Geoderma Regional最新文献

Conservation tillage, particularly the implementation of no-tillage and straw retention (NTS), has been proposed as an effective practice to enhance soil structure and improve soil quality in Northeast China. However, the impact of NTS on maize (Zea mays L.) root growth morphology and the influence of tillage practices on maize root morphology through soil physical properties and structure in Northeast China remain understudied. To address this knowledge gap, a continuous ten-year experiment was conducted to assess the effects of NTS on soil physical properties, aggregate structure, maize root morphology, and their interconnections. Our findings demonstrate that the NTS treatment significantly increased soil water content and soil bulk density at depths of 0–5 cm (1.6%) and 5–10 cm (2.2%), while decreasing soil porosity at depths of 0–5 cm (1.4%) and 5–10 cm (2.0%) compared to conventional tillage (CT). Additionally, NTS resulted in a higher content of soil macro-aggregates (> 0.25 mm) and improved soil aggregate stability compared to CT. Notably, root length, root surface area, root volume, and root biomass in the NTS treatment were 6.04%, 22.15%, 10.04%, and 9.29% higher than those in CT, respectively. However, there was no significant difference in root diameter between the two tillage practices. These results reveal that NTS induces alterations in soil physical properties, aggregate size distribution and aggregate stability, thereby affecting maize root growth morphology.

Soil properties and processes can often be estimated by soil color, which provides valuable information about composition and other properties. We focused on the morphology, particle size distribution, and chemical properties of Alfisols in Kakalachinte microwatershed, Karnataka, India. Soil redness indices were calculated: Buntley's and Westin's rate (BWR), Hurst's rate (HR); Torrent’ rates (TR1 and TR2); Barrón's and Torrent's rates (BTR1 and BTR2), and Viscarra's rate (VR). The Munsell notation was converted to the CIE (Commission Internationale del'Eclairage, CIE) Lab system, Lab model: L*, luminance, a*, redness, and b*, yellowness of moist soil samples. Forty-five soil samples were characterized for texture, soil reaction (pH), electrical conductivity, calcium carbonate content, organic carbon (OC) content, exchangeable cations (sodium, Na; potassium, K; magnesium, Mg; and calcium, Ca), base saturation (BS), and cation exchange capacity (CEC). The pH of the soils ranged from strongly acid (pH of 5.25) to strongly alkaline (pH of 8.73). These soils exhibited low CEC, with a mean of 8.36 cmol(+)/kg and a coefficient of variation of >35%. The BS was found to be >70%, with a dominance of exchangeable Ca (6.93 ± 1.08 cmol(+)/kg). Correlation analysis revealed strong positive relationships between OC and the L* value (r = 0.6**). The correlation results revealed a strong positive correlation of a* value with TR1 (r = 0.54**), significant negative relationship between L* and BTR1 (r = −0.54*), BTR2 (r = −0.525*), and VR (r = −0.53*). Regression between soil OC and the L* value showed R² of 0.40 and RMSE of 0.01; soil exchangeable K and Mg with the R² = 0.74; RMSE = 0.12 and the R² = 0.42; RMSE = 0.11 at p < 0.001. The study highlights the importance of converting Munsell color notation to the CIELab system for understanding the rubification process in relation to soil properties in Alfisols at the microwatershed level.

Pedogenic oxide ratio (POR) helps identify and understand pedogenic processes. However, POR is seldom used to study and distinguish the weathering intensity of volcanic soils that are moderately weathered in the subtropical region. The research was conducted by collecting thirty-three subtropical volcanic soils from the northeastern Tatung Volcanic Group (TVG) region in Yangmingshan National Park, near Taipei, located in northern Taiwan. These volcanic soils could be grouped into two groups with different weathering intensities based on three weathering indices, pedogenic Fe-oxides ratios (PORs) - Fe_o/Fe_d, ((Fe_d-Fe_o)/Fe_t), and total reserve in bases (TRB): the highly weathered (HW) group and the lower weathered (LW) group. The two PORs show significant differences between the two groups, but not for TRB, which indicates the PORs are a good indicator for providing quantitative measurements of weathering intensity. Except for the soil bulk density (BD) meeting andic soil properties, the soils of the HW group have a lower content of Al plus 1/2 Fe content (by ammonium oxalate) (Al_o + 1/2Fe_o) and phosphorus (P) retention, and soils of the LW group only have a lower P retention. Within those soil physicochemical properties and selective dissolution analysis results and index values, Al_o + 1/2Fe_o, pyrophosphate extractable C (C_p), P retention, and dithionite-citrate-bicarbonate (DCB) extractable aluminum (Al_d) are four critical factors for the discrimination of the soils. The soils in the study area still have relict volcanic soil properties after moderately weathered. Our research results indicate that the PO and POR are good indicators of moderately weathered volcanic soils in the subtropical region, allowing for a more precise assessment of the soil weathering process, in addition to quantitative measurements of weathering intensity.

Agricultural land abandonment is a major land use change in the Mediterranean region, especially affecting marginal areas. The fields of the abandoned village Sierra Estronad (Aragón, Spain), experienced heavy impact treatments (bulldozing) after which half of the fields were kept open and tilled without planting any crop and the other half of the fields were left fallow. From these two treatments and the surrounding natural forest 483 soil samples were collected in addition to corresponding vegetation data at 162 GPS referenced sampling points. Soil samples were analyzed using predictive models based on visible and near-infrared spectroscopy for Soil Organic Carbon (SOC), total Nitrogen, and Permanganate Oxidizable Carbon.

Comparing the fallow fields, which have had a 15-year recovery period to the tilled fields, a SOC sequestration rate of 0.64 Mg ha⁻¹ y⁻¹ was found. On tilled fields however, even after a recovery period of 5 years, very few plants were able to colonize the area, resulting in a sparse soil cover and significantly lower SOC and total N stocks.

These results show the interdependence of soil fertility proxies (SOC and /total Nitrogen) and the degree of vegetation cover, and how practices of preventing former agricultural fields from revegetating have a long-lasting impact of soil degradation, even after their termination. However, if left fallow, abandoned fields do have the potential to support a secondary succession and serve as a carbon sink thus contributing to soil fertility and climate change mitigation.

Soil aggregation controls several physical, chemical, and biological processes. Soil organic matter (SOM) and its stabilizing agents are regarded as the most important factors driving formation and stabilization of soil aggregates. However, aggregate stability in highly weathered soils may also be related to clay mineral composition and soil chemical properties. This study aims to evaluate the processes controlling soil aggregate stability and to understand the influence of soil chemical and clay mineral composition on the structural stability of highly weathered soils. Four Brazilian Oxisols were investigated: (P1) Xanthic Kandiustox, (P2) Rhodic Haplustox, (P3) Anionic Acrustox, and (P4) Typic Hapludox. Undisturbed and disturbed soil samples were collected from the Bw horizon under a native forest. Soil structural stability was evaluated using a variety of techniques and indices, including mean weight diameter (MWD) by hydraulic stress, mechanically-dispersible clay (MDC) by turbidimetry, tensile strength (TS) by crushing aggregates, and soil structural stability index (SSI) taking into account soil organic carbon (SOC). In general, P1 exhibited the highest MDC content (3.05 ± 0.54, Nephelometric Turbidity Unit, NTU/g L^‐−1), while P4 had the highest MWD (10.26 ± 0.24 mm) and the highest TS (80.42 ± 18.54 kPa) within the 8–4 mm aggregate size class. The TS for the 4–2 mm and 2–1 mm aggregate size classes was found to be equal for P2 and P4, with values ranging from 158.17 ± 24.70 kPa to 148.04 ± 38.50 kPa in the 4–2 mm class, and from 459.51 ± 189 kPa to 328.35 ± 78.22 kPa in the 2–1 mm one. The SSI was found to be inadequate for evaluating the structural stability of the Oxisols. In general, SOC was found to be the main stabilizing agent of larger aggregates, while clay mineral composition determined the stability of smaller aggregates. Goethite associated with gibbsite was more effective in increasing the structural stability of P2 and P4. Furthermore, kaolinites with low crystallinity, which are found in clayey Oxisols, resulted in a high specific surface area, particularly in Rhodic Haplustox and Typic Hapludox soils, which promoted more interactions with other clay minerals (e.g., goethite and gibbsite) and SOC, thereby increasing the tensile strength in these Oxisols. In fact, the formation and stabilization of aggregates in highly weathered soils depends on several factors, but the influence of clay mineral composition stands out as the most pronounced.

Soil salinization, impaired by climate change and poor management practices, poses a global threat, particularly in arid and semi-arid regions, leading to significant land degradation. This study aims to investigate the effects of different nitrogen (N) fertilizer sources (urea, ammonium-sulfate, and biogas waste) on CO₂, N₂O, and NH₃ emissions and soil enzyme activities in two soil types varying in salinity level (non-saline: EC = 1.15 dS m⁻¹, and saline: EC = 35.80 dS m⁻¹) in a robotized continuous-flow soil incubation system. Our results showed a sharp increase in N₂O and CO₂ emissions (up to 0.51 ± 0.02 g N₂O-N ha⁻¹ day⁻¹, 28.1 ± 3.9 kg CO₂-C ha⁻¹ day⁻¹) in non-saline soils following soil rewetting, attributed to bacterial denitrification. However, this pattern was not observed in saline soils, suggesting that salinity causes partial inhibition to the regeneration of soil organic matter mineralization and denitrification processes after rewetting. Although salinity did not alter the overall cumulative N₂O losses in any fertilizer treatment, it significantly delayed the evolution of N₂O peak during the incubation period. On the other hand, NH₃ volatilization was significantly higher in N-fertilized saline soils compared to non-saline soils (241% and 157% in ammonium-sulfate and biogas waste treatments, respectively), except for urea treatment, likely due to the decrease in nitrification rates. Furthermore, the study clearly showed lower soil enzyme activity levels for both nitrate reductase and urease activity. Interestingly, the lowest NH₃ emissions were measured in urea treatment in both soils. Overall, our findings highlight the complex interplay between soil salinity, nitrogen fertilizer sources, and microbial processes, significantly influencing gaseous nitrogen emissions and N cycling in agricultural soils. Identifying the specific fertilizer treatments that minimize or maximize gaseous nitrogen losses in varying soil salinity, may guide the selection of appropriate fertilization strategies for farmers and policymakers to mitigate environmental impacts of fertilizer use during agricultural production.

This study linked soil FT-NIR spectroscopy with soil organic carbon (SOC) and total nitrogen (TN) content in Saskatchewan (SK) agricultural soils, using a multivariate approach. Soil spectra were acquired along with laboratory measurements of SOC and TN from 1965 Saskatchewan soil samples. Spectral data were transformed using a variety of common pre-treatment approaches: Savitszky-Golay, first and second derivative, standard normal variate, multiplicative scatter correction and continuous wavelet transform. Models were next built using cubist regression tree (Cubist), support vector machine (SVM), and partial least square regression (PLSR) to evaluate the performance of the different pre-treatment/modelling approaches. The continuous wavelets transform was the best performing spectral treatment method for SK agricultural SOC and TN. For predictive model using an extensive dataset, the cubist model performed best for SOC and TN (R² = 0.80 and 0.85) followed by SVM (R² = 0.77 and 0.85) and PLSR (R² = 0.63 and 0.73). However, all models demonstrated the same correlation between predicted and observed values for SOC and TN (CCC = 0.87 and 0.93). The consistent model accuracy with extensive soil dataset suggests model's ability to generalize well beyond the data it was trained on. However, model accuracy varies if trained using different soil zones and Sk agricultural sites, and this suggest the need for careful selection of specific site or soil-zone on which model should be trained. Additionally, this study also underscores the influence of factors beyond sample size and spectra variability, such as coefficient of variation, on the accuracy of SOC and TN predictions.

In recent years, the importance of soils and soil functions has been recognised for supporting the delivery of ecosystem services and for the realisation of international initiatives, such as the UN Sustainable Development Goals. At the same time, Digital Soil Mapping (DSM) has emerged as a modelling technique that can satisfy increased end-user needs for new soil datasets by producing fine resolution soils and soil property maps to support complex digital soil and land evaluation assessments. Spatial disaggregation is a popular DSM technique that is used to transform legacy soil maps to more spatially-explicit soils datasets, which can also be used in conjunction with soil databases to generate digital soil property maps. In this study, we performed spatial disaggregation of the National Soil Map of Scotland (originally published at 1:250,000 scale) at the taxonomic level of Soil Series, with the specific objective to facilitate the production of harmonised digital soil property maps to support soil and land evaluation assessments in Scotland through linking to the Scottish Soil Database. We divided Scotland into Landscape Units of similar soil and landform characteristics and trained probability random forest models within each Landscape Unit using area-proportion random sampling of both single- and multiple- (complex) Soil Series map units and selected environmental covariates to produce Soil Series probability layers at 50 m grid resolution. The performance of the disaggregated Soil Series maps was evaluated using prediction uncertainties of individual soil types and independent soil profile classifications. Evaluation results indicated that the random forest algorithm was successful in promoting effective spatial disaggregation of both single soil and complex soil polygons and provided good prediction accuracies for most soil types with the exception of some of the least extensive soil types typically found within complex map units. This was attributed mainly to algorithm's tendency to favour dominant, more extensive classes, along with its difficulty to distinguish between similar soils within spatially diverse areas. However, training Soil Series models at a Landscape Unit level instead of nationally helped to limit both the underestimation of these minority soil types and the overestimation of the dominant ones. In addition, map evaluation results showed the usefulness of using the generated conditional Soil Series probabilities for exploring soil spatial variability, especially within complex areas such as river floodplains covered by multiple alluvial and non-alluvial soils. Overall, this study demonstrates the potential of using spatial disaggregation to extract pedological knowledge embedded in legacy soil maps and use it to generate new dynamic and harmonised soil and soil property maps by effectively using readily-available and easily-updated soils information from existing databases.

Given the natural fragility of sandy soils, Brazil's intensification of agricultural use of subtropical Acrisols must be carefully monitored. Nevertheless, reference values for microbiological properties have not yet been determined for these soils. Therefore, this study sought to determine the critical limits (CLs) and interpretative classes for soil microbiological properties, establish a minimum set of soil chemical and biological indicators based on statistical associations between these indicators and maize yield, and identify which management systems contribute to the quality of a subtropical Acrisol. Soil samples were collected from two experiments that were selected to evaluate the effects of tillage and cropping systems on maize production for >30 years. Principles to determine CLs for chemical properties were used to interpret microbiological properties, and a minimum data set was established by principal component analysis. Interpretative classes were determined for soil enzymes arylsulfatase and β-glucosidase, microbial biomass carbon and nitrogen, and soil basal respiration as a function of soil organic carbon content and maize yield. By analyzing the minimum data set, we were able to identify properties to compose a soil quality index (SQI) composed of arylsulfatase activity, microbial biomass carbon, total soil nitrogen, soil organic carbon, and pH in water. The SQI revealed that maize cultivation on subtropical Acrisols in Brazil is more sustainable under no-till and with legumes as cover crops.

Soil is an indispensable and important natural resource and it is necessary to manage and monitor soil quality with appropriate methods in order to ensure the sustainability of the soil. The aim of this study is to determine the current state of soil quality in different land uses where soil functions change. This study was carried out in the Karasu river watershed with an area of 19,178 ha where Entisols soil order is dominant, located Andırın, Kahramanmaraş. The Soil Management Assessment Framework (SMAF) method and Principal Component Analysis (PCA) was used to assess the dynamic soil quality in watershed scale. In the evaluation of soil quality, random samples were taken from a total of 360 topsoil layers (0−30) according to land use and physiography. Soil indicators such as bulk density, aggregate stability, total organic carbon, pH, electrical conductivity, available phosphorus (P), potassium (K), available water amount and water-filled pore volume were selected to create the minimum data set (MDS). The results showed that the productivity, environmental protection, waste recycling quality scores and general soil quality index of forest lands are higher than agricultural lands. According to expert opinion, general soil quality scores were found to be 0.86 in forest land, 0.78 in crop land and 0.83 in grassland, while in PCA, general soil quality scores were calculated as 0.85 in forest land, 0.79 in crop land and 0.78 in grassland. Contribution level of soil functions to management targets was observed in water relations function the most. The contribution levels of the water relations function to the productivity, environmental protection and waste recycling management targets are 0.38, 0.32 and 0.34 in forest lands, 0.42, 0.33 and 0.35 in croplands, respectively; It was showed that 0.40, 0.33 and 0.36 in grasslands. Contribution rates of productivity, environmental protection and waste recycling management targets to soil quality were 30%, 37% and 33%, respectively, in forest lands; 29%, 37% and 34% in crop lands; It has been determined as 30%, 36% and 34% in the grasslands. Negative effects such as heavy rainfall in the watershed, wrong agricultural practices (excessive fertilization and irrigation, improper tillage, and crop selection, etc.) and excessive grazing have led to soil degradation and erosion, reducing the soil function capacity of agricultural lands and meadows. In order to increase the functional capacity of the soil, a soil management approach aimed at protection, improvement and sustainability must be adopted and implemented.