Soil Quality Assessment Research Articles

Soil quality assessment of oak forests invaded by moso bamboo (Phyllostachys edulis) in the northern subtropics

Soil quality is closely related to the evolution of forest ecosystems, especially in the context of global warming; therefore, it is essential to study the changes in soil quality caused by the invasion of moso bamboo in its habitat. In this study, we selected plantation forests and natural secondary forests with typical vegetation of the northern subtropics, Quercus acutissima plantation (QAP) and oak secondary forest (OSF), collected plant community and soil physicochemical data, and established a soil quality index (SQI) to assess how moso bamboo invasion affects soil quality. In addition, the mechanisms driving the moso bamboo invasion effects on soil quality were investigated using a partial least squares structural equation model (PLS-SEM). The results demonstrated that: (1) Moso bamboo invasion significantly affected community structure and soil physicochemical properties, there were inconsistent changes in soil physicochemical indicators in oak forests of different origins, and in general the effects of moso bamboo expansion on soil physicochemical properties were concentrated in the topsoil (0–20 cm). (2) Soil bulk density (BD), total soil porosity (TSP), total nitrogen (TN), and available potassium (AK) comprised the minimum data set (MDS) used to calculate the SQI, and the SQI of oak forests of different origins ranged from 0.48 to 1, and the invasion of moso bamboo led to a significant decrease in the SQI of the surface layer of QAP. A redundancy analysis (RDA) showed that soil quality assessment factors were closely related to community structure and the PLS-SEM indicated that moso bamboo invasion indirectly affected surface soil quality through its effects on community and soil factors. The results showed that moso bamboo invasion had different effects on oak forests of different origins. They also showed that soil quality was strongly affected by aboveground biomass (AGB), the tree Shannon-Wiener index (TH'), and available potassium (AK).

Soil quality assessment in low human activity disturbance zones: a study on the Qinghai-Tibet Plateau.

The Qinghai-Tibet Plateau, located at the Third Pole and known as the "Asian water tower," serves as a crucial ecological barrier for China. Grasping the soil quality on the Qinghai-Tibet Plateau holds paramount importance for the rational and scientific exploitation of soil resources within the region and is essential for vegetation restoration and ecological reconstruction. This study, conducted in Maqin County, Qinghai Province, collected 1647 soil samples (0-20cm) within a study area of 6300km2. Sixteen soil indicators were selected that were split into beneficial (N, P, S, and B), harmful (Cr, Hg, As, Pb, Ni, and Cd), and essential (Cu, Zn, Se, Ga, K, and Ca) elements. The Soil Quality Index (SQI) was computed to assess soil quality across diverse geological contexts, land cover classifications, and soil profiles. The results indicate that the overall SQI in the study area was comparatively high, with most regions having an SQI between 0.4 and 0.6, categorized as moderately to highly satisfactory. Among the different geological backgrounds, the highest SQI was found in the Quaternary alluvium (0.555) and the lowest in the Precambrian Jinshuikou Formation (0.481). Regarding different land-use types, the highest SQI was observed in glacier- and snow-covered areas (0.582) and the lowest in other types of grassland (0.461). The highest SQI was recorded in typical alpine meadow soil (0.521) and the lowest in leached brown soil (0.460). The evaluation results have significant reference value for the sustainable utilization and management of soil in Maqin County, Qinghai Province, China.

Evaluating the Soil Quality Indicators in Various Yield Zones of Pulses in Tamil Nadu, India

Background: In recent years, there is growing demand for pulses in Tamil Nadu and the government has implemented various schemes and programmes to promote cultivation. However, the evaluation and categorization of soils for pulse production is crucial in determining the suitability of land for pulse cultivation and offer improvement through the adoption of improved crop management practices. Methods: A systematic soil quality assessment survey was taken up during 2020-2022 in the major pulse growing blocks of Virudhunagar district viz., Sattur, Aruppukottai and Thiruchuli based on past ten years yield data and it was classified into three categories viz., low yielding ( less than 400 kg ha-1), medium (400 to 700 kg ha-1) and high yielding ( greater than 700 kg ha-1) categories. Three hundred samples were collected from these zones. Results: The positive effects of soil physical, chemical, and biological qualities on the yield of pulse crops were justified through the yield data of the high yielding zone which ranged from 769 to 989 kg ha-1 with an average pulse yield of 880 kg ha-1. This may be attributed to more favourable soil physical environment in terms of soil texture (sandy clay loam), higher mean percentage of water stable aggregates (51%) and a favourable pH of 7.52. These parameters coupled with lower bulk density (1.23 Mgm-3), optimum infiltration rate (1.76 cm hr-1) and maximum mean organic carbon status (1.38 mg kg-1) contributed to higher range of soil cation exchange capacity (28.7 to 47.2 cmol p+ kg-1). In addition, the presence of avilable nitrogen and phosphorus, potassium, sulphur and availability of mean micronutrient contents viz., DTPA iron and DTPA zinc resulted in better quality of these soils contributing to maximum pulse productivity in this zone. Soil respiration rate with mean respiration rate of 4.48 mg CO2 kg-1 d-1 in high yield category compared to that of 2.34 mg CO2 kg-1 d-1 in low yield category, which is well correlated with better soil organic matter content in the former than the latter group of soil. Therefore, this study formulates a clear understanding of the variations in soil quality parameters for adopting efficient nutrient management practices towards obtaining maximum productivity of pulse crops.

Large-Scale Photovoltaic Farms in the Qinghai Desert: Soil Quality Assessment Methods

Large-Scale Photovoltaic Farms in the Qinghai Desert: Soil Quality Assessment Methods

Quantitative Soil Quality Assessment by Using Visual Soil-Field Assessment Tools (VS-FAST)

Unplanned land use reflects the changes from agricultural to urbanization and decreasing of green spaces in cities and agricultural lands surrounding cities. The Soil Adjusted Vegetation Index (SAVI), Dry Bare - Soil Index (DBSI) and Normalized Difference Built-up Index (NDBI) indices were derived to evaluate the rate of the land use/cover changes due to uncontrolled urban sprawl for the years 2001 and 2020. Field indicators were also used to assess soil properties’ degradation by using Land Degradation Assessment in Drylands (LADA) under the FAO Global Environment Facility (GEF) project. A set of 30 soil samples were randomly collected from two locations: unplanned built-up and vegetated sites in Babil Governorate, Iraq. The results showed that soil texture and salinity were the most varied indicators. The poorer areas (unplanned urban areas) were low and assessed as poor quality, while the vegetated site was assessed as moderate quality with high total score values. The findings from spectral indices show a significant rise in the built-up land use/cover category, expanding from 10.367 square kilometres in 2001 to 50.883 square kilometres in 2020. In contrast, the areas of bare-soils and vegetated decreased from 29.29 and 35.474 square kilometres in 2001 to 12.511 and 19.601 square kilometres in 2020, respectively. Various analytical techniques proved instrumental in distinguishing the different land use/cover categories in this research.

Diffuse reflectance spectroscopy for assessing soil quality indicators in cocoa production system validated using multi‐year data

AbstractDiffuse reflectance spectroscopy is emerging as a reliable soil testing approach. However, few studies reported on the applicability of DRS for estimating the overall soil quality index (SQI) in smallholder farms. Specifically, the DRS approach has not been evaluated in cocoa production systems with multi‐year soil data. We evaluated the DRS approach in typical cocoa production systems from Papua New Guinea (PNG) by collecting soil samples before and after implementing soil test‐based nutrient recommendations. Specifically, 379 soil samples were collected in 2017 from cocoa farms in East New Britain, New Ireland, and Bouganville provinces of PNG. A second soil testing campaign was undertaken in 2019 to analyse 432 soil samples from the same farms to test the consistency of the DRS approach for SQI assessment. In all these samples, 15 soil parameters were measured using the conventional wet chemistry approach. Reflectance spectra in each sample were also measured over the visible to near‐infrared (wavelength: 350–2500 nm). A minimum dataset approach was followed to estimate SQIs from laboratory‐measured soil properties. With available N, clay content, available P, and available Zn adjudged as significant soil quality parameters for the cocoa plantations of PNG, estimated mean SQI values ranged from 0.30 in the cocoa farm from Bouganville province (BOKA‐PAN) in 2017 to 0.46 for the New Ireland province (LAU‐PAN) soil samples collected during 2019. Overall, SQI values increased from about 13%–21% following the nutrient applications. Based on the validation of two commonly‐used chemometric models (PLSRFS: partial‐least‐squares regression with feature selection, SVR: support vector regression), the SQI values were estimated with the coefficient of determination (R2) values as high as .94. Our results showed that a DRS model calibrated in soil samples collected in a specific year can be used for estimating SQI values in soil samples collected in different years. These results suggest that the principal component analysis (PCA) based SQI may be estimated from the DRS data with good accuracy and SQI values may potentially capture the management effects in smallholder farms.

EFFECTS OF TEA (CAMELLIA SINENSIS) CULTIVATION ON SOIL QUALITY IN THE LAM DONG, VIETNAM

The objectives of the study were to assess soil quality and its relationship to the sustainability of tea cultivation in the Lam Dong, Vietnam. Overall soil quality declined with increasing age of the tea plantations as evidenced by decreases in soil OC, total N, K and S, available P and K, mean weight diameter of aggregates. As well, total P, bulk density and mechanical resistance increased with increasing cultivation intensity. Because these soil properties were sensitive to cultivation effects, they were considered to be good indicators of soil quality. Soil properties that were less sensitive to change, and limited as soil quality indicators included texture, clay mineralogy and sesquioxides, and effective cation exchange capacity. Soil quality changes were greatest during the first 10 years of cultivation and were generally greatest in the surface 0- to 40-cm of soil. Soil and crop management factors were considered to be the most important factors affecting soil quality. Decreases in long-term crop yields were found to correspond with decreases in soil quality. In terms of crop productivity, the most important soil quality indicators (based on a multiple regression analysis) were OC, available P, total K and PAWC. Economic analysis of the yield and production cost data indicated that, under current conditions, tea cultivation in the Lam Dong province is sustainable for periods of about 20 years. Thus, measured values of soil quality indicators in the 20-yr tea soils were considered to represent the "critical levels" for economic sustainability of tea cultivation.

Potassium and Magnesium in American Ginseng Roots as Key Factors in Monitoring Soil Quality, Yield, and Quality: Screening, Prediction, and Validation

Understanding the key roles of nutrient elements in soil–plant systems are essential for herbal medicine production and sustainable development. However, the ecological relationships between soil quality and nutrient elements, yield, saponins, or other active compounds in American ginseng remain unclear. In this study, 20 soil indicators, 10 root nutrient indicators, 9 quality indicators, and yields were investigated. The minimum dataset was constructed by principal component analysis, key factors were screened by correlation analysis and PLS-PM analysis, and the prediction model was constructed using linear fitting and tested by a validation test. The minimum dataset, constructed based on principal component analysis, comprised five indicators: SOM, TP, AK, AMg, and ACa. Correlation analysis, PLS-PM analysis, and linear fitting showed that K and Mg were the key factors relating soil quality to the yield and quality of American ginseng and that when AMg was 0.21 g/kg and AK was 0.30 g/kg, soil organic matter was 27%, total phosphorus was 1.19 g/kg in soil, K content in roots was 15.63 g/kg, Mg content was 1.91 g/kg, and the K/Mg of 8.85 could balance American ginseng yield and quality. In predicting and validating the model, predicting the DW, total ginsenoside, Rb1, Rb2, Rc, and Rd of American ginseng using K/Mg were reliable. This study provides a scientific basis for nutrient regulation, selecting planting sites, assessing soil quality, and predicting and evaluating American ginseng quality.

Soil Ecosystem Functioning through Interactions of Nematodes and Fungi Trichoderma sp.

In this review, an overview was given of the mutual interactions between nematodes and fungi of the genus Trichoderma sp. due to the potential of these fungi to protect plant roots from plant-parasitic nematodes on the one hand and the influence of nematodes (fungivores) on the efficacy of the fungus on the other. In addition, an overview of the advantages of Trichoderma sp. for agricultural production was given. The basis of sustainable agricultural production is the healthy functioning of the soil ecosystem. The diversity of organisms—bacteria, protozoa, algae, metazoans (nematodes) and fungi—improves the quality and performance of the soil by maintaining biological productivity. Root exudates in the rhizosphere support microbial communities that play a key role in regulating the dynamics of organic matter decomposition and the availability of plant nutrients. The microbial activity of organisms in the soil is interconnected and interacts to form a soil food web that reflects the condition, function and health of the soil. The energy in food webs flows through trophic chains of consumers, which are divided into energy channels. Root, bacterial and fungal channels increase soil biomass, carbon (C) and energy flow through the soil food web. The structure of the nematode community is an effective tool for the biological assessment of soil quality. This is due to a number of characteristics that nematodes have, including the following: a great diversity of species, the possibility of subdivision according to different criteria such as trophic groups and c-p groups, the duration of reproduction, the ease of sampling, the identification of genera and preservation, etc. Nematodes are involved in various ecological functions in the soil, of which the interaction between them and fungi is based on antagonism or mutualism, which is the basis for a better understanding of their impact on the ecosystem. Fungi of the genus Trichoderma sp. are successful colonizers of all habitats, secondary opportunists and fast growing.

Assessment of soil quality in irrigated arid lands under organic agriculture systems

The process of evaluating the soil quality is complex and is impacted by a variety of physical, chemical, and biological indicators as well as how they interact over time, particularly throughout different cultivation periods. The main objective of the current investigation is to examine how various organic farming practices affect the soil quality indicators at selected organic farms throughout various cultivation times. The study sites in Egypt were selected to represent two great soil groups (Torripsamments and Haplotorrerts). Moreover, the selected farms have different organic cultivation periods and management practices. Soil samples were collected from five different farms and uncultivated soil in each soil great soil group as control. After analyzing soil samples in the lab, physical, chemical, and biological indicators were assessed and used to calculate the Soil Quality Index (SOI). Principal Component Analysis (PCA) was used to obtain the minimum data set needed for soil quality evaluation. The length of organic cultivation in Torripasament raised the SQI value for physical and chemical indicators in the following order: 2 years > 6 years > 10 years > 13 years. Similar to Torripsament, the most sensitive indicators for SQI assessment in Haplotorrert were found to be soil respiration, microbial biomass C, microbial quotient, organic matter, and physical characteristics of soil quality. Results also indicated that organic farming improves most of the studied soil quality indicators of both soils (Torripsamment and Haplotorrerts). Soil respiration, Microbial Biomass (MBC), Microbial Quotient (MQ), organic matter, and physical aspects of soil quality were the most sensitive indicators of soil quality evaluation. Soil quality index (SQI) for Torripsamment was affected mostly by soil biological indicators, while soil physical indicators were the main indicators affecting SQI in Haplotorrerts. The best score for the soil quality index was at 13 years of organic cultivation periods in Torripsamment and at 17 years in Haplotorrerts.

Comparison of two soil quality assessment models under different land uses and topographical units on the southwest slope of Mount Merapi

Comparison of two soil quality assessment models under different land uses and topographical units on the southwest slope of Mount Merapi

Soil Quality under Polyhouse and Open Field Conditions as Affected by Continuous Fertilizer and Manure Applications

ABSTRACT Intensive cultivation coupled with scrupulous use of fertilizers could be detrimental to soil health and quality of the produce in the long run. Therefore, this study aimed to assess the predominant protected/polyhouse and open field vegetable production on soil quality. The changes in soil quality in comparison to control (fallow) indicate, whether the management practices being adopted under both the systems are sustainable or not. Thus, a study was conducted to identify the most appropriate soil quality indicators and finally to know about the status of soil quality both under polyhouse and open field system of cultivation in mid hills of North western Himalayas. Principal component analysis (PCA), a data reduction tool was employed to get the minimum data set for the assessment of soil quality. Geo-referenced soil samples were collected from 25 progressive farmers’ cultivating vegetables under polyhouse, as well as open field conditions and analyzed for various physical, chemical and biological indicators of soil quality. The PCA was performed which screened out the three principal components (PCs) with eigen value > 1. Average soil quality index (SQI) for different sites was observed to be on higher under polyhouse conditions (0.815) as compared to open field cultivation for vegetables (0.760) and control (0.703). Where, soil organic carbon contributed maximum to soil quality (75.6%) followed by pH (13.9%), water holding capacity (6.8%) and EC (3.6%). Conclusively, polyhouse cultivation system revealed high category of soil quality. Whereas, open field cultivation system revealed medium category of soil quality.

Soil quality reflects microbial resource availability and drives rhizosphere microbiome variation in Ghanaian cocoa farms

Cocoa (Theobroma cacao L.) is an important crop in Ghana and the source of livelihood for hundreds of thousands of smallholder farmers. Maintaining soil quality on these farms is critical to ensuring the long-term viability of cocoa farming and preventing deforestation to meet rising demand. However, increasing attention to soil health has revealed a significant knowledge gap related to the soil microbiome in cocoa production systems. Using a nested design of sixteen smallholder cocoa farms in agroforestry or monoculture, on different soil quality classes and in different agroecological zones, a study was conducted to identify 1) drivers of rhizosphere microbial diversity and community composition across scales and 2) the extent of microbial differentiation between soil quality classes. Soil quality had far greater impacts than agroecological zone or agroforestry vs. monoculture management on microbial diversity and community composition, accounting for 17 % of variation in prokaryotes and 10 % in fungi. Good-quality and poor-quality soils contrasted in soil and root parameters, creating variable microbial resources, which led to differences in microbial community composition and the relative abundance of specific taxa. Soil organic matter and root traits were key drivers of community composition in good-quality soils, while permanganate-oxidizable carbon was relatively more important in poor-quality soils. These results underscore the importance of considering rhizosphere microbial communities in assessments of soil quality and highlight taxa that may serve as biological indicators of soil health in cocoa agroforestry systems.

Concept and Assessment Methodology of Soil Quality: A Review

Soil analysis is commonly used to gauge its quality and suitability for diverse purposes from local to national evaluations. However, the complexity of soils, their site-specific traits, past land use impacts, and balancing ecosystem benefits pose significant challenges in identifying relevant parameters and interpreting measurements. This manuscript comprehensively reviews and examines the concept and assessment methodologies of soil quality. It is evaluated to learn about the effects of management practices on soil function. Sustainability of agriculture system is inwardly linked to maintenance of soil quality. Therefore, soil quality assessment is of paramount importance to know the appropriate management practices to be adopted for sustainable crop production. By just measuring yield of crop, quality of water or any other, soil quality cannot be judged. As we know, soil have different properties (chemical, physical and biological), that interact in a precipitously manner to give, soil its capacity to perform or function. Thus, soil capacity can be surmised from measuring changes in its properties or of ecosystem’ sattributes and cannot be measured directly. Assessment of Soil quality composed of three key steps (1) Selection, measurement and minimization of the set of relevant soil attributes. (2) Quantification of the selected soil attributes through direct measurement and assigning an appropriate Score. (3) Integration among the scored attributes to construct the final index, by providing criteria for defining the weight of each attribute or group of attributes. Soil quality assessment will allow interpreter to identify the attributes which are most significant, quantify the relative contribution of soil properties and subsequently assess the overall quality of soils. The review study aims to inspire researchers by integrating a detailed information on soil quality assessment and promoting the sustainable practices in preservation and enhancement of soil quality.

Effect of Bone Char Application on Soil Quality, Soil Enzyme and in Enhancing Crop Yield in Agriculture: A Review

Soil quality, in contrast to air or water, exhibits a heightened level of heterogeneity and necessitates closer examination due to its impact on the well-being of flora, fauna, and human beings. Organic carbon is considered a fundamental indicator of soil quality, as it plays a significant role in strategies aimed at mitigating climate change. The generation of bone char arises from a thermochemical conversion process involving defatted bones. Specific attention is focused on the solubility of P compounds, which serves to classify bone chars as potential slow-release P fertilizers. The introduction of P into the soil can be enhanced through an "internal activation" process facilitated by the adsorption of reduced S compounds. Additional properties of agronomic significance originate from the porosity of bone char, which promotes water retention and provides a habitat function for soil microorganisms. The evaluation of soil quality has been a longstanding practice, involving an examination of physical and chemical characteristics such as pH, nitrogen levels, soil organic carbon, bulk density, accessible water, aggregate stability, particle size distribution, and soil structure. Recently, the concept of soil quality has been expanded to encompass the notion of soil health, which is perceived as a finite, non-renewable resource that undergoes constant change. Research also demonstrates the crucial role of soil biota in the assessment of soil quality, as they exhibit rapid responsiveness to disturbances. Animal bones undergo a process of defatting, degelatinization, and subsequent incineration at temperatures ranging from 600-800°C to produce bone char (BC). Reports indicate that typical BC contains 152 g P kg<sup>-1</sup>, 280 g Ca kg<sup>-1</sup>, and 6.5 g Mg kg<sup>-1</sup>, with carbon content typically falling below 100 g kg<sup>-1</sup>. The solubility of bone char in the soil depends on factors such as pH and the soil's capacity to absorb P, situating it within the range between rock phosphate and triple super phosphate (TSP). The application of bone char to the soil can enhance soil health, resulting in increased crop yield and improved quality.

Soil Quality Evaluation in Mono and Mixed Eucalypt Plantation

Soil quality (SQ) pertains to the intricate and ongoing capacity of soil to function as a thriving ecosystem that supports the growth of plants and animals. However, there is a limited understanding of SQ assessment in mixed forest plantations. Therefore, we formulated and tested the hypothesis that the inclusion of a nitrogen-fixing tree species (such as Acacia mangium) improves SQ indicators in mixed treatments involving Eucalyptus trees. To evaluate the changes in SQ, we conducted a field experiment that employed the Soil Management Assessment Framework (SMAF) tool to analyze pure and mixed plantations of Eucalyptus grandis and A. mangium. Soil samples were collected at a depth of 0–20 cm from different treatments, including pure E. grandis without nitrogen fertilization (E), pure A. mangium (A), pure E. grandis with nitrogen fertilization (E + N), and mixed E. grandis and A. mangium (E + A). Sampling took place at 27 and 39 months after planting. We selected seven indicators of SQ: two biological indicators (soil microbial biomass carbon and β-glucosidase enzyme activity), four chemical indicators (soil organic carbon, pH, available phosphorus, and potassium), and one physical indicator (bulk density). By applying the SMAF tool, we determined the SQ scores for each indicator. The results revealed that E + A stands exhibited higher SMAF scores than pure stands, particularly in terms of pH (0.49 and 0.52 at 27 and 39 months, respectively) and phosphorus levels (0.84 and 0.82, at 27 and 39 months), respectively. Forest management practices and the sampling period had the most pronounced impact on biological and chemical indicators. Notably, significant positive correlations were observed between SMAF scores and pH, available phosphorus content, enzymes, soil organic carbon, and microbial biomass in both sampling periods. This study effectively provided novel information that introducing a nitrogen-fixing tree species in combination with eucalyptus trees enhances SQ, as indicated by the SMAF tool, which could reduce the need for external inputs (e.g., mineral fertilizers) by the farmers. Future studies should analyze the effects of A. mangium not only with other E. grandis varieties but also with other forestry essences.

Clean and accurate soil quality monitoring in mining areas under environmental rehabilitation in the Eastern Brazilian Amazon.

Soil quality monitoring in mining rehabilitation areas is a crucial step to validate the effectiveness of the adopted recovery strategy, especially in critical areas for environmental conservation, such as the Brazilian Amazon. The use of portable X-ray fluorescence (pXRF) spectrometry allows a rapid quantification of several soil chemical elements, with low cost and without residue generation, being an alternative for clean and accurate environmental monitoring. Thus, this work aimed to assess soil quality in mining areas with different stages of environmental rehabilitation based on predictions of soil fertility properties through pXRF along with four machine learning algorithms (projection pursuit regression, PPR; support vector machine, SVM; cubist regression, CR; and random forest, RF) in the Eastern Brazilian Amazon. Sandstone and iron mines in different chronological stages of rehabilitation (initial, intermediate, and advanced) were evaluated, in addition to non-rehabilitated and native forest areas. A total of 81 soil samples (26 from sandstone mine and 55 from iron mine) were analyzed by both traditional wet-chemistry methods and pXRF. The available/exchangeable contents of K, Ca, B, Fe, and Al, in addition to H+Al, cation exchange capacity at pH = 7, Al saturation, soil organic matter, pH, sum of bases, base saturation, clay, and sand were accurately predicted (R2 > 0.70) using pXRF data, with emphasis on the prediction of Fe (R2 = 0.93), clay content (R2 = 0.81), H+Al (R2 = 0.81), and K+ (R2 = 0.85). The best predictive models were developed by RF and CR (86%) and when considering pXRF data + mining area + stage of rehabilitation (73%). The results highlight the potential of pXRF to accurately assess soil properties in environmental rehabilitation areas in the Amazon region (yet scarcely evaluated under this approach), promoting a more agile and cheaper preliminary diagnosis compared to traditional methods.

Applicability of visual and analytical soil quality indicators in environmentally diverse catchments of the Ethiopian Rift

This research focuses on two science-based soil quality assessments, i.e., visual soil assessment (VSA) and analytical (lab-based) soil quality (SQ) assessment. The VSA, especially when conducted by farmers, offers a cost-effective approach to studying soil in extended study areas. However, little is known about the applicability of VSA in areas subjected to different degradation processes. Our study takes place in the lowland, midland, and highland agro-ecological zones (AEZs) of the Shafe and Sile-Elgo catchments, in the Southern Main Ethiopian Rift Valley. It aims to contribute to the state-of-art knowledge on VSA by evaluating its reproducibility when conducted by farmers and extensionists and its correlation to analytical SQ indicator scores, and to generate insight into SQ of the cropland in the various AEZs of two selected catchments. To that aim, 36 cropland fields were selected, half of these fields receiving soil and water conservation (SWC) practices, while no SWC was applied to the others. In each field, the VSA was conducted by farmers and experts, followed by laboratory analyses and calculation of an SQ index. Our finding reveals that overall VSA scores assigned by both groups were very strongly correlated (r=0.909–0.975, p<0.001) and reproducible at any level of stratification in the highly diverse, tropical study area covered in this research. However, the VSA indicator soil texture turned out to be non-reproducible. Both VSA and analytical SQ indicator scores are significantly (p<0.05) higher in fields receiving SWC practices and integrating both methods provided a clear contrast between fields that have good and poor SQ conditions in different AEZs of the catchments. The VSA method is less capable of capturing problems related to toxicities or low nutrient and water availability as a consequence of acidity, salinity and sodicity. Therefore, when the VSA method is applied to monitor SQ status of fields subjected to diverse degradation processes, it would be better to include specific indicators of pH and EC.

Machine learning-based soil quality assessment for enhancing environmental monitoring in iron ore mining-impacted ecosystems

In November 2015, a catastrophic rupture of the Fundão dam in Mariana (Brazil), resulted in extensive socio-economic and environmental repercussions that persist to this day. In response, several reforestation programs were initiated to remediate the impacted regions. However, accurately assessing soil health in these areas is a complex endeavor. This study employs machine learning techniques to predict soil quality indicators that effectively differentiate between the stages of recovery in these areas. For this, a comprehensive set of soil parameters, encompassing 3 biological, 16 chemical, and 3 physical parameters, were evaluated for samples exposed to mining tailings and those unaffected, totaling 81 and 6 samples, respectively, which were evaluated over 2 years. The most robust model was the decision tree with a restriction of fewer levels to simplify the tree structure. In this model, Cation Exchange Capacity (CEC), Microbial Biomass Carbon (MBC), Base Saturation (BS), and Effective Cation Exchange Capacity (eCEC) emerged as the most pivotal factors influencing model fitting. This model achieved an accuracy score of 92% during training and 93% during testing for determining stages of recovery. The model developed in this study has the potential to revolutionize the monitoring efforts conducted by regulatory agencies in these regions. By reducing the number of parameters that necessitate evaluation, this enhanced efficiency promises to expedite recovery monitoring, simultaneously enhancing cost-effectiveness while upholding the analytical rigor of assessments.

Assessing Soil Quality Under Different Land-Uses Through Constructing Minimum Datasets from Soil Profiles in a Fragile Ecosystem of Northeastern Region of India

ABSTRACT The conversion of forest to agricultural land has been a major worldwide concern and a cause of deterioration of soil quality. The study examined the influence of land uses on the soil quality index (SQI) using minimum datasets (MDS) constructed from soil profiles through two methods namely, additive (SQIa) and weighted (SQIw). In this study, 29 geo-referenced soil profiles (116 layers) up to a depth of 150 cm were collected in Northeastern India, representing forest, jhum, jhum fallow, and agricultural land uses. Principal component analysis (PCA) with multiple correlation analysis is found to be a suitable statistical method for selecting the key soil quality indicators. From PCA, organic carbon (OC), sand, clay, available P and available S were selected as soil quality indicators in MDS. Our results showed that these MDS significantly varied within the profiles depending on the land-uses. The estimated values of SQIs showed that SQIa was higher than SQIw across the soil depths and land-uses. For the surface soil, SQIa values were in the order of forest (2.97) > jhum fallow (2.82) > agriculture (2.68) > jhum land (1.97) and for profile soil jhum fallow (2.38) > forest (2.37) > agriculture (2.33) > jhum (1.88). Similarly, SQIw followed the trend of forest > jhum fallow > agriculture > jhum for all the three studied depths. The statistical analysis showed that the surface, subsurface, and profile soil had no significant impact on SQIw for agriculture, fallow jhum, and forest. The overall data indicated that jhum land had a significantly lower SQI than other land-uses for all soil depths. From this study, the information generated can be useful to assess the soil quality in different land-uses for managing soil health in similar areas of the Northeastern region of India. The findings are also useful in the ecosystems in which soils develop in an acid edaphic environment.