Soil Properties Research Articles

Biochars Induced Changes in CO2 Evolution and Biochemical Properties of an Alkaline Subtropical soil

Biochars Induced Changes in CO2 Evolution and Biochemical Properties of an Alkaline Subtropical soil

Research on the Mechanical Properties of EPS Lightweight Soil Mixed with Fly Ash.

Expanded polystyrene (EPS) bead-lightweight soil composites are a new type of artificial geotechnical material with low density and high strength. We applied EPS bead-lightweight soil in this project, replacing partial cement with fly ash to reduce construction costs. EPS beads were used as a lightweight material and cement and fly ash as curing agents in the raw soil were used to make EPS lightweight soil mixed with fly ash. The EPS bead proportions were 0.5%, 1%, 1.5%, and 2%; the total curing agent contents were 10%, 15%, 20%, and 25%; and the proportions of fly ash replacing cement were 0%, 15%, 30%, 45%, and 60%, respectively. Unconfined compressive strength (UCS) and scanning electron microscopy (SEM) tests were conducted. The results showed that the EPS content, total curing agent content, and proportion of fly ash replacing cement had a significant impact on the UCS of the lightweight soil. This decreased with an increase in EPS content and decrease in total curing agent content and decreased with increased proportions of fly ash replacing cement. When the proportion of fly ash replacing cement was not too high, the strength of the lightweight soil decreased less, and its performance still met engineering needs. At the same time, the soil can also consume fly ash and reduce environmental pollution. EPS lightweight soil mixed with fly ash still has advantages, and it is recommended to keep the proportion of fly ash replacing cement less than 30%. The failure patterns for lightweight soil mainly include splitting failure, oblique shear failure, and bulging failure, which are related to the material mix ratio.

Laboratory and field monitoring tests of volcanic soil (Ta-d) triggering landslides in the 2018 Hokkaido Eastern Iburi earthquake

BackgroundThe 2018 Hokkaido Eastern Iburi Earthquake triggered serious geodisasters, resulting in several landslides in volcanic soils depending on their geological features. However, there is limited investigation from the geotechnical viewpoint. Considering various volcanic soils are deposited in Hokkaido, Japan, it is crucial to ensure disaster prevention of infrastructures related to volcanic soils.MethodsTo investigate the degree of weathering, water-retention characteristics, and mechanical properties of the volcanic soil, which triggered landslides during the earthquake, called Ta-d, this study conducted laboratory tests including X-ray diffraction, water-retention, and direct shear tests under various conditions related to a type of Ta-d, saturation condition, and stress dependency. Moreover, the pore pressure of the location where the landslides occurred was monitored for over a year to investigate the effect of rainfall on the previous day of the earthquake on the landslides.ResultsThe laboratory and field monitoring test results showed that Ta-d can be categorized into three types depending on the color and physical properties, which have different degrees of weathering and shear strengths. The water content of Ta-d was high (>100 %) throughout the year, whereas it exhibited a seasonal change due to snowfall, which covered the ground surface. Furthermore, fluctuations caused by the seasonal changes are more significant than those caused by rainfall, which indicated that the rainfall on the previous day of the earthquake was not a primary factor in the occurrence of the landslidesConclusionsThis study reveals the geotechnical properties of Ta-d, which has not been well known, as comparing with those of other Hokkaido volcanic soils, and gives insights into the significant factors that can potentially cause the earthquake-induced geodisasters.

Dynamic interaction of antibiotic resistance between plant microbiome and organic fertilizers: sources, dissemination, and health risks.

Antibiotic resistance is a global health problem driven by the irrational use of antibiotics in different areas (such as agriculture, animal farming, and human healthcare). Sub-lethal concentrations of antibiotic residues impose selective pressure on environmental, plant-associated, and human microbiome leading to the emergence of antibiotic-resistant bacteria (ARB). This review summarizes all sources of antibiotic resistance in agricultural soils (including manure, sewage sludge, wastewater, hospitals/pharmaceutical industry, and bioinoculants). The factors (such as the physicochemical properties of soil, root exudates, concentration of antibiotic exposure, and heavy metals) that facilitate the transmission of resistance in plant microbiomes are discussed. Potential solutions for effective measures and control of antibiotic resistance in the environment are also hypothesized. Manure exhibits the highest antibiotics load, followed by hospital and municipal WW. Chlortetracycline, tetracycline, and sulfadiazine have the highest concentrations in the manure. Antibiotic resistance from organic fertilizers is transmitted to the plant microbiome via horizontal gene transfer (HGT). Plant microbiomes serve as transmission routes of ARB and ARGS to humans. The ingestion of ARB leads to human health risks (such as ineffectiveness of medication, increased morbidity, and mortality).

Residual Effect of Organics and Humic Acid on Physical, Chemical and Biological Property of Soil after Harvest of Succeeding Chickpea

A field experiment was conducted at Agronomy Instructional Farm, Department of Agronomy, C. P. College of Agriculture, SDAU, Sardarkrushinagar to study the effect of organics and humic acid on kharif pearlmillet and their residual effect on succeeding chickpea during kharif- 2022 to 2023 and rabi 2022-23 to 2023-24. The soil of the experimental plot was loamy sand in texture, alkaline in reaction, normal in salinity, low in organic carbon, available N, medium in available P2O5 and K2O and low DTPA- extractable Fe, Mn, Zn and Cu. The treatments were applied in kharif pearlmillet crop and their residual effect was studied in succeeding chickpea crop. Residual effect of FYM @ 10 t ha-1 recorded significantly increased OC (0.298, 0.302 and 0.300 per cent), available N (177.28, 180.10 and 178.69 kg ha-1), P2O5 (41.20, 42.04 and41.62 kg ha-1) was observed during both the year and pooled result and total bacterial count (156.26 cfu g soil-1) in pooled result soil with the application of FYM @ 10 t ha-1. While application of humic acid did not get significant result and but numerically increased above mention parameter under the soil application of humic acid 30 kg ha-1.

A Study on the Effect of Graphene Oxide on Geotechnical Properties of Soil.

Edge-oxidized graphene oxide (EOGO) is a nano-sized material that is chemically stable and easily mixed with water due to its hydrophilic properties; thus, it has been used in various engineering fields, particularly for the reinforcement of building and construction materials. In this study, the effect of EOGO in soil reinforcement was investigated. When mixed with soil, it affects the mechanical properties of the soil-GO mixture. Various amounts of the GO (0%, 0.02%, 0.06%, 0.1%) were added into the sand-clay mixture, and their geotechnical properties were evaluated via multiple laboratories testing methods, including a standard Proctor test, direct shear test, compressibility test, and contact angle measurement. The experimental results show that with the addition of EOGO in soil of up to 0.06% EOGO, the compressibility decreases, the shear strength increases, and the maximum dry density (after compaction) increases.

Soil health assessment of croplands adjacent to industrial areas in Coimbatore district, Tamil Nadu

Soil health is a vital determinant of agricultural productivity and the supply of essential ecosystem services, including water retention, nutrient cycling and the support of microbial communities. However, industrial activities near croplands can adversely affect soil health through contamination by pollutants such as heavy metals, chemicals and waste materials. This study evaluates the soil health of agricultural lands adjacent to industrial areas in Coimbatore district, Tamil Nadu, with an emphasis on physical, chemical and biological soil properties. A total of 128 soil samples were collected and categorized into three groups: Primary Impact Sites (PIS), Secondary Impact Sites (SIS) and Reference Soils (RS). The analysis revealed that soils in proximity to industrial zones exhibited significant degradation in key indicators, such as bulk density, water holding capacity (WHC), organic carbon (OC) content and microbial activity. Soils at Primary Impact Sites demonstrated the most pronounced decline, characterized by higher bulk density, reduced WHC and lower OC levels, indicative of poor structure of soil and diminished fertility. In contrast, soils at Secondary Impact Sites and Reference Soils exhibited better overall quality, with higher microbial activity and enhanced nutrient retention. The findings of this study emphasize the indirect effects of industrialization on agricultural productivity through soil degradation.

Spatial extension of soil water regime variables derived from soil moisture values using geomorphological variables in Hungary

Accurate measurement and spatial extension of soil properties are essential in geoinformatics and precision agriculture for effective resource management, particularly irrigation planning. This study addresses the challenge of extending soil moisture data and related soil water regime variables in heterogeneous agricultural landscapes by integrating geomorphological variables (GVs) derived from high-resolution digital elevation models (DEM). In digital soil mapping, machine learning and geostatistical models often struggle with validation due to data scarcity and variability across space through many geographical regions that come from the point readings of soil properties. A different approach was developed in the form of a new methodology combining two hourly Sentek soil moisture measurements from the topsoil with DEM-derived GVs to model and extend soil water regime variables. The research was conducted on an agricultural field in a hilly area with diverse geomorphological variability. The model’s performance was validated using cross-validation techniques. The monitoring and spatial extension results indicate that GVs enhance the spatial prediction of soil moisture, capturing periodic fluctuations in the upper soil layer more effectively by using in-situ, time series soil moisture sensor readings rather than traditional, on field, one time reading approaches. We observed that certain GVs, such as the slope, both type of curvatures and the convergence, were strong predictors of soil moisture variation, enabling the model to produce more accurate irrigation recommendations for agricultural areas with similar geomorphological areas. One of the soil water regime variables was validated during the preliminary validation with mixed results. The main issue was coming from the field use and spatial scarcity of the measurements. Our approach not only provides a different method for spatially extending the current soil water regime data but also offers a framework for improving irrigation decision-making with the help of other value rates and limit related soil regime variables derived from the time series readings from the soil moisture sensors. With its variables, the model allows for forecasts of soil moisture changes, which can inform better irrigation scheduling and water resource management, all based on data from the soil monitoring sensor system.

Impact of Biochar Aging on Soil Physicochemical Properties

Biochar undergoes significant transformations in soil as a result of chemical, physical, and biological processes. These alterations can impact its initial properties, influencing both its agronomic effectiveness and its capacity for carbon sequestration. Long-term observations of biochar-aging effects in soil are limited but highly relevant, as they provide a more realistic picture of the agronomic and societal benefits of biochar than short-term studies with relatively “fresh” biochar. This study aimed to describe the aging effects of biochar and their impact on a range of soil properties at a long-term biochar experiment in Bayreuth, Germany. For this purpose, soil and biochar samples were taken 13 years after application (two variants: 1. co-composted and 2. pristine biochar) and compared with a fresh variant in which the same unaged biochar was freshly mixed with the control soil. The soil quality parameters, pH and electrical conductivity, decreased significantly (p < 0.05) during biochar aging. Specifically, the pH dropped from 7.4 in freshly biochar-amended soil to 6.8 in the pristine aged biochar variant and 6.9 in the co-composted aged biochar variant. Electrical conductivity decreased from 217.0 µS cm−1 in the freshly amended soil to 81.1 µS cm−1 in the pristine aged variant and 87.6 µS cm−1 in the co-composted aged variant. Nitrogen retention was enhanced in the soil amended with co-composted aged biochar compared to the pristine aged biochar soil. Total nitrogen (TN) was higher at 1.94 g kg−1 versus 1.57 g kg−1 (p < 0.05), and ammonium-N (NH₄+-N) was slightly elevated at 35.7 mg kg−1 versus 33.0 mg kg−1, although the difference was not statistically significant. The nitrate-N (NO₃−-N) content was significantly lower in all biochar-amended soil variants compared to the control soil. Total carbon (TC) levels decreased during biochar aging in all soil variants. However, the reduction was significantly lower in the co-composted aged biochar soil (25.0 g kg−1) compared to the pristine aged biochar soil (20.5 g kg−1, p < 0.05). This study identified multiple aging effects on biochar following 13 years of exposure in loamy soil. Importantly, the results showed that compared to the amendment of pristine biochar, co-composting did not diminish the TC of the treated soil, and more N could be retained, 13 years after amendment. In fact, co-composting prior to soil application is recommended to fully realize the potential agronomic benefits.

Delimitación de zonas de gestión del riego en el cultivo del banano mediante imágenes de satélite y atributos físicos-químicos del suelo

In banana plantations, irrigation is managed in a homogeneous way, which is inadequate due to the variability of the soil in different areas, leading to significant losses in productivity. To address this issue, the delimitation of agricultural management zones (AMZ) was proposed, based on the spatial variability of the physical and chemical soil attributes, along with information obtained from spectral indices derived from satellite imagery. Additionally, the soil-climate-plant relationship was considered to improve the accuracy and reliability of the information. For this purpose, Sentinel-2 satellite images were processed, and various spectral indices were calculated using the Sen2R package. These indices allowed the generation of AMZ in QGIS using the Smart Map plugin. The satellite images facilitated the delimitation of homogeneous zones based on spectral information. Through a correlation matrix between the mean values of physical and chemical soil variables and the spectral indices per hectare, a correlation was identified between the water stress index and factors such as sand content, electrical conductivity, soil texture class, and available water. The geospatial analysis allowed for the accurate delimitation of irrigation zones, compared to those defined solely by the physical and chemical properties of the soil. The vegetation’s response to soil characteristics, such as water retention capacity, cation exchange, and base assimilation in the soil, demonstrated the effectiveness of this delimitation.

Mapping Soil Properties in Tropical Rainforest Regions Using Integrated UAV-Based Hyperspectral Images and LiDAR Points

For tropical rainforest regions with dense vegetation cover, the development of effective large-scale soil mapping methods is crucial to improve soil management practices to replace the time-consuming and laborious conventional approaches. While machine learning (ML) algorithms demonstrate superior predictability of soil properties over linear models, their practical and automated application for predicting soil properties using remote sensing data requires further assessment. Therefore, this study aims to integrate Unmanned Aerial Vehicles (UAVs)-based hyperspectral images and Light Detection and Ranging (LiDAR) points to predict the soil properties indirectly in two tropical rainforest mountains (Diaoluo and Limu) in Hainan Province, China. A total of 175 features, including texture features, vegetation indices, and forest parameters, were extracted from two study sites. Six ML models, Partial Least Squares Regression (PLSR), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Decision Trees (GBDT), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP), were constructed to predict soil properties, including soil acidity (pH), total nitrogen (TN), soil organic carbon (SOC), and total phosphorus (TP). To enhance model performance, a Bayesian optimization algorithm (BOA) was introduced to obtain optimal model hyperparameters. The results showed that compared with the default parameter tuning method, BOA always improved models’ performances in predicting soil properties, achieving average R2 improvements of 202.93%, 121.48%, 8.90%, and 38.41% for soil pH, SOC, TN, and TP, respectively. In general, BOA effectively determined the complex interactions between hyperparameters and prediction features, leading to an improved model performance of ML methods compared to default parameter tuning models. The GBDT model generally outperformed other ML methods in predicting the soil pH and TN, while the XGBoost model achieved the highest prediction accuracy for SOC and TP. The fusion of hyperspectral images and LiDAR data resulted in better prediction of soil properties compared to using each single data source. The models utilizing the integration of features derived from hyperspectral images and LiDAR data outperformed those relying on one single data source. In summary, this study highlights the promising combination of UAV-based hyperspectral images with LiDAR data points to advance digital soil property mapping in forested areas, achieving large-scale soil management and monitoring.

Effects of Seven-Year-Optimized Irrigation and Nitrogen Management on Dynamics of Soil Organic Nitrogen Fractions, Soil Properties, and Crop Growth in Greenhouse Production

Exploring the temporal evolution dynamics of different soil organic nitrogen (N) components under different water–N management practices is a useful approach to accurately assessing N supply and soil fertility. This information can provide a scientific basis for precise water and N management methods for greenhouse vegetable production. The objective of this study was to investigate the effects of optimized irrigation and nitrogen management on the dynamics of soil organic nitrogen fractions, soil properties, and crop growth. This research was conducted from 2017 to 2023 in a greenhouse vegetable field in North China. Four treatments were applied: (1) high chemical N application with furrow irrigation (farmers’ practice, FP); (2) no chemical N application with drip irrigation (DN0); (3) 50% N of FP with drip irrigation (DN1); and (4) 75% N of FP with drip irrigation (DN2). The volume in drip irrigation is 70% of that in furrow irrigation. The results showed that in 2023 (after seven years of field trials), compared with FP, the soil organic carbon (SOC), total N, and water use efficiency of the DN1 and DN2 treatments increased by 15.9%, 11.4%, and 11.3% and 7.7%, 47.2% and 44.6%, respectively. However, there was no significant difference in the total crop yield except in the DN0 treatment. Soil organic N was mostly in the form of acid-hydrolyzed N (AHN). After seven years of optimized irrigation and N management, the DN1 treatment significantly increased the content of ammonium N (AN) and amino sugar N (ASN) in AHN compared with the FP treatment. The results of further analysis demonstrated that SOC was the main factor in regulating AHN and non-hydrolyzable N (NHN), while the main regulatory factors for amino acid N (AAN) and ASN in the AHN component were dry biomass and water use efficiency, respectively. From a time scale perspective, optimization of the water and N scheduling, especially in DN1 (reducing the total irrigation volume by 30% and the amount of N applied by 50%), is crucial for the sustainable improvement of soil fertility and the maintenance of vegetable production.

Estimating Tetrachloroethene Sorption Coefficients Based on Soil Properties in Organic-Poor Soils

In the context of contaminated site remediation, the fate of chlorinated solvents in the subsurface and subsequent groundwater contamination is influenced by soil properties governing sorption. The solid–water distribution coefficient (Kd) is a key parameter for modeling contaminant distribution and transport, essential for risk assessment and remediation planning. This study evaluated tetrachloroethene sorption isotherms in 34 low-organic-carbon soils from the Czech Republic, assessing the influence of soil properties on Kd. Soil samples exhibited variability in organic carbon content (˂0.05–0.81%), with clay ranging from 0% to 64.9%, silt 5.1% to 71.2%, and sand 5.2% to 88.9%, specific surface area (0.41–64.39 m2 g−1), particle density (2.05–4.09 g cm−3), and porosity (43.5–67.3%). Batch experiments were conducted using standard procedures, with Kd values ranging from 0.379 to 2.272 L kg−1. Statistical analysis grouped the soils into three textural classes: sandy, clayey fine, and silty loam. The findings reveal that organic carbon content and specific surface area are the primary predictors of Kd, while clay and sand also play a significant role in shaping sorption behavior. Multivariate regression models explained 63.6% to 98.5% of Kd variability with high accuracy, as indicated by low root means square error (0.070–0.329) and mean absolute percentage error (3.8–28.8%) values. These models offer reliable predictions of sorption behavior, providing valuable tools for risk assessment and remediation strategies.

How Biochar Addition Affects Denitrification and the Microbial Electron Transport System (ETSA): A Meta-Analysis Based on a Global Scale

Biochar has a significant effect on denitrification, especially in agriculture. The effects of biochar and soil properties on denitrification and ETSA have been invested in individual studies but have not yet been summarized on a global scale. We conducted a meta-analysis of the data from 37 studies to examine the effects of biochar properties, soil physicochemical properties, and ecosystem types on denitrification. Biochar can decrease soil NO3− and N2O emissions by 14.16% and 76.69%, respectively, while denitrification function genes nirK, nirS, and nosZ increased by 10.98%, 34.62%, and 13.19%, respectively. Biochar enhanced ETSA by 8.65%. The results indicate that the effects of biochar on nitrogen cycling and greenhouse gas emissions vary significantly with specific properties, such as feedstock source and pyrolysis temperature, as well as soil characteristics like pH, organic matter, and cation exchange capacity (CEC). In summary, soil-specific biochar applications are necessary to realize optimized agricultural and environmental advantages of biochar, but several limitations have been recognized in this study, including variability across different types of biochar and a lack of longer-term experimental data. Future research should focus on long-term studies that can give a comprehensive understanding of how biochar interacts with microbial communities to create an accurate understanding.

Prediction of Soil Colour using Vis-NIR Spectroscopy and Machine Learning Models

Soil colour is a critical indicator of soil properties and conditions, influencing various agronomic and environmental factors. A total of 2216 surface soil samples (0-15 cm) were collected from the Kymore Plateau and Satpura Hill zone of Madhya Pradesh, using Global Positioning System (GPS) for precise location. Soil colour parameters were measured in the field using the Munsell soil colour chart, while chemical analysis was conducted in the laboratory following standard procedures. Additionally, spectra of the soil samples were recorded using a spectroradiometer under laboratory conditions. The results showed that the soil colour hues ranged from 10R, 10YR, 2.5Y, 2.5YR, 5Y, 5R, 5YR, to 7.5YR, Values and Chroma varied from 2 to 7 and 1 to 8, respectively. Correlation analysis revealed negative correlations between the RGB components and organic carbon, with r values of -0.114**, -0.071**, and -0.101* for R, G, and B, respectively. Polynomial models showed the best fit for the relationship between the value and chroma of the colour parameters and soil organic carbon (SOC), with equations Y = 0.086x² - 0.860x + 7.528 (R² = 0.982) and Y = 0.018x² - 0.249x + 6.126 (R² = 0.948), respectively. A linear relationship was observed between chroma and available phosphorus (P), with the equation Y = -0.873 + 13.92 (R² = 0.922). In addition, machine learning models, including PLSR, SVM, Random Forest, ANN, XGBoost, LightGBM, CatBoost, and ELM algorithms, were used to predict soil colour parameters. Among these, the Random Forest and XGBoost models demonstrated the best performance in predicting soil colour parameters (L*, a*, b*, R, G, and B), with model accuracies of 83.6%, 80.9%, 83.0%, 84.3%, 83.7%, and 83.4%, respectively. soil colour variation depicted in the maps generated using GIS can also serve as covariates for mapping, offering comprehensive insights into the soil's properties.

Date Palm Waste-Derived Biochar for Improving Hydrological Properties of Sandy Soil Under Saturated and Unsaturated Conditions

Water conservation and effective irrigation management are vital for sustainable agriculture in arid regions. While organic soil amendments have been widely used to enhance water retention in sandy soils, research on the use of date palm waste-derived biochar remains limited. Thus, this study aimed to explore the innovative application of biochar produced from date palm waste, focusing on its effects on the hydrological properties of sandy soil. Biochars of varying particle sizes (0.5, 1, and 2 mm) and pyrolysis temperatures (300 °C, 450 °C, and 600 °C) were produced and their impacts were assessed under both saturated and unsaturated conditions on soil hydrological properties. The biochar was incorporated into soil columns at application rates of 0%, 1%, 3%, and 5% (w/w) within a 10 cm layer on top of 35 cm deep soil columns. The soil columns were placed vertically into water basins for saturation. Evaporation, infiltration, and saturated hydraulic conductivity were measured. The findings revealed that the application of 1%, 3%, and 5% biochar significantly increased soil water retention by 36.80%, 34.18%, and 29.66%, while cumulative evaporation decreased by 7.30%, 2.00%, and 1.35%, respectively, as compared to the control. Water retained at the end of the experiment was increased by 100.63%, 112.29%, and 101.68%, while unsaturated hydraulic conductivity decreased by 21.27%, 26.15%, and 26.17% after amending the soil with 1%, 3%, and 5% biochar, respectively, as compared to the control. The water retention ranged between 30.34 and 42.51%, 22.59 and 43.20%, and 22.48 and 38.81% for biochar produced at 300 °C, 450 °C, and 600 °C, respectively. Water infiltration rate and pore size was decreased with the increased pyrolysis temperature. Overall, the application rates of 3% and 5% with particle sizes of 1 and 0.5 mm and low pyrolysis temperature were most efficient for improving soil properties such as water retention, reducing unsaturated hydraulic conductivity, reducing the rate and volume of infiltration, and enhancing the micro-porosity reduction of sandy soils. In a nutshell, this study highlights the potential of date palm waste-derived biochar as an effective soil amendment, significantly enhancing water retention by up to 112.29% and reducing evaporation. By optimizing irrigation management in sandy soils, these findings contribute to more sustainable agricultural practices.

Field Application of Mycorrhizal Inoculant Influences Growth, Nutrition, and Physiological Parameters of Corn Plants and Affects Soil Microbiological Attributes

Mycorrhizal inoculants can contribute to the development of corn crops by improving crop productivity. In this sense, the objective of this study was to evaluate the effects of a mycorrhizal inoculant on the dynamics of root system growth, gas exchange, corn crop productivity, and microbial activity in the rhizospheric soil in a no-till area with different levels of available soil phosphorus. The experiment was conducted during the 2019/2020 and 2020/2021 growing seasons. At 75 days after plant emergence, root morphological parameters (total root length (cm), average root diameter (mm), root surface area (cm2), and root volume), shoot biomass production, P content in the plant shoots, gas exchange, and microbiological attributes of the rhizospheric soil of corn were evaluated. At the end of the cycle, corn grain yield was determined. A beneficial effect of AMF inoculation was observed on the root and shoot parameters regardless of soil P level. Under conditions of evenly distributed rainfall during the experiment (2019/2020 season), AMF inoculation contributed to a 90% increase in acid phosphatase activity and a 76% increase in microbial biomass carbon (C-BIO), independent of soil P level. In contrast, under water deficit conditions (2020/2021 season), AMF inoculation provided a 29% increase in grain yield. We concluded that introducing a commercial mycorrhizal inoculant in corn benefits root system morphological parameters and physiological traits, and favors the activity of enzymes related to increased P availability, contributing to increased crop productivity in a no-till system.

Enhancing Soil Properties and Bacterial Community Dynamics in Tea Plantations through Intercropping Tea Trees and Dictyophora indusiata

Enhancing Soil Properties and Bacterial Community Dynamics in Tea Plantations through Intercropping Tea Trees and Dictyophora indusiata

Explainable Machine Learning to Map the Impact of Weather and Soil on Wheat Yield and Revenue Across the Eastern Australian Grain Belt

Understanding the causes of spatiotemporal variation in crop yields across large areas is important in closing yield gaps and producing more food for the growing global population. While there has been much focus on using data-driven models to predict crop yield, there is also an opportunity to use these empirical models to understand which factors are driving variations in yield and to quantify their contributions. This study uses a large database of 625 rainfed wheat yield maps from 14 different seasons (2007–2020) across the eastern grain belt of Australia. XGBoost models were used, with predictors including maps of soil attributes (e.g., pH and sodicity), along with weather indices (rainfall, frost, heat, growing degree days). The model and predictors could accurately predict field-scale yield, with a Lin’s concordance correlation coefficient (LCCC) of 0.78 with 10-fold cross-validation. SHapley Additive exPlanation (SHAP), a form of interpretive machine learning (IML), values were then used to assess the impact of the variables on yield. The SHAP values for each predictor were also mapped onto a grid of the study area for the 2020 season, which showed the impact of each predictor on wheat yield (t ha−1) and revenue (AUD ($) ha−1) in interpretable units. Weather variables, such as rainfall and heat events, had the largest impact on yield. Although generally less significant, soil constraints such as soil sodicity were still important in driving yield. The results also showed that despite their largely temporally stable nature, soil constraints impact yield differently, depending on seasonal conditions. Overall, data-driven models and IML proved valuable in understanding the impact of important weather and soil variables on wheat yield and revenue across the eastern Australian grain belt. This could be used to determine the magnitude and economic impact of soil constraints and extreme weather on crops across regions and to inform policies and farm management decisions.

Probabilistic Assessment of Soil–Rock Mixture Slope Failure Considering Two‐Phase Rotated Anisotropy Random Fields

ABSTRACTSoil–rock mixture (SRM) slopes consist of soils and rocks and are widely distributed globally. In addition to heterogeneity and discontinuity within SRM slopes, the inherent spatial variability can be observed in soil and rock properties. However, spatial variability in rock and soil properties and layouts has not been well considered in the stability analysis of SRM slopes. Additionally, SRM slopes commonly show a rotated anisotropic fabric pattern, while such fabric has rarely been accounted for in SRM slope stability analysis. In this study, a two‐phase rotated anisotropy random field simulation method is proposed to model these spatial variations simultaneously. The proposed approach is then integrated with the finite element method (FEM) to study the impacts of soil volume fraction and bedding dip angle (i.e., rotated anisotropy) on the probability of failure (pf) and failure mode of SRM slopes. It is found that considering only spatially varying layouts can underestimate pf by up to 97% compared to considering both spatially variable properties and layouts. The increase in soil volume fraction significantly improves pf and the likelihood of deep failure. The bedding dip angle greatly influences pf, yet deep failure remains dominant across different bedding dip angles. Furthermore, the failure mode of SRM slopes is more sensitive to the changes in soil volume fraction than to bedding dip angle.