What trees are more suitable for agroforestry implementation? A case study in Northwestern Iran

Abstract

Agroforestry is an integrative farm management approach in which trees are deliberately integrated with other crops. Agroforestry systems can be effective if appropriate trees are chosen based on particular environmental and economic factors. However, it is crucial to identify suitable trees for agroforestry implementation (AI). The objective of the current study was to recognize the most suitable trees for AI in the agricultural lands of Nazar Kahrizi (NK) rural district of Hashtroud city, located in the northwest of Iran using a multi-dimensional approach. The study area was environmentally evaluated using ArcGIS, which led to the creation of 16 classes with different features. Then, based on the preference of 126 local farmers (from 26 villages of NK), 19 native trees were selected for AI assessment. These trees were evaluated and compared considering seven criteria (i.e., frostbite resistance, salinity resistance, sensitivity to drainage, storm resistance, drought resistance, preventing soil erosion, and economic benefits). Finally, a flexible multi-criteria decision analysis (MCDA) tool (PROMETHEE II) was applied to provide a complete ranking of preferred trees from the best to the worst for each class. The findings showed that the agricultural lands should be allocated for planting elaeagnus (about 79.6%, 27,446 ha), almond (13.5%, 4619 ha), quince (4.6%, 1573 ha), apple (1.8%, 635 ha), and walnuts (0.5%, 176 ha). Measurements showed that AI with the recommended trees in the study area will lead to CO₂ sequestration of about 12.96 Mg yr⁻¹. The approach used in this study provides a valuable resource for decision-making in AI evaluations and, therefore, contributes to preserving the lands from degradation and ensures sustainable AI.