Large-scale quantification of soil organic carbon (C) and nitrogen (N) stock and their distribution between particulate (POM) and mineral-associated (MAOM) organic matter is deemed necessary to develop land management strategies to mitigate climate change and sustain food production. To this end, diffuse reflectance mid infrared spectroscopy (MIR) coupled with partial least square (PLS) analysis has been proposed as a promising method, because of its low labor and cost, high throughput and the potential to estimate multiple soil attributes. In this paper, we applied MIR spectroscopy to predict C and N content in bulk soils, and in POM and MAOM, as well as soil properties influencing soil C storage. A heterogeneous dataset including 349 topsoil samples were collected under different soil types, land use and climate conditions across the European Union and the United Kingdom. The samples were analyzed for various soil properties to determine the feasibility of developing MIR-based predictive calibrations. We obtained accurate predictions for total soil C and N content, MAOM C and N content, pH, clay, and sand (R2> 0.7; RPD>1.8). In contrast, POM C and N content were predicted with lower accuracies due to non-linear dependencies, suggesting the need of additional calibration across similar soils. These findings confirm the feasibility of MIR spectroscopy as a potential tool to estimate MAOM and enable the monitoring of soil C and N stocks.

中文翻译：

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使用漫反射光谱法作为高通量方法来定量土壤C和N及其在颗粒和矿物相关的有机物组分中的分布

为了制定减轻气候变化和维持粮食生产的土地管理策略，有必要对土壤有机碳和氮的存量进行大规模量化，以及它们在颗粒物（POM）和矿物相关（MAOM）有机物之间的分布。 。为此，已经提出了漫反射中红外光谱法（MIR）和偏最小二乘（PLS）分析相结合的方法，因为它的劳动量少，成本低，通量高并且具有估计多种土壤属性的潜力。在本文中，我们应用了MIR光谱技术来预测散装土壤，POM和MAOM中的碳和氮含量，以及影响土壤碳储量的土壤性质。在不同土壤类型下收集了349个表层土壤样品的非均质数据集，欧盟和英国的土地利用和气候条件。分析了样品的各种土壤特性，以确定开发基于MIR的预测性标定方法的可行性。我们获得了土壤总碳和氮含量，MAOM碳和氮含量，pH，粘土和沙子的准确预测（R2> 0.7； RPD> 1.8）。相反，由于非线性相关性，预测的POM C和N含量的准确性较低，这表明需要对相似土壤进行额外校准。这些发现证实了MIR光谱法作为估计MAOM并监测土壤碳和N储量的潜在工具的可行性。我们获得了土壤总碳和氮含量，MAOM碳和氮含量，pH，粘土和沙子的准确预测（R2> 0.7； RPD> 1.8）。相反，由于非线性相关性，预测的POM C和N含量的准确性较低，这表明需要对相似土壤进行额外校准。这些发现证实了MIR光谱法作为估计MAOM并监测土壤碳和N储量的潜在工具的可行性。我们获得了土壤总碳和氮含量，MAOM碳和氮含量，pH，粘土和沙子的准确预测（R2> 0.7； RPD> 1.8）。相反，由于非线性相关性，预测的POM C和N含量的准确性较低，这表明需要对相似土壤进行额外校准。这些发现证实了MIR光谱法作为估计MAOM并监测土壤碳和N储量的潜在工具的可行性。