Search Results (6,150)

Cassava mosaic begomoviruses are a major threat to cassava cultivation in Africa. The virulent Ugandan variant of the East African cassava mosaic virus (EACMV-Ug), which caused substantial damage to cassava production in Uganda in the 1990s and which was previously confined to East and Central Africa, was recently found to be well established in Guinea and Sierra Leone in West Africa. Molecular analysis of cassava leaf samples from a nationwide cassava fields survey conducted in Côte d’Ivoire in 2022 suggested the absence of EACMV-Ug in the country in 2022. Given the proximity of some confirmed EACMV-Ug infected locations in Guinea to Côte d’Ivoire, we conducted another survey in 2025 along the entire western border of Côte d’Ivoire, bordering Guinea and Liberia, to update the status of EACMV-Ug in the country. Molecular analysis of the leaf samples collected confirmed the presence of EACMV-Ug in Côte d’Ivoire for the first time, along with other begomoviruses. The infection rate of EACMV-Ug along the Liberian border was higher (28.85%) than the 17.07% observed along the Guinean border. African cassava mosaic virus (ACMV) and East African cassava mosaic Cameroon virus (EACMCMV) were detected both as a single infection and in double co-infections (ACMV+EACMCMV) in some plants, whereas EACMV-Ug was found as a double co-infection (EACMCMV+EACMV-Ug) and as a triple co-infection (ACMV+EACMCMV+EACMV-Ug). Our results also show that all the cassava varieties grown in the surveyed locations were susceptible to EACMV-Ug. Epidemiological assessment of cassava fields revealed that the incidence and severity of cassava mosaic disease (CMD) were significantly higher along the Liberian border compared to the Guinean border. However, whitefly populations were relatively low across the entire area surveyed. Furthermore, we found that the spread of CMD in the survey area was mainly through the use of infected cassava cuttings for the establishment of new farms. Based on these results, it is imperative to conduct an urgent nationwide cassava fields survey to assess the extent of EACMV-Ug spread in Côte d’Ivoire and implement containment measures to stop further spread. Full article

Anthropogenic pressures and climate change are accelerating the degradation of seagrass ecosystems and the ecological services they provide. In temperate systems, the decline of eelgrass (Zostera marina) has raised noticeable concern, particularly as restoration actions (e.g., transplantation) require accurate, nondestructive estimates of leaf biomass. Allometric power-law models can provide such proxies, but their applied value depends on whether fitted parameters remain transferable across sites and sampling periods. Here, using two extensive and independently collected datasets from San Quintín Bay (SQ) and Punta Banda estuary (PB), we evaluate three formulations: M1 (biomass–length), M2 (biomass–length–width), and M3 (biomass–area surrogate). All three models produced consistent fits in both datasets, and parameter-comparison tests detected no significant between-site differences. Reciprocal cross-projections of monthly mean leaf biomass showed high concordance, supporting practical parameter stability within the SQ–PB domain. A model-selection analysis based on goodness of fit and parsimony further identified the bivariate model M2 as the best-performing proxy across sites. Taken together, these results support a practical interpretation in which eelgrass may express phenotypic plasticity through shifts in trait distributions (length and width), while the scaling relation linking morphology to biomass remains effectively stable. For applied restoration-comparison purposes, we therefore recommend using M2—preferably with site-fitted parameters, or pooled/mean parameters when supported by reproducibility tests—to estimate aerial production non-destructively and cost-effectively. Full article

In protected horticultural production, tomato late blight shows strong environmental inducibility, with a short latent period, rapid risk accumulation, and a limited control window, which challenges conventional post-event disease monitoring. To address this, a tomato late blight risk perception and predictive control approach for protected production is proposed, integrating deep temporal modeling of environmental factors, visual symptom perception, and risk-driven greenhouse control to enable prospective assessment and proactive intervention. Based on disease mechanisms and real greenhouse conditions, an artificial intelligence (AI) framework covering perception, prediction, and regulation is constructed, moving beyond reliance on visible symptoms alone. Long-term evolution of key variables, including temperature, air humidity, leaf wetness, and light intensity, is modeled using deep temporal networks, while early weak lesions and subtle texture changes are captured by visual models. Cross-modal fusion in a unified risk space generates continuous risk scores to drive greenhouse regulation. Experiments on a multimodal dataset from a real greenhouse in Bayannur, Inner Mongolia, show that the proposed method outperforms vision-based and environment-based baselines in recognition and risk prediction. It achieves about 0.95 accuracy, 0.94 F1-score, and over 0.97 area under the receiver operating characteristic curve (AUC), while providing more than 20 h of early warning before disease onset. In environmental modeling, the deep temporal model consistently surpasses threshold-based methods, logistic regression, and long short-term memory/gated recurrent unit (LSTM/GRU) baselines in risk lead time, false alert rate, and prediction stability. Full article

Lightning is a major natural ignition source of wildfires across forest, grassland, and cropland ecosystems. Accurate prediction of lightning-ignited fire occurrence remains challenging due to uncertainties in spatiotemporal alignment caused by vegetation-dependent smoldering delays and the difficulty of representing heterogeneous fuel conditions in mixed-vegetation regions. This study proposes a semi-automated lightning–fire alignment framework that integrates land cover information and historical fire records to improve spatiotemporal matching across different vegetation types and to reduce misclassification from human-induced fires in agricultural areas. To better characterize fuel conditions, two feature-level vegetation fusion parameters—total vegetation cover and leaf area index weight—are introduced and combined with hourly meteorological variables and lightning characteristics to develop a tuned random forest prediction model. The framework is applied at a regional scale in the Greater Khingan Mountains and southwestern forest regions of China, with predictions conducted at an event-based temporal scale using hourly inputs. The vegetation-fused model achieves an AUC of 0.93, outperforming models without vegetation fusion. Analysis of model outputs indicates that hourly maximum temperature, leaf area index weight, precipitation, and wind speed are key factors influencing lightning-ignited fire occurrence. This study demonstrates the value of semi-automated alignment and vegetation feature fusion for improving lightning-ignited fire prediction in heterogeneous landscapes, supporting regional wildfire risk assessment and potential early-warning applications. Full article

Plant leaf drought tolerance is regulated by the coordinated effects of water transport efficiency, transpirational water loss, and hydraulic safety. Although cotton is considered drought-tolerant, the mechanisms that coordinate water transport and gas exchange to confer drought tolerance remain incompletely understood. In this study, four soil moisture gradients were established under field conditions and maintained consistently throughout the growing season. The relationships among leaf turgor loss point (Ψ_tlp), gas exchange, and hydraulic traits were examined in two cotton cultivars at the peak flowering stage. With increasing drought treatments, Ψ_tlp, stomatal aperture ratio (g_ratio), leaf hydraulic conductance (K_leaf), leaf hydraulic conductance inside the xylem (K_x) and leaf hydraulic conductance outside the xylem (K_ox) declined significantly, with K_x showing the greatest reduction. Both K_x and g_ratio were strongly positively correlated with Ψ_tlp. Anatomically, vein density (D_v) and vessel number (Np) increased, whereas xylem vessel area (Ap) decreased. The reduction in Ap was the primary structural factor driving the decline in K_x and contributing to lower Ψ_tlp. We conclude that cotton enhances drought tolerance through a coordinated hydraulic and osmotic strategy, by modifying xylem anatomy (reducing Ap) to downregulate K_x and by adjusting osmotically to depress Ψ_tlp. The synergistic reduction in K_x and g_ratio slows the decline in leaf water potential, thereby delaying Ψ_tlp and enhancing leaf hydraulic safety during drought. This integration optimizes stomatal regulation and water transport while ensuring hydraulic safety. The findings provide a key theoretical basis and potential breeding targets for the targeted improvement of drought tolerance and water use efficiency in cotton. Full article

Drought is the most severe natural hazard threatening agricultural production. Mulberry (Morus alba L.) is an important crop for the sericulture industry, and its drought tolerance has been extensively studied. In this study, the phenotypic and physiological responses of two different mulberry tree genotypes (711 and NS8) to drought stress were investigated, with the aim of screening potential nondestructive traits and understand interrelationships. The significant reductions of digital biomass (DB), leaf area (LA), and projected leaf area (PLA) in morphological traits indicated that drought led to a decrease in mulberry yield. The change of color traits R_FarRed and R_NIR were associated with pigments and leaf morphology. Vegetation indexes were also significantly affected by drought stress. Due to their had high correlation coefficients and good linear relationships with yield, DB and LA can be used as yield proxy traits for this measure. Drought-sensitive traits were identified using PCA and correlation analysis, and the results showed that greenness (GR) was a proxy predictor of drought stress. For antioxidant defenses, CAT activity and phenolic compound content were significantly decreased. Metabolomics analysis revealed that genotype 711 exhibited 1691 differential metabolites under drought stress; these mainly comprised amino acids, lipids, and phenolic acids, which were mainly enriched in secondary metabolism and flavonoid biosynthesis. Drought also reprogrammed carbohydrate, secondary compounds, and amino acid metabolism. The results revealed that the phenotypic response of two mulberry trees to drought, as well as the integration of phenotypic traits with metabolic traits, could help us to understand drought tolerance mechanisms and benefit efficient selection and breeding of fitter genotypes. Full article

Coffea canephora cultivation areas in Brazil are frequently exposed to successive cycles of water deficit, triggering plant stress responses. In addition to water deficit, increased air temperature can act as a second stress factor. The recurrence of these stress factors may induce plant tolerance mechanisms, potentially mitigating future stress responses even of a different stress nature. We hypothesized that repeated cycles of water deficit can trigger tolerance mechanisms that make C. canephora leaves more resilient to supra-optimal temperatures. To test this hypothesis, young C. canephora plants were grown under non-limited water conditions for seven months (Ψ_mSoil > −20 kPa), after which they were subjected to two consecutive cycles of water deficit (Ψ_mSoil < −300 kPa), followed by rehydration. Two clones were used, ‘A1’ and ‘3V’, previously classified as drought sensitive and tolerant, respectively, considering the dynamics of physiological and architectural responses. After the second cycle, leaf discs were collected from completely expanded leaves formed during the two stress cycles and exposed to heat treatments (35 °C, 40 °C, 45 °C, 50 °C, and 55 °C) for 15 min in a water bath. Chlorophyll a fluorescence emission was then monitored, and the results were analyzed using OJIP transient kinetics and the JIP_Test. High temperatures induced negative changes in both OJIP kinetics and JIP_Test-derived parameters. A significant increase in F₀ and a reduction in F_M were observed mainly at 50 °C and 55 °C, due to changes in the stages of the OJIP curve. These changes impacted the “energy connectivity” and consequently the electron transport along the electron transfer chain (ETC), increasing energy dissipation, as confirmed by the JIP_Test variables. Despite the high temperature impacts, previous water deficit induced heat tolerance in clone ‘A1’, while it increased sensitivity in clone ‘3V’. This study suggests that selecting drought-resistant varieties should consider their subsequent response to short high-temperature stress to avoid cross-sensitivity caused by selecting for a single environmental factor. Full article

Shallow-buried drip irrigation is an effective water-saving technique; however, the optimal burial depth for drip irrigation tape in peanut production remains unclear. To clarify the effects of different burial depths of drip irrigation tapes on peanut growth, photosynthetic performance, water use efficiency and yield, a two-year field experiment was conducted from 2022 to 2023 with four treatments: M0 (0 cm), M3 (3 cm), M6 (6 cm) and M9 (9 cm). The results showed that the M3 treatment produced the highest pod yield, biomass yield, kernel yield, 100-pod weight, irrigation water use efficiency, leaf area index, and photosynthetic parameter values. Compared with M0, pod yield and irrigation water use efficiency under M3 increased by 12.09% and 19.80%, respectively. Overall, under the conditions of this experiment, a drip irrigation tape burial depth of 3 cm proved most beneficial for improving peanut productivity and irrigation water use efficiency. Full article

This study investigates the prediction of the acoustic impedance of submarine sediments in the middle area of the South Yellow Sea using the Random Forest (RF) model algorithm. A predictive model for the acoustic impedance of submarine sediments was established using a Random Forest algorithm based on six characteristic factors, including density, porosity, liquid limit, moisture content, plasticity index, and median particle size. The results indicate that the highest prediction accuracy and lowest error were achieved when n_estimator was set to 27, max_depth to 8, and min_samples_leaf to 7. The model significantly outperformed traditional single-parameter regression equations. The coefficient of determination (R²) of the test set reached 0.991 after model training, the mean absolute error (MAE) was 23.14 × 10³ kg/(m²·s), and the mean absolute percentage error (MAPE) was 0.90%. This paper provides an in-depth analysis of the relationship between acoustic impedance and various physical and mechanical properties, providing valuable guidance for advancing the prediction of acoustic impedance of submarine sediments. Full article

A number of products for maintaining intimate hygiene are available on the market. They consist of a blend of components intended to cleanse, care for and protect the area of application, and support its microbiological balance. The present study reviews the compositions of international intimate hygiene product brands currently available in Poland (within the European Union) and the frequency of their components: their surfactants, plant extracts, prebiotics, postbiotics and skin care agents. The most popular surfactants in rinse-off products for women were Cocamidopropyl Betaine, present in 72% of products, followed by Coco-Glucoside (58%) and Sodium Laureth Sulfate (24%). Similarly, in the products for girls, the most common were Cocamidopropyl Betaine (55%), Lauryl Glucoside (45%), Coco-Glucoside (40%) and Sodium Laureth Sulfate (20%). The intimate wipes contained mainly nonionic surfactants: PEG-40 Hydrogenated Castor Oil (28%), Coco-Glucoside and Polysorbate 20 (20% each). Many components with protective and caring properties were identified: plant extracts (e.g., Aloe Barbadensis Leaf Juice, Chamomilla Recutita Flower Extract), prebiotics (Inulin, Alpha-Glucan Oligosaccharide) and postbiotics (Lactobacillus Ferment, Leuconostoc/Radish Root Ferment Filtrate), as well as Lactic Acid, Glycerin, Citric Acid, Panthenol and Allantoin. Full article

Soybeans with modified fatty acid compositions are widely used to improve oil quality and develop functional food products; however, physiological responses to drought stress during early growth stages remain insufficiently understood. This study compared shoot and root responses of three soybean cultivars with contrasting fatty acid profiles: Osoy (elevated linolenic acid; OS), PE529 (elevated oleic acid; PE), and Pungsannamul (PS) under drought stress conditions. Drought significantly reduced plant height, shoot biomass, and leaf area in all cultivars, although the magnitude of reduction differed among genotypes. PS exhibited the greatest decreases in plant height (39%), shoot dry weight (47%), and leaf area (78%) compared with well-watered conditions. In addition, PS showed relatively higher net carbon assimilation rate and stomatal conductance during the early phase of drought, but significantly lower values than OS and PE after 7–8 days of treatment, indicating a relatively higher sensitivity to drought stress. Root trait responses varied among cultivars. Total root length was largely maintained under drought conditions; however, all cultivars exhibited increased root distribution in deeper soil layers. Notably, PE showed a relatively higher proportion of roots at 40 cm depth. Whole-plant water use efficiency (wWUE) did not differ among cultivars under drought conditions; however, Pearson correlation analysis revealed strong associations between wWUE and root traits in PE, including total root length (r = 0.72), average root diameter (r = −0.77), and root volume (r = 0.65). Overall, PE exhibited relatively stable morphological and physiological responses under drought stress, suggesting a comparatively higher adaptive potential during early growth stages. Full article

Numerous studies have confirmed that macrophytes contribute to improving water quality; however, it remains uncertain whether they can enhance lake carbon sequestration. We conducted a mesocosm experiment to compare six macrophyte species categorized into three life form groups: submerged, floating-leaved and emergent. The results showed that the concentrations of total phosphorus and total organic carbon in water were significantly lower in the submerged macrophyte group than in the other groups (p < 0.05). The sediment total phosphorus and sediment total carbon in the emergent macrophyte group were lower than those in the other groups. Plant tissue phosphorus in the submerged macrophyte group was significantly higher, while plant tissue carbon was significantly lower than in the other macrophyte groups (p < 0.05). Although the emergent macrophyte group released the highest CH₄ flux, it absorbed the most CO₂, resulting in a significantly lower CO₂-equivalent flux. Submerged macrophytes had a higher specific leaf area but a lower leaf area index and specific root length; floating-leaved macrophytes recorded a higher root mass ratio, total biomass, and relative growth ratio; meanwhile, emergent macrophytes had a lower root mass ratio. It is therefore recommended to configure macrophyte communities differentially based on specific restoration objectives. Full article

As both an energy source and a signaling cue, light quality regulates graft healing by modulating endogenous phytohormone homeostasis, callus formation, and vascular reconnection. To elucidate the regulatory roles of red/blue (R/B) light ratios and green light supplementation on healing and seedling quality of grafted tomato (Solanum lycopersicum L.), a controlled-environment experiment was conducted in a plant factory using ‘Zhongza 105’ as the scion and ‘Zhezhen No. 1’ as the rootstock. LED lighting treatments were established with different R/B ratios (1.0, 2.5, 4.0, 5.5 and 7.0) with or without supplemental green light. The results show that moderate R/B ratios (4.0–5.5) significantly increased scion elongation, the stem diameter of both scion and rootstock, the mechanical strength of the graft union, and sap flow, while also enhancing leaf chlorophyll content, photosynthetic rate, and root activity. Under optimal R/B conditions, indole-3-acetic acid (IAA) and gibberellin (GA) levels were elevated, whereas abscisic acid (ABA) was reduced, favoring callus proliferation and vascular reconnection. Green light supplementation under moderate R/B further promoted stem thickening, leaf area expansion, water transport across the graft union, and total biomass accumulation. Overall, an R/B ratio of 4.0–5.5 combined with appropriate green light supplementation optimized the morphology, structure, and physiological performance of grafted tomato seedlings during the healing stage. The results aim to provide a scientific basis for optimizing light environments in a controlled environment, thus enhancing the stability and quality of grafted tomato seedlings. Full article

The global push to advance smart and digital forestry relies on emerging technologies to support efficient, AI-assisted, and data-driven forest management, but many forest operations occur in remote forests where reliable internet connectivity is unavailable. Low Earth Orbit (LEO) satellite constellations such as Starlink may provide reliable connectivity where cellular networks are unavailable. The performance of LEO-based solutions remains poorly understood under forest canopies, and empirical evaluations linking canopy characteristics to connectivity performance are largely lacking. In this study, the effect of forest vegetation on Starlink performance below the canopy was evaluated by placing a satellite receiver at thirty randomly selected permanent single tree inventory plots on the University of Idaho Experimental Forest and measuring connection success, connection time, and upload and download speeds along 50 m transects in all cardinal directions. LiDAR-derived stand density index (SDI), leaf area index (LAI), rumple index (RI), and vegetation cover (VC) were used to quantify canopy structure. Principal Component Analysis and survival analysis showed that higher values of PC1, primarily driven by SDI, LAI, and RI, reduced the probability of establishing a connection. Linear regression analysis indicated that higher SDI increased connection time, indicating that denser stands slowed or prevented connectivity. Linear mixed-effects models demonstrated that internet speed primarily declined with increasing distance, with download and upload rates dropping beyond 40 m from the router. LAI, RI, and VC did not influence connection time or speed, suggesting that overall stand density rather than leaf area per unit ground area has a greater impact on signal obstruction. Overall, dense forest structure and distance are the main constraints on LEO satellite connectivity and performance, and understanding these limitations supports the development and deployment of satellite-based networking to advance smart forestry operations. These results provide one of the first quantitative assessments of LEO satellite connectivity constraints in operational forest conditions, offering practical guidance for deploying satellite-based networks to support smart forestry applications in remote environments. Full article

Urban wetlands, encompassing both natural and constructed ecosystems, are vital for urban resilience. Understanding how plant functional traits adapt to these distinct habitats is crucial for ecological management. This study investigates the intraspecific variation and trait covariation patterns of the common reed (Phragmites australis) in two contrasting urban wetland types in Ganzhou City: a stagnant, engineered constructed wetland and a dynamic, natural riverine wetland. This contrast represents a key gradient in hydrological regime and anthropogenic influence. We measured 22 morphological and chemical traits to assess trait differences, variability (coefficient of variation), and correlation patterns. Volcano plot analysis revealed significant habitat effects: reed in natural wetlands exhibited higher levels of Cu, P, N, and leaf moisture content (LMC), whereas those in constructed wetlands had higher Ca content. Traits such as Na, Mn, and Al showed high intraspecific variability. Correlation analyses revealed significant trade-offs and integrations among traits, such as positive correlations between LMC and nutrients (K, Cu), and negative correlations between Ca and key leaf morphological traits. Principal component analysis (PCA) further confirmed a significant separation along PC1, driven primarily by nutrient elements (Cu, P, K) and LMC, with natural wetlands scoring higher. In contrast, PC2, associated with leaf morphological traits (e.g., leaf area, leaf width), showed no significant inter-habitat difference. Our findings demonstrate that P. australis employs distinct ecological strategies by adjusting its functional traits and resource allocation in response to different urban wetland environments. This highlights the critical role of intraspecific trait variation in plant adaptation and has important implications for wetland restoration and the design of constructed ecosystems. Full article