Soil Electrical Conductivity Research Articles

Plant functional traits mediate the response magnitude of plant-litter-soil microbial C: N: P stoichiometry to nitrogen addition in a desert steppe

Global nitrogen deposition is significantly altering the carbon (C), nitrogen (N) and phosphorus (P) stoichiometry in terrestrial ecosystems, yet how N deposition simultaneously affects plant-litter-soil-soil microbial stoichiometry in arid grassland is still unclear. In a five-year experimental study conducted in a desert steppe in Northern China, we investigated the effects of N addition on the C:N:P stoichiometry of plants, litter, soil, and soil microbes. We also used structural equation modelling (SEM) exploring the direct or indirect effects of N addition, plant species diversity, functional traits and diversity, soil microbial diversity, soil pH, soil electrical conductivity (EC) and moisture on the stoichiometry in plant-soil system. The results showed that N addition increased the N, P concentrations and N:P in plants, the N concentration and N:P in litter, and the C, N concentrations, C:P and N:P in microbes. Conversely, it decreased the C:N and C:P in plants, and litter C:N. Functional traits, functional dispersion (FDis), soil pH and EC accounted for a substantial proportion of the observed variations in elemental concentrations (from 42 % to 69 %) and stoichiometry (from 9 % to 73 %) across different components. SEM results showed that N addition decreased C:N and C:P in plants and litter by increasing FDis and leaf N content, while increased plant and litter N:P by decreasing leaf C content and increasing specific leaf area, respectively. Furthermore, N addition increased microbial C:P by increasing leaf thickness. We also found the mediating effects of soil pH and EC on C:N, C:P of litter and microbial N:P. Overall, our research suggests that plant functional traits as key predictors of nutrient cycling responses in desert steppes under N addition. This study extends the application of plant functional traits, enhances our understanding of C and nutrient cycling and facilitates predicting the response of desert steppes to N deposition.

Alfalfa cropping is superior to cotton and rapeseed cropping in improving the quality and microbial diversity of reclaimed saline soils

ABSTRACT Soil salinisation has led to increasing abandonment of farmlands in the arid region of northwest China, and the cultivation of cash crops has become an important way to reuse abandoned farmlands and remediate saline soils. In this study, the effects of cotton (Cot), alfalfa (Alf), and rapeseed (Rap) cropping on the bacterial and fungal communities in saline soil were compared based on high-throughput sequencing. The results showed that soil pH and electrical conductivity (EC) of the Cot, Alf, and Rap groups decreased significantly compared with those of bare land (CK). Especially, soil pH (8.3) and EC (0.93 mS cm-1) of the Alf group decreased the most, and the contents of total nitrogen (TN) and total phosphorus (TP) increased the most, which increased by 250% and 91.4%, respectively compared with those of the CK (p < 0.05). Actinobacteriota and Proteobacteria were the dominant bacterial phyla in the three groups, Ascomycota and Basidiomycota were the dominant fungal phyla. The diversity of soil bacterial and fungal communities in the Alf group was higher than that in the Cot and Rap groups. The results of RDA and PERMANOVA analysis showed that soil pH, EC, C/N ratio, and TN were the main factors affecting soil bacterial and fungal communities in the three groups. In general, Cot, Alf, and Rap cropping all could reduce soil pH and EC and improve soil microbial community structure and diversity, but Alf cropping had the best effects in improving saline soil quality and bacterial and fungal diversity.

Monitoring of soil EC for the prediction of soil nutrient regime under different soil water and organic matter contents

Smart farms and precision agriculture require automatic monitoring and supply of water and nutrients for crops, but sensors to monitor plant available nutrients in soil are not available. Soil electrical conductivity (EC) is related to nutrients in soil solution, which can be affected by soil organic matter, soil texture, temperature, and water content. Therefore, the objective of this study is to evaluate factors influencing soil EC sensor values by monitoring EC under different soil organic matter and water contents. Ten soil samples with various sand and clay contents, EC, pH, and organic matter contents were selected and saturated with water. Volumetric water content and EC of the soil were monitored while drying the soil. Humic acid and manure were added to soils in order to evaluate the effect of organic matter on soil EC. Soil EC values linearly increased with increasing water content at 10–25% which is favorable water content for plant growth. The EC increased when organic matter was added to soils, which was related to ions released from the organic matter. Soil EC calibration factor for soil water content increased when EC of the soil was high and organic matter was added. The sensor EC values in sandy loam and loam soils was related to the ion contents in pore water, and exchangeable ions in soil, respectively. Sensor EC values were highly correlated with organic matter and K contents in soil and can be used as an indicator for plant available nutrients in soil. Therefore, the sensor EC at optimal soil water content for plant growth can be used to monitor changes in plant available nutrients in soil.

Роль биомелиорантов в снижении электропроводности и засоленности почв

This study aimed to evaluate the impact of biomeliorants on soil electrical conductivity and salinity under the arid conditions of the Syrian Arab Republic. Field experiments were conducted using compost, biopreparations based on Bacillus bacteria, and their combination. Electrical conductivity was measured at four depths: 0-25 cm, 26-50 cm, 51-75 cm and 76-100 cm. The methodology included the use of graphite electrode cells and a conductometer calibrated with standard potassium chloride (KCl) solutions. Soil samples were collected from various depths, dried at 105°C, sieved through a 2 mm sieve, mixed with distilled water in a 1:2 ratio, stirred, and left for 24 hours. Three measurements of electrical conductivity were then taken for each sample. The results showed a significant reduction in soil electrical conductivity with the application of biomeliоrants particularly at the 0-25 cm depth. Two-factor ANOVA and Tukey’s test confirmed statistically significant differences between the experimental treatments. The combination of compost and biopreparations demonstrated the greatest effectiveness in reducing electrical conductivity. These findings indicate the high efficiency of biomeliorants in improving soil conditions and reducing salinity, which is crucial for sustainable agriculture in arid regions. The introduction of biomeliorants helped reduce the content of soluble salts and improve soil structure, ultimately leading to increased crop yields.

Impact of Jeevamrut formulations and biofertilizers on soil microbial and chemical attributes during potato cultivation

Application of jeevamrut, fortified with neem cake or vermitea, in combination with consortia of plant growth-promoting microorganisms such as Azotobacter, Vesicular Arbuscular Mycorrhiza (VAM) or Phosphate Solubilizing Bacteria (PSB) would attenuate the negative effects of synthetic fertilizers over microbial and physicochemical properties of soil. The present study was carried out during 2021–2022 and 2022-23 with two factors, namely Factor-J (Jeevamrut) of 4 levels and Factor-B (Biofertilizers) of 5 levels. Four levels of factor-J include three jeevamrut formulations with one control while five levels of factor-B include four biofertilizer combinations with one control. Observations of various soil-based parameters confirm a significant reduction in soil pH and electrical conductivity of soil after harvesting potato crops (variety Kufri Bahar) under different treatments in comparison to controls and the initial value. Further, the organic carbon, available nitrogen, phosphorus, and potassium of soil were reported to be enhanced after the application of jeevamrut fortified with vermitea or neem cake. The efficacy of fortified jeevamrut can be further improved by adding a consortia of biofertilizers consisting of PSB and Azotobactor or VAM fungi. These treatments have also a significant effect on enhancing soil microbial activities.

ANALYSIS OF LANDMINE DETECTION METHODS AND THE IMPORTANCE OF STUDYING SOIL PROPERTIES FOR THEIR EFFECTIVE APPLICATION

The review attempts to analyze the problem of mine detection and outline the prospects for using soil properties that are useful in detecting explosive objects and creating new algorithms for processing relevant data. The article analyzes five main areas in which modern landmine detection technologies are divided. Their main advantages and disadvantages are identified. Using the example of metal detectors and ground-penetrating radar technology, the necessity of a detailed study of the main physical properties of soils is justified, which will enable more efficient use of appropriate technologies for detecting landmines. Keywords: methods of mine detection, sensors, physical properties of soil, magnetic susceptibility of soil, electrical conductivity of soil, dielectric permittivity of soil, soil moisture

MENINGKATKAN PERTUMBUHAN TANAMAN MENTIMUN (Cucumis sarivus L.) DI TANAH SALIN DENGAN PELINDIAN TANAH DAN PENAMBAHAN AMELIORAN

This study aimed to assess the impact of combinations of varying soil amendments and water amounts on the leaching water requirement for cucumber growth and production. Saline soil was collected from Banongan, Situbondo, with soil electrical conductivity (EC) of 4-6 dS m-1. A 10 kg soil sifted with a diameter of 2 mm was placed in each mini lysimeter. Leaching was carried out until the soil EC reached below 2 dS m-1. The results showed that soil leaching reduced soil EC from 5 dS m-1 to 1.6-1.8 dS m-1; meanwhile, EC in leachate ranged from 2-2.3 dS m-1. Biochar treatment caused an increase in leaching water requirements. In addition, applying biochar combined with 40 mm day-1 of water produced the highest fruit number. The combination treatment of cow manure 100 g pot-1 (equivalent to 20 t ha-1) and biochar 30 g pot-1 (equivalent to 6 t ha-1) and adding 40 mm day-1 of water during each water application produced the most prominent fruit volume. Furthermore, utilizing 40 mm day-1 of leaching water produced the best number of leaves, fruit weight, length, and fruit circumference.

Leaf carbon, nitrogen, and phosphorus ecological stoichiometry of grassland ecosystems along 2,600-m altitude gradients at the Northern slope of the Tianshan Mountains.

Ecological stoichiometry of terrestrial ecosystems has been a hot issue in current research, with intense focus on the proportional relationships of nutritional elements within plants and between plants and their environment. To clarify these relationships along continuous environmental gradients is essential for a more comprehensive understanding how plants adapt to a changing environment. In arid regions, the varying plant and soil types along altitude gradients offer a unique opportunity to examine the vertical spectrum of plant and soil ecological stoichiometry. In this study, the northern slope of the Tianshan Mountains was selected as the study area to explore the carbon (C), nitrogen (N), and phosphorus (P) ecological stoichiometric characteristics of herbaceous plants along 900-m-3,500-m altitude gradients. We also investigated the variation of ecological stoichiometric characteristics among different grassland types. The results indicated that the mean C, N, and P in leaf of grassland were 342.95 g·kg-1-557.73 g·kg-1, 6.02 g·kg-1-20.97 g·kg-1, and 0.71 g·kg-1-3.14 g·kg-1, respectively. There was no significant change in leaf carbon content along the elevation gradient, and the highest and lowest leaf C concentrations were in the upland meadow and the semidesert grasslands. Both N and P concentrations obtained their highest value in the meadow steppe. The P concentration gradually increased in desert and semidesert grasslands and reached the highest value in the meadow steppe, and then decreased to the lowest value in the upland meadow and subsequently increased in the alpine meadow. The ranges of the C:N ratio, C:P ratio, and N:P ratio were 16.36-155.53, 109.36-786.52, and 2.58-17.34, respectively. Due to fluctuations in the P concentration, the C:P ratio and N:P ratio reached the lowest value in the meadow steppe and obtained their highest value in the upland meadow. Redundancy analysis showed that temperature was the dominant factor affecting the C, N, and P ecological stoichiometry of herbaceous plants, followed by soil organic carbon, mean annual precipitation, soil pH, and soil electrical conductivity. Corresponding results could enhance predictive models of nutrient cycling and ecosystem responses to climate change, particularly in arid and semiarid regions.

Características do solo e morfofisiológicas de duas espécies de violeta (Viola sp.) influenciadas por ácido giberélico e nitrogênio

ABSTRACT Violet, with its pleasant scent and attractive flower color, is also important for its medicinal and edible uses, in addition to ornamental uses. Proper nutrition of the plant (violet) with essential nutrients and growth hormones improves the quantitative and qualitative characteristics and can increase the economic value and popularity of these flowers. In this regard, the current research aims to investigate the combined effect of nitrogen (N) and gibberellic acid (GA) on the morphophysiological traits of two native Iranian violet species (Viola tricolor and Viola odorata), as a factorial experiment with three factors including two species of violets, three levels of GA (0, 150 and 300 mg/L) and three levels of N (0, 100 and 200 mg/L) in a completely randomized design with 18 treatments, 3 replications and a total of 216 plants. Some characteristics of planting medium, morphophysiological traits and activity of antioxidant enzymes were evaluated. The results of ANOVA showed that the three-way effect of experimental treatments on all evaluated traits is significant. According to the results of comparing the averages the N application reduced the soil electrical conductivity (EC) versus its non-application in both violet species and at all GA levels. The highest soil N in both species was obtained from the application of 0 mg/L GA × 100 or 200 mg/L N. In two species of violets, the highest number of leaves, flowers, and stolons was obtained from the treatment of 300 mg/L GA × 200 mg/L N. The highest chlorophyll a and total chlorophyll among the treatments were related to V. odorata × 150 mg/L GA × 200 mg/L N. The application of GA and N in both species increased peroxidase activity versus the control. The highest level of this enzyme activity was related to 300 mg/L GA × 200 mg/L N. V. odorata had higher peroxidase activity than V. tricolor. The lowest and highest levels of catalase activity were recorded by control × V. tricolor (0.13 nM/g FW/min) and V. odorata × 300 mg/L GA × 200 mg/L N (0.676 nM/g FW/min), respectively. According to the results, with the application of 300 or 150 mg/L GA × 200 mg/L N, the ornamental and edible properties of these two types of violets are improved, and their economic value and marketability are increased, and there will be a change in the sales market of these flowers.

Autocorrelação espacial de populações de plantas de soja e atributos físicos de um Latossolo

ABSTRACT Among the necessary phytotechnical adjustments, the correct recommendation of the plant population of each soybean cultivar is of paramount importance for success in crop production. Thus, the objective of this study was to analyze the distribution and spatial autocorrelation of the soybean plant population according to the spatial variability of the physical and chemical attributes of Oxisol in the Brazilian Cerrado region. The attributes studied were plant population, soybean grain yield, apparent soil electrical conductivity, soil organic matter content, cation exchange capacity, hydrogenionic potential, and clay content in the soil. The plant population varied inversely as a function of apparent soil electrical conductivity. The areas with the highest values of apparent soil electrical conductivity, soil organic matter content, cation exchange capacity, and clay content had the lowest plant populations. Soybean yield was not influenced by increase or decrease in the plant population. Thus, it is economically viable to plant with lower plant density to improve the farmer’s profit margin.

Multi-objective optimization of saline water irrigation in arid oasis regions: Integrating water-saving, salinity control, yield enhancement, and CO2 emission reduction for sustainable cotton production

Brackish water stands as a promising alternative to mitigate freshwater scarcity in arid regions. However, its application poses potential threats to agricultural sustainability. There is a need to establish a clear understanding of the economic and ecological benefits. We conducted a two-year (2021−2022) field experiment to investigate the effects of four different irrigation water salinity levels on soil electrical conductivity, cotton yield, water use efficiency, CO2 emissions, and carbon sequestration. The salinity levels were designated as CK (0.85 g L−1), S1 (3 g L−1), S2 (5 g L−1), and S3 (8 g L−1). Results indicated that using irrigation water with high salinity (≥5 g L−1) led to the accumulation of salt in the soil, and a decrease in plant biomass and seed cotton yield. Compared to CK, the S3 treatment decreased by 18.72 % and 20.10 % in the respective two years. Interestingly, using brackish water (3 L−1 and 5 g L−1) decreased the rate and cumulative CO2 emissions, and increased the carbon emission efficiency and carbon sequestration by 0.098–0.094 kg kg−1 and 871–1859 kg ha−1 in 2021, 0.098–0.094 kg kg−1 and 617–1995 kg ha−1 in 2022, respectively. To comprehensively evaluate the tradeoff between economic and ecological benefits, we employed the TOPSIS method, and S1 was identified as the optimal irrigation salinity. Through fitting analysis, the most suitable irrigation salinity levels for 2021 and 2022 were determined as 3.52 g L−1 and 3.31 g L−1, respectively. From the perspective of water conservation, salinity management, yield improvement, and reduction of CO2 emissions, it is feasible to utilize brackish water for irrigation purposes, as long as the salinity does not exceed 3.52 g L−1 (first year) and 3.31 g L−1 (second year).

Assessment of heavy metal distribution and bioaccumulation in soil and plants near coal mining areas: implications for environmental pollution and health risks.

Monitoring heavy metals (HMs) across source distance and depth distribution near coal mining sites is essential for preventing environmental pollution and health risks. This study investigated the distribution of selected HMs, cadmium (Cd2+), chromium (Cr2+), copper (Cu2+), manganese (Mn2+), nickel (Ni2+), lead (Pb2+), and zinc (Zn2+), in soil samples collected from ten sites (S-1-S-10) at two different depths (0-15 and 15-30 cm) and distances of 50, 100, and 200 m from a mining source. Additionally, three plant species, Prosopis spp., Justicia spp., and wheat, were collected to assess HM bioavailability and leaf accumulation. Coal mine activities' impact on soil properties and their HM associations were also explored. Results reveal HM concentrations except for Cr2+ exceeding World Health Organization (WHO) limits. In surface soil, Cd2+ (58%), Cu2+ (93%), Mn2+ (68%), Ni2+ (80%), Pb2+ (35%), and Zn2+ (88%) surpassed permissible limits. Subsurface soil also exhibited elevated Cd2+ (53%), Cu2+ (83%), Mn2+ (60%), Ni2+ (80%), Pb2+ (35%), and Zn2+ (77%). Plant species displayed varying HM levels, exceeding permissible limits, with average concentrations of 1.4, 1.34, 1.42, 4.1, 2.74, 2.0, and 1.98 mg kg-1 for Cd2+, Pb2+, Cr2+, Cu2+, Mn2+, Ni2+, and Zn2+, respectively. Bioaccumulation factors were highest in wheat, Prosopis spp., and Justicia spp. Source distance and depth distribution significantly influenced soil pH, electrical conductivity (EC), and soil organic carbon (SOC). Soil pH and EC increased with an increase in soil depth, while SOC decreased. Pearson correlation analysis revealed varying relationships between soil properties and HMs, showing a considerably negative correlation. Concentrations of HMs decreased with increasing depth and distance from mining activities, validated by regression analysis. Findings suggest crops from these soils may pose health risks for consumption.

Impact of Ecological Restoration on the Physicochemical Properties and Bacterial Communities in Alpine Mining Area Soils.

Ecological restoration has notably impacted microbe and soil characteristics in abandoned open pit mines, especially in alpine regions. Yet, the adaptive responses of microbial communities in the initial years of mine site restoration remain largely unexplored. This study endeavors to offer a thorough comprehension of soil properties and microbial dynamics during the initial phases of alpine mining land reclamation. It places emphasis on physicochemical properties and microbial community composition and evaluates the feasibility of phytoremediation, along with proposing subsequent measures. Our study employs spatial sequence instead of time-sequenceal sequence to investigate early-stage changes in soil microbes and physicochemical properties in alpine mining land reclamation. We used high-throughput sequencing for the 16S rRNA amplicon study. Over time, soil physicochemical properties improved noticeably. Soil pH shifted from neutral to alkaline (7.04-8.0), while soil electrical conductivity (EC) decreased to 77 μS·cm-1 in R_6a. Cation exchange capacity (CEC) initially decreased from R_2a (12.30-27.98 cmol·kg-1) and then increased. Soil organic matter increased from 17.7 to 43.2 g·kg-1 over time during mine reclamation and restoration. The dominant bacterial community consisted of Proteobacteria (33.94% to 52.09%), Acidobacteriota (4.94% to 15.88%), Bacteroidota (6.52% to 11.15%), Actinobacteriota (7.18% to 9.61%), and Firmicutes (4.52% to 16.80%) with varying relative abundances. Gene annotation of sequences from various reclamation years revealed general function prediction, translation, ribosome structure, cell wall/membrane/envelope biogenesis, nucleotide translocation, and metabolism, along with other related functions. Mine reclamation improved soil fertility and properties, with the R_6a treatment being the most effective. Starting in the 2nd year of reclamation, the effective phosphorus content and the dominance of microbial bacteria, notably the Bacillus content, decreased. Firmicute fertilization promoted phosphorus and bacterial growth. In conclusion, employing a blend of sequencing and experimental approaches, our study unveils early-stage enhancements in soil microbial and physicochemical properties during the reclamation of alpine mining areas. The results underscore the beneficial impacts of vegetation restoration on key properties, including soil fertility, pore structure, and bacterial community composition. Special attention is given to assessing the effectiveness of the R_6a treatment and identifying deficiencies in the R_2a treatment. It serves as a reference for addressing the challenges associated with soil fertility and microbial community structure restoration in high-altitude mining areas in Qinghai-Tibet. This holds great significance for soil and water conservation as well as vegetation restoration in alpine mining regions. Furthermore, it supports the sustainable restoration of local ecosystems.

Tree growth and density enhanced, while diversity and spatial clustering reduced soil mycorrhizal C and N sequestration: Strong interaction with soil properties in northeastern China

In this paper, the effects of species diversity, tree growth, and spatial clustering on mycorrhizal carbon and nitrogen sequestration and the interaction of soil physicochemical properties in Northeast China were investigated. Based on 720 10 m ∗ 10 m plots in Harbin Experimental Forest Farm of Northeast Forestry University, we determined mycorrhizal biomarkers of easily extractable Glomalin-related soil protein (EEG) and total Glomalin-related soil protein (TG). Four plant diversity indices, seven structural metrics, and five soil properties were also measured. We found that: 1) The low tree diversity plots had 1.08–1.23 times higher TG, EEG, TG-N/TN (proportion of N in TG to TN), and TG-C/SOC (proportion of C in TG to SOC) than the high plots. 2) Tree diameter was negatively correlated with EEG and TG, but positively correlated with the EEG and TG contribution to soil TN and SOC. Soil EEG and TG were positively correlated with under-branch height and tree density. W (Uniform Angle Index, higher W indicates more clustering of tree distribution in the plot) was negatively correlated with the above four ratios and positively correlated with EEG/TG. 3) pH was the most powerful explainer for the GRSP variations (6.8 %, strongest negative association with GRSP/TN, R2 > 0.13), followed by soil electrical conductance (6.5 %, positive relation with TG, p < 0.05), AP (3.2 %). 4) Plant diversity mainly affected GRSP traits through the interaction with soils (0.07), tree growth and density directly increased TG, TG-N/TN, and TG-C/SOC, while tree spatial distribution directly reduced TG-N/TN. Our finding highlighted the important effects of tree diversity and forest structural traits on GRSP amount, carbon sequestration, and nutrient retentions, and could support glomalin-related forest soil management of temperate forests in the high-latitude northern hemisphere.

Vertical Distribution of Available Plant Nutrients in Soils of Mid-Central Tableland Agro-climatic Zone of Odisha, India

Study on the vertical distribution of plant nutrients is very essential for improving crop production and productivity rather than studying the nutrient characteristics of the surface soil only. It is because many plants absorb nutrients from the subsoil. Moreover, the vertical distribution of nutrients is much more complex because of the many simultaneous processes going on in the soils such as recycling of the plant residues, leaching of nutrients, and weathering of parent materials. In this context, two pedons located in pedons in two different land types(upland and low land) of Dhenkanal district of the Mid-Central Tableland argo-climatic zone of Odisha were selected for studying the depth-wise vertical distribution of plant nutrients. Genetic horizon-wise soil samples were collected, processed, and analyzed for different soil properties viz., textural class (sand, silt, clay), pH, electrical conductivity (EC), soil organic carbon (SOC), available nitrogen (N), phosphorus (P), potassium (K), sulfur (S) and boron (B) content. The results indicated that there was a gradual decrease in the concentration of SOC, available N, P and S with soil depth. Whereas soil reaction (pH), EC and available K content increased with soil depth. Therefore, surface soils were observed to be more fertile but acidic, whereas the sub-soils were of higher pH. The findings of this study will be helpful for the scientific as well as the farming community for suggesting and uptaking suitable crop and land use plans for sustainable agricultural and land use management.

Effect of micro–nano bubbles on the remediation of saline–alkali soil with microbial agent

The widespread distribution of saline–alkali soil around the world affects the health of ecological systems and the development of the national economy by limiting the growth of plants. However, the commonly used remediation technologies have the drawbacks of low efficiency, high cost, and secondary pollution. This study investigated the feasibility and efficacy of novel combined micro–nanobubbles (MNBs) and microbial agent (MA) technology for the remediation of saline–alkali soil. The results demonstrated that the combined MA–MNBs method greatly renovated the properties of saline–alkali soil compared with the technologies of single utilization of MA or MNBs process in the laboratory. The method resulted in a reduction of soil electrical conductivity and pH levels, an improvement in soil fertility, and the formation of soil aggregates. Moreover, the method significantly impacted the growth of plants, particularly in plant length, dry weight, and rhizome elongation. Further high–throughput sequencing and gene expression analysis revealed that the MA–MNBs method enhanced the abundance of soil microbial community compared with single MA and MNBs treatment. Gene enrichment analysis revealed that the MA–MNBs method could compensate for the shortcomings of single MA treatment and enhance the expression of energy metabolism and salt stress–related genes attributed to MNBs treatment, thereby significantly improving the growth and development of plants. Consistently, 6115 kg/ha of rice was yielded in the field for the saline–alkali soils using this MA–MNBs method, with zero crops before remediation. This study provided a novel, efficient, and green strategy for the remediation of saline–alkali soil without adding any chemicals.

GIS-statically-based modelling the groundwater quality assessment coupled with soil and terrain attributes data.

In this study, we investigated the application of Geographic Information Systems (GIS) for groundwater quality assessment through the integration of statistical models with soil and topographical data. Our primary objectives were to identify soil parameters and topographical attributes contributing to groundwater quality assessment and to evaluate the potential of geostatistics and GIS for spatial analysis of groundwater resources. Groundwater samples were collected from 43 agricultural wells, and surface soil layer samples (0-20 cm) were obtained near each well. We measured groundwater quality parameters and relevant soil properties. Our approach involved the utilization of multiple linear regression (MLR) and principal component regression (PCR), combined with topographical terrain attributes and soil data, for modeling groundwater electrical conductivity (GEC). Our findings revealed significant correlations between GEC and soil electrical conductivity (EC) (r = 0.89) as well as soil carbonate (CaCO3) (r = 0.68). Among the ten topographical attributes considered, the terrain wetness index (TWI) exerted the highest influence on GEC (r = 0.57), followed by the slope (r = -0.47). Further analysis demonstrated that the MLR model outperformed the PCR model in both the development and calibration datasets, with an achieved R2value of 0.89 and a root mean square error (RMSE)of 150 μScm-1 for MLR, compared to an R2 of 0.85 and an RMSE of 170 μScm-1 for PCR when coupled with soil and attribute data for GEC prediction. The resulting GEC map generated from the MLR model displayed spatial variations, ranging from 605 μScm-1 in the northern region to 1275 μScm-1 in the central part of the study site. In conclusion, our study demonstrated the effectiveness of combining statistical modeling with geostatistics and GIS for groundwater quality assessment, providing valuable insights for resource management and environmental planning.

A microecological research reveals seasonal variation in rhizosphere-endophytic bacteria and growth and development of Codonopsis pilosula root

Codonopsis pilosula is a medicinal and edible plant. Its chemical composition and pharmacological effects have been extensively studied. However, temporal, especially seasonal variabilities of bacterial composition driving nutrient cycling are poorly understood in rhizosphere and endosphere of C. pilosula. This study aimed to investigate the relationships and distinctions among soil physicochemical characteristics, microbial diversity, and functions of C. pilosula under different seasonal conditions in Longxi, China, and how these factors influenced C. pilosula growth. Physicochemical characteristics, enzymatic activities, growth state and metabolite analysis of C. pilosula in the soil were conducted using multispectral techniques. High-throughput sequencing methods were employed to analyze the alpha- and beta-diversity of rhizospheric and endophytic bacterial communities. Seasonal changes (P < 0.05) significantly influenced physiological indicators, bacterial diversity, community structure, and ecological niche breadth of C. pilosula, which were affected by environmental variables and soil enzymes. The neutral community model indicated that stochasticity played a substantial role in bacterial community development in rhizosphere soil. Furthermore, the microbial diversity was influenced by the soil's electrical conductivity, urease, and catalase activities, as well as phosphorus and potassium concentrations. Seasonal fluctuations significantly impacted the growth, development, metabolite content, and stress resistance indicators, such as antioxidant enzymes, in C. pilosula. By analyzing the temporal characteristics between plant and soil indicators, this study provides insights into regulating environmental factors during C. pilosula growth to enhance its yield and quality.Main conclusion Both rhizosphere soil physicochemical factors and bacterial diversity indices showed strong correlations. Phylogenetic and taxonomic differences existed between rhizosphere and bulk soil bacterial communities of Codonopsis pilosula in different seasons.

Insights into electro-bioremediation of PAH-contaminated soil under polarity reversal conditions: Effect of effective current intensity and soil properties on microbial function

Electrical stimulation during electro-bioremediation has the potential to enhance the biodegradation of polycyclic aromatic hydrocarbons (PAHs) in soil. In this study, we investigated the effective current intensity, referred to as the “window condition,” which promotes the activity of functional microflora. Electro-bioremediation was performed on PAH-contaminated soil using various polarity reversal frequencies (PRF) to study the remediation mechanism. The “window condition” in this study was 20–40 mA. The enhancement of PAH degradation, particularly that of high-PAHs, increased with higher PRF, which accompanied the increasing ratio of the “window condition” to the overall electrification time (RWC). Electro-bioremediation with a 10-minute (EBPR-10 m) and 30-minute (EBPR-30 m) polarity reversal (PR) periods achieved the highest ratios of PAH degradation at 43.9±2.3 % and 37.9±1.8 %, respectively, with RWC of 0.4919 and 0.4056, respectively, after 60 days. The increase in PRF improved the soil physicochemical properties, which were conducive to maintaining effective electrokinetic and biodegradation processes. Redundancy analysis (RDA) and Pearson correlation analyses demonstrated that soil electrical conductivity (EC), dissolved organic carbon (DOC), temperature, and the proportion of macroparticle components (0.195–2.500 um) in soil colloids (Ps/Po) were key factors in explaining the persistence of microbial abundance and community structure. Structural equation models (SEMs) further illustrated the mechanism behind the enhanced PAH biodegradation, including (i) the direct stimulation of microbial activity because of increased RWC and (ii) the indirect optimization of microbial function by improving soil physicochemical properties under optimal PRF.

Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality

The traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (ECa) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 °Brix, and coefficient of determination (R2) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R2 of 0.51 and RMSE of 1.19 °Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R2 of 0.54 and a RMSE of 1.16 °Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way.