Soil Properties Research Articles

Multi-Omics Analysis Reveals Molecular Responses of Alkaloid Content Variations in Lycoris aurea Across Different Locations.

Lycoris aurea, celebrated for its visually striking flowers and significant medicinal value due to the presence of alkaloids such as lycorine and galanthamine, has intricate yet poorly understood regulatory mechanisms. This study provides a detailed examination of the transcriptomic, metabolomic and ecological dynamics of L. aurea, aiming to elucidate the underlying molecular mechanisms of alkaloid biosynthesis. Our comparative analysis across different ecological settings highlighted key genes involved in alkaloid biosynthesis, such as genes encoding aldehyde dehydrogenase and norbelladine 4'-O-methyltransferase, which were distinctively increased in the high alkaloids-producing group. We identified a total of 6871 differentially expressed genes and 915 metabolites involved in pathways like terpenoid backbone biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis. Protein interaction network analysis revealed significant upregulation of photosynthesis, photosystem and photosynthetic membrane pathways in the alkaloids-producing region. Furthermore, our research delineated the interactions among soil microbial communities, genes and plant and soil biochemical properties, noting that bacterial populations correlate with soil properties that favour the activation of metabolic pathways essential for alkaloid production. Collectively, this study advances our understanding of the genetic and metabolic alkaloid biosynthesis pathways in L. aurea, shedding light on the complex interactions that govern alkaloid production.

Overexpressing Carbonic Anhydrase Transgenic Rice Plants Maintain the Regular Soil Functions and Microbial Activities of Soil

Background: Genetically modified (GM) crops are focused on establishing desirable traits to support the food demand of increasing populations. The effect of transgenic plants on soil diversity has been highlighted by a lot of researchers. Some of them showed the negative effect of transgenic crop on soil microbes and the food chain. So there is an urgent need to develop transgenic plants with no harmful effects on environmental health. Methods: We have examined the effects salt tolerant carbonic anhydrase overexpressing transgenic rice plants on the physiology of rhizospheric microbes and their enzymatic activities. In the present study, we have used pot experiments in the greenhouse of Centurion University of Technology and management, Bhubaneswar, Odisha, India. Result: No significant variation in the soil properties viz., pH, Eh, organic C, P, K, N, Ca, Mg, S, Na and Fe+2 were observed. The population of bacteria, fungi and nematodes were remained same in the soil. The enzymatic activities like soil dehydrogenase, nitrate reductase, urease and alkaline phosphatase were not significantly affected. Further, plant growth promotion (PGP) functions such as HCN, siderophore, salicylic acid, GA, IAA, zeatin, were, not influenced considerably. The present study ecologically pertinent of salt tolerant CA transgenic rice to usual functions of the rhizospheric organisms.

Facilitating effects of plant extracts on soil health and replanted Panax ginseng growth in recession soil.

Plant extracts have been shown to be effective agricultural strategies for improving soil fertility and quality, and promoting plant growth in soil degradation remediation. The application of plant extracts improves the material cycle of soil microecology, such as the decomposition of nitrogen, phosphorus, and potassium, while increasing plant resistance. However, there is currently no experiment to demonstrate whether plant extracts have a promoting effect on the growth of ginseng and the mechanism of action. Pot experiments were carried out to investigate the effects of extracts, namely Rubia cordifolia (RC), Schisandra chinensis (SC), and Euphorbia humifusa (EH) on soil properties, enzyme activities, and plant physiological characteristics were evaluated. Results showed that compared with CK, plant extract-related treatments increased soil Organic carbon (OC), Available nitrogen (AN), Available phosphorus (AP) contents, and Soil urease activity. (S-UE), Soil sucrase activity (Soil sucrase), Soil acid phosphatase activity. (S-ACP). Meanwhile, plant extract-related treatments significantly increased plant physiological properties and TP (Total protein) content, and decreased the content of MDA (malondialdehyde) by 15.70% -36.59% and PRO (proline) by 30.13% -148.44%. Furthermore, plant extract-related treatments also significantly promote plant growth and reduce plant incidence, the fresh weight of ginseng increased by 27.80% -52.08%, ginseng root activity increased by 45.13% -90.07%, and ginseng incidence rate decreased by 20.00% -46.67%. Through correlation analysis between fresh weight of ginseng and root parameters and soil index, fresh weight is significantly positively correlated with root diameter, fiber root number, root activity, total protein (TP), catalytic activity (CAT) and superoxide dismutase activity (SOD), H, soil urea activity (S-UE), soil sucrose activity (S-SC), soil acid phosphate activity (S-ACP), and soil laccase activity (SL); The fresh weight was significantly negatively correlated with incidence rate, disease severity index, and malondialdehyde content (MDA). In summary, plant extract-related treatments improve soil quality and promote ginseng growth, further enhancing soil health and plant disease resistance. These findings provide new insights into ginseng cultivation and soil health management and highlight a new approach that can be applied to a wider range of agricultural practices and environmental sustainability.

Improvement the bearing capacity of the soil which is supporting the shallow foundation by using bored short micro-piles

In this paper , concrete micro-piles were used to improve the bearing capacity of the soil which is supporting the shallow foundation by using groups of (4; 6 and 9)bored short micro-piles which have, (D=0.125m and D=0.1m), and length to diameter ratio (L/D) equal to (6; 10 and 12) respectively. To calculate the bearing capacity of the micro-piles,(Tomlinson) and (Lamda) methods were used; also the soil properties were taken from Al-Muthana airport,(Al-Qyssi,2001) [1]. The results show that; increasing the number of piles and/ or the diameters and lengths; and the interaction between the bearing capacity of the shallow foundation with the bearing capacity of the pile group which leads to increasing the strength against the external loads; and the maximum bearing capacity was, when 9 piles with (L/D=12) was used. The improving ratio in the bearing capacity was (90%) compared with the bearing capacity of a (1×1) m shallow foundation without any piles. The results show that, the Tomlinson method of analysis produce lower results than the Lamda method by a ratio of (2-6) %. The results show also that the use of bored short micro-piles with (D=0.125m); (L/D=12) and number of 4-piles better than the (9) bored short micro-piles which have (D=0.1m).

Unveiling the main drivers of tree decline in Zagros semi-arid forests

Abstract Tree decline in arid and semi-arid forest ecosystems causes severe socioeconomic and ecological problems and thus needs to be thoroughly quantified and monitored across space and time. This study investigates tree and forest decline in Iran’s Zagros forests, considering environmental factors (e.g. topographic, soil, and climatic variables). We used field data from Chaharmahal-and-Bakhtiari (a study area covering 165 km2) and environmental data derived from freely available databases. Relationships between tree, forest decline, and environmental data were analyzed using generalized additive models. Our findings reveal that slope and the BioClim-16 variable (precipitation of the wettest quarter) significantly influence tree decline across various decline classes (P-values: slope = .009, BioClim-16 = .02). The best multivariate model for forest decline incorporated soil organic carbon and silt as predictive variables, with soil organic carbon emerging as the key factor (P-value = .04). Additionally, a spectral analysis of bare soil in declining and non-declining areas consistently demonstrated reduced reflectance values in declining regions across 10 Sentinel-2 bands, with VNIR-3, SWIR-2, red, green, and blue bands consistently showing significant differences as unveiled by the Wilcoxon test in all seasons except winter. These reduced reflectance values may indicate that forests stocked on soils with larger grain size (a higher fraction of sand) and/or higher organic carbon content may be more vulnerable to decline. This study contributes to our hitherto understanding of the main drivers of tree and forest decline in semi-arid forests, among others underscoring the potential utility of the spectral properties of bare soil in sparse semi-arid forests to predict the likelihood of tree decline.

Zeolite intervention in soil nitrate dynamics: insights from column experiments and modelling

This study aims to address the harmful effects of excessive nitrogen fertilizer by investigating the mobility-retardance effect of zeolite on nitrate movement in loam soil columns. Disturbed and undisturbed soil columns, treated with zeolite as well as control soil columns, were analysed over a span of three years. Breakthrough curves generated from the experiments were evaluated using the HYDRUS-1D code with mobile–immobile water (MIM) and dual-permeability (DP) models. Our findings highlight that zeolite application significantly reduced nitrate mobility, with recovery rates decreasing by 8% in disturbed and 11% in undisturbed columns. The DP model, which accurately reflects real soil water flow conditions, outperformed the MIM model in predictive accuracy. This research offers a novel long-term perspective on the benefits of zeolite in enhancing soil properties and mitigating nitrate leaching, recommending the DP model for superior prediction of contaminant transport in structured soils within the vadose zone.

Metals geochemistry and ecological risk assessment in lateritic soils and their transfer to tea plants

ABSTRACT Tea plant cultivation has been demonstrated to change the physicochemical properties of soils and metal bioavailability. The present study reported the geochemistry of four selected metals (Fe, Mn, Cr, and Ni) in lateritic soil cores regarding various depths cultivated with tea plants of different ages (3, 6, and 10 years old). The ecological risk and bioaccumulation of these metals in tea plants were also evaluated. The results indicate the acidic property of tea plant cultivation soils (pH < 4.5) and highlight further soil acidification due to long-term cultivation. The soils were strongly affected by farming methods and showed high levels (average from all soil cores) of total organic carbon contents (4.0% w/w), total nitrogen (2806 mg kg−1), and total phosphorus (1730 mg kg−1). We suggest that the selected metals originated naturally, supported by a high percentage in residual fractions from the results of soil sequential extraction (over 95% for Mn, Fe, and Ni and ∼ 90% for Cr). These metals exhibited no ecological risk to the adjacent environment. Mn showed the highest bioavailability, possibly due to the acidic soils and nitrogen fertiliser applications, increasing the release of Mn from soils. Mn also exhibited the highest bioconcentration factor (BCF) among four metals and 2.4 ÷ 6.5-folds higher BCFs in tea leaves than in roots. Despite the dominant content of Fe in soils (average of 20% w/w), Fe indicated lower amounts in tea leaves than Mn (average of 513 mg kg−1 vs 1354 mg kg−1). This could be attributed to the formation of Fe-rich root coatings on the tea plant root surface as a barrier to prevent Fe from translocating to other higher tea plant parts. Ni and Cr, as potentially toxic metals, were found at the lowest contents in tea leaves with low BCFs, indicating consumer safety in terms of exposure by these metals.

Residual Effect of Microbial-Inoculated Biochar with Nitrogen on Rice Growth and Salinity Reduction in Paddy Soil.

Increasing soil and water salinity threatens global agriculture, particularly affecting rice. This study investigated the residual effects of microbial biochar and nitrogen fertilizer in mitigating salt stress in paddy soil and regulating the biochemical characteristics of rice plants. Two rice varieties, Shuang Liang You 138 (SLY138), a salt-tolerant, and Jing Liang You 534 (JLY534), a salt-sensitive, were grown under 0.4 ds/m EC (S0) and 6.84 ds/m EC (S1) in a glass house under controlled conditions. Three types of biochar-rice straw biochar (BC), fungal biochar (BF), and bacterial biochar (BB)-were applied alongside two nitrogen (N) fertilizer rates (60 kg ha-1 and 120 kg ha-1) in a previous study. The required salinity levels were maintained in respective pots through the application of saline irrigation water. Results showed that residual effects of microbial biochars (BF and BB) had higher salt mitigation efficiency than sole BC. The combination of BB and N fertilizer (BB + N120) significantly decreased soil pH by 23.45% and Na+ levels by 46.85%, creating a more conducive environment for rice growth by enhancing beneficial microbial abundance and decreasing pathogenic fungi in saline soil. Microbial biochars (BF and BB) positively improved soil properties (physicochemical) and biochemical and physiological properties of plants, ultimately rice growth. SLY138 significantly had a less severe response to salt stress compared to JLY534. The mitigation effects of BB + N120 kg ha-1 were particularly favorable for SLY138. In summary, the combined residual effect of BF and BB with N120 kg ha-1, especially bacterial biochar (BB), played a positive role in alleviating salt stress on rice growth, suggesting its potential utility for enhancing rice yield in paddy fields.

Critical Review of Physical-Mechanical Principles in Geostructure-Soil Interface Mechanics

AbstractDue to the relatively different mechanical and physical properties of soils and structures, the interface plays a critical role in the transfer of stress and strain between them. The stability and safety of geotechnical structures are thus greatly influenced by the behavior at the soil–structure interface. It is therefore important to focus on the unique characteristics that set the interface apart from other geomaterials while examining the interface behaviour. Understanding the physical mechanism and modelling principles of these interfaces becomes a crucial step for the secure design and investigation of soil-structure interaction (SSI) issues. Moreover, to deal with this soil-environment interaction problem, the classical soil mechanics formulation must be progressively generalised in order to incorporate the effects of new phenomena and new variables on SSI behaviour. Considering the variety of energy geostructures that are emerging nowadays, it is crucial to comprehend the thermo-hydro-mechanical (THM) behaviour of the interface. The objective of this study is to fill this information gap as concisely as possible. A critical review is provided along with the state-of-the-art information on the thermo-hydro-mechanical behaviour of the soil-structure interface, including testing tools and measurement methods, basic principles and deformation mechanisms, constitutive models, as well as their applications in numerical simulations. This study explains how loading influences the mechanisms at the interface and critically examines the effects of boundary conditions, soil properties, environmental factors, and structure type on the THM behaviour of interface zones between soils and structural elements. The validity and reliability of the interface shear stress-displacement models are also covered in this paper. Lastly, the trends and recent advancements are also recommended for the interface research.

Groundwater quality assessment for drinking and irrigation purposes in the Ayad river basin, Udaipur (India)

Globally, about 5.25 billion people depend on groundwater for their water needs. However, groundwater quality significantly impacts human health and agriculture, influenced by factors such as land use, waste seepage, soil properties, and geological settings. In Rajasthan, the primary groundwater quality issues involve fluoride, nitrate, chloride, and calcium. This study addresses the gap in the understanding of the spatial and temporal variations of these contaminants and how the variations are linked to geology and land use. The basis for the analysis is data spanning 2000 to 2021from the Ground Water Department (GWD), the Central Ground Water Board (CGWB), and citizen science data from 2022 to 2023, focusing on the Ayad River Basin. The research aims to evaluate groundwater quality for drinking and irrigation by assessing physico-chemical parameters and using the Weighted Arithmetic Water Quality Index (WAWQI) method to calculate the Groundwater Quality Index (GWQI) from 2000 to 2023. The findings suggest a decreasing GWQI trend from west to east in the basin, with good groundwater quality (GWQI below 50) in the southern regions near the cities Umarda, Ramgiri, Undri, and Hariyab. The highest index values were near Bhoyana, Khemli, and Sisarma. The results of the salinity hazard test showed that salinity is a major issue in the eastern part of the basin. Though the groundwater is notably hard, a comprehensive analysis of various parameters nevertheless suggested its suitability for irrigation purposes. These results provide new insights in the quality of the groundwater resources in the Ayad River basin and valuable insights for policymakers and for decision-makers to develop strategies to preserve the groundwater quality.

A comprehensive review of soil organic carbon estimates: Integrating remote sensing and machine learning technologies

Abstract Purpose Accurately assessing soil organic carbon (SOC) content is vital for ecosystem services management and addressing global climate challenges. This study undertakes a comprehensive bibliometric analysis of global estimates for SOC using remote sensing (RS) and machine learning (ML) techniques. It showcases the historical growth and thematic evolution in SOC research, aiming to amplify the understanding of SOC estimation themes and provide scientific support for climate change adaptation and mitigation. Materials and Methods Employing extensive literature database analysis, bibliometric network analysis, and clustering techniques, the study reviews 1,761 articles on SOC estimation using RS technologies and 490 articles on SOC employing both RS and ML technologies. Results and Discussion The results indicate that satellite-based RS, particularly the Landsat series, is predominant for estimation of SOC and other associated studies, with North America, China, and Europe leading in evaluations with Africa is having low evaluations adopting RS technology. Trends in the research demonstrate an evolution from basic mapping to advanced topics such as carbon (C) sequestration, complex modeling, and big data utilization. Thematic clusters from co-occurrence analysis suggest the interplay between technology development, environmental surveys, soil properties, and climate dynamics. Conclusion The study highlights the synergy between RS and ML, with advanced ML techniques proving to be critical for accurate SOC estimation. These findings are crucial for comprehensive ecosystem SOC estimation, informed environmental management and strategic decision-making.

Mechanical and micro-mechanical investigations of nano-SiO2 and polypropylene fiber reinforced cement soil in marine environments

The degradation of cement-stabilized soil foundations in coastal environments is primarily caused by the corrosive effects of chloride and sulfate ions. While Nano-SiO2 enhances the mechanical properties of cemented soil, it may also increase brittleness, affecting safety and cost-effectiveness. Polypropylene fibers improve ductility by inhibiting crack propagation but contribute minimally to strength enhancement. To optimize performance, this study employed 3.6% Nano-SiO2 and 0.8% polypropylene fibers. Unconfined compressive strength (UCS) tests indicate that with increasing curing time, erosion from Cl− and SO 4 2 − significantly increases the brittleness of Nano-modified cemented soil, with compressive strength initially rising and then declining. The incorporation of polypropylene fibers further enhances both compressive strength and deformation modulus. At 60 days of curing, the composite cemented soil exhibits strength improvements greater than the sum of the individual gains in various environments, with compressive strength increases of 248.9, 159.9, and 102.9% in freshwater, chloride, and sulfate conditions, respectively. Scanning electron microscopy and X-ray diffraction analyses indicate that excessive expansion products from Cl− and SO 4 2 − reduce Nano-SiO2’s effectiveness. The C-S-H gel fills the indentations on the fiber surface and tightly envelops it, while Nano-SiO2 further enhances the mechanical interlocking between the fibers and the matrix, thereby improving durability in marine.

Genomic and Transcriptomic Analyses Identify Two Key Glycosyltransferase Genes alhH and alhK of Exopolysaccharide Biosynthesis in Pantoea alhagi NX-11.

The exopolysaccharide (EPS) produced by Pantoea alhagi NX-11, referred to as alhagan, enhances plant stress resistance, improves soil properties, and exhibits notable rheological properties. Despite these benefits, the exact bio-synthetic process of alhagan by P. alhagi NX-11 remains unclear. This study focused on sequencing the complete genome of P. alhagi NX-11 and identifying an alhagan synthesis gene cluster (LQ939_RS12550 to LQ939_RS12700). Gene annotation revealed that alhagan biosynthesis in P. alhagi NX-11 follows the Wzx/Wzy-dependent pathway. Furthermore, transcriptome analysis of P. alhagi NX-11 highlighted significant upregulation of four glycosyltransferase genes (alhH, wcaJ, alhK, and alhM) within the alhagan synthesis gene cluster. These glycosyltransferases are crucial for alhagan synthesis. To delve deeper into this process, two upregulated and uncharacterized glycosyltransferase genes, alhH and alhK, were knocked out. The resulting mutants, ΔalhH and ΔalhK, showed a notable decrease in EPS yield, reduced molecular weight, and altered monosaccharide compositions. These findings contribute to a better understanding of the alhagan biosynthesis mechanism in P. alhagi NX-11.

Soil sampling design matters - Enhancing the efficiency of digital soil mapping at the field scale

Optimisation of sampling design (methods chosen to select the samples) and sample size (number of samples) remains a key challenge in digital soil mapping, especially in the area of precision farming with the expected economic benefits from the introduction of new technologies. As the existing information is available in the form of relevant environmental covariates, its combination with non-parametric machine learning techniques requires careful planning from the initial field sampling to the final production of digital soil maps. The aim of this study is to compare widely used covariate-wise sampling designs combined with variable sample sizes for supervised prediction of common soil drivers of agricultural productivity (pH, soil organic carbon, soil macronutrients) in a real case study of a field (35 ha) with heterogeneous soil properties. From a total of 200 samples, we evaluated different sample sets where 10, 30 and 60 field samples were selected by conditioned Latin Hypercube Sampling (cLHS) and Feature Space Coverage Sampling (FSCS) to calibrate random forest (RF) models. The evaluation was performed on independently in-situ sampled test points. In addition to these datasets, we also compared the investigated methods with Simple Random Sampling (SRS) in a numerical benchmark experiment with increasing sample size, comparing the global accuracies of the predicted maps on the test points, but using interpolated maps as the artificial true population for each soil characteristic. The results of the study in both the field experiment and the numerical experiment showed slightly better results for the FSCS method, especially when the number of samples was small. At smaller training sample sizes, the risk of insufficiently accurate prediction models was slightly lower for FSCS and the difference decreased as the sample size increased. Nevertheless, sample size proved to be the most important factor in the accuracy of RF models, regardless of the sampling technique. The results suggest that a sample size between 18 and 30 training samples (0.6 to 1 sample ha−1) seems plausible for covariate-wise predictions using RF at field scale in our case study. The relative importance of each auxiliary variable for each RF calibration was also assessed for the field experiment. The results showed that the additional introduction of spatial proxies overshadowed the importance of other covariates, but only significantly improved the model calibration at larger sample sizes. The calibrated models without spatial proxies showed the strongest effect of remotely sensed surface characteristics.

Insights into the critical roles of water-soluble organic matter and humic acid within kitchen compost in influencing cadmium bioavailability

Compost has demonstrated potential as a cadmium (Cd) remediation agent, while it still remains unclear about the core components in driving the bioactive transformation of Cd. To address this issue, this study isolated three components—kitchen compost powder (KC), humic acid (HA), and water-soluble organic matter (DOM)—from kitchen compost to regulate soil properties, bacterial community structures and functions, and Cd migration risks. The results revealed that the addition of 20% KC and HA reduced the bioavailability factor of Cd by 47.20% and 16.74%, respectively, with HA contributing 35.47% of the total reduction achieved with KC. Conversely, the application of DOM increased the Cd risk through a reduction in soil pH and an increase in the abundance of Cd-activating bacteria, which adversely affected the stability of Cd complexes. However, the porous structure and organic matter in KC and HA provided adsorption sites for Cd passivation and promoted the growth of Cd-fixing bacteria. This study effectively identifies both the positive and negative effects of key compost components on Cd migration and provides scientific guidance for applying kitchen compost in soil management.

Effects of biochar application on soil properties and the growth of Melissa officinalis L. under salt stress

Soil salinization is one of the world's most seriously ecological issues. The application of biochar may enhance the properties of the soil and lessen the harm that salt stress causes to plants. In this study, we used the cuttings of Melissa officinalis as experimental materials. The method of pot experiment was used to explore to explore the effects of different concentrations of biochar (0, 10 %, and 20 % w/w) on soil properties and plant physiological characteristics under salt stress (0, 0.20 %, and 0.60 % NaCl+Na2SO4). The results indicate that the physicochemical properties of soil and the plant growth were decreased impacted with increasing salinity level, and these negative impacts were decreased traits were improved with the application of biochar. It was discovered that the application of biochar could increase the soil water holding capacity, total porosity, available P and K content, and soil enzyme activity while also decreasing the soil bulk density under salt stress. Biochar addition promoted the accumulation of plant biomass and the acquisition of nutrients, and reduced Na content in plants. With the addition of biochar, malondialdehyde (MDA) content and electrolyte leakage displayed a significant decrease under salt stress. A reduction in the osmotic substances and secondary metabolite accumulation in the leaves was also evident. The presented results reveal that biochar can contribute to protect M. officinalis against salt stress by alleviating the oxidative stress. Among the test samples, the 20 % biochar application had the best performance, suggesting that this is an advantageous method for improving soil properties and lessening the harm caused by salt stress on M. officinalis.

Influence of anthropogenic factors and soil properties on earthworm diversity in southern Mediterranean agroecosystems

This study examines the biodiversity of earthworms in agroecosystems in the Annaba region of northeastern Algeria, focusing on the impact of agricultural practices and soil environmental conditions on these species. Seven earthworm species from the Lumbricidae and Megascolecidae families have been identified, of which five are new records for this region. Areas with intensive human activity exhibited a decrease in earthworm abundance and diversity, whereas areas with less intensive agricultural practices showed higher levels of earthworm diversity. Variations in soil properties related to land use and plant diversity were notable. Intensive agricultural practices resulted in altered soil characteristics, such as higher pH, electrical conductivity (EC), and salinity, while organic amendments increased organic carbon and nutritional diversity. Essential nutrients, such as calcium and magnesium, are crucial for earthworm vitality, while high levels of pH, salinity, and EC can reduce their populations. Canonical Correspondence Analysis supports these findings. In summary, agricultural practices and soil environmental conditions significantly influence earthworm populations, underscoring the need for sustainable methods to preserve underground biodiversity and ecosystem services.

Translocation and dissipation of seven indicator polychlorinated biphenyls from contrast soils cultivated with different root vegetables

The behavior of 7 polychlorinated biphenyls (PCBs) in agricultural Chernozem and Fluvisol soils from the Czech Republic planted with radishes, onions, and carrots was studied. Only the total biomass yield of carrots was significantly reduced (P < 0.05) when compared to the control biomass yield. All tested PCBs, especially congener PCB 28, were able to accumulate in slightly higher amounts in the radish surface than in the onion one. The highest PCB content was observed in the surface layer of carrots, which could be related to the agrochemical properties of soil, vegetation period, and more intensive contact of carrot roots with PCBs in a bulk soil. The translocation factors of PCBs were lower than 1, indicating a poor ability for PCB uptake from soil. The highest relative PCB removal from soil was observed in acidic Fluvisol, accounting for 27.2%, with a maximum of 2% plant contribution to soil PCB dissipation. The low relative removal of PCBs by plants indicates that in vegetable-planted soil, the removal of PCBs could be due to autochthonous soil microorganisms in the rhizosphere of vegetables. The bioaccumulation factors for PCBs derived from the cultivation of examined root vegetables in Chernozem and Fluvisol soils contaminated with PCBs at nearly 1500 µg/kg dry weight can be considered low and negligible.

Aging of biodegradable microplastics and their effect on soil properties: Control from soil water

The ecological risks of biodegradable microplastics (BMPs) to soil ecosystems have received increasing attention. This study investigates the impacts of polylactic acid microplastics (PLA-MPs) and polybutylene adipate terephthalate microplastics (PBAT-MPs) on soil properties of black soil (BS) and fluvo-aquic soil (FS) under three water conditions including dry (Dry), flooded (FL), and alternate wetting and drying (AWD). The results show that BMPs exhibited more evident aging under Dry and AWD conditions compared to FL condition. However, BMPs aging under FL condition induced more substantial changes in soil properties, especially dissolved organic carbon (DOC) concentrations, than under Dry and AWD conditions. BMPs also increased the humification degree of soil dissolved organic matter (DOM), particularly in BS. Metagenomic analysis of PBAT-MPs treatments showed different changes in microbial community structure depending on soil moisture. Under Dry conditions, PBAT-MPs enhance the ammonium-producing process of soil microbial communities. Genes related to N nitrification and benzene degradation were enriched under AWD conditions. In contrast, PBAT-MPs do not change the abundance of genes related to the N cycle under FL conditions but significantly reduce genes related to benzene degradation. This reduction in benzene degradation genes under FL condition might potentially slow down the degradation of PBAT-MPs, and could lead to temporary accumulation of benzene-related intermediates. These findings highlight the complex interactions between BMPs, soil properties, and microbial communities, emphasizing the need for comprehensive evaluations of BMPs’ environmental impacts under varying soil water conditions.

Soil Properties Classification in Sustainable Agriculture Using Genetic Algorithm-Optimized and Deep Neural Networks

Optimization of land management and agricultural practices require precise classification of soil properties. This study presents a method to fine-tune deep neural network (DNN) hyperparameters for multiclass classification of soil properties using genetic algorithms (GAs) with knowledge-based generation of hyperparameters. The focus is on classifying soil attributes, including nutrient availability (0.78 ± 0.11), nutrient retention capacity (0.86 ± 0.05), rooting conditions (0.85 ± 0.07), oxygen availability to roots (0.84 ± 0.05), excess salts (0.96 ± 0.02), toxicity (0.96 ± 0.01), and soil workability (0.84 ± 0.09), with these accuracies representing the results from classification with variations from cross-validation. A dataset from the USA, which includes land-use distribution, aspect distribution, slope distribution, and climate data for each plot, is utilized. A GA is applied to explore a wide range of hyperparameters, such as the number of layers, neurons per layer, activation functions, optimizers, learning rates, and loss functions. Additionally, ensemble methods such as random forest and gradient boosting machines were employed, demonstrating comparable accuracy to the DNN approach. This research contributes to the advancement of precision agriculture by providing a robust machine learning (ML) framework for accurate soil property classification. By enabling more informed and efficient land management decisions, it promotes sustainable agricultural practices that optimize resource use and enhance soil health for long-term ecological balance.