Silt Content Research Articles

Advancing soil property prediction with encoder-decoder structures integrating traditional deep learning methods in Vis-NIR spectroscopy

The technology for estimating soil properties using visible and near-infrared spectroscopy has been maturing, with corresponding advances and breakthroughs in deep learning models. In this study, based on the large soil spectral library LUCAS, we explore the potential of encoder-decoder structures to improve convolutional neural network regression predictions. By introducing an encoder-decoder structure into the feature channels of a six-layer CNN model (TRNN model), we significantly enhanced the performance of shallow CNN models and successfully carried out regression predictions for seven soil properties. We employed IntegratedGradients, DeepLift, GradientShap, and DeepLiftShap methods to interpret the output of the TRNN model. Our TRNN model, built on raw spectra, demonstrated high accuracy in predicting multiple soil properties, outperforming residual architectures, LSTMs, various CNN architectures, and other traditional machine learning methods proposed in previous studies. We also investigated the impact of multi-task output structures (TRNN 1-M and TRNN M−M) and single-task output structures (TRNN 1-1) on model performance. For the TRNN model with an encoder-decoder structure, multi-task output structures resulted in a reduction in performance. The TRNN showed outstanding results in regression analysis of the seven soil properties selected in this study (cation exchange capacity, organic carbon content, calcium carbonate content, pH, clay content, silt content, and sand content), with R2 values exceeding 0.93 for all seven properties. Different soil characteristics correspond to different wavelengths, with multiple characteristic peaks commonly observed. This research convincingly demonstrates the enormous potential of combining large model architectures with traditional deep learning approaches for predicting soil properties, which could significantly advance precision agriculture.

Glomalin-Related Soil Protein Plays Different Roles in Soil Organic Carbon Pool Maintaining among Different Grassland Types

Glomalin-related soil protein (GRSP) is an important component of soil organic carbon (SOC), which can promote long-term SOC sequestration. However, GRSP distribution characteristics and its contribution to the SOC pool among different grassland types remain poorly understood. Therefore, six grassland types (alpine meadow, mountain meadow, temperate meadow steppe, temperate steppe, temperate desert steppe, and temperate desert) were chosen to evaluate the contribution of GRSP to the SOC pool and the factors that influence GRSP accumulation in the Irtysh River Basin in China. The results revealed that GRSP (EE-GRSP, T-GRSP) accumulated more in the 0–10 cm soil layer than in the 10–20 cm soil layer (p < 0.05). GRSP content was higher in alpine grasslands (15.69 mg·g−1) than in desert grasslands (5.45 mg·g−1). However, their contribution to the SOC pool exhibited an opposite trend, whereas GRSP-C/SOC even accounted for 11.88% in the desert grasslands. The redundancy analysis (RDA) showed that SOC was the top important positive regulator for GRSP accumulation both in the two layers (explanatory rate > 80%). Besides the SOC factor, the two soil layers had different factors in regulating GRSP accumulation. Changes in GRSP content in the 0–10 cm soil layer were more strongly associated with mean annual temperature (MAT), sand content, soil water content (SWC), and silt content. In contrast, in the 10–20 cm soil layer, GRSP content was more influenced by SWC, electrical conductivity (EC), and pH (p < 0.05). Additionally, the main factor in the GRSP content variation was the interaction between climate and soil in the two soil layers (explanatory rate > 80%). Our findings underscore the critical role of GRSP in facilitating SOC sequestration within desert grasslands and elucidate the primary factors driving GRSP distribution across varying soil depths.

Soil texture contributes to shaping comammox Nitrospira communities in rice-wheat rotation soils

Complete ammonia oxidizers (comammox Nitrospira) are intriguing discoveries that mark a significant milestone in the global nitrogen cycle. While numerous soil physiochemical variables have been identified as key influencers of comammox Nitrospira distribution, the role of soil texture in shaping these communities remains largely uncertain. Here, we explored the diversity, community structure of comammox Nitrospira, and their driving factors, including soil texture in 237 rice-wheat rotation soils. The results indicated that soil pH and texture were the primary factors influencing the Shannon diversity and richness of comammox Nitrospira. Comammox Nitrospira Shannon diversity and richness were positively associated with soil pH and silt content, but negatively correlated with clay content, suggesting that finer-textured soils harbored lower comammox Nitrospira diversity. Additionally, silt content emerged as the second most influential factor, after pH, shaping comammox Nitrospira community structure. Clade A.2 was found as the predominant comammox Nitrospira clade in rice-wheat rotation soils, representing 59.3 % of the total sequences. Clade A.2 exhibited a positive correlation with sand and clay contents but a negative association with silt content. Conversely, Clades A.3 and B demonstrated the opposite pattern. Overall, our study underscores the critical role of soil texture as a mediator of comammox Nitrospira diversity and community structure, emphasizing the need to consider soil texture in investigations of comammox Nitrospira.

Construction and Application of the Phaeozem Health Evaluation System in Liaoning Province, China

Soil degradation has led to a continuous decline in the quality of phaeozem, which is seriously threatening the foundation of national food security. Therefore, precisely evaluating the health status of phaeozem resources and their spatial and temporal variations are crucial for ensuring the effective implementation of soil degradation prevention and control strategies. In this study, soil data from 140 sites were collected, and eight physical and chemical indices (sand content, silt content, pedon thickness, organic matter, total potassium, total nitrogen, total phosphorus, and pH) were utilized to assess the soil health status of phaeozem in Liaoning Province. The results showed the following: (1) The minimum data set is aligned with previous research findings and effectively mirrors the soil’s health condition. (2) The substantial coefficients of variation observed for pedon thickness, sand content, and silt content across regions suggest notable differences, likely influenced by climatic variations, parent material differences, and anthropogenic activities. (3) The study area’s average soil pH of 6.39 indicates an overall acidic nature, potentially attributed to intense soil weathering and suboptimal fertilization practices. (4) The soil health index (SHI) ranged from 0.33 to 0.91, with an average of 0.62, which indicates that the majority of the phaeozem in Liaoning Province are in a sub-healthy state (SHI < 0.6 = unhealthy, SHI 0.6–0.8 = sub-healthy, SHI ≥ 0.8 = healthy). These sub-healthy areas are primarily located in hills, mountains, and the Liaohe Plain, and are significantly impacted by rainfall and wind erosion. Meanwhile, unhealthy areas are concentrated in the south and center of the province, characterized by fragile ecological environments and challenging agricultural conditions.

SOIL CONSERVATION STRATEGIES FOR THE REDUCTION OF BIODIVERSITY IN MOUNTAIN SOILS: EXAMPLE OF ULUDAĞ NATIONAL PARK

Uludag National Park in Turkey is famous for its rich biodiversity. The park serves as an essential habitat for a wide variety of flora and fauna and contributes significantly to the conservation of various species. This study examines biodiversity conservation strategies in Uludağ National Park. Soil samples were taken from 17 locations near the summit where endangered endemic plants grow. Analyses of the soil samples reveal the relationships between elevation and soil texture components. According to the soil analysis results, there was a weak positive correlation between elevation and silt content (r = 0.414) and a weak negative correlation between elevation and sand content (r = -0.375). These findings indicate that silt content tends to increase and sand content tends to decrease with increasing elevation. The geomorphological features and soil structure of Uludağ National Park were also an essential part of the research. The region's metamorphic mica schists, granites and marbles affect the soil's physical properties. For example, soils are generally sandy and permeable in areas where granite parent material is present. In contrast, areas where mica-schist parent material is distributed are less resistant and more susceptible to erosion. As a result, effective soil conservation strategies must be implemented to protect biodiversity in Uludağ National Park. These strategies are essential to reduce soil erosion, increase organic matter accumulation and minimise the impacts of climate change. Furthermore, community participation and adaptive management strategies must be adopted for sustainable development and resource management. This study provides essential information for biodiversity conservation in the Uludağ region and contributes to developing conservation strategies.

Laboratory investigation of the influence of fines content on the mechanical behavior of sand

The present article seeks to examine and discuss the experimental results that were obtained from a series of straight box shear tests. For this, different soils were prepared with clean sand, which was brought from the Wilaya (Province) of Chlef that is located in northern Algeria, mixed with silt from the same site, clay from the town of M'zilla or clayey loam that is composed half of this same clay and half of silt. The percentages of sand replacement by the selected additions were 0, 10, 20, 30, and 40% in mass ratio. The samples thus prepared were then tested with respect to dry shear, under the normal stresses equal to 100, 200, and 400 kPa. The results obtained showed that the shear strength of the sand from Chlef decreases when the silt content increases from 0 to 40%. With regard to the sand-clay and sand-silt-clay mixtures, it was found that the shear strength decreases until reaching a threshold value that coincides with the addition content of 20%. Then, beyond that value, it begins to increase as the addition fraction increases. Likewise, it turned out that cohesion increases with increasing silt content, while for sand-clay and sand-silt-clay mixtures, this cohesion increases until reaching a maximum value which coincides with the addition of 20% silt content. The same trend was observed for the variation of the direct shear strength, while the friction angle exhibited an inverse behavior.

Long-term evaluation of surface topographic and topsoil grain composition changes in an agricultural landscape

Understanding long-term changes in topography and topsoil grain composition is crucial for the management of agricultural landscapes, especially in areas prone to wind erosion. This study investigates long-term changes in topography and topsoil grain composition within an agricultural landscape in south-western Slovakia. To analyse topographic changes over time, we used high-precision positioning measurements and airborne laser scanning to create digital terrain models (DTM) for the years 2011, 2017 and 2020. To assess changes in soil grain composition, we performed grain size analyses on soil samples collected during three different periods: M1 (1961–1970), M2 (2009–2015) and M3 (2015–2016). Changes in soil texture were evaluated to understand the impact of wind erosion on soil composition. The influence of windbreaks was also analysed by comparing the accumulation and deflation processes. The results showed significant changes in both topography and soil texture over the study period. The DTMs showed marked differences in the accumulation and deflation processes, highlighting areas affected by wind erosion. Comparisons of soil samples showed a shift in dominant soil types from loam and clay loam to silty loam, highlighting the effects of wind erosion. Analysis revealed a decrease in clay and silt content and an increase in sand content, indicating wind-induced soil degradation. The presence of windbreaks played a crucial role in reducing soil erosion by reducing wind speed, promoting soil accumulation and stabilising the landscape up to 80 m windward and 20 m leeward. The study highlights the complex interplay of climate and wind factors in shaping topography and soil properties and emphasises the protective role of windbreaks in agricultural landscapes over time. Our results show that wind erosion significantly alters soil texture, which can affect agricultural productivity. However, windbreaks have proven to be an effective measure in reducing soil erosion and maintaining soil quality.

How restoration engineering measures can enhance the ecological value of intertidal flats

Restoration engineering measures, such as managed realignments or building groins, modify the environmental characteristics of coastal intertidal ecosystems. Creating physical modifications that are beneficial to an intertidal system's ecology necessitates an in‐depth understanding of the relationships between the abiotic and biotic components of a given intertidal habitat. In this study, we evaluate how hydrodynamics and sediment characteristics drive the development of the benthic macrofauna community during the first 5 years following engineering measures to enhance benthic macrofauna diversity at three locations. The creation of low‐energy habitats through groins (Knuitershoek and Baalhoek) and a managed realignment dike breach (Perkpolder) led to the accumulation of fine sediments in all three impact sites. Biomass of benthic macrofauna quickly increased between 2016 and 2020, with successional processes being more important in Perkpolder, where the habitat was started completely from scratch due to a managed realignment, than at Knuitershoek or Baalhoek, where habitat conditions were improved by adding groins. In addition, the density of benthos‐eating birds, especially oystercatchers, increased at some of the modified sites. While a low‐energy habitat may harbor more diverse assemblages of benthic macrofauna than a highly dynamic one, the extremely high silt content, which is typical for low‐energy habitats, may slow benthic community development. The observed increase of biomass at our impact sites highlights the value of the interventions, while the delays in the response of the benthic macrofauna community emphasizes the need for extensive monitoring both in time and space and the identification of underlying abiotic–biotic mechanisms.

Prediction of Soil Field Capacity and Permanent Wilting Point Using Accessible Parameters by Machine Learning

The field capacity (FC) and permanent wilting point (PWP) are fundamental hydrological properties critical for assessing water availability within soils, rather than direct measures of soil health. Due to the challenges associated with their field measurement, alternative assessment methods are necessary. In this study, global-scale accessible soil data were retrieved from the world soil database called the World Soil Information Service (WoSIS), and artificial neural network (ANN) and gene-expression programming (GEP) algorithms were used to predict soil FC and PWP based on easily obtainable parameters from the database. The best-fit variable combination for FC (longitude, latitude, altitude, sand content, silt content, clay content, and electrical conductivity) and PWP (best-fit FC combination plus pH) modeling was determined. Both ANN and GEP showed greater accuracy than linear-based models in simulating the FC and PWP from the best-fit variables. The mean absolute error (MAE) was reduced by 51.54% for the FC and 56.38% for the PWP by the ANN model, compared with the linear model used in the previous literature. The normalized root mean square error (NRMSE) evaluation indicated that the ANN model performed best for PWP prediction (NRMSE of 19.9%), while the GEP model was superior for FC prediction (NRMSE of 29.9%). Between the ANN and GEP models, the ANN model showed a slightly higher model of interpretability; however, the GEP model exhibited a similar or better ability to avoid large error, based on the error distribution. Overall, our results demonstrated that machine learning is effective in predicting the FC and PWP from easily accessible data from WoSIS, and the GEP model is more preferable for FC and PWP modeling.

A sophisticated approach to soil productivity detection using a convolutional neural network-based model

India is primarily an agricultural country where the quality of land is crucial for the livelihoods and well-being of its people. The agricultural sector plays a significant role in shaping the current state of the nation's economy. Therefore, it is essential to regularly evaluate our understanding of soil properties, such as its type, texture, color, and moisture content. Many developing countries lack sufficient knowledge and awareness about soil development. Understanding soil behavior helps farmers predict crop performance, monitor nutrient movement, and recognize soil limitations. Traditional methods for classifying soil in laboratories require significant time, staff, and financial resources. In this study, various image features, such as color, particle size, and texture, were randomly extracted and combined to predict soil fertility based on its sand, clay, and silt content using the AlexNet-CNN algorithm. We collected soil images using mobile cameras from regions such as Purulia, Hooghly, Bankura, and Burdwan to build a useful soil image dataset. The research focuses on categorizing productive and unproductive soil using convolutional neural network architectures, such as AlexNet and VGG16. Compared to previous studies, our proposed model showed better performance in terms of precision and recall. This study presents an efficient new convolutional neural network architecture for classifying soil images.

Role of aggregates in the forming process of tephric loess dunes distributed along the Pacific coast of eastern Japan

The Pacific coast of eastern Japan contains specific aeolian landforms of geomorphological interest in terms of their formation processes in humid climates and Holocene paleoenvironments. Wind erosion landforms, characterised by blowouts, were observed at the heads of coastal cliffs which consisted of marine gravel sediments, weathered volcanic ash layers (WVALs) and black soil layers (BSLs). The blowouts were accompanied by downwind tephric loess dunes. We analysed the physicochemical properties of the WVAL, BSL and dune sediment samples. The results revealed that clay and silt contents in dune sediments were 5–48% and 20–35%, respectively. The fine particles were in the form of aggregates in the WVALs, BSLs and dune sediments, with contributions from andic properties. WVAL and BSL aggregates were prone to movement in saltation, depositing into the dunes in a formation process similar to that of clay dunes in semiarid zones. The tephric loess dunes were characterised as clay dunes based on their high clay and silt contents. In addition, the dunes were characterised by durable aggregates derived from short-range-order minerals, such as allophane. The prevailing northeasterly winds caused by intense low pressures were responsible for dune formation in the study areas. Radiocarbon dating revealed two active wind periods. The first was approximately 1005–895 cal. BC to 358–281 cal. BC, and the second was 771–886 cal. AD to 1021–1155 cal. AD.

Long-term effects of conventional cultivation on soil cation exchange capacity and base saturation in an arid desert region

Land reclamation and subsequent management affect soil condition, which is critical for sustainable agricultural production. Soil cation exchange capacity (CEC) and base saturation (BS%) play an important role in the assessment of soil fertility and buffering capacity. However, the variation of these indicators in the evolution of oasis farmland in arid desert areas remains unclear. Therefore, this study was carried out aiming to evaluate the effect of desert reclamation and following long-term conventional cultivation on the CEC and BS%. For the study, we investigated the CEC and exchangeable bases (ExBas) content in oasis farmlands along a chronosequence (0–100 years) of cultivation in arid region and identified the key factors affecting CEC and BS%. The results showed that soil CEC and ExBas significantly increased after desert reclamation, whereas the BS% dramatically decreased. However, all these changes were alleviated with the conventional cultivation age. Regression analysis showed that soil CEC, ExBas, and BS% all exponentially changed with cultivation years. Based on our findings, CEC and ExBas were closely related to soil particle size composition, total nitrogen (TN), soil organic matter (SOM) and soil water content (SWC). The multiple stepwise regression further indicated that the changes in CEC and ExBas after reclamation mainly depended on the silt content, SWC, SOM, and TN. Our findings highlight that although desert reclamation increases soil CEC and ExBas in arid area, this effect tends to disappear after about 100 years of conventional cultivation, and meanwhile, the decline in BS% due to increased acids should also be noted.

Study on the effect of deformation and strength characteristics of microencapsulated phase change materials on modified silty clay under dry and wet cycles

To prevent the cracking of silty clay under cyclic wet-dry cycling (W-D), which leads to the increase of deformation and strength attenuation of silty clay, microencapsulated phase change material (mPCM) was used to improve it. The deformation and strength characteristics of silt with different dosages (1 %, 2 % and 4 %) of mPCM and their changing patterns were analyzed and studied by indoor compaction test, crack observation test, consolidation test and straight shear test, and compared with silt without modifier. The results showed that with the increase of mPCM dosage, the optimum water content of silt and the maximum dry density decreased. A 2 % dosage of mPCM inhibited the development of silty clay cracks, reduced crack width and deformation, and increased the compression modulus of the soil samples by nearly 2.3 times. Under dry and wet cycling conditions, the cohesion decay of silty clay is greater than the angle of internal friction. The addition of 2 % mPCM significantly increased the shear strength of silty clay, cohesion by nearly 2.1 times, and internal friction angle by 1.4 times. The mPCM inhibits crack development mainly by regulating the internal temperature field of soil samples, thus improving soil strength. This study provides a reference for inhibiting soil cracking from a new temperature perspective.

Effect of Silt Content on Mechanical Behavior of River Gravel Material

Effect of Silt Content on Mechanical Behavior of River Gravel Material

Mapping Soil-crop Nutrient Dynamics in Continuous Cotton Cultivation Areas of Sangareddy District, Telangana

A soil-crop nutrient survey was conducted in Sangareddy district, Telangana during Rabi, 2023 to study the fertility status of cotton growing soils at 0-15 cm and 15-30 cm depth. Georeferenced soil and leaf samples were collected from 112 sites covering 23 mandals during flowering stage. Soil physical and physico-chemical properties and cotton leaf nutrient content were analysed. In the study area, bulk density, sand, silt and clay content ranged from 1.36-1.66 Mg m-3, 41.84-78.02 %, 6.74-29.60 % and 10.76-42.76 %, respectively. The soils were strongly acidic to slightly alkaline in reaction (4.25-7.98; 4.44-7.97), non-saline (0.026-0.254 dS m-1; 0.020-0.241 dS m-1) and low to high in organic carbon (2.03 to 11.52 g kg-1; 1.13 to 8.24 g kg-1) at 0-15 cm and 15-30 cm depths, respectively. Soil Nutrient Status: Available nitrogen, phosphorus, potassium and exchangeable magnesium contents ranged from 101 to 448 kg ha-1, 7 to 41 kg ha-1, and 154 to 763 kg ha-1 at 0-15 cm depth, respectively while 91 to 319 kg ha-1, 4 to 39 kg ha-1, and 142 to 730 kg ha-1 at 15-30 cm depth, respectively. Crop Nutrient Status: Total nitrogen, phosphorus, potassium and magnesium contents in the collected cotton leaves ranged from 0.53 to 3.64 %, 0.12 to 0.68 %, 0.80 to 1.84 % and 0.27 to 0.92 %, respectively. Soil Fertility Mapping: Soil fertility maps were prepared considering the low/deficient, medium and high/sufficient range of soil macronutrients (N, P, K and Mg) at the depth of 0-15 cm under QGIS 3.36.1. Soil fertility maps showed that soils of the study area were mostly low in available nitrogen, medium in available phosphorus, high in available potassium and sufficient in exchangeable magnesium. Regular soil testing is crucial to monitor the variations in nutrient levels and formulation of appropriate management strategies to address it.

Random forest approach to estimate soil thermal diffusivity: Evaluation and comparison with traditional pedotransfer functions

Pedotransfer functions (PTFs) are widely used in science and engineering to calculate soil thermal diffusivity (κ) as a function of soil moisture content and other soil physical properties. However, available data on κ are rather scarce and already developed PTFs suffer from significant uncertainties in applicability and performance. Few studies attempted to systematically review and evaluate these PTFs with large dataset of κ. The objectives of this study were to (1) review and collate currently available PTFs for κ; (2) compile a κ dataset to evaluate performance of these PTFs, (3) build a machine learning (ML) (i.e., random forest-RF) model with three classes of soil physical properties to determine soil physical properties most appropriate to use as predictors for calculating κ, and (4) compare the RF modelling with the traditional PTFs. The correlation coefficient (r), centered root-mean-square (E'), and standard deviation (SD) were used to evaluate the performance of the above PTFs. A total of nine PTFs were synthesized and assessed with a compiled dataset consisting of 998 measurements from 106 soils with a wide range of textures. In general, the PTFs of Lukiashchenko and Arkhangelskaya (2018) (LA2018–1 and LA2018–2) perform the best. While the quadratic equation of Mady and Shein (2018) (MS2018–1 and MS2018–2) showed better performance for coarse and medium textured soil. However, the overall performance of the nine PTFs for predicting κ was not as accurate as RF. RF can satisfactorily calculate κ taking into account sand, silt, clay contents, and soil moisture content as predictors.

Interaction Effects of Vegetation and Soil Factors on Microbial Communities in Alpine Steppe Under Degradation

To clarify the regulating effect of vegetation and soil factors on microbial communities in the alpine steppe under degradation on the Qinghai-Xizang Plateau, the alpine steppe in the Sanjiangyuan area of the Qinghai-Tibet Plateau was chosen. We analyzed the differences in vegetation and soil factors in different stages of degradation （non-degradation, moderate degradation, and severe degradation） and detected the variations in microbial community characteristics in the alpine steppe under different degradation stages using high-throughput sequencing technology. Eventually, redundancy analysis （RDA） and multiple regression matrixes （MRM） based on the similarity or dissimilarity matrix were used to identify key environmental factors regulating microbial （bacterial and fungal） community changes under degradation. The results showed that the degradation of the alpine steppe significantly changed the community coverage, height, biomass, and important value of graminae； significantly reduced the contents of soil organic matter, total nitrogen, total phosphorus, and silt； and increased the soil bulk density and sand content. Degradation did not change the composition of bacteria and fungi, but their composition proportions changed and also resulted in the loss of microbial richness （Chao1 index and Richness index） but did not significantly change the microbial diversity （Shannon index）. With the occurrence of degradation, the vegetation characteristics, soil physicochemical properties, and microbial diversity showed a consistent change trend. Combined with the characteristics of the network topology changes （the number of nodes and clustering coefficient significantly decreased）, it was found that degradation of the alpine steppe led to the decline of interspecies interactions, decentralization of network, and homogenization of microorganisms, but the cooperation relations among the species were maintained （positive correlation connections accounted for more than 90% in all degradation stages）. Under the alpine steppe degradation, the vegetation-soil interaction had the greatest effect on soil bacterial community, whereas soil physicochemical properties had the greatest influence on soil fungal community. Specifically, vegetation community height, biomass, and soil bulk density were the mutual factors regulating soil microorganisms, whereas the vegetation Simpson index, important value of graminae, soil total phosphorus, total potassium, and silt content were the unique factors affecting the soil bacterial community, and soil pH and total nitrogen content were the particular factors affecting the soil fungal community.

Effects of Long-term Fertilizations on the Organic Carbon Components of Soil Macroaggregates and the Yield of Wheat in Wheat Fields on the Loess Plateau

Soil macro-aggregates are the main location for soil organic carbon （SOC） sequestration, which is of great significance to improve soil fertility. This study aimed to understand the mechanisms of the organic carbon （OC） sequestration in macroaggregates and improve crop yield in wheat fields on the loess plateau. With the aggregate-density fractionation method, an eight-year experiment was conducted to investigate the following three factors： ① the effects of long-term fertilization on OC fractions within macroaggregates； ② the variation characteristics of OC fractions within macroaggregates, including coarse particulate organic carbon （cPOC）, fine particulate organic carbon （fPOC）, intra-microaggregate particulate organic carbon （iPOC）, free silt and clay particulate carbon （s+c_f）, and intra-microaggregate silt and clay particulate carbon （s+c_m）； ③ and the relationships between them and SOC input and yield formation. The treatments included no fertilization （CK）, farmer pattern （NP）, optimized fertilizers pattern （NPK）, optimized fertilizers + organic fertilizers pattern （NPKM）, and optimized fertilizers + biological organic fertilizers pattern （NPKB）. The results showed that the application of organic and chemical fertilizer （NPKM and NPKB） improved significantly the SOC content in macroaggregates compared with that in the single fertilizer treatment （NP and NPK）, which had a greater increase in SOC content in macroaggregates than that of the soil. All fertilization treatments had a tendency to increase the content of fractions iPOC, fPOC, and iPOC in macroaggregates, but silt and clay carbon （s+c_f and s+c_m） contents were decreased. The application of manure combined with chemicals markedly increased the allocations of fractions cPOC, fPOC, and iPOC reserves, but it greatly decreased （s+c_f） reserves allocation. However, the application of chemical fertilizers only significantly increased the proportion of cPOC reserves in macroaggregates. Correlation analysis showed that there were significant positive correlations among wheat grain yield and OC fractions （cPOC and fPOC） contents, SOC content, the OC content of >0.25 mm macroaggregates, and SOC input, and the correlation coefficient was 0.645-0.883. In conclusion, long-term fertilization, especially combined with organic fertilizer, could promote the free silt and clay carbon fraction （s+c_f） to transfer into other forms of OC components through the increase in soil carbon input in the wheat field of the loess plateau. Furthermore, the OC content of macroaggregates was increased overall, providing a good soil environment for crop yield.

Kinetic and Mechanistic Modelling of Pyrimethanil Fungicide Adsorption onto Soils of Varying Physico-chemical Properties.

Pesticide transport in the environment is impacted by the kinetics of its adsorption onto soil. The adsorption kinetics of pyrimethanil was investigated in ten soil samples of varying physicochemical properties. The highest adsorption was in the soil having the maximum silt and CaCO3 contents, pH and electrical conductance but the lowest amorphous Fe oxides and CaCl2 extractable Mn. Pseudo-second order kinetics and intra-particle diffusion model best accounted the adsorption kinetics of pyrimethanil. The equilibrium adsorption estimated by pseudo-second order kinetics (q02) was significantly and positively correlated with CaCl2 extractable Cu content (r = 0.709) while rate coefficient (k02) had a negative correlation with crystalline iron oxides content (r = -0.675). The intra-particle diffusion coefficient (ki.d.) had inverse relationship with CaCl2 extractable Mn content in soils (r = -0.689). FTIR spectra showed a significant interaction of pyrimethanil with micronutrient cations. Adsorption kinetic parameters of pyrimethanil could be successfully predicted by soil properties. The findings may help to evolve fungicide management decisions.

Water retention curves of sandy soils obtained from direct measurements, particle size distribution, and infiltration experiments

AbstractAccurate information about soil water retention curves (SWRCs) of sands is essential for evaluating groundwater recharge and vulnerability to contamination in many shallow sandy aquifers which are widespread on post glacial areas in Northern Europe and North America. Pedotransfer functions (PTFs) allow to estimate SWRC from basic physical characteristics of soils, such as textural composition. However, in the case of clean sands which are dominated by a single textural fraction, PTFs should be based on more detailed information given by the particle size distribution. In this study we evaluated three parametric PTFs, which estimate parameters of the van Genuchten SWRC based on empirical correlations to the parameters of soil particle size distribution, and five semi‐physical PTFs, which derive the pore size distribution from particle size distribution. PTFs were compared to SWRCs fitted to the results of drainage experiments on sandy soil samples from six locations in Gdańsk region (northern Poland). Although in all samples the content of silt and clay fractions was low (<3.5%), the differences in actual content of fines strongly influenced the shape of SWRC. In contrast, the amount of gravel fraction (varying from 1% to 35%) did not have significant effect on SWRC. Semi‐physical PTFs were found to be more accurate than parametric PTFs. The best overall performance was shown by the semi‐physical Chang and Cheng PTF. Among the parametric PTFs the best accuracy was obtained with the Schaap and Bouten method. However, all considered functions showed limited accuracy in higher suction range. Additionally, infiltration experiments were performed on four sites. SWRCs were obtained from ring infiltrometer tests using the Beerkan estimation of soil transfer parameters (BEST) method and from the tension infiltrometer (TI) tests using numerical solution of the inverse problem based on the Richards equation. In almost all cases the wetting SWRCs were characterized by higher values of the pressure scaling parameter α compared to SWRCs measured in drainage experiments, which is consistent with the well‐known phenomenon of hysteresis in soils. However, the BEST method resulted in significantly higher α and hydraulic conductivity Ks than TI, probably due to activation of the largest soil pores during ponded infiltration.