Soil Quality Assessment Research Articles

The Productivity of Lumbricus rubellus Earthworms in Cow Manure Media with the Addition of Cricket Manure

Earthworms are one of the animals used as indicators to assess soil quality and fertility. In earthworm cultivation, several factors affect the life of earthworms, including temperature, pH, moisture, nutrient content, and media texture. This study aims to evaluate the productivity of earthworms (Lumbricus rubellus) in a mixed medium of cow manure and cricket manure. The method used in this study is a Completely Randomized Design (CRD) with 6 treatments and 3 replications, along with a Tukey posthoc test. The results showed that the addition of cricket manure to cow manure as a cultivation medium could increase earthworm productivity. The use of 90% cow manure with the addition of 10% cricket manure resulted in an average weight increase of 17 g, an average of 94 cocoons, and an average media reduction of 260.50 g. The addition of more than 10% cricket manure to cow manure could reduce earthworm productivity.

Digitized Seedbed Soil Quality Assessment from Worn and Edge Hardened Cultivator Sweeps.

Tillage tools for seedbed soil management are often subjected to low stress abrasion wear, which could negatively affect seedbed quality and crop productivity. Limited studies exist that quantify the effects of worn tillage tools on seedbed quality and crop yield. This research investigated the influence of tillage tool wear on seedbed preparation by evaluating the effect of cultivator sweep wear on soil tilth utilizing a light detection and ranging (LiDAR) sensor. The framework consists of a seedbed tillage field experiment using a Completely Randomized Design (CRD) experiment in six replicates of two-tillage treatments (new and worn cultivator sweeps). After seedbed tillage, loosely tilled soil aggregates were removed to expose the seedbed soil profile, and then seedbed roughness statistical measures were estimated from LiDAR-scanned seedbed soil surface. Three statistical analyses (Analysis of Variance (ANOVA), Kolmogorov-Smirnov (KS), and Earth Mover's Distance (EMD)) were compared to quantitatively evaluate the soil roughness estimated from the LiDAR seedbed surface data. Seedbed prepared by new and worn cultivator sweeps showed significant differences (p < 0.05) in soil roughness variables of standard deviation, coefficient of variation, and kurtosis. Data analysis from the ANOVA and KS methods revealed that LiDAR-extracted soil roughness patterns were statistically influenced by tillage treatment. EMD analysis detected noticeable disparities between the tillage treatments and new versus worn cultivator sweeps. This study concludes that tillage tool wear substantively affects seedbed quality, as evidenced by LiDAR soil profile estimated attributes of soil roughness and three statistical methods (ANOVA, KS, and EMD). Our study supports the adoption of LiDAR technology for seedbed management, highlighting its applicability to evaluate seedbed quality that accounts for the wear life cycle of cultivator sweeps.

Soil quality assessment and its response to water flow connectivity in different vegetation restoration types, eastern China

Vegetation restoration is the most widely used forest management practice for degraded soils, but the responses of soil quality in different vegetation restoration types may vary. In addition, the relationship between water flow paths connection and soil quality needs to be further explored. In this study, the soil quality index (SQI) and the index of water flow connectivity (IWFC) in the six water flow patterns (PFP, SSB, WSB, CPS, CPW, and CSW) were explored at three forest stands (oak, pine, and bamboo forests) with 50 years for enclosure. The results showed that the bamboo forest stand had the best soil quality as a whole (0.534 ± 0.135), followed by pine (0.530 ± 0.180) and oak forest stands (0.435 ± 0.205). The IWFC in PFP, CPS and CPW water flow patterns decreased gradually with increasing soil depth, while in SSB, WSB and CSB water flow patterns, the IWFC increased at first and then decreased. Finally, the IWFC in the CPW water flow pattern showed the largest positive correlation with the SQI (P < 0.05). The IWFC in the CPW water flow pattern driven by soil physical properties could mainly control the changes in the SQI indirectly (average IE = 0.825) by influencing soil nutrient (average IE = 0.447) and biological (average IE = 0.485) properties, while its direct effects could be ignored (average DE = −0.074), which demonstrated that the joint effects of preferential flow paths and weak stream buffer zones can dramatically reflect the changes in soil quality. The better soil improvement effect of bamboo and pine forest stands compared to the oak forest stand should be given more consideration. This study provided new insights for the assessment of the relationship between soil hydrology and soil quality.

Pedagogical technologies for teaching environmental engineering in the online learning system of higher education institution

The article outlines the pedagogical technologies for teaching environmental engineering in the online learning system of higher education institutions. There are proposed ways of developing educational technologies for teaching environmental engineering: active and collaborative learning, individualisation of training, use of video and audio materials, using electronic tools for evaluation of educational results, development of digital literacy, implementation of interactive online courses and professional communication and mentoring. There are presented the use of these technologies in the different aspects of environmental engineering, such as analysing water quality parameters, soil quality assessment, studying waste management practices, and studying the integration of renewable energy into the practice through virtual labs, online tests, video lectures, and collaborative projects. The proposed pedagogical technologies for teaching environmental engineering in the online learning system of higher education institutions were tested in the educational process. The Student's t-test was used for statistical calculation of the obtained experimental data. At the end of the experiment, there is a statistically significant difference between the mean values of the experimental and control groups. It can be claimed that the use of pedagogical technologies for teaching environmental engineering in the online learning system of higher education institutions is perspective.

Physico-chemical characteristics and quality assessment of soil in parts of the Banganga Basin in northeastern Rajasthan, NW India.

Soil is a vital natural resource that has a bearing on any developmental activity. A database comprising primary physicochemical characteristics and soil suitability would assist the planners in devising an appropriate land use policy and any intervention strategy for soil quality improvement. The physicochemical characteristics of soils in parts of the Banganga Basin, located in the semi-arid terrain of NW India were evaluated mainly through surface samples and sub-surface ones wherever sections were exposed in stream banks. The soil in the studied terrain is a Quaternary age, pale brown to dark brown and occasionally red, alluvial soil. Sandy loam is the dominant soil type, while sandy soil and loamy sand types also occur. The soil maintains a depth-wise consistency in physicochemical characteristics. The average soil pH value is 8.55, indicating a slightly alkaline and healthy soil. The soil has a high available nitrogen content (0.53 to 2.24%), adequate available phosphorus (23.58 and 62.18mg/kg), and low potassium (22.5 and 200mg/kg) contents. The organic carbon, Mg, and Ca levels are generally on the lower side. A consistency in overall soil characteristics is evident in the narrow range of the Soil Quality Index, varying between 10 and 14, and soil can be categorized into three categories with overlapping soil qualities.

Soil Quality Assessment of the Cultivated Land Around the Tailing Ponds in the Qinling Mountains of China

Soil Quality Assessment of the Cultivated Land Around the Tailing Ponds in the Qinling Mountains of China

Are Changes Occurring in Bacterial Taxa Community and Diversity with the Utilization of Different Substrates within SIR Measurements?

This research explores how the availability of substrates affects the regulation of soil microbial communities and the taxonomical composition of bacteria. The goal is to understand the impact of organic matter and substrate availability and quality on the diversity of soil bacteria. The study observed gradual changes in bacterial diversity in response to the addition of different substrate-induced respiration (SIR) substrates. Understanding the structure, dynamics, and functions of soil microbial communities is essential for assessing soil quality in sustainable agriculture. The preference for carbon sources among bacterial phyla is largely influenced by their life history and trophic strategies. Bacterial phyla like Proteobacteria, Bacteroidetes, and Actinobacteria, which thrive in nutrient-rich environments, preferentially utilize glucose. On the other hand, oligotrophic bacterial phyla such as Acidobacteria or Chloroflexi, which are found in lower numbers, have a lower ability to utilize labile C. The main difference between the two lies in their substrate utilization strategies. Understanding these distinct strategies is crucial for uncovering the bacterial functional traits involved in soil organic carbon turnover. Additionally, adding organic matter can promote the growth of copiotrophic bacteria, thus enhancing soil fertility.

Machine learning-assisted risk evaluation of heavy metals in the Hainan gold mining region, China.

This study employed machine learning (ML) to thoroughly investigate the impact of informal mining activities on the distribution and pollution status of heavy metals in soils near private gold mines in Hainan Province, southern China, a region known for its ecological sensitivity and economic importance. By systematically collecting surface soil samples and samples at depths of 0.5-1m from 175 drilling sites, a comprehensive quantitative analysis was conducted on major heavy metal elements, including lead (Pb), copper (Cu), cadmium (Cd), nickel (Ni), mercury (Hg), chromium (Cr), arsenic (As), and zinc (Zn). Combined with evaluation methods such as the Pollution Load Index (PLI), Normalized Pollution Index (NIPI), and Ecological Risk Index (ERI), the study revealed a high level of soil pollution at informal mining sites. The findings indicated that the average concentrations of Pb, Cd, Hg, As, and Zn in surface soils significantly exceeded the background values for soils in China, with a pronounced positive correlation observed between these heavy metal elements in both surface and deep soil profiles (r > 0.5). Furthermore, leveraging the heavy metal content in surface soils and the constructed environmental indicators, the predictive accuracy for metal content in deep soils was found to range from R2 = 0.27 to 0.68, suggesting that informal mining activities have led to substantial variations in metal content across different soil profiles. Through the application of a random forest model for predictive analysis of the PLI, NIPI, and ERI, high prediction accuracy was achieved (R2 = 0.78, 0.86, and 0.60, respectively). The study demonstrates that informal mining activities not only elevate the risk of soil pollution but also alter the distribution patterns of heavy metals. Also, this study provides a crucial foundation for the scientific assessment of soil quality and potential environmental hazards, while also affirming the efficacy of ML techniques in forecasting soil quality parameters.

Improved X-ray fluorescence spectroscopic monitoring of potentially toxic elements by multivariate analysis: A case study of soils near abandoned coal mines (NW Portugal)

Reusing soils near abandoned mines requires the assessment of soil quality, which includes determining potentially toxic elements (PTEs), such as As, Cd, Co, Cr, Cu, Fe, Mo, Ni, Pb, Sr, Zn and Zr. Levels of PTEs in soil can be harmful. Hence, the measurement of their concentrations is crucial to assess whether soil properties are reusable or it represents a potential environmental risk. Field techniques such as X-ray fluorescence (XRF) imprinting may be an option for rapid PTEs monitoring. Still, due to low sensitivity and selectivity, the partially obtained results by XRF software can be biased. This study presents an alternative solution for soil PTEs monitoring based on the advantages of multivariate analysis (MVA) principally partial least square (PLS) regression applied to orthogonally signal-corrected (OSC) XRF spectroscopic data. The developed PLS models were applied to soil samples from two regions of adjacent abandoned coal mines, in NW Portugal. High correlation coefficients obtained for As, Fe, Pb, Sr and Zn validation models (R2 = 0.79–0.99) pointed to the improved accuracy of their monitoring (compared to directly obtained XRF results) in this regional soil environment. The other PTEs (Co, Cr, Cu, Ni and Zr) showed good PLS models at local environment (R2 = 0.84–0.98). The test of these models in the contaminated regions reinforces their effectiveness in monitoring contaminated soils toward the reuse of environments near abandoned mines.

Assessing soil quality dynamics in northern Iran: a spatial modeling approach to predict and project future trends

Assessing soil quality dynamics in northern Iran: a spatial modeling approach to predict and project future trends

Assessment of High-Severity Post-Fire Soil Quality and Its Recovery in Dry/Warm Valley Forestlands in Southwest China through Selecting the Minimum Data Set and Soil Quality Index

Recurrent wildfires can negatively affect soil quality, and post-fire soil quality recovery is critical for maintaining sustainable ecosystem development. The objective of this study was to evaluate the changes and recovery of soil properties and soil quality in the forests of dry/warm river valleys in southwest China after disturbance by high-severity fires. In this study, the impact of fire on soil properties and soil quality was investigated for three years post-fire. Unburned forest land with a similar natural environment compared to the fire area was used as a control. Soil samples were collected from three different depths of 0–10 cm, 10–20 cm, and 20–30 cm, respectively. Principal component analysis (PCA) combined with the Norm value was used to select the minimum data set (MDS), thus calculating the soil quality index (SQI). The results showed that the soil properties changed significantly after high-severity fires. On average, soil bulk density (0.91 g/cm3, p = 0.001), total nitrogen (0.12 g/kg, p = 0.000), total phosphorus (0.10 g/kg, p = 0.000), and total potassium (5.55 g/kg, p = 0.000) were significantly lower in the burned areas than in the unburned areas at the first sampling. These indicators increased in the following three years but still did not recover to unburned levels. Compared with the above indicators, soil porosity and organic matter increased post-fire, but gradually decreased over time. Soil clay, geometric mean diameter, and total potassium were included in the MDS. The SQI was ranked as unburned &gt; 3 years &gt; 2 years &gt; 1 year &gt; 6 months. The SQI was significantly (p = 0.001) reduced six months post-fire by an average of 36%, and, after three years of recovery, the soil quality of the post-fire areas could be restored to 81% of soil in unburned areas. Apparently, high-severity fires caused changes in soil properties, thereby significantly decreasing soil quality. Soil quality gradually improved with increasing restoration time. However, the complete recovery of soil quality post-fire in forest land in the dry/warm river valley will take a longer time.

Assessing Soil Quality and Wheat Productivity under Manure Augmented Natural Farming

Assessing Soil Quality and Wheat Productivity under Manure Augmented Natural Farming

Predicting soil hydraulic conductivity using random forest, SVM, and LSSVM models

AbstractUnderstanding the hydraulic properties of soil is essential to solve many management problems in agriculture and the environment. Water quality affects soil hydraulic conductivity. Soil hydraulic properties play an important role in nature's water cycle and are used as basic information in designing irrigation and drainage systems, hydrological issues, and soil quality assessment. In the current study, soil sampling is performed from different areas and its hydraulic conductivity was measured using the drop load method and then predicted using support vector machine (SVM) and least‐squares support vector machine (LSSVM) models. The model inputs were: soil texture (percentage of sand, silt, and clay particles), salinity (electrical conductivity), pH, sodium adsorption ratio, soil porosity, and bulk density and the output was soil hydraulic conductivity. Correlation coefficient, root mean square error (RMSE), mean bias error (MBE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the models and compare them. Based on evaluation criteria the best performance was obtained for random forest (RF) (R = 0.89, RMSE = 0.53, mean absolute error (MAE) = 0.54, and NSE = 0.72). Following RF, the SVM with (R = 0.69, RMSE = 1.32, MAE = 0.69, and NSE = 0.48) performed better than LSSVM model.

Hyperspectral prediction model of soil Cu content based on WOA-SPA algorithm

ABSTRACT The content of heavy metal Cu in soil is an important indicator for assessing soil quality. However, soil hyperspectral data contain a large amount of redundant data, which can affect the effectiveness of spectral modelling. To address this problem, a combined method based on Whale Optimization Algorithm and Successive Projection Algorithm (WOA-SPA) is proposed to select the characteristic bands. And a prediction model of soil heavy metal Cu content was constructed using the partial least squares method. The experimental results show that: (1) The prediction accuracy of soil Cu content using the WOA-PLSR model was higher than that of the SPA-PLSR model, with RPD increased by 0.229, the R2 increased by 0.07, and RMSE decreased by 0.354. (2) Running the WOA and WOA-SPA combination algorithms 30 times respectively to prove the stability of the algorithms, the prediction accuracy of the model constructed based on the WOA-SPA combination algorithm is better than that of the model constructed based on the WOA algorithm as a whole. (3) Compared with the WOA or SPA algorithm alone, the prediction model constructed based on the WOA-SPA algorithm has a better prediction performance, with RPD of 2.259, R2 of 0.804, and RMSE of 2.396 for the prediction set. (4) Comparing the spatial distribution maps of the measured and predicted values, it can be observed that the spatial distribution between the predicted Cu content values based on the combined WOA-SPA algorithm and the measured Cu content values are basically consistent. This study indicates that the WOA-SPA-PLSR model has good stability and prediction accuracy for the prediction of Cu content in soil, which is of great practical significance for the rapid and accurate estimation of heavy metal Cu content in soil.

Experimental performance of smart irrigation systems using IoT

In agriculture field soil is a crucial determinant of plant development, primarily in irrigated environments. Nowadays there are several ways for assessing soil quality on a density or suspense basis. To develop, all plants need a specific amount of moisture level. PI involves cutting-edge technologies such as the Internet of Things (IoT), Wireless Sensor Networks (WSN), and cloud computing. In this method presents an overview of the soft computing methods based on the PI concept and architecture, including the most common wireless technologies. Then, a real-time IoT-based intelligent irrigation system is designed as a proof of concept. Several wireless sensor nodes are deployed to monitor both soil moisture and temperature. Sensed data are transmitted to the gateway through the Messaging Queuing Telemetry Transport (MQTT) communication protocol. Root based findings depict that the proposed work increases crop growth and productivity and decreases water wastage more than other state-of-the-art strategies. ANFIS method to predict the plant growth level based on the fuzzy rule. Wireless communication technology allows for the automation of different devices and real-time analysis of data collected by these sensors. The IoT (Internet of Things) system demonstrates improved plant growth. Furthermore, the application used in the plant's worst conditions and situations has provided the best sources for appropriate plant growth..

Effects of Urbanization on Soil Quality in the Rural‒Urban Gradient of Bengaluru, India

Understanding the impact of urbanization on soil quality is crucial for sustainable land management practices. This study was conducted in Bengaluru, India, to estimate the soil quality index (SQI) under different rural‒urban gradient (RUG) zones. Twenty-four sampling sites were identified along the RUG, and soil samples were collected monthly over five months during the October to February of 2020-2021. The soil quality assessment involved selecting the minimum data set (MDS) via principal component analysis (PCA) and correlation, scoring soil indicators, and combining these scores to create the soil quality index (SQI). PCA was used to identify key soil properties, which included microbial biomass carbon (MBC), SOC, N, manganese (Mn), and urease for different RUG zones derived from the MDS. The rural zones had the highest SQI (0.57), followed by the peri-urban (0.47 and 0.48) and urban (0.45 and 0.47) zones. These findings emphasize the importance of sustainable land management practices to preserve and boost soil quality across diverse regions, particularly in the face of rapid urbanization and industrialization.

Soil quality assessment and mapping in basaltic terrain of Central India for sustainable soil and crop management using integrated PCA and GIS

The cereal-based cropping system plays a vital role in ensuring food security in the Indian subcontinent. However, the productivity of these systems has seen a continuous decline due to the degradation of soil quality. This study aims to develop a Soil Quality Index (SQI) for such cropping systems. A detailed survey was conducted in the Bareli watershed of Seoni district, Madhya Pradesh, at a 1:10000 scale using high-resolution satellite data and Geographic Information System (GIS) technology. The survey identified and mapped 5 soil series: Diwartola, Diwara, Bareli-1, Bareli-2 and Bareli-3. Soil quality was evaluated based on morphological, physical and chemical properties as well as fertility parameters. Key indicators for soil quality assessment included sand, silt, clay content, bulk density, hydraulic conductivity, available water capacity and coefficient of linear extensibility (COLE). Additionally, pH, electrical conductivity, organic carbon, cation exchange capacity and nutrients like N, P, K, Fe, Mn, Cu and Zn were considered. The SQI was calculated using integrated principal component analysis, which involved selecting a minimum data set (MDS), assigning weights and scoring indicators. The results revealed that Diwartola soils had high quality (242.7 ha, 13.5 % TGA), Bareli-1 and Bareli-3 soils were of medium quality (462.8 ha, 25.7 % TGA), while Diwara and Bareli-2 soils were of low quality (966.1 ha, 53.8 % TGA). Agro-interventions such as agri-horticulture, agro-forestry, silvi-pasture, intensive cultivation and soil and water conservation measures were recommended based on the different mapping units.

Exploring the spatial relationship between soil quality index and soil ecosystem services driven by social-ecological factors: A peri-urban case study in central China

Urban expansion has profound impacts on soil quality in peri-urban areas. Understanding the intricate spatial relationship between soil quality index (SQI) and soil ecosystem services (SESs) in response to social-ecological factors is crucial for achieving sustainable soil utilization and management. In this study, a total of 485 soil samples were collected from different land-use types (including paddy field, dry land, orchard, and forest) in peri-urban areas of a city (Nanchang) in central China. The assessments of soil quality based on SQI and SESs were conducted using multi-source heterogeneous data. We determined the spatial relationships between SQI and six SESs using bivariate spatial autocorrelation analysis, and deciphered the role of 11 social-ecological factors in driving SQI–SES relationships across different zones. The results showed that SQI and SESs exhibited significant spatial clustering (P<0.01), despite certain variations in their distribution patterns. SQI had significant positive spatial correlations with soil organism habitat, food provision, erosion control, carbon storage, and filtering and buffering (r = 0.16–0.48, P<0.05). Two-step clustering categorized 183 villages with distinct SQI–SES relationships into four zones, i.e., soil ecological protection zone, food production core zone, soil quality enhancement zone, and soil ecological restoration zone. The impacts of various social-ecological factors on different zones were unveiled and corresponding development recommendations were presented. Results of this study facilitate the formulation of peri-urban-specific soil conservation policies and management strategies. Our work provides guidance on the improvement of soil quality and soil ecosystems, contributing to the future achievement of sustainable development goals.

Comprehensive assessment of soil quality in various land uses: a comparative analysis of soil quality index models

Comprehensive assessment of soil quality in various land uses: a comparative analysis of soil quality index models

A Systematic Review on Phytoremediation of Cadmium-Contaminated Soils: Identifying Hyperaccumulator Plants, Assessing Soil Quality, Analyzing Contamination Sources and Determining Health Risks

Cadmium is a highly toxic heavy metal found in soils due to various anthropogenic and natural sources, posing significant environmental and health risks. This systematic review aimed to identify plant species that can effectively accumulate cadmium, assess soil quality and contamination sources in cadmium- affected areas, and evaluate potential health risks within affected communities. Relevant studies and articles were methodically selected from databases such as PubMed, Science Direct, Google Scholar, Research Gate, and Zendy, following Prisma Guidelines. The reviewed studies, both local and international, were published after 2000. The review found that several plant species can accumulate high levels of cadmium, with Athyrium wardii (Hook.) (Makino) achieving a 55% removal rate, Linum usitatissimum L. (Flax) achieving 32-49% removal, and T. caerulescens (Alpine Pennycress) achieving 19-36% removal. These plants showed the highest cadmium concentrations in their roots, followed by stems and leaves. Anthropogenic sources of cadmium in farm soil include agricultural activities and emissions from fuel combustion, with petrochemical industries significantly contributing through oil and gas flares, leading to elevated levels of nickel and cadmium. The review highlighted that higher cadmium accumulation poses substantial health risks, with risk concentrations higher in agricultural lands than in urban areas, implying a higher carcinogenic risk for people in these regions.