In agriculture, overfertilization with liquid organic manures (LOM) is causing environmental issues including eutrophication of non-agricultural ecosystems and nitrate pollution of groundwater. To avoid such problems, a precise and demand-oriented fertilization with LOM is needed. This can only be achieved if the nutrient composition of the LOM is known. However, traditional chemical analysis is cost- and time-intensive and furthermore dependent on a representative sample. Optical spectrometry in the visible and near-infrared range could provide an efficient alternative, if a chemometric calibration assures sufficient accuracy. To improve chemometric calibration, this study investigated several spectral preprocessing and regression algorithms, and compared predictions based either on dry or wet weight concentration. In addition, the capability of low-cost spectrometers was evaluated by simulating low-resolution spectra with smaller wavelength ranges. The reflectance spectra of 391 pig manure, 155 cattle manure, and 89 biogas digestate samples were used to predict plant macronutrients (N, P, K, Mg, Ca, S), micronutrients (Mn, Fe, Cu, Zn, B), dry matter (DM) and pH. The experiments demonstrate the general aptness of optical spectrometry to accurately predict DM, pH and all nutrients except boron in pig, cattle, and digestate LOM, even with simulated low-cost spectrometers. Best results show r² between 0.80 and 0.97, ratios of performance to interquartile distance (RPIQ) between 2.1 and 7.8, and mean absolute errors normalized by the median (nMAE) between 5 and 36 %. The regression methods PLSR, LASSO, and least angle regression predominantly performed best. The innovative preprocessing methods named simple ratios (SR) and normalized differences (ND) proved to be very useful algorithms, especially for N and P predictions, outperforming the accuracy of classical techniques in several cases. Concentrations on dry weight basis improved predictions of K, Mn, and pH.

Nutrient concentrations in livestock manures and biogas digestates show a huge variability due to disparities in animal husbandry systems concerning animal species, feed composition, etc. Therefore, a nutrient estimation based on recommendation tables is not reliable when the exact chemical composition is needed. The alternative, to analyse representative fertilizer samples in a standard laboratory, is too time- and cost-intensive to be an accepted routine method for farmers. However, precise knowledge about the actual nutrient concentrations in liquid organic fertilizers is a prerequisite to ensure optimal nutrient supply for growing crops and on the other hand to avoid environmental problems caused by overfertilization. Therefore, spectrometric methods receive increasing attention as fast and low-cost alternatives. This review summarizes the present state of research based on optical spectrometry used at laboratory and field scale for predicting several parameters of liquid organic manures. It emphasizes three categories: (1) physicochemical parameters, e.g., dry matter, pH, and electrical conductivity; (2) main plant nutrients, i.e., total nitrogen, ammonium nitrogen, phosphorus, potassium, magnesium, calcium, and sulfur; and (3) micronutrients, i.e., manganese, iron, copper, and zinc. Furthermore, the commonly used sample preparation techniques, spectrometer types, measuring modes, and chemometric methods are presented. The primarily promising scientific results of the last 30 years contributed to the fact that near-infrared spectrometry (NIRS) was established in commercial laboratories as an alternative method to wet chemical standard methods. Furthermore, companies developed technical setups using NIRS for on-line applications of liquid organic manures. Thus, NIRS seems to have evolved to a competitive measurement procedure, although parts of this technique still need to be improved to ensure sufficient accuracy, especially in quality management.

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 R²s 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 R²s 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 R²s 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.

Detailed knowledge of a soil’s lime requirement (LR) is a prerequisite for a demand-based lime fertilization to achieve the optimum soil pH and thus sustainably increasing soil fertility and crop yields. LR can be directly determined by the base neutralizing capacity (BNC) obtained by soil-base titration. For a site-specific soil acidity management, detailed information on the within-field variation of BNC is required. However, soil-base titrations for BNC determination are too laborious to be extensively applied in routine soil testing. In contrast, visible and near-infrared spectroscopy (visNIRS) is a time and cost-effective alternative that can analyze several soil characteristics within a single spectrum. VisNIRS was tested in the laboratory on 170 air-dried and sieved soil samples of nine agricultural fields of a quaternary landscape in North-east Germany predicting the soil’s BNC and the corresponding lime requirement (LR_BNC) at a target pH of 6.5. Seven spectral pre-processing methods were tested including a new technique based on normalized differences (ND). Furthermore, six multivariate regression methods were conducted including a new method combining a forward stagewise subset selection algorithm with PLSR (FS-PLSR). The models were validated using an independent sample set. The best regression model for most target variables was FS-PLSR combined with the second Savitzky-Golay derivation as pre-processing method achieving R²s from 0.68 to 0.82. Finally, the performance of the direct prediction of LR_BNC (R² = 0.68) was compared with an indirect prediction that was calculated by the predicted BNC parameters. This resulted in slightly higher correlation coefficients for the indirect method with R² = 0.75.

Proper application of a fertilizer requires precise knowledge of its nutrient composition. In the case of liquid organic manures (LOM), this information is often lacking due to heterogeneous nature of these fertilizers. Published “book values” of nutrient contents present the average from a wide range of possible nutrient characteristics, but usually differ considerably from the concentration in a particular manure. Thus, chemical analyses are recommended before applying the specific LOM. Unfortunately, this is usually too costly and time-intensive in practical farming. On-farm analysis by optical spectrometry in the visible and near-infrared (Vis-NIR) range is considered as an efficient alternative. However, calibration of Vis-NIR spectrometry for LOM is challenging as shown in many studies. One reason is LOMs’ tendency to rapidly segregate into a fuzzy continuum with liquid and solid properties. By separating LOM into well-defined liquid and solid phases and measuring them separately, calibration of Vis-NIR spectrometry might be improved. In this study, the effects of four sample pre-treatment techniques on the prediction accuracy of macronutrients (N, P, K, Mg, Ca, S), micronutrients (B, Mn, Fe, Cu, Zn), dry matter and pH of LOM using visible and near infrared spectrometry were comprehensively investigated. The concentrations were referred either to wet basis or to dry matter basis. For the study, a total of 163 samples, separated in two similar LOM sets (pig, cattle, digestates), were either dried, filtered, or centrifuged and always compared to non-treated samples. The experiments demonstrate that in comparison to raw samples (Ø r² = 0.85) neither filtering (Ø r² = 0.76 for filtrates and Ø r² = 0.71 for filter residues), centrifugation (Ø r² = 0.59 for supernatants and Ø r² = 0.79 for pellets), nor drying (Ø r² = 0.74) revealed to be a helpful preparation step significantly improving prediction results, independent from referring to wet or dry basis concentrations.

Soil organic carbon (SOC) is a key indicator of soil quality and plays an important role in mitigating atmospheric carbon dioxide, while being sustainably increased through appropriate land management practices. To analyse or monitor SOC concentrations, in situ soil spectrometry is an adequate technology for fast, inexpensive, non-destructive, and accurate in situ measurements. To facilitate its practical application, we tested three miniaturized, portable, low-cost spectrometers covering the visual and near-infrared range (400–2400 nm) in the laboratory and in the field and developed a site-independent calibration model valid for a farm to directly predict soil moisture (SM) and soil organic carbon (SOC) without the need for additional sensor data calibration. Their performance was additionally compared against a high-resolution benchtop spectrometer (325–2450 nm) using rewetted samples. In total, 1003 spectra were recorded for each low-cost spectrometer and 505 for the high-resolution spectrometer.Results from the lab tests show that the three low-cost spectrometers accurately predicted SM (R² = 0.97, RMSEP = 1.5 %) and SOC (R² = 0.86, RMSEP = 0.21 %) while the high-resolution spectrometer performed only slightly better (SM: R² = 0.99, RMSEP = 1 %; SOC: R² = 0.88, RMSEP = 0.2 %). Also, during field application, the low-cost spectrometers achieved good predictions (SM: R² = 0.95, RMSEP = 1.3 %; SOC: R² = 0.85, RMSEP = 0.14 %). On average, best predictions were achieved using support vector machines (SVM) and combined spectral ratio (CSR) transformations. In contrast, Kubelka-Munk-transformations and artificial neural networks (ANN) performed significantly worse Moreover, external parameter orthogonalization (EPO) was tested in order to remove the disturbing effect from moisture variations. However, EPO did not lead to the expected improvements in prediction accuracy. Finally, excluding one low-cost spectrometer in the NIR range still resulted in satisfying accuracies, which would save costs and measurement time.