IFM-CAP
Source: Commission modelling inventory and knowledge management system (MIDAS)
Date of Report Generation: Thu Mar 06 2025
Dissemination: Public
© European Union, 2025
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Overview
Acronym
IFM-CAP
Full title
Individual Farm Model for Common Agricultural Policy Analysis
Main purpose
An EU-wide farm level model used to assess the economic and environmental impacts of the Common Agricultural Policy (CAP) by providing changes in land and input use, crop and animal production, farm income and CAP expenditures.
Summary
IFM-CAP is a micro model designed for the ex-ante economic and environmental assessment of the medium-term adaptation of individual farmers to policy and market changes. IFM-CAP was developed by JRC in close cooperation with DG AGRI starting from 2013 for the purpose to improve the quality of agricultural policy assessment upon existing aggregate (regional, farm-group, …) models and to assess distributional effects of policies over the EU farm population. Rather than providing forecasts or projections, the model aims to generate policy scenarios, or ‘what if’ analyses. It simulates how a given scenario, for example, a change in prices, farm resources or environmental and agricultural policy, might affect a set of performance indicators important to decision makers and stakeholders.
IFM-CAP is a comparative static positive mathematical programming model applied to each individual farm from the Farm Accountancy Data Network (FADN) to guarantee the highest possible representativeness of the EU agricultural sector. Farmers are assumed maximizing their expected utility at given yields, product prices and CAP subsidies, subject to resource endowments and policy constraints. The main strengths and capabilities of the model include the possibility to conduct a flexible assessment of a wide range of farm-specific policies and to capture the full heterogeneity of EU commercial farms in terms of policy representation and impacts (e.g. small versus big farms).
IFM-CAP can be applied for ex-ante economic and environmental impact assessment of agricultural and environmental policies at micro (farm) level. For example, IFM-CAP was applied to support the DG AGRI Impact Assessment accompanying the proposal for the CAP post 2020 (SWD/2018/301).
Model categories
Agriculture
Model keywords
optimisation modelagricultureCAPFarm Level ModelEUmicroeconomic analysis
Model homepage
Ownership and Licence
Ownership
EU ownership (European Commission)
Ownership details
Licence type
Non-Free Software licence
The license has one or more of the following restrictions: it prohibits creation of derivative works; it prohibits commercial use; it obliges to share the licensed or derivative works on the same conditions.
Details
Structure and approach
IFM-CAP is a static positive mathematical programming model applied to each individual FADN (Farm Accountancy Data Network) farm (83 292 farms). It assumes that farmers maximise their expected utility at given yields, product prices and CAP subsidies, subject to resource endowments (arable land, grassland and feed) and policy constraints, such as CAP greening restrictions. Farmers’ expected utility is defined following the mean-variance approach with a constant absolute risk aversion specification. Following this approach, expected utility is defined as expected income and the associated income variance. Effectively, it is assumed that farmers select a production plan that minimizes the variance in income caused by a set of stochastic variables for a given expected income level.
Farmer’s expected income is defined as the sum of expected gross margins minus a non-linear (quadratic) activity-specific function. The gross margin is the total revenue including sales from agricultural products and direct payments (coupled and decoupled payments) minus the accounting variable costs of production activities. Total revenue is calculated using expected prices and yields assuming adaptive expectations (based on the previous three observations with declining weights). The accounting costs include the costs of seeds, fertilisers and soil improvers, crop protection, feeding and other specific costs. The quadratic activity-specific function is a behavioural function introduced to calibrate the farm model to an observed base-year situation, as usually done in positive programming models. This function intends to capture the effects of factors that are not explicitly included in the model, such as farmers’ perceived costs of capital and labour, or model misspecifications.
Regarding income variance, most of the models in the literature incorporate uncertainty in the gross margin per unit of activity or in the revenues per unit of activity. The former models assume that prices, yields and costs are stochastic. The latter models either consider that costs are non-random because they are assumed to be known when decisions are made, or are less stochastic than revenues from the farmer’s perspective. Thus, the variance in the gross margin can be approximated by the variance in revenues. In the IFM-CAP framework, the second approach is applied by considering uncertainty only in prices and yields (i.e. revenues) but without differentiating between sources of uncertainty.
IFM-CAP is calibrated for the base year 2012 using cross-sectional analysis (i.e. multiple observations) and Highest Posterior Density (HPD) approach with prior information on regional supply elasticities and dual values of resources (e.g. land rental prices). The calibration to the exogenous supply elasticities is performed in a non-myopic way by taking into account the effects of changing dual values on the simulation response.
The primary data source used to parameterize and calibrate IFM-CAP is individual farm-level data available from the Farm Accountancy Data Network (FADN) database complemented by other external EU-wide data sources such as Farm Structure Survey (FSS), CAPRI database and Eurostat. All farms represented in the FADN sample for the year 2012 (83 292 farms) are included in the model. However, to obtain expected income, past observations (2007–2012) on yields, prices and input costs for these farms are also used for model parameterisation and calibration.
One needs to be aware when applying IFM-CAP that the policy simulations obviously reflect the assumptions in the model. First, the current version of IFM-CAP assumes a fixed farms structure, implying that land can be reallocated only within farms in response to the simulated policy changes. A second potential caveat of the model is that market feedback effects (output price changes) are not taken into account. Third, certain crops are defined in the model as an aggregation of a set of individual crops (e.g. ‘other cereals’). Fourth, FADN includes only commercial farms; small non-commercial farms are underrepresented in the database. A careful analysis of each of these limitations of the current version of IFM-CAP model is needed to be taken into account when analyzing the simulation results.
Input and parametrization
The following list includes the key data inputs used in the IFM-CAP model:
- Utilised Agricultural Area (FADN)
- Arable and grassland (FADN)
- Set of crop and livestock activities (FADN)
- Yields, Prices and Subsidies (FADN)
- Observed activity levels (hectares of crop area and number of livestock) (FADN)
- Farm level feed costs (FADN)
- Farm weighting factor (FADN)
- Land and milk quota rental prices (FADN)
- Prices and yields for fodder crops at MS level(FADN and CAPRI)
- Feed prices at MS level (CAPRI)
- Feed nutrient content (CAPRI)
- Nutrient requirement of animal activities (NRC , IPCC , LfL , CAPRI)
- Price and yield trends(CAPRI)
- Elasticities for feed demand at NUTS2 level (CAPRI)
- Supply elasticities for livestock activities (CAPRI)
- Supply elasticities for crops at NUTS2 level (Jansson and Heckelei, 2011)
- Carcass weights (Eurostat)
- Prices of live animals (Eurostat)
- Out-of quota prices for sugarbeet(Agrosynergie, 2011)
- MS sugarbeet in-quota production (DG-AGRI,2014)
- In- quota prices for sugar beet (Agrosynergie, 2011)
- Soil erosion cover-management factors (Panagos et al., 2015)
Main output
The main outputs/indicators generated by IFM-CAP for a specific policy scenario are the following:
Agronomic/structural indicators:
- Land allocation/crop area (ha)
- Herd size/animal number (heads)
- Livestock density (LU/ha)
- Share of arable land in Utilized Agricultural Area
- Share of grassland in Utilized Agricultural Area
- Land use change (ha)
- Agricultural production (Tons)
- Intermediate Input use (Tons)
Economic indicators:
- Agricultural output (€)
- CAP first pillar subsidies (€)
- CAP second pillar subsidies (€)
- Intermediate input costs (€)
- Variable costs (€)
- Total costs (€)
- Gross farm income (€)
- Net Farm Income (€)
Environmental indicators:
- Biodiversity index
- Soil erosion
Spatial & Temporal extent
The output has the following spatial-temporal resolution and extent:
Parameter | Description |
---|---|
Spatial extent / country coverage | EU Member states 27 |
EU-wide model covering the EU agricultural sector | |
Spatial resolution | NationalEntity |
Representative farms | |
Temporal extent | Medium-term (5 to 15 years) |
Used for medium-term comparative analyses. The time horizon (i.e. Baseline) for running simulation is 2025 and 2030 depending on the policy scenario. | |
Temporal resolution | Years |
Static model, simulations are done for a single time point (year), without any intermediate steps. |
Quality & Transparency
Quality
Model uncertainties
Models are by definition affected by uncertainties (in input data, input parameters, scenario definitions, etc.). Have the model uncertainties been quantified? Are uncertainties accounted for in your simulations?
- response
- no
- details
- The model calibration is estimated based on observed farm data. The scenario simulations are usually done for one data point. The duration of model computational time is long which does not allow to run complex analysis of model uncertainties.
- url
Sensitivity analysis
Sensitivity analysis helps identifying the uncertain inputs mostly responsible for the uncertainty in the model responses. Has the model undergone sensitivity analysis?
- response
- yes
- details
- Sensitivity analysis of model responses to different production shocks. The duration of model computational time is long which does not allow to run complex sensitivity analysis.
- url
Have model results been published in peer-reviewed articles?
- response
- yes
- details
- The model development was peer-reviewed by external experts in the field. Papers using the model were published in peer-review journals.
- url
Has the model formally undergone scientific review by a panel of international experts?
Please note that this does not refer to the cases when model results were validated by stakeholders.
- response
- no
- details
- url
Model validation
Has model validation been done? Have model predictions been confronted with observed data (ex-post)?
- response
- no
- details
- The model calibration is estimated based on observed farm data. The model predictions were not confronted with observed data.
- url
Transparency
To what extent do input data come from publicly available sources?
This may include sources accessible upon subscription and/or payment
- response
- Based on both publicly available and restricted-access sources
Is the full model database as such available to external users?
Whether or not it implies a specific procedure or a fee
- response
- no
- details
- The main model data (i.e. FADN) are confidential and are not publicly available. They are subject to confidentiality agreement with DG AGRI. They can be accessed by requesting them from DG AGRI and signing the confidentiality agreement.
- url
Have model results been presented in publicly available reports?
Note this excludes IA reports.
- response
- yes
- details
- documents
For details please refer to the 'peer review for model validation' documents in the bibliographic references
Have output datasets been made publicly available?
Note this could also imply a specific procedure or a fee.
- response
- no
- details
- Only aggregated data respecting the conditions set in the confidentiality agreement. Individual farm data are not publically available.
- url
Is there any user friendly interface presenting model results that is accessible to the public?
For instance: Dashboard, interactive interfaces...
- response
- no
- details
- url
Has the model been documented in a publicly available dedicated report or a manual?
Note this excludes IA reports.
- response
- yes
- details
Is there a dedicated public website where information about the model is provided?
- response
- no
- details
- url
Is the model code open-source?
- response
- no
- details
Can the code be accessed upon request?
- response
- yes
- details
The model’s policy relevance and intended role in the policy cycle
The model is designed to contribute to the following policy areas
- Agriculture and rural development
- Environment
The model is designed to contribute to the following phases of the policy cycle
- Anticipation – such as foresight and horizon scanning
- Formulation – such as ex-ante Impact Assessments
The model’s potential
The IFM-CAP model is designed to simulate EU-wide economic impacts of the Common Agricultural Policy and farm related policies targeted by the European Green Deal. The IFM-CAP can also be used to model environmental impacts of policies at farm level. The model provides detailed policy impacts at individual farm level on various economic and environmental indicators. More precisely, the IFM-CAP model allows a flexible assessment of a wide range of farm-specific policies; reflects the full heterogeneity of EU farms in terms of policy representation and impacts; covers all main agricultural production activities in the EU; provides a detailed analysis of different farming systems; and estimates the distributional impacts of policies across the farm population.
IFM-CAP was applied to support the following policy initiatives:
- DG AGRI assessment of CAP greening used in the Commission Staff Working Document (CSWD) 'Review of greening after one year’ (see: European Commission (2016), Impact Assessment, SWD(2016) 218 final).
- Scenar 2030 - Pathways for the European agriculture and food sector beyond 2020 (see: M’barek, et al. (2017) Scenar 2030 - Pathways for the European agriculture and food sector beyond 2020, EUR 28797 EN, Publications Office of the European Union, Luxembourg, ISBN 978-92-76-16663-4, doi:10.2760/43791, JRC108449.
- Impact assessment of "CAP post 2020" (see: European Commission (2018), Impact Assessment, SWD(2018) 301 final)
Previous use of the model in ex-ante impact assessments of the European Commission
Use of the model in ex-ante impact assessments since July 2017.
2023SWD/2023/412 final
Impact Assessment accompanying the document Proposal for a Regulation of the European Parliament and of the Council on plants obtained by certain new genomic techniques and their food and feed, and amending Regulation (EU) 2017/625
- Lead by
- SANTE
- Run by
- European Commission
- Contribution role
- baseline and assessment of policy options
- Contribution details
The model helped to assess the following impacts:
- Cost of doing business
2018SWD/2018/301 final
Impact assessment accompanying the document Proposal for a Communication: on modernising and simplifying the common agricultural policy
- Lead by
- AGRI
- Run by
- European Commission
- Contribution role
- baseline and assessment of policy options
- Contribution details
The model helped to assess the following impacts:
- Additional costs on businesses
- Equal treatment of products and businesses
- Inequalities and the distribution of incomes and wealth
- Sustainable production and consumption
- Change in land use