Soil Organic Matter Research Articles

Impact of Agricultural Activities on Soil Particle Size, pH, and Organic Matter in the Dimonika Biosphere Reserve, Mayombe (Republic of Congo)

Land-use change disrupts several soil physico-chemical parameters. This study aimed to analyze the influence of agricultural activities, topography, and localities on soil texture, pH, and organic matter in the Dimonika Biosphere Reserve. To achieve this, 90 soil samples were collected using an auger based on land use types, topography, and localities. Analyses of soil texture, pH, total organic carbon (TOC), and total nitrogen (TN) were conducted at the IRSEN laboratory in Pointe-Noire. The results showed that clay texture predominates in the studied area, which is related to the nature of the soils. The Kruskal-Wallis ANOVA test highlighted a significant effect of agricultural activities on soil acidity, with an acidic pH ranging from 4.2 to 5 in cultivated areas, compared to a very acidic pH (3.5 to 4.2) in mature forests. However, neither topography nor localities affected pH. Total organic carbon significantly decreased in old plantations (1.3±0.3%) and fallows (1.5±0.5%) compared to mature forests (1.9±0.5%) and savannahs (2.3±0.6%), while total nitrogen showed no notable variations. Topography also had no influence on organic status (TOC, TN). However, at the local level, the Makaba area stood out with significantly higher TOC (2±0.5%) and TN (0.2±0.03%) compared to Les Saras (0.14±0.03%) and Kayes (0.16±0.2%). The C/N ratio, influenced only by agricultural activities, was below 15, indicating rapid organic matter mineralization and a low TOC content. Several potential solutions were proposed for sustainable soil management.

Soil inoculation changed soil microbial communities, but did not accelerate the decomposition of European Beachgrass (Ammophila arenaria) in Point Reyes National Seashore

Legacy effects after the removal of invasive plants present significant challenges to restoration. The pivotal role of soil microbial communities in shaping these legacy effects is increasingly recognized, yet there is a lack of effective methods to mitigate altered microbial communities. In Point Reyes National Seashore (California), although the invaded European beachgrass (Ammophila arenaria [L.] Link) was successfully controlled by herbicide treatment, beachgrass litter remained undecomposed for over 5 years, leaving pronounced legacy effects on soil organic matter and microbial community composition. We hypothesized that soil inoculation from uninvaded dune scrub can accelerate the decomposition of beachgrass litter in herbicide‐treated sites by restoring the soil microbial communities and the abundance of microbial decomposers. Three litterbags containing European beachgrass litter or litter from two common shrubs at dune scrub were deployed into each plot to assess the impact of soil inoculation on litter decomposition rates. Our results revealed that soil inoculation, regardless of the inoculation level, did not accelerate the decomposition of European beachgrass. Only the decomposition of bush lupine litter, which had the highest litter quality among three types of litter, was accelerated at the highest inoculation level (approximately 11,880 g/m2). Additionally, soil inoculation increased the richness and compositional homogeneity of soil microbial communities, along with the relative abundances of wood saprotrophic fungi, soil saprotrophic fungi, and lichenized fungi. Although these findings demonstrate the potential of soil inoculation, the cost‐effectiveness of soil inoculation limits its feasibility in accelerating the delayed decomposition of European beachgrass litter in Point Reyes.

Characterization of glomalin proteins in soil: A potential indicator of erosion intensity

Abstract In this study, in a pioneering effort, glomalin proteins were extracted and geochemically characterized from soil in Serbia. Standard chemical (dry combustion, Walkley-Black) and spectroscopic methods (fourier transform infrared spectroscopy (FTIR), dynamic light scattering (DLS), scanning electron microscope (SEM)) were used to gain insight into the glomalin proteins. Samples from the City of Novi Sad (Serbia) were used for characterization and comparison. The soil contained an average of 17 mg/g of glomalin, representing ≈21% of the soil organic matter. DLS zeta potential analysis and the FTIR spectra reveal two significant differences before and after glomalin extraction, indicating a relationship between sand fractions and organic matter that affects particle aggregation and erosion potential. A comparison of SEM images and DLS results reveals that the glomalin extract consists of two particle size groups (0.08–1 and 6 µm), suggesting that the extraction process isolates only a smaller fraction of proteins and less tightly bound particles, indicating that soil aggregation is influenced by both mineralogy and the characteristics of organic matter. Hence, our research raises important questions about the role of glomalin in mitigating soil degradation processes, particularly soil erosion. Our study will enhance the overall understanding of glomalin, inspire future research, and prove beneficial for the sustainable restoration of degraded lands.

Novel adaptive control approach to fractal fractional order deforestation model and its impact on soil erosion.

Deforestation exerts profound ramifications on soil quality and biodiversity, thereby exerting substantial economic repercussions. The depletion of organic matter and structural integrity of soil following tree removal for agricultural purposes underscores the severity of this issue. In elucidating the soil pollution stemming from deforestation, this research employs a sophisticated five-compartment SDIFR model integrating fractal dimension and fractional order dynamics. The rigorous analysis, including the application of Picard Lindelof's fixed point theorem, establishes the existence and uniqueness of explicit solutions. Furthermore, the examination of local and global stability sheds light on the system's behavior, delineating between pollution-free equilibrium and pollution-extinct equilibrium states. To regulate system behavior, an adaptive control framework grounded in fractal fractional order is proposed, leveraging the Adams-Bashforth numerical approximation scheme for implementation. Through numerical simulations, the study underscores the pivotal role of parameters, thus substantiating the significance of the proposed model in comprehensively addressing the complexities of soil pollution induced by deforestation.

Prediction and pathway models for assessing soil properties influencing soil selenium enrichment and bioavailability in Aksu Prefecture, northwest China.

Selenium (Se) in soil is the primary source of human Se intake, and its content and bioavailability are influenced by soil physicochemical properties. However, the influence of soil physicochemical properties on Se enrichment and bioavailability in soil remains uncertain. Therefore, this study investigated 536 soil samples and their corresponding wheat grain samples collected from the oasis zone of Aksu Prefecture, located in northwest China. The Se content, spatial distribution, and bioaccumulation factor (BCF) in soil and wheat grains as well as soil Se fractions were examined, and the effects of soil physicochemical properties on Se enrichment and bioavailability were assessed. The results indicated that the mean Se content of soil (0.32 mg/kg) surpassed the national Se background level for Chinese soil by a factor of 1.10. The average Se content (0.13 mg/kg) in wheat grains met the national standard for Se-rich cereal products. The prediction model established using multiple linear regression showed that soil calcium carbonate (CaCO3), organic matter (OM), cation exchange capacity (CEC), and electrical conductivity (EC) were the main physicochemical factors influencing Se enrichment in soil, accounting for 29 % of the variation. The main physicochemical factors affecting Se bioavailability were CaCO3, OM, and iron, and the main Se fractions were the exchangeable and humic acid-bound Se fractions, which together explain 12 % of the variance. Additionally, a structural equation model was employed to analyze the pathways and interactions of soil factors on soil Se enrichment and bioavailability. The results indicated that soil CaCO3 and OM were the most critical factors affecting Se enrichment and bioavailability in soil. These findings can provide technical guidance for the cultivation and layout of Se-rich wheat in local and other similar regions around the world.

An explainable spatial interpolation method considering spatial stratified heterogeneity

Spatial interpolation is essential for handling sparsity and missing spatial data. Current machine learning-based spatial interpolation methods are subject to the statistical constraints of spatial stratified heterogeneity (SSH), normally involving separate modeling of each stratum and simple weighted averaging to integrate intra-stratum and inter-strata features. However, these models overlook the different contributions of inter-strata features to different locations within a stratum (heterogeneous inter-strata associations, HIA) and the explanation of spatial effects on the interpolation process, leading to suboptimal and unreliable interpolation outcomes. This article proposes a novel explainable spatial interpolation method considering SSH (X-SSHM). Spatial and environmental features are utilized to describe intra-stratum and inter-strata information, which are fed into random forest-based learners to achieve high-level semantic feature mapping. Geographically weighted regression is employed to integrate intra-stratum and inter-strata features to achieve a unified expression of SSH and HIA, obtaining the final interpolation result. Geographically weighted Shapley (GSHAP) is proposed to decompose the marginal contributions of intra-stratum and inter-strata features. Model performance is evaluated on simulated and soil organic matter datasets. X-SSHM outperformed five baselines regarding interpolation accuracy. Moreover, statistical methods validated X-SSHM’s ability to elucidate the mechanisms by which SSH, spatial autocorrelation and HIA affect the model interpolation process.

Simulated nitrogen deposition and precipitation events alter microbial carbon cycling during early stages of litter decomposition

Abstract Plant litter decomposition is a major nutrient input to terrestrial ecosystems that is primarily driven by microorganisms. Litter quality is considered a key drive of decomposition; however, human-induced global disturbance like nitrogen deposition and increasing extreme precipitation events will shift nutrient availability during litter decomposition. Little is known about how shifting nutrient availability will impact dissolved organic matter concentrations and microbially driven carbon cycling that are critical to soil organic matter formation. This study investigated the effect of simulated nitrogen deposition and repeated precipitation events on microbially-driven carbon flow during short-term litter decomposition using a ‘common garden’ experiment with microcosms containing sand and blue grama grass litter inoculated with different microbial communities. Overall, nitrogen deposition decoupled respiration and dissolved organic carbon (DOC) by increasing respiration and not affecting DOC concentrations. Moreover, nitrogen deposition had no effect on microbial carbon use efficiency (CUE). Repeated simulated precipitation events significantly increased DOC concentrations, decreased microbial CUE, increased the microbial metabolic quotient (qCO2), and altered microbial composition and diversity. These findings highlight the complex interactions and responses of surface litter decomposers to shifting nutrient availability and contradicts previous findings that nitrogen deposition will increase soil carbon sequestration from a larger supply of DOC and reduced respiration.

Machine learning-based prediction of soil organic matter via smartphone

Color is an important property of soil that indicates soil composition and fertility. Soil organic matter (SOM) of darker color soils, which have rich humus and minerals, is higher than others such as red soils. The aim of this study is to construct models to predict SOM for a range of colors using a smartphone as an image-capturing device. Random forest of classification (RFC), random forest of logical regression (RFLR), convolutional neural network (CNN) and MobileNet models are compared, which is better for SOM prediction. Soil photos were collected by smartphone in a camera obscura with a steady light. After treatment by OpenCV, photos were separated by SOM content into five groups. The prediction accuracies of RFC, RFLR, CNN and MobileNet were 0.9743, 0.5614, 0.9600 and 0.7915, respectively. Based on these results, the RFC model for SOM has the best performance both in training and validation. The proposed combination of smartphone and machine learning-based prediction models provide a fast, economic, and robust approach to monitor, detect, and predict SOM contents in precision and intelligence agriculture.

Increased stability of a subtropic bamboo forest soil bacterial communities through integration of water and fertilizer management compared to conventional management.

Conventional management (CM), substantial fertilization and flooding irrigation, hasled to soil acidification, the decrease in soil bacterial diversity in bamboo forests. Integration of water and fertilizer management (IWF) can effectively improve the efficiency of water and fertilizer use, but its effect on soil environment, especially on microbial community, is still unclear. Here, we used next-generation high-throughput sequencing to compare soil properties and bacterial communities through different fertilization and irrigation methods under IWF and CM. Compared to the control group, CM significantly reduced soil pH and bacterial diversity, while IWF improved soil nutrition status, increased soil bacterial diversity and soil pH to a level similar to the control group. Compared with CM, IWF also improved the relative abundance of beneficial bacteria and copiotrophic bacteria community in the soil, and the bacterial community in IWF was similar to CK. The structure of the bacterial community was also significantly correlated with soil organic matter, total nitrogen, hydrolyzable nitrogen, and available potassium, while soil bacterial diversity was mainly associated with soil hydrolyzable nitrogen. IWF can play an important role in preventing soil acidification, the loss of soil bacterial diversity, and improving the structure of the bacterial community under specific conditions.

Estimation of Cation Exchange Capacity for Low-Activity Clay Soil Fractions Using Experimental Data from South China

The cation exchange capacity (CEC) of the clay fraction (<2 μm), denoted as CECclay, serves as a crucial indicator for identifying low-activity clay (LAC) soils and is an essential criterion in soil classification. Traditional methods of estimating CECclay, such as dividing the whole-soil CEC (CECsoil) by the clay content, can be problematic due to biases introduced by soil organic matter and different types of clay minerals. To address this issue, we introduced a soil pedotransfer functions (PTFs) approach to predict CECclay from CECsoil using experimental soil data. We conducted a study on 122 pedons in South China, focusing on highly weathered and strongly leached soils. Samples from the B horizon were used, and eight models and PTFs (four machine learning methods, multiple linear regression (MLR) and three PTFs from publication) were evaluated for their predictive performance. Four covariate datasets were combined based on available soil data and environmental variables and various parameters for machine learning techniques including an artificial neural network, a deep belief network, support vector regression and random forest were optimized. The results, based on 10-fold cross-validation, showed that the simple division of CECsoil by clay content led to significant overestimation of CECclay, with a mean error of 14.42 cmol(+) kg−1. MLR produced the most accurate predictions, with an R2 of 0.63–0.71 and root mean squared errors (RMSE) of 3.21–3.64 cmol(+) kg−1. The incorporation of environmental variables improved the accuracy by 2–10%. A linear model was fitted to enhance the current calculation method, resulting in the equation: CECclay = 15.31 + 15.90 × (CECsoil/Clay), with an R2 of 0.41 and RMSE of 4.48 cmol(+) kg−1. Therefore, given limited soil data, the MLR PTFs with explicit equations were recommended for predicting the CECclay of B horizons in humid subtropical regions.

Effects of different sand fixation plantations on soil properties in the Hunshandake Sandy Land, Eastern Inner Mongolia, China.

Planting forests is an effective way to improve desertification. In order to elucidate the impacts of different vegetation types on soil development and restoration of degraded lands, we compared the properties of soils at different depths in three plantation forests in the Hunsandak Sandy Land in the Chinese agro-pastoral ecotone (Ulmus pumila, Pinus sylvestris var. mongolica, and Populus simonii). The results show that all three plantation forests were able to significantly improve the soil properties, and they resulted in soil nutrient enrichment in the surface layer. As the soil depth increased, the soil became progressively poorer in nutrients, the fine particle content decreased, and the bulk density and water content increased. The orders of the fractal dimension characterization and soil improvement effects of the different tree species were as follows: U. pumila > P. sylvestris var. mongolica > P. simonii. Compared with the bare sand, the soil bulk density under the U. pumila plantation was 19% lower; the soil water content was 74% higher; the soil organic matter, total N, P, and K were 336%, 207%, 106%, and 31% higher; the available N, P, and K were 41%, 125%, and 21% higher; and the clay and silt contents were 498% and 387% higher, respectively. The ranges of the soil fractal dimension were 1.67-2.08 for the bare sandy land and 2.14-2.32 for the planted forests. The soil fractal dimension was strongly correlated with the soil physicochemical properties, especially with the soil nutrients and fine particle content, which exhibited highly significant correlations (p < 0.01), and the correlation coefficients were all greater than 0.8. Therefore, we believe that U. pumila is a suitable sand-fixing plant species in this area. In addition, the soil fractal dimension can be used as an important reference index for characterizing soil properties in sandy areas.

Pulses as a Key Component in Conservation Agriculture: Impacts on Soil Health and Sustainability

Intensive agriculture poses several challenges such as soil degradation, reduced soil organic matter, and increased soil nutrient imbalances including nitrogen, phosphorus, potassium, sulfur, zinc, iron, and manganese deficiencies and nutrient toxicities (Tiwari KN, 2002). It also contributes to degradation of groundwater quality, declining groundwater levels (Humphreys E et al, 2010), and issues like sodicity and salinization (Tiwari VM et al, 2009), resulting in reduced crop productivity. Many of these limitations can be addressed by adopting conservation agriculture, which supports sustainable productivity while safeguarding natural resources (Sharma AR et al, 2012). Pulses are particularly beneficial in conservation agriculture as they align well with its principles: diversified crop rotations, minimum soil disturbance and continuous soil cover. They serve as a resource-conserving technology that mitigates the adverse impacts of modern agricultural practices.

Carbon mineralization potential and nutrient dynamics in soils under vegetation types and soil depths at Luot mountain

Soil mineralization is a crucial soil process that improves soil physical properties, enhances carbon sequestration, and provides essential minerals and available nutrients for plant growth. This study was conducted at five vegetation types and soil depths at Luot mountain area, located in VNUF campus, Hanoi city. Samples were incubated in the dark at 25°C and measured at intervals of 1, 2, 5, 10, 15, 25, 35, and 40 days in the laboratory to determine C-CO2 respiration from soils. The study showed that CO2 emissions were highest in topsoils and decreased with deeper soil depths. Mineralized C-CO2 decreased from Shrubs &gt; Acacia + Native species (NS) &gt; Pinus + NS &gt; Native species &gt; Control. CO2 emissions peaked early in the incubation period and then stabilized in the 40-day incubation period. Larger aggregates (≥ 5mm) decreased significantly under most vegetation types, except for Shrubs, where the reduction was minimal. Aggregate size ≥3mm increased post-incubation, notably under Pinus + NS and Native species, with smaller aggregates also increasing slightly. Organic matter content was highest in the topsoil but decreased post-incubation due to microbial C mineralization. There was an increase in soil organic matter at 10-20 cm and 20-40 cm layers after incubation, especially under Shrubs. Available nitrogen slightly increased in soils post-incubation for most vegetation types. Phosphorus content increased post-incubation, peaking under Shrubs, while potassium levels were generally poor but increased during incubation. The study found that C-CO2 mineralization was strongly associated with soil porosity and pH, suggesting that higher porosity and optimal pH enhance mineralization, with organic matter content being crucial for available nutrient cycles in soils.

A Systematic Review and Meta-Analysis of the Sustainable Impact of Sewage Sludge Application on Soil Organic Matter and Nutrient Content

The increasing scarcity of natural resources makes the linear production model unsustainable, highlighting the need for more sustainable practices under the umbrella of circular economic principles. Sewage sludge emerges as a promising solution to provide soil organic matter (SOM) and nutrients. This meta-analysis evaluated the impacts of three levels of sludge application (low, medium, and high) on organic matter (OM), nitrogen (N), phosphorus (P), copper (Cu), and zinc (Zn) contents, considering different areas and experimental conditions worldwide. The analysis included 37 studies and 355 comparisons, after screening 7625 records, following the 2020 PRISMA protocol. The effects of sludge concentrations, continents, and types of experiment (field or greenhouse) were evaluated. Sewage sludge application significantly increased OM, N, Cu, and Zn levels, mainly at medium and high rates. The largest effects were observed in greenhouses, suggesting variation by location and environment. Moderators explained a part of the variation in the results, but the residual heterogeneity test revealed that there is still unexplained variability.

Garlic stalk waste and arbuscular mycorrhizae mitigate challenges in continuously monocropping eggplant obstacles by modulating physiochemical properties and fungal community structure.

Continuous vegetable production under plastic tunnels faces challenges like soil degradation, increased soil-borne pathogens, and diminished eggplant yield. These factors collectively threaten the long-term sustainability of food security by diminishing the productivity and resilience of agricultural soils. This research examined the use of raw garlic stalk (RGS) waste and arbuscular mycorrhizal fungi (AMF) as a sustainable solution for these issues in eggplant monoculture. We hypothesized that the combined application of RGS waste and AMF would improve soil physicochemical properties compared to untreated soil in eggplant monoculture. The combined use of RGS and AMF was expected to suppress soil-borne pathogens, increase the abundance of soil beneficial microorganisms and alter fungal community structure. The combined application of RGS and AMF will significantly enhance eggplant yield compared to untreated plots. This study aimed to determine whether AMF and RGS, individually or in combination, can ameliorate the adverse effects of monoculture on eggplant soil. We also investigated whether these treatments could enhance eggplant yield. The experiment was arranged in a completely randomized design with four treatments: AMF, RGS, and a combined treatment of AMF + RGS (ARGS), along with a control. Each treatment was replicated three times, Eggplant seedlings inoculated with AMF and treated with RGS amendments, both individually and combined. The effects on root traits, soil physicochemical properties, soil enzyme activity, and fungal community structure were investigated. RGS amendments and AMF inoculation improved root length, volume, and mycorrhizal colonization. The combined treatment showed the most significant improvement. RGS and AMF application increased soil nutrient availability (N, P, K) and organic matter content. Enzyme activities also increased with RGS and AMF treatments, with the combined application showing the highest activity. Soil electrical conductivity (EC) increased, while soil pH decreased with RGS and AMF amendments. Sequencing revealed a shift in the fungal community structure. Ascomycota abundance decreased, while Basidiomycota abundance increased with RGS and AMF application. The combined treatment reduced the abundance of pathogenic genera (Fusarium) and enriched beneficial taxa (Chaetomium, Coprinellus, Aspergillus). Pearson correlations supported the hypothesis that soil physicochemical properties influence fungal community composition. This study demonstrates the potential of co-applying RGS and AMF in continuous cropping systems. It enhances soil physicochemical properties, reduces soil-borne pathogens, and promotes beneficial microbial communities and eggplant yield. This combined approach offers a sustainable strategy to address the challenges associated with eggplant monoculture under plastic tunnels.

Effects of Bio and water-soluble fertilizers on sweet potato yield, quality and soil properties in a continuous cropping system under plastic film-mulched drip-fertigated field conditions.

To verify an effective approach for alleviating problems associated with the consecutive monoculture of sweet potato, five fertilizer treatments were designed under plastic film-mulched, drip-fertigated conditions in fields continuously planted with sweet potato over five years. These treatments included: (1) no fertilizer application, (2) basic application of water-soluble (WS) fertilizer, (3) basic application of biofertilizer (BF) and WS fertilizer, (4) split application of WS fertilizer, and (5) combined basic application of BF and split application of WS fertilizer. The effects of BF and WS fertilizer applications on yield, quality, and soil properties were evaluated. Fertilizer applications had positive effects on increasing soil activities and nutrients compared with no fertilizer application. Split fertigation with WS fertilizer increased the activities of soil dehydrogenase (DHA) by 10.94 ~ 14.74%, alkaline phosphatase (ALP) by 4.97 ~ 5.13%, and soil organic matter (SOM) by 10.43 ~ 12.47% in the second year compared with a single application. In both years, split fertigation exerted positive effects on increasing dry matter (DM) accumulation in tuberous roots and increased the productive efficiency in nitrogen (N), phosphorus (P), and potassium (K) fertilizers. BF application for two consecutive years increased the activities of soil sucrase by 7.05 ~ 17.83%, DHA by 18.65 ~ 21.34%, ALP by 6.87 ~ 7.03%, soil available P by 18.34 ~ 28.10%, and SOM by 8.18 ~ 10.17% compared with the no BF application. BF also increased the root yield by 8.88 ~ 14.14%, the carotenoid content in tuberous roots by 20.38 ~ 30.64%, and the K utilization efficiency by 11.09 ~ 14.97%. The combination of BF and split fertigation for two consecutive years was most conducive to the activation of soil nutrients, maintenance of soil fertility, and improvement in yield and quality, which could mitigate problems associated with the consecutive monoculture of sweet potato.

Responses of Legume‐Associated Rhizobacterial Communities to Plant Diversity and Soil Traits in Alpine Grassland

ABSTRACTLegume species are essential components of plant diversity and affect soil biodiversity across various ecosystems. Their effect on the diversity and traits of soil bacteria, particularly in degraded grasslands, remains unknown. This study analysed the relationships among plant diversity, soil traits and legume‐associated rhizobacterial communities in Xiahe (XH) and Maqu (MQ) in Gansu Province, Haibei (HB) in Qinghai Province and Hongyuan (HY) in Sichuan Province in the eastern Qinghai‐Tibetan Plateau (QTP). The diversity index values (coverage, richness, Shannon index and evenness) of legume species were positively correlated with plant diversity. Several soil nutrients (ammonia‐nitrogen, nitrate‐nitrogen, total nitrogen, available potassium, available phosphorus and soil organic matter) and enzymes (urease, sucrase, peroxidase and dehydrogenase) were lower in HB and HY than in XH and MQ. The Shannon index for rhizobacterial diversity was higher in HB and HY than in XH and MQ. In contrast, the diversity index values were higher for geographical locations than for sympatric plant species. Additionally, HB and HY showed 50% fewer positive and negative associations with rhizobacteria than XH and MQ. Functional Annotation of Prokaryotic Taxa analysis indicated a higher relative abundance of nitrate reduction occurred in HB and HY than in XH and MQ, whereas nitrogen fixation occurred at a lower level in HB and HY than in XH and MQ. The Simpson index value for bacterial diversity was positively correlated with plant diversity, legume species diversity and soil multifunctionality. However, the Shannon index value was negatively correlated with these parameters. Changes in the composition of legume‐associated rhizobacteria across different geographical locations are strongly influenced by plant diversity and soil nutrients, reflecting the distribution characteristics of legumes in alpine grasslands.

Sustainable Wheat Cultivation in Sandy Soils: Impact of Organic and Biofertilizer Use on Soil Health and Crop Yield

Sandy soils are widespread globally and are increasingly utilized to meet the demands of a growing population and urbanization for food, fiber, energy, and other essential services. However, their poor water and nutrient retention makes crop cultivation challenging. This study evaluated the effects of integrating compost and plant growth-promoting rhizobacteria (PGPR; Azospirillum brasilense SWERI 111 and Azotobacter chroococcum OR512393) on wheat (Triticum aestivum L. var. Misr 1) grown in sandy soil under varying levels of recommended NPK (50%, 75%, and 100%) fertilization. Conducted over two growing seasons, the experiment aimed to assess soil health, nutrient uptake, microbial activity, and plant productivity in response to compost and PGPR treatments. The results demonstrated that combining compost and PGPR significantly improved soil chemical properties, such as reducing soil pH, electrical conductivity (ECe), and sodium adsorption ratio (SAR), while enhancing soil organic matter (SOM). Additionally, compost and PGPR improved soil nutrient content (N, P, K) and boosted the total bacterial and fungal counts. The combined treatment also increased urease and phosphatase enzyme activities, contributing to enhanced nutrient availability. Notably, plant productivity was enhanced with compost and PGPR, reflected by increased chlorophyll and reduced proline content, along with improved grain and straw yields. Overall, the results underscore the potential of compost and PGPR as effective, sustainable soil amendments to support wheat growth under varying NPK levels.

Mineral-associated organic matter is heterogeneous and structured by hydrophobic, charged, and polar interactions

The formation of mineral-associated organic matter (MAOM) is a key phenomenon that may explain the slow turnover rates of carbon in soil organic matter (SOM). Despite this, important details pertaining to the structure and dynamics of MAOM remain unknown. In the present study, we use replica-exchange molecular dynamics simulations to gain insight into the structure of MAOM on the surface of prototypical phyllosilicate clay and Fe-oxide minerals, montmorillonite and goethite, fine-grained minerals that strongly impact soil carbon dynamics in temperate and tropical regions, respectively. We examine the impact of aqueous chemistry through the presence of either Na[Formula: see text] or Ca[Formula: see text] charge balancing counterions. Our results are consistent with the hypothesized multilayer sorption ("onion-skin") model of MAOM and help to explain previous observations regarding the patchy distribution of SOM on mineral surfaces. In particular, the SOM coatings are partial and laterally heterogeneous, and water retains extensive access to mineral surfaces even when significant SOM sorption occurs. Low molecular weight neutral SOM molecules ([Formula: see text]200 Da) infrequently interact with the mineral surfaces nor their sorbed organic matter coatings and are increasingly labile with decreasing molecular weight. This observation is inconsistent with a central feature of the predominant soil continuum model of SOM and suggests that further iterations of the conceptual model may be required.

Patterns and Driving Mechanisms of Soil Organic Carbon, Nitrogen, and Phosphorus, and Their Stoichiometry in Limestone Mines of Anhui Province, China

Active vegetation restoration plays an important role in the improvement in soil organic matter (SOM), including the carbon (C), nitrogen (N) and phosphorus (P) sequestration of degraded mining ecosystems. However, there is still a lack of understanding of the key drivers of SOM pool size and dynamics in active vegetation restoration. For this study, soil was collected from five different sites (Xiaoxian, Dingyuan, Chaohu, Tongling and Dongzhi), four habitats (platforms, slopes, steps and native areas) and two soil layers (0–20 cm and 20–40 cm) in limestone mines of Anhui province to quantify the spatial distribution of SOM contents and their stoichiometric characteristics and influential factors. It was found that the top soil in Chaohu had the highest significant C, N and P contents in the ranges of 14.95–17.97, 1.74–2.21 and 0.80–1.24 g/kg, respectively. Comparing the stoichiometric ratios of the different sites revealed significant differences in C:N and N:P ratios, but C:P ratios were relatively consistent. In particular, the C:N and C:P ratios in deep soil were higher than those in top soil, whereas the N:P ratio in deep soil was lower than that in top soil, suggesting that soil N is a major limiting factor in the top soil. The SOM content did not differ significantly between the three reclaimed habitats, but was significantly higher than that in the native habitat, suggesting that mine restoration has significantly enhanced SOM accumulation. Further analysis showed that nutrient availability and enzyme activity are important factors affecting soil C, N and P content in top soil, while the relationship gradually weakens in deep soil. This was attributed to active anthropogenic management and conservation measures during the early stages of reclamation. This study shows that the ecological recovery of the mining area can be enhanced by implementing differentiated vegetation planting strategies and anthropogenic management on different habitats in the mining area.