Search Results (13,189)

Extreme rainfall events characterized by small catchments with high-velocity flows pose critical challenges to infrastructure resilience, particularly the rail infrastructure, due to its partial location near rivers and in mountainous regions, and the limited availability of alternative routes. This can lead to severe damages, often resulting in long-term route closures. To mitigate flash flood damage, detailed information about affected structures and damage processes is necessary. Therefore, this study presents a newly developed multi-criteria flash flood damage assessment framework for the rail infrastructure and a QGIS-based analysis of the most frequent damages. Applying the framework to Eifel route damages in Western Germany after the July 2021 flood disaster shows that nearly 45% of the damages affected the track superstructure, especially tracks and bedding. Additionally, power supply systems, sealing and drainage systems, as well as railway overpasses or bridges, were impacted. Approximately 30% of the railway section showed washout of ballast, gravel and soil. In addition, deposit of wood or stones occurred. Most damages were classified as minor (47%) or moderate (34%). Furthermore, damaged track sections were predominantly located within a 50 m distance to the Urft river, whereas undamaged track sections are often located at a greater distance to the Urft river. These findings indicate that the proposed framework is highly applicable to assess and classify damages. Critical elements and relations could be identified and can help to adapt standards and regulations, as well as to develop preventive measures in the next step. Full article

The growing potassium (K) demand and supply–demand imbalance in intensive agriculture require the development of multi-nutrient K fertilizers. Polyhalite (POLY), a multi-nutrient natural mineral rich in K, calcium, magnesium, and sulfur, can enhance soil nutrient diversity and fertility. However, research on its synergistic application with nitrogen (N) fertilizer remains limited. Therefore, this study was designed to apply three different fertilizer composites at four N concentration gradients through field plot experiments to evaluate crop productivity and nutrient use efficiency. Results revealed that the application of both compound fertilizers with N fertilizer increased maize yield, ranging from 1.03% to 11.53%, compared with the PK control. Moreover, 25-7-8 (MOP)(POLY26%) achieved a maximum yield of 9499.88 kg/ha at the N1 (170 kg/ha) level. This represents a significant increase of 11.53% compared with the PK control. Moreover, the application of compound fertilizer containing POLY could significantly increase the N fertilizer utilization rate; improve the quality of maize; and exert a significant effect on soil pH, EC, and nutrient content. This study paves the way for broader application of POLY by establishing its novel role as a sustainable nutrient source. It provides critical strategic guidance for advancing global resource-efficient agriculture. Full article

The Inner Mongolian Plateau is a critical region for the development of herbivorous animal husbandry in China. However, its harsh climate and poor soil quality have constrained the sustainable growth of the alfalfa industry. This 3-year field study investigated the effects of potassium (K) fertilizer on the productivity and forage quality of alfalfa (Medicago sativa L. cv. ‘WL168’) in such specific conditions of the region. Five rates of K fertilizer (0 (CK), 100, 200, 300, and 400 kg ha⁻¹ of K₂O) were applied in three split applications. Forage harvests occurred three times annually in 2023 and 2024, and yield, yield components, and forage quality were determined. The results showed that the forage yield of alfalfa increased initially and then decreased with the rising K application rates, which paralleled the changes in the plant density, and plant height, especially the mass shoot⁻¹; forage yield was mainly correlated with mass shoot⁻¹. Appropriate K fertilizer improved forage quality, especially in 2024. With increasing application, crude protein (CP) and total digestible nutrients (TDNs) first rose then declined, whereas neutral detergent fiber (NDF) and acid detergent fiber (ADF) decreased steadily, leading to a consistent rise in the relative feeding value (RFV). Comprehensively considering both yield and quality under such condition, a K fertilizer application rate of 273.2 kg ha⁻¹ of K₂O is suggested as a reference for this region. Full article

In the process of large-scale water conservancy and hydropower station construction in the southwest region of China, obtaining the deep overburden pressuremeter modulus E_m is of great significance for the calculation of foundation bearing capacity and dam foundation settlement. However, due to the complex nature of the soil properties in deep overburden layers, conducting deep-hole pressuremeter tests is challenging, time-consuming, and costly. In order to efficiently and accurately obtain the pressuremeter modulus of deep overburden, this paper takes the deep overburden in the river valley where a large hydropower station dam is located in the southwest region as the research object. It proposes a method based on data-driven prediction of the pressuremeter modulus and combines it with the physical mechanism to carry out the reliability analysis of the prediction results. By constructing a database of soil physical and mechanical parameters, including the pressuremeter modulus, the prediction performance of Random Forest (RF), Support Vector Regression (SVR), and BP Neural Network on the pressure modulus was evaluated. The Particle Swarm Optimization (PSO) was utilized for hyperparameter optimization to enhance the reliability of prediction results. The results indicate that the RF and PSO-RF models exhibit a comprehensive advantage for accurately predicting the pressuremeter modulus. The prediction results of the model for new data have a strong correlation with the results calculated by the Menard formula, which demonstrates the reliability of the model. Therefore, establishing the relationship between the conventional physical and mechanical parameters of deep overburden and the pressuremeter modulus, and predicting the pressuremeter modulus based on data-driven methods, has significant engineering value for obtaining the pressuremeter modulus of deep overburden efficiently, economically, and reliably. It also holds significant importance for the extended application of machine learning in the field of soil parameter prediction. Full article

Blow flies (Diptera: Calliphoridae) play a crucial role in the decomposition process and serve as important forensic indicators due to their predictable colonization patterns. This review focuses on the dynamics of maggot masses, highlighting their ecological roles, thermoregulation, and implications for forensics. We summarize data on the self-organizing behavior of maggot masses, which is influenced by chemical cues and environmental factors. These masses can generate internal temperatures that exceed ambient levels by 10–20 °C, accelerating larval growth and impacting competition among individuals. This localized heating complicates the estimation of the postmortem interval (PMI), as traditional models may not take these thermal influences into account. Furthermore, maggot masses contribute significantly to nutrient cycling and soil enrichment, while the behavior of the larvae includes both cooperation and competition, which is influenced by the species composition present. This review highlights challenges in PMI estimation due to heat production but also discusses advancements in molecular tools and thermal modeling that enhance accuracy. Ultimately, we identify knowledge gaps regarding species diversity, microbial interactions, and environmental variability that impact mass dynamics, suggesting future research avenues that could enhance ecological understanding and forensic applications. Full article

This study explores the complex regulatory mechanisms of nitrogen (N) and phosphorus (P) supply interactions on the growth, root architecture, and nutrient uptake of Populus × euramericana ‘Neva’ seedlings. It shows that these responses depend on nutrient concentrations and exhibit organ-specific patterns. Low P (0 mM) and sufficient N (15–30 mM) enhances plant height and aboveground biomass by promoting P acquisition processes. At moderate N levels (5–15 mM), P supply is sufficient (0.5–1.5 mM) for root and stem growth. Nitrogen application prioritizes aboveground biomass, reducing the root-to-shoot ratio. Root architecture also responds organ-specifically: sufficient N under low P promotes fine root growth to increase P absorption; under moderate P (0.5 mM), balanced N optimizes branching; and under sufficient P (1.5 mM), N increases root thickness while reducing fine root investment. In terms of P metabolism, moderate N under low P increases P concentrations by upregulating phosphate transporter genes, while sufficient N maintains P use efficiency (PUE). For N metabolism, added P under low N (0 mM) maintains N use efficiency (NUE), while higher N levels (15–30 mM) reduce NUE due to interference in nitrogen transport and enzyme activity. This study highlights the importance of organ-specific resource allocation in adapting to N–P interactions and suggests optimizing fertilization strategies based on soil nutrient status to avoid physiological imbalance. Full article

To explore strategies for the safe utilization of farmland co-contaminated with cadmium (Cd) and lead (Pb), this field study systematically evaluated the impacts of humic acid (HA) and potassium fulvate (PF) at different application rates (0, 1500, 3000, and 4500 kg·ha⁻¹) on the growth, yield, and translocation of Cd and Pb within the soil–plant system of maize (Zea mays L.). The results showed that while HA and PF did not significantly alter total soil Cd and Pb concentrations, they markedly reduced their bioavailable fractions. This mitigation of heavy metal phytotoxicity significantly promoted maize growth and yield, with the high-dose HA treatment increasing yield by a maximum of 32.9%. Both amendments dose-dependently decreased Cd and Pb concentrations, bioconcentration factors (BCF), and translocation factors (TF) in all maize tissues, particularly in the grains. At equivalent application rates, PF was slightly more effective than HA in reducing heavy metal concentrations in the grains. Notably, a significant positive correlation was observed between Cd and Pb concentrations across all plant parts, confirming a synergistic accumulation and translocation mechanism. This synergy provides a physiological explanation for the broad-spectrum immobilization efficacy of these humic substances. In conclusion, applying HA and PF presents a dual-benefit strategy for increasing yield and reducing risks in Cd- and Pb-contaminated farmlands. This study proposes a differentiated application approach: PF is the preferred option when ensuring food-grade safety is the primary goal, whereas high-dose HA is more advantageous for maximizing yield in soils with low-to-moderate contamination risk. Full article

Urban lawns are a predominant form of vegetation in sports grounds and greenbelts. Nitrogen (N) fertilization is widely used to sustain lawn productivity. However, it also promotes nitrous oxide (N₂O) emissions, a potent greenhouse gas. The microbial mechanisms underlying N₂O emissions from fertilized lawn soils remain poorly understood. In this study, we conducted a controlled incubation experiment with four N application rates [0 (N0), 100 (N100), 200 (N200), and 300 kg·ha⁻¹·yr⁻¹ (N300)] to investigate N₂O emissions and associated microbial processes in urban lawn soil. Biological inhibitors combined with high-throughput sequencing were used to quantify the inhibitor-sensitive fraction of fungi and bacteria contributing to N₂O emissions. Our results showed that N fertilizer significantly increased N₂O emissions, with the highest emission observed under N200. The fungi inhibitor-sensitive fraction accounted for ~45% of total N₂O emissions, significantly higher than that of bacteria (~31%). Dominant fungal phyla included Ascomycota, Basidiomycota, and Zygomycota, with N fertilization significantly increasing the relative abundance of Ascomycota and decreasing that of Basidiomycota. Redundancy analysis revealed strong positive correlations between Ascomycota abundance and N₂O emissions across N treatments. At the genus level, Pyrenochaetopsis, Myrothecium, and Humicola were positively associated with N₂O production and identified as key functional taxa. These findings demonstrate that moderate N fertilization can disproportionately stimulate fungal-driven N₂O emissions in urban lawns. The results provide a scientific basis for optimizing N fertilization strategies in green spaces, with implications for N policy and sustainable landscape management. Full article

This study evaluated the impact of biochar on the growth of strawberry plants, combining visual and proximal sensing monitoring. The plants were rooted in soil enriched with biochar, derived from pyrolysis of soft wood at 550 °C and applied in two doses (2 and 15 g/L), and after physical activation with CO₂ at 900 °C; there was also a treatment with no biochar (unaltered). Visual monitoring was based on data logging twice per week of plants’ height and number of flowers and ripe fruits. Proximal sensing monitoring involved a system including a low-cost multispectral camera and a Raspberry Pi 4. The camera acquired nadiral images hourly in three spectral bands (550, 660, and 850 nm), allowing calculation of the normalized difference vegetation index (NDVI). After three months, control plants reached a height of 12.3 ± 0.4 cm, while those treated with biochar and activated biochar grew to 18.03 ± 1.0 cm and 17.93 ± 1.2 cm, respectively. NDVI values were 0.15 ± 0.11 for control plants, increasing to 0.26 ± 0.03 (+78%) with biochar and to 0.28 ± 0.03 (+90%) with activated biochar. In conclusion, biochar application was beneficial for strawberry plants’ growth according to both visual and proximal-sensed measures. Further research is needed to optimize the integration of visual and proximal sensing monitoring, also enhancing the measured parameters. Full article

This article presents the development of a new pneumatic device for the precise application of mineral fertilizers, designed for use in precision agriculture systems involving farming robots. The proposed device is mounted on an autonomous agricultural platform and utilizes a machine vision system to determine plant coordinates. Its operating principle is based on accumulating a single dose of fertilizer in a chamber and delivering it precisely to the plant’s root zone using a directed airflow. The study includes a theoretical investigation of fertilizer movement inside the applicator tube under the influence of airflow and rotational motion of the tube. A mathematical model has been developed to describe both the relative and translational motion of the fertilizer. The equations, which account for frictional forces, inertia, and air pressure, enable the determination of optimal structural and kinematic parameters of the device depending on operating conditions and the properties of the applied material. The use of numerical methods to solve the developed mathematical model allows for synchronization of the device’s operating time parameters with the movement of the agricultural robot along the crop rows. The obtained results and the developed device improve the accuracy and speed of fertilizer application, minimize fertilizer consumption, and reduce soil impact, making the proposed device a promising solution for precision agriculture. Full article

Trees are vital to both environmental health and human well-being. They purify the air we breathe, support biodiversity by providing habitats for wildlife, prevent soil erosion to maintain fertile land, and supply wood for construction, fuel, and a multitude of essential products such as fruits, to name a few. Therefore, it is important to monitor and preserve them to protect the natural environment for future generations and ensure the sustainability of our planet. Remote sensing is the rapidly advancing and powerful tool that enables us to monitor and manage trees and forests efficiently and at large scale. Statistical methods, machine learning, and more recently deep learning are essential for analyzing the vast amounts of data collected, making data the fundamental component of these methodologies. The advancement of these methods goes hand in hand with the availability of sample data; therefore, a review study on available high-resolution aerial datasets of trees can help pave the way for further development of analytical methods in this field. This study aims to shed light on publicly available datasets by conducting a systematic search and filter and an in-depth analysis of them, including their alignment with the FAIR—findable, accessible, interoperable, and reusable—principles and the latest trends concerning applications for such datasets. Full article

Accurate evaluation of subgrade behaviour under dynamic loading is essential for the long-term performance of transport infrastructure. While the Light Weight Deflectometer (LWD) is commonly used to assess subgrade stiffness, it provides only a single stiffness value and may not fully capture the time-dependent response of soil. This study presents an image-based vision system developed to monitor soil surface displacements during loading, enabling more detailed analysis of dynamic behaviour. The system incorporates high-speed cameras and MATLAB-based computer vision algorithms to track vertical movement of the plate during impact. Laboratory and field experiments were conducted to evaluate the system’s performance, with results compared directly to those from the LWD. A strong correlation was observed (R² = 0.9901), with differences between the two methods ranging from 0.8% to 13%, confirming the accuracy of the vision-based measurements despite the limited dataset. The findings highlight the system’s potential as a practical and cost-effective tool for enhancing subgrade assessment, particularly in applications requiring improved understanding of ground response under repeated or transient loading. Full article

The growing demand for sustainable waste management practices has prompted interest in the land application of paper sludge as an alternative to landfilling and incineration. This study evaluates the environmental potential of paper sludge derived from recycled hygienic paper production by investigating its effects on soil respiration, seed germination, and seedling development. A comprehensive set of respirometric tests using the OxiTop^® system assessed microbial activity in soil amended with various concentrations of paper sludge (1–100%). Concurrently, bioassays using Lepidium sativum L. and Pisum sativum L. seeds examined the phytotoxicity and physiological response during germination. The results show that low to moderate sludge concentrations (1–20%) stimulated microbial activity and enhanced germination parameters, with a germination index (GI) up to 150% at 1%. However, higher concentrations (>40%) led to oxygen depletion, microbial stress, and decreased plant growth, indicating potential phytotoxicity and the need for application thresholds. For certain intermediate concentrations (e.g., 30–40%), a delay of approximately 21 days before sowing is recommended to allow microbial communities to stabilize and avoid initial stress conditions for plants. This study demonstrates that controlled application of paper sludge in soil systems can serve as a viable and sustainable disposal method, supporting circular economy principles and reducing the environmental burden of paper industry by-products. Full article

In response to water scarcity and low agricultural water-use efficiency in arid regions in Northwest China, this study conducted field experiments in Wuwei, Gansu Province, from 2023 to 2024. It aimed to develop a water stress diagnosis model for spring maize to provide a scientific basis for precision irrigation and water management. In this work, two irrigation methods—plastic film-mulched drip irrigation (FD, where drip lines are laid on the soil surface and covered with film) and plastic film-mulched shallow-buried drip irrigation (MD, where drip lines are buried 3–7 cm below the surface under film)—were tested under five irrigation gradients. Multispectral UAV remote sensing data were collected from key growth stages (i.e., the jointing stage, the tasseling stage, and the grain filling stage). Then, vegetation indices were extracted, and the leaf water content (LWC) was retrieved. LWC inversion models were established using Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Regression (SVR). Different irrigation treatments significantly affected LWC in spring maize, with higher LWC under sufficient water supply. In the correlation analysis, plant height (hc) showed the strongest correlation with LWC under both MD and FD treatments, with R² values of −0.87 and −0.82, respectively. Among the models tested, the RF model under the MD treatment achieved the highest prediction accuracy (training set: R² = 0.98, RMSE = 0.01; test set: R² = 0.88, RMSE = 0.02), which can be attributed to its ability to capture complex nonlinear relationships and reduce multicollinearity. This study can provide theoretical support and practical pathways for precision irrigation and integrated water–fertilizer regulation in smart agriculture, boasting significant potential for broader application of such models. Full article

Soil is fundamental to life on Earth through the provision of many ecosystem services. The current model of economic development exerts significant pressure on this resource, leading to degradation processes that are accelerated by the effects of climate change. This situation hinders the achievement of the UN Sustainable Development Goals, and some parts of the world have started a process to reverse this trend, among them the European Union, which has chosen the living labs approach as a strategic solution. The growing interest in this subject within the EU has led to the establishment of a new framework to design and test sustainable policies to improve soil health and management at the continental scale. This review presents State-of-the-Art information on the use of the living labs approach to improve soil health. It also introduces the SOILL Support Structure for Soil Health Living Labs (SHLLs) and Lighthouses and the significant role of the SOILL-Startup project to help establish a network of 100 such structures across the EU. Following the PRISMA methodology, the review describes the main features of SHLLs (definition, types of stakeholders, field and scale of application), as well as their current geographical distribution. The work provides information that can be used by the scientific community, policy makers, and soil stakeholders who prioritise soil health, regardless of the context in which they operate. Full article