Towards a better understanding of soil organic carbon variation in Madagascar
Towards a better understanding of soil organic carbon variation in Madagascar
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DOI：
10.1111/ejss.12473
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发表时间：
2017-10
影响因子：
4.2
通讯作者：
A. Andriamananjara;N. Ranaivoson;T. Razafimbelo;J. Hewson;N. Ramifehiarivo;A. Rasolohery;R. H. Andrisoa;M. Razafindrakoto;M. Razafimanantsoa;N. Rabetokotany;R. H. Razakamanarivo
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作者：
A. Andriamananjara;N. Ranaivoson;T. Razafimbelo;J. Hewson;N. Ramifehiarivo;A. Rasolohery;R. H. Andrisoa;M. Razafindrakoto;M. Razafimanantsoa;N. Rabetokotany;R. H. Razakamanarivo
土壤有机碳（SOC）是陆地生态系统重要的碳库。基于土壤性质和环境因子的有机碳预测有助于描述土壤有机碳的空间和垂直分布；然而，各种预测方法的有效性和准确性，包括经典的和最近开发的模型方法，需要对热带土壤和环境进行测试。采用随机森林（RF）和线性混合效应模型（LMM）预测马达加斯加东部土壤有机碳储量的空间和垂直变化。利用地形、气候、土壤类型和基于植被的变量作为预测变量，模拟了1 m以下不同土壤深度的有机碳储量。在不同深度范围内，LMM预测土壤有机碳储量最准确；海拔高度、土壤粘粒含量、土地利用和降水是预测最相关的因素。土壤有机碳模型的预测精度随着土壤深度的增加而下降，导致LMM的均方根预测误差（RMSE）在1.98 Mg ha−1 （90-100 cm深度）至5.54 Mg ha−1 （10-20 cm深度）之间，可以解决43-68%的土壤有机碳储量变化。解释变量对模型的固定效应的贡献率为2.6 - 28.2%，而随机效应对总方差的贡献率为21.7 - 35.0%。本研究强调了LMM在预测热带土壤有机碳储量方面的优势，同时考虑了采样相关的随机效应。这些结果可用于改进马达加斯加的有机碳制图。
Soil organic carbon (SOC) is an important carbon pool in terrestrial ecosystems. Prediction of SOC based on soil properties and environmental factors helps to describe the spatial and vertical distribution in SOC; however, the effectiveness and accuracy of various prediction methods, including classical and recently developed model approaches, need to be tested for tropical soil and environments. In this study, random forest (RF) and linear mixed effects model (LMM) approaches were tested to predict the spatial and vertical variation of SOC stocks in Eastern Madagascar. Topography, climate, soil types and vegetation‐based variables were used as predictor variables for modelling SOC stocks at different soil depths to 1 m. The LMM was the most accurate method for predicting SOC stocks for different depth ranges; altitude, soil clay content, land use and precipitation were identified as the most relevant factors for prediction. The accuracy of prediction in SOC modelling decreased with increasing soil depth, resulting in a root mean square prediction error (RMSE) that ranged from 1.98 Mg ha−1 (90–100‐cm depth) to 5.54 Mg ha−1 (10–20‐cm depth) for LMM, which resolved 43–68% of the variation in SOC stocks. Explanatory variables, which contributed to the fixed effect of the model, explained from 2.6 to 28.2% of the total variance, whereas the random effect contributed from 21.7 to 35.0%. This study emphasizes the strength of LMM for predicting SOC stocks in tropical soil taking into account the random effect related to sampling. These results could be used to improve SOC mapping in Madagascar.