Soil Indicators Research Articles

Differential Analysis of Biochar Effects on Soil Physicochemical Properties

For a long time, because of China’s large population and small per capita resource occupancy, the use of natural resources is unreasonable. Soil degradation problem due to the long-term irrational utilization of land resources is becoming more and more serious, which seriously damaging the ecological environment and hindering China’s modernization process. Facing the problem of soil remediation, biochar technology has become a research hotspot because of its advantages of green environmental protection, low cost, high efficiency, and wide application. And black soil in northeast China and southern region of red soil as important soil types of agroforestry in China, over the years have experienced serious soil degradation crisis. In this paper, black soil and red soil are selected as research objects to explore the impact of biochar on the physical and chemical properties of the two soils. This paper intends to use the methods of investigation and analysis, and comparison in many aspects. Through the study, it was found that the application of biochar had the same trend of influence on the organic carbon content and C/N ratio of the two soils, but the index of black soil increased more significantly. The change trend of soil bulk density and pH was also consistent with the application of biochar, and the difference was not significant. The effects of biochar applied to the two soils on greenhouse gas emissions were quite different. The soil health of black soil and red soil is of great significance in China’s agricultural production and life. This paper expects to bring reference value to the improvement of the overall soil function and the promotion of ecological restoration process of China’s biochar modification work for different soil types in the north and south, by comparing the impact of biochar on the physical and chemical properties of the two soils. A better soil improvement program can be obtained after comparative analysis, so as to provide suggestions on sustainable development and comprehensively promote ecological construction.

Assessment of the sustainability of landcovers due to artisanal mining in Jos area, Nigeria.

Environmental sustainability ensures that minerals are responsibly exploited to meet the present needs without depriving the future generations the ability to meet their needs. Unfortunately, environmental sustainability and artisanal mining seem unattainable in recent years with aggressive mining operations. It is on this premise that geospatial techniques with proven role in environmental studies were employed to assess the environmental sustainability due to artisanal mining in Jos area, Plateau State, Nigeria. Land surface temperature (LST) and seven geospatial indices used for land use land cover (LULC) estimation were generated. The mean LST values showed a steady increasing pattern from 23.98 to 25.88°C and 29.46°C in 1984, 2002, and 2020 respectively, as a result of exposed outcrops occasioned by mining and the expansion of mining communities. The mean value of the Normalized Difference Vegetation Index (NDVI) depicts a considerable increase from 0.179 in 1984 to 0.458 in 2002 and a slight decline to 0.438 in 2020. This is congruent with the mean Optimized Soil-Adjusted Vegetation Index (OSAVI) values, thus revealed that the Jos area is not densely vegetated implying that the course to revegetate the region has not been achieved to the tune of woodland populated vegetation. The resulting maps from Modified Normalized Difference Water Index (MNDWI) revealed a consistent decline in the mean values - 0.349, - 0.391, and - 0.411 in 1984, 2002, and 2020 respectively. Mineral recovery, mineral processing, and seasonal variations could be some of the reasons waterbodies are one of the most stressed natural resources in the study area. The mean values of Enhanced Built-up and Bareness Index (EBBI) decreased from 0.282 in 1984 to 0.202 in 2002, but increased from 0.202 in 2002 to 0.230 in 2020, which corroborated with the findings of NDBI. It was deduced that built-up areas in the study area are very low. This may be attributed to urban migration and the migration of artisanal miners to new mining sites across the state or country. The values of bare land mapped using Dry Bare Soil Index (DBSI) corresponds with that of Normalized Difference Bareness Index (NDBaI) and showed that bare land has reduced on the Jos Plateau due to improved vegetation growth. This study provided essential input and referential information for proper decision making on environmental sustenance, environmental management, and mineral resource conservation.

Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia

Sembilang National Park, one of the best and largest mangrove areas in Indonesia, is very vulnerable to disturbance by community activities. Changes in the dynamic condition of mangrove forests in Sembilang National Park must be quickly and easily accompanied by mangrove monitoring efforts. One way to monitor mangrove forests is to use remote sensing technology. Recently, machine-learning classification techniques have been widely used to classify mangrove forests. This study aims to investigate the ability of decision tree (DT) and random forest (RF) machine-learning algorithms to determine the mangrove forest distribution in Sembilang National Park. The satellite data used are Landsat-7 ETM+ acquired on 30 June 2002 and Landsat-8 OLI acquired on 9 September 2019, as well as supporting data such as SPOT 6/7 image acquired in 2020–2021, MERIT DEM and an existing mangrove map. The pre-processing includes radiometric and atmospheric corrections performed using the semi-automatic classification plugin contained in Quantum GIS. We applied decision tree and random forest algorithms to classify the mangrove forest. In the DT algorithm, threshold analysis is carried out to obtain the most optimal threshold value in distinguishing mangrove and non-mangrove objects. Here, the use of DT and RF algorithms involves several important parameters, namely, the normalized difference moisture index (NDMI), normalized difference soil index (NDSI), near-infrared (NIR) band, and digital elevation model (DEM) data. The results of DT and RF classification from Landsat-7 ETM+ and Landsat-8 OLI images show similarities regarding mangrove spatial distribution. The DT classification algorithm with the parameter combination NDMI + NDSI + DEM is very effective in classifying Landsat-7 ETM+ image, while the parameter combination NDMI + NIR is very effective in classifying Landsat-8 OLI image. The RF classification algorithm with the parameter Image (6 bands), the number of trees = 100, the number of variables predictor (mtry) is square root (√k), and the minimum number of node sizes = 6, provides the highest overall accuracy for Landsat-7 ETM+ image, while combining Image (7 bands) + NDMI + NDSI + DEM parameters with the number of trees = 100, mtry = all variables (k), and the minimum node size = 6 provides the highest overall accuracy for Landsat-8 OLI image. The overall classification accuracy is higher when using the RF algorithm (99.12%) instead of DT (92.82%) for the Landsat-7 ETM+ image, but it is slightly higher when using the DT algorithm (98.34%) instead of the RF algorithm (97.79%) for the Landsat-8 OLI image. The overall RF classification algorithm outperforms DT because all RF classification model parameters provide a higher producer accuracy in mapping mangrove forests. This development of the classification method should support the monitoring and rehabilitation programs of mangroves more quickly and easily, particularly in Indonesia.

Effects of sulfate attack under wet and dry cycles on strength and durability of Cement-Stablized laterite

The designed cement content for laterite soil cement base or subbase for road construction can be estimated from the unconfined compressive strength (UCS) of the compressed cement-stabilized laterite soil sample. However, at the actual construction site, several factors such as sulfate and flood adversely affect the strength and durability of the soil cement. Consequently, the cement content estimation under a normal circumstance may not be sufficient. This research aimed to investigate the effects of wetting and drying cycles (w-d cycles) and sulfate-soaked cycles on the strength and durability of cement-stabilized laterite. The result indicated that the 4 % cement content is suitable for the laterite stabilized cement. After 12 w-d cycles, the strength loss of the sample soils soaked in water and in sulfate were 44 % and 55 % respectively. The durability index (ID) of both sample soils decreased with the increase in the w-d cycles. For soil cement base soaked in water and sulfate, the durability index should be lower than 0.88 or the weight loss should not exceed 12 %. The results also indicated that for practical propose, the normalized strengths under wet and dry cycles were related by a linear relationship for the range of tested cement content, water content, and curing periods. This relationship is very useful for the calculation of the desired strength from the estimated optimal cement content for the soil cement base mainly focusing on the durability against the effects of w-d cycles soaked in water and sulfate at any repeated cycles.

Spatial simulations of soil content, storage, and quality indices in an archipelago off the Yangtze River Estuary, China

Comprehensively revealing the spatial pattern of soil condition is vital for exploring the evolution mechanism of island ecosystem and providing reference for ecological maintenance. However, it is difficult to meet the demands of comprehensiveness and accuracy in mapping the soil condition in an archipelago that contains remote islands. Such an archipelago, namely, Shengsi Archipelago off the Yangtze River Estuary, China, was selected as the study area. Field survey was conducted on part of islands that have relatively high accessibility in the archipelago, and remote sensing data that cover the entire study area was adopted. An island soil index system, including soil content, storage, and quality indices, was proposed to represent the island soil integrated condition. Soil content indices are original soil measured data, and soil storage and quality indices are composite indices that are determined based on soil content indices. Then, a predictor system based on remote sensing and the 10-fold cross-validation were used to conduct the spatial simulations of soil indices. The results validated that the combination of soil measured data sourced by field survey could respond more sensitively to remote sensing data and integrate with it better than the original soil measured data, thereby increasing the simulation accuracies of soil storage and quality indices to higher levels than the corresponding soil content indices and achieving the spatial exhibition of soil integrated condition. The spatial pattern of soil indices in Shengsi Archipelago indicated that islands or areas with good vegetation condition but low soil salinity, land surface aridity, and human interference generally had good soil integrated condition. Then, suggestions to improve the island soil integrated condition were proposed from perspectives of different essential components of the island ecosystem. The study has provided a practical method for comprehensively mapping the soil condition in areas with low accessibility.

Assessment of Remote Sensing Ecological Quality by Introducing Water and Air Quality Indicators: A Case Study of Wuhan, China

In the context of ecological protection and urban expansion, the quality of the ecological environment and ecological security are gravely at risk. A simple, effective, and comprehensive assessment method for regional environmental quality monitoring is urgently required at this time. This study proposes a comprehensive approach for evaluating regional ecological quality. Based on Landsat TM+OLI/TIRS images, indicators representing the ecological quality of land and water were constructed. Land ecological quality consists of land surface moisture (WET), normalized difference vegetation index (NDVI), normalized building bare soil index (NDBSI), and land surface temperature (LST), which represent humidity, greenness, dryness, and temperature, respectively. At the same time, the remote sensing indices of chlorophyll_a (chl_a) and suspended solids (SS) were constructed to characterize the water quality. Air quality was characterized based on aerosol optical depth (AOD) in MCD19A2. By introducing water and air quality indicators and utilizing principal component analysis, a remote sensing ecological index that improves water area assessment (WIRSEI) was established and applied to Wuhan from 2000 to 2020. The driving force of WIRSEI change was analyzed using the geographically weighted regression (GWR) model. The results revealed that (1) air quality AOD and humidity WET greatly impacted the ecological quality (WIRSEI). WIRSEIs in seven central urban areas were significantly lower than that in six remote urban regions, and the ecological quality of lakes was higher than that of rivers. (2) From 2000 to 2020, Wuhan’s overall WIRSEI showed a “rising–falling–rising–stable” trend. In most regions, the degree of ecological quality change was relatively small; most grades were “no change”, “slightly better”, and “slightly worse”, representing 88–93% of the total area. (3) The change in WIRSEI from 2000 to 2020 was closely related to urban expansion, population change, and economic development. The effects of land use and socioeconomic changes on WIRSEI were significantly different in spatial distribution. Compared to the driving factors, land use dynamics (LUCD) significantly impacted WIRSEI changes, while the effects of gross domestic product (GDP) and population (POP) were very small. This study uses WIRSEI to evaluate the regional ecological quality, providing a vital reference and basis for enhancing regional ecological quality assessment methods, promoting ecological environmental protection and restoration, regional coordination, and sustainable development. The research results show that the proposed approach is simple and effective, strongly supporting regional ecological quality and protection monitoring.

Promotion Effects of Taxus chinensis var. mairei on Camptotheca acuminata Seedling Growth in Interplanting Mode

Wild Camptotheca acuminata Decne (C. acuminata) resources are becoming endangered and face poor growth. Preliminary investigation results found that the growth of C. acuminata in an artificial mixed forest of Taxus chinensis var. mairei (Lemee et Levl.), Cheng et L. K. Fu (T. chinensis var. mairei) and C. acuminata was significantly higher than that in pure forests. Understanding the reasons for the above differences can help create a mixed forest of T. chinensis var. mairei and C. acuminata to solve the problem of depleting C. acuminata resources. In this study, the growth and soil indexes under two different modes (C. acuminata/T. chinensis var. mairei interplanted and monocultured C. acuminata seedlings) were compared. The results showed that plant height, basal diameter, photosynthesis rate and chlorophyll content of C. acuminata under the interplanting mode were higher than those under monoculture. The growth rates of plant height and basal diameter that were calculated from interplanted specimens increased by 25% and 40%, respectively, compared with those from specimens that were monocultured. Photosynthetic rates from different light intensities under interplanting were higher than those in seedlings under monoculture. The contents of chlorophylls a and b and total chlorophyll under interplanting were 1.50, 1.59, and 1.47 times higher than those under monoculture, respectively. The numbers of bacteria and fungi in the interplanted culture were higher than those in the monoculture. Furthermore, the differences in microbial diversity under different planting modes were analyzed via the amplicon sequencing method. Soil enzyme activities increased under interplanting compared with that in the monoculture. Taxane allelochemicals were detected in the range of 0.01–0.67 μg/g in the interplanting mode from April to September. T. chinensis var. mairei may increase the establishment and productivity of C. acuminata seedlings under interplanting mode through improvements in enzyme activity, changes in microorganism population structure, and release of allelochemicals.

Effect of low-density polyethylene, polyvinyl chloride, and high-density polyethylene micro-plastic contamination on the index and engineering properties of clayey soil- an experimental study

Soil health is one of the prominent features of the Sustainable Development Goals (SDGs). The SDGs seek to achieve the target of 75% healthy soils by 2030. On the contrary, the growing accumulation of microplastic contamination in soil environments is an issue of worldwide concern. The effect of a variety of microplastic accumulation on soil index and engineering properties is still unclear. Clayey soil is very important in geotechnical engineering because of its complex behaviour. This study investigates the effects of a variety of microplastics such as Low-Density Polyethylene (LDPE), Polyvinyl Chloride (PVC), and High-Density Polyethylene (HDPE) in environmental relevant concentrations (2%,4%,6% w/w) on index and engineering properties of clayey soil with respect to a number of observation days. In the present study, the non-contaminated soil treated as a control. The detailed experimentation revealed a significant change in the liquid limit, plastic limit, and shear strength values of clayey soil with an increasing percentage of microplastic contamination, whereas minute changes are recorded with respect to the number of observation days, this may be due to inert nature of microplastic. The shear strength is one of the critical engineering properties of soil that determines the strength and stability of the soil. The shear strength value of 0.94 kg/cm2 for control decreased to 0.76 kg/cm2 and 0.73 kg/cm2 and 0.85 kg/cm2 for the addition of 2%, 4%, and 6% respectively of LDPE microplastic contamination, similarly, for PVC microplastic contamination of 2%, 4% the shear strength decreases to 0.77 kg/cm2, 0.85 kg/cm2 respectively, the HDPE microplastic contamination of 2%, 4%, and 6% results in shear strength decrease to 0.52 kg/cm2, 0.53 kg/cm2 and 0.65 kg/cm2.The altered values of shear strength along with other index and engineering properties of soil due to microplastic contamination may lead to instability in structures or may increase the chances of harmful natural threats such as landslides. The present study provides evidence of significant changes in the index and engineering properties and therefore the stability of the soil which is the major contribution of the study.

Evaluating Environmental Quality and Its Driving Force in Northeastern China Using the Remote Sensing Ecological Index

As one of the three major black soil regions in the world, northeastern China has an important strategic position there. Since the 20th century, the local environment has undergone great changes under the influence of the natural economy, and it is particularly important to quantitatively assess the degree of change. However, there have been few long-term quantitative studies of environmental spatial-temporal variances in the three northeastern provinces. Therefore, in this study, four typical remote sensing indices of the normalized difference vegetation index (NDVI), land surface temperature (LST), normalized differential building–soil index (NDBSI) and wetness (WET) were employed to construct the remote sensing ecological index (RSEI) using a principal component analysis (PCA) method based on the Google Earth Engine (GEE) platform in northeastern China. The spatiotemporal variations in the eco-environmental quality were detected using linear slope and M–K test, and the direct and interactive effects of different influencing factors on the RSEI changes during 2000–2020 were explored based on geographic detection. The results show that the interannual variations in the RSEI show a fluctuating upward trend, with an increase percentage of 12.45% in the last two decades, indicating that the ecological quality of northeast China has gradually improved. Furthermore, that the western and eastern Heilongjiang provinces and western Jilin provinces contributed substantially to the improvement of environmental quality, while the environmental quality of Jilin provinces and central Liaoning provinces decreased to varying degrees. Compared with 2000, the area with a fair environmental quality grade had the greatest change, and had decreased by 60.69%. This was followed by the area with an excellent quality grade, which increased by 117%. Land-use type had the greatest impact on environmental changes in northeastern China, but the impact degree gradually decreased, while the impact of socioeconomic factors such as the gross production of agriculture, forestry, animal husbandry and fishery and population density on environmental quality gradually increased. The major reason for the decline of environmental quality in central Jilin and central Liaoning is that urbanization development had occupied a large amount of cropland. This shows that taking into account the virtuous cycle of an ecological environment while promoting urban and rural development may be an important task for northeastern China in the future.

Soil Property, Rather than Climate, Controls Subsoil Carbon Turnover Time in Forest Ecosystems across China

Subsoil (0.2–1 m) organic carbon (C) accounts for the majority of soil organic carbon (SOC), and SOC turnover time (τ, year) is an important index of soil C stability and sequestration capacity. However, the estimation of subsoil τ and the identification of its dominant environmental factors at a regional scale is lacking in regards to forest ecosystems. Therefore, we compiled a dataset with 630 observations to investigate subsoil τ and its influencing factors in forest ecosystems across China using the structural equation model (SEM). The results showed a large variability of subsoil τ from 2.3 to 896.2 years, with a mean (± standard deviation) subsoil τ of 72.4 ± 68.6 years; however, the results of one-way analysis of variance (ANOVA) showed that subsoil τ differed significantly with forest types (p = 0.01), with the slowest subsoil τ obtained in deciduous-broadleaf forests (82.9 ± 68.7 years), followed by evergreen-needleleaf forests (77.6 ± 60.8 years), deciduous-needleleaf forests (75.3 ± 78.6 years), and needleleaf and broadleaf mixed forests (71.3 ± 80.9 years), while the fastest subsoil τ appeared in evergreen-broadleaf forests (59.9 ± 40.7 years). Subsoil τ negatively correlated with the mean annul temperature, occurring about three years faster with a one degree increase in temperature, indicating a faster subsoil SOC turnover under a warming climate. Subsoil τ significantly and positively correlated with microbial activities (indicated by microbial C and nitrogen), highlighting the importance of microbial communities in regulating subsoil C dynamics. Climate, forest types, forest origins, vegetation, and soil variables explained 37% of the variations in subsoil τ, as indicated by the SEM, and the soil property was the most important factor affecting subsoil τ. This finding challenged previous perception that climate was the most important factor driving subsoil C dynamics, and that dominant drivers varied according to climate zones. Therefore, recognizing different dominant factors in predicting subsoil C dynamics across climate zones would improve our understanding and reduce the uncertainties regarding subsoil C dynamics in biogeochemical models under ongoing climate change.

Environmental assessment of soil contamination by trace metals

The main purpose. The purpose of this research is to develop a method for determining the level of heavy metal contamination in the soil. For this end, the following tasks are set: - to develop a method for the integrated assessment of heavy metal contamination in the soil at the regional and local levels; - to identify the most heavy metal contaminated regions of Ukraine; - to estimate the level of hazard of heavy metal contamination in the soil for territories with different types of land use; - to assess the pollution of soils with heavy metals as a result of hostilities in Donetsk and Luhansk regions. Methodology. The method takes into account the soil morphology, genezises, trace metals background concentrations and based on the development of the self-purification index for various soils in different regions of Ukraine. According to the proposed method soils have been classified by trace metals contamination. Results. At the local level, trace metals in soils were assessed for urban recreational areas, highways and roads, landfills and industrial zones. The soils in recreational areas were classified as ‘good’, highways and roads are classified as ‘satisfactory’ and ‘poor’, industrial and landfill was classified as ‘very poor’ according to the trace metals contamination. At the regional level, the highest contamination Class was given to the soils of Donetsk and Luhansk regions due to the highest density of industrial areas and performed military activities. The environmental assessment of trace metals contamination in soils due to the military activity was performed for these regions. The results of trace metals determination in Kharkiv according to this method indicate that the most dangerous areas are domestic waste landfills and facilities of the industrial companies. Recreation zones are ranked in Class 2. and the territory near the highways are assessed in quality Class 2 and 3. Assessment of the hazard level of trace metal contamination in the soil by the proposed method will allow making a scientifically substantiated decision on the priority of implementation of environmental measures. Scientific novelty. The paper presents a new method of assessing soil contamination with heavy metals at the regional and local level. The advantage of this method is taking into account the hazard class of pollutants and the self-cleaning index. A new classification of the level of danger of soil contamination with heavy metals has been developed. Practical significance. The use of the proposed methodological approach will contribute to obtaining comparable data when assessing the level of soil contamination by heavy metals and determining the priority of implementing environmental protection measures and increasing the level of environmental safety, which is especially relevant for the post-war recovery of our country.

Plasticity role in strength behavior of cement-phosphogypsum stabilized soils

Dredged soil and phosphogypsum are frequently regarded as wasted materials, which require further treatment to control their environmental impact. Hence, phosphogypsum is proposed as a binder to stabilize dredged soil, aiming at efficiently reducing and reusing these waste materials. In this study, the engineering properties of cement-phosphogypsum stabilized dredged soils were investigated through a series of unconfined compression tests, and the effects of plasticity index of original soils on the strength improvement were identified. Then, the microstructure test and mineralogical test were performed to understand the mechanism of physical role of original soils in strength improvement. The results revealed that the unconfined compressive strength significantly decreased with the increase in plasticity index at the same binder content. The essential factor for strength improvement was found to be the formation of cementitious materials identified as calcium silicate hydrate (CSH), calcium aluminate hydrate (CAH), and ettringite (Aft). The normalized integrated intensity of cementitious materials (CSH + CAH + Aft) by pore volume decreased with the increase in plasticity index. Consequently, the density of cementitious materials filling the soil pores controlled the effectiveness of strength improvement. More cementitious materials per pore volume were observed for the original soils with lower values of plasticity index. That is, the higher strength of stabilized soils with lower values of plasticity index was attributed to a packed structure forming by integrated fabric through denser cementitious components. It can be anticipated from the above findings that the effectiveness of stabilization treatment will significantly reduce with the increase in plasticity of origin soil. • Phosphogypsum is sufficient to stabilize dredged soil as geological material. • Establishing strength gain – normalized water content reduction relation. • Correlating strength gain with normalized cementitious materials intensity. • Proposing micro-mechanism of plasticity role in strength improvement of stabilized soils.

Drained Shear Strength Parameters Of Saprock From A Weathering Profile Over Porphyritic Biotite Granite At Km 31 Of The Kuala Lumpur - Karak Highway, Peninsular Malaysia

Three broad zones can be differentiated at the weathering profile; an upper, 9.4 m thick, pedological soil (zone I), an intermediate, 31.7 m thick, saprock (zone II) and the bottom bedrock (zone III). The saprock (zone II) comprises gravelly silty sands that distinctly preserve the minerals, textures and structures of the original bedrock and can be separated into sub-zones II A, II B, II C and II D based on differences in preservation of relict structures and content of litho-relicts (core-boulders). To characterize the drained strength of saprock, samples were collected from sub-zones II B, II C and II D, and their physical and soil index properties determined before consolidated, drained triaxial tests were carried out on remolded specimens. Three individual specimens from each sub-zone were consolidated for 24 hours and compressed at a rate of 0.152 mm/min under confining pressures of 138 kPa, 207 kPa and 276 kPa. The tests yielded effective cohesions (c’) of 30.6 kPa, 9.5 kPa, and 20.2 kPa, and friction angles of 33.2o, 31.4o and 34.4o, for the samples from sub-zones II B, II C and II D, respectively. Regression analyses show effective cohesions (c’) to increase with increasing moisture contents retained at 4.19 pF (1,500 kPa) suction; a feature indicating the influence of negative pore water pressures (matric suction). Regression analyses also show effective friction angles to increase with increasing sand, and sand and gravel, contents; a feature indicating increased inter-locking and resistance to displacement of coarse particles during shear. It is concluded that the saprock is characterized by an average effective cohesion of 14.5 kPa, and friction angle of 34.3o; these parameters influenced by the moisture content retained at 1,500 kPa suction, and the sand and gravel contents.

Numerical Modeling for Prediction of Compression Index from Soil Index Properties in Jimma town, Ethiopia

In this study, correlations are developed to predict compression index (Cc) from index parameters so that one can be able to model Jimma soils with compression index using simple laboratory tests. Undisturbed and disturbed soil samples from twelve different locations in Jimma town were collected. Laboratory tests like specific gravity, grain size analysis, Atterberg limit, and one-dimensional consolidation test for a total of twenty-four test samples were conducted. From one-dimensional consolidation tests, compressibility soil parameters (Cc and Cs) are determined. From the results of limited tests, an indicative good correlation is observed between Cc and LL, PL, and PI. However, a Poor correlation is developed between Cc and PL when related to the other parameters. The developed correlations will be important inputs in modeling Jimma clay soils with regression model and Artificial neural networks (ANN) analysis using simple index tests. In addition, the results of this study can serve as a basis for further study of such correlations on different clay soils in the country. In this study, regression analysis was used to explore the significance of individual independent (index) soil properties. Regression model and correlation of compression index for liquid limit, plastic limit, and plasticity index were obtained from the linear regression analysis and ANN. This correlation will be helpful for geotechnical engineers in developing the coefficient of compression (Cc) value of expansive/clay soil from index properties. Finally, based on the general findings of the study, suitable recommendations have been forwarded.

Effects of Mining on Urban Environmental Change: A Case Study of Panzhihua

The environment supplies water, land, biological resources, and climate resources for people’s daily life and development, dramatically affecting the subsistence and development of human beings. Panzhihua City is a representative resource-based industrial city of southwestern China. The abundant mineral resources provide the material basis for the city’s development. However, while the overdevelopment of the past decades has provided the preconditions for its rapid economic growth, it has also inevitably had a huge impact on its environmental quality and land use structure. In this study, the landsat remote sensing images, terrain data, socio-economic data, and mining resources exploitation data of Panzhihua were used to extract the NDVI (Normalized Difference Vegetation Index), NDBSI (Normalized Difference Build and Soil Index) and LST (Land Surface Temperature) of the past 20 years at 5-year intervals. We normalized four indicators by Principal Component Analysis to derive a remote sensing ecological index of each factor and build the Remote Sensing-based Ecological Index evaluation model. This research quantified the changes in environmental quality in the past 20 years through the range method, showing that the environmental quality of Panzhihua City first declined and then increased slowly. This research also analyzed the influence of land use types, terrain, mining area, and socio-economy on the environmental quality of Panzhihua City by grey relational analysis and buffer analysis. It is found that with the influence of its unique topographical factors and economic aspects, the environmental quality of Panzhihua City changed to varying degrees. The results provide a reliable basis for the future environmental planning of Panzhihua City and a reference for the ecological restoration of mining areas with different mineral species accurately.

Estimating the effect of a land parcel index using hedonic price analysis

The purpose of this study was to statistically test the effect of a parcel index – consisting of a combination of a soil index, a fertility index and a location index – intended to be used as a price-determining indicator for the sale of agricultural land at farmland markets. In the hedonic price model, the coefficients of the variables representing parcel index, population, gross return and parcel irrigation investment status were positive and statistically significant at a significance level of 0.01. There was a negative relationship between parcel size and sale price, which implied that the selling price per decare tends to decrease as the parcel size increases. In the study area, the prices of farmland with large parcel sizes and irrigation efficiency investments were higher. The population density in the region and gross income from farmlands were the major factors that generated demand for the land. The hedonic price model establishes an important link between the parcel index and the sale price of farmland. Based on this link, parcel index-based pricing can contribute significantly to the creation of a farmland market in Turkey.

Digital Mapping of Soil Organic Carbon Based on Machine Learning and Regression Kriging.

In the last two decades, machine learning (ML) methods have been widely used in digital soil mapping (DSM), but the regression kriging (RK) model which combines the advantages of the ML and kriging methods has rarely been used in DSM. In addition, due to the limitation of a single-model structure, many ML methods have poor prediction accuracy in undulating terrain areas. In this study, we collected the SOC content of 115 soil samples in a hilly farming area with continuous undulating terrain. According to the theory of soil-forming factors in pedogenesis, we selected 10 topographic indices, 7 vegetation indices, and 2 soil indices as environmental covariates, and according to the law of geographical similarity, we used ML and RK methods to mine the relationship between SOC and environmental covariates to predict the SOC content. Four ensemble models-random forest (RF), Cubist, stochastic gradient boosting (SGB), and Bayesian regularized neural networks (BRNNs)-were used to fit the trend of SOC content, and the simple kriging (SK) method was used to interpolate the residuals of the ensemble models, and then the SOC and residual were superimposed to obtain the RK prediction result. Moreover, the 115 samples were divided into calibration and validation sets at a ratio of 80%, and the tenfold cross-validation method was used to fit the optimal parameters of the model. From the results of four ensemble models: RF performed best in the calibration set (R2c = 0.834) but poorly in the validation set (R2v = 0.362); Cubist had good accuracy and stability in both the calibration and validation sets (R2c = 0.693 and R2v = 0.445); SGB performed poorly (R2c = 0.430 and R2v = 0.336); and BRNN had the lowest accuracy (R2c = 0.323 and R2v = 0.282). The results showed that the R2 of the four RK models in the validation set were 0.718, 0.674, 0.724, and 0.625, respectively. Compared with the ensemble models without superimposed residuals, the prediction accuracy was improved by 0.356, 0.229, 0.388, and 0.343, respectively. In conclusion, Cubist has high prediction accuracy and generalization ability in areas with complex topography, and the RK model can make full use of trends and spatial structural factors that are not easy to mine by ML models, which can effectively improve the prediction accuracy. This provides a reference for soil survey and digital mapping in complex terrain areas.

Mathematical Model for Estimation of Shear Parameters of Alluvial Soils of Kamrup Metro District (Assam, India) from Index Properties

ABSTRACT Projects related to Geotechnical Engineering require thorough ground study to know the soil behaviour thus making it necessary to determine the strength and stability parameters of soil. The soils of Assam are primarily of four types viz., Alluvial (old and new), Piedmont, Hill and Lateritic soils with the alluvium abundantly available in the state. The most important soil parameters are the shear strength parameters, namely, cohesion (c) and angle of internal friction (Φ) which can be obtained using triaxial tests with different drainage conditions. But one triaxial test may take weeks to complete and are very expensive. Moreover, for large projects it is practically preposterous to conduct large number of triaxial tests. Hence, an alternate way is to develop a relation between different parameters which enable us to determine others. The main objective of this study is establishing prediction models that allow the shear parameters to be predicted from soil index properties. For this, three prediction models are developed by combining various soil parameters viz., Liquid Limit (LL), Plastic Limit (PL), Specific Gravity (G) and Optimum Moisture Content (OMC) using Multivariate Regression Analysis (MRA) and Artificial Neural Network (ANN). The results show better performance for ANN than MRA.

An Open Soil Health Assessment Framework Facilitating Sustainable Soil Management.

The crucial role of healthy soil in achieving sustainable food production and environment is increasingly recognized, as is the importance of proper assessment of soil quality. We introduce a new framework, open soil index (OSI), which integrally evaluates soil health of agricultural fields and provides recommendation for farming practices. The OSI is an open-source modular framework in which soil properties, functions, indicators and scores, and management advice are linked hierarchically. Soil health is evaluated with respect to sustainable crop production but can be extended to other ecosystem functions. The OSI leverages the existing knowledge base of agronomic research and routine laboratory data, enabling its application with limited cost. The OSI is a generic framework that can be adopted for specific regions with specific objectives. As a proof of concept, the OSI is implemented for all (>700,000) Dutch agricultural fields and illustrated with 11 pairs ("good" and "poor") of local fields and 32 fields where soil quality and crop yield have been monitored. The OSI produced reasonable evaluation for most pairs when soil physical functions were refined with on-site soil visual assessment. The soil functions are sufficiently independent and yet together reflect complex multidimensionality of soil quality. The framework can facilitate designing sustainable soil management programs by bridging regional targets to field-level actions.

Short‐term effects of organic matter and compaction manipulations on soil temperature, moisture, and soil respiration for 2 years in the Oregon Cascades

AbstractUnderstanding the factors controlling nutrient dynamics can help guide forest management plans to promote their long‐term productivity. We used experimental treatments with three levels of biomass removals and two levels of compaction to monitor the impacts to soil biophysical characteristics in an intensively managed forest. Soil temperature, moisture, and respiration observations began 6 mo after treatment installation completion and continued for 2 yr. Compaction had few consistent significant effects on measured variables, and there were negligible differences in volumetric soil water content between whole tree (WT) and bole only (BO) harvesting. Compared with BO, the 10‐cm average and maximum growing season temperatures in WT significantly increased by 1.2 and 2.5 °C, respectively. The effects of WT removals resulted in whole profile (10–100 cm) increases in the average and maximum growing season soil temperatures. The WT removals resulted in an increase of 1.4 times more soil growing degree days (SGDD) at 10 cm and 1.6 times more at 100 cm compared with BO. Despite favorable temperature and moisture conditions, differences in soil respiration could not be explained by biomass or compaction treatments. The uncut reference forest was consistently cooler and drier, but respired more CO2 throughout both years of observation compared with treated areas. The large physical disturbance of forest harvesting on the site likely masked any incremental treatment differences by homogenizing the microbial response in the ensuing 2‐yr study period. Future research should continue to investigate whether these soil biophysical changes influence site productivity or more sensitive indices of soil C dynamics.