The Objectives and Priorities for the Azorean Dairy Farmers’ Decisions - The Agricultural Economics of the 21st Century - page 143

In the case study, the education level of Azores dairy farmers is low and it will be
very hard to have a suitable answer. Then, MAUT allows to estimate this function,
indirectly.
The data of Azorean dairy farms, for the years 1990-1996, were obtained on the
Farm Accountancy Data Network (FADN), complemented with INRA ( 1988 ) and
the previous research of Berbel and Barros ( 1993 ).
Based on the seven production system types of Azorean dairy farms defined by
Silva and Berbel ( 2006 ), according to specialization (meat, milk, mixed) and
intensification criteria (intensive, medium, extensive), only the dairy milk typology
was selected for this case study (Type I, II, and III—grazing systems), because
these types of farms have a bigger impact on greenhouse gas (methane emissions)
in the Azorean animal grazing system. Three groups were distinguished by Silva
and Berbel ( 2006 ), using FADN, according to its intensification: Group I—medium
intensive grazing systems (1.4-2.4 cows per ha), Group II—low intensive grazing
systems (less than 1.4 cows per ha), and Group III—high intensive grazing systems
(more than 2.4 cows per ha).
The Multi-Criteria methodology used in this research (MAUT) was developed
according to Sumpsi et al. ( 1996 ) and used by Berbel et al. ( 1999 ). It follows four
main steps:

1. To establish a set of objectives that can influence the farmers' decision. In this
research objectives were defined according to the literature (economic and
social) and the new development of CAP (environmental nature). Besides, a
survey by inquiry was done to the Azorean dairy farms to point the most
important three objectives in their decision making process. Five objectives
were found: profit maximization, risk minimization, labor seasonality minimi-
zation, leisure maximization, and deviations to the goal of total labor

minimization.
2. To determinate the square matrix, according to the number of objectives, that is
the “payoff” matrix for above five objectives (five lines and five columns),
through the optimization of each objective. The ideal (the best value in the
optimization) and anti-ideal values (the worst values in optimization) were also
defined in this phase.
3. To obtain the real values for the objective function, through the literature and
statistical data research and based on inquiries to the farmers.
4. To obtain the set of weights ( W j ) that indicates the ranking of the objectives
followed by a farmer elicited, which reproduces their behavior and reflects the
farmers' preference by solving the weighting goal programming approach.
5. If the weights found in (4) were satisfactory, the process finishes and, finally, the
utility function is estimated. If the weights weren't satisfactory, there is a need to
search another possible solution.

In order to get a solution, Amador et al. ( 1998 ) propose three alternative criteria
to get a solution: the L 1 criterion and the Manhattan utility function ( ), the

Tchebycheff function, and an intermediate criterion (a mix of Tchebycheff and
Manhattan). The first was chosen because that criterion is widely used in most

μ