Infrared spectroscopy prediction of soil microbial properties across Australian soils: Drivers and limitations

Soil microbial properties are increasingly recognised as a crucial metric of both soil condition and soil carbon dynamics. However, these properties remain underutilised due to challenges in quantifying these properties at scale. This study assesses the potential for Near (NIR) and Mid (MIR) Infrared spectroscopy, as well as Granger Ramanathan Averaging (GRA) models that integrate predictions from both spectra simultaneously, to provide reliable estimations of microbial properties when incorporating a range of soil samples collected across Australia. Given that these properties are not spectrally active, the accuracy of their predictions relies on correlations with physicochemical properties such as organic matter, clay minerals and soil moisture. Using soil samples from across Australia, the most effective models for DNA yield, microbial respiration (MR) and N-acetyl-β-D-glucosaminidase (NAG) enzyme activity achieved R 2 of 0.7, 0.63 and 0.62, though microbial biomass carbon (MBC) and β-glucosidase (BG) enzyme activity predictions were more modest with R 2 = 0.52 and 0.49, respectively. MIR consistently outperformed NIR, whereas data fusion models were comparable to MIR across all properties, except for a slight improvement in MBC from R 2 = 0.46 to R 2 = 0.52. From the correlational analysis, the key properties driving these predictions were moisture for both enzymes (r = 0.35-0.36) and organic carbon for all other properties (r = 0.34-0.56), as indicated by significant wavenumbers in the MIR spectrum. However, the reliance on the moisture peak highlights a potential limitation in predicting properties measured from field-condition soils using spectra generated from pre-processed, air-dried samples. Overall, these findings are generally encouraging for predicting microbial properties from the MIR spectrum, supporting their future use in assessing soil condition and monitoring carbon dynamics. Verifying these correlations is crucial to ensuring the wider applicability of these algorithms across diverse samples and to expanding spectral libraries. • Infrared spectroscopy shows potential to predict various soil microbial properties • MIR consistently outperformed NIR, with fusion models yielding similar results • TOC was most significant for MBC, microbial respiration and DNA yield • Moisture was significant for both BG and NAG enzyme activities • Further research should consider the variable moisture content between analyses