Evaluation Of Soil Quality Research Articles

Spatial heterogeneity of soil quality within a Mediterranean alley cropping agroforestry system: Comparison with a monocropping system

Alley cropping agroforestry systems are complex agroecosystems highlighted for their positive effects on soil quality. However, the potential spatial heterogeneity of soil quality created by tree rows at the plot scale has seldom been studied. The aim of this study was to evaluate soil quality at the plot scale, under tree rows and along transects perpendicular to the tree row and to compare alley cropping systems with monocropping systems. This study was performed on an alley cropping system that combined hybrid walnut trees (21 years old) and peas. Topsoil was sampled at tree rows between 1 and 2, 2 and 4 and 4 and 6.5 m from the tree row in the alley cropping system, as well as in a neighbouring monocropping plot. Physical, chemical and microbiological indicators of soil quality were measured. Tree row implantation induced spatial heterogeneity in the chemical indicators, microbial biomass, activities and community structure at the alley cropping plot scale. Alley cropping not only improved microbiological soil quality indicators within the tree rows but also in the interrows when compared to a monocropping system. These indicators were then integrated into one soil quality index (SQI) built through a statistical approach. The soil quality index was calculated for the monocropping plot and for each position within the alley cropping plot. After 21 years of agroforestry practice, tree rows and permanent grass cover improved the SQI until 2 m in the interrow. Weighted SQIs were calculated relative to the surface area of each location for the entire alley cropping plot (i.e., tree row + interrow positions) and for the entire alley cropping interrow (i.e., removing the tree row surface area). The weighted SQI of the entire alley cropping plot significantly increased compared with that of the monocropping plot.

Retracted: Soil monitoring and evaluation system using EDL‐ASQE: Enhanced deep learning model for IoT smart agriculture network

SummaryThe enormous growth of the Internet of Things (IoT) network provides abundant support to agriculture and development, which states the future scope of IoT‐based agriculture. In a recent scenario, agriculture IoT can be integrated with sensors, communication protocols, and microcontrollers for automated process executions to increase productivity. Moreover, deep learning effectiveness produces appropriate results and solves several real‐time issues related to agriculture‐based advancements. The proposed system presents the design of an IoT network communication system to estimate the soil conditions. Soil quality is an important factor in modernized agriculture, productivity enhancement, and hydrological cycles. By the soil quality analysis, the accurate prediction is very significant for sensible usage of resources. An enhanced deep learning model for IoT network‐based automated soil quality evaluation observes the complex soil features and meteorological factors with those concerns. Here, the real‐time samples are collected from the local area sensor network for analysis. The deep learning model is developed with big data fitting ability for soil quality prediction. The weight factors (W.F.) are derived for measuring the soil quality accurately. The proposed IoT network‐based agriculture structure allows a flexible approach to different types of crops and implementation in agricultural areas. Experimental results obtained in the laboratory and onsite confirmed the performance and reliability of the system. The result evaluations are carried out based on precision, accuracy, and processing time, and results show that the model achieves better results than compared models.

Soil biota and fertility along a gradient of forest degradation in a temperate ecosystem

Soil biota populations are good indicators for the evaluation of soil quality, which is in line with the nutrient cycles in forest ecosystems. To the best of the authors’ knowledge, responses of belowground biota to forest removal intensity have received little attention. The present study aimed to study the effect of low, moderate and high intensities of forest degradation, based on the density of standing trees, in lands covered by Carpinus betulus and Parrotia persica tree species in northern Iran on forest floor, soil properties and fauna and flora activities. In the summer season, a total of 15 forest floor and soil samples (30 × 30 × 10 cm) from each site were transferred to the laboratory. To achieve the temporal pattern of soil biota, sampling was also performed in the fall season. The activities of earthworm groups (i.e. epigeic, anecic, and endogeic) were significantly decreased in order of low > moderate > high levels of forest degradation, with higher populations in the fall season (especially epigeic earthworms). Similarly, greater activity of soil Acarina, Collembola, nematode, protozoa (especially in the fall season), bacteria and fungi (especially in the summer season) were found in forests with low intensity of degradation. Principal component analysis (PCA) indicated higher soil biological activities and fertility in the forest areas with low degradation which can be attributed to the higher density of trees, forest floor mass and quality. This research provides evidence of the strong dependence of below-ground communities on aboveground tree density in temperate forests.

Quality index of an Oxisol under different management systems in the Brazilian Cerrado

ABSTRACT Assessing soil quality under different cover crops or different management systems is essential to its conservation. This study aimed to evaluate an Oxisol cultivated with corn and cotton, after different crop successions and under no-tillage system (NTS) and conventional tillage system (CT), through the soil quality index (SQI), using an area of native Cerrado as reference. The study was carried out in the municipality of Luís Eduardo Magalhães, Western Bahia, Brazil. Soil samples with the preserved and non-preserved structure were collected in the layers of 0-0.05 m, 0.05-0.10 m, and 0.10-0.20 m to determine the macroporosity, the soil bulk density, the available water, the levels of total organic carbon, the clay dispersed in water, and the degree of flocculation. The averages of the attributes measured in the treatments and the soil quality index, which was elaborated by the method of deviations of the values of the attributes measured in the treatments concerning the reference area, followed by normalization, were compared by the Duncan test (p ≤ 0.05). The soil under CT, in all treatments, had its quality reduced when compared to the NTS. Also, the SQI used was sensitive to detect the changes caused by the management systems and assign consistent scores to the evaluated soil quality.

RETRACTED ARTICLE: Analysis of the influence of the characteristics of mountain soil and the noise in the tunnel on people: active noise control system

The production area of a mountain crop includes the resources of the land, the water, and heat coefficient, and the advantages of light and temperature are very obvious. These crops are fully developed in planting, and the pigment formation is very good, and the sugar content is high, and the damage degree of pests, and diseases are also relatively high. low. According to experts at home and abroad, this is the best planting area in the world for planting and cultivation. In this paper, a large number of samples have been collected for research and experiments, and the physical and chemical properties and biological properties of local species in a certain mountainous area have been analyzed. The growth and development of crop quality factors in the soil and the formation of products are systematically studied. After the establishment of a perfect soil quality evaluation system, it provides guidance and realistic theoretical basis for local crop industry growers. It needs to be calibrated before each data collection, especially for all sensors inside and outside the main tunnel, to ensure the accuracy and feasibility of the tunnel test in the test. The calibration of the sensor includes its sensitivity and the calibration of the sensitivity at the factory. As time goes by, its temperature, dust, and humidity will have a certain influence, which will cause its sensitivity to change. Acquired acoustic signals will cause inaccurate results along with inaccurate sensitivity and finally in the tunnel will also affect the results of inaccurate noise distribution. This experiment uses the PAI sound and vibration test analysis system in the table for data collection. The acquisition process is to obtain the analog degree of different devices on different sensors and the information of the tested unit and finally make the digital information obtain representative physical meaning.

Conversion of forest to rangelands suppress soil fauna and flora densities during long-term in mountain ecosystems

The effect of vegetation types at mountainous sites on the relationships between soil biota and environmental variables applicable for the evaluation of soil quality and functions are barely studied and remain almost unknown. In the present study, the effect of forest degradation and vegetation types changes [i.e. natural forest with dominant species of Zelkova carpinifolia (Pall.) K. Koch. and three rangelands with dominant species of Festuca ovina L. Dactylis glomerata L. and Stachys byzantina K. Koch.] on the dynamics of soil properties and density of soil organisms in northern Iran has been considered. For each studied vegetation types, three different sites (at a distance of 4–6 km) with 1 ha area were selected. In summer (August), litter or O-horizon (i.e. L, F, and H) and soil (0–10 cm) layers were collected in an area of 30 × 30 cm. Also, in order to study the dynamics of soil properties, in addition to summer, soil samples were collected in the spring (May) and autumn (November) in each of the habitats. The study area is commonly covered with snow from late December to early April every year and therefore soil sampling was not considered in the winter season. A total of 48 litters (12 samples for each of those studied vegetation types), in the summer, and a total of 144 soil [(4 vegetation types × 3 seasons × 12 samples)] samples were collected and transferred to the laboratory. According to our findings, with higher values in the autumn season, the highest density of the soil earthworm (epigeic, anecic and endogeic), microarthropods (Acarina and collembola), nematode, and protozoa were detected in the Zelkova vegetation soils. Also, the highest density of bacterial and soil fungal communities were observed, especially in summer, under the tree cover of forest habitat. Based on our data, forest degradation and vegetation type changes suppressed the density of soil biota ranked in Zelkova > Festuca > Dactylis > Stachys due to low quality of litter, soil climate, and fertility condition. Although each vegetation type plays a prominent ecological role, forest covers are worthwhile because of their capacity to create hotspots identified by soil fauna and flora densities, which play a key role in the nutrient cycle and ecosystem services, in mountain habitats.

Microbial characteristics response to the soil quality of newly created farmland on the Loess Plateau.

Microbiome plays an important role in evaluating soil quality for sustainable agriculture. However, the suitability of biological indicators in reclaimed farmland is less understood. Using high-throughput sequencing, we evaluated the soil microbial community of the newly created farmland (NF) after reclamation with two local high-yield farmlands (slope farmland (SF), check-dam farmland (CF)) on the Loess Plateau. Soil enzyme activities and the amount of culturable microorganism were also quantified to assess the soil quality. Results showed that the microbial diversity, cultural microorganism abundance, and soil enzyme activities indicated poor soil quality in NF. The dominant bacterial phyla were Proteobacteria, Bacteroidetes, Acidobacteria, and Cyanobacteria. The abundance of Acidobacteria was significantly lower in NF (13.31%) than in SF (27.25%) and CF (27.91%). Soil enzyme activities had a significant correlation with the abundance of culturable microorganism, Proteobacteria and Bacteroidetes, soil organic matter, total nitrogen, cation exchange capacity, and pH, suggesting that soil microbes have driven the formation of nutrition and further mediated crop growth. Therefore, the application of bacterial fertilizers could be a potential way to improve the soil quality of reclaimed farmland for crop growth.

The evaluate soil quality of mangrove forest in Merauke, Papua

Mangroves are very important to reduce climate change impact since they have been the indispensably portion of the environment serving different functions such as coastline assurance, obstructing salt water interruption, aquaculture, fodder for post, kindling, charcoal generation and moderating botanical and faunal species. Knowledge of soil quality is important for appropriate decision making regarding sustainable land use systems mangrove forest. Soil quality management helps to maintain biological productivity; air and water quality; and human habitation and health. The objective was to evaluate and identify an effective SQ indicator dataset from different mangrove forest types in the Payum beach Merauke area, the soil quality index are computed for basic of framework soil management system. The study was carried out from November 2019 to May 2020. This research use descriptive exploratory research method through field survey. Precise inspecting with three trancec each measuring 5 m2 were built up in both plots. The total of 9 soil samples consisting of 3 trancec type were collected from layer surface 0 to 15 cm, for evaluated quantify soil nutrient reserve and soil quality index (SQI). Soil samples were the analyzed for physical, chemical and biology indicators were used standart laboratory method. The results showed that for mangrove forest has the lowest to medium soil quality index (0.34-0.38) so natural forest has the medium soil quality index (0.39). Soil organic carbon and available phosphorus play major roles in making significant differences in the SQI among the different mangrove forest.

Next-generation sequencing of the soil nematode community enables the sustainability of banana plantations to be monitored

Uganda faces a considerable challenge to match its food production to an annual population growth rate of 3%. Cooking bananas are the country's most produced staple crop but the annual national harvest is not increasing. The crop grows on infertile soils that are normally fertilised organically and often susceptible to erosion. Soil nematodes are well-established as bioindicators of soil quality that can support environmental monitoring and assessment of the sustainability of agricultural systems. These invertebrates are a highly ranked indicator of biodiversity with molecular approaches available. Consequently, we have applied next-generation DNA sequencing of soil nematodes to evaluate soil quality of Ugandan banana plantations. The aim is to establish a method for constructing an aspect of an environmental biosafety dossier with the future aim of assessing the impact of transgenic crops and improving current cropping systems. The soil samples did not differ significantly in any of the measured soil chemistry factors, soil texture or percentage of organic matter. Thirty taxons of soil nematodes other than the plant parasites were recovered from soil supporting nine banana plantations plus three each from coffee and banana-coffee interplants from East and West Uganda. Cluster analysis correctly allocated each plantation to the crop/intercrop being grown when based on the abundance of taxa rather than taxa presence or absence. This indicates that the host has considerable effects on the abundance of specific nematode species within the soil. Overall, nematodes were more abundant in soil from coffee plantations than from banana-coffee interplants with the lowest values being from fields supporting just banana. Only the basal and trophic diversity indices and the percentage of nematodes that are rapid colonisers varied between the three plantation types. The soil of all fifteen plantations can be classified as having a mature soil web condition with low physical disturbance, limited chemical stressors, moderately high nutrient enrichment and balanced decomposition channels.

ASSESSMENT OF SOME PHYSICAL PROPERTIES OF SOIL ALONG AN EROSION PRONE WATERSHED, OWERRI-IMO STATE NIGERIA

Nworie River watershed in Owerri Imo State, Southeast Nigeria is an Urban watershed that should provide agricultural resources to the inhabitants of the area; however, the watershed has been under ecological threat as result of some activities of people within the area leading to soil degradation in the form of gully erosion. The study area was divided into Upstream, Midstream and downstream after which Five Major gullies were used for the study. Soil samples were collected from the walls of the gullies beginning from down to up according to apparent horizon differentiation. Soil profile pit was dug 100m away from erosion prone area and this served as the control. Soil data collected were subjected to statistical analysis; Variation of soil properties at various sampling points were ranked according to standard procedures. Soils of the watershed were found to be acidic. The results of some physical soil properties for soil quality evaluation were matched with FAO standard to assess the degree of degradation. Soil texture of the watershed was sandy and can be classified as an Ultisol. The watershed had low variation of SCR with mean values of 0.20,0.10 and 0.26 obtained respectively at the upstream, midstream and downstream. Bulk density was high and ranged from 1.81g/cm3 to 2.22g/cm3 at the Upstream,1.78g/cm3 to 2.71g/cm3 at the Midstream and 2.22 g/cm3 to 2.36g/cm3 at the downstream indicating moderately to highly degraded. Bulk density significantly and positively correlated with sand at P=0.01 respectively at the Upstream and Midstream. The mean values of moisture content of the watershed at the upstream, midstream and downstream were 1.02%,0.69% and 3.62%. Hydraulic conductivity of the soil ranged from 2.65cm/hr to 8.12cm/hr Upstream, 0.90cm/hr to 8.20cm/hr Midstream and 2.68cm/hr to 3.25cm/hr downstream indicating moderately slow to moderately rapid soil. Aggregate stability of the watershed was low with mean values of 0.32 ,0.38 ,0.40 at upstream, midstream and downstream respectively. Erosion within the watershed was more pronounced at the upstream and midstream compared to the downstream hence the need for appropriate conservation measures and practices within the watershed is paramount.

Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

Soil organic carbon (SOC) is a key property for evaluating soil quality. SOC is thus an important parameter of agricultural soils and needs to be regularly monitored. The aim of this study is to explore the potential of synthetic aperture radar (SAR) satellite imagery (Sentinel-1), optical satellite imagery (Sentinel-2), and digital elevation model (DEM) data to estimate the SOC content under different land use types. The extreme gradient boosting (XGboost) algorithm was used to predict the SOC content and evaluate the importance of feature variables under different land use types. For this purpose, 290 topsoil samples were collected and 49 features were derived from remote sensing images and DEM. Feature selection was carried out to prevent data redundancy. Coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), percent root mean squared error (%RMSE), ratio of performance to interquartile range (RPIQ), and corrected akaike information criterion (AICc) were employed for evaluating model performance. The results showed that Sentinel-1 and Sentinel-2 data were both important for the prediction of SOC and the prediction accuracy of the model differed with land use types. Among them, the prediction accuracy of this model is the best for orchard (R2 = 0.86 and MSE = 0.004%), good for dry land (R2 = 0.74 and MSE = 0.008%) and paddy field (R2 = 0.66 and MSE = 0.009%). The prediction model of SOC content is effective and can provide support for the application of remote sensing data to soil property monitoring.

Cacao agroforestry systems improve soil fertility: Comparison of soil properties between forest, cacao agroforestry systems, and pasture in the Colombian Amazon

The objective of our work was to evaluate soil quality in different cacao agroforestry systems (AFS) in the Colombian Amazon. We compared soil quality of AFS at the study site with soil quality of two control systems: a pasture and a secondary forest.The study was conducted at the Macagual Amazon Research Center in western Colombian Amazon. We set up eight 600 m² plots in each study system. We collected soil samples in each plot, and assessed macrofauna diversity, aggregate morphology, and physical and chemical soil properties. We integrated these variables in a General Indicator of Soil Quality (GISQ).We found GISQ values of 0.85 for forest, 0.5, 0.65 and 0.59 for AFS and 0.21 for pasture, and the values differed significantly between land uses. The establishment of cacao AFS on degraded pasture was found to significantly improve soil fertility, i.e., by 42%. The intensification level between land uses (Pasture > AFS > Forest) negatively affected macrofauna populations due to soil compaction (physical properties). Forest had the highest physical and biological quality.Our results show that AFS not only have the capacity to maintain key soil ecological functions, but also to restore soil quality of degraded pastureland. Cacao-based AFS could therefore be a key restoration strategy for degraded pastureland. These results are very important in the context of the Colombian Amazon, where cacao is currently known as the “crop of peace”.

Comparing farmers’ soil fertility knowledge systems and scientific assessment in Upper Eastern Kenya

Few soil quality studies have attempted to examine the relationship between farmer soil fertility classification indices along scientific measurements, thus calling for closer examination of this association using innovative, comprehensive and systematic approaches. This study was conducted in the upper Eastern Kenya, to compare farmers’ knowledge of soil quality and laboratory soil measurements that were undertaken in the same fields. Farmer-descriptive soil quality indicator (FD-SQI) was compared with two scientific indicators, including additive SQI (A-SQI) and Factor Analysis (FA-SQI). A total of 69 farm households were sampled and surveyed using a face to face questionnaire to collect socio-economic, agricultural and soil quality perception data. Farmers described fertile and infertile soils, using a set of 9 descriptive likert scale indicators. Further, farm fields were classified as high or low fertility based on the farmer’s soil quality rating indicators. Soil samples were georeferenced and collected from 69 farms at surface depth (0–20 cm), after which they were subjected to laboratory analyses using standard methods. Means of key soil properties were compared using ANOVAs, LSD tests and contrasts for both fertile and infertile plots while soil indices derived from farmer soil fertility measures and laboratory soil measurements were regressed using linear models. Farmer-descriptive soil quality indicators scored significantly higher in fertile fields, compared to infertile fields. There was a positive significant relationship between FD-SQI and the two scientific indices. The relationship was stronger between FD-SQI and FA-SQI. The regression of FD-SQI against A-SQI was higher in fertile plots compared to low fertility plots in the study areas. The stronger correlation between FA-SQI and FD-SQI could be attributed to the objectivity of the latter due to elimination of redundancy and bias through statistical procedures and thus greater responsiveness. The A-SQI, on the other hand, is considered subjective due to reliance on literature review and researcher’s point of view. The findings showed that there was a linkage between local and scientific soil fertility assessment paradigms. This implies that farmers’ knowledge of soil quality constituted a consistent and logical classification of soil quality. Local soil knowledge should be developed in conjunction with scientific soil methodologies to benefit resource-poor small-holder farmers in sub-Saharan Africa regions.

Soil quality assessment using erosion-sensitive indices and fuzzy membership under different cropping systems on a Ferralsol in Brazil

The objective of this study was to evaluate soil quality under different cropping systems through two erosion-sensitive indexing methodologies applying fuzzy membership functions to a minimum data set (MDS) of soil properties. The experiment was conducted in a Ferralsol, in annual growing cycles from 2007 to 2014. The evaluated cropping systems included individual and intercropped treatments using: sunn hemp (Crotalaria juncea L.), pearl millet (Pennisetum glaucum L.), jack bean (Canavalia ensiformis L.), pigeon pea (Cajanus cajan L. Huth), and maize (Zea mays L.). Sampling in an adjacent area under native forest was also performed as a reference ecosystem. Soil properties such as bulk density, micro-, macro- and total porosity, aggregates stability, laboratorial soil fertility properties, and soil organic matter content were selected as MDS of physical and chemical properties, being used to compute two soil quality indices: 1) Integrated Quality Index (IQI), and 2) Nemoro Quality Index (NQI). Soil quality results showed the lowest values of soil and water losses corresponded to the largest soil quality indices evidencing that the usage of water-erosion-sensitive indices can be useful for the prediction of soil quality status. The selected MDS of soil properties were adequate indicators of soil quality as reduced water erosion was associated with large soil quality indices, e.g. negative correlation between soil macroporosity and soil erosion processes. The fuzzy methodology was effective in predicting soil quality using a MDS of soil property indicators, which can benefit various stakeholders in their decision-making process to ensure continuing and sustainable crop production.

Formulation of soil quality index plus to support soil management in preventing soil degradation in dryland farming

Soil degradation threat to achieving sustainable agriculture is very prominent, especially in tropical areas like Indonesia. Soil degradation is a process of decreasing the function, quality, and productivity of the soil. Therefore, a soil management technique is needed to prevent soil degradation. The crucial problem faced is the unavailability of instruments and methods to assess and monitor soil quality and provide direction in maintaining the soil from soil degradation. Soil types and land suitability classes systems cannot assess soil quality and soil degradation due to land and soil management. Besides, both of the systems are not sensitive and satisfactory to evaluate changes in soil quality and soil degradation. The purpose of this study was to develop Soil Quality Index Plus (SQI-Plus) to evaluate soil quality in supporting development of soil management techniques, monitoring and evaluation of soil quality, and providing recommendations for maintaining soil quality to prevent soil degradation. SQI-Plus is presented in numerical forms and followed by letters which indicating one or more limiting factors for plant growth and production which needs to be repaired or rehabilitated to achieve optimal production and preventing soil degradation.

Soil quality evaluation under different land use types in Kersa sub-watershed, eastern Ethiopia

BackgroundSoil quality, which can be inferred using indicators that interact synergistically, is affected by land use types and agricultural management practices. This study assessed the status of soil quality under three adjacent land uses (cultivated, grazing, and fallow) in Kersa subwatershed (622 ha). Soil samples were collected from the surface soil (0–20 cm depth) of the identified land uses with three replications and the soil quality parameters were analyzed. A minimum data set of soil quality indicators were selected from physical, chemical, and biological parameters using the literature review and expert opinion method. Linear scoring functions were used to give the unitless scores for the selected data sets, which were then integrated into a soil quality index (SQI).ResultsThe results revealed that bulk density, aggregate stability, pH, cation exchange capacity (CEC), available P, and soil organic carbon (SOC) had a significant difference in SQI among the different land uses. The soil quality indices were 0.69 for grazing land, 0.62 for cultivated land, and 0.59 for the fallow land. The SQI of all the land uses falls in the intermediate soil quality (0.55 < SQI < 0.70) class.ConclusionIn almost all the quality indicators assessed, the grazing land was superior to the cultivated and fallow lands. Therefore, implementing management practices that enhance soil quality like organic matter-controlled systems is imperative for sustainable agricultural production in the study area.

Evaluation of Microbe-Driven Soil Organic Matter Quantity and Quality by Thermodynamic Theory

ABSTRACTMicrobial communities, coupled with substrate quality and availability, regulate the stock (formation versus mineralization) of soil organic matter (SOM) in terrestrial ecosystems. However, our understanding of how soil microbes interact with contrasting substrates influencing SOM quantity and quality is still very superficial. Here, we used thermodynamic theory principles and Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) to evaluate the linkages between dissolved organic matter (DOM [organic substrates in soil that are readily available]), thermodynamic quality, and microbial communities. We investigated soils from subtropical paddy ecosystems across a 1,000-km gradient and comprising contrasting levels of SOM content and nutrient availability. Our region-scale study suggested that soils with a larger abundance of readily accessible resources (i.e., lower Gibbs free energy) supported higher levels of microbial diversity and higher SOM content. We further advocated a novel phylotype-level microbial classification based on their associations with OM quantities and qualities and identified two contrasting clusters of bacterial taxa: phylotypes that are highly positively correlated with thermodynamically favorable DOM and larger SOM content versus those which are associated with less-favorable DOM and lower SOM content. Both groups are expected to play critical roles in regulating SOM contents in the soil. By identifying the associations between microbial phylotypes of different life strategies and OM qualities and quantities, our study indicates that thermodynamic theory can act as a proxy for the relationship between OM and soil microbial communities and should be considered in models of soil organic matter preservation.

Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt

Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert of Egypt. For this purpose, a total of 36 geo-referenced representative soil samples (0–0.6 m) were collected and analyzed according to standardized protocols. Principal component analysis (PCA) was used to reduce the dataset into new variables, to avoid multi-collinearity, and to determine relative weights (Wi) and soil indicators (Si), which were used to obtain the soil quality index (SQI). The zones of soil quality were determined using principal component scores and cluster analysis of soil properties. A soil quality index map was generated using a geostatistical approach based on ordinary kriging (OK) interpolation. The results show that the soil data can be classified into three clusters: Cluster I represents about 13.89% of soil samples, Cluster II represents about 16.6% of samples, and Cluster III represents the rest of the soil data (69.44% of samples). In addition, the simulation results of cluster analysis using the Monte Carlo method show satisfactory results for all clusters. The SQI results reveal that the study area is classified into three zones: very good, good, and fair soil quality. The areas categorized as very good and good quality occupy about 14.48% and 50.77% of the total surface investigated, and fair soil quality (mainly due to salinity and low soil nutrients) constitutes about 34.75%. As a whole, the results indicate that the joint use of PCA and GIS allows for an accurate and effective assessment of the SQI.

Indexing soil properties through constructing minimum datasets for soil quality assessment of surface and profile soils of intermontane valley (Barak, North East India)

Soil quality (SQ) assessment considering surface and profile soils holds great significance in sustainable soil health management. We assessed the effect of land-use systems (agriculture, forest and plantation) on SQ using three SQ indices (SQI) models namely additive (SQIa), weighted additive (SQIw) and nemoro (SQIn) from total (TDS) and minimum (MDS) datasets in the intermontane valley soils of North East India (Barak Valley). Surface samples were taken from 13 to 24 cm depth and profile samples were taken up to 102–167 cm depth from 67 profiles selected by multi-layer thematic overlay analysis of agro-physical parameters with Survey of India (SOI) topo-sheets at 1:50 K scale as base map. The estimated SQIs averaged over models and land-uses were nearly two-fold higher for surface (0.751 ± 0.03) and profile (0.722 ± 0.02) soils from TDS than the MDS (surface: 0.398 ± 0.09; profile: 0.333 ± 0.06) approach. The SQIn model underestimated the SQI values by 28–35% less than SQIa and SQIw for both datasets. The effect of land-uses on SQIs values was mostly non-significant (p > 0.05) for both surface and profile soils. We mapped (at 1: 50 K scale) spatial variability in SQ grades as poor, medium, good and very good in quality and validated by error matrix and Kappa analysis against independent variable (NDVI map derived from Landsat 8). The SQ maps developed from TDS were superior to MDS in Kappa agreement. Among the models, SQIw followed by SQIa from TDS had better Kappa agreement in representing spatial distribution of SQ across soil depth and land-use. We propose the SQIw model as a better option to evaluate soil quality of surface and profile soils with a satisfactory level of accuracy and reliability since it is independent of qualitative assumptions and superior in Kappa agreement for spatial variability mapping. The information generated can be harnessed for managing soil health in intermontane valleys and other such similar agro-ecological regions.

Study on soil physical structure after the bioremediation of Pb pollution using microbial-induced carbonate precipitation methodology

Soil structure is an important index to evaluate soil quality; however, previous researchers have only paid attention to the effect and economic benefits of soil heavy metal remediation. In this study, microbial-induced carbonate precipitation (MICP) technology was used to remediate soil Pb pollution, and its effect on soil structure was studied by sieving and X-ray computed tomography techniques. The results showed that the leaching amount of heavy metals in soil decreased by 76.34% after remediation. Interestingly, due to the addition of organic matter and microorganisms, the soil particle size changed from microaggregates to large aggregates, and the large soil particle size (diameter > 2 mm) increased significantly by 71.43%. The soil porosity increased by 73.78%, which enhanced the soil permeability and increased the soil hydraulic conductivity. Therefore, MICP bioremediation not only remediated soil heavy metal pollution but also promoted the soil aggregation structure, which has important significance for soil remediation and improvement.