III. RESULTADOS
3.3 Variables educativas de nivel secundaria y el ingreso per cápita en los
Sampling and weighting
The present weighting system is based on a stratification of the farms by farm type, farm size and region (plain, hill, mountain). This means that differences in the sample composition compared to the relevant population are adjusted as far as these criteria are concerned. The farm characteristic “organic/non- organic” however is not taken into account. Over the past years, organic farms have permanently been overrepresented. Aggregated weighted results therefore tend to be biased. Furthermore, weighted results comparing organic and non-organic farms can be influenced by the weights derived from the entire sample. As an example, farms with suckling cows are generally underrepresented in the Swiss FADN. As a consequence these farms have very high weights to adjust for this. Assuming that in the sub sample of organic farms, the proportion of farms with suckling cows corresponds to the proportion in the population of organic farms, these high weights would lead to biased results for organic farms.
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Proceedings of the 1st EISfOM Seminar, Berlin April 2004The overrepresentation of organic farms has not evolved haphazardly. The specific interest of the FADN in these farms over the past 20 years has lead to active recruitment, in some cases also supported with financial incentives. If organic farmers were more likely to adopt an accounting system with direct costing and more willing to share the results with the national FADN, this could be another reason for overrepresentation. 0% 20% 40% 60% 80% 100% Arable crops Special crops Dairying Suckling cows Other cattle Horses/sheep/goats Pigs/poultry Combined dairying/arable Combined suckling cows Combined pigs/poultry Combined others
Represented farms Population
Organic IP Conventional
Sources: Reference farms: Swiss Farm Accountancy Data Network (FAT), Year 2002 Population: AGIS (Swiss Federal Office for Agriculture)
Figure 3: Share of organic farms by type of farming in the year 2002
There are three possibilities to deal with the situation:
1. Careful interpretation of the results taking overrepresentation into account.
2. Adapt sample composition to the distribution in the population (i.e.: reduce the number of organic farms in the sample).
3. Adjust the results by including the farm characteristic “organic/non-organic” in the weighting procedure.
To date, we have handled the problem at the interpretation level (possibility 1). Given the fact that the number of organic farms is still too small for many analyses on disaggregate level, possibility 2 would lead to a loss of information and considerable resistance from data users. For the third solution, several methodological problems need to be solved.
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0 5 000 10 000 15 000 20 000 25 000 30 000 35 000 40 000 1999 2000 2001 2002 1000 CHF FADNDirect payment for Organic farming (aggregated) SFOA
Direct payment for organic farming (Agricultural Reports)
Sources: FADN - Swiss Farm Accountancy Data Network (FAT) SFOA - Swiss Federal Office for Agriculture
Figure 4: Bias in aggregated direct payment for organic farming due to overrepresentation of organic farms
Level of detail and data quality
Data recorded on organic farms are exactly the same as on other farms. At the moment, there are no plans to change that. However, a comprehensive revision of the data dictionary of the Swiss FADN has been completed in 2003. The new data dictionary improves information on para-agricultural activities. The new cost centres not only distinguish between different forms of agro tourism but also between different activities in processing of farm products and direct selling. The organic sector will particularly benefit from the new possibilities, as these activities are often major sources of added value in organic farms. Information on commodity prices is available for the major products. Using farm accounts as data source, we can rely on very accurate figures on sales. It is difficult to achieve a similar quality level for the information on sold quantities. There is some evidence that an accounting system based on direct costing will yield better physical data than financial or tax accounts, as the farmer and the accountant are interested in meaningful gross margins comparable to the benchmarks produced by the Swiss FADN. Plausibility tests and training of accountants are other key elements in quality management.
No information is collected so far on specific marketing strategies like the establishment of farm shops, Internet shops, selling at weekly markets, producing for specific labels and/or organisations, creation of own brands etc. The existing data model would allow such information to be included.
With the integration of Life cycle analysis (LCA) into the Swiss FADN, we currently focus on extending the data collection to environmental aspects of the production.
How to collect new information?
There are basically two options to satisfy new information needs:
1. Establish a new, targeted survey with all the necessary elements (survey design, data collection, quality management, data management and data analysis, including infrastructure).
2. Integrate new information items in an existing framework, such as a FADN.
Strong arguments for integration into an existing FADN are the existing organisational and structural bases, trained staff experienced in meeting high quality standards, good conditions to obtain highly consistent data with many possibilities to test plausibility, a high level of harmonisation, wide range of possibilities for cross-sectional or longitudinal studies etc.
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Proceedings of the 1st EISfOM Seminar, Berlin April 2004On the other hand, there are potential weaknesses that must be checked carefully: Limited flexibility including deadlines for implementation, data privacy aspects, compromise by accepting the existing framework with its own rules and definitions, representativity of existing sample or limited possibilities to alter sample composition etc.
Time series
Swiss FADN provides data for long periods and with a high level of harmonisation between the farms and over time. Longitudinal analyses with special emphasis on organic farms are possible, but one must carefully take the effects of continued conversion to organic farming into account.
0 10 000 20 000 30 000 40 000 50 000 60 000 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
FFI Integrated Production
FFI Organic production
FFI All Farms CHF per farm
Mountain region
Share of organic farms (% of all represented farms)
1.3% 20.3%
Source: Swiss Farm Accountancy Data Network
Figure 5: Family farm income (FFI) of farms with integrated production and organic farms
International comparability
Swiss FADN is not participating in the FADN of the European Union. Despite this fact and in order to satisfy the demand for internationally comparable results, we carry out a conversion of Swiss FADN data in accordance with EU-FADN methodology on our own. This conversion is concerned with differences in farm definition, valuation and depreciation, structure of the profit and loss account, farm typology, definition of the population and the weighting system (Meier 2002). As an example, figure 6 shows the results of dairy farms with a utilised agricultural area of 30 to 50 hectares for Switzerland and neighbouring regions. Of course, these results can also be disaggregated by organic or other farms. Selected results for farms in the hill-region are presented below. The variables correspond exactly with the variables of the EU-FADN.
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Distribution of output and subsidies on total cost and family farm income 1996-1998 0 20 000 40 000 60 000 80 000 100 000 120 000 140 000 160 000 180 000 200 000
CH All Regions CH lowland / hill region
Bavaria Schleswig- Holstein
Rhône-Alpes Austria ECU/farm Rent & interest paid (SE375+SE380)
Wages paid (SE370) Depreciation (SE360)
Other intermediate consumption (excl. SE310, SE340) Machinery & building current costs (SE340)
Feed for grazing livestock (SE310) Family Farm Income (SE420)
Source: EU Commission, FADN; Swiss FADN, FAT
Figure 6: Costs and family farm income in dairy farms of 30 to 50 ha