Reflectance spectra of soil can be used to estimate the concentrations of organic carbon in soil (SOC). The estimates are more or less imprecise, but spectroscopy is quicker, less laborious and cheaper than conventional dry combustion analysis. Are the greater economy and efficiency sufficient to justify the loss of information arising from errors in estimation? We measured soil spectra with three instruments: a bench-top mid-infrared (mid-IR) (mid-IR_b) spectrometer, a portable mid-IR (mid-IR_p) spectrometer and a portable visible–near infrared (vis–NIR_p) spectrometer. We calculated a quantity E to express the cost-effectiveness of spectroscopic estimates relative to the conventional analysis, by accounting for their inaccuracy, their cost and their capacity, namely the maximum number of samples that can be prepared and measured daily. In all, 562 samples of soil were collected from 150 locations at four depths on a farm. The samples were dried and ground to particle sizes of ≤2 and ≤0.5 mm before measurements were made by dry-combustion analysis. The machine learning algorithm Cubist was used to derive spectroscopic models of SOC concentrations and their uncertainties. We found that the mid-IR_b on the ≤0.5 mm samples was the most accurate and expensive but nevertheless sufficiently cost-effective (large value of E) for determining the organic C. The mid-IR_p was somewhat more accurate, but its E was smaller than vis–NIR_p on corresponding samples because it required more time to record the spectra. We also found that, with the portable spectrometers, the SOC predictions made on the ≤0.5 mm samples were somewhat more accurate than those made on the ≤2 mm samples, but their E was smaller because of the additional cost of sample preparation. The vis–NIR_p on the ≤2 mm samples was the most cost-effective for estimating SOC because it is cheap, accurate and has a large capacity for measurements.

中文翻译：

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反射光谱法估算土壤有机碳的成本效益

土壤的反射光谱可用于估计土壤中有机碳 (SOC) 的浓度。这些估计或多或少不精确，但光谱学比传统的干燃烧分析更快、更省力且更便宜。更大的经济性和效率是否足以证明估计错误引起的信息丢失是合理的？我们用三种仪器测量土壤光谱：台式中红外 (mid-IR) (mid-IR _b ) 光谱仪、便携式中红外 (mid-IR _p ) 光谱仪和便携式可见-近红外 (vis- NIR _p ) 光谱仪。我们计算了一个数量E表达光谱估计相对于传统分析的成本效益，通过考虑它们的不准确性、成本和容量，即每天可以制备和测量的最大样本数量。总共从农场四个深度的 150 个地点收集了 562 个土壤样本。在通过干燃烧分析进行测量之前，将样品干燥并研磨至粒径≤2 和≤0.5 mm。机器学习算法 Cubist 用于推导 SOC 浓度及其不确定性的光谱模型。我们发现≤0.5 mm 样品上的中红外_b是最准确和最昂贵的，但对于确定有机 C 而言具有足够的成本效益（E值较大）。中红外_p更准确一些，但它的E在相应样品上小于 vis-NIR _{p ，因为它需要更多时间来记录光谱。}我们还发现，使用便携式光谱仪，对≤0.5 mm 样品的 SOC 预测比对≤2 mm 样品的预测更准确，但由于样品制备的额外成本，它们的E更小。≤2 mm 样品上的 vis-NIR _p是估算 SOC 最具成本效益的方法，因为它便宜、准确且测量容量大。