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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
Phosphorus recycling from sewage sludge will be obligatory in Germany from 2029. Phosphorus content determination in sewage sludge is crucial to assess the prescribed recycling rates. Currently, German law regards sample preparation using aqua regia digestion in a microwave or under reflux conditions as well as instrumental phosphorus determination by ICP-OES, ICP-MS, or photometric determination with ammonium molybdate as equivalent. However, it is questionable whether these methods are indeed equivalent regarding phosphorus quantification in sludges near the limit of 20 g/kg for mandatory recycling. To answer this question, 15 sewage sludges of 11 different wastewater treatment plants were investigated with all permitted method (digestion and measurement) combinations. Moreover, one sewage sludge was also examined in an interlaboratory comparison (ILC) with 28 participants. This study shows that the above-mentioned methods differ in some cases significantly but across all method combinations and sludges, phosphorus recovery was between 80 and 121% after normalization to the grand mean (average of 15 sludges between 85 and 111%). The ILC and the examination of 15 sludges produced largely similar results. There is a tendency to higher phosphorus recovery after microwave digestion compared to reflux digestion and ICP-OES measurements determine higher phosphorus contents than ICP-MS and photometric phosphorus determination. As a result, the authors recommend ICP-OES determination of phosphorus in sewage sludge after microwave digestion.
Knowing the exact nutrient composition of organic fertilizers is a prerequisite for their appropriate application to improve yield and to avoid environmental pollution by over-fertilization.
Traditional standard chemical analysis is cost and time-consuming and thus it is unsuitable for a rapid analysis before manure application. As a possible alternative, a handheld X-ray fluorescence (XRF) spectrometer was tested to enable a fast, simultaneous, and on-site analysis of several elements.
A set of 62 liquid pig and cattle manures as well as biogas digestates were collected, intensively homogenized and analysed for the macro plant nutrients phosphorus, potassium, magnesium, calcium, and sulphur as well as the micro nutrients manganese, iron, copper, and zinc using the standard lab procedure. The effect of four different sample preparation steps (original, dried, filtered, and dried filter residues) on XRF measurement accuracy was examined. Therefore, XRF results were correlated with values of the reference analysis. The best R2 s for each element ranged from 0.64 to 0.92. Comparing the four preparation steps, XRF results for dried samples showed good correlations (0.64 and 0.86) for all elements. XRF measurements using dried filter residues showed also good correlations with R2 s between 0.65 and 0.91 except for P, Mg, and Ca. In contrast, correlation Analysis for liquid samples (original and filtered) resulted in lower R2 s from 0.02 to 0.68, except for K (0.83 and 0.87, respectively). Based on these results, it can be concluded that handheld XRF is a promising measuring system for element analysis in manures and digestates.
Within the framework of precision agriculture, the determination of various soil properties is moving into focus, especially the demand for sensors suitable for in-situ measurements. Energy-dispersive X-ray fluorescence (EDXRF) can be a powerful tool for this purpose. In this study a huge diverse soil set (n = 598) from 12 different study sites in Germany was analysed with EDXRF. First, a principal component analysis (PCA) was performed to identify possible similarities among the sample set.
Clustering was observed within the four texture classes clay, loam, silt and sand, as clay samples contain high and sandy soils low iron mass fractions. Furthermore, the potential of uni- and multivariate data evaluation with partial least squares regression (PLSR) was assessed for accurate Determination of nutrients in German agricultural samples using two calibration sample sets. Potassium and iron were chosen for testing the performance of both models. Prediction of these nutrients in 598 German soil samples with EDXRF was more accurate using PLSR which is confirmed by a better overall averaged deviation and PLSR should therefore be preferred.