Search Results (271)

In the context of increasing demand for sustainable floriculture, this study evaluated the effects of salicylic acid (SA) on phenotypic traits of poinsettia (Euphorbia pulcherrima Willd.). A factorial experiment was conducted in a commercial glasshouse using rooted poinsettia cuttings treated with three SA concentrations (10⁻³, 10⁻⁴, 10⁻⁵ M) applied via foliar or root application. Morphological parameters, colorimetric traits (CIELAB), canopy development, and biomass accumulation were assessed throughout the cultivation cycle. SA had no significant influence on the plant height, leaf number, or biomass of stems, leaves, and roots. However, notable phenotypic changes were observed. Foliar applications, particularly at 10⁻⁵ M, induced visible changes in leaf and bract color, including reduced brightness, saturation, and red pigmentation, especially in newly developed tissues. Conversely, root applications had milder effects and were generally associated with a more stable bract color. The 10⁻⁴ M root treatment promoted greater bract surface and color saturation. Canopy expansion and dry matter accumulation were also influenced by SA in a dose- and method-dependent manner, with high-dose foliar treatments (10⁻³ M) exerting suppressive effects. These findings suggest that the application mode and concentration of SA are critical in modulating ornamental quality traits, with low-to-moderate doses—particularly via root application—offering promising strategies to enhance plant performance in sustainable poinsettia cultivation. Full article

The unique characteristics of desert vegetation, such as different leaf morphology, discrete canopy structures, sparse and uneven distribution, etc., pose significant challenges for remote sensing-based estimation of fractional vegetation cover (FVC). The Unmanned Aerial Vehicle (UAV) system can accurately distinguish vegetation patches, extract weak vegetation signals, and navigate through complex terrain, making it suitable for applications in small-scale FVC extraction. In this study, we selected the floodplain fan with Caragana korshinskii Kom as the constructive species in Hatengtaohai National Nature Reserve, Bayannur, Inner Mongolia, China, as our study area. We investigated the remote sensing extraction method of desert sparse vegetation cover by placing samples across three gradients: the top, middle, and edge of the fan. We then acquired UAV multispectral images; evaluated the applicability of various vegetation indices (VIs) using methods such as supervised classification, linear regression models, and machine learning; and explored the feasibility and stability of multiple machine learning models in this region. Our results indicate the following: (1) We discovered that the multispectral vegetation index is superior to the visible vegetation index and more suitable for FVC extraction in vegetation-sparse desert regions. (2) By comparing five machine learning regression models, it was found that the XGBoost and KNN models exhibited relatively lower estimation performance in the study area. The spatial distribution of plots appeared to influence the stability of the SVM model when estimating fractional vegetation cover (FVC). In contrast, the RF and LASSO models demonstrated robust stability across both training and testing datasets. Notably, the RF model achieved the best inversion performance (R² = 0.876, RMSE = 0.020, MAE = 0.016), indicating that RF is one of the most suitable models for retrieving FVC in naturally sparse desert vegetation. This study provides a valuable contribution to the limited existing research on remote sensing-based estimation of FVC and characterization of spatial heterogeneity in small-scale desert sparse vegetation ecosystems dominated by a single species. Full article

Research on young trees’ adaptation to shade has predominantly focused on leaf-level responses, overlooking critical structural and functional adaptations in branch systems. In this study, we address this gap by investigating hierarchical branch morphology–physiology integration in 20-year-old Pinus koraiensis specimens across four distinct light conditions classified by photosynthetic photon flux density (PPFD): three in the understory (low light, LL: 0–25 μmol/m²/s; moderate light, ML: 25–50 μmol/m²/s; and high levels of light, HL: 50–100 μmol/m²/s) and one under full light as a control (FL: 1300–1700 μmol/m²/s). We measured branch base diameter, length, and angle as well as chlorophyll and NSCs content in branches and needles. Branch base diameter and length were more than 1.5-fold higher in the FL Korean pine trees compared to the understory-grown ones, while the branching angle and ratio in the LL Korean pine trees were more than two times greater than those in the FL trees. As light levels increased, Chlorophyll a and b and total chlorophyll (Chla, Chlb, and Chl) concentrations in the needles all significantly decreased. Starch, glucose, and NSC (Starch + Soluble Sugars) concentrations in both needles and branches were the highest in the trees under FL and lowest under ML (except for soluble sugars in branches). Understory young P. koraiensis trees morphologically and physiologically adapt to limited light conditions, growing to be more horizontal, synthesizing more chlorophyll in needles, and attempting to increase their light-foraging ability. We recommend gradually expanding growing spaces to increase light availability for 20-year-old Korean pine trees grown under canopy level. Full article

Global challenges such as climate change and population growth require improvements in crop monitoring models. To address these issues, this study advances the identification of potato crop phenological stages using satellite remote sensing, a field where cereals have been the primary focus. We introduce a methodology using Sentinel-1 (S1) and Sentinel-2 (S2) time series data to pinpoint critical phenological stages—emergence, canopy closure, flowering, senescence onset, and harvest timing—at the field scale. Our approach utilizes analysis of NDVI, fAPAR, and IRECI2 from S2, alongside VH and VV polarizations from S1, informed by domain knowledge of the spectral and morphological responses of potato crops. We propose the integration of NDVI and VH indices, NDVI_VH, to improve stage detection accuracy. Comparative analysis with ground-observed stages validated the method’s effectiveness, with NDVI proving to be one of the most informative indices, achieving RMSEs of 12 and 14 days for emergence and closure, and 17 days for the onset of senescence. The integrated NDVI_VH approach complemented NDVI, particularly in harvest and flowering stages, where VH enhanced accuracy, achieving an overall R² value of 0.80. The study demonstrates the potential of combining SAR and optical data for post-season crop phenology analysis, providing insights that can inform the development of new methods and strategies to enhance on-season crop monitoring and yield forecasting. Full article

To investigate whether blue-dominant spectra from end-of-day (EOD) lighting can regulate crop morphological and flowering responses, chrysanthemum (Chrysanthemum × morifolium; obligate short day), geranium (Pelargonium × hortorum; day neutral), calibrachoa (Calibrachoa × hybrida; facultative long day), and gerbera (Gerbera jamesonii; facultative short day) plants were grown under different light-emitting diode (LED) spectrum treatments from January to April 2020, in Guelph, Canada. The spectrum treatments were (1) no EOD lighting, (2) narrowband blue from LEDs (B), (3) a combination of narrowband blue, red, and far-red LEDs with a photon flux ratio of 47:3:1 (blue:red:far-red; BRFR). The B and BRFR treatments ran daily from 0.5 h to 4.5 h after dusk. Compared to the control without EOD lighting, chrysanthemum flower initiation was completely inhibited under BRFR. Flowering time was slightly delayed, but flower bud number increased under B. Side branch number, leaf area, and main stem length and diameter increased under B and BRFR. In the geranium B and BRFR did not affect flowering, but increased side branch number and length and diameter of the main stem. Both spectrum treatments promoted earlier flowering in the calibrachoa, but BRFR produced more flower buds. The calibrachoa aerial dry biomass and main stem length increased under B and BRFR. The gerbera leaf chlorophyll index and leaf thickness increased under BRFR. Both spectrum treatments increased the gerbera flower bud size, despite having little effect on flowering time. In all species, at least one of the LED treatments increased canopy size. Therefore, low levels of B or BRFR can be potentially used for EOD lighting to regulate the flowering and morphology of potted ornamentals. Full article

As a globally important cash crop, the optimization of tomato yield and quality is strategically significant for food security and sustainable agricultural development. In order to address the problem of missing point cloud data on fruits in a facility agriculture environment due to complex canopy structure, leaf shading and limited collection viewpoints, the traditional geometric fitting method makes it difficult to restore the real morphology of fruits due to the dependence on data integrity. This study proposes an adaptive symmetry self-matching (ASSM) algorithm. It dynamically adjusts symmetry planes by detecting defect region characteristics in real time, implements point cloud completion under multi-symmetry constraints and constructs a triple-orthogonal symmetry plane system to adapt to multi-directional heterogeneous structures under complex occlusion. Experiments conducted on 150 tomato fruits with 5–70% occlusion rates demonstrate that ASSM achieved coefficient of determination (R²) values of 0.9914 (length), 0.9880 (width) and 0.9349 (height) under high occlusion, reducing the root mean square error (RMSE) by 23.51–56.10% compared with traditional ellipsoid fitting. Further validation on eggplant fruits confirmed the cross-crop adaptability of the method. The proposed ASSM method overcomes conventional techniques’ data integrity dependency, providing high-precision three-dimensional (3D) data for monitoring plant growth and enabling accurate phenotyping in smart agricultural systems. Full article

In a hypothetical climate change scenario, zoysiagrass species could be a good choice for turfgrass areas due to their adaptation to heat conditions and the great variability in species and cultivars. Knowledge of the root system’s characteristics is paramount for predicting cultivar adaptation to different heat–drought scenarios and therefore for designing proper turfgrass management programs, especially irrigation. A field experiment was conducted in the Mediterranean environment of Valencia (Spain) to study the root weight density (RWD), root length density (RLD), and average root diameter (RDI) at three different soil depths (0–5, 5–15, and 15–30 cm) of five new zoysiagrass genotypes (Zoysia matrella (L.) Merr., Zoysia japonica Steud., and their hybrid), relating these parameters to the performance of these experimental lines during their establishment. All the tested experimental lines had a higher RWD and RLD in the upper soil layer (0–5 cm), while the RDI was higher in the lowest layer of the sampled soil profile (0.269 mm compared with 0.249 mm and 0.241 mm in the upper layers). All the tested genotypes showed the same RWD and RLD, while the Zoysia matrella experimental line A showed a higher RDI value (0.2683 mm) than those for the Z. japonica (0.2369 mm) and the hybrid (0.2394 mm) genotypes. This last finding could have influenced its more rapid establishment, although it was not linked to its NDVI values during autumn. In conclusion, different morphological root characteristics were detected among new zoysiagrass genotypes and soil depths, which could have affected their canopy performance, and they are expected to affect irrigation management in a possible future drought scenario. Full article

This study aimed to determine the optimal nitrogen (N) fertilization rate per regrowth cycle for Megathyrsus maximus cv. BRS Tamani by evaluating its effects on forage production, nutrient uptake, bromatological composition, and in vitro degradation kinetics. A randomized complete block design with five N doses (0, 40, 80, 120, and 160 kg N ha⁻¹) and seven replications was conducted over two rainy seasons. From December 2019 to April 2020, canopy height and light interception were measured weekly. When canopy height reached 95% of light interception the grass was harvest and productive and morphological structure were measured. Nitrogen fertilization increased forage mass and yield up to the dose of 40 kg N ha⁻¹, resulting in 1959 and 9798 kg DM ha⁻¹, respectively, while nitrogen use efficiency declined at higher doses. Weed mass was decreased at 0 kg ha⁻¹, and chlorophyll index increased with the N dose. Nitrogen and potassium were the most extracted nutrients, with nitrogen uptake being highest at 80 kg ha⁻¹. Fertilization elevated the levels of crude protein, NDIP, cell content, and cell wall-bound protein, while ash content decreased. In vitro fermentation showed a reduced gas volume at higher N doses and improved degradation and digestibility up to 40 kg ha⁻¹. Nitrogen fertilization enhanced the forage yield and quality of BRS Tamani, with 40 kg ha⁻¹ maximizing efficiency and digestibility. Full article

For crop protection, electrostatic spraying technology significantly improves deposition uniformity and pesticide utilization through the “wraparound-adsorption” effect of charged droplets. However, existing electrostatic nozzles using hydraulic atomization suffer from low charge-to-mass ratios due to unclear principles for optimizing electrode parameters. To this end, this study designs and evaluates a novel air-assisted hydraulic-atomization hollow-cone electrostatic nozzle. First, the air-assisted hollow-cone nozzle was designed. High-speed imaging was then employed to obtain morphological parameters of the liquid film (length: 2.14 mm; width: 1.96 mm; and spray angle: 49.25°). Based on these parameters, an electric field simulation model of the electrostatic nozzle was established to analyze the influence of electrode parameters on the charging performance and identify the optimal parameter combination. Finally, feasibility and efficiency evaluation experiments were conducted on the designed electrostatic nozzle. The experimental results demonstrate that cross-sectional dimensions of the electrode exhibit a positive correlation with the surface charge density of the pesticide liquid film. In addition, optimal charging performance is obtained when the electrode plane coincides with the tangent plane of the liquid film leading edge. Based on these charging laws, the optimal electrode parameters were determined as follows: 2.0 × 2.0 mm cross-section with an electrode-to-nozzle tip distance of 3.8 mm. With these parameters, the nozzle achieved a droplet charge-to-mass ratio of 4.9 mC/kg at a charging voltage of 3.0 kV. These charged droplets achieved deposition coverages of 12.19%, 5.72%, and 5.91% on abaxial leaf surfaces in the upper, middle, and lower soybean canopies, respectively, which is a significant improvement in deposition uniformity. This study designed a novel air-assisted hollow-cone electrostatic nozzle, elucidated the optimization principles for annular induction electrodes, and achieved improved spraying performance. The findings contribute to enhanced pesticide application efficiency in crops, providing valuable theoretical guidance and technical references for electrostatic nozzle design and application. Full article

Reliable source term prediction for hazardous pollutant puffs in urban microenvironments is challenging, especially for risk management under strict time constraints. Puff movement is highly stochastic due to atmospheric turbulence, intensified by complex urban canopies. This complexity, combined with time limitations, makes advanced computational modeling impractical. A more efficient approach is leveraging past and present data using Machine Learning (ML) techniques. This study proposes an ML-based method, enriched with simplified physical modeling, for source term estimation of unforeseen hazardous air releases in monitored urban areas. The Random Forest Regression, commonly used in meteorology and air quality studies, has been selected. A novel variable selection method is introduced, including the following: (a) a model-derived Exposure Burden Index (EBI) reflecting plume–morphology interactions; (b) a plume travel time indicator; (c) the standard deviation of input variables capturing stochastic behavior; and (d) the total dosage-to-mass released ratio at sensor locations as the target variable. The case study examines JU2003 field experiments involving SF₆ puffs released at street level in Oklahoma City’s urban core, a challenging scenario due to the limited number of sensors and historical data. Results demonstrate the approach’s effectiveness, offering a promising, realistic alternative to traditional computationally intensive methods. Full article

The selection of suitable growing media plays a vital role in the successful adaptation of high-altitude plant species to lowland urban landscapes. This study assessed the morphological and physiological performance of two native perennial species, Alchemilla mollis and Geranium psilostemon, under low-altitude conditions using three different soil–sand (mil) mixtures: TA (50% soil–50% sand), TB (75% soil–25% sand), and TC (100% soil). Over a 17-month period, key plant growth parameters—including height, canopy diameter, leaf number, and chlorophyll concentration—were systematically monitored. Additionally, soil samples were analyzed before and after cultivation to determine pH, total nitrogen, organic matter, organic carbon, phosphorus, and electrical conductivity levels. To evaluate overall performance, the PROMETHEE multi-criteria decision-making (MCDM) method was applied, incorporating 11 criteria spanning plant development, soil quality, and economic considerations. Results revealed that the TC medium offered the most favorable outcomes for both species, particularly in terms of chlorophyll content and biomass accumulation. Conversely, the TB medium supported higher retention of nitrogen and organic matter. While A. mollis exhibited greater resilience under suboptimal conditions, G. psilostemon demonstrated rapid development under favorable settings. These findings underscore the potential of native perennial species in sustainable landscape design and validate the use of MCDM approaches in optimizing plant–soil interactions in horticultural applications. Full article

The accurate definition of tree growth descriptors is a crucial step in enhancing orchard management, allowing cultivar identification within an orchard and in new genotype selection for breeding programs. In apple, almond, and olive orchards, Terrestrial Laser Scanning (TLS) technologies have been already used to identify different architectural groups, but not in hazelnut yet. This study utilized TLS to investigate the canopy structure of hazelnut trees of four different Italian varieties, with and without leaves. TLS proved to be a sensor capable of collecting three-dimensional data from hazelnut field trials and allowed the definition and selection of hazelnut plant descriptors by morphological traits and morphological indexes. Nineteen descriptors, eight morphologic traits and 11 morphological indexes have been identified as reliable suitable descriptors of hazelnut cultivar and in breeding evaluations, according to Biodiversity, FAO and CIHEAM. Many of the selected descriptors are related to the tree habit, vigour and branching density. Two useful indexes have also been defined: Canopy Uprightness (CU) Index and the Index of Canopy Opening (ICO). The descriptors allowed us to distinguish the four studied hazelnut cultivars based on their growth habit; in particular the cultivar Tonda Gentile delle Langhe showed a growth habit that is a lot different from that of the other ones. Full article

Urban forests are remnants of forest habitats within urban areas. Their structural alterations create stressful microclimatic conditions that can influence the morphology of sensitive plants, such as ferns. This study analyzed variations in the morphological traits of ferns in four urban forest sites in central Veracruz, Mexico, considering the microclimatic differences arising from vegetation structure. Temperature, humidity, canopy openness, and radiation were measured, along with eight foliar traits, while assessing the impact of site and habit (terrestrial or epiphytic) on the response. Sites with greater alterations in vegetation structure exhibited increased canopy openness, solar radiation, temperature, and a higher number of days with lower relative humidity. In these sites, leaves showed an increase in dry matter content and vein density, indicating a greater investment in resource storage and structural resistance. In the less-disturbed sites, terrestrial ferns demonstrated larger leaf area and specific leaf area, suggesting greater growth potential. Conversely, epiphytes generally had smaller leaves, which could represent an adaptive advantage for these species. The results also suggest a process of biotic homogenization within this plant group, reflecting a similar morphological response, except for indicator species restricted to less disturbed sites. Thus, this study reveals that microclimatic variations induced by urbanization significantly affect plant morphology and, ultimately, species diversity. Full article

This study collected site and spatial morphological data from 63 typical aging community activity spaces and extracted 12 spatial types through statistical analysis. A parametric modeling tool was used to generate spatial models. Based on clearly defined design variables and constraints, the NSGA-II multi-objective optimization algorithm was applied to minimize summer thermal discomfort, maximize winter thermal comfort, and maximize annual average sunlight duration, resulting in 342 Pareto optimal solutions. The study first explored the linear relationships between spatial morphology and environmental performance using the Spearman method. It then integrated ensemble learning and the interpretable machine learning model SHAP to reveal nonlinear relationships and boundary effects. The results of the two methods complemented and reinforced each other. Based on a comparison of these two approaches, morphological indicators showing significant differences were selected for attribution and sensitivity analyses, clarifying the mechanisms by which spatial morphological parameters influence environmental performance and identifying their critical thresholds. Key findings include the following: (1) the UTCI-S exhibits significant negative linear correlations with the open space ratio (OSR) and spatial crowding density (SCD); the UTCI-W shows negative linear correlations with canopy coverage (CVH) and wind speed (WS); and a positive linear correlation exists between the sky view factor (SVF) and AV.SH. (2) Boundary effects and threshold intervals of critical morphological parameters were identified as follows. The open space ratio should be controlled to 10–15%, the shrub–tree layer coverage to 0.013–0.0165%, and the average building height to 3.1–3.8 m. (3) Spatial layout principles demonstrate that placing fully enclosed spaces (E-2) and semi-enclosed spaces (S-1/S-3) on the northern side, as well as semi-enclosed spaces (S-1/S-2) and circulation spaces (C-3) on the southern side, significantly enhance microclimatic performance. These findings provide quantitative guidelines for community space design in cold regions and offer data support for creating outdoor environments that meet the comfort needs of the elderly. Full article

Cowpea (Vigna unguiculata (L.) Walp.) is a vital legume crop recognized for its nutritional value and adaptability to various growing conditions. However, exposure of cowpea to drought stress during the early growth stages can significantly restrict growth and yield potential. Therefore, identifying cowpea genotypes tolerant to drought during early growth and development is essential for maintaining yield potential. This study characterized 15 diverse cowpea genotypes for various physiological, pigment, and morphological traits that may contribute to drought tolerance. At the V2 stage, the cowpea genotypes were subjected to two moisture regimes: control (100% irrigation) and drought (50% irrigation) for 22 days to assess trait responses and their relationship to drought tolerance. Drought-stressed plants decreased stomatal conductance by 79%, negatively correlating with a 2.9 °C increase in canopy temperature. Under drought, the photochemical reflectance index (PRI) was strongly associated with the quantum yield of PSII and electron transport rate. Shoot biomass decreased by 51% and root biomass by 32% under drought. Leaf area and shoot weight were correlated with root traits such as total length, surface area, and weight. Among all genotypes, 280785-11 and UCR 1004 demonstrated superior rooting vigor under drought, emphasizing their efficiency in resource utilization. These findings highlight the relevance of utilizing drought-adaptive traits to improve early-season drought tolerance. Full article