Measured Soil Properties Research Articles

Scaling up taxon-specific microbial traits to predict community-level microbial activity in agricultural systems

Soil microorganisms perform many important ecosystem functions including nitrogen (N) cycling which dictates plant productivity in agricultural ecosystems. Despite the importance of these communities, connecting microbial composition with ecosystem function has been a long standing challenge. Taxon-specific substrate assimilation traits, measured with quantitative stable isotope probing (qSIP), may provide a means to scale from microbial community composition to community-level process rates. To test the potential for scaling up taxon-specific N assimilation to predict community-level rates of carbon mineralization, N mineralization, and N immobilization we measured soil properties, microbial activity, and N assimilation using 15N qSIP in soils from six distinct farms. N assimilation, measured as DNA 15N enrichment, varied among taxa and within taxa across farms. Taxon specific N assimilation was aggregated to calculate a community-weighted mean, which when combined with measures of microbial biomass was used to estimate new microbial biomass N production. This estimate of new microbial biomass production reflects the growth of active microbes over the incubation period and related to microbial activity. The new microbial biomass N produced was predictive of soil C and N mineralization rates, explaining 37-47% of the observed variation across the six farming systems. This approach highlights the ability of trait-based methods to relate microbial community structure data to microbially-mediated functional process rates. Such advances may enhance our ability to understand and manage microbially mediated processes, such as N cycling, in both natural and agricultural ecosystems.

Compacted Soil Liners and Covers for Waste Containment

An effective waste disposal system is essential in preventing the infiltration of leachate into the ground and achieving the sustainable development goal of ensuring access to clean and potable water. Liner is an important component of landfill in waste disposal facilities as it impedes the flow of leachate from the landfill. The choice of liner material depends on factors, such as the type and availability of soil, the site conditions, sustainability, and cost-effectiveness. This paper focuses on different soil types as compacted liners in waste containment facilities. The main purpose of this paper is to present the findings of laboratory investigations and measurements of hydraulic conductivity and other important soil properties extracted from previous literature with the aim of providing a guide in material selection for use as compacted soil liners and covers in waste containment applications. The key results of many laboratory studies previously conducted on soils of different properties, gradation, plasticity, and mineralogy, untreated or stabilized with various additives such as waste materials from industries, agricultural by-products and municipal waste materials are reported. The paper also presents the potential of using biopolymer-treated soils (soils modified by polymers) derived from plants and animals, as potential liner materials.

Significant Differences in Microbial Soil Properties, Stoichiometry and Tree Growth Occurred within 15 Years after Afforestation on Different Parent Material.

The mineralogical composition of the parent material, together with plant species and soil microorganisms, constitutes the foundational components of an ecosystem's energy cycle. Afforestation in arid-semi arid regions plays a crucial role in preventing erosion and enhancing soil quality, offering significant economic and ecological benefits. This study evaluated the effects of afforestation and different parent materials on the physicochemical and microbiological properties of soils, including microbial basal respiration (MR), as well as how these changes in soil properties after 15 years influence plant growth. For this purpose, various soil physicochemical parameters, MR, soil microbial biomass carbon (Cmic), stoichiometry (microbial quotient = Cmic/Corg = qMic and metabolic quotient = MR/Cmic = qCO2), and tree growth metrics such as height and diameter were measured. The results indicated that when the physicochemical and microbiological properties of soils from different bedrock types, along with the average values of tree growth parameters, were analyzed, afforestation areas with limestone bedrock performed better than those with andesite bedrock. Notably, sensitive microbial properties, such as Cmic, MR, and qMic, were positively influenced by afforestation. The highest values of Cmic (323 μg C g-1) and MR (1.3 CO2-C g-1 h-1) were recorded in soils derived from limestone. In contrast, the highest qCO2 was observed in the control plots of soils with andesite parent material (7.14). Considering all the measured soil properties, the samples can be ranked in the following order: limestone sample (LS) > andesite sample (AS) > limestone control (LC) > andesite control (AC). Similarly, considering measured plant growth parameters were ranked as LS > AS. As a result, the higher plant growth capacity and carbon retention of limestone soil indicate that it has high microbial biomass and microbial activity. This study emphasizes the importance of selecting suitable parent material and understanding soil properties to optimize future afforestation efforts on bare lands.

Spatiotemporal patterns of greenhouse gas fluxes in the subtropical wetland ecosystem of Indian Himalayan foothill.

The study characterized the temporal and spatial variability in greenhouse gas (GHG) fluxes (CO2, CH4, and N2O) between December 2020 and November 2021 and their regulating drivers in the subtropical wetland of the Indian Himalayan foothill. Five distinct habitats (M1-sloppy surface at swamp forest, M2-plain surface at swamp forest, M3-swamp surface with small grasses, M4-marshy land with dense macrophytes, and M5-marshy land with sparse macrophytes) were studied. We conducted in situ measurements of GHG fluxes, microclimate (AT, ST, and SMC(v/v)), and soil properties (pH, EC, N, P, K, and SOC) in triplicates in all the habitat types. Across the habitats, CO2, CH4, and N2O fluxes ranged from 125 to 536mgm-2h-1, 0.32 to 28.4mgm-2h-1, and 0.16 to 3.14mgm-2h-1, respectively. The habitats (M3 and M5) exhibited higher GHG fluxes than the others. The CH4 flux followed the summer > autumn > spring > winter hierarchy. However, CO2 and N2O fluxes followed the summer > spring > autumn > winter. CO2 fluxes were primarily governed by ST and SOC. However, CH4 and N2O fluxes were mainly regulated by ST and SMC(v/v) across the habitats. In the case of N2O fluxes, soil P and EC also played a crucial role across the habitats. AT was a universal driver controlling all GHG fluxes across the habitats. The results emphasize that long-term GHG flux monitoring in sub-tropical Himalayan Wetlands has become imperative to accurately predict the near-future GHG fluxes and their changing nature with the ongoing climate change.

Soil hydraulic property maps for the contiguous United States at 100‐m resolution and seven depths: Code design and preliminary results

AbstractEstimates of the van Genuchten (1980, abbreviated as VG) parameters and saturated hydraulic conductivity (Ks) were made for the contiguous United States at a resolution of 100 m and seven soil depths by combining the SoilGrids+ (SG+) soil property maps of Ramcharan et al. with the R3H3 member of the Rosetta3 hierarchical pedotransfer functions (PTFs) of Zhang et al. To this end, we developed multi‐threaded code that significantly speeds up computation (up to a factor 25) depending on the level of parallelism. We verified estimates first by calculating simple summary statistics of estimated basic properties of SG+ with actual measured soil properties for 14,113 pedons in the National Cooperative Soil Survey (NCSS) (2023) labsample database. Next, we computed summary statistics of PTF‐estimated moisture contents for NCSS and SG+ data. The results show estimation errors are dominated by intrinsic errors of the PTF, and that (potentially correctable) systematic errors are present in SG+ soil properties and PTF estimates. The resulting hydraulic property maps contain well over 750 million points for each of the seven layers and show considerable horizontal and depth variation for each VG parameter and Ks, except the VG “n” parameter, which is dominated by values between 1.25 and 1.6. The hydraulic property maps are 99.9% complete, and we demonstrate that plausible profiles and uncertainty information can be generated for virtually each point. The maps are available as two multi‐channel GeoTIFF maps per SG+ layer: one with the five hydraulic parameters and one with the corresponding covariances.

Predicting the soil bulk density using a new spectral PTF based on intact samples

Sample collection and measurement of soil bulk density (BD) are often labor-intensive and expensive in large regions. Conversely, soil spectra are easy to measure and facilitate BD prediction. However, the literature suggests that the damage to the physical structure of soil during scanning spectra on the ground and/or sieved samples might hinder the capacity of spectral technology to accurately predict BD. In addition, because some soil properties that have high correlations with BD, such as the soil organic matter (SOM), are routinely measured and available in most soil databases, coupling them with soil spectra may improve BD prediction compared to using soil properties or spectra. Therefore, in this study, we propose a novel spectral pedo-transfer function (spectral PTF) that couples the measured visible and near-infrared spectra of soils on intact samples and other soil properties to accurately predict the BD (BD = f (soil spectra, soil properties)), which is different from the traditional PTF that uses only soil properties (BD = f (soil properties)) or spectra alone (BD = f (soil spectra)). In this study, we collected topsoil (0–20 cm) and subsoil (20–40 cm) samples from 586 sites in Northeast China, covering a large area of 1.09 million km2 characterized by black soils with high SOM contents. Five routinely measured soil properties were selected: SOM, moisture content (MC), Sand, Silt, and Clay, and various spectral PTFs with one, two, and three soil properties were calibrated using the partial least square regression. The cross-validation results show that the traditional PTF can only predict BD for subsoil with an R2 of 0.51 and an RMSE of 0.11 g·cm−3 when using SOM + MC + Silt or SOM + MC. Compared to subsoil, topsoil and all layers (topsoil + subsoil) had a lower BD prediction accuracy, and a saturation effect was observed for BD values above 1.5 g·cm−3. Unexpectedly, the soil spectra did not provide a higher BD prediction accuracy than traditional PTFs, although the spectra were measured on intact samples. However, adding soil properties to the spectral PTF improved the prediction accuracy and saturation effect for high BD values. The optimal spectral PTF with a single soil property (MC) showed an acceptable BD prediction performance with R2≥0.49, RPD>1.4, and RPIQ>1.7 regardless of whether the sample was topsoil, subsoil, or all layers. Furthermore, the spectral PTF with two or three soil properties yielded a slightly better prediction performance and a more stable prediction among different combinations of soil properties. These results indicate that soil properties and spectra are irreplaceable for BD prediction. Our study demonstrates the potential of spectral PTFs for the accurate prediction of BD and offers insights into the prediction of other soil properties using soil spectra.

Macro-Nutrient Prediction of Paddy Field Soil Using Artificial Neural Network and NIR Spectroscopy

Understanding soil fertility, influenced by macronutrients like nitrogen, phosphorus, and potassium, is essential for adaptive agriculture implementation based on various soil conditions. Near-infrared spectroscopy technology provides non-destructive, rapid soil property measurements without chemicals, applicable both in-field and in-laboratory. However, the wide NIR spectrum range and neural network complexities can hinder Artificial Neural Network (ANN) training and inference, leading to time and resource inefficiency, especially without sophisticated computing devices. This study examines data reduction methods to enhance ANN performance in predicting soil macronutrients using NIR spectra. Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) were applied to select wavelengths from the 1000–2500 nm for ANN input, comparing their performance. About 237 NIR reflectance data from paddy soil were transformed into absorbance data. MLR used forward selection to identify wavelengths with correlations higher than 0.9, while PCA selected wavelengths corresponding to the loading factor peaks for each principal component. These selected wavelengths served as inputs for the ANN model. The ANN’s performance was assessed using correlation and determination coefficients, RMSE, RPD, and model consistency. For nitrogen, the PCA+ANN model with reflectance spectra performed better (RPD 2.4-4.8) than the MLR+ANN model (RPD 2.2-2.6) using fewer wavelengths (5-9 for PCA+ANN vs. 9-12 for MLR+ANN). For phosphorus estimation, the PCA+ANN model also excelled (RPD 2.3-7.0 vs. 2.3-2.4) with fewer wavelengths (4-7 vs. 7). For potassium estimation, the PCA+ANN model showed superior performance (RPD 4.3-9.5 vs. 4.2-4.4), using the same number of wavelengths (4-8 vs. 4-6).

Pedotransfer functions development for modeling FC and PWP using Vis-NIR spectra combined with PLSR and regression models

The utilization of the soil pedotransfer functions (PTFs) developed based on the basic soil propertied is an alternative, fast, cost-effective and applicable approach for the prediction of field capacity (FC) and permanent wilting point (PWP). In addition, the Visible–Near-Infrared (Vis-NIR) spectra in soil science has gained prominence due to its practicality and relevance. In this paper, we used the data of the soil PWP and FC of 135 soil samples, easily measurable soil properties and Vis-NIR spectroscopy. The multiple linear regression (MLR) model was utilized to formulate PTFs model and Vis-NIR spectroscopy combined with MLR and partial least-squares (PLSR) was used to develop Spectrotransfer Function (STF). Results showed that among the easily measurable soil properties, particle-size diameter (dg) with Beta of −0.72 and −0.63 the most influential parameters for predicting FC and PWP, respectively, followed by the clay content. Developed PTFs for both FC and PWP with a R2 of 0.71 and 0.68, respectively, had a better performance than other previous developed PTFs. Results also revealed that the PLSR with a higher R2 (0.81) and lower RMSE (4 %) significantly performed better in comparison to STF for both FC and PWP prediction.

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

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

Variations in soil erodibility (K-factor) for the Chernozems depending on the method of texture determination

Soil erodibility (K-factor) is an important parameter in erosion modeling, is one of five factors of the Revised Universal Soil Loss Equation (RUSLE), and generally represents the soil's response to rainfall and run-off erosivity. The erodibility could be determined based on direct measurements of soil properties and mathematical calculations. In this study, the K-factor was calculated based on a formula from RUSLE, proposed by Renard et al. (1997). All input parameters: soil organic carbon (SOC), soil structure, and permeability classes were measured by one method, but particle size distribution – in two ways by sedimentation and laser diffraction methods to assess the impact the K-factor variability and the values of soil erosion rates. The 107 soil samples of Chernozems from Kursk Oblast (Russia) were studied. The texture for the most of samples was classified as silty loam in both analyses. However, the laser diffraction underestimates the clay content by an average of 13.2% compared to the pipette method. The average K-factor estimated based on laser diffraction data was 0.050, and 0.034 t ha h ha−1 MJ−1 mm−1 – sedimentation method. Thus, depending on the method of soil texture analysis, the RUSLE calculated soil loss could underestimated/overstated by 32% (or 4 t ha-1 yr-1 on average in the study site). Therefore, we propose a regression equation-based conversion method of laser diffraction data to sedimentation method data for Chernozems.The Laska-TM laser analyzer measured on ∼ 13% less clay fraction (more on ∼ 8% silt and ∼ 5% fine sand) compared with sedimentation method data.For erosional researchers/modelers it is suggested to state the method of soil texture analysis (based on sedimentation law or laser diffraction) was used for RUSLE K-factor calculations.To convert K-factor values (for Chernozems) calculated and based on data of the sedimentation method to laser sedimentation – it suggested utilize the coefficient 1.47 (0.68 – vice versa).

Mapping Topsoil Behavior to Compaction at National Scale from an Analysis of Field Observations

Soil compaction is one of the most important and readily mitigated threats to soil health. Digital Soil Mapping (DSM) has emerged as an efficient method to provide broad-scale maps by combining soil information with environmental covariates. Until now, soil information input to DSM has been mainly composed of point-based quantitative measurements of soil properties and/or of soil type/horizon classes derived from laboratory analysis, point observations, or soil maps. In this study, we used field estimates of soil compaction to map soil behavior to compaction at a national scale. The results from a previous study enabled clustering of six different behaviors using the in situ field observations. Mapping potential responses to soil compaction is an effective land management tool for preventing future compaction. Random forest was used to make spatial predictions of soil behavior to compaction over cultivated soils of mainland France (about 210,000 km2). Modeling was performed at 90 m resolution. The map enabled us to spatially identify clusters of possible responses to compaction. Most clusters were consistent with known geographic distributions of some soil types and properties. This consistency was checked by comparing maps with both national and local-scale external sources of soil information. The best spatial predictors were available digital maps of soil properties (clay, silt, sand, organic carbon (SOC) content, and pH), some indicators of soil structural quality using SOC and clay content, and environmental covariates (T °C and relief-related covariates). Predicted maps were interpretable to support management recommendations to mitigate soil compactness at the soil–scape scale. Simple observational field data that are usually collected by soil surveyors, then stored and available in soil databases, provide valuable input data for digital mapping of soil behavior to compaction and assessment of inherent soil sensitivity to compaction.

Revealing Topsoil Behavior to Compaction from Mining Field Observations

Soils are a finite resource that is under threat, mainly due to human pressure. Therefore, there is an urgent need to produce maps of soil properties, functions and behaviors that can support land management and various stakeholders’ decisions. Compaction is a major threat to soil functions, such as water infiltration and storage, and crops’ root growth. However, there is no general agreement on a universal and easy-to-implement indicator of soil susceptibility to compaction. The proposed indicators of soil compaction require numerous analytical determinations (mainly bulk density measurements) that are cost prohibitive to implement. In this study, we used data collected in numerous in situ topsoil observations during conventional soil survey and compared field observations to usual indicators of soil compactness. We unraveled the relationships between field estimates of soil compactness and measured soil properties. Most of the quantitative indicators proposed by the literature were rather consistent with the ordering of soil compactness classes observed in the field. The best relationship was obtained with an indicator using bulk density and clay (BDr2) to define three classes of rooting limitation. We distinguished six clusters of topsoil behaviors using hierarchical clustering. These clusters exhibited different soil behaviors to compaction that were related to soil properties, such as particle-size fractions, pH, CaCO3 and organic carbon content, cation exchange capacity, and some BDr2 threshold values. We demonstrate and discuss the usefulness of field observations to assess topsoil behavior to compaction. The main novelty of this study is the use of large numbers of qualitative field observations of soil profiles and clustering to identify contrasting behavior. To our knowledge, this approach has almost never been implemented. Overall, analysis of qualitative and quantitative information collected in numerous profiles offers a new way to discriminate some broad categories of soil behavior that could be used to support land management and stakeholders’ decisions.

Development of near-infrared spectroscopy (NIRS) for estimating organic matter, total carbon, and total nitrogen in agricultural soil

The analysis of soil organic matter (OM), total carbon (TC), and total nitrogen (TN) using traditional methods is quite time-consuming and involves the use of hazardous chemical reagents. Absorbance spectroscopy, especially near-infrared (NIR), is becoming more popular for soil analysis. This method requires little sample preparation, no chemicals, and a single spectral analysis to evaluate soil properties. Thus, this research aimed to develop an NIR spectroscopy method for the analysis of OM, TC, and TN in agricultural soils. These findings can provide a good concept of using PLS regression with NIR techniques. The method is as follows:•Topsoil (0–20 cm) samples were collected from various agricultural fields. OM, TC, and TN were analyzed using traditional methods and NIR spectroscopy.•NIR spectra were obtained using an FT-NIR spectrometer, original spectral including with Savitzky–Golay smoothing, standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing method were used to create a predicted model through Partial Least Squares (PLS) regression with 65 % calibration, and the rest 35 % for validation.•The results showed significant relationships between measured soil properties (SOM and TC) and NIR absorbance spectra in agricultural soil (R2 of calibration and validation higher than 0.80).

Unravelling the impact of soil data quality on species distribution models of temperate forest woody plants

Soil properties influence plant physiology and growth, playing a fundamental role in shaping species niches in temperate forest ecosystems. Here, we investigated the impact of soil data quality on the performance of species distribution models (SDMs) of 41 woody plant species in Swiss forests. We compared models based on measured soil properties with those based on digitally mapped soil properties on regional (Swiss Forest Soil Maps) and global scales (SoilGrids). We first calibrated topo-climatic SDMs with measured soil data and plant species presences and absences from mature temperate forest stand plots. We developed further models using the same soil predictors, but with values extracted from digital soil maps at the nearest neighbouring plots of the Swiss National Forestry Inventory. The predictive power of SDMs without soil information compared to those with soil information, as well as measured soil information vs digitally mapped, was evaluated with metrics of model performance and variable contribution. On average, models with measured and digitally mapped soil properties performed significantly better than those without soil information. SDMs based on measured and Swiss Forest Soil Maps showed higher performance, especially for species with an ‘extreme’ niche position (e.g., preference for high or low pH), compared to those using SoilGrids. Nevertheless, if no regional soil maps are available, SoilGrids should be tested for their potential to improve SDMs. Moreover, among the tested soil predictors, pH, and clay content of the topsoil layers most improved the predictive power of SDMs for forest woody plants. In conclusion, we demonstrate the value of regional soil maps for predicting the distribution of woody species across strong environmental gradients in temperate forests. The improved accuracy of SDMs and insights into drivers of distribution may support forest managers in strategies supporting e.g. biodiversity conservation, or climate adaptation planning.

Spatial-temporal variability in nitrogen use efficiency: Insights from a long-term experiment and crop simulation modeling to support site specific nitrogen management

Within-field soil heterogeneity can lead to large variation in nitrogen use efficiency (NUE). Crop simulation models provide a multi-faceted approach to management considering both soil and plant interactions. However, research using crop models for investigating within field variation in NUE is limited, in part because of challenges quantifying spatially variable soil model parameters. Here soil apparent electrical conductivity (ECa) and measured soil properties were used to map spatial variations in soil characteristics across a Long-Term Experiment in Norfolk, England. The relationship between plot ECa across the 3 ha experiment and agronomic data across three different nitrogen rates (0, 110, and 220 kg N ha-1) over five wheat years (2010–2020) was quantified. The Sirius crop model was parameterized for two soils representing the extremes of ECa. Sirius was validated using recorded plot data. Site-specific optimal nitrogen and associated leaching risks were simulated across 29 years of weather data. Variation in soil properties had significant impact on measured NUE. At 220 kg N ha-1 mean observed yields across 5 years ranged from 9.0 to 10.7 t ha-1 and grain protein from 11.6% to 11% on the low EC and high EC plots, respectively. On average fertiliser grain N recovery was 19.7 kg N ha-1 lower on the low ECa plots. Sirius simulated the variation in yield, grain protein and grain N recovery to a good level of accuracy with RRMSE of 19.5%, 15.4% and 19.5%, respectively. Simulated optimal nitrogen on the low EC soils was on average 12 kg N ha-1 lower, with >1 in 4 years with optimal nitrogen <200 kg N ha-1. Our work demonstrated that using a combination of proximal soil EC scans and targeted soil sampling we can optimize the data requirements for model parameterisation to support site-specific N management.

Determination of a pedotransfer function for specific air–water interfacial area in sandy soils: A pore network‐informed multigene genetic programming approach

AbstractUnderstanding specific air–water interfacial area (SAWIA) is essential for characterizing and modeling various phenomena in vadose zone hydrology, such as virus and colloid transport, contaminant dissolution, evaporation, and the hydro‐mechanical behavior of unsaturated soils. Traditional measurement methods, including X‐ray imaging and tracer techniques, often encounter challenges, leading to a scarcity of studies that provide a reliable relationship for SAWIA. Currently, no pedotransfer function in the literature links SAWIA with saturation and suction using readily measurable soil properties such as median grain size and porosity. In this study, we initially developed a pore network model capable of predicting SAWIA by calibrating it with corresponding soil‐water retention curves (SWRCs). We then used these models to compile a comprehensive database of SAWIA for six sandy soils, for which experimental SWRCs were available, covering a range of median grain sizes and porosities. Utilizing this database, we established a pedotransfer function through multigene genetic programming. The accuracy of this function was validated against experimental data not previously used in its training and testing. Our parametric study indicated that increases in either porosity or median particle size led to a decrease in the regions exhibiting higher SAWIA in terms of saturation and suction.

Prediction of cadmium content using machine learning methods

Heavy metals are the most environmentally hazardous pollutions in agricultural soils, threatening humans and several ecosystem services. Cadmium (Cd) is a highly toxic element but distinctively different from other heavy metals with its high mobility in soil environments. The study aimed to evaluate the Cd concentration of soils in the Konya plain with a specific attribute to soil fertilization, mainly phosphorous fertilizers. A total of 538 surface (0–20 cm) soil samples were analyzed to determine basic physical and chemical properties and total phosphorus (P) and Cd concentrations. Descriptive statistics, machine learning, and regression models were used to assess the accumulation of Cd in soils. Decision Trees, Linear Regression, Random Forest, and XGBoost machine learning methods were used in Cd prediction. The XGBoost model proved to be the best prediction model, with a coefficient of determination of 98.1%. Electrical conductivity, pH, CaCO3, silt, and P were used in the Cd estimation of the XGBoost model and explained 56.51% of the total variance in relation to measured soil properties. The results revealed that a machine learning algorithm could be useful for estimating Cd concentration in soils using basic physical and chemical soil properties.

Response of Soil Microbial Community Structure and Diversity to Mixed Proportions and Mixed Tree Species in Bamboo–Broad-Leaved Mixed Forests

Bamboo and broad-leaved mixed forests have been widely recognized for their advantages in maintaining ecological balance, improving soil fertility, and enhancing biodiversity. To understand the effects of mixed broad-leaved tree species and mixing ratios on soil microbial communities in bamboo and broad-leaved mixed forests, we quantified the structure and diversity responses of soil microbial communities to tree species and mixing ratios using high-throughput sequencing of the 16 S rRNA gene. Three bamboo and broad-leaved tree mixed forests were studied, including bamboo–Castanopsis chinensis Hance mixed forest (CCB), bamboo–Alniphyllum fortune (Hemsl.) Makino mixed forest (AFB), and bamboo–Choerospondias axillaris (Roxb.) B. L. Burtt &amp; A. W. Hill mixed forest (CAB). We assessed the impact of tree species and mixing ratios on soil microbial communities by measuring soil properties and the diversity and composition of soil microbes. The results indicate that soil properties and the diversity and composition of microbial communities are highly dependent on broad-leaved tree species in mixed forests. The mixing ratios had a more pronounced effect on microbial diversity than on richness. In CAB, diversity peaked at mixing ratios of 10%–20% and 20%–40%. The presence of broad-leaved trees significantly altered the relationships among soil bacteria, with CAB showing the highest stability, likely due to the increased diversity and quantity of litter from Choerospondias axillaris. Our results show that the choice of broad-leaved tree species and their mixing ratios significantly influence soil microbial diversity and composition in bamboo–broad-leaf mixed forests. An optimal mixing ratio in CAB can maximize bacterial diversity and stability, providing insights for forest management and promoting ecosystem health and sustainability.

European topsoil bulk density and organic carbon stock database (0–20 cm) using machine-learning-based pedotransfer functions

Abstract. Soil bulk density (BD) serves as a fundamental indicator of soil health and quality, exerting a significant influence on critical factors such as plant growth, nutrient availability, and water retention. Due to its limited availability in soil databases, the application of pedotransfer functions (PTFs) has emerged as a potent tool for predicting BD using other easily measurable soil properties, while the impact of these PTFs' performance on soil organic carbon (SOC) stock calculation has been rarely explored. In this study, we proposed an innovative local modeling approach for predicting BD of fine earth (BDfine) across Europe using the recently released BDfine data from the LUCAS Soil (Land Use and Coverage Area Frame Survey Soil) 2018 (0–20 cm) and relevant predictors. Our approach involved a combination of neighbor sample search, forward recursive feature selection (FRFS), and random forest (RF) models (local-RFFRFS). The results showed that local-RFFRFS had a good performance in predicting BDfine (R2 of 0.58, root mean square error (RMSE) of 0.19 g cm−3, relative error (RE) of 16.27 %), surpassing the earlier-published PTFs (R2 of 0.40–0.45, RMSE of 0.22 g cm−3, RE of 19.11 %–21.18 %) and global PTFs using RF models with and without FRFS (R2 of 0.56–0.57, RMSE of 0.19 g cm−3, RE of 16.47 %–16.74 %). Interestingly, we found that the best earlier-published PTF (R2 = 0.84, RMSE = 1.39 kg m−2, RE of 17.57 %) performed close to the local-RFFRFS (R2 = 0.85, RMSE = 1.32 kg m−2, RE of 15.01 %) in SOC stock calculation using BDfine predictions. However, the local-RFFRFS still performed better (ΔR2 &gt; 0.2) for soil samples with low SOC stocks (&lt; 3 kg m−2). Therefore, we suggest that the local-RFFRFS is a promising method for BDfine prediction, while earlier-published PTFs would be more efficient when BDfine is subsequently utilized for calculating SOC stock. Finally, we produced two topsoil BDfine and SOC stock datasets (18 945 and 15 389 soil samples) at 0–20 cm for LUCAS Soil 2018 using the best earlier-published PTF and local-RFFRFS, respectively. This dataset is archived on the Zenodo platform at https://doi.org/10.5281/zenodo.10211884 (S. Chen et al., 2023). The outcomes of this study present a meaningful advancement in enhancing the predictive accuracy of BDfine, and the resultant BDfine and SOC stock datasets for topsoil across the Europe enable more precise soil hydrological and biological modeling.

Effect of intercropping maize and sunn hemp at different times and stand densities on soil properties and crop yield under in-field rainwater harvesting (IRWH) tillage in semi-arid South Africa

Abstract Background Evidence suggests that manipulating intercropping timing and stand density within intercropping systems could enhance crop yields. However, our current understanding of the effects of intercropping a cover crop on soil chemical properties and moisture still needs to be improved. This study investigates the effects of intercropping sunn hemp with maize at different timings and stand densities on selected soil properties and crop yield. Materials and methods A split-plot experiment was conducted under the in-field rainwater harvesting (IRWH) tillage. The trial had three intercropping times (simultaneously with maize planting, at V15 maize growth stage, and R1 maize growth stage) as the main plot factors and three stand densities (16, 32, and 48 plants m−2) as the subplot factors, with three replicates for both the 2019/20 and 2020/21 seasons. Changes in soil properties were assessed within the uppermost layer (0-30 cm). Soil moisture content was continuously monitored throughout the growing season and specific soil chemical properties were analyzed at harvest. Results The results showed that the interaction of sunn hemp intercropping period and stand densities did not significantly influence most of the measured soil properties. The early planting of sunn hemp had significantly 32.4% higher soil organic matter (SOM) than the last planting date at low stand density. After two growing seasons SOM, nitrogen, potassium, and manganese were significantly enhanced by 39.7%, 19.0%, 21% and 60.6% respectively. However, during the same period calcium, sodium and iron were significantly reduced by 13.4%, 46.1% and 78.0% respectively. The management of sunn hemp crop had significant effect on maize grain yield across the two seasons. The maize yields in the medium and high stand densities in the first season were significantly 15.3% and 34.3% higher than in the second season, respectively. Conclusion Due to the intercropping treatments, the retention of sunn hemp residues with varying quantities and qualities may have influenced the soil nutrient dynamics in the short-term. Significant changes in soil chemical properties and yield may need more time, and future research should be conducted out in agricultural regions with different soil mineral matrices.