Distortions in a multi-level co-financing system: the case of the agri-environmental programme of Saxony-Anhalt



Agrarwirtschaft 56 (2007), Heft 7

und standortangepasste Landbewirtschaftung, MSL)”. Fed-
eration and federal states share the funding of such meas-
ures at the ratio of 60% (federation) and 40% (federal
state). Other measures, which are not part of the GAK, do
not receive federal grants.

Urfei (1999: 140) characterised this mixed co-financing
system of agri-environmental measures between EU, federa-
tion, and federal states as follows:

The EU has a high financial responsibility and a low
impact on the objectives.

The federation has a strong impact on the objectives and a
small share in total financing.

The federal states have the biggest impact on the objec-
tives, decision-making, and on the development of agri-
environmental measures, while the financial responsibility
is very small.

When the case study was carried out there were the follow-
ing conditions in Saxony-Anhalt, which is an “objective 1”
region: the EU covered 75%, the federation covered 15%,
and Saxony-Anhalt covered 10% of the expenditures on
MSL-measures. For the other measures the EU and the
federal states shared the expenditures at the ratio of 75%
and 25%. The structure of co-financing and the intergov-
ernmental grants are of no direct importance for farmers
taking part in agri-environmental programmes. However,
there are important implications for the regional budget and
regional policy-making as will be shown in the following
chapters.

2.2 The linear programming approach

The following analysis is based on an interactive linear
programming approach which was developed for support-
ing budgeting decisions about the agri-environmental pro-
gramme of Saxony-Anhalt (Kirschke et al., 2004a und
2004b). In order to decide about priorities and to determine
budget allocations, relevant political measures need to be
chosen, consensus about the most important objectives
needs to be reached amongst stakeholders, the impact of
measures on the objectives has to be assessed, and relevant
restrictions for decision-making have to be considered.

This task can be tackled step by step in discussions with
stakeholders and decision-makers using the method of inter-
active programming. Kirschke and Jechlitschka (2002,
2003) as well as Jechlitschka, Kirschke and Schwarz
(2007) report how to implement a linear programming
approach in MS-Excel© for formulating structural and agri-
environmental programmes.

Under the assumption of constant marginal and average
coefficients the following linear objective function can be
defined:

n

(1) Z1 =zu- Bi

i =1

with:

Z1         objective 1

Bi        budgetary expenses for measure i

i = 1, ..., n Index of agri-environmental measures considered
z1i        constant marginal and average coefficient of the

objective function describing the impact of the
budgetary expenses for measure i on objective 1.
For considering two objectives, an aggregated objective
function can be formulated as follows:

(2)   Z = (1 - α )Z1 + α∙Z 2

with (1-α) and α being weighting factors and 0 ≤ α ≤ 1.

Hence, the programming approach can be formulated as
follows:

nn

(3) max Z = (1 - α y∑ z υBi + αz 2 iBi

B1,...,Bn

subject to:


n

Σ ari∙Bi

i =1

br


for r = 1,


..., m and


Bi 0 for i =1, ..., n

where:

r = 1, ..., m index of restrictions (equations or inequalities)
a
ri          coefficient of restriction r for measure i

br          right hand side of restriction r.

The idea of interactive programming is to develop and use
such a linear programming model in a communication
process using the knowledge and the assumptions of rele-
vant actors. The aim is to support the decision-making
process and to increase the transparency of underlying
assumptions for the results. The approach also helps to
facilitate the learning process of actors and policy makers,
thus improving the basis for decision-making. The perspec-
tive is not to replace decision-making of actors and policy
makers coming up with “an optimal policy” (Geurts and
Joldersma, 2001; Walker et al., 2001; Munda, 2004),
but to support actors and policy-makers in an effective way.
The modelling approach, used for the calculations in this
article, was applied to design the agri-environmental pro-
gramme of Saxony-Anhalt for the financial period from
2004 to 2008. It has been developed and used in several
workshops based on the assumptions of stakeholders and
decision-makers in the region. In the following a brief out-
line of the specific model structure is given which is also
illustrated in table 1.

Nine groups of measures have been used as activities in the
modelling approach which consist of several single meas-
ures each. Thus, the modelling approach was used to con-
sider the strategic situation on an aggregated level. The
measures are defined as follows:

general extensive grassland use (including all grassland of
the farm) (M1);

specific extensive grassland use (single grassland areas
and sheep grazing) (M2);

specific extensive grassland use (single grassland areas
and cattle grazing) (M3);

organic farming (M4).

These measures belong to the group called “Market-
oriented and locally adapted land management” (MSL)
(MLU, 2003). Another measure is:

Environmental protective cultivation of special cultures
(vegetables, medicinal and spice herbs, pip and stone fruit
as well as vine and hop) (M5) (MLU, 2002a).

And finally, special nature conservation measures (VNS -
“Vertragsnaturschutz”) are considered (MLU, 2002b):

298




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