Search Results (188)

Drought poses a severe challenge to ornamental tree growth under climate change. This study employed a 2 × 4 factorial design—with two soil moisture levels (80–85% vs. 50–55% field capacity) and four nitrogen treatments (NN: no nitrogen; NO: nitrate nitrogen; NH: ammonium nitrogen; MN: mixed nitrate-ammonium nitrogen)—to examine the efficacy of nitrogen addition in enhancing drought resistance in Machilus yunnanensis seedlings. Results revealed that (1) drought stress leads to the acidification of rhizosphere soil, resulting in a decrease of 7.67%, 29.51%, 14.07%, and 44.09% in the content of soil organic matter (SOM), available phosphorus (AP), available potassium (AK), and dissolved organic nitrogen (DON), respectively. This adverse change directly impacts plant growth; it is manifested by a significant reduction of 45% in total chlorophyll (T Chl), a 67.18% decrease in photosynthetic rate (Pn), as well as reductions of 10.61%, 27.59%, 14.81%, and 12.35% in plant height, leaf, stem, and total biomass, respectively. (2) The application of all three forms of nitrogen helps alleviate drought stress, as evidenced by the recovery of photosynthetic levels and the reduction in malondialdehyde (MDA) content, with ammonium-N exhibiting superior efficacy over nitrate-N across most metrics. (3) Strikingly, the mixed nitrogen form outperformed singular applications by demonstrating multifaceted advantages: It maintains soil pH levels and rhizosphere nutrient availability under drought conditions, particularly with a 10.99% and 33.44% increase in dissolved organic nitrogen and available phosphorus content, respectively. More importantly, under drought stress, it increased leaf water content by 20.31%, nitrogen use efficiency by 15.67%, and photosynthetic nitrogen use efficiency by 439.44%, promoted the accumulation of osmolytes, while upregulating antioxidant enzyme activity to counteract osmotic imbalance and alleviate oxidative damage. These findings highlight that nitrogen supplementation, particularly mixed nitrogen application, enhances drought resistance in M. yunnanensis, offering a viable management strategy to sustain urban tree landscapes in water-limited environments. Full article

Saline–alkali soils represent a significant reserve of arable land, playing a vital role in ensuring national food security. Given that saline–alkali soil has low soil organic matter (SOM) and soil nutrient contents, and that soil quality degradation poses a threat to regional high-quality agricultural development and ecological balance, this study took coastal saline–alkali land as a case study. It adopted the extreme gradient boosting (XGB) model optimized by the tree-structured Parzen estimator (TPE) algorithm, combined with in situ hyperspectral (ISH) and spaceborne hyperspectral (SBH) data, to predict and map soil organic matter and four soil nutrients: alkali nitrogen (AN), available phosphorus (AP), and available potassium (AK). From the research outputs, one can deduce that superior predictive efficacy is exhibited by the TPE-XGB construct, employing in situ hyperspectral datasets. Among these, available phosphorus (R² = 0.67) exhibits the highest prediction accuracy, followed by organic matter (R² = 0.65), alkali-hydrolyzable nitrogen (R² = 0.56), and available potassium (R² = 0.51). In addition, the spatial continuity mapping results based on spaceborne hyperspectral data show that SOM, AN, AP, and AK in soil nutrients in the study area are concentrated in the northern, eastern, southern, and riverbank and estuarine delta areas, respectively. The variability of soil nutrients from large to small is phosphorus, potassium, nitrogen, and organic matter. The SHAP (SHapley Additive exPlanations) analysis results reveal that the bands with the greatest contribution to the fitting of SOM, AN, AP, and AK are 612 nm, 571 nm, 1493 nm, and 1308 nm, respectively. Extending into realms of hierarchical partitioning (HP) and variation partitioning (VP), it is discerned that climatic factors (CLI) alongside vegetative aspects (VEG) wield dominant influence upon the spatial differentiation manifest in nutrients. Meanwhile, comparatively diminished are the contributions possessed by terrain (TER) and soil property (SOIL). In summary, this study effectively assessed the significant variation patterns of soil nutrient distribution in coastal saline–alkali soils using the TPE-XGB model, providing scientific basis for the sustainable advancement of agricultural development in saline–alkali coastal regions. Full article

In the lower and middle sections of the Yangtze River Basin Region (YRBR) in China, challenges posed by climate change and delayed harvesting of preceding crops have hindered the timely sowing of wheat, leading to an increasing prevalence of late-sown wheat fields. This trend has emerged as a significant impediment to achieving high and stable production of wheat in this area. During the growing seasons of 2022–2023 and 2023–2024, an unmanned aerial vehicle (UAV)-based multispectral camera was used to monitor different wheat materials at various growth stages under normal sowing treatment (M1) and late sowing with increased plant density (M2). By assessing yield loss, the wheat tolerance to late sowing was quantified and categorized. The correlation between the differential vegetation indices (D-VIs) and late sowing resistance was examined. The findings revealed that the J2-Logistic model demonstrated optimal classification performance. The precision values of stable type, intermediate type, and sensitive type were 0.92, 0.61, and 1.00, respectively. The recall values were 0.61, 0.92, and 1.00. The mean average precision (mAP) of the model was 0.92. This study proposes a high-throughput and low-cost evaluation method for wheat tolerance to late sowing, which can provide a rapid predictive tool for screening suitable varieties for late sowing and facilitating late-sown wheat breeding. Full article

Non-destructive diameter estimation of cabbage heads and yield prediction employing Unmanned Aerial Vehicle (UAV) imagery are superior to conventional approaches, which are labor intensive and time consuming. This approach assesses spatial variability across the field, effective allocation of resources, and supports variable application rates of fertilizer and supply chain management. Here, individual cabbage head diameters were estimated using deep learning-based pose estimation models (YOLOv8s-pose and YOLOv11s-pose) using high spatial resolution RGB images acquired from UAV 6 m during the cabbage-growing season in 2024. With a mean relative error (MRE) of 4.6% and a high mean average precision (mAP) 98.5% at 0.5, YOLOv11s-pose emerged as the best-performing model, verifying its accuracy for pragmatic agricultural use. The approximated diameter was then combined with climatic variables (temperature and rainfall) and canopy reflectance indices (normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green chlorophyll index (CIg)) that were extracted from the multispectral images with 6 m resolution and fed into AI models to develop individual cabbage head fresh weight. Among the machine learning models (MLMs) tested, CatBoost achieved the lowest Mean Squared Error (MSE = 0.025 kg/cabbage), highest R² (0.89), and outperformed other models based on the Diebold–Mariano statistical test (p < 0.05). This finding suggests that an integrated AI-powered framework enhances non-invasive and precise yield estimation in cabbage farming. Full article

With the escalating frequency of urban and forest fires driven by climate change, the development of intelligent and robust fire detection systems has become imperative for ensuring public safety and ecological protection. This paper presents a comprehensive multi-module fire detection framework based on visual computing, encompassing image enhancement and lightweight object detection. To address data scarcity and to enhance generalization, a projected generative adversarial network (Projected GAN) is employed to synthesize diverse and realistic fire scenarios under varying environmental conditions. For the detection module, an improved YOLOv8n architecture is proposed by integrating BiFormer Attention, Agent Attention, and CCC (Compact Channel Compression) modules, which collectively enhance detection accuracy and robustness under low visibility and dynamic disturbance conditions. Extensive experiments on both synthetic and real-world fire datasets demonstrated notable improvements in image restoration quality (achieving a PSNR up to 34.67 dB and an SSIM up to 0.968) and detection performance (mAP reaching 0.858), significantly outperforming the baseline. The proposed system offers a reliable and deployable solution for real-time fire monitoring and early warning in complex visual environments. Full article

The high-elevation ecosystems of the Tibetan Plateau provide crucial ecosystem services including watershed protection and water provision for downstream human and wildlife communities. Thus, understanding the relationship between soil properties and vegetation under different management regimes is important as a warming climate alters these systems. This study assessed vegetation cover, quantified the distribution of soil nutrients, and examined the relationships among soil chemical properties and plant cover in the high-elevation shrublands (3300 to 3700 m) in the Qilian Mountains on the northeastern Tibetan Plateau of China. These vegetation surveys and soil sample collections were conducted on 15 shrubland plots at different soil depths and soil chemical properties were investigated at each elevation. The content of soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), and available potassium (AK) fluctuated along the elevational gradient, while soil pH was close to neutral (pH 7.4). At our sites, SOM and TN contents generally increased with elevation, and AK was positively correlated with Salix plant cover. Using PCA, we determined that PC1 captured 43% of the total variance, and SOM and TN were the top contributing features. As climate in the region warms and precipitation becomes more variable, understanding the current soil–vegetation equilibria and how vegetation may migrate in future years is important to predicting changes in this region, especially at high elevations. From a managerial perspective, our goal was to provide additional information for restoring and managing subalpine and alpine shrubland vegetation in the Qilian Mountains to ensure the future sustainable use of these systems. Full article

With its unique climate and geographical advantages, Yunnan Province in China has become one of the country’s most important citrus-growing regions. However, the dense foliage and large fruit size of citrus trees often result in significant occlusion, and the fluctuating light intensity further complicates accurate assessment of fruit maturity. To address these challenges, this study proposes an improved model based on YOLOv8, named ORD-YOLO, for citrus fruit maturity detection. To enhance the model’s robustness in complex environments, several key improvements have been introduced. First, the standard convolution operations are replaced with Omni-Dimensional Dynamic Convolution (ODConv) to improve feature extraction capabilities. Second, the feature fusion process is optimized and inference speed is increased by integrating a Re-parameterizable Generalized Feature Pyramid Network (RepGFPN). Third, the detection head is redesigned using a Dynamic Head structure that leverages dynamic attention mechanisms to enhance key feature perception. Additionally, the loss function is optimized using InnerDIoU to improve object localization accuracy. Experimental results demonstrate that the enhanced ORD-YOLO model achieves a precision of 93.83%, a recall of 91.62%, and a mean Average Precision (mAP) of 96.92%, representing improvements of 4.66%, 3.3%, and 3%, respectively, over the original YOLOv8 model. ORD-YOLO not only maintains stable and accurate citrus fruit maturity recognition under complex backgrounds, but also significantly reduces misjudgment caused by manual assessments. Furthermore, the model enables real-time, non-destructive detection. When deployed on harvesting robots, it can substantially increase picking efficiency and reduce post-maturity fruit rot due to delayed harvesting. These advancements contribute meaningfully to the quality improvement, efficiency enhancement, and digital transformation of the citrus industry. Full article

Biochar has garnered considerable attention as a soil amendment due to its unique physicochemical properties. Its application not only enhances soil carbon sequestration but also improves nutrient availability. Incorporating biochar into soil is regarded as a promising strategy for mitigating global climate change while delivering substantial environmental and agricultural benefits. In this study, biochar was extracted from Siraitia grosvenorii and subsequently modified through alkali treatment. A laboratory incubation experiment was conducted to assess the effects of unmodified (JMC) and modified (GXC) biochar, applied at different rates (1%, 2%, and 4%), on organic carbon mineralization and soil nutrient dynamics. Results indicated that, at equivalent application rates, JMC-treated soils exhibited lower CO₂ emissions than those treated with GXC, with emissions increasing alongside biochar dosage. After the incubation, the 1% JMC treatment exhibited a mineralization rate of 17.3 mg·kg⁻¹·d⁻¹, which was lower than that of the control (CK, 18.8 mg·kg⁻¹·d⁻¹), suggesting that JMC effectively inhibited organic carbon mineralization and reduced CO₂ emissions, thereby contributing positively to carbon sequestration in Siraitia grosvenorii farmland. In contrast, GXC application significantly enhanced soil nutrient levels, particularly increasing available phosphorus (AP) by 14.33% to 157.99%. Furthermore, partial least squares structural equation modeling (PLS-SEM) identified application rate and pH as the key direct factors influencing soil nutrient availability. Full article

Renovating existing buildings is a key strategy for achieving the EU’s climate targets, as over 75% of the current building stock is energy inefficient. This study evaluates the environmental impacts of three façade renovation scenarios for an office building at the Technical University in Zvolen (Slovakia) using a life cycle assessment (LCA) approach. The aim is to quantify and compare these impacts based on material selection and its influence on sustainable construction. The analysis focuses on key environmental indicators, including global warming potential (GWP), abiotic depletion (ADE, ADF), ozone depletion (ODP), toxicity, acidification (AP), eutrophication potential (EP), and primary energy use (PERT, PENRT). The scenarios vary in the use of insulation materials (glass wool, wood fibre, mineral wool), façade finishes (cladding vs. render), and window types (aluminium vs. wood–aluminium). Uncertainty analysis identified GWP, AP, and ODP as robust decision-making categories, while toxicity-related results showed lower reliability. To support integrated and transparent comparison, a composite environmental index (CEI) was developed, aggregating characterisation, normalisation, and mass-based results into a single score. Scenario C–2, featuring an ETICS system with mineral wool insulation and wood–aluminium windows, achieved the lowest environmental impact across all categories. In contrast, scenarios with traditional cladding and aluminium windows showed significantly higher impacts, particularly in fossil fuel use and ecotoxicity. The findings underscore the decisive role of material selection in sustainable renovation and the need for a multi-criteria, context-sensitive approach aligned with architectural, functional, and regional priorities. Full article

Agricultural greenhouse gas (GHG) emissions contribute significantly to climate change and underline the importance of reliable measurements and mitigation strategies. This life cycle assessment (LCA)-based study evaluates the environmental impacts of four key Mediterranean agricultural products, namely olives, sweet potatoes, corn, and grapes using GHG measurements at four pilot fields located in different regions of Greece. With the use of a cradle-to-gate approach six environmental impact categories, more specifically acidification potential (AP), eutrophication potential (EP), global warming potential (GWP), ozone depletion potential (ODP), photochemical ozone creation potential (POCP), and cumulative energy demand (CED) as energy-based indicator are assessed. The functional unit used is 1 ha of cultivated land. Any potential carbon offsets from mitigation practices are assessed through an integrated low-carbon certification framework and the use of innovative, site-specific technologies. In this context, the present study evaluates three life cycle inventory (LCI)-based scenarios: Baseline (BS), which represents a 3-year crop production period; Field-based (FS), which includes on-site CO₂ and CH₄ measurements to assess the effects of mitigation practices; and Inventoried (IS), which relies on comprehensive datasets. The adoption of carbon mitigation practices under the FS scenario resulted in considerable reductions in environmental impacts for all pilot fields assessed, with average improvements of 8% for olive, 5.7% for sweet potato, 4.5% for corn, and 6.5% for grape production compared to the BS scenario. The uncertainty analysis indicates that among the LCI-based scenarios evaluated, the IS scenario exhibits the lowest variability, with coefficient of variation (CV) values ranging from 0.5% to 7.3%. In contrast, the FS scenario shows slightly higher uncertainty, with CVs reaching up to 15.7% for AP and 14.7% for EP impact categories in corn production. The incorporation of on-site GHG measurements improves the precision of environmental performance and supports the development of site-specific LCI data. This benchmark study has a noticeable transferability potential and contributes to the adoption of sustainable practices in other regions with similar characteristics. Full article

Understanding morphological descriptions of plants documented by ancient peoples over 1000 years ago and identifying the species they described are critical for reconstructing the natural geographic distribution of plant taxa, tracking taxonomic variations, and inferring historical climate dynamics. Analyzing shifts in plant communities and climatic conditions during this period is essential to unravel the interplay among floristic composition, climate fluctuations, and anthropogenic impacts. However, research in this field remains limited, with greater emphasis placed on plant taxa from hundreds of millions of years ago. Investigations into flora and climate during the last two millennia are sparse, and pre-millennial climatic conditions remain poorly characterized. In this study, a historical text written 1475 years ago was analyzed to compile plant names and morphological features, followed by taxonomic identification. The research identified three gymnosperm species (one in Pinaceae, two in Cupressaceae), 1 Tamaricaceae species (dicotyledon), and 19 dicotyledon species. However, three plant groups could only be identified at the genus level. Using textual analysis and woody plant coexistence methods, the climate of 1475 years ago in western Henan Province, located in the middle-lower Yellow River basin in East Asia, was reconstructed. Results indicate that the mean temperature of the coldest month (MTCM) was approximately 1.3 °C higher than modern values. In comparison, the mean temperature of the warmest month (MTWM) and mean annual temperature (MAT) were lower than present-day levels. This suggests slightly cooler overall conditions with milder seasonal extremes in ancient Luoyang—a finding supported by contemporaneous studies. Furthermore, annual precipitation (AP), precipitation of the warmest quarter (PWQ), and precipitation of the coldest quarter (PCQ) in the Luoyang region 1475 years ago exceeded modern measurements, despite the area’s monsoonal climate. This suggests significantly higher atmospheric moisture content in ancient air masses compared to today. This study provides floristic and climatic baseline data for advancing our understanding of global climate variability at millennial scales. Full article

With the acceleration of global climate warming and agricultural modernization, the energy and carbon emission issues of agricultural parks (APs) have drawn increasing attention. An AP equipped with biogas-based combined heat and power (CHP) generation and photovoltaic systems serves as a prosumer terminal in a distribution network (DN). This paper introduces carbon emission flow (CEF) theory into the coordinated dispatch of APs and DNs. First, a CEF model for APs is established. Then, based on this model, a carbon–energy coordinated dispatch is carried out under bidirectional CEF interaction between the park and DN. A bidirectional carbon tax mechanism is adopted to explore the low-carbon synergy potential between them. Finally, the Stackelberg game approach is employed to address the pricing of electricity purchase/sale and carbon taxes in a DN, and the particle swarm optimization algorithm is used for rapid generating solutions. The case study shows that the proposed CEF model can effectively determine CEF distribution in the park. Moreover, the proposed bidirectional carbon tax mechanism significantly enhances the low-carbon economic benefits of both the AP and the DN. Full article

(1) Background: Global grain production faces challenges such as increasing demands due to population growth, limited arable land resources, and climate change, with natural resource and environmental constraints becoming increasingly stringent. Traditional smallholder economies struggle to meet the increasing demand for grain, resulting in a tight balance between grain supply and demand. Therefore, to improve grain production efficiency (GPE), clarifying the specific effects of agricultural production services (APS), a new driving force on farmers’ GPE, is critical for ensuring grain security and achieving sustainable grain production. (2) Methods: Through the super-efficiency Data Envelopment Analysis (DEA) and Tobit models, and utilizing microdata from 747 farmers from the China Rural Revitalization Survey (CRRS), we analyzed the differences in farmers’ operating scales and types of agricultural production services to determine the extent and specific implementation effects of agricultural production services on the farmers’ GPE. (3) Results: agricultural production services enhanced the farmers’ GPE. Specifically, labor-intensive services (LIS) markedly improved the GPE of smallholder farmers but not large-scale farmers; technology-intensive services (TIS) did not have a substantial influence on either the smallholder farmers or large-scale farmers. There were significant regional differences in the threshold effect of agricultural production services on the GPE of the farmers. (4) Conclusions: Providers of agricultural production services should enhance their service capabilities to meet farmers’ diverse service needs. Government departments should establish uniform service standards and regulate industry development. Village and community organizations should leverage their grassroots coordination functions to facilitate the efficient operation of services. In addition, tailored development models should be developed for farmers of different scales, and they should be provided with financial and technical support as well as institutional guarantees. Full article

In recent years, China has been promoting aquaculture, but extensive water pollution caused by production activities and climate changes has resulted in losses exceeding 4.6 × 10⁷ kg of aquatic products. Widespread water pollution from production activities is a key issue that needs to be addressed in the aquaculture industry. Therefore, dynamic monitoring of water quality and fish-specific solutions are critical to the growth of fry. Here, a low-cost, small, and real-time monitorable bionic robotic fish based on YOLO-PWSL (PConv, Wise-ShapeIoU, and LGFB) is proposed to achieve intelligent control of aquaculture. The bionic robotic fish incorporates a caudal fin for propulsion and adaptive buoyancy control for precise depth regulation. It is equipped with various types of sensors and wireless transmission equipment, which enables managers to monitor water parameters in real time. It is also equipped with YOLO-PWSL, an improved underwater fish identification model based on YOLOv5s. YOLO-PWSL integrates three key enhancements. In fact, we designed a multilevel attention fusion block (LGFB) that enhances perception in complex scenarios, to optimize the accuracy of the detected frames, the Wise-ShapeIoU loss function was used, and in order to reduce the parameters and FLOPs of the model, a lightweight convolution method called PConv was introduced. The experimental results show that it exhibits excellent performance compared with the original model: the mAP@0.5 (mean average precision at the 0.5 IoU threshold) of the improved model reached 96.1%, the number of parameters and the amount of calculation were reduced by 1.8 M and 3.1 G, respectively, and the detected leakage was effectively reduced. In the future, the system will facilitate the monitoring of water quality and fish species and their behavior, thereby improving the efficiency of aquaculture. Full article

The aim of this study was to observe soil erosion by wind, depending on the soil physical properties, climatic conditions, and plant canopy, for three representative soil series in the reclaimed tidal flats. Soil samples were collected from the Ap horizon of three soil series to analyze soil physical properties and particle distribution. Precipitation and wind velocities were measured by the weather station installed at the filed. The particle distribution curves showed that the actual proportions of erodible soil particle were in the order of 74.7%(TH), 66.1%(PS), and 62%(JB). The instantaneous and daily maximum wind speeds exceeded the threshold friction velocity (5.78 m s⁻¹) suggested by Chepil. However, the dynamic velocities, depending on the radius of 0.125 mm and 0.42 mm belonging to erodible particle size, were much lower than the threshold friction velocity suggested by Chepil. The wind profile increases logarithmically with height, just above the plant canopy. The vertical gradients of wind velocity for the winter wheat plot were smaller than that of the bare plot due to the relatively rough canopy, and U(Z)c of the bare plot was slightly higher than that of the winter wheat plot with a plant canopy for the given U(Z)m. Conclusively, the actual proportion of erodible particles was much less than that of the particle size limit. Full article