Prediction of peat properties from transmission mid-infrared spectra

Abstract

A better understanding of peatland dynamics requires more data on more peat properties than provided by existing databases. These data needs may be addressed with resource-efficient measurement tools, such as models that predict peat properties from mid-infrared spectra (MIRS). High-quality spectral prediction models are already used for mineral soils, but similar developments for peatland-focused research lag behind. Here, we present transmission-MIRS prediction models for peat that are openly available, easy to use, include quality checks to assess prediction quality, and propagate prediction errors. The models target element contents (C, N, H, O, P, S, K, Ca, Si, Ti), element ratios (C/N, H/C, O/C), isotope values (δ13C, δ15N), physical properties (bulk density, loss on ignition (LOI), macroporosity, non-macroporosity, volume fraction of solids, hydraulic conductivity, specific heat capacity, dry thermal conductivity), thermodynamic properties (Gibbs free energy of formation (ΔGf0)), and nominal oxidation state of carbon (NOSC). They are representative for a more diverse set of peat samples than currently existing peat-only models while having a competitive predictive accuracy. Relatively accurate predictions can be made, for example, for many element contents (C, N, O, S, Si, Ca, ΔGf0, O/C, H/C, bulk density, and LOI). Many of these properties are not predicted by existing high-quality prediction models focusing on mineral soils. For some of the target variables, high-quality prediction models focused on mineral soils exist. These models may be more accurate, but reported predictive accuracies are not directly comparable because the training data is imbalanced in the number of organic versus mineral soil samples. We suggest that some soil properties are easier to predict for peat, whereas others are easier to predict for mineral soils, emphasizing that we need new approaches to meaningfully compare prediction errors of spectral models computed on datasets with variable amounts of organic soil samples. Our tests also indicate that replacing δ13C and δ15N measurements with MIRS models probably is unlikely to be feasible due to large prediction errors. Future studies should address the lack of open training and validation data for some peat properties (O, H, NOSC, ΔGf0, LOI, H/C, O/C), the lack of mineral-rich peat samples, and improve and standardize model validation and comparison for models trained on data with very different proportions of peat soils. This study is a step to catch up with high quality standards set by models for mineral soils and provides models for several peat properties for which we could not find descriptions of previous models in the literature. By filling data gaps in the Peatland Mid-Infrared Database, we make a step towards providing the data required to better understand peatland dynamics.