Preprints
https://doi.org/10.5194/soil-2022-27
https://doi.org/10.5194/soil-2022-27
 
01 Jun 2022
01 Jun 2022
Status: this preprint is currently under review for the journal SOIL.

Improving Models to Predict Holocellulose and Klason Lignin Contents for Peat Soil Organic Matter with Mid Infrared Spectra

Henning Teickner and Klaus-Holger Knorr Henning Teickner and Klaus-Holger Knorr
  • ILÖK, Ecohydrology and Biogeochemistry Group, University of Münster, Heisenbergstr. 2, 48149 Münster, Germany

Abstract. To understand global soil organic matter (SOM) chemistry and its dynamics, we need tools to efficiently quantify SOM properties, for example prediction models using mid infrared spectra. However, the advantages of such models rely on their validity and accuracy. Recently, Hodgkins et al. (2018) developed models to quantitatively predict organic matter holocellulose and Klason lignin contents, two indicators of SOM stability and major fractions of organic matter. The models may have the potential to understand large-scale SOM gradients and have been used in various studies.

A research gap to fill is that these models have not been validated in detail yet. What are their limitations and how can we improve them? This study provides a validation with the aim to identify concrete steps to improve these models. As a first step, we provide several improvements using the original training data.

The major limitation we identified is that the original training data are not representative for a range of diverse peat samples. This causes both biased estimates and extrapolation uncertainty under the original models. In addition, the original models can in practice produce unrealistic predictions (negative values or values >100 mass-%). Our improved models partly reduce the observed bias, have a better predictive performance for the training data, and avoid such unrealistic predictions. Finally, we provide a proof-of-concept that holocellulose contents can also be predicted for mineral-rich samples.

A key step to improve the models will be to collect training data that is representative for SOM formed under various conditions. This study opens directions to develop operational models to predict SOM holocellulose and Klason lignin contents from mid infrared spectra.

Henning Teickner and Klaus-Holger Knorr

Status: open (until 06 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Henning Teickner and Klaus-Holger Knorr

Model code and software

irpeat: Functions to analyse mid infrared spectra of peat samples Henning Teickner, Suzanne Hodgkins https://github.com/henningte/irpeat/tree/dev

Executable research compendia (ERC)

hklmirs: Reproducible Research Compendium for "Improving Models to Predict Holocellulose and Klason Lignin Contents for Peat Soil Organic Matter with Mid Infrared Spectra" and "Predicting Absolute Holocellulose and Klason Lignin Contents for Peat Remains Challenging" Henning Teickner, Klaus-Holger Knorr https://zenodo.org/record/6348561

Henning Teickner and Klaus-Holger Knorr

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Short summary
The chemical quality of biomass can be described with holocellulose (relatively easily decomposable by microorganisms) and Klason lignin (relatively recalcitrant) contents. Measuring both is laborious. In a recent study, models have been proposed which can predict both quicker from mid infrared spectra. However, it has not been analyzed if these models make correct predictions for biomass in soils and how to improve them. We provide such a validation and a strategy for their improvement.