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The application of visual and near infrared soil spectroscopy (vis–NIR) is an easy and cost-efficient way to gain a wide variety of soil information to cover high spatial and temporal resolution in large-scale soil surveys and in local field-scale studies. However, unlike for conventional methods, the prediction accuracy of vis–NIR spectral models cannot yet be estimated before the data collection, which hampers its application at the local scale where often a high precision is required (e.g., field experiments). In this study we used soil data from six agricultural fields in Eastern Switzerland and calibrated i) field-specific (local) models and ii) general models (combining all fields) for organic carbon, total carbon, total nitrogen, permanganate oxidizable carbon and pH using partial least squares regression. 24 out of 30 local models showed an accurate or even excellent performance (ratio of performance to deviation (RPD) > 2) and the root mean square errors (RMSE) of prediction were, except for pH, maximum five times higher than the lab measurement error. The variability of a specific soil property and the mean carbonate concentration in the dataset were the two factors influencing the performance of the local models. We found a significant relationship between the coefficient of variation in the dataset and the metrics for model performance (R2, percental RMSE and RPD). Starting from a tolerable prediction error for the spectral measurements, the regressions can be used to develop a sampling design that matches the corresponding target variability. The five inaccurately performing local models with RPD < 2 were on the two fields with highest carbonate content raising the question if local vis–NIR models are suitable for soils with high carbonate concentration. General models combining the datasets from all six fields showed an accurate overall performance but the RMSE on the field level were higher compared to the local models. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) originates from a complex mixture of organic materials, and to better understand its role in soil functions, one must characterise its chemical composition. However, current methods, such as solid‐state 13C nuclear magnetic resonance (NMR) spectroscopy, are time‐consuming and expensive. Diffuse reflectance spectroscopy in the visible, near infrared and mid‐infrared regions (vis–NIR: 350–2500 nm; mid‐IR: 4000–400 cm−1) can also be used to characterise SOC chemistry; however, it is difficult to know the frequencies where the information occurs. Thus, we correlated the C functional groups from the 13C NMR to the frequencies in the vis–NIR and mid‐IR spectra using two methods: (1) 2‐dimensional correlations of 13C NMR spectra and the diffuse reflectance spectra, and (2) modelling the NMR functional C groups with the reflectance spectra using support vector machines (SVM) (validated using 5 times repeated 10‐fold cross‐validation). For the study, we used 99 mineral soils from the agricultural regions of Sweden. The results show clear correlations between organic functional C groups measured with NMR and specific frequencies in the vis–NIR and mid‐IR spectra. While the 2D correlations showed general relationships (mainly related to the total SOC content), analysing the importance of the wavelengths in the SVM models revealed more detail. Generally, models using mid‐IR spectra produced slightly better estimates than the vis–NIR. The best estimates were for the alkyl C group (R2 = 0.83 and 0.85, vis–NIR and mid‐IR, respectively), and the O/N‐alkyl C group was the most difficult to estimate (R2 = 0.34 and 0.38, vis–NIR and mid‐IR, respectively). Combining 13C NMR with the cost‐effective diffuse reflectance methods could potentially increase the number of measured samples and improve the spatial and temporal characterisation of SOC. However, more studies with a wider range of soil types and land management systems are needed to further evaluate the conditions under which these methods could be used. Highlights: Diffuse reflectance spectroscopy was used to characterise and model SOC functional chemistry.NMR derived C functional groups could be modelled with vis‐NIR and mid‐IR diffuse reflectance spectra.The methods allow for characterisation of SOC chemical composition on whole mineral soil samples.The approach can improve the spatial and temporal characterisation of SOC composition. [ABSTRACT FROM AUTHOR]

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The interface between decaying plant residues and soil minerals represents an essential soil microenvironment at which soil organic matter forms. The high amount of microbial products and residues within this hot spot of microbial activity fosters the formation of mineral-associated organic matter. Besides classical quantitative analyses, our understanding of processes controlling soil organic matter formation greatly benefits from microscopic observations and measurements, which provide spatially resolved information at a meaningful scale for microbial processes and for the association between organic and mineral particles. We studied carbon and nitrogen transfer from fresh-plant residues to the mineral soil, through a litter decomposition experiment in an artificial soil mixture. Needles of Norway spruce (Picea abies L.) were placed in microbatch containers filled with an artificial soil mixture free of soil organic matter. Containers were buried in fresh organic layer material from a Norway spruce stand and incubated for 14 and 42 days. We applied nanoscale secondary ion mass spectroscopy (NanoSIMS) to investigate the spatial distribution of mineral and organic compounds at the needle vicinity and into the mineral soil (0–550 μm from the needle). After 14 days, we depicted the formation of mineral-associated organic matter in the surrounding of the decaying needles. After 42 days, we observed substantial colonization of the needles and the detritusphere by saprotrophic fungi. The fungal hyphae extended into the mineral matrix of the artificial soil acting as vectors for the transfer of litter-derived carbon and nitrogen into the bulk soil. This resulted in an increase of the area covered by organic matter in the detritusphere, with up to 10% of the total investigated area classified as organic matter closely associated with mineral surfaces. Our results provide evidence that the carbon and nitrogen derived from litter decomposition transformed by microorganisms is transferred as mineral-associated organic matter, heterogeneously distributed from the litter source, and still detected 550 μm away from the latter. The close association of newly formed soil organic matter and fine sized minerals suggests that the formation of mineral-associated OM and likely also microaggregates is directly driven by microbial activity in the vicinity of hot spots for plant carbon input (e.g. the detritusphere). • Fine spatial resolution few hundreds of micrometers from the plant residue. • Formation of mineral-associated organic matter in the vicinity of decaying needles. • Fungal hyphae extend into the mineral matrix of the artificial soil. • Fungi are vectors for the transfer of litter-derived organic matter into soil. • Important microbial pathway for organic matter formation in substrate hot spots. [ABSTRACT FROM AUTHOR]