Evaluation Of Soil Quality Research Articles

Evaluating soil quality and carbon storage in Western Ghats Forests, Karnataka, India, for sustainable forest management.

Monitoring soil quality index (SQI) and soil organic carbon (SOC) stock status of the Western Ghats (WG) forests in India is crucial for providing vital ecosystem services alongside sustainable forest management practices. However, comprehensive profile data on SQI and SOC stock across different forest types under WG forests are limited. The study evaluated SQI and SOC stock under three forest types, i.e. tropical wet evergreen (TWE), tropical semi-evergreen (TSE), and tropical moist deciduous (TMD) across WG in Karnataka. SQI was assessed using principal component analysis with two indexing approaches and scoring methodologies, with weightage indexing through nonlinear scoring functions (NLSF) showing superiority over other methodologies. TMD forests exhibited the highest SQI, followed by TWE and TSE, while the lowest was observed in Rippon Pet RF (0.36 surface, 0.28 control section), primarily due to limitations in organic carbon and clay content. SOC stock mirrored SQI trends (TMD > TWE > TSE), with the highest values in Kollegal RF (339.3 MG ha-1) and lowest in Rippon Pet RF (102.5 MG ha-1). Although SOC and SQI were established to be ideal indicators for dynamic ecosystem services (ESs), high OC content in surface soils of Poomale NF induces pedogenic acidification and Al toxicities, indicating potential forest soil degradation. Significant correlation with control section SQI and SOC (p < 0.05) emphasises monitoring subsurface soil status to identify soil degradation, sustainable forestry practices, and complex ESs in forest systems.

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

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

Impacts of land use and management methods on soil quality dynamics in central highlands of Ethiopia.

Land degradation often results in poor soil quality in many parts of Ethiopia, including the study area. To address this issue and promote sustainable land management practices, various land use and management methods (LUMMs) have been implemented. However, little information is available regarding how these management practices influence overall soil quality dynamics of the study area. This study aimed at evaluating soil quality dynamics in the Urago micro-watershed, central highlands of Ethiopia, under major LUMMs: barren land (BL), grassland (GL), established farm boundary (EFB), restored degraded land (RDL), and stone-supported soil bund (SSB). Forty-five disturbed and fifteenundisturbed soil samples were collected from the ploughed soil layer (0-20cm) of each LUMM and analysed for selected physicochemical properties to be used as indicators of soil quality. Principal component analysis and multiple correlation were used to select the minimum data set (MDS) to evaluate the overall soil quality index (SQI). The MDS included SOC, clay content, exchangeable Mg2+, and available P, which could replace other indicators for assessing the overall soil quality dynamics of the study watershed. The result showed notable variations in particle-size fractions, soil organic carbon (SOC), total nitrogen (TN), available P (av. P), and exchangeable Na+, K+, and Mg2+ levels among the LUMMs. RDL had higher sand and silt contents than SSB, whereas SSB had higher clay content compared to RDL, GL, and BL. GL, RDL, and EFB showed significantly higher levels of SOC, TN, and av. P, respectively, compared to other LUMMs. The obtained SQI showed that GL had the highest score (0.847), followed by SSB (0.703), RDL (0.701), EFB (0.644), and BL (0.628). This underscores the significance of stone-supported soil bund and restored degraded land as an efficient management method to enhance soil quality and agro-ecosystem through conserving soil and encouraging sustainable farming practices.

A comprehensive evaluation of soil quality in the Three River Headwaters Region, China

Soil quality in the Three River Headwaters Region (TRHR) has a direct connection with the vegetation restoration and ecosystem health of the whole region, however, the evaluation of soil quality on a regional scale is still lacking. We analyzed the distribution pattern of the regional soil quality index (SQI), explored the dominant factors affecting SQI, and proposed measures to improve soil quality based on 638 soil samples collected at surface soil and subsurface soil. The results showed that in the TRHR, the SQI was higher in the southeast, and lower in the northwest. The average SQI were 0.52 for surface soil and 0.50 for subsurface soil, indicating better soil quality in the surface soil. Among different vegetation types within the TRHR, the SQI was ranked as follows: the alpine shrub (0.52–0.55) > the alpine meadow (0.51–0.52) > the alpine steppe (0.44–0.45). The study explored that NDVI was the dominant factor driving SQI, with geographical and climatic factors indirectly affected it through vegetation. This study proposes the corresponding strategies based on the distribution of SQI in different vegetation types and its main driving factor for the better development of soil quality in the TRHR, and it can provide practical guidance for remediating degraded soil, restoring degraded vegetation, and alleviating the impact of climate change.

Evaluation of soil quality of cultivated lands with classification and regression-based machine learning algorithms optimization under humid environmental condition

In soil science, machine learning algorithms are preferred for pedotransfer functions due to their rapid data acquisition and high prediction accuracy. The current study aims to evaluate the prediction of soil quality in agricultural lands dominated by the humid Black Sea climate using various algorithms. Both classification and regression-based algorithms (Random Forest-RF, Light Gradient Boosting-LGB, Extreme Gradient Boosting-XGBoost, k-nearest neighbors-kNN, Logistic Regression, multilayer perceptron-MLP, Linear Regression-LR and Bayesian Ridge- BR) were used in the method. The comparison of soil maps is also included. Furthermore, the present study evaluates the Grid Search optimization method with K-Fold Cross Validation (K = 5) for both classification and regression-based algorithms. The prediction of soil quality was performed using class-based and regression-based algorithms. As a result of the study, the RF and XGBoost algorithms achieved an approximate accuracy rate of 92 % in the class-based prediction. In regression-based predictions, the most successful algorithms were BR and LR, with an R2 Score of 0.84. The Grid Search optimization method was used to improve the R2 Score, resulting in an increase to 0.90 and 0.88 for BR and LR, respectively. The optimized hyperparameters showed improved performance in predicting the soil quality index. The present study found that Gaussian and Spherical models had the lowest prediction errors in spatial distribution maps. Tree-based algorithms were found to be suitable for class-based prediction of soil quality, while the linear regression method was appropriate for regression predictions. This study is characterized by a rainy climate resulting in acidic soils with high organic matter content. Planning of new studies in different climates and soil properties is recommended.

Changes in fungal abundance and biochemical properties of soil through a temporal restoration of forests

Changes due to the exploratory management of the soil and cultivated plant species can interfere with the cycling of nutrients vital for soil fertility. This biogeochemical cycling of soil is an interaction of physical, chemical, and biological processes that can be altered by land use change and management. Therefore, the objective of this study was to evaluate soil quality indicators in sand-extraction-degraded areas that had been reforested at different times and compare them to an unrestored area and native forest fragments. The five areas reforested with native Atlantic Forest species were 20, 17, 10, 7, and 5 years old. The fungal abundance, microbial respiration rate, enzyme activity, and C content were evaluated in soil samples collected at a depth of 10 cm. In addition, the total organic carbon, soluble carbon content, total carbohydrates, moisture, organic matter, and pH of these soils were studied. Our results verified a significant improvement in the fertile fraction of these soils due to the action of microorganisms in the different reforestation areas. The longer the reforestation period, the more the soil exhibited similar characteristics to native forests and approached the level of primary forests. The unrestored sand mining degraded soil area had the poorest fungal community and the least biochemical activity, which was less than the fungal abundance and activity of the most recent reforestation. These results suggest that the use of microbial and enzymatic activities as soil quality indicators is a viable approach to assess the impact suffered from past uncontrolled exploitation in areas that are undergoing ecological restoration processes of native forests.

Soil Quality Assessment and Influencing Factors of Different Land Use Types in Red Bed Desertification Regions: A Case Study of Nanxiong, China

Soil environmental issues in the red bed region are increasingly conspicuous, underscoring the critical importance of assessing soil quality for the region’s sustainable development and ecosystem security. This study examines six distinct land use types of soils—agricultural land (AL), woodland (WL), shrubland (SL), grassland (GL), bare rock land (BRL), and red bed erosion land (REL)—in the Nanxiong Basin of northern Guangdong Province. This area typifies red bed desertification in South China. Principal component analysis (PCA) was employed to establish a minimum data set (MDS) for calculating the soil quality index (SQI), evaluating soil quality, analyzing influencing factors, and providing suggestions for ecological restoration in desertification areas. The study findings indicate that a minimal data set comprising soil organic matter (SOM), pH, available phosphorus (AP), exchangeable calcium (Ca2+), and available copper (A-Cu) is most suitable for evaluating soil quality in the red bed desertification areas of the humid region in South China. Additionally, we emphasize that exchangeable salt ions and available trace elements should be pivotal considerations in assessing soil quality within desertification areas. Regarding comprehensive soil quality indicators across various land use types, the red bed erosion soils exhibited the lowest quality, followed by those in bare rock areas and forest land. Within the minimal data set, Ca2+ and pH contributed the most to overall soil quality, underscoring the significance of parent rock mineral composition in the red bed desertification areas. Moreover, the combined effects of SOM, A-Cu, and AP on soil quality indicate that anthropogenic land management and use, including fertilization methods and vegetation types, are crucial factors influencing soil quality. Our research holds significant implications for the scientific assessment, application, and enhancement of soil quality in desertification areas.

Soil quality assessment in the Araban plain across various land use types

BackgroundSoil is an important resource for improving the quality of human well-being and creating sustainable environmental awareness. Therefore, it is imperative to protect the soil health. This study determined quality of the soils in the Araban plain, Türkiye using three different methods, i.e., principal component analysis (PCA), analytic hierarchy process (AHP), and expert opinion (EO). The major aim was to determine the most appropriate method for determining soil quality and recommending soil protection practices for improving quality. MethodsThis study was conducted on an area of approximately 25,000 ha. A total of 230 soil samples (7 pasture areas, 56 orchards, and 167 arable crops) were analyzed to determine key soil quality indicators. A minimum dataset consisting of parameters that best represent the soils of the region was created. Subsequently, PCA was used to predict soil quality with the optimal accuracy using the least amount of data. The EO was incorporated to validate the PCA results. The AHP was used to assign weights to the parameters. Indicators at all sampling points were converted to unitless scorers. These scores were then formulated as soil quality index (SQI) using the weighted summation method. ResultsOrganic matter (OM) exerted the highest impact on soil quality. Significant variations in SQI values were observed among arable land, orchards land and pastureland (P < 0.01). Significant variations were recorded in SQI values of the assessment methods employed in this study. The lowest (0.552) and highest (0.829) SQI values were recorded for AHP and EO, respectively. Overall, streamlining indicators through PCA gave the most accurate result for SQI value. Subsequently, SQI determination should involve weighing with AHP, particularly towards EO. ConclusionThe AHP was found to be the most appropriate method for evaluating soil quality in Araban. This integrated approach ensures a robust evaluation of soil quality, facilitating more accurate and nuanced insights for informed land management decisions. Overall, 92.61 % of the region’s soil had poor and medium soil quality. Therefore, increasing OM content and aggregate stability are suggested to improve soil quality in the study region.

Application of soil quality evaluation indices and multivariate statistics to assess soil health: a case study

Application of soil quality evaluation indices and multivariate statistics to assess soil health: a case study

Soil quality enhancement by multi-treatment in the abandoned land of dry-hot river valley hydropower station construction area under karst desertification environment.

The medium-intensity karst desertification environment is typically characterized by more rocks and less soil. The abandoned land in the construction areas of the dry-hot river valley hydropower station has more infertile soil, severe land degradation, and very low land productivity. Therefore, it is urgent to improve the soil quality to curb the increasingly degrading land and reuse the construction site. Few studies have focused on the effect of soil restoration and comprehensive evaluation of soil quality with multi-treatment in abandoned land in the dry-hot valley hydropower station construction area. Here, 9 soil restoration measures and 1 control group were installed at the Guangzhao Hydropower Station construction in Guizhou Province, China, for physical and chemical property analysis. In total, 180 physical and 90 chemical soil samples were collected on three occasions in May, August, and December 2022. Soil fertility and quality were evaluated under various measures using membership functions and principal component analysis (PCA). This study showed that almost all measures could enhance soil water storage capacity (The average total soil porosity of 9 soil treatments was 57.56%, while that of the control group was 56.37%). With the increase in soil porosity, soil evaporation became stronger, and soil water content decreased. Nevertheless, no decrease in soil water content was observed in the presence of vegetation cover (soil water content: 16.46% of hairy vetch, 13.99% of clover, 13.77% of the control). They also proved that manure, synthetic fertilizer, and straw could promote total and available nutrients (Soil total nutrient content, or the total content of TN、TP、TK,was presented as: synthetic fertilizer (11.039g kg-2)>fowl manure (10.953g kg-2)>maize straw (10.560g kg-2)>control (9.580g kg-2);Total available nutrient content in soil, or the total content of AN,AP,A,was shown as:fowl manure (1287.670 mg kg-1)>synthetic fertilizer (925.889 mg kg-1)>sheep manure (825.979 mg kg-1)>control (445.486 mg kg-1). They could also promote soil fertility, among which the first two reached the higher comprehensive soil quality. Fertilizer was conducive to improve soil quality and fertility, yet long-term application could cause land degradation like soil non-point source pollution, compaction, and land productivity decline. Ultimately, combining fertilizer with biochar or manure is recommended to improve soil fertility. Biochar and green manure could play an apparent role in soil improvement only when there is abundant soil water. The above views provide theoretical support for curbing soil degradation, improving soil fertility and quality, enhancing land productivity, and promoting the virtuous cycle of the soil ecosystem.

How Can Soil Quality Be Accurately and Quickly Studied? A Review

Evaluating soil quality is crucial for ensuring the sustainable use of agricultural lands. This review examines the definition, evaluation methods, indicator selection, and relevant case studies. The concept of soil quality supplements soil science research by deepening our understanding of soils and aiding in the allocation of resources as agriculture intensifies to meet rising global demand. Soil quality provides a framework for educating stakeholders about the essential functions of soils and offers a tool for assessing and comparing different management techniques. Regular evaluation of soil quality is vital for maintaining high crop yields and addressing the gap between production and consumption. Nowadays, many researchers have explored machine learning (ML) and deep learning (DL) techniques and various algorithms to model and predict soil quality with satisfactory results. These chosen indicators can be influenced by chemical, biological, or physical features. This paper compares ML and DL with traditional methods, examining their features, limitations, different categories of machine learning, and their applications in soil quality assessment. Finally, we show that predicting soil quality has the potential to be extremely accurate and efficient with ML and DL. This distinguishes the application of DL and ML from other approaches since they can anticipate the soil quality index without the need for more intricate computations. Our suggestion for future studies is to evaluate soil quality over broader regions and predict it by using more accurate, modern, and faster methods, using a variety of activation functions and algorithms.

A method for estimating the bulk density and particle density of granite residual soil based on the construction of pedotransfer functions

AbstractParticle density (ρs) and bulk density (ρb) are key factors in the calculation of total soil porosity. However, direct measurements of ρs and ρb are labour‐intensive, time‐consuming, and sometimes impractical. Pedotransfer functions (PTFs) provide alternative methods for indirect estimation of ρs and ρb. In this paper, the accuracy of typical 12 ρs and 9 ρb PTFs was evaluated using easily measurable soil properties (sand, silt, clay, and soil organic matter (SOM) content) from granitic residual soils collected from six study areas in subtropical China, and the accuracy of PTFs constructed based on multiple linear stepwise regression (MSR) and machine‐learned algorithms (backpropagation neural network, k‐nearest neighbour algorithms, random forests, support vector machines, and gradient boosted decision trees) was compared to determine the accuracy of PTFs. The results show that typical PTFs have poor accuracy (R2adjusted &lt; 0.020) and are not applicable to the indirect estimation of ρs and ρb in granitic residual soils. The PTFs constructed by machine learning algorithms all performed better than MSR, with the highest estimation accuracy of the PTFs constructed by the random forest algorithm, with R2adjusted values of 0.923 and 0.933 for the ρs and ρb PTFs, respectively, and root‐mean‐square error of 0.020 g·cm−3 and 0.023 g·cm−3, respectively. Compared with MSR, the random forest algorithm has greater accuracy and eliminates the restriction of PTFs on predictors, which provides support for understanding the changing rules of ρs and ρb in granite residual soils in subtropical regions, evaluating soil quality and improving soil structure.

Assessing the Alteration of Soil Quality under Long-Term Fertilization Management in Farmland Soil: Integrating a Minimum Data Set and Developing New Biological Indicators

The key role of soil quality improvement in achieving sustainable agricultural development based on highly intensive use of farmland is increasingly being recognized, as is the ponderance of suitable evaluation of the soil quality. The overarching goal of this study was to determine an accurate assessment framework by the comparison of the scoring function (linear and non-linear) and integration method (area and weighted additive), which integrally evaluates the soil quality of an eleven-year field fertilization experiment (including CK, no fertilizer; CF, conventional fertilization; SF, formulated fertilization; SFO, SF with organic fertilizer). Thirty-three properties, including eighteen physiochemical-related and fifteen biological-related properties, associated with soil functions were measured as potential soil quality indicators, and the soil multifunctionality (SMF) was applied to validate the soil quality indices (SQIs). Principal component analysis and relationship analysis were used with indicators sensitive to management to determine a minimum data set (MDS). The results showed that the electrical conductivity, large macroaggregate-associated total nitrogen, small macroaggregate-associated organic carbon, carbon fixation, and enzyme activities of phenol oxidase and cellulase were chosen as the MDS. All the SQIs were significantly correlated with the SMF (p &lt; 0.05). The fertilization strategies affected most indicators in different ways, and the index developed using the non-linear function and weighted additive integration method (SQI-NL) had the best sensibility and discriminability. The SQI value with the SQI-NL-MDS method was higher following the fertilization treatments than that of no fertilizer (p &lt; 0.05), and the treatment of the organic fertilizer had the highest SQI value (0.66). Soil quality evaluation in long-term fertilized farmland suggested that the soil quality constraints between treatments of synthetic and organic fertilizer are related to the soil functions of nutrient cycling and sustain biological activity due to their higher contribution rates to the SQI in the organic fertilizer treatment, which provides insights into ways to reduce the gap in soil quality. The framework method can provide an accurate quantitative tool for the evaluation of soil quality from the target indicators by bridging management objectives and field-level actions.

Soil quality and land capability evaluation for agriculture in Balat area, El Dakhla Oasis, western Desert, Egypt

This research aimed to evaluate the land capability and soil health of the Balat area, El-Dakhla Oasis, Egypt. To achieve this objective, thirty soil profiles were chosen to represent the study area and soil samples were collected from each layer of each soil profile. Soil samples were analyzed for their physical, chemical, and fertility properties. These soils were characterized and classified. The obtained results were mapped for spatial variability distribution using ArcGIS software. The land capability was evaluated using the modified Storie index and the Microcomputer Land Elevation Information system (MicroLEIS). The soil quality index (SQI) was also calculated based on soil physical, chemical, and fertility indices. The obtained results revealed that the studied soils were alkaline, slightly saline to moderately saline, light to heavy textured, non-calcareous to calcareous, and poor in organic matter and nutrients. These soils were classified into two orders (Entisols and Aridisols), whereas they were found in three subgroups (Typic Torripsamments, Typic Torriorthents, and Typic Haplosalids). The modified Storie index was found to be better than the MicroLEIS Cervatana model in evaluating land capability. Four land capability grades were obtained from the Storie index (Grade 2 = Good; Grade 3 = Fair; Grade 4 = Poor; and Grade 5 = Non-agricultural), while two grades were recorded in MicroLEIS (S3 and N). The main limitations of the study area were soil texture, salinity, organic matter, and ESP. Regarding the soil quality, two grades were obtained from the applied method (moderate = 0.35–0.59 and good = 0.60–0.79). Spatial variability maps were generated for land capability methods as well as the soil quality. Therefore, these results and maps can be utilized as a guide for better planning and land resource management in the Balat area.

Bioindicators for Assessing Soil Quality in Ecuador’s Jun Jun Micro-Watershed

The evaluation of soil quality in different altitudinal gradients, with vegetative and non-vegetative subareas, is crucial for proper soil functioning and optimal crop growth, thus contributing to the sustainability of agroecosystems. Although the altitudinal gradient significantly influences soil quality, the ability to predict this quality, expressed through an index, in soils with and without vegetative cover, is still insufficiently explored. This study employed the simple additive index (SQI) method to evaluate soil quality in Ecuador’s Jun Jun micro-watershed, Tungurahua region. Three altitude categories (&lt;2800, 2800–2900, &gt;2900 masl) were investigated, with 24 soil samples collected across both vegetated and non-vegetated areas. The indicators used included chemical and biological parameters such as soil organic carbon (OC), earthworm density (WD), earthworm biomass (WB), organic matter (OM), pH, and total nitrogen (TN). The results revealed that in areas with altitudinal gradients below 2800 masl, the soil quality index values were higher compared to other altitudes. In vegetated areas, a decrease in index values was observed as the altitudinal gradient increased, indicating a deterioration in soil quality with increasing altitude. These findings are significant in providing a quantitative assessment of the effects of altitudinal gradient and vegetative cover influence on soil quality.

Evaluating dynamic soil quality by the soil management assessment framework (SMAF) in the watershed scale in a semi-arid Mediterranean ecosystem in Turkey

Soil is an indispensable and important natural resource and it is necessary to manage and monitor soil quality with appropriate methods in order to ensure the sustainability of the soil. The aim of this study is to determine the current state of soil quality in different land uses where soil functions change. This study was carried out in the Karasu river watershed with an area of 19,178 ha where Entisols soil order is dominant, located Andırın, Kahramanmaraş. The Soil Management Assessment Framework (SMAF) method and Principal Component Analysis (PCA) was used to assess the dynamic soil quality in watershed scale. In the evaluation of soil quality, random samples were taken from a total of 360 topsoil layers (0−30) according to land use and physiography. Soil indicators such as bulk density, aggregate stability, total organic carbon, pH, electrical conductivity, available phosphorus (P), potassium (K), available water amount and water-filled pore volume were selected to create the minimum data set (MDS). The results showed that the productivity, environmental protection, waste recycling quality scores and general soil quality index of forest lands are higher than agricultural lands. According to expert opinion, general soil quality scores were found to be 0.86 in forest land, 0.78 in crop land and 0.83 in grassland, while in PCA, general soil quality scores were calculated as 0.85 in forest land, 0.79 in crop land and 0.78 in grassland. Contribution level of soil functions to management targets was observed in water relations function the most. The contribution levels of the water relations function to the productivity, environmental protection and waste recycling management targets are 0.38, 0.32 and 0.34 in forest lands, 0.42, 0.33 and 0.35 in croplands, respectively; It was showed that 0.40, 0.33 and 0.36 in grasslands. Contribution rates of productivity, environmental protection and waste recycling management targets to soil quality were 30%, 37% and 33%, respectively, in forest lands; 29%, 37% and 34% in crop lands; It has been determined as 30%, 36% and 34% in the grasslands. Negative effects such as heavy rainfall in the watershed, wrong agricultural practices (excessive fertilization and irrigation, improper tillage, and crop selection, etc.) and excessive grazing have led to soil degradation and erosion, reducing the soil function capacity of agricultural lands and meadows. In order to increase the functional capacity of the soil, a soil management approach aimed at protection, improvement and sustainability must be adopted and implemented.

The Effects of Different Vegetation Restoration Models on Soil Quality in Karst Areas of Southwest China

Rocky desertification is a devastating process in Karst areas of Southwest China and induces serious fragmentation in ecosystems. Therefore, vegetation restoration and the scientific evaluation of soil quality are key restorative strategies in these areas. In this study, a natural closed forest and a disturbed forest with three restoration models, including an evergreen broad-leaved forest, mixed forest, and deciduous forest, were investigated in Huanjiang County. More than nineteen soil properties (including physical, chemical, and biotic properties) were analyzed across treatments, and principal component analyses (PCA) were combined with a minimum data set (MDS) applied to evaluate the soil quality. Our study sought to identify a vegetation restoration model to improve the soil quality in this area. We demonstrated that soil physical and chemical properties, microbial biomass, and enzyme activities significantly differed across all of the models. Soil water content, capillary porosity, total porosity, organic carbon, total phosphorus, available phosphorus, and urease activity were high in the mixed forest, leading to better physical soil properties. Also, relatively high soil total nitrogen, total potassium, available nitrogen, available potassium, microbial biomass C and N, catalase, sucrose, and alkaline phosphatase levels were observed in the deciduous broad-leaved forest, resulting in improved soil chemical properties. Based on the minimum data set (MDS) method, six indicators, including non-capillary porosity, organic carbon, total phosphorus, pH, microbial biomass nitrogen, and urease activity, were selected to evaluate the soil quality across the models. Our data showed that, among the five models, the deciduous broad-leaved forest had the highest soil quality index (0.618), followed by the mixed forest (0.593). Stepwise regression analysis showed that soil organic carbon explained 79.9% of the variations in the soil quality indices, suggesting it was a major factor affecting the soil quality. Thus, vegetation restoration models mainly comprised of native tree species effectively improved the soil quality in Karst rocky desertification areas, with deciduous broad-leaved forests displaying the best effects, followed by mixed forests.

Evaluation of Soil Quality of Pingliang City Based on Fuzzy Mathematics and Cluster Analysis

Pingliang City has a complex topography and diverse soil types. To realize the improvement of soil according to local conditions and the reasonable and sustainable use of soil resources, an evaluation of soil quality in Pingliang City was carried out, based on the soil distribution situation in Pingliang City, adopting a method combining fuzzy mathematics and cluster analysis of the main evaluation factors, such as soil organic matter, topsoil depth, soil erosion intensity, soil moisture regime, effective soil thickness, soil texture, soil profile structure, soil nutrient status and topographical parts, to carry out a comprehensive evaluation. A comprehensive evaluation of soil quality was conducted in seven counties under the jurisdiction of Pingliang City, and the evaluation results were compared and analyzed against the national standard, “Cultivated land quality grade”, to provide a basis for the selection of scientific soil improvement methods. The results of the arable land quality grades indicate that the quality of farmland in Pingliang City is divided into three to ten grades, and the average quality grade of farmland is 6.83, which is in the middle–lower level, and the overall grade distribution shows the characteristics of low in the middle and high in the east and west. The results of fuzzy mathematics combined with cluster analysis indicated the following trends in soil quality for the 12 soil genera: Chuan black gunny soil &gt; yellow moist soil &gt; sandy soil &gt; silt soil &gt; mulching helilu soil&gt; loessal soil&gt; loamy soil &gt; slope loessal soil &gt; arenosol &gt; tillage leaching gray cinnamon soil &gt; calcareous gray cinnamon soil &gt; red clay soil. The results of the combination of fuzzy mathematics and clustering were significantly correlated with the results of the evaluation of the soil quality of arable land; the correlation coefficient was 0.884. This indicates that the method can accurately and objectively review the advantages and disadvantages of arable land soil and can be effectively applied to the evaluation of the soil quality of agricultural soils in other regions. It is a complement to the existing evaluation of the soil quality of arable land and at the same time provides a reference for the improvement of soil quality in agricultural regions.

Assessment of Soil Spatial Variability in Agricultural Ecosystems Using Multivariate Analysis, Soil Quality Index (SQI), and Geostatistical Approach: A Case Study of the Mnasra Region, Gharb Plain, Morocco

Accurate assessment of soil quality is crucial for sustainable agriculture and soil conservation. Thus, this study aimed to assess soil quality in the agricultural ecosystem of the Mnasra region within the Gharb Plain of Morocco, employing a comprehensive approach integrating multivariate analysis and geostatistical techniques. Thirty soil samples were collected from the surface layers across thirty selected sites. The results showed significant variations in soil properties across the study area, influenced by factors such as soil texture, parent material, and agricultural practices. Pearson correlation and principal component analysis (PCA) were employed to analyze the relationships among soil properties and compute the Soil Quality Index (SQI). The SQI revealed values ranging from 0.48 to 0.74, with 46.66% of sampled soils classified as “Good” and 53.33% as “Fair”. Geostatistical analysis, particularly ordinary kriging (OK) interpolation and semivariogram modeling, highlighted the spatial variability of soil properties, aiding in mapping soil quality across the landscape. The integrated approach demonstrates the importance of combining field assessments, statistical analyses, and geospatial techniques for comprehensive soil quality evaluation and informed land management decisions. These findings offer valuable insights for decision-makers in monitoring and managing agricultural land to promote sustainable development in the Gharb region of Morocco.

Long-Term Straw Incorporation under Controlled Irrigation Improves Soil Quality of Paddy Field and Rice Yield in Northeast China.

Soil quality is an indicator of the ability to ensure ecological security and sustainable soil usage. The effects of long-term straw incorporation and different irrigation regimes on the yield and soil quality of paddy fields in cold regions remain unclear. This study established four treatments: controlled irrigation + continuous straw incorporation for 3 years (C3), controlled irrigation + continuous straw incorporation for 7 years (C7), flooded irrigation + continuous straw incorporation for 3 years (F3), and flooded irrigation + continuous straw incorporation for 7 years (F7). Analysis was conducted on the impact of various irrigation regimes and straw incorporation years on the physicochemical characteristics and quality of the soil. The soil quality index (SQI) for rice fields was computed using separate datasets for each treatment. The soil nitrate nitrogen, available phosphorus, soil organic carbon, and soil organic matter contents of the C7 were 93.51%, 5.80%, 8.90%, and 8.26% higher compared to C3, respectively. In addition, the yield of the C7 treatment was 5.18%, 4.89%, and 10.32% higher than those of F3, C3, and F7, respectively. The validity of the minimum data set (MDS) was verified by correlation, Ef and ER, which indicated that the MDS of all treatments were able to provide a valid evaluation of soil quality. The MDS based SQI of C7 was 11.05%, 11.97%, and 27.71% higher than that of F3, C3, and F7, respectively. Overall, long-term straw incorporation combined with controlled irrigation increases yield and soil quality in paddy fields in cold regions. This study provides a thorough assessment of soil quality concerning irrigation regimes and straw incorporation years to preserve food security and the sustainability of agricultural output. Additionally, it offers a basis for soil quality diagnosis of paddy fields in the Northeast China.