Consideración final

In the context of simulating GHG emissions and their interaction with corresponding control systems with the economy, a range of economic models has been conceived. The economic part of those models has been developed as programming model optimizing single enterprises or as market models balancing supply and demand. The coexistence of both approaches suggests that each one features specific advantages and disadvantages. These particularities shall be shortly described in the following by giving an assortment of (programming) models that are concerned with the same topic as the current study.

3.1.3.1 FASOM

LEE and MCCARL (2005) ran a market model, a partial equilibrium model, the so-

called Forest and Agricultural Sector Optimisation Model (FASOM), to analyse the mitigation potential in the Kyoto relevant LULUCF sector (land use, land use change, and forestry), including land-use change, in the United States of America. Among other issues, the research questions addressed sequestration potential, price development of food and non-food products, and labour market.

The model’s objective function maximises the producers’ and consumers’ surplus and uses a calculation of market equilibrium based on econometrically deduced price elasticities of demand and supply. The maximisation is subject to resource limitations, and policy constraints, but also to a number of ecological parameters. The eligible activities under carbon sequestration Articles 3.3 and 3.4 of the Kyoto Protocol can be analysed for forests, cropland and grassland management.

By its multi-periodic dynamic formulation, FASOM makes it easy to analyse perennial crops and forest growth while including predicted trends of price or yield developments. FASOM, like other market models, features the endogenous simulation of prices by clearing supply and demand (market model). Against this extensive simulation of macro-economic coherences stand the uncertainty of long- term predictions in dynamic programming models, and the aggravated integration of the latest technologies in case time-series data is lacking.

3.1.3.2 CAPRI

The model applied by PÉREZ DOMÍNGUEZ (2006) is a combination of a macro-

economic equilibrium module and a micro-economic supply module. He simulated GHG abatement costs for the EU-15’s agricultural sector. Designed at the end of the

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nineties to analyse the impact of the CAP (Common Agricultural Policy) on regional agricultural income, the so-called CAPRI-model (Common Agricultural Policy Regional Impact) has been undergoing various steps of refinement and amplified application until the current day.

CAPRI’s core is constituted by a market module and supply module that are iteratively25 coupled and brought to an equilibrium condition. The supply module simulates regional agricultural supply under the assumption of profit maximising producers. Profit maximisation thereby is achieved by determining the optimal combination of production, optimal intensities, and minimal cost combinations under a set of constraints arising from nutrient requirements, scarcity of production factors, and so forth. Within the market module, world-wide agriculture and agro-industry is broken down into twelve trading zones, each one featuring systems of supply, human consumption, animal feed and processing functions. The parameters of the market module are based on elasticities. Consistency between the supply and the market module, the supply module operating on administrative regions and the market module operating on national or higher scale, is obtained by the aggregation of the supply module’s coefficients and the subsequent calibration through PMP (Positive Mathematical Programming) methods. Results of the market module are vice versa scaled down to fit the supply module.

Advantageous to this approach is the consideration of the entire agricultural sector including verification through top-down statistics and endogenous simulation of sales prices. However, policy impacts on the farm level are more difficult to capture than they are by pure linear programming models, due to the PMP assumptions. Farm type specific impacts cannot be analysed.

3.1.3.3 EU-EFEM

The foundation for EU-EFEM was laid down in the late nineties with a model on the impact of regional agricultural and environmental policies (KAZENWADEL, 1999).

Within an integrated approach, environmental parameters from precedent studies (KRAYL, 1993) were integrated into an economic framework. The economic

framework was provided by an optimisation model of the linear programming type, maximising the total gross margin of farms. The regional scope was on the Southern German state of Baden-Württemberg, where the model was simulated within eight

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homogeneous regions with respect to natural conditions. By that time the modelling units were “region typical farms” for which all relevant farm activities of the plant and animal lines of production were represented (SCHÄFER et al., 2003).

This original model was enhanced by ANGENENDT (2003) to form the so-called

EFEM (Economic Farm Emission Model). She complemented further environmental coefficients, especially emission factors, and made the model ready for the analysis of climate relevant gas emissions. Further refinement was achieved by

SCHÄFER (2006) through coupling EFEM with a biophysical model, thereby integrating

site-specific emission estimates. Like Kazenwadel, he modelled the Baden- Wurttemberg agricultural sector on the basis of region typical farms for a range of political scenarios.

The EU-EFEM model, a further extension of EFEM, allows for the variety of factors necessary to accurately depict the EU-15’s agricultural diversity to be taken into account. The bottom-up approach features the simulation of region typical farms and the consecutive extrapolation of single farm results to regional results. Through maintaining region typical farms as modelling units, similarity to real farms with respect to the resource endowment is assured. Extrapolation controls the representation of the regional production capacities and at the same time observes farm structure.

EU-EFEM is a Mixed-Integer Programming (MIP) model that maximises the farm gross margin. In contrast to FASOM and CAPRI, EU-EFEM is a pure supply model, i.e. prices are exogenous. The model features high regional resolution, and a deep disaggregation of agricultural production and framework conditions. The bottom-up approach, from farms to regions, allows for an accurate farm level modelling and leaves open the option of dismantling the trade-off between high resolution and regional coverage. By the promotion of data exchange with other model types, via ad hoc established interfaces, some of the disadvantages of micro-economic modelling are surpassed. The data exchange is via linkages that are described in section 3.2.3.

The applied linear programming respective MIP is popular for the planning and optimisation of complex farm level production decisions. A standard linear programming problem for k farms, n production activities and m constraints can be formulated as:

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Objective function: max Фk (xk) = gk×xk

Subject to: Ak×xk ≤ zk

xk ≥ 0

Xk denotes the n-vector of production activities of a farm and gk the n-vector of

objective values. Ak defines the matrix of production coefficients for all constraints,

whereby zk determines the m capacities. Summarising the single gross margins of

production activities, the objective value Фk expresses a farm’s total gross margin.

An applicability criterion of LP-models is the constancy of input-output- coefficients, a precondition which can be partially relaxed by introducing binary decision variables. MIP, in contrast to linear programming, allows for the integration of binary variables. Binary variables (or decisions) are sometimes necessary to depict agricultural policies on farm-level, for example. A prominent example from agricultural policy is the European set-aside obligation of the AGENDA 2000, which is only mandatory for farms exceeding a certain total cereal production quantity. This jumping relation between cereal production and set-aside obligation can be depicted by the MIP approach. MIP is also a valuable tool for the integration of fixed costs dependent upon the realisation of a certain project or production alternative.