INFORME Y CRONOGRAMAS DE RECOJOS DE RESIDUOS SOLIDOS EN EL DISTRITO DE MORO
DISPOSICIONES COMPLEMENTARIAS, TRANSITORIAS Y FINALES Primera Coordinación transectorial
international comparative analysis of policy and technology impacts proposed by a working group of the agricultural research institute FAL
(Bundesforschungsanstalt für Landwirtschaft) in Germany. However, this approach was further developed and substantially adapted to the specific situation of organic farms and the present research in particular. A theoretical reasoning for selecting this concept was discussed in detail in section 2.1 to 2.4. Section 2.5 describes the research process and adaptations made in detail.
What distinguishes this approach from other common approaches to farm level impact analysis is the fact that it uses a mixture of methodologies (typical farms; based on focus groups, and the simulation model TIPI-CAL). The individual elements will be discussed in the light of criteria that are used to evaluate empirical research processes (Table 6-1).
Table 6-1: Criteria for the evaluation of empirical research processes
...applicable mainly to quantitative research ...applicable mainly to qualitative research
Reliability: Operational instructions are precise and objective, results are repeatable
Appropriateness of the chosen approach
Validity: Operational instructions measure those criteria that should be measured
Sensitivity of the chosen approach
Representativity of selection: Sample allows conclusions with regard to the whole population
Systematic conduct of analyses and application of criteria
While some criteria refer mainly to the evaluation of quantitative empirical research processes others are suggested for evaluating qualitative research approaches. As appropriate, these criteria will be applied to the different methodological elements. However, only crucial criteria will be discussed in detail. Finally, the long-term objective of the approach (IFCN), chosen, to establish an international network of representative farms, the International Farm
Comparison Network (IFCN), with the long-term objective of establishing a sustainable structure for policy and technology impact analysis, is discussed in light of the experience gathered during the research process.
6.1.1
Defining typical farms
Defining model farms is a crucial issue for farm economics and policy impact analysis, especially with regard to the representativity of selection and the possibility of generalising results to other farms in the sector.
At the time the research took place, statistics on the regional distribution of production or farm types for the organic farming sector existed in very few countries in Europe. Therefore, the regional focus of a certain organic production system or farm type could not be identified via statistical data bases. Similarly, model farms, e.g. as average farms, aggregated regional farms or single model farms, could not be defined via statistical data. Selecting model farms according to the typical farm concept therefore seemed appropriate to facilitate economic analyses of organic farms in Europe (section 2.3).
When the empirical, expert-based, research phase was completed three different databases became available, containing information that would have facilitated the selection of typical farms at the outset:
• Data on organic holdings, land use and livestock for all EU countries at the NUTS 1 or 2 level (Eurostat 2002).
• Data on organic holdings, land use and livestock at a district level for Germany (Statistische Landesämter 2003).
• Anonymous farm-specific data for all farms (approx. 3600) belonging to one of the largest organic producers’ organisations in Germany for the year 1997 (Bioland 2000), covering nearly 44% of all organic farms in Germany (Foster & Lampkin 2000). These data are now used to cross-check the reliability of farm selection with regard to: • the regional focus of farm types within the chosen countries,
• the nature of typical farms,
and thus validate the process chosen for selecting typical model farms.
Selection of case-study regions
Regionalised data are available only for the case-study countries Germany, Italy and the UK for the year 2000, while in Denmark data is only available at the country level (Table 6-2).
In Germany, in the year 1999 more than 50% of all organic dairy cows reared in the country are located in the two southern federal states of Baden-Württemberg (25%) and Bavaria (33%), while no other federal state contributed more than 9% of all dairy cows. Similarly, high densities of organic dairy cows per total UAA are observed in Baden-Württemberg (1.5 cows/ha UAA-100) and Bavaria (0.9 cows/ha UAA-100).
Table 6-2: Regional distribution of organic dairy cows in the case study countries
Country Region dairy cows Organic (No.)
Organic dairy cows in each region as % of all organic dairy cows
Organic dairy cow density (cows/100 ha total UAA) Denmark 66.570 100% 2.5 Germany 85.250 100% 0.5 Bayern 28,130 33% 0.9 Baden-Württemberg 21,350 25% 1.5 Hessen 7,630 9% 1.0 Brandenburg 6,160 7% 0.5 Mecklenburg-Vorpommern 5,600 7% 0.4 Nordrhein-Westfalen 3,680 4% 0.2 Niedersachsen 3,700 4% 0.1 Schleswig-Holstein 2,140 3% 0.2 Thüringen 1,920 2% 0.2 Sachsen 2,120 2% 0.2 Saarland 540 1% 0.7 Rheinland-Pfalz 1,030 1% 0.1 Sachsen-Anhalt 1,150 1% 0.1
Hamburg, Bremen, Berlin 0 0% 0.0
Italy 87.150 100% 0.7 Emilia Romagna 28,790 33% 2.6 Puglia 15,170 17% 1.2 Lombardia 7,050 8% 0.7 Sicilia 6,720 8% 0.5 Piemonte 4,530 5% 0.4 Sardegna 4,100 5% 0.4 Lazio 3,130 4% 0.4 Calabria 3,030 3% 0.6 Campania 2,640 3% 0.5 Veneto 2,750 3% 0.3 Umbria 2,120 2% 0.6 Marche 1,700 2% 0.3 Toscana 1,310 2% 0.2 Trento 750 1% 0.5 Molise 540 1% 0.3 Friuli-Venezia Giulia 560 1% 0.2 Bolzano-Bozen 550 1% 0.2 Basilicata 860 1% 0.2 Liguria 370 0% 0.6 Abruzzi 390 0% 0.1 Valle d‘Aosta 0 0% 0.0
Table 6-2: Regional distribution of organic dairy cows in the case study countries (continued)
Country Region Organic dairy cows (No.)
Organic dairy cows in each region as % of all organic dairy cows
Organic dairy cow density (cows/100 ha total UAA)
UK 32.380 100% 0,2
South West (UK) 12.140 37% 0,7
Wales 3.820 12% 0,3
Scotland 3.370 10% 0,1
London, South East 2.800 9% 0,3
North West 2.720 8% 0,3
West Midlands 2.400 7% 0,3
East Midlands 2.180 7% 0,2
Northern Ireland 1.480 5% 0,1
Yorkshire and Humberside 890 3% 0,1
Eastern 300 1% 0,0
North East 0 0% 0,0
Source: Eurostat (2002)
In Italy, the region with the highest number and density of organic dairy cows (33% of all Italian organic dairy cows, 2.9 cows/ha UAA-100) is Emilia Romagna,
the region which was chosen as case-study region at the outset of the study. In the UK in the year 2000, nearly 50% of all organic dairy cows were located in the south-west (37%) and Wales (12%). Similarly, fairly high dairy cow densities were observed in these regions: 0.7 and 0.3 cows/ha UAA-100, for the
South West and Wales, respectively. During the research process, Wales was selected as the case study region despite the knowledge that the south-west would have been the more appropriate in terms of organic dairy cow density and farms. However, Wales was chosen for practical reasons (see section 1.1.13).
In summary, these examples of regions typical for organic dairy farming confirm that the chosen expert-based selection of typical case-study regions results in selection of the same case-study regions as those that would be selected on the basis of analysing statistical data.
For the German dairy farm this can even be shown at a lower level of aggregation, because additional data of high quality was available. As an example, the density of organic dairy cows is given at district level for Baden- Württemberg (Figure 6-1). This confirms that the typical farm is located in a region (Hohenlohe district) with a high density of dairy cows although higher organic cow densities are observed in other regions.
Figure 6-1: Density of organic dairy cows per district in Baden- Württemberg in 1999 (dairy cows per 100 ha)
Typical farm selection & definition of production system
The representativity of the chosen typical farm in terms of farm size and land use was cross-checked via two sources: the data-base containing farm-level data of all Bioland farms (Bioland 2000) and data on organic holdings, land use and livestock at a district level for Germany (Statistische Landesämter 2003). The latter show that the most common average organic dairy herd size in Germany is 20 to 40 cows (Figure 6-2), while the farm-specific data further confirms the correct choice of typical farm (Table 6-3). With the exception of total UAA, this database would have resulted in a „typical farm“ similar to the one selected by focus groups (Table 6-3).
Figure 6-2: Number of cows on organic farms per district in Germany
<= 10 10 - <= 20 20 - <= 40 40 - <= 60 > 60 no data
Dairy cows per farm
Source: Osterburg & Zander (2003) based on Stat. Bundesamt (1999)
Table 6-3: Typical farms defined via a database and focus groups in comparison*
Bioland database 1997 Focus group selection
Dairy cows (no.) 35-40 38
Average FCM (kg) 4,500-5,000 5,062
UAA (ha) 30-35 55
...of which grassland (ha) 25-30 28
* Assumption: Changes in farming structure are only minor in two years. Both selection processes applied the following criteria: i) full-time organic dairy farm, ii) with a minimum of 20 dairy cows, and iii) a minimum average milk yield of 4500 kg/year. These criteria applied to 237 farms.
Source: Own data, based on Bioland (2000)
Although data of sufficient quality for cross-checking at various level of aggregation was only available in one of the eight cases studied, these results nevertheless give a first indication of the validity of the chosen expert-based method for defining farms. The resulting typical farms would have been similar if quantitative data had been available at the time of research.
However, the chosen approach relies on a wide range of experts in the field, and the time required is considerable. Nevertheless, in data-poor sectors, such as the organic farming sector, the approach discussed can be an indispensable tool to facilitate the definition of model farms, applying clear criteria in the selection process.
Additionally, if data on production processes needs to be compiled, either due to a lack of available data or because available data might not fit the „typical farm“, input by focus group becomes indispensable.
Furthermore, farm definition by focus groups facilitates a rapid up-date of typical farms on a regular basis. Statistical data-bases usually lag behind by several years. Focus groups enable farm data to be updated at any point in time. In conclusion, both the process of selecting case-study regions via diverse experts in the field and by means of defining typical model farms gave the same results that would have been provided by classical, quantitative approaches. Nevertheless, extrapolation of the results should be carefully evaluated. However, the time required for expert-based approaches is considerable. Thus, for the selection of case-study regions, typical farm size and land use, such approaches are only the preferred method where no data is available. Detailed definition of typical farm data and production processes is more satisfactorily defined via focus groups. This way the time required for the selection of case- study regions, typical farms and data on production processes can be reduced in the future.
6.1.2
Adaptation strategies and policy impacts
Adaptation strategies & policy impact discussion via focus groups
Based on the demand for analysing policy objectives and measures together with those directly affected (Köhne 1998), focus groups were tested as a means to depict the multiple goals of farmers decision making in the analysis of policy impacts on organic farms. A theoretical reasoning for choosing such an approach was discussed in section 2.2.1.
In this research, focus groups showed considerable facility in proposing a range of possible adaptation strategies of farms to current or close policy environments. Strategies developed by focus groups seem to be based on an excellent information pool as the group takes more factors into account than a single individual would be capable of doing. The most important factors in this respect seem to be the participation of particularly interested individuals, group results and the additional input given by an advisor knowledgeable in the field. Nevertheless, the quality of decisions on the strategic development of farms may be poor due to a lack of overview of all influencing factors and the complexity of such decisions: farm strategies proposed by focus groups were declared to be expected to bring about an improvement in farm profitability in the long term - except a few retirement strategies (see Chapter 4). However, Table 6-4 shows that only some of the envisaged farm adaptation strategies (12 out of 31 modelled strategies) can - according to the modelling results - be expected actually to improve profitability in the long term.
Table 6-4: Strategies and their success by 2008 compared to the base farm
Farms UK DE DK IT
Dairy Extensification Herd reduction Minor herd
expansion Herd expansion Herd expansion Milk yield increase Major herd expansion Cereal seeds Rear stores Herd expansion Field vegetables Direct market meat Field vegetables Finish steers Finish pigs Herd reduction Arable Field vegetables Cereal seeds Finish pigs Medicinal plants/ seeds Direct market
meat Field vegetables Seeds Fruit
Breeding sows Laying hens Field vegetables Direct market fruit
Landlording Finish pigs n.a. Reforestation
There may be two reasons for this. On the one hand, focus groups might not have been able to consider several factors simultaneously, although all assumptions on prices, yields, required investments etc. for farm strategy simulations were known and discussed by focus group sessions. Simulation modelling may take more factors into account and thus produce other results. On the other hand, the contrasting results of modelling and focus-group expectations could be due to „intrinsic“ factors that were not taken into account in modelling procedures but might counteract profit maximisation, either because they are not declared by focus groups or because they cannot be depicted in modelling procedures.
The first reason is especially relevant for discussion of farm adaptation strategies to potential future policy scenarios(see Chapter 5). Increasing distance in the future increases uncertainty and the ability to judge the impact of policies and envisage farm strategies. Furthermore, the ability to abstract is limited by current policy discussions. For example, at the time of focus-group meetings, there was a great deal of discussion of the EU policy package „Agenda 2000“ in the media and among farmers. It soon became obvious that farmers’ underlying assumptions for proposed farm strategies were dominated by these discussions. Abstracting to the level of potential future policy scenarios therefore seemed difficult, although simple and precise scenario descriptions were presented to and discussed with the focus groups. Therefore, only very general qualitative assessment of likely farm adaptations to policy scenarios resulted from these policy discussions, and detailed farm development strategies could not be provided.
Although the quality of proposed farm adaptation strategies could not be evaluated conclusively, potential drawbacks may be compensated for by the time sensitivity of the results.
Focus groups may help to depict reactions to dynamic policy developments in a timely manner. A comprehensive evaluation must consider the combined approach with simulation modelling and compare it with other commonly used methods of forecasting farm developments. This was not possible in the framework of this research due to time constraints.
It may be concluded that groups are based on an excellent information pool but they nevertheless lack the capacity to overview all relevant factors that might be depicted by simulation models. In the future, the validity of prognoses relating to farm adaptations may be evaluated by comparing actually observed developments with forecasts.
Simulation modelling
The simulation model TIPI-CALis an easily understandable simulation model with a basic structure potentially applicable to many farm types. Thus, compared to optimising farm planning models, TIPI-CAL© has the advantage
that it already exists and farms only have to be adapted to the model, while Successful strategy: profit per FWU by 2008 a minimum of
optimising farm planning models tend to be redesigned for each research problem and are often used only once. The use of TIPI-CALrather than another modelling approach was therefore expected to reduce the time required to obtain results.
At the time of the present study, available programmed elements were designed to consider dairy and arable farming activities. This limits the feasibility of using the model for organic farms because most organic farms are not limited to dairy or arable activities. Mixed farms, the most representative organic farm type, therefore had to be omitted from the research from the outset. Additional farm activities in animal production could only be introduced via the gross margin for each year. Input and output price developments and annual subsidies for these activities had to be inserted manually and could not be simulated, as were prices for other activities.
Despite its current limitations in terms of use on organic farms, TIPI-CAL© is
nevertheless a valuable tool for simultaneous simulation of interacting factors influencing the farming sector (e.g. yield or price developments), which experts are not always able to depict in their full range. However, this simulation model only serves to simulate and illustrate the impact of changes in farm management and „direct“ policy impacts. It does not depict optimisation of the farm organisation and, therefore, has only limited value as a planning and forecasting tool.
6.1.3
Summary
The methodological approach adopted for this research is characterised by a mix of methodological elements (typical farms, based on focus groups, and the simulation model TIPI-CAL). This methodological mix seems to be useful in addressing the research objectives under the constraints previously outlined at the beginning of the study.
The selection of typical farms proved to be appropriate: the representativity of the selected farms was confirmed by statistical data at a later stage, thus confirming the validity of the selection process.
The interaction of the model TIPI-CAL© and focus groups seemed appropriate
to „replace“ optimising farm planning models:
• Factors which are not influenced by farmers and which are complex in their interaction for assessment by humans (e.g. simultaneous policy changes, price and yield developments) are depicted well by the simulation model TIPI-CAL©.
• Farm development and adaptation strategies to policy changes are depicted via expert assessments. This way the multiple factors of farmers’ economic decision making is taken into account and a more complete assessment of policies can be given. Furthermore, the integration of focus groups in modelling procedures also provides an excellent tool for validation and evaluation of the data used and the results produced.
However, although less time is required for construction of an optimisation model, focus groups are very time-intensive – not only for the researcher, but especially for the focus groups. Compared to other approaches, the combination of the simulation model TIPI-CAL© with focus groups
nevertheless represents a valuable option for compiling policy impact analysis quickly. Unfortunately, an objective cost-benefit assessment cannot be carried out due to a lack of comparative data.