Soil Analysis Research Articles

The Effect of Anaerobic Composting Method of Coffee Waste on The Growth of Robusta Coffee Seedlings (Coffea canephora L.)

Coffee is one of the plantation commodities that has significant economic value. A factor that needs to be considered in coffee plant cultivation is good planting media composition. This study aims to determine the effect of planting media composition on the growth of robusta coffee seedlings. This study used a Completely Randomized Design with treatments M0= Soil: 100%, M1= Soil: 50% + Coffee Husk Compost: 25% + Rice Husk: 25%, M2= Soil: 25% + Coffee Husk Compost: 50% + Rice Husk: 25%, M3= Soil: 25% + Coffee Husk Compost: 25% + Rice Husk: 50%. The analyses conducted were compost analysis and initial and post-incubation soil analysis. Observation data were analyzed using the F test followed by DNMRT at the 5% level. The research results show that the provision of coffee husk compost and rice husks affects the chemical properties of the soil and the growth of coffee seedlings. The planting media composition of Soil: 25% + Coffee Husk Compost: 50% + Rice Husk: 25% proved to increase the pH of the planting media from 5.5 to 6.6, increase in total N-value from 0.2% to 0.5%, available P from 18.6 ppm to 38.5 ppm and the soil CEC from 7.6 me/100g to 36.6 me/100g. This planting media composition also makes it possible to enhance the growth of robusta coffee seedlings in plant height, stem diameter, number of leaves, leaf width, and root volume. Thus, this planting media composition is a suitable medium for the growth of robusta coffee seedlings.

PTC-NH2@MOFs-AuNCs: A novel bifunctional signal probe for ratiometric electrochemiluminescence/colorimetry dual-mode aptasensing of lead ion

The development of a dual-mode sensing method for lead ions (Pb2+) is crucial and desirable. Herein, a single component-based bifunctional signal probe (perylene derivative-modified MOFs-gold nanoclusters, PTC-NH2@MOFs-AuNCs), which was capable of simultaneously generating an electrochemiluminescence (ECL) signal and exhibiting nanoenzyme activity, was prepared to fabricate a novel ratiometric ECL/colorimetry dual-mode platform for the visual, reliable, and sensitive analysis of Pb2+. Specifically, the PTC-NH2@MOFs-AuNCs were firstly synthesized via the electrostatic adsorption of AuNCs on the surface of PTC-NH2@MOFs. Upon the addition of Pb2+, the ECL signal of PTC-NH2@MOFs-AuNCs, which had been quenched by the adsorbed aptamer, could be restored at both the anode and cathode. Simultaneously, the peroxidase-like activity of PTC-NH2@MOFs-AuNCs was also recovered, accompanied by a distinct color transition from light blue to dark blue. The linear ranges of ratiometric ECL/colorimetric dual-modal assay for Pb2+ were 1 pM ∼ 5 nM and 1 nM ∼ 2 μM, with the limits of detection of 0.1 pM and 0.31 nM, respectively. More importantly, by introducing the screen-printed electrode and paper chip as sensing platform, a portable sensor for the convenient detection of Pb2+ was further established, demonstrating its promising practicability of rapid visual screening and accurate quantitative analysis in farmland soils.

Co-application of Parthenium biochar and urea effectively mitigate cadmium toxicity during wheat growth

Environmental contamination by cadmium (Cd), a highly toxic heavy metal, poses significant health risks to plants and humans. Biochar has been effectively used to promote plant growth and productivity under Cd stress. This study presents an innovative application of biochar derived from the invasive weed Parthenium hysterophorus to promote plant growth and productivity under Cd stress. Our study includes detailed soil and plant analyses, providing a holistic perspective on how biochar and urea amendments influence soil properties, nutrient availability, and plant physiological responses. To address these, we established seven treatments: the control, Cd alone (5 mg kg−1), biochar alone (5 %), urea alone (3 g kg−1), biochar with Cd, urea with Cd, and a combination of biochar and urea with Cd. Cd stress alone significantly reduced plant growth indicators such as shoot and root length, fresh and dry biomass, chlorophyll content, and grain yield. However, the supplementation of biochar, urea, or their combination significantly increased shoot length (by 48%, 34%, and 65%), root length (by 73%, 46%, and 70%), and fresh shoot biomass (by 4%, 31%, and 4%), respectively. This improvement is attributed to enhanced soil properties and improved nutrient absorption. The biochar–urea combination also enhanced Cd tolerance by improving total chlorophyll content by 14 %, 13 %, and 16 % compared to the control, respectively. Similaly, these treatments significantly (p < 0.05) boosted the activity of antioxidant enzymes such as catalase, peroxidase, and superoxide dismutase by 51 %, 30 %, and 51 %, respectively, thereby mitigating oxidative stress as a defensive mechanism. The Cd tolerance was improved by biochar, urea, and their combinations, which reduced Cd content in the shoots (by 60.5 %, 38.9 %, and 51.3 %), roots (by 47.5 %, 23.9 %, and 57.6 %), and grains (by 58.1 %, 30.2 %, and 38.3 %) relative to Cd stress alone, respectively. The synergistic effects of biochar and urea are achieved through improved soil properties, nutrient availability, activating antioxidant defense mechanisms, and minimizing the accumulation of metal ions in plant tissues, thereby enhancing plant defenses against Cd stress. Conclusively, converting invasive Parthenium weed into biochar and combining it with urea offers an environmentally friendly solution to manage its spreading while effectively mitigating Cd stress in crops.

Geospatial Approach to Identify the Indicators of Wetland Change: A Study for Kabartal (Ramsar Wetland), India

This study assesses the trend of LULC change in Wetlands from 1989 to 2023 using LANDSAT data for the core wetland zone of 26.02 km2 and the extended buffer zone of 623.08 km2. A physicochemical analysis of soil and water quality was conducted, and geospatial tools such as NDVI, MNDWI, and LULC were used in the study. ICP-OES study shows high levels of Ca, Mg, K, Fe and Cu in soil. Water quality analysis reveals that all parameters are within the permissible limit. Pearson correlation analysis shows a significant positive correlation between precipitation and vegetation cover in the buffer zone and with the water in wetland in the core zone. Nitrate and phosphate have a strong positive correlation with each other in both seasons, implying the same source, which could be due to agricultural runoff. Higher electrical conductivity indicates the presence of mineral ions and is strongly correlated with pH, temperature alkalinity and TDS. NDVI, MNDWI and LULC studies show decreased vegetation and water areas in core and buffer zones. The driving forces of wetland degradation are agriculture and built-up in core and buffer zones. The results of the kappa coefficient show an accuracy of 88%. Conservation strategies should be developed based on the indicators of the driving forces of degradation.

Rhizobacteria Isolated from Amazonian Soils Reduce the Effects of Water Stress on the Growth of Açaí (Euterpe oleracea Mart.) Palm Seedlings.

Euterpe oleracea Mart., also known for its fruit açaí, is a palm native to the Amazon region. The state of Pará, Brazil, accounts for over 90% of açaí production. Demand for the fruit in national and international markets is increasing; however, climate change and diseases such as anthracnose, caused by the fungus Colletotrichum sp., lead to decreased production. To meet demand, measures such as expanding cultivation in upland areas are often adopted, requiring substantial economic investments, particularly in irrigation. Therefore, the aim of this study was to evaluate the potential of açaí rhizobacteria in promoting plant growth (PGPR). Rhizospheric soil samples from floodplain and upland açaí plantations were collected during rainy and dry seasons. Bacterial strains were isolated using the serial dilution method, and subsequent assays evaluated their ability to promote plant growth. Soil analyses indicated that the sampling period influenced the physicochemical properties of both areas, with increases observed during winter for most soil components like organic matter and C/N ratio. A total of 177 bacterial strains were isolated from rhizospheres of açaí trees cultivated in floodplain and upland areas across dry and rainy seasons. Among these strains, 24% produced IAA, 18% synthesized ACC deaminase, 11% mineralized organic phosphate, and 9% solubilized inorganic phosphate, among other characteristics. Interestingly, 88% inhibited the growth of phytopathogenic fungi of the genera Curvularia and Colletotrichum. Analysis under simulated water stress using Polyethylene Glycol 6000 revealed that 23% of the strains exhibited tolerance. Two strains were identified as Bacillus proteolyticus (PP218346) and Priestia aryabhattai (PP218347). Inoculation with these strains increased the speed and percentage of açaí seed germination. When inoculated in consortium, 85% of seeds germinated under severe stress, compared to only 10% in the control treatment. Therefore, these bacteria show potential for use as biofertilizers, enhancing the initial development of açaí plants and contributing to sustainable agricultural practices.

Green tech soil analysis: a comparison of potentially toxic element contents among urban, agricultural, and gold mining areas in the Eastern Brazilian Amazon.

Artisanal gold mining can lead to soil contamination with potentially toxic elements (PTEs), necessitating soil quality monitoring due to environmental and human health risks. However, determining PTE levels through acid digestion is time-consuming, generates chemical waste, and requires significant resources. As an alternative, portable X-ray fluorescence (pXRF) offers a faster, more cost-effective, and sustainable analysis. This study compared total As, Ba, Cr, Cu, Fe, Mn, Ni, Pb, Sr, Ti, V, and Zn obtained from pXRF with their pseudo-total contents obtained through acid digestion (USEPA method 3051A) in areas influenced by artisanal gold mining in the Eastern Amazon, Brazil. pXRF data and machine learning algorithms were used to predict extractable Cu, Fe, Mn, and Zn. Linear regression models were fitted to compare the two methods, and random forest and support vector machine techniques were used to predict extractable contents. The best regression model fits for the pseudo-total PTE contents were those for Cu, Fe, Mn and Pb in agricultural areas (R2 > 0.80); Fe and Mn in gold mining residue (R2 > 0.70); and Ba, Cu and Mn in urban areas (R2 > 0.80). The best models for predicting the extractable PTE contents were those for Cu (R2 = 0.72; RMSE = 2.58mgdm-3) and Zn (R2 = 0.71; RMSE = 1.44mgdm-3) in agricultural areas and for Zn (R2 = 0.72; RMSE = 0.43mgdm-3) in gold mining residue. The results demonstrated that pXRF can characterize and predict PTE contents in mining-impacted areas, offering a sustainable approach to soil quality analysis.

Assessing bioavailability risks of heavy metals in polymetallic mining regions: a comprehensive analysis of soils with varied land uses.

To accurately assess the bioavailability risk of heavy metals (HMs) in a representative polymetallic mining region, we undertook an exhaustive analysis of Cu, Pb, Ni, Co, Cd, Zn, Mn, and Cr in soils from diverse land-use types, encompassing agricultural, forest, residential, and mining areas. We employed speciation analysis methods and a modified risk assessment approach to ascertain potential ecological threats posed by the HMs. Our findings reveal that both the total potential ecological risk and the modified bioavailability risks are most pronounced in the soil of the mining area. The modified bioavailability threats are primarily caused by Pb, Ni, Cd, and Co. Although the total potential ecological risk of Cu is high in the local soil, the predominance of its stable forms reduces its mobility, thereby mitigating its detrimental impact on the ecosystem. Additionally, medium modified bioavailability risks were identified in the peripheries of agricultural and forest areas, potentially attributable to geological processes and agricultural activities. Within the urban district, medium risks were observed in residential and mining areas, likely resulting from mining, metallurgy, industrial operations, and traffic-related activities. This study provides critical insights that can assist governmental authorities in devising targeted policies to alleviate health hazards associated with soils in polymetallic mining regions.

Soil moisture content estimation of drip-irrigated citrus orchard based on UAV images and machine learning algorithm in Southwest China

Soil moisture content (SMC), as a pivotal component in the energy and matter exchange processes within the soil-plant-atmosphere continuum, plays a crucial role in surface water dynamics, energy fluxes, and carbon cycling within ecosystems. The development of remote sensing technology has offered new perspectives for monitoring soil moisture at regional scales. Unmanned aerial vehicles (UAV) equip with multispectral have distinct advantages for vegetation monitoring, including rapidity and cost-effectiveness, which has superior applicability and practicality. Therefore, in a 5a "Daya" late-maturing citrus orchard, the vegetation index (VI) and texture feature (TF) information of citrus canopy based on UAV multi-spectral images were extracted, and soil and plant analyzer development (SPAD) of citrus was collected. These different data sources were integrated into the framework of the random forest algorithm (RF) and genetic algorithm-optimized random forest (GA-RF) to evaluate the accuracy of surface SMC (SSMC) estimation in citrus orchard. The Biswas model was utilized to simulate the root zone SMC (RSMC). The spatiotemporal variations of SMC in citrus orchard were analyzed, and the potential of low-cost sensor-equipped drones in rapidly acquiring spatial and temporal distribution information of SMC at a large regional scale was explored. The results indicated that the GA-RF models outperformed the RF models in estimating citrus orchard SMC (with R2 ranging from 0.502 to 0.949 and RMSE ranging from 0.552 % to 3.166 % for GA-RF, compared to R2 ranging from 0.430 to 0.936 and RMSE ranging from 0.587 % to 3.449 % for the RF). The GA-RF models using VI+SPAD as inputs exhibited the best performance for SMC at depths of 5 cm, 10 cm, 20 cm and 40 cm (SMC5, SMC10, SMC20 and SMC40) across citrus growth stages (R2 ranging from 0.793 to 0.949 at 5 cm, R2 ranging from 0.702 to 0.938 at 10 cm, R2 ranging from 0.714 to 0.927 at 20 cm). In bud bust to flowering, young fruit and fruit maturation stages (stage Ⅰ, ⅠⅠ and ⅠⅤ), all models demonstrated good accuracy in estimating SMC at depth of 10 cm (R2 ranging from 0.567 to 0.908 in stage Ⅰ, with R2 ranging from 0.681 to 0.916 in stage ⅠⅠ and R2 ranging from 0.579 to 0.938 in stage ⅠⅤ). In fruit expansion stage (stage III), the models performed best in predicting SMC5 (R2 ranging from 0.698 to 0.861). The Biswas model was constructed to simulate SMC40 by utilizing the inverted SMC10 and SMC20, thereby generating spatiotemporal distribution maps of SMC at different depths in citrus orchard. The SSMC was susceptible to environmental factors, exhibiting significant spatiotemporal heterogeneity. In summary, this study illustrated that the integration of multiple data sources into GA-RF enhanced the estimation performance of SMC at different growth stages of late-maturing citrus orchard in the Southwest China. Additionally, it enabled the rapid and efficient monitoring of spatiotemporal variations in SMC, providing an effective method and practical foundation for precision irrigation and improved water use efficiency.

3D Stability Analysis of Soil–Rock Tunnel Faces: Pore Water Pressure and Partial Failure Perspectives

3D Stability Analysis of Soil–Rock Tunnel Faces: Pore Water Pressure and Partial Failure Perspectives

Development of self-efficacy in high school students through soil analysis in the protected natural area "Estero El Soldado"

Teaching various scientific disciplines involves both hands-on and theoretical methods aimed at sparking students' curiosity in learning concepts. Students enhance their comprehension by actively engaging in direct activities rather than solely relying on explanations provided by teachers. The development of a research project carried out by 12th grade students during the semester allowed them to develop scientific or technological research to understand nature and act on it. The research project involved taking soil samples of natural protected area “Estero El Soldado” and analyzing it using a microscope, increasing student’s self-efficacy using the microscope. Self-efficacy was measured before and after the proposed research project using the General Self-Efficacy Scale instrument (Baessler and Schwarzer, 1996), which consists of 10 items and measures a person's perception when facing a variety of stressful situations. The participation of 43 12th grade students was analyzed. After the completion of the research project students, the results from the pre-test (M = 66.89, SD = 19.52) and post-test (M = 80.34, SD = 19.84) self-efficacy evaluation indicate that carrying out the research project resulted in an improvement in self-efficacy, t(84) = 2.01, p = 0.0008. We can conclude that integrating hands-on research projects like soil analysis using different laboratory equipment boosts 12th-grade students' scientific self-efficacy, as evidenced by improved scores on the General Self-Efficacy Scale. These findings underscore the value of experiential learning in nurturing both scientific skills and confidence in students.

Seedling Growth and Nutritional Status of Elaeagnus angustifolia and Robinia pseudoacacia as Response to Arbuscular Mycorrhizal Fungi and K-Humate

ABSTRACT This study aimed to reveal the effects of arbuscular mycorrhizae and K-humate on some of the morphological characteristics and growth of Russian olive (RO; Elaeagnus angustifolia L.) and black locust (BL; Robinia pseudoacacia L.). The indigenous mycorrhizal spores (Claroideoglomus claroideum, Claroideoglomus etunicatum, Claroideoglomus luteum, and Funneliformis mosseae) collected from rhizospheres of RO and BL trees in afforestation sites located in Central Anatolia. In addition, commercial mycorrhizal mixture and K-humate were used as treatments. Five treatments (1—indigenous mycorrhizal fungi, 2—K-humate, 3—indigenous mycorrhizal fungi + K-humate, 4—commercial mycorrhizal fungi, and 5—control) were assigned in a completely randomized design for both tree species. Four months after the treatments, inoculation rate, above-ground seedling height, fresh and dry weight, root collar diameter, length, fresh, and dry weight, leaf area index, shot-to-root dry weight ratio, seedling height to root collar diameter ratio, and Dickson quality index were determined. Plant and soil analyses were carried out to determine the effects of treatments on plant and soil nutrition. Indigenous arbuscular mycorrhizal fungi and K-humate combinations had positive effects on the morphological characteristics and nutritional status of the seedlings. The indigenous mycorrhizal and K-humate interaction showed the most pronounced effects on RO growth and nutrition.

Influence of soil degradation on radiation-chemical transformations of oil

Protecting soil from pollution by oil and petroleum products is an important environmental task. Sources of oil contamination of soil are not only oil fields, but also industrial facilities that directly or indirectly use petroleum products. Of interest are studies aimed at studying the processes of changes in oil during its degradation in the soil. When oil degrades, polycondensation processes occur on the soil surface, which leads to structural changes in the oil. We compared the composition and properties of oil after production and long-term presence on the soil surface. An analysis of oil-contaminated soils was carried out at a distance of 0–5 and 0–10 m from the source of pollution and from a depth of 0–15 cm. Oils degraded in the soil are also susceptible to the effects of natural radionuclide background. In this regard, the work investigated the impact of gamma radiation on oil degraded in the soil of the Surakhani oil field of Azerbaijan. Oil samples were taken from a well and from oil-contaminated soil and separated into three components: oils, resins, and asphaltenes. The results of mass spectrometric and IR spectrometric studies of the indicated fractions of crude and degraded oil samples are presented, and the patterns of formation of gaseous products during their radiolysis are established.

Role of Artificial Intelligence in Vegetable Production: A Review

Role of Artificial Intelligence (AI) in vegetable production, emphasizing its potential to address critical challenges such as climate change, population growth, and resource scarcity. AI technologies, including machine learning, computer vision, and robotics, are revolutionizing agricultural practices. AI-driven innovations in crop management, pest control, and soil analysis enhance productivity, reduce labour costs, and ensure sustainable farming practices. Notable advancements include precision spraying by Blue River Technology, significantly reducing herbicide use, and deploying autonomous tractors and drones for efficient farm management. AI applications, such as PEAT's Plantix and Trace Genomics, provide accurate diagnostics for soil health and pest management. Satellite-based solutions like Farm Shots and aWhere offer real-time crop monitoring and weather prediction, optimizing resource use and mitigating risks. The review highlights the importance of making AI technologies more affordable and accessible to farmers, particularly in developing regions. Collaboration between researchers, industry stakeholders, and policymakers is crucial to harness AI's full potential in agriculture. As AI continues to evolve, its integration into vegetable production promises a more efficient, resilient, and sustainable agricultural sector, contributing to global food security and environmental preservation. The aim of the study is to evaluate the impact and effectiveness of Artificial Intelligence (AI) in vegetable production, focusing on how AI technologies enhance productivity, efficiency, and sustainability. The objectives are to assess current AI applications, analyze their benefits and challenges, and provide recommendations for future improvements and wider adoption in the agricultural sector. The research methodology for the study on the role of Artificial Intelligence in vegetable production involves a comprehensive literature review of existing AI technologies and their applications in agriculture, coupled with the analysis of case studies to evaluate real-world implementations. Additionally, expert interviews and surveys with farmers and industry professionals will be conducted to gather insights on the benefits, challenges, and future potential of AI in this sector. The theoretical implications of the study on the role of Artificial Intelligence in vegetable production include advancing the understanding of AI's capabilities in agricultural optimization and contributing to the academic discourse on sustainable farming practices. Practically, the study provides actionable insights for farmers and agribusinesses on implementing AI technologies to enhance crop yields, reduce resource wastage, and improve overall farm management efficiency.

Carbohydrate accumulation patterns in mangrove and halophytic plant species under seasonal variation

This study investigates the impact of carbohydrate accumulation in mangrove and halophytic plants on their response to abiotic stress. Using soil analysis and FT-IR spectroscopy, key species (Sueda nudiflora, Aeluropus lagopoides, Avicennia marina) were examined for seasonal changes in sugar content (reducing sugars, total soluble sugars, starch). The elevated carbohydrate levels may serve as an indication of the plant’s ability to adapt to different environmental conditions throughout the year. This accumulation enables plants to adapt to variations in their environment, assuring their survival and functionality during periods of environmental fluctuation. Halophytic plants’ sugar content peaked during the monsoon, suggesting biotic adaptations. The mangrove Avicennia marina had year-round sugar levels. PCA and Hierarchical Cluster Analysis revealed sugar accumulation trends across species and seasons. Partial Least Squares (PLS) analysis revealed correlations between soil characteristics and sugar content, suggesting plant-microbe interactions. K-means clustering and correlation analysis of FT-IR data revealed sugar composition and resource allocation trade-offs. These findings shed light on the role of carbohydrate metabolism in enabling coastal plants to endure stress. Gaining insight into these mechanisms can enhance sustainable agriculture in challenging environments and shed light on plant adaptations to evolving environmental conditions, especially biotic interactions.

Effect of Nitrogen and Humic Acid on Growth and Yield of Pakcoy (Brassica rapa subsp. chinensis) Plants

Decreased soil quality due to reduced nutrient content is one of the causes of decreasedquality and yield in plants. The use of humic acid organic matter with N fertilizer is one of the effortsto increase nutrients in the soil while increasing plant growth and yield. This research was conductedin Heuleut Village, Leuwimunding District, Majalengka Regency, from May to June 2024. Theresearch method used was the experimental method with a group-randomized design. The treatmentconsisted of 16 treatments that were repeated twice. The variables observed were plant height,number of leaves, root volume, plant fresh weight, leaf area, and plant growth rate. The resultsshowed that the use of N fertilizer at a dose of 115 kg/ha (urea 250 kg/ha) and humic acid 3 g/planthad a good effect on all variables observed. The results of soil analysis showed an increase in N-totaland C-organic.

Soil Quality Evaluation and Analysis of Driving Factors of Pinus tabuliformis in Loess Hilly Areas

Soil Quality Evaluation and Analysis of Driving Factors of Pinus tabuliformis in Loess Hilly Areas

Metagenomic analysis of soil from landfill site reveals a diverse microbial community involved in plastic degradation

In this study, we have investigated microbial communities structure and function using high throughput amplicon sequencing and whole metagenomic sequencing of DNA extracted from different depths of a plastic-laden landfill site. With diverse taxonomic groups inhabiting the plastic-rich soil, our study demonstrates the remarkable adaptability of microbes to use this new substrate as a carbon source. FTIR spectroscopic analysis of soil indicated degradation of plastic as perceived from the carbonyl index of 0.16, 0.72, and 0.44 at 0.6, 0.9 and 1.2 m depth, respectively. Similarly, water contact angles of 108.7 degree, 99.7 degree, 62.7 degree, and 77.8 degree of plastic pieces collected at 0.3, 0.6, 0.9, and 1.2 m depths respectively showed increased wettability and hydrophilicity of the plastic. Amplicon analysis of 16S and 18 S rRNA revealed a high abundance of several plastic-degrading bacterial groups, including Pseudomonas, Rhizobiales, Micrococcaceae, Chaetomium, Methylocaldum, Micromonosporaceae, Rhodothermaceae and fungi, including Trichoderma, Aspergillus, Candida at 0.9 m. The co-existence of specific microbial groups at different depths of landfill site indicates importance of bacterial and fungal interactions for plastic. Whole metagenome analysis of soil sample at 0.9 m depth revealed a high abundance of genes encoding enzymes that participate in the biodegradation of PVC, polyethylene, PET, and polyurethane. Curation of the pathways related to the degradation of these materials provided a blueprint for plastic biodegradation in this ecosystem. Altogether, our study has highlighted the importance of microbial cooperation for the biodegradation of pollutants. Our metagenome-based investigation supports the current perception that consortia of fungi-bacteria are preferable to axenic cultures for effective bioremediation of the environment.

Portable X‐ray fluorescence spectrometry accurately measures metal concentrations in aqueous Mehlich III soil extraction solutions

AbstractAccurate measurement of metal concentrations in soil and water is vital for healthy crop production and decision making for environmental surveys. While there are a multitude of laboratory‐based soil analysis methods, such as inductively coupled plasma‐optical emission spectroscopy (ICP‐OES), flame emission spectrometry, and atomic absorption spectroscopy, most are time and resource intensive. Additionally, there is a lack of information for rapid analysis of elements for aqueous soil extractions. The goal of this research is to establish elemental correlations between portable X‐ray fluorescence (pXRF) measurements of Mehlich III soil extractions and traditional elemental measurements via ICP‐OES. We hypothesize that certain metals can be accurately measured in aqueous soil extraction solutions by pXRF to the same degree as they are measured by ICP‐OES. To test this hypothesis, Mehlich III and 2% nitric acid solutions with known elemental concentrations were analyzed via ICP‐OES and pXRF. Soil samples extracted using Mehlich III were compared between ICP‐OES and pXRF to verify correlations. High correlations were found for As, Ca, Cd, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Se, V, and Zn in both the Mehlich III and 2% nitric acid solutions at concentrations between 5 and 85 mg L−1. P, S, and Si did not show high correlations at concentrations &lt;100 mg L−1. These results indicate that between 5 and 85 mg L−1, pXRF analysis of aqueous solutions and soil extractions is a reliable technique; however, at low concentrations (i.e., &lt;5 mg L−1 for metals and &lt;100 mg L−1 for P and S), pXRF is not well suited.

Soil and root microbiome analysis and isolation of plant growth-promoting bacteria from hybrid buffaloberry (Shepherdia utahensis 'Torrey') across three locations.

The effects of climate change are becoming increasingly hazardous for our ecosystem. Climate resilient landscaping, which promotes the use of native plants, has the potential to simultaneously decrease the rate of climate change, enhance climate resilience, and combat biodiversity losses. Native plants and their associated microbiome form a holo-organism; interaction between plants and microbes is responsible for plants' growth and proper functioning. In this study, we were interested in exploring the soil and root microbiome composition associated with Shepherdia utahensis, a drought hardy plant proposed for low water use landscaping, which is the hybrid between two native hardy shrubs of Utah, S. rotudifolia and S. argentea. The bulk soil, rhizosphere, root, and nodule samples of the hybrid Shepherdia plants were collected from three locations in Utah: the Logan Campus, the Greenville farm, and the Kaysville farm. The microbial diversity analysis was conducted, and plant growth-promoting bacteria were isolated and characterized from the rhizosphere. The results suggest no difference in alpha diversity between the locations; however, the beta diversity analysis suggests the bacterial community composition of bulk soil and nodule samples are different between the locations. The taxonomic classification suggests Proteobacteria and Actinobacteriota are the dominant species in bulk soil and rhizosphere, and Actinobacteriota is solely found in root and nodule samples. However, the composition of the bacterial community was different among the locations. There was a great diversity in the genus composition in bulk soil and rhizosphere samples among the locations; however, Frankia was the dominant genus in root and nodule samples. Fifty-nine different bacteria were isolated from the rhizosphere and tested for seven plant growth-promoting (PGP) traits, such as the ability to fix nitrogen, phosphates solubilization, protease activity, siderophore, Indole Acetic Acid (IAA) and catalase production, and ability to use ACC as nitrogen source. All the isolates produced some amount of IAA. Thirty-one showed at least four PGP traits and belonged to Stenotrophomonas, Chryseobacterium, Massilia, Variovorax, and Pseudomonas. We shortlisted 10 isolates that showed all seven PGP traits and will be tested for plant growth promotion.

Sustainable Paddy Farming in Edirne: Evaluating the Impacts of Excessive Fertilizer and Pesticide Use

This study examines the environmental and economic impacts of chemical fertilizer and pesticide use in paddy fields in Edirne Province, Türkiye. Chemical fertilizers, especially nitrogen and phosphorus, are crucial for boosting paddy yield and ensuring food security. However, recent trends indicate significant potassium deficiency and excessive pesticide use, leading to environmental degradation and higher production costs. Incorrect fertilization techniques and pesticide usage can lower product quality and yield, adversely affecting farmers’ incomes and the national economy. Surveying 139 paddy farmers, the research found that farmers apply the herbicidal active ingredients Tefuryltrione, Clomazone, Tembotrione, and Quinclorac at rates of 129.35%, 34.27%, 18.06%, and 155.29% above the recommended levels, respectively. The excessive use of pesticides costs each farmer an additional USD 925.67, totaling USD 5.32 million for Edirne. The majority of farmers (69.2%) do not conduct soil analysis, indicating a potential gap in adopting the best practices for soil management and fertilization. Farmers use 84% less potassium than recommended while applying nitrogen and phosphorus 13% and 14% above the recommended levels, respectively. The extra cost of fertilizer use in Edirne is calculated as USD 833,135.49. This situation harms the economy and may have adverse effects on the environment and potential health effects on consumers.