Australian Soil Carbon Research Program

This data collection contains measurements of the carbon content and composition in the 0 to 30 cm layer of soil under a range of agricultural management practices in numerous regions around Australia. It also contains the data required to calculate the stocks of soil organic carbon and its components and various climate, topographic and management data associated with each soil collection site.Lineage: Soil carbon contents (total, organic and inorganic) and for some samples total nitrogen content were measured directly. The distribution of soil organic carbon (SOC) in three major SOC fractions (e.g. particulate, humus and resistant organic carbon) was directly measured on a subset of samples using a combination of physical size fractionation and solid-state 13C nuclear magnetic resonance (NMR). All soil carbon and total nitrogen contents were also predicted from mid infrared (MIR) spectra collected for all soil samples. The measured analytical data was used in association with the associated MIR spectra in a partial least squares regression (PLSR) analysis to derive predictive algorithms, and these algorithms were used to generate predicted values. Detailed management histories were collected where available and other potential determinants of SOC content and composition such as climate, soil type and topography were obtained.本数据集收录了澳大利亚多个区域内，多种农业管理措施下0至30厘米土层的碳含量与组成测定数据，同时包含用于计算土壤有机碳及其组分储量所需的相关数据，以及各土壤采样点配套的气候、地形与管理数据。 数据溯源：土壤碳含量（涵盖总碳、有机碳与无机碳）及部分样品的总氮含量均为直接实测值。针对部分样品，研究人员采用物理粒径分级结合固态13C核磁共振波谱法（solid-state 13C nuclear magnetic resonance, NMR），直接测定了土壤有机碳（soil organic carbon, SOC）在三大主要组分（如颗粒态碳、腐殖质碳与惰性有机碳）中的分布情况。此外，针对所有土壤样品采集的中红外（mid infrared, MIR）光谱，被用于预测全部土壤碳与总氮含量。研究人员将实测分析数据与配套的中红外光谱相结合，通过偏最小二乘回归（partial least squares regression, PLSR）分析构建预测算法，并依托这些算法生成预测值。研究人员收集了可获取的详细管理历史，同时获取了其他可能影响土壤有机碳含量与组成的潜在影响因素数据，包括气候、土壤类型与地形条件。