Key Soil Parameters Research Articles

Evaluation of the Suitability of Horticultural Crops under Prevailing Environmental Conditions - A Case Study of Punjab

Alternate land use options such as horticultural crops help in ensuring efficient land resource utilization. Thus, it is essential to evaluate the agri-environmental conditions for economically viable and sustainable horticultural production. A study was carried out for land use planning of Rajpura block in Patiala district of Punjab. Under this study, soil-site suitability of horticultural crops were evaluated by considering key soil parameters such as texture, depth, slope, EC, pH, organic carbon content, erosion, drainage, and climate factors viz., rainfall and temperature, etc. The soils of the block were interpreted to assess their suitability for various horticultural crops viz., mango, grapes, banana, sapota, guava, pomegranate and citrus fruits. The mango cultivation was found suitable for almost 50% area and moderately suitable for 34% area. Soil-site suitability characteristics evaluation for grape cultivation revealed that around 34% area of the block is suitable while 50% area is moderately suitable. The banana and sapota crop cultivation was found to be highly suitable for approximately 45% area, whereas moderately suitable for 40% area. In context of guava and pomegranate, nearly 34% area was covered under suitable class and 50% area was covered under moderately suitable class, respectively. The citrus crops are highly suitable for this block which covers the maximum area of the block about 61% and moderately suitable for almost 23% area, respectively. The small fraction of the block, only 1.68% (476 ha) exhibits marginal suitability for the cultivation all fruit crops. The horticultural crop suitability maps were generated by using remote sensing and GIS tools. Hence, it can be concluded, the Rajpura block has vast dimensions for the horticultural farming which will lead to land resource management, sustainable land use planning and livelihood security of farmers of the block. This study will also help policy makers and stake holders for the horticultural developmental planning of this block.

Key Chemical Soil Parameters for the Assembly of Rhizosphere Bacteria Associated with Avocado Cv Hass Grafted on Landrace Rootstocks

Avocado cultivation holds significant economic importance in many countries, ranking Colombia as the fifth largest global producer. Particularly, the Hass cultivar plays a pivotal role in Colombia’s avocado industry, especially in the Department of Antioquia, the primary export region. This cultivar is grown under diverse soil and climate conditions and exhibits considerable genetic polymorphism due to the hybridization of varieties of agronomic significance, leading to a diverse array of landrace rootstocks. However, the role of soil conditions and rootstock genotype in structuring rhizosphere bacterial communities is still lacking. In addressing this knowledge gap, we investigated the influence of two soil conditions on the structure of rhizosphere bacterial communities associated with two landrace genotypes of Persea americana cv. Hass, utilizing 16S rRNA sequencing. Notably, no significant differences related to genotypes were observed. This study reports that the rhizosphere bacterial microbiome remains consistent across avocado landrace rootstocks, while variations in key parameters such as phosphorus, pH, Mg, and Ca drive distinct rhizosphere effects. Our results reveal that despite the soils having similar management, increases in these crucial parameters can lead to bacterial communities with lower alpha diversity and a more complex co-occurrence network. In addition, we found substantial variations in beta diversity, bacterial composition, and metagenome predictions between the two farms, underscoring the role of soil variables in shaping the bacterial microbiome. These findings provide valuable insights into the factors influencing the bacterial communities that may play a role in the health and productivity of crops with agro-industrial potential, such as Hass avocado.

Oat/Soybean Intercropping Reshape the Soil Bacterial Community for Enhanced Nutrient Cycling

ABSTRACTIntercropping, particularly within legume‐based systems, has been shown to enhance yields and optimize resource use efficiency. Yet, the potential contribution of intercropping on soil microbial communities and functions to soil nutrients cycling are not fully understood. We conducted the same field experiments at Youyu (Site1) and Zhangbei (Site2) in Northern China to evaluate the impact of oat/soybean intercropping on the rhizosphere soil bacterial community structure, composition, and co‐occurrence networks. Our results indicated that intercropping significantly modified the bacterial community structure for both oat and soybean at Site1, with changes observed only in the oat community at Site2. Specifically, intercropping led to a substantial increase in the relative abundance of Bacteroidetes and Patescibacteria in the oat rhizosphere by 48.3% and 65.4% (Site1), respectively. Conversely, in the soybean rhizosphere at Site1, there was a notable decrease in the abundance of Patescibacteria and Nitrospirae by 32.4% and 40.0%, respectively. The soil bacterial functional groups demonstrated robust positive correlations with key soil parameters such as available nitrogen (Nmin), available phosphorus (Avail‐P), and the activities of nitrogen‐ and phosphorus‐acquiring enzymes in the rhizosphere. In conclusion, intercropping is an effective agricultural practice for enhancing nitrogen and phosphorus cycling by reshaping the soil bacterial community, offering a distinct advantage over monoculture practices. This insight underscores the potential of intercropping to foster sustainable soil nutrient management, highlighting the importance of integrating such practices into modern agricultural strategies to ensure long‐term productivity and environmental sustainability.

Comprehensive Spatial-Temporal and Risk Factor Insights for Optimizing Livestock Anthrax Vaccination Strategies in Karnataka, India.

Anthrax, a zoonotic disease affecting both livestock and humans globally, is caused by Bacillus anthracis. The objectives of this study were the following: (1) to identify environmental risk factors for anthrax and use this information to develop an improved predictive risk map, and (2) to estimate spatial variation in basic reproduction number (Ro) and herd immunity threshold at the village level, which can be used to optimize vaccination policies within high-risk regions. Based on the anthrax incidences from 2000-2023 and vaccine administration figures between 2008 and 2022 in Karnataka, this study depicted spatiotemporal pattern analysis to derive a risk map employing machine learning algorithms and estimate Ro and herd immunity threshold for better vaccination coverage. Risk factors considered were key meteorological, remote sensing, soil, and geographical parameters. Spatial autocorrelation and SaTScan analysis revealed the presence of hotspots and clusters predominantly in the southern, central, and uppermost northern districts of Karnataka and temporal cluster distribution between June and September. Factors significantly associated with anthrax were air temperature, surface pressure, land surface temperature (LST), enhanced vegetation index (EVI), potential evapotranspiration (PET), soil temperature, soil moisture, pH, available potassium, sulphur, and boron, elevation, and proximity to waterbodies and waterways. Ensemble technique with random forest and classification tree models were used to improve the prediction accuracy of anthrax. High-risk areas are expected in villages in the southern, central, and extreme northern districts of Karnataka. The estimated Ro revealed 11 high-risk districts with Ro > 1.50 and respective herd immunity thresholds ranging from 11.24% to 55.47%, and the assessment of vaccination coverage at the 70%, 80%, and 90% vaccine efficacy levels, all serving for need-based strategic vaccine allocation. A comparison analysis of vaccinations administered and vaccination coverage estimated in this study is used to illustrate difference in the supply and vaccine force. The findings from the present study may support in planning preventive interventions, resource allocation, especially of vaccines, and other control strategies against anthrax across Karnataka, specifically focusing on predicted high-risk regions.

Combating Soil Salinity in Sugarcane Farming: Integrated Approaches and Bio-saline Agriculture Innovations

Sugarcane, a vital commercial crop globally, faces significant challenges due to soil salinity, particularly in India where regions such as Gujarat, Uttar Pradesh, Maharashtra, West Bengal, and Rajasthan are notably affected. Salinity impairs sugarcane growth through reduced water uptake, ion toxicity, nutrient imbalance, stunted growth and soil structure degradation. To combat these issues, various strategies have been proposed, including improving drainage, leaching, selecting salt-tolerant varieties, optimizing irrigation, and using soil amendments. Bio-saline agriculture, incorporating plant growth-promoting rhizobacteria (PGPR) and salt-tolerant plants, offers an innovative approach by enhancing soil fertility and crop resilience under saline conditions. Additionally, research indicates that long-term irrigation practices can exacerbate soil salinity, altering key soil parameters such as pH, electrical conductivity and sodium levels. Studies on spent wash application show variable impacts on sugarcane yield and quality, with optimal results observed at moderate application rates. Effective management of soil salinity through these combined approaches can mitigate adverse effects and improve sugarcane productivity and sustainability.

Impact of Biotic and Abiotic Factors on Listeria monocytogenes, Salmonella enterica, and Enterohemorrhagic Escherichia coli in Agricultural Soil Extracts.

Outbreaks of Enterohemorrhagic Escherichia coli (EHEC), Salmonella enterica, and Listeria monocytogenes linked to fresh produce consumption pose significant food safety concerns. These pathogens can contaminate pre-harvest produce through various routes, including contaminated water. Soil physicochemical properties and flooding can influence pathogen survival in soils. We investigated survival of EHEC, S. enterica, and L. monocytogenes in soil extracts designed to represent soils with stagnant water. We hypothesized pathogen survival would be influenced by soil extract nutrient levels and the presence of native microbes. A chemical analysis revealed higher levels of total nitrogen, phosphorus, and carbon in high-nutrient soil extracts compared to low-nutrient extracts. Pathogen survival was enhanced in high-nutrient, sterile soil extracts, while the presence of native microbes reduced pathogen numbers. A microbiome analysis showed greater diversity in low-nutrient soil extracts, with distinct microbial compositions between extract types. Our findings highlight the importance of soil nutrient composition and microbial dynamics in influencing pathogen behavior. Given key soil parameters, a long short-term memory model (LSTM) effectively predicted pathogen survival. Integrating these factors can aid in developing predictive models for pathogen persistence in agricultural systems. Overall, our study contributes to understanding the complex interplay in agricultural ecosystems, facilitating informed decision-making for crop production and food safety enhancement.

Parametric Study of the Deep Excavation Performance of Underground Pumping Station Based on Numerical Method

Environmental responses to deep excavations are combined results of numerous factors. The effects of some factors are relatively straightforward and can be considered carefully during the design. On the other hand, more features impact excavation-induced performances indirectly, making their influences difficult to be clearly understood. Unfortunately, the complexity and non-repeatability of practical projects make it impossible to thoroughly understand these issues through realistic deep excavation projects. Therefore, parametric studies based on repeatable laboratory and numerical tests are desired to investigate these issues further. This work examines the influence of several key features on excavation-induced displacements through a series of 3D numerical tests. The study includes the choice of soil constitutive models, the modeling method of the soil–wall interface, and the influences of various key soil parameters. The comparison shows that the MCC model can yield a displacement field similar to the HSS model, while its soil movement is greatly improved compared to the MC model. Both the soil–wall interface properties and soil parameters impact the excavation-induced displacement to a large extent. In addition, the influence mechanisms of these parameters are analyzed, and practical suggestions are given. The findings of this paper are expected to provide practical references to the design and construction of future deep excavation projects.

A new method for satellite-based remote sensing analysis of plant-specific biomass yield patterns for precision farming applications

AbstractThis study describes a new method for satellite-based remote sensing analysis of plant-specific biomass yield patterns for precision farming applications. The relative biomass potential (rel. BMP) serves as an indicator for multiyear stable and homogeneous yield zones. The rel. BMP is derived from satellite data corresponding to specific growth stages and the normalized difference vegetation index (NDVI) to analyze crop-specific yield patterns. The development of this methodology is based on data from arable fields of two research farms; the validation was conducted on arable fields of commercial farms in southern Germany. Close relationships (up to r > 0.9) were found between the rel. BMP of different crop types and study years, indicating stable yield patterns in arable fields. The relative BMP showed moderate correlations (up to r = 0.64) with the yields determined by the combine harvester, strong correlations with the vegetation index red edge inflection point (REIP) (up to r = 0.88, determined by a tractor-mounted sensor system) and moderate correlations with the yield determined by biomass sampling (up to r = 0.57). The study investigated the relationship between the rel. BMP and key soil parameters. There was a consistently strong correlation between multiyear rel. BMP and soil organic carbon (SOC) and total nitrogen (TN) contents (r = 0.62 to 0.73), demonstrating that the methodology effectively reflects the impact of these key soil properties on crop yield. The approach is well suited for deriving yield zones, with extensive application potential in agriculture.

Sustainable basmati rice yield and quality enhancement through long-term organic nutrient management in the Indo-Gangetic Plains

Context or problemThe extensive use of chemical fertilizers and pesticides in modern agriculture, particularly since the green revolution, has led to profound consequences for agricultural ecosystems. This approach, while initially boosting yields, has disrupted soil health, biodiversity, and environmental balance. Moreover, it contributes silently to global climate change by releasing greenhouse gases. These challenges underscore the urgent need for a shift towards sustainable agricultural practices to safeguard soil health, biodiversity, and human nutrition while mitigating the impacts of climate change. Objective or research questionThe primary objective of this study is to assess the impact of the long-term (seventeen years) application of organic nutrient management on the physicochemical properties of basmati rice soil and its nutritional quality. The study aims to understand the effectiveness of different organic nutrient management practices in mitigating the negative consequences associated with conventional agricultural practices. MethodsThe experiment used a strip-plot design with three replications, assigning two cropping systems (basmati rice-wheat-green gram and basmati rice-wheat-sesbania) to vertical strips. Seven nutrient management practices, including control, farmyard manure (FYM), vermicompost, farmyard manure + crop residues, vermicompost + crop residues, farmyard manure + crop residues + biofertilizers, and vermicompost + crop residues + biofertilizers [nitrogen-fixing Azospirillum, phosphorus-solubilizing bacteria (PSB), potassium-solubilizing bacteria (KSB), and a cellulolytic culture (Aspergillus awamori, Trichoderma viride, Phanerochaete chrysosporium, and Aspergillus wululens)] were applied to the horizontal strips. ResultsThe findings show that the cropping system with sesbania green manure significantly improved soil physicochemical attributes, nutritional content, and basmati rice yield compared to the green gram-based system. Continued application of organic manures, crop residues, and biofertilizers notably enhanced soil fertility, grain quality, and basmati rice productivity. The combined use of vermicompost, wheat residue, biofertilizers, and sesbania green manure treatment increased organic carbon content by 78.7% over the control, and soil available nitrogen, phosphorus, and potassium by 38.3–54.9%, 57.6–143.8%, and 27.9–64.1%, respectively. Additionally, it augmented diethylenetriamine pentaacetic acid extractable iron, zinc, manganese, and copper content by up to 44.2%, 28.5%, 57.9%, and 71.0%, respectively. Co-application of the above organic sources also significantly enhanced grain and straw yields by 74.5–80.1% and 46.1–50.0%, respectively, compared to the control. ConclusionsEnhancing Basmati rice yield and quality in the Indo-Gangetic Plains can be sustainably achieved through sesbania green manuring and organic practices. Positive correlations with key soil parameters emphasize the significance of organic farming for long-term sustainability. Implications and significanceThe study suggests that adopting organic practices, including sesbania green manuring and combining organic inputs, can alleviate the adverse effects of conventional farming on soil health, biodiversity, and climate change. This aligns with the broader goal of establishing sustainable production and consumption in basmati rice-based cropping systems.

A data-driven approach for assessing the wind-induced erodible fractions of soil

To develop an effective strategy for controlling wind erosion and soil degradation, it is necessary to identify the regions with the greatest wind erosion potential. In this regard, many wind erosion models are available that can be used to estimate the rate of wind erosion, allowing erosion control strategies to be assessed. A major factor in all wind erosion models is the inherent erodibility of soil. As it has been proven that the wind erodible fraction of soil (EF) is closely related to its erodibility, this parameter is of importance and used in many wind erosion models such as WEQ, RWEQ, EPIC, and APEX. To evaluate the effect of key soil parameters such as contents of sand, silt, clay, organic matter, and calcium carbonate on the EF, a dataset consisting of 293 samples was compiled from peer-reviewed studies. Initially, to evaluate the relationship between main soil parameters and the EF, Pearson correlation coefficients were calculated for the soil parameters. The results indicate that soil texture has a more significant impact on the EF than the contents of organic matter and calcium carbonate. Moreover, a total of six equations have been identified within the existing body of the literature for the purpose of predicting the soil EF. Through an evaluation of the performance of the existing equations, it was observed that their accuracy varies, often overestimating or underestimating the EF of soil, indicating their overall unsatisfactory performance. To address this issue, regression analysis was employed to propose an equation based on the compiled dataset, which provides satisfactory accuracy in EF prediction.

Micronutrients modulate the structure and function of soil bacterial communities

Soil micronutrients are increasingly recognized as critical regulators of biogeochemical cycling and terrestrial ecosystem processes. Despite substantial efforts establishing how belowground microbial communities respond to macronutrients such as N and P, responses to micronutrients remain poorly understood. This is of particular interest in tropical soils, where micronutrients are heterogeneously distributed and often deficient. Using nearly 300 soil samples that span broad gradients in soil micronutrients, we aimed to determine how differences in micronutrients (Ca, Mg, S) and secondary macronutrients (Mn, Fe, Zn) shape soil bacterial communities and their metabolic capabilities. Along with key soil parameters including pH and climatic predictors, we found that Ca, Mg, and Mn were important in shaping the composition and function of soil microbiomes. We also identified lineages that were consistently responsive to specific micronutrients, with those taxa found in low micronutrient conditions often associated with oligotrophic life history strategies. We detected a subset of metabolic attributes related to nutrient cycling that were strongly associated with specific micronutrients. Notably, Mn was important in predicting the abundance of gene systems related to P metabolism including control of Pho regulon and alkylphosphonate utilization, suggesting that it may be important mediators of these responses. Taken together, our results begin to establish a broader understanding of the extent to which belowground systems are shaped by micronutrients and the strategies that soil microbes employ in variable micronutrient environments.

Effects of different tillage and fertilizer on soil quality under wheat‐maize rotation in the North China Plain

AbstractThe North China Plain plays an important role in China's crop cultivation regions, with the wheat‐maize rotation serving as the predominant cropping system. Nonetheless, a dearth of research refers to the impact of tillage and fertilizer employment on soil quality (SQ) in this region. In this study, the primary objective centered on utilizing the soil quality index (SQI) derived from both minimum and total datasets (MDS, TDS) by the linear and nonlinear scoring criteria to assess SQ. The results indicated that there was a significant correlation between wheat‐maize yield and SQI, revealing a significant association between yield and key soil parameters such as bulk density, soil porosity, total phosphorus, alkali‐hydrolyzed nitrogen, available phosphorus and cation exchange capacity at 0–60 cm soil layer. The study identifies that substantially elevated SQI mean values were completely under T8 whose values were 0.980 (linear), 0.672 (nonlinear) under MDS and 0.908 (linear), 0.701 (nonlinear) under TDS at 20–40 cm. Importantly, the annual yields were the highest when the SQI values were the maximum. Furthermore, SQ levels within this locale demonstrate a moderate to highly favorable status, ultimately underscoring the area's propitious conditions for crop cultivation. This research provides indispensable theoretical guidance for agricultural practitioners and offers a foundational framework supporting enhanced yields in local grain crops.

Predicting soil cone index and assessing suitability for wind and solar farm development in using machine learning techniques

This study proposes a novel approach that combines machine learning models to predict soil compaction using the soil cone index values. The methodology incorporates support vector regression (SVR) to gather input data on key soil parameters, and the output data from SVR are used as inputs for additional machine learning techniques such as Gradient Boosting, Decision Tree, Artificial Neural Networks, and Adaptive Neuro-Fuzzy Inference System. Evaluation of Artificial Intelligent techniques shows that the XGBoost model outperforms others, exhibiting high accuracy and reliability with low mean square error and high correlation coefficient. The effectiveness of the XGBoost model has implications for soil management, agricultural productivity, and land suitability evaluations, particularly for renewable energy projects. By integrating advanced AI techniques, stakeholders can make informed decisions about land use planning, sustainable farming practices, and the feasibility of renewable energy installations. Overall, this research contributes to soil science by demonstrating the potential of AI techniques, specifically the XGBoost model, in accurately predicting soil compaction and supporting optimal soil management practices.

Bayesian updating for ground surface settlements of shield tunneling

Accurate prediction of ground surface settlements induced by shield construction is of great significance for ensuring the safety of shield construction. This paper proposes a ground surface settlement prediction method for shield tunneling based on Bayesian updating. The sequential observation data during the advance of excavation is utilized to update the key soil parameters, leading to a more accurate settlement prediction for the subsequent excavation stages. Response surfaces are constructed to replace the finite element model as the forward models for higher computational efficiency. A tunnel excavation project in Hangzhou, China, is selected to illustrate the effectiveness of the proposed method. The shield excavation face passes through four soil layers, and two soil parameters (i.e., Young’s modulus and friction angle) of these soil layers are selected as random variables to be updated. The results show that the soil parameters can be effectively updated based on the observation data at multiple points and various excavation stages. The predictions of ground surface settlements are improved by using the updated soil parameters. The prediction accuracy of the proposed method increases as more stages of observation data are sequentially obtained and incorporated.

Unveiling the soil physicochemical dynamics of bare soils in Southeast Kazakhstan: A comprehensive study in the Akdala Massif

This study addresses desertification in Kazakhstan's Akdala region, aiming to propose sustainable solutions by examining the effects of various plants on soil properties and nutrient dynamics. Desertification poses a threat to land productivity in arid areas, and this research aims to determine its impact on soil and identify plants for mitigation. Field experiments over three years in the Akdala region utilized crops such as rice, corn, soybean, sudan grass, and sorghum to assess their influence on key soil parameters. Results revealed diverse effects on soil bulk density, agronomically valuable aggregates, water-stable aggregates, labile and total organic carbon, easily hydrolyzable nitrogen, nitrate, available phosphorus, and exchangeable potassium. While no significant differences in bulk density were observed among crops, variations in surface and subsurface soil layers emphasized the importance of depth-specific considerations. Sorghum stood out as a particularly influential crop, significantly increasing labile and total organic carbon levels, highlighting its potential role in enhancing soil quality. The experiments were conducted on the fields of "Birlik" LLP in the Balkhash district of the Almaty region from 2015 to 2017. The chosen crops, each with distinct characteristics, provided a comprehensive understanding of their impact on soil dynamics. Advanced techniques for soil sampling and analyses ensured accurate measurements of various soil parameters. The study site's sharply continental climate, marked by temperature variations, snowy winters, and hot, dry summers, added complexity to the investigation due to its influence on plant growth and soil interactions. In conclusion, this comprehensive study offers valuable insights into the intricate relationships between different crops and soil parameters for combating desertification. The findings contribute significantly to the development of sustainable soil management practices, providing a foundation for identifying suitable crops for soil improvement in arid regions. By understanding how different plants impact soil properties, this research supports informed decision-making in agricultural practices, promoting the long-term sustainability of farming in regions vulnerable to desertification.

IoT-Based Agro-Toolbox for Soil Analysis and Environmental Monitoring.

The agricultural sector faces numerous challenges in ensuring optimal soil health and environmental conditions for sustainable crop production. Traditional soil analysis methods are often time-consuming and labor-intensive, and provide limited real-time data, making it challenging for farmers to make informed decisions. In recent years, Internet of Things (IoT) technology has emerged as a promising solution to address these challenges by enabling efficient and automated soil analysis and environmental monitoring. This paper presents a 3D-printed IoT-based Agro-toolbox, designed for comprehensive soil analysis and environmental monitoring in the agricultural domain. The toolbox integrates various sensors for both soil and environmental measurements. By deploying this tool across fields, farmers can continuously monitor key soil parameters, including pH levels, moisture content, and temperature. Additionally, environmental factors such as ambient temperature, humidity, intensity of visible light, and barometric pressure can be monitored to assess the overall health of agricultural ecosystems. To evaluate the effectiveness of the Agro-toolbox, a case study was conducted in an aquaponics floating system with rocket, and benchmarking was performed using commercial tools that integrate sensors for soil temperature, moisture, and pH levels, as well as for air temperature, humidity, and intensity of visible light. The results showed that the Agro-toolbox had an acceptable error percentage, and it can be useful for agricultural applications.

Hydraulic conductivity of fine-grained soils subjected to freeze-thaw cycles

This paper presents laboratory data from tests on four fine-grained soils: reconstituted Kaolinite, destructured Bothkennar clay, reconstituted Bothkennar clay, and reconstituted Gault clay. The soil samples were conditioned in an oedometer cell while being subjected to varying numbers of freezing and thawing cycles. The influence of freeze-thaw cycles on key soil parameters, including the hydraulic conductivity, Atterberg limits, compression and swelling index was studied. The experimental results were then compared with the analysis of a previously published database of hydraulic conductivity measurements on fine-grained soils called FG/KSAT-1358. The paper demonstrates that while multiple cycles of freezing and thawing affect some of the studied soil parameters, such as the Atterberg limits and the compression characteristics, the effects on the hydraulic conductivity transformation model parameters, linking the water content ratio to hydraulic conductivity are less apparent. The results are useful for geotechnical and pavement engineers when making assessments of freeze-thaw effects on subgrade materials in cold regions.

Litter Decomposition Is Not Affected by Perfluorobutane Sulfonate (PFBS) in Experimental Soil Microcosms

Perfluorobutane sulfonate (PFBS) has been found in increasing concentrations in the environment. However, its effect on litter decomposition in soils is still unclear. Therefore, the effect of PFBS on the decomposition of various litter types was tested, as well as on selected aspects of soil quality. Soil samples were treated with different concentrations of PFBS (0, 1, and 10 µg g–1) and five organic litter materials were used with various C:N ratios. A soil microcosm experiment was performed at 20 °C for 6 weeks. Litter decomposition, soil respiration, enzyme activities, soil pH, water-stable aggregates (WSA), and soil total C and N contents were measured. PFBS treatments were observed to have negligible effects on litter decomposition as well as on other soil properties. This means that in the concentration range examined, this substance has no observable effects on the key soil parameters examined. The present result was inconsistent with the findings of a previous study with similar experimental microcosms but different soils. This study suggests that the effects of PFBS may be less pronounced in the tested soil, but it cannot be concluded that PFBS is harmless in soil ecosystems. A wider range of soil types and PFBS levels should be tested in future studies.

Enhanced soil methane oxidation in both organic layer and topsoil during the succession of subtropical forests

Although (sub)tropical forests account for 10–20% of the atmospheric methane (CH4) uptake by soils, the study of soil CH4 oxidation rates and the controlling factors during the chronosequences of forest succession remains poorly understood. The objectives of this study were to characterize the vertical distribution patterns and dynamics of CH4 oxidation among the early-, mid-, and late-successional stages of subtropical forests, and to investigate the main drivers of soil CH4 fluxes. Three successional forests soils were collected and the ambient and potential CH4 oxidation rates, the related enzymes as well as the key soil parameters were determined in the laboratory. The soils at mid- and late-successional stages functioned exclusively as a CH4 sink while the soil at early-succesional stage was either a CH4 source or sink. Soil CH4 oxidations showed significant vertical distributions along with the successional gradients. The highest rate of ambient CH4 oxidation was observed in the A-horizon of the mid-successional stage (forest age ∼ 100 years), increased by 26-fold compared to the early successional stage, while the highest rate of potential CH4 oxidation was detected in the O-horizon of the late-successional stages (forest age > 300 years), which increased the CH4 oxidation by 29% and 21% respectively compared to the early- and mid-successional stages. Soil CH4 oxidation enhanced with decreasing of soil nitrite and nitrate content but weakened with declining of soil moisture at the successional chronosequence. Collectively, subtropical forests have the potential to increase the soil sink capacity for CH4 oxidation along the successional gradient, and thereby providing the negative feedbacks to ecosystems under climate changing.

Clues of wildfire-induced geotechnical changes in volcanic soils affected by post-fire slope instabilities

Wildfires can significantly affect mountain hillslopes through the combustion of trees and shrubs and changes in soil properties. The type and magnitude of the associated post-fire effects depend on several factors, including fire severity and soil physical–mechanical-hydraulic features that, coupled with climate and topographic conditions, may cause increased runoff, erosion, and slope instability as consequence of intense rainfall. The post-fire response of slopes is highly site-specific. Therefore, in situ surveys and laboratory tests are needed to quantify changes in key soil parameters. The present study documents the post-fire physical and hydromechanical properties of pyroclastic topsoil collected from three test sites that suffered wildfires and rainfall-induced post-fire events in 2019 and 2020 in the Sarno Mountains (Campania Region, southern Italy). The tested pyroclastic soils in burned conditions show (i) no significant changes in grain size distribution, soil organic matter, and specific gravity; (ii) a deterioration in shear strength in terms of decreased soil cohesion caused by the fire-induced weakening of root systems; and (iii) a decrease in hydraulic conductivity. Accordingly, it can be argued that the documented post-fire erosion responses were mainly caused by the reduced cohesion and hydraulic conductivity of the burned topsoil layer, as well as by the loss of vegetation cover and the deposition of fire residues. Although deserving further deepening, this study can represent the necessary background for understanding the initiation mechanism of post-fire erosion processes in the analyzed area and on several natural slopes under similar conditions.