Agronomic characterization of anaerobic digestates with near-infrared spectroscopy. (1st September 2022)

• Record Type: Journal Article
• Authors: Zennaro, Bastien; Marchand, Paul; Latrille, Eric; Thoisy, Jeanne-Chantal; Houot, Sabine; Girardin, Cyril; Steyer, Jean-Philippe; Béline, Fabrice; Charnier, Cyrille; Richard, Charlotte; Accarion, Guillaume; Jimenez, Julie
• Is Part Of: Journal of environmental management. Volume 317(2022)
• Publication Date: 2022-09-01
• Subjects: Near infrared spectroscopy -- Anaerobic digestion -- Digestate -- Characterization -- Agronomic value -- Chemometrics; AD anaerobic digestion -- NIR Near infrared -- PLSR partial least squares regression -- OM organic matter -- DM dry matter -- TOC total organic carbon -- N nitrogen -- Norg organic nitrogen -- P phosphorus -- K potassium -- TKN total Kjeldahl nitrogen -- SNV standard normal variate -- RPD ratio of performance to deviation -- RMSE Root Mean Square Error -- SEL Standard Error of Laboratory; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2022.115393
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: Anaerobic digestion is an increasingly widespread process for organic waste treatment and renewable energy production due to the methane content of the biogas. This biological process also produces a digestate (i.e., the remaining content of the waste after treatment) with a high fertilizing potential. The digestate composition is highly variable due to the various organic wastes used as feedstock, the different plant configurations, and the post-treatment processes used. In order to optimize digestate spreading on agricultural soils by optimizing the fertilizer dose and, thus, reducing environmental impacts associated to digestate application, the agronomic characterization of digestate is essential. This study investigates the use of near infrared spectroscopy for predicting the most important agronomic parameters from freeze-dried digestates. A data set of 193 digestates was created to calibrate partial least squares regression models predicting organic matter, total organic carbon, organic nitrogen, phosphorus, and potassium contents. The calibration range of the models were between 249.8 and 878.6 gOM.kgDM −1, 171.9 and 499.5 gC.kgDM −1, 5.3 and 74.1 gN.kgDM −1, 2.7 and 44.9 gP.kgDM −1 and between 0.5 and 171.8 gK.kgDM −1, respectively. The calibrated models reliably predicted organic matter, total organic carbon, and phosphorus contents for the whole diversity of digestates with root mean square errors of prediction of 70.51 gOM.kgDM −1, 34.84 gC.kgDM −1 and 4.08 gP.kgDM −1, respectively. On the other hand, the model prediction of the organic nitrogen content had a root mean square error of 7.55 gN.kgDM −1 and was considered as acceptable. Lastly, the results did not demonstrate the feasibility of predicting the potassium content in digestates with near infrared spectroscopy. These results show that near infrared spectroscopy is a very promising analytical method for the characterization of the fertilizing value of digestates, which could provide large benefits in terms of analysis time and cost. Highlights: NIR spectroscopy was successfully used for digestate composition prediction. A fast and cost-efficient agronomic characterisation of digestates was proposed. Organic matter, C, N and P contents were accurately predicted from NIR spectra. The feasibility of predicting K content in digestates with NIR was not demonstrated. Regular characterization of digestates can enhance their management in agriculture.