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- Soil (2)
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Detailed knowledge about soil composition is an important prerequisite for many applications, for example precision agriculture. Current standard laboratory methods are complex and time-consuming but could be complemented by non-invasive optical techniques. Its capability to provide a molecular fingerprint of individual soil components makes Raman spectroscopy a very promising candidate. A major challenge is strong fluorescence interference inherent to soil, but this issue can be overcome effectively using shifted excitation Raman difference spectroscopy (SERDS). A customized dual-wavelength diode laser emitting at 785.2 and 784.6 nm was used to investigate 117 soil samples collected from an agricultural field along a distance of 624 m and down to depths of 1 m. To address soil spatial heterogeneity, a raster scan approach comprising 100 measurement spots per sample was applied. Based on the Raman spectroscopic fingerprint extracted from intense fluorescence interference by SERDS, 13 mineral soil constituents were identified, and even closely related molecular species could be discriminated, for example polymorphs of titanium dioxide and calcium carbonate. For the first time, the capability of SERDS is demonstrated to predict the calcium carbonate content as an important soil parameter using partial least squares regression (R2 = 0.94, root mean square error of cross-validation RMSECV = 2.1%). Our findings demonstrate that SERDS can extract a wealth of spectroscopic information from disturbing backgrounds enabling qualitative and quantitative soil analysis. This highlights the large potential of SERDS for precision agriculture but also in further application areas, for example geology, cultural heritage and planetary exploration.
Mobile proximal gamma-ray spectrometry (GS) is an emerging approach to estimate soil texture directly in the field. It relies on gamma radiation emitted during the natural decay of potassium-40 (K-40) and other isotopes. The K-40 counts are correlated with total K content, mineralogical soil composition and therefore with soil texture. Yet, it is not clear if K fertilisation impacts the ratio of K-40 to total K which would necessitate to take the fertilisation history into account for soil sensing applications. To elucidate this question, a well-documented long-term experiment was selected. The soils of the Rengen grassland experiment (55 plots) were investigated for total K (XRF-K) and for K-40 via mobile proximal GS. No significant differences in XRF-K and K-40 were found between the treatments with and without K fertilisation, although formely published results point to negative and positive K balances, respectively. Fertiliser analysis revealed a ratio of K-40 to total K that was almost identical to the natural abundance of 0.0117%. It was concluded that it is not necessary to take K fertilisation history into account when predicting soil texture from mobile proximal GS on agricultural land for, e.g., precision farming purposes.
In precision agriculture, the estimation of soil parameters via sensors and the creation of nutrient maps are a prerequisite for farmers to take targeted measures such as spatially resolved fertilization. In this work, 68 soil samples uniformly distributed over a field near Bonn are investigated using laser-induced breakdown spectroscopy (LIBS). These investigations include the determination of the total contents of macro- and micronutrients as well as further soil parameters such as soil pH, soil organic matter (SOM) content, and soil texture. The applied LIBS instruments are a handheld and a platform spectrometer, which potentially allows for the single-point measurement and scanning of whole fields, respectively. Their results are compared with a high-resolution lab spectrometer.
The prediction of soil parameters was based on multivariate methods. Different feature selection methods and regression methods like PLS, PCR, SVM, Lasso, and Gaussian processes were tested and compared. While good predictions were obtained for Ca, Mg, P, Mn, Cu, and silt content, excellent predictions were obtained for K, Fe, and clay content. The comparison of the three different spectrometers showed that although the lab spectrometer gives the best results, measurements with both field spectrometers also yield good results. This allows for a method transfer to the in-field measurements
This study uses conventional 87Sr/86Sr and 143Nd/144Nd isotope and interelement ratios of Ca, Sr, K, Mn, Mg and Ti as fingerprints for provenancing ordinary Portland cements (OPC). Herein, the first database of Sr and Nd isotope ratios investigated in OPCs, stemming from 29 cement plants located worldwide, was created. The results show that the Sr isotope ratios of OPCs are higher than those of seawater from the observed geological period. The spread of 143Nd/144Nd in OPCs is not as large as the spread for 87Sr/86Sr isotope ratios. However, the combination of both Sr and Nd isotope ratios provides the potential for distinguishing between cements of different production sites. Most of the OPCs investigated have measurable differences in their 87Sr/86Sr and 143Nd/144Nd isotope ratios, which can be employed as a valuable analytical fingerprinting tool. In the case of equivocal results, divisive hierarchical clustering was employed to help overcome this issue. The construction of geochemical profiles allowed the computing of suitably defined distances between cements and clustering them according to their chemical similarity. By applying this methodology, successful fingerprinting was achieved in 27 out of the 29 ordinary Portland cements that were analysed.