DRAFT IRENA Methodology / Data Fact Sheet Indicator 5b organic farm incomes

Transcription

1 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, April 2004 (see for details). A working group in the seminar is specifically addressing farm income issues. Headline graphs and maps 1

2 % 180 Profits per ha UAA AT CH DE DK FI GB IT NL SE % Profits per FWU AT CH DE DK FI IT NL SE Dot size is corresponding to the number of farms analysed: 2-9 farms, farms, > 20 farms, Expert assessment 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 ( averages), Source: Offermann & Nieberg, 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 2

3 Introduction DRAFT IRENA Methodology / Data Fact Sheet 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 3

4 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 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 reflects the heterogeneity of farming before the of farms is drawn, Liaison Agencies stratify the field of according to three criteria: region, economic size and type of farming 1. Farms are selected in the according to a selection plan that guarantees its representativeness 2. An individual weight is applied to each farm in the, this corresponding to the number of farms in the 3-way stratification cell of the field of s divided by the number of farms in the corresponding cell in the. 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 oversupplied. 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 : Organic farms occur in the FADN 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 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 ). 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. 4

5 representative 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 sizes, it is unlikely to be possible to achieve a reliable, representative 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, 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- 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. 5

6 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 (+/ %) similar milk quota/milk production (+/ %) 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, QLK , 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, April 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. Since , 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 s containing at least 15 farms. Table 5-2 provides an overview of the respective sub-s available in the FADN accounting year On an 'EU'-level 4, the s 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, QLK ). 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

7 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): 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 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 s EU15 AT BE DE DK ES FI GB LU NL PT All Arable Horticultural Wine Perm. Crops Dairy Graz.livestock Pigs/Poultry Mixed 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 reduced only reduced only Available spring 2004 Source: FAL Braunschweig (pers. comm.) (of which 367 fully organic) avail. soon (67 IRS FADN) (67 IRS + ca. 150 FADN) 7

8 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. s 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) and 2) 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 s are supplied below, including data used to create the header graphs and maps. 8

9 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 Organic Comp. conv. Organic Comp. conv. BMLF (1995) average of different frmtypes BMLF (1995) >40 % of standard grss mrgin from cropping BMLF (1996) average of different farm types BMLF (1996) >40 % of standard gross margin from cropping BMLF (1997) average of different farm types BMLF (1997) >40 % of standard gross margin from cropping

10 Denmark Organic Comp. conv. Organic Comp. 2 farms conv. farms per hour DIAFE (1998) all farms 1996/ (818) (81) (20.95) (79) DIAFE (1998) average of arable and dairy farms 1996/ DIAFE (1998) Arable 1996/ DIAFE (1998) Dairy 1996/ conventional farms). Finland 2 Figures in brackets refer to comparisons with all conventional farms (as opposed to comparable Organic Comp. conv. Organic Comp. conv. per hour AERI (1996) average of different farm types AERI (1996) Arable AERI (1997) Arable Expert estimate Arable AERI (1996) Dairy AERI (1997) Dairy Expert estimate Dairy

11 France Trouilloud (1990) average of different farm types Trouilloud (1990) average of different farm types Germany Farpe of farms in Organic Comp. conv. Organic Comp. conv. Comp. conv. farms 2 Organi c farms BMELF (1994) average of different farm types 1992/ BMELF (1995) average of different farm types 1993/ BMELF (1996) average of different farm types 1994/ BMELF (1997) average of different farm types 1995/ BMELF (1998) average of different farm types 1996/ LBA (1997) average of different farm types 1995/ LBA (1998) average of different farm types 1996/ Landwirtschaftskammer average of different farm types 1996/ (559) (58) (20857) (78) Westfalen-Lippe (1998) Nieberg (1997) average of different farm types 1992/ Nieberg (1997) average of different farm types 1993/ Nieberg (1999) average of different farm types 1994/ Nieberg (1999) average of different farm types 1995/ Germany (continued) Co mp. v. far ms 11

12 DRAFT IRENA Methodology / Data Fact Sheet Organic Comp. conv. Organic Comp. conv. 2 Zerger (1995) average of different farm types 1988/ Zerger (1995) average of different farm types 1989/ Zerger (1995) average of different farm types 1990/ Zerger (1995) average of different farm types 1991/ Zerger (1995) average of different farm types φ Nieberg (1997) Arable 1992/ Nieberg (1997) Arable 1993/ Nieberg (1999) Arable 1994/ Nieberg (1999) Arable 1995/ Köhne and Köhn (1998) Arable Köhne and Köhn (1998) Arable Zerger (1995) Arable 1988/89 ca Zerger (1995) Arable 1989/90 ca Zerger (1995) Arable 1990/91 ca Zerger (1995) Arable 1991/92 ca Zerger (1995) Arable φ Stolze (1998) Arable continued on next page

13 Germany (continued) Organic Comp. conv. Organic Comp. conv. 2 Stolze (1998) Dairy Nieberg (1997) Nieberg (1997) Nieberg (1999) Nieberg (1999) grazing livestock (mainly dairy) grazing livestock (mainly dairy) grazing livestock (mainly dairy) grazing livestock (mainly dairy) Köhne and Köhn (1998) grazing livestock / / / / Köhne and Köhn (1998) grazing livestock Zerger (1995) grazing livestock 1988/89 app Zerger (1995) grazing livestock 1989/90 app Zerger (1995) grazing livestock 1990/91 app Zerger (1995) grazing livestock 1991/92 app Zerger (1995) grazing livestock φ Nieberg (1997) pigs and poultry 1992/ Nieberg (1997) pigs and poultry 1993/ conventional farms). 2 Figures in brackets refer to comparisons with all conventional farms (as opposed to comparable 13

14 Great Britain Fowler, Lampkin and Midmore (1998) Fowler, Lampkin and Midmore (1998) Organic Comp. conv. Organic Comp. conv. average of different farm types 1995/96 38 NFI: average of different farm types 1995/96 38 ONI: Murphy (1992) average of different farm types Fowler, Lampkin and Midmore (1998) Fowler, Lampkin and Midmore (1998) Arable 1995/96 6 NFI: Arable 1995/96 6 ONI: Murphy (1992) Arable Fowler, Lampkin and Midmore (1998) Fowler, Lampkin and Midmore (1998) Haggar and Padel (1996) Haggar and Padel (1996) Haggar and Padel (1996) continued on next page Dairy 1995/96 6 NFI: Dairy 1995/96 6 ONI: Dairy Dairy Dairy 3rd year after conversion 3rd year after conversion 3rd year after conversion 10 ONI: NFI: MII:

15 Great Britain (continued) Haggar and Padel (1996) dairy 4th year after conversion Haggar and Padel (1996) dairy 4th year after conversion Haggar and Padel (1996) dairy 4th year after conversion Organic Comp. conv. Organic Comp. conv NFI: MII: 190 Murphy (1992) dairy Lampkin and Batemen (1993) mainly dairy (Wales) Lampkin and Batemen (1993) mainly dairy (Wales) NFI: Fowler, Lampkin and Midmore (1998) Fowler, Lampkin and Midmore (1998) horticulture 1995/96 5 NFI: horticulture 1995/96 5 ONI: Murphy (1992) horticulture NFI: 310 Fowler, Lampkin and Midmore (1998) Fowler, Lampkin and Midmore (1998) grazing livestock 1995/96 12 NFI: grazing livestock 1995/96 12 ONI: Lampkin and Batemen (1993) grazing livestock Lampkin and Batemen (1993) grazing livestock NFI: continued on next page 15

16 Great Britain (continued) Fowler, Lampkin and Midmore (1998) Fowler, Lampkin and Midmore (1998) Organic Comp. conv. Organic Comp. farms conv. farms mixed farms 1995/96 9 NFI: mixed farms 1995/96 9 ONI : Murphy (1992) mixed farms The Netherlands Organic Comp. conv. Organic Comp. conv. Dutch FADN average of different farm types Dutch FADN Arable Dutch FADN Dairy Dutch FADN horticulture Dutch FADN mixed farms

17 Italy Zanoli, Fiorani and Gambelli (1998) Zanoli, Fiorani and Gambelli (1998) Zanoli, Fiorani and Gambelli (1998) Chiorri and Santucci (1997) Organic Comp. conv. Organic Comp. conv. average of different farm types average of different farm types average of different farm types average of different farm types Zonin (1996) average of different farm types Zonin (1996) average of different farm types Piani (1995) average of different farm types Salghetti (1997) Dairy Santucci and Chiorri (1996) Santucci and Chiorri (1996) Santucci and Chiorri (1996) Santucci and Chiorri (1996) mixed farms mixed farms mixed farms mixed farms φ Furnari (1994) citrus farms φ

18 Luxembourg Expert estimate Arable Expert estimate Dairy Expert estimate grazing livestock Expert estimate mixed livestock Organic Comp. conv. Organic Comp. conv. Expert estimate mixed farms Norway Organic Comp. conv. Organic Comp. conv. Vittersø (1995) Dairy Sweden Danielsson and Arnesson (1998) Organic Comp. conv. Organic Comp. conv. 1996/

19 Switzerland Organic Comp. conv. Organic Comp. conv. FAT (1992a) average of different farm types FAT (1992b) average of different farm types FAT (1994) average of different farm types FAT (1995) average of different farm types FAT (1996b) average of different farm types FAT (1996c) average of different farm types FAT (1997a) average of different farm types FAT (1996b) dairy (mountain area) FAT (1996c) dairy (mountain area) FAT (1997a) dairy (mountain area) FAT (1996b) mixed farms (flat land) FAT (1996c) mixed farms (flat land) FAT (1997a) mixed farms (flat land)

20 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