## 3 Results and Discussion  
Principally, the conventional method of evaluating agricultural land suitability depends on the biophysical properties of the soil. This approach often adheres to two primary methodologies: qualitative and quantitative. Qualitative approaches classify land suitability into predetermined categories, such as \"very highly suitable\", \"moderately suitable\", or \"not suitable\", according to diagnostic criteria [83]. By contrast, quantitative approaches involve mathematical formulae to calculate an index, therefore providing a more accurate assessment of land capacity [84]. Modern technologies combine expert knowledge, machine learning, and customized models to improve the ability to assess crop suitability in different scenarios with more precision and adaptability [85]. In our case, the results belong to the category of studies that use a quantitative model to categorize all land parcels into suitability classes, taking into account current and future conditions. The implementation of the suggested model yields findings which allow the classification of the study's area into distinct favorability categories for grassland, orchards, and pasture. For grassland use, the favorability value ranges from 2.1 to 100 (Figure 3). Favorability class IV (low) occupies nearly 42% of the study area, while the medium favorability class (III) occupies 20.54%, the high favorability class (II) occupies 11%, and the very high favorability class (I) occupies 14.77%. This supports an average favorability for this area considering the topography, soil, and climatic characteristics. According to the results of the GIS model, the favorability for pasture ranges from 11.7 to 100, being classified according to the methodology into five favorability classes. The 11 administrative territorial units are covered with 30.65% favorability class III (medium), 25.65% favorability class II (high), and 20.36% favorability class (very high). This fact supports the favorable suitability of this use in the study area (Figure 4). Figure 4: Map of actual favorability classes for pastures. Figure 3: Map of actual favorability classes for grassland. The use of fruit trees is obtained by arithmetic average of the maximum value of the six crops: apple, pear, plum, cherry, apricot, and peach s.a. This operation results in the above map showing the low to very low favorability of this use. The scores given to the use of fruit trees, as a result of the calculation, range from 0 to 50.1, which classifies the use of fruit trees into three favorability classes. Very poor favorability (class V) covers 82.48% of the study area, followed by poor favorability (class IV) with 14.78%, while medium favorability (class III) occupies 3%. This emphasizes that the study area is not suitable for orchard use (Figure 5). The effective identification of suitable agro-ecological zones and the mitigation of climate change effects on land suitability are essential for improving crop yields and promoting the financial stability of small-scale farmers. By precisely identifying these zones, farmers can maximize resource utilization and adjust to evolving environmental conditions, resulting in healthier crop production [86; 87]. A statistical analysis of the fit of the 6385 pasture, grassland, and orchard plots in the study area to the five favorability classes indicates a Receiver Operating Characteristic (ROC) curve value (which is a tool used to evaluate the performance of a model) of 0.823 for orchards, 0.708 for pastures, and 0.806 for grassland (Figure 6). The results obtained from the application of the GIS model for the study area were validated using the ROC curve. The values obtained were 0.823 for orchards, 0.708 for pastures, and 0.806 for grasslands. These values indicate that there is an 83% chance that the model correctly identifies a favorable and unfavorable location for an orchard, a 70.8% chance for pastures, and an 80.6% chance for grasslands.