Forest stand and soil types determine soil organic carbon storage in the Middle Atlas region of Morocco using machine learning models
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 pro...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | All Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/27669645.2024.2400432 |
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| author | Mohamed El Mderssa Meysara Elmalki Joann K. Whalen Hicham Ikraoun Fatima Zahra Aliyat Youssef Dallahi Younes Abbas Laila Nassiri Jamal Ibijbijen |
| author_facet | Mohamed El Mderssa Meysara Elmalki Joann K. Whalen Hicham Ikraoun Fatima Zahra Aliyat Youssef Dallahi Younes Abbas Laila Nassiri Jamal Ibijbijen |
| author_sort | Mohamed El Mderssa |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-29e96309fd2742d9b320792d76baf103 |
| institution | Kabale University |
| issn | 2766-9645 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | All Earth |
| spelling | doaj-art-29e96309fd2742d9b320792d76baf1032024-12-09T07:46:39ZengTaylor & Francis GroupAll Earth2766-96452024-12-0136111010.1080/27669645.2024.2400432Forest stand and soil types determine soil organic carbon storage in the Middle Atlas region of Morocco using machine learning modelsMohamed El Mderssa0Meysara Elmalki1Joann K. Whalen2Hicham Ikraoun3Fatima Zahra Aliyat4Youssef Dallahi5Younes Abbas6Laila Nassiri7Jamal Ibijbijen8Polyvalent Unit in Research and Development, Polydisciplinary Faculty, Sultan Moulay Sliman University, Beni Mellal, MoroccoNational Water and Forests Agency, Rabat, MoroccoDepartment of Natural Resource Sciences, Macdonald Campus of McGill University, Ste-Anne-de-Bellevue, CanadaUnit “Environment and Valorization of Microbial and Plant Resources”, Faculty of Sciences, Moulay Ismail University, Meknes, MoroccoUnit “Environment and Valorization of Microbial and Plant Resources”, Faculty of Sciences, Moulay Ismail University, Meknes, MoroccoPlant Physiology and Biotechnology Team, Center of Plant and Microbial Biotechnology, Biodiversity and Environment, Faculty of Sciences, Mohamed V University in Rabat, MoroccoPolyvalent Unit in Research and Development, Polydisciplinary Faculty, Sultan Moulay Sliman University, Beni Mellal, MoroccoUnit “Environment and Valorization of Microbial and Plant Resources”, Faculty of Sciences, Moulay Ismail University, Meknes, MoroccoUnit “Environment and Valorization of Microbial and Plant Resources”, Faculty of Sciences, Moulay Ismail University, Meknes, MoroccoForest 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.https://www.tandfonline.com/doi/10.1080/27669645.2024.2400432Soil typologyCentral Middle Atlasmachine learningclimate changesoil organic carbon |
| spellingShingle | Mohamed El Mderssa Meysara Elmalki Joann K. Whalen Hicham Ikraoun Fatima Zahra Aliyat Youssef Dallahi Younes Abbas Laila Nassiri Jamal Ibijbijen Forest stand and soil types determine soil organic carbon storage in the Middle Atlas region of Morocco using machine learning models All Earth Soil typology Central Middle Atlas machine learning climate change soil organic carbon |
| title | Forest stand and soil types determine soil organic carbon storage in the Middle Atlas region of Morocco using machine learning models |
| title_full | Forest stand and soil types determine soil organic carbon storage in the Middle Atlas region of Morocco using machine learning models |
| title_fullStr | Forest stand and soil types determine soil organic carbon storage in the Middle Atlas region of Morocco using machine learning models |
| title_full_unstemmed | Forest stand and soil types determine soil organic carbon storage in the Middle Atlas region of Morocco using machine learning models |
| title_short | Forest stand and soil types determine soil organic carbon storage in the Middle Atlas region of Morocco using machine learning models |
| title_sort | forest stand and soil types determine soil organic carbon storage in the middle atlas region of morocco using machine learning models |
| topic | Soil typology Central Middle Atlas machine learning climate change soil organic carbon |
| url | https://www.tandfonline.com/doi/10.1080/27669645.2024.2400432 |
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