Search Results (16,181)

Jasmonic acid (JA) and its derivative, methyl jasmonate (MeJa), are naturally occurring plant hormones involved in alleviating abiotic stresses, such as exposure to extreme temperatures (cold or heat), flooding and drought. JA content increased following MeJa applications at pre- or postharvest, regulating several physiological and biochemical processes during fruit growth and ripening. As a preharvest treatment, MeJa increased crop yield and improved the organoleptic quality of the fruit. Regarding postharvest applications, MeJa reduced the chilling injury symptoms in sensitive fruits when they were stored at cold temperatures. In addition, there is some evidence of crosstalk between JA and other plant hormones. In this review, we highlight the mechanisms by which jasmonates contribute to plant stress resistance, regulating the biosynthesis and metabolism of abiotic stress and improving fruit quality. Full article

Barley remains the fourth most cultivated cereal crop worldwide and is valued for its versatility in malting and brewing, animal feed, human nutrition, and dietary supplements. The identification of genotypes suitable for breeding or specific end-use applications requires multi-environment testing to evaluate agronomic performance, grain quality, and trait stability. In this study, a panel of 50 winter barley genotypes (two-row and six-row) originating from diverse genetic backgrounds was evaluated over three growing seasons (2021–2023) under the environmental conditions of southeastern Romania. Seven traits were analyzed, including three phenological traits (heading time, flowering time and plant height), grain yield, and three quality parameters (thousand-grain weight, protein content, and starch content). Environmental conditions had a strong influence on phenological development and grain yield, whereas grain quality traits showed relatively greater stability, indicating a stronger genetic control. Multivariate analyses (Principal Component Analysis (PCA) and Genotype plus Genotype-by-Environment interaction biplot (GGE biplots)) revealed clear relationships among traits and highlighted contrasting adaptive strategies between the two barley types. In two-row barley, genotypes such as Idra and Sandra combined favorable yield performance with stable grain quality traits and therefore represent promising candidates for breeding programs and large-scale cultivation. In six-row barley, SU-Ellen and LG Zebra showed high productivity and strong starch accumulation, making them valuable genetic resources for yield-oriented breeding, although further improvement in nitrogen use efficiency may be beneficial. The 2022–2023 growing season represented the most restrictive environment, emphasizing the importance of stability under stress conditions. Genotypes located close to the Average Environment Coordination axis (AEC axis) during that season, such as Ametist (six-row) and Lardeya (two-row), may represent promising material for breeding programs targeting drought resilience. Overall, the results expand the phenotypic characterization of winter barley germplasm and identify valuable genetic resources that can support pre-breeding efforts and the development of climate-resilient barley cultivars. Full article

Potassium (K⁺) is a vital macronutrient for plant growth and stress resilience, with KT/HAK/KUP transporters playing a central role in its homeostasis. Although these transporters are known to influence photosynthesis, the molecular mechanisms by which fertilization promotes assimilate accumulation in peach crops remain poorly understood. In this study, 17 PpHAK genes were identified based on the peach genome and classified into four distinct clades through phylogenetic analysis, a classification further supported by conserved gene structures and motifs. Interspecific collinearity analysis revealed that transporters are highly conserved among Rosaceae species. Physiological measurements demonstrated that foliar application significantly enhanced photosynthetic capacity, as evidenced by a 33% increase in net photosynthetic rate (Pn) and improved photoelectron yield (Y(II)). At the same time, the transcript levels of the transporters PpHAK1, PpHAK5, and PpHAK9 were significantly upregulated, as confirmed by quantitative real-time RT-PCR (qRT-PCR) analysis. Furthermore, the expression of genes involved in sugar metabolism and transport, particularly PpPLT5-1, was significantly induced. Collectively, these results indicate that foliar K⁺ application enhances photosynthesis and promotes assimilate accumulation by modulating the expression of both K⁺ and sugar transporters. These findings offer a theoretical basis for optimizing nutrient management to improve fruit quality in stone fruit production. Full article

Assessing crop water status during the reproductive development of winter wheat is challenging because soil–plant–atmosphere interactions are strongly influenced by soil physical conditions, and measured soil water content (SWC) does not necessarily reflect plant-accessible water. This study applied an integrated, process-based multisensor approach to evaluate functional crop water status and its relationship to grain yield, combining hyperspectral canopy reflectance, atmospheric observations, in situ SWC, and pedological characterization. Five winter wheat cultivars were monitored at two contrasting pedoclimatic sites in continental Croatia during the 2022/2023 growing season. Hyperspectral canopy reflectance (350–2500 nm) was measured at reproductive stages (BBCH 61–83), and seventeen vegetation indices describing canopy water status, structure, pigments, and senescence were derived. Principal component analysis (PCA) identified location as the dominant source of spectral variability, while cultivar effects were secondary. Although atmospheric conditions were broadly comparable, the sites differed markedly in soil physical properties, resulting in contrasting soil water–air regimes. Despite consistently higher volumetric SWC at one site, hyperspectral indicators revealed lower canopy water status, reduced canopy structure, earlier senescence, and lower grain yield across all cultivars. Water-sensitive indices exploiting near-infrared (700–1300 nm) and shortwave infrared (1300–2400 nm) bands (NDWI, NDMI, NMDI, MSI) consistently indicated greater physiological stress. Conversely, the site with lower SWC but more favorable soil physical conditions exhibited higher values of water- and structure-related indices and achieved higher grain yield, with a mean increase of 669 kg ha⁻¹. The results demonstrate that hyperspectral canopy reflectance captures yield-relevant water stress that cannot be inferred from soil moisture alone, highlighting the importance of multisensor integration for interpreting soil–plant–atmosphere interactions under heterogeneous soil conditions. Full article

As one of the world’s leading cotton-producing countries, China frequently experiences severe yield reductions due to crop diseases and pest infestations, with losses often exceeding 20%. Although computer vision models can identify diseased plants, they currently fail to connect visual symptoms to the diagnostic reasoning process used by agronomists. This leads to text descriptions that ignore the biological causes of the damage. To fix this, we built Multimodal Knowledge Graph-Enhanced Cross Vision Transformer-18-Dagger-408 and Text-to-Text Transfer Transformer for Cotton Disease and Pest Image Captioning (MKG-CottonCapT6), a model that uses a local knowledge database to generate professional diagnostic reports from field images. The technical core consists of a Multimodal Knowledge Graph (MMKG) containing 14 types of entities (such as Pathogens and Control Agents) and 12 types of relations. We use a Cross Vision-Transformer-18-Dagger-408 (CrossViT) encoder to capture both the overall leaf shape and microscopic details of pests. Through a Visual Entity Grounding (VEG) module, the model maps visual features directly to specific triplets in the graph. These triplets are then turned into text sequences and fused with image data in a Text-to-Text-Transfer-Transformer (T5) decoder. To train the model, we collected a dataset of cotton images paired with expert descriptions of lesions, colors, and affected plant parts. Tests show that MKG-CottonCapT6 performs better than standard models, reaching an Information-based Metric for Image Captioning (InfoMetIC) score of 72.6%. Results prove that by using a specific alignment loss (𝓛_align), the model generates reports that correctly name the disease stage and recommend specific chemicals, such as Carbendazim or Triadimefon. This framework provides a practical tool for farmers to record and treat cotton diseases with high precision. Full article

The analysis of water and emission efficiency in cropping systems is vital for sustainable agriculture in Mediterranean regions, which face increasing water shortages. This study offers a site-specific assessment of the Water Footprint (WFP) and Carbon Footprint (CFP) of organic cotton grown under Mediterranean conditions, integrating environmental indicator measurements with economic valuation of greenhouse gas (GHG) emissions via the EU Emissions Trading System (ETS) and the Social Cost of Carbon (SCC). Experiments were carried out at three sites with different soil types, testing two cultivars (Armonia and ST-318) under three irrigation scenarios: severe water deficit (I30), moderate water deficit (I70), and full irrigation (I100). The results reveal significant site-specific variability, with average WFP_lint values ranging from about 1.440 m³ per ton at the most productive site to over 4.100 m³ per ton at the least productive site. Similarly, CFP_lint is lower under high-yield conditions, emphasizing the strong influence of yield on mass-based indicators. At the Carboj and Primosole sites, shifting from (I30) to I100 results in roughly a 50% reduction in emissions, while at Buonfornello, increased irrigation does not consistently produce benefits. The cultivar response is key: Armonia shows greater resilience to water stress, while ST-318 performs best with full irrigation. Overall, the findings highlight that the sustainability of the Mediterranean cotton system depends on factors such as yield performance, site-specific conditions, and cultivar choice. Full article

Climate variability amplifies temporal heterogeneity in crop production, challenging uniform varietal recommendations and highlighting the need to integrate genotype × environment interactions. This study evaluated the yield performance and stability of sixteen triticale (×Triticosecale Wittmack) genotypes over three consecutive growing seasons (2022/2023, 2023/2024, 2024/2025) at a single location with pronounced interannual climatic variability. Grain yield ranged from 3.49 to 6.68 t/ha in the least productive season (2022/2023) and from 7.71 to 9.92 t/ha in the most favorable season (2024/2025), with overall genotype means varying between 6.67 and 8.12 t/ha. Stability was assessed using regression-based parameters (regression coefficient and variance of deviations from regression), Shukla’s stability variance, and derived indices describing responsiveness (RI), predictability (PI), genetic risk (GRI), stress robustness (SRI), and yield opportunity (YOI). Results revealed substantial genotype × year interaction, with yield strongly dependent on seasonal conditions. Four genotypes combined high mean yield with stable performance and low interaction-related risk, indicating broad adaptability across years. Another four exhibited strong responsiveness to favorable seasons or elevated instability, increasing production risk despite high yield potential. The derived indices enabled risk-oriented genotype profiling, identifying contrasting adaptation strategies. Multivariate AMMI and GGE biplot analyses confirmed these patterns, providing a comprehensive view of interaction structure and stability. This integrated framework translates stability metrics into practical, decision-oriented descriptors, supporting risk-aware genotype selection under variable climates. Full article

The bollworm/corn earworm, Helicoverpa zea (Boddie) (Lepidoptera: Noctuidae), is one of the most economically damaging crop pests in North America. Colonies of H. zea are notoriously difficult to maintain and frequently collapse in laboratory rearing. The persistent difficulty in maintaining healthy H. zea colonies has become a major obstacle to performing many research activities on the insect. To optimize colony maintenance, six populations were evaluated across three trials and six tests examining pupal maturity at diet removal, adult emergence synchrony, and cohort size at mating and reproduction. Females emerging from mature pupae produced more eggs than those from mid-aged (5–7 d) or young pupae (0–2 d). Synchronizing male and female emergence within one day yielded higher mating frequency, spermatophore transfer, and progeny, whereas a two-day difference reduced these metrics by 45–67%. Adult cohort size also influenced the outcomes, with ≥10 males and ≥10 females per cage enhancing reproductive success. Most matings occurred on nights 2–3, peaking within 2.5 h after lights off. Positive correlations were observed among mating frequency, spermatophore transfer, and progeny production. Overall, optimal performance was achieved by removing pupae only at maturity, synchronizing adult emergence within one day, and maintaining larger adult cohorts. These findings should establish key conditions to improve the mating success, reproduction, and laboratory rearing of H. zea. Full article

Soybean (Glycine max L. Merr.) is a globally significant oilseed crop. The number of four-seeded pods in the lower part (FSPL) serves as a critical yield component under high-density planting. To date, numerous crop-specific traits have been investigated in multiple breeding studies of soybean; however, little attention has been paid to studies on FSPL. Hence, in this study, we investigated the genetic basis of FSPL using a recombinant inbred line population (RIL3613) across four environments. The segregated genetic mapping population was cultivated during the field experiments, and the collected phenotypic dataset of FSPL exhibited quantitative genetics and high broad-sense heritability (0.724), indicating stable genetic control. Further, we performed quantitative trait locus (QTL) mapping using raw means in each environment and identified 10 QTL, explaining phenotypic variations (PVE) ranging from 0.10% to 2.94%. Among the identified environmentally stable QTL, qFSPL-15-1 was consistently detected across all environments. Two candidate genes [Glyma.15G034100 (encoding lysophosphatidic acid acyltransferase 2) and Glyma.15G034200 (encoding an RNA-binding protein)] were predicted within the flanking genomic interval. The allele frequencies of haplotype combinations of Hap1: Pro2 + CDS1 for Glyma.15G034100 and Hap3: Pro3 + CDS1 for Glyma.15G034200 in wild soybeans (26.6–30.0%) were larger than improved cultivars (52.6–53.4%). We believe that our current findings elucidate the molecular mechanisms regulating lower-pod formation and provide precise genetic targets for marker-assisted selection in high-yield soybean breeding. Full article

To elucidate the regulatory mechanisms underlying the interaction between straw return and nitrogen (N) fertilization on yield formation, nutrient uptake, and soil N cycling in a continuous maize cropping system, a two-year positioning experiment was conducted. The study established two straw treatments (S0: 0 g/box; S1: 84 g/box) combined with three N levels (N0: 0 g/box; N1: 1.24 g/box; N2: 2.47 g/box). (The box refers to the cylinder used for planting maize.) The responses of maize yield, plant nutrient accumulation and partitioning, fertilizer-derived N ratio, nitrogen fertilizer use efficiency (NUE), and soil microenvironment were analyzed. Results indicated that under N1 conditions, straw return had a negligible effect on crop growth and yield formation. Conversely, under N2 conditions, straw return significantly enhanced maize yield and promoted the accumulation of N, phosphorus (P), and potassium (K) in plant tissues. ¹⁵N isotope tracing revealed a novel mechanism: rather than significantly altering direct fertilizer nitrogen use efficiency, straw return improved crop yield primarily by elevating indigenous soil N content and boosting the activities of N-transforming enzymes, thereby beneficially altering the ultimate environmental fate of the fertilizer N. Furthermore, straw return significantly boosted the activities of enzymes involved in N transformation and optimized the soil microenvironment. Collectively, straw return coupled with increased N application (specifically the S1N2 treatment) significantly maximizes maize yield, providing a theoretical basis for rational straw utilization and N management. Full article

Baby maize (Zea mays L.) is a high-value horticultural crop widely cultivated due to its short growth cycle and strong market demand. However, intensive production systems often rely heavily on chemical fertilizers, leading to reduced nutrient use efficiency and potential soil degradation. The present study investigated the potential of the Priestia megaterium Thr45 to enhance soil fertility, improve crop performance, and optimize fertilizer management in baby maize cultivation. A field experiment was conducted using a three-factor factorial design consisting of bacterial inoculation, different urea application rates, and different KCl rates. Soil chemical properties, plant growth parameters, yield components, and nutrient composition of edible cobs were evaluated. The results showed that inoculation with P. megaterium Thr45 significantly increased available phosphorus and exchangeable potassium in soil compared with the non-inoculated control. Inoculated plants exhibited higher chlorophyll content, greater leaf development, and increased plant height during early growth stages. Bacterial inoculation also significantly improved yield components, including ear number, ear yield, edible cob yield, and plant biomass. Furthermore, the nutritional quality of baby corn was enhanced, as reflected by increased protein and mineral (N, P, and K) concentrations in edible cobs. Significant interactions between bacterial inoculation and fertilizer treatments indicated that the beneficial effects of P. megaterium Thr45 were closely associated with nutrient management practices. Notably, comparable yield and nutritional quality were achieved under reduced nitrogen and potassium fertilizer inputs when combined with bacterial inoculation. These findings highlight the novel potential of P. megaterium Thr45 as an effective biofertilizer for improving nutrient availability, maintaining high productivity, and supporting sustainable baby maize production with reduced chemical fertilizer inputs Full article

Moringa oleifera Lam. (moringa) is a desirable crop for intensive cultivation because of its multiple uses in human and animal nutrition, medicine, and ecological applications. Its resilience and adaptability to various environmental conditions make it an attractive option for farmers seeking alternative cash crops that can thrive in challenging agricultural environments. While its resilience is well documented in tropical and subtropical climates, limited information exists on its growth dynamics and adaptation mechanisms to prolonged cold stress, which constrains its expansion and cultivation in temperate regions. This review synthesises current knowledge on cold stress adaptation mechanisms and the coordinated functional roles of primary and secondary metabolites in response to cold stress in plants, with a focus on moringa. Although considerable progress has been made in understanding morphological adjustments to cold stress in moringa plants, limited attention has been given to elucidating the physiological, metabolic, and genetic regulatory mechanisms underlying its cold-adaptive responses. Moreover, despite the potential roles of primary and secondary metabolites in coordinating protective functions against cold stress in plants, specific metabolites and their functional roles against cold stress remain insufficiently characterised in moringa. While genetic improvement and selective breeding have improved key agronomic traits, including growth rate, biomass yield, and nutritive value, breeding for enhanced cold stress tolerance remains insufficiently explored. Future studies should focus on integrative metabolite profiling, as well as the identification and selection of cold-tolerant provenances, to support the development of cold-tolerant gene pools to expand the cultivation range of moringa into temperate regions. Full article

Global agricultural drought is evolving into a compound crisis threatening food security and ecological stability that is characterized by increased frequency, intensity, duration, and spatial extent. Since 2000, the global number of drought events has increased by 29% compared with the previous two decades, and 82% of drought-related losses in developing countries are concentrated in agriculture. The UNCCD (2022) projects that drought may affect up to three quarters of the world’s population by 2050. Climate change and human activities jointly drive this escalation through higher atmospheric evaporative demand, altered precipitation regimes, land use change, groundwater overexploitation, and pollution emissions. Their interaction forms amplifying feedback loops across ecology-agriculture and climate–agriculture systems, resulting in ecosystem degradation, crop yield loss, and rising socioeconomic inequality. Addressing this crisis requires a three-dimensional framework integrating mitigation, adaptation, and collaborative governance. This review synthesizes coupled driving mechanisms, cross-system impacts, and response pathways, and it also highlights priorities in compound-drought attribution, region-specific technology scaling, cross-scale governance, and resilience building in vulnerable regions. Full article

Flax (Linum usitatissimum L.) is among the earliest domesticated crops and remains agronomically and economically important due to its dual use for fibre and seed (oil) production. In recent years, renewed interest in flax has emerged from its role in diversified and sustainable agriculture, human nutrition, and bio-based industrial applications. This review provides a comprehensive agronomic synthesis of global flax production, integrating worldwide production trends, genetic resource availability, and the main agronomic drivers governing crop establishment, growth, yield, and quality. Particular emphasis is placed on climatic requirements, soil and nutrient management, crop management practices, and water use, as well as on the contrasting requirements of fibre flax and seed flax. Despite growing research efforts, agronomic knowledge on flax remains fragmented across environments, production purposes, and management strategies, limiting the translation of experimental findings into robust, environment-specific crop management recommendations. Sustainable intensification of flax production will therefore depend on integrating optimized agronomic practices with breeding strategies that exploit existing genetic diversity to improve yield stability, quality, and resilience under increasing climatic variability. 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