Sustainable Soil Use Research Articles

Review of Root–Stubble Characteristics and Root–Stubble Crushing and Clearing Technologies for Conservation Tillage

Conservation tillage (CT) is an agricultural technology for sustainable soil use, and clearing the root–stubble remaining in the seedbed and nursery bed is a core aspect of CT. In this paper, the characteristics and the testing methods of roots stubble and their growth environments were reviewed, which could provide a reference for the setting of parameters in numerical simulations and the design of stubble treatment devices. The methods for the restoration of the environment in CT are described. Moreover, the root–stubble crushing and clearing technologies and the methods for the evaluation of device performance are summarized. Furthermore, the prospects for the physical properties of the roots stubble soil, the reduction of soil adhesion when a cutter crushes the roots, the tracking of the long-term effects of different methods on soil, intelligent devices for the crushing of roots stubble, and challenges and strategies regarding the application of new root–stubble crushing and clearing technologies are discussed. This paper provides a reference for the development of devices for the crushing of roots stubble from the perspective of CT.

Mapping the soil C:N ratio at the European scale by combining multi-year Sentinel radar and optical data via cloud computing

Spatial information on the soil carbon-to-nitrogen (C:N) ratio is essential for sustainable soil use and management. The unprecedented availability of Sentinel optical and radar data on cloud computing platforms, such as the Google Earth Engine (GEE), has created new possibilities for developing soil prediction models from the local scale to the planetary scale. However, there is a paucity of literature on the effects of Sentinel sensor selection and integration and radar data utilization strategies on mapping the C:N ratio. In this study, we explored the use of multiyear Sentinel-1 radar and Sentinel-2 optical data obtained from the GEE platform combined with the digital soil mapping (DSM) technique to map the soil C:N ratio at the European scale. The performance of soil prediction models, which were constructed using two modeling techniques (random forest and support vector machine), derived under multiple scenarios based on optical, radar and commonly used auxiliary data (climatic and topographic variables) combined with the LUCAS 2018 soil dataset, was evaluated by a cross-validation technique. The results showed that the modeling performance varied with the selection and integration of Sentinel observations, as well as the configuration of the radar data. Models based on single polarization performed the worst across all scenarios related to Sentinel-1, with cross-polarization performing better than copolarization. Models that utilized Sentinel-1 data from ascending orbits outperformed those that utilized data from descending orbits. The application of Sentinel-1 backscatter information derived from different orbits and polarization modes resulted in improved prediction accuracy. Our study also demonstrated the potential of integrating multiyear Sentinel satellite data via the GEE to map the continental-scale C:N ratio. The model based on Sentinel-1 data outperformed the one built on Sentinel-2 data, whereas combining Sentinel-2 optical data with Sentinel-1 radar data led to more accurate predictions. The variable importance results indicated that optical data and backscattering information from Sentinel observations are the most important groups of variables for soil C:N ratio mapping compared to the other variable groups (terrain and climate data). The digital soil maps generated under the different scenarios exhibited detailed patterns with significant spatial variation, with similar overall trends but slightly different details.

Tillage and land use management effects on soil organic matter and soil microbial biomass in a field network of practical farms in France, Romania, and Sweden

A reduction in tillage intensity, organic farming, and introduction of ley-grass periods into the crop rotation are means to improve the sustainable use of arable soils. The current study uses an on-farm approach to investigate soils from different practical farms in Northern France, Central Sweden, and Romania in comparison with previously published results from randomized field-experiments nearby. No-tillage generally increased the mean SOC and total N contents of arable fields in comparison with ploughing. However, this increase was only significant for the 70 % increase at 0–10 cm depth in France. No-tillage had no general effects on MBC or MBN and increased the ergosterol content at 0–10 cm depth in France and Sweden but not in Romania. Averaging depths and tillage systems, median MBC significantly increased in the order France (220 μg g−1 soil), Romania (324 μg g−1 soil), and Sweden (384 μg g−1 soil), whereas the median MB-C/N ratios followed the order France (5.6), Sweden (8.3), and Romania (10.4). The study region-specific changes in soil pH significantly affected covariate MBC and MBN in the regression equations for equal slopes ANCOVA models. In Northern France, organic farming and ley-grass implementation into the crop rotation increased SOC and total N contents to the level of the no-tillage fields. On-farm research gives similar results to long-term field experiments in France, Romania, and Sweden. For this reason, on-farm research on practical fields is an important, less expensive alternative to randomized field experiments, with a high potential for investigating actual and relevant research objectives. This will intensify exchange in knowledge between scientists and farmers.

Remote Sensing Inversion of Soil Organic Matter Content in Straw-Returned Fields in China’s Black Soil Region

China’s black earth region is the country’s corn golden belt, and returning corn straw to the field not only helps improve the Soil Organic Matter (SOM) content and soil fertility, but also resolves environmental pollution caused by straw burning. To study the effects of different years and amounts of straw returned to the field on SOM content, this study used soil sampling data from a conservation tillage experimental base in Gaojia Village, Lishu County, combined with indoor measurements of imaging spectral data, to establish a prediction model of SOM content by applying partial least squares regression, and inverting the SOM content in the study area. The results showed that the PLSR model accuracy using indoor measured soil imaging spectral data as the independent variable was high. The accuracy coefficients of samples with different field return and different field return amounts, R2, were 0.9176 and 0.8901, respectively, which better predicted SOM content. In the 0–50 cm tillage layer, the highest average SOM content of 39.73 g/kg was found under the NT-1 treatment with different no-tillage straw return year treatments. The depth of the tillage layer in the typical black soil region of Northeast China is around 0–20 cm, and the most significant increase in SOM content was observed in the experimental samples under the NT-1 treatment. SOM content in NT-1 treatment increased by 31.83% compared with CK-1, 68.24% compared with CK-2, 72.18% compared with NT-0, 699.48% compared with NT-2, and 311.44% compared with NT-3, respectively. The highest SOM content of 31.9 g/kg was found in NT-100 under the different treatments of different years of field return. At the 0–20 cm soil layer, the SOM content increases most significantly under NT-100 treatment, which is the most suitable treatment method for straw return to the field. And NT-100 is 22.09% higher than CK-1, 55.36% higher than CK-2, 58.99% higher than NC-0, 115.95% higher than NT-33, and 48.72% higher than NT-67, respectively. This study provides data that can support the conservation of soil ecosystem diversity and sustainable soil use, and it also enriches the application of the PLSR model application.

Mapping Topsoil Carbon Storage Dynamics of Croplands Based on Temporal Mosaicking Images of Landsat and Machine Learning Approach

Understanding changes of soil organic carbon (SOC) in top layers of croplands and their driving factors is a vital prerequisite in decision-making for maintaining sustainable agriculture. However, high-precision estimation of SOC of croplands at regional scale is still an issue to be solved. Based on soil samples, synthetic image of bare soil and geographical data, this paper predicted SOC density of croplands using Random Forest model in the Black Soil Region of Jilin Province, China in 2005 and 2020, and analyzed its influencing factors. Results showed that random forest model that integrates bare soil composite images improve the accuracy and robustness of SOC density prediction. From 2005 to 2020, the total SOC storage in croplands decreased from 89.96 to 82.79 Tg C with an annual decrease of 0.48 Tg C yr−1. The mean value of SOC density of croplands decreased from 3.42 to 3.32 kg/m2, and high values are distributed in middle parts. Changes of SOC represented significant heterogeneity spatially. 62.14% of croplands with SOC density greater than 4.0 kg/m2 decreased significantly, and 38.60% of croplands with SOC density between 2.5 and 3.0 kg/m2 significantly increased. Climatic factors made great contributions to SOC density, however, their relative importance (RI) to SOC density decreased from 44.65% to 37.26% during the study period. Synthetic images of bare soil constituted 23.54% and 26.29% of RI in the SOC density prediction, respectively, and the contribution of each band was quite different. The RIs of topographic and vegetation factors were low but increased significantly from 2005 to 2020. This study can aid local land managers and governmental agencies in assessing carbon sequestration potential and carbon credits, thus contributing to the protection and sustainable use of black soils.

Vegetation degradation alters soil physicochemical properties and potentially affects ecosystem services in green spaces of a tropical megacity (Lubumbashi, DR Congo)

Urban soils are degraded by various human pressures, including vegetation degradation, leading to changes in physical and chemical characteristics and affecting important ecosystem services. Soil physical properties are an important fertility control parameter, providing the basis for sustainable soil use in urban conditions; however, they do not receive sufficient attention in work on tropical cities. We assessed the impact of vegetation degradation in six urban green spaces (bare soils versus vegetated soils) on the physical (texture, soil bulk density and structure) and chemical (pH, cation exchange capacity (CEC), organic carbon (OC), nitrogen (N), phosphorus (P), potassium (K), copper (Cu) and manganese (Mn)) qualities of soils in a tropical megacity (Lubumbashi, DR Congo). Vegetated soils presented better physical and chemical qualities than bare soils. Vegetated soils were characterized by a high clay and silt content and a good consistency (soil structure), while bare soils were characterized by a high sand content and high bulk density. Vegetated soils were characterized by higher pH, OC, N, C/N ratio, CEC, P, and K. There was no significant difference in Mn or Cu between bare and vegetated soils. Cu was highly variable between sites (from 99 ± 61 mg.kg−1 in VS to 8559 ± 151 mg.kg−1 in BS). Our results demonstrate that the destruction of vegetation, leading to bare soil, negatively affects soil properties and may interfere with ecosystem services provided by urban soils in tropical climates. The physical properties observed in bare soils in this study, including silt, clay, and sand content, soil structure, and soil bulk density, along with chemical properties such as soil pH, cation exchange capacity, and soil organic carbon, can influence the ecosystem services provided by urban soils. These services include regulating water flow and nutrient cycling, enhancing nutrient availability, and supporting ecosystem functions through the cycling of water and nutrients.

A transdisciplinary approach for assessing connections between soil, food, and people in Aotearoa New Zealand

AbstractWith increasing urbanisation, there is a growing disconnect between soil, food, and people, centred around those relying on Western food production models. This ‘wicked’ problem is challenging sustainable soil use and food insecurity. Knowledge of soils through scientific studies alone are unable to address this problem, and we need to extend the boundaries of soil science for real progress to be made. We present a conceptual framework for understanding the reciprocal connections between soil, food, and people in local food production systems in Aotearoa New Zealand, applying principles and practices from a transdisciplinary research methodology. The resultant Food‐Landscape Networks framework consists of seven factors that support understanding the inherent connections between soil, food, and people. The factors are: soil health, land suitability, climate, productivity, food quality, well‐being, and engagement. These factors have been identified from weaving together mātauraka (the Kāi Tahu mita [dialect] is used in this text, where the ‘ng’ diagraph is replaced with ‘k’, e.g., mātauranga = mātauraka) Māori (Māori knowledge, culture, worldview, and values) and soil science using the He Awa Whiria, Braided Rivers, epistemological framework. Mātauraka Māori guides the traditionally reductionist discipline of soil science into a more holistic space, in an Aotearoa New Zealand context.

Influence of Land Use Types on Soil Properties and Soil Quality in Karst Regions of Southwest China

Establishing a suitable and useful soil quality index (SQI) is the key to accurately evaluating changes in soil quality (SQ) under different land use types. In the present study, a suitable and useful SQI using a minimum data set (MDS) with two scoring methods (linear scoring method and nonlinear scoring method) and two additive models (simple additive model with same weighting value and weighted additive model with significant different weighting value) was established to compare SQ under different land uses in Longtan valley. Soil samples were collected under one dryland (DRYL), one paddy (PADD), one orchard (GRA), and one natural forest (FORE), and 13 soil properties were measured. The four land use types had the same soil type and similar environmental conditions. Land use types had significant effects on the measured 12 soil properties. The top two principal components in Principal Component Analysis were chosen, and their cumulative variance was more than 90%. Soil indicators of soil labile carbon, C/N ratio, and microaggregates were chosen as members of MDS in this study. Significant (p < 0.001) positive correlations among the four establishing SQIs were found. The values of the sensitive index ranged from 47.17% to 82.12% for the four SQIs, and the SQI established using the nonlinear scoring method and weighted additive model (SQI-NLW) had the highest values. Among the four land use types, the four SQIs had similar change trends, and the average values of SQ under FORE (0.73) and PADD (0.68) were significantly higher than those under GRA (0.54) and DRYL (0.43). These results indicated that the SQI-NLW was an effective and precise tool to assess SQ under different land uses in similar regions, and the FORE and PADD were the suitable land use types for the sustainable use of soils in karst regions.

Enhancing soil properties through sustainable agronomic practices reduced the occurrence of kiwifruit vine decline syndrome

AbstractKiwifruit vine decline syndrome (KVDS) is an emerging challenge related to various factors, including water stagnation, soil compaction, root hypoxia, imbalanced soil redox potential, soil nutrient imbalance and fungal pathogens, yet its definitive causes remain unconfirmed. This study compared soils and roots of healthy plants (CTRL group) and plants affected by KVDS (KVDS group) at a kiwifruit orchard (Actinidia chinensis cv. ‘Zesy002’) in Sermoneta (Lazio region), Italy. Our findings indicate that soil redox potential, a parameter related to soil oxidation, and soil macroporosity were significantly lower in the KVDS soils. The water table was also found to be higher in the KVDS block of the orchard, demonstrating a higher soil water content. This suggests less aeration and more water content in KVDS‐affected soils compared to the control. Root analyses showed that KVDS led to browning and decomposition of rhizodermis, decay of the central stele, detachment of the cortex, reduced cell size and the presence of starch granules in the parenchyma, with significant variations in root morphometric parameters. To mitigate KVDS, sustainable agronomic practices, including precision irrigation, green manure, compost addition and water drainage root pruning, were carried out on some KVDS‐symptomatic vines (SUST group). Molecular analyses revealed the presence of 10 fungal species from treated root plants, with Paraphaeosphaeria michotii, Fusarium oxysporum and Ilyonectria vredenhoekensis being frequently isolated from symptomatic KVDS‐affected plants but less from SUST plants. Also, Beauveria bassiana and Bacillus amyloliquefaciens, showed an effect against I. vredenhoekensis and P. michotii, and may be suitable candidates as biocontrol agents. The adoption of sustainable agronomic practices resulted in significantly more healthy plants (46% increase) by the end of the trial, demonstrating that a sustainable soil use and water management at kiwifruit orchards can mitigate KVDS through restoration of natural growth conditions.

Effect of Pumping Speeds on the Fate of Aniline in Different Soil Layer

Helan Mountain is an important ecological safety barrier in northwest China. In this study, a heterogeneous site polluted by aniline on Helan Mountain was the research object, and the TMVOC (A Simulator For Multiple Volatile Organic Chemicals) model of aniline restoration by pumping was optimized by employing a column experiment. Four typical layers of the soil medium were selected to explore the influence of soil settlement caused by different pumping speeds on the fate of aniline in different zones. The results show that the optimal pumping speed at the site is 3.24 × 106 m3/month and the latest remediation time is the 10th month after the start of the remediation. The larger the pumping speed is, the more obvious the sedimentation effect is. When the remediation is carried out at 5.18 × 106 m3/month, the NAPL (Non-Aqueous-Phase Liquid) phase removal rate decreases by 33.75% and the distribution of aniline to the NAPL phase increases, compared to that without considering the soil settlement. The fate of aniline in the source zone is the least affected by sedimentation, while that in the vadose zone is the most affected. The phase redistribution phenomenon is the most obvious in the water table fluctuation zone, and the NAPL phase aniline changes into gas and liquid phases. In addition, the NAPL phase concentration in the water table fluctuation zone is two orders of magnitude higher than that at 0.2 m below the water table. NAPL is the most sensitive to the relative settlement in the aquifer. The simulation results can provide a technical reference for the future application of P&T (Pump-and-Treat) technology in the remediation of organically contaminated sites to facilitate the sustainable use of soil. It is suggested that more attention should be paid to the water table fluctuation zone during the remediation of contaminated sites.

Soil fertility mapping of a cultivated area in Resunga Municipality, Gulmi, Nepal.

Soil fertility maps are crucial for sustainable soil and land use management system for predicting soil health status. However, many regions of Nepal lack updated or reliable soil fertility maps. This study aimed to develop the soil fertility map of agricultural areas in Resunga Municipality, Gulmi district of Nepal using the geographical information system (GIS) technique. A total of 57 composite geo-referenced soil samples from the depth (0-20 cm) were taken from the agricultural land of an area of 52 km2. Soil samples were analyzed for their texture, pH, organic matter, total nitrogen, available phosphorous, available potassium, available boron, and available zinc. These parameters were modelled to develop a soil quality index (SQI). Using the kriging tool, obtained parameters were interpolated and digital maps were produced along with soil quality and nutrient indices. The result showed that the study area lies within the fair (0.4 to 0.6) and good (0.6 to 0.8) range of SQI representing 96% and 3% respectively. Soil organic matter and nitrogen showed moderate variability exhibiting a low status in 95% and 86% of the total study area. Phosphorous and potassium showed medium status in 88% and 75% of the study area, respectively. Zinc was low and boron status was medium in most of the area. To maintain soil fertility is by improving the rate of exogenous application of fertilizers and manures. The application of micronutrients like boron and zinc is highly recommended in the study area along with organic manures. The soil fertility map can be used as a baseline for soil and land use management in Resunga Municipality. We recommend further studies to validate the map and assess the factors affecting soil fertility in this region. Soil fertility maps provide researchers, farmers, students, and land use planners with easier decision-making tools for sustainable crop production systems and land use management systems.

Comparing Laboratory and Satellite Hyperspectral Predictions of Soil Organic Carbon in Farmland

Mapping soil organic carbon (SOC) accurately is essential for sustainable soil resource management. Hyperspectral data, a vital tool for SOC mapping, is obtained through both laboratory and satellite-based sources. While laboratory data is limited to sample point monitoring, satellite hyperspectral imagery covers entire regions, albeit susceptible to external environmental interference. This study, conducted in the Yuncheng Basin of the Yellow River Basin, compared the predictive accuracy of laboratory hyperspectral data (ASD FieldSpec4) and GF-5 satellite hyperspectral imagery for SOC mapping. Leveraging fractional order derivatives (FODs), various denoising methods, feature band selection, and the Random Forest model, the research revealed that laboratory hyperspectral data outperform satellite data in predicting SOC. FOD processing enhanced spectral information, and discrete wavelet transform (DWT) proved effective for GF-5 satellite imagery denoising. Stability competitive adaptive re-weighted sampling (sCARS) emerged as the optimal feature band selection algorithm. The 0.6FOD-sCARS RF model was identified as the optimal laboratory hyperspectral prediction model for SOC, while the 0.8FOD-DWT-sCARS RF model was deemed optimal for satellite hyperspectral prediction. This research, offering insights into farmland soil quality monitoring and strategies for sustainable soil use, holds significance for enhancing agricultural production efficiency.

Retraction Notice: Internet of Things Technology in Monitoring System of Sustainable Use of Soil and Land Resources

Retraction Notice: Internet of Things Technology in Monitoring System of Sustainable Use of Soil and Land Resources

Multifunctional Use of Agricultural Land with Support for Selected Ecosystem Services in the Territory Protected Water Management Area Žitný Ostrov

Agroecosystems represent one of the largest managed systems in the world and are also considered to be the largest surface polluters. This is particularly evident in highly vulnerable areas such as the model area assessed in our study, the Protected Water Management Area Žitný Ostrov, which contains the most productive soils in the Slovak Republic. At the same time, the Žitný Ostrov represents the largest groundwater reserves in Central Europe. We based this study on the information system of valued soil ecological units (VSEUs) and their spatial extension to the agricultural soils of Slovakia. We used the synthetic–parametric method for a summary (aggregated) evaluation of the production potential of provisioning soil services, which we have classified into 10 categories (1—very low potential; 10—very high potential). Overall, there were 3000 individual VSEUs identified in the area of interest. By eliminating repetitive VSEUs, we evaluated 72 subtypes and, based on similarity parameters, they were further aggregated into 40 soil subtypes. The result is a spatial representation of the proposed zones within the model area (zones for sustainable economic use of soils with the support for ecologization of agricultural technologies, non-productive land use, cultural and social development of rural areas, and the protection and revitalization of the landscape and its components). The approach is also suitable for use in other areas.

Inhibition mechanism of atrazine on soybean growth insight from the plant nitrogen fixation and rhizobia diversity inhabiting in nodules and rhizosphere soil

Atrazine [2-chloro-4-(ethylamino)-6-(isopropylamino)-s-triazine] is a herbicide widely used in corn production. However, when used under corn-soybean rotation system, it decreases soybean yield. The toxicology mechanisms of atrazine on soybean are rarely studied from the perspective of rhizobia-soybean symbiont formation and nitrogen fixation. In this study, under atrazine stress, high-throughput sequencing of rpoB was performed to evaluate rhizobial diversity in the nodules and soybean rhizosphere, and the correlation between physiological indicators of plant growth and rhizobial diversity was explored. The results revealed that atrazine inhibited soybean growth by disrupting glutamate synthetase activity to reduce total nitrogen (TN) content in the plant. Atrazine altered the nodule structure and reduced leghemoglobin content. High-throughput sequencing of rpoB revealed that the inhibition rate of Afipia genosp. 3 in the nodules under atrazine stress (20 mg·kg−1) was >80 % higher than that in the control (no atrazine). The abundance of Rhizobium leguminosarum and Rhizobium sp. CF142 decreased in the nodules but increased in rhizosphere soil. The abundance of Afipia genosp. 3 and Bradyrhizobium sp. AD1_1_nodule in rhizosphere soil was negatively correlated with TN content and glutamate synthetase activity in soybean. Moreover, soil nitrate nitrogen (NO3−-N) content was the essential factor influencing the rhizobial community diversity under atrazine stress. This study contributed to clarifying the molecular biological mechanism of the effect of atrazine on rhizobia-soybean symbiont formation and nitrogen fixation. Moreover, this study provided new directions and ideas for the subsequent development and application of related prevention and control measures, which can contribute to the more comprehensive development of corn-soybean crop rotation system and ensure the sustainable use of black soil.

Evaluating short-term effects of rice straw management on carbon fractions, composition and stability of soil aggregates in an acidic red soil with a vegetable planting history

Red soils are characterised by acidic pH and limitations in carbon, nitrogen, water, and soil structure. To overcome such limitations, improved soil aggregation is the key to improving the physical and chemical properties of soil. Applying organic amendments such as straw can lead to corresponding soil aggregation and stability changes. Therefore, we explored the short-term effects of rice straw amendment, either alone or in combination with biochar, on improving the carbon fractions, stability, and composition of soil aggregates in red soil with a history of vegetable planting. The study consisted of four treatments: control (no organic material, CK), biochar alone (5% homemade straw biochar, B), straw alone (12% rice straw, S), and biochar with straw (5% homemade straw biochar + 12% rice straw, BS). Our results showed that equal amounts of straw and biochar substantially reduced the number of mechanically stable aggregates (MSA), mean weight diameter (MWD), and geometric mean diameter (GMD) of the soil. BS treatment reduced >0.25 mm aggregate content (R0.25), MWD and GMD by 24.06%, 56.81%, and 62.19%, respectively, compared with that of the control. The addition of straw greatly enhanced the water-stable macromolecular content and stability coefficient of the soil, but treatment B had no obvious effect. The S treatment had the greatest effect on R0.25, MWD and GMD, increasing them by 143.94%, 246.67%, and 181.82%, respectively, compared with that of the control. Soil organic carbon (SOC) was significantly increased by straw addition and carbonisation treatment, and the effect of the BS treatment was the best, with an increase of 325.63% compared with that of the control. The organic carbon content in the aggregates of different particle sizes treated with different organic materials also increased significantly. In the soil reactive organic carbon fraction, applying biochar alone did not affect microbial biomass carbon (MBC), dissolved organic carbon (DOC), or easily oxidized organic carbon (EOC) but could increase the particulate organic carbon (POC) content. All the treatments with straw application significantly increased the MBC, DOC, EOC, and POC content, and the highest effect was obtained by applying both straw and biochar in an integrated form, i.e., the BS treatment. In conclusion, the co-application of biochar and straw sequestered more carbon and revamped soil C pools than either biochar or straw alone and could be a promising option for the sustainable use of red soils to ameliorate the aforementioned limitations associated with this soil type.

Digital soil characteristics mapping for aiding site-specific management practices in the West Nile Delta, Egypt

Soil survey system is the key word for sustainable soil use and management. This study aimed to generate a digital soil mapping (DSM) using various spatial resolutions of freely available digital elevation models (DEMs) i.e., (30 m ALOS “Advanced Land Observing Satellite”, 12.5 m ALOS, 30 m SRTM “Shuttle Radar Topography Mission”, and 90 m SRTM) to assess the spatial variability of soil physio-chemical characteristics in El-Beheira Province, Western Nile Delta. A total of 196 geo-referenced soil profiles were collected from agricultural fields, and the soil's contents of total nitrogen (TN), potassium (K), available phosphorous (P), calcium (Ca), sulfur (S), magnesium (Mg), zinc (Zn), copper (Cu), manganese (Mn), and boron (B) were analyzed. The study revealed that the soil's contents of TN, P, S, B, and Cu were low, and Mg was suspected to be higher than K, leading to an Mg-induced K shortage. The study also found wide-ranging spatial variability structures of inverse distance weight (IDW) interpolated maps for several soil physio-chemical characteristics. The DSM further revealed that poor status was present in 85.4% of TN, 91.7% of P, 76.9% of S, and 87% of both B and Cu. The conclusion is that soil variability, as demonstrated in DSM, outweighs uniform treatment, and this discovery will assist extension workers, scientists, and decision-makers in implementing site-specific nutrient management strategies. Validation of the DSM is recommended for crop and variety-specific nutrient treatment rates. Understanding the geographical variance in the soil is critical for long-term nutrient management and higher output.

Spatial assessment of the soil organic carbon content under different types of land use in the Ohrid valley

Spatial assessment of key soil properties is a basic prerequisite for the evidence-based decision making and sustainable use and management of soil. The aim of this work was to estimate the spatial distribution of SOC under different types of land use, by the means of Digital Soil Mapping techniques. A site-specific soil data collection for the Ohrid valley was integrated with continuous and discrete datasets of environmental covariates, serving as predictors. The selected test area outlines the variability of factors influencing the SOC content and spatial distribution. Soil sampling locations were randomly distributed within a predefined mesh with a 1-sq.km spatial resolution and further stratified to outline different types of land use within each mash square. Soil samples were collected from 93 locations at three depths, each 20 cm apart, covering the total area of 10 thousand ha of arable land, forestland, and land under natural vegetation. A set of additional environmental dataset was collected, namely the soil map, land use map, geology map, digital terrain model and its derivatives, satellite images, climate data, as well as a set of indices NDVI, SAVI, BI etc., developed from the remote sensing datasets. Multiple linear regression was used for evaluating the regression pattern between the environmental predictors and the target variable. To estimate spatial variability, several regression tree methods were used. The results obtained using this approach have given a better spatial overview of the most vulnerable areas regarding SOC depletion. Out of 21 locations examined, the content of soil organic carbon in the top layer (0-20 cm.) of forestland was on average 6.81%, while at 22 locations examined under grassland, the average content was 4.07%. The arable land, which is under continuous human impact, had the lowest content of SOC of 2.5% under field crops and 2.61% under perennials.

Land evaluation approaches comparing TOPSIS and SAW with parametric methods for rice cultivation.

Population growth has resulted in an increase in land exploitation on a large scale. Therefore, to increase crop yield and sustainable use of soil, it is necessary to exploit the land according to its potential. Due to land suitability assessment's multifactor nature, it needs a method for evaluating the factors simultaneously; in this case, multi-criteria decision models can be used. Therefore, this study aimed to compare the efficiency of the parametric method (square root) with multi-criteria decision-making approaches (technique for order of preference by similarity to ideal solution (TOPSIS) and simple additive weighting (SAW)) for evaluating land suitability in some rice cultivated areas in Khuzestan province, southwest Iran. A total of 28 rice farms were selected in the study area, and a pedon was dug, examined, and sampled in each. Several physicochemical land characteristics were used for the evaluation process, such as soil, climate, and topographical factors. According to the results, soil texture is the main limiting factor for rice farming in the study region, and organic carbon, salinity, and alkalinity ranked next. The range of land index for rice cultivation calculated by the square root method was from 19.3 to 70.9, from 49 to 95.3 by TOPSIS, and from 3.57 to 74.7 by SAW. The calculated explanatory coefficients between the actual yield and land indices for rice products estimated by the square root method, the TOPSIS approach, and the SAW method were 0.44, 0.63, and 0.60, respectively. This result confirms the high accuracy of TOPSIS method compared to SAW and square root methods. TOPSIS is therefore the ideal method for prioritizing options based on the simulation of the ideal answer because it is highly technical and robust in its decision-making approach. Furthermore, it uses the standardization method, equations, mathematical matrices, and suitable weights. Overall, it can be recommended as a suitable efficiency approach for land suitability evaluation.

Investigation on the impact of curing conditions on the behavior of controlled low-strength material as basement of rural roads in the Mekong Delta

This study investigates the performance of controlled low-strength material (CLSM) in the challenging Mekong Delta climate, which experiences rapid weather fluctuations, acid rain erosion, uneven mixture gradation, and subpar aggregate quality. Additionally, difficulties in sieving and quality control have led to the disposal of dredged soil in construction sites. The research aims to enhance the practical use of CLSM as a foundation for rural roads in the Mekong Delta by incorporating dredged soil and mineral additives (6% silica fume, 25% bottom ash) under various curing conditions, including normal, acidic, and salted environments. The study assesses CLSM strength through flowability, setting time, and unconfined compressive strength tests. Findings highlight the significant influence of curing conditions on both early and 28-day CLSM strength, with harsh environments compromising cement hydration. Optimizing dredged soil content and mineral components can bolster CLSM strength and promote sustainable use of dredged soil in construction.