Journal
JOURNAL OF GLOBAL OPTIMIZATION
Volume 42, Issue 2, Pages 143-155Publisher
SPRINGER
DOI: 10.1007/s10898-007-9235-0
Keywords
farmland appraisal; multicriteria; AHP; ANP
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This paper presents an application of the Analytic Network Process (ANP) to farmland appraisal. The purpose of this new methodology is to solve some of the drawbacks found in comparative and capitalisation methods, called classical appraisal methods, which cannot deal with contexts where only partial information is available and/or qualitative variables are used. The ANP is a method based on the Multiple Criteria Decision Analysis (MCDA). Previous works have already applied other MCDA techniques to the appraisal context, such as the Analytic Hierarchy Process (AHP), however they have not been able to handle all the complexities of many real world appraisal problems. The ANP provides a more accurate approach for modelling complex environment because it allows the general study of the quantitative and qualitative explanatory variables of the price and the incorporation of feedback and interdependence relationships among variables. The new proposed methodology has been applied to a case study of a farm located in Valencia (Spain) in order to demonstrate its goodness. Both quantitative and qualitative variables, such as the age of the trees, productivity or water quality, have been considered to assess the market value of the farm. Six farms from the same region have been selected as reference assets. The appraisal problem has been solved in three different ways in order to study the influence of each model on the value of the problem farm. In this study it has been proved that the more information is incorporated into the model, the higher accuracy of the solution. From the results of this work we can conclude that the approach proposed stands out as a good alternative to current farmland appraisal approaches, as it has proven to be useful when data are only partially available, qualitative variables are used and influences among the explanatory variables are present.
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