Heterogeneity Of Soil Properties Research Articles

Soil sampling design matters - Enhancing the efficiency of digital soil mapping at the field scale

Optimisation of sampling design (methods chosen to select the samples) and sample size (number of samples) remains a key challenge in digital soil mapping, especially in the area of precision farming with the expected economic benefits from the introduction of new technologies. As the existing information is available in the form of relevant environmental covariates, its combination with non-parametric machine learning techniques requires careful planning from the initial field sampling to the final production of digital soil maps. The aim of this study is to compare widely used covariate-wise sampling designs combined with variable sample sizes for supervised prediction of common soil drivers of agricultural productivity (pH, soil organic carbon, soil macronutrients) in a real case study of a field (35 ha) with heterogeneous soil properties. From a total of 200 samples, we evaluated different sample sets where 10, 30 and 60 field samples were selected by conditioned Latin Hypercube Sampling (cLHS) and Feature Space Coverage Sampling (FSCS) to calibrate random forest (RF) models. The evaluation was performed on independently in-situ sampled test points. In addition to these datasets, we also compared the investigated methods with Simple Random Sampling (SRS) in a numerical benchmark experiment with increasing sample size, comparing the global accuracies of the predicted maps on the test points, but using interpolated maps as the artificial true population for each soil characteristic. The results of the study in both the field experiment and the numerical experiment showed slightly better results for the FSCS method, especially when the number of samples was small. At smaller training sample sizes, the risk of insufficiently accurate prediction models was slightly lower for FSCS and the difference decreased as the sample size increased. Nevertheless, sample size proved to be the most important factor in the accuracy of RF models, regardless of the sampling technique. The results suggest that a sample size between 18 and 30 training samples (0.6 to 1 sample ha−1) seems plausible for covariate-wise predictions using RF at field scale in our case study. The relative importance of each auxiliary variable for each RF calibration was also assessed for the field experiment. The results showed that the additional introduction of spatial proxies overshadowed the importance of other covariates, but only significantly improved the model calibration at larger sample sizes. The calibrated models without spatial proxies showed the strongest effect of remotely sensed surface characteristics.

Improved 3D characterization of in-situ soil desiccation cracking by multi-source data integration

Desiccation cracking is a common and natural phenomenon under a drought climate. The geometric and morphologic characteristics of the crack pattern are critical to understanding the response of soil mechanical and hydraulic properties to drought climate. It is always a big challenge to obtain the refined geometric structure of the in-situ soil desiccation crack network. This study proposes an integrated method named ERT+ for determining the three-dimensional geometry of in-situ soil desiccation crack networks by integrating multi-source data from electrical resistivity tomography (ERT), surface image analysis, and depth investigation. The proposed method was applied to three in-situ expansive soil sites with different crack geometries and soil properties. The results showed that the joint application of ERT with other investigations reduced the ambiguity of interpreting each technique independently. The crack network model characterized the three-dimensional geometric structure of the desiccation crack accurately and quantitatively, verifying the effectiveness and feasibility of the ERT+ method. Field and laboratory experiments showed that heterogeneity in soil properties resulted in different cracking morphologies (width, depth, and width-depth ratio). The crack geometric data suggested that the width-depth ratio of the crack network was related to the cracking modes and the soil properties, indicating the non-homology of different crack networks. In addition, the correction gradients calculated from the ERT+ method also varied with the cracking modes and soil properties, further suggesting the reliability and prospect of the proposed method.

Geotechnical Stability Analysis of the Tiga Dam, Nigeria on the Assessment of Downstream Soil Properties, Erosion Risk, and Seasonal Expansion

The Tiga Dam, a primary hydraulic structure in northern Nigeria, is subjected to intense hydrological stress during the rainy season, posing potential risks to its structural integrity. This study investigates the geotechnical properties and stability of the Tiga Dam in Kano State, Nigeria. Twelve soil samples from the downstream area were analyzed for specific gravity, grain size distribution, Atterberg limits, compaction parameters, permeability, and shear strength. The dam’s stability was assessed using Plaxis 2D under various reservoir conditions. Soil erodibility was evaluated using the Revised Universal Soil Loss Equation (RUSLE), and a linear regression model with noise was developed to predict soil expansion rates. The results showed heterogeneous soil properties, with specific gravity ranging from 2.11 to 2.63 and permeability from 3.40 × 10−9 to 1.49 × 10−7 m/s. Stability analysis revealed factors of safety of 1.322, 1.006, 1.002, and 1.147 for high reservoir, rapid drawdown, slow drawdown, and low reservoir conditions, respectively. The RUSLE K factor ranged from 0.055 to 0.145, indicating low to moderate soil erodibility. The expansion rate model demonstrated high accuracy (R2 = 0.989) in predicting seasonal and long-term soil expansion trends, with peak rates increasing from 16.94 mm/month in 2010–2013 to 19.45 mm/month in 2017–2020. This comprehensive analysis provides crucial insights into the Tiga Dam’s geotechnical behavior, highlighting potential vulnerabilities and the need for targeted management strategies to ensure long-term stability and safety.

Unraveling microbial community structure–function relationships in the horizontal and vertical spatial dimensions in extreme environments

A fundamental challenge in soil macroecology is to understand how microbial community structure shapes ecosystem function along environmental gradients of the land surface at broad spatial scales (i.e. the horizontal dimension). However, little is known about microbial community structure–function relationships in extreme environments along environmental gradients of soil depth at finer spatial scales (i.e. the vertical dimension). Here, we propose a general spatial dimension partitioning approach for assessing the patterns and drivers of soil microbial community structure–function relationships across horizontal and vertical spatial gradients simultaneously. We leveraged a 200‐km desert soil salinity gradient created by a 12‐year saline‐water irrigation in the Tarim basin of Taklamakan Desert. Specifically, using a general linear model, hierarchical variance partitioning, and a path model, we assessed the patterns and key ecological processes controlling spatial turnover in microbial community structure (i.e. β‐diversity) and enzymatic activity relevant to carbon, nitrogen, and phosphorus cycling along soil salinity gradients across study sites (horizontal dimension) and soil depths (vertical dimension). We found a decoupled relationship between soil microbial β‐diversity and enzymatic activity. Differences in soil depth (on the scale of meters) were as important as geographic distance (on the scale of kilometers) in shaping bacterial and fungal β‐diversity. However, the vertical and horizontal turnover in enzymatic activity was largely attributed to an increase in the heterogeneity of soil properties, such as soil texture, water content, and pH. Our findings suggest that dispersal limitation controls microbial community β‐diversity and that environmental heterogeneity, rather than soil salinization, controls enzymatic activity. Taken together, this work highlights that in the face of ongoing environmental alterations, soil depth is an under‐explored spatial dimension that must be considered in soil conservation efforts as a critical factor in determining microbial community structure and function in extreme environments.

Initial microbiome and tree root status structured the soil microbial community discrepancy of the subtropical pine-oak forest in a large urban forest park.

Plant-microbe-soil interactions control over the forest biogeochemical cycling. Adaptive plant-soil interactions can shape specific microbial taxa in determining the ecosystem functioning. Different trees produce heterogeneous soil properties and can alter the composition of soil microbial community, which is relevant to the forest internal succession containing contrasting stand types such as the pine-oak forests. Considering representative microbial community characteristics are recorded in the original soil where they had adapted and resided, we constructed a soil transplant incubation experiment in a series of in situ root-ingrowth cores in a subtropical pine-oak forest, to simulate the vegetational pine-oak replacement under environmental succession. The responsive bacterial and fungal community discrepancies were studied to determine whether and how they would be changed. The pine and oak forest stands had greater heterogeneity in fungi composition than bacteria. Original soil and specific tree root status were the main factors that determined microbial community structure. Internal association network characters and intergroup variations of fungi among soil samples were more affected by original soil, while bacteria were more affected by receiving forest. Specifically, dominant tree roots had strong influence in accelerating the fungi community succession to adapt with the surrounding forest. We concluded that soil microbial responses to forest stand alternation differed between microbiome groups, with fungi from their original forest possessing higher resistance to encounter a new vegetation stand, while the bacteria community have faster resilience. The data would advance our insight into local soil microbial community dynamics during ecosystem succession and be helpful to enlighten forest management.

Patterns of bacterial distance decay and community assembly in different land-use types as influenced by tillage management and soil layers

Land use and cover change are major factors driving global change and greatly impact terrestrial organisms, especially soil microbial diversity. Little is known, however, about bacterial diversity, distribution patterns and assembly processes across different land use types. In this study, therefore, we conducted a large-scale field survey of 48 sampling sites, encompassing different land use types in Xuancheng city, China, with different degrees of soil disturbance and different soil horizons. The distance-decay relationships (DDRs), assembly processes and the spatial patterns of soil bacterial communities were investigated based on high-throughput sequencing data. We found that the DDRs might be weakened by anthropogenic disturbances, which were not observed in tilled soils, while a decreasing trend was observed along the soil horizon in untilled soils. The relative importance of environmental factors and geographic distance varied with soil tillage. Specifically, bacterial communities in tilled soils were driven by non-spatially autocorrelated environmental factors, while untilled soils were more susceptible to geographic distance. In addition, the heterogeneity of soil properties, as well as the differences in soil bacterial niche width and niche overlap, determined the assembly processes of the bacterial community, resulting in opposite trends along the soil layers in tilled and untilled soils. These findings expand the current understanding of the biogeography of soil bacterial communities across different land use types.

Quantitative Evaluation of the Spatial Variation of Surface Soil Properties in Continuous Paddy Growing Fields

Soil degradation caused by poor land management practices is a major impediment to optimal land productivity. Soil spatial variability is required for agricultural productivity, food safety and environmental modeling. Rice is one of the important food resources for most of the world’s population, especially in India and feeds more than 60 per cent population of the country. Telangana is on track to become India's rice bowl as rice production is expected to reach 1.3 crore tons in 2019–20.The present study was conducted in continuous paddy cultivated field of Machapur village of Siddipet district, Telangana, India to know the spatial variability of soil properties with a help of geostatistical model. For this, a total of 100 composite samples at 20*20 m grids in an area of 4 ha were collected. The pH of the soil, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), phosphorus (P) and potassium (K) were all determined. The semivariogram model was used to create surface maps of soil properties using the ordinary kriging technique. The skewness values showed a normal distribution for all analyzed parameters except for Available K. Coefficient of variation ranged from 1.92% for pH to 34.08% for EC in topsoil indicating the heterogeneity of soil properties. Spherical model fits well with experimental semivariogram of pH, EC and AK. Exponential model better described the variation of soil OC and AN while the variation of AP was best described by Gaussian model. The soil pH, OC and available P were moderately spatially dependent whereas EC, available N and K were strongly spatially dependent. The cross validation results demonstrated the spatial prediction's smoothing effect. According to the findings of this study, a geostatistical model can directly reveal the spatial variability of lateritic soils and will assist farmers and decision makers in improving soil-water management.

Soil Organic Carbon Stock Prediction: Fate under 2050 Climate Scenarios, the Case of Eastern Ethiopia

Soil Organic carbon (SOC) is vital to the soil’s ecosystem functioning as well as improving soil fertility. Slight variation in C in the soil has significant potential to be either a source of CO2 in the atmosphere or a sink to be stored in the form of soil organic matter. However, modeling SOC spatiotemporal changes was challenging due to lack of data to represent the high spatial heterogeneity in soil properties. Less expensive techniques, digital soil mapping (DSM) combined with space-for-time substitution (SFTS), were applied to predict the present and projected SOC stock for temperature and rainfall projections under different climate scenarios represented by the four Representative Concentration Pathways (RCPs): RCP2.6, RCP4.5, RCP6, and RCP8.5). The relationship between environmental covariates (n = 16) and measured SOC stock (148 samples) was developed using a random forest model. Then, the temporal changes in SOC stock over the baseline were developed for the top 30 cm soil depth of the selected districts (Chiro Zuria, Kuni, Gemechis and Mieso) of West Hararghe Zone at 30 m resolution. The model validation using the random sample of 20% of the data showed that the model explained 44% of the variance (R2) with a root mean square error (RMSE) of 8.96, a mean error (ME) of 0.16, and a Lin’s concordance correlation coefficient (CCC) of 0.88. Temperature was the most important predictor factor influencing the spatial distribution of SOC stock. An overall net gain of SOC stock over the present C stock was expected in the study area by 2050. The gain in areas with the lower baseline SOC stock counterbalanced the loss in areas with the higher baseline stock. The changes in the SOC stock depended on land use land cover (LULC), soil type, and agro-ecological zones. By 2050, cropland is supposed to lose its SOC stock under all RCPs; therefore, appropriate decisions are crucial to compensate for the loss of C.

Seismic stability analysis of heterogeneous slopes reinforced by inclined soil nails

This study aims to propose an effective approach to evaluate the seismic stability of heterogeneous slopes reinforced by inclined soil nails. The modified pseudo dynamic approach is applied to properly describe the characteristics of seismic loads with time and space. The discretization-based failure mechanism generated from the slope crest is divided horizontally into many blocks to incorporate the heterogeneous soil properties and depth-dependent seismic accelerations into the analysis of the stability issue. The critical seismic acceleration coefficient is then determined to assess the slope stability using the kinematic approach of limit analysis. The proposed method is validated by comparisons with existing solutions and numerical results for some available cases. A systematic parametric analysis is conducted to discuss the effects of model parameters on the slope stability. Results show that there exist optimal relative nail lengths and optimal incline angles of soil nails to obtain the maximum critical seismic acceleration coefficient for reinforced slopes. Finally, an application of the proposed model to spatially variable soil strength properties is performed to illustrate that the proposed method has the potential to serve as a benchmark for the slope stability under complex geological conditions.

Three-dimensional blow-out stability analysis of shield tunnel face in anisotropic and heterogeneous soils

Many published experimental tests and field observations have shown that blow-out failure is potentially more likely to happen during the excavation of shallowly buried tunnels. However, limited research has been conducted to assess the three-dimensional (3D) blow-out stability analysis of shield tunnel faces, especially in anisotropic and heterogeneous soils. To resolve this shortcoming, a 3D blow-out failure mechanism is developed to make it possible to incorporate the influences of the anisotropies and heterogeneities of the soil properties into the determination of the limit blow-out pressure and the limit failure surface in the framework of upper-bound limit analysis theory. For verification purposes, the proposed method is tested through comparisons with existing analytical solutions and numerical results for some special cases. Then, the effects of the anisotropic and heterogeneous soil properties on the normalized limit blow-out pressure and the limit failure surface are presented and discussed. Finally, applications of the proposed method to blow-out stability analyses considering spatially variable soil properties and inclined ground surfaces are performed to illustrate that the proposed method has the potential to serve as a benchmark for the blow-out stability of tunnel faces under complex geological and geometrical conditions.

Waxy Barley Yield and Grain β-Glucan Content Affected by Heterogeneous Soil Properties in Differently Managed Fields

Waxy Barley Yield and Grain β-Glucan Content Affected by Heterogeneous Soil Properties in Differently Managed Fields

UAV Remote Sensing for Detecting within-Field Spatial Variation of Winter Wheat Growth and Links to Soil Properties and Historical Management Practices. A Case Study on Belgian Loamy Soil

Intra-field heterogeneity of soil properties, such as soil organic carbon (SOC), nitrogen (N), phosphorous (P), exchangeable cations, pH, or soil texture, is a function of complex interactions between biological factors, physical factors, and historic agricultural management. Mapping the crop growth and final yield heterogeneity and quantifying their link with soil properties can contribute to an optimization of amendment/fertilizer application and crop yield in a management variable zones (MVZ) approach. To this end, we studied a field of 17 ha consisting of four former fields that were merged in early 2017 and cropped with winter wheat in 2018. Historical management practices data were collected. The topsoil characteristics were analyzed by grid-based sampling and kriged to create maps. We tested the capacity of a multispectral MicaSense® RedEdge-MTM camera sensor embedded on an unmanned aerial vehicle (UAV) to map in-season growth of winter wheat. Relating several vegetation indices (VIs) to the plant area index (PAI) measured in the field highlighted the red-edge NDVI (RENDVI) as the most suitable to follow the crop growth throughout the growing season. The georeferenced final grain yield of the winter wheat was measured by a combine harvester. The spatial patterns in RENDVI at three phenological stages were mapped and analyzed together with the yield map. For each of these images a conditional inference forest (CI-forest) algorithm was used to identify the soil properties significantly influencing these spatial patterns. Historical management practices of the four former fields have induced significant heterogeneity in soil properties and crop growth. The spatial patterns of RENDVI are rather constant over time and their Spearman rank correlation with yield is similar along the growing season (r ≃ 0.7). Soil properties explain between 87% (mid-March) to 78% (mid-May) of the variance in RENDVI throughout the growing season, as well as 66% of the variance in yield. The pH and exchangeable K are the most significant factors explaining from 15 to 26% of the variance in crop growth. The methodology proposed in this paper to quantify the importance of soil parameters based on the CI-forest algorithm can contribute to a better management of amendment/fertilizer inputs by stressing the most important parameters to take into consideration for site-specific management. We also showed that heterogeneity induced by the soil properties can be described by a crop map early in the season and that this crop map can be used to optimize soil sampling and thus amendment/fertilizer management.

Environmental variation drives the decoupling of leaf and root traits within species along an elevation gradient.

Plant performance is enhanced by balancing above- and below-ground resource uptake through the intraspecific adjustment of leaf and root traits. It is assumed that these organ adjustments are at least partly coordinated, so that analogous leaf and root traits broadly covary. Understanding the extent of such intraspecific leaf-root trait covariation would strongly contribute to our understanding of how plants match above- and below-ground resource use strategies as their environment changes, but comprehensive studies are lacking. We measured analogous leaf and root traits from 11 species, as well as climate, soil and vegetation properties along a 1000-m elevation gradient in the French Alps. We determined how traits varied along the gradient, to what extent this variation was determined by the way different traits respond to environmental cues acting at different spatial scales (i.e. within and between elevations), and whether trait pairs covaried within species. Leaf and root trait patterns strongly diverged: across the 11 species along the gradient, intraspecific leaf trait patterns were largely consistent, whereas root trait patterns were highly idiosyncratic. We also observed that, when compared with leaves, intraspecific variation was greater in root traits, due to the strong effects of the local environment (i.e. at the same elevation), while landscape-level effects (i.e. at different elevations) were minor. Overall, intraspecific trait correlations between analogous leaf and root traits were nearly absent. Our study suggests that environmental gradients at the landscape level, as well as local heterogeneity in soil properties, are the drivers of a strong decoupling between analogous leaf and root traits within species. This decoupling of plant resource acquisition strategies highlights how plants can exhibit diverse whole-plant acclimation strategies to modify above- and below-ground resource uptake, improving their resilience to environmental change.

Probabilistic modelling of the inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian belief networks

Abstract. Pesticides are contaminants of priority concern that continue to present a significant risk to drinking water quality. While pollution mitigation in catchment systems is considered a cost-effective alternative to costly drinking water treatment, the effectiveness of pollution mitigation measures is uncertain and needs to be able to consider local biophysical, agronomic, and social aspects. We developed a probabilistic decision support tool (DST) based on spatial Bayesian belief networks (BBNs) that simulates inherent pesticide leaching risk to ground- and surface water quality to inform field-level pesticide mitigation strategies in a small (3.1 km2) drinking water catchment with limited observational data. The DST accounts for the spatial heterogeneity in soil properties, topographic connectivity, and agronomic practices; the temporal variability of climatic and hydrological processes; and uncertainties related to pesticide properties and the effectiveness of management interventions. The rate of pesticide loss via overland flow and leaching to groundwater and the resulting risk of exceeding a regulatory threshold for drinking water was simulated for five active ingredients. Risk factors included climate and hydrology (e.g. temperature, rainfall, evapotranspiration, and overland and subsurface flow), soil properties (e.g. texture, organic matter content, and hydrological properties), topography (e.g. slope and distance to surface water/depth to groundwater), land cover and agronomic practices, and pesticide properties and usage. The effectiveness of mitigation measures such as the delayed timing of pesticide application; a 10 %, 25 %, or 50 % reduction in the application rate; field buffers; and the presence/absence of soil pan on risk reduction were evaluated. Sensitivity analysis identified the month of application, the land use, the presence of buffers, the field slope, and the distance as the most important risk factors, alongside several additional influential variables. The pesticide pollution risk from surface water runoff showed clear spatial variability across the study catchment, whereas the groundwater leaching risk was uniformly low, with the exception of prosulfocarb. Combined interventions of a 50 % reduced pesticide application rate, management of the plough pan, delayed application timing, and field buffer installation notably reduced the probability of a high risk of overland runoff and groundwater leaching, with individual measures having a smaller impact. The graphical nature of BBNs facilitated interactive model development and evaluation with stakeholders to build model credibility, while the ability to integrate diverse data sources allowed a dynamic field-scale assessment of “critical source areas” of pesticide pollution in time and space in a data-scarce catchment, with explicit representation of uncertainties.

Information systems in geotechnics

The article considers the information modeling of buildings together with the foundation within the information system and the stages of its implementation. The workflow for building a 3D geotechnical model includes surface relief data, field and laboratory test data, soil lithology, geometric characteristics of the foundation structure and load. Automated systems with processing and interpretation of test data are used to determine the characteristics of soils. Mathematical modeling of the behavior of the foundations of the foundations with various input data is performed using analytical solutions and numerical methods. The natural heterogeneity of soil properties and its impact on the behavior of buildings is estimated by the sensitivity indicator of the foundation-foundation system by introducing virtual workings between the existing normative ones and the subsequent calculation of the precipitation and roll of the foundation.

How Did Soil Depth and Sampling Time Influence on Soil Organic Carbon, Soil Nitrogen, and Soil Biological Properties in a Mediterranean Olive Grove?

ABSTRACT The objective of this study was to determine the effects of sampling time and depth on some soil properties in a natural olive grove. Soil samples were collected at 0–10 cm (S0) and 10–20 cm (S1) depths in February, May, August, and November between 2013 and 2015 in Cukurova University Campus, Adana, Turkey. The soil properties determined were soil organic carbon (SOC), total nitrogen (TN), C/N, soil C mineralization and rate (Cmin) under laboratory conditions, aerobic bacteria and fungi counts. Effect of soil depth on these properties was more significant than sampling time. SOC, TN, soil aerobic bacteria and fungi counts, and Cmin were generally decreased as soil depth increased. Carbon mineralizations in S0 and S1 were lowest in May 2014 and highest in May 2015. In general, there were no significant differences between depths and sampling times in soil bacteria counts and C/N while fungi counts in S0 were significantly higher in S1 in every sampling time (P < .05). SOC was significantly correlated with all soil properties in S1 and positively correlated with TN in both depths (P < .05). In conclusion, heterogeneity of soil properties was more comprehensible in depth than in sampling times.

Synthesis of montmorillonite-supported nano-zero-valent iron via green tea extract: Enhanced transport and application for hexavalent chromium removal from water and soil

The nano-zero-valent iron composite (nZVI@TP-Mont) was successfully prepared using a low-cost and environmental-friendly green synthesis via tea leaves extract (tea polyphenols, TPs) and the montmorillonite (Mont). The batch and column experiments and characterization were conducted to investigate the transport behavior and Cr(VI) remediation by nZVI@TP-Mont in water/soil. Due to its particular surface characteristics and morphology (i.e., the Fe0 core wrapped by TPs, the doped sulfur, and interlayer structure), the nZVI@TP-Mont composite showed a great removal capacity of Cr(VI) and sufficient mobility under different soil conditions. We opine the increase in the Cr(VI) reduction of nZVI@TP-Mont was attributed to the tethering of Fe2O3 on the surface of Fe0 core by the support of Mont interlayer, especially the TP-coverage around nZVI@TP-Mont surface unwrapped, thereby increasing the regenerated reactive Fe2+ and the exposed reaction sites of Fe0 cores to Cr(VI). The increased transportability of nZVI@TP-Mont slightly depends on the heterogeneous soil properties (i.e., ionic strength, sand/soil ratio, and pH). The two-site kinetic attachment model fitting results suggest Cr(VI)/Cr(III) speciation associated with the agglomerated nZVI@TP-Mont were efficiently immobilized in soil. Therefore, this study would benefit the efficient application of the green-synthesized nZVI@TP-Mont in in-situ remediation of soils contaminated by Cr(VI).

Capturing the Scale Dependency of Erosion-Induced Variation in CO2 Emissions on Terraced Slopes

Net soil CO2 emissions are not independent of topography but tend to decline with increasing slope gradients. Such decline has been attributed to increased runoff and greater soil loss on steep slopes, leaving the soil less habitable for microorganisms. However, the specific variations of slope gradients and thus the associated soil properties relevant for CO2 emissions, especially from terraced slopes, are often disguised by the coarse resolution of digital terrain models (DTMs) based on commonly available open-source data. Such misrepresentation of the relationship between topography and soil CO2 emissions carries the risk of a wrong assessment of soil-atmosphere interaction. By applying a slope dependent soil CO2 emission model developed from erosion plots to nearby sloping and partially terraced cropland using two DTMs of different spatial resolutions, this study tested the significance of these resolution-induced errors on CO2 emission estimates. The results show that the coarser-resolution Shuttle Radar Topography Mission (SRTM) underestimated CO2-C emission by 27% compared to the higher-resolution DTM derived from Unmanned Aerial Vehicles (UAV) imagery. Such difference can be mostly attributed to a better representation of the proportion of flat slopes in the high-resolution DTM. Although the observations from erosion plots cannot be directly extrapolated to a larger scale, the 27% underestimation using the coarser-resolution SRTM DTM emphasizes that it is essential to represent microreliefs and their impact on runoff and erosion-induced soil heterogeneity at an appropriate scale. The widespread impact of topography on erosion and deposition on cropland, and the associated slope-dependent heterogeneity of soil properties, may lead to even greater differences than those observed in this study. The greatly improved estimation on CO2 emissions by the UAV-derived DTM also demonstrates that UAVs have a great potential to fill the gap between conventional field investigations and commonly applied coarse-resolution remote sensing when assessing the impact of soil erosion on global soil-atmosphere interaction.

Effect of vegetation mosaic on spatial heterogeneity of soil organic carbon mineralization and nitrification in an alpine meadow

Patchy distribution of herbaceous species is common in degraded grasslands on the Qinghai-Tibetan Plateau, but the effects of such a mosaic pattern on soil biochemical properties have rarely been studied. We compared soil organic carbon (SOC) mineralization, nitrogen (N) availability, ammonia oxidation and plant N uptake between the following vegetation patch types (plant communities) in an alpine meadow: grass (dominated by Elymus nutans, Poa crymophila and Leymus secalinus), sedge (Kobresia capillifolia and Kobresia humilis), Polygonum viviparum (P. viviparum only), Artemisia smithii (A. smithii only) and Oxytropis kansuensis (O. kansuensis only). All patches had a diameter ranging from about tens of centimeters to several meters. SOC mineralization was faster in patches beneath grass or A. smithii than in those beneath sedge or O. kansuensis, but the faster SOC mineralization beneath grass and A. smithii patches did not result in a difference in SOC storage in the top 30 cm of soil compared with that in other patches. Greater litter production beneath grasses or belowground carbon input (largest root/shoot ratio) beneath A. smithii may have offset the negative effect of rapid SOC mineralization on SOC storage under grass and A. smithii patches. Productive grasses were associated with the highest level of N availability in the soil, consistent with their greater demands for N uptake. Sedge species and A. smithii inhibited nitrification by decreasing the abundance of ammonia oxidation bacteria in soil, in contrast to grasses. The inhibition of biological nitrification under A. smithii explained the finding that the soil beneath this vegetation patch type had the lowest nitrate content as a percentage of total inorganic N. However, the inhibition of nitrification beneath sedges did not induce a decrease in the nitrate content as a percentage of total inorganic N under sedge patches compared with that under grass patches. This suggests that sedges preferentially use ammonium instead of nitrate as their N source. It is concluded that patchy distribution of herbaceous species in alpine meadows created significant spatial heterogeneity in soil biological and biochemical properties at the field scale. Sampling in ecosystem studies clearly needs to take into account such heterogeneity of soil properties and processes in grasslands with patchily distributed vegetation.

Anthropogenic erosion-induced small-scale soil heterogeneity in South African rangelands

Land-use-induced soil erosion in semi-arid drylands induced by land-use changes creates patchworks of soils and vegetation different from those of natural conditions. Knowledge of the highly dynamic spatial heterogeneity in soil properties, not depicted in conventional soil maps, is important for managing land uses sustainably, and for understanding soil-climate interactions. This analysis of soil redistribution in a degraded rangeland in South Africa assessed the significance of anthropogenic soil heterogeneity within a small catchment with a silted reservoir. This study carries out analysis of soil redistribution in a degraded rangeland in South Africa, to assess the significance of anthropogenic soil heterogeneity within a small catchment with a silted-up reservoir. Surface soil (N = 51) and soil profile (N = 29) samples were collected from areas of various degrees of degradation and analysed for texture, pH, total nitrogen (TN), total organic carbon (TOC), available phosphorus (P), and potassium (K). Diverse vegetation cover is reflected in a high soil heterogeneity, showing differences in soil texture. Average surface soil nutrient content was significantly higher in vegetated areas (grassland: TOC 1.08 %, TN 0.10 %, P 20.12 mg kg −1 , mixed vegetation: TOC 0.93 %, TN 0.08 %, P 13.54 mg kg −1 , depositional: TOC 1.68 %, TN 0.18 %, P 34.67 mg kg −1 ) than at degraded sites (TOC 0.47 %, TN 0.06 %, P 7.52 mg kg −1 ). K content was low to moderate but did not show any significant difference between landscape units. Potential exists for the formation of distinct young anthropogenic soils on the silted-up reservoir where deposited sediments differed in TOC, TN, P, and profile depth from the shallow natural soils. Consequently, azonal soils on dam-deposits are different from those occurring naturally, and not depicted on conventional soil maps. Their different water and nutrient cycling may affect vegetation and biogeochemical fluxes. Land-use management should consider such soils, as they play an important role in rangeland ecology.