Silt Fractions Research Articles

Speciation of Trace Metals in the Bottom Sediments of the Mozhaisk Reservoir and the Moskva River

The speciation of heavy metaf Geology, ls (Mn, Fe, Co, Ni, Cu, Zn, Cd, and Pb) in the bottom sediments of the Mozhaisk Reservoir and the Moskva River is described. They were characterized using the Tessier sequential selective extraction procedure trace element concentrations determined by inductively coupled plasma–mass spectrometry (ICP-MS). The bottom sediments of the Mozhaisk Reservoir are characterized by higher concentrations of the examined metals compared to the channel alluvium of the Moskva River. In this case, the most widespread metal compounds in the bottom sediments of the Mozhaisk Reservoir are firmly bound (stable form) to the mineral matrix. High concentrations of the firmly bound forms of metals (Co, Ni, Cu, Zn, Cd, Pb, and Fe) in the bottom sediments are due to an increased proportion of the silt fraction (0.1–0.01 mm) entering the reservoir due to abrasion of its shores. The only exceptions are Mn and Cd, which are present in labile compounds with carbonates and hydroxides of iron and manganese. In the bottom sediments of the Moskva River, strongly bound forms prevail for most metals—for Ni, Zn, and Cd, they are complex compounds with Fe and Mn hydroxides; for Co, Cu, Pb, and Fe, they are compounds with stable silicate minerals. The proportion of labile bioavailable forms of metals in the bottom sediments of the Moskva River is higher than in the reservoir due to anthropogenic input. Among the labile forms of the metal compounds, carbonates predominate. The proportion of elements in the most mobile exchange form and in compounds with organic matter is not large and does not exceed 14% for most elements. The only exceptions are Co and Cd, for which the concentration of the exchange form reaches 25%.

Impact of Conservation Agriculture on Soil Quality and Cotton–Maize System Yield in Semi-Arid India

Intensive agriculture is the chief cause of soil degradation, particularly in regions with low soil organic carbon status, such as semi-arid southern India. In the quest to attain sustainable yield and improved soil quality, conservation agriculture (CA) is being advocated and adopted globally, including in India. In this experiment, CA was implemented to investigate the synergistic impacts of tillage and weed management on soil quality index and system yield and to identify a remunerative treatment combination that can sustain system yield and enhance soil quality. Contrasting tillage practices (main plots) included the T1: conventional tillage with cotton–conventional tillage with maize–fallow, i.e., no Sesbania rostrata (Farmers’ practice), T2: conventional tillage with cotton–zero tillage with maize–zero tillage with Sesbania rostrata and T3: zero tillage with cotton + Sesbania rostrata residues–zero tillage with maize + cotton residues–zero tillage with Sesbania rostrata + maize stubbles. Weed management tactics (sub-plots) were W1: chemical weed control, W2: herbicide rotation, W3: integrated weed management and W4: single hand-weeded control in a split-plot design with cotton–maize–Sesbania cropping system over 3 years, in a split-plot design. Principal component analysis (PCA) was performed using the soil quality index (SQI)-CAL Version 1.0 software tool to extract minimum datasets from measured soil properties. A total of 40 soil variables were analyzed at 60 DAS and after the maize harvest, then subjected to principal component analysis (PCA) and subjected to PCA in soil quality index (SQI)-CAL software as to choose variables, minimum dataset and obtain soil quality index. The following soil properties, soil organic carbon (SOC), silt fraction, available soil zinc (Zn), iron (Fe), potassium (K), nitrogen (N), pH, electrical conductivity (EC), soil carbon to nitrogen (C:N) and cation exchange capacity (CEC), were selected as indicators based on correlations, calculated PCA and adept opinions on texture and lime concretions of experimental soil. The soil quality index improved by 23.34% in the T3W4 compared to T1W1. The system yield was 51.79% higher with the adoption of T3W3 compared to T3W4 combinations. Therefore, considering both system yield and soil quality index, T3 and W3 were remunerative and the best treatment combination among all others to sustain both soil and crop productivity in this region.

A Framework for High-Spatiotemporal-Resolution Soil Moisture Retrieval in China Using Multi-Source Remote Sensing Data

High-spatiotemporal-resolution and accurate soil moisture (SM) data are crucial for investigating climate, hydrology, and agriculture. Existing SM products do not yet meet the demands for high spatiotemporal resolution. The objective is to develop and evaluate a retrieval framework to derive SM estimates with high spatial (100 m) and temporal (<3 days) resolution that can be used on a national scale in China. Therefore, this study integrates multi-source data, including optical remote sensing (RS) data from Sentinel-2 and Landsat-7/8/9, synthetic aperture radar (SAR) data from Sentinel-1, and auxiliary data. Four machine learning and deep learning algorithms are applied, including Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and Ensemble Learning (EL). The integrated framework (IF) considers three feature scenarios (SC1: optical RS + auxiliary data, SC2: SAR + auxiliary data, SC3: optical RS + SAR + auxiliary data), encompassing a total of 33 features. The results are as follows: (1) The correlation coefficients (r) between auxiliary data (such as sand fraction, r = −0.48; silt fraction, r = 0.47; and evapotranspiration, r = −0.42), SAR features (such as the backscatter coefficients for VV-pol (σvv0), r = 0.47), and optical RS features (such as Shortwave Infrared Band 2 (SWIR2) reflectance data from Sentinel-2 and Landsat-7/8/9, r = −0.39) with observed SM are significant. This indicates that multi-source data can provide complementary information for SM monitoring. (2) Compared to XGBoost and LSTM, RFR and EL demonstrate superior overall performance and are the preferred models for SM prediction. Their R2 for the training and test sets exceed 0.969 and 0.743, respectively, and their ubRMSE are below 0.022 and 0.063 m3/m3, respectively. (3) The SM prediction accuracy is highest for the scenario of optical + SAR + auxiliary data, followed by SAR + auxiliary data, and finally optical + auxiliary data. (4) With an increasing Normalized Difference Vegetation Index (NDVI) and SM values, the trained models exhibit a general decrease in prediction performance and accuracy. (5) In 2021 and 2022, without considering cloud cover, the IF theoretically achieved an SM revisit time of 1–3 days across 95.01% and 96.53% of China’s area, respectively. However, SC1 was able to achieve a revisit time of 1–3 days over 60.73% of China’s area in 2021 and 69.36% in 2022, while the area covered by SC2 and SC3 at this revisit time accounted for less than 1% of China’s total area. This study validates the effectiveness of combining multi-source RS data with auxiliary data in large-scale SM monitoring and provides new methods for improving SM retrieval accuracy and spatiotemporal coverage.

Comparison of laser diffractometry and pipetting methods for particle size determination: A pilot study on the implications of result discrepancies on soil classification

AbstractIn recent decades, the determination of particle size distribution (PSD) using the laser diffraction method (LDM) has become increasingly common, supplanting traditional sedimentation techniques. Advances in everything from sample preparation to software settings have been realized globally, whether through recommendations from laser diffraction (LD) manufacturers or through user experiences. These developments seek to enhance accuracy and diminish the uncertainties associated with new methodologies. Particularly in the determination of PSD using LDM on various LD instruments and in comparison with the sieve–pipette method (SPM), discrepancies in PSD frequently arise. This article aims to mitigate these discrepancies by predefining parameters, specifically through the adjustment of LD software settings and sample preparation (employing a uniform set of dispersed samples in potassium hydroxide) on two widely used LD instruments for soil measurements: Mastersizer 3000 and Analysette 22. Additionally, these samples were analyzed using the traditional SPM (ISO 11277, 1998), with the results from LDM and SPM subsequently compared. The paper also explores the impact, range of user options, and limitations of predefined software settings on both LD instruments. Eighty soil samples were analyzed for PSD, collected from arable land in the cadastral area of Hrušky, district of Břeclav (Czech Republic), in spring 2022, from depths of 0‐ to 10‐cm and 10‐ to 20‐cm. Significant differences in PSD were confirmed, although the trends of the grain size distribution curves were very similar to those of LDM. Results from the Mastersizer underestimated the clay fraction by an average of 17% compared to SPM, at the expense of the sand fraction, whereas the silt fraction was underestimated by a maximum of 4%. Conversely, Analysette 22 overestimated the silt fraction by an average of 37% at the expense of the sand fraction, confirming only a slight difference in the clay fraction: 3%. Moreover, the quantity of sample entering the dispersion unit was identified as a significant issue when comparing LD instruments, despite the obscuration value being nearly identical, that is, 20%–30%. Therefore, it was not possible to achieve the same or similar weight when introducing suspension into circulation. The robustness of the obtained results underscores the importance of understanding input parameters when interpreting results between different methods.

The Natural Radioactivity and Accompanying Radiation Hazard in the Tigris River Sediments in Baghdad, Iraq

The natural radioactivity in the sediments of the Tigris River was measured using gamma spectrometry using an HPGe detector. Ten sediment samples were collected, and the target radionuclides are 226Ra, 232Th and 40K. Radioactivity in sediment was investigated in clay and silt fractions beside the hazard indices. The 226Ra concentration ranged between 6.5 and 11.2 Bq/kg, 232Th from 4.1 to 9.6 Bq/kg, and 40K from 133.8 to 260.2 Bq/kg. The average values of the natural radionuclides of these elements are within the worldwide average or less and comparable to those of the rivers in other countries; accordingly, the radiological hazard indices such as absorbed dose rate, annual effective dose equivalent, hazard indices and gamma index are below the recommended international values, indicating no significant hazard. The study concluded a positive relationship between the radioactivity and clay and silt content.

Methods to identify and distinguish the effects of weathering and landslides on sediment granulometry

Urbanization has resulted in the widespread development of built-up areas, often without considering the local geology and geomorphology. To improve risk assessments related to landslides, it is essential to determine the physical and chemical properties of sediments. The aim of this study is to exemplify an already mobilized and reworked layer based on granulometric properties of the sediments and characterize the chemical and physical properties which have changed during or after the mass movement. Ten red clay layers were sampled near Kulcs (Hungary) from a sliding surface and its environment. We measured grain size distribution, major element and modal composition, furthermore, conduct particle shape analysis, respectively. Grain size distribution suggest that samples from the sliding surface are weathered, with the exhibiting reworking characteristics. We calculate the weathering index from the major elements, which, in combination with the particle shape analyses of the silt fractions, indicate that the morphological parameters of the samples are in relation with weathering index. According to our results, while a fresh sliding event leaves marks on the granulometric properties, the main process which affects the grain morphology is the chemical abrasion (post the mass movement) and not the landslide event.

Predicting USCS soil texture classes utilizing soil spectra and deep learning

PurposeSoil texture identification is vital for various agricultural and engineering applications but generally involves rigorous laboratory work, especially for estimating USCS (Unified Soil Classification System) soil texture classes. Soil texture influences soil water storage capacity, soil fertility, compaction characteristics, and soil strength. Soil spectroscopy offers a reliable approach that is non-destructive, rapid, and cost-effective to estimate several soil properties including texture. For engineering applications, the USCS soil texture classes are preferred, but very few studies have focussed on estimating USCS soil texture using soil spectroscopy or remote sensing data in general.MethodsTwo large soil spectral libraries (SSLs), viz., Kellog Soil Spectral Library (KSSL) and Open-source Soil Spectral Library (OSSL), as well as three deep learning algorithms (VGG-16, ResNet-16, and Swin transformers), were used in this study to predict six USCS soil texture classes and three USCS soil texture groups. The USCS soil texture classes and groups were derived by grouping clay, sand, and silt fractions that are closely associated with the corresponding USCS soil texture classes.ResultsThe results indicate that the Swin transformer model performed the best with an accuracy of 67% for six USCS soil texture class predictions and 81% for three USCS soil texture group predictions. Cohen’s kappa value implies a moderate agreement (0.55) for soil texture class predictions and a substantial agreement (0.64) for soil texture group predictions.ConclusionThe proposed methodology offers a novel approach for USCS soil texture class predictions utilizing SSLs and deep learning techniques.

Computation of Particulate Emission Load from Vehicular Traffic on Road Network

The present study on traffic volume monitoring aims to compute total particulate emission load from urban road vehicular traffic and to find average emission factor. An industrial area Pandesara was selected as study area for its commercial and industrial activity, the contribution of public and private vehicle emissions in this area were high. Vehicle emissions include tailpipe emissions and road dust resuspensions. Tailpipe emission was the highest in off- road vehicles even their traffic volume was the least of all. Road dust resuspension due to vehicular speed was highest in 2 wheelers. Road dust resuspension concentrations due to silt fraction was found to be maximum in heavy motor vehicles. Total particulate matters concentrations emitted from vehicles and average emission factor were found to be 251616 gm/hr and 0.79 gm/Veh/Km respectively for entire Pandesara zone. Road dust resuspension plays a significant role in vehicular emission, still having less awareness among people worldwide. Keywords: Particulate Matter, Tailpipe Emission, Traffic Volume, Speed, Road Dust Resuspension, Turbulent Wake Height

Prediction of thermal conductivity of frozen soils from basic soil properties using ensemble learning methods

Thermal conductivity is one of the important properties required for understanding the frozen soils behavior. There are several models available in the literature for the prediction of thermal conductivity of frozen soils based on the proportions of unfrozen water, ice, gas, and soil particles. In this study, two ensemble learning methods-based models; namely, the Random Forest (RF) model and the Least Squares Boosting (LSB) model, are extended to estimate the thermal conductivity of frozen soils. These models utilize basic soil properties as input parameters that include water content, dry density, temperature, and fractions of gravel, sand, silt, and clay, can be measured easily, or determined. Additionally, seven widely used thermal conductivity models, referred to as the traditional models for frozen soils, were evaluated. Both the RF and LSB models, as well as the traditional models, were assessed using data of 823 tests derived from 43 soils with different textures that were gathered from the literature. The results highlight that the traditional models have their strengths and limitations in terms of their use for different types of soils. In contrast, the proposed ensemble learning methods-based models provide higher prediction accuracy compared to the traditional models and can be applied to all soil types and temperature ranges. Furthermore, estimation from the ensemble learning methods-based models can be used to provide probability of multi-dimensional analysis of frozen soils.

Mineral Composition and Distribution of Silt and Oxides in Shatt Al-Arab Deposits, Basra, Iraq

Understanding the mineral composition and distribution of river deposits is crucial for environmental management, resource utilization, and geological studies. This study aims to investigate the mineral composition and distribution of silt and oxides in the deposits of the Shatt al-Arab River, specifically in the Faw area of Basra Governorate. Samples were collected from depths of 0-30, 30-60, and 60-90 cm. The silt fraction (2-53 micrometers) was isolated, and mineral analysis was conducted using XRD and a point-counting device. Morphological characteristics were determined using a polarized light microscope. XRD analysis revealed the presence of minerals such as quartz, calcite, dolomite, smectite, kaolinite, chlorite, illite, hematite, magnetite, and albite at varying depths. Polarized light microscopy identified additional minerals within light and heavy fractions. The study found a dominance of monocrystalline quartz and opaque minerals, with magnetite being more prevalent than hematite across all depths and a high correlation coefficient with depth (0.96). Depth-specific variations in mineral composition suggest the need for further research in different locations and at greater depths. These findings provide insights into sedimentary processes and potential resource utilization in the region, making this study significant for regional geological and environmental research. Future research should focus on the temporal changes in mineral composition and their impact on sediment dynamics to enhance the understanding of regional geological history and resource management.

Soil degradation and herbicide pollution by repeated cassava monoculture within Thailand's conservation region.

In a national park in Northeast Thailand, agricultural land has been converted from natural forest by small-scale farmers for cassava agriculture. We hypothesise that long-termed cassava monoculture leads to the degradation of soil properties. To test the hypothesis, we conducted a five-year (2016-2020) study on the physical and chemical properties of soil in cassava farmland, and also examined the soil properties of its adjacent natural forests, as a control. The examined cassava farmland was converted from the natural forest during the five years from 2011 to 2015. The significant decrease in organic carbon and the increases in exchangeable potassium and bulk density were found in 2016, indicating that these soil properties varied quickly following the farmland conversion. On the other hand, the significant increase in soil nitrogen and the decrease in pH were found later in 2020, indicating that these soil properties were gradually altered by repeated agricultural activities, such as fertilizer application and trampling. In contrast, there were no significant differences in available phosphate, electrical conductivity, cation exchange capacity, and the soil texture (the fractions of sand, silt, and clay) among the forest and farmland soils. The cation exchange capacity was positively correlated to the fraction of clay, the organic carbon, and pH. The use of glyphosate and paraquat herbicides is prohibited within national parks in Thailand. However, in 2020, glyphosate was detected in farmland soil (up to 5.0 mg kg-1) during both the rainy and dry seasons, and glyphosate (up to 2.5 mg l-1) was detected in stream water from the farmland during the dry season at least in 2020. Soil degradation and herbicide pollution may carry a high risk of causing irreversible changes in terrestrial ecosystems. We discuss the root causes of this issue from perspectives of agricultural production, economy, and the environmental impact, and propose effective policy measures.

Context specific effects of substrate composition on the taxonomic and functional diversity of macroinvertebrate communities in temperate lowland streams.

Substrate composition has been widely recognised as a primary variable shaping lotic macroinvertebrate communities at the habitat unit level. However, fundamental understanding of how communities inhabiting mineralogical habitats (i.e., gravel, sand and silt) are structured across differing rivers is lacking. Moreover, research largely focusses on gravel beds and fine sediment in general (<2 mm) and as a result detailed field observations specifically of the sand and silt fractions are lacking. Using data from five UK streams collated from published studies, we assess taxonomic and functional biodiversity (alpha and beta diversity) at the habitat unit level (as defined by substrate composition of sand, silt and gravel). We found that the composition of taxonomic communities were clearly different in all habitat units for each individual stream (and at the landscape scale), with comparable, but less strong, distinctions between substrates for functional macroinvertebrate community composition. However, alpha diversity metrics and Local Contribution to Beta Diversity (LCBD) recorded among the different habitat units varied significantly across individual rivers, and the amount of variation explained by the habitat unit for taxonomic and functional composition demonstrated considerable differences suggesting strong context dependence. The depositional fine sediment habitats of sand and silt were found to support a discrete community composition and differing levels of alpha and beta diversity within and between rivers. We advocate that care should be taken when seeking to generalise biodiversity patterns at a landscape scale as our study highlights the high degree of context dependency when considering the role of the habitat template. Moreover, our results provide evidence that discriminating between the size fractions of fine sediment habitats (sand or silt) is important to fully elucidate the wider ecological importance of these habitats and the distinct taxonomic and functional biodiversity they support.

Preservation of Organic Carbon Associated with Iron on Continental Shelves Influenced by Hydrodynamic Processes.

Understanding the environmental fate of organic carbon associated with iron (OC-Fe) is critically important for investigating OC preservation in aquatic systems. Here, we first investigate 13C and 14C isotopes of OC-Fe within grain size-fractionated sediments retrieved from the East China Sea and estimate their sources and reactivities of OC-Fe through isotope-mixing models and thermal pyrolysis approaches in order to reveal the fate of OC-Fe on continental shelves influenced by hydrodynamic processes. Our results show that the OC-Fe proportion in total OC (fOC-Fe) in the sortable silt fractions (20-63 μm) is the highest among three grain size fractions, likely suggesting that hydrodynamics may enhance the iron protection on OC. In addition, Δ14COC-Fe values fall within the range of from -358.73 to -64.03‰, and both Δ14COC-Fe values and ancient OC-Fe% exhibit strong positive linear relationships with fOC-Fe. This emphasized that the hydrodynamic processes may cause the ancient OC to be tightly associated with Fe, accompanying OC-Fe aging. Our findings shed new light on the preservation of OC-Fe in marginal seas to advance the recognition of carbon "rusty sinks" in seafloor sediments.

Dissolved carbon flow to particulate organic carbon enhances soil carbon sequestration

Abstract. Particulate organic carbon (POC) and mineral-associated organic carbon (MAOC), which are two primary components of the soil carbon (C) reservoir, have different physical and chemical properties as well as biochemical turnover rates. Microbial necromass entombment is a primary mechanism for MAOC formation from fast-decaying plant substrates, whereas POC is typically considered the product of structural litter via physical fragmentation. However, emerging evidence shows that microbial by-products derived from labile C substrates can enter the POC pool. To date, it is still unclear to what extent dissolved C can enter the POC pool and how it affects the subsequent long-term soil organic carbon (SOC) storage. Our study here, through a 13C-labeling experiment in 10 soils from 5 grassland sites as well as a modeling analysis, showed that up to 12.29 % of isotope-labeled glucose C (i.e., dissolved C) was detected in the POC pool. In addition, the glucose-derived POC was correlated with 13C-MBC (microbial biomass carbon) and the fraction of clay and silt, suggesting that the flow of dissolved C to POC is dependent on interactions between soil physical and microbial processes. The modeling analysis showed that ignoring the C flow from MBC to POC significantly underestimated soil C sequestration by up to 53.52 % across the 10 soils. The results emphasize that the soil mineral-regulated microbial process, besides the plant structural residues, is a significant contributor to POC, acting as a vital component in SOC dynamics.

Provenance of clay-sized detrital sediments in the North Sea and the Skagerrak region based on radiogenic Nd-Sr-Hf isotopes and clay mineral compositions: assessing the impact of coastal and seabed erosion

The Skagerrak basin represents the main sink area for fine-grained sediment in the North Sea region and constitutes a natural deposition center for sediments that are supplied from the Atlantic, the Baltic Sea and the surrounding continental margins and coasts. However, the exact sources and their proportional contributions to the North Sea sediments and to the Skagerrak deposits are not well understood. To trace the predominant sources of the sediment and to gain a better understanding of the sedimentary processes in the North Sea and the Skagerrak basin, radiogenic Sr, Nd, and Hf isotope signatures and clay mineral compositions of the detrital clay fraction of surface sediment samples from the North Sea, the Scandinavian margins and the Baltic Sea were measured. The results indicate that the major source for Skagerrak clay-size sediments is the northern North Sea but Scandinavia as well as the southern North Sea including the southern England coast also contribute material. Seabed and coastal erosion in the northern North Sea are enhanced by the inflowing Atlantic Currents, which provide the Skagerrak with high amounts of clay size sediments. In contrast, the southern North Sea, the Baltic Sea and mid-European rivers such as Weser, Elbe and Ems are only minor contributors. As Skagerrak deposits are dominated by clay sized material (up to 60%), the reconstructed sediment processes in this study deviate from findings in previous sediment budget studies, which were based on both clay and silt fraction and indicated predominant influences from the southern North Sea. These results highlight that coastal and seabed erosion in the North Sea is a previously underestimated source of fine-grained sediments for depocenters in the entire North Sea. With regard to climate change, the global sea-level rise will likely enhance erosional processes and can therefore significantly influence the sediment budget of the entire North Sea.

Fine Sediment in Mixed Sand‐Silt Environments Impacts Bedform Geometry by Altering Sediment Mobility

AbstractGeometric characteristics of subaqueous bedforms, such as height, length and leeside angle, are crucial for determining hydraulic form roughness and interpreting sedimentary records. Traditionally, bedform existence and geometry predictors are primarily based on uniform, cohesionless sediments. However, mixtures of sand, silt and clay are common in deltaic, estuarine, and lowland river environments, where bedforms are ubiquitous. Therefore, we investigate the impact of fine sand and silt in sand‐silt mixtures on bedform geometry, based on laboratory experiments conducted in a recirculating flume. We systematically varied the fraction of sand and silt for different discharges, and utilized an acoustic Doppler velocimeter to measure flow velocity profiles. The final bed geometry was captured using a line laser scanner. Our findings reveal that the response of bedforms to an altered fine sediment percentage is ambiguous, and likely depends on, among others, bimodality‐driven bed mobility and sediment cohesiveness. When fine, non‐cohesive material (fine sand or coarse silt) is mixed with the base material (medium sand), an increased dune height and length is observed, possibly caused by the hiding‐exposure effect, resulting in enhanced mobility of the coarser material. However, weakly cohesive fine silt suppresses dune height and length, possibly caused by reduced sediment mobility. Finally, in the transition from dunes to upper stage plane bed, there are indications that the bed becomes unstable and dune heights vary over time. The composition of the bed material does not significantly impact the hydraulic roughness, but mainly affects roughness via the bed morphology, especially the leeside angle.

Irrigation-Initiated Changes in Physicochemical Properties of the Calcisols of the Northern Part of Fergana Valley

Agriculture in Central Asia and in the Fergana Valley in general strongly depends on irrigation and drainage of agricultural lands. The Fergana Valley includes about 45% of the irrigated area in the Syr Darya River basin. Active use of irrigation in agriculture can lead to changes in the soil’s natural composition, as well as pollution and changes in the soil’s physical and chemical properties. Soil degradation in the process of irrigation can lead to a decrease in crop yields and, as a consequence, to a decrease in food security in the region. In this study, a comparative analysis of three main types of Calcisols (Dark, Light, and Typical) before (uncultivated soil) and after agricultural use (surface-irrigated agricultural soil) was carried out. Irrigation leads to increment of SOC stocks in Typical (from 113.8 to 126.3 t/ha) and Light (from 62.8 to 100.1 t/ha) Calcisols and to decreasing of SOC stocks in Dark Calcisols (from 160.1 to 175.3 t/ha). In general, the content of biophilic elements (SOC and TN) is lower in irrigated soils, and their distribution in the soil profile is close to the functional relationship (r2 0.98 to 0.99). In uncultivated Calcisols, the profile distribution of SOC and TN is more heterogeneous (r2 0.67 to 0.97). Changes in the humification processes of soil organic matter are also identified; in soils after irrigation the carbon ratio of humic/fulvic acids (CHA/CFA) is lower (&lt;1) compared to their uncultivated counterparts (~1). The alteration of the soil water regime also resulted in transformation of the individual compositions of amino acids. All studied types of Calcisols are characterized by changes in particle-size distribution of soils especially in the number of the silt fraction (0.01–0.05 mm) and the difference between uncultivated and irrigated soils, 10–20%, which is associated with the processes of colmatage by accumulation of a fine fraction and replacement of sub-fractions in the fraction of sand. The highest concentrations of nutrients are characteristic of the upper soil horizons (P up to 231, K up to 2350 mg/kg), which indicate their pedogenic and agrogenic origins rather than inheritance from the parent material. Soil P and K availability is rather high, with non-labile forms prevailing, although of near reserve. The surface irrigation results in apparent accumulation of water-soluble Mg2+ (1.6–2.1 meq/100 g) and K+ (0.6–0.9 meq/100 g), but the cation of Ca2+ predominates in the base cations’ composition, which is the most favorable in terms of soil agrogenic property formation. Data obtained will be useful for development of strategies for effective land use in arid, subtropical, overpopulated regions of Central Asia that have deficient water sources and intensive soil degradation.

Estimation of ammonia emissions from rice fields under different N inputs in China

Rice production is essential for ensuring food security. Nitrogen fertilizers applied in rice fields are prone to loss through NH3 emissions. We need to elucidate the responses of NH3 emissions to different N inputs. With a national dataset, we identified that N application rate, N application frequency, soil temperature, and soil silt fraction were the key driving factors of NH3 emissions. 190−210 kg N ha−1 was suitable for simultaneously reducing NH3 emissions and increasing rice yields. The model showed that under the actual N application rate and N application frequency of 3, national NH3 emissions were 0.852 Mt N each rice growing season, while national NH3 emission factor was 12.7 %. We developed a portable software to conveniently assess China's NH3 emissions from rice fields under different N inputs. The findings can help to optimize N inputs in rice fields to reduce NH3 emissions and improve ecosystem health and agricultural sustainability.

Effect of Soil Texture on Agricultural Machine Performance in Sylhet, Bangladesh.

Soil texture is one of the primary soil physical properties that largely affects agricultural machine performance. Before implementing an agricultural machine at the farm level, it is important to test the machine to gain an understanding of its technical functionality and economic viability to get the best output from it. The research was carried out to assess how soil texture influences the performance of machinery, aiming to determine the effectiveness of agricultural equipment on a particular type of land. Soil samples were collected from nine different fields, and their textures were determined using hydrometer analysis. To correlate soil textural classes with machine performance, machinery data—effective field capacity, forward speed, and field efficiency—were calculated by operating a combine harvester on the study areas during the harvesting season of Boro rice. Two types of soil were identified: loamy sand and sandy loam. The findings of the study showed that machine efficiency has a positive correlation with sand proportion and a negative correlation with clay and silt fractions. It was observed that soil with loamy sand-type textural classes demonstrated better field efficiency, higher effective field capacity, and higher forward speed compared to sandy loam soil. Small and irregularly shaped fields also caused variation in performance. The study identified significant variation in speed between these two types of soils but no significant effect on effective field capacity or field efficiency at the 5% level of significance. The outcomes of this research will help farmers make informed decisions when selecting appropriate equipment for their agricultural operations.

Upper limit of mineral-associated organic carbon in temperate and sub-tropical soils: How far is it?

Soil organic carbon (SOC) is the main terrestrial C pool and is roughly divided into stable mineral-associated organic carbon (MAOC) and particulate organic matter carbon (POC). However, the sequestration capacity of MAOC remains unquantified. Currently, soils are considered to have a limited capacity to accumulate MAOC, primarily due to the presence of fine fractions, i.e. silt and clay particles. However, it is unlikely that saturation will occur in all soils due to land use. Therefore it is necessary to investigate whether soils from different latitudes and their silt and clay fractions that comprise MAOC are saturated. We selected 71 soil samples that were physically fractionated from a Mexican inventory of vegetation with a wide range of SOC (4–186 g kg−1 soil) and clay (126–777 g kg−1 soil) contents. Soils were grouped into non-volcanic soils and allophanic Andosols. In the first group, we found a positive and highly significant linear relationship between SOC and MAOC contents, but no upper limit was detected. We found a common slope of 0.86 (R2 = 0.97, p < 0.01), i.e. 86% of the SOC was contained in MAOC irrespective of soil types and management, a value very similar to that in other studies. Conversely, for allophanic Andosols, which had SOC content twice that of non-volcanic soils, a second-order polynomial function (R2 = 0.98, p < 0.01) was established, the upper limit of which exceeded 186 g SOC kg−1 for a MAOC of 134 g kg−1. This was also supported by an increasing polynomial function of particulate organic carbon (POC) as the SOC increased. In both types of soils, maximum MAOCs were found between 700 and 800 g kg−1 of silt and clay in medium-textured soils. The amount of silt and clay particles was limited by SOC accumulation in the silt fraction as clay became saturated. These results are influenced by the mass proportion of silt and clay that follows a unique pattern in allophanic Andosols and non-volcanic soils. An investigation of a comprehensive database comprising 21,315 soil profiles sampled 0–30 cm across Mexico showed that most soils are below 186 g SOC kg−1 the bending point above which the upper limit does exist.