Livelihood alternatives model for sustainable rangeland management: a review of multi-criteria decision-making techniques

Although a set of appropriate livelihood alternatives has already been developed to approach sustainable rangeland management (SRM), determining an appropriate livelihood model for supporting policy makers still remains to be a challenge. Livelihood alternatives are affected by multiple factors such as livelihood capital, vulnerability contexts as well as policies, institutions and processes which can be identified by stakeholders from different perspectives. Accordingly, determining appropriate livelihood alternatives is a multifaceted challenge that requires multi-criteria decision-making (MCDM) techniques. This paper aims to review MCDM methods that have the potential to be applied in SRM. It discusses how different MCDM techniques can be used and which techniques are well matched to determine appropriate livelihood alternatives. First, it justifies the need for decision support systems followed by an explanation of the most common MCDM techniques. Among them, two techniques, namely analytic hierarchy process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), are found to be the most suitable MCDM in the case of SRM. Furthermore, based on the reviews on different hybrid approaches, AHP-TOPSIS is introduced as a superior approach to select appropriate livelihood alternatives. Accordingly, AHP is introduced to elicit the relative importance of livelihood criteria and TOPSIS is employed to provide a score for livelihood alternatives. As a conclusion, the application of AHP-TOPSIS approach is proposed where many decision criteria, alternatives and stakeholders are involved. Subsequently, a methodological framework to determine a livelihood model is also developed. This study concludes that, as well as recognizing the theory of appropriate livelihood alternatives, the application of MCDM techniques can be further pursued toward devising a workable policy framework for SRM. At the end, we have elaborated future methodological issues to be considered when selecting feasible alternatives to resolve the current challenges in SRM.