Soil spectroscopy was applied for predicting soil organic carbon (SOC) in the highlands of Ethiopia. Soil samples were acquired from Ethiopia’s National Soil Testing Centre and direct field sampling. The reflectance of samples was measured using a FieldSpec 3 diffuse reflectance spectrometer. Outliers and sample relation were evaluated using principal component analysis (PCA) and models were developed through partial least square regression (PLSR). For nine watersheds sampled, 20% of the samples were set aside to test prediction and 80% were used to develop calibration models. Depending on the number of samples per watershed, cross validation or independent validation were used. The stability of models was evaluated using coefficient of determination ( ), root mean square error (RMSE), and the ratio performance deviation (RPD). The (%), RMSE (%), and RPD, respectively, for validation were Anjeni (88, 0.44, 3.05), Bale (86, 0.52, 2.7), Basketo (89, 0.57, 3.0), Benishangul (91, 0.30, 3.4), Kersa (82, 0.44, 2.4), Kola tembien (75, 0.44, 1.9), Maybar (84. 0.57, 2.5), Megech (85, 0.15, 2.6), and Wondo Genet (86, 0.52, 2.7) indicating that the models were stable. Models performed better for areas with high SOC values than areas with lower SOC values. Overall, soil spectroscopy performance ranged from very good to good. 1. Introduction Ethiopia is one of the largest countries in Africa. The vast majority of the society (~80%) depends on agriculture which contributes around 42% of the growth domestic product (GDP) [1]. Despite its strategic importance for the country’s economic development, the agricultural sector suffers from low efficiency, population pressure, ineffective land management, and unfavourable land use practices leading to widespread land degradation. Still, the country is strongly committed to environmental protection, rehabilitation, and sustainable land management as evident from Article 92 of its constitution [2], and various internationally supported initiatives have been launched aiming to stop and reverse this trend. Among others, the Growth and Transformation Plan (GTP) is the major one. To address these challenges, efficient and affordable land management practices are required in all regions of the country and their effectiveness should be analysed and monitored. Alongside, there is pronounced need to improve knowledge on soil resources and to collect reliable data on soil’s state and dynamics, allowing for operational assessment and monitoring of this important resource more precisely [3]. Such information is critically important for
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