Effect of Structural Variation in Selected Woody Species on Selected Soil Chemical Properties in the Mountain of Hangadi Watershed, Genale Dawa Basin, Ethiopia

Human‐driven land‐use changes have reduced species diversity and soil quality, altering ecosystems within the Hangadi watershed. Imagery was preprocessed, features were extracted, training data were collected, and the Random Forest classifier was applied to produce land‐cover maps. The maps were compared over time to monitor land‐use changes, which were further analyzed in conjunction with soil and vegetation data for a comprehensive land system assessment. One hundred georeferenced composite soil samples were collected at 0–30 cm depth from 100 plots (20 m × 20 m) along 1 km apart transects, analyzed in a laboratory, and mapped using Random Forest. Findings indicate a 41.2% increase in agroforestry and a 47.2% decrease in forested land. Tree heights and diameter at breast height (DBH) in forest and agroforestry areas were comparable, except for specific species: P. adolfi-friederici in agroforestry and C. macrostachyus and D. afromontana in forested areas. Rarefaction analysis of shade coffee trees estimated species richness from selected samples. Higher TN on upper slopes (high = 0.586; low = 0.215) was likely due to household waste, minimal tillage, and liming, improving soil conditions for nitrogen‐fixing microbes. Forest land use showed higher values for available potassium (AvK) (high = 300.7; low = 47) and electrical conductivity (EC) (high = 0.4797; low = 0.1176). Conversely, agroforestry had relatively higher available phosphorus (AvP) (high = 9.03; low = 2.29), possibly due to improved soil management and parent material characteristics. This study highlights shifts in land use and land cover (LULC) in the Hangadi watershed, emphasizing the growth of agroforestry and decline of forested areas, impacting ecosystem services. Habitat changes have adversely affected woody species. To enhance predictions of soil properties and support ecological restoration, incorporating additional predictors is recommended.