European Journal of Soil Science最新文献

Replacing the burnt sugarcane harvesting system with unburnt sugarcane is important for the sustainability of the sugarcane sector in Brazil. Thus, quantifying the impact of the change in the sugarcane harvesting system on soil organic carbon (SOC) stock in Brazil is necessary, as it will allow the refinement of data on SOC, which is essential for the preparation of the national inventory of emissions and removal of greenhouse gases (GHGs), in addition to contributing to national public policies. We used data from both soil sampling and literature review in this study, resulting in 210 pairs of comparisons: 84 for the conversion from burnt sugarcane to unburnt sugarcane; 95 for the conversion from native vegetation to unburnt sugarcane; and 31 for the conversion from native vegetation to burnt sugarcane (NV–burnt), which we analysed using a mixed linear model. In Brazil and the South-Centre region, burnt–unburnt conversion results in a progressive increase in SOC stocks over time, in surface and subsurface layers. Over 20 years, the NV–burnt conversion showed SOC losses between 15% and 32%, and the NV–unburnt conversion showed losses between 27% and 35%. SOC change rates showed gains of 0.32 and 0.59 Mg C ha⁻¹ year⁻¹ for burnt–unburnt, and losses ranging from 0.82 to 1.06 Mg C ha⁻¹ year⁻¹ for conversions from native vegetation. The time required to offset the negative carbon balance of the NV–unburnt conversion is 6.4 and 8.2 years, being shorter than the payback time of the NV–burnt conversion, which is 9.9 and 9.2 years, in the 0–30 and 0–50 cm layers, respectively.

The present European Union (EU) Sewage Sludge Directive (86/278/EEC) is undergoing modifications aimed at enhancing its applicability in the agricultural sector. The Directive's existing limit values for heavy metal concentrations in soils are in the process of being revised. However, to comprehensively understand their effects on EU agricultural lands, additional evaluations are necessary. This is particularly important given that ecological risk assessments are often performed on a site-specific basis, potentially overlooking broader regional implications. The main objective of the current work is to introduce a methodological approach to quantify the impact of sewage sludge (SS) application on agricultural soils in the EU and the United Kingdom. Concentrations of heavy metals (HMs) (Cd, Cu, Hg, Ni, Pb and Zn) in agricultural land from Land Use/Land Cover Area Frame Survey (LUCAS) 2009 topsoil database were used as a baseline. Maximum quantities of SS that can be safely applied to agricultural lands were obtained by a modeling procedure was used to determine the maximum safe quantities of SS that can be applied to agricultural lands for each country within the European Member States and the United Kingdom. Accumulation of HMs in soils was modelled by using a representative SS composition, distributed over 10 successive years at 5 Mg ha⁻¹ year⁻¹ rate. Ecological risk impact was assessed by using both the single ecological risk index (E_r) and the integrated potential ecological risk index (RI). Maximum quantities of SS applied on agricultural soils in EU + UK were estimated to be 45 Mg ha⁻¹ at the country level. We found that 19% of agricultural land (around 28,471,900 ha) in the EU + UK shows a higher RI than moderate risk after long time application of the representative SS. We show that the combination of the HM concentrations from the LUCAS topsoil survey and assumptions on the SS composition and soil HM partitioning can be used to define the actual and potential soil pollution rate in EU + UK. We demonstrate that the proposed methodology can be used by policymakers, farmers, regional authorities and other stakeholders, with possible adaptions based on local in-depth soil and SS knowledge.

This study demonstrates that phosphate oxygen isotope (δ¹⁸O_PO4) analysis effectively detects and monitors fire-induced transformation in soil phosphorus (P). Fires increase bioavailable P, potentially limiting primary production in terrestrial ecosystems. However, understanding the effects of fire on soil P dynamics in the field remains challenging due to the interaction between fire spread and soil properties with high spatial heterogeneity. Soil burning experiments were conducted using a surface soil sample collected in central Japan. The soil was burned in an electric furnace from 50 to 550°C for 3 h, and P concentrations and δ¹⁸O_PO4 values were determined. The results revealed that high temperatures (>350°C) depleted the soil of organic P (P_o) and increased labile and stable inorganic P (P_i) concentrations while significantly decreasing δ¹⁸O_PO4 values. By contrast, low temperatures (150°C) increased labile P_i and P_o concentrations without isotopic shift, indicating that low-intensity fires could increase bioavailable P while conserving soil organic matter. These findings indicate that δ¹⁸O_PO4 analysis can provide insight into the relationship between P transformations and fire intensity and track subsequent changes in P dynamics over time. Our research highlights the potential of δ¹⁸O_PO4 in predicting and managing postfire ecological and agricultural impacts.

In digital soil mapping, modelling soil thickness poses a challenge due to the prevalent issue of right-censored data. This means that the true soil thickness exceeds the depth of sampling, and neglecting to account for the censored nature of the data can lead to poor model performance and underestimation of the true soil thickness. Survival analysis is a well-established domain of statistical modelling that can deal with censored data. The random survival forest is a notable example of a survival-related machine learning approach used to address right-censored soil property data in digital soil mapping. Previous studies that employed this model either focused on mapping the probability of soil thickness exceeding certain depths, and thereby not mapping soil thickness itself, or dismissed it due to perceived poor performance. In this study, we propose an alternative survival model to map soil thickness that is based on the inverse probability of censoring weighting. In this approach, calibration data are weighted by the inverse of the probability that soil thickness exceeds a certain depth, that is, a survival probability. These weights can then be used with most machine learning models. We used the weights with a regular random forest, and compared it with a random survival forest, and other strategies for handling right-censored data, through a comprehensive synthetic simulation study and two real-world case studies. The results suggest that the weighted random forest model produces competitive predictions, establishing it as a viable option for mapping right-censored soil property data.

For nature conservation and planning, terrestrial ecosystems are commonly classified based on their plant communities. Although soils are fundamental to ecosystem functioning, ecosystem classifications based on soil organisms are rare, and it is poorly understood whether their assemblage compositions follow existing classification schemes. We examined whether commonly used ecosystem types capture variation in earthworm (Lumbricidae) assemblages—a crucial biotic component of soil ecosystems. To this end, we created four ecosystem classifications by combining large-scale climatic classifications (Biogeographic Regions [BGR] and Holdridge Life Zones [HLZ]) with small-scale land cover classifications (CORINE Land Cover [CLC] and European Nature Information System [EUNIS]). European earthworm assemblage data from the sWORM and Edaphobase databases were analysed for variation in composition within and among ecosystem types, using Permutational Analysis of Variance and Analysis of Similarities. Additionally, we used Typical Species Analysis to establish typical earthworm assemblages (TAs) for each ecosystem type. Ecosystem classifications using the BGR explained more variance than HLZ, but HLZ showed a higher separation of assemblages between ecosystem types. The differentiation between Atlantic and Continental climates in the BGR could explain the superiority over the HLZ, which had only one category for the cool temperate zone of our study region. The typical assemblages contained on average six species, with some habitat generalists present in most. This study shows that combinations of ecosystem properties from different spatial scales can be used to distinguish between earthworm assemblages at the European level. However, earthworm assemblages across Europe were highly similar due to low species richness and the dominance of a few widespread species. This limits the possibility of applying TAs on large spatial scales, for example, for environmental monitoring. We suggest that future studies should explore the use of more species-rich groups of soil organisms to characterize ecosystem types.

Urban soils exhibit a wide diversity of properties that have no equivalent in other environments but are overwhelmingly perceived as degraded by decision makers. As a result, their potential is not considered in urban planning even though they can deliver a range of ecosystem services. This paper describes a decision support tool for assessing soil ecosystem services to support urban land use planning. An overview of existing methods for assessing ecosystem services provided by urban soils has been detailed. Destisol, a model developed specifically for the urban context, is presented in detail. Destisol was then applied to 37 urban soils under various situations and pedoclimates. The main innovations of Destisol lie in the consideration of soil properties throughout the whole pedon and in the evaluation of the compatibility of the soil with different land covers. It is based on the acquisition of soil indicators, which are transformed into scores of soil functions, then into compatible land covers and finally into scores of ecosystem services. The architecture of the model is based on 20 physico-chemical-biological soil indicators used to score 15 soil functions, based on a detailed set of decision rules. The soil functions scores allow the calculation of a percentage of compatibility for 13 land covers. Finally, the resulting scores are used to evaluate 18 ecosystem services, as a function of land cover. The tests show a wide distribution of scores depending on the soil studied, but also depending on the soil function, land cover or ecosystem service considered. Technosols show the largest dispersion of soil function scores, and no correlation was found between the anthropization gradient and the mean soil function score. The main results of Destisol are an assessment of the soil suitability, that is the compatibility between a soil and a land cover, and an explicit assessment of the ecosystem services provided depending on the land covers.

Gas exchange in the soil is determined by the size and connectivity of air-filled pores. Root mucilage reduces air-filled pore connectivity and thus gas diffusivity. It is unclear to what extent mucilage affects soil pore connectivity and tortuosity. The aim of this study was to gain a better understanding of gas diffusion processes in the rhizosphere by explaining the geometric alterations of the soil pore space induced by mucilage. We quantified the effect of a root mucilage analogue collected from chia seeds without intrinsic respiratory activity on oxygen diffusion at different water contents during drying–rewetting cycles in a diffusion chamber experiment. Quantification of oxygen diffusion showed that mucilage decreased the gas diffusion coefficient in dry soil without affecting air-filled porosity. Without mucilage, a hysteresis in gas diffusion coefficient during a drying–rewetting cycle was observed. The effect depended on particle size and diminished with increasing mucilage content. X-ray computed tomography imaging indicated a hysteresis in the connectivity of the gas phase during a drying–rewetting cycle for samples without mucilage. This effect was attenuated with increasing mucilage content. Furthermore, electron microscopy showed that mucilage structures formed in drying soil increase with mucilage content, thereby progressively reducing the connectivity of the gas phase. In conclusion, the effect of mucilage on soil gas diffusion highly depends on soil texture and mucilage content. The diminishing hysteresis with the addition of mucilage suggests that plant roots secrete mucilage to balance oxygen availability and water content, even under fluctuating moisture conditions.

Growing cover crops promotes soil health as they retain nutrients during autumn/winter and provide organic matter to the soil biota, which in turn supplies nutrients to the main crop upon mineralisation in spring. Different cover crops have varying impacts on soil biology and nutrient dynamics due to the quantity and quality of plant material returned to the soil. To understand these effects, high-resolution data on crop responses is required. In this study, remote sensing was used to provide such data. The temporal dynamics of soil nitrogen (N) availability and N uptake in barley were studied in response to different cover crop monocultures and mixtures. This was achieved using high-resolution multispectral images of the main crop acquired from an unmanned aerial vehicle. Alongside this, in-situ collected plant and soil parameters were used in this 5-year cover crop field experiment. The results showed that cover crop legacies significantly affected barley N uptake, biomass, and canopy N content. In early June, at peak canopy N, the highest values were observed in barley grown after vetch-radish or oat-radish mixtures (84 kg N/ha) and the lowest in barley grown after fallow (63 kg N) or oat (53 kg N/ha on 23rd of June). At the start of the barley growing season, soil microbial biomass was not significantly affected by the cover crop legacies. However, differential N mineralisation between cover crop legacies can be attributed to differences in microbial activity associated with cover crop quantity and quality. This research demonstrates the potential of remote sensing to monitor and understand temporal and spatial variation of crop canopy N in response to cover crop N mineralisation by the soil biota which is an important component of soil health. This approach can contribute to more efficient N use by enabling fine-tuning of the type, quantity, timing, and location of fertilisation.

The rapid increase in nitrogen (N) and phosphorus (P) availabilities in terrestrial ecosystems has led to sustained shifts in soil microbial communities and microbially-mediated N-cycling. However, the specific effects of N and P amendments on N-cycling microbes are poorly understood. This meta-analysis synthesizes the effects of N and/or P amendments on the abundances and functional potentials of N-cycling genes involved in N₂ fixation, organic N mineralization, nitrification, and denitrification across natural ecosystems and diverse soil conditions in China. Our findings indicate that ammonia-oxidizing bacteria (AOB) showed greater responsiveness to N amendment than ammonia-oxidizing archaea (AOA), and AOB amoA abundance increased while AOA amoA abundance decreased with P amendments. Additionally, the abundance of nirS declined, while nirK abundance remained unresponsive to both N and P amendments. These findings highlight the distinct ecological niches occupied by microbial groups with equivalent functions in response to N and P amendments. Moreover, our findings indicate that soil N and P availabilities, along with soil acidification induced by N additions and microbial biomass carbon content, are key factors regulating N-cycling gene abundances and potentials. The driving mechanisms for N-cycling genes and their corresponding potentials appear to be distinct, with gene abundance showing only a limited influence on functional potentials. This suggests that factors such as soil properties and microbial community compositions may be more critical determinants of N-cycling processes than functional gene abundances with regard to scenarios of increasing N and P deposition.

Determining the temperature and water content of soil, at a given instant or along time, is fundamental to understand several soil-related phenomena and processes. Evaporation, aeration, chemical-reaction rates and types, biological processes such as germination and growth of seeds, root development, nutrient and water uptake by roots, and decomposition of organic matter by microbes, are all strongly influenced by soil temperature. On the other hand, infiltration of water through the soil surface allows soil to temporarily store water, making it available for uptake by plants and organisms living in soil. Furthermore, soil water content is closely related to physical and chemical properties of soil, such as oxygen content and demand, which impacts root breathing, microbial activity and soil chemical balance. The accurate evaluation of these two parameters and their interconnection is even relevant in semi-arid regions, where climate conditions are particularly difficult, such as the north-eastern zone of Brazil. Thus, the use of computational models and coupled approaches are imperative for rigorous descriptions. This work presents a contribution to estimate soil temperature and water content, by solving the heat transfer equation and the Richards equation, respectively, through finite differences. As input, the model uses the experimental material composition of the soil, the time-dependent temperature profile at the surface and information about the regional rain regime. Three different numerical approaches were implemented: explicit, simple implicit and the Crank–Nicolson method. The calculations for temperature and water content of the soil obtained with these computational models were compared with the results from Computational Fluid Dynamics (CFD). The relative differences between the numerical methods were less than 0.006% by solving the heat transfer equation and less than 2.75% using the Richards equation. The maximum relative differences within the model, including both a constant and a variable water-content profile, were 3.28%. The results from the computational model using the CFX tool have maximum relative differences of 0.6%, which contributes to verifying the accuracy of the implemented methods.