Topsoil Properties Research Articles

Improving the spatial prediction of topsoil properties in a typical grazing area using multi-season PlanetScope spectral covariates and data mining techniques

Understanding the spatial distribution of topsoil properties in grassland ecosystems is essential for improving soil ecosystem services, quality, and erosion resilience. The availability of free, high-resolution satellite imagery and advanced data mining techniques offers new opportunities for efficient soil property assessment. This study aimed to evaluate the potential utility of multi-season PlanetScope imagery to predict soil organic carbon (SOC), pH, and calcium carbonate (CaCO3). Using random sampling, 121 topsoil samples (0–30 cm depth) were collected with an auger across grasslands, bare soil, and eroded areas within a typical grazing land use. Three data mining techniques: random forest (RF), extreme gradient boosting (XGB), and support vector machines (SVM), were applied and evaluated using a 10-fold cross-validation. The results indicated that multi-season spectral covariates considerably improved the accuracy of the target soil properties compared to single-season imagery. SVM was the most effective algorithm for predicting SOC, achieving a root mean square error (RMSE) of 0.52%, mean absolute error (MAE) of 0.24%, and R² of 0.92. RF was the best-performing algorithm for predicting soil pH (RMSE = 0.22, MAE = 0.17, and R² = 0.97) and CaCO3 (RMSE = 0.55%, MAE = 0.42%, and R² = 0.96). While XGB failed to capture the variability in soil pH, the other models generated interpretable maps that accurately represented the distribution of soil properties across different land cover categories. The green-red vegetation index (GRVI) was the most critical covariate for predicting SOC, while elevation and the topographic wetness index (TWI) were key predictors for soil pH and CaCO3, respectively. This study underscores the potential of multi-season PlanetScope imagery for accurately predicting soil properties and recommends conducting similar studies in diverse geographical settings to validate these findings and develop more generalizable models.

Seismic Site Characterization of Coimbatore city, Tamil Nadu, India using the Multi-channel Analysis of Surface Waves (MASW) test and Correlations between shear-wave velocity and SPT - N

Seismic Site Characterization involves the qualitative assessment of top-soil properties that have the capability of amplifying the generated earthquake ground motions. The geotechnical properties of topsoil refer to the top 30-m subsurface profile which plays a vital role in seismic microzonation and Ground Response Analysis (GRA) studies. Among various geotechnical parameters, shear-wave velocity (Vs) of the top 30 m subsurface is mainly linked to seismic site characterization and amplification studies. The average shear-wave velocity of the top 30 m subsurface, Vs(30), has been used for seismic site classification in accordance with the National Earthquake Hazard Reduction Program (NEHRP) and various building codes. In this study, an attempt has been made to retrieve the geospatial variation of average shear-wave velocity for Coimbatore city using the active Multi-channel Analysis of Surface Waves (MASW) test which is one of the non-destructive geophysical tests. To retrieve the spatial distribution of shear-wave velocity (Vs), the test was carried out at 35 locations in the vicinity of important structures, schools, colleges, and hospitals within the city. The seismic records have been acquired in the field and analyzed using the winMASW software. From the one-dimensional MASW test, the study area has an average Vs(30) in the range of 640 m/s to 909 m/s and has been classified as site-class “BC” (soft rock) according to NEHRP standards. These test results have been validated using the collected SPT bore log data from various locations, including 40 sites in the vicinity of the conducted MASW tests. The site-specific correlation between the shear-wave velocity (Vs) and the corrected SPT N- Value, N1(60), and between Vs and shear modulus (G) have been developed for Coimbatore city with a regression coefficient of 0.79 and 0.83 respectively. From the fundamental site period map, the study area has a site period in the range of 0.1 to 0.2 s, which indicates that 1- to 2- storey buildings that are densely distributed throughout the city may lead to damage in case of probable future earthquakes. This study bridges the connectivity from the evaluated bedrock acceleration using the Seismic Hazard Analysis (SHA) and provides insights for evaluating surface acceleration using GRA studies.

Soil-forming factors controlling Technosol formation in historical mining and metallurgical sites in the high-alpine environment of the Tatra Mountains, southern Poland

The Tatra Mountains are a unique Central European alpine ecosystem where the non-anthropogenic soil cover and soil-forming processes are well recognized. However, the Technosols in the area’s high-mountain environment have not been studied in detail to date. Therefore the aim of this study was to identify the most important soil-forming factors controlling the properties of Technosols developed in historical mining and metallurgical sites in the Tatra Mountains of southern Poland active from the 15th Century until the end of the 19th. The present paper is one of the first attempts to study the genetic aspects of high-mountain Technosols in the temperate climatic zone. The study involved determining soil morphology and classification, soil properties, magnetic susceptibility, mineral composition, optical microscopic observations and total concentration of major elements. The studied Technosols were poorly developed soils with simple soil morphology (mainly A horizon in the topsoil and C horizons in the subsoil). There was a high content of rock fragments. The research has shown that the properties of Technosols in the Tatra Mountains were primarily determined by past human activities like mining and metallurgy as well as the type of anthropogenic parent material, which included mining wastes and metallurgical slags and determined soil properties together with mineral and chemical composition. Soil formation was significantly influenced by vegetation which was conditioned by the relief and climatic conditions dependent on altitudinal zonation. Vegetation and plant-derived soil organic matter shaped topsoil properties. The lengthy soil-forming process acting since a few centuries in some Technosols led to the formation of Bw horizons.

Soil properties of overburden and topsoil in limestone mining area: A preliminary study

Limestone mining removes topsoil and produces overburden piles, which is improper for plant growth. The objectives of this study were to compare the texture, certain chemical properties, and bacterial population of overburden to the topsoil and to observe the effects of compost amendment on the growth of corn seedlings on the potted overburden. The overburden characterization was performed using a quantitative descriptive method with purposive sampling. Samples were taken from a 6-month-old overburden pile and a pine-vegetable agroforestry ecosystem. The data were further analyzed by using the Student’s t-test. A bioassay was set up in a randomized block design with various concentration compost treatments and five replications. The results showed significant differences between texture, chemical characteristics, and bacterial counts of overburden and topsoil were recorded. The topsoil was more acidic and had higher organic carbon, potential phosphorus, potential potassium, and cation exchange capacity, while the overburden had higher levels of calcium; however, the total N and Magnesium content in topsoil and overburden was similar. The bacterial population of the overburden was lower than in the topsoil. A total of 16 gram-negative and one gram-positive bacteria have been identified from both samples. Applying huge amounts of compost enhanced corn seedlings' growth in the overburden. The study suggested that overburden was more unfertile compared to the topsoil. Therefore, the improvement of overburden properties by compost amendment is needed to green the area.

The Effect of Compost, Cow, and Goat Manure on Inceptisols Soil Characteristics From Smallholder Plantation, Karangploso District, Malang Regency

Inceptisol soils mainly have medium to high physical properties. However, these physical properties can be degraded if there is a limiting factor in the terrain, which is the degree of slope and low SOC. On steep slopes, soil infiltration rates decrease, which leads to the limited available soil moisture in root zone subsoil. Application of compost or manure into the soil can improve soil physical properties and increase the soil organic carbon (SOC), it improves soil porosity and improve available water capacity (AWC). This study used the factorial completely randomized design with 8 treatments and 4 replications. The treatments included (Control + Topsoil), (Control + Subsoil), (Compost + Topsoil), (Compost + Subsoil), (Cow manure + Topsoil), (Cow manure + Subsoil), (Goat manure + Topsoil), (Goat manure + Subsoil). The results showed that (1) the addition of compost and manure can improve the physical properties of topsoil and subsoil, reducing macro porosity, increasing meso and microporosity, and increasing field capacity. (2) The addition of compost and manure can increase the water holding capacity of topsoil by 36.72% at 1 MAI to 41.12% at 5 MAI and inceptisol subsoil by 35.27% at 1 MAI to 36.42% at 5 MAI. (3) The addition of compost and manure can increase the C-organic content of topsoil by 1.62% at 1 MAI to 1.72% at 5 MAI and subsoil by 1.24% at 1 MAI to 1.38% at 5 MAI.

Estimation of top soil properties by Sentinel-2 imaging

ABSTRACT This study evaluated the feasibility of using free multispectral remote sensing data from Sentinel-2A satellites to predict soil properties in Northern Karnataka, India. Sentinel-2A images were downloaded for selected sites, covering Vertisol, Ultisol, and Alfisol soils. Multiple linear regression (MLR) models incorporated four Sentinel-2 bands and six spectral indices (NDVI, GNDVI, SAVI, TVI, EVI, and BI) as independent variables, with soil properties as dependent variables. Surface samples (0–15 cm depth) were collected from March to May 2022. The analysis showed significant correlations between individual bands and soil properties, with variations in Organic Carbon (OC) compared to sand, silt, clay, and pH. Sand positively correlated with all spectral indices, while silt, clay, and pH were negatively correlated. The red and Near-Infrared (NIR) bands showed a non-significant relationship with OC. No significant correlation was found between EVI and the soil properties. Strong regression coefficients were observed between Sentinel-2 predictions and laboratory measurements: sand (r² = 0.63), silt (r² = 0.73), clay (r² = 0.59), and pH (r² = 0.59). These results demonstrate the potential of Sentinel-2 data for predicting soil properties, offering a valuable tool for managing unsampled agricultural fields.

Effects of occasional tillage on soil physical and chemical properties and weed infestation in a 10-year no-till system

Few studies have investigated how one-time targeted tillage of long-term no-till fields impacts topsoil properties and weed dynamics. An on-farm trial was implemented in 2020 to test the effects of occasional tillage (OT) in Morocco with a long-term no-tillage (NT) system and rainfed field crops: durum wheat (Triticum durum), faba bean (Vicia faba minor), and chickpea (Cicer arietinum). Four treatments were established, namely, continuous NT with crop residues maintained (“NT + residue”); continuous NT with crop residues not maintained (“NT-residue”); shallow inversion tillage (“shallow OT”); and deep non-inversion tillage (“deep OT”). We assessed the effect of these treatments on soil physical and chemical properties in 0–10 and 10–20 cm soil depths after crop harvest of the 2020–2021 (year 1) and 2021–2022 (year 2) growing seasons corresponding to 1 and 2 years after OT, respectively. In addition, we evaluated the effect of the treatments on weed populations and the effect of the legume crop rotated with wheat on soil nitrogen (N) and weed density. In year 1, deep OT reduced the water content at field capacity and available water capacity at 0–10 cm compared to continuous NT; the cation-exchange capacity (CEC) under deep OT was lower than in NT-residue and NT + residue at 0–10 cm and 10–20 cm, respectively. Furthermore, deep OT increased ammonium-N (NH4-N) at 0–10 and 10–20 cm compared to NT + residue but reduced exchangeable potassium (K) at 10–20 cm depth compared to NT-residue. In year 2, shallow OT had lower total porosity at 10–20 cm than NT + residue, while shallow and deep OT recorded higher water-stable aggregates at 0–10 cm than NT + residue; at 10–20 cm, deep OT recorded lower CEC than NT + residue. However, deep OT had higher nitrate-N (NO3-N) and available sulfur (S) than NT-residue at 10–20 cm. Occasional tillage did not significantly affect 10 out of 19 of the soil properties evaluated, including soil organic matter (SOM), in all the years and did not help reduce the stratification of soil nutrients in NT. In year 1, 50 days after OT, deep OT reduced the weed density by 46% compared to NT + residue, while in year 2, 406 days after OT, shallow OT reduced weed density by 53% compared to NT-residue. Regarding the effect of the legume rotated with wheat, faba bean appeared to be the better preceding or following wheat crop as it resulted in higher residual soil mineral N and lower weed infestation than chickpea.

Comparing LUCAS Soil and national systems: Towards a harmonized European Soil monitoring network

A recent assessment states that 60–70% of soils in Europe are considered degraded. Protecting such valuable resource require knowledge on soil status through monitoring systems. In Europe, different types of monitoring networks currently exist in parallel. Many EU Member states (MS) developed their own national soil information monitoring system (N-SIMS), some being in place for decades. In parallel in 2009, the European Commission extended the periodic Land Use/Land Cover Area Frame Survey (LUCAS) led by EUROSTAT to sample and analyse the main properties of topsoil in EU in order to develop a homogeneous dataset for EU.Both sources of information are needed to support European policies on soil health evaluation. However, a question remains whether the assessment obtained by using soil properties from both monitoring programs (N-SIMS and LUCAS Soil) are comparable, and what could be the limitations of using either one dataset or the other.Conducted in the context of European Joint Programme (EJP) SOIL, this study shows the results of a comparison between N-SIMS and LUCAS Soil programs among 12 different EU member states including BE, DE, DK, EE, ES, FR, DE, HU, IT, NL, PL, SE and SK. The comparison was done on: (i) the sampling strategies including site densities, land cover and soil type distribution; (ii) the statistical distribution of three soil properties (organic carbon, pH and clay content); (iii) two potential indicators of soil quality (i.e. OC/Clay ratio and pH classes). The results underlined substantial differences in soil properties statistical distributions between N-SIMS and LUCAS Soil in many member states, particularly for woodland and grassland soils, affecting the evaluation of soil health using indicators. Such differences might be explained by both the monitoring strategy and sampling or analytical protocols exposing the potential effect of data source on European and national policies. The results demonstrate the need to work towards data harmonization and in the light of the Soil Monitoring Law, to carefully design the future of soil monitoring in Europe taking into account both LUCAS Soil and N-SIMS considering the significant impact of the monitoring strategies and protocols on soil health indicators.

Effect of vegetation introduction versus natural recovery on topsoil properties in the dried Aral Sea bed

AbstractDesiccation of the Aral Sea has resulted in the emergence of vast saline and flat terrains, jeopardizing human health and agricultural activities because of sand and dust storms. Vegetation, mainly indigenous Haloxylon species, has been introduced to ameliorate the soil. As a critical indicator of rehabilitation, the physicochemical properties of soil after the introduction of vegetation remain poorly understood. This study examined (1) the changes in topsoil properties after vegetation establishment based on a 30‐year chronosequence and employed (2) site comparisons of topsoil properties between two cases of natural versus introduced vegetation on a dried Aral Sea bed. Twelve paired areas that were naturally or artificially vegetated during 1990, 2000, 2005, 2008, 2013, and 2017 were selected for examination. Irrespective of vegetation type, increases in organic matter and nutrients (TOC >90%, TN >143%, and P2O5 >23%) were detected in the surface soil (0–10 cm) along the 30‐year chronosequence. In addition, decreases in ECe (92% and 69%), CEC (41% and 11%), Ca2+ (38% and 12%), and TIC (81% and 11%) were observed in both natural and introduced vegetation, respectively. The introduced vegetation was associated with a slightly greater accumulation of soil K+ and TN than the natural vegetation. Our results indicate vegetation‐derived nutrient and organic matter accumulation as well as the possible removal of salts by root exudates in the surface soil. Overall, vegetation contributed to soil amelioration, with similar effects observed at naturally and artificially vegetated sites.

Particle and bulk media characteristics of food waste compost-based biomedia by dynamic high-temperature aerobic composting process

The dynamic high-temperature aerobic composting (DHAC) process, as a decentralized system, is considered a sustainable strategy for renewable resource-based raw materials. The food waste compost-based biomedia (BMFWCs) produced within five days of the DHAC process exhibited the physical properties of peaty topsoil with acceptable maturity under the meso-thermophilic phase. To understand the particle and bulk media characteristics of the BMFWCs, organic matter (OM) fraction–density–porosity parameters, particle size distribution, and uniformity coefficient were considered. Operating the DHAC process produced end products characteristics of relatively high total porosity (φb,T) and low dry bulk density (ρb) compared to the conservative mixture theory due to binary mixing and shape effects. A significant change in total porosity was estimated as an increase in non-spherical fibrous particles within the OM fraction of 0.4. Appropriate organic–mineral additives led to the effective arrangement of the variable porosity structure and improvement of aggregate stability (fom,spent coffee grounds≥0.2, fm,oyster shell powder≤0.4). The Forced aeration supply contributed to the expansion of the open space and macropore structures that serve as gas passages in the BMFWCs, while the introduction of microbes (Bacillus subtilis) promoted the biodegradation of medium and small macroaggregates (0.25–2 mm). The BMFWCs produced by the DHAC process was expected to have application potential in sustainable nature-based solution technology, due to appropriate particle properties, uniformity, and maturity.

A data‐driven topsoil classification framework to support soil health assessment in Minnesota

AbstractSoil health assessments aim to quantify soil health status using indicators linked to ecosystem services such as yield, nutrient cycling, water cycling, or carbon storage. Many indicators are related to soil biological processes, which can be challenging to interpret because they are sensitive not only to management, but also to nonmanagement variables such as soil inherent properties, topography, and climate. Existing studies address this challenge by grouping similar soils by taxonomy, geography, or a combination of these and other variables for soil health assessment. We investigated whether grouping soils based on multiple quantitative topsoil properties could be an alternative to taxonomic or geographic groups. We used an unsupervised classification algorithm, k‐means, to cluster publicly available soil and climate data for Minnesota. Clustering into eight conceptual groups (“clusters”) based on 10 topsoil properties was determined to be the optimal algorithm output. We evaluated the ability of our soil clusters and other grouping methods to explain variance in eight soil health indicators. We found the combination of Major Land Resource Area (MLRA) and soil cluster performed best, explaining as much or more variance than other groupings for five of the eight indicators. The clusters distinguish zones of topsoil variation at the field scale, and MLRAs account for broader scale variation in climate and other landscape factors. The approach we describe is flexible and could be applied at different locations and scales to produce conceptual soil groups and associated maps to support soil health test sampling and interpretation at the field scale.

Predicting soil nutrients with PRISMA hyperspectral data at the field scale: the Handan (south of Hebei Province) test cases

ABSTRACT This research investigates the suitability of PRISMA and Sentinel-2 satellite imagery for retrieving topsoil properties such as Organic Matter (OM), Nitrogen (N), Phosphorus (P), Potassium (K), and pH in croplands using different Machine Learning (ML) algorithms and signal pre-treatments. Ninety-five soil samples were collected in Quzhou County, Northeast China. Satellite images captured soil reflectance data when bare soil was visible. For PRISMA data, a Linear Mixture Model (LMM) was used to separate soil and Photosynthetic Vegetation (PV) endmembers, excluding Non-Photosynthetic Vegetation (NPV) using Band Depth values at the 2100 nm absorption peak of cellulose. Sentinel-2 bare soil reflectance spectra were obtained using thresholds based on NDVI and NBR2 indices. Results showed PRISMA data provided slightly better accuracy in retrieving topsoil nutrients than Sentinel-2. While no optimal predictive algorithm was best, absorbance data proved more effective than reflectance. PRISMA results demonstrated potential for predicting soil nutrients in real scenarios.

Long-term cotton stubble return and subsoiling improve soil organic carbon by changing the stability and organic carbon of soil aggregates in coastal saline fields

Cotton stubble return and subsoiling significantly enhanced soil fertility and cotton yield in coastal saline soils. Despite these benefits, the mechanisms influencing soil organic carbon (SOC) sequestration remain poorly understood. To address this gap, a comprehensive ten-year study was undertaken, focusing on the effects of stubble management (either removal or return) and tillage (either non-subsoiling or subsoiling) on the physicochemical properties of both topsoil (0–20 cm) and subsoil (20–40 cm) layers, as well as on cotton yield. The objectives were to (i) quantify interannual variability (2013–2022), stubble return, and subsoiling on the soil physicochemical properties of the topsoil (0–20 cm) and subsoil (20–40 cm) and cotton yield and (ii) elucidate the mechanism by which these agricultural practices affect SOC sequestration. Findings from this decade-long investigation indicated that ten consecutive years of cotton stubble return and subsoiling at three-year intervals significantly ameliorated soil conditions by reducing the salt content, pH, and the proportion of silt plus clay, while increasing macro-aggregates, mean weight diameter (MWD), aggregates organic carbon (AOC) across various particle sizes, SOC, total nitrogen (TN) in both soil layers, and ultimately, the cotton lint yield. Interestingly, the effect of stubble return on micro-aggregates varied between the two soil layers, leading to a reduction in micro-aggregates by 11.16% in the topsoil, in contrast to a 5.7% increase in the subsoil, when compared to conditions where stubble was removed. The partial least squares path modeling (PLS-PM) indicated that the enhancements in MWD attributable to stubble return and subsoiling were primarily due to significant reductions in soil pH and salt content in both layers, which in turn fostered an increase in AOC, ultimately contributing to higher SOC levels. However, the pathways through which AOC influenced SOC varied significantly between the topsoil and subsoil, with stubble return and subsoiling increasing SOC through an increase in the macro-AOC in the topsoil, and micro-AOC in the subsoil. In summary, long-term cotton stubble return and subsoiling significantly optimize the aggregate particle size distribution and enhance the carbon sequestration potential in coastal saline cotton fields.

Topsoil's magnetic and electrical properties in a volcanic and tropical region.

Environmental monitoring by measuring topsoil's magnetic and electrical properties is one practical, quick, and low-cost approach. This method has been used worldwide as a proxy for the presence of potentially toxic elements. However, additional research must be conducted on diverse soil types, geology, and climates. We determined the magnetic and electrical properties of urban and agricultural topsoils in a volcanic region and analyzed them as possible proxies of potentially toxic elements for environmental monitoring. To identify topsoil characteristics, we employed the measurements of magnetic susceptibility and hysteresis, electrical conductivity, total dissolved solids, power of hydrogen (pH), particle morphology, and element contents that were statistically analyzed to identify relevant properties. The result was able to differentiate volcanic soils from urban, industrial, and agricultural areas as well. The value of low-frequency magnetic susceptibility (χLF) in soils from urban areas is higher than 866.0±249.9 ×10-8 m3 kg-1, while the value of χLF in agricultural areas is 208.0±67.8 ×10-8 m3 kg-1. This is reinforced by the relationship between low-frequency and frequency-dependent magnetic susceptibility (χLF-χFD%) in samples from urban areas that fall within the same cluster dominated by coarse-grained magnetic minerals originating from anthropogenic processes. In contrast, the agricultural area forms a separate cluster primarily influenced by pedogenic processes from acid igneous rock minerals. Caution is required for interpreting the magnetic signal due to the high contents of lithogenic magnetic particles inherited from the parent materials of Andisols.

Drivers of tree regeneration in coniferous monocultures during conversion to mixed forests in Central Europe – Implications for forest restoration management

In Central Europe, anthropogenic coniferous monocultures have been subject to conversion to more diverse mixed forests since the 1990s, however, they are still abundant across many forest landscapes. Artificial and natural tree regeneration both play a key role during conversion by determining the species composition and structure of the future forests. Many abiotic and biotic factors can potentially influence the regeneration process and its specific combinations or interactions may be different among tree species and its developmental stages. Here, we aimed to identify and quantify the effect of the most important drivers on the density of the most abundant regenerating tree species (i.e., Norway spruce and European beech), as well as on species and structural diversity of the tree regeneration. We studied tree regeneration in four former monospecific coniferous stand types (i.e., Norway spruce, Scots pine, European larch, and Douglas fir) in Southwest Germany that have been under conversion to mixed forests since the 1990s. We sampled tree regeneration in four growth height classes together with a variety of potentially influencing factors on 108 sampling plots and applied multivariate analyses. We identified light availability in the understorey, stand structural attributes, browsing pressure, and diaspore source abundance as the most important factors for the density and diversity of tree regeneration. Particularly, we revealed species-specific differences in drivers of regeneration density. While spruce profited from increasing light availability and decreasing stand basal area, beech benefited either from a minor reduction or more strikingly from an increase in overstorey density. Increasing diaspore source abundance positively and a high browsing pressure negatively affected both species equally. Our results suggest that humus and topsoil properties were modified during conversion, probably due to changes in tree species composition and silvicultural activities. The species and structural diversity of the tree regeneration benefitted from increasing light availability, decreasing stand basal area, and a low to moderate browsing pressure. We conclude that forest managers may carefully equilibrate among the regulation of overstorey cover, stand basal area, and browsing pressure to fulfil the objectives of forest conversion, i.e., establishing and safeguarding a diverse tree regeneration to promote the development of mature mixed forests in the future.

Elevational patterns of warming effects on plant community and topsoil properties: focus on subalpine meadows ecosystem

Elevational patterns of warming effects on plant community and topsoil properties: focus on subalpine meadows ecosystem

The synthesis of potential factors contributing to the asynchronous warming between air and shallow ground since the 2000s on the Tibetan Plateau

Atmospheric conditions, topsoil properties and land cover conditions play essential roles in ground surface temperature (GST), surface air temperature (SAT) and their differences (GST-SAT). They determine the strength of the thermal forcing of the lower atmospheric boundary and the distributions of frozen ground in cold regions. However, the relative importance of these factors at various time scales and the underlying physical mechanisms remain less well understood. Here, we investigate the spatiotemporal patterns of GST-SAT and examine 11 potential factors in three categories in influencing the GST-SAT variations from 1983 to 2019 over the Tibetan Plateau (TP) using boosted regression tree models. The results show that the TP has experienced asynchronous warming in GST and SAT since 2001: a warming hiatus in SAT but continued warming in GST, resulting in a significantly increasing trend in GST-SAT. The relative importance of the three categories that influence the GST-SAT spatial variation was: atmospheric variables (56.1 %) > shallow soil properties (24.4 %) > interfacial land cover features (19.5 %). The importance of the factors also varied with the combinations of annual, seasonal, daily, day-time and night-time time scales, manifested by positive or negative effects. The interdecadal changes of net radiation, precipitation, wind speed and soil moisture amplified the asynchronous warming between air and shallow ground over the TP since the 2000s. These findings provide an in-depth understanding of the spatiotemporal variations of GST-SAT and the underlying mechanisms. This study will benefit the development of the Earth system models on the TP.

Evaluating the extrapolation potential of random forest digital soil mapping

Spatial soil information is essential for informed decision-making in a wide range of fields. Digital soil mapping (DSM) using machine learning algorithms has become a popular approach for generating soil maps. DSM capitalises on the relation between environmental variables (i.e., features) and a soil property of interest. It typically needs a training dataset that covers the feature space well. Mapping in areas where there are no training data is challenging, because extrapolation in geographic space often induces extrapolation in feature space and can seriously deteriorate prediction accuracy. The objective of this study was to analyse the extrapolation effects of random forest DSM models by predicting topsoil properties (OC, clay, and pH) in four African countries using soil data from the ISRIC Africa Soil Profiles database. The study was conducted in eight experiments whereby soil data from one or three countries were used to predict in the other countries. We calculated similarities between donor and recipient areas using four measures, including soil type similarity, homosoil, dissimilarity index by area of applicability (AOA), and quantile regression forest (QRF) prediction interval width. The aim was to determine the level of agreement between these four measures and identify the method that had the strongest agreement with common validation metrics. The results indicated a positive correlation between soil type similarity, homosoil and dissimilarity index by AOA. Surprisingly, we observed a negative correlation between dissimilarity index by AOA and QRF prediction interval width. Although the cross-validation results for the trained models were acceptable, the extrapolation results were unsatisfactory, highlighting the risk of extrapolation. Using soil data from three countries instead of one increased the similarities for all measures, but it had a limited effect on improving extrapolation. Also, none of the measures had a strong correlation with the validation metrics. This was particularly disappointing for AOA and QRF, which we had expected to be strong indicators of extrapolation prediction performance. Results showed that homosoil and soil type methods had the strongest correlation with validation metrics. The results for this case study revealed limitations of using AOA and QRF as measures of extrapolation effects, highlighting the importance of not relying on these methods blindly. Further research and more case studies are needed to address the effects of extrapolation of DSM models.

Altered intra-annual precipitation patterns affect the N-limitation status of soil microorganisms in a semiarid alpine grassland

The ongoing intensification of the hydrological cycle due to global climate change alters intra-annual precipitation variability. Changes in precipitation patterns lead to disparities in soil moisture; however, the responses of soil extracellular enzyme activities (EEAs) and microbial metabolism limitations to them is unclear. This study conducted an in situ field experiment in the alpine grasslands of the Kunlun Mountains to simulate the same amount of precipitation but change the distribution time of precipitation within the plant growing season. We examined the effects of variability in intra-annual precipitation patterns on the stoichiometry of topsoil (0–5 and 5–20 cm) properties, microbial biomass, and EEAs. Results showed that altered precipitation patterns significantly increased soil moisture’s disparity index (D) by 57 %–89 %. The activity of nitrogen-acquiring enzymes (L-leucine aminopeptidase) was increased by 20.82–32.08 % (P < 0.05) with increased variability of precipitation, while phosphorus-acquiring enzymes (Acid phosphatase) and carbon-acquiring enzymes (β-glucosidase) both decrease by 9.25–23.35 % and by 17.86–33.04 %, respectively (P < 0.05). We determined that soil microorganisms in the study region were metabolism-limited by nitrogen (vector angles < 45◦), and increased precipitation variability exacerbated nitrogen limitation (decrescent vector angles) and alleviated carbon limitation (shortened vector length). In addition, soil EEAs can be both interactively influenced by biotic and abiotic factors, with abiotic factors (i.e., D, soil organic carbon content, and soil total nitrogen content) having a stronger effect. Our study provides an effective strategy for regulating soil EEAs, microbial biomass, and microbial metabolism limitation and contributes to developing nutrient management and restoration strategies in semiarid alpine grassland ecosystems.

Importance of soil fertility for climate-resilient cropping systems: The farmer's perspective

Healthy and productive agricultural soils are the basis for global food security as they are a prerequisite for yield-stable cropping systems under climate change. Despite the expansion of agricultural research activities in this area through field experiments, lab analyses, and modelling frameworks, current empirical insights from farming practice on a more national scale are still rare. For this reason, the agronomic importance of soil fertility for farming practice was the focus of this nationwide empirical study conducted in Germany (winter/spring 2022) with a total sample size of 585. The views and needs of 370 farmers and 215 agricultural institutionalists were evaluated, i.a., regarding the importance of soil fertility and related soil properties, as well as preferred agronomic management strategies and needs for the promotion of soil fertility. The results showed that most farmers and institutionalists consider soil fertility to be very important. Moreover, it was emphasized that the importance of this factor will increase in the future due to changing climatic conditions (e.g., heat/drought stress) and the need for more sustainable land use including the protection of biodiversity. The main motivations for agronomic investments in greater soil fertility were improving the climate resilience and yield stability of cropping systems. In this context, the top soil properties of interest were ranked by the respondents as follows: (1) water storage capacity, (2) rootability, (3) biological activity, and (4) water infiltration rate. To promote soil fertility, farmers mainly considered catch cropping, diversified crop rotations with a positive humus balance, and year-round ground plant cover/mulch as the most useful agronomic measures. In terms of methods for the assessment of soil fertility, soil structure analyses, biological indicators, yield/biomass production, soil nutrient analyses, and field methods were most important, whereas sensor systems and apps/digital tools were of minor importance. For the future improvement of soil fertility promotion in farming practice, simple indicators and reference values for assessing soil fertility as well as 'workshops, field days, and field schools' for training aspects were suggested by the participants. Overall, there were few differences between the perceptions of farmers and agricultural institutionalists. Both groups pointed out the need for improved communication between politics, science, and practice such that agriculture can respond more quickly to changing climatic conditions in the future.