AbstractEven in developing countries that are faced with ecological and socio-economic problems, agricultural areas are constrained by land-use laws to be developed in particular ways. This being the case, the major issues in these areas are a better quality of life and sustainable use of the limited resources. This necessitates planning for sustainable development and evaluating various qualitative and quantitative data in a single framework. Multicriteria or multipurpose decision analysis methods are appropriate for this purpose. Using these methods, physical, economical and social data, as well as planning goals, can be combined and evaluated in the context of sustainable development. These multicriteria methods have been documented widely in a variety of problem areas, but two multicriteria methods, namely AHP (analytic hierarchy process) and ELECTRE II (elimination and choice translating reality), have not been used extensively in agricultural land-use decisions in developing countries. However, in situations where decision-making criteria are non-commensurable, non-comparable or non-countable, and when it is necessary to evaluate the criteria together, as in agricultural land-use decisions, AHP and ELECTRE II methods are warranted. This study reviews these methods briefly and suggests their potential application in the agricultural land-use decision process in a developing country. For this purpose, these methods were sampled in Ziyaret Stream Basin in Adiyaman, which is part of the Güneydoğu Anadolu Projesi (GAP) (South-eastern Anatolian Project) in the Republic of Turkey. The area could be characterized by its rural and agricultural features, although it is under the pressure of Adiyaman urban development. This study shows that both AHP and ELECTRE II methods can be applied successfully for the determination of agricultural land-use priorities, which are an essential part of the quality of life and of sustainable land-use studies.
Heterogeneity in agricultural land use decisions in Argentine Rolling Pampas: The effects on environmental and economic indicators