European Journal of Soil Science最新文献

Soil plays a paramount role in addressing complex challenges related to climate change, the agri-food system, and ecosystem services. This importance makes soil research data highly relevant for meta-analysis, research synthesis, modelling, and assessment. As data-intensive techniques proliferate in studying global change impacts on agricultural systems, effective data management and reuse are essential. Repositories that adhere to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles are crucial for maximizing the value and efficiency of research data. While publishing in an Open Access repository is necessary for data reusability, it alone is not sufficient. Specialized repositories enhance data reuse potential by addressing discipline-specific needs through targeted metadata and technical frameworks. The BonaRes Repository was developed for agricultural soil research data and is guided by the FAIR principles, with a focus on data reusability. Here, we introduce the repository's infrastructures and services, including specialized tools for data quality assurance and the management of soil profile as well as long-term field experiment data. We emphasize the ability of these infrastructures and services to promote data publication and reuse specifically in soil and agricultural sciences. We review examples of data reuse, highlighting their scientific contributions to the understanding of soil and agricultural systems. Finally, we discuss the remaining challenges in achieving FAIR and open soil data publication and reusability. From 2018 to date, the BonaRes Repository has facilitated 815 data publications; 62 papers have reused the published data. Reuse applications range widely—from extracting study site metadata or environmental covariates to reanalysing (meta)data in light of new research questions, to developing scenarios and conducting model calibration and evaluation. A key insight from our review of data reuse is that researchers frequently apply reused data to advance method development. Initiatives such as reciprocal metadata harvesting and integration into larger national and international research data infrastructure will further expand the scope and reuse of the repository's data, including in broader agrosystems science.

A series of modelling scenarios were employed to determine the influence of raster resolution on soil erosion risk maps using both the Water and Tillage Erosion Model (WaTEM) and the Revised Universal Soil Loss Equation (RUSLE) in the regions of Flanders (Belgium) and Lower Austria (Austria) using field-specific data from the Integrated Administration and Control System (IACS). The impact of these maps on farms when used as areas for regulatory measures was also investigated. Three different resampling techniques were employed to assess the impact of varying data resolution on the accuracy of soil erosion risk maps. These techniques included (i) resampling input data, (ii) resampling RUSLE factors and (iii) resampling the output erosion risk map. The resampling of input data resulted in the most pronounced discrepancies in erosion values in both regions. The impact analysis, assessing the effect of data resolution and resampling techniques, was conducted with the objective of identifying fields and farms that were most affected by erosion. This was achieved by applying erosion thresholds of 11 and 2 t ha⁻¹ year⁻¹. The results indicate that raster resolution has a significant influence on model accuracy, with lower resolutions resulting in substantial deviations in erosion estimates. The analysis reveals that lower resolution data and certain resampling methods have a disproportionate impact on smaller farms, resulting in high erosion values in regions with a generally high erosion potential. The study highlights the necessity of utilising the best available data and robust modelling techniques to generate reliable soil erosion risk maps. These findings have significant policy implications, suggesting that erosion control measures and agricultural regulations should be informed by accurate, high-resolution data to ensure fair and effective soil conservation practices.

Decision support tools (DSTs) are crucial in aiding agricultural decision-making, particularly in improving soil health by enhancing nutrient management, soil organic matter (SOM) and water retention. Despite the availability of numerous DSTs in Europe, their adoption, effectiveness and development needs are not well understood, as most research is based on literature reviews rather than direct feedback from stakeholders. This study aims at filling this gap by conducting an expert survey of the most widely used digital DSTs across Europe on SOM, water retention and nutrient use efficiency in agriculture. We aimed at evaluating the current use, limitations and development needs of DSTs and offering recommendations to improve the effectiveness and adoption of DSTs in the context of soil health. A questionnaire was distributed to experts in 24 countries. Answers were received from 18 countries, including 14 European Union (EU) nations, Norway, the UK, Switzerland and Turkey. A total of 115 DSTs were identified aligning with our definition of DST, with agronomists, consultants and farmers being the primary users. Adoption of DSTs was rated moderate (score: 3.1/5), with tools featuring user-friendly interfaces and alignment with farmer goals achieving higher adoption rates. DSTs were rated better suited to achieve farm-level goals (score: 4.1/5) than regional (score: 3.6/5) or national objectives (score: 3.5/5). Major barriers to adoption included limited end-user involvement in DST development, which may hinder alignment with practical needs. Considering all the received questionnaires, the most frequently cited areas for improvement were nutrient use efficiency (45%), SOM (24%) and water retention (18%). Respondents emphasised the need for better integration of new farming systems (e.g., organic farming, agroforestry), more detailed process descriptions, integration of multiple processes, inclusion of economic modules and improved user interfaces. This study presents the first comprehensive evaluation of DSTs in Europe, revealing a diverse yet moderately adopted landscape. Increasing user engagement, enhancing technical integration and improving accessibility are essential for promoting a wider use of DSTs to improve soil health. By adopting these recommendations, DSTs can play a key role in achieving the EU's sustainability goals, fostering resilient agricultural systems and addressing environmental challenges such as soil degradation and climate change.

Understanding the effects of agricultural soil management on the soil system and its functions is crucial to ensure the sustainable use of soil. Due to the countless ways in which soil can be managed, it is not an easy task to compare soil management practices across different locations and over time. One approach to making soil management comparable is the use of numerical soil management indicators. However, due to the lack of standardisation of soil management data and indicators, the comparability of results across studies remains limited. To address these shortcomings, we developed SoilManageR, an accessible R package. The first version of SoilManageR calculates numerical soil management indicators for carbon (C) input, tillage intensity, soil cover duration, nitrogen (N) fertilisation, equivalent livestock units per area, and plant diversity. In this paper, we present the functionality of SoilManageR and demonstrate its capabilities with three case studies. The cases were selected to compare soil management across space, time and context, as well as to relate soil management to soil quality. For this, we calculated soil management indicators for 16 experimental treatments from six agricultural long-term experiments and for 18 farmers' fields in Switzerland. We found that experimental treatments were representative of the management of the farmers' fields in terms of tillage intensity and soil cover, but that farmers' fields tended to exhibit higher livestock integration, leading to higher C and N inputs through organic amendments. We related soil management indicators to selected soil quality indicators in experimental treatments and showed that tillage intensity is the most important management driver of earthworm biomass, whereas C and N inputs were the best predictors of the organic carbon content of the topsoil. Finally, we applied SoilManageR to three sites of the Swiss Soil Monitoring Network and identified significant reductions of N inputs across time in two sites. We demonstrate that SoilManageR is a versatile tool for quantifying multiple aspects of soil management intensity, which can be useful to analyse how policy changes affect soil management. Additionally, SoilManageR can be used to assess soil management impacts on soil quality and provide guidance based on these insights.

Flux chamber methodologies are used at the global scale to measure the exchange of trace gases between terrestrial surfaces (soils) and the atmosphere. These methods evolved as a simplistic necessity to measure gas fluxes from a time when gas analysers were limited in capability and costs were prohibitively high, since which thousands of studies have deployed a wide variety of chamber methodologies to build vast datasets of soil fluxes. However, analytical limitations of the methods are often overlooked and are poorly understood by the flux community, leading to confusion and misreporting of observations in some cases. In recent years, the number of commercial suppliers of gas analysers claiming to be capable of measuring trace gas fluxes from chambers has drastically increased, with a myriad of analysers (and low-cost sensors) now on offer with a wide variety of capabilities. While chamber designs and the capabilities of analysers vary by orders of magnitude, the rudimentary analytical uncertainties of individual flux measurements can still be standardised for direct comparison of methods. This study aims to serve as a guide to calculate the analytical uncertainty of chamber flux methodologies in a standardised way for direct comparisons. We provide comparisons of a variety of chamber measurement methodologies (closed static and dynamic chamber methods) to highlight the impact of analytical noise, chamber size, enclosure time and number of gas samples. With the associated tools, researchers, commercial suppliers and other stakeholders in the flux community can easily estimate the limitations of a particular methodology to establish and tailor the suitability of particular chambers and instruments to experimental requirements.

Riparian ecosystems, through their anoxic properties driven by floods, play a crucial role in favouring denitrification. The absence of nitrous oxide (N₂O) reductase activity in the denitrification process provokes the emission of a potent greenhouse gas (GHG), N₂O, into the atmosphere. Our understanding of the contribution of denitrification to N₂O emissions is limited by the difficulties in capturing peak N₂O events and measuring dinitrogen gas (N₂), the final product of the process under soil flooding. In this study, we describe the GHG-Aquacosme, a new laboratory-based and ecosystem-relevant approach to simulate flood conditions and investigate GHG flux dynamics in intact riparian soil cores, focusing on N₂O. The system capabilities were tested on two different riparian soils with simultaneous monitoring of N₂O, carbon dioxide and porewater chemistry. We also used a simple mass balance approach to estimate the N₂ emissions. The GHG-Aquacosme proved efficient in the incubation of soil samples under atmospheric conditions, preserving the initial soil structure and heterogeneity and providing a high temporal resolution of N₂O emission dynamics upon flooding. This translated into heterogeneous outputs in terms of N₂O dynamics and denitrification-related parameters such as N₂O yield and nitrate removal efficiency. Finally, accounting for nitrogen (N) species diffusion within the system is recommended, and the setup can easily accommodate isotopic N tracer methodologies to investigate other N cycle pathways. Further research is encouraged to determine how the results from the GHG-Aquacosme application can be utilised in predictive models of N₂O emissions, particularly in relation to future scenarios and projections of riparian flooding.

Acid sulfate soils (ASS) pose a significant environmental risk, yet their systematic characterisation is often overlooked in conservation areas, leaving an important gap in understanding their distribution and management. This study characterises ASS in three temperate coastal wetland vegetation communities—mangroves, saltmarshes and paperbark forests—located in southern Australia. Soil samples were collected from two sites, Rhyll and Corner Inlet, representing typical low-energy embayment environments. The study aimed to assess the acidification risk by analysing key soil properties, including pH, electrical conductivity, organic carbon, nitrogen content and the presence of sulfidic materials. Results indicate that mangrove soils exhibited the highest concentrations of chromium reducible sulfur (CRS), while saltmarsh and paperbark forest soils displayed varying levels of acid neutralising capacity (ANC), largely influenced by seawater intrusion and organic matter decomposition. Net acidity was highest in mangrove and deeper saltmarsh layers, indicating a significant potential acidification risk if disturbed. This study highlights the spatial variability in ASS characteristics and acidification risks across different vegetation zones in temperate coastal environments. The findings underscore the need to consider management strategies in conservation areas to mitigate acidification hazards, particularly in light of ongoing sea-level rise and climate change, which may alter the distribution of coastal vegetation and the formation of ASS. These insights provide critical baseline data for the conservation and management of temperate coastal ecosystems in southern Australia.

In situ soil erosion monitoring is essential to investigate the effects of soil erosion control measures and to provide effective management strategies to maintain soil health and for future climate change adaptation. However, reliable soil erosion monitoring in the field depends on the accuracy of the installed measurement equipment under a range of environmental conditions. This study evaluated how slot position in a multislot divisor, runoff intensity and soil type affect runoff and sediment measurements of tipping bucket measurement boxes for soil erosion monitoring. A controlled experimental setup simulated runoff events using two different soil types to analyse potential differences in collected runoff, sediment and its textural composition among slot positions. Based on the results, we present a calibration strategy for tipping bucket measurement boxes to adjust for deviations in collected runoff and sediment. The results reveal that tipping stability declines at frequencies over 40 tips per minute, with the central slot collecting up to 4% of water volume, exceeding other slots. Water and sediment collection at the central slot maintained a consistent pattern under 40 tips per minute, while deviations in sand content between collected and parent soils were observed but did not impact overall sediment mass significantly. Calibration functions applied to a measurement plot during a period of natural runoff events under field conditions exhibited underrepresentation of runoff and sediment levels in uncalibrated records. Runoff calibration results in more accurate total erosion estimates, especially crucial for high-frequency runoff events where uncalibrated results overestimated soil loss by up to 13%.

Sustainable land management can play an important role in climate change mitigation by reducing soil organic carbon (SOC) losses or even by sequestering C in soils. This can be achieved through practices that increase C inputs to the soil and/or improve the quality of these inputs, thereby facilitating the removal of atmospheric carbon dioxide (CO₂) and storing it in the soil as SOC. In this study, we investigated the potential of an increased share of legumes in crop rotations to enhance SOC accrual—defined as the increase in SOC stocks at a given land unit compared to the baseline scenario—using data from 30 mid-term (MTEs, 5–20 years) and long-term (LTEs, 20+ years) field experiments across Europe. Our findings indicate that increasing the proportion of forage legumes in rotations (based on 21 experiments and 39 paired comparisons) led to SOC accrual of up to 13.25 Mg ha⁻¹ (0.44 Mg ha⁻¹ year⁻¹), while grain legumes (based on nine experiments and 28 paired comparisons) resulted in a decrease in SOC stocks of up to 14.37 Mg ha⁻¹ (−0.48 Mg ha⁻¹ year⁻¹) compared to the reference treatment. For forage legumes, the largest SOC gains were achieved at sites with the smallest reference SOC stocks and greater share of forage legumes in the rotation. Our observations suggested that the duration of crop growth of the forage legumes (annual vs. perennial) did not exert a significant impact on SOC stock increase, while pedoclimatic zone did. Positive effects on SOC stocks were more pronounced in the Atlantic climatic zone in contrast to the Mediterranean climatic zone. For grain legumes, larger SOC losses were observed with a greater share of grain legumes in the rotation. Overall, integrating forage legumes in cropping systems can enhance their sustainability and present a viable option for climate change mitigation. Finally, we present a regression equation to derive emission factors (EFs) for estimating SOC changes due to the increase of the share of forage legumes in a rotation, and another due to the increase of the share of grain legumes in the rotation. The first can be used to support the assessment of management impacts for the purpose of rewarding carbon farming and the estimation of a national-scale SOC accrual potential, while the second can be used for estimating national-scale SOC losses.