Search Results (42,144)

Chrysanthemum morifolium Ramat. is a major ornamental crop that suffers from diverse fungal, bacterial, viral, and insect pests, causing significant yield and quality losses. Between 2015 and 2025, rapid progress in molecular biology, genomics, and ecological regulation has advanced both fundamental research and applied control strategies. Multi-locus sequencing, multiplex PCR, and next-generation sequencing refined the identification of fungal and bacterial pathogens, while functional studies of WRKY, MYB, and NAC transcription factors revealed key resistance modules. Hormone-mediated signaling pathways, particularly those of salicylic acid, jasmonic acid, and abscisic acid, were shown to play central roles in host defense. Despite these advances, durable genetic resistance against bacterial pathogens and broad-spectrum defense against viruses remains limited. Novel technologies, including virus-free propagation, RNA interference, and spray-induced gene silencing, have shown promising outcomes. For insect pests, studies clarified the damage and virus-vectoring roles of aphids and thrips, and resistance traits linked to trichomes, terpenoids, and lignin have been identified. Biocontrol agents such as Trichoderma spp., Bacillus spp., predatory mites, and entomopathogenic fungi have also demonstrated efficacy. Future efforts should integrate molecular breeding, genome editing, RNA-based tools, and microbiome management to achieve sustainable chrysanthemum protection. Full article

Making effective decisions about scaling up on-farm irrigation practices to the district level requires a comprehensive assessment of irrigation management at the farm level. In this context, a bucket-type water mass balance model was developed, calibrated, and validated over five irrigation seasons on a 121-hectare rice farm located in the lower Ter River valley (north-east Spain), to assess the water use efficiency and the impact of different irrigation practices on water savings. The model was implemented considering the spatial variability of the soils within the farm. It showed a satisfactory performance in both the calibration (2020, 2021, 2022) and validation (2023, 2024) cropping seasons, with NSE values greater than 0.50, PBIAS lower than ±20%, and RSR lower than 0.70. After model validation, the simulation of alternative water management practices revealed that the 10-day fixed-turn irrigation reduced irrigation water use by 30% compared to the traditional water management, although it may negatively impact rice yield. Simulations of an early irrigation cut-off at the end of the season and dry seeding with delayed flooding accounted for 17% and 15% irrigation water savings, respectively. The implementation of the no-runoff practice only accounted for a 6% reduction in water use. The water-saving potential of the simulated strategies was mainly driven by shortening the flooded period of rice paddies, thus demonstrating that managing the ponding water level is critical to diminishing water use in rice irrigation. Full article

Salt stress is a major abiotic factor limiting crop productivity worldwide, as it disrupts plant growth, metabolism, and survival. In this study, we report that the genes PvPR10-2 and PvPR10-3 were significantly up-regulated in bean leaves and stems in response to combined salt and jasmonic acid (NaCl–JA) treatment. Foliar application of JA with salt induced physiological alterations, including stem growth inhibition, H₂O₂ accumulation, and activation of antioxidant enzymes. To investigate the role of PvPR10-3 in response to salt and phytohormones, we introduced this gene into Arabidopsis and found that its heterologous expression conferred salt tolerance to the transgenic lines. Interestingly, exogenous JA contributed to salt tolerance by reducing H₂O₂ levels, inducing ROS-scavenging enzymes, and promoting the accumulation of phenolic compounds and ABA. Furthermore, gene expression analysis of the transgenic lines revealed that PvPR10-3 expression under NaCl–JA stress is associated with the induction of JA-related genes like MYC2, JAZ2, JAZ11, and JAZ12, as well as SA-responsive genes, like ALD1 and TGA2, and two ABA-independent components DREB2A and ERD1, suggesting potential coordination between JA, ABA, and SA signaling in salt stress response. Additionally, key flowering regulators (FT, GI) were upregulated in transgenic lines under NaCl–JA treatment, suggesting a previously unexplored link between salt tolerance pathways and the regulation of flowering time. Taken together, our findings suggest a role of PvPR10-3 in enhancing salt stress tolerance and the involvement of exogenous JA in tolerance potentially by modulating ROS balance, hormone-associated gene expression, and protective secondary metabolites. Full article

Calcium-dependent protein kinases (CDPKs) are key Ca²⁺ sensors in plants, mediating responses to abiotic stresses via phosphorylation signaling. In the halophyte Nitraria sibirica, which thrives in saline soils, we identified 19 CDPK genes (NsCDPKs) and classified them into four canonical angiosperm clades, highlighting conserved functional modules. Promoter analysis revealed diverse cis-acting elements responsive to light, hormones (ABA, MeJA, auxin, GA, SA), and abiotic stresses (drought, cold, wounding), along with numerous MYB binding sites, suggesting complex transcriptional regulation. Transcriptome profiling under salt stress (100 and 400 mM NaCl) showed induction of most NsCDPKs, with several genes significantly upregulated in roots and stems, indicating coordinated whole-plant activation. These salt-responsive NsCDPKs were also upregulated by cold but repressed under PEG-simulated drought, indicating stress-specific regulatory patterns. Fifteen MYB transcription factors, differentially expressed under salt stress, were predicted to interact with NsCDPK promoters, implicating them as upstream regulators. This study identified a potential salt- and cold-responsive CDPK regulatory module and a MYB-mediated transcriptional hierarchy in N. sibirica, providing insights into the molecular mechanisms of salinity adaptation and highlighting candidate genes that could be explored for improving salt tolerance in crop species. Full article

Walnuts (Juglans regia L.) are an economically important woody crop, but spring frost poses a serious threat to their growth and productivity. However, the molecular mechanisms underlying walnut responses to freezing stress remain largely unknown. In this study, transcriptome analyses were performed on cold-tolerant and cold-sensitive walnut varieties subjected to freezing stress. A total of 9611 differentially expressed genes (DEGs) responsive to freezing stress were obtained, of which 2853 were common up-regulated and 2880 were common down-regulated in both varieties. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed 15 significantly enriched pathways in both varieties, including flavonoid biosynthesis. A simplified walnut flavonoid biosynthesis pathway was constructed, encompassing 36 DEGs encoding 13 key enzymes, demonstrating that flavonoid biosynthesis in walnut is significantly activated under freezing stress. Furthermore, weighted gene co-expression network analysis (WGCNA) identified a regulatory network centered on the JrCBF genes and uncovered 34 potential interacting genes. Collectively, these findings provide novel insights into the molecular responses of walnut to freezing stress and establish a foundation for elucidating the mechanisms underlying walnut cold tolerance. Full article

Nuts, including tree nuts such as almonds, walnuts, cashews, and macadamias, as well as peanuts, are widely consumed for their health benefits owing to their high-quality protein content. Globally, the nut industry represents a multi-billion-dollar sector, with increasing demand driven by consumer interest in nutrition, functional foods, and plant-based diets. Recent advances in proteomic technologies have enabled comprehensive analyses of nut seed proteins, shedding light on their roles in nutrition, allergenicity, stress responses, and food functionality. Seed storage proteins such as 2S albumins, 7S vicilins, and 11S legumins, are central to nutrition and allergenicity. Their behavior during processing has important implications for food safety. Proteomic studies have also identified proteins involved in lipid and carbohydrate metabolism, stress tolerance, and defense against pathogens. Despite technical challenges such as high lipid content and limited genomic resources for many nut species, progress in both extraction methods and mass spectrometry has expanded the scope of nut proteomics. This review underscores the central role of proteomics in improving nut quality, enhancing food safety, guiding allergen risk management, and supporting breeding strategies for sustainable crop improvement. Full article

Plant growth-promoting bacteria (PGPBs) offer a sustainable alternative for enhancing crop productivity in low-fertility tropical soils. In this study, 30 bacterial isolates were screened in vitro for multiple PGP traits, including phosphate solubilization (from aluminum phosphate—AlPO₄ and thermophosphate), potassium release from phonolite rock, siderophore production, indole-3-acetic acid (IAA) synthesis, ACC deaminase activity, and antagonism against Fusarium spp. Statistical analysis revealed significant differences (p < 0.05) among the isolates. The most efficient isolates demonstrated a solubilization capacity ranging from 24.0 to 45.2 mg L⁻¹ for thermophosphate and 21.7 to 23.5 mg L⁻¹ for potassium from phonolite. Among them, Pseudomonas azotoformans K22 showed the highest AlPO₄ solubilization (16.6 mg L⁻¹). IAA production among the isolates varied widely, from 1.34 to 9.65 µg mL⁻¹. Furthermore, 17 isolates produced carboxylate-type siderophores, and only Pseudomonas aeruginosa SS183 exhibited ACC deaminase activity, coupled with strong antifungal activity (91% inhibition). A composite performance index identified P. azotoformans K22, E. hormaechei SS150, S. sciuri SS120, and B. cereus SS18 and SS17 as the most promising isolates. This study provides a valuable foundation for characterizing plant growth-promoting traits and identifies key candidates for future validation and the development of microbial consortia. Full article

Acetolactate synthase (ALS) is an important enzyme in plant branched-chain amino acid biosynthesis and the target of several major herbicide classes. Despite its agronomic importance, the role of ALS genes in stress adaptation in the invasive weed Amaranthus palmeri remains unstudied. In this study, four ApALS genes with high motif conservation were identified and analyzed in A. palmeri. Phylogenetic analysis classified ApALS and other plant ALS proteins into two distinct clades, and the ApALS proteins were predicted to localize to the chloroplast. Gene expression analysis demonstrated that ApALS genes are responsive to multiple stresses, including salt, heat, osmotic stress, glufosinate ammonium, and the ALS-inhibiting herbicide imazethapyr, suggesting roles in both early and late stress responses. Herbicide response analysis using an Arabidopsis thaliana ALS mutant (AT3G48560) revealed enhanced imazethapyr resistance, associated with higher chlorophyll retention. Furthermore, high sequence homology between AT3G48560 and ApALS1 suggests a conserved role in protecting photosynthetic function during herbicide stress. This study provides the first comprehensive analysis of the ALS gene family in A. palmeri and offers important insights into its contribution to stress resilience. These findings establish a vital foundation for developing novel strategies to control this pervasive agricultural weed and present potential genetic targets for engineering herbicide tolerance in crops. Full article

Halophytes are increasingly recognized as sustainable crops that offer a wide range of nutrients. This study provides a nutritional characterization of annual (Salicornia europaea) and perennial (Sarcocornia fruticosa, Arthrocaulon macrostachyum) species of glasswort, collected from different coastal habitats in southern Italy. S. europaea was also cultivated under non-saline conditions. Results showed differences in mineral content, and bioactive compounds among genotypes, but they were modulated by environmental conditions, leading to significant site-specific variation. S. europaea, regardless of the collecting sites, exhibited the highest concentration of minerals (K, Ca, and Mg), chlorophylls, carotenoids, and phenolic compounds as well as antioxidant activity. A. macrostachyum stood out for its high flavonoid and sterol content, exhibiting other nutritional traits comparable to S. europaea when collected in a more arid site. A. macrostachyum and S. fruticosa displayed similar compositional features, showing the highest anthocyanin and iodine (187.8 µg 100 g⁻¹ FW, on average) content. Sodium and potassium—critical for hypertension management—varied, exceeding the recommended Na/K ratio (1) for human consumption, especially in A. macrostachyum grown close to the sea. The most promising result was observed in non-saline S. europaea and in an A. macrostachyum sample (1.7, on average). Overall findings confirm the potential of both annual and perennial glassworts as nutritionally rich, sustainable crops for marginal environments. Full article

Seasonal fires in northern Thailand are a persistent environmental and public health concern, yet existing fire probability mapping approaches in Thailand rely heavily on subjective multi-criteria analysis (MCA) methods and temporally static data aggregation methods. To address these limitations, we present a flexible, replicable, and operationally viable seasonal fire probability mapping methodology using a Random Forest (RF) machine learning model in the Google Earth Engine (GEE) platform. We trained the model on historical fire occurrence and fire predictor layers from 2016–2023 and applied it to 2024 conditions to generate a probabilistic fire prediction. Our novel approach improves upon existing operational methods and scientific literature in several ways. It uses a more representative sample design which is agnostic to the burn history of fire presences and absences, pairs fire and fire predictor data from each year to account for interannual variation in conditions, empirically refines the most influential fire predictors from a comprehensive set of predictors, and provides a reproducible and accessible framework using GEE. Predictor variables include both socioeconomic and environmental drivers of fire, such as topography, fuels, potential fire behavior, forest type, vegetation characteristics, climate, water availability, crop type, recent burn history, and human influence and accessibility. The model achieves an Area Under the Curve (AUC) of 0.841 when applied to 2016–2023 data and 0.848 when applied to 2024 data, indicating strong discriminatory power despite the additional spatial and temporal variability introduced by our sample design. The highest fire probabilities emerge in forested and agricultural areas at mid elevations and near human settlements and roads, which aligns well with the known anthropogenic drivers of fire in Thailand. Distinct areas of model uncertainty are also apparent in cropland and forests which are only burned intermittently, highlighting the importance of accounting for localized burning cycles. Variable importance analysis using the Gini Impurity Index identifies both natural and anthropogenic predictors as key and nearly equally important predictors of fire, including certain forest and crop types, vegetation characteristics, topography, climate, human influence and accessibility, water availability, and recent burn history. Our findings demonstrate the heavy influence of data preprocessing and model design choices on model results. The model outputs are provided as interpretable probability maps and the methods can be adapted to future years or augmented with local datasets. Our methodology presents a scalable advancement in wildfire probability mapping with machine learning and open-source tools, particularly for data-constrained landscapes. It will support Thailand’s fire managers in proactive fire response and planning and also inform broader regional fire risk assessment efforts. Full article

Occluded person re-identification (Re-ID) faces significant challenges, mainly due to the interference of occlusion noise and the scarcity of realistic occluded training data. Although data augmentation is a commonly used solution, the current occlusion augmentation methods suffer from the problem of dual inconsistencies: intra-sample inconsistency is caused by misaligned synthetic occluders (an augmentation operation for simulating real occlusion situations); i.e., randomly pasted occluders ignore spatial prior information and style differences, resulting in unrealistic artifacts that mislead feature learning; inter-sample inconsistency stems from information loss during random cropping (an augmentation operation for simulating occlusion-induced information loss); i.e., single-scale cropping strategies discard discriminative regions, weakening the robustness of the model. To address the aforementioned dual inconsistencies, this study proposes the unified Multi-Aligned and Multi-Scale Augmentation (MA–MSA) framework based on the core principle of ”synthetic data should resemble real-world data”. First, the Frequency–Style–Position Data Augmentation (FSPDA) module is designed: it ensures consistency in three aspects (frequency, style, and position) by constructing an occluder library that conforms to real-world distribution, achieving style alignment via adaptive instance normalization and optimizing the placement of occluders using hierarchical position rules. Second, the Multi-Scale Crop Data Augmentation (MSCDA) strategy is proposed. It eliminates the problem of information loss through multi-scale cropping with non-overlapping ratios and dynamic view fusion. In addition, different from the traditional serial augmentation method, MA–MSA integrates FSPDA and MSCDA in a parallel manner to achieve the collaborative resolution of dual inconsistencies. Extensive experiments on Occluded-Duke and Occluded-REID show that MA–MSA achieves state-leading performance of 73.3% Rank-1 (+1.5%) and 62.9% mAP on Occluded-Duke, and 87.3% Rank-1 (+2.0%) and 82.1% mAP on Occluded-REID, demonstrating superior robustness without auxiliary models. Full article

Plant-based residues generated within the agri-food system represent an abundant resource with significant potential for sustainable valorization. However, they are still underutilized and place a substantial burden on the environment and climate. This review discusses research trends over the past decade, combining bibliometric analysis with an overview of emerging technologies applied to the processing of residues generated from conventional crops and medicinal and aromatic plants. The bibliometric analysis reveals main valorization pathways, ranging from energy production to recovery of high-value bioactive compounds. Recent advances in this field are discussed in detail, with emphasis on low-energy and non-thermal processing (ultrasound, microwave, cold plasma), green solvents (natural deep eutectic solvents, bio-based solvents), biological pretreatments (with ligninolytic microorganisms and enzymes), thermochemical technologies (hydrothermal carbonization, pyrolysis), and emerging cascade strategies applied for multi-product recovery. Published research proves that these approaches have a great potential for sustainable valorization, while process optimization and economic feasibility remain a challenge at industrial scales for wider adoption. By providing an integrated perspective on diverse types of plant-based residues, this review highlights the importance of developing cascade and circular processing strategies, which align with global sustainability goals and encourage innovation in bio-based industries. New knowledge and advances in this field are highly required and will further help the transition of the current agri-food system towards greater circularity and sustainability. Full article

Under the condition of straw returning to the field, appropriate nitrogen fertilizer application is one of the key factors used to improve crop yield and ensure environmental safety. Therefore, an experiment with different rates of nitrogen fertilization was conducted with a randomized block design in Harbin, China. The straw was deeply plowed back into the field after harvest in the autumn. The nitrogen application rates were 0, 75, 150, 180, 225, and 300 kg·ha⁻¹. The purpose of this study is to clarify the appropriate amount of nitrogen fertilizer under the condition of straw returning to the field and to provide technical support for high-yield and high-efficiency maize in cold regions. The results indicated that the yield of maize first increased and then stabilized as the amount of nitrogen fertilizer increased, while the economic benefits first increased and then decreased. When the nitrogen application rate exceeds 225 kg·ha⁻¹ or is lower than 150 kg·ha⁻¹, the economic benefits significantly decrease. When high-nitrogen fertilizer rates of 225 kg·ha⁻¹ and 300 kg·ha⁻¹ were applied, the residual nitrate nitrogen in the soil was increased by 2.1 times and 2.3 times, respectively, compared to before sowing. With the increase in the nitrogen application rate, the nitrogen fertilizer utilization efficiency and agronomic efficiency decreased, and the apparent nitrogen loss and nitrogen surplus significantly increased. Comprehensively considering the maize yield, benefits, and environmental risk factors the suitable nitrogen application rate was in a range of 170.2 kg·ha⁻¹ to 178.2 kg·ha⁻¹ in the first year and 150.0 kg·ha⁻¹ to 171.3 kg·ha⁻¹ in the second year. This work provides a theoretical basis and technical support for the rational application of nitrogen fertilizer and high-yield and high-efficiency spring maize under the condition of straw returning to the field. Full article

Understanding wild bee diversity is critical for pollinator conservation, particularly in understudied tropical regions like coastal Ecuador. This preliminary study provides a photography-based assessment of bee diversity at Finca Botánica, an organic and regenerative farm on Ecuador’s central Pacific coast. Over a 10-day survey in December 2024, researchers documented 51 bee species across four families, with Apidae being the most represented. The study highlights a predominance of solitary, ground-nesting bees and a lower-than-expected diversity of Meliponini (stingless bees) and Euglossini (orchid bees) compared to other regions of Ecuador. Many species were found in forest patches, ecological corridors, and cover-cropped maize fields, underscoring the role of sustainable farming practices in supporting pollinator diversity. While photographic methods provided valuable preliminary data, they also revealed limitations in species-level identification, reinforcing the need for future specimen-based surveys. These findings suggest that Ecuador’s dry coastal forests may harbor a richer bee community than previously recognized and that organic farms can serve as important refuges for native pollinators. Full article

The integration of unmanned aerial vehicles (UAVs) and deep learning (DL) has significantly advanced crop disease detection by enabling scalable, high-resolution, and near real-time monitoring within precision agriculture. This systematic review analyzes peer-reviewed literature indexed in the Web of Science Core Collection as articles or proceeding papers through 2024. The main selection criterion was combining “unmanned aerial vehicle*” OR “UAV” OR “drone” with “deep learning”, “agriculture” and “leaf disease” OR “crop disease”. Results show a marked surge in publications after 2019, with China, the United States, and India leading research contributions. Multirotor UAVs equipped with RGB sensors are predominantly used due to their affordability and spatial resolution, while hyperspectral imaging is gaining traction for its enhanced spectral diagnostic capability. Convolutional neural networks (CNNs), along with emerging transformer-based and hybrid models, demonstrate high detection performance, often achieving F1-scores above 95%. However, critical challenges persist, including limited annotated datasets for rare diseases, high computational costs of hyperspectral data processing, and the absence of standardized evaluation frameworks. Addressing these issues will require the development of lightweight DL architectures optimized for edge computing, improved multimodal data fusion techniques, and the creation of publicly available, annotated benchmark datasets. Advancements in these areas are vital for translating current research into practical, scalable solutions that support sustainable and data-driven agricultural practices worldwide. Full article