Visible and near‐infrared (vis–NIR) spectroscopy is a promising technology for the analysis of different soil quality parameters. In this study, we used in‐situ vis–NIR spectroscopy in association with partial least squares regression to predict the total and the mineral (nitrate + ammonium) nitrogen content, the permanganate oxidizable carbon (POXC), as well as the ratio of soil organic carbon‐to‐clay content in different agricultural soils in Switzerland. These parameters can indeed be used as indicators of soil quality in response to agronomic practices. To this goal, a total number of 134 soil samples were used for carbon‐, total nitrogen‐ and clay‐related parameters, whereas 69 soil samples were used for the mineral nitrogen‐related parameters. We found that the partial least squares regression model can successfully predict the total nitrogen and the POXC content as well as the ratio of soil organic carbon‐to‐clay content (ratio of performance to interquartile range, RPIQ > 2.62, R2 > 0.73, Lin's concordance correlation coefficient > 0.83). As concerns the mineral nitrogen, it was not possible to successfully predict this parameter by vis–NIR spectroscopy. By demonstrating the possibility to reliably predict POXC content and the soil organic carbon‐to‐clay ratio, we show that vis–NIR can be also used to analyse soil parameters associated with both the quality of organic carbon and the structural quality of agricultural soils.

中文翻译：

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通过原位光谱预测农业土壤中的氮、活性炭和有机碳与粘土的比率


可见光和近红外（vis-NIR）光谱是分析不同土壤质量参数的一项有前途的技术。在本研究中，我们使用原位可见近红外光谱与偏最小二乘回归来预测总氮和矿物（硝酸盐+铵）氮含量、高锰酸盐可氧化碳（POXC）以及土壤比例瑞士不同农业土壤中有机碳与粘土的含量。这些参数确实可以用作响应农艺实践的土壤质量指标。为了实现这一目标，总共 134 个土壤样本用于碳、总氮和粘土相关参数，而 69 个土壤样本用于矿质氮相关参数。我们发现偏最小二乘回归模型可以成功预测全氮和 POXC 含量以及土壤有机碳与粘土含量之比（性能与四分位距之比，RPIQ > 2.62，R2 > 0.73，Lin's一致性相关系数 > 0.83）。至于矿物氮，不可能通过可见近红外光谱成功预测该参数。通过证明可靠预测 POXC 含量和土壤有机碳与粘土比率的可能性，我们表明 vis-NIR 还可用于分析与有机碳质量和农业土壤结构质量相关的土壤参数。