Search Results (1,888)

Nanoparticles of the aminopolysaccharide chitosan (ChNPs) are effective delivery platforms for biologically active substances for agrobiotechnological applications and hold great promise for solving precision problems in sustainable and efficient agriculture. This review presents an analysis of research publications during the past 20 years examining methods for producing ChNPs through ionotropic gelation using sodium tripolyphosphate for cross-linking macrochains, which are of practical interest for agriculture. Key aspects of the nanostructure formation process are analyzed, including the influence of the physicochemical characteristics of the aminopolysaccharide, the concentration and ratio of reagents, and ionic cross-linking conditions on the average size, size distribution (polydispersity), and zeta potential of nanoparticles. Particular attention is paid to several approaches proposed in the literature for determining optimal gelation conditions to obtain ChNPs with pre-specified size characteristics. Potential applications of nanostructured preparations based on these nanoparticles for agrobiochemical purposes are considered, including the encapsulation of antifungal, antiviral and antimicrobial agents, pesticides, NPK fertilizers, metal ions, plant extracts, essential oils, etc., to develop biodegradable stimulants for seed germination and plant growth, increased crop yields, and improved agricultural product quality. It is concluded that blocking the protonated amino groups of chitosan with tripolyphosphate anions is undesirable due to the reduced biological activity of the macromolecules and the nanostructured preparations obtained therefrom. An alternative approach for producing ChNPs with high biological activity with neither use of cross-linking agents nor encapsulation of agrochemicals is described. Full article

The agricultural industry largely relies on conventional pesticides to maintain healthy, pest-free crops. Application of conventional insecticides is the go-to method for cultivating important food crops, such as corn and sorghum, free of Spodoptera frugiperda (fall armyworm) infestations. However, conventional insecticides have purported negative environmental and health impacts. Natural plant extracts, such as essential oils, are viewed as a promising alternative to conventional insecticides. In the current study, Dysphania ambrosioides (epazote) essential oil was embedded into an artificial diet and fed at two different concentrations to fall armyworms during a 10-day period. Final weights of the 5% epazote treatment group were statistically less (F₆₃₄₃ = 136.2 p < 0.001) than control groups. The 5% epazote treatment group also experienced the highest mortality rate (62%) of any treatment group (X² = 831.4, DF = 6, p < 0.001). Findings suggest that epazote essential oil has potential as an effective, natural insecticidal ingredient. This research is of importance to the fields of agronomy and health sciences. Full article

Genome editing, particularly CRISPR/Cas-based systems, has emerged as a key tool for functional genomics and trait improvement in flax (Linum usitatissimum L.), an important fiber and oilseed crop. This review focuses specifically on flax as an emerging target species and distinguishes experimentally validated applications from approaches adapted from model plants. Recent progress includes the characterization of endogenous U6 promoters, which improved guide RNA expression and contributed to enhanced genome editing performance under optimized conditions. Reported studies demonstrate efficient targeted mutagenesis in flax; however, editing outcomes remain strongly dependent on genotype, construct design, and regeneration capacity, and stable homozygous edited lines are still limited. Target genes include pathways involved in lignin and cellulose biosynthesis, fatty acid metabolism, and stress responses, influencing fiber quality, oil composition, and stress adaptation. Despite current bottlenecks such as low homologous recombination efficiency and regeneration constraints, base editing, prime editing, and multiplex CRISPR systems provide promising avenues for precision breeding in flax. Full article

Soybean is a strategic crop for global protein and vegetable oil supply chains; however, genetic improvement remains constrained by genotype-dependent regeneration, variable transformation efficiency, and regulatory concerns regarding stable transgene integration. This review synthesizes emerging DNA-free and genotype-independent genome-editing frameworks for soybean, where genotype independence is defined as the ability to recover fertile, non-chimeric edited plants across elite germplasm. We critically examine the soybean genome-editing toolbox, including CRISPR-Cas9, Cas12a, multiplex editing systems, base editing, and prime editing, and discuss persistent bottlenecks associated with target selection, off-target assessment, editability, and plant recovery. Particular emphasis is placed on artificial intelligence (AI)-assisted approaches that integrate genomic, epigenomic, chromatin-accessibility, and multi-omics datasets to improve target prioritization, guide RNA design, off-target prediction, and locus- and genotype-specific editability assessment. We further evaluate DNA-free genome-editing technologies, including CRISPR-Cas ribonucleoproteins, transient RNA-based systems, and nanocarrier-mediated delivery platforms, highlighting their potential to generate non-integrative edits while reducing prolonged nuclease exposure. In addition, we discuss regeneration reprogramming strategies based on developmental regulators and morphogenic modules, including BBM-WUS, GRF-GIF, de novo meristem induction, and somatic embryogenesis, as enabling technologies for overcoming cultivar-dependent regeneration barriers. Importantly, this review proposes an integrated AI-to-field framework that connects target discovery, editability prediction, DNA-free editing, regeneration reprogramming, phenotypic validation, and breeding deployment into a unified soybean improvement pipeline. We further highlight emerging opportunities in multi-omics-guided target discovery, genotype-aware prediction models, regeneration-aware editing strategies, and closed-loop machine-learning systems that continuously improve editing decisions through experimental feedback. Collectively, these convergent innovations provide a practical foundation for accelerating the development of climate-resilient, nutritionally enhanced, and industry-ready soybean cultivars. Full article

Climate change is increasingly constraining agricultural productivity by intensifying drought, accelerating soil degradation, and increasing pest and disease pressure. In this context, biostimulants are emerging as sustainable tools to improve crop resilience and maintain yield under suboptimal conditions. This study evaluated the effects of microbial and non-microbial biostimulants on chickpea (Cicer arietinum L.) growth, grain yield, seed quality, root traits, and soil properties under low-fertility and water-limited conditions in a marginal field in southern Italy. Treatments included an untreated control and biostimulants based on microelements, arbuscular mycorrhizal fungi (AMF), microbial consortia, ozonated oil, and humic substances. Biostimulants significantly affected agronomic traits. Humic substances increased plant height, while microelements markedly enhanced reproductive performance, with pod number increasing from 13 in the control to 23 pods plant⁻¹. Root traits were also improved, particularly under microbial, humic, and AMF treatments. Grain yield was highest in the ozonated oil treatment (430.6 kg ha⁻¹), whereas seed nutritional composition showed only limited variation among treatments. Biostimulants also induced treatment-specific changes in soil fertility indicators. Overall, the results indicate that selected biostimulants can improve chickpea performance and modulate soil fertility under marginal conditions, although multi-year studies are needed to confirm the stability of these responses under variable environments. Full article

Price volatility in fresh food commodities can weaken supply-chain coordination, disturb market expectations, and increase short-term risks to food availability and affordability. This issue is more pronounced for specialty crops with seasonal production, concentrated supply, limited storability, and high sensitivity to climate, trade, energy, and online-attention shocks. This study develops a trajectory-regularized physics-informed multi-source forecasting framework for daily wholesale prices of garlic, scallion, and ginger in China from 2014 to 2024. The framework, denoted as STL–ETO–EMA–PILSTM, integrates Seasonal-Trend decomposition using LOESS (STL), Efficient Multi-scale Attention (EMA), Long Short-Term Memory (LSTM), an economically motivated physics-informed trajectory residual constraint, and Exponential-Trigonometric Optimization (ETO), using production, climate, macroeconomic, trade, crude-oil, and online-attention indicators. In this framework, the physics-informed component is implemented as a trajectory residual constraint inspired by price-adjustment inertia and local continuity, rather than as a conventional PINN based on strict governing physical equations. In one-step-ahead forecasting, the model outperformed conventional machine learning baselines and additional time-series baselines, including naive persistence, Transformer Encoder, and PatchTST, with MAE values of 0.0853, 0.0581, and 0.1409 for garlic, scallion, and ginger, respectively, and R² values above 0.996. Leakage-prevention procedures, walk-forward validation, multi-horizon forecasting, and Diebold–Mariano tests were used to strengthen result credibility. Multi-step forecasting showed clear performance degradation as the horizon increased, supporting the positioning of the framework as a short-term market-monitoring tool rather than a long-horizon structural projection model. Permutation-based feature-importance and interaction analyses revealed crop-specific price drivers. The framework provides an interpretable tool for fresh food price forecasting, market stability monitoring, and short-term operational risk monitoring in fresh food supply chains. Full article

Rapeseed is one of the most important oil crops in the world. Its yield and quality are severely restricted by biotic stress and abiotic stress. Rapeseed seeds play a crucial role in the propagation process, and the microorganisms in the seeds can be vertically passed on to the next generation, which greatly affects the quality, yield and growth of rapeseed. However, from a group perspective, there is currently a lack of systematic research on the composition of seed-associated microbiome within rapeseed seeds. This study utilized the transcriptome data of 218 rapeseed seeds that have been published, focusing on analyzing and comparing the dynamic changes and functional differences in the composition of seed-associated microbiome in rapeseed seeds under normal growth and development, biologic stress and abiotic stress conditions. Since we used public transcriptome data without surface sterilisation control, we refered to the detected microorganisms as seed-associated microbiome. The advantage of this study lies in its application of this method to a large-scale sample of rapeseed populations, which systematically revealed the response characteristics of seed-associated microbiome under different stress conditions. Interestingly, some widely distributed genera were not detected, while rare taxa were found under specific conditions, warranting further verification. Since these microorganisms originated from the seeds, their compatibility with plants and colonization ability may far exceed those of soil-derived agents. In the future, high-throughput screening of strains with excellent antagonistic or repellent effects against major diseases and pests of rapeseed can be conducted from these unique seed-associated microbiome. These strains that were confirmed by culture-based, amplicon or metagenomic approaches can then be used to develop seed coating agents or soil inoculants. Full article

Soybean is an important crop for food, oil and feed production in China, and improving its yield is a major national goal. Salt stress severely restricts soybean production. XTH genes participate in plant growth and stress adaptation, yet the functions of most soybean XTH members are unclear. In this study, we cloned the soybean GmXTH-like26 gene previously identified via transcriptome sequencing, and successfully constructed its overexpression vector and CRISPR/Cas9 gene-editing vector. Subcellular localization analysis confirmed that GmXTH-like26 is localized to the cell wall. The gene was transformed into soybean via the Agrobacterium-mediated method. Under 100 mM NaCl stress, the GmXTH-like26-overexpressing lines exhibited markedly enhanced salt tolerance at both germination and seedling stages compared with the control group. Physiological and biochemical assays showed that the overexpression plants had higher activities of superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT), lower malondialdehyde (MDA) content and higher chlorophyll content under salt stress, while the gene-edited lines displayed the opposite trends. These results indicate that GmXTH-like26 improves salt tolerance in soybean by reducing reactive oxygen species accumulation and effectively enhances the resistance of soybean to salt stress. Full article

Aflatoxin contamination is the main risk factor in grain and oil crops, which brings serious threats to food and feed safety. Exploring a green and safe way to reduce aflatoxin is meaningful. In this study, six strains with aflatoxin removal ability are screened from traditional Chinese fermented vegetables. It was found that Lactiplantibacillus plantarum YS-718, as fermentation probiotics, showed the best performance on the aflatoxin B₁ mitigation with the removal rate of 78.15% in liquid fermentation. To investigate the mechanism of removal, the aflatoxin B₁ reduction tests by different components of Lactiplantibacillus plantarum YS-718 demonstrated that the bacterial suspension of Lactiplantibacillus plantarum YS-718 fermentation exhibited stronger adsorption ability compared to the removal ability of the supernatant of YS-718 fermentation. In addition, the Lactiplantibacillus plantarum YS-718 and aflatoxin B₁ complex retained 43.74% of adsorption ability after four times repeated elution with PBS and 37.22% of adsorption after digestion with simulated gastric fluid for four hours. Moreover, Lactiplantibacillus plantarum YS-718 could be used to reduce aflatoxin B₁ in peanut meal. By evaluating the contents of protein, amino acids, total sugars, and fatty acids after the fermentation treatment, it was found that Lactiplantibacillus plantarum YS-718 fermentation could increase the contents of protein, fatty acids, and amino acids in peanut meal. This study might provide useful information for constructing a green, safe, and efficient method for removing aflatoxin from peanut meal. Full article

Soybean is an important global crop used for oil, food, and feed production. To increase yield and land-use efficiency, growers often plant soybean at a high density or use intercropping systems. Under these systems, soybeans frequently experience shade stress, which directly affects agronomic traits such as plant height. Although researchers have well documented the genetic basis of plant height under normal conditions, the loci responsible for height variation under shade stress remain largely unexplored. Therefore, we performed a restricted two-stage multi-locus multi-allele genome-wide association study (RTM-GWAS) using SNP linkage disequilibrium block (SNPLDB) markers to identify QTLs associated with soybean plant height under shade stress. We evaluated a natural population of 181 soybean accessions for plant height traits under both normal and shaded conditions across four environments for three years. Using the Soybean40K chip, we derived 11,463 SNPLDB markers and identified 42, 33, and 28 significant SNPLDBs associated with plant height, average internode length, and number of main-stem nodes, respectively. For each SNPLDB, we estimated haplotype (allele) effects and assembled QTL–allele matrices to summarize the population’s genetic composition. Four SNPLDB loci proved stable across multiple environments, exhibiting high −lg(p) values and explaining substantial phenotypic variation. Finally, we projected that 80 candidate genes resided within 180 kb of these stable loci, and we identified four strong candidate genes linked to plant height traits based on combined positional and functional evidence. These results clarify genetic factors that influence soybean height under shading and could aid development of high-yielding soybean varieties. Full article

Mounting acaricide resistance in Tetranychus mexicanus (McGregor) (Acari: Tetranychidae), among the most damaging phytophagous mites in tropical and subtropical crops, has intensified the search for botanical alternatives. An oil-in-water nanoemulsion of Thymus vulgaris essential oil (TVEO-NE) was developed and evaluated for lethal and sublethal effects on adult females of T. mexicanus. TVEO, composed mainly of thymol (45%) and p-cymene (37%), was formulated by low-energy emulsification yielding stable dispersions (~200 nm; PDI < 0.25; zeta potential of −22.2 mV). At 30.0 mg a.i./mL, TVEO-NE caused 68.3% corrected mortality at 72 h and suppressed fecundity by ~44–52%; vehicle controls exerted only moderate effects, identifying the essential oil as the primary bioactive driver. Morphological examination revealed collapse of female idiosomata and disruption of excretory pellet architecture, corroborating the bioassay data. Molecular docking against a cathepsin L homology model revealed that thymol and p-cymene interact exclusively via hydrophobic contacts and display substantially lower ChemPLP fitness scores than the reference cysteine protease inhibitor E64, indicating weak predicted binding affinity and arguing against enzyme inhibition as the primary mechanism. Taken together, bioassay, morphological, and docking are consistent with supporting membrane partitioning as a plausible primary mode of action, positioning TVEO-based nanoemulsions as promising botanical tools for T. mexicanus management. Full article

Peanut (Arachis hypogaea L.) is a major oil and economic crop, yet genetic transformation remains inefficient and time-consuming, hindering functional genomics and molecular breeding. In this study, we found that the use of GRF, GIF and WOX genes improved the efficiency of Agrobacterium-mediated peanut hairy-root transformation. Here, we identified multiple peanut Growth-Regulating Factor (GRF) genes, GRF-Interacting Factor (GRF-GIF) fusion genes and WUSCHEL-related homeobox (WOX) genes, constructed high-expression vectors, and delivered them into A. rhizogenes to infect 3–5 cm peanut stem segments cut from 30-day-old seedlings. Statistical analysis of the data showed that, relative to the empty-vector control, expression of these developmental regulators markedly enhanced hairy-root growth: the number of roots per explant increased by 1.3–2.4-fold. Observations using reporter constructs showed that growth factors (besides 2S-PL-GUS and GRF-2A-T-GUS) improved the transformation efficiency of hairy roots, among which the highest transformation efficiency of GRF-2A (396)-GIF-GUS was 85.14 ± 2.94%. Collectively, these findings provide an efficient and rapid platform for the study of peanut gene function. Full article

Soybean is an important food and oil crop, and rapid nondestructive identification of seed cultivars is crucial for seed purity inspection, varietal certification, breeding management and food-quality control. However, the global spectral profiles of individual soybean seeds from different cultivars are often highly similar, making it difficult for single-representation models to simultaneously capture spectral sequential dependency and inter-band relational structure. To address this issue, this study proposes a GADF–Mamba Fusion Network (GMFNet) for soybean seed hyperspectral classification. Hyperspectral images of 24,800 seeds from eight cultivars were acquired, and reflectance spectra in the range of 900–1700 nm were collected. After preprocessing, 200 effective bands were retained. The preprocessed one-dimensional spectral sequence was fed into a Mamba-based branch to model continuous wavelength dependency and global spectral evolution, while the same sequence was transformed into a GADF image, resized to 208 × 208, and input into a ResNet18-based structural branch to extract inter-band relational features. The two heterogeneous representations were then integrated through a weighted feature fusion module for final classification. Experimental results showed that Mamba achieved the best test accuracy (0.8721) among the raw spectral models, whereas ResNet18 achieved the best test accuracy (0.8737) among the GADF-based structural models. More importantly, the proposed weighted fusion strategy achieved the best overall performance, reaching validation and test accuracies of 0.9039 and 0.9011, respectively. These results demonstrate that spectral sequential information and GADF-based structural semantics are highly complementary. Overall, the proposed framework provides an effective hyperspectral solution for single-seed soybean cultivar identification and shows potential for non-destructive automated quality control in food-industry applications. Full article

Lavender (Lavandula angustifolia Mill.) is a globally significant crop, with Bulgaria maintaining a leading position in essential oil production. This study presents the first comprehensive, multi-regional analysis of commercial lavender plantations in Bulgaria, integrating phenotypic, biochemical, and genetic data. A new field quality index (FQI) was developed to evaluate production efficiency by intergating yield, essential oil quality, and intra-field homogeneity. Genetic profiling of 285 individual plants via Start-Codon-Targeted (SCoT) markers revealed significant genetic diversity and a population structure derived from two primary clusters (Delta K = 2), with high intra-field heterogeneity (64%). Our results demonstrate that peak FQI values are achieved in fields with moderate genetic diversity (genetic homogeneity index HI = 0.6–0.7) and high polymorphic information content (PIC ≥ 0.35), whereas excessive clonal uniformity compromises both yield and phytochemical complexity. The production areas in Northeastern Bulgaria (Shumen, General Toshevo, Shabla) outperformed the traditional areas (Chirpan, Kazanlak), demonstrating higher yields of high-quality essential oil. Furthermore, macroclimatic variations across the studied areas showed significant correlations with the main terpenoid components. Association analysis suggested six SCoT loci as preliminary candidates for Marker-Assisted Selection (MAS), explaining up to 33.97% of the variation in key terpenoids within this study. Furthermore the FQI is proposed as a promising conceptual framework for genome-informed management providing a strategic basis for the sustainable production of high-value lavender oil in a changing climate. The SCoT markers hold potential as useful tools for yield and quality assessment, with the possibility of inclusion in future breeding programs aimed at improving lavender production. Full article

Paeonia ostii, a significant perennial woody oil crop in China, is notable for its seeds’ high oil content and elevated levels of unsaturated fatty acids. However, there is currently a lack of scientific fertilisation protocols and targeted nutrient management for P. ostii. The concentrations of macronutrients (N, P, K, Ca, and Mg) and micronutrients (Fe, Mn, Zn, and Cu) were determined in the leaves at five distinct growth stages: flowering, initial fruit set, fruit expansion, late fruiting, and foliar senescence. The levels of N and P were found to be at their highest point during the flowering stage, after which they declined significantly. In contrast, the levels of K remained relatively stable throughout the growth phase, while Mg levels increased significantly to peak at fruit expansion. The level of Ca increased, reaching its peak at the late fruiting stage. The annual average content of micronutrients in P. ostii leaves was as follows: Fe > Mn > Zn > Cu. Furthermore, it was observed that the concentrations of Fe and Mn oscillated, while the concentration of Cu decreased significantly after flowering. Additionally, Zn concentrations remained stable throughout the various stages. Multivariate analyses, including PCA, nutrient ratio analysis, and an integrated nutrient stability index, further revealed coordinated shifts in leaf nutrient composition and indicated that May and June were relatively stable periods for nutrient assessment. Considering both the nutrient stability and the phenological relevance, June, corresponding to the fruit expansion stage, was considered a practical sampling window for foliar nutrient diagnosis. These findings contribute to the definition of an appropriate sampling window for foliar nutrient diagnosis, thereby providing a useful basis for nutrient monitoring and future fertilisation studies in P. ostii. Full article