Optimal soil carbon sampling designs to achieve cost-effectiveness: a case study in blue carbon ecosystems

Researchers are increasingly studying carbon (C) storage by natural ecosystems for climate mitigation, including coastal ‘blue carbon’ ecosystems. Unfortunately, little guidance on how to achieve robust, cost-effective estimates of blue C stocks to inform inventories exists. We use existing data (492 cores) to develop recommendations on the sampling effort required to achieve robust estimates of blue C. Using a broad-scale, spatially explicit dataset from Victoria, Australia, we applied multiple spatial methods to provide guidelines for reducing variability in estimates of soil C stocks over large areas. With a separate dataset collected across Australia, we evaluated how many samples are needed to capture variability within soil cores and best methods for extrapolating C to 1 m soil depth. We found that 40 core samples are optimal for capturing C variance across 1000’s of kilometres but higher density sampling is required across finer scales (100-200 km). Accounting for environmental v...研究人员日益关注利用自然生态系统固碳以实现气候减缓目标，其中便包括滨海‘蓝碳’（blue carbon）生态系统。遗憾的是，当前鲜有针对如何获得可靠且具成本效益的蓝碳储量估算结果以支撑碳库存编制的指导性方案。本研究依托现有492个岩芯数据，针对获取可靠蓝碳储量估算所需的采样强度提出优化建议。我们采用澳大利亚维多利亚州的大尺度空间显式数据集，通过多种空间分析方法，为降低大尺度区域土壤碳储量估算的变异性提供实践指南。我们借助全澳范围内采集的另一套数据集，评估了捕捉土壤岩芯内部碳储量变异性所需的样本量，以及将碳储量外推至1米土层深度的最优方法。研究结果表明，在数千公里的大尺度范围内，40个岩芯样本为捕捉碳储量变异的最优样本量；但在100-200公里的精细尺度下，则需采用更高密度的采样方案。若纳入环境变量……