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Soils are the nexus of water, energy and food, which illustrates the need for a holistic approach in sustainable soil management. The present study therefore aimed at identifying a bioindicator for the evaluation of soil management sustainability in a cross-disciplinary approach between soil science and multi-omics research. For this purpose we first discuss the remaining problems and challenges of evaluating sustainability and consequently suggest one measurable bioindicator for soil management sustainability. In this concept, we define soil sustainability as the maintenance of soil functional integrity. The potential to recover functional and structural integrity after a disturbance is generally defined as resilience. This potential is a product of the past and the present soil management, and at the same time prospect of possible soil responses to future disturbances. Additionally, it is correlated with the multiple soil functions and hence reflecting the multifunctionality of the soil system. Consequently, resilience can serve as a bioindicator for soil sustainability. The measurable part of soil resilience is the response diversity, calculated from the systematic contrasting of multi-omic markers for genetic potential and functional activity, and referred to as potential Maximum Ecological Performance (MEPpot) in this study. Calculating MEPpot will allow to determine the thresholds of resistance and resilience and potential tipping points for a regime shift towards irreversible or permanent unfavorable soil states for each individual soil considered. The calculation of such ecosystem thresholds is to our opinion the current global cross-disciplinary challenge.

In soil spectroscopy a series of strategies exists to optimise multivariate calibrations. We explore this issue with a set of topsoil samples for which we estimated soil organic carbon (OC) and total nitrogen (N) from visible-near infrared (vis-NIR) spectra (350-2500 nm). In total, 172 samples were collected to cover the soil heterogeneity in the study area located in western Rhineland-Palatinate, Germany. There, soils with varying properties developed from very diverse parent materials, e.g., ranging from very acidic sandstone to dolomitic marl. We defined four sample sets each of a different size and heterogeneity. Each set was subdivided into a calibration and a validation set. The first strategy that we tested to improve prediction accuracies was spectral variable selection using competitive adaptive reweighted sampling (CARS) and iteratively retaining informative variables (IRIV), both in combination with partial least squares regression (PLSR). In addition, continuous wavelet transformation (CWT) with the Mexican Hat wavelet was applied to decompose the measured spectra into multiple scale components (dyadic scales 2 1-2 5) and thus to represent the high and low frequency features contained in the spectra. CARS was then applied to select wavelet coefficients from the different scales and to introduce them in the PLSR approach (CWT-CARS-PLSR). Regarding prediction power, CWT-CARS-PLSR outperformed the other approaches. For the smallest data set with 30 validation samples, prediction accuracy for OC increased from approximately quantitative with full spectrum-PLSR (r 2 = 0.81, residual prediction deviation (RPD) = 2.27) to excellent when using wavelet decomposition and CARS-PLSR (r 2 = 0.93, RPD = 3.60). For N, predictions improved from unsuccessful (r 2 = 0.63, RPD = 1.36) to approximately quantitative (r 2 = 0.84, RPD = 2.03). In case of OC, predictions were worst for the largest dataset with 57 validation samples: CWT-CARS-PLSR achieved approximately quantitative predictions (r 2 = 0.82, RPD = 2.31), whereas full spectrum-PLSR provided estimates that allowed only separating between high and low values (r 2 = 0.72, RPD = 1.88). Accuracy of N estimation for this dataset using CWT-CARS-PLSR was also approximately quantitative. Concerning the tested spectral variable selection techniques, both methods provided similar results in the prediction. The application of IRIV was limited due to long processing times.

Abstract:We explored the potentials of both non-imaging laboratory and airborne imaging spectroscopy to assess arable soil quality indicators. We focused on microbial biomass-C (MBC) and hot water-extractable C (HWEC), complemented by organic carbon (OC) and nitrogen (N) as well-studied spectrally active parameters. The aggregation of different spectral variable selection strategies was used to analyze benefits for reachable estimation accuracies and to explore spectral predictive mechanisms for MBC and HWEC. With selected variables, quantification accuracies improved markedly for MBC (laboratory: RPD = 2.32 instead of 1.33 with full spectra; airborne: 2.35 instead of 1.80) and OC (laboratory: RPD = 3.08 instead of 2.36; airborne: 2.20 instead of 1.94). Patterns of selected variables indicated similarities between HWEC and OC, but significant differences between all other soil variables. This agreed to our results of indirect approaches in which both (i) wet-chemical data of OC and N and (ii) spectra fitted to measured OC and N values were used to estimate MBC and HWEC. Compared to these approaches, we found marked benefits of laboratory and airborne data for a direct spectral quantification of MBC (but not for HWEC). This suggests specificity of spectra for MBC, usable for the determination of this important soil parameter.