Search Results (4,925)

Hemp (Cannabis sativa L.) is an important crop, yet little is known about how herbivory and soil microbial communities interact to influence plant performance. In this study, two hemp cultivars, BaOx and Cherry Citrus, were grown under identical greenhouse conditions and exposed to naturally occurring background populations of the two-spotted spider mite (Tetranychus urticae). Plant traits were measured, and rhizosphere soil was sampled for 16S rRNA gene sequencing to compare bacterial community composition and diversity between cultivars. Spider mite injury was assessed using a standardized 0–5 visual damage scale commonly applied in integrated pest management studies. Although the cultivars did not differ significantly in growth traits, Cherry Citrus experienced significantly less spider mite damage than BaOx, suggesting greater tolerance or resistance to herbivory under shared conditions. Rhizosphere bacterial communities differed markedly between cultivars despite identical soil and environmental conditions. BaOx rhizospheres were enriched in Actinobacteria, including taxa associated with decomposition and antimicrobial compound production, whereas Cherry Citrus rhizospheres were enriched in Alphaproteobacteria, particularly nitrogen-cycling and root-associated taxa such as Rhizobium and Reyranella. Alpha diversity metrics did not differ between cultivars; however, beta diversity analyses revealed significant cultivar-level separation, particularly in phylogenetic community structure. Because herbivore pressure and microbial communities were not experimentally manipulated, this observational study identifies ecological associations rather than direct causal relationships. Nevertheless, the results demonstrate that hemp cultivar identity is associated with distinct rhizosphere microbiomes and differential susceptibility to spider mite damage. These findings highlight the potential for integrating cultivar selection and microbiome-informed strategies into sustainable pest management programs for hemp. Full article

Accurate extraction of crop planting structures is important for crop area and yield estimation, but complex and fragmented cropping patterns with overlapping phenology in the Hetao Irrigation District hinder reliable crop discrimination. This study proposes a hierarchical workflow that integrates vegetation masking with multi-source feature optimization for crop mapping. First, dual-temporal Sentinel-2 imagery (May and August) is used to generate a vegetation region-of-interest(ROI) mask via Otsu thresholding applied to the Normalized Difference Vegetation Index (NDVI), combined with pixel-wise maximum-value fusion to reduce phenology-driven omissions and background interference. Second, within the vegetation mask, Sentinel-2 spectral, vegetation-index, and texture features are combined with Sentinel-1 synthetic aperture radar (SAR) backscatter and SAR texture features to construct a multi-source feature set. Random Forest(RF) feature-importance ranking is used to select an effective feature subset, and four classifiers (RF, support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and convolutional neural network (CNN)) are compared under the same training/validation setting. The vegetation extraction achieves an overall accuracy of 91% (Kappa = 0.80). Using Sentinel-2 features only, the optimized subset with CNN attains the best performance (overall accuracy = 95%, Kappa = 0.93). Adding Sentinel-1 SAR texture features provides an additional improvement (overall accuracy = 96%, Kappa = 0.94), particularly for classes prone to confusion in fragmented plots. Area proportions derived from the final map are consistent with statistical yearbook data (percentage errors: maize 3.45%, sunflower 2.66%, wheat 0.11%, tomato 0.92%) under the study conditions. This workflow supports practical crop-structure monitoring in complex irrigation districts. Full article

As sessile organisms, plants must dynamically allocate resources between growth and stress resilience. This review focuses on Ethylene Response Factor (ERF) transcription factors as central regulators of this fundamental balance. We evaluate the molecular basis of ERF function, highlighting their modular structure, dynamic post-translational regulation, and ability to form context-specific protein complexes that integrate diverse signals. While ERF family members show functional redundancy, certain ERF subgroups, such as the ERF-VIIs, exhibit clearer evidence of dual roles in coordinating both developmental programs and adaptive responses to stress. We further elucidate the mechanisms underlying ERF-mediated trade-offs, explaining how these factors direct spatial resource allocation and enable temporal switching between growth and defense states. Finally, we explore how emerging technologies, such as CRISPR-based genome editing and various synthetic biology tools, can harness ERF regulatory networks. These approaches offer promising strategies for engineering crops with precisely tuned adaptive capacity, supporting sustainable agriculture even in changing climate conditions. This synthesis highlights specific ERF subgroups as pivotal integrators and future targets for crop improvement. Full article

Cadmium (Cd) contamination severely threatens crop productivity and food safety, particularly in maize (Zea mays L.), which exhibits relatively high capacities for metal uptake and translocation. Metal tolerance proteins (MTPs) play essential roles in metal homeostasis and detoxification; however, the functions of maize MTP under Cd stress remain poorly understood. In this study, a comprehensive expression analysis of the maize MTP gene family revealed that two Zn-CDF members, ZmMTP1-1 and ZmMTP1-2, displayed the strongest and most consistent transcriptional induction in response to Cd stress, especially in roots. Phylogenetic and structural analyses confirmed that both genes are closely related to MTP1 homologs from other plant species, while exhibiting distinct gene structures and regulatory features. Functional characterization in transgenic Arabidopsis thaliana demonstrated that overexpression of ZmMTP1-1 or ZmMTP1-2 significantly enhanced tolerance to Cd and Zn stress, as reflected by improved seed germination, root growth, survival, and biomass accumulation. Enhanced metal tolerance was associated with elevated antioxidant enzyme activities, reduced oxidative damage, and coordinated upregulation of endogenous metal transporter genes. Moreover, heterologous expression of ZmMTP1-1 in yeast further supported its conserved role in Cd tolerance. Collectively, these findings indicate that ZmMTP1-1 and ZmMTP1-2 contribute to Cd detoxification through coordinated metal transport and stress-response pathways, providing potential genetic resources for improving heavy metal tolerance in maize. Full article

Compost-based biostimulants (CBB) have emerged as a promising tool in sustainable agriculture, offering an eco-friendly approach to improving soil health, crop productivity, and environmental resilience. Derived from the controlled biodegradation of organic waste, CBB contains a diverse array of beneficial microorganisms, humic substances, and bioactive compounds that act synergistically to stimulate plant growth and soil biological activity. Mechanistically, CBB enhances nutrient acquisition by increasing plant-available nitrogen and phosphate solubility, promoting root development through phytohormone synthesis, and improving stress tolerance by modulating plant defense pathways and antioxidant activity. Additionally, their application enhances soil structure, microbial diversity, and carbon sequestration, making them integral to climate-smart agriculture. Despite their growing relevance, several challenges impede the widespread adoption of CBB. Variability in compost quality, lack of standardized production protocols, limited field-scale validation, and inconsistent regulatory frameworks hinder reproducibility and commercialization. Addressing these gaps requires interdisciplinary research that integrates microbiology, biochemistry, agronomy, and data science to better understand how microbial metabolites interact and optimize formulation strategies. Future research should prioritize the standardization of composting methods, long-term multi-crop field evaluations, and integration with precision agriculture tools for real-time soil monitoring. Policy harmonization, quality assurance frameworks, and farmer education are also vital for ensuring safe and effective use of CBB. Full article

Non-specific Lipid Transfer Proteins (nsLTPs) constitute a ubiquitous family of plant proteins that play a critical role in mediating plant adaptation and tolerance to abiotic stress. While their functions have been extensively characterized in model plants such as Arabidopsis thaliana and rice (Oryza sativa L.), they remain largely unexplored in Cucurbitaceae crops. We identified 31 CmnsLTP genes in the melon (Cucumis melo L.) genome, these genes were unevenly distributed across 11 chromosomes and classified into 8 subfamilies. Members of the same subfamily have similar gene structures and conserved domains, with all family members having motif 1 and motif 3. The promoter region contains cis elements that respond to light, hormones (ABA and MeJA response elements), and abiotic stress, suggesting that this gene is involved in melon growth, development, and stress response. Previous studies have identified copper resistant candidate MELO3C031073.2 through forward genetics, which belongs to the nsLTP family and was named CmnsLTPY.9 in this study. The RT qPCR results showed that the CmnsLTPY.9 exhibited specific expression in different tissues, The expression levels of CmnsLTPY.9 in leaves ranged from 0.3 to 3.2. Under copper stress, the ‘M625’ (copper-sensitive) showed a 3.2-fold increase, indicating marked upregulation. Additionally, CmnsLTPY.9 was localized to the endoplasmic reticulum, and the position remains unchanged after copper stress. This study provides the first systematic analysis of the CmnsLTP gene family in melon; these findings provide fundamental insights into their specific functions in plant development and stress response, as well as valuable genetic resources for future research on copper-tolerant molecular breeding. Full article

Ascorbate peroxidase (APX) plays a crucial role in both the removal of hydrogen peroxide and chloroplast development in response to light. To clarify the function of the APX gene family in oat (Avena sativa L.), we identified the family members and systematically analyzed their characteristics, phylogenetic relationships, promoter cis-elements, and expression patterns. Overall, 27 oat APX (AsAPX) members were identified in oat, and all encoded products had a peroxidase or peroxidase-like heptapeptide structure and motif. The genes were distributed unevenly across 15 chromosomes, with amino acid sequences ranging from 112 to 510 and molecular weights varying between 11.83 and 55.45 kDa. A phylogenetic analysis revealed that AsAPXs can be categorized into five branches, while an intra-group syntenic analysis identified 17 pairs of duplicate segments. Furthermore, 41 cis-element recognition sites were identified in the promoter regions of AsAPX genes, primarily comprising light-responsive and phytohormone-responsive elements. Moreover, qRT-PCR results indicated that AsAPX genes respond to light. Based on these results, our research establishes a foundation for exploration of AsAPX gene functionality and offers light-inducible candidate genes for chloroplast development to enhance A. sativa and improve crop production. Full article

This study examined nectar and pollen production as well as pollinator visitation in Nigella damascena (Ranunculaceae), an annual ornamental and seed crop, over two flowering seasons. Flower anthesis lasted 6–7 days, with protandry: the male phase began on the first day, and pollen presentation continued until corolla senescence. Peak stigma receptivity occurred in 5-day-old flowers, resulting in a partial overlap of male and female functions between days 5 and 7. Nectar was secreted by petal-derived structures, with secretion beginning in 1-day-old flowers and steadily increasing, peaking on the day of maximum stigma receptivity. The nectar sugar composition differed between floral phases; it was sucrose-dominant in the male phase and sucrose-rich in the female phase. Significant year effects were observed for flowering abundance, nectar traits (volume, sugar production, concentration), and pollen output. Flowers were visited predominantly by honey bees, but bumblebees, solitary bees, and dipterans were also recorded. These results demonstrate that floral reward traits vary between years and contribute to differences in the temporal availability of nectar and pollen resources. Full article

Accurate weed mapping in cereal fields requires pixel-level segmentation from unmanned aerial vehicle (UAV) imagery that remains reliable across fields, seasons, and illumination. Existing multispectral pipelines often depend on thresholded vegetation indices, which are brittle under radiometric drift and mixed crop–weed pixels, or on single-stream convolutional neural network (CNN) and Transformer backbones that ingest stacked bands and indices, where radiance cues and normalized index cues interfere and reduce sensitivity to small weed clusters embedded in crop canopy. We propose VISA (Vegetation Index and Spectral Attention), a two-stream segmentation network that decouples these cues and fuses them at native resolution. The radiance stream learns from calibrated five-band reflectance using local residual convolutions, channel recalibration, spatial gating, and skip-connected decoding, which preserve fine textures, row boundaries, and small weed structures that are often weakened after ratio-based index compression. The index stream operates on vegetation-index maps with windowed self-attention to model local structure efficiently, state-space layers to propagate field-scale context without quadratic attention cost, and Slot Attention to form stable region descriptors that improve discrimination of sparse weeds under canopy mixing. To support supervised training and deployment-oriented evaluation, we introduce BAWSeg, a four-year UAV multispectral dataset collected over commercial barley paddocks in Western Australia, providing radiometrically calibrated blue, green, red, red edge, and near-infrared orthomosaics, derived vegetation indices, and dense crop, weed, and other labels with leakage-free block splits. On BAWSeg, VISA achieves 75.6% mean Intersection over Union (mIoU) and 63.5% weed Intersection over Union (IoU) with 22.8 M parameters, outperforming a multispectral SegFormer-B1 baseline by 1.2 mIoU and 1.9 weed IoU. Under cross-plot and cross-year protocols, VISA maintains 71.2% and 69.2% mIoU, respectively. The full BAWSeg benchmark dataset, VISA code, trained model weights, and protocol files will be released upon publication. Full article

Plant growth regulators (PGRs) are extensively used in modern agriculture to modify plant developmental processes, enhance productivity, and improve crop quality under increasingly variable environmental conditions. While their agronomic benefits are well established, growing attention has been directed toward understanding their broader environmental implications. In this current review, we analyze recent research published over the last five years to evaluate the environmental behavior and ecological impacts of widely used natural and synthetic plant growth regulators. Particular emphasis is placed on their persistence and mobility in soil and water, their interactions with soil microbial communities, and their effects on non-target terrestrial and aquatic organisms. Recent advances in analytical detection and ecotoxicological assessment have revealed that several PGRs, despite being applied at low doses, may exhibit prolonged environmental residence and subtle biological effects, particularly following repeated applications. Alterations in soil enzyme activity, shifts in microbial community structure, and growth disturbances in non-target plants and aquatic primary producers have been increasingly reported. The review also discusses emerging strategies aimed at reducing environmental risks, including precision application technologies, the development of biodegradable regulators, and improved regulatory frameworks. Overall, these findings highlight the need for integrated risk assessment approaches and long-term field studies to support the sustainable use of plant growth regulators in agroecosystems. Full article

The proposed study investigates the key factors influencing UAV adoption and proposes an integrated educational–operational framework to enhance implementation in agricultural practice. A case study in Sibiu County, Romania, combined survey-based empirical analysis (n = 80), strategic environmental assessment and the deployment of a demonstration aerial crop monitoring system for educational purposes (ACMS-E). We integrated the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) to examine adoption intentions, revealing perceived usefulness (β = 0.355, p = 0.021) and positive attitudes (β = 0.382, p = 0.005) as the strongest predictors, explaining 44.1% of variance. Based on these findings, a modular training curriculum was designed, combining theoretical instruction, flight operation exercises, remote sensing techniques, data analytics and farm-management integration. ACMS-E provides hands-on training and promotes capacity-building, bridging the gap between technological availability and real-world adoption. By linking technological capabilities with structured training, ACMS-E bridges the gap between UAV availability and effective implementation, offering a scalable model for precision agriculture. This framework provides a pathway to accelerate UAV adoption, optimize field-level monitoring and support evidence-based, resource-efficient farm management in emerging and developed agricultural contexts. Full article

Understanding the intricate interrelationships among ecosystem services (ESs) is fundamental to advancing sustainable ecological management. This study focuses on the Taihu Basin and examines five representative ESs, including water yield (WY), carbon sequestration (CS), soil retention (SR), habitat quality (HQ), and crop production (CP), for the years 2000, 2010, and 2020. Spatial distribution characteristics and spatiotemporal dynamics were quantified through the combined application of the InVEST model, a food production model, and ArcGIS. Spearman correlation analysis and K-means clustering were then applied to characterize trade-offs and synergies among ESs and to delineate ecosystem service bundles at multiple spatial scales, including 1 km × 1 km grids, 10 km × 10 km grids, and the county level, while GeoDetector was used to identify the associated driving mechanisms. The results indicated that (1) between 2000 and 2020, the spatial distribution pattern of the ESs in the Taihu Basin underwent significant changes, with WY and SR increasing by 48.97% and 51.89%, respectively, while HQ, CS, and CP decreased by 17.2%, 15.5%, and 47.6%. (2) From an overall perspective of trade-offs and synergies, the interactions among ESs shifted from trade-offs (r < 0) to synergies (r > 0) as the scale increased. From the perspective of the spatial characteristics of trade-offs and synergies, the intensity of these interactions varied significantly with increasing scale, but the trend remained relatively stable. (3) The Taihu Basin can be categorized into six ES bundles (ESBs). ESB 1, ESB 3, ESB 4, and ESB 5 have relatively stable ES structures, whereas ESBs 2 and 6 display significant variations. (4) The primary factors influencing ESs vary significantly across different spatial scales, with land use/land cover (LULC) and the proportions of arable land, forestland, and buildings exhibiting strong explanatory power. This highlights the critical role of coupled natural and anthropogenic processes in shaping the spatial patterns of ESs. This study considers the spatiotemporal variation and scale dependence of ecosystem services, providing management recommendations tailored to different regions and spatial scales, and offering a scientific basis for regional ecological planning and watershed governance. Full article

RNA tailing, the non-templated addition of nucleotides to RNA 3′ ends, is a conserved post-transcriptional modification that plays a critical role in regulating RNA metabolism. In plants, this process is primarily mediated by nucleotidyltransferase proteins (NTPs). In this review, we analyze current knowledge of plant NTPs by integrating evidence from genetic, biochemical, and phylogenetic analyses of the gene-family across model plants and crops. We summarize the composition and evolutionary diversification of the plant NTP gene family, with emphasis on lineage-specific expansion and conservation patterns. Using Arabidopsis thaliana as a reference framework, we then describe the molecular roles of NTPs in the tailing of distinct RNA classes, emphasizing how tail type and length confer context-dependent regulatory outcomes including stabilization versus degradation and processing/maturation versus clearance. We further examine the determinants of substrate choice, focusing on RNA type, terminal structure, and subcellular localization. Finally, we discuss the biological functions of NTP-mediated RNA tailing in plants, linking RNA tailing to development, stress responses, antiviral immunity, and agronomic traits in crops. We conclude by outlining key mechanistic and physiological challenges that define future directions for understanding and harnessing NTP-mediated RNA regulation. Collectively, this review provides an integrated framework for understanding how RNA tailing by NTPs shapes plant RNA metabolism and biological fitness. Full article

Background: The DOF transcription factor family is involved in plant growth, development, and stress responses, but systematic comparative genomics studies across legume species are lacking. Methods: We identified the whole genome of the DOF gene family of five legume plants: Medicago truncatual (43), Cicer arietinum (43), Phaseolus vulgaris (44), Glycine max (79), and Lotus japonicus (32). Genome-wide identification of DOF genes was performed in five legume species, followed by phylogenetic analysis, gene structure characterization, duplication event identification, promoter element prediction, synteny analysis, and expression pattern profiling. Results: Phylogenetic comparison with Arabidopsis thaliana (47) and Oryza sativa (37) classified them into four subfamilies (Groups I–IV). The five legumes all had no more than 30% members of the subgroup. The same subfamily has similar protein structures and gene structures, and most of its members have motif1, with most plants having more than 30% of genes intronic. Gene duplication events were evenly distributed among the members of the DOF gene in all five legumes, and played an important role in its evolution. Moreover, the majority of the DOF genes showed tissue specificity in the five legumes, with most of these members being upregulated in flowers. Additionally, expression pattern analysis under abiotic stress in soybean revealed that members of different subfamilies exhibit divergent expression dynamics under salt, alkali, and cold stresses. The DOF gene family in legumes expanded primarily through segmental duplication and evolved under purifying selection. Conclusion: The subfamily-specific responses to abiotic stress and tissue-specific expression patterns provide candidate gene resources for functional studies aimed at improving stress tolerance and agronomic traits in legume crops. Full article

Drone-mounted ground-penetrating radar (GPR) systems offer new opportunities for integrating subsurface characterization into remote sensing workflows. However, the interaction between flight parameters, surface conditions, and vegetation characteristics remains poorly understood. This study investigates the impact of flight altitude, surface topography, crop presence, and canopy water content on the stability and interpretability of GPR signals collected using a drone. Field experiments were conducted under controlled conditions using agricultural plots with variable canopy cover and soil moisture regimes. Radargrams were processed to evaluate signal amplitude, reflection continuity, and attenuation patterns in relation to terrain slope and vegetation structure derived from co-registered RGB drone imagery. The results reveal that lower flight altitudes and smoother surfaces yield higher signal coherence and greater subsurface penetration, while increased canopy water content and biomass reduce signal strength and clarity. Integrating drone-based GPR observations with surface spectral and thermal data improved discrimination between soil and vegetation-induced signal distortions. The findings highlight the potential of drone–GPR systems as a complementary layer in a multi-sensor remote sensing framework for precision agriculture, environmental monitoring, and 3D soil mapping. Full article