Search Results (531)

Heavy machinery degrades agricultural soils, with severity influenced by wheel type, contact area, and soil moisture. Tropical agriculture is characterized by the constant maintenance of straw on the ground. This permanent cover, among other benefits, can mitigate the stress imposed by wheels on the physical structure of the soil. This study aimed to evaluate the effect of tire types and straw amounts on soil stresses. Static studies were carried out under controlled conditions in a static tire test unit (STTU), equipped with standardized sensors and systems that simulated real farming conditions. Three tire models were tested: road truck double wheelset—2 × 275/80R22.5 (p1); agricultural radial tire—600/50R22.5 (p2); and bias-ply tire—600/50-22.5 (p3) on four contact surfaces (rigid surface; bare soil; soil with 15 and 30 Mg ha⁻¹ straw cover). We performed comparative statistical tests and subsurface stress simulations for each tire and surface condition. On the hard surface, the contact areas were 4.7 to 6.8 times smaller than on bare soil. Straw increased the tire’s contact area, reducing compaction and subsoil stresses. Highest pressure was imposed by the road tire (p1) and lowest by the radial tire (p2). Adding 15 Mg ha⁻¹ of straw reduced soil SPR by 18%, while increasing it to 30 Mg ha⁻¹ led to an additional 8% reduction. Tire selection and effective straw management improve soil conservation and agriculture sustainability. Full article

In response to the growing demand for lightweight and high-efficiency industrial equipment, this study addresses the critical issue of lubrication failure in high-speed, heavy-duty gear reducers, which often leads to reduced transmission efficiency and premature mechanical damage. A three-dimensional transient multiphysics-coupled model of oil-jet lubrication is developed based on computational fluid dynamics (CFD). The model integrates the Volume of Fluid (VOF) multiphase flow method with the shear stress transport (SST) k−ω turbulence model. This framework enables the accurate capture of oil-jet interface fragmentation, reattachment, and turbulence-coupled behavior within the gear meshing region. A parametric study is conducted on oil injection velocities ranging from 20 to 50 m/s to elucidate the coupling mechanisms between geometric configuration and flow dynamics, as well as their impacts on oil film evolution, energy dissipation, and thermal management. The results reveal that the proposed method can reveal the dynamic evolution characteristics of the gear injection lubrication. Adopting an appropriately moderate injection velocity (30 m/s) improves oil film coverage and continuity, with the lubricant transitioning from discrete droplets to a dense wedge-shaped film within the meshing zone. Optimal lubrication performance is achieved at this velocity, where oil shear-carrying capacity and kinetic energy utilization efficiency are maximized, while excessive turbulent kinetic energy dissipation is effectively suppressed. Dynamic monitoring data at point P further corroborate that a well-tuned injection velocity stabilizes lubricant-velocity fluctuations and improves lubricant oil distribution, thereby promoting consistent oil film formation and more efficient heat transfer. The proposed closed-loop collaborative framework—comprising model initialization, numerical solution, and post-processing—together with the introduced quantitative evaluation metrics, provides a solid theoretical foundation and engineering reference for structural optimization, energy control, and thermal reliability design of gearbox lubrication systems. This work offers important insights into precision lubrication of high-speed transmissions and contributes to the sustainable, green development of industrial machinery. Full article

To overcome the absence of a standardized budget quota system for high-standard farmland projects and the resultant extended compilation cycles and high workloads, this study systematically analyzes quota consumption and innovatively proposes an integrated DEMATEL-ISM-BN and gray clustering analytical model. Through a literature review and engineering feature analysis, a hierarchical factor system was established, encompassing six dimensions (environmental, technical, labor, machinery, material, and management) and 24 indicators. The DEMATEL-ISM method quantified factor weights and structured them into a five-level hierarchy, while Bayesian networks (BNs) enabled probabilistic productivity predictions (29% conservative, 45% moderate, and 26% advanced). Gray clustering was integrated to derive a comprehensive representative consumption value, and validation across six regions demonstrated a comprehensive productivity index of 0.986 (CV = 2.6%) for 17 earthwork projects, confirming model robustness. This research constructs a standardized “factor structure analysis–probabilistic deduction–regional clustering” framework, providing a theoretical foundation for precise budget compilation in high-standard farmland and proposing a novel methodological paradigm for quota consumption research. Full article

Nitrogen (N) and potassium (K) are essential macronutrients for plants whose functions and interactions profoundly influence plant physiological metabolism, environmental adaptation, and agricultural production efficiency. This review summarizes research advances in plant N and K nutrition and their interaction mechanisms, elucidating the key physiological functions of N and K individually and their respective absorption and transport mechanisms involving transporters such as NRTs and HAKs/KUPs. The review discusses the types of nutrient interactions (synergism and antagonism), with a primary focus on the physiological basis of N–K interactions and their interplay in root absorption and transport (e.g., K⁺-NO₃⁻ co-transport; NH₄⁺ inhibition of K⁺ uptake), photosynthesis (jointly optimizing CO₂ conductance, mesophyll conductance, and N allocation within photosynthetic machinery to enhance photosynthetic N use efficiency, PNUE), as well as sensing, signaling, co-regulation, and metabolism. This review emphasizes that N–K balance is crucial for improving crop yield and quality, enhancing fertilizer use efficiency (NUE/KUE), and reducing environmental pollution. Consequently, developing effective N–K management strategies based on these interaction mechanisms and implementing Balanced Fertilization Techniques (BFT) to optimize N–K ratios and application strategies in agricultural production represent vital pathways for ensuring food security, addressing resource constraints, and advancing green, low-carbon agriculture, including through coordinated management of greenhouse gas emissions. Full article

In Taiwan, hydro-meteorological data are fragmented across multiple agencies, limiting the effectiveness of coordinated flood response. To address this challenge and the increasing uncertainty associated with extreme rainfall, a real-time disaster prevention platform has been developed. This system integrates multi-source data and geospatial information through a cluster-based architecture to enhance pluvial flood management. Built on a Service-Oriented Architecture (SOA) and incorporating Internet of Things (IoT) technologies, AI-based convolutional neural networks (CNNs), and 3D drone mapping, the platform enables automated alerts by linking sensor thresholds with real-time environmental data, facilitating synchronized operational responses. Deployed in New Taipei City over the past three years, the system has demonstrably reduced flood risk during severe rainfall events. Region-specific action thresholds and adaptive strategies are continually refined through feedback mechanisms, while integrated spatial and hydrological trend analyses extend the lead time available for emergency response. Full article

The electrification of road vehicles is a relatively mature sector, while other areas of mobility, such as construction machinery, are just beginning their transition to electric solutions. This work presents the design and realization of an integrated drive system specifically developed for retrofitting fan drives in heavy machinery, like bulldozers and tractors, utilizing existing 48 VDC batteries. By replacing or complementing internal combustion and hydraulic technologies with electric solutions, significant advantages in efficiency, reduced environmental impact, and versatility can be achieved. Focusing on the fan drive system addresses the critical challenge of thermal management in high ambient temperatures and harsh environments, particularly given the high current requirements for $3\mathrm{k}\mathrm{W}$-class applications. A sensorless architecture has been selected to enhance reliability by eliminating mechanical position sensors. The developed fan drive has been extensively tested both on a braking bench and in real-world applications, demonstrating its effectiveness and robustness. Future work will extend this prototype to electrify additional onboard hydraulic motors in these machines, further advancing the electrification of heavy-duty equipment and improving overall efficiency and environmental impact. Full article

Tunnel construction poses significant safety challenges due to confined spaces, limited visibility, and the dynamic movement of labourers and machinery. This study addresses a critical gap in real-time, bidirectional proximity monitoring by developing and validating a prototype early-warning system that integrates real-time location systems (RTLS) with long-range (LoRa) wireless communication and ultra-wideband (UWB) positioning. The system comprises Arduino nano microcontrollers, organic light-emitting diode (OLED) displays, and piezo buzzers to detect and signal proximity breaches between workers and equipment. Using an action research approach, three pilot case studies were conducted in a simulated tunnel environment to test the system’s effectiveness in both static and dynamic risk scenarios. The results showed that the system accurately tracked proximity and generated timely alerts when safety thresholds were crossed, although minor delays of 5–8 s and slight positional inaccuracies were noted. These findings confirm the system’s capacity to enhance situational awareness and reduce reliance on manual safety protocols. The study contributes to the tunnel safety literature by demonstrating the feasibility of low-cost, real-time monitoring solutions that simultaneously track labour and machinery. The proposed RTLS framework offers practical value for safety managers and informs future research into automated safety systems in complex construction environments. Full article

Chinese hamster ovary (CHO) cells are the most common protein production platform for glycosylated biopharmaceuticals due to their relatively efficient secretion systems, post-translational modification (PTM) machinery, and quality control mechanisms. However, high productivity and titer demands can overburden these processes. In particular, the endoplasmic reticulum (ER) can become overwhelmed with misfolded proteins, triggering the unfolded protein response (UPR) as evidence of ER stress. The UPR increases the expression of multiple genes/proteins, which are beneficial to protein folding and secretion. However, if the stressed ER cannot return to a state of homeostasis, a prolonged UPR results in apoptosis. Because ER stress poses a substantial bottleneck for secreting protein therapeutics, CHO cells are both selected for and engineered to improve high-quality protein production through optimized UPR and ER stress management. This is vital for optimizing industrial CHO cell fermentation. This review begins with an overview of common ER-stress related markers. Next, the optimal UPR profile of high-producing CHO cells is discussed followed by the context-dependency of a UPR profile for any given recombinant CHO cell line. Recent efforts to control and engineer ER stress-related responses in CHO cell lines through the use of various bioprocess operations and activation/inhibition strategies are elucidated. Finally, this review concludes with a discussion on future directions for engineering the CHO cell UPR. Full article

Erosion gullies can reduce arable land area and decrease agricultural machinery efficiency; therefore, automatic gully extraction on a regional scale should be one of the preconditions of gully control and land management. The purpose of this study is to compare the effects of the grey level co-occurrence matrix (GLCM) and topographic–hydrologic features on automatic gully extraction and guide future practices in adjacent regions. To accomplish this, GaoFen-2 (GF-2) satellite imagery and high-resolution digital elevation model (DEM) data were first collected. The GLCM and topographic–hydrologic features were generated, and then, a gully label dataset was built via visual interpretation. Second, the study area was divided into training, testing, and validation areas, and four practices using different feature combinations were conducted. The DeepLabV3+ and ResNet50 architectures were applied to train five models in each practice. Thirdly, the trainset gully intersection over union (IOU), test set gully IOU, receiver operating characteristic curve (ROC), area under the curve (AUC), user’s accuracy, producer’s accuracy, Kappa coefficient, and gully IOU in the validation area were used to assess the performance of the models in each practice. The results show that the validated gully IOU was 0.4299 (±0.0082) when only the red (R), green (G), blue (B), and near-infrared (NIR) bands were applied, and solely combining the topographic–hydrologic features with the RGB and NIR bands significantly improved the performance of the models, which boosted the validated gully IOU to 0.4796 (±0.0146). Nevertheless, solely combining GLCM features with RGB and NIR bands decreased the accuracy, which resulted in the lowest validated gully IOU of 0.3755 (±0.0229). Finally, by employing the full set of RGB and NIR bands, the GLCM and topographic–hydrologic features obtained a validated gully IOU of 0.4762 (±0.0163) and tended to show an equivalent improvement with the combination of topographic–hydrologic features and RGB and NIR bands. A preliminary explanation is that the GLCM captures the local textures of gullies and their backgrounds, and thus introduces ambiguity and noise into the convolutional neural network (CNN). Therefore, the GLCM tends to provide no benefit to automatic gully extraction with CNN-type algorithms, while topographic–hydrologic features, which are also original drivers of gullies, help determine the possible presence of water-origin gullies when optical bands fail to tell the difference between a gully and its confusing background. Full article

This research aims to analyze the evolution of productivity and efficiency in tractors featuring varying propulsion system designs through the development of a parametric modeling approach. Recognizing that large row-crop tractors represent a significant capital investment—ranging from USD 0.4 to over 0.8 million for current-generation models—and that machinery costs constitute a substantial share of farm production expenses, this study addresses the urgent need for data-driven decision-making in agricultural enterprises. Utilizing consolidated OECD Code 2 tractor test data for all large row-crop John Deere tractors from the MFWD era to the latest generation, the study evaluates tractor performance across multiple productivity and efficiency indicators. The analysis culminates in the creation of a robust, user-friendly parametric model (R² = 0.9337, RMSE = 1.0265), designed to assist stakeholders in making informed decisions regarding tractor replacement or upgrading. By enabling the optimization of productivity and efficiency while accounting for agronomic and timeliness constraints, this model supports sustainable and profitable management practices in modern agriculture. Full article

Soil compaction is a critical challenge in perennial fruit production, limiting root growth, water infiltration, and nutrient uptake—factors essential for climate-resilient and sustainable orchard systems. In subtropical Asian pear (Pyrus pyrifolia Nakai) orchards under the annual top-working system, intensive machinery traffic exacerbates subsurface hardpan formation and tree performance. This study evaluated the effectiveness of pneumatic subsoiling, a minimally invasive method using high-pressure air injection, in alleviating soil compaction without disturbing orchard surface integrity. Four treatments varying in radial distance from the trunk and pneumatic application were tested in a mature orchard in central Taiwan. Pneumatic subsoiling 120 cm away from the trunk significantly reduced soil penetration resistance by 15.4% at 34 days after treatment (2,302,888 Pa) compared to the control (2,724,423 Pa). However, this reduction was not sustained at later assessment dates, and no significant improvements in vegetative growth, fruit yield, and fruit quality were observed within the first season post-treatment. These results suggest that while pneumatic subsoiling can modify subsurface soil physical conditions with minimal surface disturbance, its agronomic benefits may require longer-term evaluation under varying moisture and management regimes. Overall, this study highlights pneumatic subsoiling may be a potential low-disturbance strategy to contribute to longer-term soil physical resilience. Full article

Soil compaction from mechanized agriculture is a major concern, as frequent machinery use degrades soil structure, reduces porosity, and ultimately impairs crop productivity. Among potential mitigation strategies to enhance soil resilience to machinery-induced compaction, biochar has shown promise in laboratory settings but remains untested under real field conditions. To address this, we monitored soil in a Tuscan vineyard where biochar was applied at 16 and 32 Mg ha⁻¹, compared to control, on both flat and sloped plots. Soil compaction was induced by 20 passes of a wheeled orchard tractor. Soil bulk density (BD) was measured before, immediately after, and one year following the initial passes, during which 19 additional machine passes occurred as part of the vineyard’s routine agronomic management. Initial results showed a significant BD increase (up to 12.8%) across all treatments, though biochar significantly limited soil compaction, regardless of the applied dose. After one year, in which the soil underwent further compaction, BD further increased across all treatments (up to 20.2%), with the steepest increase observed on the sloped terrain. At this stage, the mitigating effect of biochar on soil compaction was no longer evident. Our findings suggest that biochar may offer some short-term relief from compaction, but further investigations are needed to clarify its long-term effectiveness under field conditions. Full article

Background: Drones are rapidly establishing themselves as one of the most critical technologies. Robotics, automated machinery, intelligent manufacturing, and other high-impact technological research and applications bring up pressing ethical, social, legal, and political issues. Methods: The present research aims to present the results of a qualitative investigation that looked at perceptions of the growing socio-ethical conundrums surrounding the development of drone applications. Results: According to the obtained results, participants often share similar opinions about whether different drone applications are approved by the public, regardless of their level of experience. Perceptions of drone applications appear consistent across various levels of expertise. The most notable associations are with military objectives (73%), civil protection (61%), and passenger transit and medical purposes (56%). Applications that have received high approval include science (8.70), agriculture (8.78), and disaster management (8.87), most likely due to their obvious social benefits and reduced likelihood of ethical challenges. Conclusions: The study’s findings can help shape the debate on drone acceptability in particular contexts, inform future research on promoting value-sensitive development in society more broadly, and guide researchers and decision-makers on the use of drones, as people’s attitudes, understanding, and usage will undoubtedly impact future advancements in this technology. Full article

Livestock contributes to global warming through greenhouse gas (GHG) emissions. Reducing these emissions is an ongoing challenge for the small ruminant sector. Despite its significant role in national economies, limited studies on the carbon footprint (CF) of dairy small ruminants in Mediterranean countries exist. The study aimed to achieve the following: (a) estimate the GHG emissions of eleven semi-extensive sheep and goat farms in a mountainous region of southern Greece, using the Tier 1 and Tier 2 methodologies; (b) compare the outcomes of both methods; and (c) calculate farms’ CF, as a means of their environmental impact evaluation. All on-farm activities (except machinery or medicine use) related to sheep or goat production were considered to estimate GHG emissions. The results show differences between Tier 1 and Tier 2 estimates, reflecting the simplified computational approach of Tier 1. The average CF values estimated via Tier 1 for goat and sheep farms were 2.12 and 2.87 kg CO₂-eq./kg FPCM, respectively. Using Tier 2, these values increased to 2.73 and 3.99 kg CO₂-eq./kg FPCM. To mitigate environmental impact, farms could enhance productivity by improving herd management and feeding strategies. Full article

Identifying the key factors influencing agricultural carbon emissions and accurately predicting future trends are essential for achieving carbon peak and carbon neutrality goals. This study aims to assess carbon emissions in agriculture from 1997 to 2022, construct an accurate model to identify the key influencing factors, and predict carbon emissions in agriculture from 2023 to 2030 with an intelligent prediction system to discuss risk management. Additionally, the Dagum method was employed to explore regional differences in agricultural carbon emissions across China. The results reveal that China’s agricultural carbon emissions exhibited a fluctuating trend from 1997 to 2022, peaking in 2015, followed by a period of decline and a moderate rebound in recent years. Elastic Net Regression identified eleven key variables, including Agricultural Machinery Level (MA), Numbers of Agricultural Tools (AT), and Agricultural Industrial Structure Upgrading (AICE), as major determinants of agricultural carbon emissions. Furthermore, the RF-PSO method demonstrated the highest predictive accuracy, forecasting a minor peak in agricultural carbon emissions in 2027, followed by stabilization. Regionally, imbalances in emissions were observed, with the intensity of transvariation accounting for 37.078% of the disparity. Therefore, the Chinese government is advised to implement region-specific strategies for controlling agricultural carbon emissions, cultivate new high-quality agricultural productivity, and promote advanced technologies. Full article