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.

Optimised use of liquid organic manures (LOM) can reduce the consumption of mineral fertilisers and help reduce the emission of nutrients into nonagricultural ecosystems. To achieve this, farmers need to be able to measure the greatly variable nutrient composition of LOMs as accurately as possible on-farm. Since existing on-farm test methods either need to be precisely adapted to each LOM type or take a long time to perform, a test kit was developed to measure the nutrients of different LOM types within a short time. For the study, 619 LOMs (391 pig slurries, 139 cattle slurries, and 89 digestates) were collected from farms in northwest Germany and analysed in the laboratory for total N, ammonium, phosphorus, and potassium. The samples were analysed in parallel using the on-farm test kit consisting of ion-selective ammonium and potassium electrodes and an automatic moisture analyser to evaluate the comparability of the data. Each measurement could be performed in less than 15 min. Regardless of LOM type, regressions with an R² > 0.9 could be generated for total nitrogen, ammonium, and potassium, while the models for phosphorus were not as reliable.

Farmers need to know the actual nutrient concentrations in organic manures in order to enable optimal crop nutrition and to avoid nutrient losses into the environment. Physicochemical quick tests offer a way to estimate nutrient concentrations on a farm but require statistical calculation models to be applied by the farmer. A total of 391 pig slurries, were sampled from practical farms in northwest Germany and ammonium nitrogen (AN), total nitrogen (TN), total phosphorus (TP), total potassium (TK) concentrations, and dry matter were analysed in a laboratory. Furthermore, on-farm quick tests were used to determine electrical conductivity and specific density (SG) and the ammonium concentration with a Quantofix-N-Volumeter. Simple and multiple linear regression models for all lab analysed nutrients and on-farm determined parameters were calculated. The best regression models for all slurries were found for AN based on Quantofix-N-Volumeter (R² = 0.92), TN based on Quantofix-N-Volumeter, and dry matter (R² = 0.95), TP based on dry matter (R² = 0.88), and TK based on Quantofix-N-Volumeter (R² = 0.70). An application for mobile devices is being developed that will allow farmers to use these statistical models in a user-friendly way. Future regression models from other studies might be integrated into the app database so that farmers can calculate nutrient concentrations in pig slurries based on regionalised data.