Methodological tool to evaluate farm performance
This tool, from the FACEPA project, explores the suitability of a range of methodologies used to evaluate various facets of farm performance, including through the use of Operational Competitiveness Ratings Analysis (OCRA), Data Envelopment Analysis (DEA), Stochastic Frontier Analysis (SFA) and Positive Mathematical Programming (PMP).
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Output Description
FACEPA develops, implements, and validates a ‘general cost of production model’ (GECOM) based on German, Italian, Dutch, French and Bulgarian national FADN data and EU aggregate FADN data. Besides this core model, FACEPA explored the suitability of other methodologies, including:
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OCRA can calculate appropriate performance measurements, analyse the relationship between production cost structures and farm performance, examine the efficiency, and make specific reference to economies of scale and competitiveness of the crop and dairy sectors in the participant countries. OCRA can be used when data in the FADN database are missing or contain extremely low or high input/output values.
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The relationship between efficiency and performance is frequently investigated with DEA, which constructs an efficient production frontier using the best performing farm business of the sample. The FADN database is an essential source of information for DEA. For this reason, the FADN database has been used in a wide range of performance analyses using DEA and including the study of transition economies and the access of New Member States.
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SFA is a parametric production frontier method that yields efficiency estimates or efficiency scores of individual producers. Efficiency scores vary across producers, and the sources of inefficiency can be identified. SFA provides a powerful tool for examining the effects of policy interventions. Many SFAs use FADN data to explore the impact of various agricultural policy measures or changes in the EU or international policy regimes such as the trade regime.
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The methodology based on PMP is used for recovering the production-specific costs for agricultural activities collected by the FADN database. The FADN, at the European level, doesn’t contain information about the variable costs associated with the different farm activities but only the total variable costs at the farm level. As a result, all analyses aiming to evaluate the production allocation decisions cannot be carried without costs derived from external sources. The use of PMP can mitigate this restriction.
Relevance for monitoring and evaluation of the CAP
Method to support the evaluation of measures and policies on farm performance:
FACEPA showcases the use of FADN data to measure farm performance, analyse the relationships between production cost structures and farm performance, examine farm efficiency and inefficiency, and make specific reference to economies of scale and competitiveness. The various methods can overcome specific data difficulties such as outliers, to lift data constraints or fill in data gaps. These methods can be useful in evaluating indicators related to farming performance and competitiveness or examining the impacts of various measures on a farm’s performance.
The cost production model (GECOM) is a valuable source of information for competitiveness analysis. FADN data is the basis for product-level data so that evaluators can show the gross margin of one product or unit cost for another product. In the case of gross margin, they can allocate variable costs to different products, and in the case of unit cost, they need to allocate the total cost to different products. A sophisticated methodology is needed for this procedure, and the cost production model is such a methodology. The data can also be used for agricultural policymaking, for instance, in Hungary, gross margin and total cost data has been used to build the income stabilisation policy.
The tool describes a range of econometric evaluation methods using real-life examples of FADN data from products and Member States that address existing evaluation issues. Each of the techniques presented and examined can be adapted to address specific policy evaluation questions in any Member State. However, all methods require considerable knowledge of econometrics, mathematical programming and quantitative techniques in general and access to farm level FADN data.
Relevance of the output per CAP Objectives
- Specific Objective 1 - Ensure a fair income for farmers
- Specific Objective 2 - Increase competitiveness
Additional output information
Data collection systems used:
- FADN(FSDN)
Type of output:
- Methodology
Associated evaluation approaches:
- Cost and benefit analysis
- Impact evaluation ex post
Spatial scale:
- Farm holding
Project information
Farm Accountancy Cost Estimation and Policy Analysis of European Agriculture. FACEPA developed tools and methods to analyse production costs in European agriculture using FADN data. It has provided information, expertise and economic models on the cost of production for various agricultural products.
Specific objectives:
- To address the usefulness and appropriateness of the FADN data to measure the cost of production for agricultural commodities.
- To develop a ‘general’ cost of production model for EU agriculture.
- To test and implement this cost model in an EU context on a large scale.
- To assess the relationship between cost structure and farm performance, farm technology, environmental quality and farm heterogeneity.
- To provide methodological improvements to the ‘general’ cost of production model.
- To evaluate agricultural policy measures using models based on the production cost estimates.
Project’s timeframe: 2008 – 2011
Contacts of project holder: Yves Surry, Swedish University of Agricultural Sciences (yves.surry@slu.se)
Territorial coverage: Belgium, Bulgaria, Estonia, France, Hungary, Italy, Sweden, The Netherlands