Soil Quality Assessment Research Articles

Analysis of Andisol Soil Quality Index in Scallion (Allium fistulosum L.) Agricultural Land in Tosari District, Pasuruan Regency

Soil quality assessment can be done by monitoring the dynamic conditions of various indicators that influence it. Measuring these indicators produces a soil quality index (IKT), which is a tool to evaluate the impact of land management practices (Mas'udi et al., 2021). This research aims to determine the distribution of the soil quality index on shallot (Allium fistulosum L) agricultural land in Tosari sub-district, Pasuruan district, East Java. The research was carried out in Tosari sub-district, Pasuruan district at an altitude of 1700 meters above sea level. In September 2023 – January 2024. This research uses a purposive sampling method for taking soil samples. Next, it was analyzed in the laboratory for texture, volume weight, porosity, C-organic, pH, P-available, K-exchangeable and the rooting depth was measured. The soil quality index is calculated using the criteria of Mausbach and Seybold (1998), which can be adjusted to field conditions using the Minimum Data Set (MDS). Based on the research results, it shows that there are differences in the Soil Quality Index on Onion Leaf land in several villages in Tosari sub-district. Leek fields in Tosari sub-district have two criteria, namely medium and good criteria. Tosari Village, Ngawidono Village, Mororejo Village and Kandangan Village are classified as Good (B) while Podokoyo Village is classified as Medium (S).

Integrated assessment of soil quality and contaminant risks in salinized farmland adjacent to an oil-exploitation zone: insights from the Yellow River Delta

Intensified industrial activities significantly threaten farmland soil integrity, particularly in salinized regions. However, comprehensive evaluations of soil fertility and contamination by polycyclic aromatic hydrocarbons (PAHs) remain limited. In this study, we assessed soil quality in China’s Yellow River Delta (YRD) by quantifying 13 indicators of soil physicochemical and biological properties, along with 11 PAHs. Our findings reveal that the minimum data set approach provides a robust and comprehensive representation of overall soil fertility. Salinity emerged as the primary limiting factor, with strong correlations between salinity and key ions, highlighting its adverse effects on soil structure and function. Additionally, significant PAH contamination was detected, particularly from benzo[a]anthracene (BaA), fluoranthene (Flu), and chrysene (Chr), as indicated by the Nemerov pollution index. A pronounced negative correlation between the soil quality index (SQI) and the soil environmental index (SEI) underscores the substantial role of PAH pollution in soil degradation. Notably, the SQI integrates both SEI and soil fertility, providing a holistic assessment of soil health. These findings highlight the utility of SQI as a diagnostic tool for evaluating soil degradation and emphasize the need for targeted remediation strategies to address salinity and PAH contamination, thereby promoting soil restoration and agricultural sustainability.

Ecological impact and community perception of Phoenix acaulis (Roxb.) management in Shorea robusta (Garten. f.) forest of Udayapur district, Nepal

Phoenix acaulis (Roxb.), a common understory shrub in Nepal's Chure region, has remained largely understudied until now. Therefore, this study aims to examine the ecological impact of P. acaulis on Shorea robusta (Gaertn. f.) forest properties and to explore associated community perceptions in S. robusta forest of Udayapur district, Nepal. Stratified random sampling was adopted for this study where P. acaulis density (high, low and absent) was considered as basis of stratification. Altogether, 45 rectangular plots of 10 m x 10 m area (15 each category) were established for regeneration survey and soil sample collection (up to 30 cm). Soil quality index (SQI) method was used for soil quality assessment using indicators on the basis of prior studies conducted in Nepal. Using random sampling, a total of 52 households from the community forest user group were interviewed to gather their insights on the perceived effects of P. acaulis and its management. Highest S. robusta seedling and sapling density was observed in P. acaulis absent area (1132 ± 9.65 ha⁻¹ and 60 ± 0.63 ha⁻¹) where lowest in P. acaulis dense area (548 ± 7.4 ha⁻¹ and 4 ± 0.2 ha⁻¹). Similarly, higher SQI was in areas with high P. acaulis density (0.49) followed by low (0.45) and absent area (0.39). Most respondents advocated for removing P. acaulis from the forest, highlighting significant concerns among stakeholders. Our study suggests a positive impact P. acaulis on soil quality but indicates a negative impact on S. robusta regeneration. Therefore, further research to explore management strategies that balance the positive impact on soil quality with the observed negative influence on regeneration in P. acaulis presence areas is recommended.

Prospects of artificial intelligence for the sustainability of sugarcane production in the modern era of climate change: An overview of related global findings

By analysing biochemical composition, assessing soil quality, projecting yields, predicting productivity, identifying illnesses, and predicting productivity, artificial intelligence (AI) has greatly improved sugarcane cultivation. This study discusses the latest research on artificial intelligence (AI) in sugarcane farming, with particular attention given to soil biochemistry, disease detection, climate-smart technology for greenhouse gas emissions, yield and water productivity prediction, and cane juice biochemistry. Artificial intelligence (AI) tools, such as machine learning algorithms, can optimize irrigation, increase yields, save water, and properly estimate sugarcane production. Because of the effectiveness, affordability, and efficiency of AI, it is essential in sugarcane agriculture for accurate yield forecasting as well as for streamlining resource allocation and crop management. AI facilitates prompt interventions by assisting in early disease identification and production prediction. AI can also forecast soil organic carbon (SOC) levels, which can help guide sustainability and soil health initiatives. The comprehensive global review identifies research gaps in the literature and suggests potential avenues and directions for future research.

Relationships between yield, enzymatic activity, and chemical properties across different soil layers and phenological stages of grapevines in southern Brazil

The activities of the enzymes β-glucosidase (GLU) and arylsulfatase (ARYL) in annual crop soils can now be determined in several commercial laboratories in Brazil. The results are used to assess soil quality and recommend management practices. However, this technology is not yet available for vineyards. The objective of this study was to evaluate whether the activities of soil GLU and ARYL enzymes are related to grapevine yield and chemical properties in sandy vineyard soils, and to define the phenological stage of the crop and the soil sampling layer that best characterize these relationships. Soil and plant samples were collected in a commercial Vitis vinifera L. vineyard located in the extreme south of Brazil during the 2022/23. The soil was sampled in three layers (0–0.05, 0–0.10, and 0–0.20 m) and at four phenological stages (bud burst, full flowering, veraison, and post-harvest). The activities of the soil enzymes GLU and ARYL, the nutrient content of the soil and the leaf, the quality of the must, and the relative yield (RY) of the grapevine were evaluated. The RY was related to the activity of these enzymes. The best regression model adjustments were observed after harvest and in the 0–0.10 m layer. Enzyme activity was also significantly related to soil organic C and total N, as well as other soil properties, mainly P, K, Ca, Mg, Zn, and Mn. The activities of GLU and ARYL enzymes were related to grapevine yield and chemical properties in sandy vineyard soils and soil sampling in the 0–0.10 m layer after harvest is recommended.

An Investigation into the Viability of Portable Proximal Sensor X-Ray Fluorescence Data for Assessing Heavy Metal Contamination in Urban Soils: A Case Study in Changchun, China.

In order to validate the applicability of pXRF for rapid in situ detection of heavy metals in urban soils and to accurately obtain an assessment of soil quality in Changchun, a city in northeast China, 164 soil samples from within the main urban area of Changchun were collected for pXRF analysis. The main stable elements Si and Ti were used to establish a matrix effect correction model, and the values of Cr (64.2 mg⋅kg-1), Cu (43.8 mg⋅kg-1), Zn (96.2 mg⋅kg-1), As (20.9 mg⋅kg-1), and Pb (57.4 mg⋅kg-1) were predicted. The empirical findings indicate that the quality of soil data from the pXRF was improved to different degrees under the correction model, and it became a relatively reliable dataset; the order of improvement was Cu > Pb > Cr > Zn > As. A comprehensive assessment indicated that Changchun City is primarily contaminated by the heavy metals As, Pb, and Cu, with the main sources being automobile manufacturing and pharmaceutical chemical production. These findings align with previous studies and have produced favorable outcomes in practical applications. This rapid, non-destructive and economical detection method is very applicable and economical for the sustainable monitoring and control of heavy metals in large cities. This study provides a basis for rapid large-scale prediction of urban soil safety and protection of local human health.

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.

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.

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.

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.