Search Results (1,135)

Flow regulation and water quality maintenance are considered ecosystem services, as they provide environmental benefits with a measurable economic value to society. Distributed or semi-distributed hydrological models can help identify where land use decisions yield the greatest economic and environmental returns related to water resources. For these reasons, this study integrated simulations performed with the SWAT (Soil and Water Assessment Tool) model under varying land use conditions, aiming to balance potential benefits with the loss of ecosystem services. Among the tested parameters, those associated with surface runoff showed the highest sensitivity in simulating streamflow for the Jundiaí River Basin. Based on the statistical indicators R², Nash–Sutcliffe efficiency (NS), and Percent Bias (PBIAS), the SWAT model demonstrated a reliable performance in replicating observed streamflows on a monthly scale, even with limited spatially distributed input data. Scenario 2, which involved converting 15% of pasture/agricultural land into forest, yielded the most favorable hydrological outcomes by increasing soil water infiltration and aquifer recharge while reducing surface runoff and sediment yield. These findings highlight the value of reforestation and land use planning as effective strategies for improving watershed hydrological performance and ensuring long-term water sustainability. Full article

With the increasing severity of global climate change and soil salinization, the development of microorganisms that enhance crop salt tolerance has become a critical focus of agricultural research. In this study, we explored the potential of a novel Streptomyces species Qhu-G9 as a plant growth-promoting rhizobacterium (PGPR) under salt stress conditions, employing whole-genome sequencing and functional annotation. The genomic analysis revealed that Qhu-G9 harbors various genes related to plant growth promotion, including those involved in phosphate solubilization, indole-3-acetic acid (IAA) biosynthesis, antioxidant activity, and nitrogen fixation. A total of 8528 coding genes were annotated in Qhu-G9, with a significant proportion related to cell metabolism, catalytic activity, and membrane transport, suggesting its broad growth-promoting potential. In vitro experiments demonstrated that Qhu-G9 exhibited strong iron siderophore production, IAA synthesis, phosphate solubilization, and 1-aminocyclopropane-1-carboxylate (ACC) deaminase activity, all of which correlate with its plant growth-promoting capacity. Further plant growth trials revealed that Qhu-G9 significantly enhances the growth of Avena sativa and Onobrychis viciifolia seedlings under salt stress conditions, improving key physiological parameters, such as chlorophyll content, relative water content, and photosynthetic efficiency. Under salt stress conditions, inoculation with Qhu-G9 resulted in notable increases in total biomass, root length, and plant height. Biochemical analyses further confirmed that Qhu-G9 alleviates the oxidative damage induced by salt stress by boosting antioxidant enzyme activities, reducing peroxide levels, and promoting the accumulation of osmotic regulators. These findings suggest that Qhu-G9 holds great promise as a PGPR that not only promotes plant growth, but also enhances plant tolerance to salt stress; thus, it has significant agricultural potential. Full article

The strength of cement-treated soil (CTS) is influenced by a range of complex factors. Investigating the microstructure of cement-treated soil provides a fundamental understanding of its strength evolution. This study explores the micro-mechanism of strength in cement-treated soil through qualitative and quantitative analyses. Unconfined compressive strength (UCS) tests were conducted on two types of cement-treated soils with different curing ages. Microstructural images were obtained via Scanning Electron Microscopy (SEM), from which micro-parameters—including image porosity ($n_s$), mean shape factor ($F$), particle grading entropy ($G_e$), and directional probability entropy ($H_m$)—were obtained. Finally, the Grey Relation Analysis (GRA) method was employed to evaluate the relative importance of these micro-parameters in influencing strength. And the micro-mechanism of strength was discussed. Results show that these variations of cement-treated soil strength are primarily governed by porosity, particle shape, size, and arrangement. GRA results indicate that particle grading entropy ($G_e$) has the greatest impact on CTS strength, followed by image porosity ($n_s$). In contrast, mean shape factor ($F$) and directional probability entropy ($H_m$) have relatively lower impacts. The order of influence is $G_e>n_s>F \mathrm{a}\mathrm{n}\mathrm{d} H_m$, suggesting that changing particle grading entropy ($G_e$) is recommended to achieve higher CTS strength more efficiently. Finally, a polynomial relation between $G_e$ and strength is proposed and the sensitivity analysis indicates that the best value of $G_e$ for Hangzhou CTS is 0.3125 and for Taizhou CTS is 0.5. The corresponding UCSs are 56.96 kPa and 425.8704 kPa, respectively. These findings contribute to optimizing the strength of cement-treated soils and provide insights into the micro-mechanisms underlying macro-scale material properties. Full article

In recent years, droughts and extreme weather events have witnessed increasing frequency around the globe, leading to a growing number of issues related to soil cracking that severely impact the stability of engineering projects and ecological environments. This study mainly investigates the cracking of clay in arid areas of Xinjiang. An experiment is conducted in a climate simulation laboratory, in which we explored the evolution of soil drying cracks under different (straight/curved) boundary constraints. The crack development process is quantitatively described through the use of digital imaging technology and by combining parameters such as crack rate, fractal dimension, and water-holding performance at different boundaries. The following conclusions were drawn: Under curved boundary conditions, cracks expand in a curve-like manner, and the cracks in the boundary area are densely distributed. However, under straight boundary conditions, penetrating straight cracks are formed, and the soil blocks present as regular polygons. Boundary conditions significantly impact the residual water content. Under straight boundary conditions, the residual masses of strongly and weakly bound water are significantly higher than those under curved boundary conditions. The complexity of the crack network under the curved boundary condition is higher, and its fractal dimension is significantly greater than that under the straight boundary condition. A quantitative analysis based on the use of digital image processing technology indicates that the boundary geometry has a significant controlling effect on the crack propagation path. This research provides guidance on the prevention and control of soil engineering cracking and damage in arid regions. Full article

In a hypothetical climate change scenario, zoysiagrass species could be a good choice for turfgrass areas due to their adaptation to heat conditions and the great variability in species and cultivars. Knowledge of the root system’s characteristics is paramount for predicting cultivar adaptation to different heat–drought scenarios and therefore for designing proper turfgrass management programs, especially irrigation. A field experiment was conducted in the Mediterranean environment of Valencia (Spain) to study the root weight density (RWD), root length density (RLD), and average root diameter (RDI) at three different soil depths (0–5, 5–15, and 15–30 cm) of five new zoysiagrass genotypes (Zoysia matrella (L.) Merr. cultivars, Zoysia japonica Steud., and their hybrid), relating these parameters to the performance of these cultivars during their establishment. All the tested cultivars had a higher RWD and RLD in the upper soil layer (0–5 cm), while the RDI was higher in the lowest layer of the sampled soil profile (0.269 mm compared with 0.249 mm and 0.241 mm in the upper layers). All the tested cultivars showed the same RWD and RLD, while the Zoysia matrella cultivar ZG18003 showed a higher RDI value (0.2683 mm) than those for the Z. japonica cultivar (0.2369 mm) and the hybrid (0.2394 mm). This last finding could have influenced its more rapid establishment, although it was not linked to its NDVI values during autumn. In conclusion, different morphological root characteristics were detected among new zoysiagrass genotypes and soil depths, which could have affected their canopy performance, and they are expected to affect irrigation management in a possible future drought scenario. Full article

The quality of water in the Red River is a complex interplay between human-induced changes and inherent natural variables. This research utilized the snapshot sampling approach, garnering water quality data from 45 sampling sites in the Red River and crafting 24 environmental indicators related to land use and inherent natural determinants at the catchment scale. Through Spearman rank correlation and redundancy analyses, relationships among land use, natural variables, and water quality were elucidated. Our variance partitioning revealed differentiated impacts of land use and natural factors on water quality. Pivotal findings indicated superior water quality in the Red River, driven mainly by land use dynamics, which showed a distinct geomorphic gradient. Specific land use attributes, like cropland patch density, grassland’s largest patch index, and urban metrics, were pivotal in explaining variations in parameters such as total nitrogen, ammonia, and temperature. Notably, the configuration of land use had a more profound influence on water quality than merely its components. In terms of natural influences, while topography played a dominant role in shaping water quality, other factors like soil and weather had marginal impacts. Elevation was notably linked with metrics like total phosphorus and suspended solids, whereas precipitation and slope significantly determined electrical conductivity and chlorophyll-a models. In sum, incorporating both land use configurations and natural determinants offers a more comprehensive understanding of water quality disparities in the Red River’s ecosystem. For holistic water quality management, the focus should not only be on the major contributors like croplands and urban areas but also on underemphasized areas like grasslands. Tweaking cropland distribution, recognizing the intertwined nature of land use and natural elements, and tailoring land management based on topographical variations are essential strategies moving forward. Full article

Remotely sensed cropland abandonment monitoring is crucial for providing spatially explicit references for maintaining sustainable agricultural practices and ensuring food security. However, abandoned cropland is commonly detected based on multi-date classification or the dynamics of a single vegetation index, with the interactions between vegetation and soil time series often being neglected, leading to a failure to understand its full-life-cycle succession processes. To fill this gap, we propose a new full-life-cycle modeling framework based on the interactive trajectories of vegetation–soil-related endmembers to identify abandoned and reclaimed cropland in Jinan from 2000 to 2022. In this framework, highly accurate annual fractional vegetation- and soil-related endmember time series are generated for Jinan City for the 2000–2022 period using spectral mixture models. These are then used to integrally reconstruct temporal trajectories for complex scenarios (e.g., abandonment, weed invasion, reclamation, and fallow) using logistic and double-logistic models. The parameters of the optimization model (fitting type, change magnitude, start timing, and change duration) are subsequently integrated to develop a rule-based hierarchical identification scheme for cropland abandonment based on these complex scenarios. After applying this scheme, we observed a significant decline in green vegetation (a slope of −0.40% per year) and an increase in the soil fraction (a rate of 0.53% per year). These pathways are mostly linked to a duration between 8 and 15 years, with the beginning of the change trend around 2010. Finally, the results show that our framework can effectively separate abandoned cropland from reclamation dynamics and other classes with satisfactory precision, as indicated by an overall accuracy of 86.02%. Compared to the traditional yearly land cover-based approach (with an overall accuracy of 77.39%), this algorithm can overcome the propagation of classification errors (with product accuracy from 74.47% to 85.11%), especially in terms of improving the ability to capture changes at finer spatial scales. Furthermore, it also provides a better understanding of the whole abandonment process under the influence of multi-factor interactions in the context of specific climatic backgrounds and human disturbances, thus helping to inform adaptive abandonment management and sustainable agricultural policies. Full article

The shallow, soft subsea formations, characterized by low strength and poor stability, lead to complex interactions between the screw motor drilling tool and the wellbore wall during directional drilling, complicating the accurate evaluation of the tool’s deflection capability. To address this issue, this paper proposes an integrated mechanical analysis method combining three-dimensional finite element analysis and transient dynamic analysis. By establishing a finite element model using 12-DOF (degree-of-freedom) spatial rigid-frame Euler–Bernoulli beam elements, coupled with well trajectory coordinate transformation and Rayleigh damping matrix, a precise description of drill string dynamic behavior is achieved. Furthermore, the introduction of pipe–soil dynamics and the p-y curve method improves the calculation of contact reaction forces between drilling tools and formation. Case studies demonstrate that increasing the tool face rotation angle intensifies lateral forces at the bit and stabilizer, with the predicted maximum dogleg severity within the first 10 m ahead of the bit progressively increasing. When the tool face rotation angle exceeds 2.5°, the maximum dogleg severity reaches 17.938°/30 m. With a gradual increase in the drilling pressure, the maximum bending stress on the drilling tool, maximum lateral cutting force, and stabilizer lateral forces progressively decrease, while vertical cutting forces and bit lateral forces gradually increase. However, the predicted maximum dogleg severity increases within the first 10 m ahead of the bit remain relatively moderate, suggesting the necessity for the multi-objective optimization of drilling pressure and related parameters prior to actual operations. Full article

This study presents a robust methodology for the indirect estimation of groundwater abstraction for irrigation at the scale of individual wells, addressing a key gap in data-scarce agricultural settings. The approach combines NDVI time series, crop water requirement modelling, and spatial analysis of irrigation systems within a GIS environment. A soil water balance model was applied to Homogeneous Units of Analysis, and irrigation requirements were estimated using an ensemble approach accounting for key sources of uncertainty related to phenology detection, soil moisture at sowing (%SAW), and irrigation system efficiency. A spatial linkage algorithm was developed to associate individual wells with the irrigated areas they supply. Sensitivity analysis demonstrated that 10% increases in %SAW resulted in abstraction reductions of up to 1.98%, while 10% increases in irrigation efficiency reduced abstractions by an average of 6.48%. These findings support the inclusion of both parameters in the ensemble, generating eight abstraction estimates per well. Values ranged from 33,000 to 115,000 m³ for the 2023 season. Validation against flowmeter data confirmed the method’s reliability, with an R² of 0.918 and an RMSE equivalent to 9.3% of the mean observations. This approach offers an accurate, spatially explicit estimation of groundwater abstractions without requiring direct metering and offers a transferable, cost-effective tool to improve groundwater accounting and governance in regions with limited monitoring infrastructure. Full article

The seasonal and interannual irregularity of temperature and precipitation is a feature of the Mediterranean climate that is intensified by climate change and constitutes a relevant driver of water and soil degradation. This study was developed during three years in a hydro-agricultural area of the Alqueva irrigation system (Portugal) with Mediterranean climate conditions. The sampling campaigns included collecting water samples from eight irrigation hydrants, analyzed four times yearly. The analysis incorporated meteorological data and indices (precipitation, temperature, and drought conditions) alongside chemical parameters, using multivariate statistics (factor analysis and cluster analysis) to identify key water quality drivers. Additionally, machine learning models (Random Forest regression and Gradient Boosting machine) were employed to predict electrical conductivity (ECw), sodium adsorption ratio (SAR), and pH based on chemical and climatic variables. Water quality evaluation showed a prevalence of a slight to moderate soil sodification risk. The factor analysis outcome was a three-factor model related to salinity, sodicity, and climate. The cluster analysis revealed a grouping pattern led by year and followed by stage, pointing to the influence of inter-annual climate irregularity. Variations in water quality from the reservoirs to the distribution network were not substantial. The Random Forest algorithm showed superior predictive accuracy, particularly for ECw and SAR, confirming its potential for the reliable forecasting of irrigation water quality. This research emphasizes the importance of integrating time-sensitive monitoring with data-driven predictions of water quality to support sustainable water resources management in agriculture. This integrated approach offers a promising framework for early warning and informed decision-making in the context of increasing drought vulnerability across Mediterranean agro-environments. Full article

Egypt’s rapid population increase has resulted in higher water demand. It may significantly reduce the amount of water available for agriculture, increasing the chance of using saline water in agriculture. Using saline water certainly poses a major threat to maize growth and may severely affect the growth and productivity of this important crop. Therefore, the aim of this study was to isolate newly native salt-tolerant bacteria from Egyptian saline soils and assess their ability to produce growth-promoting substances under salinity stress, as well as test the mitigating impact of these isolated salt-tolerant bacteria along with arbuscular mycorrhizal fungi (AMF) in maize plants under salinity stress. We isolated ninety-seven salt-tolerant bacterial isolates, and these isolates show a high ability to grow under different concentrations of NaCl. The nine most efficient isolates show a high ability to produce indole acetic acid (IAA), gibberellic acid (GA), P-solubilized exopolysaccharides (EPS), proline, and antioxidants under different NaCl concentrations. Using the 16S rRNA gene, the most effective isolate STB 89 was identified, and its impact, along with AMF, on the growth of salinity-stressed maize was tested in a pot experiment. Our results showed that the growth parameters (shoot length, root length, dry weight, and leaf area), photosynthetic-related pigments (Chlorophyll a, b, and carotenoids), NPK content, and antioxidant enzymes (PPO, POX, and CAT) were improved significantly at p ≤ 0.05 due to the bioinoculant applications, while reduced proline accumulation, Na uptake, and the Na⁺/K⁺ ratio in maize plant tissues were observed compared to the control plants. Moreover, the indices of AMF colonization in maize roots and the count of bacteria in the rhizosphere were enhanced due to the bioinoculant applications under salinity stress. In addition, we found that the combined application was more pronounced than the individual application impact. Hence, our results recommended that salt-tolerant bacteria (STB 89) could support salinity, mitigating the impact of AMF in maize plants, as well as allowing better practical techniques for maize cultivation and soil sustainability under salinity stress. Full article

The Mixteca Oaxaqueña region has historically suffered from soil and vegetation loss. However, since the last decade of the previous century, successful reforestation efforts have been carried out in many areas. As a result, there is now a need to select phenotypes with superior growth and good wood quality. This study aimed to estimate genetic parameters and identify superior families of Apulco pine (Pinus pseudostrobus var. apulcensis (Lindl.) Shaw) based on growth and stem quality traits. After four years, growth and stem quality traits were evaluated in 64 open-pollinated families. Different selection intensities were tested using two evaluation methods. All traits showed significant differences among families (p ≤ 0.0016), with genetic coefficients of variation ranging from 8.01% to 18.84%. Heritability estimates for growth traits were high ($h_i^2$ = 0.42−0.66; $h_f^2$ = 0.55−0.63), whereas heritability for stem quality traits was slightly lower ($h_i^2$ = 0.01−0.38; $h_f^2$ = 0.01−0.38). Genetic correlations (r_g) among growth traits were high and positive (r_g ≥ 0.857), while correlations among stem quality traits and between quality and growth traits were more variable (r₉ = −0.498 to 0.899). Based on both evaluation methods and a 25% selection intensity, sixteen superior families were identified, showing estimated genetic gains of approximately 4% for growth-related traits. These families are recommended for use in timber plantations, whereas a broader set is suggested for reforestation efforts in order to maintain genetic diversity. Full article

Fractal theory of particle size distribution (PSD) is a widely used approach in soil science. However, fractal studies on sandstone PSDs are scarce in sedimentology and geology. Taking littoral sandstones in the Carboniferous Donghetang Formation of the Hade Oilfield as an example, fractal dimensions of 115 fine sandstone and 150 silty sandstone PSDs are calculated and compared with particle size compositions and traditional statistical parameters in this paper. The results show that fractal dimension values in fine sandstones, 1.69–2.17 averaged at 1.99, are usually lower than that in silty sandstones, 2.12–2.73 averaged at 2.37. Fractal dimension and sandy content of littoral sandstones show a strong negative linear relationship. Significant logarithmic correlations are implied between fractal dimension and silty and clayey contents of littoral sandstones, which is different from linear relations in soil PSDs. The relationships between fractal dimension and mean, sorting, and skewness of silty sandstone PSDs are better than those of fine sandstones. Fractal dimension and kurtosis of silty sandstones and fine sandstones exhibit weak negative and positive linear relationships, respectively. Fractal dimension values in lower-shoreface facies, 2.05–2.47 averaged at 2.33, are generally higher than that in upper-shoreface facies, 1.79–2.30 averaged at 2.11. Fractal dimension values in bar and beach microfacies are commonly lower than those in trough microfacies. Combined with additional sedimentary information from various clastic deposits, the fractal dimension can serve as a new depositional environment indicator. Full article

These days, one of the main issues preventing agricultural development is salinized soils. Potassium fulvic acid (PFA) not only regulates plant growth, but also improves the soil nutrient content and physical structure, which makes it a soil conditioner worth promoting. Nevertheless, the research conducted thus far on the subject of PFA with regard to plant growth and inter-root microbial communities remains somewhat limited in scope. In this study, a pot experiment was conducted to simulate both the normal environment and salt stress environment. The objective of this experiment was to verify the effect of PFA on the growth of blueberry (Vaccinium corymbosum L.) as well as its effect on the soil physical and chemical indices and the soil microbial community structure. The findings demonstrated that the implementation of potassium fulvic acids exhibited a minimal impact on the growth of blueberry plants under standard environmental conditions. However, it was observed to exert a substantial effect on enhancing various physiological parameters, including plant height, root activity, and chlorophyll synthesis, particularly in response to salt stress. PFA led to a substantial augmentation in the soil organic matter content, alongside a notable rise in the alkali-hydrolyzable nitrogen (AN) and available potassium (AK) content. Concurrently, PFA caused a notable escalation in the activities of soil urease, sucrase, acid phosphatase, and catalase (p < 0.05) in the salt-stressed environment. PFA increased the abundance of Acidobacteria, Myxococcota, Ascomycota, and Fungi_phy_Incertae_sedis under salt stress, which was mainly related to the decrease in electrical conductivity (EC) values and increase in soil acid phosphatase (S-ACP) activity. It is evident that the implementation of PFA is advantageous in enhancing the saline environment, mitigating the impact of salt damage on blueberries and establishing a foundation for the expansion of cultivated areas and the sustainable cultivation of blueberries. Full article

In order to test within-group homogeneity for numerical or ordinal variable groupings, we have introduced a family of discrete probability distributions, related to the Gini mean difference, that we now study in a deeper way. A member of such a family is the law of a statistic that operates on the ranks of the values of the random variables by considering the sums of the inter-subgroups ranks of the variable grouping. Being so, a law of the family depends on several parameters such as the cardinal of the group of variables, the number of subgroups of the grouping of variables, and the cardinals of the subgroups of the grouping. The exact distribution of a law of the family faces computational challenges even for moderate values of the cardinal of the whole set of variables. Motivated by this challenge, we show that an asymptotic result allowing approximate quantile values is not possible based on the hypothesis observed in particular cases. Consequently, we propose two methodologies to deal with finite approximations for large values of the parameters. We address, in some particular cases, the quality of the distributional approximation provided by a possible finite approximation. With the purpose of illustrating the usefulness of the grouping laws, we present an application to an example of within-group homogeneity grouping analysis to a grouping originated from a clustering technique applied to cocoa breeding experiment data. The analysis brings to light the homogeneity of production output variables in one specific type of soil. Full article