Search Results (1,850)

With the acceleration of urbanization in China, water resources have become a key factor restricting regional sustainable development. Current research primarily examines the temporal or spatial variations in the water resources ecological footprint (WREF), with limited emphasis on the integration of both spatial and temporal scales. In this study, we collected the data and information from the 2005–2022 Statistical Yearbook and Water Resources Bulletin of the Yangtze River Delta Urban Agglomeration (YRDUA), and calculated evaluation indicators: WREF, water resources ecological carrying capacity (WRECC), water resources ecological pressure (WREP), and water resources ecological surplus and deficit (WRESD). We primarily analyzed the temporal and spatial variation in the per capita WREF and used the method of Geodetector to explore factors driving its temporal and spatial variation in the YRDUA. The results showed that: (1) From 2005 to 2022, the per capita WREF (total water, agricultural water, and industrial water) of the YRDUA generally showed fluctuating declining trends, while the per capita WREF of domestic water and ecological water showed obvious growth. (2) The per capita WREF and the per capita WRECC were in the order of Jiangsu Province > Anhui Province > Shanghai City > Zhejiang Province. The spatial distribution of the per capita WREF was similar to those of the per capita WRECC, and most areas effectively consume water resources. (3) The explanatory power of the interaction between factors was greater than that of a single factor, indicating that the spatiotemporal variation in the per capita WREF of the YRDUA was affected by the combination of multiple factors and that there were regional differences in the major factors in the case of secondary metropolitan areas. (4) The per capita WREF of YRDUA was affected by natural resources, and the impact of the ecological condition on the per capita WREF increased gradually over time. The impact factors of secondary metropolitan areas also clearly changed over time. Our results showed that the ecological situation of per capita water resources in the YRDUA is generally good, with obvious spatial and temporal differences. Full article

The intensification of anthropogenic pressures, particularly those related to agriculture driven by increasing demands for food and cash crops, generates negative environmental externalities. Assessing these externalities is essential to better identify and implement measures that promote the environmental sustainability of rural landscapes. This study aims to develop a multi-criteria assessment method of the negative environmental externalities of rural landscapes in the northern Benin agricultural basin, based on satellite-derived data. Starting from a 12-class land cover map produced through satellite image classification, the evaluation was conducted in three steps. First, the 12 land cover classes were reclassified into Human Disturbance Coefficients (HDCs) via a weighted sum model multi-criteria analysis based on nine criteria related to the negative environmental externalities of anthropogenic activities. Second, the HDC classes were spatially aggregated using a regular grid of 1 km² landscape cells to produce the Landscape Environmental Sustainability Index (LESI). Finally, various discretization methods were applied to the LESI for cartographic representation, enhancing spatial interpretation. Results indicate that most areas exhibit moderate environmental externalities (HDC and LESI values between 2.5 and 3.5), covering 63–75% (HDC) and 83–94% (LESI) of the respective sites. Areas of low environmental externalities (values between 1.5 and 2.5) account for 20–24% (HDC) and 5–13% (LESI). The LESI, derived from accessible and cost-effective satellite data, offers a scalable, reproducible, and spatially explicit tool for monitoring landscape sustainability. It holds potential for guiding territorial governance and supporting transitions towards more sustainable land management practices. Future improvements may include, among others, refining the evaluation criteria and introducing variable criteria weighting schemes depending on land cover or region. Full article

The discharge behavior of agricultural materials from silos is significantly influenced by external driving forces. Pressurized discharge from silos is an effective method for analyzing localized stress distribution and controlling flow rates. In this study, a combined approach of experiments and Discrete Element Method (DEM) simulations was employed to investigate the forced flow behavior of rice particle beds. Detailed analyses were conducted on flow patterns, velocity distributions, mass flow rates, dynamic arch formation, bottom stress distribution, and load propagation. Furthermore, the dissipative power during discharge was calculated based on the local shear at the silo wall, and a master curve for the dissipative power was presented. This curve facilitates the prediction of energy dissipation during silo discharge under various conditions. The findings provide a foundation and data support for the structural optimization of silos. Full article

The Fit for 55 package introduced by the European Union aims to achieve a 55% reduction in greenhouse gas emissions by 2030. In parallel, increasingly stringent exhaust gas regulations have intensified research into alternative fuels. Ethanol presents a promising option due to its compatibility with gasoline, higher octane rating, and lower exhaust emissions compared to conventional gasoline. Additionally, ethanol can be derived from agricultural waste, further enhancing its sustainability. This study examines the impact of two ethanol–gasoline blends (E10, E20) on emissions and performance in a turbocharged gasoline direct injection (TGDI) spark-ignition (SI) engine. The investigation is conducted using three-dimensional computational fluid dynamics (3D CFD) simulations to minimize development time and costs. This paper details the model development process and presents the initial results. The boundary conditions for the simulations are derived from one-dimensional (1D) simulations, which have been validated against experimental data. Subsequently, the simulated performance and emissions results are compared with experimental measurements. The E10 simulations correlated well with experimental measurements, with the largest deviation in cylinder pressure being an RMSE of 1.42. In terms of emissions, HC was underpredicted, while CO was overpredicted compared to the experimental data. For E20, the IMEP was slightly higher at some operating points; however, the deviations were negligible. Regarding emissions, HC and CO emissions were higher with E20, whereas NOx and CO₂ emissions were lower. Full article

The integration of a real-time image processing system using multiple webcams with a variable rate spraying system mounted on the back of an unmanned tractor presents an effective solution to the labor shortage in agriculture. This research aims to design and fabricate a low-cost, variable-rate, remote-controlled sprayer specifically for use in sugarcane fields. The primary method involves the modification of a 15-horsepower tractor, which will be equipped with a remote-control system to manage both the driving and steering functions. A foldable remote-controlled spraying arm is installed at the rear of the unmanned tractor. The system operates by using a webcam mounted on the spraying arm to capture high-angle images above the sugarcane canopy. These images are recorded and processed, and the data is relayed to the spraying control system. As a result, chemicals can be sprayed on the sugarcane accurately and efficiently based on the insights gained from image processing. Tests were conducted at various nozzle heights of 0.25 m, 0.5 m, and 0.75 m. The average system efficiency was found to be 85.30% at a pressure of 1 bar, with a chemical spraying rate of 36 L per hour and a working capacity of 0.975 hectares per hour. The energy consumption recorded was 0.161 kWh, while fuel consumption was measured at 6.807 L per hour. In conclusion, the development of the remote-controlled variable rate sprayer mounted on an unmanned tractor enables immediate and precise chemical application through remote control. This results in high-precision spraying and uniform distribution, ultimately leading to cost savings, particularly by allowing for adjustments in nozzle height from a minimum of 0.25 m to a maximum of 0.75 m from the target. Full article

In the context of worsening climate change and biodiversity loss, forest carbon offset projects are viewed as important nature-based solutions to mitigate these trends. However, there is limited evidence on whether these projects provide net benefits for biodiversity conservation. This study uses a staggered difference-in-differences model with balanced panel data from 128 counties in Sichuan Province, China, spanning from 2000 to 2020, to examine whether these projects bring biodiversity conservation co-benefits. The results show that the implementation of forest carbon offset projects leads to a 55.1% decrease in the ecosystem service value of forest biodiversity, with the negative impact particularly pronounced in areas facing agricultural land use and livestock pressures. The dynamic effect tests indicate that the benefits of biodiversity conservation generally begin to decline significantly 5 years after project implementation. Additional analyses show that although projects certified under biodiversity conservation standards also exhibit negative effects, the magnitude of decline is substantially smaller compared to uncertified projects, and certified projects achieve greater carbon stock gains. Heterogeneity analysis demonstrates that projects employing native tree species show significant positive effects. Moreover, spatial econometric results demonstrate significant negative spillover effects within an 80 km radius surrounding the project sites, with the effect attenuating over distance. To maximize the potential of forest carbon offset projects in addressing both climate change and biodiversity loss, it is important to mitigate the negative impacts on biodiversity within and beyond project boundaries and to enhance the continuous monitoring of projects that have been certified for biodiversity conservation. Full article

Understanding the carbon emission efficiency of apple production (APCEE) is critical for promoting green and low-carbon agricultural development. However, the spatiotemporal dynamics and driving factors of APCEE in China remain inadequately explored. This study employs life cycle assessment, super-efficiency slacks-based measures, and a panel Tobit model to evaluate the carbon footprint, APCEE, and its determinants in China’s two major production regions from 2003 to 2022. The results reveal that: (1) Producing one ton of apples in China results in 0.842 t CO₂e emissions. Land carbon intensity and total carbon emissions peaked in 2010 (28.69 t CO₂e/ha) and 2014 (6.52 × 10⁷ t CO₂e), respectively, exhibiting inverted U-shaped trends. Carbon emissions from various production areas show significant differences, with higher pressure on carbon emission reduction in the Loess Plateau region, especially in Gansu Province. (2) The APCEE in China exhibits a W-shaped trend (mean: 0.645), with overall low efficiency loss. The Bohai Bay region outperforms the Loess Plateau and national averages. (3) The structure of the apple industry, degree of agricultural mechanization, and green innovation positively influence APCEE, while the structure of apple cultivation, education level, and agricultural subsidies negatively impact it. Notably, green innovation and agricultural subsidies display lagged effects. Moreover, the drivers of APCEE differ significantly between the two major production regions. These findings provide actionable pathways for the green and low-carbon transformation of China’s apple industry, emphasizing the importance of spatially tailored green policies and technology-driven decarbonization strategies. Full article

Mangoes are an important part of Hainan’s tropical characteristic agriculture. In response to the requirements of building an ecological civilization pilot demonstration zone in Hainan, China, green and sustainable development will be the future development trend of the mango planting system. However, the economic benefits and environmental impact during its planting and management process remain unclear. This paper combines emergy, life cycle assessment (LCA), and economic analysis to compare the system sustainability, environmental impact, and economic benefits of the traditional mango cultivation system (TM) in Dongfang City, Hainan Province, and the early-maturing mango cultivation system (EM) in Sanya City. The emergy evaluation results show that the total emergy input of EM (1.37 × 10¹⁶ sej ha⁻¹) was higher than that of TM (1.32 × 10¹⁶ sej ha⁻¹). From the perspective of the emergy index, compared with TM, EM exerted less pressure on the local environment and has better stability and sustainability. This was due to the higher input of renewable resources in EM. The LCA results showed that based on mass as the functional unit, the potential environmental impact of the EM is relatively high, and its total environmental impact index was 18.67–33.19% higher than that of the TM. Fertilizer input and On-Farm emissions were the main factors causing environmental consequences. Choosing alternative fertilizers that have a smaller impact on the environment may effectively reduce the environmental impact of the system. The economic analysis results showed that due to the higher selling price of early-maturing mango, the total profit and cost–benefit ratio of the EM have increased by 55.84% and 36.87%, respectively, compared with the TM. These results indicated that EM in Sanya City can enhance environmental sustainability and boost producers’ annual income, but attention should be paid to the negative environmental impact of excessive fertilizer input. These findings offer insights into optimizing agricultural inputs for Hainan mango production to mitigate multiple environmental impacts while enhancing economic benefits, aiming to provide theoretical support for promoting the sustainable development of the Hainan mango industry. Full article

The Santurbán paramo is a sensitive high-mountain ecosystem exposed to pressures from extractive and agricultural activities, as well as increasing tourism. In response, this study presents a context-aware recommendation system designed to support sustainable tourism through the integration of deep neural networks and ontology-based semantic modeling. The proposed system delivers personalized recommendations—such as activities, accommodations, and ecological routes—by processing user preferences, geolocation data, and contextual features, including cost and popularity. The architecture combines a trained TensorFlow Lite model with a domain ontology enriched with GeoSPARQL for geospatial reasoning. All inference operations are conducted locally on Android devices, supported by SQLite for offline data storage, which ensures functionality in connectivity-restricted environments and preserves user privacy. Additionally, the system employs geofencing to trigger real-time environmental notifications when users approach ecologically sensitive zones, promoting responsible behavior and biodiversity awareness. By incorporating structured semantic knowledge with adaptive machine learning, the system enables low-latency, personalized, and conservation-oriented recommendations. This approach contributes to the sustainable management of natural reserves by aligning individual tourism experiences with ecological protection objectives, particularly in remote areas like the Santurbán paramo. Full article

This article analyzes statistics on the failure of technological equipment, assemblies, and mechanisms of agricultural (and other) machines associated with the breakdown or failure of gear pumps. It was found that the leading causes of gear pump failures are the opening of gear teeth contact during pump operation, poor assembly, wear of bushings, thrust washers, and gear teeth. It has also been found that there is a problem related to the restoration, repair, and manufacture of parts in the conditions of enterprises serving the agro-industrial complex of the Republic of Kazakhstan (AIC RK). This is due to the lack of necessary technological equipment, tools, and instruments, as well as centralized repair and restoration bases equipped with the required equipment. This work proposes to solve this problem by applying AM technologies to the repair and manufacture of parts for agricultural machinery and equipment. The study results on the stress–strain state of support bushings under various pressures are presented, showing that a fully filled bushing has the lowest stresses and strains. It was also found that bushings with 50% filling and fully filled bushings have similar stress and strain values under the same pressure. The difference between them is insignificant, especially when compared to bushings with lower filling. This means that filling the bushing by more than 50% does not provide a significant additional reduction in stresses. In terms of material and printing time savings, 50% filling may also be the optimal option. Full article

Hydrogels are widely known for their ability to increase soil water retention and for their potential slow nutrient release mechanism. They have been constantly improved to meet the growing demand for sustainability in agriculture. Research focused on the development of biodegradable hydrogels, produced from industrial cellulose waste, are an ecological and efficient alternative soil ameliorant for the improvement of agricultural land. The objective of this study was to evaluate the impacts of two types of hydrogel (processed in a glass reactor versus a twin-screw extruder) on soils with different textures (clay and sandy loam), testing their water retention capacity, nitrogen leaching, and effects on seed germination. The methodology included the evaluation of water retention capacity at different pressures with different hydrogel addition rates in the soil, leaching tests in columns filled with soil and hydrogel layers, and germination tests of sorghum and corn. The results indicated that the addition of hydrogel significantly improved water retention, especially in sandy loam soils. The hydrogels also reduced nitrogen leaching, acting as nitrification inhibitors and limiting the conversion of ammonium to nitrate, with greater effectiveness in clayey soils. In the tested formulations, it was observed that the hydrogel doses applied to the columns favored nitrogen retention in the region close to the roots, directly influencing the initial stages of germination. This behavior highlights the potential of hydrogels as tools for directing nutrients in the soil profile, indicating that adjustments to the C:N ratio, nutrient release rate, and applied doses can optimize their application for different crops. Full article

Potatoes are one of the world’s most widely cultivated crops, but their yield is coming under mounting pressure from early blight, a fungal disease caused by Alternaria solani. Early detection and accurate identification are key to effective disease management and yield protection. This paper introduces a novel deep learning framework called ZeroShot CNN, which integrates convolutional neural networks (CNNs) and ZeroShot Learning (ZSL) for the efficient classification of seen and unseen disease classes. The model utilizes convolutional layers for feature extraction and employs semantic embedding techniques to identify previously untrained classes. Implemented on the Kaggle potato disease dataset, ZeroShot CNN achieved 98.50% accuracy for seen categories and 99.91% accuracy for unseen categories, outperforming conventional methods. The hybrid approach demonstrated superior generalization, providing a scalable, real-time solution for detecting agricultural diseases. The success of this solution validates the potential in harnessing deep learning and ZeroShot inference to transform plant pathology and crop protection practices. Full article

Information and communication technologies (ICTs) are becoming increasingly important for sustainable rural development through the smart village concept. This study aims to model ICT’s potential for public services management in European rural areas. It identifies ICT applications across rural service domains, analyzes how these technologies address specific rural challenges, and evaluates their benefits, implementation barriers, and future prospects for sustainable rural development. A qualitative content analysis method was applied using purposive sampling to analyze 79 peer-reviewed articles from EBSCO and Elsevier databases (2000–2024). A deductive approach employed predefined categories to systematically classify ICT applications across rural public service domains, with data coded according to technology scope, problems addressed, and implementation challenges. The analysis identified 15 ICT application domains (agriculture, healthcare, education, governance, energy, transport, etc.) and 42 key technology categories (Internet of Things, artificial intelligence, blockchain, cloud computing, digital platforms, mobile applications, etc.). These technologies address four fundamental rural challenges: limited service accessibility, inefficient resource management, demographic pressures, and social exclusion. This study provides the first comprehensive systematic categorization of ICT applications in smart villages, establishing a theoretical framework connecting technology deployment with sustainable development dimensions. Findings demonstrate that successful ICT implementation requires integrated urban–rural cooperation, community-centered approaches, and balanced attention to economic, social, and environmental sustainability. The research identifies persistent challenges, including inadequate infrastructure, limited digital competencies, and high implementation costs, providing actionable insights for policymakers and practitioners developing ICT-enabled rural development strategies. Full article

Weeds pose a significant challenge to agricultural productivity, requiring control strategies that are both effective and environmentally sustainable. Therefore, this study evaluated the inhibitory potential of aqueous extracts from Ricinus communis L. leaves to manage the weeds Oryza sativa L. (weedy rice) and Cyperus ferax. Extracts were obtained through pressurized liquid extraction using water as the solvent. Bioassays were conducted during pre- and post-emergence stages by foliar spraying 15 and 30 days after sowing (DAS). The effect of extraction time (1–30 min) on inhibitory efficacy was also assessed. Chemical profiles of the extracts were characterized using high-performance liquid chromatography. The extracts significantly inhibited seed germination, with suppression rates reaching 92.7%. Plant growth was also diminished, particularly with earlier treatments (at 15 DAS), resulting in reductions of up to 32% and 53% in shoot length, and 69% and 73% in total dry mass for O. sativa L. and C. ferax, respectively. Mortality rates of O. sativa L. and C. ferax reached 64% and 58%, respectively. Phenolic compounds were identified in the extracts, and higher concentrations were observed at shorter extraction times. These findings underscore the potential of R. communis L. leaf extracts as an ecologically sustainable alternative for weed management, providing an effective and natural approach that may reduce reliance on synthetic herbicides and mitigate their environmental impact. Full article

Central Asia (CA) faces growing Water–Energy–Food (WEF) Nexus challenges, due to its complex transboundary water management, legacy Soviet-era water infrastructure, and increasing climate and socio-economic pressures. This study presents the development of a System Dynamics Model (SDM) to evaluate WEF interdependencies across the Aral Sea Basin (ASB), including the Amu Darya and Syr Darya river basins and their sub-basins. Different downscaling strategies based on the area, population, or land use have been applied to process open-access databases at the national level in order to match the scope of the study. Climate and socio-economic assumptions were introduced through the integration of already defined Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). The resulting SDM incorporates more than 500 variables interacting through mathematical relationships to generate comprehensive outputs to understand the WEF Nexus concerns. The SDM was successfully calibrated and validated across three key dimensions of the WEF Nexus: final water discharge to the Aral Sea (Mean Absolute Error, MAE, <5%), energy balance (MAE = 4.6%), and agricultural water demand (basin-wide MAE = 1.2%). The results underscore the human-driven variability of inflows to the Aral Sea and highlight the critical importance of transboundary coordination to enhance future resilience. Full article