Search Results (90)

Corydalis yanhusuo W. T. Wang, commonly known as Yanhusuo, is an important and rare medicinal plant resource in China. Its habitat integrity is facing severe challenges due to climate change and human activities. Establishing an integrative quality zoning system for this species is of significant practical importance for resource conservation and adaptive management. This study integrates multiple data sources, including 121 valid distribution points, 37 environmental factors, future climate scenarios (SSP126 and SSP585 pathways for the 2050s and 2090s), and measured content of tetrahydropalmatine (THP) from 22 sampling sites. A predictive framework for habitat suitability and spatial distribution of effective components was constructed using a multi-model coupling approach (MaxEnt, ArcGIS spatial analysis, and co-kriging method). The results indicate that the MaxEnt model exhibits high prediction accuracy (AUC > 0.9), with the dominant environmental factors being the precipitation of the wettest quarter (404.8~654.5 mm) and the annual average temperature (11.8~17.4 °C). Under current climatic conditions, areas of high suitability are concentrated in parts of Central and Eastern China, including the Sichuan Basin, the middle–lower Yangtze plains, and coastal areas of Shandong and Liaoning. In future climate scenarios, the center of suitable areas is predicted to shift northwestward. The content of THP is significantly correlated with the mean diurnal temperature range, temperature seasonality, and the mean temperature of the wettest quarter (p < 0.01). A comprehensive assessment identifies the Yangtze River Delta region, Central China, and parts of the Loess Plateau as the optimal integrative quality zones. This research provides a scientific basis and decision-making support for the sustainable utilization of C. yanhusuo and other rare medicinal plants in China. Full article

This study investigates the operational performance of fruit-picking robots under varying terrain slopes and soil moisture conditions, with a focus on comparing wheeled and tracked locomotion systems. A modular robot platform was designed and tested in both controlled environments and actual mountainous orchards in Shandong, China. The experiments assessed key performance metrics—average speed, slip rate, and path deviation—under combinations of four slope levels (0°, 8°, 18°, 28°) and three soil moisture levels (dry 10%, moderate 20%, wet 35%). Results reveal that wheeled robots perform optimally on dry and flat terrain but experience significant slippage and path deviation under steep and wet conditions. In contrast, tracked robots maintain better stability and terrain adaptability, demonstrating lower slip rates and more consistent trajectories across a wide range of conditions. A synergistic deterioration effect was observed when high slope and high soil moisture co-occur, significantly degrading the performance of wheeled systems, while tracked systems mitigated these effects. Complementary semi-structured interviews with 20 orchard stakeholders—including farmers, growers, and hired pickers—highlighted key user expectations: robust traction, terrain adaptability, reduced physical labor, and operational safety. The findings suggest that future agricultural robots should adopt adaptive hybrid mobility systems and integrate environmental perception capabilities to enhance performance in complex agricultural scenarios. These insights contribute practical and theoretical guidance for the design and deployment of intelligent fruit-picking robots in diverse field environments. Full article

New quality productive force is a crucial driver for rural transformation. Exploring the impact of this new quality productive force on rural transformation in Shandong Province and enhancing the positive role of regional new quality productive force are significant in promoting high-quality development in this area. Based on urban panel data from 16 prefecture-level cities in Shandong Province, China, spanning from 2010 to 2022, the levels of new quality productive force and rural transformation in Shandong Province are measured separately and an econometric model is constructed to analyze, in depth, the impact of new quality productive force on rural transformation and its mechanism of action. The results show the following. (1) New quality productive force can significantly increase the level of rural transformation in Shandong Province. (2) The urbanization rate of new quality productive force significantly promotes rural transformation, but increases in the average wage of urban workers and the over-advancement of industrial structure significantly inhibit rural transformation. (3) New quality productive force significantly affects the level of rural transformation, mainly by improving the quality of the population. (4) There is regional heterogeneity in the impact of new quality productive forces on rural transformation in the three economic circles of Shandong Province. New quality productivity force provides new dynamic energy for rural transformation in Shandong Province, which can provide new research perspectives and practical guidance for better rural development in China and the rest of the world. Full article

Accurate diagnostics of crop yields are essential for climate-resilient agricultural planning; however, conventional datasets often conflate environmental covariates during model training. Here, we present HHHWheatYield1km, a 1 km resolution winter wheat yield dataset for China’s Huang-Huai-Hai Plain spanning 2000–2019. By integrating climate-independent multi-source remote sensing metrics with a Random Forest model, calibrated against municipal statistical yearbooks, the dataset exhibits strong agreement with county-level records (R = 0.90, RMSE = 542.47 kg/ha, MRE = 9.09%), ensuring independence from climatic influences for robust driver analysis. Using Geodetector, we reveal pronounced spatial heterogeneity in climate–yield interactions, highlighting distinct regional disparities: precipitation variability exerts the strongest constraints on yields in Henan and Anhui, whereas Shandong and Jiangsu exhibit weaker climatic dependencies. In Beijing–Tianjin–Hebei, March temperature emerges as a critical determinant of yield variability. These findings underscore the need for tailored adaptation strategies, such as enhancing water-use efficiency in inland provinces and optimizing agronomic practices in coastal regions. With its dual ability to resolve pixel-scale yield dynamics and disentangle climatic drivers, HHHWheatYield1km represents a resource for precision agriculture and evidence-based policymaking in the face of a changing climate. Full article

The excessive use of chemical fertilizers has resulted in a decline in soil quality, crop yield, and crop quality. Partial substitution of chemical fertilizers with organic fertilizers is a sustainable practice that can alleviate these issues. However, a comprehensive evaluation of the effects of partial organic substitution on muskmelon yield, quality, soil fertility, and economic benefits remains unclear. We conducted a greenhouse experiment with muskmelon production in Shandong, China, involving five treatments: no fertilization (CK); total chemical fertilizer (CON); only replacing base fertilizer with organic fertilizer (OPT); 15% (OF15) and 30% (OF30) organic substitution of chemical fertilizers based on optimized fertilization. Our results indicated that the partial organic substitution treatments (OF15 and OF30) improved yield by 5.60–11.9% compared to CON. Furthermore, the Vitamin C, soluble protein, and sugar content in muskmelon were higher in the OF15 and OF30 treatments than in the CON. Compared to the CON, organic substitution treatments significantly increased soil organic matter, total N, total K, alkaline-hydrolyzable, available P, and available K. Additionally, the economic benefit analysis revealed that OF15 and OF30 increased net benefits by 5.60–14.9% respectively, compared to CON. Collectively, these findings suggest that partial substitution of mineral fertilizer with organic fertilizer improves muskmelon productivity, enhances soil nutrients, and increases economic benefits. Full article

The equitable distribution of basic educational services is crucial for attaining educational fairness and promoting balanced demographic and economic growth. This research leverages point-of-interest (POI) data to analyze the spatial arrangement of basic educational service facilities in the Yellow River Basin of China. Employing kernel density analysis and spatial autocorrelation with a geographic information system tool, this study examines the spatial distribution of these facilities. It also applies geographically weighted regression to identify the primary factors influencing their spatial layout. This study reveals a pronounced disparity between the four downstream and five upstream provinces of the Yellow River Basin in terms of basic educational facility availability. In the downstream provinces, facilities constitute 82.45% of the total, markedly surpassing the level of 17.55% in the upstream provinces. The kernel density analysis shows that areas with a high concentration of educational facilities often align with provincial capitals, including Taiyuan in Shanxi Province, Xi’an in Shaanxi Province, Zhengzhou in Henan Province, and regions around Shandong Province. Significant regional differences exist within the Yellow River Basin. Preprimary, primary, and secondary education facilities exhibit strong spatial clustering, with Moran’s I indices of 0.26, 0.19, and 0.09, respectively. High–high clusters of preprimary education are predominantly found in the western region of the basin, whereas low–low clusters appear in some eastern and northern areas. Primary and secondary educational facilities show high–high clustering in the north. The spatial distribution of these educational facilities is chiefly influenced by the permanent population and the proportion of the tertiary industry. Per capita gross domestic product (GDP) and educational fiscal expenditure play secondary roles in influencing the spatial layout. The results have important practical significance for promoting the equalization of basic education public services and equal educational opportunities for the school-age population in the Yellow River Basin. Full article

Timely sowing is a crucial cultivation practice for enhancing crop productivity. In Shandong Province, inadequate supporting cultivation techniques are the primary factors limiting the yield and quality improvement of high-quality strong-gluten wheat (Triticum aestivum L.). A promising strategy for achieving synergistic improvements in both yield and quality involves matching the sowing date and density to the specific ecological conditions of each region. To explore this approach, we conducted continuous field experiments at three testing stations—Jining, Dezhou, and Yantai—across the major wheat-growing regions of Shandong Province from 2019 to 2021. Four sowing dates (T1: October 5; T2: October 15; T3: October 25; and T4: November 5) and seven planting densities (ranging from 135 × 10⁴ plants ha⁻¹ to 405 × 10⁴ plants ha⁻¹, denoted as D1–D7) were tested at each location. The results revealed that the wheat yield in each ecological zone initially increased, then decreased as the sowing dates were delayed. In Jining and Dezhou, high grain yields were typically observed at all densities under T3, while Yantai showed optimal yields under T2. Specifically, Jining achieved the highest grain yield of 9326.6 kg ha⁻¹ with 315 × 10⁴ plants ha⁻¹ on October 25 (T3D5), while Dezhou and Yantai reached their maximum yields under 225 × 10⁴ plants ha⁻¹ on October 15 (T2D3), with yields of 8784.0 kg ha⁻¹ and 9366.3 kg ha⁻¹, respectively. Except in Dezhou, the wheat quality compliance rate at all sites followed an increasing trend initially, which then declined with later sowing dates. In Jining and Yantai, high-quality compliance rates were most frequently achieved under T2, while Dezhou showed optimal quality rates under T1. In conclusion, selecting appropriate sowing dates and densities can lead to synergistic improvements in both grain yield and quality of strong-gluten wheat across Shandong’s wheat-growing regions. Full article

Resource-based cities are vulnerable to the depletion of natural resources and urgently need to undergo a green transformation to avoid the so-called “resource curse” and achieve sustainable development. At present, there is a lack of a scientific and reasonable indicator system and theoretical model to guide the evaluation of the effectiveness of green transformation of resource-based cities. To undertake a comprehensive analysis of the causality of indicators, this study employs the DPSIR (Driving Force–Pressure–State–Impact–Response) model to construct an evaluation index system for the effectiveness of green transformation of resource-based cities. The DEMATEL (Decision-Making Trial and Evaluation Laboratory) method is employed to explore the level of importance of the evaluation indicators and the causal relationships between the evaluation indicators. The VIKOR (‘VlseKriterijumska Optimizacija I Kompromisno Resenje’ in Serbian) method is introduced to evaluate the effectiveness of green transformation of resource-based cities. This study selects Shandong Province, a strategically important energy resource region in China, as an example. The results of the study show that the effectiveness of green transformation of resource-based cities in Shandong Province as a whole was on an upward trend from 2013 to 2021. This study categorizes the phases into rapid start-up period, adjustment and optimization period, and recovery and acceleration period. Currently, the green transformation shows remarkable effectiveness. The effectiveness of green transformation within each dimension of the DPSIR model reveals a ranking of response > pressure > driving force > state > impact. By incorporating regional characteristics, this study explores and proposes recommendations to enhance the green transformation of resource-based cities. It can not only furnish policy references for green transformation of similar resource-based cities, but also offer case study practices for different types of resource-based cities to achieve sustainable development. Full article

Spatial and temporal information about cropping patterns of single and multiple crops is important for monitoring crop production and land-use intensity. We used time-series MODIS NDVI 8-day composite data to develop annual cropping pattern products at a 250 m spatial resolution for China, covering the period from 2001 to 2023. To address the potential impacts of varying parameters in both data pre-processing and the peak detection algorithm on the accuracy of cropping pattern mapping, we employed a grid-search method to fine-tune these parameters. This process focused on optimizing the Savitzky–Golay smoothing window size and the peak width parameters using a calibration dataset. The results highlighted that an optimal combination of a five to seven MODIS composite window size in Savitzky–Golay smoothing and a peak width of four MODIS composites achieved good overall mapping accuracy. Pixel-wise accuracy assessments were conducted for the selected mapping years of 2001, 2011, and 2021. Overall accuracies were between 89.7% and 92.0%, with F1 scores ranging from 0.921 to 0.943. Nationally, this study observed a fluctuating trend in multiple cropping percentages, with a notable increase after 2013, suggesting shifts toward more intensive agricultural practices in recent years. At a finer spatial scale, the combination of Mann–Kendall and Sen’s slope analyses revealed that approximately 12.9% of 3 km analytical windows exhibited significant changes in cropping intensity. We observed spatial clusters of increasing and decreasing crop intensity trends across provinces such as Hebei, Shandong, Shaanxi, and Gansu. This study underscores the importance of data smoothing and peak detection methods in analyzing high temporal resolution remote sensing data. The generation of annual single/multiple cropping pattern maps at a 250 m spatial resolution enhances our comprehension of agricultural dynamics through time and across different regions. Full article

Leveraging the transformative potential of digital technologies to support the farmers’ green production transformation (FGPT) is a critical catalyst for facilitating the accelerated green transformation and upgrading of agricultural systems, thereby achieving high-quality agricultural development. Using survey data from major watermelon- and muskmelon-producing regions in Henan, Shandong, and Xinjiang, and employing Ordinary Least Squares (OLS), Propensity Score Matching (PSM), and the Logit model, this study examines the mechanisms through which digital technology influences FGPT. The findings reveal that the adoption of digital technology significantly reduces farmers’ use of pesticides and fertilizers, with a particularly pronounced effect on pesticide reduction. Heterogeneity analysis indicates that the impact of digital technology on reducing pesticide and fertilizer usage varies across regions: while its adoption leads to significant reductions in Henan and Shandong, the positive effects in Xinjiang remain statistically insignificant. Mechanism analysis shows that digital technology enhances FGPT by reducing information asymmetry, improving market access, and enhancing precision management practices. Based on these findings, this study recommends expanding awareness campaigns to accelerate the adoption of digital technology, enhancing digital infrastructure to bridge the urban–rural digital divide, and optimizing digital technology promotion systems. These actions can be taken alongside the implementation of economic incentives and compensation mechanisms. The insights and policy recommendations from this research provide valuable guidance for China and other countries seeking to leverage digital technology for sustainable agricultural development. Full article

Conservation tillage, particularly no tillage (NT), has been recognized as an efficient farming practice, particularly in dryland agriculture, as it significantly enhances crop yields, improves soil health, and contributes to environmental sustainability. However, the influence of NT on winter wheat radiation interception and utilization, biomass, and yield under NT in irrigated fields, especially under drip fertigation, is unclear. A field experiment was carried out for two growing seasons in Shandong province, China, using a split-plot design with the tillage method as the main plot (no tillage, NT; rotary tillage, RT; and first plowing the soil and then conducting rotary tillage, PRT), and water–nitrogen management as the sub-plot (N fertilizer broadcasting and flood irrigation, BF and drip fertigation, DF). Our results showed that DF increased yield by 11.0–28.5%, but the yield response to DF depended on the tillage methods. NT had the highest response in yield of 26.3–28.5%, followed by RT of 14.6–15.1% and PRT of 11.0–11.9%. Both increased grains per ear and ear number, a result of the greater maximum stems number donating to the yield gain by DF under NT. This gain was also due to the substantially promoted post-anthesis biomass (36.7–47.3%), which resulted from the increased interception of solar radiation and radiation use efficiency after anthesis. In addition, the extended post-anthesis duration also benefited biomass and yield. To conclude, our findings underscore the critical need to optimize water and nitrogen management strategies to maximize yield under conservation tillage systems. Full article

Hydrological models serve as essential tools in hydrological research, allowing us to address practical hydrological issues. This study focuses on the Xunhe Watershed in Shandong Province, China, constructing a distributed Xin’anjiang hydrological model. Furthermore, traditional manual calibration and automatic calibration using the Particle Swarm Optimization (PSO) algorithm were employed to determine model parameters, followed by hydrological simulations, with the aim of investigating the applicability of the distributed Xin’anjiang model in this watershed. The research findings indicate that the distributed Xin’anjiang model accurately simulates the hydrological processes in the Xunhe Watershed. There is a high level of agreement between the observed data and the simulated results, including key indicators such as peak discharge, runoff volume, and peak time. After optimizing the model parameters using the PSO algorithm, the distributed Xin’anjiang model demonstrates improved simulation performance in the Xunhe Watershed. During the calibration period, the mean relative peak discharge error (RPE) is 4.1%, the mean relative runoff error (RRE) is 4.34%, and the average Nash–Sutcliffe efficiency (NSE) for simulating the flood events is 0.89. During the validation period, the mean RPE is 3.82%, the mean RRE is 6.1%, and the average NSE for the process is 0.83. This indicates that the distributed Xin’anjiang model has good applicability in this watershed, providing a reliable reference for flood control and disaster reduction in the Xunhe Watershed. Full article

This paper aims to investigate the indoor environmental conditions and energy use behaviours of older individuals in rural cold climates of China, with a specific focus on cooling practices during the summer months in the Shandong region. This study employs a mixed-method approach, combining quantitative indoor environmental monitoring with qualitative interviews and observations, to explore the relationship between environmental factors, household living conditions, and energy use patterns across five types of elderly households: three generations living together, older people living with grandchildren, older people living with children, older couples living together, and older people living alone. Data collection was conducted over five weeks during the summer of 2023 using HOBO UX100-003 data loggers, while external weather conditions were monitored by the China Meteorological Administration. Face-to-face interviews were conducted to gain deeper insights into daily cooling behaviours and energy use. The results reveal that cooling practices and indoor environmental conditions vary significantly among the different household types. Multigenerational households showed more complex energy use dynamics, with younger family members frequently operating high-energy appliances like air conditioners, while older individuals tended to rely on natural ventilation and electric fans to reduce energy costs. In contrast, older couples and solitary older individuals demonstrated more conservative cooling behaviours, often enduring higher indoor temperatures due to limited financial resources and a desire to minimize energy expenditures. Despite the high energy use intensity in some households, many homes failed to achieve comfortable indoor environments, particularly in dwellings with minimal insulation and older building materials. This study concludes that economic status, household structure, and building characteristics play crucial roles in shaping cooling behaviours and indoor comfort during the summer. Full article

Leaf diseases such as Mosaic disease and Black Rot are among the most common diseases affecting apple leaves, significantly reducing apple yield and quality. Detecting leaf diseases is crucial for the prevention and control of these conditions. In this paper, we propose incorporating rotated bounding boxes into deep learning-based detection, introducing the ProbIoU loss function to better quantify the difference between model predictions and real results in practice. Specifically, we integrated the Plant Village dataset with an on-site dataset of apple leaves from an orchard in Weifang City, Shandong Province, China. Additionally, data augmentation techniques were employed to expand the dataset and address the class imbalance issue. We utilized the EfficientNetV2 architecture with inverted residual structures (FusedMBConv and S-MBConv modules) in the backbone network to build sparse features using a top–down approach, minimizing information loss. The inclusion of the SimAM attention mechanism effectively captures both channel and spatial attention, expanding the receptive field and enhancing feature extraction. Furthermore, we introduced depth-wise separable convolution and the CAFM in the neck network to improve feature fusion capabilities. Finally, experimental results demonstrate that our model outperforms other detection models, achieving 93.3% mAP@0.5, 88.7% Precision, and 89.6% Recall. This approach provides a highly effective solution for the early detection of apple leaf diseases, with the potential to significantly improve disease management in apple orchards. Full article

With the development of modernization, traditional fossil energy reserves are decreasing, and the power industry, as one of the main energy consumption forces, has begun to pay attention to increasing the proportion of clean energy generation. With the deepening of electrification, the peak-valley difference of residential electricity consumption increases, but photovoltaic and wind power generation have fluctuations and are manifested as reverse peak regulation. Thermal power plants as the main force of peak regulation gradually reduce the market share, making nuclear power plants bear the heavy responsibility of participating in peak regulation. The traditional method of adjusting operating power by inserting and removing control rods has great safety risks and wastes resources. Therefore, this paper proposes a new energy storage system that can keep the nuclear power plant running at full power and produce hydrogen to synthesize ammonia from excess power. A comprehensive evaluation model of energy storage based on z-score data standardization and objective parameter assignment AHP (analytic hierarchy process) analysis method was established to evaluate energy storage systems according to a multi-index system. With an AP1000 daily load tracking curve as the input model, the simulation model built by Aspen Plus V14 was used to calculate the operating conditions of the system. In order to provide a construction basis for practical engineering use, Haiyang Nuclear Power Plant in Shandong Province is taken as an example. The system layout scheme is proposed according to the local environmental conditions. The accident tree analysis method is combined with ALOHA 5.4.1.2 (Areal Locations of Hazardous Atmospheres) hazardous chemical analysis software and MARPLOT 5.1.1 geographic information technology. A qualitative and quantitative assessment of risk factors and the consequences of leakage, fire, and explosion accidents caused by hydrogen and ammonia storage processes is carried out to provide guidance for accident prevention and emergency rescue. The design of an “Electric-Hydrogen-Ammonia” energy storage system proposed in this paper provides a new idea for zero-carbon energy storage for the peak shaving of nuclear power plants and has a certain role in promoting the development of clean energy. Full article