Organic Carbon Content Research Articles

Has lake brownification ceased? Stabilization, re-browning, and other factors associated with dissolved organic matter trends in eastern Canadian lakes

The increase in dissolved organic carbon (DOC) concentrations in freshwater systems has received considerable attention due to its implications for drinking water treatment and numerous limnological processes. While past studies have documented the influence of recovery from acidification and climate change on long-term DOC trends, the emerging importance of these explanatory factors remains less understood. In addition, few studies have followed up on recent trends in sites that have undergone increases in DOC. Using a dataset from 1980 to 2020, we investigated interannual variations in DOC and dissolved organic nitrogen (DON) in 49 lakes across four eastern Canadian regions with a history of increases in DOC. We identified recent shifts in DOC patterns using LOESS smoothing and piecewise regression. We observed a stabilizing pattern or even a decrease (p < 0.001) in high acidification regions (Dorset and Nova Scotia), where increases in DOC were previously documented. At the low acid deposition region, IISD-Experimental Lakes Area, an increasing pattern in DOC stabilized in the early 2000s; however, DOC appears to be increasing again in recent years (p = 0.03). Our analysis identified precipitation and SO4 deposition as the primary explanatory variables for DOC patterns (explaining 56–71% of variance). However, because acid deposition has declined substantially, climate and local watershed factors are becoming increasingly influential, leading to the emergence of new DOC patterns. Long-term changes in DOC and DON were not always synchronous, as these were often correlated with different factors (e.g., DON with ammonium deposition). This resulted in observable shifts in DOC:DON ratios, indicative of changes in dissolved organic matter (DOM) composition. We underscore the importance of ongoing monitoring in diverse regions because of the changing nature of environmental variables and new emerging trends.

Predominant Cropping Systems affect Soil Organic Carbon Content in the Soil Profile of North–West India

Predominant Cropping Systems affect Soil Organic Carbon Content in the Soil Profile of North–West India

Status of Trace Metals Contamination and Ecological Risk Assessment in Intertidal Sediments Along the Gulf of Guinea

Surface sediments from the intertidal area of Côte d’Ivoire were sampled to investigate trace metals (Hg, Cd, Cr, Cu, Pb, Zn, and As) concentrations to illustrate their spatial distribution and ecological risk of pollution. The average total organic carbon content was 2.04 ± 0.01%. Except for Cr and Zn, the spatial distribution exhibited a similar pattern for TOC, Hg, Cd, Cu, Pb, and As. The average concentrations of trace metals were less than their corresponding upper continental crust (UCC) values, except for Cd and Cr. The pollution indices indicated that sediment quality in the studied area has been impacted by Cd and the Assinie and Bleron sites were the most contaminated. A few ecological risks were posed by Hg, Cd, Cr, and Cu, based on sediment quality guidelines (SQGs). The Pearson correlation showed a significant (p < 0.05) correlation between As, Cd, Pb, Cr, and Hg, suggesting a common origin in sediments. The principal component analysis (PCA) identified As, Cr, Pb, Zn, and Hg might originate from natural source, Cd and Cu might be from natural and anthrogenic sources. Although this study presents the current status of littoral trace metals contamination level in sediments, further studies should integrate the statistical multivariate approach to determine the anthropogenic sources of the trace metals in the intertidal area of Côte d’Ivoire.

CHIMBO Air Quality Modeling System: Verification and Processes Analysis

This study presents an evaluation of the CHIMBO modeling chain applied to the Italian domain, specifically focusing on the Po Valley subdomain over the one-year period of 2019. The comparison between simulated and observed data indicates that the performance of the CHIMBO model aligns well with existing literature on other state-of-the-art models. The results demonstrate that the CHIMBO chain is particularly effective for regional-scale quantitative assessments of pollutant distribution, comparable to that of CAMS ensemble models. The analysis of key chemical species in particulate matter reveals that the CHIMBO model accurately represents the average concentrations of organic and elemental carbon, as well as secondary inorganic compounds (sulfate, nitrate, and ammonium), particularly at background monitoring stations in the flat terrain of the Po Valley, with the exception of Aosta, a city located at about 500 m asl. However, seasonal discrepancies were identified, especially during winter months, when significant underestimations were observed for several species, including elemental and organic carbon, predominantly at background sites. These underestimations are likely attributed to various factors: (i) inadequate estimations of primary emissions, particularly from domestic heating; (ii) the limited effectiveness of secondary formation processes under winter conditions characterized by low photochemical activity and high humidity; and (iii) excessive dilution of pollutants during calm wind conditions due to overestimation of wind intensity. In conclusion, while the CHIMBO modeling chain serves as a robust tool for mesoscale atmospheric composition investigations, limitations persist related to emissions inventories and meteorological parameters, which remain critical drivers of atmospheric processes.

Restoration from grazing on the Tibetan plateau: Pathway-specific soil MAOC sequestration in meadow and peat wetlands

Mineral-associated organic matter (MAOM) constitutes a significant portion of carbon (C) pools in wetlands, determining their role as C sinks or sources. Nevertheless, detailed knowledge of the response of MAOM formation pathways to wetland restoration is lacking. Here, we collected 0–20 cm soil samples from two typical wetlands (meadow and peat wetlands) subjected to both grazing and restoration treatments (through micro dam construction) in the Zoige area. Lignin phenols and amino sugars within the MAOM fraction were analysed to clarify the MAOM formation pathways and their potential contributions to MAOM formation. The results demonstrated that both meadow and peat wetland restoration enhanced MAOM sequestration. Compared to grazed sites, restored meadow wetlands significantly increased the mineral-associated organic carbon (MAOC) content in soils but not the C content of the MAOM fraction; restored peat wetlands increased the C content of the MAOM fraction by 58% but not the MAOC content in soils. Notably, plant-derived C and microbial necromass C strongly and dominantly influenced the C content of the MAOM fraction in the meadow (path coefficient = 0.73) and peat wetlands (path coefficient = 0.75), respectively. Compared with those in the grazed peat wetlands, the MAOM fraction in restored peat wetlands contained 1.93, 1.61, and 2.09 times higher total, bacterial, and fungal necromass C contents, respectively. Moreover, soil properties influence lignin phenols and necromass C in conjunction with the C content of the MAOM fraction rather than directly. It seems reasonable to assume that meadow wetland restoration from grazing enhanced MAOM formation in soils via the accumulation of lignin phenols (through the “direct sorption” pathway). Peat wetland restoration from grazing enhanced MAOM formation in soils via the accumulation of lignin phenols (through the “direct sorption” pathway) and the “microbial turnover” pathway.

Determination of Organic Carbon Content of the Soils within the Greenhouses Built on Pyroclastic Deposits in Isparta Settlement Area

Soil organic carbon (SOC) is an important indication of soil health and helps to sustain soil fertility. As a result, determining its composition and the factors that influence it is critical for long-term soil nutrient management, especially in controlled conditions such as greenhouses. This study utilizes machine learning to classify SOC content in greenhouses built on pyroclastic deposits in the Isparta region. A dataset of 276 samples and eight variables—clay (%), silt (%), sand (%), soil electrical conductivity (EC), pH, elevation, slope, and aspect—were used to model SOC values. SOC content was classified into five classifications: very low (2.3%). In this study, five machine learning models—Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)—were evaluated using cross-validation to determine their classification accuracy, precision, recall, F-score, and ROC area. Random Forest (RF) and Decision Tree (DT) outperformed the other models, with RF achieving the highest overall accuracy (76.4%), precision (77.3%), and AUC (0.904), followed by DT at 75.4% and AUC of 0.874. This study shows the practicality of machine learning models in categorizing SOC content, highlighting their importance for long-term soil health and fertility control in greenhouse conditions. To improve model efficacy, future studies should include more auxiliary variables, such as soil physical and chemical qualities and lithological data, as well as a wider range of soil types.

Soil Physico-chemical Responses to Integrated Nutrient Management in Lentil Intercropped with Bhimal (Grewia optiva) in Agroforestry vs. Open Systems

The growing concern over health risks associated with the heavy use of chemical fertilizers and pesticides in India, which have been linked to rising cancer cases, underscores the need for sustainable agricultural practices. Integrated Nutrient Management offers a promising solution to enhance soil fertility and support crop health, especially for lentil cultivation within agroforestry systems. This study, conducted at Dr. Y. S. Parmar University of Horticulture and Forestry in Nauni, Solan, Himachal Pradesh, examined the effects of INM on the soil’s physical and chemical properties when lentils were intercropped within a Grewia optiva (Bhimal) agroforestry system in the mid-hills region of Himachal Pradesh. The experiment evaluated various combinations of organic and inorganic fertilizers, including T1: Recommended Dose of Fertilizer, T2: Farmyard Manure, T3: Vermicompost, T4: Goat Manure, T5: 50% Recommended dose of fertilizer + 50% Farm yard manure, T6: 50% Recommended dose of fertilizer + 50% Vermicompost, T7: 50% Recommended dose of fertilizer + 50% Goat Manure, and T8: Control. These treatments were applied in plots both under the Bhimal-based agroforestry system (S1) and in open field conditions (S2), with three replications per treatment following a factorial randomized block design. Key physical parameters such as bulk density, particle density, porosity, and soil moisture were measured alongside chemical properties, including soil pH, electrical conductivity, organic carbon content, and the availability of nitrogen, phosphorus, and potassium. Data collected over two years was analyzed using R Statistical Software, revealing that INM significantly improved soil properties in the agroforestry system compared to the control. Notably, 100% FYM emerged as the most effective treatment, enhancing soil porosity, moisture, pH, and nutrient availability (N, P, and K). Improvements in soil structure and nutrient levels were particularly evident in the second year, positively impacting lentil growth and yield. The study highlights the potential of INM to promote sustainable, chemical-free agriculture in hilly areas, offering valuable recommendations for farmers and policymakers focused on sustainable agriculture. Specifically, 100% FYM is recommended for improving soil health and crop productivity in the mid-hill region of Himachal Pradesh, providing a model for similar agro-ecological zones aiming to reduce chemical inputs and support human health.

In-depth insights into carbohydrate-active enzyme genes regarding the disparities in soil organic carbon after 12-year rotational cropping system field study

Carbohydrate-active enzymes (CAZymes) play a crucial role in plant-derived carbon utilization and decomposition and are influenced by the crop rotation system; however, our knowledge of how different agricultural systems impact CAZyme functionality is still limited. We conducted a metagenomic analysis to evaluate the functional genes of CAZymes in a 12-year in situ farmland with three commonly used crop rotation systems: wheat-maize rotation (WM), wheat-cotton rotation (WC), and wheat-soybean rotation (WS). We aimed to study the impact of long-term use of crop rotation, especially crop rotation involving soybean, on soil organic carbon (SOC) content and to gain an in-depth understanding of the CAZyme genes in context of the disparities in SOC. After 12 years, the SOC content was significantly higher in WS than in WC (5.44 %) and WM (17.6 %). Furthermore, the crop rotation system had a significant effect on the soil microbial communities and CAZyme function genes. Detailly, WS increased the phyla abundance of Proteobacteria, Actinobacteria, and Firmicutes and enriched the glycoside hydrolase (GH) and carbohydrate-binding modules (CBM) genes; WC increased the abundance of Acidobacteria and Bacteroidota and enriched the polysaccharide lyase gene; WM increased the abundance of Nitrospirae, Candidatus_Rokubacteria, Chloroflexi and Gemmatimonadetes and enriched the gene abundance of glycosyltransferases and auxiliary activity genes. Additionally, Acidobacteria, Proteobacteria, and Actinobacteria are key phyla involved in soil carbon cycling and collectively contribute >70 % of the total CAZyme functional genes, which highlights their importance. In addition, our results indicated that total nitrogen content played a major role in influencing genes related to CAZymes, especially those belonging to the GH family. Our study demonstrates that WS conferred the advantage of increasing SOC across the three crop rotation systems. CAZyme analysis revealed that WS's could potentially support the increased abundance of Proteobacteria, Actinobacteria and Firmicutes in the soil community, at the same time potentially leading to increased number of GH and CBM genes in the soil, which may bolster the decomposition and transformation of plant-derived carbon, thus promoting an increase in SOC content. The findings of this study offer new insights into the microbial factors contributing to SOC enhancement in rotation systems.

Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale.

We utilized remote sensing and ground cover data to predict soil organic carbon (SOC) content across a vast geographic region. Employing a combination of machine learning and deep learning techniques, we developed a novel data fusion approach that integrated Digital Elevation Model (DEM) data, MODIS satellite imagery, WOSIS soil profile data, and CHELSA environmental data. This combined dataset, named GeoBlendMDWC, was specifically designed for SOC prediction. The primary aim of this research is to develop and evaluate a novel optimization algorithm for accurate SOC prediction by leveraging multi-source environmental data. Specifically, this study aims to (1) create an integrated dataset combining remote sensing and ground data for comprehensive SOC analysis, (2) develop a new optimization technique that enhances both machine learning and deep learning model performance, and (3) evaluate the algorithm's efficiency and accuracy against established optimization methods like Jaya and GridSearchCV. This study focused on India, Australia, and South Africa, countries known for their significant agricultural activities. We introduced a novel optimization technique for both machine learning and deep neural networks, comparing its performance to established methods like the Jaya optimization technique and GridSearchCV. The models evaluated included XGBoost Regression, LightGBM, Gradient Boosting Regression (GBR), Random Forest Regression, Decision Tree Regression, and a Multilayer Perceptron (MLP) model. Our research demonstrated that the proposed optimization algorithm consistently outperformed existing methods in terms of execution time and performance. It achieved results comparable to GridSearchCV, reaching an R2 of 90.16, which was a significant improvement over the base XGBoost model's R2 of 79.08. In deep learning optimization, it significantly outperformed the Jaya algorithm, achieving an R2 of 61.34 compared to Jaya's 30.04. Moreover, it was 20-30 times faster than GridSearchCV. Given its speed and accuracy, this algorithm can be applied to real-time data processing in remote sensing satellites. This advanced methodology will greatly benefit the agriculture and farming sectors by providing precise SOC predictions.

Organic matter accumulation under the Middle-Upper Ordovician tectonic transition on the western margin of the North China platform

The tectonic of the Middle and Late Ordovician in the western margin of the north China Platform is complex, and the accumulation models of organic matter of the Wulalike Formation formed during this period are still unclear. Total organic carbon (TOC) content, mineral composition, organic carbon isotope composition, as well as the major and trace elements in the shale samples were all measured in this study. The Wulalike Formation was formed during a tectonic transition from a passive continental margin to an active continental margin. Hydrocarbon-forming organisms are mainly algae. Primary productivity, terrigenous input, and sedimentation rates influence input, dilution, and degradation of organic matter. The degree of degradation, redox, and basin restriction control the preservation of organic matter. The TOC of the Wulalike Formation is determined by the input and preservation degree of organic matter at different sedimentary periods. The organic matter accumulation model in the Wulalike Formation is controlled by regional structures, dominated by sedimentation rate and preservation conditions.

Effect of exogenous thermophilic biocontrol agent inoculum on the high temperature chicken manure composting.

Aerobic composting is an effective method for utilizing chicken manure. However, its low carbon to nitrogen (C/N) ratio leads to slow heating and short high-temperature phases, which reduce composting efficiency and product quality. To address this issue, splinted mushroom cultivation residues were added to adjust the C/N ratio, and exogenous thermophilic composting strains were introduced to increase composting temperature. This study analyzed the relationship between physicochemical metabolites and microbial community structure during high-temperature chicken manure composting. Based on metagenomic and physicochemical analyses, results showed that the exogenous microbial agents extended the thermophilic phase by three-times, reduced the heating phase duration by 75%, and increased nitrogen, phosphorus, potassium, and soluble organic carbon contents by 3.61, 21.63, 7.21, and 39.03%, respectively. Genes associated with amino acid metabolism were significantly enriched during the heating phase, while genes involved in the tricarboxylic acid cycle were more active in the thermophilic phase. During the thermophilic phase, bacterial diversity and richness decreased compared to the heating and cooling phases. Functional microbes such as Bacillus, Caldicoprobacter, and Virgibacillus showed a positive correlation with the key differential metabolites. While Actinomadura, Saccharomonospora, Paenibacillus, and Aneurinibacillus displayed an opposite correlation. Further experiments demonstrated that the increased temperature during the thermophilic phase triggered the upregulation of oleic acid metabolism and piperidine metabolism pathways in functional microorganisms, leading to the production of heat stabilizers and protective agents like oleic acid, gallic acid, and 2-piperidone. This phenomenon helped maintain microbial viability during the thermophilic phase and improved composting efficiency.

Variation in microbial communities and network ecological clusters driven by soil organic carbon in an inshore saline soil amended with hydrochar in Yellow River Delta, China

Char materials (e.g., hydrochar) can enhance carbon sequestration, improve soil quality and modulate soil microbial communities to recuperate soil health. However, little is known about the soil organic carbon (SOC) content, as well as the microbial communities and co-occurrence networks in response to hydrochar amendment in an inshore saline soil. Here, the effect of Sesbania cannabina (a halophyte) straw derived hydrochar (SHC) amendment on SOC and labile organic carbon (LOC) fractions and the potential associations among SOC content change, soil C-cycling enzyme activities and microbial communities were illustrated using a pot experiment. SHC effectively improved the contents of SOC and LOC, particularly particulate organic carbon (POC), and stimulated the activities of C-cycling enzymes. Furthermore, SHC induced shift in microbial community compositions and co-occurrence networks, result in decrease in relative abundance of Actinobacteriota and its corresponding ecological cluster, which may favor SOC accumulation. Functional annotation of prokaryotic taxa (FAPROTAX) analysis also revealed a decrease in microbial ecological function related to carbon degradation. These findings provided a deeper insight about the hydrochar-induced SOC enhancement and suggested an efficient approach to improve C sequestration and improve soil health in the coastal salt-affected soil.

Influence of vegetative cover on snowpack mercury speciation and stocks in the greening Canadian subarctic region

A notable greening and warming of the Arctic and Subarctic due to climate change has uncertain implications for the global cycling of mercury (Hg). Snowpacks are dynamic reservoirs for Hg susceptible to solar radiation and wind pumping, with vegetative cover potentially altering Hg photochemistry. However, the impact of northern greening on the transformation of major Hg species and on Hg stocks remain poorly understood. Temporal surface snow and snowpit sampling was conducted under tree canopies and open tundra sites at the boreal-tundra ecotone in Nunavik, Canada. Maximum (mean) concentrations of 69.1 ng/L (8.8 ng/L) total mercury (HgT) and 46.9 ng/L (5.5 ng/L) reactive mercury (HgR) were measured in forest surface snow, with maximums attributed to rapid atmospheric oxidation events. Significant post-depositional reductions were recorded in the bay, tundra, and forest (67–99% HgR) and suggested greater Hg sequestration may occur under tree canopies. Increasing methylmercury (MeHg), HgT, and dissolved organic carbon (DOC) concentrations were detected across a vegetation gradient shifting towards humic-like organic matter. Notably, springtime depth profiles presented an approximate 12-fold greater accumulation of HgT under tree canopies compared to open tundra (p < 0.01), with up to 16-times higher stocks (HgT, MeHg, DOC) at elevated vegetation density (p < 0.05). In the North, increasing vegetation cover and surface warming may favor Hg accumulation and methylation in snowpacks, facilitated by interactions with organic matter, and further enriched by the reduced wind and solar exposure experienced under forest canopies.

Carbon fate and potential carbon metabolism effects during in-situ cyanobacterial inhibition by artemisinin sustained-release algaecides

Carbon pools and microbial carbon metabolism can be significantly altered due to the diverse organic matter properties and low pH characteristics of artemisinin sustained-release algaecides (ASAs). These effects have still not been systematically studied, leading to uncertainty in the application of ASAs for cyanobacterial management in natural waters. This study assessed the effects of ASAs on carbon fate, carbon metabolism and potential ecological impacts during in-situ cyanobacterial inhibition. In the initial phase of ASAs-induced cyanobacterial inhibition (2–10 days), the carbon pool underwent significant changes due to the increased proportion of C4 plant-derived organic matter (97 %), humification level (FI =0.9–1.5), and inorganic carbon concentration (TIC = 80–110 mg/L). The cyanobacterial apoptosis triggered by ASAs produced particulate organic carbon, which provided a bioavailable carbon source for bacterial metabolism. ASAs enhanced the connection between bacteria and the carbon pool, as well as their carbon metabolism capabilities, by increasing the relative abundance of Proteobacteria (33.1 %−37.3 %), bacterial diversity, the proportion of Alphaproteobacteria and Betaproteobacteria (19.3 %−29.5 %) involved in carbon metabolism, and the complexity of the environment-bacteria co-occurrence network. These effects contributed to maintaining long-term ecosystem stability and resistance to cyanobacterial proliferation. Our findings elucidate the influence of bacterial carbon metabolism by ASAs as one of an important mechanism for achieving cyanobacterial inhibition in natural water, and emphasize the important role of bacteria in maintaining ecological stability during in situ cyanobacterial management.

Prediction of soil organic carbon and total nitrogen affected by mine using Vis–NIR spectroscopy coupled with machine learning algorithms in calcareous soils

The utilization of visible-near infrared (Vis–NIR) spectroscopy presents a nondestructive, fast, reliable and cost-effective approach to predicting total nitrogen (TN) and organic carbon (OC) levels. This study employed a combination of Vis–NIR spectroscopy, partial least-squares regression (PLSR), and support vector machine (SVM) models to investigate the effects of mining on TN and OC stocks in both the topsoil (0–10 cm) and subsoil (10–40 cm). 105 soil samples were collected from agricultural areas near an iron mine, polluted, moderately-polluted, and non-polluted sites. Results indicated that soils at the non-polluted site had the highest of soil OC stocks (7.5 kg m–2) and total nitrogen (2.5 kg m–2), followed by the moderately-polluted site. Furthermore, it was observed that soils from the polluted site displayed the highest spectral reflectance. The spectral bands in the range of 500–700 nm showed the strongest correlation with soil organic carbon content. Notably, the SVM method utilizing Vis–NIR spectroscopy provided superior predictions for both subsoil and topsoil organic carbon and total nitrogen compared to the PLSR methods. Additionally, SVM demonstrated better performance in predicting topsoil soil organic carbon (R2 = 0.87, RMSE = 0.13%, and RPD = 2.8) and total nitrogen (R2 = 0.91, RMSE = 0.13%, and RPD = 2.4) compared to the subsoil, owing to the larger OM content in the topsoils.

Evaluating Wheat Cultivation Potential in Ethiopia Under the Current and Future Climate Change Scenarios

Land suitability analyses are crucial for identifying sustainable areas for agricultural crops and developing appropriate land use strategies. Thus, the present study aims to analyze the current and future land suitability for wheat (Triticum aestivum L.) cultivation in Ethiopia. Twelve variables including soil properties, climate variables, and topographic characteristics were used in the evaluation of land suitability. Statistical methods such as Rotated Empirical Orthogonal Functions (REOF), Coefficient of Variation (CV), correlation, and parametric and non-parametric trend analyses were used to analyze the spatiotemporal variability in current and future climate data and identified significant patterns of variability. For future projections of land suitability and climate, this study employed climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) framework, downscaled using regional climate model version 4.7 (RegCM4.7) under two different Shared Socioeconomic Pathway (SSP) climate scenarios: SSP1 (a lower emission scenario) and SSP5 (a higher emission scenario). Under the current condition, during March, April, and May (MAM), 53.4% of the country was suitable for wheat cultivation while 44.4% was not suitable. In 2050, non-suitable areas for wheat cultivation are expected to increase by 1% and 6.9% during MAM under SSP1 and SSP5 climate scenarios, respectively. Our findings highlight that areas currently suitable for wheat may face challenges in the future due to altered temperature and precipitation patterns, potentially leading to shifts in suitable areas or reduced productivity. This study also found that the suitability of land for wheat cultivation was determined by rainfall amount, temperature, soil type, soil pH, soil organic carbon content, soil nitrogen content, and elevation. This research underscores the critical importance of integrating spatiotemporal climate variability with future projections to comprehensively assess wheat suitability. By elucidating the implications of climate change on wheat cultivation, this study lays the groundwork for developing effective adaptation strategies and actionable recommendations to enhance management practices. The findings support the county’s commitment to refining agricultural land use strategies, increasing wheat production through suitability predictions, and advancing self-sufficiency in wheat production. Additionally, these insights can empower Ethiopia’s agricultural extension services to guide farmers in cultivating wheat in areas identified as highly and moderately suitable, thereby bolstering production in a changing climate.

Effects of Straw Addition on Soil Priming Effects Under Different Tillage and Straw Return Modes.

This study aims to investigate the impact of straw addition on soil activation effects under different tillage practices, providing a scientific basis for establishing reasonable straw return measures in the southern Northeast Plain, thus enhancing soil fertility, and mitigating greenhouse effects. Soil samples were collected from various straw return practices that were conducted continuously for two years as follows: rotary tillage without straw return (RTO), deep tillage combined with straw incorporation (PT), rotary tillage with straw incorporation (RT), and no-till with straw cover (NT). The samples were incubated in the dark at 25 °C for 70 days. We measured the CO2 release rate and cumulative release, apparent activation effect, soil organic carbon, active microbial biomass organic carbon, soluble organic carbon, and easily oxidizable organic carbon to clarify the effects of straw addition on soil activation under different tillage practices. The results indicate that a straw addition promotes the mineralization of soil organic carbon while also increasing the content of active organic carbon components. The CO2 release rates and cumulative release under different tillage practices were as follows: PT > NT > RT. The contents of the active microbial biomass organic carbon, soluble organic carbon, and easily oxidizable organic carbon increased by 16.62% to 131.88%, 4.36% to 57.59%, and 12.10% to 57.97%, respectively, compared to the control without the straw addition. Except for the RT practice, the addition of straw significantly enhanced the instability of soil organic carbon in the PT, NT, and RTO practices, with increases of 51.75%, 48.29%, and 27.90%, respectively. Different straw return practices altered the physical and chemical properties of the soil, resulting in significant differences in the strength of the apparent activation effect. Notably, the apparent activation effect of RT was reduced by 86.42% compared to RTO, while that of NT was reduced by 36.99% compared to PT. A highly significant positive correlation was observed between the apparent activation effect and the unstable carbon components in the soil, indicating that higher levels of easily decomposable organic carbon corresponded to stronger apparent activation effects. In conclusion, it is recommended that in this region, rotary tillage should be adopted for straw return in the first 2 to 3 years, as this practice is beneficial for the formation and stabilization of organic carbon in the short term. As the duration of straw return increases, adjustments can be made based on the degree of soil organic carbon retention and soil fertility status.

Sustainable Nitrogen Management in Rice Farming: Spatial Patterns of Nitrogen Availability and Implications for Community-Level Practices

Sustainable nitrogen management is crucial for long-term food security and environmental protection in rice farming systems. However, the spatial patterns of nitrogen availability at the community level remain poorly understood, hindering the development of effective sustainable management strategies. This study introduces a novel application of spatial autoregressive analysis to investigate available nitrogen distribution in paddy soils across a rice farming community in Kyoto, Japan. Soil samples from 61 plots, including organically farmed ones, were analyzed for available nitrogen and various physicochemical properties. Contrary to the hypothesis of high variability between adjacent plots, significant positive spatial autocorrelation in available nitrogen was observed, revealing previously unrecognized community-level patterns. The spatial Durbin model outperformed traditional regression approaches and revealed complex spatial interactions in soil properties. Water-soluble organic carbon and humus content showed strong but opposing effects, with a positive direct impact but negative spatial interaction, suggesting topography-driven accumulation processes. Water-soluble nitrogen exhibited reverse patterns with negative direct effects but positive spatial interaction, indicating potential nutrient transport through water movement. These findings highlight the importance of considering both direct and indirect spatial effects in understanding soil fertility patterns, challenging the conventional plot-by-plot management approach. This methodological advancement provides new perspectives for more effective, community-scale soil management strategies in rice farming systems. Moreover, it demonstrates an innovative approach to maximizing the value of outsourced soil analysis data, providing a model for more comprehensive utilization of such data in agricultural research. By enabling more targeted and efficient nitrogen management practices that consider both plot-level processes and landscape-scale interactions, this study potentially contributes to the development of more sustainable and resilient rice production systems.

Pertubations of Physicochemical Properties and Heavy Metal in the Soil of Coastal Areas of Akwa Ibom State, Nigeria

This study aimed at assessing the concentrations and perturbations of the physicochemical properties and heavy metals in the soil of the coastal regions of Akwa Ibom State, Nigeria. Sampling was carried out across three mangrove locations (Iko Town, Okoroutip and Uta Ewa) in Eastern Obolo, Ibeno and Ikot Abasi Local Government Areas respectively. Four vegetation plots were randomly selected, and within each plot, three belt transects were established. Physicochemical properties [Sand, Clay, Silt, organic carbon (OC), total nitrogen, sulphate and chlorine content] and heavy metals (Arsenic, Silver, Chromium, Copper, Nickel, Lead, Mercury, Cadmium, Titanium, Vanadium and Zinc) were analyzed using standardized methods. Results from this study revealed considerable variations in the physicochemical properties of soil in the study area. The aforementioned heavy metals were present in both wet and dry seasons in considerable concentrations in the soil samples within the study areas. However, were within and below WHO limits. The variations in the physicochemical properties and the presence of these heavy metals in the samples are indicative of an impacted environment unsuitable for the growth and survival of the endemic flora and fauna species of this unique ecosystem. In view of the current knowledge of the role of mangrove ecosystems in blue-economy of the nation and mitigation of ongoing climate change, we advocate a tripartite alliance between the government, multinational companies operating within the area and the various impacted communities that leads to a concerted efforts of constant monitoring and remediation in the reduction of current and future pollutant levels.

Micro-CT Analysis of Pore Structure in Upland Red Soil Under Different Long-Term Fertilization Regimes

This study hypothesized that long-term fertilization alters the pore structure and aggregate stability in upland red soil. A long-term fertilization experiment in Qiyang, Hunan, was conducted with three treatments: no fertilization (CK), nitrogen–phosphorus–potassium fertilization (NPK), and nitrogen–phosphorus–potassium combined with pig manure (NPKOM). X-ray computed tomography (CT) scanning technology was used to assess three-dimensional pore structures at both the soil column (50 mm diameter and 50 mm height) and aggregate scales (diameter 3–5 mm), alongside the evaluation of the soil’s physical and chemical properties. Results showed that the soil organic carbon content (SOC) increased by 44.8% in NPK and 112.5% in NPKOM compared to CK. NPKOM improved the aggregate stability by 51.6%, whereas NPK had no significant effect. At the soil column scale, NPK increased the total porosity by 13.7% but reduced larger pores (&gt;0.06 mm), whereas NPKOM decreased the total porosity by 7.8% and increased larger pores. At the aggregate scale, NPKOM increased the porosity for pores &gt;0.098 mm by 7.6 times compared to CK and 9.5 times compared to NPK. In conclusion, long-term NPKOM significantly enhances the SOC and aggregate stability and promotes larger pore formation, unlike NPK, which mainly increases SOC but does not improve the soil structure.