Determining nutrients, dry matter, and pH of liquid organic manures using visual and near-infrared spectrometry

Michael Horf; Robin Gebbers; Hans-Werner Olfs; Sebastian Vogel

doi:10.1016/j.scitotenv.2023.168045

Determining nutrients, dry matter, and pH of liquid organic manures using visual and near-infrared spectrometry

Sci Total Environ. 2024 Jan 15:908:168045. doi: 10.1016/j.scitotenv.2023.168045. Epub 2023 Nov 2.

Authors

Michael Horf¹, Robin Gebbers², Hans-Werner Olfs³, Sebastian Vogel⁴

Affiliations

¹ Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Agromechatronics, Max-Eyth-Allee 100, 14469 Potsdam, Germany. Electronic address: mhorf@atb-potsdam.de.
² Martin Luther University Halle-Wittenberg, Chair of Agricultural Business Operations, Karl-Freiherr-von-Fritsch-Straße 4, 06120 Halle, Germany.
³ University of Applied Sciences, Plant Nutrition and Crop Production, Am Krümpel 31, 49090 Osnabrück, Germany.
⁴ Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department of Agromechatronics, Max-Eyth-Allee 100, 14469 Potsdam, Germany.

PMID: 37923277
DOI: 10.1016/j.scitotenv.2023.168045

Abstract

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.

Keywords: Biogas digestates; Chemometrics; Low-cost spectrometers; Optical spectroscopy; Preprocessing; Slurry.