Taru Sandén

Soil biota play a crucial role in maintaining soil biophysical processes fundamental to soil structure and health. However, intensive agricultural practices, such as tillage, exert pressure not only on soil structure but also on soil biodiversity – particularly on larger soil organisms, which can act as ecosystem engineers. The loss of biodiversity can impair key soil biophysical processes, including aggregation and biopore formation. To investigate how gradual biodiversity loss affects the capacity of soil biota to support these processes, we conducted a six-month mesocosm experiment using a sieve-based approach to exclude soil organisms by size. We compared how biological communities originating from long-term conventional (CT) and minimum tillage (MT) soil management evolved after exclusion and how they influenced soil structure. Faunal communities were assessed by morphotyping, while microbial communities were characterized using amplicon sequencing (16S rRNA gene, ITS region) and quantitative PCR. Changes to soil structure were measured through the use of X-ray tomography and wet sieving to quantify pore formation and aggregate stability, respectively. Our results show that biodiversity simplification significantly reduced soil aggregate and biopore formation as well as plant primary productivity. Structural equation modeling revealed that earthworms played a key role in biopore and aggregate formation, while bacterial biomass and diversity were significantly and positively associated with aggregate formation. In addition, mesocosms containing communities originating from MT systems exhibited higher aggregate mean weight diameter and porosity in pores < 360 µm at the strongest size exclusion level. Soil communities from MT had higher resilience to withstand impacts of soil biodiversity loss. In this study, we could show that biodiversity enhances soil aggregation and biopore formation. These biophysical soil processes are maintained by the interaction of multiple soil biota – with earthworms as well as bacterial and fungal diversity as direct key players, while nematodes show indirect effects.

Long‐term management of crop residues strongly influences soil organic carbon (SOC) dynamics, what may also influence nitrous oxide (N ₂ O) emissions, two key components determining the impact of agricultural systems on climate change. We investigated N ₂ O fluxes in a long‐term experiment after four decades of continuous residue retention or residue export. Gas flux measurements were collected over 28 months, encompassing both crop‐growing and bare‐soil periods. Cumulative N ₂ O emissions between crop residue management strategies were compared against long‐term SOC stock differences. Soil microbial biomass and extractable C and N were also investigated. Residue retention showed lower total N ₂ O fluxes and higher SOC stocks and microbial biomass in the top 25 cm of soil. Our results suggest that an increased microbial immobilization of N and enhanced microbial processing of soil organic matter accumulated over the years reduced the availability of C and N for denitrification. This was supported by the enhanced levels of microbial biomass and slightly higher porosity under crop residue retention, being the main link to the lower N ₂ O emissions, despite higher SOC. When expressed in CO ₂ equivalents, the yearly difference in N ₂ O emissions between the residue treatments accounts for only 1.8% (≈373 kg CO ₂ ‐eq ha ⁻¹ a ⁻¹ ) of the long‐term climate mitigation benefit from higher SOC stocks (≈21 Mg CO ₂ ‐eq ha ⁻¹ ) after 40 years of continuous management. Our findings indicate that long‐term residue retention in this site contributes to climate change mitigation by synergically increasing long‐term SOC storage and reducing N ₂ O emissions. In this sense, managing and monitoring both SOC and N ₂ O emissions are a crucial basis for continuous climate change mitigation in croplands. Further mitigation studies looking for this aim should also evaluate additional strategies for more ambitious management of N input, especially when increases in SOC may diminish after approaching a new steady state.

The development of harmonized, standardized, and integrated environmental observation systems is a key challenge in Earth system science. Such capability is essential for advancing the interdisciplinary research needed to improve understanding of the Earth system and support global sustainability. The Integrated European Long‐Term Ecosystem, Critical Zone and Socio‐ecological Research Infrastructure (eLTER RI) is a recently developed pan‐European network of in situ research sites that facilitates the collection long‐term, comprehensive observation, analysis, and modeling of environmental and ecosystem change. This initiative focuses on Europe's primary ecosystems, encompassing the atmosphere, geosphere, hydrosphere, biosphere, and their socio‐ecological interactions with the anthroposphere. A fundamental prerequisite for effective environmental monitoring and observation is a standardized and harmonized design that facilitates consistent and comparable environmental data across diverse spatial and temporal scales. The objective of this paper is to introduce the eLTER Framework of Standard Observations (eLTER SO) as a harmonized conceptual and operational standard for long‐term, integrated in situ environmental observations, and to demonstrate how it supports consistent cross‐sphere monitoring and international collaboration in environmental research. The eLTER SO delineates essential ecosystem variables, their measurement methods, and protocols. These Standard Observations (SOs) constitute the conceptual foundation of eLTER RI and provide a basis for overcoming existing disciplinary barriers to the international harmonization of environmental research and a foundation for cross‐sphere observation concepts. The eLTER SO combines the scientific‐academic perspective, as known from “classical” Essential Variable concepts, with the operational perspective required for the establishment and long‐term operation of in situ observatories.

Enhancing soil organic carbon (SOC) in croplands is fundamental to climate‑smart agriculture (CSA), yet the mechanisms by which labile and recalcitrant carbon (C) inputs build and stabilize SOC remain unclear. Crop residues supply labile C that improve microbial activity and particulate organic matter (POM) formation, whereas biochar is considered a recalcitrant, negative‑emission amendment. This study used ¹³C‑labelled maize crop residues and its derived biochar, applied at 1.5% w/w (1.46 atom% ¹³C) to trace amendment-derived C into different soil C pools such as particulate organic C (POC), mineral-associated organic C (MAOC), microbial biomass C (MBC), and microbial respiration. Soil was sampled (Grabenegg, Austria) from two long-term management practices: one fertilized with mineral NPK since 1954 (SM1) and one receiving the same NPK regime plus wood‑derived biochar since 2022 (SM2). In a 44‑day laboratory incubation we quantified how the laboratory amendment of 13C-crop residue and 13C-biochar, alone and interacting with the long-term soil management practices, affect microbial utilization and C stabilization.The results showed that field biochar treated soil (SM2) had higher SOC (by 51%) than non-biochar amended field soil (SM1), indicating that longer-term biochar application enhanced soil C stocks. In the short-term, at day 44 of the incubation, SOC under laboratory applied biochar was highest in both soils (SM1: 21.1g C kg-1; SM2: 26.8 g C kg-1), followed by crop residue (SM1: 15.2 g C kg-1; SM2: 21.1 g C kg-1), compared to soil with no laboratory C amendment (SM1: 11.8 g C kg-1; SM2: 17.8 g C kg-1). This highlights that in the short-term SOC gains were higher for biochar (SOC gains of 9.0-9.3 g C kg-1) than for crop residue (SOC gains of 3.0-3.3 g C kg-1). In addition, both soils showed a strong positive relationship between SOC and POC (R² = 0.783) but not with MAOC, indicating that SOC increases were largely driven by changes in the POM. The 13C tracing will allow to partition and follow the amendment-derived allocation of C into microbial biomass, respiratory use, and the transfer from the POC into the MAOC pool by microbial turnover (data evaluation ongoing). Overall, biochar proved to be the most effective CSA amendment for enhancing SOC in both long-term and in the short-term.

We provide an overview of the accuracy of soil property predictions using the most common proximal sensing (PSS) techniques in precision agriculture (PA), both standalone and in combination with one another or with environmental covariates. Based on 114 scientific papers, we evaluate the accuracy of soil property estimates by calculating the normalized root mean square error (NRMSE) using RMSE values and the range of the predicted soil property. Soil properties, PSS techniques, covariate types, and the model employed for predictions are the factors used to sort the accuracy results. We estimate PSS service costs using both the literature and a market study with questionnaires from private companies in PA. Our analysis indicates that diffuse reflectance spectroscopy (DRS) can estimate the greatest number of soil properties with high accuracy among the PSS techniques. Popular DRS applications include determining soil organic matter, nutrients, and soil texture. X-ray fluorescence (XRF) is the second-most popular technique for estimating soil properties. XRF is widely used in the field to determine elemental concentrations. On-the-go techniques such as electromagnetic induction (EMI) or gamma-ray spectroscopy (γ-ray) yield lower accuracy than point-based techniques. They are widely used by companies because they can delineate PA management zones in the field and are suitable for on-the-go mapping of soil properties such as mineralogy, texture, salinity, water content, cation exchange capacity, and soil depth. The combined use of PSS techniques generally doesn’t outperform the singular application, although the number of samples collected for calibration and the specific combinations of sensors, covariates, and modeling techniques, when correctly applied, may enhance the predictions of soil properties using PSS techniques applied singularly. These outcomes tend to depend on local site characteristics.According to data, the estimated cost of surveying a hectare with PSS oscillates between 15.5€/ha and 130€/ha, whereas our company survey yielded an interval of 142-362€/ha. Price variability was influenced by personnel costs, fieldwork, data and reporting, sample analysis, and equipment. Increases in final prices can be attributed to accessibility and difficulties related to fieldwork and travel to the area of interest. This work aims to serve as a reference for the adoption of sensing technologies by farmers, policymakers, and companies, providing insights into the suitability of different PSS techniques for soil mapping, their associated costs, and what is available in the market. We foresee that PSS will become the standard approach for producing high-resolution maps and affordable soil property information in the future. AcknowledgmentsWork funded by the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement Nº 862695, and from the Tillämpninggsklivet Precisionsodling RUN 2021-00020 Region Västra Götaland

The rise in demand for soil data and information calls for quick and cost-effective methodologies to quantify soil properties. This is particularly important in the realm of restoring soil health in Europe. Near-infrared (NIR) spectroscopy has demonstrated the ability to predict specific soil properties with high accuracy whilst being less costly and time-consuming than traditional methods. To fill gaps in national spectroscopic soil data, we compiled the first Austrian NIR soil spectral library (680–2500 nm) based on legacy samples (n=2129), covering all environmental zones of Austria. We then employed partial least square regression (PLSR) modelling to test the usability of the dataset for soil health assessments at its current stage. Our analysis revealed that the application of the PLSR is not suitable for accurately estimating soil health indicators compared to routine laboratory analysis. Nevertheless, among the 14 soil properties tested, total nitrogen, CaCO3, soil organic carbon and clay exhibited moderate predictive accuracy (R2>0.7). Most importantly, the dataset containing sample meta-data (e.g., land use type, environmental zone or zip code), laboratory reference values and NIR spectra with 1 nm resolution can be used as a foundation for further spectral analysis and modelling. We make this work openly accessible to actively contribute to closing soil data gaps and promote the expansion of soil spectral libraries as a basis for soil health assessments.

The rise in demand for soil data and information calls for quick and cost-effective methodologies to quantify soil properties. This is particularly important in the realm of restoring soil health in Europe. Near-infrared (NIR) spectroscopy has demonstrated the ability to predict specific soil properties with high accuracy whilst being less costly and time-consuming than traditional methods. To fill gaps in national spectroscopic soil data, we compiled the first Austrian NIR soil spectral library (680–2500 nm) based on legacy samples (n=2129), covering all environmental zones of Austria. We then employed partial least square regression (PLSR) modelling to test the usability of the dataset for soil health assessments at its current stage. Our analysis revealed that the application of the PLSR is not suitable for accurately estimating soil health indicators compared to routine laboratory analysis. Nevertheless, among the 14 soil properties tested, total nitrogen, CaCO₃, soil organic carbon and clay exhibited moderate predictive accuracy (R²>0.7). Most importantly, the dataset containing sample meta-data (e.g., land use type, environmental zone or zip code), laboratory reference values and NIR spectra with 1 nm resolution can be used as a foundation for further spectral analysis and modelling. We make this work openly accessible to actively contribute to closing soil data gaps and promote the expansion of soil spectral libraries as a basis for soil health assessments (https://doi.org/10.5281/zenodo.15772618, Fohrafellner et al., 2025).

The rise in demand for soil data and information calls for quick and cost-effective methodologies to quantify soil properties. This is particularly important in the realm of restoring soil health in Europe. Near-infrared (NIR) spectroscopy has demonstrated the ability to predict specific soil properties with high accuracy whilst being less costly and time-consuming than traditional methods. To fill gaps in national spectroscopic soil data, we compiled the first Austrian NIR Soil Spectral Library (680–2500 nm) based on legacy samples (n = 2129), covering all environmental zones of Austria. We then applied partial least square regression modelling to test the usability of the dataset for soil health assessments at its current stage. Our analysis revealed that, at the present time, the Austrian NIR Soil Spectral Library is not suitable to predict most of the 14 soil properties with sufficient accuracy. Nevertheless, total nitrogen, CaCO₃ organic carbon and clay showed satisfactory results (R² > 0.7). Most importantly, the dataset containing sample meta-data (e.g., land use type, environmental zone or zip code), laboratory reference values and NIR spectra with 1 nm resolution can be used as a foundation for further spectral analysis and modelling. We make this work openly accessible to actively contribute to closing soil data gaps and promote the expansion of soil spectral libraries as a basis for soil health assessments.

Transitioning towards soil health‐oriented farming systems is fundamental to mitigate future challenges such as climate change, soil degradation, and increasing global food demands. In this study, we evaluated soil health, crop yields, and greenhouse gas (GHG) emissions at a long‐term experimental site in Central Europe that comprised two cropping systems: a conventional system with regular tillage, low‐diversity crop rotation, and minimal cover cropping, and a conservation system with shallow tillage, diverse crop rotation, and extensive cover cropping. We assessed soil health using 13 physico‐chemical and biological parameters, calculated field‐scale GHG emissions, and analysed yield dynamics over an eight‐year period to evaluate potential crop yield penalties under conservation farming. We observed significant soil health advances (+7%) and reductions in GHG emissions (−43%) with conservation farming, while crop yields for all cultivated crops remained stable. Improvements in soil health were particularly pronounced for nitrogen cycling and microbial‐driven processes. For several measured soil health parameters, we found a larger effect of crop species compared to farming system. Further, positive management effects on soil were apparent particularly for winter wheat and to a lesser extent for maize and sugar beet, strongly emphasizing the need for standardized soil health assessments that take crop species into account. Our study demonstrates that easily implementable conservation farming measures such as reduced tillage, increased crop diversity, and enhanced cover cropping can substantially improve soil health and long‐term agricultural sustainability without compromising crop yields. Conservation farming thus emerges as a viable strategy to support resilient crop production in temperate regions.