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Für 13 000 der insgesamt 50 000 Hektaren Wald des Kantons Zürich wird von einer starken Bodenversauerung ausgegangen. Saure Böden verarmen an Nährstoffen, was das Pflanzenwachstum und die Bodenbiologie hemmt. Von 2013 bis 2022 liess der Kanton 4800 Hektaren der am stärksten betroffenen Flächen bodenkundlich kartieren. Mit der gewählten Kartiermethodik wurden Versauerung und Basensättigung bis 180 cm Bodentiefe erfasst. Nebst den klassischen Bodenkarten werden auch Produkte zu spezifischen Fragestellungen erarbeitet, zum Beispiel bezüglich Wasserhaushalt oder Verdichtungsempfindlichkeit. Die erhobenen Daten machen das Ausmass und Fortschreiten der Versauerung sichtbar. Sie helfen, Gegenmassnahmen zu ergreifen und weitere Herausforderungen, wie die Anpassung an den Klimawandel, zu bewältigen.

Despite the importance of soil microorganisms for ecosystem services, long‐term surveys of their communities are largely missing. Using metabarcoding, we assessed temporal dynamics of soil bacterial and fungal communities in three land‐use types, i.e., arable land, permanent grassland, and forest, over five years. Soil microbial communities remained relatively stable and differences over time were smaller than those among sites. Temporal variability was highest in arable soils. Indications for consistent shifts in community structure over five years were only detected at one site for bacteria and at two sites for fungi, which provided further support for long‐term stability of soil microbial communities. A sliding window analysis was applied to assess the effect of OTU abundance on community structures. Partial communities with decreasing OTU abundances revealed a gradually decreasing structural similarity with entire communities. This contrasted with the steep decline of OTU abundances, as subsets of rare OTUs (

Soil microbial diversity has major influences on ecosystem functions and services. However, due to its complexity and uneven distribution of abundant and rare taxa, quantification of soil microbial diversity remains challenging and thereby impeding its integration into long-term monitoring programs. Using metabarcoding, we analyzed soil bacterial and fungal communities at 30 long-term soil monitoring sites from the three land-use types arable land, permanent grassland, and forest with a yearly sampling between snowmelt and first fertilization over five years. Unlike soil microbial biomass and alpha-diversity, microbial community compositions and structures were site- and land-use-specific with CAP reclassification success rates of 100%. The temporally stable site core communities included 38.5% of bacterial and 33.1% of fungal OTUs covering 95.9% and 93.2% of relative abundances. We characterized bacterial and fungal core communities and their land-use associations at the family-level. In general, fungal families revealed stronger land-use associations as compared to bacteria. This is likely due to a stronger vegetation effect on fungal core taxa, while bacterial core taxa were stronger related to soil properties. The assessment of core communities can be used to form cultivation-independent reference lists of microbial taxa, which may facilitate the development of microbial indicators for soil quality and the use of soil microbiota for long-term soil biomonitoring., FEMS Microbiology Ecology, 97 (12), ISSN:0168-6496, ISSN:1574-6941

Information on soils' composition and physical, chemical and biological properties is paramount to elucidate agroecosystem functioning in space and over time. For this purposes we developed a national Swiss soil spectral library (SSL; n = 4374) in the mid-infrared (mid-IR), calibrating 17 properties from legacy measurements on soils from the Swiss biodiversity monitoring program (n = 3778; 1094 sites) and the Swiss long-term monitoring network (n = 596; 71 sites). General models were trained with the interpretable rule-based learner CUBIST, testing combinations of {5, 10, 20, 50, 100} committees of rules and {2, 5, 7, 9} neighbors to localize predictions with repeated by location grouped ten-fold cross-validation. To evaluate the information in spectra to facilitate long-term soil monitoring at a plot-level, we conducted 71 model transfers for the NABO sites to induce locally relevant information from the SSL, using the data-driven sample selection method rs-local. Eleven soil properties were estimated with discrimination capacity suitable for screening (R2 > 0.6), out of which total carbon (C), organic C (OC), total N, organic matter content, pH, and clay showed accuracy eligible for accurate diagnostics (R2 > 0.8). Cubist and the spectra estimated total C accurately with RMSE = 0.84 % while the measured range was 0.1–⁠58.3 %, and OC with RMSE = 1.20 % (measured range 0.0–⁠27.3 %). Compared to general estimates of properties from Cubist, local modeling on average reduced the root mean square error of total C per site fourfold. We found that the selected SSL subsets were highly dissimilar in terms of both their spectral input space and the measured values. This suggests that data-driven selection with RS-LOCAL leverages chemical diversity in composition rather than similarity. Our results suggest that mid-IR soil estimates were sufficiently accurate to support many soil applications that require a large volume of input data, such as precision agriculture, soil C accounting and monitoring, and digital soil mapping. This SSL can be updated continuously, for example with samples from deeper profiles and organic soils, so that the measurement of key soil properties becomes even more accurate and efficient in the near future.

Traditional laboratory methods of acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modelling can link and massively scale up these methods for many soil characteristics in a cost-effective and timely manner. In Switzerland, only 10 % to 15 % of agricultural soils have been mapped sufficiently to serve spatial decision support systems, presenting an urgent need for rapid quantitative soil characterization. The current Swiss soil spectral library (SSL; n = 4374) in the mid-infrared range includes soil samples from the Biodiversity Monitoring Program (BDM), arranged in a regularly spaced grid across Switzerland, and temporally-resolved data from the Swiss Soil Monitoring Network (NABO). Given the relatively low representation of organic soils and their organo-mineral diversity in the SSL, we aimed to develop both an efficient calibration sampling scheme and accurate modelling strategy to estimate soil carbon (SC) contents of heterogeneous samples between 0 m to 2 m depth from 26 locations within two drained peatland regions (HAFL dataset; n = 116). The focus was on minimizing the need for new reference analyses by efficiently mining the spectral information of SSL instances and their target-feature representations. We used partial least square regressions (PLSR) together with a 5 times repeated, grouped by location, 10-fold cross validation (CV) to predict SC ranging from 1 % to 52 % in the local HAFL dataset. We compared the validation performance of different calibration schemes involving local models (1), models using the entire SSL spiked with local samples (2) and 15 subsets of local and SSL samples using the RS-LOCAL algorithm (3). Using local and RS-LOCAL calibrations with at least 5 local samples, we achieved similar validation results for predictions of SC up to 52 % (R2 = 0.94–0.96, bias = −0.6–1.5, RMSE = 2.6 % to 3.5 % total carbon). However, calibrations of representative SSL and local samples using RS-LOCAL only required 5 local samples for very accurate models (RMSE = 2.9 % total carbon), while local calibrations required 50 samples for similarly accurate results (RMSE R2, bias and RMSE. Hence, we show that a simple and comprehensible modelling approach using RS-LOCAL together with a SSL is an efficient and accurate strategy when using infrared spectroscopy. It decreases field and laboratory work, the bias of SSL-spiking approaches and the uncertainty of local models. If adequately mined, the information in a SSL is sufficient to predict SC in new and independent study regions, even if the local soil characteristics are very different from the ones in the SSL. This will help to efficiently scale up the acquisition of quantitative soil information over space and time.

Soil data at different scales are needed for assessments and monitoring of soil functions. Soil diffuse reflectance spectroscopy using visible–Near Infrared and mid-Infrared energies can be used to estimate a range of soil properties, rapidly and inexpensively. However the spectroscopic modeling is challenging because of the large soil diversity and its complex composition. We developed a National Soil Spectral library (SSL) (n = 4339) using samples from (i) the Swiss Soil Monitoring Network (NABO; 7 sampling campaigns at 71 agricultural locations since 1985, n = 592) and (ii) the National Biodiversity Monitoring (BDM) Program (n = 4295, 1094 locations across a 5x5 km grid). The SSL will provide spectroscopic models for estimation of functional soil properties at different scales (e.g. total carbon (C) and nitrogen, organic C, texture, pH and cation exchange capacity). We used a rule-based algorithm, Cubist, for the modelling. The models were tuned across full combinations of {5, 10, 20, 50, 100} committees and {2, 5, 7, 9} neighbors, using 5 times repeated 10-fold cross-validation grouped by location. Further, transfer learning with RS-LOCAL tuning was performed for each of the 71 monitoring sites separately by a hold out approach in order to select optimal instances from the remaining SSL. Total soil C in the reference data ranged from 0.1% to 58.3% C and the best Cubist model had a cross-validated RMSE of 0.82% C. The RS-LOCAL approach (RMSEmean = 0.14 %) was on average 2.5 times more accurate for the estimation of C over time at each of the 71 NABO sites compared to the general Cubist approach. Our results suggest that data-driven selection of SSL instances targeted to closely related soils produces less biased estimation of soil properties over time at smaller geographic extents. The general Cubist calibration models are useful when reference analyses in a new study area are scarce. In conclusion, the Swiss SSL models can be used to cost-efficiently estimate a range of soil properties for a diverse applications and purposes in Switzerland.

Land use change strongly affects soil organic carbon (SOC), which is a critical driving force for soil quality and soil services. However, the influence of detailed land use dynamics on SOC remains an active research field. We investigated the impact of long-term grass-/cropland conversion dynamics on SOC for the Swiss agroecosystem (8‵500 km2). In Switzerland multi-temporal SOC observations are sparse, while the spatio-temporal resolution of available land use data is inadequate to capture small structured and dynamic land use. Thus, we stratified one-time SOC observations of two periods (n1995–1999 = 1096; n2011–2015 = 502) into typical land use classes of grass-/cropland conversion regimes to analyze states and trends of SOC. First, based on spectral imagery and auxiliary variables, we established a Random Forest classifier to detect annual grass-/cropland distributions in a spatial resolution of 30 × 30 m for 2000–2015. Second, we used the annual land use maps to derive classes of typical land use dynamics based on a pattern description of conversion regimes. Third, we comparatively assessed the SOC covariate importance (IMP) and SOC sample representation of land use dynamics using terrain and climate covariates. Subsequently, we statistically analyzed SOC across land use dynamics and two periods. The land use classifier shows an Overall Accuracy of 86%, while the annual land use maps deviate by 6%–11% from census data. 46% of the farmland underlies frequent grass-/cropland conversions. The SOC covariate importance of land use dynamics is increased by at least 2.4 times compared to the SOC covariates. The SOC observations consistently represent land use dynamics across SOC covariates. In both periods, SOC increased as the grassland share within the land use regimes increased. The mean SOC for permanent grasslands is 35 g kg−1 and 17 g kg−1 for permanent croplands. Areas of land use conversions where grassland is dominant show increased SOC (29–30 g kg−1) compared to areas where cropland was dominant (23–25 g kg−1). The mean differences of SOC between the periods and classes are statistically not significant except for permanent cropland, for which the SOC is decreased by 5.2 g kg−1.

The temporal evolution of soil organic carbon (SOC) is of major importance given its status as a key parameter in many soil functions. Furthermore, soils constitute an important reservoir of carbon in our environment. In light of climate change, consistent SOC data over extended periods in combination with information on agricultural management are much required, but still scarce. We report SOC changes in the topsoil (0–20 cm) of Swiss cropland measured at well-defined monitoring sites resampled every 5 years from 1990 to 2014 by the Swiss Soil Monitoring Network NABO using consistent sampling protocols and quality assurance. Data on agricultural management practices were retrieved from farmers. Overall, SOC remained stable for the ensemble of monitoring sites, although increasing and decreasing trends were observed for individual sites, ranging from − 11 to + 16% relative change per decade. Changes in the agricultural management of cropland triggered substantial changes in SOC contents for some sites. Moreover, sites with a low ratio of SOC/clay (

Soil provides ecosystemservices, supports human health and habitation, stores carbon and regulatesemissions ofgreenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agroecologicalbalances and food security. It is important that we learn more about soil to sustainably manage andpreserve it for future generations. To this end, we developed and analyzed a global soil visible–near infrared(vis–NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the informationencoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness ofthe global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand andiron contents, cation exchange capacity, and pH. Usingwavelets to treat the spectra, whichwere recorded in differentlaboratories using different spectrometers and methods, helped to improve the spectroscopic modelling.We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationshipsin the data to produce accurate predictions of soil properties. The spectroscopic models that we derivedare parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, whichmight serve to facilitate research on soil at the global scale. This spectroscopic approach should help to dealwith the shortage of data on soil to better understand it and to meet the growing demand for information to assessandmonitor soil at scales ranging fromregional to global.New contributions to the library are encouraged sothat this work and our collaboration might progress to develop a dynamic and easily updatable database withbetter global coverage. We hope that this work will reinvigorate our community's discussion towards larger,more coordinated collaborations. We also hope that use of the database will deepen our understanding of soilso that we might sustainably manage it and extend the research outcomes of the soil, earth and environmentalsciences towards applications that we have not yet dreamed of.

Spatial information on soil function fulfillment (SFF) is increasingly being used to inform decision-making in spatial planning programs to support sustainable use of soil resources. Soil function maps visualize soils abilities to fulfill their functions, e.g., regulating water and nutrient flows, providing habitats, and supporting biomass production based on soil properties. Such information must be reliable for informed and transparent decision-making in spatial planning programs. In this study, we add to the transparency of soil function maps by (1) indicating uncertainties arising from the prediction of soil properties generated by digital soil mapping (DSM) that are used for soil function assessment (SFA) and (2) showing the response of different SFA methods to the propagation of uncertainties through the assessment. For a study area of 170 km2 in the Swiss Plateau, we map 10 static soil sub-functions for agricultural soils for a spatial resolution of 20 × 20 m together with their uncertainties. Mapping the 10 soil sub-functions using simple ordinal assessment scales reveals pronounced spatial patterns with a high variability of SFF scores across the region, linked to the inherent properties of the soils and terrain attributes and climate conditions. Uncertainties in soil properties propagated through SFA methods generally lead to substantial uncertainty in the mapped soil sub-functions. We propose two types of uncertainty maps that can be readily understood by stakeholders. Cumulative distribution functions of SFF scores indicate that SFA methods respond differently to the propagated uncertainty of soil properties. Even where methods are comparable on the level of complexity and assessment scale, their comparability in view of uncertainty propagation might be different. We conclude that comparable uncertainty indications in soil function maps are relevant to enable informed and transparent decisions on the sustainable use of soil resources., SOIL, 4 (2)

Although polycyclic aromatic hydrocarbons (PAH) are of concern due to their carcinogenic, mutagenic, and teratogenic properties and their ubiquitous occurrence in environmental compartments, only few studies assessed the temporal evolutions of PAH contents of soils over extended time periods. The Swiss Soil Monitoring Network NABO runs long-term monitoring sites resampled every five years since the 1980s. In the present study, soil (0–20 cm) samples collected from 1985 through 2013 at 25 selected monitoring sites were analysed for the 16 priority PAH according to the U.S. EPA and five PAH marker substances. We observed divergent trends for light PAH, such as naphthalene and phenanthrene, compared with heavy PAH, such as benzo[a]pyrene and benzo[ghi]perylene. Whereas the former showed decreasing concentrations since the late 1980s, no significant trends were found for the latter. Furthermore, the analyses showed that naphthalene contents decreased most strongly at rural sites featuring low population densities, while phenanthrene contents generally decreased most strongly at semi-rural sites. The deviating evolutions of light and heavy PAH were mainly attributed to their differing physico-chemical properties. Temporal evolutions in soils contradict emission inventory data suggesting PAH emissions to decline since the 1980s.