The International Farm Comparison Network (IFCN) - bridging the gap between farmers, science and policy

Transcription

1 The International Farm Comparison Network (IFCN) - bridging the gap between farmers, science and policy Claus Deblitz Institute of Farm Economics, Federal Agricultural Research Centre (FAL), Bundesallee 50, Braunschweig, Germany. Summary This paper introduces the International Farm Comparison Network (IFCN). IFCN is an international network applying internationally harmonised methods of data collection, calculation and presentation for the purpose of providing policy advice. The key elements and characteristics of IFCN are described, with particular emphasis on the method and process of data collection; areas of most interest to the European Livestock Policy Evaluation Network (ELPEN). In addition, possible areas of information exchange and co-operation with ELPEN are identified. The characteristics, objectives and instruments of IFCN The International Farm Comparison Network (IFCN) is an international network of scientists, advisors and farmers. The objectives of IFCN are: to create and maintain an infrastructure enabling the long-term analysis of agricultural production systems around the world, to improve analysis of structural, technological and policy changes around the world, to facilitate communication and data exchange between economists interested in farm level analysis and related issues. IFCN is a long-term project. The network was started in 1997 and it will take a period of at least 3 years until the basic network is established. The main components of IFCN are shown in Figure 1 and briefly explained below: The components of IFCN are: 1 The network itself, consisting of scientists, advisors, consultants and farmers. IFCN builds up an infrastructure that generates a continuous exchange of information between scientists and farmers on an international scale, thereby facilitating the analysis of alternative policy impacts at farm-level. A unique methodology, involving the use of groups of real farmers to develop datasets for analysis, makes IFCN a valuable approach. 2 A unique, realistic and up-to-date data-base of typical farms, representing different farm types and various regions. This data is developed from so-called panels, each consisting of four to

2 six farmers, one advisor and one scientist. Further explanation of the panel process is given below. 3 Complex production and accounting models (TIPI-CAL and FLIPSIM) are used to simulate a farm s financial performance in the future. The models allow status quo analysis of production costs as well as projections about the farm under different strategy, risk and policy conditions. The performance measurements are standardised allowing easy international comparison. Figure 1. The vision of the International Farm Comparison Network The Network of Typical Macro and Micro Europe Prices Market (FAPRI, GAPsi, GTAP) Input Output North Profit -Costs -... Farm (TIPI-CAL, FLIPSIM) Scenario Other - New Zealand - Australia - CIS Countries - South America -... Baselin Scenario IFCN FAL- HEMME/DEBLITZ The results of the 10-year farm predictions are, of course, highly dependent upon assumptions made about price developments. It is, therefore, necessary to combine the microeconomic approach of IFCN with agricultural sector and macro-economic models capable of developing longer-term forecasts of variables such as price, production and consumption. For the time being, price forecasts will be taken from the following sources: Forecasts that are published regularly by certain institutions (e.g. USDA, OECD, FAPRI, European Commission) Co-operation with consortiums that are applying highly aggregated models in order to assess future developments on a world-wide scale (e.g. GTAP) Ad-hoc expert assessments in special cases (particularly when price forecasts at regional or local levels rather than national or international levels are required)

3 In the long run, research institutes that provide macro-economic and model-based forecasts of international trade will be linked to the network. In addition, commodity experts, specialists in the field of processing, marketing and international trade, will eventually be integrated into IFCN to provide analytical capabilities that extend across industry sectors that will become increasingly linked together. This vision is outlined in Figure 2. Figure 2. Vision of a link between IFCN and other networks Experts Processing/Marketing/Trade Dairy products Macroeconomic Projections and World Trade Models Arable products Beef products IFCN Dairy Arable crops Beef Source: Own illustration FAL-BW ISERMEYER (1998) There is no doubt that the establishment of such an infrastructure is a great challenge, but is there any alternative? In past decades, many ad hoc studies have been carried out with the aim of analysing both the international competitiveness of farms and farm adjustments to changing conditions. Every study started from scratch; the data used was not comparable and became out of date too quickly. Moreover, almost all the studies covered only a very limited set of countries and commodities. Furthermore, there were usually no sustainable structures established or permanent staff employed to continue the work. Hence, the overall impact of these studies (e.g. at WTO level) has been very limited.

4 IFCN will help to overcome the problems of ad hoc-studies: IFCN uses an approach, which has proved to be successful for more than 15 years in the United States. For the purposes of international comparisons IFCN uses experience that the European Dairy Farmers (EDF) Network has built up since IFCN applies internationally compatible and harmonised methodologies. IFCN produces high quality research in which the quality of scientific results is checked by a network of farmers and advisors. IFCN provides a permanently active, world-wide network of experts with an up-to-date data-base and is consequently capable of producing results within a very short period of time. For these reasons, we believe that the IFCN will provide an infrastructure that makes farmer's experience and micro-economic knowledge more readily available for the analysis of national policies related to agriculture, for enhancing the quality of world trade models and assessments, for the analysis of international agricultural trade policies. The concept of typical farms The concept of typical farms provides a unique, realistic and up-to-date data-base of different farm types in several different regions. In order to select typical farms, the region(s) in a country where the required farm type, let us say a dairy farm, is most important in terms of volume of production and/or density of dairy cows is first identified. A typical dairy farm is representative of the dairy farms within the region in terms of size, crops grown, livestock systems, labour organisation and production technology used. The technical and economic data used to describe the typical farm is neither individual farm data nor statistical averages, but is based on a consensus achieved in a panel meeting. In each region and for each relevant farm type it is intended that one moderate (average) sized farm and one large farm will be chosen to represent typical farms in the area and to capture economies of scale. The large farm should represent the largest (in size) 10% of farms in the area. The Panel process The panel is a vital component of IFCN. For each typical farm, panels consisting of four to six farmers, one advisor and one scientist will be used (a) to develop and update the typical farm data for use in calculations using the models, (b) to identify and discuss strategies and adjustments to changes in markets, technology or policy conditions and (c) to review the results of the modelling. Both physical and economic data will be collected in detail and will cover:

5 Land use, including up to 20 different crops (area, yields, fertiliser input, quotas, prices, direct payments, variable costs; all per ha) Dairy data (milk yield, livestock weights, herd management, feed rations, milk quota, prices, direct payments, variable costs per cow, valuation of livestock inventory) Fixed costs of the farm An inventory of up to 100 machines and equipment as well as 20 buildings (purchase price, lifetime, replacement values, market values, book values) An inventory of up to 10 short-term, mid-term, long-term loans (amount, period, interest) Tax payments (farm taxes, income taxes) and management income for up to 10 partners Income from other enterprises (e.g. tourism) and off-farm income The farms of farmers participating in IFCN should be approximately the same size as the typical farm size selected. However, none of the farmers is obliged to disclose his own individual farm data. Each statistic is determined by discussing the typical farm and not the farmers' own farms. The advisor s role is principally to iron out any biases which individual farmers may show and this he is able to do by knowing more farms and farmers than those participating in IFCN and thus having a greater overview of the situation. Thus, the data obtained reflects an agreement between the panel participants and gives a far more accurate picture of reality and where the data has come from than could be gained from statistical averages derived from existing or original surveys. The data collected in the panel meetings is run through the TIPI-CAL and FLIPSIM models and a profit and loss account as well as a summary of the most important indicators describing the farm is returned to the panel members for checking. This process is repeated until the panel agrees on the results achieved (see Figure 3). Panels will not only be used to set up and update typical farms for use in model calculations, but in the next stage will also be used in the identification and specification of basic farm strategies. Such strategies might include, for example keeping production and quota constant and reducing number of cows due to yield increase or keeping the number of cows constant and renting or buying quota as a result of the increase in milk production. They may also include different growth strategies, such as growth without major investment (e.g. an increase of 60 to 65 cows by filling the barn to full capacity) or growth with investment (e.g. an increase of 60 to 80 or even 120 cows). The results of these calculations are discussed with the farmers and used for policy analysis, given different farm strategies. Moreover, if a certain policy requires a specific adjustment strategy, this will also be discussed in the panel and incorporated into the models for further analysis.

6 Figure 3. The panel process A 1 Advisor and 5 Farmers A Panel 1 Panel 2 Farm data for typical farm in the region Check Input data Plausibility of the results Simulation of farm data Result Panel farm 1 Panel farm 2 Simulation of Scenarios Policy Market Technology Farm strategies Results FAL-BW HEMME (1997)

7 The farmers (and everybody else) find participating in the panels very fruitful, as they provide the opportunity to obtain and exchange information quickly in a structured way. In addition farmers receive all the results for which their panel data has been used. It is the opinion of the IFCN network that that there is no better way of obtaining this information than asking the farmers or decision-makers themselves. Results IFCN and its models can produce different types of results. These include: Measures to compare competitiveness at farm-level, e.g. international cost comparisons. Figure 4 shows the result of a recently completed study on the total cost of milk production in typical dairy farms world-wide. The impacts of different farm development strategies under a given policy. Figure 5 provides such an example for a typical 60-cow dairy farm in Germany. Forecasts of how farms will change under different policy, technology and market scenarios over a period of 10 years. Figure 6 illustrates an example of how a typical 60-cow dairy farm in Germany will change under Agenda Figure 4. Milk price and total costs if milk production in typical dairy farms world wide 1996/97 (US-$/100 kg) 70 Opportunity costs Costs from P&L account - returns from by-products 60 Milk Price US $ / per 100 kg milk FCM Break-even point I Break-even point II A-23av I-29um D-28by F-30br I-90em D-75wm D-60BRv F-70br NL-70 UK-65wa UK-165wa D-800sc US-70wi US-600wi US-500id US-1800id H-400 PO-35 PO-543 BU-50 CZ-31 AR-120sf AR-220co BR-157sp BR-260sa UR-90 NZ-225 NZ-480 AU-150sv AU-250sv AU-150nv AU-250nv ZA-100 ZA-121 European Union USA C-Europe S-America Oceania Africa Country codes: A=Austria, I=Italy, D=Germany, F=France, NL=Netherlands, UK=United Kingdom, US=USA, H=Hungary, PO=Poland, BU=Bulgaria IFCN CZ=Czech Rep.,AR=Argentina, BR=Brazil, NZ=New Zealand, AU=Australia, ZA=South Africa FAL-BW Break even point I- Milk price necessary to cover all economic costs (Total costs - returns from by-products (cull cows, calves, heifers, dir. Payments) DEBLITZ / HEMME Break even point II - Milk price necessary to cover all costs from the profit and loss account - the farm genrerates a positve family farm income GOERTZ / JACOBI Source: IFCN Meeting, April 1998 Braunschweig, Germany (1998)

8 Figure 5. Development of family farm income on a 60-cow dairy farm under different farm strategies 1996 to 2005 Baseline Projection Dairy farm 60 cows Germany Bremervörde Analysis of Farm Strategies under current Policy Family Farm Income in DM/ farm Quota constant Cows constant Growth to 80 cows Growth to 120 Cows Quit farming Source: TIPI-CAL calculations IFCN/TIPI-CAL FAL-BW HEMME/GOERTZ (1998) Figure 6. Income development under Agenda 2000 in a typical 60-cow dairy farm in Northern Germany (DM per farm and year) Policy Analysis 60 cows-dairy-farm Germany-Bremervörde Policy= Agenda Strategy= Keep no. Cows constant Family Farm Income in DM/ farm Baseline Agenda old-7/97 Agenda new 3/ Source: TIPI-CAL calculations IFCN/TIPI-CAL FAL-BW HEMME/GOERTZ (1998)

9 The potential of IFCN IFCN is an excellent farm-level tool for reinforcing and supplementing sector and market oriented analysis (and vice versa). IFCN is not intended to replace or compete with these types of analysis. Rather IFCN provides an opportunity to bridge the gap between farming reality on the one hand and science and policy on the other. The features of IFCN, i.e. (a) being based on the expert knowledge of farmers and advisors, (b) using internationally harmonised methodologies, (c) being continually upto-date, (d) providing a structure for long-term analysis, and (e) resulting in forecasts for up to 10 years into the future, has meant that it has largely received a positive response from policy-makers, institutions and agribusiness. The potential of IFCN includes research, studies and an answer to the question, "What happens to typical farms world-wide if policy, technology or market conditions change?". How IFCN and ELPEN could benefit from each other Before drawing conclusions on the mutual benefits of IFCN and ELPEN, it is first necessary to look comparatively at both networks. The following is based upon the networks as they currently stand and future developments may alter them. The question which needs to be discussed is "what does ELPEN cover that IFCN doesn't and vice versa?" The following paragraphs go some way to answering this. Based on the information available, both systems aim to cover the full range of livestock production systems within the agricultural sector. The methodology and design of ELPEN has taken all these systems into account from the outset, whereas IFCN has so far only included milk production and arable systems. However, expansion is already under way to include pig and poultry systems. ELPEN represents a regional or aggregated approach, whereas IFCN represents a farmlevel approach with additional, expert knowledge on down-stream and input industries. As a result ELPEN will be statistically more representative, whilst IFCN will provide typical rather than representative results and in greater depth. IFCN is single-farm oriented and product-related. Both physical and economic data is taken into account. ELPEN, on the other hand uses less detailed physical and economic data to describe farm activities but more detailed non- economic information such as environmental issues, ethical aspects and activities beyond production (in particular marketing). IFCN involves variables and calculations on investment activities, country specific taxes (on farm and income), management and off-farm incomes. ELPEN uses aggregate statistics as a data base whereas IFCN uses panel data collected from the network participants. It can therefore be concluded that IFCN data is usually more up to date than ELPEN data. IFCN provides access to decision-makers and the expert knowledge which exists within farming. It is a tool which enables the adjustment strategies of farmers to be rapidly assessed. IFCN is forward-looking, but can also be used for ex-post studies

10 Bringing these features together, the following areas of potential co-operation between IFCN and ELPEN can be identified: Cross-checking of data: Is ELPEN data realistic compared with IFCN data? Is IFCN data typical or representative if compared to ELPEN data? Cross-checking of results: Bringing ELPEN's results more up to date by using IFCNresults. Validating IFCN models and results by using ex-post data from ELPEN. New partners for IFCN might originate from ELPEN and vice versa. IFCN could provide ELPEN with information for the proposed "knowledge tables", in particular expert knowledge on adjustment strategies. References Deblitz C, Hemme T, Isermeyer F, Knutson R, Anderson D, Goertz D, Möller C, and Riedel J Report on the first IFCN meeting. IFCN-Report 1/1998. FAL, Braunschweig-Völkenrode. also published under IFCN-homepage: Deblitz C, Hemme T, Isermeyer F, Knutson R, Anderson D, Goertz D, Möller C, and Riedel J New comparison of total milk production costs worldwide. Agra-europe (London ed.),