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Empower Innovations in Routine Soil Testing

Conventional soil tests are commonly used to assess single soil characteristics. Thus, many different tests are needed for a full soil fertility/soil quality assessment, which is laborious and expensive. New broad-spectrum soil tests offer the potential to assess many soil characteristics quickly, but often face challenges with calibration, validation, and acceptance in practice. Here, we describe the results of a 20 year research program aimed at overcoming the aforementioned challenges. A three-step approach was applied: (1) selecting and establishing two contrasting rapid broad-spectrum soil tests, (2) relating the results of these new tests to the results of conventional soil tests for a wide variety of soils, and (3) validating the results of the new soil tests through field trials and communicating the results. We selected Near Infrared Spectroscopy (NIRS) and multi-nutrient 0.01 M CaCl2 extraction (1:10 soil to solution ratio; w/v) as broad-spectrum techniques. NIRS was extensively calibrated and validated for the physical, chemical, and biological characteristics of soil. The CaCl2 extraction technique was extensively calibrated and validated for ‘plant available’ nutrients, often in combination with the results of NIRS. The results indicate that the accuracy of NIRS determinations is high for SOM, clay, SOC, ECEC, Ca-CEC, N-total, sand, and inorganic-C (R2 ≥ 0.95) and good for pH, Mg-CEC, and S-total (R2 ≥ 0.90). The combination of the CaCl2 extraction technique and NIRS gave results that related well (R2 > 0.80) to the results of conventional soil tests for P, K, Mg, Na, Mn, Cu, Co, and pH. In conclusion, the three-step approach has revolutionized soil testing in The Netherlands. These two broad-spectrum soil tests have improved soil testing; have contributed to increased insights into the physical, chemical, and biological characteristics of soil; and have thereby led to more sustainable soil management and cropping systems.

UF/IFAS Standardized Nutrient Recommendations for Vegetable Crop Production in Florida

This publication presents the fertilization recommendations for vegetable crops based on soil tests performed by the UF/IFAS Extension Soil Testing Laboratory (ESTL). It contains the basic information from which ESTL soil test reports and fertilization recommendations are generated. The audiences for this information include commercial and small farmers, crop advisers and consultants, state and local agencies, fertilizer industry, and any interested individuals interested in sustainable nutrient and environmental management. Major revision by Rao Mylavarapu, George Hochmuth, and Guodong Liu; 12 pp. https://edis.ifas.ufl.edu/cv002

Agricultural Advisory Diagnostics Using a Data-Based Approach: Test Case in an Intensively Managed Rural Landscape in the Ganga River Basin, India

technology adoption through agricultural extension may be a consequence of providing generic information without sufficient adaptation to local conditions. Data-rich paradigms may be disruptive to extension services and can potentially change farmer-advisor interactions. This study fills a gap in pre-existing, generic advisory programs by suggesting an approach to “diagnose” farm-specific agricultural issues quantitatively first in order to facilitate advisors in developing farm-centric advisories. A user-friendly Farm Agricultural Diagnostics (FAD) tool is developed in Microsoft Excel VBA that uses farmer surveys and soil testing to quantify current agricultural performance, classify farms into different performance categories relative to a localized performance target, and visualize farm performance within a user-friendly interface. The advisory diagnostics approach is tested in Kanpur, representative of an intensively managed rural landscape in the Ganga river basin in India. The developed open-source tool is made available online to generate data-based agricultural advisories. During the field testing in Kanpur, the tool identifies 24% farms as nutrient-limited, 34% farms as water-limited, 27% farms with nutrient and water co-limitations, and the remaining farms as satisfactory compared to the localized performance target. It is recommended to design advisories in terms of water and nutrient recommendations which can fulfill the farm needs identified by the tool. The tool will add data-based value to pre-existing demand based advisory services in agricultural extension programs. The primary users of the tools are academic, governmental and non-governmental agencies working in the agricultural sector, whose rigorous scientific research, soil testing capacity, and direct stakeholder engagement, respectively, can be harnessed to generate more data-based and customized advisories, potentially improving farmer uptake of agricultural advisories.

Estimating cation exchange capacity and clay content from agricultural soil testing data

Clay content and the ability to reversibly retain cations affect many essential chemical and physical properties of soil, such as pH buffering and carbon sequestration. Cation exchange capacity (CEC) and base saturation are also commonly used as criteria in soil classification. However, determination of CEC and particle-size distribution is laborious and not included in routine soil testing. In this study, pedotransfer functions including soil test cations (STCat; Ca2+ + Mg2+ + K+), pH and soil organic carbon (SOC, %) as explanatory variables were developed for estimating CEC, titratable acidity (TA; H+ + Al3+) and clay content (clay, %). In addition, reference values for potential CEC and its components were determined for Finnish mineral and organic soils. The mean of potential CEC extracted by 1 M ammonium acetate at pH 7.0 ranged from 14 (range 6.4−25) in coarse soils to 33 (21−45) cmol(+) kg-1 in heavy clay soils, and from 42 (24−82) in mull soils to 77 (25−138) cmol(+) kg-1 in peat soils. The average CEC of clay and SOC were 27 and 160 cmol(+) kg-1, respectively. Titratable acidity occupied 53% and around 40% of the CEC sites in organic and mineral soils, respectively, evidencing that it is a prominent component of the potential CEC in these predominantly acidic soils. STCat, pH and SOC explained 96% of the variation in potential CEC. STCat and pH can be used in estimating the clay content especially for soils containing over 30% clay. In coarse textured soils, in contrast, SOC hampers the STCat based estimation of clay content.

Testing the accuracy of Soil Testing Kit® Transchem

Soil testing is key to soil fertility management as it serves as a fertilizer application guide to farmers, scientists and consultants. It gives information on soil nutrient status and its supplying capacity. Laboratory (LB) procedures have been the most reliable approach for soil nutrients analyses. However, it is costly and nonpoint. Thus, the use of in–situ testing kit emerges and becomes prominent. Notwithstanding, applicability of soil testing kit must be validated by laboratory test. This work aimed to examine the reliability/suitability of Soil Testing Kit® Transchem (SK) in determining selected soil nutrients in Sahel Savannah, Nigeria. Twentyfive replicate soil samples were collected from 12°47’86’’-12°20’96’’N and 4°38’37’’-4°188’02’’E, Kebbi State Nigeria and used to test soil pH, N, P, K and soil organic carbon (SOC) by SK and LB. The SK uses colour chart and comparator for rating nutrients status qualitatively into; low, medium and high and up to very high for P. The LB results were transformed to qualitative data by corresponding the values with soil rating standardinto low, medium and high. To perform statistics, weighting was done by assigning weight load to each category; low = 1, medium = 2 and high = 3. The two methods were compared using t-test, regression and descriptive analyses. Results showed non-significant difference between the two methods for soil contents of N, P and K. However, SK poorly estimated soil pH and SOC. Correlation and regression coefficients (r = 0.915 and R2 = 0.838, respectively) indicated reliability of the SK. It is concluded that SK can be reliably used for N, P, and K but not soil pH and SOC estimation for soils in Sahel savannah of Nigeria.

ANALYSIS OF EXPERIMENTAL DATA OF TESTING OF SOILS BY THE METHOD OF THREE-AXIAL COMPRESSION

The article analyzes the data obtained during soil testing in the ASIS-1 measuring and computational complex, the GT 1.3.1-05 triaxial compression device, conclusions are drawn about the possibility of determining the structural strength of soils in this device, graphic results are analyzed, and conclusions are drawn.

Livelihoods, Technological Constraints, and Low-Carbon Agricultural Technology Preferences of Farmers: Analytical Frameworks of Technology Adoption and Farmer Livelihoods

In the context of achieving carbon neutrality, it is scientifically important to quantitatively explore the relationships among livelihoods, technological property constraints, and the selection of low-carbon technologies by farmers to promote agricultural modernization and carbon neutrality in the agricultural sector of China. Based on the scientific classifications of farmer capital and low-carbon agricultural technologies, a farmer technology selection theory model considering capital constraints was developed in this study. Microcosmic survey data were collected from farmers in the Jiangsu province for empirical testing and analyses. A total of four low-carbon technologies related to fertilizer usage and three types of farmers’ livelihoods and their relationships were examined by using a logistic model. The results showed the existence of a significant coupling relationship between the intrinsic decision mechanism involved in selecting low-carbon agricultural technology and the properties of low-carbon agricultural technology for different types of farmers. Significant differences exist in the selection of different low-carbon technologies among large-scale farmers, mid-level part-time farmers, and low-level (generally small) part-time farmers. (1) When selecting technology, large-scale farmers are more inclined to accept capital-intensive, low-carbon technologies, such as new varieties, straw recycling, soil testing, and formulated fertilization. Mid-level part-time farmers are more inclined to accept capital intensive, labor saving, or low risk low-carbon agricultural technologies. In contrast, low-level part-time farmers are inclined to accept labor intensive technologies to reduce capital constraints and agricultural risks. (2) Large-scale farmers and low-level part-time farmers are influenced by household and plot characteristics, while mid-level part-time farmers are more influenced by plot characteristics. (3) Households with capital constraints created by differentiated livelihoods face challenges adopting capital-intensive low-carbon agricultural technologies, such as straw recycling, new varieties, soil testing, and formulated fertilization. However, farmers with stronger constraints in the areas of land and labor are more inclined to accept labor-saving technologies, such as soil testing and formulated fertilization technology. Moreover, farmers with stronger risk preferences tend to accept high-risk technologies, such as new technologies like straw recycling. The results of this study can provide a scientific basis for formulating carbon emission reduction policies and low-carbon technology policies for the agricultural sector.

Improvement of soil testing techniques for detecting spores of potato wart disease Synchytrium endobioticum using molecular methods

Synchytrium endobioticum (Schilb.) Percival. is a pathogen of potato wart disease and has a restricted distribution on the territory of the Russian Federation. Its main pathways are infected potato tubers and different planting material containing soil particles infected with spores of the fungus. One of the main problems is the use of toxic chemicals during detecting the disease in laboratory methods of direct soil testing to identify resting spores. This paper presents the assessment of molecular methods of soil diagnosis for detection of S. endobioticum by direct extraction of fungal DNA from soil samples using the MetaGen reagent kit. Identification was performed using the Fitoskrin. Synchytrium endobioticum-RT kit. The kit was pre-tested using DNA isolated from potato warts by various commercial kits. It was found that the optimal method of DNA isolation from the warts was using the FitoSorb-Avtomat 48 kit at the Tecan robotic station. Studies have shown that the sensitivity of the direct DNA extraction method from soil samples with various infection levels is the same as that of flotation method using carbon tetrachloride. Moreover, this method makes it possible to work with soil samples of different types, including peaty soils.

Identifying barriers to routine soil testing within beef and sheep farming systems

Sensors and their application in precision agriculture.

The paper depicts sensors in precision agriculture. It encompasses the most significant and frequently used sensors in agriculture. Furthermore, the paper explains the main sensor types according to their design, the recorded range of electromagnetic spectrum, as well as the way of detection, recording, measuring, and representation of the detected energy. The development of remote research has provided deeper understanding of remote sensors and their advantages. The sensors installed on soil testing equipment, fertilizing and crop protection machinery, as well as crop picking machinery have been analyzed relative to precision farming. The paper depicts widely known sensors OptRx, ISARIA and VRT technology. The results of the paper assess the data collected by sensors and processed in order to produce maps for agrotechnical operations. The application of maps decreases the employment of human resources, heightens the capacity of data collection, increases the precision of agricultural activities, and finally results in decreasing the cost of final products. The technological progress over the past decade has enabled the development of technology with variable application standards (VRT) that, according to current needs, enables input optimization.

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• Published: 25 August 2020

The concept and future prospects of soil health

• Johannes Lehmann   ORCID: orcid.org/0000-0002-4701-2936 1 , 2 , 3 ,
• Deborah A. Bossio   ORCID: orcid.org/0000-0002-2296-9125 4 ,
• Ingrid Kögel-Knabner 3 , 5 &
• Matthias C. Rillig   ORCID: orcid.org/0000-0003-3541-7853 6 , 7  

Nature Reviews Earth & Environment volume  1 ,  pages 544–553 ( 2020 ) Cite this article

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• Biogeochemistry
• Environmental sciences
• Sustainability

Soil health is the continued capacity of soil to function as a vital living ecosystem that sustains plants, animals and humans, and connects agricultural and soil science to policy, stakeholder needs and sustainable supply-chain management. Historically, soil assessments focused on crop production, but, today, soil health also includes the role of soil in water quality, climate change and human health. However, quantifying soil health is still dominated by chemical indicators, despite growing appreciation of the importance of soil biodiversity, owing to limited functional knowledge and lack of effective methods. In this Perspective, the definition and history of soil health are described and compared with other soil concepts. We outline ecosystem services provided by soils, the indicators used to measure soil functionality and their integration into informative soil-health indices. Scientists should embrace soil health as an overarching principle that contributes to sustainability goals, rather than only a property to measure.

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Acknowledgements

J.L. acknowledges the Hans Fischer Senior Fellowship of the Institute for Advanced Study (Technical University Munich) and a TNC-ACSF project (Cornell University), D.A.B. the support by the Craig and Susan McCaw Foundation, I.K.-K. the support by the German Federal Ministry of Education and Research (BMBF) in the framework of the funding measure ‘Soil as a Sustainable Resource for the Bioeconomy’ (BonaRes project), BonaRes Centre for Soil Research (FKZ 031B0516A; BonaRes, Module A) and M.C.R. an ERC Advanced Grant (694368) and the BMBF for the project ‘Bridging in Biodiversity Science (BIBS)’ (01LC1501A). Sincere thanks to Else Bünemann-König for sharing raw data.

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Lehmann, J., Bossio, D.A., Kögel-Knabner, I. et al. The concept and future prospects of soil health. Nat Rev Earth Environ 1 , 544–553 (2020). https://doi.org/10.1038/s43017-020-0080-8

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AI and machine learning for soil analysis: an assessment of sustainable agricultural practices

• Muhammad Awais 1 , 2 ,
• Syed Muhammad Zaigham Abbas Naqvi 1 , 2 ,
• Hao Zhang 1 , 2 ,
• Linze Li 1 , 2 ,
• Wei Zhang 1 , 2 ,
• Fuad A. Awwad 3 ,
• Emad A. A. Ismail 3 ,
• M. Ijaz Khan 4 , 5 ,
• Vijaya Raghavan 6 &
• Jiandong Hu 1 , 2  

Bioresources and Bioprocessing volume  10 , Article number:  90 ( 2023 ) Cite this article

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Sustainable agricultural practices help to manage and use natural resources efficiently. Due to global climate and geospatial land design, soil texture, soil–water content (SWC), and other parameters vary greatly; thus, real time, robust, and accurate soil analytical measurements are difficult to be developed. Conventional statistical analysis tools take longer to analyze and interpret data, which may have delayed a crucial decision. Therefore, this review paper is presented to develop the researcher’s insight toward robust, accurate, and quick soil analysis using artificial intelligence (AI), deep learning (DL), and machine learning (ML) platforms to attain robustness in SWC and soil texture analysis. Machine learning algorithms, such as random forests, support vector machines, and neural networks, can be employed to develop predictive models based on available soil data and auxiliary environmental variables. Geostatistical techniques, including kriging and co-kriging, help interpolate and extrapolate soil property values to unsampled locations, improving the spatial representation of the data set. The false positivity in SWC results and bugs in advanced detection techniques are also evaluated, which may lead to wrong agricultural practices. Moreover, the advantages of AI data processing over general statistical analysis for robust and noise-free results have also been discussed in light of smart irrigation technologies. Conclusively, the conventional statistical tools for SWCs and soil texture analysis are not enough to practice and manage ergonomic land management. The broader geospatial non-numeric data are more suitable for AI processing that may soon help soil scientists develop a global SWC database.

Graphical Abstract

Introduction

Organisms need water as a compulsory part of routine metabolic activities, and especially plants require a continual supply of soil–water to maintain their turgor and transport mechanisms (Huang et al. 2020 ). The predominant allocation of water within plants is dedicated to the hydrolysis process, which serves as a means of generating energy required for the sustenance of a diverse array of chemical reactions and physiological processes (Bhunia et al. 2023 ). Soil–water contents (SWCs) are often confused with soil–moisture contents (SMCs), which in general are different. SWCs account for a ratio between the volume of water present in a unit of soil volume (Hueso et al. 2012 ). It has been discovered that the rate of mineralization significantly affects the microbial contents and activity, which helps to regulate plant growth (Ma et al. 2022 ). Moreover, the soil porosity and SWC saturation in designated soil pores may hinder O 2 diffusion, because the rate of O 2 diffusion is about one hundred times less when compared with air.

Hence, the ideal soil–water contents SWCs are limited in their ability to sustain crop growth as a result of the impeded diffusion caused by saturated soil pores (Zhang et al. 2021 ). Similarly, when the saturation of soil pores decreases from an optimal level, it results in significant damage to the microbial flora residing within these pores. Consequently, this leads to a decline in nitrogen and carbon mineralization (Schlüter et al. 2098 ). Furthermore, when drought is induced in soil pores, the water films surrounding soil particles become thinner, and dryness prevails in the soil, which primes water channels in the soil pores to become disconnected (Dwevedi et al. 2017 ).

The optimal SWCs are closely associated with carbon allocation, plant growth, nutrient recycling, photosynthetic rate, and microbial activity. The regulation of these parameters has also invariably been linked with the physicochemical properties of water that are held in soil (Wang et al. 2023 ). There is a fuzzy concept that all of the water present in soil can be taken up by plants. However, this is not a true concept; holding the water against the gravitational pull is a soil art that most often depends on the type of soil. Another indicator of the soil's overall ability to store water is its porosity. Measuring soil–water content and potential is the initial step in doing soil research, since they are important to state quantities of soil (Heiskanen 1997 ; Vereecken et al. 2015 ). Therefore, this water-holding capacity against the gravitational pull feeds the crop during water scarcity posed by low precipitation.

Though, due to complicated laboratory protocols and high cost, it remains a mere challenge to analyze available water contents for plant growth (Zhao et al. 2015 ). Soil–water holding capacity (SWHC) is mainly affected by soil texture, which is further dependent upon pH, temperature, microbial community, precipitation, type of soil, and other relatable factors. For example, an elevated environmental temperature will lead to the thawing of permafrost, which leads to many fluctuations in soil properties finally nutrient availability to plants is compromised (Månsson et al. 2014 ). Changes in crop rotation and land use can make the soil better in ways, such as C/N ratio, bulk density, tillage (Beheshti et al. 2012 ), and soil–organic carbon distribution is influenced by general topography (Sun et al. 2015 ). Moreover, carbon stocks are influenced by precipitation. There are very few studies available on the subject that encapsulate multifactorial factors influencing soil’s porosity and texture (Jamil et al. 2016 ). For instance, nutrient movement, pore size, and soil structure are influenced by the soil physical properties. Soil fertility is improved by particle surface absorption of ions in clay (Wang et al. 2022 ). Hence, the comprehensive examination of environmental factors pertaining to soil texture, porosity, and water content availability necessitates a laborious endeavor (Dragone et al. 2020 ).

The electrical conductivity (EC) and SWC measurements with minimal damage to soil have been anticipated by scientists for many years (Masha et al. 2021 ). While from the previous literature, it has been found that these factors are greatly influenced by soil porosity, which in turn is compromised by fluctuating environmental influencers (Bittelli 2011 ). Besides, the effects of environmental influencing variables are a time-demanding and complex task (Ruszczak and Boguszewska-Mańkowska 2022 ) that has not been reviewed and has also not been experimentally evaluated in a single manuscript (Heiskanen 1997 ).

Due to the diversified data available, it is very difficult to establish and study the effects of all influencing factors on soil parameters (Zhao et al. 2020 ). This creates a huge hurdle to getting a complete insight into the influencing parameter and making further decisions for intelligent agricultural practices manually (Pastén-Zapata et al. 2014 ). The accurate outcomes and decisions may be facilitated by considering the processing time and prerequisites of both conventional and advanced statistical techniques (Clauser et al. 2022 ). However, on the other hand, the critical time to perform a management response may have surpassed. For instance, the SWCs have been recorded on the landmark, and it is time to irrigate. While conventional statistics play a significant role in this irrigation decision-making process, the relative analysis is consistently and flawlessly present (Blanco and Lal 2023 ).

Artificial intelligence (AI) has been found to process non-numerical data, such as images, videos, text, and voice data with greater perfection. Therefore, there is a need to align the geophysical influence data on soil quality with artificially intelligent systems to process the decision-making more robustly (Liu et al. 2014 ). The AI does not even require the data to be large enough to process and suggest a crop management practice. The AI tools have previously been found to be smart enough to remove the noise from SWC, EC, and DC data for soil parameters. The noise removal was found to give an accurate SWC measurement that is actually available for the crop to be taken (Ratshiedana et al. 2023 ). The current study explains a review of soil–water content data and its possible processing using AI tools. The false positivity in SWC results and bugs in advanced detection techniques are also evaluated, which may lead to wrong agricultural practices. This review first covers the soil–water relationships and advancements in measurement techniques for SWC and soil texture. Second, the initial efforts for the development of global SWC and soil texture databases using AI networks have been discussed. Furthermore, the conventional statistical and AI analysis platforms have been compared, and conclusions are drawn for future recommendations.

The diverse SWCs inside the soil

The soil has the property of water anchorage, which may change with physicochemical texture and climate (Singh and Nair 2023 ). The water-holding capacity of soil can vary among different types of soil. However, the comprehensive depiction of soil–water is insufficient to elucidate the scientific principles governing water absorption by field crops (Adhikari et al. 2022 ). The soil–water can be attributed to different types, i.e., hygroscopic soil–water (HSW), gravitational soil–water (GSW), and capillary soil–water (CSW). These various varieties of soil–water are subject to distinct and variable forces that degrade the soil (Rayne and Aula 2020 ). The soil architecture is quite variable and is affected by various environmental factors that regulate the soil pore distribution; likewise, the soil–water distribution is affected (Jian et al. 2015 ). The HSW contents are held by soil–particle physical interactions in vapor form, which is more often hydrogen bonding. These contents are very unlikely to be strained by the crops for their growth due to strong soil binding (Wuddivira et al. 2012 ). After the precipitation, the GSW is rapidly increasing but is drained with more speed than any other type of soil–water due to its humongous gravitational pull. The gravitational forces drag GSW contents sharply to the larger pores deep down in the soil and often add to the water table (Fu et al. 2021 ). Due to shorter root lengths, the crops are also much less likely to use this GSW. As a general concept, GSW contents are temporarily available to the crops only before they are drained. Ideally, plants can easily access the soil–water when water contents are -33 bar; this only happens after all of the GSW drainage is completed and is termed field capacity (Leucci 2012 ). Water is usually considered the most important factor for crop growth, but actually, the soil–water content causes plants to wilt if all of the soil pores are filled with water (saturation). Total saturation hinders oxygen diffusion and halts the respiration of roots, which destroy the whole crop lot (Bhattarai et al. 2005 ). Therefore, the actual and readily available water contents utilized by crops are the CSW contents that make up the maximum field capacity. Moreover, when field capacity is not accessible by the plants due to the strong bonding of remaining water with soil, the permanent wilting point is achieved which is actually the point of no water uptake by the plants (Ben-Noah et al. 2021 ). At this point, the crop water uptake forces cannot overcome the available soil–water, which is often calculated at -15 bar. Hence, the water draw point for crops lies between field capacity and the permanent wilting point.

Soil and water interactions affect nutrient uptake

The water storage in soil has a direct relationship with the movement of water in soil pores, making water potential and SWCs relatable (Richards 2004 ). The soil texture and layering profiles also influence the water flow (Khaled and Fawy 2011 ). The manner in which soil–water interacts with soil particles has varying effects on the absorption of water and nutrients by crops. The disparity between soil and crop root water potential serves as a determining factor in facilitating the process of crop water uptake (Vico et al. 2023 ).

The instantaneous water concentration and force generated by water inside the crop roots are called crop root water potential. This is the key determinant for the direction of water movement in or out of the plant, because water movement is always explained as spontaneous from higher to lower potentials (Agegnehu et al. 2016 ). The pressure potential, turgor pressure, and solute potentials are also the denominators for crop root water potential (Boyer 2015 ). However, the real factor that governs water flow is the interaction of water molecules with soil (adhesion) and with each other (cohesion). Gravitational pull is another key factor that restricts uphill water flow (Miranda-Apodaca et al. 2018 ).

In general, water molecules are associated with one another, and plants are only able to uptake soil–water when they overcome the adhesion and gravitational pull. Therefore, the water uptake by the plants, even at more feasible bar pressure is difficult with varying soil textures and porosity. The water movement from soil to plant roots is justified by the suction phenomena elucidated by TACT theory (transpiration, adhesion, cohesion, and tension). The present theory elucidates the mechanism underlying water transport within the xylem and the generation of negative water potential within plant roots, resulting in the development of suction forces that facilitate the uptake of water into the plant roots (Lambers et al. 2019 ). The supporters of this theory are of the view that water suction is only possible when the TACT forces overcome the gravitational pull and other physical interactions established by water. The schematic of TACT and water–soil interaction is explained in Fig.  1 .

Schematics of water–water and water–soil interaction affecting the movement and availability of SWCs to plant roots (Drawn using Biorender.com)

The disparities in nutrient composition between soil and crop roots facilitate the process by which crops absorb nutrients. The plant nutrient concentration refers to the quantity of nutrients found in the sap of a plant, which is measured as a ratio of mass or molarity per unit volume (Chen et al. 2022 ). Plant nutrient concentration varies with plant species, growth stage, and environmental conditions. Crop uptake of water and nutrients also depends on the characteristics of the root system, such as root branching pattern, root surface area, root diameter, root length, root hair density, root depth distribution, etc. (Dotaniya and Meena 2015 ). The characteristics of the root system affect the contact area between roots and soil particles as well as the transport capacity of roots (Pregitzer and King 2005 ).

Crops need adequate amounts of both water and nutrients for optimal growth and yield. The interaction of soil–water with soil particles depends on various factors, such as soil texture, structure, organic matter, pH, cation exchange capacity, fertilizer application, climate, crop species, growth stage, environmental conditions, root system characteristics, etc. Therefore, understanding these factors and their effects on soil–water–plant relationships is important for managing soil fertility and irrigation practices effectively (Fu et al. 2019 ).

The relative proportions of clay, silt, and sand are expressed as soil texture (Barman and Choudhury 2020 ). The texture of soils is considered to influence nutrient availability either by changing the water holding capacity or by manipulating the cation exchange capacity (Sharma et al. 2015 ). The water retained by soil for plant usage is subjected to water holding capacity, as this is the most crucial feature that supports nutrient uptake and transport mechanisms in plants. In general, higher water-holding capacity is a feature of finer-textured soils (clayey soils) than coarser textured soils (sandy soils), because they have 8–10 times more total pore space and smaller pores that hold water more tightly (Li et al. 2014 ). This means that clayey soils can provide more water and nutrients to plants than sandy soils, especially during drought periods. However, clayey soils can also become waterlogged or anaerobic if drainage is poor, which can limit nutrient availability and plant growth (Wang et al. 2021a ).

The concentration of soil nutrients exhibits variability based on factors, such as soil texture, structure, organic matter content, pH levels, cation exchange capacity, and the application of fertilizers (Bouajila et al. 2023 ). In general, finer-textured soils (finer than 1 mm) have higher soil nutrient concentrations than coarser-textured soils, because they have a larger surface area and a more negative charge on their surfaces that can adsorb cations (positively charged nutrients). However, finer-textured soils can also bind some nutrients too strongly or make them unavailable by forming insoluble compounds with other elements.

Cation exchange capacity (CEC) reflects the availability of cationic nutrients present in soil–water, such as ammonium, calcium, potassium, magnesium, iron, zinc, etc. Cover crops in seed maize or soybean treatment (SCCC) had a significant effect on soil exchangeable K in the topsoil (0–5 cm soil layer) (Emamgolizadeh et al. 2015 ). These positively charged ions are found in close interaction with negatively charged organic soil constituents. The positively charged ions are absorbed by the plant root using an anti-port cation exchange mechanism (Ulusoy et al. 2016 ). In general, finer-textured soils have a higher CEC than coarser-textured soils, because they have a larger surface area and more negative charge on their surfaces. This means that clayey soils can store more cations and prevent their leaching than sandy soils. However, clayey soils can also bind some cations too strongly or make them unavailable by forming insoluble compounds with other elements, such as phosphorus.

Soil texture also affects the mobility and retention of negatively charged nutrients (anions), such as nitrate, phosphate, sulfate, etc. Previously, 10% clay soil addition was nearly as effective in reducing N and P leaching as 20% clay soil. Adding only 10% clay soil to a sandy soil is likely to be less expensive than 20%. (Yan et al. 2022 ). Anions are not held by the soil particles but move freely with the soil–water. In general, coarser-textured soils have higher anion leaching potential than finer-textured soils, because they have larger pores that allow more water flow. This means that sandy soils can lose more anions by leaching than clayey soils, especially under high rainfall or irrigation conditions (Ali et al. 2020 ). However, sandy soils can also allow more anion uptake by plant roots than clayey soils, because they have lower anion adsorption potential. Convincingly, soil texture affects nutrient availability by influencing the water-holding capacity and the cation exchange capacity of the soil. Finer-textured soils tend to have higher nutrient retention and lower nutrient leaching than coarser-textured soils, but they may also have lower nutrient availability and aeration under certain conditions (Liu et al. 2021 ). Therefore, soil texture needs to be considered when managing soil fertility and applying fertilizers.

SWCs in different soil types

The predominant land textures in arable areas primarily depend on precipitation to sustain SWCs, which is crucial for supporting arid vegetation and facilitating the ecohydrological cycle (Xu et al. 2023 ). The SWCs in arid and semi-arid regions exhibit increased heterogeneity in response to variations in precipitation patterns and vegetation types (Obade and Gaya 2021 ). Due to the exhaustive evaporation factor, the smaller precipitation index has insignificant effects on SWC (Wilson et al. 2004 ). SWC is reported to decrease with increasing soil depth due to lessening influence of precipitation factors on deep soils. Such as, the coefficient of variation was found to be high for SWC in the horizontal direction (48%), but was relatively small for SWC in the vertical direction (9%) (Zhao et al. 2017 ). Therefore, it can be established that precipitation and SWCs are strongly associated with local climate (Mei et al. 2019 ). Diverse microbial communities coexist in various soil types to maintain their textural heterogeneity at microscale (Huang et al. 2023 ). Soil textures mainly hinge on soil heterogeneity, which has a direct linkage to soil pores distribution (Rooney et al. 2022 ). The differential pore distribution affects the SWC and pore saturation at large; therefore, soil–microbial flora can also influence the SWC measurement techniques that involve EM waves. The development of more microbial communities in soil pores often uplifts the water density (Amarasekare 2003 ) and also adds to the relative water volume, which later on results in false positivity for volumetric SWC observations (Vos et al. 2013 ). Scientists believe that this microbial flora is involved in the natural biogeochemical cycles and offers colonization resistance to the soil (Stein et al. 2014 ).

The water matrix suction by the crops has been a recent topic of research in environmental science and agriculture engineering (Xu and Yang 2018 ; Rahardjo et al. 2019 ; Tian et al. 2020 ). Measurement of SWCs in specific soil types is limited to detection techniques whether recent or advanced (Wang et al. 2021 ; Karakan 2022 ; Ojeda Olivares et al. 2020 ). These studies have presented the idea of hydraulic retention and SWC strength using quantitative analytical techniques. Soil texture (Liu et al. 2012 ) and pore conformations (Chen et al. 2017 ) have been found to pose a significant influence on total SWCs. Moreover, the initial soil wetting has also been investigated to impact the water content at large (Zhang et al. 2021 ). However, the rapid draining of water due to gravitational pull generates a sufficient number of atrocities in data collection and further analytical processing.

In another study, it was found that out of 120 samples, the water contents of more fine clayey soils were significantly higher compared with those of more sandy soils (Li et al. 2016 ). Nonetheless, the finer and clayey soil holds water sufficiently well and halts water mobility for crops even at ideal water bar of -30 units. Furthermore, when the soils are tuned to be finer, the porosity may increase, but the crop efficacy to drag water from these pores is significantly reduced (Fig.  2 ). Moreover, the suction matrix fractal analysis model for VSWC in various soil types confirms the significantly differential VSWCs (Fig.  3 ).

Physical parameters of differently textured soils in relation to water contents (Redrawn from data source) open access license (Obade and Gaya 2021 )

Results of fractal model showing the association between VSWC and matrix suction from diverse soil textures (Reprint from open access license) (Obade and Gaya 2021 )

In general, sandy soils have higher hydraulic conductivity than clayey soils, because they have larger pores that offer less resistance to water flow (Hao et al. 2019 ). This means that sandy soils can drain faster than clayey soils after rainfall or irrigation. However, this also means that sandy soils can lose more water by evaporation or transpiration than clayey soils, because they have a lower matric potential and cannot retain water against atmospheric demand. Because clayey soils have smaller pores that give more barrier to water movement, they have poorer hydraulic conductivity than sandy soils. This means that clayey soils can hold more water after rainfall or irrigation than sandy soils. However, because clayey soils have a larger matric potential, which prevents gravitational drainage, they can become waterlogged or anaerobic if drainage is poor (Mulla et al. 2023 ). SWCs vary with soil texture due to differences in pore space and pore size distribution. Clayey soils have a higher SWC and lower hydraulic conductivity than sandy soils under all moisture conditions. This affects the water holding capacity and water movement in the soil, which in turn affect various processes, such as plant growth, nutrient cycling, water balance, and soil erosion. Therefore, understanding the relationship between SWC and soil texture is important for managing soil and water resources effectively.

Advancements in SWCs measurements

Soil–water content (SWC) is a crucial parameter that affects various biophysical processes, such as plant growth, nutrient cycling, and water balance (Pereira et al. 2020 ). Measuring SWC accurately and efficiently is important for many applications in ecology, agriculture, hydrology, and engineering. The SWC is often termed wetness of soil, which in general has no specified but relative value (Lal and Shukla 2004 ). This relative value is often a ratio, often calculated as water over soil volume (V w /V S ) and volumetric SWC (VSWC), is usually represented as θ (García-Gamero et al. 2022 ). Moreover, as a general convention, this VSWC is often referred to as relative soil volume filled with water. Being a basic quantity for soil research initiation, SWC measurement is a routine analysis for soil examination. There are diverse methods that have been reported to measure SWC and have their own pros and cons. One of the basic, or so-called absolute, methods of measuring SWC is the gravimetric method (GM), which is noncalibrated and the most basic method.

The GM is a destructive approach that often fails to provide real-time knowledge and cannot measure the same sample area again (Villalobos et al. 2008 ). This method may also leave a space from where the sample has been taken, which will abruptly change the SWC, bulk density, and relative volume of nearby areas. These factors might leave the GM method unreliable and nonrepresentative if we are more involved in the real-time measurement of SWC. The neutron method (NM) was then developed for more likely real-time and non-destructive SWC measurements. This method accounts for only elastic collisions between neutrons and water molecules. Consequently, the presence of tightly bound hygroscopic water molecules and neutron dissipation can lead to the acquisition of false-positive outcomes.

The determination of the actual water content accessible for crops may continue to lack definitive findings (Drizo et al. 2022 ). Due to high radiation levels, this method also remains banned in most countries. This method is non-destructive, continuous, and capable of measuring SWC at different depths and large volumes of soil. However, it is expensive, hazardous, and requires a license to operate. It also requires calibration with other methods and correction for soil bulk density and temperature. With the research and development of SWC measurement technology, more accurate and noninvasive methods were also introduced, which require little calibration and are more sensitive. These methods either rely on electrical or electromagnetic (EM) signal travel in the soil and water, and then the resistance, capacitance, frequency, or time of travel can be compared as a measure of SWC. These methods are more robust and require prior installation and calibration (Karimi, et al. 2020 ). However, once installed, real-time and noise-free results can be obtained.

It remains a key factor that electromagnetic wave propagation is inhibited by air gaps, so the techniques utilizing EM waves are limited to certain soil types. For instance, the ground penetration radar (GPR) method involves EM wave propagation in soil and then a reflection of these waves by soil entities. It uses a transmitter and a receiver that are moved along the soil surface or placed in boreholes and measure the travel time, amplitude, frequency, or envelope of the reflected waves (Pandya 2021 ). This method is non-destructive, high-resolution, and capable of measuring SWC at different depths and large areas of soil. However, it is complex, expensive, and affected by soil texture, structure, salinity, and surface roughness. It also requires inversion models and calibration with other methods (Sharma and Sen 2022 ). The comparative analysis of the techniques discussed is also summarized in Table 1 .

Conclusively, there are various techniques available for measuring SWC at different scales and for different purposes. Each technique has its own strengths and weaknesses that need to be considered when choosing the most suitable one for a given situation. There is no single best technique that can measure SWC universally and accurately. Therefore, it is often necessary to combine or compare different techniques to obtain reliable and representative estimates of SWC. Future development of SWC measurement techniques may focus on improving their accuracy, precision, resolution, cost-effectiveness, ease of use, and integration with other sensors, models, and programs developed by AI.

Soil texture prediction and analysis using artificial intelligence

Soil texture has been found to play a crucial role in ecosystem health, agricultural production, and sustainable farmland management (Zhai et al. 2006 ). Among the diverse soil properties, the texture plays a pivotal role in decision-making for the planning and management of agricultural land. The conventional approaches with agriculture sensors and statistical analysis were found to be non-robust, time-consuming, non-instantaneous, and expensive (Bormann 2010 ). However, with advanced AI processing tools and ML applications, new avenues for texture prediction and revolutionized soil management practices have been opened.

Conventional soil texture analysis is performed by sieving, sedimentation, and other hydrometric laboratory methods. Later, the results from these experiments are statistically analyzed, and conclusions are drawn manually. The complexity of this analysis can be presumed from the variable soil textures and environmental attributes that affect it. This creates heaps of data that cannot be translated into a single conclusion for correct decision-making (Riese and Keller 2019 ). Therefore, all of these manual dealings require skilled professionals, a significant amount of time, and specialized instruments. However, AI tools are a promising set of alternatives for these limitations that otherwise confine soil management.

AI techniques that include machine learning (ML) and deep learning (DL) are potentially remarkable for accurate and efficient soil texture predictions. The inputs utilized by these algorithms are compositional, spectral, and geographical data sets that can be in non-numerical form (Johnson et al. 2020 ). AI processing of these data sets mainly reduces the cost, time, and labor involved compared with conventional laboratory protocols. The complexity of relationships among the data sets is quickly learned and applied using the ML and DL algorithms (Wang et al. 2021b ). This is not the only scale available with this technology; cloud systems and mobile applications are another step forward. The wider scalability of AI enables farmers and land managers to access and process land management operations with ease.

The subjective and error-prone data analysis from traditionally practiced laboratory protocols is then dazzled by the objective, more accurate, and real-time data processing using AI tools (Hassan-Esfahani et al. 2015 ). By leveraging AI techniques, we can overcome the limitations of traditional laboratory-based methods and enable real-time decision-making in soil management practices. However, addressing challenges related to data quality, interpretability, and system integration will be crucial for the successful implementation of AI-based soil texture analysis (Liu et al. 2022 ). With continued research, collaboration, and innovation, AI-driven soil analysis can contribute significantly to sustainable land management, agricultural productivity, and environmental conservation. Integrating AI models seamlessly into existing soil management practices and decision support systems requires collaboration among scientists, engineers, and policymakers. This integration would ensure the practical implementation of AI-based soil texture analysis on a broader scale.

Artificial intelligence and SWC measurement

The highest cadre of the information technology revolution is AI, which has influenced and reshaped every field of life. AI has a robust approach, where the computers learn from already existing data sets and get themselves trained enough to solve complex problems. This AI resides on very complex modules that are cladistical in their linkage and are very complex to understand (Barros et al. 2022 ). AI is generally compared with human neurons for its signal-processing complexity. The inputs, processing, decision-making, and outputs are similar to those of human neurological systems (Fig.  4 ).

Comparison of artificial and biological neuron (Barros et al. 2022 ) (Open Access)

A variety of fields have benefited from AI, for instance, robotics, medical imaging, disease detection, and flight control systems. AI has also been found to be capable of solving key issues in agronomy, meteorology, and hydrology. The science of SWC measurement has also benefited from AI source codes recently. It has presented itself as a water- and soil-state manager with high performance, correlation, and statistical correctness (Gao et al. 2022 ). This is a very smart add-on to robust agricultural decision-making under the influence of various environmental factors. This section of the manuscript will review AI strategies for agriculture biosensing, with a special focus on SWC management.

Climate variability can readily impact the outcomes of the FDR and TDR techniques for measuring SWC. This has inspired scientists to report a novel method in 2020 (Mallet et al. 2020 ), which included measuring the temperature response and heating the surrounding soil in short bursts. Due to its high degree of automation and being less influenced by climate, this actively heated optical fiber (AHOF) method can be applied, where conventional TDR and FDR might not be enough (Ciocca et al. 2012 ). However, the analysis performed by AHOF still requires correction due to unpredicted errors, types of soil, and other climatic variables. Therefore, the artificial neural network (ANN) utilized for removing the errors generated by the AHOF method to improve the effectiveness of acquired results (Liu et al. 2023 ). The prescribed model was recommended for usage, which contained the use of a climate layer or cover layer that was found to be highly correlated with SWC contents.

The field of agriculture sensing has now been closely linked with ANN models to rectify and filter the most accurate results and forecast the future for irrigation and agricultural management. Precision agriculture has now been supplemented with pH, humidity, SWC, mechanical, and airflow sensors that provide enough results for robust decision-making using ANN tools. Considerable efforts have been made to forecast the future of wheat crops using ANN model training based on previous sensor readings (Roshan et al. 2022 ). The scientists then concluded multilayered perceptron model was the most effective, with lower MSE and RMSE when compared with other ANN models. Accurate assessment of SWCs has grabbed the attention of researchers over the past few years.

Many efforts have been made to contribute to in-situ data collection and the development of remote databases for SWCs (Owe et al. 2008 ). However, these efforts are still in progress, and to date, we do not have such a database on a global scale. SWC measurement using pseudo-transfer functions (PTFs) is a solution to maximize the right soil data acquisition from lands, where there are no data available. Regression model analysis usually generates PTF data that can be fed to an adaptive neuro-fuzzy interference system (ANFIS) that gives a non-explicit insight into SWC variables (Liu et al. 2020 ). The research found the ANFIS model to be more efficient, robust, and quick compared to conventional statistical models that have higher standard errors (Hosseini et al. 2021 ). Understanding soil texture and water content is key to proper crop management. Precipitation and droughts considerably account for more than anything else that can affect soil textures (Keller and Håkansson 2010 ).

The main components of a Bragg grating AH–FBG moisture sensor are a resistance wire, an optical fiber with quasi-distributed FBGs for temperature monitoring, and an enclosed tube. The capacity of an FBG to reflect light waves with a specific center wavelength is affected by temperature and strain. The AH–FBG sensor's center wavelength shift is unaffected by strain, because a corundum tube encircles it, taking the strain out of the equation. If the sensor is placed in the ground, the soil temperature at the relevant measurement point may be calculated by examining the FBG reflection spectrum (Fig.  5 ). This superior technology, however, is strictly confined to certain situations. For example, when heated, the AH–FBG sensor may be seen as an endless linear heat source. Furthermore, the soil being tested is expected to be homogenous and isotropic (Liu et al. 2023 ).

Artificial intelligence-based fiber optic sensing for soil moisture measurement (Liu et al. 2023 ) ( a ) an optical fiber with quasi-distributed FBG for temperature measurement ( b ) example of result plot from the fiber with quasi-distributed FBG

Role of AI in smart agricultural irrigation systems

AI has enabled computer science to create artificial systems that work similar to the human brain. These artificial systems or machines can help humans perform more precise learning, logical reasoning, problem-solving, and decision-making. Due to its enormous potential, AI has been integrated into many research fields, such as agriculture, where it can help farmers optimize their use of water, land, and other resources, increase their productivity and profitability, and reduce their environmental impact (Kose et al. 2022 ). Agriculture faces the key challenge of water scarcity in regions with low precipitation and where droughts are prevalent.

The Food and Agriculture Organization (FAO) states that agriculture accounts for about 70% of global freshwater withdrawals, and it can go up to 15% by 2050 to meet the mounting demand for food by a growing population (Pernet and Ribi Forclaz 2019 ). However, water availability is unevenly distributed across the world, and climate change is expected to exacerbate the variability and impulsiveness of rainfall patterns, distressing crop yields and quality. To address this challenge, many researchers and practitioners have proposed and implemented smart irrigation systems that use sensors, controllers, actuators, communication networks, and data analysis tools to monitor and control the delivery of water to crops according to their needs and environmental conditions (Jong et al. 2021 ).

These systems aim to improve water use efficiency, reduce water waste and runoff, enhance crop growth and quality, and save energy and labor costs (Penghui et al. 2020 ). However, smart irrigation systems face some limitations, such as high maintenance cost, a lack of interoperability and standardization among different devices and platforms, the complexity of data processing and interpretation, and the uncertainty and variability of crop responses to irrigation. Moreover, traditional irrigation scheduling methods based on fixed rules or thresholds may not be intelligent enough to detach the dynamic and nonlinear interactions between soil, crops, water, management practices, and weather (Kouadio et al. 2018 ). Therefore, AI is considered to play a significant role in enhancing of functionality and performance of smart irrigation systems. Agriculture, when factionalized with AI systems, can help farmers identify instantaneous and accurate measurements as well as decisions for fertilization, irrigation, SWC, pH, and related factors (Raja and Shukla 2021 ). AI can also help farmers optimize their irrigation schedules and distribution using machine learning algorithms that can learn from historical and current data, forecast future scenarios, and adapt to changing conditions.

There are a variety of insights available, where AI can help smart irrigation systems obtaining optimal results. Fuzzy logic is one of the examples that deals with imprecise and uncertain information using linguistic variables and rules instead of numerical values. Fuzzy logic can be used to model complex systems, such as SWC, soil–total moisture, nutrient dynamics, or crop water requirements by incorporating expert knowledge and human intuition (Mahmoudi et al. 2022 ). Second, the Internet of Things (IoT) is a network of interconnected devices that can collect, transmit, process, and act on data without human intervention. IoT can be used to implement smart irrigation systems by integrating sensors, controllers, actuators, communication modules, cloud computing services, and mobile applications (Kodali and Sahu 2016 ).

Finally, ML is another smart advancement that is empowered by ANNs, DL, and reinforcement learning (RL). These attributes of ML have the advantage of processing soil imagery to extract information about soil textures, SWCs, nutrition, and other parameters (Pham et al. 2018 ). Therefore, utilizing these resources efficiently over conventional statistical approaches and vigorous data analysis can be the new era approach for smarter agriculture practices, especially for soil management. In the last couple of years, numerous researchers and engineers worked on different types of materials subject to biological sciences, agricultural sciences and environments.

Statistical and AI tools comparison

The statistical approaches have served the science from its beginning and have presented enormous ways of data analysis for further decision-making and drawing conclusions (Phoon et al. 2010a ). Most mathematical or statistical approaches involve numerical data processing, stigmatization, and drawing data correlations. Rather, the AI tools rely on computational modules to carry out such challenging tasks that otherwise require human intelligence. Drawing the line between statistical analysis and AI processing is difficult due to the reliability of both in various conditions (Henderson et al. 1992 ). However, AI processing for the same subject of experiments can be more advantageous, robust, and decisive compared to statistics. Statistical analysis usually requires structured and large numerical data inputs, such as SWCs, soil properties, weather data, etc. (Phoon et al. 2010b ). However, on the other hand, AI tools can efficiently operate with non-structured and qualitative data, such as images, videos, texts, voice data, etc. (Yu and Kumbier 2018 ). In addition, the AI tools are smart enough to handle data augmentation, regularization, and ensemble learning of small and noisy data sets (Friedrich et al. 2022 ). More specifically, ML tools are data-driven models and novel algorithms for training, processing, and making predictions for given data sets that are not addressed while using predefined statistical models. The interpretation and visualization of data have been simplified for human interface in AI tools, but the statistical approach is not robust and time-consuming for this purpose. Statistical tools can be used for descriptive and inferential purposes, such as describing the distribution of soil–water content, identifying the factors that affect soil–water content, testing the differences or relationships among soil–water content variables (Wadoux et al. 2020 ). On the other side, AI tools can be used for predictive and prescriptive purposes, such as predicting SWCs based on various inputs, optimizing the irrigation schedule based on SWC goals, recommending the best management practices, etc. Statistical and AI tools are known to have different strengths and limitations for SWC analysis. Depending on the research question and objective, one may choose to use either or both of them to obtain comprehensive and accurate insights from the data. The link between statistical and AI tools is an advanced key to opening the doors for robust management and agricultural biosensing (Table 2 ).

Development of a global dataset for soil texture and SWC data

Many ecological processes, water availability, and agricultural productivity depend on the type and strength of agricultural land. Soil texture and SWC are the more crucial parameters to determine effective land usage, sustainable agriculture, and water administration (Maino et al. 2022 ). However, due to the vast earth’s surface and spatial soil heterogeneity, it is very difficult to obtain comprehensive and reliable soil information (Martinelli and Gasser 2022 ). The global data set of SWC and soil texture information has the potential to enable farmers, policymakers, and land managers to timely engage in smart agricultural practices. Such data sets can also help in crucial hydrological analysis and climate modeling.

Soil–water dynamics, climate change, flood prediction, and water movement modeling can be reshaped after the development of these global data sets. Environmental conservation is another crucial concern that can be smartly managed after developing global data sets for soil parameters (Zhang and Shi 2019 ). This will help in the assessment of soil erosion, fertility, and ecological health. Moreover, areas with vulnerable soils can be identified more accurately, and targeted conservation and land restoration efforts can be practiced more efficiently. Considerable efforts have been made in past toward the development of a global soil parameter database. These efforts involve everything from general laboratory analysis of soil from various localities to the installation of advanced sensors globally or geospatial technologies (Mallah et al. 2022 ).

Remote sensing technologies, such as satellite imagery and airborne sensors, can provide valuable information on soil properties indirectly. Spectral signatures obtained from these sensors can be correlated with soil texture and soil–water content data collected from ground-based measurements. Proximal sensing techniques, such as electromagnetic induction and ground-penetrating radar, also contribute to the acquisition of soil data on a larger scale. Machine learning algorithms, such as random forests, support vector machines, and neural networks, can be employed to develop predictive models based on available soil data and auxiliary environmental variables. Geostatistical techniques, including kriging and co-kriging, help interpolate and extrapolate soil property values to unsampled locations, improving the spatial representation of the dataset (Naimi et al. 2022 ).

Ensuring an adequate distribution of soil samples across different regions, soil types, and land cover categories is essential for capturing the spatial heterogeneity of soils globally. Sampling biases and limited access to certain regions can pose challenges in achieving a representative data set. Soil data collected using different protocols, laboratory methods, and instruments needs to be standardized and harmonized to ensure consistency and compatibility. Developing robust quality control procedures and data harmonization protocols is necessary to integrate diverse data sets into a coherent global database. Encouraging data sharing among researchers, institutions, and national soil agencies is crucial for developing a comprehensive global data set. Collaboration at regional and international levels can help overcome data gaps and promote data exchange, leading to a completer and more reliable dataset.

The need for robust, quick, and accurate soil analysis using AI technology holds a great and promising future for sustainable agricultural practices and efficient natural resource management (Pandey et al. 2023 ). The nonuniform geospatial distribution of SWCs and soil textures is a big impediment to the development of a global soil database. Even advanced statistical data processing is time-consuming and has delayed decision-making to practice intelligent agriculture. However, by leveraging AI, DL, and ML techniques, researchers can overcome these challenges and obtain more efficient and reliable soil analysis results. Artificial neural networks (ANNs) and other related AI modules have shown promising results in achieving robustness in SWC and soil texture analysis. These techniques allow for the processing of non-numeric geospatial data, providing valuable insights for soil scientists and aiding in the development of a global SWC database.

Machine learning algorithms, such as random forests, support vector machines, and neural networks, can be employed to develop predictive models based on available soil data and auxiliary environmental variables. In addition, geostatistical techniques such as kriging and co-kriging play a significant role in interpolating and extrapolating soil property values, improving the spatial representation of the data set. However, challenges such as false positivity in SWC results and bugs in advanced detection techniques need to be addressed to ensure accurate and reliable soil analysis. Further research and development are required to refine AI models and improve their performance in soil analysis applications. It is evident that conventional statistical tools alone are insufficient for robust SWC and soil texture analysis. The integration of AI and related technologies provides a promising pathway to enhance soil analysis efficiency, enable intelligent decision-making, and facilitate sustainable land management practices. By harnessing the power of AI, researchers can make significant strides in understanding soil–water relationships, improving agricultural productivity, and developing a comprehensive global SWC database to support sustainable agriculture and resource management.

Availability of data and materials

Data sharing not applicable to this article as no data sets were generated or analyzed during the current study.

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Researchers Supporting Project number (RSPD2024R1060), King Saud University, Riyadh, Saudi Arabia. The authors are thankful to Henan agriculture university for providing the research facilities.

Researchers Supporting Project number (RSPD2024R1060), King Saud University, Riyadh, Saudi Arabia. Also, this work was supported by the National Natural Science Foundation of China, Grant Number 32071890, 31671581 and supported by the Henan Center for Outstanding Overseas Scientists, Grant Number GZS2021007.

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Awais, M., Naqvi, S.M.Z.A., Zhang, H. et al. AI and machine learning for soil analysis: an assessment of sustainable agricultural practices. Bioresour. Bioprocess. 10 , 90 (2023). https://doi.org/10.1186/s40643-023-00710-y

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• Intelligent agriculture
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Segregation test—a standardisable test for suffusion assessment of granular soils

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• Open access
• Published: 23 May 2024
• Volume 17 , article number  186 , ( 2024 )

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• Peter To 1 ,
• Soo Vang 1 ,
• Shenese Dempsey 1 ,
• Jayden van Donderen-Livock 1 &
• Rico Saayman 1  

Suffusion is arguably the most complicated type of internal erosion. Although there are several popular assessment methods, the most realiable assessment is possibly still laboratory testing. However, there is not a standardised test for suffusion yet as different laboratories use different equipment and test configurations. Hence, a reliable comparison of outcomes across laboratories may not be able to achieve yet. This paper presents a new and simple, but very effective, way to test the susceptibility of soil to internal erosion using a novel segregation test. The test employs standard equipment which can be easily found in any geotechnical laboratory. There are some common characteristics of internal erosion and transport segregation, where fine particles are transported through the pore constrictions formed by the soil’s primary fabric. In segregation, particles are transported by gravitational/mechanical force to the bottom of the soil mass. Meanwhile, they are washed out of the soil mass by hydraulic force in internal erosion. Laboratory testing for internal erosion often requires specific equipment and a long duration. Meanwhile, segregation test could be undertaken with standardised sieving tower, which is available in any geotechnical laboratory. The approach was verified with an acrylic setup and some 3D-printed details. Later, the tests of 25 mixtures were undertaken with standard sieving sets. The correlation of laboratory results shows good agreement and prompts the common application of the new approach. The new test may not be able to completely replace the conventional suffusion test yet as it overlooks the critical hydraulic gradients at this stage, but it can be very useful if the research focuses on only the erodible mass and susceptibility to suffusion. In addition, it is a standardisable test with no specific requirements on equipment. The new approach may also be a starting point to study other types of internal erosion.

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Introduction

Suffusion is an insidious hazard occurring under the ground surface. As not all soil particles contribute equally to the stress transfer (Skempton and Brogan 1994 ), soils may have a bimodal structure. That is, coarse particles form the primary fabric, while fine particles embedded loosely in pores can be mobilised and transported by seepage. When fine particles are washed out of the soil mass, the porosity and hydraulic conductivity of the soil may increase. As a sequence, seepage is enhanced and may facilitate the destruction of the primary fabric.

Although there are plenty of suffusion assessment methods, their accuracy may need to be scrutinised (To and Scheuermann 2021 ). Field test is way too expensive, and numerical simulation is limited by number of particles. The laboratory test may be still the most reliable method to confirm the internal stability. However, the laboratory approach requires a special setup, including an erosion unit with diffusers, a hydraulic system for water cycle and water head control, and a collecting system for fine particles. These devices are not often available in geotechnical lab and may need to be manufactured. As a sequence, different laboratories may employ different testing configurations and equipment.

In general, suffusion tests could be classified in two categories. The first category of suffusion tests adapts existing laboratory setups, which are usually triaxial tests (Xiao and Shwiyhat 2012 ; Ke and Takahashi 2014 ) or odo-permeameter test (Sail et al. 2011 ). Hence, the adaptation is able to investigate the behaviours of soils under significant loadings, including cyclic axial stress (Mehdizadeh et al. 2019 ). This simulates the real behaviour of soils in field and hydraulic structures. As the small loading plate of the triaxial setup limits the sample size up to roughly 70 mm diameter, larger samples may need an additional support system (Moffat et al. 2011 ; Chang and Zhang 2011 ). The second category of suffusion test consists of unique setups created for specific purposes. To study 2D defects, the erosion unit can be a flat rectangular box instead of a conventional cylinder (Indiketiya et al. 2017 ). Water can be injected via small holes on a tube buried in the sample to study concentrated flow due to internal cracks (Sato and Kuwano 2015 ). For the development of piping, a slot or hole can be preformed (Wan and Fell 2004 ; Benahmed and Bonelli 2012 ).

Up to date, there is no standardised unit for the use in commercial laboratories yet because the lab setup and testing conditions are frequently alternated for specific monitoring (To et al. 2018 ). It is authorial experience that professional dam engineering practice are based on some traditional assessment methods, which have not been standardised. There is a need of a standardised setup, which may study fewer parameters but can be applied with minimum nuances. Thereby, test results can be verified by an independent third party.

A possible parameter to be removed is the critical hydraulic gradient. In laboratory tests, the hydraulic gradient is usually changed instantaneously. Meanwhile, it varies gradually in real incidents, except pipe cracking/leaking. The exact estimation of the critical hydraulic gradient in a gradual change is an actual challenge (To and Scheuermann 2021 ). Besides, the impact of this gradient on internal erosion may depend on the deviation of seepage direction (Goldin and Rasskazov 1992 ), which could vary widely in porous medium at both macro and particle scales (To et al. 2020 ). When the spotlight on hydraulic force is off, the similarity between internal erosion and segregation attracts attention. Segregation is a phenomenon when fine particles are transported through pore constrictions to form local particle size distributions (PSD) that are distinct from the global PSD. Although the segregation is well known as a common issue for soils transported and dumped by trucks (To et al. 2016 ), it also can be caused by seepage in internal erosion and clogging, which is a type of internal instability of soil (Goldin and Rasskazov 1992 ).

This paper presents a new test in the first category of suffusion tests. The new setup is adapted from sieving test and named segregation test, where the particle transportation is driven by gravity and shaking impact instead of seepage force (Fig.  1 ). The initial verifying setup was transparent to observe the similarity of segregation due to shaking and suffusion due to seepage. Later, 25 mixtures were tested with a steel sieving set. The nets of sieves, except the bottom sieve, were removed to facilitate the free transportation of particles. The laboratory results of the new segregation tests showed that the new test still can estimate the erodible mass with a similar accuracy to the traditional suffusion test but with much less effort. No water management is required, and the testing devices can be found easily in any geotechnical laboratory.

Forces in particle transportation in suffusion (left) and segregation (right)

• Laboratory setup

The original idea came from an experimental experience in a suffusion test with downward seepage direction (To and Van Thinh 2021 ). The test investigated the maximum proportion of fine particles, which can fully fill the pores. This way, fine particles clog themselves and cannot be washed out (Indraratna et al. 2011 ). When the erosion unit (Fig.  2 ) was accidentally shaken, the soil particles were rearranged and transported. A time-lapse video showed some similarities between transportation due to shaking and internal erosion due to seepage. This discovery led to the idea that it may be feasible to test the susceptibility of soils to suffusion with standard sieving equipment. The new test is named in this paper as segregation test.

Universal internal erosion apparatus

There may be one problem with segregation test. The shaking impact may be way too large and destruct the soil primary fabric from the beginning, which does not occur in suffusion. Hence, different amplitudes must be tested to find a good value that does not cause any change in soil primary fabric. As the normal sieves are made from steel, it is difficult to notice the change inside the tower. Hence, a transparent acrylic setup was made to verify the idea.

Transparent segregation setup

The acrylic segregation tower was made from up to 6 layers with a compressing top cap and a bottom pan (Fig.  3 ). All components were tightened together with bolts and wing nuts. The external and internal diameters of the tower were 200 mm and 194 mm, respectively. These are the closest dimensions of commercially available acrylic tubes to the size of laboratory standard sieves at 200/203 mm (ISO 2016 , 2020 ). The thickness of each layer was 50 mm, which was roughly 10 \(d_{100}\) . A net was placed at the top of the bottom pan to keep the sample above shaken-off particles. This helped to simulate the internal erosion process, where particles are washed out of the soil mass. The net was tested against both static and dynamic loads of 340 and 260 N, respectively (Fig  4 ). This is the proposed cyclic impact from the vibration with 20 kg of soil. It is authorial experience that good quality sieves in general soil laboratories could hold 20 kg of soil without problems. As there are hundreds of wires, each steel wire carries just a few grams. Nevertheless, if the net is anchored with only four wing bolts, each bolt has to carry 5 kg. Hence, small cracks may occur in acrylic, near the bolts. Preformed cracks may help to reduce the damage.

In the first trial, a gap-graded soil was used, and the whole tower was shaken for half an hour with 1 mm amplitude. A total of 1.27 kg of the erodible soil mass was shaken off in the first 15 min. During the last 15 min, only 20 g were collected. A visual observation confirmed the similarity of the particle transportation in a 30-min segregation test and a 48-h suffusion test (Fig.  5 ). After the test, the soil was removed by layer to estimate the local PSD due to segregation. Although the tower was shaken well, not all fine particles are dropped to the lower layers. They were trapped at pore constrictions. Soil primary fabric moved collectively, and pore constrictions were not significantly widened, given the small amplitude of vibration and large compressing loads. The ‘eroded’ soil mass for the gap-graded soil increased slightly from 15.76% in suffusion test to 17.19% in segregation test. A repeated test with poorly graded soil showed a negligible increase from 1.62 to 1.67%. Note that, due to the heterogeneity, suffusion test results of the same soil with the same hydraulic erosion unit may still vary slightly.

Transparent acrylic tower

Loading test for the net: static load (left) and dynamic load (right)

There may be some interesting notes of the transparent setup. Firstly, although the vibration amplitude was small, the coated layer of the acrylic tower was scratched, and particles could not be observed easily after ten tests. A replaceable thin film may be placed inside to protect the tower from scratches. Secondly, as the tower was fully filled, the total soil mass was roughly 19.7 kg. This required a powerful shaker, which might damage the acrylic tower, especially the near-bottom layer with the net. Nevertheless, a normal 300W Octagon shaker carried the test well without any noticeable cracks induced. Thirdly, a small amplitude from 0.5 to 1.5 mm is recommended. The sieve shaker has amplitude varied from 0 to 3 mm with 0.1 mm steps. When the amplitude was 0.4 mm, fine particles did not drop out well. In contrast, when the amplitude was more than 1.6 mm, soil seemed to be tossed up. Therefore, the amplitude of 1 mm was selected in this research as the average for the tested soils. The vibration should be hardly noticed in the recorded FHD videos from a distance of 60 cm. Fourthly, if there was not any constraint at the boundary, contact erosion loss may occur at the gap between soil particles and the acrylic wall. A rubber donut of 5 mm width and 200 mm in diameter can stop contact erosion. Last but not least, when suffosion—an extreme type of suffusion with volume change (Fannin R, Slangen 2014 )—occurred, the top cap could not compress the soil well. It even may not touch the soil’s upper surface (Fig.  6 ). To study the susceptibility of compacted soils to suffusion, a compressed thick sponge may be placed underneath the top cap to ensure that there is always a sufficient surcharge on the sample.

Backward move of a void in a segregation test—a common phenomenon in suffusion tests : top-left: initial soil; top-right: a void appears; bottom-left: the void is developed and moves backwards; bottom-right: the movement progresses further backwards

Top and bottom layers in a segregation test

In general, the acrylic setup worked well as a transparent prototype to observe the transportation and confirm the feasibility of the new laboratory approach for suffusion assessment.

Standard sieving setup

To obtain a durable device, a steel segregation tower was made from two standard sieving sets. The nets in the first set were cut to make the middle layers of the tower (Fig.  7 ). Note that a metal ring is deliberately left/attached to the sieve to avoid contact erosion along the tower wall. Based on the PSD of the tested soil, a sieve with suitable net size will be selected from the second set to be used as the near-bottom layer above the pan.

Standard sieving setup: 1 - top cap; 2 - sponge; 3 - middle layer without net; 4- near bottom layer with net; 5 - pan; 6 - metal ring

If the soil is gap-graded with the gap ratio \(G_r\) (equation 1 ) larger than 2, the largest standard sieve size within the size gap is selected to be the opening size of the net, \(d_{net}\) .

where \(d_{begin}\) and \(d_{end}\) are the sizes at the lower and upper boundary of the size gap, respectively (To et al. 2018 ).

For continuously graded soils, \(d_{net}\) is selected to be the closest standard sieving size (ISO 2016 ) smaller than

where 0.77 is the ratio between the diameter of transportable granular particles and the pore constriction size (Goldin and Rasskazov 1992 ).

If there is no close standard size, a smaller standard size can be accepted if it allows particles of \(d_{40}\) passing through (Fig.  8 ).

This estimation may not be recommended in some special cases of very well graded soils, where \(d_{40}\) can be very small. In general, dry sieving may be effective if the opening size is no less than 0.075 mm, which is the sieve #200 (To et al. 2023 ). However, cohesive soils with the proportion of fines larger than 12.5% still could be tested as long as \(d_{40}\) is large enough for sieving. When \(d_{40}\) is very small, the soil is unlikely to be suffusive because the hydaulic

Net size selection

Tested soils could be compressed if required (Fig.  9 ). However, the influence of relative density is not in the scope of this research. Hence, all tested mixtures below were not compressed so that the results could be compared with the traditional erosion test (Fig.  2 ). The testing procedure for both segregation setups can be summarised as below:

Prepare the desired mass of soils or mixtures. The moisture content must be low to avoid any possible cohesion/adhesion.

Estimate \(d_{60}\) to find the recommended net size. Stack the tower.

Carefully place the soil by layer into the tower to ensure the homogeneity of the soil. (Optional) Compress each layer to the design stress and place a hard sponge on the top before closing the tower to maintain the desired stress.

Shake the tower for 30 min.

Measure the soil mass dropped into the bottom pan and compare to the total soil mass.

(Optional) Remove the soil by layer and undertake the sieving test for each layer.

Soil compression

Soil particle size distribution

In order to investigate the work of the new testing setup, mixtures were made from four different soils: fine, gravel mix, filter, and CSR (Fig.  10 ). CSR is the technical name of a construction sand. Although the PSDs are different, these soils have similar roundness and sphericity (Fig.  11 ) to avoid the unknown possible effect of different particle shapes in different soils. The characteristics of the particle shape were estimated with charts (Santamarina and Cho 2004 ). In general, the sphericity and roundness of soil particles were 0.7 ± 0.2 and 0.4 ± 0.1, respectively.

Particle size distribution of basic soils

Soil particles zoomed with digital microscope

In the first step, PSDs were predicted with a MATLAB code for a given proportion. Then, the proportion was amended with ‘trial and error’ to reach the desired PSD. The number of mixtures was 25 to maintain the maximum difference between PSDs no less than 5%. After that, the soils were mixed thoroughly with a concrete mixer and shovels. Before the test, the PSDs had been verified with standard sieving test because the actual PSD may be different to the proposed one. Very fine fractions were analysed with Mastersizer 3000, a laser diffraction particle size analyser. The resultant PSDs are presented in Fig.  12 . For the convenience of readers, main characteristics of mixtures are listed in Table  1 . Due to the heterogeneity, the local PSD of each layer may vary within 3% from the estimated PSD.

Particle size distribution of tested soils

A preliminary study on the required vibrating time found that the duration of the test might strongly depend on the PSD. Gap-graded soils may take 15 min or less, while continuously graded soils may require a longer time. Besides, the amplitude of vibration can also influence the ‘eroded’ mass. High amplitude might shake more fine particles off as shaking impact may destruct the primary fabric of soils. For all of the below tests, the amplitude was set at 1 mm, and the shaking time was set at 30 min. For most of the tests, fine particles were not shaken off after 25 min.

Results and discussion

Contact erosion calibration.

As mentioned above, if there is no boundary constraint, contact erosion may occur between the soil mass and the wall. This phenomenon occurs with both erosion unit (Fig.  2 ) and segregation tower (Fig.  7 ). A comparison for three typical PSDs was undertaken to assess the mass loss with and without contact erosion (Table 2 ). To assess the influence of contact erosion, acrylic segregation tower and normal erosion unit were employed. To eliminate the contact erosion, a filter-paper ring was placed in the erosion unit, and standard sieves were used to build the segregation tower.

When contact erosion occurs, fine particles were transported down along the wall, while they are still trapped in pores in the middle of the acrylic tower (Fig.  13 ). The mass loss in the contact erosion test was increased significantly to nearly four times in comparison with the traditional suffusion test for gap-graded soil (Table 2 ). A similar increase was also found in the segregation test with the acrylic tower. Nearly all fine particles have been transported out of the gap-graded soil. For soil ‘19’, the stability was changed to unstable as the eroded mass went over 5%. This may be caused by the larger diameter of the tower. This milder curvature may create larger gaps and allow more fines to pass. Nevertheless, the suffusion loss and segregation loss were close. Hence, this paper will focus on only suffusion.

There was a concern that segregation due to vibration could form layers perpendicular to the particle flow, while segregation due to seepage could form layers parallel to the flow. However, lab results showed that they are both parallel to the flow. The segregation due to vibration may be sharper.

Erodible mass

The laboratory experience showed some advantages of the new test over the conventional approach. An undeniable advantage of the new segregation test is its simplicity. The test does not require any special equipment. Old standard sieving devices are popular in geotechnical laboratories and may be replaced after a few years. In general, a suffusion test for a soil sample of 300 mm height may take 1 or 2 days. Meanwhile, a similar sample in segregation test requires just 30 min to shake. If the soil is wet, a drying process in oven may be required before the test. However, the suffusion test may take an even longer time to dry and prepare for sieving after the test. It is the authors’ experience that the segregation test may be 8–10 times faster than the conventional suffusion test.

Moreover, segregation test may be more accurate in measuring the ‘eroded’ amount. If the water is recycled due to the long suffusion test duration, a proportion of fine particles may be pumped back to the erosion unit. This trouble does not happen in segregation tower. Also, the erosion unit may experience leaks as rubber seals may deteriorate.

In contrast, the suffusion test is still irreplaceable. It can study the effect of many factors, including the applied hydraulic gradient and stability of the pathway in piping, a later stage of suffusion. If soil primary fabric is compressed and moves collectively, the hydraulic gradient may have a certain relation with shaking amplitude and frequency. However, it is challenging to study piping in segregation test because the pathways often collapse due to strong shaking impact.

Contact erosion along the wall

To assess the accuracy of the new test with the standard sieving setup, the mixtures were assessed with segregation test and traditional erosion tests. PSDs were also assessed with four key-size methods (Table 3 ), which use only PSD as the input (Wan and Fell 2008 ; Burenkova 1993 ; Kenny and Lau 1985 ; To et al. 2018 ). Other methods may be difficult to be compared as they may require some unknown factors, e.g. relative density or hydraulic gradient (To et al. 2016 ; To and Scheuermann 2021 ). The laboratory test result is classified as ‘stable’ if less than 3% of soil mass was shaken off and ‘unstable’ if more than 5% of soil mass dropped out (Goldin and Rasskazov 1992 ). The transitional result means the susceptibility to suffusion may depend on uncontrollable factors, e.g. the spatial distribution of particles. Transitional soil is generally not recommended for the construction of hydraulic structures.

Although the PSD was widely alternated to have about 28% of unstable soils, which would not satisfy Burenkova’s stable condition (Eq.  4 ), just only three mixtures were estimated as ‘unstable’ or ‘transitional’ after the laboratory tests.

However, the results showed a good coherence between segregation test and erosion test (Table 3 ). Although the difference spreads from -1% to +1%, erosion loss is frequently less than segregation loss (Fig.  14 ). The mode of difference is close to zero with \(\mu \) = 0.1648% and \(\sigma \) = 0.401%. It is understandable that segregation may transport slightly more particles because the vibration impact rearranges and reorients fine particles better than a consistent seepage flow. As a sequence, transportation can better avoid particle jam at the pore constrictions. Note that erosion loss is always larger than 0.15% of soil mass. This may be caused by some initial loss during the water filling process.

Difference in mass loss between segregation and erosion

There may be a concern about a larger difference of losses if soils are unstable. However, the results seem to be consistent for tests with less than 10% of mass loss. If the loss is bigger, it may be out of interest as the soil is obviously unstable in many assessment methods. Hence, there is no need to test those soils in laboratory to confirm the internal stability.

It is interesting that the method of Wan and Fell ( 2008 ) assesses all tested soils as stable and makes three major misalignments, when an ‘unstable’ or ‘transitional’ soil is assessed as ‘stable’. In contrast, the method of Burenkova (Burenkova 1993 ) makes no major but five minor misalignments, when a ‘stable’ soil is assessed as ‘unstable’ or ‘transitional’. These two methods might be cheated by complicated PSDs as they use just several fixed key sizes (To et al. 2018 ). Methods of Kenney and Lau ( 1985 ) and To ( 2018 ) are consistent, except one extra major misalignment with the method of Kenny and Lau ( 1985 ). Summary of the comparison is given in Table 4 . Note that the lab result of mixture 16 is very close to transitional zone. If this mixture is considered as transitional, method of To et al. ( 2018 ) makes just one major misalignment.

• Segregation

Although the sieving test after the main segregation test is optional, sieving results provided some good understanding of the transportation. When a gap-graded soil was segregated (Fig.  15 a), a large shift of PSD was found in layer 6 as it is the bottom layer. There is not any below layer to provide resistance to the transport. However, the largest shift in PSD shape was found in layer 1 because it had no fine particles dropped from above. Moreover, when suffosion occurred, the top layer might not have any firm constraint applied from above (Fig.  6 a). Hence, soil particles can be tossed up to allow more fines to drop into the below pores. Although the segregation seems to be continuing because PSDs of layers 2, 3, 4, and 5 were still close to the original PSD, the transportation was actually stopped at the end of the test as no more fine particles dropped out. All erodible particles were shaken off via formed transportation pathways. Meanwhile, the trapped fine particles fully filled pores in clogged pathways. Hence, layers 3 and 4 had more fine particles than the original mixture.

Local PSDs after segregation test

A similar result was found for erodible continuously graded soils (Fig.  15 b). Although the PSD of the bottom layer was close to the original PSD, nearly 1 kg of fine particles were shaken off. Note that continuously graded soils may require a longer testing time. Mixture 17 had nearly 1.2% of soil mass eroded after 30 min. Then, the particle flow was stifled, but it still could reach 1.8 % of soil mass after approximately 2 h.

For stable mixtures, the local PSDs of layers were close (Fig.  15 c) with the maximum deviation of less than 5%. This difference may also be caused by soil heterogeneity. The consistent PSDs of layers 2, 3, and 4 might hint that the number of middle layers could be reduced to four layers.

Note that segregation test is a disturbed test as soil has been dried in oven and placed into the segregation tower by layer. A careless preparation may have some initial segregation. However, as the tested soil mass is large, this initial segregation might not significantly influence the segregation. In case the soil is naturally segregated, it is obviously suffusive and can be detected by the test results.

DEM simulation of segregation for a non-suffusive soil: before (left) and after(right) the vibration

Although many conventional suffusion testing setups used granular materials to simulate the continuation of soil (Nguyen et al. 2019 ; To and Scheuermann 2021 ), this may make constriction size hard to be controlled. Hence, many other suffusion testing setups used a net after the tested soil (Xiao and Shwiyhat 2012 ; Rochim et al. 2017 ; Kodieh et al. 2021 ). However, they often used a fixed size and neglected the net size determination.

The current research initially proposed to use \(d_{point}\) —the size at the kinking point on soil PSD (To and Scheuermann 2014 ) as the net size. However, this approach may not be applicable to some straight-like PSDs. Kenny and Lau ( 1985 ) proposed that particles sized d are kept by particles sized 4 d , and the maximum amount of erodible fine particles is 20% of the soil mass. Hence, 4 \(d_{20}\) may be a good choice as this size does not allow coarse particles to drop through. Nevertheless, some other studies proposed that the fraction of fine may occupy up to 40% of soil mass (Li and Fannin 2008 ). A numerical study pointed out that the size of transportable particles can go up to a high value, less than \(0.25d_{100}\) (To et al. 2015 ). However, the transportation of these particles may terminate very quickly after a few pores. Another possible selection was the use of a delimiting point so that fine particles would fully fill the pores (Indraratna et al. 2011 ). However, this approach requires the porosity of the primary fabric, which must be calculated approximately with a sophisticated computer program. In addition, suffusive soils do not have this condition. Hence, the real delimiting size may be smaller.

This paper does not deny any of the above possible selections. It simply agrees with some experimental studies that the fraction of coarse particles should be presented by \(d_{60}\) (Burenkova 1993 ; Wan and Fell 2008 ). Hence, Eq.  2 was proposed with a hypothesis that the maximum effective opening can be up to \(d_{60}\) . Nevertheless, laboratory experience showed that smaller sieve sizes may still work, if they allow particles of \(d_{40}\) passing through (To et al. 2023 ). The only small difference in eroded mass was found in a thin slice at the bottom layer. The middle-size particles in upper layers are trapped by self-filtration effect. Future study may focus more on the selection of the appropriate sieve size.

Stress transfer

A possible drawback of the segregation test is the application of large loads. Sieving meshes are often made of thin wires to maximise the sieving ability. Hence, the mesh may deform under high stress. However, it is evidenced that relative density and compaction could influence the internal stability of soils (Israr and Aziz 2019 ; To et al. 2020 ). Therefore, the application of high stress is a considerable need. Nevertheless, this is also the significant problem with all other apparatus for large specimens. A steel plate with large holes can be placed underneath the mesh (Sarmah and Watabe 2023 ). If the holes are close, the plate may not be strong enough. However, if the holes are discrete, the plate may block a series of mesh holes in a local area. Another good solution may be the use of round gravels or large glass beads in the bottom pan. These large particles may still block some mesh holes, but discretely. If the apparatus is used to test the filter work, a mesh may not be necessary. The tested setup with supporting glass beads could bear 50 kPa static load at ease. This is the stress on soil at a few meters beneath the ground level. If the load is much higher, triaxial setup may be a better choice, given the soil is fine.

As the load is not monitored and maintained automatically, it may be varied due to the volume change due to the test. A DEM simulation showed that vibration could cause some short transportation and possibly a very small volume change in most segregation tests of wide graded soils (Fig.  16 ), where fine particles are relatively small to the pores formed by coarse particles. When fine particles are transported, the coarse particles have more room to move horizontally. However, the observed change is insignificant for non-suffusive representative volume (Fig.  16 ). Note that the number of particles in DEM simulation is roughly 100,000. This number is limited by computational resources. High performance computing cluster can simulate millions particles of general shape, but the computation may take weeks or even months (To et al. 2016 ).

The influence of boundary wall to the stress transfer in segregation test may be less than in conventional tests because the tested soil samples are large. Standard sieves in geotechnical laboratory are normally 200 mm in diameter and could be 300 mm for large sieve (ISO 2020 ). Meanwhile, most conventional triaxial setups employed specimens of less than 100 mm in diameter. In addition, the o-ring keeps in place a thin layer of soil particles along the wall. Hence, the boundary contact could be considered as soil-soil contact at some level.

The paper has presented a novel approach to test the susceptibility of granular soils to suffusion. The only significant difference in mechanism is claimed to be the mechanical driving force, not the seepage force.

The new test is generally 8–10 times faster than the conventional suffusion test, including adequate preparation. Laboratory results showed a good agreement with the conventional erosion test with 100% matched for tested soils. In general, the new test showed a slight increase in the amount of mass loss because vibration generally works better than seepage in driving fine particles out of pores. Hence, it provides some safety in assessment.

The biggest advantage of the new test is a consistent and unified setup. The new test employs only standard equipment available in common geotechnical laboratories. The vibration could be accurately controlled with digital controller. Hence, it may have some good potential to be standardised.

As the only crucial difference is the driving force, the new approach may also be applied for other types of internal erosion, such as contact erosion or filter design.

The test may not completely replace the conventional suffusion test yet as it cannot study some specific factors, e.g. critical hydraulic gradient. However, it may be good if only the internal stability is focused on. More tests may be required to correlate the impact of vibrating magnitude and hydraulic gradient.

Future studies will aim to discover the applicability of the new test to investigate soil behaviour under various loadings.

Data Availability

All associated data and informations have been included in the paper. Any other inquiries can be satisfied on request.

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To, P., Vang, S., Dempsey, S. et al. Segregation test—a standardisable test for suffusion assessment of granular soils. Arab J Geosci 17 , 186 (2024). https://doi.org/10.1007/s12517-024-11988-3

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Soil testing time saver predicts key soil health characteristics

by Brittaney Mann, University of Arkansas

Farmers in a time crunch have a new, speedier option for analyzing the texture and organic matter content of the soil on their fields.

Gerson Drescher, assistant professor of soil fertility for the Arkansas Agricultural Experiment Station, has led a study to create prediction models for these key soil health indicators based on standard tests already being used to analyze soil samples .

"We want to provide people with the maximal amount of information that they can get from samples they are already submitting without the additional cost and time of analysis," Drescher said.

The newly developed prediction model can help add information about the soil's properties, which can guide fertilization, irrigation, and herbicide decisions, Drescher added. Standard soil testing evaluates plant-available nutrient content and soil pH. However, these properties are also affected by soil texture and organic matter in the soil, which require additional expensive and time-consuming tests.

"The traditional methods used for both soil texture analysis and organic matter determination are relatively time-consuming, and generally, producers are interested in a quick turnaround when they submit a sample so they can make their nutrient management decisions and make plans for the amount of fertilizer they need to apply," Drescher said.

The research article titled "Soil texture and organic matter prediction using Mehlich-3 extractable nutrients" is published in Agrosystems, Geosciences & Environment . Co-authors include Trent Roberts, professor and interim department head of crop, soil, and environmental sciences; Nathan Slaton, associate vice president for agriculture and assistant director of the Arkansas Agricultural Experiment Station; and Alden D. Smartt, program associate in the crop, soil, and environmental sciences department.

The models are accurate for predicting the most common soils used in crop production in Arkansas. However, the models are less accurate for sandy soils and soils with very high organic matter, Drescher said. The state's common agricultural soils are fine and medium categories, which include silt loams and silty clays.

The experiment station is the research arm of the University of Arkansas System Division of Agriculture.

What's in the soil?

The time a soil test takes depends on the information requested, Drescher said. Each additional test on top of the routine soil analyses can add days or weeks to the total testing time. Drescher's prediction model for organic matter and soil texture cuts those additional tests and can reduce turnaround time by at least half.

Soil samples sent to a lab for analysis are dried, ground and sieved for uniformity to ensure the precision and accuracy of the analysis. In Arkansas, the experiment station's Marianna Soil Test Laboratory provides routine soil testing services, which are free for Arkansas residents.

Routine soil testing calls for measurement of soil pH and the Mehlich-3 soil analysis, which extracts and identifies the available nutrients in soil samples.

The two properties predicted by Drescher's model—soil organic matter and soil texture—require more extensive tests, Drescher said. Testing for soil organic matter is determined by a method known as "loss on ignition," which requires weighing samples, subjecting them to high temperatures to combust the organic matter, and reweighing the sample to measure weight loss.

Soil texture is measured using a device called a hydrometer, which determines soil particle sizes, Drescher said. Understanding soil texture is an important aspect of crop production because it dictates the ability of the soil to hold nutrients, the rates of herbicide or fertilizer application, irrigation scheduling and soil sampling protocols.

These tests help producers understand the health of their soil and guide fertilizer applications and crop management decisions, Drescher said.

Better than a map

Maps, such as the Web Soil Survey offered by the U.S. Department of Agriculture's Natural Resources Conservation Service, can be used to provide information on soil type in different areas, but the resolution of the map might not indicate changes in soil texture in a small amount of space within a field, Drescher said. The new prediction model is more precise than a map because it uses field-specific information.

The Marianna Soil Test Lab already provided soil texture predictions, but this new research fine-tuned the model to make the estimation more accurate, Drescher said.

The Marianna Soil Test Lab analyzes about 200,000 soil samples every year. Routine soil tests for nutrients and pH are free for Arkansas residents through the support of Fertilizer Tonnage Fees. Non-routine tests of soil texture or organic matter analysis, however, have a fee and take additional time for analysis.

The prediction models

Drescher used Mehlich-3 nutrient and soil pH data from soil samples submitted to the Marianna Soil Test Lab to calibrate and validate the prediction models.

For the clay and sand prediction models , he used data from 409 soil samples, representing different production systems and soils throughout Arkansas. About half were from row crop fields and the other half from forage systems.

When developing the model for soil organic matter, he used data from 604 samples from row crop fields and 415 samples from forage production areas.

After the calibration, Drescher validated the models by using 103 different soil samples from row crop, forage, vegetable, fruit, turfgrass and ornamental production areas.

Over time, the models can be evaluated for accuracy and optimized with new soil samples to improve soil texture and organic matter prediction. Drescher said the models could be used in areas outside of Arkansas if they have similar soils.

Drescher said he appreciated the work performed by the Marianna Soil Test Laboratory personnel who assisted with soil analysis and provided the data for the models' calibration.

Provided by University of Arkansas

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• J Genet Eng Biotechnol
• v.19; 2021 Dec

Isolation, characterization, and molecular identification of soil bacteria showing antibacterial activity against human pathogenic bacteria

R. prashanthi.

Department of Biotechnology and Genetics, M. S. Ramaiah College of Arts, Science and Commerce, Karnataka 560054 Bengaluru, India

Shreevatsa G.K.

Krupalini s., associated data.

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

The present study dealt with the screening of soil bacteria with antibacterial activity from different locations in Bangalore, India. Antibiotics play the role of self-defense mechanism for the bacteria and are produced as secondary metabolites to protect themselves from other competitive microorganisms. The need for new antibiotics arose as the pathogenic bacteria acquire resistance to various antibiotics meant for treating human diseases. Given the importance of antibiotics of bacterial origin, standard techniques have been used to isolate and characterize the soil bacteria which showed antibacterial activity.

The isolated bacteria were tested against human pathogenic bacteria like Staphylococcus aureus , Escherichia coli , Pseudomonas aeruginosa , and Klebsiella pneumoniae by primary and secondary screening methods. The isolates PR1, PR2, and PR3 were confirmed to have antibacterial activity against S. aureus , E. coli , P. aeruginosa , and K. pneumoniae by both methods. Studies on the effect of filter sterilization, autoclaving, and proteinase K treatment on culture filtrates showed filter sterilization as the best method. The effect of different carbon and nitrogen sources on the antibacterial activity showed that preference by each isolate differed for carbon and nitrogen requirements. The isolates PR1, PR2, and PR3 were identified as Bacillus aryabhattai strain PR-D07, Arthrobacter humicola strain PR-F07, and Neomicrococcus lactis strain PR-F11 through 16S rRNA sequencing.

Findings from this research work are encouraging and could proceed further to applied aspects. Only 3 bacterial isolates out of 263 isolates from soil samples displayed antibacterial activity against human pathogens S. aureus , E. coli , P. aeruginosa , and K. pneumoniae . They were identified as B. aryabhattai , A. humicola , and N. lactis by 16S rRNA studies and all of them are Gram-positive. Each isolate preferred different carbon and nitrogen sources for their enhanced antibacterial activity. Efficacy of the culture filtrates of these isolates was tested by filter sterilization, autoclaving, and proteinase K treatment. Filter-sterilized culture filtrates showed higher antibacterial activity than other treatments. A comparison of the antibacterial activity of culture filtrates and antibiotic streptomycin produced an inhibition zone of 18.5 mm and 15.5 mm respectively. This is the first report on the antibacterial activity of all the 3 bacterial strains ( B. aryabhattai strain PR-D07, A. humicola strain PR-F07, and N. lactis strain PR-F11), against all the human pathogens, mentioned earlier. It is also found that the antibiotic factor is proteinaceous as proteinase K considerably reduced the antibacterial activity of the culture filtrates. With the above significant results, these 3 bacteria are considered to be promising candidates for the isolation of new antibacterial agents.

The existence of a large microbial community in the soil is supported by an enormous group of organic matters in the earth. Most of these microorganisms are bioactive and survive at the top few inches of the agricultural soils [ 1 ]. The microorganisms can live in several habitats along with humans and also in the utmost condition such as inside the rocks of the oceanic crust [ 2 ], cold temperature [ 3 ], hot springs [ 4 ], and miles deep in the ocean [ 5 ]. Abiotic and biotic factors are involved in regulating the activity and diversification of soil microorganisms. The presence of microbes in soil is based on the existence of ambient conditions provided by the types of vegetation, the texture and chemical nature of the soil, nutrients availability, pH, moisture content, climate, and temperature. The physiology of the soil is also determined by all these conditions as it varies across the same place between different seasons. Further, the dumping of the organic wastes from agricultural fields ensures the availability of the high nutrient content in soil for the growth of the microorganisms [ 6 ]. Some bacterial communities in the soil such as Thiobacillus , Rhizobium , Nitrosomonas , Clostridium , Nitrobacter , Caulobacter , Pseudomonas , and Frankia carry out an essential task in nutrient cycling [ 7 ].

During the past decades, an enormous number of bacteria that produce a diverse kind of primary and secondary metabolites, enzymes, antibiotics, and novel compounds, etc., were isolated [ 8 , 9 ], identified, and exploited by several research groups in human health care, agriculture, and animal husbandry, etc. As these compounds have a unique structure, microorganisms continue to be the essential source of secondary metabolites. The uniqueness of the secondary metabolite is that they act as an antimicrobial agent towards pathogenic bacteria. Antibiotics are not necessary for the growth of bacteria but they help the survival of bacteria [ 10 ]. The bacterial community remains the major source of antibiotic production which is widely used in human health care. Each year, nearly 500 antibiotics are discovered, in which most of the antibiotics are obtained from the soil bacteria [ 11 , 12 ]. Antibiotics are low-molecular-mass (< 1500 kDa) products of secondary metabolism, usually produced during the late growth phase (idiophase) of a relatively small group of microorganisms [ 10 ]. Antibiotics play the role of a self-defense mechanism for the bacteria and are produced as secondary metabolites to protect themselves from other competitive microorganisms [ 13 ]. Most of the antibiotics which are currently in use are produced from a small group of microorganisms belonging to the genera Penicillium , Streptomyces , Cephalosporium , Micomonospora , and Bacillus [ 14 ]. Members of the species Bacillus generally produce polypeptide-type bacteriocins, and these antibiotics are generally effective against several Gram-positive bacteria [ 15 , 16 ].

India was the largest user of antibiotics with 12·9 × 10 9 units (10·7 units per person) as per the data available for 2010 [ 17 ]. The demand for bacterial antibiotics continues to increase globally owing to the pathogenic bacteria acquiring resistance to existing antibiotics and many antibiotics proved that they are no longer potent against the infections [ 18 , 19 ]. Multidrug resistance in bacteria raised serious concerns among pharmaceutical and healthcare researchers. It puts greater pressure among researchers to find alternative antibacterial substances that can be used for use in clinics, food preservation, and dairy products [ 20 , 21 ]. The indiscriminate use of antibiotics and their improper disposal lead to drug resistance in pathogenic bacteria and the antibiotics become less effective for their use. At present, modern medicine is facing a tremendous challenge to combat the antibiotic resistance acquired by several pathogenic species. Antibiotics are aimed to inhibit the growth or kill the microbes that cause infectious diseases and drug resistance is considered a threat to health security [ 22 ]. To survive the adverse environment, bacteria evolve mechanisms to modify or acquire new genes through natural ways and eliminate the effectiveness of drugs [ 23 ].

An incomplete dose of any prescribed antibiotic also facilitates the pathogenic bacteria to develop resistance. This necessitates a situation to find new antibiotics to meet the drug resistance challenges. Considering these in mind, this study was focused on isolation and characterization of antibiotic-producing bacteria from the soil in 7 different sites like the garden, the playground, near the canteen, and near the sewage sump covered with vegetation. In this study, we have found that some soil bacteria displayed antibacterial activity and they are characterized and identified using molecular methods.

Collection of soil sample

The soil collection site is located in and around with a lot of native vegetation at an altitude of 949 m with a latitude and longitude of 12.87° N, 77.59° E. The types of soil in the location consist of red laterite and fine loamy to clayey. The debris from the soil surface was removed before the collection of soil samples. The soil was dug into 5–10-cm depth. About 20 g of the soil samples were collected and stored in an icebox before transporting to the laboratory.

Pathogenic bacteria and culture conditions

The standard serial dilution technique was used for the isolation of bacteria from soil samples collected from 7 different sites like the garden, the playground, near the canteen, near the sewage sump, near the biotechnology department, near RIT, and RUAPS. One gram of soil sample was mixed with l0 ml of sterile water and serially diluted (10 −1 to 10 −4 ). From the serially diluted soil sample, 100 μl was mixed with warm nutrient agar medium and poured into Petri plates. Natamycin (Sigma-Aldrich, USA) at 20 μg/ml was amended with a molten nutrient agar medium at 50 C to prevent fungal growth [ 24 , 25 ]. After 48 h, the plates had a lawn of mixed bacterial colonies. The individual colonies were picked using sterile toothpicks and streaked onto fresh nutrient agar plates to get pure cultures. The pure culture was stored and used for testing antibacterial activity against human pathogenic bacteria Staphylococcus aureus (MTCC 96), Escherichia coli (MTCC 739), Pseudomonas aeruginosa (MTCC 741), and Klebsiella pneumoniae (MTCC 3040). The concentration of all pathogenic bacteria was adjusted to obtain the OD = 0.8 using a UV/Vis spectrophotometer at 600 nm.

Primary screening

A total of 263 bacterial colonies were isolated from soil samples collected from 7 different sites. Initial screening of the 263 soil bacteria for antagonistic activity was done in an in vitro condition against pathogenic bacteria like Staphylococcus aureus (MTCC 96), Escherichia coli (MTCC 739), Pseudomonas aeruginosa (MTCC 741), and Klebsiella pneumoniae (MTCC 3040) through perpendicular streaking [ 26 ] and seed overlay method [ 27 ]. The bacteria from the soil sample were individually streaked as a single straight line through the central point of the nutrient agar plates. All the pathogenic bacteria were perpendicularly streaked to the soil bacteria [ 26 ]. The plates were incubated for 24 h to find any inhibition zone between soil bacteria and the pathogenic bacteria. The bacterial strains showing an inhibition zone against test pathogens were chosen for secondary screening.

Seed overlay method

The isolated soil bacteria were inoculated using a sterile toothpick in a nutrient agar plate and incubated for 48 h followed by the addition of 2 ml of chloroform to arrest the growth of the inoculated bacteria. The plates were incubated for 1 h to ensure only the secondary metabolites from the inoculums remain active on the nutrient agar plate. The plates were kept open for 20 min for the evaporation of the chloroform. Then, 100 μl of each pathogenic bacterium (OD = 0.8) was mixed with 2 ml of nutrient broth and mixed thoroughly. The medium was transferred to the above agar plate and incubated for 24 h. The activity of the secondary metabolites against pathogenic bacteria was indicated by the diameter of the inhibition zone [ 27 ]. The bacteria which produced the inhibition zone were chosen for secondary screening.

Secondary screening to confirm antibacterial activities

All the active bacteria selected from the primary screening method were grown separately in Nutrient broth at 30 °C under shaking conditions. After 24 h, the nutrient broth with cells was adjusted to get an OD of 0.8 at 600 nm using a UV/Visible spectrophotometer (SYSTRONICS, India, Model: AU-2702). It was centrifuged at 5000 × g for 10 min in a centrifuge (Remi, Model: CPR-24Plus) and cell free-culture filtrate was collected and stored at 4 °C.

The minimum inhibitory concentrations (MICs) of the culture filtrate were determined by using the Agar Well Diffusion method. Nutrient agar plates were inoculated with 100 μl of the pathogenic organism by the spread plate method. Using a 6-mm-diameter cork borer, 2 wells were made in agar plates at equal distances and the wells were filled with 50 μl, and 100 μl, of cell-free culture filtrates of PR1, PR2, and PR3 separately. Then, the filter paper disc about 6 mm in diameter impregnated with streptomycin (20 μl,) was placed on the agar surface. Streptomycin discs were used as a positive control. The agar plates were incubated for 2 days at 30 °C for bacterial growth. Antibacterial activity of culture filtrate was determined by measuring the zone of inhibition (Kirby-Bauer Test) around the well [ 28 ].

Effect of filter sterilization, autoclaving, and proteinase K treatment on culture filtrate

To test the efficacy of culture filtrates of PR1, PR2, and PR3 as a sustainable antibacterial agent, they were subjected to (a) filtering through a 0.45-μm Millipore filter (b), autoclaving at 121 °C for 20 min, and (c) treating with proteinase K (0.02 mg/ml) at 50 °C for 1 h. Antibacterial activity was tested using the following: (1) crude cell-free culture filtrate, (2), filter-sterilized culture filtrate, (3), filter-sterilized + heat-sterilized, and (4), filter-sterilized + proteinase K. Inhibition zone for each treatment was measured and presented.

Effect of carbon and nitrogen sources on antibacterial activity

The effect of different carbon and nitrogen sources on the antibacterial activity of the culture filtrates of PR1, PR2, and PR3 was studied. Fifty milliliters of the synthetic medium amended with various carbon (1%) and nitrogen (0.3%) sources was distributed into each 250-ml Erlenmeyer flask and sterilized. The composition of the synthetic medium was sucrose 10 g, K 2 HPO 4 1.2 g, KH 2 PO 4 0.8 g, MgSO4 7H 2 O 0.2 g, NH 4 NO 3 0.3 g, water 1000 ml, and pH 6.8–7.00. Arabinose, fructose, galactose, glucose, lactose, maltose, mannitol, and sucrose were used as carbon sources. Casein, NH 4 Cl, NH 4 NO 3 , NaNO 3 , NH 4 H 2 PO 4 , KNO 3 , (NH4) 2 SO4, and urea were used as nitrogen sources. After inoculation with PR1, PR2, and PR3, the flasks were incubated at 30 °C for 48 h. under shaking conditions. At the end of 48 h, the liquid cultures of PR1, PR2, and PR3 were centrifuged at 5000 × g in a centrifuge. Cell-free culture filtrates were collected and stored at 4 °C.

Morphological and biochemical analysis of bacteria

Morphological and biochemical characterizations of the bacteria that showed antibacterial activity was carried out using standard techniques described in Bergey’s Manual of Determinative Bacteriology. Bacteria grown for 24 h in the nutrient broth were used for Gram staining and biochemical characterization.

Molecular identification of bacteria

Overnight-grown cultures of PR1, PR2, and PR3 were used for DNA isolation. About 2.0 ml of bacterial suspension was transferred to a microcentrifuge tube and centrifuged for 2 min at 10,000 × g to collect the pellet. The above process was repeated twice with 2.0 ml of bacterial suspension to obtain a sufficient number of cells. The cells were washed with 0.9% saline and 0.2 ml protease was added to digest and remove the protein and cellular materials to release the genomic DNA. The centrifuged tubes were inverted 5–6 times and kept in a boiling water bath for 1 h at 55 °C. After 1 h, 0.1 ml of the DNA Salt solution was added and centrifuged for 5 min at 5,000 × g. Finally, 0.8 ml of the precipitated solution was added slowly and the centrifuge tube inverted several times to mix the components. 70% ethanol was used to wash the cells. The collected DNA were dried and suspended in TE buffer and stored at 4 °C. The quality and integrity of the isolated genomic DNA were quantified at wavelength 260 and 280 nm using a spectrophotometer. The purity of the extracted DNA was checked on 08% agarose gel.

For the amplification of the 16S rRNA gene fragments the universal primers were used (forward primer 5′-AGAGTTTGATCCTGGCTCAG-3′ and reverse primer 5′-GGTTACCTTGTTACGACT-3′). PCR parameters were initial denaturation at 95 °C for 5 min; followed by 30 cycles of denaturation at 94 °C for 1 min; annealing at 54 °C for 2 min; extension at 72 °C for 2 min; final extension at 72 °C for 5 min. The amplified PCR products were electrophoresed on an agarose gel [ 29 ].

Amplified 16S rRNA gene fragments were purified and sequenced using a DNA sequencing service. The obtained 16S rRNA gene sequences were uploaded to the Basic Local Alignment Search Tool (BLAST) to identify nucleotide sequence matching with the reference sequences.

Soil sample

The soil samples from 7 sites were serially diluted and spread on Nutrient agar plates and incubated at 30 C for 3 days. A total of 263 bacterial colonies were isolated from all the sites mentioned earlier. Individual colonies were picked and inoculated into Petri plates containing nutrient agar medium and incubated at 30 °C. A maximum number of colonies were noticed in soil samples collected from the garden, near the sewage sump, and near the canteen compared to other soil samples (Table ​ (Table1 1 ).

Bacteria from soil samples

Primary screening for antibacterial activity

All the 263 bacterial isolates were tested for their antibacterial activity against pathogenic bacteria S. aureus , E. coli , P. aeruginosa , and K. pneumoniae using primary screening. The results of the primary screening showed that only 2 isolates (PR1 and PR2) from garden soil and one isolate (PR3) from the soil collected near the sewage sump had antibacterial activity against all the tested bacteria (Table ​ (Table2). 2 ). The isolates PR1, PR2, and PR3 were selected for secondary screening.

Antibacterial activity against pathogenic bacteria by primary screening

Secondary screening for antibacterial activity 

The 3 isolates PR1, PR2 & PR3 which were confirmed to have antibacterial activity through primary screening were subjected to secondary screening for further confirmation of their activity against human pathogens S. aureus , E. coli , P. aeruginosa , and K. pneumoniae . All culture filtrates at 100 μl showed a maximum inhibition zone except E. coli indicating that Gram (+) bacteria are susceptible to antibiotics than Gram (−) bacteria. The filter-sterilized culture filtrate of PR1 showed a maximum zone of inhibition with 15.5 mm, 10.0 mm, 15.0 mm, and 14.0 mm compared to PR2 and PR3 against the tested pathogens (Table ​ (Table3). 3 ). Although all the isolates showed antibacterial activity against all pathogenic bacteria, autoclaved and proteinase K-treated culture filtrates lost their activity by 30 to 40% (Table ​ (Table4) 4 ) indicating that filter sterilization of crude culture filtrate is the best option for testing antibacterial activity. The inhibition zone for standard antibiotic streptomycin was 17.5 to 18.5 mm depending on the pathogens.

Antibacterial activity of culture filtrate of PR1, PR2, and PR3 against pathogenic bacteria

Results are expressed as antagonistic activity (mm) of the isolate bacteria against pathogenic compared to control (mean ± SD, n = 3). Values significantly different from control if * ρ  < 0.05 as analyzed by Student’s t -test. Control value for all the pathogenic bacteria 6 mm

PR1 —Bacillus aryabhattai strain PR-D07, PR2 —Arthrobacter humicola strain PR-F07, PR —Neomicrococcus lactis strain PR-F11

Results are expressed as antagonistic activity (mm) of the isolate bacteria against pathogenic compared to control (mean ± SD, n = 3). Values significantly different from control if * ρ  < 0.05 as analyzed by student t-test. Control value for all the pathogenic bacteria 6 mm

PR1 —Bacillus aryabhattai strain PR-D07, PR2 —Arthrobacter humicola strain PR-F07, PR3 —Neomicrococcus lactis strain PR-F11

Studies on the effect of different carbon and nitrogen source on the antibiotic activity revealed that Glycerol and Urea were the preferred carbon and nitrogen source for the isolate PR1. Isolate PR2 preferred Glucose and Casein while PR3 preferred sucrose and casein. Nutrient preference varied among all the isolates tested. Among the 4 test organisms, the inhibition zone for the Gram-negative bacterium E. coli was smaller than all other bacteria (Table ​ (Table5). 5 ). Commercial antibiotic streptomycin showed a larger inhibition zone than all the culture filtrates. A synthetic medium with sucrose and ammonium nitrate as carbon and nitrogen source was used as a control.

Effect of carbon and nitrogen sources on antibacterial activity by PR1, PR2, and PR3

++ 5–10 mm, +++ 10–15 mm, ++++ above 15 mm

Morphological and biochemical characterization of antagonistic bacteria

The morphological and biochemical characterization of bacterial isolates PR1, PR2, and PR3 which showed antibacterial activity were done, and results are tabulated (Table ​ (Table6 6 ).

The morphological and biochemical characterization of bacterial isolates

The isolation of genomic DNA was carried out to obtain pure DNA from the isolates PR1, PR2, and PR3. The 16sRNA was amplified from the isolated DNA sample using PCR. 1.2% of agarose gel was used to verify the amplified products which showed a fragment of 1.5 kb .

The amplified 16s r RNA of PR1, PR2, and PR3 were subjected to purification and sequencing. The sequences of PR1, PR2, and PR3 were submitted to NCBI. The accession numbers of the sequence PR1, PR2, and PR3 are {"type":"entrez-nucleotide","attrs":{"text":"MT453908","term_id":"1840474010","term_text":"MT453908"}} MT453908 — Bacillus aryabhattai strain PR-D07, {"type":"entrez-nucleotide","attrs":{"text":"MT453911","term_id":"1840474013","term_text":"MT453911"}} MT453911 — Arthrobacter humicola strain PR-F07, and {"type":"entrez-nucleotide","attrs":{"text":"MT453912","term_id":"1840474014","term_text":"MT453912"}} MT453912 — Neomicrococcus lactis strain PR-F11.

The sequence of {"type":"entrez-nucleotide","attrs":{"text":"MT453908","term_id":"1840474010","term_text":"MT453908"}} MT453908 — Bacillus aryabhattai strain PR-D07

TATCCCCGGGAGCCCGACCCGGCGCCGCAAGTCGGAACCAGGTACCCGTATAGTTTGATCCTGGCTCAGG

ATGAACGCTGGCGGCGTGCCTAATACATGCAAGTCGAGCGAACTGATTAGAAGCTTGCTTCTATGACGTT

AGCGGCGGACGGGTGAGTAACACGTGGGCAACCTGCCTGTAAGACTGGGATAACTTCGGGAAACCGAAGC

TAATACCGGATAGGATCTTCTCCTTCATGGGAGATGATTGAAAGATGGTTTCGGCTATCACTTACAGATG

GGCCCGCGGTGCATTAGCTAGTTGGTGAGGTAACGGCTCACCAAGGCAACGATGCATAGCCGACCTGAGA

GGGTGATCGGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTAGGGAATCTTCC

GCAATGGACGAAAGTCTGACGGAGCAACGCCGCGTGAGTGATGAAGGCTTTCGGGTCGTAAAACTCTGTT

GTTAGGGAAGAACAAGTACAAGAGTAACTGCTTGTACCTTGACGGTACCTAACCAGAAAGCCACGGCTAA

CTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGCGCGC

GCAGGCGGTTTCTTAAGTCTGATGTGAAAGCCCACGGCTCAACCGTGGAGGGTCATTGGAAACTGGGGAA

CTTGAGTGCAGAAGAGAAAAGCGGAATTCCACGTGTAGCGGTGAAATGCGTAGAGATGTGGAGGAACACC

AGTGGCGAAGGCGGCTTTTTGGTCTGTAACTGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGGATTA

GATACCCTGGTAGTCCACGCCGTAAACGATGAGTGCTAAGTGTTAGAGGGTTTCCGCCCTTTAGTGCTGC

The sequence of {"type":"entrez-nucleotide","attrs":{"text":"MT453911","term_id":"1840474013","term_text":"MT453911"}} MT453911 — Arthrobacter humicola strain PR-F07

TCAAACTCCCTTAGATTTGATCCTGGCTCAGGACGAACGCTGGCGGCGTGCTTAACACATGCAAGTCGAA

CGATGATCCGGTGCTTGCACCGGGGATTAGTGGCGAACGGGTGAGTAACACGTGAGTAACCTGCCCTTGA

CTCTGGGATAAGCCTGGGAAACCGGGTCTAATACCGGATATGACTTCCTGCCGCATGGTGGGGGGTGGAA

AGATTTTTTGGTTTTGGATGGACTCGCGGCCTATCAGCTTGTTGGTGGGGTAATGGCCTACCAAGGCGAC

GACGGGTAGCCGGCCTGAGAGGGTGACCGGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAG

GCAGCAGTGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCGACGCCGCGTGAGGGATGACGGCCT

TCGGGTTGTAAACCTCTTTCAGCAGGGAAGAAGCGGAAGTGACGGTACCTGCAGAAGAAGCGCCGGCTAA

CTACGTGCCAGCAGCCGCGGTAATACGTAGGGCGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCTC

GTAGGCGGTTTGTCGCGTCTGCTGTGAAAGCCCGGGGCTCAACCCCGGGTCTGCAGTGGGTACGGGCAGA

CTGGAGTGCAGTAGGGGAGACTGGAATTCCTGGTGTAGCGGTGAAATGCGCAGATATCAGGAGGAACACC

GATGGCGAAGGCAGGTCTCTGGGCTGTAACTGACGCTGAGGAGCGAAAGCATGGGGAGCGAACAGGATTA

GATACCCTGGTAGTCCATGCCGTAAACGTTGGGCACTAGGTGTGGGGGACATTCCACGTTTTCCGCGCCC

GTAGCTAACGCCC

The sequence of {"type":"entrez-nucleotide","attrs":{"text":"MT453912","term_id":"1840474014","term_text":"MT453912"}} MT453912 — Neomicrococcus lactis strain PR-F11

GAGAATTCCACGTTTTTCCGCGCCGTAGCTAACGCATTAAGTGCCCCGCCTGGGGAGTACGGCCGCAAGG

CTAAAACTCAAAGGAATTGACGGGGGCCCGCACAAGCGGCGGAGCATGCGGATTAATTCGATGCAACGCG

AAGAACCTTACCAAGGCTTGACATGGGCCGGATCGCCGCAGAAATGCGGTTTCCCTTCGGGGCCGGTTCA

CAGGTGGTGCATGGTTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCC

TCGTTCTATGTTGCCAGCGGTTCGGCCGGGGACTCATAGGAGACTGCCGGGGTCAACTCGGAGGAAGGTG

GGGACGACGTCAAATCATCATGCCCCTTATGTCTTGGGCTTCACGCATGCTACAATGGCCGGTACAAAGG

GTTGCGATACTGTGAGGTGGAGCTAATCCCAAAAAGCCGGTCTCAGTTCGGATTGAGGTCTGCAACTCGA

CCTCATGAAGTCGGAGTCGCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCCTTGT

ACACACCGCCCGTCAAGTCACGAAAGTTGGTAACACCCGAAGCCGGTGGCCTAACCCTTTTGGGAGGGAG

CCGTCGAAGGTGGGACCGGCGATTGGGACTAAGTCGTAACAAGGTAACCGATAAGG

Phylogeny of PR1, PR2, and PR3

The obtained sequences were compared with sequences in the Genbank nucleotide database. The identification of the species is done with the phylogenetic analysis neighbor with 98 – 100 % similarity. Phylogenetic analysis and sequence alignment were carried out using the http://www.phylogeny.fr/simple_phylogeny.cgi . This indicates that collected strains are B. aryabhattai (PR1 ) , A. humicola (PR2), and N. lactis (PR3) (Figs. ​ (Figs.1, 1 , ​ ,2, 2 , and ​ and3 3 ).

Neighbor-joining tree showing the phylogenetic relationships of 16S rRNA gene sequences of B. aryabhattai . The scale bar indicates evolutionary distance

Neighbor-joining tree showing the phylogenetic relationships of 16S rRNA gene sequences of A. humicola . The scale bar indicates evolutionary distance

Neighbor-joining tree showing the phylogenetic relationships of 16S rRNA gene sequences of N. lactis . The scale bar indicates evolutionary distance

Soil is found to be a rich source for various types of bacterial communities when compared to other environments and they are very well adapted to constantly varying soil environments. Competition among them for survival necessitates producing antibacterial compounds to eliminate the competitor. This is one of the reasons why soil bacteria are preferred for screening of antibacterial activity [ 30 ]. Numerous scientists have selected soil for the isolation of countless antibiotic-producing bacteria. Arifuzzaman et al. [ 31 ], a revealed 20 soil bacterial strains were active against the pathogenic microbes. Denizci [ 32 ] isolated 356 Streptomyces isolates from the soils in several regions of Turkey and screened for antibacterial activity. Dehnad et al. [ 33 ] have isolated 150 actinomycetes from soil samples of West of Iran for screening antibacterial activity against the test pathogens. Falkinham et al. [ 34 ] reported that soil bacteria form the basis for the production of nearly 500 antibiotics each year. The demand for bacterial antibiotics continues to increase globally because the pathogenic bacteria continue to acquire resistance to the antibiotics and many antibiotics proved that they are no longer potent against the infections [ 18 , 19 ]. It is reported that some Staphylococcus spp., Streptococcus spp., pseudomonads, and Enterobacteria, responsible for several human health issues, have developed resistance to many antibiotics [ 35 ].

This study is aimed to isolate soil bacteria that exhibit antibacterial activity. Out of 263 bacterial colonies isolated from soil samples, only 3 had antibacterial potential. The 3 active bacterial strains ( B. aryabhattai strain PR-D07, A. humicola strain PR-F07, and N. lactis strain PR-F11), isolated and identified through molecular methods in this study are the first report on their antibacterial activity against all the human pathogens mentioned earlier. Any reports of antibacterial activity by these isolates and their preferential carbon and nitrogen source are not available so far.

Nike et al. [ 36 ] and Kaur et al. [ 37 ] have used the primary and secondary screening methods as done in our work for the isolation and screening of bacterial isolates. Many scientists have adopted the agar well diffusion method for secondary screening of bacteria using cell-free culture filtrates [ 38 – 40 ]. Similar studies on different bacteria were carried out by Rafiq et al. [ 41 ] and their study suggested that most of the species belonging to the genus Bacillus are potential for the production of antibiotics. Our study also found that one of the active bacteria was B. aryabhattai .

In this study, all culture filtrates at 100 μl inhibited the growth of all bacterial pathogens listed earlier. It shows that PR1, PR2, and PR3 displayed broad-spectrum activity against both the Gram (+) and Gram (−) bacteria. However, the zone of inhibition for S. aureus , P. aeruginosa , and K , pneumoniae was bigger than the inhibition zone for E. coli indicating that Gram (+) bacteria are more vulnerable to antibiotics than Gram (−) bacteria. The difference in membrane constituents of Gram (+) and Gram (−) is responsible for the difference in susceptibility, outer polysaccharide membrane possessed by Gram (−) did not permit the entry of lipophilic solutes whereas the peptidoglycan layer of Gram (+) bacteria is not an effective barrier [ 42 ]. In their work, Ray et al. [ 43 ] reported that culture filtrates of B. aryabhattai LS11, isolated from wetland soils failed to inhibit E.coli at all concentrations.

In our studies, the filter-sterilized culture filtrate of B. aryabhattai (PR1) showed maximum antibacterial activity against all tested pathogens which is similar to the results of Yoshida et al. (38), who used the filter-sterilized culture filtrate of B. amyloliquefaciens against many bacteria. However, Onajobi et al. [ 44 ] reported that the culture filtrate of B. aryabhattai KNUC205 isolated from farmland soil showed antibacterial activity only against P. aeruginosa and failed to show any activity against S. aureus , E. coli , and K. pneumoniae , tested by them.

Autoclaving and proteinase K treatment of culture filtrate reduced the antibacterial activity against all tested pathogens. Reduction in the antibacterial activity of culture filtrate as a result of proteinase K treatment indicated that the antibacterial principle is proteinaceous [ 45 ]. Members of the genus Bacillus were found to produce different types of peptides which are responsible for the broad spectrum of antibacterial activity against pathogenic bacteria [ 46 ]. Proteins, whether they are a simple or complex group of polypeptides make pathways with several enzymatic steps using polyketide synthases and peptide synthetases to produce antibiotics (10). Meng et al. [ 47 ] and Siahmashteh et al. [ 48 ] have shown that Bacillus species are found to be a robust source of antibiotics against various pathogens.

The antibacterial activity shown by soil bacteria is governed by the nutritional status of the soil which includes carbon and nitrogen sources. Therefore, optimization of essential substrates is required for the production of a high level of antibacterial compounds [ 14 ]. The effect of different carbon and nitrogen sources on antibacterial activity was studied as these have significant effects on bacterial metabolism. The priority of nutritional substrates widely varies among every isolate. In our studies, 3 different isolates preferred 3 different carbon and nitrogen sources. B. aryabhattai strain PR-D07 (PR1), A. humicola strain PR-F07 (PR2), and N. lactis strain PR-F11 (PR3) preferred glucose, glycerol, and sucrose as carbon sources respectively. Hence, there is a difficulty in arriving at a common formula regarding the nutritional requirement for all isolates. Our results revealed that 1% glucose as the sole carbon source stimulated increased antibacterial activity more than other sugars in the case of the isolate B. aryabhattai strain PR-D07 (PR1). These results were in agreement with the earlier reports which stated that 1% glucose was the optimum carbon source for antibiotic production in Streptomyces sp. [ 49 , 50 ] in Brevibacillus laterosporus EA62 [ 51 ] and Bacillus subtilis [ 52 ]. However, Pandey et al. [ 53 ] found that 2% of Dextrose is the preferred source by S. kanamyceticus. Dunia et al. [ 54 ] reported enhanced production of antibiotics by wheat bran as a carbon source in Streptomyces sp. whereas Alev Usta et al. [ 51 ] found that the antibiotic activity of Brevibacillus laterosporus EA62 was repressed by wheat bran but a higher growth rate was observed. Zhang et al. [ 55 ] found enhanced antibacterial activity if sucrose was used for the growth of B. amyloliquifaciens . El-Banna [ 56 ] had tested 5 different strains of B. megaterium , and their preference to carbon source significantly varied for enhanced antibacterial activity. According to their results, B. megaterium NB-3 and NB-6 utilized Glycerol, B. megaterium NB-4 and NB-5 used Glucose, and B. megaterium NB-7 preferred Fructose for enhanced antibiotic activity against tested bacteria. Glycerol as a carbon source displayed higher antibacterial activity by B. firmus and B. circulans , starch for B. stearothermophilus , and an unknown strain [ 57 ].

Similarly, preference for nitrogen source also differed among all the 3 isolates tested in the present study. B. aryabhattai strain PR-D07 (PR1) showed maximum antibacterial activity using urea than all other nitrogen sources. A. humicola strain PR-F07 (PR2) preferred casein and N. lactis strain PR-F11 (PR3) used NH 4 H 2 PO 4 for antibacterial activity. Although the isolate B. aryabhattai strain PR-D07 (PR1) preferred urea, Zhang et al. [ 55 ] reported urea led to the loss of antibacterial activity in B. amyloliquifaciens and it preferred NH 4 Cl as the best nitrogen source for antibiotic activity.

In a study by Dunia et al. [ 54 ], yeast extract, ammonium sulfate, and beef extract as a supplement to wheat bran produced 249 U/g, 240 U/g, 220 U/g of antibiotic respectively by Streptomyces sp. AS4 which is comparatively higher than the wheat bran alone. Oskay [ 58 ] and Al-Ghazali and Omron, [ 59 ] have reported peptone as the excellent nitrogen source for Streptomyces sp. and Streptomyces sp. KGG32 respectively for antibiotic production.

Morphological and biochemical characterization of isolates PR1, PR2, and PR3 were carried out as it is a tool for preliminary identification of bacteria, and it is a conventional method followed by microbiologists all over the world [ 37 , 60 ]. For the past several decades, most laboratories adopt microscopic identification and biochemical characterization to identify the bacteria [ 61 ]. As the bacteria do not have sufficient morphological features to confirm their identity, several procedures have been formulated based on their nutrition, metabolic activities, metabolic products, or enzymatic reactions which help in grouping and identifying the bacteria up to genus and species level [ 62 , 63 ]. The Gram staining results showed that the isolates PR1, PR2, and PR3 are Gram (+). The biochemical tests carried out in our studies matched with these isolates.

Phylogenetic analysis and sequence alignment of the isolates PR1, PR2, and PR3 revealed them as B. aryabhattai , A. humicola , and N.lactis . The isolate B. aryabhattai PR-D07 (PR1) displayed a high antibacterial activity than other isolates. Several reports confirmed the fact that many species of the genus Bacillus are potential antibiotic producers [ 41 ]. Ours is the first report on the antibacterial activity of B. aryabhattai against human pathogens S. aureus , E. coli , P. aeruginosa , and K. pneumoniae .

Apart from its antibacterial property, B. aryabhattai was reported to be the producer of many value-added products. Yaraguppi et al. [ 64 ] have reported that B. aryabhattai could be a promising candidate for exploring the production of biosurfactants relevant to the pharmaceutical industry. Paz et al. [ 65 ] have obtained several value-added products from B. aryabhattai by using different media. They suggested this bacterium could be used to degrade lignocellulose wastes and treating dyes from the textile industry. It indicates that B. arybhattai could be a potential organism to study in detail. The antibacterial activity shown by B. aryabhattai (isolate PR1) would be as effective as that of the commercial antibiotic provided further purification and characterization of antibiotic factor is carried out.

In the present work, the isolate PR2 was identified as A. humicola PR-F07 exhibited antibacterial activity on all test bacteria; however, its activity was comparatively lower than B. aryabhattai PR-D07vNo report is available on the antibiotic activity of A. humicola and ours is the first report of antibiotic activity by A. humicola . However, Munaganti et al. [ 66 ] have reported that modified yeast extract malt extract dextrose broth enhanced the bioactive compound formation in A. kerguelensis . According to their experimental results, lactose and peptone were the best carbon and nitrogen sources. Bacteria of the genus Arthrobacter are commonly found in the soil environment and Kageyama et al. [ 67 ] isolated this bacteria from the paddy field. The bioflocculants produced by A. humicola were reported to be used for sewage wastewater treatment replacing the chemically produced flocculants [ 68 ].

The isolate PR3 which showed antibacterial activity was identified as N. lactis PR-F11. This new species was proposed and described by Prakash et al. [ 69 ]. There is no report on the production of antibiotics by N. lactis so far. Ours is the first report on antibacterial activity by N. lactis. Biscupiak et al. [ 70 ] reported that Micrococcus luteus produced an antibiotic named Neoberninamycin . In another report by Kumari et al. [ 71 ], the crude yellow pigment of Micrococcus sp. OUS9 was active against both Gram (+) and Gram (− ) bacteria, i.e . , B. subtilis , K. pneumoniae. Salmonella sp., S. aureus , P. aeruginosa , and E. coli , No literature has been available on N. lactis on its antibacterial activity except for the proposal for the creation of a new genus Neomicrococcus by Prakash et al. [ 70 ].

Our attempt to isolate soil bacteria having antibacterial activity yielded encouraging results. There are 3 bacterial isolates, hitherto not reported for antibiotic activity have been characterized and identified as B. aryabhattai , A. humicola , and N. lactis . These isolates were tested against human pathogens, S. aureus , E. coli , P. aeruginosa , and K. pneumoniae . Among the pathogens tested, the Gram-negative bacteria E. coli was slightly resistant compared to other Gram-positive pathogens. Filter-sterilized culture filtrates had more antibacterial activity than autoclaved and proteinase K-treated culture filtrates. All isolates preferred different carbon and nitrogen sources for their enhanced activity. In our study, B. aryabhattai showed good antibacterial activity than the other two isolates. All the isolates showed broad-spectrum antibiotic activity, and further purification and standardization processes are required to compare the efficacy with all available antibiotics. We conclude that all these 3 bacteria are potential candidates for further research on their antibacterial properties.

Acknowledgements

The authors wish to express their gratitude to the Department of Biotechnology and Genetics, M. S. Ramaiah College of Arts Science and Commerce, Bengaluru, for providing the laboratory facility.

Authors’ contributions

PR: Carried out molecular identification of bacteria and sequencing and analysis of 16S rRNA

SGK: Selection of study area and soil sample collection. Primary screening, isolation, and maintenance of bacteria. Performed experiments on perpendicular streaking method and seed overlay method

KS: Secondary screening of antimicrobial activities of pure isolates. Analyzed the effect of filter sterilization, carbon and nitrogen sources, and proteinase K treatment on antibiotic activity

ML: Carried our morphological and biochemical characterization of bacteria, isolated genomic DNA from bacterial samples

All authors read and approved the final manuscript.

Authors’ information

Not applicable

Availability of data and materials

Declarations.

The authors declare that they have no competing interests.

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Model test research of wave-induced submarine landslide based on Fibre Bragg Grating sensing technology

• Cheng, Shuai
• Wang, Sheng
• Chen, Yuxue

Submarine landslides are common marine disasters that pose significant threats to human safety. However, there is no established method for monitoring submarine landslides. To effectively prevent and control such disasters, this study conducted a series of investigations based on the Zhujiajian Landslide. Based on optical fibre sensing and numerical simulation technology, this study proposed a sensor optimisation method for a submarine landslide simulation experiment. Three types of fibre-optic sensors with high sensitivity and corrosion resistance were developed, and the designed sensors have broad application prospects for monitoring complex environments. A wavelength-division multiplexing sensor-networking method for submarine landslide simulation tests was proposed. A simulation test of a submarine landslide under wave action was conducted to verify the practicability of the optical fibre monitoring system, and experimental data were collected. The variation characteristics of the seepage pressure, displacement, and velocity fields were studied. The results showed that submarine landslides are the products of liquefaction and shear failures. Continuous wave action causes the accumulation of pore water pressure in the soil mass inside the slope, which leads to a decrease in the shear strength. The shearing action of waves is the driving force for submarine landslides. When a submarine landslide occurred, both displacement and pore water pressure showed a sudden change, but the sudden change in pore water pressure occurred approximately 5 s earlier than that of the sudden change in displacement. A 50:1 on-site prototype was obtained by converting the time similarity scale, and the on-site pore water pressure mutation time was approximately 30s earlier than the submarine landslide occurrence time. Therefore, when conducting on-site submarine landslide monitoring, pore water pressure can be prioritised as an evaluation index for the stability of submarine slopes. The research results provide effective technical support for the prevention and control of submarine landslides and have reference significance and application value for similar projects.

• Submarine landslide;
• Optical fibre sensor;
• Physical model tests;
• Fibre Bragg grating;
• Stability analysis

• MyU : For Students, Faculty, and Staff

News Roundup Spring 2024

CEGE Spring Graduation Celebration and Order of the Engineer

Forty-seven graduates of the undergraduate and grad student programs (pictured above) in the Department of Civil, Environmental, and Geo- Engineering took part in the Order of the Engineer on graduation day. Distinguished Speakers at this departmental event included Katrina Kessler (MS EnvE 2021), Commissioner of the Minnesota Pollution Control Agency, and student Brian Balquist. Following this event, students participated in the college-wide Commencement Ceremony at 3M Arena at Mariucci. 

UNIVERSITY & DEPARTMENT

The University of Minnesota’s Crookston, Duluth, and Rochester campuses have been awarded the Carnegie Elective Classification for Community Engagement, joining the Twin Cities (2006, 2015) and Morris campuses (2015), and making the U of M the country’s first and only university system at which every individual campus has received this selective designation. Only 368 from nearly 4,000 qualifying U.S. universities and colleges have been granted this designation.

CEGE contributed strongly to the College of Science and Engineering’s efforts toward sustainability research. CEGE researchers are bringing in over $35 million in funded research to study carbon mineralization, nature and urban areas, circularity of water resources, and global snowfall patterns. This news was highlighted in the Fall 2023 issue of  Inventing Tomorrow  (pages 10-11). https://issuu.com/inventingtomorrow/docs/fall_2023_inventing_tomorrow-web

CEGE’s new program for a one-year master’s degree in structural engineering is now accepting applicants for Fall 2024. We owe a big thanks to DAN MURPHY and LAURA AMUNDSON for their volunteer work to help curate the program with Professor JIA-LIANG LE and EBRAHIM SHEMSHADIAN, the program director. Potential students and companies interested in hosting a summer intern can contact Ebrahim Shemshadian ( [email protected] ).

BERNIE BULLERT , CEGE benefactor and MN Water Research Fund founder, was profiled on the website of the University of Minnesota Foundation (UMF). There you can read more about his mission to share clean water technologies with smaller communities in Minnesota. Many have joined Bullert in this mission. MWRF Recognizes their Generous 2024 Partners. Gold Partners: Bernie Bullert, Hawkins, Inc., Minnesota Department of Health, Minnesota Pollution Control Agency, and SL-serco. Silver Partners: ISG, Karl and Pam Streed, Kasco, Kelly Lange-Haider and Mark Haider, ME Simpson, Naeem Qureshi, Dr. Paul H. Boening, TKDA, and Waterous. Bronze Partners: Bruce R. Bullert; Brenda Lenz, Ph.D., APRN FNP-C, CNE; CDM Smith; Central States Water Environment Association (CSWEA MN); Heidi and Steve Hamilton; Jim “Bulldog” Sadler; Lisa and Del Cerney; Magney Construction; Sambatek; Shannon and John Wolkerstorfer; Stantec; and Tenon Systems.

After retiring from Baker-Tilly,  NICK DRAGISICH  (BCE 1977) has taken on a new role: City Council member in Lake Elmo, Minnesota. After earning his BCE from the University of Minnesota, Dragisich earned a master’s degree in business administration from the University of St. Thomas. Dragisich retired in May from his position as managing director at Baker Tilly, where he had previously served as firm director. Prior to that, he served as assistant city manager in Spokane, Washington, was the city administrator and city engineer in Virginia, Minnesota, and was mayor of Chisholm, Minnesota—all adding up to more than 40 years of experience in local government. Dragisich was selected by a unanimous vote. His current term expires in December 2024.

PAUL F. GNIRK  (Ph.D. 1966) passed away January 29, 2024, at the age of 86. A memorial service was held Saturday, February 24, at the South Dakota School of Mines and Technology (SDSM&T), where he started and ended his teaching career, though he had many other positions, professional and voluntary. In 2018 Paul was inducted into the SDSM&T Hardrocker Hall of Fame, and in 2022, he was inducted into the South Dakota Hall of Fame, joining his mother Adeline S. Gnirk, who had been inducted in 1987 for her work authoring nine books on the history of south central South Dakota.

ROGER M. HILL  (BCE 1957) passed away on January 13, 2024, at the age of 90. His daughter, Kelly Robinson, wrote to CEGE that Roger was “a dedicated Gopher fan until the end, and we enjoyed many football games together in recent years. Thank you for everything.”

KAUSER JAHAN  (Ph.D. 1993, advised by Walter Maier), PE, is now a civil and environmental engineering professor and department head at Henry M. Rowan College of Engineering. Jahan was awarded a 3-year (2022- 2025), $500,000 grant from the U.S. Department of Environmental Protection Agency (USEPA). The grant supports her project, “WaterWorks: Developing the New Generation of Workforce for Water/Wastewater Utilities,” for the development of educational tools that will expose and prepare today’s students for careers in water and wastewater utilities.

SAURA JOST  (BCE 2010, advised by Timothy LaPara) was elected to the St. Paul City Council for Ward 3. She is part of the historic group of women that make up the nation’s first all-female city council in a large city.

The 2024 ASCE Western Great Lakes Student Symposium combines several competitions for students involved in ASCE. CEGE sent a large contingent of competitors to Chicago. Each of the competition groups won awards: Ethics Paper 1st place Hans Lagerquist; Sustainable Solutions team 1st place overall in (qualifying them for the National competition in Utah in June); GeoWall 2nd place overall; Men’s Sprint for Concrete Canoe with rowers Sakthi Sundaram Saravanan and Owen McDonald 2nd place; Product Prototype for Concrete Canoe 2nd place; Steel Bridge (200 lb bridge weight) 2nd place in lightness; Scavenger Hunt 3rd place; and Aesthetics and Structural Efficiency for Steel Bridge 4th place.

Students competing on the Minnesota Environmental Engineers, Scientists, and Enthusiasts (MEESE) team earned second place in the Conference on the Environment undergraduate student design competition in November 2023. Erin Surdo is the MEESE Faculty Adviser. Pictured are NIKO DESHPANDE, ANNA RETTLER, and SYDNEY OLSON.

The CEGE CLASS OF 2023 raised money to help reduce the financial barrier for fellow students taking the Fundamentals of Engineering exam, a cost of $175 per test taker. As a result of this gift, they were able to make the exam more affordable for 15 current CEGE seniors. CEGE students who take the FE exam pass the first time at a rate well above national averages, demonstrating that CEGE does a great job of teaching engineering fundamentals. In 2023, 46 of 50 students passed the challenging exam on the first try.

This winter break, four CEGE students joined 10 other students from the College of Science and Engineering for the global seminar, Design for Life: Water in Tanzania. The students visited numerous sites in Tanzania, collected water source samples, designed rural water systems, and went on safari. Read the trip blog: http://globalblogs.cse.umn.edu/search/label/Tanzania%202024

Undergraduate Honor Student  MALIK KHADAR  (advised by Dr. Paul Capel) received honorable mention for the Computing Research Association (CRA) Outstanding Undergraduate Research Award for undergraduate students who show outstanding research potential in an area of computing research.

GRADUATE STUDENTS

AKASH BHAT  (advised by William Arnold) presented his Ph.D. defense on Friday, October 27, 2023. Bhat’s thesis is “Photolysis of fluorochemicals: Tracking fluorine, use of UV-LEDs, and computational insights.” Bhat’s work investigating the degradation of fluorinated compounds will assist in the future design of fluorinated chemicals such that persistent and/or toxic byproducts are not formed in the environment.

ETHAN BOTMEN  (advised by Bill Arnold) completed his Master of Science Final Exam February 28, 2024. His research topic was Degradation of Fluorinated Compounds by Nucleophilic Attack of Organo-fluorine Functional Groups.

XIATING CHEN , Ph.D. Candidate in Water Resources Engineering at the Saint Anthony Falls Laboratory is the recipient of the 2023 Nels Nelson Memorial Fellowship Award. Chen (advised by Xue Feng) is researching eco-hydrological functions of urban trees and other green infrastructure at both the local and watershed scale, through combined field observations and modeling approaches.

ALICE PRATES BISSO DAMBROZ  has been a Visiting Student Researcher at the University of Minnesota since last August, on a Doctoral Dissertation Research Award from Fulbright. Her CEGE advisor is Dr. Paul Capel. Dambroz is a fourth year Ph.D. student in Soil Science at Universidade Federal de Santa Maria in Brazil, where she studies with her adviser Jean Minella. Her research focuses on the hydrological monitoring of a small agricultural watershed in Southern Brazil, which is located on a transition area between volcanic and sedimentary rocks. Its topography, shallow soils, and land use make it prone to runoff and erosion processes.

Yielding to people in crosswalks should be a very pedestrian topic. Yet graduate student researchers  TIANYI LI, JOSHUA KLAVINS, TE XU, NIAZ MAHMUD ZAFRI  (Dept.of Urban and Regional Planning at Bangladesh University of Engineering and Technology), and Professor Raphael Stern found that drivers often do not yield to pedestrians, but they are influenced by the markings around a crosswalk. Their work was picked up by the  Minnesota Reformer.

TIANYI LI  (Ph.D. student advised by Raphael Stern) also won the Dwight David Eisenhower Transportation (DDET) Fellowship for the third time! Li (center) and Stern (right) are pictured at the Federal Highway Administration with Latoya Jones, the program manager for the DDET Fellowship.

The Three Minute Thesis Contest and the Minnesota Nice trophy has become an annual tradition in CEGE. 2023’s winner was  EHSANUR RAHMAN , a Ph.D. student advised by Boya Xiong.

GUANJU (WILLIAM) WEI , a Ph.D. student advised by Judy Yang, is the recipient of the 2023 Heinz G. Stefan Fellowship. He presented his research entitled Microfluidic Investigation of the Biofilm Growth under Dynamic Fluid Environments and received his award at the St. Anthony Falls Research Laboratory April 9. The results of Wei's research can be used in industrial, medical, and scientific fields to control biofilm growth.

BILL ARNOLD  stars in an award-winning video about prairie potholes. The Prairie Potholes Project film was made with the University of Delaware and highlights Arnold’s NSF research. The official winners of the 2024 Environmental Communications Awards Competition Grand Prize are Jon Cox and Ben Hemmings who produced and directed the film. Graduate student Marcia Pacheco (CFANS/LAAS) and Bill Arnold are the on-screen stars.

Four faculty from CEGE join the Center for Transportation Studies Faculty and Research Scholars for FY24–25:  SEONGJIN CHOI, KETSON ROBERTO MAXIMIANO DOS SANTOS, PEDRAM MORTAZAVI,  and  BENJAMIN WORSFOLD . CTS Scholars are drawn from diverse fields including engineering, planning, computer science, environmental studies, and public policy.

XUE FENG  is coauthor on an article in  Nature Reviews Earth and Environment . The authors evaluate global plant responses to changing rainfall regimes that are now characterized by fewer and larger rainfall events. A news release written at Univ. of Maryland can be found here: https://webhost.essic. umd.edu/april-showers-bring-mayflowers- but-with-drizzles-or-downpours/ A long-running series of U of M research projects aimed at improving stormwater quality are beginning to see practical application by stormwater specialists from the Twin Cities metro area and beyond. JOHN GULLIVER has been studying best practices for stormwater management for about 16 years. Lately, he has focused specifically on mitigating phosphorous contamination. His research was highlighted by the Center for Transportation Studies.

JIAQI LI, BILL ARNOLD,  and  RAYMOND HOZALSKI  published a paper on N-nitrosodimethylamine (NDMA) precursors in Minnesota rivers. “Animal Feedlots and Domestic Wastewater Discharges are Likely Sources of N-Nitrosodimethylamine (NDMA) Precursors in Midwestern Watersheds,” Environmental Science and Technology (January 2024) doi: 10.1021/acs. est.3c09251

ALIREZA KHANI  contributed to MnDOT research on Optimizing Charging Infrastructure for Electric Trucks. Electric options for medium- and heavy-duty electric trucks (e-trucks) are still largely in development. These trucks account for a substantial percentage of transportation greenhouse gas emissions. They have greater power needs and different charging needs than personal EVs. Proactively planning for e-truck charging stations will support MnDOT in helping to achieve the state’s greenhouse gas reduction goals. This research was featured in the webinar “Electrification of the Freight System in Minnesota,” hosted by the University of Minnesota’s Center for Transportation Studies. A recording of the event is now available online.

MICHAEL LEVIN  has developed a unique course for CEGE students on Air Transportation Systems. It is the only class at UMN studying air transportation systems from an infrastructure design and management perspective. Spring 2024 saw the third offering of this course, which is offered for juniors, seniors, and graduate students.

Research Professor  SOFIA (SONIA) MOGILEVSKAYA  has been developing international connections. She visited the University of Seville, Spain, November 13–26, 2023, where she taught a short course titled “Fundamentals of Homogenization in Composites.” She also met with the graduate students to discuss collaborative research with Prof. Vladislav Mantic, from the Group of Continuum Mechanics and Structural Analysis at the University of Seville. Her visit was a part of planned activities within the DIAGONAL Consortium funded by the European Commission. CEGE UMN is a partner organization within DIAGONAL, represented by CEGE professors Mogilevskaya and Joseph Labuz. Mantic will visit CEGE summer 2024 to follow up on research developments and discuss plans for future collaboration and organization of short-term exchange visits for the graduate students from each institution. 

DAVID NEWCOMB  passed away in March. He was a professor in CEGE from 1989–99 in the area of pavement engineering. Newcomb led the research program on asphalt materials characterization. He was the technical director of Mn/ROAD pavement research facility, and he started an enduring collaboration with MnDOT that continues today. In 2000, he moved from Minnesota to become vice-president for Research and Technology at the National Asphalt Pavement Association. Later he moved to his native Texas, where he was appointed to the division head of Materials and Pavement at the Texas A&M Transportation Institute, a position from which he recently retired. He will be greatly missed.

PAIGE NOVAK  won Minnesota ASCE’s 2023 Distinguished Engineer of the Year Award for her contributions to society through her engineering achievements and professional experiences.

The National Science Foundation (NSF) announced ten inaugural (NSF) Regional Innovation Engines awards, with a potential $1.6 billion investment nationally over the next decade. Great Lakes ReNEW is led by the Chicago-based water innovation hub,  Current,  and includes a team from the University of Minnesota, including PAIGE NOVAK. Current will receive $15 mil for the first two years, and up to $160 million over ten years to develop and grow a water-focused innovation engine in the Great Lakes region. The project’s ambitious plan is to create a decarbonized circular “blue economy” to leverage the region’s extraordinary water resources to transform the upper Midwest—Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin. Brewing one pint of beer generates seven pints of wastewater, on average. So what can you do with that wastewater?  PAIGE NOVAK  and her team are exploring the possibilities of capturing pollutants in wastewater and using bacteria to transform them into energy.

BOYA XIONG  has been selected as a recipient of the 2024 40 Under 40 Recognition Program by the American Academy of Environmental Engineers and Scientists. The award was presented at the 2024 AAEES Awards Ceremony, April 11, 2024, at the historic Howard University in Washington, D.C. 

JUDY Q. YANG  received a McKnight Land-Grant Professorship Award. This two-year award recognizes promising assistant professors and is intended to advance the careers of individuals who have the potential to make significant contributions to their departments and their scholarly fields. 

Professor Emeritus CHARLES FAIRHURST , his son CHARLES EDWARD FAIRHURST , and his daughter MARGARET FAIRHURST DURENBERGER were on campus recently to present Department Head Paige Novak with a check for $25,000 for the Charles Fairhurst Fellowship in Earth Resources Engineering in support of graduate students studying geomechanics. The life of Charles Fairhurst through a discussion with his children is featured on the Engineering and Technology History Wiki at https://ethw.org/Oral-History:Charles_Fairhurst#00:00:14_INTRODUCTION

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