Soil Texture Research Articles

Hydrophobic Nanostructured Coatings of Colloidal Lignin Particles Reduce Nutrient Leaching and Enhance Wheat Agronomic Performance and Nutritional Quality.

Traditional farming practices are increasingly being replaced with more sustainable approaches, including the development of slow-release fertilizers (SRFs), to mitigate environmental stress and ensure food security for the ever-growing global population. Despite the rising focus on eco-friendly materials like biopolymers for fertilizer coatings, optimizing their hydrophobicity remains a significant challenge. In this context, nanotechnology offers a promising route toward achieving hydrophobicity and sustainability. In this study, hydrophobic colloidal lignin particles (20-50 nm) were synthesized using a straightforward acid precipitation method involving the coprecipitation of lignin (LGe) and sodium dodecyl sulfate (SDS). This strategy aimed to reduce particle size, enhance stability, and increase hydrophobicity by incorporating the nonpolar SDS alkyl chains onto the surface of the nanomicelles. TEM and STEM microscopy confirmed the formation of core-shell hybrid micelles, which were incorporated into a cross-linked carboxymethyl cellulose (CMC) matrix at various ratios to produce a series of waterborne coating formulations and films. The spherical morphology and new surface features, along with their integration into an interpenetrating cross-linked network, led to the formation of nanostructured coating films with good hydrophobicity (WCA ∼ 106.1°) and slow biodegradability in soil. When applied to diammonium phosphate (DAP) granular fertilizer, the coatings revealed good interfacial adhesion, enhanced hardness (2.5-fold), and improved water-holding capacity in soil (18%). Most importantly, a 100 day nutrient leaching study revealed an impressive nutrient-release longevity, showing a 75% reduction in N-P leaching. Subsequently, these SRFs were evaluated in a 6 month wheat (Triticum aestivum)) cultivation trial across different soil textures, demonstrating substantial enhancements in leaf area (150-200%), total root length (160%), biomass production (575%), grain yield (115-264%), and quality-related parameters. These findings highlight a robust solution for addressing nutrient deficiencies and promoting sustainable agricultural practices, especially for crops with extended growth cycles, while inspiring novel nanostructured coatings for broader applications.

Assessment of the Influence of Aluminum, Iron, and Manganese Forms on the Phytocenoses of Post-Mining Lands in the Lengerskoye Brown Coal Mine

Post-mining land in areas where mineral extraction has occurred may constitute a significant portion of the land used for various purposes. Such land serves as soil-forming parent material for developing anthropogenic soils, which sometimes exhibit unfavorable physicochemical properties. The toxicity of the waste generated during lignite mining is due to a number of factors, whose determination permits the identification of its origin for the subsequent design of technologies for the waste reclamation. The purpose of the study, in consistence with sustainable development, is to identify the causes of the toxicity of brown coal waste from the Lengerskoye deposit, in southern Kazakhstan. These studies have provided the results essential for planning remedial actions necessary to improve the well-being of the local population, in accordance with the principles of sustainable development. The studies were performed using single extraction; forms of Al, Fe, and Mn; soil texture; elemental analysis; phytocoenosis analysis; and diffractometric, IR spectroscopic, SEM, route reconnaissance, and comparative statistical methods. A decrease in the biodiversity of plant species was noted, with a gradual increase with distance from the waste storage sites. The most resistant plant species in the vicinity of the waste dump were Cynodon dactylon (L.) Pers and Alhagi pseudalhagi (M. Bieb.) Desv. ex B. Keller & Shap., while Dodartia orientalis (L.) was the only plant species found at the edge of the waste dump. The high toxicity of lignite waste is determined by such factors as low pH values, about 3.0; high content of active forms of aluminum, iron, and manganese (344.0, 0.90, and 20 mg/kg); high electrical conductivity—2835 µS/cm; waste composition poor in nutrients; and climate aridity. It has been observed that a content of exchangeable aluminum above 100 mg/kg resulted in an almost complete lack of vegetation.

A Global Meta-Analysis of Soil Carbon Stock in Agroforestry Coffee Cultivation

Given the climate crisis, the search for sustainable production with potential to reduce excess of carbon dioxide (CO2) in the atmosphere has been the subject of global agreements. Soils are fundamental carbon storage systems, with a relevant role in CO2 mitigation emissions. Considering coffee as an important commodity for several countries and agroforestry systems (AFSs) as important allies for mitigating greenhouse gases emitted by the agricultural sector, this study aimed to investigate the ability of coffee plantations in AFSs to mitigate greenhouse gas emissions, through soil carbon sequestration. For this purpose, we performed a meta-analysis of 45 AFSs, including simple and diversified ones, from a detailed literature search of scientific research investigating soil organic carbon in AFSs including coffee cultivation. Overall, no effect of AFSs on carbon stock change rates was found, but an increment of soil carbon storage was observed when comparing AFSs with conventional coffee cultivation. Generally, climatic variables and soil texture positively affect soil carbon stock. When comparing diversified and simple AFSs, the first had a positive effect on carbon stock change rates. Agroforestry coffee showed capacity to mitigate climate effects through carbon storage in the soil, especially when the system is diversified. This is a climate-smart strategy and should be implemented in preference to conventional coffee cultivation.

Enhancing Rainwater Harvesting Sustainability in the Al-Basrah Basin, Iraq: A Two-Phased Approach Combining GIS-AHP for Site Suitability and Fog Computing for Dynamic Management

The increasing issue of water scarcity worldwide calls for research into sustainable water management solutions. Using a two-phase methodology that makes use of geographic information systems (GIS) and analytical hierarchy process (AHP) method for multi-attributes decision making (MADM), integrated with fog computing. This study explores the potential of rainwater harvesting (RWH) in the Al-Basrah Basin, Iraq. In the first phase, weights are assigned to the evaluation attributes represented by the thematic layers (elevations, slope, river buffer, well pads/urban area, and soil texture). The results demonstrate the feasibility of the southeast region in the study area to be used for RWH. Furthermore, a comparison between AHP and the fuzzy analytic hierarchy process (FAHP), the merit of AHP to reconcile discrepancies and expedite the collection of expert opinions, reduces the problems of uncertainty and inconsistency, and improves the appropriateness assessment credibility and reliability. In addition, a novel conceptual second phase integrates fog computing for dynamic RWH management in the candidate RWH site area. At the selected RWH sites, fog nodes with sensors might be deployed to enable real-time data collection of rainfall, water quality, tank levels, and maintenance demands. Local preprocessing and analysis of data by fog nodes facilitate near-real-time decision-making for the best possible use of water resources and system health. Long-term water security in the Al-Basrah Basin may be enhanced by this strategy, which has the ability to enable informed water management. Received: 27 September 2024 |Revised: 25 November 2024 |Accepted: 28 November 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Alauldeen Taher Najm: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft, Visualization. Wei-Koon Lee: Resources, Data curation, Writing – review & editing, Supervision. Abdulhussain Abdulkarim Abbas: Validation, Formal analysis, Data curation. Suzana Ramli: Writing – review & editing, Supervision.

Compositional shifts and co-occurrence patterns of topsoil bacteria and micro-eukaryotes across a permafrost thaw gradient in alpine meadows of the Qilian Mountains, China.

Soil microorganisms play a pivotal role in the biogeochemical cycles of alpine meadow ecosystems, especially in the context of permafrost thaw. However, the mechanisms driving microbial community responses to environmental changes, such as variations in active layer thickness (ALT) of permafrost, remain poorly understood. This study utilized next-generation sequencing to explore the composition and co-occurrence patterns of soil microbial communities, focusing on bacteria and micro-eukaryotes along a permafrost thaw gradient. The results showed a decline in bacterial alpha diversity with increasing permafrost thaw, whereas micro-eukaryotic diversity exhibited an opposite trend. Although changes in microbial community composition were observed in permafrost and seasonally frozen soils, these shifts were not statistically significant. Bacterial communities exhibited a greater differentiation between frozen and seasonally frozen soils, a pattern not mirrored in eukaryotic communities. Linear discriminant analysis effect size analysis revealed a higher number of potential biomarkers in bacterial communities compared with micro-eukaryotes. Bacterial co-occurrence networks were more complex, with more nodes, edges, and positive linkages than those of micro-eukaryotes. Key factors such as soil texture, ALT, and bulk density significantly influenced bacterial community structures, particularly affecting the relative abundances of the Acidobacteria, Proteobacteria, and Actinobacteria phyla. In contrast, fungal communities (e.g., Nucletmycea, Rhizaria, Chloroplastida, and Discosea groups) were more affected by electrical conductivity, vegetation coverage, and ALT. This study highlights the distinct responses of soil bacteria and micro-eukaryotes to permafrost thaw, offering insights into microbial community stability under global climate change.IMPORTANCEThis study sheds light on how permafrost thaw affects microbial life in the soil, with broader implications for understanding climate change impacts. As permafrost degrades, it alters the types and numbers of microbes in the soil. These microbes play essential roles in environmental processes, such as nutrient cycling and greenhouse gas emissions. By observing shifts from bacteria-dominated to fungi-dominated communities as permafrost thaws, the study highlights potential changes in these processes. Importantly, this research suggests that the stability of microbial networks decreases with permafrost degradation, potentially disrupting the delicate balance of these ecosystems. The findings not only deepen our understanding of microbial responses to changing climates but also support the development of strategies to monitor and potentially mitigate the effects of climate change on fragile high-altitude ecosystems.

Context Matters: Soil Ecosystem Status Varies across Diverse Conservation Agriculture Systems

Abstract Conservation agriculture promotes soil health across different management and environmental contexts. While soil ecosystem status (health and functioning) serves as a key indicator of soil health, it remains understudied, with most evidence coming from long-term trials that may not reflect on-farm conditions. Therefore, this study evaluated and compared the long-term soil ecosystem status (health and functioning) of farmer croplands practicing conservation agriculture under two distinct management and environmental contexts. Two sites near Vrede and Reitz (South Africa) were investigated, focusing on conservation agriculture systems, with conventional agriculture and grazed grassland as references systems. Selected ecological indicators (nematode-based indices, organic matter, permanganate-oxidizable carbon, and soil respiration) and physico-chemical properties (particle size distribution, pH, electrical conductivity, and macro- and micronutrients) were assessed to measure soil ecosystem status and the environmental context. At Vrede, pasture and conservation agriculture systems presented elevated organic matter content and microbial activity due to continuous organic cover and minimal physical disturbance. Furthermore, the nematode Maturity Index in these systems was higher, indicating more balanced and healthier soil ecosystems. In contrast, at Reitz, differences between conservation agriculture systems were strongly associated with soil texture differences, influencing organic matter and respiration rates. Additionally, fine-textured soils consistently exhibited greater permanganate-oxidizable carbon values, reflecting the role of soil texture in carbon retention and ecosystem functioning. This study underscores the relevance of both agricultural management and environmental contexts, particularly soil texture, when implementing conservation agriculture systems. It highlights the need for tailored agricultural systems to complement on-farm options and local conditions.

Evaluation of the Potential for Estimating Backscattering Coefficients over Bare Agricultural Soils at the Intra-Plot Scale

The objective of this study is to model backscattering coefficients over bare soils at intra-plot spatial scales (from almost 80 to 2800 m2), in a context where the plot is the reference spatial scale in most past studies. A statistical modeling approach, based on a random forest algorithm, is proposed to overcome the limits of semi-empirical or physical models pointed out in the literature and to reduce discrepancies observed between the satellite-derived backscattering coefficients and the predicted values. The experimental device was set up on a network of agricultural plots located in southwestern France during the Multispectral Crop Monitoring (MCM) experiment. The dataset combines high spatial resolution satellite images (acquired by TerraSAR-X and Radarsat-2) together with synchronous geo-located measurements of key soil parameters (i.e., top soil moisture, surface roughness, and soil texture) on consistent spatial areas. Backscattering coefficients are estimated at six intra-plot spatial scales (from ~80 to ~2800 m2), showing an exponential increase in modeling performance, and reaching higher levels of accuracy than previous work performed at the plot spatial scale (i.e., 50% of variance explained in the literature, in the best cases). The increase in signal quality with the spatial scale mainly explains the higher performance observed in the 2800 m2 area, with a correlation of 0.91 and RMSE of 0.83 dB in the X-band (for backscattering coefficients acquired with the HH polarization state). In the C-band, the values of correlation range from 0.74 to 0.80, and the RMSE from 1.65 to 1.85 dB (depending on the considered polarization state). The results also showed that the developed statistical algorithm is mainly influenced by the surface roughness and the top soil moisture, as semi-empirical or physical-based models. Soil texture does not significantly affect the algorithm.

Unmanned Aerial Vehicle-Based Hyperspectral Imaging and Soil Texture Mapping with Robust AI Algorithms

This paper explores the integration of UAV-based hyperspectral imaging and advanced AI algorithms for soil texture mapping and stress detection in agricultural settings. The primary focus lies on leveraging multi-modal sensor data, including hyperspectral imaging, thermal imaging, and gamma-ray spectroscopy, to enable precise monitoring of abiotic and biotic stressors in crops. An innovative algorithm combining vegetation indices, path planning, and machine learning methods is introduced to enhance the efficiency of data collection and analysis. Experimental results demonstrate significant improvements in accuracy and operational efficiency, paving the way for real-time, data-driven decision-making in precision agriculture.

Pore‐Based Modeling of Hydraulic Conductivity Function of Unsaturated Rooted Soils

ABSTRACTMualem's approach has been widely used to predict hydraulic conductivity functions (HCFs) of bare soils if a soil water retention curve (SWRC) model is available. The assumption that Mualem's approach holds is that the distribution of soil pores is spatially completely random. Under this assumption, relative hydraulic conductivity (Kr) is determined by the continuance probability of water‐filled pores. However, this assumption is not valid for rooted soils, as root growth causes soil particle rearrangement, and thus soil pore rearrangement, altering the probability of pore connectivity. After reconsidering Mualem's assumption, this study attempts to develop a new approach for predicting HCF of rooted soils by modeling the root‐induced pore rearrangement and the resultant change in the continuance probability of water‐filled pores. Two approaches mentioned were incorporated with a root‐dependent SWRC model to express HCF as a function of matric suction. The proposed model was validated against nine sets of measured HCFs from published studies. It was found that the proposed model reduced the root mean square error (RMSE) of Kr and lg Kr by 33% and 53%, respectively, as compared to traditional Mualem's model. Physically, the model's effectiveness depended on soil texture and root type. In fine‐textured soils, roots were capable of displacing soil particles, thereby causing soil pore rearrangement. Also, coarse roots with high strength tend to alter pore distribution. After considering the effects of pore‐level root‐soil interaction on pore rearrangement, the proposed model provided a significant improvement in the prediction of HCF of unsaturated rooted soils.

Modeling metal uptake by selected vegetables from urban soils in Europe: uncovering key soil factors using partial least squares regression (PLS-R)

Partial Least Squares Regression (PLS-R) was introduced as a method for modeling the uptake of six potentially toxic elements (PTEs)- Ba, Cd, Cu, Ni, Pb, and Zn- by lettuce, chard, and carrot. Data were obtained from a pot experiment where these crops were cultivated in urban soils of various characteristics. The models consider soil concentrations of PTE, Al, Ca, Fe, K, Mg, Mn, Na, P, S and pH, SOM, CEC, and soil texture as predictors. Initially, eighteen metal- and crop-specific models with all predictors were developed, using selectivity ratios (SRi) to identify influential variables for predicting PTE soil-to-crop transfer. Reduced models were then created using only predictors with high SRi. Key variables for predicting PTE soil-to-crop transfer included soil PTE concentration, pH, Fe and Mn soil concentrations, and soil texture. Out of eighteen models, sixteen were suitable for predicting correlations and assessing PTE accumulation in crops, while eight were accurate for quantitative predictions. This study shows that PLS-R is a robust method for modeling soil-to-crop transfer of metal contaminants, even with multicollinear predictors. PLS-R also helps identify key variables, providing insights into the mechanisms of PTE accumulation in crops, which is crucial for effective risk assessments.

Characterization and DNA barcoding of Zambian plant species used as inoculum in the traditional fermentation of Munkoyo; a cereal-based beverage

BackgroundMunkoyo, a non-alcoholic fermented beverage, is traditionally prepared in Zambia and neighbouring countries using cooked grains and the uncooked roots of wild plant species, collectively called ‘Munkoyo’ plants. The drink, valued for its refreshing taste and nutritional contribution, is made using roots of several wild plant species resulting in variations in the taste and quality of the beverage. However, comprehensive information on the specific plant species used in different regions of Zambia, as well as their occurrence in terms of habitat and soil type, is missing. This gap limits our understanding of the factors contributing to Munkoyo’s heterogeneity. The present study sought to identify the Zambian plant species used as an inoculum in Munkoyo fermentation and to characterize the soil in which they occur.ResultsPlant and soil samples were collected from four districts in Zambia known for Munkoyo production. Using morphological taxonomy, three Fabaceae species were identified as commonly used Munkoyo plants: Rhynchosia insignis (O.Hoffm.) R.E.Fr., Rhynchosia heterophylla Hauman, and Eminia holubii (Hemsl.) Taub. Root colour differed among these species, with the Rhynchosia species having yellowish roots and E. holubii having whitish roots. To validate their identification, we evaluated three DNA barcoding markers (matK, rbcL, and ITS2) for species discrimination. All markers showed 100% PCR amplification and sequencing success rates, with ITS2 displaying the highest genetic variability and species-level resolution. Phylogenetic analyses further confirmed ITS2 as the most effective marker. Validation using samples from a fifth district reaffirmed ITS2’s suitability for species-level discrimination. Soil analysis revealed significant associations between soil texture and plant occurrence: R. insignis and E. holubii were prevalent in sandy soils, while R. heterophylla was more prevalent in soils with lower sand content.ConclusionsThis study identified three common Munkoyo plant species and demonstrated ITS2 as a robust DNA barcode for their identification. It also established the influence of soil texture on the distribution of these plants, contributing to the understanding of Munkoyo production’s biological and environmental determinants.

GIS-based multi-criteria decision making for identifying rainwater harvesting sites

The Middle East region, with its arid and semi-arid climate, is one of the regions most affected by climate change and water scarcity. To address the severe issue of water scarcity in the western region of Iraq, this study identifies optimal potential rainwater harvesting (RWH) locations. Geographic Information System (GIS) and multi-criteria decision-making (MCDM) techniques were employed to generate themed layers for RWH. The nine primary criteria considered were rainfall, elevation, slope, stream order, soil texture, land use, groundwater depth, distance from the lake, and runoff depth. A weighted overlay assessment was used to identify probable RWH locations. The analytical hierarchical process was used to weight criteria depending on the study region, hydrological and socioeconomic parameters, and literature. The consistency ratio (CR = 3.16%) was calculated to validate the optimum weights of the comparison components, from which it was found that the weights assigned to each criterion were appropriate for comparative purposes. The results indicated that the optimum location (very high suitability) for RWH is mostly in middle regions of the study area, covering 286 km2 (13%), while for the other categories, high suitability is at 23% (498 km2), medium suitability at 29% (636 km2), low suitability at 21% (462 km2), and very low suitability at 14% (305 km2). Sensitivity analysis was used to identify the relative importance of the parameters and determine how each of the nine criteria influences the optimal RWH sites. These findings can assist decision makers and planners in devising strategies to mitigate the effects of climate change and increase any reclaimed area for agriculture.

High-Resolution Mapping of Topsoil Sand Content in Planosol Regions Using Temporal and Spectral Feature Optimization

Soil sand content is an important characterization index of soil texture, which directly affects soil water regulation, nutrient cycling, and crop growth potential. Therefore, its high-precision spatial distribution information is of great importance for agricultural resource management and land use. In this study, a remote sensing prediction method based on the combination of time-phase optimization and spectral feature preference is innovatively proposed for improving the mapping accuracy of the sand content in the till layer of a planosol area. The study first analyzed the prediction performance of single-time-phase images, screened the optimal time-phase (May), and constructed a single-time-phase model, which achieved significant prediction accuracy, with a coefficient of determination (R2) of 0.70 and a root mean square error (RMSE) of 1.26%. Subsequently, the model was further optimized by combining multiple time phases, and the prediction accuracy was improved to R2 = 0.77 and the RMSE decreased to 1.10%. At the feature level, the recursive feature elimination (RF-RFE) method was utilized to preferentially select 19 key spectral variables from the initial feature set, among which the short-wave infrared bands (b11, b12) and the visible bands (b2, b3, b4) contributed most significantly to the prediction. Finally, the prediction accuracy was further improved to R2 = 0.79 and RMSE = 1.05% by multi-temporal-multi-feature fusion modeling. The spatial distribution map of sand content generated by the optimized model shows that areas with high sand content are primarily located in the northern and central regions of Shuguang Farm. This study not only provides a new technical path for accurate mapping of soil texture in the planosol area, but also provides a reference for the improvement of remote sensing monitoring methods in other typical soil areas. The research results can provide a reference for mapping high-resolution soil sand maps over a wider area in the future.

Effects of nitrogen fertilizer basal-to-top-dressing ratios on maize straw decomposition, soil carbon and nitrogen, and bacterial community structure in different soil textures on the north china plain.

Straw return is a recognized agricultural practice that improves soil quality, reduces reliance on chemical fertilizers, and supports sustainable agriculture. Its effectiveness is influenced by microbial changes under varying soil properties and fertilization practices. In a wheat-maize rotation system, field experiments were conducted over 2 years in loam and clay loam soils with five fertilizer (N) application treatments (i.e., no N fertilizer (N0) and N fertilizer basal-to-top-dressing ratios of 3:7 (N3:7), 4:6 (N4:6), 5:5 (N5:5), and 6:4 (N6:4)) to investigate the dynamics of maize straw decomposition, changes in soil organic carbon (SOC) and total nitrogen (TN) concentrations, soil bacterial diversity and abundance, and their interactions. Our results showed that the optimization of N fertilizer basal-to-top-dressing ratios enhanced SOC and TN by accelerating maize straw decomposition and nutrient release, as well as increasing plant carbon and nitrogen inputs. At the wheat maturity stage, the decomposition rate of maize straw reached 69.48-75.04%. The N4:6 and N5:5 ratios exhibited higher decomposition rates and C and N release rates in both soil textures. Compared to N0, N application treatments increased SOC and TN concentrations by 7.90-14.17% and 7.94-33.60%, respectively. The effects were most pronounced with the N4:6 ratio in loam and the N5:5 ratio in clay loam. Both soil textures had the same dominant bacterial phyla, but species abundance differed significantly. Loam had a significantly higher relative abundance of Proteobacteria and lower relative abundances of Gemmatimonadetes, Actinobacteria, and Chloroflexi than clay loam. N application significantly influenced bacterial diversity, with higher diversity observed with the N4:6 ratio in loam and the N5:5 ratio in clay loam. Structural equation modeling indicated that different N application treatments in loam influenced maize straw decomposition by altering the soil C/N ratio and bacterial community diversity, while in clay loam, N application treatments influenced maize straw decomposition mainly by altering the soil C/N ratio. Overall, the N4:6 treatment in loam and the N5:5 treatment in clay loam accelerated the decomposition and nutrient release of maize straw, enhanced SOC, TN, and bacterial community abundance, and provided a scientific basis for efficient straw utilization and sustainable agricultural development in the North China Plain region.

Soil Texture, Soil Moisture, and Sentinel-1 Backscattering: Towards the Retrieval of Field-Scale Soil Hydrological Properties

Soil Texture, Soil Moisture, and Sentinel-1 Backscattering: Towards the Retrieval of Field-Scale Soil Hydrological Properties

Properties of Soils of Medak District in Telangana State, India

For the management of soil and plant growth the physicochemical study of parameters are crucial.The objective of this study is to do the physical and chemical analysis of soil samples collected from agricultural fields of Medak district and to provide information to the farmers. Soil samples collected from twenty mandals at three samples per mandal from 0-15 cm depth. Soil parameters like soil texture, pH, EC, organic carbon available nitrogen, phosphorus, potassium and Sulphur tested and recorded. Results shows texture varies from sandy clay loam to sandy loam. pH shows slightly acidic, neutral to slightly alkaline. Electric conductivity remains within the safe limits. The organic carbon recorded maximum low to medium in content, low in nitrogen content, medium to high range in phosphorous, potassium. The available sulphur recorded deficient to sufficient values. The findings suggest that farmers should adopt appropriate soil management techniques, such as crop rotation and conservation tillage which will add to maintain the soil physical characteristics to ensure the sustainability of agricultural practices and the long-term health of the soil.

Changes in Soil Properties, Organic Carbon, and Nutrient Stocks After Land‐Use Change From Forests to Grasslands in Kumaun Himalaya, India

ABSTRACTLand‐use changes are anticipated to be a substantial contributor to global change climate, substantially causing significant modifications in soil characteristics. This study addressed the impact of land‐use change from native forests to grasslands on the soil physico‐chemical properties in entirely replicated grasslands of three different forest zones (Oak, Pine and Cypress) in temperate region of Kumaun Himalaya. A total of 162 soil samples (6 sites × 3 plots × 3 seasons × 3 depths = 162 samples) were randomly collected from each site in triplicates from depths. The soil texture, bulk density (bD), porosity, water holding capacity, soil moisture content, pH, organic carbon (SOC), total nitrogen (TN), available phosphorus (P) and available potassium (K) were determined at different depths in forest and grassland sites. Results showed that soil bD, pH, SOC, TN, P and K significantly (p < 0.05) decreased with increasing depth. Moreover, conversion of forests into grassland reduced nutrient concentrations, physical qualities (bD and porosity), and pH levels. The decreasing trend of nutrient along the soil depth explains that the zone of nutrient accumulation is not well established in these grasslands because of the substantial leaching effect. Our findings indicate that conversion of natural forests into grasslands resulted in significant losses of SOC and TN stocks which can be attributed to the disturbance of natural forests. Therefore, while making land‐use change plans, the impact of these alterations on soil nutrients must be considered. These findings emphasize the value of establishing natural vegetation (forests) in these areas to retain nutrients and safeguard soil against runoff and erosion. However, anticipating the physico‐chemical impacts of land‐use alteration necessitates a better comprehension of its relations with other drivers of global change, such as changing climate and nitrogen deposition.

Biochar reduces early-stage mineralization rates of plant residues more in coarse-textured soils than in fine-textured soils – an artificial-soil approach

Abstract. Quantifying the impact of biochar on carbon persistence across soil textures is complex, owing to the variability in soil conditions. Using artificial soils with precise textural and mineral compositions, we can disentangle the effects of biochar from the effects of soil particle size. We can show that biochar application significantly reduces the early-stage carbon mineralization rates of plant residues in various soil textures (from 5 % to 41 % clay) but more significantly in sandy soils. Clay and silt particles alone also reduce C mineralization, but the magnitude of the changes is negligible compared to the impact of biochar. This finding suggests that biochar can compensate for the lack of clay in promoting C persistence in soil systems. This short report contributes substantially to understanding soil texture and biochar application interactions.

Migration and Transformation of Nitrogen in Clay-Rich Soil Under Shallow Groundwater Depth: In Situ Experiment and Numerical Simulation

Excessive use of nitrogen fertilizer in agricultural activities can easily induce nitrogen pollution in groundwater, which may deteriorate groundwater quality. Generally, nitrogen fertilizer passes through the unsaturated zone to groundwater. Therefore, it is of great significance to investigate the migration and transformation of nitrogen pollutants in unsaturated zones for the prevention and control of groundwater nitrogen pollution. Clay-rich soil is often considered a barrier layer to prevent pollutant leakage because of its lower relative permeability, while its prevention capacity is seldom reported under shallow groundwater table conditions. Motivated by this, an in situ experiment and numerical simulation were conducted to investigate the migration and transformation of nitrogen fertilizer in a clay-unsaturated zone with a shallow groundwater table. Systematic measurements and numerical simulation results revealed that nitrogen can pollute groundwater via the infiltration through clay-rich soil in the in situ experiment site. This finding clarified that the difference in hydraulic head under the shallow groundwater table, rather than soil permeability, is the dominant factor in controlling the downward migration of nitrogen pollutants in the clay-unsaturated zone. More importantly, the nitrogen migration is convection dominant during precipitation in this experiment, indicating nitrogen polluted groundwater much faster in humid climate areas. These findings suggest that nitrogen contaminates groundwater easily under shallow groundwater tables in humid climate areas, even with clay-rich soil texture.

Effect of different moisture content on nitrous oxide production in aggregated soil of Shintoku, Japan

Agricultural soils are the major source of the potent greenhouse gas and ozone-depleting substance, N2O. An incubation study was carried out using the volcanic ash fine textured soil of Shintoku. Soil samples used in this study were taken from managed grassland at Shintoku Experimental Livestock Farm of Hokkaido University in Southern Hokkaido, Japan (N43º05′, E142º51′). Soil aggregates were air-dried, and sieved with 4.5 mm and 2 mm and adjusted the soil moisture of 60% and 80% of field water capacity (FWC). Just after the moistening, the aggregates were incubated for 9 days under a temperature of 20°C. Just after starting the incubation, the flush of N2O production was observed. Similar flushes of carbon dioxide (CO2) and nitric oxide (NO) productions were also observed. All of the gas productions were higher in larger aggregates with 80% of field water capacity. The concentrations of Water Extractable Organic Carbon (WEOC), NH4+-N, pH and total N were significantly different before and after incubation. Shintoku soil showed a significant correlation between before and after incubation for all soil chemical properties except pH. Especially WEOC and NH4+-N changed immediately after the addition of water and this situation continued during the incubation. Larger aggregates showed higher amounts of NH4+-N and NO3-–N and were responsible for higher N2O production compared to smaller aggregates. In Shintoku the results of N2O-N/NO-N ratio in both moisture contents indicated nitrification as a main process of N2O production. It is very well known that N2O is produced more from the denitrification process than nitrification. Poor aeration and less diffusion of NO3-N and WEOC from the aerobic area to the anaerobic area reduce N2O production in the denitrification process of fine textured Shintoku soil. Int. J. Agril. Res. Innov. Tech. 14(2): 99-110, December 2024