MidDRIFTS-based partial least square regression analysis allows predicting microbial biomass, enzyme activities and 16S rRNA gene abundance in soils of temperate grasslands

Abstract

The objective of this study was to underpin the capability of diffuse reflectance Fourier transform mid-infrared spectroscopy (midDRIFTS)-based partial least squares regression (PLSR) analyses to accurately predict soil microbiological properties across six temperate grassland ecosystems differing in their land-use intensity; three sites of low land-use intensity (low LUI) and three fertilized mown meadows (high LUI). In addition, the potential of midDRIFTS-PLSR analyses for spatial studies between soils of contrasting grassland ecosystems was evaluated. 304 samples were subjected to midDRIFTS-PLSR-based predictions of soil microbial biomass, activities of beta-d-glucosidase, xylosidase and urease, as well as bacterial abundance based on 16S rRNA gene quantification. Accuracies of midDRIFTS-PLSR-based predictions across both LUI were, on basis of the residual prediction deviation (RPD), ‘acceptable’ for all soil microbiological properties, in particular soil microbial biomass (coefficient of determination (R2) = 0.92; RPD = 3.55), urease (0.91; 3.42) and beta-d-glucosidase (0.89; 3.01). Predictions of ‘moderately successful’ accuracy were developed for 16S rRNA gene copies (0.88; 2.93) and xylosidase (0.84; 2.55). Spatial midDRIFTS-PLSR-based predictions between grassland ecosystems were only ‘acceptable’ for soil microbial biomass, while the other studied soil microbiological properties revealed only ‘moderately successful’ predictions. The potential of midDRIFTS-PLSR to predict a range of soil microbiological properties including molecular data with ‘acceptable’ accuracies across the two investigated grassland ecosystems with contrasting land-use intensities was substantiated. However, the prevailing ‘moderately successful’ prediction accuracies between grassland ecosystems were probably due to the dependence on land use-specific data sets for calibration and validation. For prospective application of cost- and time-efficient midDRIFTS-PLSR-based approaches, predictions of soil microbiological properties considering particularly, but not yet ‘acceptable’ molecular data will greatly advance the understanding on the abundance and functional dynamics of soil microbial communities across spatial and temporal scales of contrasting ecosystems.