Agriculture Reference
In-Depth Information
2000') will be completed. Production costs are estimated taking into
account genetic and agronomic progress regarding all arable crops as well
as technology advances resulting in economies of scale at the biomass
transformation level. Spatial diversity of arable cropping farms cultivating
energy crops is considered.
A systemic analysis has been implemented where all bio-fuel
chains compete for the agricultural land in obligatory set aside regime and
share the earmarked budget fixed by the government. A bi-level
mathematical programming model determines optimal bio-fuel chains'
production levels (quantities of energy crops produced, transformation
units and bio-fuel quantities). Its results include values of total public
expenditure and agents' surpluses as well as GHG emission savings
corresponding to any proposed activity level.
French government fixes both unitary tax exemptions and
production levels for each bio-fuel chain. For this reason, for any unitary
tax exemption set, the model provides the decision-maker (DM) all
possible (resulting in benefits greater than zero for both chains) alternative
industry activity levels per chain. Considering alternatives for all sets of tax
exemptions the DM can select a tax exemption set and a bio-fuel
production level scheme that ensures chain viability and respects budgetary
constraints while including other aspects such as environmental targets.
Thus, a methodology able to propose efficient
4
compromise solutions has
to be applied. For this purpose an interactive multi-criteria method based
on the reference point approach (Wierzbicki 1982) has been implemented.
The chapter is organised as follows. First the context of the
analysis and the case study is presented. A bi-level micro-economic model,
that consists of two parts concerning the agricultural sector and bio-fuel
industry, is presented in the second section. Next, an appropriate decision
support methodology is proposed that integrates multiple criteria, followed
by illustrative examples and concluding remarks.
2. THE CASE STUDY
Energy crops are cultivated mainly in two types of arable crop farms:
sugar-beet producing exploitations and cereal oriented exploitations
producing also rape-seed. Farm Accounting Data Network (FADN) data
(orientations OTEX 13 and 14) on number of farms per type, surfaces
cultivated, and land set aside concerning the above farm types have been
used in this exercise along with detailed data on inputs of arable crops used
by each farm (Sourie
et al.
2000). The year 1996 has been chosen as the
basis because the percentage of land set aside then fixed by the C.A.P. at
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