Accessing and modelling soil organic carbon stocks in Prairies, Savannas, and forests - Ruiz Potma Gonçalves, Daniel; Massao Inagaki, Thiago; Gustavo Barioni, Luis; La Scala Junior, Newton [UNESP]; Roberto Cherubin, Maurício; Carlos de Moraes Sá, João; Eduardo Pellegrino Cerri, Carlos; Anselmi, Adriano (Elsevier, 2024)
Soils are the third largest carbon pool on Earth and play a crucial role in mitigating climate change. Therefore, understanding and predicting soil carbon sequestration is of major interest to mitigate climate change globally, especially in countries with strong agricultural backgrounds. In this study, we used a new database composed of 5029 samples collected up to 1-meter depth in three biomes that are most representative of agriculture, Pampas (Prairie), Cerrados (Savanna), and Atlantic Forest (Forest), to explore soil organic carbon (SOC) stocks and its environmental drivers. The Cerrado (Savanna) biome was the only one where croplands presented higher SOC stocks than native vegetation (Native vegetation 121.23 Mg/ha and croplands 127.85 Mg/ha or 5 % higher). From the tested models, the Random Forest outperformed the others, achieving an R2 of 0.64 for croplands and 0.56 for native vegetation. The accuracy of the models varied with soil depth, showing better predictions in shallow layers for croplands and deeper layers for native vegetation. Our results highlight the importance of clay content, precipitation, net primary production (NPP), and temperature as key predictors for soil carbon stocks in the studied biomes. The findings emphasize the importance of protecting the surface layers, especially in the Cerrado biome, to enhance SOC stocks and promote sustainable land management practices. Moreover, the results provide valuable insights for the development of nature-based carbon markets and suggest potential strategies for climate change mitigation. Enhancing our understanding of SOC dynamics and adopting precise environmental predictors will contribute to the formulation of targeted soil management strategies and accelerate progress toward achieving climate goals.
Keywords: Atlantic forest; Cerrados; Land use change; Machine learning; Pampa; Soil carbon prediction