A fuzzy-multi criteria evaluation approach for the evaluation of forest capability for beekeeping use | ||
| تحقیقات جنگل و صنوبر ایران | ||
| Article 5, Volume 30, Issue 1 - Serial Number 87, March 2022, Pages 39-56 PDF (833.79 K) | ||
| Document Type: Research article | ||
| DOI: 10.22092/ijfpr.2022.352367.2026 | ||
| Authors | ||
| Ali Najafifar* 1; M. Mohammadpour2; M. Tahmasebi2 | ||
| 1Corresponding author, Assistant Prof., Research Division of Natural Resources, Ilam Agricultural and Natural Resources Research and Education Center, AREEO, Ilam, Iran | ||
| 2Assistant Prof., Research Division of Natural Resources, Ilam Agricultural and Natural Resources Research and Education Center, AREEO, Ilam, Iran | ||
| Abstract | ||
| Integrated forest ecosystem management plays an important role in controlling degradation and balancing its various functions for the sustainable use of all available capabilities. In this regard, beekeeping is one of the most suitable multiple uses of the forest. The purpose of this study is to introduce a fuzzy multi-criteria Evaluation approach (F-MCE) to determine the forest's capability for beekeeping and compare it with the conventional non-fuzzy approach. In this regard, beekeeping capability in 16825 hectares of Zagros forests in Kabirkouh region of Ilam province, Iran was evaluated by the two approaches in four classes and the results were compared. Evaluation criteria were the same in both approaches and included the percentage of the canopy of plant attractiveness classes, flowering period, slope, air temperature, relative humidity, prevailing wind speed, and access to road and water. The weight of the criteria was determined using the Analytic Network Process (ANP). In F-MCE, the values of the criteria were determined using fuzzy membership functions and the criteria were combined by the Weighted Linear Combination (WLC) method. Then, beekeeping capability was digitally evaluated using the Multi-Criteria Evaluation method (MCE). The results showed that 40% of the area of beekeeping classes differed in the two evaluation approaches and the main part of this difference (35.6%) is related to the areas that were evaluated in the F-MCE approach in the good class (S1) and in the traditional approach in Moderate class (S2). This difference in results can be attributed to the different methods used in F-MCE in which the adverse effect of boolean and subjective criteria evaluation in the traditional method is avoided through their digital evaluation with fuzzy logic. | ||
| Keywords | ||
| ANP; fuzzy logic; MCE; Zagros forests | ||
| References | ||
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