Dry Soil Samples Research Articles

Restoration of the properties of organomineral gels in dried soil samples

It was previously established that soil drying changes their properties and, in particular, the characteristics of a specific soil organic substance – humic substances (HS). HS is the basis of soil organomineral gels that cover and bind soil particles. When water is removed from the soil, hydrophobization and compression of gels occur, as a result of which the properties of soil samples may change. The restoration of soil gels of air-dry samples should reduce the discrepancy between the data obtained when studying the soil properties of dried and non-dried soil samples. The purpose of the work is to find ways to restore the structure of soil gels. Samples of 6 types of soils were studied. Methods of vibration viscometry, laser diffractometry, scanning electron microscopy (SEM), photocolorimetry and conductometry were used in the work. It has been found that drying of soil samples increases the size of supramolecular formations (SMFs) from the soil and reduces the viscosity of soil pastes, a parameter characterizing the structure and ability of gels to swell. To restore the structure of soil gels, it is proposed to reduce the size of the SMFs from the HS to the initial ones. SMFs separation of air-dry samples was carried out by moistening the soils and subsequent treatment with various influences: temperature, ultrasound and freezing. Using SEM, it is shown that heating and ultrasound treatment do not reduce, but increase the size of the SMFs. Humidification of air-dry soils, exposure to moisture for 2 weeks and subsequent freezing bring the viscosity of pastes of a number of studied soils closer to the condition of samples that have not been dried. This process occurs due to the return of the SMFs size to the values of the initial soils, as evidenced by the data on the distribution of the size of suspended particles on a laser diffractometer. Thus, a method for restoring gel structures in dried soils to the state of the original soils is proposed.

Comparative Study of Back-Propagation Artificial Neural Network Models for Predicting Salinity Parameters Based on Spectroscopy Under Different Surface Conditions of Soda Saline–Alkali Soils

Soil salinization typically exerts a highly negative influence on soil productivity, crop yields, and ecosystem balance. As a typical region afflicted by soil salinization, the soda saline–alkali soils in the Songnen Plain of China demonstrate a clear cracking phenomena. Nevertheless, the overall spectral response to the cracked soil surface has scarcely been studied. This study intends to study the impact of salt parameters on the soil cracking process and enhance the spectral measurement method used for cracked salt-affected soil. To accomplish this goal, a controlled desiccation cracking experiment was carried out on saline soil samples. A gray-level co-occurrence matrix (GLCM) was calculated for the contrast (CON) texture feature to measure the extent of cracking in the dried soil samples. Additionally, spectroscopy measurements were conducted under different surface conditions. Principal component analysis (PCA) was subsequently performed to downscale the spectral data for band integration. Subsequently, the prediction accuracy of back-propagation artificial neural network (BP-ANN) models developed from the principal components of spectral reflectance was compared for different salt parameters. The results reveal that salt content is the dominant factor determining the cracking process in salt-affected soils, and that cracked soil samples had the highest model prediction accuracy for different salt parameters rather than uncracked blocks and 2 mm comparison soil samples. Furthermore, BP-ANN prediction models combining spectral response and CON were further developed, which can significantly enhance the prediction accuracy of different salt parameters with R2 values of 0.93, 0.91, and 0.74 and a ratio of prediction deviation (RPD) of 3.68, 3.26, and 1.72 for soil salinity, electrical conductivity (EC), and pH, respectively. These findings provide valuable insights into the cracking mechanism in salt-affected soils, thereby advancing the field of hyperspectral remote sensing for monitoring soil salinization. Furthermore, this study also aids in enhancing the design of spectral measurements for saline–alkali soils and is also helpful for local soil remediation with supporting data.

Alleviation of Drought Stress in Zea Mays Seeding by Extracellular Polysaccharides Produced by Azotobacter

Azotobacter bacteria are Gram-negative, rod-shaped soil bacteria that can fix atmospheric nitrogen and promote plant growth. They are known to produce exopolysaccharides (EPS), which can help plants to retain water and tolerate drought stress. In this study, 20 Azotobacter isolates were isolated from dry and semi-dry soil samples. The isolates were characterized morphologically and microscopically and tested for their tolerance to drought using PEG6000 at concentrations of 0%, 5%, 10%, 15%, 20%, and 25%. They were also tested for their ability to produce EPS. The isolates that were most tolerant to drought and produced the most EPS were used as biofertilizers to improve the drought tolerance of maize seedlings. The seedlings were irrigated every 24, 48, or 72 hours. The results showed that the biofertilizers containing the two most drought-tolerant and EPS-producing Azotobacter isolates (Azotobacter 1 and Azotobacter 2) significantly improved the germination rate, dry weight, stress tolerance index, chlorophyll stability, and membrane damage index of the maize seedlings.

Effect of vegetation cover and soil moisture on water repellency persistence of drained peat soils

AbstractPeatlands play an essential role in the environment and their conservation requires knowledge of water-related processes. Their hydrophobicity is an important factor influencing the flow of water in soils. Drainage of fens and adapting them for agricultural production have contributed to the degradation of peat soils. The main goal of this study was to evaluate the effect of vegetation cover (meadows, alder swamp and birch forest) of drained peat soils on the persistence of their water repellency as a function of their moisture content. Soil samples (Rheic Fibric Histosol and Rheic Murshic Hemic Histosol) were collected from four sites in the Biebrza Wetlands (Poland). The sites include extensive meadows, intensive meadows, alder swamp forest, and secondary birch forest. The study used the Water Drop Penetration Time (WDPT) to assess the persistence of soil water repellency during the drying of soil samples in laboratory conditions. The study proposed a three-straight lines model to describe the relationship between WDPT and soil moisture content. Results revealed that the peat soils in intensive meadows showed the highest WDPT, followed by the secondary birch and alder swamp forest. and then by semi-natural meadows, which has the lowest WDPT.

The soil sample conservation method and its potential impact on ammonium, nitrate and total mineral nitrogen measurements

Scientific literature for mineral soil nitrogen content (ammonium and nitrate) measurements usually differs regarding the chosen soil sample conservation method (fresh, frozen or air-dry) before soil extraction and analysis. In addition, most of the commonly used methodologies focus on the definition of the analysis processes, regardless of the previous sample conservation methodology. With the aim to fill this gap, we performed an analysis of the conservation method effect on the nitrate, and ammonium content in frozen or air-dried samples, comparing the results with direct fresh extraction. Moreover, the effects of additional soil parameters, such as soil texture, organic matter content or mineral nitrogen content range were also studied. The results showed that both, frozen and air drying soil samples, were capable of reliably preserving the N content of soil samples in most cases. However, some ammonium losses may occur in frozen samples when a high N content (>30 mg kg−1) is present. It was also observed that air-dried soil samples can reduce the soil nitrate and increase ammonium content in samples with a high N content. It was also observed that significant amounts of organic matter in soil can alter the mineral N measured depending on the conservation method chosen. On the other hand, the soil texture presented small effects on the mineral N measurements. In any case, a broader range of soils conditions (including i.e. pH or different natural organic matter content) should be further tested to confirm our findings.

Recovery of Organomineral Gel Properties in Dried Soil Samples

Recovery of Organomineral Gel Properties in Dried Soil Samples

Conversion of Marginalized Tea Lands into Timber Plantations and its Impact on Soil Organic Carbon Content: A Case Study in Central Highlands of Sri Lanka

Tea (Camellia sinensis) plantations can be in production for about 100 years under well-managed conditions. However, soil degradation makes the tea lands marginalized and uneconomical. Land use conversion is an option for marginalized tea lands that could gain numerous environmental and economic benefits. This study estimated and compared the soil organic carbon stocks (SOC) in the surface soil layer (0–30 cm) of marginalized tea lands against those converted to timber plantations in the Badulla and Nuwaraeliya districts. Disturbed and undisturbed soil samples were collected from the top layer (0-30 cm) of the selected sites of marginalized tea lands and those converted to timber plantations for analysis. Organic matter percentage in the site-level dry soil samples was analysed using the loss on ignition method. The final carbon stocks (t C ha-1) for samples from both land use types were then estimated using the SOC percentages and bulk density estimates. The carbon stocks for the soil samples collected at marginalized tea lands and the marginalized tea lands converted to timber plantations were then statistically analysed (descriptive statistics, normality tests and two-sample t-tests) using Minitab 21.2 software. The pH of marginal tea lands (5.34±0.14) and the marginal tea lands converted to timber plantations (5.20±0.13) was slightly acidic in nature. The bulk density estimates of marginalized tea lands and marginalized tea lands converted to timber plantations were very close and estimated as 1.15±0.02. The statistical analysis showed that overall mean SOC was significantly higher under the marginalized tea lands converted to timber plantations (155.3±10.0 t C ha-1) compared to the marginalized tea lands (126.7±5.8 t C ha-1). Land use conversion has increased the SOC stocks gradually after forest establishment. The increased carbon stocks could be attributed to consistent addition of litter layer on the soil, less soil disturbance, and retarded soil erosion contributing to the buildup of soil organic matter and SOC. On the other hand, low carbon stocks in the marginalized tea land could be attributed to unsustainable agronomical practices. Our findings suggest that converting marginalized tea lands in central highlands into timber plantations could enhance the sequestration of atmospheric CO2, significantly contributing to climate change mitigation.
 Keywords: Soil organic carbon, Land use change, Climate change, Tea plantations

Smart soil image classification system using lightweight convolutional neural network

In the agriculture sector, soil classification plays a significant task, as it helps in soil tillage, crop selection, moisture level estimation, and automation. Conventionally, soil classification is carried out with the help of physical, chemical, and biological characteristics of the geo-referenced and mapped soil. Soil classification by conventional and laboratory methods is time-consuming, high-cost, and requires proficiency. This study presents a quick and cost-effective prediction of soil type by using soil images. A soil image dataset has been created to classify the soil type using images. To create the soil image dataset, 392 soil samples are collected from different agricultural fields in Andhra Pradesh, India. The collected samples are dried and the soil type is identified using a sieve and hydrometer analysis in the laboratory. An imaging setup has been made to capture the images of the dried soil samples using a smartphone camera. The captured images are pre-processed using: RGB extraction, and V extraction from HSV bins, and adaptive histogram are applied to highlight the texture features of the soil images. A novel lightweight convolutional neural network called Light-SoilNet is proposed to classify five soil sample images: sand, clay, loam, loamy sand, and sandy loam. The proposed network is designed to take care of the imbalanced soil image dataset. The proposed network is tested and compared with state-of-the-art lightweight and pre-trained deep learning networks. The proposed Light-SoilNet network architecture has produced an overall accuracy of 97.2% in classifying the soils. The comparison of the results shows the performance of the proposed model using the image and deep learning techniques in classifying the soil types.

Novel experimental technique to evaluate soil thermal conductivity in a transient state

novel experimental technique to evaluate soil thermal conductivity in a transient state

Isolation and morphological characterisation of Bacillus spp. from different cereal rhizoshere soil

Bacillus spp. are widely used in agriculture in order to promote the plants growth. Considering this, the present study was carried out to isolate and characterize the Bacillus spp. from different cereal rhizosphere soil. Twenty Bacillus spp. were isolated from rhizosphere soil samples collected from Raichur and Gulbarga district. Dried soil samples were analysed for the pH, EC, organic carbon content and enzyme activity. All the isolates were Gram positive, rod shaped and motile in nature.

Development of an express method for measuring soil nitrate, phosphate, potassium, and pH for future in‐field application

AbstractBackgroundIn practical farming, there is often a need for short‐term availability of information on the soil nutrient status.AimsTo develop a new express method for the extraction of major plant‐available nutrients and measurement of soil nutrients. In future, this method shall serve for in‐field measurements of soil samples with an ion‐sensitive field‐effect transistor (ISFET).MethodsVarious extraction conditions such as type of extractant, soil‐to‐solution ratio, time, and intensity were investigated on a broad selection of dried soil samples in the laboratory. Based on 83 field‐moist soil samples with varying clay contents, these conditions were compared to standard laboratory methods.ResultsWith increasing extraction time, the nutrient concentrations increased. When the soil‐to‐solution ratio was reduced, a greater share of nutrients was extracted, independent of soil type. H2O and 0.01 M CaCl2 and standard calcium‐acetate‐lactate (CAL) solution proved to be too weak in the short period to reach the ISFET sensor measurement range. Higher concentrated CAL solutions performed much better. Finally, a 5‐min CaCl2 extraction followed by the removal of an aliquot for the determination of soil pH and NO3− was found to be effective. The remaining solution was then mixed with 0.20 M CAL solution for the analysis of H2PO4− and K+ at 10 min of extra extraction time. This extraction method showed very good correlations with the values based on the German laboratory reference methods for pH (R2 = 0.91) and for nitrate (R2 = 0.95). For phosphorus and potassium, we obtained an R2 of 0.70 and 0.81, respectively, for all soils. When soils were grouped according to clay content higher correlations were found.ConclusionsA new express method based on a wet‐chemical approach with a soil preparation procedure was successfully developed and validated. This seems to be a valuable basis for future in‐field measurements via ISFET.

Behavior of Reinforced Gypseous Soil Embankment Model under Cyclic Loading

The construction of embankment for roadway interchange system at urban area is restricted due to the large geometry requirements, since the value of land required for such construction is high, and the area available is limited as compared to rural area. One of the optimum solutions to such problem is the earth reinforcement technique which requires a limited area for embankment construction. Gypseous soil from Al-Anbar governorate area was obtained and subjected to various physical and chemical analysis to determine it is properties. A laboratory model box of 50x50x25 cm was used as a representative embankment; soil has been compacted in five layers at maximum dry density (modified compaction) and an aluminum reinforcement strips were introduced between layers. The model was subjected to cyclic loading and the vertical and lateral deformations were detected at different stages of loading cycles using LVDT. The reinforced soil embankment under soaking condition exhibited vertical settlement at the top surface was (12.55 mm) while the lateral displacements at (1st, 3rd layer) were (2.18, 1.32) mm respectively at (47 load cycles).For reinforced gypseous soil, embankment without soaking cured for 24 hours, the Number of load cycles was found to be (165) loading cycles with vertical displacement (9.12 mm), that means an improvement of 59%. Accordingly, the lateral displacement in 1st and 3rd layers were (3.28, 2.59) mm respectively which observes improvement by (28% and 5%) respectively. The rates of improvement are taken with respect to the reinforced pure dry soil sample.

Modelling Soil Color and Soil Fertility Relations on Cultivated Semi-Arid Sloping Landscapes

Soil color is a critical property, providing important information on soil properties. Soil color highly spatially varies on cultivated semi-arid sloping landscapes, indicating differences in soil properties that affect soil fertility. This study evaluated the relationships between color variables (L*: soil brightness, a*: redness, and b*: yellowness) and some basic soil properties on air dry and wet (around field capacity) soil samples, in a semi-arid sloping landscape having been under wheat cultivation for a long time. The values of color variables and soil properties were graphed and relationships between them were modeled using most proper regression models. The soil properties were poorly related to values of a* and b*, while CaCO3, sand, clay, and K contents and EC were highly significantly correlated with values of L*-wet (L*-values obtained on moist soil samples). Soil EC and CaCO3 content can be safely predicted by L*-wet in the study area. Also, the L*-wet should be preferred over L*-dry in predicting soil properties from soil color components in the study area and similar soils.

MART-soil: A modified analytical radiative transfer mode for simulating multi-angular reflection of soils with different particle size

Understanding the angular reflection properties of soils and quantifying their particle size distribution through physical models is valuable for ecological and agricultural applications in arid and semi-arid regions. An analytical radiative transfer (ART) model has considered the absorptivity, k, as a constant to simulate the angular reflection of soil at two measurement directions and invert the soil particle size. However, the limited viewing directions cannot reflect the key role of ART in characterizing the effects of particle size on angular reflection. In this study, an ART model was first assessed based on a multi-angular soil reflection dataset from six types of dry soil samples with different particle size. The absorptivity, k, an inverted model parameter, was found to be strongly dependent on the angular distribution of soil reflection for different soil types. Subsequently, we coupled the ART model with a semiempirical function that can characterize the angular distribution of k, to improve the model’s ability in simulating the multi-angular reflection of soil. The modified ART model (defined as MART-Soil) made it possible to generate satisfying simulated multi-angular soil reflections with different particle sizes from all six soil types. Moreover, the model parameters of MART-Soil have the potential for representing different soil types in the simulation. These results indicate that MART-Soil model can quantify angular reflection properties of soils with different particle size and may also be useful for characterizing the radiative transfer process in land surfaces.

Influence of terms and methods of storage on changes in enzyme activity of meadow chernozem-like soil on Zeya-Bureya plain

The analysis of the biological and enzymatic properties of soils is an important aspect of soil ecology, but the results of studies can be strongly influenced by the storage conditions of the samples. Variation in storage methods and duration studies reduces the ability to accurately interpret data and compare results. This work presents a study of the influence of various storage conditions and time on the results of the activity of enzymes of hydrolase class (urease, phosphatase), and oxidoreductases class (catalase, peroxidase and polyphenoloxidase) of meadow chernozem-like soil of the Zeya-Bureya plain. For the research, a laboratory experiment was performed, in which naturally moist and air-dry samples were taken and stored under various conditions: room temperature (+23 – +25 °С), low positive temperature (refrigerator, +10 °C), negative temperature (freezer, –10 °С) within 7, 28, 90 and 365 days. As a result, we found that drying soil samples immediately after sampling increases the activity of urease, phosphatase, peroxidase and polyphenoloxidase. Storage conditions do not significantly affect the activity of enzymes. To assess the actual enzymatic activity, we recommend using naturally moist soil samples immediatel y after sampling; to assess potential enzymatic activity, drying the soil and storing it for no more than 7 days are recommended. The results of this study provide useful information on the impact of sample storage conditions for researchers of enzyme activity in similar climates and contribute to further consideration and discussion of the implications of sample storage.

Optimization of headspace solid-phase microextraction of volatile organic compounds from dry soil samples by porous coatings using COMSOL Multiphysics

Headspace solid-phase microextraction (HSSPME) is one of the simplest and cost-efficient sample preparation approaches for determination of volatile organic compounds (VOCs) in soil. This study was aimed at the development of the model for numerical optimization of HSSPME of volatile organic compounds from dry soil samples by porous coatings using COMSOL Multiphysics (CMP). ‘Transport of Diluted Species in Porous Medium’ physics was used for modeling. Effect of sample mass, pressure, fiber-headspace and soil-headspace distribution constants on extraction profiles and time of 95% equilibrium has been studied using the developed model. Equilibrium extraction under atmospheric pressure (1 atm) can take up to 97 min, while under vacuum (0.0313 atm) – 2.3 min. Equilibration time under vacuum was 42-43 times lower than under 1 atm at all studied distribution constants and sample masses. The developed model was modified for optimization of pre-incubation time using ‘Transport of Diluted Species’ physics. According to the obtained plots, 95% equilibration time can reach 13.3 min and depends on both sample mass and soil-headspace distribution constant of the analyte. The developed model can be recommended for optimization of pressure, preincubation and extraction time when fiber-headspace and soil-headspace distribution constants, soil porosity and density are known.

Development of a Method for Preservation, Extraction, and Quantitation of 4,4’-Methylenedianiline in Soils of Varied Texture and Organic Matter Content

ABSTRACT Despite the reactivity and degradability of 4,4’-methylenedianiline (4,4’-MDA) with organic matter in soils, the improvement of Brunet et al. method makes it possible to monitor the substance in the environment. In fact, 4,4’-MDA can be found in soils, used for agriculture for example, if 4,4’-methylene diphenyl diisocyanate (4,4’-MDI) based fertilizers and pesticides are used. The possible degradation of 4,4’-MDI-based polyurethane could result in formation of 4,4’-MDA. To have a soil where 4,4’-MDA could be uniformly distributed, an impregnation method was developed. The method was validated using 1 g of the impregnated soil ranging 2,4% to 10% of organic matter, and the addition of both internal and surrogate standard to correct any losses of the substance over the different steps of the method. The limit of detection (LOD) and limit of quantification (LOQ) for the determination of 4,4’-MDA in soil are 0.107 and 0.358 μg/kg (dry weight), respectively, with a dynamic range between 5 and 250 μg/kg. For the intra-day precision, the result was 4,28% and for the inter-day, it was 9,32%. The accuracy was 96,4% and the total recovery obtained was 82.02%. Moreover, the stability of 4,4’-MDA was demonstrated in dry soil samples or in samples completely immersed in methanol over a 14-day period.

Dongia deserti sp. nov., Isolated from the Gurbantunggut Desert Soil.

A Gram-stain-negative, aerobic, short rod-shaped strain, designated as SYSU D60009T, was isolated from a dry sandy soil sample collected from the Gurbantunggut Desert in Xinjiang, northwest China. Strain SYSU D60009T was observed to grow at 15-42°C (optimum at 37°C), pH 4.0-10.0 (optimum at 7.0), and with 0-0.5% (w/v) NaCl (optimum, 0%). The strain grew well on R2A agar, and colonies were smooth, white-pigmented, and circular with low convexity. The polar lipids consisted of phosphatidylethanolamine, aminolipid, aminophospholipid, and unknown lipids. The major cellular fatty acid (> 10%) was C16:0 and the predominant respiratory quinone was Q-10. Whole genome sequencing of strain SYSU D60009T revealed 6,132,710bp with a DNA G + C content of 63.6%. The ANI and dDDH values of strains SYSU D60009T to Dongia mobilis CGMCC 1.7660T were 72.8% and 19.0%, respectively. Based on the phenotypic, phylogenetic, and chemotaxonomic properties, strain SYSU D60009T represents a novel species of the genus Dongia, for which the name Dongia deserti sp. nov. is proposed, the type strain is SYSU D60009T (= CGMCC 1.16441T = KCTC 52790T).

Using Nix color sensor and Munsell soil color variables to classify contrasting soil types and predict soil organic carbon in Eastern India

Optimal soil management depends on rapid and frequent monitoring of key soil properties, which are conventionally measured in the laboratory using laborious wet-chemistry protocols. The Nix color sensor has recently exhibited promise for predicting several soil properties using soil color. This study evaluated the relationship between the Munsell Soil Color Chart (MSCC) color values of dry and ground surface soil samples to those reported by the Nix color sensor with (NixSTD) and without MSCC standardization (NixNON-STD) to classify 371 samples collected from three contrasting soil types, collected from three agro-climatic zones (coastal saline zone, red and laterite zone, and Gangetic alluvial zone) and to predict soil organic carbon (OC) using different multivariate data mining algorithms. Comparing the CIEL*a*b* color values reported by the MSCC and the NixSTD, an acceptable mean color difference (ΔE*ab) value of 5.20 was obtained, indicating the potential accuracy of the Nix sensor. Principal component analysis efficiently clustered the soil types using the RGB variables extracted from the MSCC color chips in tandem with the NixSTD/NixNON-STD data. Both classification tree and linear support vector machine algorithms perfectly classified all three contrasting soil types using NixNON-STD data alone. Besides, the combination of the MSCC and the NixNON-STD datasets produced the best OC prediction (R2 = 0.66) via random forest (RF) algorithm and indicated the potential of Nix in digital soil morphometrics. In most of the RF models, redness (a*), yellowness (b*), and yellow (Y) variables appeared influential, presumably because of their negative correlation with OC in red and laterite soils. More research is warranted to measure the impacts of variable soil moisture and other confounding soil morphological features on the soil classification and OC prediction performance to extend the approach for classifying soil types and predicting OC in-situ.

Diagnostic and Survey Study of Toxoplasma gondii in the Soil in Mosul City-Iraq

The present study deals to diagnose Toxoplasma gondii cysts in the soil of Mosul city, with a total of 50 samples were collected from each side of the city and three replications using flotation methods and microscopic methods. The study aims to detect contamination Toxoplasmosis oocysts, in the soil with making a comparison between the right and left side of the city and determine the effect of environmental factors such as humidity on the presence of the parasite through making a comparison between wet and dry soil samples. Molecular methods confirmed the presence of infection where the microscopic examination explained the presence of 20 positive samples out of 100 samples for both sides of the city where 11 positive cases found in the right side and 9 positive samples in the left side. The difference was clear between the wet samples with a rate of (14/48) positive samples, while the number of positive dry samples was (6/52) that confirmed infected 8 of the positive samples were subjected to a molecular examination using the polymerase chain reaction (PCR). Furthermore, the results showed the presence of 3 positive samples for B1 gene of the Toxoplasma parasite. The study proved the presence of soil contamination with parasite oocysts, which gives a measure of the high incidence of toxoplasmosis in Mosul city and the possibility of transmitting the infection to the rest of the intermediate hosts including humans.