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DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Indicator 5b - organic farm incomes (R)
Indicator Definition
The overall focus of this indicator is the relative financial
performance and viability over time of organic farms
(which use practices and systems designed to achieve
high environmental outputs), on the basis that financial
viability is a key determinant of both uptake of and
continued organic management. The indicator is split into
two parts:
5a: organic producer prices and premiums premiums
relative to conventional prices, and/or market share/retail
sales value (to indicate levels of consumer demand for
organic products and market signals to organic producers)
5b: organic farm incomes compared to similar
conventional farms (to indicate combined impacts of
prices, agri-environmental support payments and other
factors on financial viability of organic holdings)
This fact sheet is focused specifically on Indicator 5b:
Organic Farm Incomes.
Indicator links
Input Indicator Links:
No. 1 ‘Agri-environmental support’
Output Indicator Links:
No. 7 ‘Organic land area’,
No. 8 ‘Fertiliser consumption’,
No. 13 ‘Cropping/livestock patterns’,
No.14 ‘Management practices’, and
No. 15 ‘Intensification/extensification’
Key message
The relative financial performance and long-term viability of organic farming is a key
consideration both for producers considering conversion to organic farming and for policy makers.
Farm income in absolute terms and relative to conventional farms provides the most important
indicator for this, but it is important to understand the contribution that key components (including
prices, yields, costs and support payments) make to this value.
Further modifications to FADN methodology may be required to ensure data reliability.
The farm income results presented are taken from studies conducted during the 1990s as
reviewed by Offermann and Nieberg (2000) and are based primarily on national studies. These
show that organic producers were in general terms able to achieve similar incomes to
conventional producers, but this is not consistent between countries and farm types.
An analysis of results up to 2001/2002 is currently in progress and results will be included in the
final draft of this fact sheet. The purpose of including the older data is to illustrate for discussion
the types of data available and possible formats for presentation.
The issues raised in this DRAFT factsheet will be revisited during a seminar on organic farming
statistics organised as part of the European Information System for Organic Farming (EISFOM)
Concerted Action seminar in Berlin, 26-27 April 2004 (see www.eisfom.org for details). A working
group in the seminar is specifically addressing farm income issues.
Headline graphs and maps
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DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
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Figure 5-1: Profits (family farm income) per ha utilisable agricultural area (UAA) and per
family work unit (FWU) of organic farms relative to comparable conventional farms in
different countries, all farm types (sample averages), 1992-1997
Source: Offermann & Nieberg, 2000 - for data see Table 5.4
The analysis of this earlier data indicates that although organic crops received higher price
premiums than livestock, the incomes of organic crop producers was not always as high relative
to conventional as for livestock producers. However, the development of the market for organic
livestock products is relatively recent, and it is expected that the performance of organic livestock
systems will show an improvement, while the situation for crop producers may show some
deterioration. It should also be noted that some of the studies recorded here were carried out
before or in the early stages of the introduction of agri-environmental support payments for
organic producers, so that the effects of these are not necessarily included.
Methodological Approach
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DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Introduction
Organic agriculture can be defined as production system which puts the highest emphasis on
environmental protection and animal welfare by reducing or eliminating the use of synthetical
chemical inputs such as fertilisers, pesticides, growth promoters/regulators and GMOs, and by
using cultural and agro-ecosystem management practices to achieve production, health and
sustainability objectives. The environmental benefits of this approach to agriculture are now well
documented (e.g. Stolze et al., 2000; DEFRA, 2003), so that an economically sustainable
expansion of organic farming can be seen to have direct benefits across a wide range of
environmental issues.
The aim of the indicator is to identify underlying economic factors (“driving forces") that are
behind the development of organic farming in the EU by influencing the decision of farmers to
start or to continue an organic production system. This relates in particular to the income they can
receive from the production and marketing of organic products and the potential for incomes to be
sustained in the longer-term.
The potential growth in EU organic production can be attributed to a combination of supply and
demand side factors. The organic market currently provides one of the few ways through which
consumers can express preferences for more environmentally friendly agricultural practices, even
if health concerns appear to be very important too. The price difference between conventional
and organic products therefore indicates the consumers’ willingness to pay higher prices for
organic products. Thereby, producers are given signals from the market via the price premium
that they should change production and management. But there are several other factors
influencing relative prices for organic and conventional foods, which limit the value of the price
premium as an indicator. A potential alternative indicator is the absolute value and relative market
share of organic farming products in total food sales. A market share indicator would have the
advantage that the trends would not fluctuate as much as price indicators while also indicating the
total level of consumer interest in organically farmed products. While the price indicator is
considered in further detail in this document, it is strongly recommended that the market share
indicator is developed as an alternative for the reasons discussed below.
While the price received is an important component of income and therefore affects the relative
viability of organic farming, incomes are also significantly affected by yields, production costs and
support payments. Prices and support payment levels may provide a key stimulus to farmers
converting, but the actual incomes generated over time are likely to influence whether farmers
remain in organic production. Therefore, income is the most important indicator, but its
interpretation requires an understanding also of the underlying components.
Methods and tools
Datasets relating to organic farming are generally at an early stage of development and cannot
be considered to be comprehensive across all EU member states. Only since 2000 have pan-
European datasets started to become available through FADN, the Farm Structure Survey and
EC Reg. 2092/91 reporting (see IRENA Indicator 7) and the availability and reliability of this data
still varies markedly from country to country. Most of the currently available data has been drawn
instead from national level studies collated as part of EU-funded research programmes on
organic farming policy and organic marketing initiatives (see Data Sources below). Many of these
studies, by their nature, are time limited and therefore cannot provide a basis for long-term, time
series data. However, the new pan-European initiatives do provide the potential to address this
problem and should provide a better basis for implementation of this indicator in future years.
Until recently relevant comparable farm income data can only be found in some national data
collection systems (see below for further information on data sources). Moreover, it is difficult to
make direct comparisons about economic profitability relative to conventional systems, with the
selection of the comparable conventional farms having a large influence on results. More
promising is the development of a pan-European approach to data collection though the Farm
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DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Accountancy Data Network (FADN). The use of the FADN also enables a stratification of results
by farm type as well as the selection of comparable conventional farms.
The aim of FADN is to gather accountancy data from farms for the determination of incomes and
business analysis of agricultural holdings. Currently, the annual sample covers approximately
60,000 (conventional and organic) holdings. They represent a population of about 4,000,000
farms in the 15 Member States, which cover approximately 90% of the total utilised agricultural
area (UAA) and account for more than 90% of the total agricultural production of the Union. To
ensure that this sample reflects the heterogeneity of farming before the sample of farms is drawn,
Liaison Agencies stratify the field of observation according to three criteria: region, economic size
1
and type of farming . Farms are selected in the sample according to a selection plan that
2
guarantees its representativeness . An individual weight is applied to each farm in the sample,
this corresponding to the number of farms in the 3-way stratification cell of the field of
observations divided by the number of farms in the corresponding cell in the sample. However,
there are some specific problems that need to be resolved if the FADN database (or similar
national databases) is to be used reliably:
2.1 Identification of organic holdings: Since 2000, an identifier variable for organic holdings has
been included in the FADN system which is intended to indicate whether the holding/land
area is either a) organic or b) in-conversion or part conventional/part organic and the
resulting total. The availability of data resulting from this indicator is summarised below, but
particular questions arise concerning the reliability of data thus derived:
a) how is organic or in-conversion status defined? Is it left to self-identification by the
farmer, with a risk that holdings claim to be organic when they are not, or is it based on
certification in accordance with EU-Regulation 2092/91? In some cases, producers may be
familiar only with their specific certification/inspection organisation and not with the
underlying regulations, but the question may not be clear enough to ascertain this. In
others, producers may be organic but market their products conventionally – this applies
particularly in the situation of policy-support but uncertified organic production, for example
in Sweden, but is also common in situations where markets are undeveloped or over-
supplied.
b) if a holding is part-organic/part-in-conversion, or part-organic/part-conventional, how are
the different enterprises and costs separated – is the indicator applied to each production
enterprise separately? Alternatively, should a minimum organic proportion threshold be
defined, and should this be based on land area or a financial measure such as standard
gross margin? What happens in the case of larger holdings consisting of separately
managed units, one or more of which is actually fully organic? The potential complexity of
this is illustrated by the six Italian codes for organic farming: 1) partially organic, fully
converted, 2) partially organic, fully converting; 3) fully organic, partly converted, partly
converting; 4) fully organic, fully converted; 5) fully organic, fully converting and 6) partially
organic, converting or fully converted.
2.2 Obtaining a representative sample: Organic farms occur in the FADN sample as part of the
broader selection of farms to meet the farm type, size and region requirements of FADN –
there is no specific methodology in place to ensure that any organic sample thus derived is
representative of organic farms overall. In countries such as DK, AT, SE, where organic
holdings represent a higher proportion of farms (but still typically around 10%), there may
be a large enough pool of holdings that this problem is less serious or can be addressed
directly, but in most countries that will not be the case. Therefore in order to ensure that a
1
Stratification in the FADN is used to increase sampling efficiency (i.e. to minimise the number of farms
required to represent the variety of farms in the field of observation). The Commission makes extensive use
of this technique and uses three criteria for stratification: region, economic size and type of farming.
2
The FADN covers the agricultural holdings having an economic size equal to, or greater than, a threshold
expressed in European size units (ESU). This threshold is not the same in all Member States. However, at
least 90 % of agricultural production should be included in the FADN field of survey.
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DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
representative sample is obtained, it is likely to be necessary for supplementary holdings to
be recruited at national level, and it may be necessary to consider whether organic
management should be made a stratification criterion within the FADN system. With
current sample sizes, it is unlikely to be possible to achieve a reliable, representative
sample to permit breakdown of data to NUTS 2 or 3 level, but NUTS 1 may be possible in
some countries.
2.3 Defining farm type/size: Within FADN, types of farming are defined in terms of the relative
importance of the different enterprises on the farm. Specialisation is determined on the
basis of the contributions of the different lines of production to the total standard gross
margin (SGM). To determine the total standard gross margin, coefficients established at
the level of the different regions of the Union for the different lines of productions are taken
as a basis: e.g. standard gross margin for one hectare of wheat or for one dairy cow. For
each holding, the number of hectares of wheat or dairy cows is multiplied by the
corresponding coefficients and the total SGM is calculated. The standard gross margin
coefficients are calculated at regular intervals and correspond to three-year averages. The
coefficients used for SGM calculations are based on conventional farming practice, which
limits their usefulness for the classification of organic farms. This may be particularly
problematic for specialist horticultural holdings which might be classified as to small to be
included in the FADN statistics on the basis of conventional gross margins, but due to the
higher value of organic production, would justify inclusion as a larger sized business.
Another related problem arises as farms progress through conversion, as their
classification might change from specialist to mixed holdings of a particular type, because
for example stock numbers are reduced, even though the financial value of the enterprise
remains the same. However, as separate SGM’s for organic farming are not yet available,
the farm type classification of the EU FADN can be used as an approximation of the farm
type and business of organic farms.
2.4 Selecting comparable conventional farms: It is necessary to ensure that any comparative
data used is genuinely comparable. It is not sufficient simply to compare the average for
the organic farms with the average for all farms in the FADN sample, as the composition in
terms of type, size and locality may be very different. This is an issue that has been
discussed at length by both Lampkin and Padel (1994) and Offermann and Nieberg (2001).
In order to focus attention on differences in performance arising from differences in the
management system, comparable farms should be similar in terms of production potential
or resource endowment (land quality/area, farm type, region, capital infrastructure (e.g.
buildings, quotas) as well as management capacities of the producer. Other inputs,
including labour, need not be similar as they will reflect the decisions made about
production intensity and how the fixed resources are used for specific activities to achieve
the desired objectives. Some studies have taken the average for the organic group and
compared this with the most closely fitting sub-sample of FADN data, but this is also not
appropriate as the ‘average’ organic farm may be mix of several different and unrelated
farm types. An alternative approach is to select paired organic and conventional farms to
create a similar group of both types, but this can lead to some significant imbalances in the
case of management ability, which is more difficult to account for in pairs.
The authors’ preferred approach is to select a group of similar conventional farms to
compare with each individual organic farm, so that the impact of differences in
management ability can be minimised. These data can be grouped for comparison
purposes (the average for the organic group being compared with the average of the
averages for the selected conventional comparison groups. The selection of the
comparison groups may be done on the basis of clustering (by minimising Euclidean
distance) or by selecting groups of farms that fall within a specified range of values for
defined parameters. Both approaches have been applied by the Institute of Rural Sciences
and FAL Braunschweig teams in their national level studies, the latter proving easier in
terms of implementation.
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DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
The key variables that have been identified for group selection are:
• same region (e.g. NUTS1 or other statistical region used for national data)
• same altitude zone
• same (not) less favoured (mountain) area status
• same farm type (using FADN typology)
• similar size in hectare UAA (+/- 15-20%)
• similar milk quota/milk production (+/- 15-20%)
• given the issue of using conventional SGMs for organic farms, it is debatable whether
grouping according to similar business size is also relevant – this variable is included by
IRS Aberystwyth, but not FAL Braunschweig.
The methodological issues outlined above are currently being reviewed as part of a concerted
action project ‘European information system for organic markets’ (EISfOM, QLK5-2002-02400,
www.eisfom.org); results of a country by country survey are expected to be available late April
and will be discussed at a seminar on organic farming statistics due to be held in Berlin, 26-27
April 2004. The results of this discussion will be reflected in the final draft of this fact sheet.
Data sources
Financial data relating to organic farming has only recently started to become the focus of
attention for governments and researchers, with the situation varying significantly from country to
country. There have been two international reviews of the financial performance. Lampkin and
Padel (1994) presented results from a number of studies from different countries in the 1980s and
early 1990s. At this stage, most studies were one-off research projects, covering one to three
years’ data. Only Switzerland and Germany had started to collate data from their official FADN
sources. During the 1990s, other countries with significant organic farming sectors, such as
Austria and the Netherlands, started to generate data from official FADN systems, while others,
such as the UK and Denmark, continued to rely on special studies. Offermann and Nieberg
(2000) analysed and reviewed the organic farming data available from FADN and other sources
from the mid-1990s as part of an EU-funded research project ‘Organic Farming and the CAP’
(OFCAP, contract no.), some of the results are presented below.
3
Since 2000 , there has been an effort to gather more data on organic farming from FADN at the
European level. All member states have been asked to identify which of their FADN holdings are
organic or in-conversion, although there are some potential problems with the indicator variability
as discussed above. A preliminary analysis of the data availability and specifically the issue of
support payment levels on conventional and organic farms was conducted by Offermann as part
of a report for DG Environment (Haering et al. 2004). Since the accounting year had started in
almost all member states when the respective Commission Regulation 1122/2000 entered into
force, this identification code was not yet available for all member states. Table 5-1 provides an
overview on the availability of the organic farming identification code for 2000; the situation in
later years is expected to improve significantly. For confidentiality reasons, results may be
published only for farm samples containing at least 15 farms. Table 5-2 provides an overview of
the respective sub-samples available in the FADN accounting year 2000. On an 'EU'-level4, the
samples are large enough to allow an analysis for most farm types, but it is more problematic at a
regional or national level.
In the near future (mid 2004), the FADN data for 2001 from selected countries (see Table 5.3) will
be analysed by Offermann et al. at FAL Braunschweig as part of the EU-funded research project
‘Further development of organic farming policies in Europe’ (EU-CEE-OFP, QLK5-2002-00917).
The results from this analysis will be integrated in the final version of this fact sheet.
3
The actual time period covered differs by member state, as accounting years are defined according to
national standards. See European Communities (2003a) for details.
4
Here and in the following paragraphs, 'EU'-results are referring to the results based on the ten countries
where organic farms can be identified in the accounting year 2000.
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DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Table 5-1: Identification of organic farms in the FADN accounting years 2000, 2001
AT BE DE DK ES FI FR GB GR IE IT LU NL PR SE
FADN 2000 X (x) X x X x (x) (x) x x
FADN 2001 X ? X x X x ? X ? ? x x
National A B c d e f g ?
(x): sample too small for publication
a: BMLF Gruener bericht, since 1995
b: BMVEL Agrarbericht, since 1981
c: DIAFE, since 1996
d: AERI: arable, dairy since 1996
e: England and Wales, Institute of Rural Sciences, University of Wales, Aberystwyth see
www.organic.aber.ac.uk from 1995/1996 to 2003/04
f: INEA: since 1997
g: BIN-LEI: arable from 1990, dairy from 1995
DK and AT also provide detailed information on off-farm income and household finances
National time series data is also available for CH
Sources: FAL (Haering et al., 2004); LEI (TAPAS); IRS (EISFOM)
Table 5-2: Number of organic farms in the FADN accounting year 2000
Farm types EU15 AT BE DE DK ES FI GB LU NL PT
All 645 316 11 127 75 25 58 9 1 7 16
Arable 110 29 30 15 11 17 5 3
Horticultural 18 6 9 2 1
Wine 5 2 1
Perm. Crops 22 3 2 1 10 6
Dairy 316 200 4 41 42 19 6 1 1 2
Graz.livestock 80 51 6 7 2 8 3 1 2
Pigs/Poultry 2 1 3
Mixed 85 26 1 39 5 2 10 2
Samples with at least 15 farms are highlighted by bold figures.
Source: FAL Braunschweig (Haering et al., 2004)
Table 5.3: Basis of data to be analysed in EUCEEOFP project (combining national
and FADN sources) – income results expected by June 2004
AT CH DK DE IT UK (England & Wales)
overall Good good Good Good good limited
1996 80 216 41
1997 210 119 250 221 41
1998 422 291 153 237 257 41
1999 449 320 173 215 620 reduced sample only
2000 440 339 216 248 921 reduced sample only
1181
(of which
2001 410 322 281 279 (67 IRS + 100 FADN)
367 fully
organic)
Available avail.
2002 402 334 (67 IRS + ca. 150 FADN)
spring 2004 soon
Source: FAL Braunschweig (pers. comm.)
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DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Results
Describe the results (ranges, means, trends, spatial dimension and anomalies)
A brief comment on the results presented as been included with the headline graph above. A full
description of the results will be prepared once some of the analysis in progress has been
completed, as the currently available data sets to not permit either comment on trends over time
or on the implications of recent growth in the organic farming sector.
References
Häring, A. M.; Dabbert, S.; Aurbacher, J.; Bichler, B.; Eichert, C.; Lampkin, N.; Tuson, J.; Olmos,
S.; Offermann, F.; Zanoli, R.; Gambelli, D. (2004) Impact of CAP Measures on
Environmentally Friendly Farming Systems. Report for DG Environment, European
Commission. To be published as: Organic Farming and Measures of European Agricultural
Policy. Organic Farming in Europe: Economics and Policy, Volume 11. University of
Hohenheim, Stuttgart.
Lampkin, N. H. and S. Padel (eds.) (1994): The Economics of Organic Farming – an international
perspective. CAB International; Wallingford.
Offermann, F. and Nieberg, H. (2000): Economic performance of organic farms in Europe.
Organic Farming in Europe: Economics and Policy, Vol. 5, University of Hohenheim, Stuttgart.
Stolze, and other references to be completed
At the Community level: Regulations 2092/91 and 1804/1999: 1) http://europa.eu.int/eur-
lex/en/consleg/main/1991/en_1991R2092_index.html and 2)
http://europa.eu.int/servlet/portail/RenderServlet?search=DocNumber&lg=en&nb_docs=25&doma
in=Legislation&coll=&in_force=NO&an_doc=1999&nu_doc=1804&type_doc=Regulation
Data
Format, title and location of data files
A spread sheet containing all available data will be provided with the final draft of this fact sheet
as some of the data is still being analysed and key methodological issues identified above need
to be resolved.
Data samples are supplied below, including data used to create the header graphs and maps.
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DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Table 5.4: Profits (family farm incomes) of organic and comparable conventional farms
(No recent data on farm profits was available for BE, ES, F, IE, PT, GR). Source: Offermann and Nieberg, 2000
Austria
Profit in ECU per ha UAA Profit in ECU per FWU
Reference Farm type Year of No of farms in Organic Comp. conv. % 1 Organic Comp. conv. % 1
observation sample farms farms farms farms
BMLF (1995) average of different frmtypes 1994 168 1138 1006
BMLF (1995) >40 % of standard grss mrgin 1994 26 1181 1208 98 9136 9655 95
from cropping
BMLF (1996) average of different farm types 1995 240 1361 13252
BMLF (1996) >40 % of standard gross 1995 27 1223 1135 108 13364 13792 97
margin from cropping
BMLF (1997) average of different farm types 1996 348 1290 12241
BMLF (1997) >40 % of standard gross 1996 27 1129 1239 91 12051 10573 114
margin from cropping
1
as a percentage of comparable conventional farms
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DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Denmark
Profit in ECU per ha UAA Profit in ECU per FWU
Reference Farm type Year of No of farms in Organic Comp. conv. % 1 Organic Comp. %1
observation sample farms farms2 farms conv. farms
per hour
DIAFE (1998) all farms 1996/97 80 665 (818) (81) 16.50 (20.95) (79)
DIAFE (1998) average of arable and dairy farms 1996/97 60 728 768 95 18.69 16.03 117
DIAFE (1998) Arable 1996/97 26 497 376 132 10.42 7.94 131
DIAFE (1998) Dairy 1996/97 34 843 949 89 24.34 19.73 123
1 2
as a percentage of comparable conventional farms Figures in brackets refer to comparisons with all conventional farms (as opposed to comparable
conventional farms).
Finland
Profit in ECU per ha UAA Profit in ECU per FWU
Reference Farm type Year of No of farms in Organic Comp. conv. % 1 Organic Comp. conv. % 1
observation sample farms farms farms farms
per hour
AERI (1996) average of different farm types 1995 22 671 807 83 7.12 7.55 94
AERI (1996) Arable 1994 5 399 510 78
AERI (1997) Arable 1995 6 412 475 87 7.63 10.55 72
Expert estimate Arable 1996 316 373 85
AERI (1996) Dairy 1994 10 787 1089 72
AERI (1997) Dairy 1995 16 766 937 82 7.02 7.15 98
Expert estimate Dairy 1996 636 789 81
1
as a percentage of comparable conventional farms
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DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
France
Profit in ECU per ha UAA Profit in ECU per FWU
Reference Farm type Year of No of farms in Organic Comp. conv. % 1 Organic Comp. conv. % 1
observation sample farms farms farms farms
Trouilloud (1990) average of different farm types 1988 2 601
Trouilloud (1990) average of different farm types 1990 3 400
1
as a percentage of comparable conventional farms
Germany
Reference Farpe Year of Comp. Organi Co %1
observation conv. c farms mp.
of farms in farms2 v.
sample far
ms
BMELF (1994) average of different farm types 1992/93 1
BMELF (1995) average of different farm types 1993/94 1
BMELF (1996) average of different farm types 1994/95 1
BMELF (1997) average of different farm types 1995/96 1
BMELF (1998) average of different farm types 1996/97 1
LBA (1997) average of different farm types 1995/96
LBA (1998) average of different farm types 1996/97
Landwirtschaftskammer Westfalen-Lippe (1998) average of different farm types 1996/97
Nieberg (1997) average of different farm types 1992/93 1
Nieberg (1997) average of different farm types 1993/94 1
Nieberg (1999) average of different farm types 1994/95
Nieberg (1999) average of different farm types 1995/96
Germany (continued)
Profit in ECU per ha UAA Profit in ECU per FWU
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DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Reference Farm type Year of No of farms in Organic Comp. conv. %1 Organic Comp. conv. %1
observation sample farms farms2 farms farms
Zerger (1995) average of different farm types 1988/89 24 394 16845
Zerger (1995) average of different farm types 1989/90 35 448 19795
Zerger (1995) average of different farm types 1990/91 32 387 15140
Zerger (1995) average of different farm types 1991/92 20 404 16956
Zerger (1995) average of different farm types 1988-1992 60 411 17347
Nieberg (1997) Arable 1992/93 39 571 388 147 34893 18905 185
Nieberg (1997) Arable 1993/94 39 553 304 182 34116 15152 225
Nieberg (1999) Arable 1994/95 22 1452 645 225 100806 39249 257
Nieberg (1999) Arable 1995/96 22 1292 711 182 96776 44320 218
Köhne and Köhn (1998) Arable 1995 4 589
Köhne and Köhn (1998) Arable 1996 4 642 52081
Zerger (1995) Arable 1988/89 ca. 12 175 10724
Zerger (1995) Arable 1989/90 ca. 17 381 21955
Zerger (1995) Arable 1990/91 ca. 16 376 14413
Zerger (1995) Arable 1991/92 ca. 10 295 14706
Zerger (1995) Arable 1988-1992 23 332 16385
Stolze (1998) Arable 1994 6 554 254 218 220078 66216 332
continued on next page
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DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Germany (continued)
Profit in ECU per ha UAA Profit in ECU per FWU
Reference Farm type Year of No of farms in Organic Comp. conv. % 1 Organic Comp. conv. % 1
observation sample farms farms2 farms farms
Stolze (1998) Dairy 1994 10 182 198 91 35352 26183 135
Nieberg (1997) grazing livestock (mainly 1992/93 61 563 540 104 14170 14543 97
dairy)
Nieberg (1997) grazing livestock (mainly 1993/94 61 594 429 139 15273 11965 128
dairy)
Nieberg (1999) grazing livestock (mainly 1994/95 32 1313 855 154 32528 25904 126
dairy)
Nieberg (1999) grazing livestock (mainly 1995/96 32 1119 808 138 30060 24983 120
dairy)
Köhne and Köhn (1998) grazing livestock 1995 6 433
Köhne and Köhn (1998) grazing livestock 1996 6 325 36630
Zerger (1995) grazing livestock 1988/89 app. 12 484 19365
Zerger (1995) grazing livestock 1989/90 app. 17 516 17636
Zerger (1995) grazing livestock 1990/91 app. 16 401 16090
Zerger (1995) grazing livestock 1991/92 app. 10 606 21134
Zerger (1995) grazing livestock 1988-1992 36 491 18310
Nieberg (1997) pigs and poultry 1992/93 5 671 452 148 21958 12040 182
Nieberg (1997) pigs and poultry 1993/94 5 1084 142 761 33380 4115 811
1 2
as a percentage of comparable conventional farms Figures in brackets refer to comparisons with all conventional farms (as opposed to comparable
conventional farms).
13
DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Great Britain
Profit in ECU per ha UAA Profit in ECU per FWU
Reference Farm type Year of No of farms in Organic Comp. conv. % 1 Organic Comp. conv. % 1
observation sample farms farms farms farms
Fowler, Lampkin and average of different farm types 1995/96 38 NFI: 306 362 84
Midmore (1998)
Fowler, Lampkin and average of different farm types 1995/96 38 ONI: 345 424 81
Midmore (1998)
Murphy (1992) average of different farm types 1989 117 58
Fowler, Lampkin and Arable 1995/96 6 NFI: 428 324 132
Midmore (1998)
Fowler, Lampkin and Arable 1995/96 6 ONI: 473 422 112
Midmore (1998)
Murphy (1992) Arable 1989 8 -179
Fowler, Lampkin and Dairy 1995/96 6 NFI: 659 661 100
Midmore (1998)
Fowler, Lampkin and Dairy 1995/96 6 ONI: 576 672 86
Midmore (1998)
Haggar and Padel Dairy 3rd year after 10 ONI: 145
(1996) conversion
Haggar and Padel Dairy 3rd year after 10 NFI: 313 497 63
(1996) conversion
Haggar and Padel Dairy 3rd year after 10 MII: 183 377 49
(1996) conversion
continued on next page
14
DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Great Britain (continued)
Profit in ECU per ha UAA Profit in ECU per FWU
Reference Farm type Year of No of farms in Organic Comp. conv. % 1 Organic Comp. conv. % 1
observation sample farms farms farms farms
Haggar and Padel (1996) dairy 4th year after 10 219
conversion
Haggar and Padel (1996) dairy 4th year after 10 NFI: 347
conversion
Haggar and Padel (1996) dairy 4th year after 10 MII: 190
conversion
Murphy (1992) dairy 1989 8 124
Lampkin and Batemen (1993) mainly dairy (Wales) 1989 6 470
Lampkin and Batemen (1993) mainly dairy (Wales) 1989 6 NFI: 606 273 222
Fowler, Lampkin and Midmore horticulture 1995/96 5 NFI: 1696 2994 57
(1998)
Fowler, Lampkin and Midmore horticulture 1995/96 5 ONI: 1806 3093 58
(1998)
Murphy (1992) horticulture 1989 61 NFI: 310
Fowler, Lampkin and Midmore grazing livestock 1995/96 12 NFI: -60 227
(1998)
Fowler, Lampkin and Midmore grazing livestock 1995/96 12 ONI: 10 241 4
(1998)
Lampkin and Batemen (1993) grazing livestock 1989 5 -48 0
Lampkin and Batemen (1993) grazing livestock 1989 5 NFI: 109 48 228
continued on next page
15
DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Great Britain (continued)
Profit in ECU per ha UAA Profit in ECU per FWU
Reference Farm type Year of No of farms in Organic Comp. conv. % 1 Organic Comp. %1
observation sample farms farms farms conv. farms
Fowler, Lampkin and Midmore mixed farms 1995/96 9 NFI: 245 333 74
(1998)
Fowler, Lampkin and Midmore mixed farms 1995/96 9 ONI 296 402 74
(1998) :
Murphy (1992) mixed farms 1989 39 42 0
1
as a percentage of comparable conventional farms
The Netherlands
Profit in ECU per ha UAA Profit in ECU per FWU
Reference Farm type Year of No of farms in Organic Comp. conv. % 1 Organic Comp. conv. % 1
observation sample farms farms farms farms
Dutch FADN average of different farm types 1995 30 1744 1187 147 41089 30630 134
Dutch FADN Arable 1995 7 1931 1006 192 46859 42327 111
Dutch FADN Dairy 1995 12 1356 1481 92 38010 28505 133
Dutch FADN horticulture 1995 6 3657 784 466 60472 15872 381
Dutch FADN mixed farms 1995 5 1235 1303 95 23169 38234 61
1
as a percentage of comparable conventional farms
16
DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Italy
Profit in ECU per ha UAA Profit in ECU per FWU
Reference Farm type Year of No of farms in Organic Comp. conv. % 1 Organic Comp. conv. % 1
observation sample farms farms farms farms
Zanoli, Fiorani and average of different farm types 1994 28 502 405 124 8139 5511 148
Gambelli (1998)
Zanoli, Fiorani and average of different farm types 1995 28 654 544 120 11412 7631 150
Gambelli (1998)
Zanoli, Fiorani and average of different farm types 1996 28 720 12146
Gambelli (1998)
Chiorri and Santucci average of different farm types 1996 30 525 23136
(1997)
Zonin (1996) average of different farm types 1990 47 2980
Zonin (1996) average of different farm types 1990 12 3135
Piani (1995) average of different farm types 1990 30 2642
Salghetti (1997) Dairy 1995 33 1412 2898 49 30193 48040 63
Santucci and Chiorri mixed farms 1992 19 530 13243
(1996)
Santucci and Chiorri mixed farms 1993 19 429 11712
(1996)
Santucci and Chiorri mixed farms 1994 19 491 11339
(1996)
Santucci and Chiorri mixed farms 1992-1994 19 482 638 75 12613 8210 154
(1996)
Furnari (1994) citrus farms 1991-1993 15 14596
1
as a percentage of comparable conventional farms
17
DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Luxembourg
Profit in ECU per ha UAA Profit in ECU per FWU
Reference Farm type Year of No of farms in Organic Comp. conv. % 1 Organic Comp. conv. % 1
observation sample farms farms farms farms
Expert estimate Arable 1997 493
Expert estimate Dairy 1997 740
Expert estimate grazing livestock 1997 740
Expert estimate mixed livestock 1997 1234
Expert estimate mixed farms 1997 666 508 131
1
as a percentage of comparable conventional farms
Norway
Profit in ECU per ha UAA Profit in ECU per FWU
Reference Farm type Year of No of farms in Organic Comp. conv. % 1 Organic Comp. conv. % 1
observation sample farms farms farms farms
Vittersø (1995) Dairy 1989-1992 11 1909 2026 94
1
as a percentage of comparable conventional farms
Sweden
Profit in ECU per ha UAA Profit in ECU per FWU
Reference Farm type Year of No of farms in Organic Comp. conv. % 1 Organic Comp. conv. % 1
observation sample farms farms farms farms
Danielsson and 1996/97 4 472 423 112 12203 10932 112
Arnesson (1998)
1
as a percentage of comparable conventional farms
18
DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Switzerland
Profit in ECU per ha UAA Profit in ECU per FWU
Reference Farm type Year of No of farms in Organic Comp. conv. % 1 Organic Comp. conv. % 1
observation sample farms farms farms farms
FAT (1992a) average of different farm types 1990 2751 2777 99 28835 29541 98
FAT (1992b) average of different farm types 1991 2726 3044 90 31309 32038 98
FAT (1994) average of different farm types 1992 2186 1940 113 26953 23247 116
FAT (1995) average of different farm types 1993 2399 2245 107 28792 28741 100
FAT (1996b) average of different farm types 1994 2164 1889 115 27294 25395 107
FAT (1996c) average of different farm types 1995 2238 1738 129 31290 23239 135
FAT (1997a) average of different farm types 1996 75 2044 1849 111 25380 22847 111
FAT (1996b) dairy (mountain area) 1994 1736 1430 121 23852 19816 120
FAT (1996c) dairy (mountain area) 1995 1773 1501 118 24547 22180 111
FAT (1997a) dairy (mountain area) 1996 35 1515 1416 107 20497 19798 104
FAT (1996b) mixed farms (flat land) 1994 2459 2206 111 30896 30365 102
FAT (1996c) mixed farms (flat land) 1995 2596 1920 135 33678 24652 137
FAT (1997a) mixed farms (flat land) 1996 40 2588 2292 113 29560 25397 116
1
as a percentage of comparable conventional farms
19
DRAFT IRENA Methodology / Data Fact Sheet
Indicator 5b – organic farm incomes
Meta data to be completed
Provide information for the following items
Technical information
1. Data source:
2. Description of data:
3. Geographical coverage:
4. Temporal coverage:
5. Methodology and frequency of data collection:
6. Methodology of data manipulation:
Quality information
7. Strength and weakness (at data level):
8. Reliability, accuracy, robustness, uncertainty (at data level):
9. Overall scoring (give 1 to 3 points: 1=no major problems, 3=major reservations):
Relevancy:
Accuracy:
Comparability over time:
10. Comparability over space
20
