European Journal of Soil Science最新文献

Climate change alters rainfall patterns and increases temperatures, which disrupt soil processes, enhance CO₂ emissions, and reduce the capacity of soils to store carbon. Soil respiration, the CO₂ released into the atmosphere from the soil, is a vital process in the terrestrial carbon cycle. We performed a two-year study investigating the seasonal variation of soil CO₂ efflux in two typical oak-dominated Mediterranean ecosystems, a deciduous and a broadleaf evergreen one, as we lack sufficient information on this topic. To understand the drivers of soil respiration, we also monitored soil water content and temperature, as well as organic matter input by sampling litterfall and fine roots and by applying in parallel a litter and root exclusion approach. We found a 30%–54% higher soil CO₂ efflux in broadleaf evergreens vs. deciduous oaks, depending on the season. We also identified significant effects of all tested drivers on soil respiration. Soil water content controlled the dependency of soil respiration on temperature and resulted in the highest CO₂ emissions in spring, when these conditions were optimal. The high litterfall input and turnover rate in spring further supported the peak of CO₂ respired by broadleaf evergreens' soil in this period. On the contrary, low water availability limited soil respiration during summer in both ecosystems. The litter and fine root exclusion resulted in a 69.9% and 38.7% reduction in CO₂ efflux in spring, for deciduous and evergreen oaks, respectively, verifying the important contribution of these organic inputs to soil respiration. However, it led to overestimation of soil respiration in summer and in the second year of the study, probably due to water retention. We developed a polynomial regression model that predicts CO₂ efflux with soil temperature and water content as multipliers, and it is novel in including carbon fluxes of litterfall and fine root production as explanatory variables. The model predictions are good for broadleaf evergreen oaks (R² = 0.64) and lower, but fair, for deciduous oaks (R² = 0.48) and can efficiently illustrate how microclimate in combination with organic input and affects soil respiration. Our findings can improve our knowledge of soil CO₂ effluxes and their drivers in typical oak-dominated Mediterranean ecosystems and support their climate-adapted management.

No-till may have the potential to improve the resilience of agricultural systems to climate change by enhancing soil structure and soil health. However, the experimental evidence for this is inconclusive because few field trials have been established long enough for the soil to reach a new (quasi-) equilibrium state upon adopting no-till practices. Soil-crop models should be useful tools to fill this knowledge gap, but most neglect the dynamics of soil properties and so cannot predict long-term changes in soil health. One exception is Uppsala model of Soil Structure and Function (USSF), which accounts for soil structure dynamics due to physical (e.g., swell-shrink, tillage) and biological (e.g., faunal activity, aggregation) processes. In this study, we used the USSF model to evaluate the potential long-term impacts of no-till systems on soil structure, soil organic matter (SOM), water balance, and yields of winter wheat based on data obtained from a long-term farming systems trial near Zürich, Switzerland. The model was first calibrated against field measurements during one growing season of soil water contents, leaf area index, and grain yield and aboveground biomass of winter wheat. The calibrated model was then used to simulate a baseline period (1985–2015) and 18 transient future climate scenarios for the period 2020 to 2090 for continuous winter wheat in conventionally cultivated and no-till systems. In the simulations driven by future climate projections, SOM stocks decreased by 3%–15% in the tilled soil, whereas they were maintained under no-till despite rising temperatures. Enhanced physical protection associated with soil aggregation and improved thermal regulation from the surface residue cover were identified as mechanisms contributing to the maintenance of SOM stocks under no-till. Wheat yields increased slightly and were similar for tilled and no-tilled treatments, as simulated drought stress rarely occurred at the site, which has a wet climate, despite reductions in summer rainfall. The no-till system also showed an improved water balance, with smaller losses by surface runoff and soil evaporation, suggesting that conservation agriculture should be a promising strategy for sustaining soil health and soil functions in the face of a warming climate.

Bulk density (BD) is among the most important physical soil properties, influencing many other soil properties, functions and services. An earlier study revealed that BD observations in the Hungarian Soil Information and Monitoring System (SIMS) contain errors and need correction. The objective of this research was to correct BD measurements in SIMS using advanced pedotransfer functions (PTFs) (i.e., multiple linear regression, generalized additive model, cubist, random forest, and artificial neural networks) developed based on the Hungarian Detailed Soil Hydrophysical Database and environmental covariates serving as proxies for the soil forming factors. The developed PTFs were evaluated and compared using cross-validation. It was found that random forest (RF) outperformed other techniques, with RMSE and model efficiency coefficient values of 0.099 g cm⁻³ and 0.539, respectively. An RF-based PTF was used to correct BD measurements in SIMS and to provide quantitative information on the uncertainty associated with the corrected BD values. The corrected dataset consists of information on the profile and layer ID of the SIMS monitoring sites, the upper and lower depth boundaries of each soil genetic horizon, the corrected BD values, as well as the uncertainty associated with them. The dataset and the developed codes are freely available on Zenodo and GitHub, respectively. The use of the corrected BD dataset is recommended not only for soil scientists but also for researchers from various disciplines. The shared dataset can be of interest not only for Hungarian applications but also for continental and global initiatives aimed at soil health monitoring, land degradation neutrality, and sustainable agriculture.

The EU aims to harmonise soil health monitoring across Member States with the Soil Monitoring Law. Selection of appropriate soil health indicators remains a key challenge, however. Total organic carbon (TOC) content, a key factor in soil health, may be related to indicators of microbial soil health. The aim of this study was to assess the relationship between various microbial soil health indicators and TOC in the topsoil of arable fields in Flanders (northern Belgium). Carbon (C) input from exogenous organic matter (C input) was also explored as a proxy for TOC. Four microbial soil health indicators were examined: (1) Hot-water extractable C (HWC), (2) Total biomass according to phospholipid fatty acid analysis (PLFA), (3) Bacterial (DivB) and (4) Fungal (DivF) Shannon-Wiener diversity. Five medium- to long-term field trials with different field histories and spatial variability were selected based on different C inputs. Results showed that both TOC and C input were good predictors for HWC and PLFA. A positive relationship between C input and TOC was found. This supports the use of C input as a practical proxy for monitoring TOC changes in soils (e.g., for carbon farming and soil health assessments). Significant within-field spatial variability was observed for TOC, HWC and PLFA, suggesting that spatial differences in soil health assessments should be addressed via sampling design. DNA-based indicators (DivB and DivF) were less influenced by spatial or management factors and also correlated weakly with TOC. These findings highlight the complex interplay among field history, current management and spatial variability when determining soil health.

This study is a proof-of-concept for a practical implementation of the global pedogenon map (GPM) in Europe. A pedogenon is a concept that classifies soil based on similar soil-forming factors at a given reference time rather than on soil properties. Pedogenon classes can potentially be further divided into genosoils and phenosoils, where genosoils represent soils with minimal disturbance and phenosoils reflect soils with greater disturbance due to anthropogenic pressure. This study evaluates two approaches for delineating genosoils and phenosoils across 38 European countries, using the GPM: (1) combining CORINE land cover data with a Human Modification Index (HMI) layer at a 0.2 threshold, and (2) selecting the 5% least disturbed area per pedogenon across the analysed European countries. Soil organic carbon (SOC) values from the LUCAS soil spectral library were used to assess genosoil stability. Results show that while the CORINE+HMI method delineated larger undisturbed areas, using the HMI with 5% least disturbed per pedogenon produced a more spatially balanced genosoil distribution. This second method was a more efficient strategy, as it also showed considerably lower SOC standard deviations in the genosoils, indicating that it identified more stable reference states for monitoring soil capacity and condition in Europe. By accounting for Europe's rich pedodiversity, the GPM provides a consistent basis for detecting changes over time and supports informed land and environmental management.

Visible and near-infrared (Vis–NIR) spectroscopy provides a rapid approach to assess soil properties in situ, reducing the need for labour-intensive analyses of large-scale soil surveys. However, research-grade spectrometers may present financial and logistical challenges for widespread deployment, particularly in field applications. This study evaluated the performance of two emerging low-cost spectrometers from OtO Photonics: a visible-range spectrometer (HummingBird, HB) operating at 350–1020 nm and a NIR-range spectrometer (SideWinder, SW) operating at 900–2500 nm. These spectrometers cost less than half the price of the research-grade Vis–NIR spectrometers. A total of 386 soil samples from Eastern Australia were used in the study to build models for pH, total carbon, clay, sand, and cation exchange capacity. HB, SW, and two Vis–NIR spectrometers (AgriSpec from Malvern Panalytical and PSR+ from Spectral Evolution) were tested in this study. The spectra acquired by different spectrometers were characterized by identical shapes, and the features of organic matter and iron oxides were clear. When constructing models for soil properties, the combination of HB and SW achieved performance comparable to research-grade spectrometers. Together, HB and SW provided the best predictions for clay content (R² = 0.82, RMSE = 6.93%) and sand (R² = 0.72, RMSE = 8.62%). The variable importance in projection scores indicated that the models recognized the same features for HB, SW, and standard Vis–NIR spectrometers. Given their capacity to predict soil properties, the low-cost spectrometers were expected to undertake versatile tasks, such as mapping of soil organic carbon and high-resolution monitoring of soil conditions.