Search Results (65)

Urban gardening plays diverse social, cultural and economic roles; its further development appears to be worthwhile, provided that soil contamination does not compromise ecosystem services. This study was conducted at a complex of urban gardens in Wroclaw (Poland) where topsoil screening indicated significant spatial differentiation of trace elements content, presumably related to the history of the site. Urbic Technosols cover the reclaimed section of the gardens, where industrial and urban waste materials, such as ash, slag, construction and demolition, and household waste, were used to fill former clay and sand mines. Although the topsoil layers, comprised of transported external soil, exhibited beneficial physicochemical properties and high fertility, they were seriously contaminated with trace elements (up to 1700, 920, 740, 5.1, 7.4, and 5.1 mg kg⁻¹ zinc, lead, copper, cadmium, mercury, and arsenic, respectively). The trace elements were likely transferred from technogenic materials used for mine infilling, which now underlie the thin humus layers of the garden soils. The results suggest that the quality of soils in urban gardens located at reclaimed post-mining sites, while seemingly beneficial for horticulture based on physicochemical soil properties and fertility indices, can be seriously and permanently compromised by soil contamination from inappropriate materials used for site reclamation, thereby affecting soil quality and posing potential health and ecological risks. Full article

Soil texture, defined by the proportions of sand, silt, and clay particles in the soil, is one of the most essential physical properties of soil. High-resolution soil texture data can provide critical parameter support for soil hydrological modeling, agricultural production management, and ecosystem assessment. In digital soil mapping, previous studies often predicted the sand, silt, and clay contents in soil and then indirectly calculated soil texture. Currently, approaches that directly map soil texture by classification modeling are gaining increasing attention due to the decreased error from data conversion, but few studies have systematically compared these two methods yet. In this study, we comprehensively assessed the performance of direct and indirect predicting soil texture using four machine learning algorithms (e.g., extreme gradient boosting, random forest, gradient boosting decision tree, and extremely randomized tree) with 190 covariates from the Digital Elevation Model, Sentinel-1/2 satellite images, and classification maps and generated a 10 m resolution soil texture map based on 405 topsoil (0–20 cm) sample data collected in Suichuan County, China. The results showed that compared with indirect predictions, direct predictions improved overall accuracy (OA) by 20.57–44.19% and the Kappa coefficient (Kappa) by 0.220–0.402. Among the models used, the XGB model achieved the highest accuracy (OA: 0.948; Kappa: 0.931) and the lowest uncertainty (confusion index: 0.052). The direct prediction map (nine classes recorded) exhibited more detailed and diverse spatial distribution patterns than the indirect prediction map (six classes recorded), aligning better with the actual environment. Based on accuracy validation and spatial distribution, the performance of the XGB model was best during direct prediction. The Shapley additive explanation from the XGB model revealed that the normalized height and stream power indices were the most significant factors driving the soil texture in the study area. Our results provide a reference for future studies on soil texture mapping using machine learning models. Full article

The phytoaccumulation of Fe, Mn, Cu, Zn, and Pb in Tilia cordata flowers and soils from six locations with varying degrees of anthropopressure in Bydgoszcz city and its surroundings (Poland) was assessed. Additionally, metal concentrations and soil enzymatic activity were analyzed. Enrichment Factor analysis revealed significant Zn enrichment at only one locality, supported by a geoaccumulation index value indicating moderate soil pollution. Total metal content in soils correlated with total organic carbon (TOC), while total iron content correlated with the clay fraction (<0.002 mm). Metal concentrations were comparable to the geochemical background levels for soils in Poland. Assessment of total metal contents in the topsoil surface layer from the six locations indicated that concentrations did not exceed permissible limits established by applicable legislation. The study showed that sampling locations influenced the activities of dehydrogenase (DHA), fluorescein diacetate hydrolysis (FDA), β-glucosidase (GL), and arylsulfatase (AR), and these activities correlated more strongly with pedogenic factors than with metal content. No elevated metal levels were detected in the dry mass of T. cordata flowers. Lead content did not exceed 10 mg·kg⁻¹ dry matter, in accordance with World Health Organization (WHO) recommendations. Continued monitoring of trace element levels in soils and T. cordata flowers, particularly in urban environments, is advisable. Full article

It has been assumed that the long-term impact of a diversified soil use system (SUS) and the continuous application of manure and/or mineral fertilizers (NPK) affects the sustainability of soil fertility components. This influence is manifested through the content and distribution of nutrients, as well as some bioavailable heavy metals in the soil. This hypothesis was verified in 2022 in a long-term field experiment that started in 1957. It consisted of a seven-course crop rotation: potato–spring barley–winter triticale–alfalfa–alfalfa–winter wheat–winter rye and monocultures of these crops plus black fallow. The studies were carried out on three separate fields: black fallow (BF), winter wheat grown in monoculture (WW-MO), and crop rotation (WW-CR). Each of these experimental objects consists of five fertilizer variants (FVs) fertilized in the same way every year: absolute control (AC)—variant without fertilizers for 75 years; farmyard manure—FM; mineral fertilizers—NPK; mixed variant—NPK + FM; mineral fertilizers plus annually applied lime—NPK + L. The second factor was the soil layer: 0.0–0.3 m, 0.3–0.6 m, or 0.6–0.9 m. The obtained results clearly indicate that long-term fertilization with NPK + FM, especially in rotation with legumes, strengthens the eluviation/illuviation processes, decreasing the sustainability of soil fertility. Liming is a factor stabilizing the content and distribution of silt and clay particles in the soil. The key factor determining the content and distribution of micronutrients and heavy metals in the soil was the content of organic carbon (C_org). Its content decreased in the following order: WW-CR (13.2 ± 5.8) ≥ WW-MO (12.3 ± 6.9) > BF (6.6 ± 2.8 g·kg⁻¹). The large variability resulted from a distribution trend with soil depth, which increased as follows: MO ≥ CR > BF. FVs with FM had the highest C_org content. NPK, regardless of the long-term soil use system (SUS), had the lowest content. Among the elements studied, the key one impacting the content of both micronutrients and heavy metals was iron (Fe). The Fe content decreased in the order BL (100%) > WW-MO (90.5%) > WW-CR (85%). The opposite tendency was found for the remaining elements, the content of which was consistent with the content of C_org, which was the highest in CR. The strongest impact of Fe, modified by the SUS, was found for Zn, Pb, and Cd. Despite the differences observed between SUSs, fertilization variants, and soil layers, the content of Fe and Mn was in the medium class, while Zn and Cu were in the high class of availability. The content of Ni was the highest for NPK + FM in WW-CR. The content of Pb was weakly affected by the long-term SUS but showed a strong tendency for accumulation in the topsoil layer. The content of Cd was the highest in BF, where it exceeded the threshold of 0.27 mg·kg⁻¹. The long-term diversified SUS, as the main factor determining the sustainability of soil fertility, makes it possible to indicate the directions of humus accumulation and its distribution in the soil. It turned out to be a key factor, but in cooperation with Fe, it determined the content of micronutrients and bioavailable heavy metals in the soil. Full article

Tea (Camellia sinensis (L.) Kuntze) plantations have been rapidly expanding in recent years in developing countries, but there is a lack of knowledge about the effects of woodland conversion to tea plantations and tea plantation aging on soil organic carbon (SOC) accumulation in subtropical regions, which may be a critical issue for accurately estimating the regional C balance in tea planting areas. Thus, in this study, we selected four tea plantations with ages ranging from 5 to 23 years, along with an adjacent mature pine forest (PF, more than 60 years of age), to investigate the effects of woodland conversion to tea plantations and stand age on SOC. Lignin phenols and amino sugars were used to distinguish the contributions of plant-derived C and microbial-derived C to SOC. The results showed that when PF is converted to a tea plantation, 54.12% of the SOC content in the topsoil is lost, with reductions of 84.77% in plant-derived C and 10.23% in microbial-derived C; however, there is a slight increase in microbial-derived C in the deep-layer soil. The tea planting age only has a negative effect on microbial-derived C in the topsoil. Additionally, the plant aboveground biomass, ratio of carbon to nitrogen, total nitrogen concentration, and clay–silt content are key environmental variables influencing SOC accrual, explaining 59.8% of the total variance. SOC and plant-derived C are thus implicated in the quick response to decreasing plant inputs with land conversion and do not accumulate with increasing tea plantation age under the current tea plantation management practices. Generally, more attention should be focused on SOC loss with woodland conversion to tea plantations at the regional scale, and more effective practices can be applied to enhance SOC accrual in subtropical tea plantations. Full article

The composition of clay minerals in soils, and more particularly the presence of montmorillonite (as part of the smectite family), is a key factor in soil swell–shrinking as well as off–road vehicle mobility. Detecting these topsoil clay minerals and quantifying the montmorillonite abundance are a challenge since they are usually intimately mixed with other minerals, soil organic carbon and soil moisture content. Imaging spectroscopy coupled with unmixing methods can address these issues, but the quality of the estimation degrades the coarser the spatial resolution is due to pixel heterogeneity. With the advent of UAV-borne and proximal hyperspectral acquisitions, it is now possible to acquire images at a centimeter scale. Thus, the objective of this paper is to evaluate the accuracy and limitations of unmixing methods to retrieve montmorillonite abundance from very-high-resolution hyperspectral images (1.5 cm) acquired from a camera installed on top of a bucket truck over three different agricultural fields, in Loiret department, France. Two automatic endmember detection methods based on the assumption that materials are linearly mixed, namely the Simplex Identification via Split Augmented Lagrangian (SISAL) and the Minimum Volume Constrained Non-negative Matrix Factorization (MVC-NMF), were tested prior to unmixing. Then, two linear unmixing methods, the fully constrained least square method (FCLS) and the multiple endmember spectral mixture analysis (MESMA), and two nonlinear unmixing ones, the generalized bilinear method (GBM) and the multi-linear model (MLM), were performed on the images. In addition, several spectral preprocessings coupled with these unmixing methods were applied in order to improve the performances. Results showed that our selected automatic endmember detection methods were not suitable in this context. However, unmixing methods with endmembers taken from available spectral libraries performed successfully. The nonlinear method, MLM, without prior spectral preprocessing or with the application of the first Savitzky–Golay derivative, gave the best accuracies for montmorillonite abundance estimation using the USGS library (RMSE between 2.2–13.3% and 1.4–19.7%). Furthermore, a significant impact on the abundance estimations at this scale was in majority due to (i) the high variability of the soil composition, (ii) the soil roughness inducing large variations of the illumination conditions and multiple surface scatterings and (iii) multiple volume scatterings coming from the intimate mixture. Finally, these results offer a new opportunity for mapping expansive soils from imaging spectroscopy at very high spatial resolution. Full article

Soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), and total potassium (TK) are important indicators for evaluating soil fertility. Exploring the content and spatial distribution of these indicators is of great significance for optimizing cropland management measures and developing sustainable agriculture. Yunnan Province is one of the most important agricultural regions in southwestern China, characterized by large variations in the topography and an uneven distribution of soil fertility. In this study, the data of 8571 topsoil (0–20 cm) samples selected from Yunnan Province and a portion of related spatial data were used to carry out electronic mapping of the spatial distribution of soil sand content, clay content, silt content, SOC, TN, TP, TK content, and C:N ratio at 1 km resolution using the Random Forest (RF) model. The results indicated that the average measured contents of SOC, TN, TP, TK, and C:N ratio in the topsoil of Yunnan Province were 18.78 ± 0.09 g/kg, 1.78 ± 0.01 g/kg, 0.98 ± 0.01 g/kg, 13.89 ± 0.08 g/kg, and 10.56 ± 0.02, respectively. The spatial analysis showed that higher SOC was mainly distributed in northern and eastern Yunnan, and the spatial distribution pattern of TN and TP was similar to that of SOC. While higher TK was mainly distributed in southwestern Yunnan Province. There was a significant positive correlation between SOC and TN and TP contents with correlation coefficients of 0.889 and 0.463, however, there was a significant negative correlation between SOC and TK content with correlation coefficients of −0.060. It was also indicated that elevation, temperature, precipitation, clay content, sand content, and silt content were the most important factors affecting SOC, TN, TP, and TK content. The present study provided an understanding of soil nutrients characteristics and their affecting factors, which is helpful to optimize agricultural management practices and develop sustainable agriculture according to soil fertility. Full article

Soil heavy metal (HM) control has become a pressing global ecological and environmental issue, with a significant focus on the western Dabie Mountain in China, a region that is recognized for its ecological barriers and water sources. We analyzed the concentrations, pollution status, and potential ecological risks of 10 HMs (i.e., Cu, +Pb, Zn, Cr, Ni, Co, Cd, As, Hg, and Mn) in the topsoil of different ecosystems in this region. The effects of four soil groups (Anthrosol, Fluvisols, Gleysol, and Alisols) and three soil texture types (sandy, clayey, and loamy soils) on the above aspects were also analyzed. Positive Matrix Factorization (PMF) models were performed to identify and quantify the potential sources and contribution rates of these HMs. Results showed that the level of HM pollution may be negligible, and there was no potential ecological risk. Of all metals, Pb and Cd exhibited the highest levels of pollution. The potential ecological risk associated with soil HMs decreased from croplands to urban and further to forests. Cu and Co were more likely to accumulate in Anthrosols, while Alisols were more likely to accumulate Pb, Cd, and Hg and had a higher overall potential ecological risk. The overall risk in clayey and loamy soils is both higher than in sandy soils. The HM accumulation in topsoil was closely related to the soil organic matter and clay contents. The potential main sources of HMs in this region can be agricultural activities, soil parent material, mining, industrial activities, and vehicle emissions. These findings suggest that special attention should be paid to soil Pb and Cd in western Dabie Mountain. Future efforts can focus on reducing mining, smelting, and industrial emissions, as well as strengthening the research on the migration patterns and mechanisms of HMs. Full article

Soils are a finite resource that is under threat, mainly due to human pressure. Therefore, there is an urgent need to produce maps of soil properties, functions and behaviors that can support land management and various stakeholders’ decisions. Compaction is a major threat to soil functions, such as water infiltration and storage, and crops’ root growth. However, there is no general agreement on a universal and easy-to-implement indicator of soil susceptibility to compaction. The proposed indicators of soil compaction require numerous analytical determinations (mainly bulk density measurements) that are cost prohibitive to implement. In this study, we used data collected in numerous in situ topsoil observations during conventional soil survey and compared field observations to usual indicators of soil compactness. We unraveled the relationships between field estimates of soil compactness and measured soil properties. Most of the quantitative indicators proposed by the literature were rather consistent with the ordering of soil compactness classes observed in the field. The best relationship was obtained with an indicator using bulk density and clay (BDr₂) to define three classes of rooting limitation. We distinguished six clusters of topsoil behaviors using hierarchical clustering. These clusters exhibited different soil behaviors to compaction that were related to soil properties, such as particle-size fractions, pH, CaCO₃ and organic carbon content, cation exchange capacity, and some BDr₂ threshold values. We demonstrate and discuss the usefulness of field observations to assess topsoil behavior to compaction. The main novelty of this study is the use of large numbers of qualitative field observations of soil profiles and clustering to identify contrasting behavior. To our knowledge, this approach has almost never been implemented. Overall, analysis of qualitative and quantitative information collected in numerous profiles offers a new way to discriminate some broad categories of soil behavior that could be used to support land management and stakeholders’ decisions. Full article

Climate and land use changes are causing trees line to shift up into mountain meadows. The effect of this vegetation change on the partitioning of soil carbon (C) between the labile particulate organic matter (POM–C) and stable mineral-associated organic matter (MAOM–C) pools is poorly understood. Therefore, we assessed these C pools in a 10 cm topsoil layer along forest–meadow ecotones with different land uses (reserve and pasture) in the Northwest Caucasus of Russia using the size fractionation technique (POM 0.053–2.00 mm, MAOM < 0.053 mm). Potential drivers included the amount of C input from aboveground grass biomass (AGB) and forest litter (litter quantity) and their C/N ratios, aromatic compound content (litter quality), and soil texture. For both land uses, the POM–C pool showed no clear patterns of change along forest–meadow ecotones, while the MAOM–C pool increased steadily from meadow to forest. Regardless of land use, the POM–C/MAOM–C ratio decreased threefold from meadow to forest in line with decreasing grass AGB (R² = 0.75 and 0.29 for reserve and pasture) and increasing clay content (R² = 0.63 and 0.36 for reserve and pasture). In pastures, an additional negative relationship was found with respect to plant litter aromaticity (R² = 0.48). Therefore, shifting the mountain tree line in temperate climates could have a positive effect on conserving soil C stocks by increasing the proportion of stable C pools. Full article

The soils of the high-elevation mountains along the East African Rift Valley are poorly understood. Assessing the potential of soils for agriculture, climate change mitigation, and environmental functioning requires insight into how they relate to the factors influencing soil formation. Between 3000 and 4120 m a.s.l., 85 soil profiles of Mount Guna were described and sampled. Standard physicochemical analyses were done on all pedons. Additionally, X-ray diffraction, Al_ox and Fe_ox content, and P fixation were performed on six selected profiles. Soils on Mount Guna included Andosols, Phaeozems, Leptosols, Regosols, Cambisols, Luvisols, and Vertisols. With increasing elevation, clay content, bulk density, and pH decreased while the C:N ratio remained constant. In contrast, sand, silt, silt-to-clay ratio, SOC, Ntotal, and SOCS increased. With a factor analysis, the soil-forming factors’ elevation/climate could be disentangled from the factor’s parent material as these affect topsoil and subsoil differently. In the ordination based on climate/elevation and parent material, Andosols and Vertisols stood out while other Reference Soil Groups (RSG) showed indistinct patterns. Soil erosion appeared as an additional soil-forming factor not accounted for by the factor analysis. The distribution of the RSG was significantly associated with elevation belts (p < 0.001), lithology (p < 0.001), and landcover (p < 0.003). On the summital ridge, the Andosols were crucial for groundwater storage due to high precipitation. Shallow and stony soils in the mid-elevation belt contributed to runoff generation. Average soil carbon stock ranged from 8.1 to 11 kg C m⁻² in the topsoil and from 29.2 to 31.9 kg C m⁻² in the upper meter, emphasizing the global importance of high-elevation areas for carbon sequestration. Full article

The deployment of remote sensing platforms has facilitated the mapping of soil properties to a great extent. However, the accuracy of these soil property estimates is compromised by the presence of non-soil cover, which introduces interference with the acquired reflectance spectra over pixels. Therefore, current soil property estimation by remote sensing is limited to bare soil pixels, which are identified based on spectral indices of vegetation. Our study proposes a composite mapping approach to extend the soil properties mapping beyond bare soil pixels, associated with an uncertainty map. The proposed approach first classified the pixels based on their bare soil fractional cover by spectral unmixing. Then, a specific regression model was built and applied to each bare soil fractional cover class to estimate clay content. Finally, the clay content maps created for each bare soil fractional cover class were mosaicked to create a composite map of clay content estimations. A bootstrap procedure was used to estimate the standard deviation of clay content predictions per bare soil fractional cover dataset, which represented the uncertainty of estimations. This study used a hyperspectral image acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor over cultivated fields in South India. The proposed approach provided modest performances in prediction ($R_{val}^2$ ranging from 0.53 to 0.63) depending on the bare soil fractional cover class and showed a correct spatial pattern, regardless of the bare soil fraction classes. The model’s performance was observed to increase with the adoption of higher bare soil fractional cover thresholds. The mapped area ranged from 10.4% for pixels with bare soil fractional cover >0.7 to 52.7% for pixels with bare soil fractional cover >0.3. The approach thus extended the mapped surface by 42.4%, while maintaining acceptable prediction performances. Finally, the proposed approach could be adopted to extend the mapping capability of planned and current hyperspectral satellite missions. Full article

Geochemical indices used to identify the emerging anomalies of potentially harmful elements in topsoil depend on background values (BVs). For urban sites, it is reasonable to estimate native BVs through the targeted selection of peri-urban sampling sites or by distinguishing a useful background subset (BS) within the peri-urban dataset. Here, the goals were to examine the influence of Quaternary deposits on various types of topsoil variables, identify the variables most helpful for cluster analysis intended for the choice of background subset (BS), and compare background values (BVs) based on different background subsets. Composite topsoil samples from a peri-urban area were used for the determination of the following variables: contents of 26 elements and components of the bulk mineralogical composition, as well as the sand, silt, and clay fractions and loss-on-ignition (LOI) at 550 °C and at 950 °C. Although Quaternary lithology influences topsoil elemental contents or granulometric fractions, percentages of illite, kaolinite, orthoclase, quartz, albite, dolomite, and LOI at 550 °C, the choice of BS, according to it, is not recommended, as BVs based on topsoil texture are superior. However, cluster analysis using topsoil fractions < 2, <63, and >63 μm or the contents of Al, Fe, K, Ti, Ga, Nb, Rb, and Si are preferable. It is recommended to use these reference variables for the selection of BS. Full article

Humans can potentially be exposed to Enterocytozoon bieneusi and Encephalitozoon spp. from topsoils when playing or spending time in recreational areas. Two hundred and seventy-seven topsoil samples were collected across Alcalá de Henares (Spain) in July 2017: one hundred and fifty-five urban, sixty industrial and twelve from a public garden. Simultaneous detection was performed using a SYBR Green real-time PCR following the appropriate extraction of DNA with Fast-Prep for Soil^®. The organic matter content (OM), pH, electrical conductivity (EC) and soil texture (percentages of sand, clay and silt) were also determined. E. bieneusi was detected in nine (five urban, four industrial); meanwhile, Encephalitozoon spp. was detected in 22 topsoil samples (sixteen urban, six industrial; ten E. intestinalis, nine E. intestinalis/E. hellem and three E. cuniculi). The presence of E. bieneusi was associated with urban soils that presented lower EC (0.50 vs. 0.71 dS/m; p-value = 0.0110), as this factor may provide a richer environment for the survival of spores. The presence of microsporidian spores was higher in those topsoils with a higher OM content for E. bieneusi (6.96% vs. 4.98%; p-value = 0.0342) and E. intestinalis/E. hellem in one of the four quadrants into which the urban area was divided (5.54% vs. 3.12%; p-value = 0.0007). E. intestinalis is present in industrial topsoils with significantly lower contents of sand (14.5% vs. 21.74; p-value = 0.00003) but higher contents of silt (78.5% vs. 64.9%; p-value = 0.0229), which might be attributed to the differences in the capacity of topsoils to retain moisture, depending on their texture. Moreover, the provision of enough oxygen might play a role in the higher presence of E. intestinalis/E. hellem in urban topsoils with lower contents of clay (11% vs. 19%; p-value = 0.0200). A better understanding of these potential associations is critical in selecting appropriate decontamination techniques and strategies to prevent and minimise human exposure. Full article

There is a growing realization among policymakers that in order to pave the way for the development of evidence-based conservation recommendations for policy, it is essential to improve the capacity for soil-health monitoring by adopting multidimensional and integrated approaches. However, the existing ready-to-use maps are characterized mainly by a coarse spatial resolution (>200 m) and information that is not up to date, making their use insufficient for the EU’s policy requirements, such as the common agricultural policy. This work, by utilizing the Soil Data Cube, which is a self-hosted custom tool, provides yearly estimations of soil thematic maps (e.g., exposed soil, soil organic carbon, clay content) covering all the agricultural area in Lithuania. The pipeline exploits various Earth observation data such as a time series of Sentinel-2 satellite imagery (2018–2022), the LUCAS (Land Use/Cover Area Frame Statistical Survey) topsoil database, the European Integrated Administration and Control System (IACS) and artificial intelligence (AI) architectures to improve the prediction accuracy as well as the spatial resolution (10 m), enabling discrimination at the parcel level. Five different prediction models were tested with the convolutional neural network (CNN) model to achieve the best accuracy for both targeted indicators (SOC and clay) related to the R${}^2$ metric (0.51 for SOC and 0.57 for clay). The model predictions supported by the prediction uncertainties based on the PIR formula (average PIR 0.48 for SOC and 0.61 for clay) provide valuable information on the model’s interpretation and stability. The model application and the final predictions of the soil indicators were carried out based on national bare-soil-reflectance composite layers, generated by employing a pixel-based composite approach to the overlaid annual bare-soil maps and by using a combination of a series of vegetation indices such as NDVI, NBR2, and SCL. The findings of this work provide new insights for the generation of soil thematic maps on a large scale, leading to more efficient and sustainable soil management, supporting policymakers and the agri-food private sector. Full article