Soil Organic Matter Research Articles

Land Use and Its Impact on Phosphorus Adsorption Capacity in a Tropical Dryland Agricultural Frontier: Insights and Predictive Modeling

ABSTRACT Land use significantly impacts the physical-chemical properties of soil, particularly in tropical regions where the maximum phosphorus adsorption capacity (MPAC) plays a crucial role in agricultural sustainability. This study evaluates the influence of land uses (cropland, grassland, and forest) on MPAC in dryland soils of Sergipe, Alagoas, and Bahia (SEALBA) agricultural frontier in Northeast Brazil. Soil samples from nine sites were analyzed for their physical-chemical characteristics (potential of hydrogen (pH), available phosphorus (P), remaining P, soil organic matter (SOM), calcium and magnesium content, exchangeable aluminum, potential acidity and texture) in the 0–20 cm soil depth. MPAC was determined using two isotherm models, and three predictive models were applied. The results revealed that cropland soils exhibit the highest MPAC, contrary to expectations of lower values due to legacy P from P inputs. However, MPAC varied slightly across sites, indicating specific dependencies on soil physical-chemical properties and suggesting that land use may have a more significant influence over the long term. Random forest and decision tree models identified SOM and available P as the most significant predictors of MPAC, yet with these routine soil fertility indicators alone, the maximum explanatory power (R2) was 75% with a precision (RMSE) of 77 mg kg−1 using the random forest model. Consequently, the conversion of forest to cropland can increase MPAC, and predicting MPAC can be a valuable tool for guiding land use and assisting in the formulation of more sustainable agricultural and environmental policies, particularly in dryland agricultural frontiers.

Enhancement of Fertilizer Efficiency Through Chinese Milk Vetch and Rice Straw Incorporation

The incorporation of rice straw (RS) and Chinese milk vetch (CMV) with reduced chemical fertilizers (CFs) is a viable solution to reduce the dependency on CF. However, limited research has been conducted to investigate the impact of CMV and RS with reduced CF on rice production. A field trial was conducted from 2018 to 2021 with six treatments: CK (no fertilizer), F100 (100% NPK fertilizer (CF)), MSF100 (100% CF+CMV and RS incorporation), MSF80 (80% CF+CMV+RS), MSF60 (60% CF+CMV+RS), and MSF40 (40% CF+CMV+RS). The results revealed that compared with the F100, the MSF80 treatment maintained a significantly higher mean grain yield over the four years, with an increase of 5.8~24.5%. MSF80 treatment also improved nitrogen (N), phosphorus (P), and potassium (K) use efficiencies, sustainable yield index, and partial factor productivity. Soil organic matter (SOM), total nitrogen (TN), ammonium N (NH4+-N), nitrate N (NO3−-N), available phosphorus (AP), and available potassium (AK) contents were significantly enhanced under MSF80 across different growth stages in both 2020 and 2021 seasons over F100. Pearson correlation analysis revealed a strong positive correlation among SOM, TN, NH4+-N, AP, AK, and rice yield. Additionally, Partial Least Squares Path Modeling (PLS-PM) demonstrated significant relationships between organic amendments, soil nutrients, nutrient uptake, and yield. The above findings suggest that combining RS returning with CMV incorporation is a long-term sustainable strategy for maintaining soil health, and it could reduce fertilizer addition by 20% without prejudicing rice grain yield under a rice-green manure rotation system.

Synthesis, Characterization, and Impact of Different Carbon-Based Nanomaterials on Gram (Cicer arietinum) Plant Growth and Soil Properties

The ecology and general public health are badly impacted by the prolonged usage of chemical fertilizers. Applying carbon-based nanomaterials is one of the best options available for accelerating plant growth while reducing harm to the environment. The current study aims to assess the effects of graphene oxides (GO), functionalized carbon nanotubes (FCNTs), and carbon nanotubes (CNTs) on plant growth and soil nutrient content. To observe the impact on gram plant growth and soil parameters, we synthesized and applied GO, FCNTs, and CNTs at a rate of 100µg/mL (120 g per kg soil) in the corresponding pots. After 90 days of seed sowing, GO-treated crops showed a 41% increase in crop height compared to the control (no nanomaterials), but this increase was 33% and 40% in CNTs - and FCNTs-treated crops, respectively. When compared to the control, the GO-treated plants shown a twofold increase in root length; in contrast, the FCNTs and CNTs-treated plants showed increases of 60% and 25%, respectively. The highest increases in plant biomass, soil organic matter, total nitrogen, microbial biomass, and enzymatic activity were observed in plants treated with GO. A 52% increase in SDA was seen in the GO-treated soil as compared to the control; in the FCNTs and CNTs-treated soils, this increase was 32% and 19%, respectively. An organic material with a carbon base is a carbon-based nanomaterial, which has the ability to control the soil microenvironment and activate soil enzyme activity. The results verified that incorporating carbon-based nanomaterials, particularly GO, into the soil might enhance the growth of gram plants and the sustainability of the soil.

Assessing heavy metal contamination in agricultural soils: a new GIS-based Probabilistic Pollution Index (PPI) – case study: Guarda Region, Portugal

ABSTRACT Soil contamination by heavy metals is a global agricultural problem, as these elements can be absorbed by plants and transferred to humans through the food pathway. Current contamination assessment methods rely on in situ sample collection, which restricts the evaluation process due to the number of samples that can be analysed and the associated costs. This study addresses the need for a more efficient and cost-effective approach to identifying areas at risk of heavy metal contamination without the logistical constraints of physical sampling. To meet this challenge, we developed a new Probabilistic Pollution Index (PPI), calculated by integrating GIS tools with an 8-parameter probability-risk matrix to identify agricultural areas potentially contaminated by heavy metals. The factors considered included roads, industrial sites, pH levels, soil organic matter content, terrain slope, soil texture, mining areas, and drainage. Each parameter was classified and reclassified to produce a contamination risk map, categorizing each pixel into five levels of risk. To test the PPI, data analysis, parameter classification, and reclassification were applied in the district of Guarda, Portugal. The PPI map revealed that the central portion of the Guarda municipality, along with specific zones in Celorico da Beira, Sabugal, Mêda, and Pinhel, exhibited a high-probability risk of contamination. Given the agricultural expanse within each municipality, enhanced monitoring of heavy metal levels was recommended for Mêda, Pinhel, and Vila Nova de Foz Côa. This index provides a scalable, cost-effective, and easily replicable tool for identifying potential contamination hotspots and the sources and processes of contamination. Designed as a first-line method for large-scale assessments, it supports governmental decision-making and facilitates targeted risk mitigation strategies through a low-cost approach.

The Soil Food Web Model as a Diagnostic Tool for Making Sense out of Messy Data: A Case of the Effects of Tillage, Cover Crop and Nitrogen Amendments on Nematodes and Soil Health

Tillage, cover crops (CC) and nutrient amendments are regenerative agricultural practices (RAPs) which enhance desirable ecosystem services (DESs), including the beneficial nematode community structure (BNCS), soil organic matter (SOM), pH, and available nitrogen, and the Ferris et al. soil food web (SFW) model relates changes in the BNCS to biophysicochemical conditions generating DESs. However, the SFW model’s power to identify soil health conditions influencing DESs’ outcomes has been limited. We tested how tillage, winter rye CC, and 0, 112, or 224 kg N/ha from inorganic and compost sources affected the DESs after four years of corn production. The SOM and NO3 was much greater in the no-till than the tilled soil, and the SOM in the 224 kg organic source, compared with the rest of the N rates, was significantly increased. The N recovery was not proportional to what was applied. The variable effects of the RAPs on the DESs suggest either changing or continuing treatments until suitable outcomes are achieved, all without knowing the source(s) of variability. The SFW model revealed primarily resource-limited and structured (Quadrant C) conditions, suggesting that (1) nutrient cycling needs biological activities and (2) the presence of a process-limiting factor may have contributed to the variable results. The impacts of the SFW model as a diagnostic tool are outlined.

Quantification of Soil–Water Erosion Using the RUSLE Method in the Mékrou Watershed (Middle Niger River)

Despite nearly a century of research on water-related issues, water erosion remains one of the greatest threats to soil health and soil ecosystem services around the world. Yet, to date, data on water erosion needed to develop mitigation strategies are scarce, especially in the Sahelian regions. The current study therefore sets out to estimate annual soil losses caused by water erosion and to analyze trends over the period of 1981–2020 in the Mékrou watershed, located in the Middle Niger river sub-basin in West Africa. The Revised Universal Soil Loss Equation, remote sensing, and the Geographic Information System (GIS) were deployed in this study. Several types of data were used, including rainfall data, sourced from meteorological stations and reanalysis datasets, which capture the temporal variability of erosive forces. Soil properties, including texture and organic matter content, were derived from FAO global soil databases to assess soil erodibility. High-resolution digital elevation models (30 m) provided detailed topographic information, crucial for calculating slope length and steepness factors. Land use and land cover data were extracted from satellite imagery, enabling the analysis of vegetation cover and anthropogenic impacts over four decades. By integrating and treating these data, this study reveals that the estimated average annual amount of water erosion in the Mékrou watershed is 6.49 t/ha/yr over 1981–2020. The dynamics of the ten-year average are highly variable, with a minimum of 3.45 t/ha/yr between 1981 and 1990, and a maximum of 8.50 t/ha/yr between 1991 and 2000. Even though these average soil losses in the Mékrou basin are below the tolerable threshold of 10 t/ha/yr, mitigation actions are needed for prevention. In addition, the spatial dynamics of water erosion are noticeably heterogeneous. The study reveals that 72.7% of the surface area of the Mékrou watershed is subject to slight water erosion below the threshold, compared with 27.3%, particularly in the mountainous south-western part, which is subject to intense erosion above the threshold. This research is the first study of soil erosion quantification with the RUSLE method and GIS in the Mékrou watershed, and fills a critical knowledge gap of the water erosion in this watershed, providing insights into erosion dynamics and supporting future sustainable land management strategies in vulnerable Sahelian landscapes.

Soil smoldering in temperate forests: a neglected contributor to fire carbon emissions revealed by atmospheric mixing ratios

Abstract. Fire is regarded as an essential climate variable, emitting greenhouse gases in the combustion process. Current global assessments of fire emissions traditionally rely on coarse remotely sensed burned-area data, along with biome-specific combustion completeness and emission factors (EFs). However, large uncertainties persist regarding burned areas, biomass affected, and emission factors. Recent increases in resolution have improved previous estimates of burned areas and aboveground biomass while increasing the information content used to derive emission factors, complemented by airborne sensors deployed in the tropics. To date, temperate forests, characterized by a lower fire incidence and stricter aerial surveillance restrictions near wildfires, have received less attention. In this study, we leveraged the distinctive fire season of 2022, which impacted western European temperate forests, to investigate fire emissions monitored by the atmospheric tower network. We examined the role of soil smoldering combustion responsible for higher carbon emissions, locally reported by firefighters but not accounted for in temperate fire emission budgets. We assessed the CO/CO2 ratio released by major fires in the Mediterranean, Atlantic pine, and Atlantic temperate forests of France. Our findings revealed low modified combustion efficiency (MCE) for the two Atlantic temperate regions, supporting the assumption of heavy smoldering combustion. This type of combustion was associated with specific fire characteristics, such as long-lasting thermal fire signals, and affected ecosystems encompassing needle leaf species, peatlands, and superficial lignite deposits in the soils. Thanks to high-resolution data (approximately 10 m) on burned areas, tree biomass, peatlands, and soil organic matter (SOM), we proposed a revised combustion emission framework consistent with the observed MCEs. Our estimates revealed that 6.15 Mt CO2 (±2.65) was emitted, with belowground stock accounting for 51.75 % (±16.05). Additionally, we calculated a total emission of 1.14 Mt CO (±0.61), with 84.85 % (±3.75) originating from belowground combustion. As a result, the carbon emissions from the 2022 fires in France amounted to 7.95 MtCO2-eq (±3.62). These values exceed by 2-fold the Global Fire Assimilation System (GFAS) estimates for the country, reaching 4.18 MtCO2-eq (CO and CO2). Fires represent 1.97 % (±0.89) of the country's annual carbon footprint, corresponding to a reduction of 30 % in the forest carbon sink this year. Consequently, we conclude that current European fire emission estimates should be revised to account for soil combustion in temperate forests. We also recommend the use of atmospheric mixing ratios as an effective monitoring system of prolonged soil fires that have the potential to re-ignite in the following weeks.

A Novel High-Conjugated Probe of 4-(Acridone-10-yl)-Phenylethyl Chloroformate (APE-Cl) for Rapid Detection of Amino Compounds Using HPLC with Fluorescence Detection.

The fluorescence detection of amino compounds and the evaluation of their content in environmental samples are vital, not only for assessing food quality but also for studying soil organic matter. Here, we present the synthesis and application of a novel fluorescent probe, 4-(9-acridone)benzylmethyl carbonochloride (APE-Cl), for detecting amino compounds via a chloroformate reaction with fluorescence detection. The complete derivatization reaction of APE-Cl with amino compounds can be accomplished in aqueous acetonitrile within 5min at room temperature, using 0.2M borate buffer (pH = 9.0). APE-amine derivatives exhibited intense fluorescence with an excitation maximum at λex 254nm and an emission maximum at λem 418nm. All derivatives demonstrated high stability, strong fluorescence, and elevated ionization potential under atmospheric pressure chemical ionization (APCI-MS) in positive ion detection mode. The method, combined with gradient elution, provides baseline resolution of common amine derivatives on a reversed-phase C18 column. The LC separation for the derivatized amines shows good reproducibility with aqueous acetonitrile as the mobile phase. The relative standard deviations (n = 6) for each amine derivative are < 3.99%. The detection limits (at a signal-to-noise ratio of 3) per injection ranged from 1.68 to 11.2 femtomole. The established pre-column derivatization method for determining amino compounds in practical samples proved to be satisfactory.

Parameters of labile organic carbon as the indicators of the stability of soil organic matter under different land use

Parameters of labile organic carbon as the indicators of the stability of soil organic matter under different land use

Multiple Environmental Variables as Covariates to Improve the Accuracy of Spatial Prediction Models for SOM on Karst Aera

ABSTRACTAims accurately predicting the spatial distribution of soil organic matter (SOM) is essential for environmental management and carbon storage estimation. However, the diversity of sources of variables poses a challenge in studying the spatial distribution of SOM. Methods in order to address this issue, we propose leveraging multiple environmental variables and employing machine learning models, specifically Lightweight gradient boosting machine learning (LightGBM) and random forest (RF), for predicting SOM spatial distribution. 128 soil samples were collected from the Caohai National Nature Reserve, and their SOM content was measured. Results the study found that the average SOM content was 36.75 g/kg. Compared to traditional linear regression models such as ordinary kriging (OK), ordinary least squares (OLS), and geographically weighted regression (GWR), the machine learning models based on nonlinear regression, LightGBM and RF, demonstrated higher cross‐validated coefficients of determination (R2) of 0.62 and 0.60, respectively, outperforming the other models. Additionally, RF exhibited lower mean absolute error (MAE) and root mean square error (RMSE), indicating higher stability and generalization capability. The spatial distribution of SOM among the models showed consistency, with higher SOM content observed in southern and near‐Caohai Lake regions and lower SOM content in northern and farther regions from Caohai Lake. Results from the Shapley additive explanations (SHAP) model highlighted agricultural land (AL), pH, and Elevation (ELV) as primary covariates influencing SOM spatial distribution. Conclusions this study provides valuable insights and support for environmental management and carbon storage estimation in the karst plateau region.

Phytochemical characterization of the date palm (Phoenix dactylifera L.) linked to chemical characteristics of the soil of backyard orchards

The fruits of Phoenix dactylifera L. contain bioactive compounds such as phenols, flavonoids, carotenoids, vitamins and minerals. However, its composition and content exhibit great variability depending on genetic and environmental factors, especially the physico-chemical properties of the soils where date palms develop. To elucidate the above, samples of dates from three backyard orchards and soil samples from each orchard were collected to determine the content of bioactive compounds and chemical characteristics of the substrate and to determine the correlations between these. For total contents of polyphenols and flavonoids, antioxidant activity and soluble solids, the following methods were used - Folin-Ciocalteu reagent based total polyphenol estimation; aluminium chloride based total flavonoid estimation; 2, 2-Diphenyl-1-picrylhydrazyl (DPPH) free radical inhibition and a hand-held refractometer for soluble solid content determination. Each orchard was considered as a separate treatment and a completely randomized design was used, with the Tuckey’s test (?=0.05) applied to compare means. Pearson's linear correlation analysis was applied to establish the link between bioactive compounds of dates and chemical characteristics of the soils. The hypotheses were that the bioactive compounds present in dates obtained in different backyard orchards could vary depending on the chemical characteristics of their soils. The results reflected significant differences only for the soluble solids content of the dates. Statistically significant correlations were observed between two soil characteristics; organic matter (OM) and electrical conductivity (EC); and the soluble solids content of dates. It was concluded that higher organic matter in the soils is associated with higher soluble solids content in the fruit, while higher electric conductivity in the soils is linked to lower soluble solids content in the dates.

A Review on Conservation Agriculture: Challenges, Opportunities and Pathways to Sustainable Farming

A cutting-edge kind of sustainable agriculture, conservation agriculture (CA) involves reducing tillage, maintaining constant soil cover, using a variety of cropping techniques, and effectively utilizing the resources at hand. For instance, CA in India has made impressive strides in the past 20 years, particularly in the Indo-Gangetic plain's rice-wheat rotation. There are many disadvantages that limit the use of CA, despite its many benefits, which include cost savings, improved crop production, water and nutrient conservation, and environmental advantages. Numerous issues include the lack of appropriate seeders for small and medium-sized farmers, agricultural leftovers that compete with livestock feed for CA, burning of these residues, which destroys soil organic matter, a lack of labor, and a general bias in favor of traditional tillage. Therefore, in order to effectively promote CA, we advise immediate policy development and planning. CA reduces soil erosion, preserves SOM, suppresses weeds, conserves soil moisture, avoids compaction, promotes good soil structure, and improves biological processes and biodiversity both above- and below-ground. This article looks at the new problems that are coming from traditional farming practices, as well as the constraints, opportunities, regulations, and areas that need more study in order to expand conservation agriculture in India. This emphasizes that for conservation agriculture to be successfully implemented, governments, researchers, farmers, and other groups must collaborate. Therefore, we have the ability to get past these obstacles and reap the rewards of this kind of farming.

Geochemical insights into plant uptake of Technology-critical elements: A case study on lettuce from European soils.

While vegetable uptake of traditional metal contaminants is a well-studied pathway to human exposure and risk, a paucity of information exists on the uptake of emerging metal contaminants. This study evaluated the uptake of the Technology-critical elements (TCEs) gallium (Ga), germanium (Ge), niobium (Nb), tantalum (Ta), thallium (Tl), and rare earth elements (REEs) into lettuce cultivated in 21 European urban soils. For comparison, the uptake of cadmium (Cd) was also analysed. First, the uptake was predicted by multiplying soil concentrations with previously established bioconcentration factors (BCFs). Subsequently, multiple regression models incorporating geochemical variables as predictors were used to determine whether prediction accuracy could be improved. A "3-predictor model" incorporated soil TCE concentration, pH, and organic matter (OM), and a "7-predictor model" added data on clay content and the soil concentrations of Fe, Al, and Mn as well. With the exception of Cd, Ge, and Tl, the BCF approach provided unsatisfactory predictions (R2<0.5), while the 7-predictor models yielded the best predictions, even when accounting for the greater number of predictors. While the most important predictors of uptake varied somewhat between the TCEs, the concentrations of TCEs in the soil generally explained the largest proportion of the variation. The least influential predictors in our dataset were [Mnsoil], [Fesoil], and soil OM. Incorporating geochemical data generally improved the predictions of uptake by lettuce, and these findings underscore the need for more detailed characterisations of the uptake potential of TCEs by food plants and subsequent consequences for human health.

Polyethylene microplastics hamper aged biochar’s potential in mitigating greenhouse gas emissions

While biochar effectively reduces greenhouse gas emissions, the coincident microplastics will alter these benefits. To assess the long-term efficacy of biochar application in reducing emissions amidst microplastic interference, we investigated the interactive effects of polyethylene microplastics (1%–5% wt) and decadal biochar addition (aged biochar) on C and N stability in a Fluvic Cambisol. Aged biochar and polyethylene individually reduced CO2 emissions by 49% and 18%, respectively, over 91-day incubation. This was due to decreased soil aggregation, dissolved organic matter (DOM) content, and increased DOM aromaticity, which reduced microbial biomass and chitinase activity associated with soil organic matter (SOM) decomposition. This ultimately led to increased accumulation of microbial necromass carbon (MNC) to soil stable carbon pool. Interestingly, the coexistence of polyethylene diminished the efficacy of biochar in mitigating CO2 emissions (+ 44 ~ 82%) and stabilizing MNC (-18 ~ 23%). This was because the interaction between polyethylene and biochar facilitated macroaggregate formation and DOM accumulation and decreased DOM aromaticity, which increased microbial biomass and chitinase activity for SOM decomposition. Similar to soil C dynamics, aged biochar largely reduced N2O emissions by 54%, due to decreased nirK but increased nifH genes. Polyethylene increased N2O emissions by 5% and 25% in biochar-free and aged biochar soil, respectively, likely through upregulation of nirS and nirK but downregulating nifH gene expression. Thus, polyethylene microplastics may undermine the benefits of biochar in mitigating climate change, highlighting the necessity to recognize microplastics as a global change factor and to incorporate their role in elemental cycling alongside their global transport.Graphic

The Grain for Green Program Promotes Soil Organic Matter Accumulation and Improves Soil Fungal Diversity in the Southwestern Karst Region

Soil microorganisms play pivotal roles in terrestrial ecological processes. However, how soil microbial biomass and community characteristics respond to changes in land utilization in karst regions remains largely unknown. The present study investigated the impacts of land-use change on soil chemical properties, microbial community structure, and biomass in a karst region of southwest China across four land-use types: shrubland (natural vegetation restoration), plantation forest (managed vegetation restoration), orchards, and croplands. Vegetation restoration increased microbial biomass carbon and microbial biomass nitrogen. Shrubland had the highest bacterial and fungal abundance and fungal diversity; in addition, the soil microbial community structure differed significantly among land-use types. The dominant bacterial phyla were Actinobacteria, Proteobacteria, Acidobacteria, and Chloroflexi, whereas Ascomycota was the predominant fungal phylum, with its abundance declining significantly following vegetation restoration. Soil properties, including soil organic matter and available phosphorus, were strongly associated with microbial community composition and diversity in karst areas. The findings of this study are essential for gaining a deeper understanding of how changes in land-use affect soil properties and microbial dynamics, and provide valuable insights for ecological restoration and agricultural management in karst regions.

Unveiling the impact of biodegradable polylactic acid microplastics on meadow soil health.

Soil microplastics (MPs) pollution has garnered considerable attention in recent years. The use of biodegradable plastics for mulching has led to significant quantities of plastic entering agro-ecosystems. However, the effects of biodegradable polylactic acid (PLA) plastics on meadow soils remain underexplored. This study investigates the impacts of PLA-MPs of varying particle sizes and concentrations on soil physicochemical properties, enzyme activities, and microbial communities through a 60-day incubation experiment. PLA-MPs increased the pH, soil organic matter, total nitrogen (TN) and available potassium (AK) content, as well as enhanced the activities of superoxide dismutase (S-SOD), peroxidase (S-POD), soil catalase (S-CAT), β-glucosidase (S-β-GC) and urease (S-UE) activities. Conversely, a decrease in alkaline phosphatase (S-ALP) activity was observed. The influence of PLA-MPs on soil physicochemical properties was more pronounced with larger particle sizes, whereas smaller particles had a greater effect on enzyme activities. Additionally, PLA-MPs led to an increase in the abundance of Acidobacteriota, Chloroflexi, and Gemmatimonadota, while the abundance of Proteobacteria, Actinobacteriota, and Patescibacteria declined. Mantel test analysis showed that changes in microbial community composition affected soil properties such as pH, AK, S-UE and S-β-GC. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) analysis demonstrated that PLA-MPs modify bacterial metabolic pathways. Our results suggest that particle size and concentration of PLA-MPs differentially affect soil nutrients and microbial community structure and function, with more significant effects observed at larger particle sizes and higher concentrations.

Sorption and Transport of Antibiotics in Manured Upland Agricultural Soils

Sorption and transport are important environmental behaviors of antibiotics in soils and can determine the fate of antibiotics in environments； however, limited relevant studies have been conducted on long-term manured soils. In this study, batch and repacked soil column experiments were conducted to examine the sorption and transport behavior of four veterinary antibiotics, including sulfamethazine （SMT）, florfenicol （FFC）, doxycycline （DOX）, and enrofloxacin （ENR）, in red soils, yellow soils, and calcareous soils with long-term amendment of chicken or pig manure collected in Zhejiang Province. The results showed that the sorption isothermal data of the four target antibiotics all conformed well to the linear and Freundlich models. The performance of the linear model was better for the weakly-sorbing antibiotics （SMT and FFC）, whereas the performance of the Freundlich model was better for the strongly-sorbing antibiotics （DOX and ENR）. The sorption of target antibiotics was governed by both soil and manure types. The linear sorption parameter （Kd） of SMT and FFC were positively related to the organic carbon content and pH of soils, and their sorption was mainly controlled by soil organic matter, indicating that non-electrostatic interactions （e.g., hydrogen bonding, hydrophobic interactions） played dominant roles in their sorption to the soils. Contrastingly, DOX and ENR did not show significant relationships with soil pH, organic carbon content, and cation exchange capacity, indicating that multiple mechanisms （e.g., hydrophobic interactions, hydrogen bonding, and electrostatic interactions） worked jointly in their sorption into the soils. SMT and FFC exhibited high recoveries of breakthrough curves （in ranges of 37.5%-92.3% and 45.8%-112.6%, respectively） in column transport experiments, reflecting their high leaching potentials. Contrastingly, no soil column breakthrough of DOX and ENR was observed, reflecting that the injected antibiotics were completely retained inside the soil columns. The maximum relative concentrations and breakthrough recoveries of SMT and FFC showed significant negative linear relationships with Kd. This indicates that the leaching potential of antibiotics can be predicted from sorption parameters using the regression equations established by batch and column experiments.

Ecosystem productivity and carbon dynamics in Keibul Lamjao National Park, Manipur, India: a gray relational analysis perspective.

An in-depth understanding of carbon dynamics and ecosystem productivity is essential for conservation and management of different ecosystems. Ecosystem dynamics and carbon budget are assessed by estimating net ecosystem production (NEP) across different global ecosystems. An ecological productivity assessment of forest and floating meadow ecosystems in Keibul Lamjao National Park (KLNP), Manipur, North East India, was conducted using the multi-criteria decision-making process namely, gray relational analysis (GRA). The analysis was performed on 24 selected criterions classified either as "higher-the-better" or "lower-the-better" based on their degree of influence on the carbon budget. Floating meadows exhibited a higher production of aboveground and belowground biomass and a higher total mortality and decay. Furthermore, the study found that floating meadows exhibited a higher soil organic carbon (SOC) and net soil organic matter (SOM) than the forest ecosystem. The forest ecosystem showed higher total respiration (RT), heterotrophic respiration (RH), and autotrophic respiration (RA) than floating meadows. Floating meadows exhibited a higher net primary productivity (NPP) of 616.49 ± 33.87 gCm-2year-1 than the forest ecosystem, which has a NPP of 566.64 ± 65.26 gCm-2year-1. Similarly, floating meadows have higher NEP (495.25 ± 36.46 gCm-2year-1) than forest ecosystems (418.39 ± 65.76 gCm-2year-1). These characteristics have a significant influence on the carbon budget in floating meadows as compared to forest ecosystems, as shown by larger values of gray relational coefficient (GRC) in GRA. The floating meadows ecosystem (0.82) obtained 54.72% gain in gray relational grades (GRG) value with the forest ecosystem (0.53). This study might help in improving KLNP and other adjutant areas for conservation and management policies from the vital information given on the importance of wetlands in carbon dynamics and ecosystem productivity.

Improving spatial prediction of soil organic matter in central Vietnam using Bayesian-enhanced machine learning and environmental covariates

ABSTRACT Soil organic matter (SOM) has a vital role in maintaining soil quality and ecosystem functions. However, predicting its spatial distribution remains a challenging task since it was affected by various environmental covariates. To address this limitation, a novel approach by integrating Bayesian technique into the random forest (RF) algorithm was proposed in this research. A total of 94 surficial soil samples from the top 30 cm and eight key environmental covariates were utilized for training and testing with a 70:30 ratio. According to the results, the enhanced RF model demonstrated a significant improvement in accuracy (RMSE = 0.31%; MAE = 0.25%, R2 = 0.79, Acc = 0.81) compared to the traditional RF model (RMSE = 0.66%; MAE = 0.48%, R2 = 0.10, Acc = 0.61). The four environmental covariates including rainfall, distance to the sea, distance to water bodies, and altitude explained 74.07%, and 75.37% of the variability in SOM content in traditional and enhanced RF models, respectively. Locations with high SOM content were characterized by abundant rainfall, a greater distance from the sea, proximity to rivers, and low elevations. These findings introduce a reliable approach for predicting the spatial distribution of SOM in the context of complex environmental changes.

Responses of fungal communities at different soil depths to grazing intensity in a desert steppe.

Grazing can alter the physicochemical properties of soil and quickly influence the composition of microbial communities. However, the effects of grazing intensity on fungal community composition in different soil depth remain unclear. On the Inner Mongolia Plateau, we studied the effects of grazing intensity treatments including no grazing (NG), light grazing (LG), moderate grazing (MG), heavy grazing (HG), and over grazing (OG) on the physicochemical properties and fungal community composition of surface (0-20 cm) and subsurface (20-40 cm) soil layers. The α-diversity of fungi in subsurface soil decreased under the influence of grazing. The relative abundance of Ascomycota in the subsoil was higher than that in the topsoil, while the situation of Basidiomycota was the opposite. This was caused by the differences in the soil carbon (C) environment for the growth of oligotrophic and copiotrophic fungi. In the subsoil, grazing affected nutrient contents such as soil organic matter (SOM) and total nitrogen (TN), resulting in significantly lower relative abundance of Ortierellomycota under LG, HG, and OG than in the NG. HG showed much higher relative abundance of Glomeromycota. Results of a multiple regression tree (MRT) analysis revealed that TN and nitrate nitrogen affected the fungal α-diversity in top- and subsoils, respectively; the main driving factor regulating fungal community changes was soil water content (SWC) in the topsoil, while it was ammonium nitrogen and nitrate nitrogen in the subsoil. The results of our study indicate that grazing changes the soil environment by changing TN, SWC, nitrate nitrogen, ammonium nitrogen, and affects the diversity and community structure of soil fungi. This provides empirical support for coping with the impact of grazing on soil microbiomes in desert steppes.