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Soil organic carbon fractions in the Great Plains of the United States: an application of mid-infrared spectroscopy
Biogeochemistry ( IF 3.9 ) Pub Date : 2021-02-02 , DOI: 10.1007/s10533-021-00755-1
Jonathan Sanderman , Jeffrey A. Baldock , Shree R. S. Dangal , Sarah Ludwig , Stefano Potter , Charlotte Rivard , Kathleen Savage

Spectroscopy is a powerful means of increasing the availability of soil data necessary for understanding carbon cycling in a changing world. Here, we develop a calibration transfer methodology to appropriately apply an existing mid infrared (MIR) spectral library with analyte data on the distribution of soil organic carbon (SOC) into particulate (POC), mineral-associated (MAOC), and pyrogenic (PyC) forms to nearly 8000 soil samples collected in the Great Plains ecoregion of the United States. We then use this SOC fraction database in combination with a machine learning-based predictive soil mapping approach to explore the controls on the distribution of fractions through soil profiles and across the region. The relative abundance of each fraction had unique depth distribution profiles with POC fraction dropping exponentially with depth, the MAOC fraction having a broad distribution with a maxima at 35–50 cm, and the PyC fraction showed a slight subsurface maxima (10–20 cm) and then a steady decline with increasing depth. Within the Great Plains ecoregion, clay content was a strong control on the total amount and relative proportion of each fraction in both the surface and subsoil horizons. Sandy soils and soils in cool semi-arid regions contained significantly more POC relative to the MAOC and PyC fractions. Cultivated soils had significantly less SOC than grassland soils with losses following a predictable pattern: POC > MAOC ≫ PyC. This SOC fraction database and resulting maps can now form the basis for improved representation of SOC dynamics in biogeochemical models.



中文翻译:

美国大平原的土壤有机碳组分:中红外光谱法的应用

光谱学是一种强大的手段,可以提高了解不断变化的世界中碳循环所需的土壤数据的可用性。在这里,我们开发了一种校准转移方法,以将现有的中红外(MIR)光谱库与分析物数据适当地应用于关于土壤有机碳(SOC)分布到颗粒(POC),矿物相关(MAOC)和热解(PyC)中的分析物数据)形成在美国大平原生态区收集的近8000个土壤样品。然后,我们将此SOC分数数据库与基于机器学习的预测性土壤测绘方法结合使用,探索通过土壤剖面和整个区域对分数分布的控制。每个馏分的相对丰度具有独特的深度分布曲线,POC馏分随深度呈指数下降,MAOC馏分分布广泛,最大值在35–50 cm,PyC馏分显示出轻微的地下最大值(10–20 cm),然后随着深度的增加而稳定下降。在大平原生态区内,粘土含量是表层和地下土壤层中各个部分的总量和相对比例的强有力控制。相对于MAOC和PyC组分,沙质土壤和凉爽的半干旱地区的土壤含有更多的POC。耕作土壤的SOC显着低于草原土壤,其损失遵循可预测的模式:POC> MAOC≫ PyC。现在,该SOC分数数据库和生成的图可以构成在生物地球化学模型中改进SOC动力学表示的基础。PyC分数显示出轻微的地下最大值(10–20 cm),然后随着深度的增加而稳定下降。在大平原生态区内,粘土含量是表层和地下土壤层中各个部分的总量和相对比例的强有力控制。相对于MAOC和PyC组分,沙质土壤和凉爽的半干旱地区的土壤含有更多的POC。耕作土壤的SOC显着低于草原土壤,其损失遵循可预测的模式:POC> MAOC≫ PyC。现在,该SOC分数数据库和生成的图可以构成在生物地球化学模型中改进SOC动力学表示的基础。PyC分数显示出轻微的地下最大值(10–20 cm),然后随着深度的增加而稳定下降。在大平原生态区内,粘土含量是表层和地下土壤层中各个部分的总量和相对比例的强有力控制。相对于MAOC和PyC组分,沙质土壤和凉爽的半干旱地区的土壤含有更多的POC。耕作土壤的SOC显着低于草原土壤,其损失遵循可预测的模式:POC> MAOC≫ PyC。现在,该SOC分数数据库和生成的图可以构成在生物地球化学模型中改进SOC动力学表示的基础。粘土含量是地表和地下土壤层中各部分总量和相对比例的有力控制。相对于MAOC和PyC组分,沙质土壤和凉爽的半干旱地区的土壤含有更多的POC。耕作土壤的SOC显着低于草原土壤,其损失遵循可预测的模式:POC> MAOC≫ PyC。现在,该SOC分数数据库和生成的图可以构成在生物地球化学模型中改进SOC动力学表示的基础。粘土含量是地表和地下土壤层中各部分总量和相对比例的有力控制。相对于MAOC和PyC组分,沙质土壤和凉爽的半干旱地区的土壤含有更多的POC。耕作土壤的SOC显着低于草原土壤,其损失遵循可预测的模式:POC> MAOC≫ PyC。现在,该SOC分数数据库和生成的图可以构成在生物地球化学模型中改进SOC动力学表示的基础。

更新日期:2021-02-02
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