Forest stand and soil types determine soil organic carbon storage in the Middle Atlas region of Morocco using machine learning models

• Published in: All Earth Vol. 36; no. 1; pp. 1 - 10
• Main Authors: El Mderssa, Mohamed, Elmalki, Meysara, Whalen, Joann K., Ikraoun, Hicham, Aliyat, Fatima Zahra, Dallahi, Youssef, Abbas, Younes, Nassiri, Laila, Ibijbijen, Jamal
• Format: Journal Article
• Language: English
• Published: Taylor & Francis Group 31.12.2024
• Subjects: Central Middle Atlas; climate change; machine learning; soil organic carbon; Soil typology
• ISSN: 2766-9645; 2766-9645
• DOI: 10.1080/27669645.2024.2400432

Forest soils often contain more carbon (C) than living trees, with significant variation in soil organic carbon (SOC) stocks due to stand type and soil characteristics. This study evaluates SOC stocks in the Moroccan Middle Atlas forests using field measurements and machine learning models. Soil profiles across 16 forest types were analysed, identifying soil typology and measuring SOC stocks. Spatial variation in SOC stocks was influenced by stand type and substratum nature, as determined through supervised extrapolation analysis. SOC stocks ranged from 35 t SOC ha-1 on tree-free land to 252 t SOC ha-1 under mixed cedar (Cedrus atlantica) and zeen oak (Quercus canariensis) stands. To enhance estimation accuracy, Random Forest (RF) and Gradient Boosting Machine (GBM) models were tested. The GBM model outperformed the RF model, with an RMSE of 6.97 t C ha-1 and R2 of 0.99, compared to RF’s RMSE of 10.28 t C ha-1 and R2 of 0.44. For better SOC stock assessment in deeper soil layers, a strategy involving more surface soil samples (0–30 cm) combined with numerical modelling of proximal soil properties is recommended.