish farmer had made it difficult for the statistical offices to define the universe of aqua41
culture producers and that the omission of many companies belonging to the target population had
resulted in a certain under-coverage, especially in Bavaria. Meanwhile (as per 2006) the number of fulltime commercial companies in the annual report has come down to 678 while the number of part-time
companies remained at a similar level (21,865).
Compared with the annual report the survey of 2004 had covered only about 15% of aquaculture
producers, at that time representing 52% of the total freshwater aquaculture yield (data as per 2003). This
means that the remaining 19,260 freshwater aquaculture entities, that were considered as non-commercial
and therefore not obliged to take part, shared among themselves a production in the magnitude of 19,000
tonnes which is slightly less than one ton per producer (corresponding to a market value of an estimated
4,300 Euro/ton in the case of trout and 3,100 Euro/ton in the case of carp). In order to cover 90% of the
total freshwater aquaculture production including the output of the non-commercial entities (= appr.
40,000 tonnes) data need to be obtained that are representative of about 18,600 producers or 70% of the
total number.
6.2.3.
Questionnaires posted
For the purpose of the survey specific questionnaires were designed, tested and finally sent to 267 fish
farmers - representing about 9% of the universe of commercial fish farms as established by the inland
fisheries survey (3,343, with threshold described above) or 40% of the 688 (source: Jahresbericht zur deutschen
Binnenfischerei) main activity commercial units, which were chiefly reached by the survey. The sample had
been drawn largely at random from a list of ca. 600 firms that was previously compiled from various
sources, among others telephone directories, websites, advertisements and member lists of professional
organisations. The sample comprised of carp and trout producers at about equal shares but in the end
turned out of being lopsided to the favor of the larger companies while the smaller, often side-lined
enterprises were clearly under-represented.
6.2.4.
Response
Of these 267 farmers 12 returned the questionnaire. In view of the notorious reluctance of German fish
farmers to shed light on their financial matters in general and, more specifically, the rather disapproving
attitude shown during the preparation of this survey as well as the considerable effort required to fill in the
forms, this poor response rate did not come as a surprise (see 6.3).
Six of the respondents were carp and five were trout farmers; one operated a recirculation system and,
therefore, was not included in the statistical evaluation. Due to the biased sample the average yearly fish
production of the sample farms was well above the total mean, amounting to 104 tons of carp and 215
tons of trout, respectively, against 1.3 tons and 2.3 tons per farm that may be computed from the statistics
published in the Jahresbericht when dividing the total production by the total number of units; the sample
average is even higher than the average size of main activity units of 80 tons for carp and 55 tons for
trout, calculated by dividing the total production only through the number of main activity units (thus
disregarding the sideline-activity units and attributing their production to the main activity units, too). The
responses account for 1.9% of the main activity aquaculture firms and 4.1% and 4.4% of the total national
carp and trout production, but only to about 0.3% of the units covered by the inland fisheries survey.
6.2.5.
Processing, quality control and extrapolation
The questionnaires were cross-checked for plausibility, minor obvious errors (e.g. data in wrong lines or
columns) were corrected and missing data completed from other sources as far as possible. The economic
and employment data were calculated as an average per one tonne of production.
The results were then extrapolated to sector level relative to total production volumes (as published in the
Jahresbericht zur deutschen Binnenfischerei 2006) rather than total number of units. Despite the obvious shortcomings and limitations it was assumed that such an approach would lead to more realistic results (see
Chapter 6 below).
42
This assumption proved to be sound when finally verifying the results against the outcomes of other
recent studies or data compilations on the economic performance of German aquaculture (e.g. WINKEL,
SEBASTIAN: Ökonomik der Karpfenteichwirtschaft. Schriftenreihe der Sächsischen Landesanstalt für
Landwirtschaft. Dresden 2005), the key indicators of which were in a similar order. Although the sample
size was extremely limited, the information thus gained can therefore be considered of being largely
representative.
6.2.6.
General Approach of the Testbetriebsnetz (TBN)
The concept and methodology of Testbetriebsnetz (TBN) is in line with the FADN to which it contributes.
The TBN consists of sample agricultural holdings that are selected among the pool of bookkeeping
entities which form the “universe” of farms. The survey concentrates on commercial farms, participation
is voluntarily. Commerciality is defined on the basis of standard Gross Margin (SGM) and expressed in
terms of European Size Units (ESU). The accountancy data are gathered from the tax advisors, who
against a fee produce standardized annual statements of accounts (“BMELV-statement”). The data,
collected trough the chambers of agriculture or the respective district offices of the federal states and
forwarded to BMELV for processing, are statistically extrapolated in order to obtain representative
average and aggregate values. Representativeness is achieved through stratification of the farms under
observation (in line with Decision 85/377/EEC) and random or - more often - quota selection on the
grounds of economic size and type of farming. The selection is carried out by subject matter committees
of the Länder and governed by sampling plans and guidelines issued by BMELV.
The yearly sample covers about 12,000 companies, representing about 300,000 agricultural holdings and
450 fishing vessels.
6.2.7.
Administration and management
It is proposed that aquaculture data collection, verification, processing and flow would be fully integrated
into the existing TBN-system and thus organised in the following fashion:
Table 6.1
Level
Länder
Federal
Germany, Organization of aquaculture data collection under the TBN
Activity
Establishment of universe
(structural survey)
Responsibility
Statistical Offices
Selection of sample
Data collection
TBN-Committee
Agricultural chambers,
district offices and others
BMELV (Dept. 426)
Definition of standard gross
margin
Definition and stratification of
field of observation
Elaboration off sampling plan
Data compilation, verification,
processing and submission to EC
6.2.8.
BMELV (Dept. 426)
BMELV (Dept. 426)
Co-operation
Länder Fisheries offices
Fed. Statistical Office
BMELV
various authorities, sector
Aquaculture companies
accountants
KTBL
Länder
Laender
Commission (approval of
sampling plan)
Research institutes of BMELV
Establishment of the universe of aquaculture companies (Grundgesamtheit)/of the field of observation
Establishing the number of aquaculture units under enquiry – the universe – as well as criteria for
inclusion in the sample and appropriate thresholds are closely related to the overall rational of a future
survey and its acceptance. Perceptions of the national aquaculture universe vary widely and have been
subjected to considerable controversy.
43
Table 6.2
Germany, Universe of aquaculture firms
Species
Technology
No. total
Carp
pond
12,076a)
Trout
ponds & tanks
10,421a)
No. total with threshold
inland fisheries survey
2,210d)
(1.705 only carp ponds)b)
1,424d)
(895 only trout ponds)b))
9c)
11b)e)
No. main activity units
192a)
440a)
Mussel
on bottom
9c)
9c)
a)
Various
recirculation
23
?
Trout and others
net-cages
23a)
27 (4 only net-cages)
a) Source: Jahresbericht zur deutschen Binnenfischerei 2006;
b) Source: Inland fisheries survey 2004;
c) Source: Own inquiry;
d) main species not speciefied, units may fall into more than one category; total number of units (all species) is 3,343;
in the follwoing, we assume that 2,065 units mainly produce carp and 1,278 trout.
e) only table fish production, without fry and fingerling production
It is therefore proposed that the future survey in Germany covers the universe as negotiated between the
national parties concerned. If a wider universe than defined by common criteria of a European survey is
deemed appropriate, data to be communicated to the Commission can be extracted from the overall
survey results. The TBN would provide enough flexibility to facilitate the different requirements of the
Länder and Federal Government and the Commission as to the numbers of units under enquiry as well as
the quality and amount of data to be collected.
In the absence of an agreement concerning the field of observation, this study proposes to follow, except
for the criteria on commerciality, the definitions set forth by the inland fisheries survey. The number of
aquaculture units under enquiry can then be identified and monitored in the frame of the current inland
fisheries surveys. For this purpose the survey should be carried out more frequently, perhaps every five
years instead of ten (every second survey could cover aquaculture alone). According to the last Binnenfischereierhebung, the field of observation would in this case be around 3.350 units, broken down to the
different species and production systems as outlined in the table above.
However, the commerciality criteria used in the survey would need reconsideration. Commerciality had
namely not been defined in terms of output, size or any other objectively verifiable indicator but by less
precise criteria, whereupon aquaculture was deemed commercial when:
• the level of production went beyond subsistence needs;
• products were given away against returns, be these monetary or in-kind, regardless of quantities and
frequency
This definition included units operated as a main or as a sideline activity, but left apparently enough room
for many of the small commercial companies (that exceeded the size thresholds outlined above) to evade
survey participation. In case the aquaculture data collection scheme materializes the structural survey
should therefore adopt the concept of European Size Units (ESU) to facilitate a more effective
delimitation of the field of observation (and stratification of samples). ESUs are also applied under the
TBN.
6.2.9.
Defining Standard Gross Margins (Standard-Deckungsbeiträge) and economic sizes
Standard Gross Margins (SGM) are defined as “the value of output from one hectare or from one animal”
(FADN). SGMs have been established in Germany by KTLB (Association for Technology and Structures
in Agriculture) on behalf of BMELV for 70 different kinds of crops and livestock but not for aquatic
products.
The SGM for aquaculture can be expressed as the value of fish etc. produced:
• per unit area (in carp farming: Euro/ha of ponds/year) or
• per unit weight (trout and others: Euro/ton/year) or, alternatively,
44
•
per unit water supply (trout: Euro/litre/second/year)
minus direct expenditures, computed as three year averages. Prices, yields and turnovers as well as average
costs are empirical data and are, for agriculture, usually drawn from statistics and book-keeping results.
Such information are yet not or not readily available for aquaculture but could be generated and/or
systematically compiled and updated under the TBN.
There are currently 38 regions (district level) for which SGMs have been calculated individually.
Considering the relatively small number of companies and their distributional pattern it seems appropriate
to use, in the context of aquaculture, federal states or groups thereof as reference regions. The regions can
be selected in accordance with the specific geography of the segments under survey. As to carp culture
these would be Bayern (48% of total production) and Sachsen (27%) and for trout Bayern (40%) and
Baden–Württemberg (27%); the remaining states could for both segments be clustered into two regions,
namely newly-formed Länder (17% of carp, 10% of trout) and old Länder (7% of carp, 23% of trout). The
two mussel producing federal states Niedersachsen and Schleswig-Holstein may form one region, if
mussel aquaculture would come under the scheme.
In order to define the economic size of an aquaculture company in compliance with FADN guidelines the
total SGM must be divided by the value of one ESU which presently is fixed at 1,200 Euro. The current
German economic size thresholds (see above) would be equivalent to approximately 1 and 2 ESU which is
quiet low when compared to the 8 ESU thresholds set forth for inclusion in the TBN and the 16 ESU that
are applied by the Commission for German agricultural holdings.
6.2.10. Stratification
The field of observation would be stratified by BMELV along the three standard criteria type of aquaculture, economic size and region. A further break-down of strata may be desirable but the relatively small
number of companies does not support the invention of many categories, particularly since only a part of
the companies would be capable and/or willing to serve as a sample. Striking a balance between the need
to adequately represent the variety of aquaculture on one side and to meet with the statistical (and
confidentiality) requirements on the other side would be one of the greater challenges of the survey.
6.2.11. Selection of samples, sampling and handling of data
The selection of samples would be carried out on regional level by TBN-committees and based on
sampling plans, prepared by BMELV and approved by the Commission. These committees would be
composed of representatives of the relevant administrative bodies, of the financial administration, and of
the professional organisations and chaired by one leading fisheries officers of the state or states that form
one region.
The small number of companies in the stratified field of observation would most possibly not allow for a
random draw of samples as stipulated by the TBN guidelines, so that the survey would have to rely on
quota sampling instead.
Once that the data collection system has been set up annual accounting data of the sample companies
would be obtained from tax advisors against a fee. All procedures involved in the definition, collection,
verification, quality assurance and processing of data would be those of the TBN, using to the extent
possible the existing personnel and technical capacities.
45
6.3.
SIZE OF PRESENT AND FUTURE SURVEY
For the overall survey it is presently difficult if not impossible to determine the number of samples with
any accuracy, as the key features of an aquaculture data collection scheme under TBN-conditions are not
known, i.e. number of firms in the populations, thresholds and size classes based on SGM, etc. However,
on the grounds of the respective figures of the Inland Fisheries Survey of 2004 and on the 3-way-stratification “type of aquaculture”, “economic size” and “region” (plus 1 sub-set “intensity”) the sample size
has been preliminarily estimated as follows:
Table 6.3
Germany, Estimation of sample size
Criteria
Carp
No. strata/ Min no. of
subsets/
units total
units
1
1
2
2
3
6
4
24
15
360
Trout
Mussel
No. strata/ Min no. of No. strata/ Min no. of
subsets/
units total
subsets/
units totald)
units
units
Type of aquaculture
1
1
1
1
- Intensity a)
2
2
1
1
Size classes b)
3
6
1
1
Regions c)
4
24
1
1
Companies in sample
15
360
9
9
Total samples (n)
729 (720 without mussels)
Companies in population
ca. 2,065
ca. 1,278
9
Share of population in sample
17%
28%
9
100%
a) trout: semi-intensive (ponds), intensive (Rec, T&R); carp: extensive (ponds under agri - environment prg.), semiintensive (ponds not under agri-environment prg.).
b) small, medium, large by pond surface.
c) Bayern, Baden-Württemberg, Sachsen, other newly-formed and old federal states.
d) If mussel culture should be included; this study argues that the economic situation of the small sector could
alternatively be covered by a less formal survey and that problems concerning data protection are to be expected
due to the small size of the sector.
The following table shows the sample size (number of respondents) of the survey under this study and the
recommended future surveys against the number of firms within the main segments of the national
aquaculture sector:
In case the share of 17% for carp farming is deemed insufficient, the sample size may be increased, e.g. to
580, in order to cover the same share as in case of trout farming. On the other hand, any more restrictive
definition of the field of observation (higher thresholds, restriction to units operated as a main activity
and/or to enterprises which generate most value added from aquaculture, not from farming or other
activities, may allow to reduce the sample size and even the stratification.
Table 6.4
Germany, Main segments of the national aquaculture sector (number of firms)
Segment
(Species / technology)
Carp (and complementary),
ponds
Trout (and complementary),
ponds and tanks
Mussels, on bottom
Various, recirculation systems
Population
Present survey
Recommended future
survey
Without
With
threshold
threshold
360
360
Without
threshold
12,076a)
With
threshold
ca. 2,065b)
Number in sample
10,421a)
ca. 1,278b)
5
360
360
9c)
23a)c)
9c)
23 (?)c)
0
- (1 response, not
taken into
consideration)
-
9
-
9
-
6
Trout and others, net-cages
23a)d)
19 (?)d)
a) Source: Jahresbericht zur deutschen Binnenfischerei 2006;
b) Source: Binnenfischereierhebung 2004; in case of units producing more than on especies, the survey did not
specifiy which was the main species; this may result in some shifts between carp an trout units.
46
c) Source:
Own inquiry;
case of recirculation systems and net-cages, the Jahresbericht does not distinguish between units operated
independently or as part of the activities of an aquaculture enterprise. Among the netcages, at least four produce 1
ton per year or less and therefore might, as independent activities, be exclude from the inland fisheries survey.
d) In
The population without threshold includes units operated semi-commercially or on a recreational, rather
non-commercial basis and it can be safely assumed that these are largely excluded when using the threshold. The population of aquaculture units with threshold would therefore be almost , if not fully identical
with the population of commercial units.
Information on intensity levels can be established in the survey and results then be broken down
accordingly, rendering the pre-identification of strata on intensity level oblivious. In order to obtain a
sufficient number of units in each segment of the survey, intensity levels have nevertheless been included
in the calculation of the sample size (with factor two).
By applying the 3+1 criteria to the set of companies that had been identified as commercial aquaculture
undertakings, the field of observation would be divided into 49 groups averaging 70 companies. The
samples would cover 21% of the field of observation, the average weight of one sample company would
come to 5. In practise, however, the group sizes would differ from each other considerably with the small
companies forming the biggest groups.
6.4.
ESTIMATION OF COSTS
The total costs estimate for the national aquaculture data collection scheme under discussion in this report
and as outlined above are presented in the table below. Not included are costs of the statistical offices in
the context of structural surveys.
Table 6.5
Germany, Estimation of costs
Item description
Without threshold
Accounts
Accounts
available
not
available
With threshold
Accounts Accounts
available
not
available
Investment costs
• Staff (training, etc.)
• Hardware (computers, office equipment, etc.)
• Software (data compilation and processing)
Total investment costs
96,000
50,000
12,000
158,000
Annual operational costs
• Data collection (labour)
• Date collection (other expenses)
• Data processing (labour)
• Data processing (other expenses)
• Sub-contracted work
Total annual operational costs
168,000
193,000
154,000
147,000
45,000
707,000
On the side of BMELV alone current operational expenditures of the TBN for comparison amount to
about 5,000,000 Euro/year or to an approximate average of 417 Euro/sample/year. This covers the
following types of costs:
47
Table 6.6
Germany, Costs of TBN per sample
Type of costs
Euro/year
Sample farmer fee
55
Accountant fee*
ca 300 (250-410)
Software
Meetings
ca. 62
Training of accountants and staff
Other
Total
417
*The range of fees for accountants is determined by whether or not the sample is a book-keeping farm (250 Euro) or
not (410 Euro). Because most accountants have many sample farmers among their clients, up to several hundreds,
the fees can be kept at a moderate level. This is not very likely in the case of aquaculture and the average, therefore,
would be higher, presumably at about 470 EURO.
As to the personnel costs of BMELV, of the participating federal states and of the research institutes
involved in data analysis, the calculation is based on the following:
Table 6.7
Germany, Estimated personnel costs/staff
Type of costs
Personnel costs
Cost of working place
Supplementary costs
Total
Euro/year/staff
45,000
15,000
10,000
70,000
It is assumed that one aquaculture specialist would be employed (full-time) by BMELV and the present 5
staff at the TBN would each devote 0.1 full-time-equivalent while 12 staff of the 12 participating Länder
and 3 staff of 2 institutes involved would have to contribute work in the order of 3 full-time equivalents.
6.5.
AVAILABILITY OF FUNDING
According to statements by Federal and Länder authorities there are currently no funds available or
earmarked for the purpose of aquaculture statistics.
6.6.
PROBLEMS AND SOLUTIONS
6.6.1.
Extrapolation of the sample to total population
As indicated above; extrapolation was undertaken in relation to total production, with information on
sector production taken from the Jahresbericht zur Deutschen Binnenfischerei. Reasons for this decision were
that
•
•
•
overall production appears to be the most reliable and up-to-date data available on the sector;
results of the project survey were biased, as the average size of the surveyed units was far above
the sector average; an extrapolation on the grounds of total units would therefore be misleading,
data for stratification along size-classes were not available – neither for the sample nor for the
overall sector – so that no correction of the size-bias could be undertaken.
48
Table 6.8
Germany, Share or sample in value and volume of production of the total segment
On-growing technique
Species
Population (no. firms)
Sample (no. firms)
Share of sample in total population
Value of production population (million €)
Value of production survey (million €)
Share of sample in total value of the segment (%)
Volume of production population (tons)
Volume of production survey (tons)
Share of sample in total volume of the segment (%)
Ponds
Carp + by-species
2,065
6
0.3%
49.2
1.3
2.7%
15,206
626
4.1%
Ponds & tanks
Trout + by-species
1,278
5
0.4%
123.5
3.5
2.8%
23,889
1,075
4.5%
With a share of more than 4% on the total volume of the sector, it is assumed that the survey is, at least in
broad line, representative for the sector.
6.6.2.
Evaluation of individual indicators
The following tables show the evaluation of the individual indicators obtained in the survey:
Table 6.9
Germany, Statistical indicators, carp in ponds)
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Sample
mean
(1000 Euro
or %)
Absolute values
340.553
100.281
191.487
289.492
164.701
885.065
7,5
Relative
standard
deviation
(%)
Relative
standard
error
(%)
129%
150%
98%
169%
201%
111%
68%
53%
61%
40%
69%
82%
55%
28%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
90,9%
5%
Total capital costs
9,1%
51%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
22%
56%
Unpaid labour
16%
104%
Energy costs
9%
41%
Live raw material costs
17%
59%
Feed raw material costs
15%
17%
Repair and maintenance
5%
54%
Other operational costs
16%
56%
2%
21%
23%
43%
17%
24%
7%
22%
23%
49
Table 6.10 Germany, Statistical indicators, trout in ponds and tanks
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Sample
mean
(1000 Euro
or %)
Absolute values
750.9
146.8
360.4
399.2
244.8
1.170.4
7,0
Relative
standard
deviation
(%)
Relative
standard
error
(%)
106%
126%
127%
101%
100%
101%
89%
47%
56%
57%
45%
45%
58%
40%
Relative costs composition
Level 1 – Aggregated costs as% of total costs
Total operational costs
85%
11%
Total capital costs
15%
11%
Level 2 – Details of operational costs as% of total operational costs
Personnel costs
21%
59%
Unpaid labour
8%
144%
Energy costs
8%
38%
Live raw material costs
18%
72%
Feed raw material costs
23%
30%
Repair and maintenance
6%
53%
Other operational costs
16%
59%
5%
5%
26%
64%
17%
32%
13%
24%
27%
The German aquaculture sector is extremely heterogeneous, which is only partially reflected in the survey
results. As far as the share of individual cost factors etc. are concerned, results are less scattered
nevertheless, i.e. relative standard deviation and error are much lower. It is believed that at least along
general lines, the survey was able to provide a realistic picture of the sectors’ economic situation.
6.6.3.
Present and future response rate
When setting out to introduce an ongoing aquaculture data collection scheme, the perhaps greatest
impediment to be expected relates to its acceptance by both the administration on Federal/Länder levels
and the aquaculture companies.
When preparing the survey under this report, not much of a response was therefore expected, so that the
poor response rate of about 4% did not come as a surprise. The reasons presumably were:
• a general reluctance of fish farmers to provide economic information
• the considerable efforts required on the side of the farmers to fill in the forms
• the view that such a survey would be of neither immediate nor potential benefit for the farmers.
Only 3 out of 12 fish farmers in the survey felt that results of such a survey might be of use for them (4
no, 4 don’t know), and only 1 was prepared to invest more time in statistics (9 no, 2 don’t know). The
time currently spent in the context of aquaculture data collection was estimated by the respondents at
between 0.25 and 5 working days per year.
The fisheries administrators and the aquaculture associations were at best ambivalent about the practical
use of an economic survey. While there was a common wish of more and especially more reliable
information, the attitude prevailed, that better statistical data would not necessarily open up new options
to assist the sector any better or more efficiently.
The common concerns held against an aquaculture statistics that would go beyond the current extent are
best exemplified by decision 30/07 of the German Bundesrat (federal council representing the Laender) of
March 9, 2007: In its comments on the Commissions’ proposal for a regulation on the submission by
50
Member States of statistics on aquaculture COM(2006) 864 final the Bundesrat pointed out, among
others, that
• Germanys’ share in the total European (15) aquaculture output amounts to only 4.4% which is
small when compared to the combined share of 80% of Spain, France, UK, Italy, and Greece. But
the atomistic structure of production would, despite its marginality, require excessive, unjustifiable
administrative and financial efforts when surveyed in accordance with the proposed regulation.
• Current data collection on the grounds of Council Regulation (EC) No 788/96 provides, with a
view to the implementation of the CFP and the sustainable development of European
aquaculture, sufficient information. The introduction of additional statistical obligations would
contradict the efforts of Federal and state institutions as well as of the Commission (action plan
of 24th January 2004) to reduce unnecessary burdens on the administration and businesses. 8
The proposed regulation has been justified by the need of a broader range of data to facilitate a rational
development and management under the CFP of the aquaculture sector that has grown greatly since the
current legislation was adopted a decade ago. But there is no denial that this rational does not apply to
Germany, where aquaculture production since 1996 has been either declining (carp, mussels) or almost
stagnating (trout).
As to the aquaculture data collection scheme under discussion, however, it has been indicated by the
fisheries authorities concerned that they would not object or even support its establishment as long as it
comes under the TBN and their inputs would not go beyond the contributions that are outlined in table
6.1. It has also been stressed that financial inputs from the side of the state fisheries offices cannot be
expected.
The question of a sufficient participation of companies in the TBN, which would be on a voluntary basis,
is difficult to answer and therefore constitutes a substantial risk. Especially the smaller companies are
generally very hesitant to disclose economic data even when obliged to do so. According to experiences,
however, the problem is less prevalent within the TBN where data are obtained from the accountants with
little or no direct involvement of the company owners.
6.6.4.
Standard of balance sheets and book-keeping
Balance sheets of fish farmers follow German standards. EBIT, for instance, is not a position included in
ordinary German balance sheets, so that the data given here for EBIT had to be calculated from other
information, following the standards set for this study.
Another issue was that most indicators and values that were asked for could obviously be retrieved by
most respondents from their annual accounts (profit and loss account and balance sheets), but not all
positions of the accounts were included in the questionnaires (which were following the standards agreed
upon within the consortium). Many annual accounts contained additional, mostly extraordinary other
positions. In consequence, standard EBIT calculations produced other – more positive – results than
would be expected from indicators contained in the accounts, such as result of ordinary activities (Ergebnis
der gewöhnlichen Geschäftstätigkeit) or annual net profit.
One reason for discrepancies between actual results of accounting and survey results was that “value of
unpaid labour” was, of course, not included in the accounts, but considered here.
If aquaculture statistics come under the TBN, such problems would become largely irrelevant, as the use
of standardised accounts would be obligatory.
8
The decision concludes with the advice that the Federal Government should, in the course of consultations, work
towards a regulation that would not impose additional statistical obligations but would fully recognise (under
article 8) the current reporting system, or, in case of failure, should reject the entire proposal.
51
6.6.5.
Cross-check with other sources
Volume figures of the annual report on German fisheries and of FAO and Eurostat show only minor
differences as far as the main species carp and trout are concerned. For these two branches of aquaculture,
the annual report breaks down volume figures into table fish of main species (carp or trout), stocking
material, and by-species, where only the first segment is covered by FAO and Eurostat figures. For values,
however, only a total for all three types of products is given for each of the two branches of aquaculture,
which obviously has to be higher than that for table-size fish of the main species alone.
Results of the project survey have been extrapolated to sector level relative to total production volumes as
published in the annual report and therefore are in line with this source.
Values of carp production from the extrapolated project survey are very close to those given in the annual
report (both sources include by-species), but higher than Eurostat-figures (without by-species). Project
survey values of trout production are in between those given by the annual report and by Eurostat. This
may be attributed to by-species, an above-average size of the units covered by our survey (which implies
limited possibilities of direct marketing and therefore a price below average) and/or to shortcomings of
the other surveys. Nevertheless, cross-checking shows in both cases that results of our project survey are
in a plausible range.
Table 6.11 Germany - Comparison of the extrapolation of the survey data to other sources, 2006
Volume (1000 t)
Annual reporta)
FAO
Eurostat
table fish
incl. by-fish,
main spec.
stocking m.
Carp
10.5
15.2
10.6
10.6
Trout
18.9
23.9
19.0
21.3
a) Source: Jahresbericht zur deutschen Binnenfischerei 2007
Value (mln Euro except FAO)
Project
FAO
Annual
Eurostat
(mln US$)
Survey
report a)
49.2
123.5
49.7
83.4
46.6
83.5
36.9
66.4
52
7. GREECE
7.1.
SUITABLE ORGANIZATION
The obligation to collect data and to compile aquaculture statistics has been so far an obligatory task (EU
Reg. 788/96). This task has been implemented by the Ministry of Agricultural Development and Food
(MAD&F). The services of this organisation have been restricted to the collection of production data and
value of the production. They have never covered the cost distribution. For this reason, the MAD&F was
obliged to scan the aquaculture sector in regular time intervals in order to have an accurate view of the
economic performance of the sector.
One of the organizations which participated in those studies was the Hellenic Centre for Marine Research
which has developed the network and expertise to collect the data. The same organization is also
responsible for the implementation of the fishery data collection program. Therefore the most suitable
organization to collect the data for the aquaculture sector seems to be the Hellenic Centre for Marine
Research.
7.2.
METHOD OF DATA COLLECTION
HCMR implemented the present data collection program by means of direct surveys to fish and shell
farmers. The data collected include, financial data of firms having aquaculture as their main activity. Three
main activities have been surveyed with a relative good response rate.
• Saltwater culture (Response Rate 34.8%)
• Freshwater culture (Response Rate 44.3%)
• Shellfish culture (Response Rate 12.5%)
•
No hatcheries have been surveyed separately as they are, for 99%, part of the fattening units. Although all
farmers who provided information communicated the basic financial data (turnover, overall cost, assets
and personnel) the most of them were not in the position to provide a cost breakdown.
The survey was mixed: by telephone and personal visits. The data of the large companies (Limited and
Anonymous) have been cross checked with the published balance sheets. The public balance sheet data
have been provided by a specialized private company (ICAP S.A.). Incomplete questionnaires have been
filled properly with additional information provided after telephone calls. Only 2% of the mailed
questionnaires where completed 100%.
7.3.
SIZE OF PRESENT AND FUTURE SURVEY
The present survey is carried out in 2008 and had as reference year the year 2006. This requirement
created a problem because the situation has changed drastically in saltwater culture. Additionally, the
survey had as target three populations with different characteristics. For this reason different approaches
have been applied.
• The saltwater culture production is highly concentrated. The large companies, (17 of 201 in 2006)
with revenues above 5 million euro contribute by 82% to the total revenues of the segment. The
medium size companies having revenues one to five million euro (37 of 201 in 2006) contribute by
15.4% to the total revenues of the segment. Finally the remaining 147 companies contribute by 2.6%
to the total revenues of the segment. In the current survey, the large and medium size companies have
been considered as one sub-segment and the small ones as the second sub-segment.
• The freshwater culture production is scattered in a large number of small companies having a family
organization character. Many of them do not sell any product to the fish market. Instead they use
their entire production to their own business (restaurants). Many of those producers could be difficult
classified according to the Standard Industrial Classification (SIC). In this segment, all information has
53
•
been collected by telephone interviews. Additional information has been gained by the local fishery
inspectors who have a good view of the performance of each firm. Not a single firm was able to
provide a complete cost item break down. All information concerning the cost has been estimated by
the interviewers in collaboration with the farmers. The sample size was 54 of 122 companies (44.3%).
The shellfish culture has a similar character as the freshwater one. The sample size for this activity is
was 29 out 232 companies (12.5%).
The tracing of the economic performance of the aquaculture sector in the future has to take into
consideration the fact that a large number of the producers in freshwater farming are small business not
classified in SIC as aquaculture firms. Despite their small contribution to the overall performance it is
important to include their data in the production statistics.
Attention has to be paid also to the illegal or not declared production in all segments of the aquaculture
sector. In fact the declarations of production create a false image of the sector that produce nice statistics
but the market rules with their price forming mechanisms are merciless, bring things in their proper
perspective.
Data collection and related cost is not proportional to the importance of the activity in terms of economic
impact. Sampling activities with large number of firms, not having accounting systems, requires multiple
efforts in labour and costs compared to the sampling of business having account data. A reliable data
collection in such cases requires a visit to the site.
In activities with high diversification of the population in terms of production size or turnover the
stratified sampling design has to be applied. In the Greek case, this is the situation for the saltwater culture
sector where at least two strata should be specified.
The detailed disaggregation of cost as it has been imposed by the Regulation is not available in the existing
account data. The problem is more intense in firms without accounting system. Therefore, a cost specific
survey has to be designed.
Almost all aquaculture firms in Greece have as exclusive activity the culture of fish or shellfish. For this
reason the question of putting a threshold for the definition of the population is not applicable. The
sample sizes of the current and the proposed future surveys are presented in
Table 1.
Table 7.1
Main segments of the Greek aquaculture sector (number of firms)
Segment
(Species/Technology)
Saltwater farming in cages:
Seabass –Seabream
Freshwater farming in ponds
and raceways:
Trout, Salmon, Carp
Shellfish farming:
1)
2)
a)
b)
Population 2008
Present survey
1/2
Without
With
threshold threshold
Sample size
Recommended survey
a/b
Without
With
threshold
threshold
Exhaustive/ Exhaustive/
20
20
107
107
54/19
214
214
54
45
45
232
232
29
50
50
Companies having turnover > 1 million Euro (Based on the population of 2006)
Companies having turnover < 1 million Euro (Based on the population of 2006)
Companies having turnover > 5 million Euro
Companies having turnover < 5 million Euro
The population in the future survey will be based on the list of aquaculture producers provided by the
Greek Ministry of Agricultural Development and Food – (Directorate of Fisheries). The list of the
Ministry is regularly updated. The strict licensing system that is applied currently in Greece makes it
impossible to establish a new fattening unit in the saltwater culture. Inevitably, the existing firms will try to
take over existing companies in the ongoing consolidation process. This is shown clearly in the sharp
decrease of the number of firms between 2006 and 2008. It is expected that in the near future we will face
54
a further decrease of the number of saltwater culture firms. Similar trends are expected in the freshwater
farming and in the shellfish farming. In this case the restricting factor is the environmental problem
(scarcity of sites and pollution). For this reason, a mechanism has to be created in side the Ministry to
follow the mutations and establish a company oriented register.
7.4.
ESTIMATION OF COSTS
The experience gained by the implementation of the Greek DCR but also by the current survey showed
that the best way to collect reliable data is to establish direct contact with the business unit and to visit the
site. The data collected in this way cross checked with the accountant data and the declarations given to
the Ministry services, provide a realistic financial picture of the firm. The estimation of costs for the data
collection is based on the above mentioned conditions. In all activities it is preferable to choose the
exhaustive data collection scenario. The threshold scenario has a meaning because in all segments there
are very small units. In order to provide precise estimation we choose for a large sample or apply
stratified sampling.
The initial cost of the survey covers the start up cost and it includes the creation of infra-structure
(computers, software, database etc) and the training of the personnel. The operational cost covers the
actual data collection, mainly labour cost and travel expenses.
Table 7.2
Budget for data collection of aquaculture data (Euro)
Without threshold
Accounts
Accounts
not
available
available
Item description
Investment costs
• Staff (training, etc.)
• Hardware (computers, office equip., etc.)
• Software (data compilation and processing)
Total investment costs
Annual operational costs
• Data collection (labour)
• Date collection (other expenses)
• Data processing (labour)
• Data processing (other expenses)
Total annual operational costs
7.5.
With threshold
Accounts
Accounts
not
available
available
5.000
10.000
10.000
25.000
5.000
10.000
10.000
25.000
5.000
10.000
10.000
25.000
5.000
10.000
10.000
25.000
30.000
15.000
25.000
10.000
105.000
45.000
25.000
30.000
10.000
135.000
20.000
10.000
20.000
10.000
85.000
30.000
20.000
25.000
10.000
110.000
AVAILABILITY OF FUNDING
The large scale data collection programs applied in Greece have been co-funded by EU funds. This was
the case with the fishery data collection programs. HCMR, as a public organization, has no funds available
for a regular data collection program for the aquaculture sector. Therefore an external source of funding
should provide the resources for the data collection. The ministry of Agricultural Development and Food
should be requested to make funds available in the mid term planning. As it has been experienced so far,
there is not strong intention to spend funds for data collection if there is not any must. In case of
application of a Regulation the situation is different. The repetition of the national fishery data collection
initiative with co-funding from national and European funds seems to be the best funding practice.
55
7.6.
PROBLEMS AND SOLUTIONS
7.6.1.
Extrapolation of the sample to total population
In order to make any estimation it became necessary to define the population. The problem with the
definition of the population in each segment was the identification of the firms which could be classified
as aquaculture firms. It is a complex issue because many companies registered as aquaculture companies,
exist only in paper because they have hired their production units to other companies. Other companies
have developed other activities and they can not keep the same classification any more. This investigation
became even more difficult because investigation has been implemented in 2008 but the reference year
was 2006.
The survey on the account and production statistics for 2006 resulted in a sample of 153 aquaculture
companies. Three segments have been identified according to the on-growing technique: sea cage farming,
freshwater farming, and shellfish farming. The sample size coverage for each segment has been 34.8% for
the cage farming, 44.3% for the freshwater farming and 12.5% for the shellfish farming.
After the collection of the account data the companies have been classified by size according to their
turnover. After the classification, the production data and the cost disaggregation data have been
collected. From the sampling data collected, the averages of all indicators within a segment have been
calculated. From the averages calculated in the previous step, the estimated values of the indicators have
been calculated by simple rising to the total population of each segment. This practice has been applied
for the estimation of the indicators of the freshwater farming and the shellfish farming. In the sea cage
farming the population is divided in two sub-segments: the firms having a turnover above one million
Euro and the firms below that threshold. In this case each indicator is calculated separately for each subsegment and the final estimation is the sum of the two values. Table 3 shows the distribution of the
sampling effort.
Table 7.3
Share or sample in value and volume of production of the total segment
On-growing technique
Species
Population (no. firms)
Sample (no. firms)
Share of sample in total sector value
of the segment (%)
Share of sample in total sector volume
of the segment (%)
7.6.2.
Cages
Seabass Seabream New Species
201
70
87.7%
93.8%
71.1%
93.8%
Tanks and
raceways
Off bottom
Trout - Eels Carp - Salmon
Mussels Oysters
122
54
1.5%
44.3%
1.5%
44.3%
232
29
0.3%
11.4%
2.6%
12.5%
Assumptions
The underlying assumptions in this approach are:
The turnover is a reliable indicator for the classification of a certain company. This is not always true
because even a wealthy company can loose the entire production due to natural disasters, pollution or
diseases. Such accidents have been reported in the shellfish farming and in the freshwater farming during
the year 2006.
There is a linear relation between all indicators in all companies within a segment. This is never true
because the costs are influenced by a variety of factors and even two companies with identical turn over
can have a very different cost distribution.
56
7.6.3.
Data collection problems
The main problem that has been encountered in this survey was the reluctance of the majority of the
farmers to provide financial information to the interviewers. The reasons are different in each segment but
converge to one point: the fear that the results of the investigation will be exploited either by the
competition or by the tax authorities. Additionally, the break down of the cost to the items defined in this
investigation was not possible since most of the farmers do not keep detailed register of their expenses.
Consequently, the answers included a lot of guess work. Only the large firms with accounting systems
were in the position to give a complete cost break down.
The solution to this problem is to charge a neutral organization with the data collection task. A research
organization with a good reputation in the society seems to be the preferable one. Additionally, prior to
the data collection survey an extensive briefing of the farmers should be initiated in order to convince
them about the necessity of reliable data reporting.
Table 7.4
Statistical indicators, segment 1: Euryaline fish (cages)
Sample mean
(1000 Euro or %)
Relative
standard
deviation
(%)
Relative
standard error
(%)
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
7,044.6
259%
1,036.6
93%
3,334.3
75%
1,652.6
86%
2,190.1
18%
14,064.2
279%
88.1
118%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
91%
Total capital costs
9%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
15%
Unpaid labour
Energy costs
2%
Live raw material costs
8%
Feed raw material costs
47%
Repair and maintenance
1%
Other operational costs
17%
31%
11%
9%
10%
2%
33%
14%
57
Table 7.5 Statistical indicators, segment 2: Freshwater fish (tanks and raceways)
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Sample mean
Relative standard Relative standard
(1000 Euro or %)
deviation (%)
error (%)
Absolute values
128.8
186%
25%
7.7
308%
42%
40.1
194%
26%
2.1
Relative costs composition
Level 1 – Aggregated costs as % of total costs
100%
90%
12%
Total operational costs
Total capital costs
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
5%
Unpaid labour
35%
Energy costs
Live raw material costs
Feed raw material costs
Repair and maintenance
Other operational costs
Table 7.6 Statistical indicators, segment 3: Shellfish (tanks and raceways)
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Sample mean
Relative standard Relative standard
(1000 Euro or %)
deviation (%)
error (%)
Absolute values
59.4
186%
25%
24.7
308%
42%
27.5
194%
26%
195.2
2.5
90%
12%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
100%
Total operational costs
Total capital costs
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
5%
Unpaid labour
35%
Energy costs
Live raw material costs
Feed raw material costs
Repair and maintenance
Other operational costs
7.6.4.
Cross check with other sources
The data provided in the national chapter are different than the data from FAO and Eurostat. The
estimations made in this work show higher values in both the volume and value of landings in all three
segments.
58
8. HUNGARY
8.1.
SUITABLE ORGANIZATION
Data collection is suggested to be performed by the Agricultural Economics Research Institute
(Agrárgazdasági Kutató Intézet, AKI). It has substantial experience in statistical data collection being the
Hungarian Liaison Agency of the EU FADN system. Moreover AKI receives and processes natural data
recently collected in the field of aquaculture.
8.2.
METHOD OF DATA COLLECTION
The presently ongoing data collection is organized on county (NUTS III) level. Data is gathered on yearly
basis in February. Fish inspectors are maintaining the actualized database of farms involved in aquaculture
and perform the data collection. Collected data is then forwarded to the Agricultural Economics Research
Institute. On the basis of these data the field of observation can be worked out.
Present project
Prior to data collection for this project a workshop was organized by AKI with the representatives of the
biggest fish producers who are members of the Hungarian Fish Producers Organization. Not all the
managers were co-operative and some of them hesitated to take part in the data collection. Later an act of
consent was worked out and sent to the fish farmers thought to be co-operative. Potential sample farms
were selected according to their location, economic size and legal form. Questionnaires were sent out by
regular mail to some 35 fish farmers, followed by a telephone call. Besides that one third of the farms
were visited personally. Despite the prior agreement a substantial number of farmers have refused to
complete the questionnaire. Data quality was assured by finding the most prepared individuals for filling in
the questionnaire. Afterwards AKI experts have checked the data for potential errors.
Future
Farmers providing data for the project are willing to take part in future data collections as well. They find
this data collection useful and are hoping to have a common EU regulation. Some farmers could not
supply data as they have other farm activities (crop production, animal husbandry) and were not able to
separate their costs. However, they would make the effort in the future to separate their invoices to be
able to provide data on their fish production. This data collection has proved that personal interviews are
far more effective compared to data collection through mail.
8.3.
SIZE OF SURVEY
In Hungary there are only two segments in terms of the on-growing technique: pond farming and
recirculation systems. However, the size of the second segment is so small and the number of farms is
very low (10).This makes it impossible to sample such a small segment. We suggest concentrating only to
pond farming as this is by far the most important. The existing segments and the proposed samples are
shown in the following table.
The size of pond surface of fish farms in Hungary is ranging from 1-2 hectares to above a thousand
hectares, making the sector very heterogeneous. Therefore it would make sense to leave out the smallest,
mainly subsistence fish farmers from the field of observation by applying a certain threshold. The
recommended sample size for the present and the future surveys are indicated in the following table.
59
Table 8.1
Hungary, Main segments of the national aquaculture sector (number of firms)
Segment
(Species / technology)
Carp / ponds
Population
Without
With
threshold
threshold
220
170
Present survey
Number in
sample
21
Recommended future survey
Without
With
threshold
threshold
40
35
Source: estimation by AKI experts
8.4.
ESTIMATION OF COSTS
Estimation was made with the supposition of surveying the total population (without threshold some 220 farms) and
the field of observation (with threshold some 170 farms) employing two people full time and supplying them with
required equipment (one furnished and equipped office room). Estimated values were determined on the basis of
similar costs in the Hungarian FADN system.
Table 8.2
Hungary, Estimation of costs (Euro)
Item description
Without threshold
Accounts
Accounts
available
not
available
With threshold
Accounts Accounts
available
not
available
Investment costs
• Staff (training, etc.)
• Hardware (computers, office equipment, etc.)
• Software (data compilation and processing)
Total investment costs
1,500
6,000
8,000
15,500
1,500
6,000
8,000
15,500
1,500
6,000
8,000
15,500
1,500
6,000
8,000
15,500
Annual operational costs
• Data collection (labour)
• Date collection (other expenses)
• Data processing (labour)
• Data processing (other expenses)
Total annual operational costs
88,000
8,000
25,000
10,000
131,000
98,000
10,000
30,000
12,000
150,000
72,000
6,000
20,000
8,000
106,000
80,000
8,000
25,000
10,000
123,000
Source: estimation by AKI experts
8.5.
AVAILABILITY OF FUNDING
National or own sources are not available at present.
8.6.
PROBLEMS AND SOLUTIONS
It is foreseeable that farms willingness to cooperate is very low. Selection and surveying of the farms is planed to be
executed with the assistance of the Hungarian Fish Producers Organisation. Convincing the farmer to provide data is
only possible by acquaintance. Working out of some kind of incentive scheme would be advisable.
8.6.1.
Extrapolation of the sample to total population
Extrapolation of the sample values to the total population was made through the quantity of produced
fish. As the quantity of fish produced is known for the sample and for the total population, a ratio was
calculated and used for extrapolating other indicators from sample level to the level of the total population
(See Table 8.3.).
60
Table 8.3
Hungary, Share or sample in value and volume of production of the total segment
On-growing technique
Ponds
Species
Carp
Population (no. firms)
220
Sample (no. firms)
21
Share of sample in total value of the segment (%)
28.45
Share of sample in total volume of the segment (%)
28.45
Source: calculation on the basis of the sample and the total population
8.6.2.
Evaluation of individual indicators
One of the biggest problems was the limited willingness of co-operation of the farmers. Somehow they
are afraid of providing data, especially data concerning economic performance. Better communication of
the purpose of data collection and its possible positive effects on their business would most likely change
their attitude.
Problems occurred with small scale single holders as they were not able to provide balance sheet data. The
lack of sufficient registers (accounting) makes it very difficult to collect reliable data from them. In EU
FADN this problem (at least in Hungary) is solved by the use of accountancy offices as data collectors.
Heterogeneity in terms of size is a problem. The size of farms is ranging from 1 ha pond surface area to
more than 1000 ha. We suggest to stratify the farms at least to small, medium and large farms’ groups
taking samples from each.
Another problem was in several cases that farms are involved in other activities as well (i.e. crop
production, animal husbandry). That made it practically impossible to separate income and cost figures
related to fish production. A possible solution would be signing longer term contract with the farms in
which they would commit themselves to provide separated data.
Table 4.1. describes the mean, the relative standard deviation (RSD) and the relative standard error (RSE)
of selected indicators as well as the relative cost composition (RCC). As in the sample of 21 farms there
were very large and very small farms the RSD values are quite high over 100%, in one case even 200%. At
the same time RSE values are also high exceeding 30%. According to these statistical indicators the
heterogeneity of the sample is obvious. Solution could be the increase of the sample size and the
stratification of the population according to farms size.
61
Table 8.4
Hungary, Statistical indicators, carp in ponds
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
162.7
221.1
149.5
179.9
179.2
167.2
199.5
35.5
48.2
32.6
39.6
39.1
36.5
43.5
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
637.4
186.0
317.6
382,8
192.5
1186.0
25.6
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
88.9
171.5
Total capital costs
11.1
160.4
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
36.8
221.1
Unpaid labour
0.3
199.7
Energy costs
10.3
134.6
Live raw material costs
4.9
237.4
Feed raw material costs
17.7
179.8
Repair and maintenance
14.8
138.4
Other operational costs
15.2
242.9
Source: calculations on the basis of the sample
37.4
35.0
48.2
43.6
29.4
51.8
39.2
30.2
53.0
Regarding the relative composition of costs the share of personal costs are considerably high while at the
same time the share of unpaid labour is very low. This is due to the fact that corporate farms 9 are bigger
in size compared to individual farms 10 and do not apply unpaid labour. Individual farms on the other
hand are usually operated by the family and only in the peak season hire additional workforce.
8.6.3.
Cross check with other sources
Production volume of marketed fish in 2006 according to Eurostat was 14,686 tones in Hungary.
According to our data collection total fish production in 2006 was 20,762 tones. The difference between
the two numbers is due to the fact that while Eurostat counts only marketed fish, our statistics include
also juveniles.
9
Corporate farms are usually limited liability companies or joint-stock companies (legal entities).
Individual farms refer to individual persons involved in aquaculture often referred as “single holders”.
10
62
9. IRELAND
9.1.
SUITABLE ORGANIZATION
Bord Iascaigh Mhara (BIM) provides grants and technical advice and delivers quality and environment
programmes as well as marketing and training supports to promote the development of the fish farming
industry in a sustainable manner. It has carried out, since 2003, an annual report reviewing the status of
Irish aquaculture, in collaboration with the other two main State Agencies that provide support services in
the areas of research and development to the industry – the Marine Institute (MI) and Udaras na
Gaeltachta/Taighde Mara Teoranta (TMT). One of the objectives of this report is to show trends in the
production, employment, and export and market statistics for the industry. BIM carries out an annual
survey of all producers on the volume and value of production and employment. As such, the agency
already has in place the necessary access to the contact details of the entire aquaculture population and the
relevant experience and relationships built with the industry which are required for collecting data of this
nature.
9.2.
METHOD OF DATA COLLECTION
9.2.1.
Present data collection by BIM
BIM, the seafood development agency, conducts an annual survey on production volumes and value,
directly from aquaculture operators. BIM has also conducted an annual employment survey since 2002.
The method for collecting this information involves the distribution of a species-specific questionnaire to
all license holders, on BIM’s database. The list of license holders is provided to BIM each year from the
Department of Communications, Marine and Natural Resources.
The production and employment survey form is sent out on a species-specific basis every January with a
deadline for return of forms on the 28th February. BIM does a preliminary “once off” call-around in early
March and in April; BIM starts to follow up on survey forms via repeated telephone calls. Much of the
time on phones is spend aiding operators to fill in the forms. The forms are usually all collected and
inputted to the database by end of July.
For this survey, employment is defined as follows:
• Full Time Staff: >30 hours / week throughout the year or > 40 weeks / year;
• Part Time Staff: between 10 & 30 hours / week throughout the year or between 13 & 39 weeks of
working 40 hours / week;
• Casual Staff: < 10 hours / week throughout the year or < 13 weeks of working 40 hours / week.
The results of the survey feed into an annual publication, The Status of Irish Aquaculture, which is a
collaborative effort between Bord Iascaigh Mhara (BIM), the Marine Institute (MI) and Údarás na
Gaeltachta/Taighde Mara Teoranta (TMT). The overall aim of the report is to provide useful reference
material for the industry, trade customers, investors, researchers and interested parties.
9.2.2.
Future data collection
The proposed data collection approach is to expand on the existing survey that is sent out to producers
on production/employment. However, this survey is a mail survey only and from our experience in
carrying out this feasibility survey, the best approach would be to visit the companies directly. Therefore,
a sample of producers would be randomly selected and visited by an assigned data collector from BIM.
Of the 28 companies surveyed, it was not possible to meet personally with 7 of them. BIM requested that
these companies fill out the form themselves and mail it to BIM. After much chasing via phone calls by
BIM, one producer returned a completed form, two producers returned partially completed forms and
four producers did not reply. As part of the feasibility survey, BIM asked each of the companies it met
63
with if they would, in the future, complete this survey form by mail or email if they received it. The vast
majority replied that they probably would not do so, or that they would have to hand it over to their
accountants to fill. They felt that the best option for maximum return would be to visit them directly.
9.3.
Table 9.1
SIZE OF PRESENT AND FUTURE SURVEY
Ireland, Segments of the national aquaculture sector (number of firms)
Segment
(Species / technology)
Population
Present survey
Recommended future survey
Without
With
Without
With
Number in
threshold
threshold
threshold
threshold
sample a)
Saltwater (salmon/trout)
11
4 (4)
10
Mussels/ On bottom
37
6 (4)
15
Mussels / Off bottom
61
7 (5)
25
Oysters (gigas and native)
110
8 (6)
40
Hatcheries
12
3 (3)
10
a) Between brackets is the number of firms which provided also costs and earnings data, while for the others only
balance sheet information (assets and liabilities) was available.
60% of licence holders are sole-traders, with 40% registered as limited companies. It is proposed that the
survey be stratified by species and/or on-growing technique. Where there are less than 10 firms in a
segment, and where the species is an economically important species for the Irish aquaculture segment
(for e.g. salmon), it is proposed to survey on a “sub-sector” level, for e.g. marine finfish.
9.4.
Table 9.2
ESTIMATION OF COSTS
Ireland, Estimation of costs
Item description
Investment costs
• Staff (training, etc.)
• Hardware (computers, office equipment, etc.)
• Software (data compilation and
processing/database development)
Total investment costs
Annual operational costs
• Data collection & processing (labour)
• Date collection (other expenses)
• Data processing (other expenses)
Total annual operational costs
Without threshold
Accounts
Accounts
available
not
available b
With threshold a
Accounts
Accounts
available
not
available b
18,500
7,500
50,000
18,500
7,500
50,000
18,500
7,500
50,000
18,500
7,500
50,000
76,000
76,000
76,000
76,000
169,000
49,722
1,500
220,222
169,000
49,722
1,500
220,222
124,000
19,124
1,500
144,624
124,000
19,124
1,500
144,624
Notes:
a:
The threshold applied is to leave out segments with under 10 companies and to sample a total of 100 out of the
remaining 232 firms.
b: The provision of full access to accounts, via company accountants, is not foreseen as a viable option. Abridged
balance sheets are available for limited companies (40% of population) and will be used as a means of verifying the
quality of data provided.
64
9.5.
AVAILABILITY OF FUNDING
Are there own or national funds to support such data collection?
9.6.
PROBLEMS AND SOLUTIONS
9.6.1.
Table 9.3
Extrapolation of the sample to total population
Ireland, Share or sample in value and volume of production of the total segment a)
On-growing technique
Pens or
Off
On
Bags &
HatcheCages
bottom
bottom
Trestles
ries
Species
Salmon /
Mussels
Mussels
Oysters
Salmon
trout
smolt
Population (no. firms)
11
61
37
110
12
Sample (no. firms)
4
7 (5)
6 (4)
8 (6)
3
Share of sample in total value of the segment (%)
64%
14%
19%
14%
25%
Share of sample in total volume of the segment (%)
63%
18%
19%
13%
25%
a) Figures between brackets indicate how many firms provided costs and earnings data, if different from the total
number of firms in the sample.
The following section 1.6.2 shows that the data from the samples showed often very large dispersion (high
relative standard deviation and error). This means that a straightforward extrapolation of costs and
earnings from the sample average to the population was not possible. As population information is
available on average earnings, the ratio between population average and sample average was used to
‘calibrate’ all cost items to the segment total, presented in the Part 1 of the report ‘National chapters’.
The formula used can be expressed as : TPCi = ASCi * APE / ASE
Where:
TPCi = Total population costs i
ASCi = Average / firm costs i of the sample
ASE = Average / firm sample revenues
APE = Average / firm population revenues
In view of the fact that the relative standard deviation ranges between 76% and 165% it is clear that the
followed approach provides a very rough estimate only. Substantially larger samples will have to be taken
in the future in order to derive smaller confidence interval. However, it must be pointed out that in
heterogeneous populations larger samples will not necessarily lead to narrower confidence interval. Rather
it may be necessary to stratify the population into smaller, more homogeneous segments. This latter
solution may be appropriate for the larger populations of mussel and oyster firms, but not for the
relatively small population of salmon growers and hatcheries.
65
9.6.2.
Table 9.4
Evaluation of individual indicators
Ireland, Statistical indicators, Salmon / Cages
Sample mean
(1000 € or %)
Relative standard
deviation
(%)
Relative standard
error
(%)
Absolute values
7,854.7
105%
384.8
100%
6,245.4
108%
1,609.3
-4%
1,224.5
-104%
6,799.8
92%
8.5
117%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
6,630.1
101%
Total capital costs
391.8
139%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
25%
156%
Unpaid labour
Energy costs
0%
177%
Live raw material costs
7%
128%
Feed raw material costs
43%
80%
Repair and maintenance
1%
200%
Other operational costs
23%
51%
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Table 9.5
52%
50%
54%
-2%
-52%
46%
59%
50%
70%
78%
88%
64%
40%
100%
25%
Ireland, Statistical indicators, Oysters / on bottom
Sample mean
(1000 € or %)
Relative standard
deviation
(%)
Relative standard
error
(%)
Absolute values
Total turnover (incl. other income)
347.6
76%
Personnel costs (excl. unpaid labour)
31.2
131%
Operational costs (excl. labour)
206.0
100%
Gross value added
141.6
-25%
Gross cash flow
110.3
-156%
Total assets
783.7
97%
Engaged persons
6.8
103%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
237.2
83%
Total capital costs
46.3
88%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
31%
46%
Unpaid labour
Energy costs
3%
56%
Live raw material costs
32%
52%
Feed raw material costs
17%
136%
Repair and maintenance
4%
184%
Other operational costs
35%
48%
31%
54%
41%
-10%
-64%
39%
42%
34%
36%
23%
23%
21%
96%
75%
20%
66
Table 9.6
Ireland, Statistical indicators, Mussels / on bottom
Sample mean
(1000 € or %)
Relative
standard
deviation
(%)
Relative
standard error
(%)
Absolute values
Total turnover (incl. other income)
796.9
165%
Personnel costs (excl. unpaid labour)
53.1
Operational costs (excl. labour)
495.4
164%
Gross value added
301.4
1%
Gross cash flow
248.3
1%
Total assets
958.8
140%
Engaged persons
2.8
91%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
548.5
167%
Total capital costs
116.0
194%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
3%
200%
Unpaid labour
Energy costs
7%
80%
Live raw material costs
31%
102%
Feed raw material costs
11%
140%
Repair and maintenance
4%
117%
Other operational costs
47%
61%
Table 9.7
83%
82%
1%
1%
70%
45%
84%
97%
100%
40%
51%
70%
59%
30%
Ireland, Statistical indicators, Mussels / off bottom
Sample mean
(1000 € or %)
Relative
standard
deviation
(%)
Relative
standard error
(%)
Absolute values
Total turnover (incl. other income)
475.9
141%
Personnel costs (excl. unpaid labour)
51.3
Operational costs (excl. labour)
189.3
114%
Gross value added
286.6
27%
Gross cash flow
235.4
27%
Total assets
843.1
114%
Engaged persons
4.5
82%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
240.6
117%
Total capital costs
112.0
172%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
18%
134%
Unpaid labour
Energy costs
8%
140%
Live raw material costs
6%
175%
Feed raw material costs
6%
123%
Repair and maintenance
6%
161%
Other operational costs
57%
64%
63%
51%
12%
12%
51%
37%
52%
77%
60%
63%
78%
55%
72%
29%
67
Table 9.8
Ireland, Statistical indicators, Salmon smolt / hatcheries
Sample mean
(1000 € or %)
Relative
standard
deviation
(%)
Relative
standard error
(%)
Absolute values
Total turnover (incl. other income)
767.5
77%
Personnel costs (excl. unpaid labour)
47.8
104%
Operational costs (excl. labour)
634.5
101%
Gross value added
132.9
-25%
Gross cash flow
85.2
-129%
Total assets
716.0
70%
Engaged persons
4.3
35%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
682.3
89%
Total capital costs
26.3
117%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
14%
87%
Unpaid labour
Energy costs
7%
97%
Live raw material costs
26%
104%
Feed raw material costs
8%
36%
Repair and maintenance
3%
38%
Other operational costs
42%
38%
9.6.3.
44%
60%
59%
-14%
-74%
41%
20%
52%
67%
50%
56%
60%
21%
22%
22%
Cross check with other sources
The results of the survey were compared to the national total of value of production and employment
which have been collected by BIM since 2002. As stated in the section 1.6.1 major inconsistencies
appeared when comparing the average revenues per firm which follow from the population and from the
sample. Extrapolation of the costs has been adapted to these inconsistencies. However, future surveys will
have to address this issue in greater detail and achieve consistency on a desired level of statistical
confidence.
68
10. ITALY
10.1.
SUITABLE ORGANIZATION
The complexity and diversity of the aquaculture sector and the need for reliable information, require an
organisation with adequate experience of advanced statistical techniques and management of permanent
statistical frameworks. These requirements can be fulfilled by organisations belonging to the National
Statistics System (SISTAN) which follow standard certified procedure in terms of the quality of
information produced. IREPA fulfils these requirements, having the proper qualifications and experience,
and could be the institution capable of carrying out the survey of aquaculture economic data.
The Institute has a long-standing experience in fishery data collection. and As a member of SISTAN,
IREPA has full access to the required information. Moreover, the Institute has been working closely with
the national professional organization API (Associazione Piscicoltori Italiana) for a long time.
At present IREPA is responsible for the production of Italian fisheries statistics. The methodology has
been validated by ISTAT, the national statistical office. Since 1983, IREPA has developed a monitoring
system capable of providing a permanent statistical framework for the collection of fisheries data
regarding the European Common Fisheries Policy. Such a system is currently utilized to assist the General
Directorate of Fisheries and Aquaculture in developing national management policy and it focuses on:
• evaluation of various fishing fleet activities and fishing effort monitoring;
• data from commercial fisheries;
• collection of prices related to landings;
• data for economic monitoring of fishing enterprises.
National Statistic Institute (ISTAT) does not carry out any specific SBS survey on aquaculture sector. In
this framework we have reported IREPA as an official suitable organization, even if there is no direct
involvement in aquaculture data collection. In fact, as stated in Part 2 section 10.1, Irepa is an institutional
member of the National Statistics System (SISTAN) and is responsible for the production of Italian
fishery statistics. As such, it could have been able to carry out the survey as a suitable organization.
Anyway, within the DCR Programme, this task has recently been attributed to Unimar that will carry out
data collection on aquaculture.
10.2.
METHOD OF DATA COLLECTION
Present situation
The most recent data collection was carried out through a census of the population. A database of
aquaculture enterprises was initially developed in order to allow a direct survey of fish farmers. Six data
collectors have been involved in contacting the fish farmers. To date, the data collection has been
performed by interviewing the fish farmers and only technical data (e.g. on growing technique, volume of
cage, farming density, etc..) has been recorded on an Access database.
At present, the database records a population of 979 farms (held by 715 firms), including both productive
and non- productive units. Considering such a broad spectrum as the starting point and the intention of
defining the universe of active enterprises, the database first needed to be updated. With this aim, as a
preliminary step, all installations which had not carried out activities during 2006 were eliminated. The
double records of all companies which managed more than one farm were also eliminated. In this way the
number of active companies to be included in the population to be stratified has been defined. In order to
define the segment (combination between species and farming technique), the population of active
enterprises, farming more than one species, has been successively distributed on the basis of the prevalent
species production.
69
Present Project
The sample of the present project was chosen following a stratified approach based on the proportional
division of the various strata. Two different analyses were performed. The first was aimed to develop the
balance sheet analysis and the second was aimed to collect data in order to obtain operational costs. In the
case of the balance sheet analysis, data were collected from official sources and analysed as far as possible,
according to the items included. As for the survey needed to obtain operational and technical parameters,
a direct interview approach was adopted.
Future project
In the future, a direct interview sample approach will be adopted. This will entail significant costs and a
high risk of non-response and errors.
The sample size for the stratified universe (as is that of the set of fish farms) will be determined using the
Bethel algorithm. It is currently the procedure adopted by the Italian National Statistical Institute which
has developed a specific software. Many other Statistical Institutes have used the Bethel algorithm to solve
the problem of sample allocation in multivariate surveys.
10.3.
SIZE OF THE PRESENT AND FUTURE SURVEY
The segmentation of the population of the present survey was based on the elaboration of data from a
census which considered all farms present in all companies which in some way were involved in the
aquaculture sector. In order to define the proposed size of the sample survey, a review of the database has
been carried out. The updating process has led the proposed survey to focus on 715 enterprises, which
have been identified according to the following criteria and thresholds:
• aggregation of companies managing more than one farm,
• exclusion of companies producing less than 1 ton/year.
As for the preliminary approach, the sample size has been proportional to the stratification of the
population of the 715 enterprises, which represent the structure of the sector, and the results has been
recorded in Table 10.1.
In consideration of the synthetic analysis of the structure of the Italian aquaculture sector and the
subsequent highlighting of the predominant production divisions, the field survey was carried out for the
6 main segments for which a sampling strategy has been calculated: 46 units.
These main combinations account for 95% of the total production and 88% of the gross saleable
production. With regards to this, it is to be noted that no survey results are reported for the eel farming
sector because, even though the universe is composed of 16 companies, most production is concentrated
in three companies and production is limited to 0.7% of the farmed fish. All other segments with less then
20 firms have been excluded.
The future approach to the survey will also imply the utilisation of the list including all producers from
which a sample will be drawn. The field survey will be based on a stratified random sampling
methodology. The method of data collection will consist in a field survey (monthly interview) aimed at
collecting data on disaggregate economic variables.
The table of the sample size according to Bethel is given below. For the sample of 199 units, data relating
to the survey described was used. From this, a preliminary variability estimate for both quantity and
production value was carried out, and the sample size needed to obtain a 10% precision rate, (relative
standard deviation), was estimated for these two variables.
70
The shellfish segment (clams and mussels) is highly differentiated, has cooperative legal structure and
requires a large sample.
All the segments (population strata) with less than 20 units could also be eliminated, as they represent a
marginal share of the total production. In this case the size survey will be reduced to 177 units.
Table 10.1
Italy, Main segments of the national aquaculture sector
Segment (Species/technology)
Seabass & sea-bream/ T&R
Seabass & sea-bream/ E&P
Seabass & sea-bream/ Cgs
Eel/ T&R
Trout/ T&R
Carp/ T&R
Catfish/ T&R
Ornamental fish/ T&R
Clams/ OnB
Mussels/ OffB
Other species/ T&R
Trout/ H&N
Seabass & sea-bream/ H&N
TOTAL
10.4.
Number of companies in
population
Without
With threshold
threshold
41
108
17
34
23
16
330
226
10
8
21
16
20
18
184
94
259
224
14
13
9
6
1
1
979
715
Present
survey
Recommended future
survey
Without
With
threshold
threshold
7
7
3
3
5
5
3
0
13
13
3
0
3
0
3
0
80
80
79
79
0
0
0
0
0
0
199
177
3
2
2
0
16
0
0
0
7
16
0
0
0
46
ESTIMATION OF COSTS
The calculation of the financial requirement for the future sampling survey was differentiated on the basis
of the presence or absence of thresholds. In the case of surveys without thresholds, the calculation is
based on the inclusion of the 199 sampling units resulting from the application of the Bethel algorithm to
the population of aquaculture companies. In the case of surveys with thresholds, the sample size is
reduced to the 177 units included in the 6 production segments which raise more than 95% of the total
production. At the same time, the planning of activities, in terms of hiring staff, also took into account
whether the accounts data separated into single cost items was available or not.
Bearing this in mind, the calculation of the relative costs of the financial requirement for the future
sampling survey takes into consideration the average costs used within the fisheries DCR. As regards
labour costs, the calculation is based on the average hourly cost relative to the level of placement of staff
and the amount of time designated to the planning of activities. In operational detail, the planning costs
were estimated considering the development of the following activities:
• “Data collection (labour)”: definition of the sampling plan and the co-ordination of the operational
phase of collecting data.
• “Data processing (labour)”: quality control of data and development procedure of expansion to the
universe.
Table 10.2 Italy, Required labour input (man-hours)
Staff reference
Category and grade
Scientist II
(Data collection)
Scientist I
(Data processing)
Without threshold
With threshold
Average
hourly cost
Accounts
available
(Man/hours)
Accounts
not available
(Man/hours)
Accounts
available
(Man/hours)
Accounts not
available
(Man/hours)
37.5
796
1061
708
944
40.0
796
1194
708
1062
71
The calculation of costs relating to the item “Data Collection (other expenses)” refers to the phase of data
collection through monthly interviews carried out at the companies. In this case the unit cost of the
monthly questionnaire is also obtained from the values used in fisheries DCR. This cost of € 34, covers a
monthly interview at the company (estimated time – an hour and a half). Considering that the compilation
of 12 monthly questionnaires is envisaged for each aquaculture company included in the sample, the total
annual cost is around € 400.
Example
A
Cost of monthly interview €
B
No. interviews in a year
C
ANNUAL COMPANY COST € (AxB)
D
No. sample companies
E
DATA COLLECTION (OTHER EXPENSES) € (CxD)
34
12
400
199
79,600
The estimate of the cost item “Data processing (other expenses)” is a lump sum related to the cost of
work involved in the elaboration of data.
The estimate of investment costs refers mainly to the supply of hardware and software needed to carry
out the survey. As regards hardware, the data collectors are to be supplied with 8 laptop computers at a
unit cost of € 1,350 so that data management is computerized. With this aim the development of a
database is to be implemented on a server at a unit cost of € 2,700. The software costs refer both to the
development of survey software and to the implementation and maintenance of the database.
Table 10.3.
Italy, Estimation of costs (Euro)
Item description
Without threshold
Accounts
Accounts
available
not available
With threshold
Accounts
Accounts
available
not available
Investment costs
• Staff (training, etc.)
• Hardware (computers, office equipment, etc.)
• Software (data compilation and processing)
Total investment costs
1,500
13,500
18,000
33,000
5,000
13,500
18,000
36,500
1,500
13,500
18,000
33,000
5,000
13,500.
18,000
36,500
Annual operational costs
• Data collection (labour)
• Data collection (other expenses)
• Data processing (labour)
• Data processing (other expenses)
Total annual operational costs
29,850
59,700
31,840
15,741
137,131
39,800
79,600
47,760
21,691
188,851
26,550
53,100
28,320
13,996
121,966
35,400
70,800
42,480
19,228
167,968
10.5.
AVAILABILITY OF FUNDING
In the light of previous experience in aquaculture data collection, the Italian Directorate for Fisheries and
Aquaculture, following a call for tender, has appointed a new entity (UNIMAR) in order to fulfil
requirements of Reg. (EC) N. 788/96. The programme is valid for 2007 only. 250,000 Euro of national
funding has been allocated to implement this activity. The programme is, however, limited to the
collection of information on quantities and values, broken down by species and technique used.
10.6.
PROBLEMS AND SOLUTIONS
Two surveys has to be carried out: one analysing both balance sheets and Profit/Loss statement, and a
second one to obtain operational costs.
72
While the former can be performed through the collection of available public accounting documents, the
second requires a direct survey using a sample which significantly represents the sector structure. Given
the sector structure, some doubts arise when considering the reliability of balance sheet analysis, as many
small producers are not required to prepare a balance sheet. Within this framework, the preliminary
approach to the survey implies the utilisation of the list including all producers from which a sample will
be drawn.
In very general terms, the number of units to be interviewed is obtained by first establishing the maximum
sampling error to be allowed, or, equivalently, the coefficient of variation. Obviously, before conducting
the survey, preliminary data needs to be defined to understand the variability of data available.
The higher that variability is (that is to say, the standard deviation), the greater the sample size must be to
obtain a pre-determined level of relative standard deviation. Once the survey has been carried out, the data
gathered is then used to verify the effective variability found and thus, the reliability of the estimate
measured through the coefficient of variation. In the case that this is higher than that pre-determined,
sample size must be increased in future surveys.
As for the data quality collected through the sample, a control of both the sampling error and nonsampling error needs to be performed.
The sampling error is reduced by setting the sample to certain levels that ensure a certain precision of the
estimate. In order to solve the problem, the data reported in Table 1 (Size of Survey) is obtained through
the Bethel criterion (1989), which is a generalization for a stratified sample of the Neyman criterion. This
means that for every strata (main species and farming technique), the number of sampling units required
to obtain estimates, with a given level of the coefficient of variation, will be calculated (in this case, 10%
for the volume and value of production for the main species).
Software for calculating the sample size can be downloaded freely from the website of the National
Statistics Institute of Italy (ISTAT). The consistency of the data will be verified using appropriate indexes
and graphical tools.
Table 10.4. Italy, Share or sample in value and volume of production of the total segment
On-growing technique
R&T
R&T
E&P
CGS
Species
Seabass/ Seabass/
Seabass/
Trout
seabrea
seabrea
seabream
m
m
Population (no. firms)
226
41
17
34
Sample (no. firms)
16
3
2
2
Share of sample in total value of the segment (%)
0.44%
2.44%
5.88%
2.94%
Share of sample in total volume of the segment (%)
0.44%
2.44%
5.88%
2.94%
Off B
On B
Mussel
s
Clams
224
16
0.45%
0.45%
94
7
1.06%
1.06%
10.6.1. Extrapolation of sample to population
The data collection is carried out through a simple random sample without replacement. The procedure
for the extrapolation of the sample to the universe was performed according to this statistical
methodology.
Specifically, for each sample unit of the same strata, the same weight of Nh / nh was then associated. Nh is
the number of population units in the strata h, while nh is the number of units in the sample.
So, generally, the estimate of the total in the strata h for a generic variable Y was based on the formula:
nh
h
h
hi
i =1
h
N
Yˆ = ∑ y ∗
n
73
Evaluation of individual indicators
To assess the quality of the estimates the relative standard deviation was calculated for each estimated
variable, considering the values achieved for individual strata. It was known beforehand that the sample
used was lower than that required 11 to reach a level of relative standard deviation less than 10%, and
generally higher levels were achieved. Such a situation implies that in future survey a higher size sample is
mandatory.
10.6.2. Cross check with other sources
The production of Italian aquaculture statistics is a rather recent activity and is on the way to choose the
best solution to these complex issue. Estimates available have been depending on two different sources.
The first being the API/ICRAM longstanding time series , which have been produced since 1994 and are
still ongoing. Data are produced on estimates based on feed consumption and conversion rates, broken
down for different species. Data also include production available and not yet sold.
As unique and homogenous statistics available in Italy for the aquaculture production these data have
been forwarded to Istat, which sent them to Eurostat.
The second source is Idroconsult a specialised company which was appointed for the service following a
tender launched by the Ministry of Agriculture, food and forestry policies (Mipaf). The period covered by
the tender has been 2002/2006. Starting on 2004, Mipaf officially forwarded these data to Istat who has
been sending them to Eurostat. In this period there is no difference between Italian and Eurostat data.
Data are produced on annual direct interviews with aquaculture farmers.
Of course, depending on the different approach some differences in data exist. In particular, the first
survey seems to allow for data slightly higher than the second. However, the two outcomes are quite
coherent when inventories, indirectly included in the first survey, are included in the second survey which
explicitly excludes any stocked fish.
Finally, it should be considered that EU and national programmes have been utilising the API/Icram data
due to the their time extension and homogeneity.
It must be also admitted that FAO aquaculture statistics have been produced following their own
methodology and approach. As far as we know, data published by FAO are the sum of different sources
based on an array of national correspondents.
11
Bethel procedures required about 200 units
74
Table 10.5 Italy, Statistical indicators, Trout in tanks and raceways
Sample mean
(1000 Euro or
%)
Relative
standard
deviation
(%)
Relative
standard error
(%)
141%
141%
127%
153%
178%
86%
na
38%
38%
34%
41%
48%
23%
na
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
530.5
83.2
389.8
169.7
82.6
1,237.2
3.1
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
89.3%
128%
Total capital costs
10.7%
113%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
17.5%
Unpaid labour
0.8%
Energy costs
10.3%
132%
Live raw material costs
10.9%
134%
Feed raw material costs
42.3%
137%
Repair and maintenance
1.9%
135%
Other operational costs
16.3%
102%
35%
31%
40%
32%
38%
34%
27%
Table 10.6 Italy, Statistical indicators, Seabass and seabream in tanks and raceways
Sample mean
(1000 Euro or
%)
Relative
standard
deviation
(%)
Relative
standard error
(%)
69%
74%
95%
64%
55%
62%
na
48%
51%
65%
44%
38%
43%
na
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
1,725.2
358.1
1,084.3
727.4
342.5
3,698.9
13.78
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
90%
84.0%
Total capital costs
16%
15.9%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
24.4%
Unpaid labour
1.8%
Energy costs
5.3%
129%
Live raw material costs
13.7%
131%
Feed raw material costs
19.8%
132%
Repair and maintenance
0.4%
128%
Other operational costs
34.6%
54%
62%
11%
85%
90%
96%
88%
37%
75
Table 10.7 Italy, Statistical indicators, Seabass and seabream in enclosures and pens
Sample mean
(1000 Euro or
%)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
222.9
151.9
54.7
177.6
21.6
326.8
5.5
12%
74%
23%
51%
114%
90%
na
8%
49%
16%
34%
75%
60%
na
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
48%
95.7%
Total capital costs
87%
4.3%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
72.1%
Unpaid labour
1.9%
Energy costs
6%
4.3%
Live raw material costs
5%
1.8%
Feed raw material costs
7%
1.9%
Repair and maintenance
8%
5.8%
Other operational costs
58%
12.2%
32%
58%
6%
8%
5%
4%
38%
Table 10.8 Italy, Statistical indicators, Seabass and sea bream in cages
Sample mean
(1000 Euro or
%)
Relative
standard
deviation
(%)
Relative
standard error
(%)
86%
118%
78%
104%
80%
87%
na
59%
81%
54%
71%
55%
60%
na
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
1.375.6
338.2
1.072.0
724.8
354.9
4.152.7
11
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
89%
85.9%
Total capital costs
81%
14.1%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
31.6%
Unpaid labour
2.9%
Energy costs
70%
4.3%
Live raw material costs
76%
24.5%
Feed raw material costs
78%
22.4%
Repair and maintenance
64%
3.2%
Other operational costs
109%
11.2%
61%
55%
60%
42%
45%
57%
75%
76
Table 10.9 Italy, Statistical indicators. Mussels - off bottom)
Sample mean
(1000 Euro or
%)
Relative
standard
deviation
(%)
Relative
standard error
(%)
69%
55%
89%
58%
113%
80%
na
20%
16%
26%
17%
33%
23%
na
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
361.0
219.0
117.7
253.2
29.5
324.7
14.2
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
66%
93.6%
Total capital costs
96%
6.5%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
64.2%
Unpaid labour
1.4%
Energy costs
162%
7.5%
Live raw material costs
98%
10.0%
Feed raw material costs
0%
0.00%
Repair and maintenance
109%
8.9%
Other operational costs
91%
8.0%
19%
28%
48%
29%
0%
32%
27%
77
11. LITHUANIA
11.1.
SUITABLE ORGANIZATION
The data could be collected by the Fisheries Department under the Ministry of Agriculture of the
Republic of Lithuania as it is responsible for fishery data collection. Economic data collection is
demanding specific knowledge and education, so the data could be collected by the Fisheries Department
with the cooperation with research institution, such as the Lithuanian Institute of Agrarian Economics, or
other suitable organization with analytical and statistical experience in the fishery economy.
11.2.
METHOD OF DATA COLLECTION
The present DCR data collection is coordinated by the Fisheries Department under the Ministry of
Agriculture with the cooperation of two institutions: the Lithuanian State Pisciculture and Fisheries
Research Centre and the Lithuanian Institute of Agrarian Economics. The volume of aquaculture
production is collected by the Fisheries Department with cooperation with the Lithuanian State
Pisciculture and Fisheries Research Centre. Economic data could be collected through post survey of
enterprises.
For the purpose of this project, the Lithuanian Institute of Agrarian Economics produced survey forms
and tried to collect the data through the National Association of Aquaculture and Producers of Fish
Products. The Association provided the data of all 18 aquaculture enterprises, but instead of economic
data of values in LTL, the half of the data have been in quantities (e.g. kW, average salary per month, fish
feed in tonnes, etc.). After this data submission the Lithuanian Institute of Agrarian Economics has sent
the questionnaires directly to all the enterprises once more and got 4 answers with precise information
necessary for the project. Additionally the accountants were asked to fill in the questionnaire about the
time spent for filling in the economic data questionnaires and possible payment.
The lack of responses is explained by the confidentiality of economic data and fear of directors to provide
complete economic data to any other organization.
The data collection approach used during the survey is suitable for economic data collection, but the
enterprises should be interested in the studies produced and trust the data collection first. The other
solution could be obligation to provide the data by national law, but it could still be a problem. The other
problem is that the Department of Statistics collects some similar data for general statistics and private
sector is very dissatisfied with requirement to provide similar data twice or even more times.
11.3.
SIZE OF PRESENT AND FUTURE SURVEY
The segmentation of the Lithuanian aquaculture sector is based on the main activity and the volume of
production per specie. As the volume of other species is very small in most of the enterprises, there is no
marginal difference to segment by volume or value of production. The main segment of the sector is carp
ponds. There is 1 state-owned organization with 6 hatcheries as a separate segment. But due to a high
variety of its functions it is impossible to separate the economic data per unit at this moment. One
enterprise raises eel in a re-circulation system, but due to data confidentiality it is impossible to collect and
provide data for such a small segment.
As the Lithuanian aquaculture sector is so small, there it is not advisable to separate the population with
threshold at this moment.
78
Table 11.1
Lithuania, Main segments of the national aquaculture sector (number of firms)
Segment
(Species / technology)
Population
Present survey
Recommended future survey
Without
With
Number in
Without
With
threshold
threshold
sample
threshold
threshold
Carp ponds
18
18 (4)*
18
Hatcheries
1
0
1
Eel re-circulation
1
0
1
* Some parameters e.g. volume and value of production, number of persons employed, capacity, subsidies, average
salaries, energy used, volume of feed used have been collected for all enterprises.
11.4.
ESTIMATION OF COSTS
According to DCR practice, there is a need to have at least one full or half-part employed in aquaculture
data collection to be ready for additional work which can appear during preparation of national programs,
data procession, implementation of regulation, different administrative work and aquaculture analyses.
The employed person could always be ready for different meetings or other work in line with DCR.
Otherwise external experts for each task could be hired to avoid full time employment and save the
money for data collection.
For the purpose of this report, official data of the Fisheries Department about the number of enterprises
have been used. But it is recommended to make a pilot study of agriculture farmers which could own
ponds and produce fish for production. Due to a lack of funding, the survey could not be produced
during this project.
Table 11.2 Lithuania, Estimation of costs
Item description
Without threshold
Accounts
Accounts
available
not
available
Investment costs
• Staff (training, etc.)
• Hardware (computers, office equipment, etc.)
• Software (data compilation and processing)
Total investment costs
3,000
10,000
30,000
43,000
4,500
10,000
30,000
44,500
Annual operational costs
• Data collection (labour)
• Data collection (other expenses)
• Data processing (labour)
• Data processing (other expenses)
Total annual operational costs
2,000
5,000
12,000
1,000
20,000
3,000
7,000
12,000
1,000
23,000
According to DCR practice, there have to be at least one full time employed to deal with DCR guidelines,
data analysis, produce positions and national program. So the labour costs will be similar and not
depended to the total number of the enterprises in this case.
As the aquaculture sector in Lithuania is very small and aquaculture activity is run only in ponds, there is
no need to collect all economic data annually. It could be done each 3-5 years by surveys.
11.5.
AVAILABILITY OF FUNDING
There are no national funds to support such data collection. The data is collected only at the level of
volume and value of landings at this moment. The other parameters collected are mostly technical and
capacity information.
79
11.6.
PROBLEMS AND SOLUTIONS
11.6.1. Extrapolation of the sample to total population
The most part of economic data has been collected via questionnaires, but also there were other sources
of information. Therefore the extrapolation of some data was made by recalculation of average rates from
the sample and extrapolation of it to the entire segment. For example, to calculate the costs of energy, all
the data were used about consumed energy from the National Association of Aquaculture and Producers
of Fish Products for each enterprise. So the average costs of one kW have been calculated from the
sample and this price have been used to calculate the costs for other enterprises as the volume of energy
consumed has been collected exhaustively. Similar calculations have been done for recalculation of feed
costs from the volume of feeds used. To calculate personal costs from average wages of the enterprises,
the number of persons employed has been multiplied by the average wage plus 31% social costs.
Table 11.3 Lithuania, Share or sample in value and volume of production of the total segment
On-growing technique
Species
Population (no. firms)
Sample (no. firms)*
Share of sample in total value of the segment (%)
Share of sample in total volume of the segment (%)
Ponds
Carp
18
4-18
18
15
Part of the data (turnover, number of persons employed, equity capital) have been collected for the total
population.
11.6.2. Evaluation of individual indicators
No problems have been encountered to find the data in enterprises accounts. However, for the stateowned enterprises with no profit-loss report it was difficult to produce the data.
As the questionnaire is based on the main accounting reports – the balance sheet and profit-loss report - it
could be easily filled in, but low response rate causes problems because of the small number of enterprises
in the segment, competition and lack of trust. For insiders it becomes quickly clear which companies have
provided the data and confidentiality is compromised.
FTE – the parameter have been calculated as the total number of hours worked per year divided by 2000.
The relative standard deviation and standard error of the sample is rather high, as the sampled enterprises
are of different sizes. But as the most of indicators have been calculated from volumes (kW, volume of
feed used, average salary*number of persons employed*social security costs) which have been translated
exhaustively to values by using average prices of the sample, the population is well represented due to the
method of calculation.
80
Table 11.4 Lithuania, Statistical indicators, carp in ponds
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
73
58
118
115
119
88
50
17
14
59
58
59
44
12
Absolute values
Total turnover (incl. other income)*
Personnel costs (excl. unpaid labour)**
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons*
409
95
175
154
70
894
19
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
92
4
2
Total capital costs
8
42
21
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
38
55
28
Unpaid labour
0
…
…
Energy costs
5
108
54
Live raw material costs
7
88
44
Feed raw material costs
33
59
29
Repair and maintenance
3
75
38
Other operational costs
15
194
59
* the data is collected exhaustively for all population (sample=population);
** the averages salaries in the enterprises collected for all population, personnel costs calculated from average salaries
and number of persons engaged which have been collected for all population..
11.6.3. Cross check with other sources
The volume and value of production for 2007 is not available at FAO or Eurostat yet, the differences
could appear due to price used for calculation of value of landings and some additional volume produced
by Lithuanian State Pisciculture and Fisheries Research Centre. There is no major difference in volume of
production as the source of information for FAO, Eurostat and this study is Fishery Department,
however there could be minor difference in value of production due to the reasons pointed above.
81
Table 11.5 Lithuania, Statistical indicators, Clams, on bottom
Sample mean
(1000 Euro or
%)
Relative
standard
deviation
(%)
Relative
standard error
(%)
72%
82%
69%
77%
95%
76%
na
28%
32%
27%
31%
38%
30%
na
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
2.329.4
1.280.9
998.7
1.354.8
6.950.3
238.7
25.7
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
97.1%
76%
Total capital costs
2.9%
99%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
56.2%
Unpaid labour
0.0%
Energy costs
18.0%
78%
Live raw material costs
18.9%
86%
Feed raw material costs
0.0%
0%
Repair and maintenance
0.0%
0%
Other operational costs
6.8%
51%
30%
39%
38%
29%
0%
0%
37%
82
12. NETHERLANDS
12.1.
SUITABLE ORGANIZATION
LEI will collect the data of the aquaculture sector. LEI is the main institute for economic research in
agriculture, horticulture, fisheries and forestry in the Netherlands, having a staff of 300 people and being
part of Wageningen University and Research (WUR). The research programs of the various divisions of
the Institute are particularly directed to the provision of relevant quantitative and qualitative information
on micro and macro level. The Fisheries Economics Division employs 11 persons in total, of whom 8 are
professional research staff and 3 support staff for the collection of financial statistics of the sector and
administrative tasks. Since 1948 the Division has built up solid experience in all fields of fisheries
economics, such as financial analysis, market research, techno- and bio-economics, management policies,
aquaculture and fisheries development. Part of the research program of the Division is regular extraction,
processing and publication of the economic results of Dutch fisheries and aquaculture. A panel of
skipper-owners covering about one quarter of the sea fishing fleet is annually making these data available
on a voluntary basis. Another part consists of specific research projects requested by the Dutch fishing
industry or the Fisheries Directorate of the Ministry of Agriculture, Nature Management and Food
Quality. Finally, the Division carries out studies contracted by the European Union and government
institutions in developed and developing countries or private firms.
Since 2005 LEI is collecting some data regarding the freshwater fish farming sector. This includes data
from the European eel farms and African catfish farms. LEI did collect data from the mussel sector until
2002 and started collecting data again from 2005 onwards.
12.2.
METHOD OF DATA COLLECTION
Data of the sea-fishing fleet is collected using a panel of skipper-owners which covers about 25% of the
total fleet. Data is collected using financial results, balance sheets and invoices collected from accounts. So
far data collection of aquaculture data has focused on collection of financial results only from a panel of
mussel farms, eel farms and catfish farms. Data collected includes net and gross revenues, total costs,
depreciation costs, interest, debts and investments.
For this project data was collected from 14 mussel farms, 5 oyster farms, 3 European eel farms and 2
catfish farms. Compared to data collection in 2006 the response is lower because 2 European eel farms
and 3 catfish farms have since stopped production. Unfortunately the response in eel and catfish farms
could not be expanded. All non-cooperating farms working in this sector were contacted by telephone and
several farms were visited in person but none of these farms were willing to supply data. To enhance to
familiarity of LEI research under these farmers an article was published in the Dutch Journal Aquaculture
and in cooperation with this journal a meeting will be organized in the spring of 2009 at which LEI can
present some of their work. Hopefully these efforts will increase the cooperation between the sector and
LEI.
12.3.
SIZE OF PRESENT AND FUTURE SURVEY
The table below shows the proposed survey size. To get reliable estimates of the entire sector, the panel
should cover about 25% of the sector. In the European eel sector, 3 companies are significantly larger
then the rest and preferably at least one of these companies should take part in the survey. In the blue
mussel sector, 4 companies are significantly larger then the rest of the sector. LEI already has 2 of these
companies in its panel and these are expected to also respond favourably to this survey request. About
half of the oyster companies (17) also produce blue mussels, these companies therefore are counted twice
in the table below. It will be important to try to get some of the companies that produce both mussels and
oysters in the panel and some companies that produce solely either mussels or oysters.
83
The current estimation is that all farms in the aquaculture sector are sufficiently large that it is not
necessary to apply a threshold for the Dutch data collection of aquaculture data.
Table 12.1 Netherlands, Main segments of the national aquaculture sector (number of firms)
Segment
(Species / technology)
Catfish
European Eel
Blue mussels
Oysters
12.4.
Population
Without
With
threshold
threshold
37
37
50
50
50
50
32
32
Present survey
Number in
sample
2
3
17
5
Recommended future survey
Without
With
threshold
threshold
7-10
7-10
10-15
10-15
10-15
10-15
6-12
6-12
ESTIMATION OF COSTS
The table below presents estimated costs of setting up a data collection system for aquaculture. The costs are
calculated similarly to the costs of collecting data for the sea fishing fleet. On average the costs of data collection will
be equal to 2,000 Euro per company in the panel in the first year. A significant part of the cost in the first year will
be the cost of setting up a panel. Previous experience has learned that companies in the fresh fish farming sector are
not very forthcoming in providing detailed financial information therefore it will be time consuming to find a
sufficiently large panel.
Because of the large initial costs of finding participants to the survey we propose to make use of a fixed panel instead
of random sampling every year. If possible some sort of rotation of participants of the panel would be preferable.
However, this will fully depend on the willingness of companies to participate.
The table below only presents the estimated costs in the scenario without threshold and with accounts available. All
aquaculture farms are sufficiently large that it will not be necessary to apply a threshold in this sector. All companies
will have accounts available, although the company will have to be willing to supply their accounts.
Table 12.2 Netherlands, Estimation of costs (Euro)
Item description
Investment costs
• Staff (training, etc.)
• Hardware (computers, office equipment, etc.)
• Software (data compilation and processing)
Total investment costs
Annual operational costs
• Data collection (labour)
• Date collection (other expenses)
• Data processing (labour)
• Data processing (other expenses)
Total annual operational costs
12.5.
Without threshold
Accounts
Accounts
available
not
available
With threshold
Accounts Accounts
available
not
available
5,000
5,000
5,000
15,000
na
na
na
na
na
na
na
na
na
na
na
na
15,000-20,000
5,000-10,000
35,000-50,000
5,000-10,000
60,000-90,000
na
na
na
na
na
na
na
na
na
na
na
na
na
na
na
AVAILABILITY OF FUNDING
Presently limited data about the mussel sector, European eel sector and African catfish sector is collected by LEI and
this data collection process is financed by the Ministry of Agriculture. This data collection only involves limited data
from balance sheets and will only provide a small part of the data requested in this project. This data collection is
funded for about 10,000 Euro / year.
84
12.6.
PROBLEMS AND SOLUTIONS
12.6.1. Extrapolation of the sample to total population
The sample population has been aggregated to the total population by multiplying the mean per firm in
the survey by the total number of firms in the population. This approach assumes that the survey is
representative for the sample. In the European eel sector, it is known that only the small companies
responded to the survey request and that therefore the survey is not representative for the sector. To
correct the estimated totals for this sector both the costs and the benefits are multiplied by a factor such
that the total value of the catch of the sector is equal to values the sector produces itself. By using a simple
factor we do assume that the costs structure of larger companies is the same as the cost sector of smaller
companies.
Table 3 shows how much the sample covers of the value and volume produced by the total sector.
Especially for the catfish and European eel sector the sample is unfortunately far too small to be
representative.
Table 12.3 Netherlands, Share of sample in value and volume of production of the total segment
On-growing technique
Species
Population (no. firms)
Sample (no. firms)
Share of sample in total value of the segment (%)
Share of sample in total volume of the segment (%)
Rec
Catfish
37
2
5%
5%
Rec
Europ. eel
50
3
1.5%
1.7%
On bottom
Mussels
50
17
34%
34%
On bottom
Oysters
32
5
16%
16%
12.6.2. Evaluation of individual indicators
Like mentioned before, the response in the catfish and European eel sector is insufficient. We have still
hopes that the response can be increased but the sector so far is not very willing to cooperate. To increase
the response, a lot of effort will have to be put into convincing the sector to cooperate. It may, however,
prove extremely difficult to get a homogeneous panel, especially for the catfish sector. In this sector there
are several small companies and one very large company. The very large company has absolutely no
intention the cooperate with our data request and it is doubtful whether it ever will if cooperation is
voluntary.
Tables 4.1 to 4.4 show the relative standard deviation and the relative standard error of the survey results.
These tables show quite large standard deviations and standard errors, especially for the European eel
farms and catfish farms. Considering the number of responses in these sectors these results are not
surprising.
85
Table 12.4 Netherlands, Statistical indicators, European eel in recirculation
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
36.5
141.4
64.7
-1302.3
-4550.3
81.5
137.8
17.2
66.6
30.5
-613.9
-2145.0
38.4
64.9
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
308.9
11.3
280.5
-15.4
-4.1
1,062.1
0.2
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
81.4
7.9
Total capital costs
18.6
34.5
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
2.5%
112.1
Unpaid labour
12.1%
77.7
Energy costs
14.0%
52.5
Live raw material costs
26.1%
25.5
Feed raw material costs
27.1%
33.1
Repair and maintenance
3.2%
48.7
Other operational costs
14.9%
10.9
3.7
16.3
52.9
36.6
24.8
12.0
15.6
22.9
5.1
Table 12.5 Netherlands, Statistical indicators, Catfish in recirculation
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
130.5
141.4
112.4
122.0
122.0
10.1
65.3
70.7
56.2
61.0
61.0
5.1
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
233.4
0.3
140.4
107.5
107.8
346.1
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
79.5
8.3
Total capital costs
20.5
32.2
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
0.1
141.4
Unpaid labour
20.8
8.0
Energy costs
4.7
150.1
Live raw material costs
20.7
47.4
Feed raw material costs
35.2
41.5
Repair and maintenance
6.2
91.5
Other operational costs
12.5
37.3
4.1
16.1
70.7
4.0
75.0
23.7
20.7
45.7
18.7
86
Table 12.6 Netherlands, Statistical indicators, Mussels – On bottom
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
113.0
74.2
160.8
103.7
114.8
234.4
38.6
29.1
19.1
41.4
26.7
29.6
60.4
9.9
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
909.9
101.3
290.3
622.9
521.5
2436.1
3.3
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
72.6
28.1
Total capital costs
27.4
74.2
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
25.3
76.7
Unpaid labour
23.0
100.3
Energy costs
9.8
27.5
Live raw material costs
5.6
247.0
Feed raw material costs
Repair and maintenance
12.3
41.8
Other operational costs
23.9
37.9
7.2
19.1
19.8
25.8
7.1
63.6
10.8
9.8
Table 12.7 Netherlands, Statistical indicators, Oyster - On bottom
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
22.4
49.9
36.1
26.8
71.3
42.5
63.9
9.0
20.0
14.4
10.7
28.5
17.0
25.6
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
348.2
90.2
124.0
208.9
118.8
221.1
3
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
93.8
5.9
Total capital costs
7.3
74.7
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
30.6
34.8
Unpaid labour
25.0
75.6
Energy costs
6.1
58.0
Live raw material costs
5.6
98.5
Feed raw material costs
Repair and maintenance
11.0
103.0
Other operational costs
21.7
34.7
2.4
29.9
13.9
30.2
23.2
39.4
41.2
13.9
87
12.6.3. Cross check with other sources
Data published by Eurostat and FAO, based on figures provided by Statistics Netherlands, show quite
some inconsistencies with the data provided by the Dutch Ministry of Agriculture (shown in the table 1a
to 1d). Based on expert opinion and results of a survey held in 2005 by LEI, the data provided by the
Ministry seems to present a more accurate picture of the sector then the data shown by Eurostat. LEI
started to collect data about the aquaculture sector in 2005. Since that time LEI knows fairly accurately
how many firms are operating in the sector. It is difficult to determine exactly how many firms were
operating in the aquaculture sector before that time.
88
13. POLAND
13.1.
SUITABLE ORGANIZATION
The Inland Fisheries Institute has got sufficient experience with large scale data collection in general and
aquaculture in particular. Using specific questionnaires production of trout farms has been surveyed since
the 80ties, carp farms since the beginning of the 90ties, and pond-lake enterprises since 1995.
13.2.
METHOD OF DATA COLLECTION
Staff of the institute is experienced in collecting and analyzing data on aquaculture, and is well equipped
with suitable software. Present data collection system is based on two kinds of surveys conducted by the
Inland Fisheries Institute, Department of Pond Culture and Department of Fishery Bioeconomics:
1. Voluntary questionnaire surveys. There are three questionnaires used in these surveys: for trout farms,
carp farms and pond-lake enterprises. Using these questionnaires trout farming has been surveyed since
the 80ties, carp farms since the beginning of the 90ties, and pond-lake enterprises since 1995. In 2006
totally 331 questionnaires were collected and analyzed: 144 from trout farms (i.e. 72% of the total number
of trout farms in Poland), 146 questionnaires from carp farms (i.e. 22% of the total number, but as much
as 60% of the total pond area used for carp farming), 41 questionnaires from pond-lake enterprises (i.e.
84% of such companies in the whole country). In case of trout and carp farms the questionnaires embrace
the main questions connected with fish production, usage of feeds, production of stocking material, health
problems, additional food fish species production. In case of pond-lake enterprises the questionnaires
comprise the following topics: table fish production, stocking into lakes, simplified accountancy (turnover, income from lake, carp and trout production, income from fishing licenses sale, other incomes, total
costs, investments). Figures calculated for each category of fish farms have been extrapolated to the total
number of enterprises (trout farms) or total area of ponds (carp farms) in the country. The system of
voluntary questionnaires operates with sufficient effectiveness, and allows estimations of the total food
fish production in Poland.
2. Obligatory questionnaire surveys. The system has been implemented since 2004, and its main purpose is
delivery of data on aquaculture for the National Statistical Office. Two kinds of questionnaires have been
used:
• RRW-22 for typical aquaculture (carp and trout) enterprises. The questions cover the following
issues: number and area of fish ponds, kind and volume of devices used for fish culture, production
of fish for consumption, production of stocking material, stocking for on-growing in ponds and
other devices, employment.
• RRW-23 for fishery users of natural running waters (lakes, rivers, dam reservoirs). The questions
embrace the following issues: commercial fish catches, stocking material introduced into natural
running waters, employment. Although this survey does not refer to typical aquaculture enterprises,
data on stocking allow estimation of production of various life stages of stocking material produced
in aquaculture units in the whole country.
Although the system is obligatory, in case of typical aquaculture enterprises a declining trend (lack of
response) was observed as regards the number of collected questionnaires: 530 were collected for 2004,
355 for 2005, and only 96 for 2006. On contrary, in case of fishery users of natural running waters an
increasing trend was observed: 207 questionnaires were collected for 2004, 235 for 2005, and 329 for
2006.
The results of the survey have been subsequently presented during 33 National Conferences of Salmonid
Fish Producers, 13 National Conferences of Carp Producers, and 13 National Conferences of Fishery
Users of Lakes, Rivers and Dam Reservoirs.
89
There are following sources of lists of aquaculture producers in Poland: Polish Fisheries Society,
Headquarters of the Polish Anglers’ Association, Agency of Agricultural Properties (responsible for
privatization of fisheries and aquaculture sector), Regional Offices of Water Management, historical data
of Inland Fisheries Institute in Olsztyn 12 and personal relationships between staff of the institute and fish
producers.
This project was also based on questionnaire survey. During the survey it was planned to collect economic
data from at least 30 fish farms.
Size of survey
The survey conducted under the project embraced totally 31 fish farms, among them 18 carp farms and 13
trout farms (Table 13.1). As a principle mainly those enterprises were surveyed which provide full
accountancy. Following this principle the questionnaires were sent by post to the farms which employ
accountants, and full financial data were given mainly by the accountants. The obtained data were
extrapolated to the overall sector i.e. to the total production of carp and trout in Poland in 2007.
Implementation
The usual approach to data collection is questionnaire survey, and this kind of survey was implemented
for necessary data collection. The questionnaires were sent by post, and collected by Department of
Fishery Bio-economics, Inland Fisheries Institute in Olsztyn. During the survey there was strict
cooperation with 27 accountants employed by surveyed enterprises.
Quality control
The survey was based on mutual trust between fish producers and the staff of the Inland Fisheries
Institute. This trust has been based on multiyear cooperation in collection of production data, as well as
personal relationships between partners of this cooperation. These relationships have been established
during joint fisheries studies at universities, and during national conferences of carp producers, salmonid
producers and fishery users of lakes, rivers and dam reservoirs. To ensure sufficient quality of the
collected data, there were implemented cross checking with data available from other sources, cross
checking of the individual data with group average and recontacting the farmers when large outliers were
identified, and comparisons with the results of similar producers in other countries.
13.3.
SIZE OF PRESENT AND FUTURE SURVEY
The sample was based on two following principles:
• Aquaculture was the main activity of the surveyed fish farms, i.e. generated most value added for the
enterprise,
• The threshold for carp farms has been established on the level over 50 ha of pond area of each farm.
This size was selected because farms over 50 ha represent approx. 15% of the total number of carp
farms, but they produce over 70% of the total common carp production in the whole country. The
threshold of trout farms was established on the level of 50 tonnes of annual rainbow trout
production. This volume was selected because such farms represent approx. 44% of the total number
and as much as over 80% of the total rainbow trout production in Poland.
12
www.pankarp.pl/wspeirajacy.swf, www.rybobranie.pl, http://katalog.pf.pl/Ryby-hurt-hodowla
90
Table 13.1 Poland, Main segments of the national aquaculture sector (number of firms)
Species
Common carp/ponds
Rainbow trout/ponds,
raceways, tanks
Population
Without
With
threshold
threshold
670
100
200
88
Present survey
Number in
sample
18
13
Recommended future survey
Without
With
threshold
threshold
30
20
It was planned to survey carp and trout farms, as these two segments produced as much as 95% of the
total volume, and 93.4% of the total value of food fish production in Polish aquaculture sector in 2005.
Although the sample size is not numerous comparing to the total number of fish farms, it was planned to
cover in the survey more than 25% of the total carp and more than 20% of the total trout production in
Poland.
13.4.
ESTIMATION OF COSTS
Estimated costs of the system are presented in table 2 (data in Euro). The costs are based on market prices
(software) and usual wages paid by the Inland Fisheries Institute (labour). The questionnaire used during
the survey comprised questions requiring knowledge about full standard accountancy, hence it was too
complicated for private holders. Therefore the questionnaires were sent mostly to enterprises employing
accountants. Approx. 108 Euro was paid for each accountant engaged in cooperation. It is planned to
cooperate with accountants also in future studies.
Table 13.2 Poland, Estimation of costs
Item description
Investment costs
• Staff (training, etc.)
• Hardware (computers, office equipment, etc.)
• Software (data compilation and processing)
Total investment costs
Annual operational costs
• Data collection (labour)
• Data collection (other expenses)
• Data processing (labour)
• Data processing (other expenses)
• Sub-contracted work
Total annual operational costs
13.5.
Present study
With threshold
Accounts
available
Future study
With threshold
Accounts
available
300
300
600
900
600
900
1,000
6,000
3,000
800
2,900
14,600
1,500
8,000
4,000
1,000
5,400
20,800
AVAILABILITY OF FUNDING
It was highly recommended to find external funds to support data collection, especially cooperation with
accountants. As filling in questionnaires properly was very difficult by single holders, additional external
funding to accountants was available. In that case the costs of the data collection in cooperation with
accountants was 2,900 Euro. Part of the costs (labour) was paid by the project, and part was covered by
the Inland Fisheries Institute in Olsztyn within frames of usual scientific activities. In future studies it also
is planned to find external funds to cover cooperation with accountants. It is expected to support future
surveys from national funds e.g. fees paid by farmers and enterprises for participation in national
conferences of carp, trout and lake fishery users.
91
13.6.
PROBLEMS AND SOLUTIONS
13.6.1. Extrapolation of the total sample to total population
The total area of ponds used by surveyed carp farms (16,079 ha) embraced approx. 31.1% official area of
ponds in the whole country. The total fish production in the surveyed farms was 4,700 tonnes. The
production results obtained by the surveyed enterprises embraced 28.1% of the total common carp
production in the whole country, which amounted to 15,432 tonnes in 2007 (Table 13.3). Extrapolation of
the survey results to the overall sector (15,432 tonnes of common carp production) gave the total turnover
of 42.28 million Euro in 2007. In the same way the rest of indicators were calculated.
The total fish production in the surveyed trout farms was 3,791 tonnes. The total rainbow trout
production obtained by the surveyed enterprises embraced 22.8% of the total production of this species in
the whole country, which amounted to 16,650 tonnes in 2007 (Table 13.3). Extrapolation the survey
results to the overall sector (16,650 tonnes of rainbow trout production) gave the total turnover of 52.1
million Euro in 2007. Other incomes were much lower and amounted only to 0.93 million Euro. In the
same way the rest of indicators were calculated.
13.6.2. Evaluation of individual indicators
Based on own experience it was assumed that only limited number of companies would be willing to
cooperate. Polish fish farmers are very reluctant to deliver detailed data on economic performance of their
farms. To encourage directors, owners and managers of fish farms to cooperate with the staff of the
institute obtained preliminary results of the survey were presented during XIII National Conferences of
Fishery Users of Lakes, Rivers and Dam Reservoirs.
In case of few farms, especially private carp and trout farms without accountants, there were problems
with collection certain variables e.g. labour costs and depreciation. To overcome this limitation it was
planned to explain fish farmers necessity of filling in all the elements of the accounts in mailed
questionnaires. This ensured sufficient number of fish farms available to calculate comprehensive
economic performance of aquaculture enterprises.
Table 13.3 Poland, Share of sample in value and volume of production of the total segment
On-growing technique
Species
Population (no. firms)
Sample (no. firms)
Share of sample in total value of the segment (%)
Share of sample in total volume of the segment (%)
Carp
Common carp +
supplementary species
100
18
28.12
28.12
Trout
Rainbow trout +
supplementary species
88
13
22.77
22.77
13.6.3. Statistical indicators
To describe the surveyed sample of the enterprises following statistical parameters were calculated for
each segment: sample mean (in 1000 Euro), relative standard deviation (%) and relative standard error
(%). The results of the analyses are presented in Tables 13.1 (carp) and 13.2. (trout).
Carp farms
Generally, almost all calculated means revealed relatively high differences in relative standard deviation. In
case of absolute values this index was varying between 92.9% (total turnover) and 93.2% (gross cash flow)
to 113.0% (engaged persons). The second measured parameter – relative standard error did not vary to a
large extent. It’s minimal value was 21.9% in case of total turnover, and maximal value was the item
“engaged persons (26.6%). These indicators reflect that the surveyed sample embraced enterprises which
were different as regards volume and value of production, but as a principle it embraced mostly the
biggest carp farms in Poland. Calculated statistical indicators were much more differentiated in case of
92
costs composition; relative standard deviation varied between 34.9% (personnel costs) to 309.1% (unpaid
labour). This phenomenon is quite obvious as the latter item was mentioned only by two surveyed
enterprises. Also relative standard error was minimal in case of personnel costs (8.2%) and maximal in
case of unpaid labour (72.9%).
Trout farms
Generally, also in case of trout farms calculated indicators revealed relatively high differences. Taking into
account absolute values the greatest relative standard deviation amounted to 155.0% in case of gross cash
flow. The minimal value of this index was 75.6 in case of total assets. The second measured parameter –
relative standard error varied rather narrowly; it’s minimal value was 21.8% in case of total assets, and
maximal value amounted to 44.7% in case of gross cash flow. Although most of calculated parameters
reflect that the sample was not very homogenous, but as a principle it embraced mostly the biggest
rainbow trout farms in Poland. Calculated statistical indicators were much more differentiated in case of
costs composition; relative standard deviation varied between 30.3% (feed) to 249.4% (unpaid labour).
The highest level of the latter indicator is quite obvious as the item “unpaid labour” was mentioned only
by two surveyed trout farms. Also relative standard error was minimal in case of feed raw material (8.8%)
and maximal in case of unpaid labour (72.0%).
Table 13.4 Poland, Statistical indicators, carp
Sample mean
(1000 Euro)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
706.8
259.3
356.9
340.0
88.7
1460.0
92.9
111.5
97.9
97.5
93.2
98.4
21.9
26.3
23.1
23.0
22.0
23.2
572
113.0
26.6
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
618.1
100.7
Total capital costs
44.7
102.8
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
41.9
34.9
Unpaid labour
0.3
309.1
Energy costs
1.8
77.3
Live raw material costs
5.4
141.0
Feed raw material costs
27.1
39.2
Repair and maintenance
8.5
96.5
Other operational costs
15.0
50.1
23.7
24.2
8.2
72.9
18.2
33.2
9.3
22.8
11.8
93
Table 13.5 Poland, Statistical indicators, Trout
Sample mean
(1000 Euro)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
987.0
208.3
639.0
352.0
137.9
1302.6
78.4
99.6
81.0
86.9
155.0
75.6
22.6
28.8
23.4
25.1
44.7
21.8
275
84.7
24.4
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
849.0
82.4
Total capital costs
36.4
108.8
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
24.5
46.7
Unpaid labour
0.2
249.4
Energy costs
3.9
43.4
Live raw material costs
12.2
90.9
Feed raw material costs
44.7
30.3
Repair and maintenance
4.8
143.2
Other operational costs
9.7
70.0
23.8
31.4
13.5
72.0
12.5
26.3
8.8
41.3
20.2
13.6.4. Cross check with other sources
Quality of the data was addressed by comparison to the data collected in the surveys by DGPA. However,
most of the data obtained by the project survey is not addressed in the DGPA survey, or any other survey.
It was expected to obtain different values as anonymity in the project survey is guaranteed as opposed to
the current surveys, where it is possible to identify the source of information. In general terms, all
companies revealed better economic performance in the project survey than they do in the official data.
As was referred in 14.2, this is especially true for the seabass and seabream segment, where both value and
volume are significantly higher than declared data. As the data contains more detail than is normally
requested in the normal surveys, it will not be possible to verify the quality of many indicators. Future
surveys should be carried out using the detailed economic data available. For the time being, the great
majority of units are single holders which does not allow for the desired scrutiny of their economic
performance. This situation will improve in the future so the data collected will be increasingly
homogenous
94
14. PORTUGAL
14.1.
SUITABLE ORGANIZATION
The Directorate General for Fisheries and Aquaculture (DGPA) is, and has always been, the sole
institution responsible for all data collection regarding the fisheries sector. DGPA not only collects but
also processes this information, forwards it to the Institute of National Statistics (INE), who then
publishes the data. All data concerning the performance of the national aquaculture sector stems from
DGPA.
DGPA was consulted concerning the collection and processing of data in accordance to the present
survey. Although access to the economic data of aquaculture companies is presently possible, protocol
must be established with INE regarding data acquisition and treatment, especially due to the necessity of
harmonizing with the data already being collected. DGPA informed that it will only be ready to publish
information in the beginning of 2011.
14.2.
METHOD OF DATA COLLECTION
The usual approach to data collection is by direct survey to all the active production units. A reply is
obligatory and there are financial penalties for those who do not complete and return the form. The
survey asks for the production volume, value of sales, the various production costs and the number of full
time and part time personnel. A DGPA employee then uploads the data to the database where it becomes
available for internal consultation and treatment. Although a reply is obligatory, the inclusion of all the
information requested in the survey seams not to be.
The present survey was carried out through personal interviews. It was possible to carry out interviews
with various companies on single occasions for both the seabass/seabream, trout and clams segment. The
turbot segment was carried out by interviews with individual companies.
14.3.
SIZE OF PRESENT AND FUTURE SURVEY
Table 14.1 Portugal, Main segments of the national aquaculture sector (number of firms)
Segment
(Species / technology)
Population
Present survey
Without
With
Number in
threshold
thresholds
sample
Trout/raceways and cages
38
13
12
Clams/on bottom
1253
894
30
Seabass & bream/ponds & cages
177
96
30
Turbot/recirculation a
3
3
3
a – the construction of 2 units is underway, so the future survey will be of 5 companies
Recommended future
survey
Without
With
threshold
threshold
15
15
30
30
100
50
5
5
There are two thresholds applied to the survey.
Threshold 1 is applied to all segments and is relative to active/inactive companies. If a company does not
respond do the DGPA survey for two consecutive years, it is considered inactive. However, not
responding to the survey does not mean the company is inactive. Therefore, Threshold 1 differentiates
between active and inactive companies.
Threshold 2 applies only to the trout segment. The majority of the companies in this segment are also
licensed as hatcheries. When reviewing statistical data, the two different licences are considered as separate
companies, which falsely increases the number of companies available in the segment. However, only on
company carries out the complete reproductive cycle, the only true hatchery of the segment. The
95
remaining farms buy fertilized eggs, hatch them and grow the fish to commercial size, but they may also
re-sell the eggs and juveniles, or use the fish for repopulation purposes. Therefore, Threshold 2
differentiates makes a separation between hatcheries and on-growing companies.
14.4.
ESTIMATION OF COSTS
Table 14.2 Portugal, Estimation of costs
Item description
Investment costs
• Staff (training, etc.)
• Hardware (computers, office equipment, etc.)
• Software (data compilation and processing)
Total investment costs
Without threshold
Accounts
Accounts
available
not
available
0
0
With threshold
Accounts Accounts
available
not
available
0
0
0
0
0
0
250,000 1
250,000 1
Annual operational costs
0
0
0
0
• Data collection (labour)
2
3
2
3,000
300
3,000
3003
• Date collection (other expenses)
4
4
0/3,000
0/3,000
• Data processing (labour)
0
0
0
0
• Data processing (other expenses)
Total annual operational costs
6300/3,300
6,300/3,300
1This is the estimated value for the acquisition of an individual database that will house all the information available
and allow for various data treatments to be carried out.
2Cost of continued mailing survey but reduced to a sample, not the entire population as is presently done.
3Access to accounts has a cost of 3.00 Euro per company, considering a sample of 100 companies in all sectors.
Presently, this value is excessive but covers companies that are in the final licensing stages. If this information is
requested by DGPA, there may be no data acquisition costs. The information is paid for online and received online.
4Data processing (entering information into the database) is a task which may be done free by DGPA employees or
at a cost of 5 Euro per survey correctly loaded.
14.5.
AVAILABILITY OF FUNDING
The national budget has 100,000 Euro destined for data collection in the fisheries sector which is
sufficient to cover operational costs. It is not sufficient to include the acquisition of a new database for
information. This is deemed necessary, as the present system is considered to unreliable. It is sufficient to
contribute towards the acquisition of an European database, as well as its maintenance and development.
There are EU funds available for these expenses, covering 50% of the costs, which means Portugal could
have 200,000 Euro available.
14.6.
PROBLEMS AND SOLUTIONS
14.6.1. Extrapolation of the sample to total population
A simple linear extrapolation was used for the total population. Due to the small number of suppliers (one
feed factory and 2 hatcheries), it was assumed that values would be identical and proportional to the size
of the units, which is valid for all segments except for the seabass/seabream where different unit
dispositions and construction influence energy, labour and maintenance costs.
96
Table 14.3 Portugal, Share or sample in value and volume of production of the total segment
On-growing technique
Species
Population (no. firms)
Sample (no. firms)
Share of sample in total value of the segment (%)
Share of sample in total volume of the segment (%)
Ponds
Seabass & bream
96
30
47,9%
44,3%
On bottom
Clams
894
30
20,7%
10,3%
Recirculation
Turbot
3
3
100%
100%
Raceways
Trout
13
12
99%
99%
14.6.2. Evaluation of individual indicators
The data obtained for the indicators is based on personal interviews. The sample was homogenous for the
clam and turbot segment, but heterogeneous in the remaining segments as the companies interviews were
composed mainly of the larger producers, as they were the only ones willing to give the necessary
information.
It is not possible to determine the majority of the indicators for the clam segment as it is composed of
single holders which have no accounting. This segment will continue to have very few data available in the
near future. All other segments have a significant number of companies from which the necessary data
can be extracted. The information can then be extrapolated to determine the indicator values of the single
holders.
Accounts of all companies (not single holders) are available to the general public, for a fee, a fact which
the producers are unaware of and so were the entities collecting data. In order to compare the information
volunteered by the farmers with that of their accounts, this information was paid for and included in the
survey. The inclusion of this information resulted in a higher standard error and deviation than would be
expected. The information forwarded by the producers was very different from the tax returns.
It is not possible to compare the values obtained as this is the first time these indicators are addressed.
Although they may not give a correct characterization of the various indicators, the extrapolation of the
sample to the total population indicates that the production values and volumes are correct, with
exception to the seabass and seabream segment. Despite being almost three times higher than the
officially declared values, the majority of the companies interviewed indicated that a large percentage of
sales occurs through parallel markets. Therefore, the value of the extrapolation could be closer to the
reality than the declared numbers.
Most importantly, the information collected tells that it is possible to have access to the majority of the
indicators for all segments, as it is now possible to obtain detailed information regarding accounts. In the
long term, as the clam segment evolves and more companies are created, it will also be possible to access
detailed accounts, thereby obtaining a more detailed image of the segment.
97
Table 14.4 Portugal, Statistical indicators, Seabass and Seabream/Ponds
Total turnover (incl. other income)
Personnel costs (excl. unpaid labor)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Sample mean
Relative standard deviation
(% or 1000 Euro)
(%)
Absolute values
375.0
64.9%
58.3
65.2%
232.2
52.4%
160.2
82.0%
84.5
99.0%
1736.6
84.0%
3.6
70.0%
Relative standard error
(%)
10.3%
10.3%
8.3%
13.0%
15.7%
13.3%
11.1%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
91.0%
1.1%
Total capital costs
9.0%
2.3%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
20.1%
1.5%
Unpaid labour
Energy costs
20.2%
2.7%
Live raw material costs
14.5%
2.7%
Feed raw material costs
39.8%
1.6%
Repair and maintenance
0.8%
6.3%
Other operational costs
4.7%
5.8%
0.2%
0.4%
0.2%
0.4%
0.4%
0.3%
1.0%
0.9%
Table 14.5 Portugal, Statistical indicators, Clams/Bottom culture
Sample mean
(1000 Euro)
Relative standard deviation
(%)
Relative standard error
(%)
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
25.3
58.8%
9.3%
10.5
14.9
25.3
92.8%
48.0%
23.0%
14.7%
7.5%
3.7%
1.1
27.0%
4.3%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
100.0%
100.0%
Total operational costs
Total capital costs
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
Unpaid labour
Energy costs
3.9%
1.0%
Live raw material costs
91.1%
1.1%
Feed raw material costs
Repair and maintenance
Other operational costs
5.0%
0.9%
15.8%
0.2%
0.2%
0.1%
98
Table 14.6 Portugal, Statistical indicators, Turbot/Recirculation
Total turnover (incl. other income)
Personnel costs (excl. unpaid
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Sample mean
Relative standard deviation
(1000 Euro)
(%)
Absolute values
804.9
59.2%
179.9
29.5%
644.4
72.3%
181.5
113.0%
-19.4
-679.0%
3360.6
77.0%
11.7
35.0%
Relative standard error
(%)
34.2%
17.0%
41.7%
65.5%
-392.0%
44.7%
20.3%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
94.4%
1.1%
Total capital costs
5.6%
10.4%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
21.8%
0.8%
Unpaid labour
Energy costs
7.4%
2.5%
Live raw material costs
31.8%
2.3%
Feed raw material costs
35.5%
1.8%
Repair and maintenance
3.1%
0.7%
Other operational costs
0.4%
2.1%
0.6%
6.0%
0.5%
1.4%
1.3%
1.0%
0.4%
1.2%
Table 14.7 Portugal, Statistical indicators, Trout/Raceways
Total turnover (incl. other income)
Personnel costs (excl. unpaid
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Sample mean
Relative standard deviation
(1000 Euro)
(%)
Absolute values
276.5
159.1%
57.2
109.2%
167.4
114.7%
101.5
139.0%
51.8
358.0%
467.0%
Relative standard error
(%)
86.0%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
100.0%
100.0%
Total operational costs
Total capital costs
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
25.5%
1.2%
Unpaid labour
Energy costs
2.5%
2.4%
Live raw material costs
3.4%
1.8%
Feed raw material costs
50.8%
1.8%
Repair and maintenance
4.5%
1.8%
Other operational costs
13.4%
2.1%
91.9%
63.0%
66.2%
80.1%
206.7%
49.6%
57.7%
0.7%
1.4%
1.0%
1.0%
1.0%
1.2%
14.6.3. Cross check with other sources
Quality of the data was addressed by comparison to the data collected in the surveys by DGPA. However,
most of the data obtained by the project survey is not addressed in the DGPA survey, or any other survey.
It was expected to obtain different values as anonymity in the project survey is guaranteed as opposed to
the current surveys, where it is possible to identify the source of information. In general terms, all
companies revealed better economic performance in the project survey than they do in the official data.
99
As was referred in 14.2, this is especially true for the seabass and seabream segment, where both value and
volume are significantly higher than declared data.
As the data contains more detail than is normally requested in the normal surveys, it will not be possible to
verify the quality of many indicators. Future surveys should be carried out using the detailed economic
data available. For the time being, the great majority of units are single holders which does not allow for
the desired scrutiny of their economic performance. This situation will invert in the future so the data
collected will be increasingly homogenous.
100
15. SPAIN
15.1.
SUITABLE ORGANIZATION
The Ministerio de Medio Ambiente y Medio Rural y Marino (Ministry of Environment, Rural and Marine
Medium, MMAMRM) could collect this data. It is the institution that is managing this task currently. This
institution has experience with large scale data collection in general for fishing activity and aquaculture
activity in particular.
At the present MMAMRM is responsible for the production of Spanish fishery statistics. Such a system is
currently utilized to assist the Secretaria General del Mar in developing national management policy and it
has its focus on:
• evaluation of various fishing fleets activities and fishing effort monitoring;
• data from commercial fisheries;
• collection of prices related to landings;
• data for economic monitoring of fishing enterprises.
15.2.
METHOD OF DATA COLLECTION
The most recent data collection was carried out through census of the population. A database of
aquaculture enterprises was initially developed in order to allow a direct survey of farmers. Six data
collectors have been involved in contacting the farmers. So far, the data collection has been performed by
interviewing the fish farmers and data has been recorded on Access database.
The usual approach to data collection is through direct survey of farmers, but the methodological
approach currently used relies on both a combination of direct surveys of companies and cooperation
with accountants. MMAMRM plays a major role within the national statistical system set up by the
regional administrations of Spain, that implies that although regional governments have their own
aquaculture statistics, MMAMRM (through the department of Secretaria General Tecnica) collates all the
datasets and provides a global picture of the aquaculture statistics in Spain. The Ministerio has available
suitable software and people, as well as regional Government.
15.3.
SIZE OF PRESENT AND FUTURE SURVEY
Table 15.1 Spain, Main segments of the national aquaculture sector
Segment
(Species / technology)
Turbot /T&R
Seabream and Seabass / E&P
Trout / T&R
Tuna / Cages
Mussel / Off-b
Oyster / Off-b
Number of firms in
population
19
110
50
14
2,065
107
Present survey
Recommended future survey
5
27
12
4
3 POs + 1 firm
2 POs
15
100
40
10
3POs
60
It is not possible to make a distinction between a situation with threshold and without threshold, as
information available about the firms is not enough to do that in a reliable way.
101
15.4.
ESTIMATION OF COSTS
The budget of the survey put in place by Ministerio de Medio Ambiente y Medio Rural y Marino in Spain
for sampling the main economics indicators in the extractive fisheries amounts to 219,993 Euro. Given
the fact that the main fisheries regions are also the main aquaculture regions in Spain, it is reasonable to
assume that the unitary cost of sampling is the same for aquaculture and fisheries. In this sense, the
aquaculture sampling schemes represents a 30% in value, that implies that an efficient and system should
be around 66,000 Euro.
The technical expenses include the preparatory works, quality analysis of data and collating the data.
The estimation of costs is based on the budget of existing Spanish surveys, among which the collection of
economic data for fisheries. According to the cost information available, we could distinguish a situation
with threshold and without threshold. Within these two groups, we could distinguish between accounts
available and not available.
The application of a threshold limiting the sampled leads to a more significant cut of the budget as a
whole, with a total cost of 66,000 Euro in the best scenario.
In all the possible scenarios foreseen in the following table, labour cost is generally the main cost in the
collection system. This is due to the scattered distribution of aquaculture entities in Spain.
(Estimation derived from on-going fisheries economic data collection. 1000 Euro)
Table 15.2 Spain, Estimation of costs (1000 Euro)
Item description
Without threshold
Accounts
available
Accounts not
available
Accounts
available
Accounts not
available
4
17
25
46
6
17
25
48
3
17
25
45
5
17
25
47
58
32
16
9
97
95
77
24
15
182
35
32
13
14
66
71
77
23
18
153
Investment costs
Staff (training, etc.)
Hardware (computers, office equipment, etc.)
Software (data compilation and processing)
Total investment costs
Annual operational costs
Data collection (labour)
Data collection (other expenses)
Data processing (labour)
Data processing (other expenses)
Total annual operational costs
15.5.
With threshold
AVAILABILITY OF FUNDING
There will be Spanish national funding available for carrying out an experimental sampling.
102
15.6.
PROBLEMS AND SOLUTIONS
15.6.1. Extrapolation of the sample to total population
The extrapolation has been carried out based on the total number of aquaculture facilities in the directory
of the MMAMRM (P) and on the number of questionnaires obtained in the final sample (S) (1). The
elevation factor for each segment has been obtained as follows:
Ei = Pi/Si
Ei = Elevation factor for segment i.
Pi = Population (total number of aquaculture facilities) of segment i.
Si = Number of questionnaires obtained in the final sample of segment i.
In order to check the final result, the volume of sales (date available) has been compared to that one
published by the MMAMRM for the year 2006.
Table 15.3 Spain, Share or sample in value and volume of production of the total segment
On-growing technique
Species
Population (no. firms)
Sample (no. firms)
Off-b
Off-b
T&R
Mussel
Oyster
Turbot
2,065
3 POs(2)
and 1 firm
107
POs(3)
2
Cages
T&R
Tuna
Trout
19
E&P
Seabream /
Seabass
220
14
50
5
27
4
12
Share of sample in total value of the
93%
56%
24%
15%
29%
19 %
segment (%)
Share of sample in total volume of the
89%
54%
23%
19%
30%
16%
segment (%)
(1) (National Statistics Institute, INE; Galician Statistics Institute, IGE; Statistics Institute of Andalucía, IEA).
(2) Mussel POs represent a total of 1,880 producers (single holders).
(3) Oyster POs represent a total of 96 producers (single holders).
15.6.2. Evaluation of individual indicators
With reference to trout, and given the characteristics of the sector in which the small businesses or family
nature predominates, it has been impossible to obtain data on economic accounts due to the fact that
official registers are not publicly available databases, and the accounting information that is required for
the project is considered as confidential. Therefore, it has been rejected systematically to fulfil economic
data either due to distrust for the final destination of the data or due to the lack of personnel specialised in
these affairs.
The oyster sector is characterised by associations of small producers which hold a rudimentary and basic
accounting register of the data for income and production costs. Some producers did not provide any
collaboration for the realisation of surveys neither fulfil the accounting information requested in the
questionnaire.
The sectors of turbot, seabass and seabream were willing to collaborate. Their structure is based on well
organized and structured companies, they are involved and participate regularly in groups of interest
and committees linked to the aquaculture management and therefore they are aware of the
state and Community subsidies. It has been acknowledged that these companies are the ones
that develop more innovative business in the Spanish national aquaculture sector.
103
In case of trout farmers, where 75% of businesses are officially registered in the familiar sites, having small
production, and very low emphasis innovation, and very limited access to financial services (more
specifically: start-up capital, venture capital and working capital).
It has been observed, in spite of the lack of submission of data that trout companies are located in rural
areas and often unable to respond to market demand and compete in a globalized market. They rarely
participate in exchanges of experience and knowledge on best practices among policy makers. It seems
clear that the trout sector in Spain needs to give more strength to collective actions to improve current
entrepreneurial strategies.
Table 15.4 Spain, Statistical indicators, Mussel / off bottom
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
29.00
13.98
9.22
28.81
39.92
5.96
0.35
1.19
0.64
0.59
1.50
1.62
1.50
0.81
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
35.31
11.58
10.96
25.24
13.65
0.49
1.15
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
96.23
2.29
Total capital costs
3.77
2.65
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
51.39
2.53
Unpaid labour
Energy costs
11.37
1.06
Live raw material costs
9.40
2.10
Feed raw material costs
Repair and maintenance
27.85
2.80
Other operational costs
0.04
0.02
0.06
-0.00
0.02
0.03
104
Table 15.5 Spain, Statistical indicators, Oyster / off bottom
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
15.73
11.46
10.42
15.71
18.26
-36.67
2.38
1.68
2.13
3.37
3.34
-10.71
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
67.39
16.58
15.35
52.20
35.60
0.37
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
99.48
14.78
Total capital costs
0.52
3.65
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
51.93
4.62
Unpaid labour
-Energy costs
0.10
4.63
Live raw material costs
10.55
16.11
Feed raw material costs
-Repair and maintenance
37.51
13.06
Other operational costs
4.36
0.75
0.84
-0.05
3.33
-2.39
Table 15.6 Spain, Statistical indicators, Turbot in tanks and raceways
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
2.88
9.49
9.02
9.70
9.89
10.25
27.19
1.38
7.83
5.01
7.01
8.37
3.07
23.01
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
2,260.31
246.69
1,241.05
1,027.04
780.37
1,472.91
10.56
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
80.37
20.28
Total capital costs
19.63
1.73
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
16.58
36.79
Unpaid labour
-Energy costs
12.15
4.12
Live raw material costs
Feed raw material costs
31.35
18.50
Repair and maintenance
8.84
21.49
Other operational costs
31.07
6.76
18.36
1.09
19.69
-2.94
10.27
15.68
3.74
105
Table 15.7 Spain, Statistical indicators, Seabream/seabass in enclosures and pens
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
14.75
13.35
0.94
1.23
1.20
9.64
13.93
6.36
3.42
0.31
0.30
0.29
3.58
3.94
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
573.61
68.34
209.14
377.10
308.77
22.61
5.67
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
88.25
6.12
Total capital costs
11.75
9.36
Level 2 – Details of operational costs as % of total operational cost
Personnel costs
24.63
3.65
Unpaid labour
-Energy costs
0.65
7.69
Live raw material costs
Feed raw material costs
54.52
4.77
Repair and maintenance
2.34
7.26
Other operational costs
17.86
3.36
1.11
8.84
0.94
-7.40
1.31
6.25
3.02
Table 15.8 Spain, Statistical indicators, Tuna in cages
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
3.65
3.08
9.73
11.08
10.87
16.65
1.10
2.74
2.35
8.84
8.68
9.41
14.37
0.80
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
3,723.80
498.14
2,855.94
875.56
377.22
1,315.25
51.94
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
95.56
1.99
Total capital costs
4.44
18.02
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
14.85
19.53
Unpaid labour
-Energy costs
7.97
13.80
Live raw material costs
68.75
11.64
Feed raw material costs
Repair and maintenance
5.76
17.36
Other operational costs
2.66
15.05
1.88
16.89
15.82
-11.92
10.18
15.63
13.17
106
Table 15.9 Spain, Statistical indicators, Trout in tanks and raceways
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
314.06
47.11
172.04
142.80
95.70
13.82
16.05
19.90
21.88
21.46
9.37
22.99
17.46
17.03
17.25
1.56
21.34
18.93
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
90.59
7.09
Total capital costs
9.41
11.69
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
21.50
1.86
Unpaid labour
-Energy costs
12.90
9.30
Live raw material costs
Feed raw material costs
61.31
1.47
Repair and maintenance
4.30
4.65
Other operational costs
5.60
11.04
0.81
-8.95
0.85
4.03
Brief discussion about the quality of indicators:
The three statistical indicators show some variability data in the surveyed sample, especially in the trout
segment. The survey has covered aquaculture firms with significantly different scale of production. In the
future, some stratification of firms according scale of production would provide more homogenous
results.
15.6.3. Cross check with other sources
Survey data show significant differences with FAO and Eurostat data, but they are quite similar to the
Ministerio de Medio Ambiente y Medio Rural y Marino data, which is our main reference.
Table 15.10 Spain, Comparison of the extrapolation of the survey data to other sources
Species
Mussels
Oysters
Turbot
Seabream/Seabass
Tuna
Trout
Volume
(1000
tonnes)
301.9
4.8
6.2
27.3
2.9
24.9
Survey data
Value
Employ(million
ment
Euro)
120.6
8,319.0
10.4
981.0
51.8
331.0
129.4
1,566.0
53.5
2,814.0
65.0
434.0
FAO/ Eurostat data
MMAMRM data
Volume
Value
Employment
Volume
Value
Employment
158.1
4.9
5.6
21.2
3.4
26.0
44.5
6.7
31.4
80.4
40.6
45.9
na
na
na
na
na
na
228.9
6.2
6.4
22.0
2.6
24.9
120.6
12.3
48.6
106.5
36.5
58.1
na
na
na
na
na
na
107
16. SWEDEN
16.1.
SUITABLE ORGANIZATION
The Swedish Board of Fisheries (SBF) is responsible for the official aquaculture statistics. So far the
statistics on aquaculture production has been based on data from Statistics Sweden which yearly collects
and calculates data on the sector. However, data has so far only been presented in broad outlines and
rough estimates due to the fact that the current surveys have not been detailed enough for the needs
specified in this pilot study.
Data has traditionally been separated into two main segments: fish and shellfish for consumption, and fish
and crayfish for stocking purposes. As from the year 2000, Statistics Sweden collects data for both fish
and shellfish for consumption, and fish and crayfish for stocking. From 1996 to 1999, only data on fish
and shellfish for consumption was collected which mainly affects the time series for freshwater since fish
for stocking is mostly farmed in freshwater. Since not all companies have provided information on value
of production, Statistics Sweden calculates the value of production as the surveyed volume of production
average value of the species of interest. Also, information on employment was not collected until the year
2000.
Statistics Sweden has however stated that the volume of production of crayfish probably is substantially
underestimated. Specific studies indicate about ten times the volume of production compared to what is
presented in the national statistics each year. Hence, a new method must be found.
Because of the above stated problems, data collection on aquaculture must be further improved and
compiled in a manner that gives a more detailed picture. In this pilot study, information provided by
Statistics Sweden has been supplemented by information gathered by Fiskhälsan AB (Fish Health
Services) which legally is a private company, owned by the industry. Information from this source covers
energy costs, costs for live and feed raw material as well as the amount of full time, seasonal and unpaid
labour. Since Fiskhälsan AB apparently can retrieve information on a number of economic indicators not
available through Statistics Sweden, Fiskhälsan AB should continue to gather these data in the future
surveys. In the forthcoming surveys, Fiskhälsan AB should also gather data on the volume of production
since there is a large difference between Statistics Sweden and the figures referred to by the industry.
Fiskhälsan AB has calculated an estimated total volume that is 56 percent higher than the figures provided
by Statistics Sweden. However, since Fiskhälsan AB only covers the companies in their fish health
programme (the majority of all aquaculture firms producing fish in Sweden), additional surveys are needed
to retrieve data for firms producing shellfish (i.e. crayfish, mussels and oysters).
Concluding, the SBF is responsible and should continue to be responsible for compiling and reporting
statistics on aquaculture. The SBF should furthermore make an annual survey, covering all companies in
the aquaculture sector in Sweden, in cooperation with Fiskhälsan AB. The statistical population consists
of all companies that have a permission to run an aquaculture business. The production of smolts by the
hydroelectric power companies, in order to compensate for the loss of natural migration routes for
diadrome fish due to the construction of hydroelectric power stations, will in the future be included in the
official statistics.
16.2.
METHOD OF DATA COLLECTION
Project: Data was collected by Statistics Sweden and Fiskhälsan AB. Statistics Sweden conducted an
exhaustive survey where the statistical population consisted of all companies that had a permission by the
SBF, or by the County Administrative Boards in Sweden, to run an aquaculture business. Statistics Sweden
annually collects data on the aquaculture sector, on behalf of the SBF, using a postal survey to all
aquaculture producers that are registered as a legal business in Sweden. The firms that indicate that they
are no longer active are deleted from the list. New firms that are registered are added to the register
through cooperation with the County Administrative Boards. According to Statistics Sweden, almost all
108
firms asked have answered the survey throughout the years. When needed, the survey is completed by
telephone calls to the producers to sort out any misunderstandings or information gaps.
As a part of this pilot study, Statistics Sweden compiled/processed the results of their postal survey along
with data from the aquaculture firms’ annual declarations of income. One general problem (facing all
segments) was that these declarations were not detailed enough, in terms of costs, for the purpose of this
project, thereby making a second source (Fiskhälsan AB) necessary. Furthermore, some firms, mainly
smaller ones, do not specify all types of costs in detail. There were also a number of firms that had several
sources of income (i.e. not only income from aquaculture), which, in some cases, made it difficult to
allocate costs and incomes to the right type of activity. Hence, in order to retrieve information on the
requested economic indicators that were not available through Statistics Sweden, Fiskhälsan AB
conducted a survey while visiting a number of firms as part of its fish health programme. Through this
survey information on energy costs, costs for live and feed raw material, and amount of paid, unpaid and
seasonal work was gathered, thereby supplementing the information available through Statistics Sweden’s
survey.
The survey encompassed 206 firms in total, whereas it was possible to process data from 165 firms by
technique as well as by species, environment, and end-use. For the segment consisting of firms producing
rainbow trout in freshwater using a combination of techniques (cages, ponds and raceways) mean values
have been extracted from the primary/original segments of rainbow trout for consumption produced in
freshwater.
During the pilot study it became clear that the data from the declarations of income were not in total
accordance with the number of companies; about 85% of the declarations of income of identified
aquaculture companies were found and the remaining probably belongs to firms with low, or no, income
from aquaculture. The general threshold of an aquaculture firm was set to 40 percent, meaning that only
firms with income from aquaculture representing at least 40 percent of total income were included.
However, as crayfish production is generally undertaken as a side line/ancillary (activity) to other business
activities (usually agriculture) the threshold of 16 percent was applied for firms producing signal crayfish.
In order to compensate for the non-response level, a correction factor was introduced. The correction
factor has been calculated dividing the average sales value for the total population by the average sales
value of the firms that responded to the survey. A correction factor of 1 means that no correction is
needed as the non-response level was zero. A correction factor less than 1 means that the average sales
value is less in the non-response group compared to the total group. Correspondingly, a correction factor
more than 1 means that the average sales value of the non-response group is higher than the average sales
value of the total population.
16.3.
SIZE OF PRESENT AND FUTURE SURVEY
In this study the survey is exhaustive, i.e. all companies with permits for aquaculture production are
included. The same is planned for future surveys; at least as long as the total number of companies is
relative small. Segments can be presented in different detail, depending on the number of companies in
each segment. Many segments are too small to be divided and presented into species, environment
(freshwater or saltwater) and technique.
109
Table 16.1 Sweden, Main segments of the national aquaculture sector (number of firms)
Segment
(Species / technology)
Population*
Without
threshold
With
threshold
Present
survey
Number in
sample
Recommended future
survey
Without
With
threshold** threshold
*
20
38
22
5
25
Rainbow trout, freshwater, ponds
7**
7**
Rainbow trout, freshwater, cages
23**
23**
Rainbow trout, saltwater, cages
21**
21**
Rainbow trout, freshwater, raceways
Rainbow trout, freshwater, cages, ponds and
16**
16**
raceways
Brown trout, freshwater, cages
23
23
23
Arctic char, freshwater, cages
7
7
7
Crayfish, freshwater, ponds
48
48
48
Signal crayfish, freshwater, ponds
15
15
15
Blue mussels, long-lines/off bottom
5
5
5
Total
165
165
208
*The population is defined as all firms having an aquaculture license. However, the firms having an income from
aquaculture that is less than 40% of the total income (for all segments but firms producing signal crayfish, where a
minimum requirement of 16% has been applied) are not included in the survey and, hence, constitute part of the
non-response level.
**The population of firms producing rainbow trout has, in the pilot study, been segmented with respect to
environment and technique as well as with respect to the end-use of the fish; i.e. if the firm is producing fish for
consumption or for stocking purposes. If the end-use of the fish is not taken into account it is possible to get a larger
population for each technique.
***If not separating between firms producing fish for consumption and firms producing fish for stocking the
populations (number of firms in each segment based on species, environment and technique) provided in the
column are possible.
16.4.
ESTIMATION OF COSTS
Table 16.2 Sweden, Estimation of costs (Euro)
Item description
Investment costs
• Staff (training, etc.) 14
• Hardware (computers, office equipment, etc.)
• Software (data compilation and processing) 15
Total investment costs
Annual operational costs
• Data collection (labour) 16
• Date collection (other expenses)
• Data processing (labour)
• Data processing (other expenses)
• Sub-contracted work
Total annual operational costs
Without threshold 13
Accounts
Accounts
available
not
available
With threshold
Accounts Accounts
available
not
available
30,480
89,064
119,544
8,711
72,158
80,869
Due to a small population of firms in some segments, and earlier experience of low response levels, the total
population is suggested to be surveyed in order to receive a tolerable number of answers. However, firms having an
income from aquaculture that constitute less than 40% of the total income are not included (16% is applied for firms
producing signal crayfish), thus constituting part of the non-response level.
14 Includes training of staff
15 Includes the costs of setting up of a new system for data compilation and data processing
16 Annual cost of data collection: annual visits to firms
13
110
16.5.
AVAILABILITY OF FUNDING
The costs have been delivered as a part of the Swedish package for the Data Collection Regulation (DCR).
16.6.
PROBLEMS AND SOLUTIONS
16.6.1. Extrapolation of the sample to total population
Sweden has in principle conducted an exhaustive survey as the population consisted of all firms having an
aquaculture license. However, firms having an income from aquaculture that was less than 40% (for all
segments but firms producing signal crayfish were a minimum of 16% has been applied) were not
included in the part of the survey conducted by Statistics Sweden, thus constituting part of the nonresponse level. Hence, there is in general no extrapolation of the sample to the total population. However,
as the survey conducted by Fiskhälsan AB was not an exhaustive survey, the data provided was
extrapolated to the population defined by Statistics Sweden. The aquaculture firms have been segmented
based on species, environment (freshwater or saltwater), end-use (consumption or stocking purposes) 17
and, where possible, technique employed.
Table 16.3 Sweden, Share or sample in value and volume of production of the total segment
On-growing technique
Cages
Ponds
Cages, ponds
Cages
and raceways
Species
Rainbow
Rainbow
Rainbow
Rainbow
trout
trout
trout
trout
(freshwater, (freshwater, (freshwater, (saltwater,
consumption) restocking) restocking) consumption)
Population (no. firms)
23*
7*
16*
21*
Sample (no. firms)
23
7
16
21
Share of sample in total value of the segment (%)
100%
100%
100%
100%
Share of sample in total volume of the segment (%)
100%
100%
100%
100%
On-growing technique
Cages
Cages
Species
Brown trout Arctic char
(freshwater) (freshwater)
Ponds
Crayfish
(freshwater)
Ponds
Signal
crayfish
(freshwater)
Population (no. firms)
23
7
15
48
Sample (no. firms)
23
7
15
48
Share of sample in total value of the segment (%)
100%
100%
100%
100%
Share of sample in total volume of the segment (%)
100%
100%
100%
100%
*Due to the segmentation also taking the end-use of the fish into account; i.e. if the aquaculture firm is producing
fish for consumption or for stocking purposes, the population size is not as big as it could otherwise have been.
Long-lines
(off bottom)
Blue mussels
16.6.2. Evaluation of individual indicators
Since not all Swedish companies are obliged to have an accountant, asking the accountants for the
information is not a possible alternative. This problem is more likely among the firms producing fish for
stocking than among firms producing fish for consumption as the former often are managed by nonprofit organisations rather than enterprises. The population appears to be rather heterogenic since there is
a (small) number of large firms dominating the sector while there many very small firms (i.e. representing
a small share of the total value).
There is also some firms where it was not possible to determine if the main share of the fish produced was for
consumption or for stocking purposes. These firms have their own group named consumption and stocking.
17
111
5
5
100%
100%
Although using two sources poses certain problems and makes need for assumptions in order to compile
the data. However cooperating with Statistics Sweden as well as with Fiskhälsan AB has provided the
survey with more detailed information than would otherwise have been possible.
Table 16.4 Statistical indicators, Rainbow trout (for consumption), freshwater, cages
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Segment
mean
(1000 Euro
or %)
Absolute values
11,333
702
7,980*
2,536*
2,597*
8,605
96**
Relative
standard
deviation
(%)
Relative
standard
error
(%)
697%
610%
692%
NA
NA
695%
145%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
95%
695%
Total capital costs
5%
598%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
8.0%
610%
Unpaid labour
0.6%
173%
Energy costs
0.7%
NA
Live raw material costs
5.7%
NA
Feed raw material costs
53.6%
NA
Repair and maintenance
0.2%
698%
Other operational costs
31.0%
703%
*Energy costs, costs for feed raw material and costs for live raw material are estimations based on data gathered by
Fiskhälsan AB.
**Estimated as FTEs based on data gathered by Fiskhälsan AB
Due to the survey being an exhaustive survey, it is not possible to calculate standard deviation and
standard error. The non-response level for the segment was 57% consisting of firms lacking organization
numbers or personal identification numbers, and firms where the share of income from aquaculture was
less than 40 percent of the firm’s total income. In order to compensate for the non-response level, a
correction factor of 0.468 was used, implying that the firms constituting the non-response level had an
average sales value that was less than the sales value of the population.
112
Table 16.5 Statistical indicators, Rainbow trout, freshwater, ponds
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Segment
mean
(1000 Euro
or %)
Absolute values
366
49
258*
205*
40*
8**
Relative
standard
deviation
(%)
Relative
standard
error
(%)
145%
195%
152%
NA
NA
186%
131%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
86%
164%
Total capital costs
14%
156%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
15.2%
195%
Unpaid labour
5.7%
128%
Energy costs
8.7%
NA
Live raw material costs
13.2%
NA
Feed raw material costs
28.3%
NA
Repair and maintenance
3.5%
212%
Other operational costs
25.5%
149%
*Energy costs, costs for feed raw material and costs for live raw material are estimations based on data gathered by
Fiskhälsan AB.
**Estimated as FTEs based on data gathered by Fiskhälsan AB
The non-response level for the segment was 43% consisting of firms lacking organization numbers or
personal identification numbers, and firms where the share of income from aquaculture was less than 40
percent of the firm’s total income. In order to compensate for the non-response level, a correction factor
of 0.728 was employed, implying that the firms constituting the non-response level had an average sales
value that was somewhat less than the sales value of the population.
113
Table 16.6 Statistical indicators, Rainbow trout, freshwater, ponds, cages and raceways
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Segment
mean
(1000 Euro
or %)
Absolute values
5,689
1,296
3,555*
2,214*
630*
7,269
122**
Relative
standard
deviation
(%)
Relative
standard
error
(%)
188%
256%
191%
NA
NA
179%
95%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
90%
206%
Total capital costs
10%
205%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
25.6%
256%
Unpaid labour
4.1%
254%
Energy costs
3.5%
NA
Live raw material costs
4.9%
NA
Feed raw material costs
31.2%
NA
Repair and maintenance
-0.3%
190%
Other operational costs
30.9%
182%
*Energy costs, costs for feed raw material and costs for live raw material are estimations based on data gathered by
Fiskhälsan AB.
**Estimated as FTEs based on data gathered by Fiskhälsan AB
The non-response level for the segment was 40% consisting of firms lacking organization numbers or
personal identification numbers, and firms where the share of income from aquaculture was less than 40
percent of the firm’s total income. The data in this segment has been compiled through subtracting the
mean values of the following subgroups; “rainbow trout, fish for consumption, freshwater, cages” and
“rainbow trout, fish for stocking, freshwater, ponds”, from the main segments “rainbow trout, fish for
consumption, freshwater” and “rainbow trout, fish for stocking purposes, freshwater” respectively and
adding the mean values from “rainbow trout, fish for consumption and stocking, freshwater”. It is
therefore not possible to provide a correction factor for the segment. However, correction factors are
readily available for the main segments. For the main segment “rainbow trout, fish for consumption,
freshwater” a correction factor of 0.377 was employed, implying that the average sales value of the firms
in the non-response level was less than the average sales value of the firms in the population. For the main
segment “rainbow trout, fish for stocking purposes, freshwater” a correction factor of 0.695 was used,
meaning that the average sales value of the firms in the non-response level was less than the average sales
value of the firms in the population. The correction factor used in “rainbow trout, fish for consumption
and stocking, freshwater” was 0.708, also implying the firms in the non-response level having a sales value
that was less than the sales value among the firms in the population.
114
Table 16.7 Statistical indicators, Rainbow trout, saltwater, cages
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Segment
mean
(1000 Euro
or %)
Absolute values
5,322
862
3,585*
2,085*
785*
6,194
23**
Relative
standard
deviation
(%)
Relative
standard
error
(%)
107%
109%
92%
NA
NA
177%
134%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
91%
95%
Total capital costs
9%
145%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
19.0%
109%
Unpaid labour
2.0%
245%
Energy costs
0.5%
NA
Live raw material costs
13.2%
NA
Feed raw material costs
47.6%
NA
Repair and maintenance
0.1%
115%
Other operational costs
17.5%
91%
*Energy costs, costs for feed raw material and costs for live raw material are estimations based on data gathered by
Fiskhälsan AB.
**Estimated as FTEs based on data gathered by Fiskhälsan AB
The non-response level for the segment was 38%, all attributed to firms where the share of income from
aquaculture was less than 40 percent of the firm’s total income. In order to compensate for the nonresponse level, a correction factor of 1.280 was employed, implying that the firms in the non-response
level had an average sales value that was larger than the average sales value of the population.
115
Table 16.8 Statistical indicators, Brown trout, freshwater, cages
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Segment
mean
(1000 Euro
or %)
Absolute values
1,758
415
1,236*
848*
-3*
746
23**
Relative
standard
deviation
(%)
Relative
standard
error
(%)
192%
233%
197%
NA
NA
264%
110%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
94%
Total capital costs
6%
246%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
23.6%
233%
Unpaid labour
6.2%
147%
Energy costs
7.8%
NA
Live raw material costs
20.2%
NA
Feed raw material costs
15.8%
NA
Repair and maintenance
0.7%
234%
Other operational costs
25.6%
161%
*Energy costs, costs for feed raw material and costs for live raw material are estimations based on data gathered by
Fiskhälsan AB.
**Estimated as FTEs based on data gathered by Fiskhälsan AB
The non-response level for the segment was 65%, where the majority (39%) was attributed to firms
lacking organization or personal identification number and the remaining share (26%) consisted of firms
that had a share of income from aquaculture less than 40 percent of the firms’ total income. In order to
compensate for the non-response level, a correction factor of 0.507 was employed, implying an average
sales value among the firms constituting the non-response level that was less than the average sales value
of the population.
116
Table 16.9 Statistical indicators, Arctic char, freshwater, cages
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Segment
mean
(1000 Euro
or %)
Absolute values
1,897
411
1,811*
660*
-504*
1,562
13**
Relative
standard
deviation
(%)
Relative
standard
error
(%)
156%
100%
172%
NA
NA
262%
125%
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
96%
Total capital costs
4%
177%
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
17.1%
100%
Unpaid labour
4.6%
147%
Energy costs
4.1%
NA
Live raw material costs
27.9%
NA
Feed raw material costs
31.0%
NA
Repair and maintenance
0.2%
220%
Other operational costs
15.2%
170%
*Energy costs, costs for feed raw material and costs for live raw material are estimations based on data gathered by
Fiskhälsan AB; **Estimated as FTEs based on data gathered by Fiskhälsan AB
The non-response level for the segment was 43%, where the majority (29%) consisted of firms that had a
share of income from aquaculture less than 40 percent of the firms’ total income and the remaining share
(14%) was attributed to firms lacking organization number or personal identification number. In order to
compensate for the non-response level, a correction factor of 0.597 was employed, implying that the firms
constituting the non-response level had an average sales value that was less than the average sales value of
the population.
Statistical indicators could not be presented for segment 7 (species: signal crayfish, environment:
freshwater, on-growing technique: ponds) and segment 8 (species: crayfish, environment: freshwater, ongrowing technique: ponds) due to the data being highly uncertain; the volume of crayfish production is
believed to be substantially underestimated and specific studies indicate the volume of crayfish production
to be 10 times the volume presented in the annual official statistics.
No statistical indicators could be provided for segment 9 (species: blue mussels, marine environment, ongrowing technique: long-lines/off bottom) due to the population being small (only 5 firms) and the nonresponse level being 50%.
16.6.3. Cross-check with other sources
The Swedish official statistics on aquaculture published by Statistics Sweden only give data on production
in terms of geographical location, technique, species and volume and value. These statistics are also the
basis for the reporting to Eurostat and FAO. The “sales value” in the official statistics differs from
“turnover” in the costs and earnings survey. “Sales value” is collected on a yearly basis directly from the
aquaculture companies through a questionnaire whereas “turnover” is taken from the yearly income
declarations. Turnover may include sales of products other than aquaculture and there are some
differences in population size between the two surveys. For rainbow trout this difference amounts to
approx 10%, where the cost and earning survey gives the highest value. Another methodological challenge
is that the official statistics refer to production sites whereas the cost and earnings survey is based on
companies.
117
17. UNITED KINGDOM
17.1.
SUITABLE ORGANIZATION
Data collection in the UK is currently undertaken by CEFAS (England and Wales), FRS (Scotland) and
DARDNI (Ireland) under their health-related farm inspection remit and is thus presently limited to
production variables. With the increasing emphasis on devolution of powers to national (rather than UKwide) fisheries administrations, this division of labour is likely to remain unchanged. In addition the Office
of National Statistics (ONS) also conducts an ‘Annual Business Inquiry’ (see below) which is UK-wide
and is proposed to be the main future vehicle for collecting economic data in aquaculture, as CEFAS nor
FRS have the requisite skills. The current FADN data collection remit is with DEFRA.
17.2.
METHOD OF DATA COLLECTION
17.2.1. Project data collection methodology
Five different aquaculture segments were examined in the UK under this study:
Table 17.1 UK, Main segments of the national aquaculture sector (number of firms)
Segment
(Species / technology)
Salmon (cages)
Trout (freshwater)
Oyster (on bottom)
Mussel (on bottom)
Mussel (rope)
Population
Without
With
threshold
threshold
44
29
202
101
94
19
67
38
58
17
Present survey
Number in
sample
15
48
12
18
16
Recommended future survey
Without
With threshold
threshold
15
10
40
30
10
5
15
10
15
5
Salmon
It was planned to collect data via a structured questionnaire issued via the internet (on Survey Monkey)
and by email (hard copy of Excel spreadsheet used for data compilation) to UK salmon producers.
Endorsement of the survey was sought from Scottish Salmon Producers Organisation (SSPO), but a
change in personnel meant that the relevant person was not in post during the survey. Due to the lack of
SSPO endorsement and several recent industry consultation exercises it was agreed that the consultants
would populate the company survey based on published accounts (available via the Merlin Scott
database 18 ), existing data and company responses to the consultants in relation to other recent projects.
The survey sample included a disproportionate number of large-scale operators. Extrapolation from the
survey sample therefore over-estimates sector totals. Official sources for total turnover and FTE are
quoted throughout the report, while survey totals are used to derive indicators.
Trout
The main data collection was via a structured questionnaire issued via the internet (on Survey Monkey) or
by mail (hard copy of Excel spreadsheet used for data compilation) to UK trout farms. The survey was
endorsed by the British Trout Association (BTA) who wanted to update their own understanding of the
industry (the last economic survey was conducted in 2001 by Nautilus Consultants).
The response was generally good. Of the 55 farms invited (out of 202 firms), 18 responded (32%),
although a number gave only production data and declined to give economic information. The share of
the sample was 10% and five% by volume and value respectively.
18
See www.merlinscottassociates.co.uk
118
The extrapolation of the sample to the segment total was based on a straightforward multiplication of the
mean average response to the total universe. Where possible, a stratified approach was utilised (large
>200t per annum, intermediate 51-200t per annum and small <50t per annum). The number of FTE was
estimated based on a standard working year of 2,086 hours per annum.
Oysters
As with trout, the main data collection was via a structured questionnaire issued via the internet (on
Survey Monkey) or by mail (hard copy of Excel spreadsheet used for data compilation) to UK trout farms.
Whilst the survey methodology was presented to the Shellfish Association of Great Britain (SAGB), no
support or assistance in contacting member was provided beyond a membership contact list.
The response was very poor. Of the 20 farms invited (out of 94 firms), only 2 responded, although these
were two of the larger organisations who accounted for 20% of both volume and value. The low response
was largely due to the large number of small operations in the industry, many of which are sole traders
without the detailed accounts required of limited companies. In order to expand the sample, accounting
information on a further seven farms was obtained through the Merlin Scott database (see salmon above).
The extrapolation of the sample to the segment total was based on a straightforward multiplication of the
mean average response to the total universe. The number of FTE was estimated based on a standard
working year of 2,086 hours per annum.
Mussels
As with trout, the main data collection was via a structured questionnaire issued via the internet (on
Survey Monkey) or by mail (hard copy of Excel spreadsheet used for data compilation) to UK mussel
producers. Whilst the survey methodology was presented to FRS, the Association of Scottish Shellfish
Growers ( ASSG) and Scottish Shellfish Marketing Group (SSMG) to seek endorsement, no support or
assistance in contacting members was provided, partly due to data protection issues. Instead internet
searches and industry contacts resulted in a contact list of 34 mussel producers, who were approached
directly.
The response to the survey was poor. Of the 34 farms contacted (out of an estimated 125 companies), 5
responded. These represented the larger companies who accounted for around 23% by both volume and
value. In order to expand the sample, accounting information on a further seven farms was obtained
through the Merlin Scott database (see salmon above). The low response was largely due to the large
number of small operations in the industry, many of which are sole traders without the detailed accounts
required of limited companies. Additionally, through the consultation process it was established that
consolidation of the industry had been ongoing with a number of producers contacted now producing for
a larger parent company.
The extrapolation of the sample to the segment total was based on a straightforward multiplication of the
mean average response to the sector total as no other data are available. This however inevitably leads to
an overestimation of sector totals for some categories.
17.2.2. Current aquaculture data collection
Aquaculture data from all three fisheries departments (see below) is collated by the Marine Fisheries
Agency (MFA) in London. This is then made available in the appropriate formats for external bodies, such
as the EC, FAO and OECD.
England and Wales: the current voluntary reporting system will become mandatory. However,
information will continue to be collected by veterinary health inspectors who visit all the UK fish and
shellfish farms and thus collect data by direct survey. Information is collated by CEFAS in Weymouth and
data collated in-house. At present this is confidential to CEFAS, although is reported in summary form
119
through the ‘Shellfish News’. However with the advent of the new Aquatic Animal Heath Regulations
(responding to EC Council Directive 2006/88/EC) will require that the names of all approved (or
registered) establishments be publically available.
Scotland: in Scotland, the Fisheries Research Service (FRS), an agency of the Scottish Government Marine
Directorate (SGMD), collects the aquaculture data for all finfish and shellfish species in Scotland. The
scheme is voluntary, but the final return approaches 100% and therefore essentially covers all production
(Ron Smith, FRS, pers. comm.). Although the main purpose of the data collection is for FRS to prepare
the annual summaries, it is also sent to SEERAD so that they can compile UK-wide aquaculture data for
SEERAD (prices); FAO (production, prices/kg, number of sites by facility type); OECD Fisheries
Statistical Review (tonnage by species) as well as various NGOs.
The new Marine Directorate (was part of the Scottish Executive) is the portal for aquaculture data
collected in Scotland 19 . Once the FRS data are signed off it is made available to policy makers and a copy
sent to the Marine and Fisheries Agency (MFA) in London.
Northern Ireland: animal health inspections – and therefore aquaculture statistics verification - is
undertaken by the Fisheries Division of the Department of Agriculture and Rural Development
(DARDNI), who usually undertake at least two visits to each farm annually. Therefore direct survey will
be likely to continue in Northern Ireland.
UK ONS Annual Business Enquiry (ABI): collects employment and financial information on every sector
of UK industry, including aquaculture, to feed into the production of ‘input-output annual supply and use’
tables. The latest revision to the ‘Standard Industry Classification (SIC 2007) divides aquaculture into two
classes, saltwater aquaculture (03.21) and freshwater aquaculture (03.22), and whilst identifying segments
within these, does not attempt to disaggregate them. The businesses are registered with the InterDepartmental Business Register (IDBR), which is a list of UK businesses maintained by the Office for
National Statistics (ONS) and combines the former Central Statistical Office (CSO) VAT based business
register and the former Employment Department (ED) employment statistics system. It complies with
European Union regulation 2186/93 on harmonisation of business registers for statistical purposes. Only
businesses that are VAT registered (companies with a turnover of more than 82,000 Euro) or taxed
through the ‘Pay as you earn’ PAYE scheme are included.
The private sector uses published accounts submitted to Companies House by VAT registered businesses
to provide financial indicators such as gross profit margin (%), operating margin (%), pre-tax margin (%),
return on capital, employed return on investment current ratio, debt ratio stock turnover (per year), fixed
asset turnover (per year), value added (% of turnover), average sales per employee (£) , average wage per
employee (£), total sales (% of base year) and operating profit (% of base year). These can be collated on a
company by company or on a broad sectoral basis.
17.2.3. Current proposals for accessing economic data from UK aquaculture businesses
The Marine and Fisheries Agency is currently leading UK discussions as to how the different fisheries
departments will collect economic data not currently captured by the health inspectorates. The main
premise for their approach has been to both (i) not increase the compliance burdens upon aquaculture
businesses though increased surveys and (ii) avoid the need to develop a new and separate desiccated
exercise to collect economic data 20 . The current proposal is therefore to make increasing use of the results
of the Annual Business Inquiry (ABI) for this industry to allow the requirements under the data collection
regulations to be met with the minimum cost to UK business and to UK administrations.
Essentially this would entail linking economic data from the IDBR ‘Annual Business Enquiry’ with
segmented production data from the existing CEFAS /DARDNI veterinary inspectorates and the Scottish
production surveys.
19
20
Heather Holmes, MD, pers. comm.
Kevin Williamson, MFA UK National Correspondent for the DCR, pers. comm., Oct 2008
120
This linkage would then allow the fisheries departments to recast the ONS data from the ABI into the
appropriate industry segmentations. This has a number of advantages:
• The majority of economic data would be collected from existing sources and would thus obviate the
need for separate, dedicated data collection
• A similar system would be proposed for the economic assessment of fish processing businesses
However, a number of problems are anticipated, including:
• The IDBR only contains information on VAT registered businesses or those registered through the
PAYE scheme – this would immediately impose minimum business size thresholds
• It will be a challenge to reconcile data from two very different surveys and produce a single set of
economic data that corresponds to the different segments involved. This is currently the focus on
MFA in developing this approach.
• Some variables required by the DCR are not covered by the ABI. These are currently being
determined by MFA, but are likely to include a breakdown of the different goods and other cost
elements relevant to aquaculture (e.g. livestock and feed costs). In such cases additional data collection
may be required to provide complete compliance with the DCR.
An alternative approach, but one not currently being considered by the MFA, is to delegate responsibility
for economic data collection to the Farm Business Economics Unit (FBEU) of Defra, who currently
collect the FADN data on agriculture. This unit is extremely experienced in farm-level economic data
collection and reporting but lacks the specific expertise related to aquaculture. In such a case it would be
necessary to expand the database system to include an aquaculture module, but this could be modelled on
the FADN approach as it is broadly compatible.
17.3.
SIZE OF PRESENT AND FUTURE SURVEY
Salmon farming
Threshold: overall production was dominated by 11 companies (of 44) in 2006, which between them
accounted for over 90% of the salmon production in Scotland. Of the rest, around a third (18) produce
between 100-2,000 tonnes and the remaining third (13) less than 100 tonnes. It is therefore suggested that
a threshold of >100 tonnes per annum is used, thus still capturing 99.95% of production when
extrapolated.
Survey size: at present FRS surveys all finfish farms in Scotland as part of their ‘Annual Production
Survey’. We understand that although the mailed survey is voluntary, dedicated staff resources ensure
return levels are very high. The introduction of financial data would compromise this survey response as
(i) financial data may be compiled some time after production data, depending upon the financial year end
and (ii) such information may be less readily available than the production data currently collected. Given
the comparatively small number of companies involved, and their high individual turnover, utilisation of
the ABI would capture the vast majority of the segment’s participants. It is considered that if no threshold
were adopted a sample size of 15 companies would be sufficient to capture 44% of production.
Trout farming (freshwater pond and cage)
Although trout farming is largely freshwater and therefore excluded from the new DCR elements, it is an
important component of UK aquaculture and is therefore covered by this report.
Threshold: the major share of trout production in England and Wales is from larger farms – 76% of
production is from farms >50t per annum, despite these representing only 21% by number (37 out of 178
farms). Of the remaining farms producing <50t per annum, over half by number (76) produce less than 10
tonnes per annum, mainly for restocking. It is therefore suggested that the threshold for trout aquaculture
121
is set at 25 tonnes per annum. 101 farms (67 in England and Wales and 34 in Scotland) represent around
half the total number yet produce 85-90% of the UK’s trout.
Survey size: with the above threshold applied, a stratified survey accommodating the 26-50; 51-100, 101200 and >200 tonnes per annum sub-segments will be required. It is suggested that 10 farms each of the
two smaller segments and 5 farms each of the two larger sub-segments are surveyed, giving a total of 30
farms. Should the whole population be sampled (e.g. with the 1-25 tonnes per annum farms below the
proposed threshold), then an extra 10 farms should be included.
Oyster farming (on bottom)
Thresholds: UK oyster aquaculture is dominated by very small, privately owned businesses producing very
low volumes of shellfish e.g. less than 5 tonnes per annum. Of the 94 business in the UK, only about 10
produced more than 50 tonnes each. Many of these businesses have insufficient accounting procedures to
generate the financial information required by the proposed DCR and thus thresholds would be essential
in order to make the programme achievable. CEFAS has expressed grave reservations to the consultants
over their ability to capture meaningful information on shellfish aquaculture economics due to the small
size of the majority of the producers, especially in England. As a result it is considered that a size
threshold of around 25 tonnes annual production would be practically feasible, capturing the majority of
production even if it only includes a quarter of the actual producers.
Sample size: given the need to implement a threshold of at least 25 tonnes production, the recommended
sample size for oyster farms would be around 5 companies. The majority of these would be in the 11-50t
per annum production range (which represents around 28% of production, with the remainder being the
small number of larger farms. Even this small sample would represent over 80% of UK oyster farm
production.
Mussel farming (on bottom and rope culture)
Thresholds: Like oyster farming, mussel production is dominated by a small number of large producers –
the quarter of the companies producing over 100 tonnes per year represent around 86% of all UK mussel
production. Equally around half the companies produce less than 25 tonnes per annum, or only 5% of the
total production. It is therefore suggested that a threshold of 100 tonnes be applied.
Sample size: Applying a 100t threshold to future surveys would give a population of on bottom
production accounting for 95% of production by volume, but only 33% of producers by number. For
rope culture a 100t threshold would account for 71% of production by volume and 29% of producers by
number (17). With the threshold applied, the whole sample universe is around 55 companies. It is
therefore suggested that 15 companies are included in the survey divided between on bottom (10) and
rope culture (5).
17.4.
ESTIMATION OF COSTS
As stated previously, the operational responsibility for data collection in England and Wales currently falls
under the CEFAS fish veterinary inspectorate, the FRS annual production survey in Scotland and the
DARDNI fish veterinary inspectorate in Northern Ireland. The English / Welsh / Northern Irish services
conduct site visits to all registered fish farming operations whilst the FRS depends upon mailed
questionnaires. However, all have the organisation, infrastructure and personnel to collect aquaculture
production and employment data.
As discussed in the previous section, the UK is currently opting towards linking the current production
surveys with economic data collected under Annual Business Inquiry. This has a number of important
cost advantages, including minimal additional IT (hardware and software) costs and relatively little change
to the existing labour costs involved. The only significant incremental cost item will be the need to
purchase data from the Office of National Statistics (e.g. sub-contracted work).
122
As with the other Member States, two cost scenarios have been generated, (i) a survey of the total
population and (ii) a survey of the ‘field of observation’ e.g. the population after application of a threshold
(see earlier section). The costs have been based upon the current FADN data collection costs in England
and Wales 21 as well as discussions with MFA 22 .
Table 17.2 UK, Estimation of costs (Euro)
Without threshold
Accounts
Accounts
available
not
available
Item description
With threshold 23
Accounts Accounts
available
not
available
Investment costs
•
Staff (training, etc.)
•
Hardware (computers, office equipment, etc.)
•
Software (data compilation and processing)
Total investment costs
10,000
10,000
5,000
25,000
4,000
4,000
2,000
10,000
10,000
10,000
5,000
25,000
-
Annual operational costs
•
Data collection (labour)
•
Data collection (other expenses)
•
Data processing (labour)
•
Data processing (other expenses)
•
Sub-contracted work
Total annual operational costs
12,000
5,000
30,000
10,000
10,000
67,000
4,000
2,000
15,000
4,000
25,000
12,000
5,000
30,000
10,000
10,000
67,000
-
17.5.
AVAILABILITY OF FUNDING
At present the UK utilises around 3-4 million Euro of EC funding related to work carried out under the
DCR. This represents around 50% of the total funding required for capture fisheries, aquaculture and
other fisheries sector DCR requirements. In terms of incremental funding for integrating economic data,
additional funding will be sought from the Commission as part of their overall DCR work programme,
with remaining costs coming from MFA funds. This will be justified on the basis that MFA will be using
the ONS work to help update the EFF statistics and indicators.
17.6.
PROBLEMS AND SOLUTIONS
17.6.1. Extrapolation of the sample to total population
A simple linear approach to extrapolate survey data to the whole population was required. However, for
all three segments it was important to first stratify the sampling universe and extrapolate within these
rather than using the entire sample population which in each case was highly heterogeneous.
This current exercise has highlighted a number of issues relating to the collection of financial and
employment data from the UK’s aquaculture industry. These are summarised briefly below:
1. Some aquaculture segments, in particular the Scottish salmon industry, have undergone intense
regulatory scrutiny over the past five years (particularly looking at cost structures) and are highly
resistant to further data collection exercises. Therefore it is important to integrate any additional
requirements into current data collection approaches and minimise the time and effort needed for
incremental reporting needs.
Andrew Woodend, Defra FBEU, pers. comm., Oct 2008
Kevin Williamson, MFA UK National Correspondent for the DCR, pers. comm., Oct 2008
23 Presumes that all businesses produce audited accounts
21
22
123
2. Whilst the salmon industry is mainly composed of larger, limited liability companies, the oyster and
trout and mussel producers are highly variable in size and management capacity. Many are smaller
companies – often sole traders or partnerships – with a restricted capacity to collect and a more
limited requirement to report financial data. Therefore the application of thresholds would still
capture the majority of production volume, but would eliminate a relatively large number of small
producers from the survey.
3. The amount of industry-level representation varies between segments. The trout segment in particular
is well represented and would support additional data collection if this resulted in a better
understanding of the needs of the segment, improved EU level and national planning and more
targeted fiscal and regulatory support. Others – such as shellfish – are more fragmented and thus are
less willing to encourage participation in data collection programmes.
4. In the UK, most aquaculture data collection is tied into fish health inspection programmes. This has
been a reasonable approach, in that the information covered – mainly production and movement
related variables – has mutual benefits. Furthermore participation has been voluntary, although due to
the direct visit approach, has resulted in near 100% returns. However the expansion into financial and
employment data collection will have institutional implications, with potential opportunities for
conflicts of interest.
Table 17.3 UK, Share or sample in value and volume of production of the total segment (without
threshold)
On-growing technique
Species
Population (no. firms)
Sample (no. firms)
Share of sample in total value of the segment (%)
Share of sample in total volume of the segment (%)
Cages
Ponds
Salmon
44
15
44%
44%
Trout
202
40
20%
20%
On
bottom
Oyster
94
10
85%
85%
On
bottom
Mussel
67
15
85%
85%
Rope
Mussel
58
15
85%
85%
Table 17.4 UK, Share or sample in value and volume of production of the total segment (with
threshold)
On-growing technique
Species
Population (no. firms)
Sample (no. firms)
Share of sample in total value of the segment (%)
Share of sample in total volume of the segment (%)
Cages
Ponds
Salmon
29
10
44%
44%
Trout
101
30
20%
20%
On
bottom
Oyster
19
5
80%
80%
On
bottom
Mussel
38
10
80%
80%
Rope
Mussel
17
5
71%
71%
17.6.2. Evaluation of individual indicators
Likelihood of a sufficient response
This study concurs with current MFA thinking that the collection of economic data through survey would
be both partially effective and expensive. As discussed earlier, the main problems arise from (i) the large
number of small producers, especially in the trout, oyster and bottom-grown shellfish segments that are
geographically dispersed and rarely record robust economic information on their operations and (ii) all
four segments, inc. salmon, would consider additional economic surveys to be an unjustified extra burden
on their operations. As a result, they are opting to link the current production surveys with existing data
collection under the Annual Business Inquiry.
This said, one finding of the present work was that engaging the cooperation of the segment’s
professional organisation could significantly boost survey returns. For instance the positive response from
the UK trout segment was largely due to the strong endorsement and encouragement provided by the
British Trout Association, which clearly recognised the benefits of obtaining financial indicators that
improved targeting of sectoral assistance (e.g. via EFF or BTAs own promotion activities).
124
17.6.3. Identification of specific problems
Again as indicated above, the proposed ABI approach is not without its challenges. These include:
• The IDBR is restricted to those businesses that are VAT and / or PAYE registered. As such it will
automatically exclude a large number of smaller businesses. However, as discussed in Section 3 above,
even when thresholds are applied such an approach will still capture the majority of the volume and
value of production. However, it may exclude businesses that might have social and community
importance, even if their wider economic contribution is negligible.
• The current the ‘Standard Industry Classification’ (SIC 2007) utilized by the ABI divides aquaculture
into two classes, saltwater aquaculture (03.21) and freshwater aquaculture (03.22), and whilst
identifying segments within these, does not attempt to disaggregate them. Therefore MFA would need
to develop a methodology for cross-linking the ABI data to the different segments.
• A number of variables required by the new DCR are not disaggregated by the ABI e.g. production
costs are simply ‘Total purchases of goods materials and services’, whilst the DCR requires a
breakdown of energy, livestock, feed, repair and maintenance and other operational costs. This again
is currently being analysed by the MFA and as possible candidates for consideration under separate
statistics exercises that already exist or extra questions, either in the ABI or by adapting the other
sources under the DCR.
Tables 4.1 to 4.5 show the sample mean, relative standard deviation and relative standard error for the five
segments surveyed. These are discussed on a per segment basis below.
Table 17.5 UK, Statistical indicators, Salmon in cages
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
35,822
2,939
27,668
15,814
12,876
21,619
97
128
114
145
122
125
135
116
33
29
38
32
32
35
30
8
8
2
2
23
0
3
6
27
2
33
6
0
1
2
7
1
9
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Total operational costs
Total capital costs
Personnel costs
Unpaid labour
Energy costs
Live raw material costs
Feed raw material costs
Repair and maintenance
Other operational costs
Relative costs composition
Level 1 – Aggregated costs as % of total costs
87
13
Level 2 – Op. costs as % of total operational costs
10
0
3
12
54
3
19
The salmon sector (Table 17.1) is characterised by a small number of producers that include three or four
very large producers and the remainder at a (comparatively) smaller scale, which creates the large relative
deviations found. Despite the scale differences, the production process and technology employed is
similar. Few report any income streams other than from salmon production. Feed costs are the most
significant operational cost and can amount to as much as two thirds of a producers operational costs.
The sample means appear to be broadly representative of the sector as a whole.
125
The statistical quality of the sample is reasonable compared to some of the other sectors surveyed as these
large companies submit full accounts on an annual basis. Variation in the absolute values will fluctuate
year on year due to the price of salmon and variation between companies due to company structure and
investment cycles.
Table 17.6 UK, Statistical indicators, Trout in ponds
Sample
mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
483
109
265
174
55
155
4
80
76
93
111
134
94
61
20
19
23
28
33
23
15
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
92
5
Total capital costs
8
5
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
27
51
Unpaid labour
0
Energy costs
5
83
Live raw material costs
13
147
Feed raw material costs
35
65
Repair and maintenance
4
62
Other operational costs
18
82
1
1
13
21
37
16
15
21
The trout sector generally operates at a far smaller scale than the salmon sector and in a more labour
intensive way. This is reflected in personnel costs representing 27% of total costs compared to 10% for
salmon. Companies within the trout sector appear to operate in very different ways to each other with
stocking from hatcheries showing the largest cost variations. This may be a result of some operating
hatcheries themselves for restocking and table production. The income base of trout producers is more
varied with the potential to sell for the table, sell for restocking rivers and lakes, with a number also
operating recreational fisheries. The trout sample presented the most consistent statistical results of all the
surveys undertaken, resulting from a good standard and level of response backed-up by association
endorsement.
126
Table 17.7 UK, Statistical indicators, Oysters /on bottom culture
Sample
mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
1,709
1,408
307
2,102
694
594
55
474
93
55
80
54
272
96
335
66
39
57
39
193
68
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
86
4
Total capital costs
14
4
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
27
30
Unpaid labour
0
0
Energy costs
3
52
Live raw material costs
15
97
Feed raw material costs
0
0
Repair and maintenance
7
82
Other operational costs
40
85
3
3
21
0
37
68
0
58
60
The statistical results of the oyster sample are highly variable due to a small sample size and one
respondent showing a high degree of diversification outside of production. This company remains one of
the largest oyster producers within a sector characterised by owner-operators, but 91% of total income
was from restaurant and retail sales rather than oyster production. This accounts for the extremely large
relative standard deviation and errors observed.
Table 17.8 UK, Statistical indicators, Mussels on rope
Sample
mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard error
(%)
275
66
141
172
106
68
5
34
78
11
62
148
100
23
24
55
8
44
105
71
17
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
80
10
Total capital costs
20
10
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
25
66
Unpaid labour
8
100
Energy costs
21
40
Live raw material costs
0
0
Feed raw material costs
0
0
Repair and maintenance
15
29
Other operational costs
0
0
7
7
46
71
28
0
0
21
0
127
Tables 17.4 and 17.5 present the means and statistical results from the mussel sector. The statistical quality
is poor due to the small sample sizes, but the means do broadly reflect the sub-sector averages. Both subsectors benefit from no purchase of stock or feed costs as they are reliant on natural spatfall and naturallyoccurring feed. They do, however, incur energy (rope) and ‘other operational costs’ (bottom) through the
use of vessels to tend to lines (rope) or gather and relay spat (bottom). Repair and maintenance costs are
the largest in proportion of operational costs for the mussel sector primarily due to this reliance on
vessels.
Rope production is undertaken on a variety of scales, as indicated by the variation in persons employed,
but employing very similar processes and technologies. The most variable value is seen to be cash flow
followed by total assets. Only one respondent included a value for unpaid labour; this may result from a
differing interpretation of inputs by owner/operators i.e. the inclusion of work over and above FTE
hours.
Table 17.9 UK, Statistical indicators, Mussels - on bottom
Sample mean
(1000 Euro
or %)
Relative
standard
deviation
(%)
Relative
standard
error
(%)
2,720
368
882
2,271
1,904
4,043
7
64
59
25
33
39
57
29
46
42
18
23
28
40
20
Absolute values
Total turnover (incl. other income)
Personnel costs (excl. unpaid labour)
Operational costs (excl. labour)
Gross value added
Gross cash flow
Total assets
Engaged persons
Relative costs composition
Level 1 – Aggregated costs as % of total costs
Total operational costs
74
4
Total capital costs
26
4
Level 2 – Details of operational costs as % of total operational costs
Personnel costs
35
3
Unpaid labour
0
0
Energy costs
10
36
Live raw material costs
0
0
Feed raw material costs
0
0
Repair and maintenance
20
30
Other operational costs
20
30
3
3
2
0
26
0
0
21
21
17.6.4. Cross check with other sources
The Eurostat figures for UK salmon production in 2006 are 131,973 tonnes, some 126t more than the
UK production figures from FRS & industry sources. This is very close to UK reported figures, and the
small discrepancy may be due to inaccurate reporting of the small amount of salmon production from
Northern Ireland, which represents a very small fraction (<0.05%) of total production.
The Eurostat figures for UK trout production in 2006 are 13,458 tonnes, some 3,242t less the UK
production figures from FEAP and UK industry sources. The major part of this discrepancy is the 3,100t
produced for restocking which are excluded from the Eurostat figures.
Employment estimates generated by the study were 10% higher than those reported by Cefas and FRS in
their annual surveys and is due to sampling error (our sample was 20% of total number of companies
whilst Cefas / FRS are usually 100%).
128
The Eurostat figures for UK oyster production in 2006 are 730 tonnes, just under half of the UK
production figures from FEAP and UK industry sources. However the latter organisations also include
production from the Channel Islands - where Jersey produced 730t in 2006 (Eurostat data), thus bringing
the two estimates to parity.
The Eurostat figures for UK mussel production in 2006 are 14,711 tonnes, just over half the UK
production figures reported by CEFAS. The discrepancy between the two figures is unknown, but one
contributory factor may be the recording of depurated product from B and C grade waters. A proportion
of UK mussel production is sold to the Netherlands and elsewhere prior to depuration and subsequent
sale. This product flow may not be captured by the Eurostat statistics for UK and instead may form part
of Dutch production.
129