Beltrán-Dávalos Andrés A, Ayala Izurieta Johanna Elizabeth, Echeverría Magdy, Jara Santillán Carlos Arturo, Verrelst Jochem, Delegido Jesús, Merino Agustín, Otero X L
Departament of Soil Science and Agricultural Chemistry, Unit for Sustainable Environmental and Forest Management, University of Santiago de Compostela, 27002 Lugo, Spain.
Group of Research for Watershed Sustainability (GISOCH), Faculty of Sciences, Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba, 060155 Ecuador.
J Soils Sediments. 2025;25(5):1578-1597. doi: 10.1007/s11368-025-04017-7. Epub 2025 Apr 8.
Large soil organic carbon (SOC) reserves and a high soil capacity for SOC storage within an ecosystem contribute to mitigating the release of carbon into the atmosphere. Developing new spatially-explicit SOC estimation methods at local and micro-watershed scales is essential for gaining landscape understanding of SOC variability.
This study provides new insights into the spatial variability of SOC in the Andean páramo soils. A range of variables from different sources (i.e., geophysical, meteorological, topographic, and spectral) were analyzed to identify driving variables to explain the SOC dynamic in the Andean páramo highlands of the Real range in the central region of Ecuador. This information was used to calibrate a SOC prediction model using Classification and Regression Trees (CART) and soil data samples from the 0-30 cm soil horizon.
Eight key variables linking with the SOC storage were used to calibrate the model for SOC estimation with an accuracy of 67% with an RMSE value of 2.17%. Results reveal that sand content emerged as the most significant variable, while taxonomic suborder and protected area variables provided crucial supplementary information. This study improves the ability to detect changes in SOC, particularly in smaller areas where traditional predictors, often more suitable for regional or national assessments, may exhibit insufficient explanatory power.
The Andean páramo highlands of the Real range show high capacity for storing SOC, with values ranging from 3.5% to 19%. This variability highlights the ecosystem's importance as a globally relevant carbon reservoir.
生态系统中大量的土壤有机碳（SOC）储量以及较高的土壤SOC储存能力有助于减少碳向大气中的释放。在局部和微观流域尺度上开发新的空间明确的SOC估算方法对于全面了解SOC变异性至关重要。
本研究为安第斯山地土壤中SOC的空间变异性提供了新的见解。分析了来自不同来源（即地球物理、气象、地形和光谱）的一系列变量，以确定驱动变量，从而解释厄瓜多尔中部地区雷亚尔山脉安第斯山地高地的SOC动态。这些信息被用于使用分类与回归树（CART）和来自0-30厘米土壤层的土壤数据样本校准SOC预测模型。
与SOC储存相关的八个关键变量被用于校准SOC估算模型，准确率为67%，均方根误差值为2.17%。结果表明，砂含量是最显著的变量，而分类亚纲和保护区变量提供了关键的补充信息。本研究提高了检测SOC变化的能力，特别是在较小区域，在这些区域传统预测因子（通常更适合区域或国家评估）可能表现出解释力不足。
雷亚尔山脉的安第斯山地高地显示出较高的SOC储存能力，值范围为3.5%至19%。这种变异性突出了该生态系统作为全球相关碳库的重要性。
