Wang Shuai, Xu Li, Adhikari Kabindra, He Nianpeng
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; College of Land and Environment, Shenyang Agricultural University, Shenyang, Liaoning Province 110866, China; Earth Critical Zone and Flux Research Station of Xing'an Mountains, Chinese Academy of Sciences, Daxing'anling 165200, China.
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Earth Critical Zone and Flux Research Station of Xing'an Mountains, Chinese Academy of Sciences, Daxing'anling 165200, China.
Sci Total Environ. 2023 Dec 20;905:167292. doi: 10.1016/j.scitotenv.2023.167292. Epub 2023 Sep 22.
Understanding soil organic carbon (SOC) stocks and carbon sequestration potential in cultivated lands can have significant benefit for mitigating climate change and emission reduction. However, there is currently a lack of spatially explicit information on this topic in China, and our understanding of the factors that influence both saturated SOC level (SOC) and soil organic carbon density (SOC) remains limited. This study predicted SOC and SOC of cultivated lands across mainland China based on point SOC measurements, and mapped its spatial distribution using environmental variables as predictors. Based on the differentiation between SOC and SOC, the soil organic carbon sequestration potentials (SOC) of cultivated land were calculated. Boosted regression trees (BRT), random forest (RF), and support vector machine (SVM) were evaluated as prediction models, and the RF model presented the best performance in predicting SOC and SOC based on 10-fold cross-validation. A total of 991 topsoil (0-20 cm) SOC measurements and 12 environmental variables explaining topography, climate, organism, soil properties, and human activity were used as predictors in the model. Both SOC and SOC suggested higher SOC levels in northeast China and lower levels in central China. The cultivated lands in China had the potential to sequester about 2.13 ± 0.96 kg m (3.25 Pg) SOC in the top 20 cm soil depth. Northeastern China had the largest SOC followed by Northern China, and Southwestern China had the lowest SOC. The primary environmental variables that affected the spatial variation of SOC were mean annual temperature, followed by clay content and normalized difference vegetation index (NDVI). The assessment and mapping of SOC in China's cultivated lands holds significance importance as it can provide valuable insights to policymakers and researchers about SOC, and aid in formulating climate change mitigation strategies.
了解耕地土壤有机碳（SOC）储量和碳固存潜力对于缓解气候变化和减排具有重大意义。然而，目前中国在这一主题上缺乏空间明确的信息，并且我们对影响饱和SOC水平（SOC）和土壤有机碳密度（SOC）的因素的理解仍然有限。本研究基于点SOC测量预测了中国大陆耕地的SOC和SOC，并使用环境变量作为预测因子绘制了其空间分布。基于SOC和SOC之间的差异，计算了耕地的土壤有机碳固存潜力（SOC）。对提升回归树（BRT）、随机森林（RF）和支持向量机（SVM）作为预测模型进行了评估，基于10折交叉验证，RF模型在预测SOC和SOC方面表现最佳。总共991个表层土壤（0 - 20厘米）SOC测量值和12个解释地形、气候、生物、土壤性质和人类活动的环境变量被用作模型中的预测因子。SOC和SOC均表明中国东北地区的SOC水平较高，而中部地区较低。中国的耕地在土壤深度0 - 20厘米处有潜力固存约2.13±0.96千克/平方米（3.25Pg）的SOC。东北地区的SOC最大，其次是华北地区，而西南地区的SOC最低。影响SOC空间变异的主要环境变量是年均温度，其次是粘土含量和归一化植被指数（NDVI）。中国耕地SOC的评估和制图具有重要意义，因为它可以为政策制定者和研究人员提供有关SOC的宝贵见解，并有助于制定气候变化缓解策略。
