Optical Spectrometry to Determine Nutrient Concentrations and other Physicochemical Parameters in Liquid Organic Manures: A Review

Abstract

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.

Growth and Yield Responses of Leafy Vegetables Grown in Organic and Conventional Agriculture Systems

“…Some liquid fertilizers are evaluated during the growing season which are top-dressed and reported increased organic matter content, microorganism populations in the soil and stable soil pH [27]. Liquid fertilizer is a prerequisite to ensure optimal nutrient supply for growing crops and on the other hand to avoid environmental problems caused by over fertilization [28].…”

Nutrient quantification of livestock slurry and digestate using a low-cost handheld NIR spectrometer and multi-biomass calibration models

“…Any commercial NIR sensor installable on slurry tankers can be certified to prove its accuracy in predicting TS, TKN, TAN, P, and K content in cattle manures, pig manures, and digestates. This certification sealed by the German Agricultural Society (DLG) assures that at least 60% of the measured samples differ less than 25% from the reference lab value and no measured sample exceeds the 35% of relative deviation (Horf et al, 2022). The performance of NeoSpectra was evaluated using the validation set for both PLS and SVM models, distinguishing its predictive capacity on TS, TKN, TAN, P for each type of slurry but also for all the slurries together.…”

“…These results demonstrate that NeoSpectra is eligible for DLG certification, meeting the criteria for the entry level of certification. The achievement of this threshold should be considered positively as commercial NIR sensors that have obtained DLG certification still present a high variability in the performance they can guarantee (Horf et al, 2022). Finally, it is important to point out that the scanning of samples in lab performs comparably with the on-field real-time analyses of slurry, because both measurement systems are based on scans in reflectance; in addition, during the field distribution the machines normally scan a flow of slurry, making available several scans that compensate a possible lower stability of the measurement.…”

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms

“…In recent years, horticultural and postharvest crops have been studied for the classification of phenotypes, varieties, and genetic crop plant analysis [ 9 , 10 ]. In this way, the use of remote sensing tools such as RGB tools, Multispectral Image Sensors (MSI), Hyperspectral Imaging Sensors (HSI), and Visible–Near-Infrared–Shortwave-Infrared (VIS-NIR-SWIR) spectroscopy combined with artificial intelligence algorithms (AIAs) can improve the accuracy and precision of crop classification [ 10 , 11 ]. This is of great importance, considering that the global food economy is estimated at approximately $750 billion annually, and AIAs can help reduce food waste and improve consumer satisfaction.…”