Collected Soil Samples Research Articles

Coupling remote sensing and eDNA to monitor environmental impact: A pilot to quantify the environmental benefits of sustainable agriculture in the Brazilian Amazon.

Monitoring is essential to ensure that environmental goals are being achieved, including those of sustainable agriculture. Growing interest in environmental monitoring provides an opportunity to improve monitoring practices. Approaches that directly monitor land cover change and biodiversity annually by coupling the wall-to-wall coverage from remote sensing and the site-specific community composition from environmental DNA (eDNA) can provide timely, relevant results for parties interested in the success of sustainable agricultural practices. To ensure that the measured impacts are due to the environmental projects and not exogenous factors, sites where projects have been implemented should be benchmarked against counterfactuals (no project) and control (natural habitat) sites. Results can then be used to calculate diverse sets of indicators customized to monitor different projects. Here, we report on our experience developing and applying one such approach to assess the impact of shaded cocoa projects implemented by the Instituto de Manejo e Certificação Florestal e Agrícola (IMAFLORA) near São Félix do Xingu, in Pará, Brazil. We used the Continuous Degradation Detection (CODED) and LandTrendr algorithms to create a remote sensing-based assessment of forest disturbance and regeneration, estimate carbon sequestration, and changes in essential habitats. We coupled these remote sensing methods with eDNA analyses using arthropod-targeted primers by collecting soil samples from intervention and counterfactual pasture field sites and a control secondary forest. We used a custom set of indicators from the pilot application of a coupled monitoring framework called TerraBio. Our results suggest that, due to IMAFLORA's shaded cocoa projects, over 400 acres were restored in the intervention area and the community composition of arthropods in shaded cocoa is closer to second-growth forests than that of pastures. In reviewing the coupled approach, we found multiple aspects worked well, and we conclude by presenting multiple lessons learned.

Soil Moisture Sensor-Based Landslide Monitoring: A Laboratory-Based Approach for Guwahati City

Various techniques for landslide mapping, monitoring and modelling are being employed in a variety of studies to keep people safe from landslides. Guwahati, a city in Assam (India) is surrounded by hills, with varied slope angles, become prone to landslide during monsoon season. Relative increase in the moisture content of soil is a major parameter for determining the occurrence of landslides that are induced by rainfall. An experimental model with varying slope angles is demonstrated to witness some proportionality behaviour of soil moisture value for the collected soil sample from landslide prone areas. The soil moisture sensor value increases with increase in slope angle. The toe position of moisture value also shows a significant display of data during landslide. This early warning module can be incorporated with the help of Blynk Application to send messages to the residents of landslide prone areas. This study would be a cost effective alternative for landslide early warning hazard monitoring and fast emergency response process and the model may be considered as a miniature version of real-life slope conditions for the hills of Guwahati city, Assam, India.

Time-Lapse Electromagnetic Conductivity Imaging for Soil Salinity Monitoring in Salt-Affected Agricultural Regions

In this study, the temporal variation in soil salinity dynamics was monitored and analyzed using electromagnetic induction (EMI) in an agricultural area in Port Said, Egypt, which is at risk of soil salinization. To assess soil salinity, repeated soil apparent electrical conductivity (ECa) measurements were taken using an electromagnetic conductivity meter (CMD2) and inverted (using a time-lapse inversion algorithm) to generate electromagnetic conductivity images (EMCIs), representing soil electrical conductivity (σ) distribution. This process involved converting EMCI data into salinity cross-sections using a site-specific calibration equation that correlates σ with the electrical conductivity of saturated soil paste extract (ECe) for the collected soil samples. The study was performed from August 2021 to April 2023, involving six surveys during two agriculture seasons. The results demonstrated accurate prediction ability of soil salinity with an R2 value of 0.81. The soil salinity cross-sections generated on different dates observed changes in the soil salinity distribution. These changes can be attributed to shifts in irrigation water salinity resulting from canal lining, winter rainfall events, and variations in groundwater salinity. This approach is effective for evaluating agricultural management strategies in irrigated areas where it is necessary to continuously track soil salinity to avoid soil fertility degradation and a decrease in agricultural production and farmers’ income.

Soil properties evaluation under different irrigation sources in the Semi-arid Region of Kandahar, Afghanistan

‌Wastewater reuse is a non-conventional source of water containing high amounts of nutrients but also other undesirable components. However, little is known about the impact of wastewater irrigation on farmland soil properties, particularly in Afghanistan. Therefore, this paper reports for the first time a comprehensive understanding of the presence and effect of soil properties under wastewater irrigation and freshwater irrigation in Kandahar, Afghanistan. Accordingly, the properties of soil were evaluated by collecting soil samples from the sites of Haji-Arab (HA), Bala-Karz (BKZ), Mahal-e-Nejat (MN), and Char-Bagh (CB) in Kandahar province. The agricultural fields at the HA, BKZ, and MN sites are irrigated with wastewater, while at the CB site farms are entirely irrigated by freshwater. The results of this study revealed that the lowest pH and maximum EC levels were recorded in the BKZ soil samples, followed by the HA, MN, and CB soil samples. The soil texture class was silt loam and sandy loam at the wastewater-irrigated and freshwater-irrigated sites, respectively. Discoveries from this study have identified implications for informing policymakers on the need for appropriate wastewater treatment systems and regulations to ensure the safe and sustainable use of wastewater for irrigation purposes.

Distribution pattern and influencing factors of bacterial communities in different soil depths of Caragana jubata shurb in Luya Mountain, China.

Soil microorganisms are important components of terrestrial ecosystems, affecting soil formation and fertility, plant growth and stress tolerance, nutrient turnover and carbon storage. In this study, we collected soil samples (humus layer, 0-10 cm, 10-20 cm, 20-40 cm, and 40-80 cm) from Caragana jubata shrubland in Shanxi subalpine to explore the composition, diversity, and assembly of soil bacterial communities at different depths across the soil profile. The results showed that Actinomycota (19%-28%), Chloromycota (10%-36%) and Acidobacteria (15%-24%), and Proteobacteria (9%-25%) were the dominant bacterial phyla. α-diversity of soil bacterial community significantly decreased with the increases of soil depth. Soil bacterial β-diversity varied across different soil depths. Soil pH, water content, and enzyme activity were the main ecological factors affecting the distribution of soil bacterial communities. Soil bacterial communities had more complex interactions in humus layer and 0-10 cm layer. On the whole, soil bacterial communities were dominated by coexistence in C. jubata shrubland, and the soil bacterial community assembly was driven by random process.

Micro-Environmental Variation in Soil Microbial Biodiversity in Forest Frontier Ecosystems—Implications for Sustainability Assessments

Soil health indicators based on microbial biodiversity are increasingly used in agricultural sustainability assessments. However, little is known about how microbial communities vary within micro-environmental gradients across different land uses, which is crucial for designing field sampling and monitoring protocols. Our objective was to assess how soil microbial communities changed with soil depth and spatial distance across land-use types. We sampled soils in four spatial distances (within 0.1–70 m) and four depths (within 0–40 cm) in forests, grasslands, and horticultural lands, and combined 16S rRNA gene sequencing, DNA quantification and soil chemical characterization to explore micro-environmental variation in microbial biomass, α-β-diversity, and communities’ assembly processes. Depth and spatial distance had differential effects on microbial biodiversity within different land uses. Microbial biomass was most sensitive to depth, α-diversity to spatial distance, and β-diversity to both depth and spatial distance. Deterministic processes dominate microbial communities’ assembly along depth in all land uses, which is a promising result for developing soil quality indicators based on microbial biodiversity. Overall, our results suggest that collecting soil samples separated by at least 12 m is adequate to capture biodiversity changes across land uses. However, collecting randomly within the first 10 cm is recommended for native forests, while systematic sampling within the first 20 cm is advised for grasslands and horticultural lands. Our findings underscore the need for land use-specific sampling frameworks in soil life-based sustainability assessments for meaningful regional comparisons.

Lead exposure and its relationship with fecal cortisol levels in black howler monkeys (Alouatta pigra).

Efficiently detecting early environmental threats to wildlife is vital for conservation. Beyond obvious dangers like habitat loss or deforestation, our study focuses on one of the most hazardous toxic metals for wildlife: lead (Pb). Pb is a widespread, cumulative, and insidious environmental pollutant that can trigger a wide range of physiological, biochemical, and behavioral disorders. In fact, Pb can cause permanent dysfunction of the major stress system, the hypothalamic pituitary adrenal (HPA) axis. We analyzed Pb and cortisol concentrations in fecal samples from Alouatta pigra in southern Mexico. Fecal samples were collected across six sites categorized as free-ranging (n = 65; conserved and disturbed) and from captive animals (n = 58). Additionally, we collected soil samples (n = 35). We found that Pb was present in 28% of fecal samples and 83% of soil samples. There was a positive relation between fecal and soil Pb levels, and fecal Pb concentration was negatively associated with cortisol levels. However, the claim of Pb being a direct interference with HPA axis requires further investigation. Given our findings, assessing wildlife exposure can be a valuable tool for understanding potential Pb exposure levels in the environment and its possible implications for human health. It can also serve as an early warning system of these consequences.

Identifying the nitrate transport and transformations under apple orchards in the loess depositusing stable isotopes of water and nitrate

Understanding the behavior of nitrate in the unsaturated zone is critical for predicting groundwater nitrate pollution. Although this issue has been intensively investigated for shallow soil profiles, it remains poorly understood in deep soils. We collected soil samples up to 20 m depth under farmland and apple orchards with trees that were 16 and 22 years old (A16 and A22). We then determined soil-water content, stable isotopes (δ2H and δ18O) and nitrate content under two land use types. The stable isotopes of nitrate under two apple orchards were measured to a depth of 16 m. The mean nitrate contents in the shallow soils (0–6 m) under apple orchards (78.8 ± 87.8 mg kg−1 for A16, 180 ± 155 mg kg−1 for A22) were significantly higher than that of farmland (2.40 ± 1.56 mg kg−1) (p < 0.01), whereas the nitrate contents in the deep soils (6–20 m) were almost similar between apple orchards and farmland. Ammonium or urea-based nitrogen (N) fertilizers (30–61%) were the predominant contributors to nitrate in the shallow soil layers, followed by soil organic N and manure & sewage, whereas the soil organic N (mean 34%) was the primary source of nitrate in deep soils. Isotopic data revealed that the N transformation was controlled by nitrification, and denitrification was not apparent throughout the soil profiles. The downward transport of nitrate lagged behind soil-water. Nitrate was leached by piston flow in the vadose zone, and the nitrate from N application since the 1980 s has only infiltrated to 6 m depth. The difficulty in recharging deep soils mitigates the transport of nitrate to lower depths. The combination of water-nitrate stable isotopes provides new insights for identifying nitrate sources, transformation, and transport in the thick unsaturated zone.

Geospatial Variability of Soil Properties of the Different Villages in Arajiline Block of Varanasi District of Uttar Pradesh

The current study was conducted at the different villages of the Arajiline block of Varanasi district, Uttar Pradesh, India during November, 2022–June, 2023 to evaluate the variability of soil properties and nutrient indexof soils. The random sampling technique was used to collect 40 soil samples at different locations with the help of GPS at 0–15 cm depth. The collected soil samples were air-dried in shade at room temperature, passed through a 2 mm filter paper, and analyzed for different physico-chemical properties. The result revealed that the TS (range 8.2–81.3%), VFS (range 4.2–35.5%), Silt (range 10.1–54.2%), Clay (range 7.7–38.8%), BD (range 1.08–1.45 g cm-3), PD (range 2.04–2.33), Porosity (range 30.24–51.56%) pH (range 6.4-7.8), EC (range 0.1-0.3 dS m-1), OC (0.2-0.6%), and available N, P, K, S, Ca, and Mg varied from 130.12–260.30 kg ha-1, 14.2-23.2 kg ha-1, 145.0–240.0 kg ha-1, 7.5–17.6 kg ha-1, 3.8–8.2 cmol(P+)/kg, and 2.1-4.1 cmol(P+)/kg, respectively. The findings indicated that the studied soils were slightly acidic to moderately acidic and free from salinity hazards; 70% of the soil samples were low in organic carbon, 30% were medium in organic carbon, and 100% of the soil samples were low in available N and medium in available P, K, and S. 100% of soil samples were found to be sufficient in exchangeable Ca and Mg. The findings of this research could help in crop nutrient management, fertilizer recommendation, and decision-making for increasing agricultural production and farmer profitability.

Effects of cropping patterns on the distribution, carbon contents, and nitrogen contents of aeolian sand soil aggregates in Northwest China

The long-term physicochemical responses of aeolian sandy soil aggregates to different crop rotation patterns are poorly understood. Here, we collected soil samples from the 0 to 20 cm tillage layer of continuous maize crop and alfalfa–maize rotation plots situated on the edge of the Zhangye Oasis, Northwest China. These samples were analyzed to quantify the influence of both cropping patterns on the structure, carbon content, and nitrogen content of aeolian sandy soils. When compared with long-term continuous maize cropping, planting alfalfa–maize rotation system significantly increased the mass fraction of macro-aggregates with sizes of > 2 mm and 0.25–2 mm from 8.7 to 12.1% and 19.1 to 21.2%, respectively, but decreased the mass fraction of micro-aggregates (0.053–0.25 mm) from 8.1 to 6.2%. Further, there was no significant difference in the content of silt and clay particles between each system. The alfalfa–maize rotation increased the stability of aggregates from 32 to 37%, representing an increase of 15.6%. Soil organic carbon, inorganic carbon, and total nitrogen were mainly enriched in macro-aggregates with sizes of > 2 mm, and silt and clay fractions for both cropping patterns. Implementation of a rotation pattern increased organic carbon contents by 27.2%, 25.6%, 26.7%, and 27.6%, inorganic carbon contents by 14.4%, 4.5%, 53.3%, and 21.0%, and total nitrogen contents by 29.7%, 7.0%, 4.2%, and 50.0% in aggregate particle sizes of > 2 mm, 0.25–2 mm, 0.053–0.25 mm, and < 0.053 mm, respectively, when compared to continuous maize cropping. The alfalfa–maize crop rotation can therefore effectively improve soil aggregate composition and aggregate stability, alongside organic carbon content, inorganic carbon content, total nitrogen content, and their storage capacity. This system thus represents a soil cultivation technique that can increase the soil carbon sequestration capacity in the oasis zone of Northwest China.

Removal of Crop Ion Components in Relation to Mollisol Acidification under Long-Term Management

Crop removal of cations and anions is one of the main factors causing soil acidification. Mollisols, also known as black soils, which are inherently neutral soils, have been acidified due to irrational land use. However, the magnitude of acidification of black soils in relation to crop removal under crop rotation and field management remains unclear. In this study, we collect soil samples from 9- and 10-year long-term trials with seven treatments of soybean and corn monoculture, and corn–soybean rotation under different fertilization or straw return in the Mollisol region of Northeast China. Total removal of cations and anions is 371–508 and 92–125 kg ha−1 in corn but 166–250 and 56–82 kg ha−1 in soybean, which leads to the production of 7.06–10.2 kmol ha−1 H+ for corn and 4.43–5.77 kmol ha−1 H+ for soybean. Among the four cations (K+, Ca2+, Na+, and Mg2+) analyzed in different tissues of crops, K+ contributes more to the total H+ production, where K+ in corn stem contributes more than 55% H+, while K+ in soybean seed contributes more than 64% H+. The sum of Ca2+ and Mg2+ removal for soybean is 37.6–66.9 kg ha−1, 1.26–2.07 times (p &lt; 0.05) greater than for corn. Net H+ produced by corn and soybean removal may potentially lead to a soil acidification of 0.053–0.074 and 0.032–0.045 year−1 units pH, respectively. The greater the corn or soybean biomass, the higher the net H+ production. Continuous soybean and combination of chemical fertilizer for corn but no chemical fertilizer for soybean reduce the soil pH buffering capacity (pHBC), while other treatments increase the soil pHBC, but there is no effect for continuous corn. Compared to normal corn–soybean rotation, soil pH in 9-year continuous corn declines from 5.76 to 5.63, while 10-year continuous soybean declines from 5.68 to 5.43 (p &lt; 0.05). However, soil pH with chemical fertilizer for corn and dairy manure for soybean increases by 0.60 and 0.89 units (p &lt; 0.05). Changes in pH might not be fully related to crop ion removal. Manure application with crop rotation is an effective approach to alleviate soil acidification.

SHAP values accurately explain the difference in modeling accuracy of convolution neural network between soil full-spectrum and feature-spectrum

Acquiring soil nutrient content quickly and accurately through remote sensing is the key to advance precision agriculture. The development of deep learning has provided new technical means for soil hyperspectral modeling. However, the problem of poor interpretability of deep learning models limits its development. Although SHapley Additive exPlanations (SHAP) values based on game theory have been successfully applied to the interpretation of deep learning soil spectral modeling, whether they can accurately explain the differences in deep learning model accuracy remains to be verified. Based on this, we explored whether SHAP values can accurately explain the differences in convolutional neural network (CNN) modeling accuracy. We collected soil samples from agricultural land in the Liangshui River Basin in the southern mountainous and hilly areas of China, and measured the soil total nitrogen (STN) content and soil spectral data in the laboratory. We compared the effects of full-spectrum and feature-spectrum on the accuracy of deep learning models, and obtained the contribution of wavelengths in the CNN modeling process by calculating SHAP values. The results showed that combining different spectral pre-processing methods can play their respective advantages and help improve modeling accuracy. Among them, the CNN model obtained the highest prediction accuracy under the first-derivative Savitzky-Golay smoothing combination standard normal variate (SG1-SNV) spectral pre-processing in full-spectrum modeling. Compared with the feature-spectrum selected for modeling by Mutual information (MI) and competitive adaptive reweighted sampling (CARS), the CNN model achieved higher accuracy in most pre-processed spectra in full-spectrum modeling, and SHAP values accurately explained this reason. This is because the contribution is usually higher at most wavelengths with a high correlation with STN content. The feature-spectrum selected by CARS is more widely distributed but lacks continuity, and some wavelengths with high correlation and high contribution will also be missed. Meanwhile, some wavelengths with low correlation also have high contributions, which are usually not involved in the feature spectrum modeling of MI, thus affecting the modeling accuracy. Therefore, the deep learning model is more suitable for full-spectrum modeling due to its strong feature extraction and self-learning capabilities, and SHAP can obtain the wavelength contribution of the CNN model in soil spectral modeling, and then explain the differences in modeling accuracy. This study further proves the interpretability of deep learning, provides an important basis for the application of deep learning in soil hyperspectral modeling.

Core microbiota drive multi-functionality of the soil microbiome in the Cinnamomum camphora coppice planting

BackgroundCinnamomum camphora (L.) Presl (C. camphora) is an evergreen broad-leaved tree cultivated in subtropical China. The use of C. camphora as clonal cuttings for coppice management has become popular recently. However, little is known about the relationship between soil core microbiota and ecosystem multi-functionality under tree planting. Particularly, the effects of soil core microbiota on maintaining ecosystem multi-functionality under C. camphora coppice planting remained unclear.Materials and methodsIn this study, we collected soil samples from three points (i.e., the abandoned land, the root zone, and the transition zone) in the C. camphora coppice planting to investigate whether core microbiota influences ecosystem multi-functions.ResultsThe result showed a significant difference in soil core microbiota community between the abandoned land (AL), root zone (RZ), and transition zone (TZ), and soil ecosystem multi-functionality of core microbiota in RZ had increased significantly (by 230.8%) compared to the AL. Soil core microbiota played a more significant influence on ecosystem multi-functionality than the non-core microbiota. Moreover, the co-occurrence network demonstrated that the soil ecosystem network consisted of five major ecological clusters. Soil core microbiota within cluster 1 were significantly higher than in cluster 4, and there is also a higher Copiotrophs/Oligotrophs ratio in cluster 1. Our results corroborated that soil core microbiota is crucial for maintaining ecosystem multi-functionality. Especially, the core taxa within the clusters of networks under tree planting, with the same ecological preferences, had a significant contribution to ecosystem multi-functionality.ConclusionOverall, our results provide further insight into the linkage between core taxa and ecosystem multi-functionality. This enables us to predict how ecosystem functions respond to the environmental changes in areas under the C. camphora coppice planting. Thus, conserving the soil microbiota, especially the core taxa, is essential to maintaining the multiple ecosystem functions under the C. camphora coppice planting.

Analyzing soil enzymes to assess soil quality parameters in long-term copper accumulation through a machine learning approach

Soil contamination by agrochemicals is a big concern for soil health and ecosystem functioning. This is especially true for non-degradable substances like heavy metals (HM) that, because of their long-term use, are reaching significant values today due to soil accumulation. Among agrochemicals, copper (Cu) has been important in fighting fungal diseases on perennial crops for centuries.Laboratory experiments can be useful to understand the highest potential toxic effect of Cu but need to reflect what happens with long-term application in a dynamic and living agroecosystem. This study uses multivariate data analysis and machine learning to investigate long-term Cu accumulation on soil quality parameters, especially soil extracellular enzymatic activities. We collected soil samples from 21 apple orchards in South Tyrol, Italy. The orchards had different concentrations of Cu. We took 315 samples in total and analyzed them for various soil properties. We also measured the concentrations of elements in apple leaves and the activities of soil extracellular enzymes. We depicted the effect of Cu on several enzymatic activities, shedding light on the effect of Cu on the soil microbial communities functionality. Our results show that Cu concentrations in the study area affect only phosphatase activity, showing effects above 60 mg kg−1 of available Cu. Protease activity was positively correlated with Cu, while soil organic matter and management mainly influenced the carbon (C) cycle enzymes. Phosphatase decrease could be of concern for the potential disruption of the Phosphorus (P) cycle in the soil and plays a role in plant nutrition, as seen by P concentration in apple trees' leaves.We demonstrated how machine learning can help interpret complex and multivariate environmental data and overcome some downsides of traditional statistical models.

Methylocystis dominates methane oxidation in glacier foreland soil at elevated temperature.

Methane-oxidizing bacteria (methanotrophs) play an important role in mitigating methane emissions in various ecological environments, including cold regions. However, the response of methanotrophs in these cold environments to extreme temperatures above the in-situ temperature has not been thoroughly explored. Therefore, this study collected soil samples from Longxiazailongba (LXZ) and Qiangyong (QY) glacier forelands and incubated them with 13CH4 at 35°C under different soil water conditions. The active methanotroph populations were identified using DNA stable isotope probing (DNA-SIP) and high throughput sequencing techniques. The results showed that the methane oxidation potential in LXZ and QY glacier foreland soils was significantly enhanced at an unusually high temperature of 35°C during microcosm incubations, where abundant substrate (methane and oxygen) was provided. Moreover, the influence of soil water conditions on this potential was observed. Interestingly, Methylocystis, a type II and mesophilic methanotroph, was detected in the unincubated in-situ soil samples and became the active and dominant methanotroph in methane oxidation at 35°C. This suggests that Methylocystis can survive at low temperatures for a prolonged period and thrive under suitable growth conditions. Furthermore, the presence of mesophilic methanotrophs in cold habitats could have potential implications for reducing greenhouse gas emissions in warming glacial environments.

Spatial soil heterogeneity rather than the invasion of Spartina alterniflora drives soil bacterial community assembly in an Eastern Chinese intertidal zone along an estuary coastline

The invasion of Spartina alterniflora (S. alterniflora) significantly influences the stability and transformation of soil organic carbon in coastal ecosystems. However, the impact of this invasion on the assembly of soil bacterial communities along estuary coastlines and soil profiles remains unclear. This study involved collecting soil samples from different environments (areas covered with S. alterniflora vs. barren flats) along the coastline of an estuary. The samples were taken at different depths (0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, 80–100 cm) within a 1-meter soil profile in the intertidal area of Xiaoyangkou Estuary in Jiangsu, China. The invasion of S. alterniflora had a notable impact on soil SOM (soil organic matter), DOC (dissolved organic carbon), POC (particulate organic carbon), and MAOC (mineral-associated organic carbon). Concurrently, the soil layer primarily influenced the levels of soil DOC and MAOC. The dominant bacterial communities inside the soil consisted of Proteobacteria (27.4–50.2 %), Bacteroidota (5.2–23.3 %), and Desulfobacterota (9.5–19.8 %), which participates in several elemental cycles, such as the carbon, nitrogen, sulfur, and phosphorus cycles. The changes in the patterns and functions of bacterial communities related to carbon cycling were mostly associated with the estuary shoreline, rather than being influenced by the invasion of S. alterniflora or variations in soil depths. Soil total carbon (TC) and SOM displayed a greater influence than other soil organic fractions on the distribution of the bacterial community. Furthermore, elements such as As, Cr, and Zn also exerted a significant impact on the assembly of soil bacterial communities. Our research findings suggest that the influence of S. alterniflora invasion on the composition and functioning of soil bacterial communities is contingent upon the specific features of the area. These characteristics, in turn, control the stability of soil carbon and the transformation of various types of soil carbon.

Effects of Different Forest Types on Stoichiometric Characteristics of Carbon, Nitrogen, and Phosphorus in Tropical Soils, China

The stoichiometric characteristics of carbon (C), nitrogen (N), and phosphorus (P) in soil can directly reflect the level of soil fertility. In this study, a secondary forest, two kinds of plantation forests (Dacrycarpus imbricatus and Pinus caribaea Morelet), and a primary forest in tropical areas were selected for soil sample collection and analysis. We found that the soil [leucine aminopeptidase (LAP) + β-N-acetylglucosidase (NAG)]/acid phosphatase (APho) value was less than 1, indicating that there was P limitation. In addition, we also found that compared with primary forest, the β-glucosidase (BG) activity and (LAP + NAG) activity of the secondary forest were lower, and the microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) contents were higher. The contents of total nitrogen (TN) and MBN in the Dacrycarpus imbricatus forest soil were significantly higher than those in the primary forest, but the activity of (LAP + NAG) was lower. The structural equation model showed that the factors that significantly affected the total nutrients were the available nutrients (0.396**) and the enzyme activities (0.600**). For the long-term requirements of sustainable development, ecological adjustment and soil nutrient optimization of plantations should be carried out scientifically.

Impacts of wildfire on soil characteristics in subtropical Chir Pine forests of Murree, Pakistan

Wildfires are a natural component of forest ecosystem. These fires are a major cause of significant perturbations in the forest habitat due to temperature extremes and drought conditions particularly in summer. Wildfires occur frequently during summer in the subtropical Chir pine forests of Murree, Pakistan. These fires influence soil characteristics as well as tree-species growth and survival. The present study was thus conducted to investigate soil characteristics of wildfire-affected and non-affected areas. Four sites were selected based on wildfire incidents in the study area. Soil samples were collected from the fire-affected and adjacent non-affected areas. The collected soil samples were analyzed for physical and chemical characteristics using appropriate methods. The study showed that bulk density, moisture content, electrical conductivity, and soil nitrogen and soil carbon content declined in the burnt soil. However, soil pH, and phosphorus and potassium contents were higher in the burnt soils than those in the non-burnt soils. Generally, low intensity forest fire (surface fire) is an important component of forest ecosystem and it enhances natural regeneration as well as soil fertility. It is suggested that government forest departments should take into account burn severity while developing hazard reduction and restoration strategies of wildfire affected areas.

Tree canopy area-dependent changes in soil properties: a comparative study in the southern limit of boreal forest distribution

The forest distribution in relation to mountain slope position and the relationships between forest canopy area and soil physical and chemical properties in Mongolia attract the attention of scientists. In this study, we aimed to determine (1) how the slope aspect affects the forest soil physical and chemical properties, (2) how the soil variables correlate with projected canopy area. For comparative study, we established a total of 18 (20 × 25 m2 sized) sample plots in upper forest edge (UFE), upper slope of the forest (USF), lower slope of the forest (LSF), lower forest edge (LFE), sparse forest (SF), and degraded forest area (DFA) stand types with three replications. We measured the tree height, diameter and crown projection area of each growing tree in the sample plots, and collected soil samples for further soil physical and chemical analyses in the laboratory. Our results showed a high variation in growing stock (p < 0.0001), stand density and projected canopy area (p < 0.0001) in relation to mountain slope position and land-use management. We found that the slight changes in stand canopy area projection cause a high variation in soil physical and chemical properties. Among studied stand types higher means of soil temperature and bulk density and lower moisture content were observed on SF and DFA stand types. Contrary, relatively higher content of soil moisture and nutrient elements were recorded in USF, LSF, LFE. We conclude that tree canopy area becomes key determinant factor that positively influences forest soil moisture and fertility by increasing organic matter content in Mongolia.

An assessment of soil microbial community environmental stress in alpine marginal ecosystems using microbial indicators

Alpine marginal ecosystems above the natural tree line are considered to be the most vulnerable habitats for maintaining local biodiversity. The soil microbial community is crucial for ecosystem stability and function. However, our comprehension of the overall soil microbial community in alpine marginal ecosystems remains limited, particularly regarding environmental stress. We selected the Baima Mountain National Nature Reserve (BMNNR) in the northwest Yunnan provinces as our study area and collected soil samples from three different depth layers, in alpine marginal ecosystems. The phospholipid fatty acid (PLFA) analysis method was utilized to evaluate alterations in soil microbial communities. The results showed that: (1) the PLFA content gradually decreased with increasing soil depth; (2) on the whole, the variations in PLFA across alpine marginal ecosystems were obviously, the highest in alpine meadow (AM) and the lowest in alpine screes (AS); (3) the site effect exerted a greater direct influence than microhabitat effect and elevation (0–5 cm, 32 %, 16 % and 9 %, respectively; 20–40 cm, 46 %, 29 % and 0 %, respectively), with the direct impact of elevation decreasing as soil depth increased (0–5 cm, 5–20 cm and20-40 cm, 9 %, 1 % and 0 %, respectively); and (4) the ratio of cyclopropyl to precursors (Cy:Pre) in all communities was > 0.1, indicating that the soil microbial community was under environmental stress. This study demonstrates the potential of PLFA analysis to profile soil microbial communities and their stress status under field conditions and also provides a better understanding of the correlation between soil microbial communities and the function and health of alpine marginal ecosystems.