Search Results (55,593)

Selenium nanoparticles (SeNPs) synthesized through green routes have emerged as promising nanobiostimulants in sustainable agriculture due to their ability to enhance plant growth and antioxidant defense. The aim of this study was to evaluate the biostimulant effect of SeNPs on Capsicum annuum at two stages of crop development to characterize the response to SeNP exposure and identify concentration-dependent effects and application methods. Physiological indicators, including growth, photosynthetic pigment content, and antioxidant activity, were evaluated. Different concentrations of SeNPs were tested during germination, and dosage and two types of application were compared during the vegetative phase in a hydroponic experiment. SeNPs at concentrations of 1.25, 2.5, 5, 10, 20, 40, and 80 µM were applied to chili seeds for 20 days. The plants were exposed to SeNPs concentrations ranging from 1.25 to 80 µM, applied through the roots and leaves. Germination parameters were not significantly affected except for the seed vigor index, which increased at all concentrations, particularly at 20 µM. Low to moderate doses (1.25–20 µM) acted as biostimulants, enhancing plant height, root length, biomass accumulation, photosynthetic pigment content, and phenolic and flavonoid compound synthesis. Conversely, high doses (80 µM) induced phytotoxic effects, especially via root exposure, reflected by growth inhibition, and reduced chlorophyll content. Foliar application demonstrated a systemic biostimulant response, improving root growth and photosynthetic activity without toxicity symptoms. Antioxidant assays (DPPH and ABTS) revealed dose-dependent modulation of redox balance, suggesting adaptive responses to SeNP-induced oxidative conditions. These findings highlight the potential of SeNPs as biostimulants that improve physiological performance in chili plants, while emphasizing the importance of an optimal dosing and application method for sustainable nanotechnology-based crop management. Full article

Managing tillage intensity and diversifying crop rotation are important sustainability levers for conservation agriculture (CA) with the potential to enhance crop resilience, resource efficiency, and yield stability. Accordingly, this study aimed to determine the effect of reduced tillage intensities and cereal–legume rotation systems on the agronomic and physiological performance of rainfed durum wheat grown under Mediterranean semi-arid conditions. To this end, a two cropping seasons field experiment was conducted in northeast Tunisia where the combined effects of two reduced tillage intensities (minimum and no-tillage; MT and NT) and two legume-based crop rotation systems (biennial and triennial; B and T) were compared to the more traditional conventionally tilled monocropping system (CT and M). Crop rotation, particularly when integrated with no-tillage (NT), significantly improved wheat development and grain yield, along with key yield attributes such as thousand-kernel weight and spike density. The interaction between tillage and crop sequence was highly influential; for instance, the NT × T (no-tillage × triennial rotation) combination achieved the highest grain yields (240 and 236 g m⁻² in 2020–2021 and 2021–2022, respectively), while the CT × M (conventional tillage × monoculture) interaction resulted in the lowest productivity (143 and 135 g m⁻²). Physiologically, the integration of reduced tillage and legume–cereal rotations optimized the photosynthetic apparatus, as evidenced by significantly improved chlorophyll fluorescence parameters. However, a prominent trade-off was identified: while NT × T maximized productivity, conventional tillage (CT) maintained superior grain protein (18.6%) and gluten concentrations, indicating a nitrogen dilution effect in high-yielding conservation systems. These results demonstrate that while no-tillage and triennial rotations (faba bean–wheat–barley) are robust strategies for climate-resilient yields in semi-arid environments, they must be coupled with optimized nitrogen management to offset quality declines. Consequently, this study establishes the NT × T interaction as a superior model for sustainable rainfed farming, provided that nutrient synchronization is addressed to ensure nutritional security under increasingly unpredictable Mediterranean climates. Full article

The synergistic impacts of land use/land cover (LULC) transformations and weather pattern variabilities (WPV) represent a primary driver of hydro-geological instability, threatening agricultural productivity, soil conservation, and water quality. Disentangling the discrete contributions of these stressors to runoff and sediment yield (SY) remains a significant challenge, particularly in complex, confluence-proximal watersheds lacking major hydraulic regulations. This study investigates the Tirumakudalu Narasipura watershed in Karnataka, India, an agriculturally intensive system undergoing rapid peri-urbanization. Leveraging the process-based geospatial interface of the Water Erosion Prediction Project (GeoWEPP), we analyzed hydrological responses over a 24-year period (2000–2023) and projected future trajectories through 2030. To overcome the traditional constraints of GeoWEPP, which was developed for small-scale watersheds (<260 ha), we present a novel upscaling framework utilizing a multi-site multivariate temporal calibration of hydrological response variables to exploit its process-based precision in capturing distributed soil erosion and landscape heterogeneity. This approach is further reinforced by an ancillary data validation to minimize error propagation while model-upscaling. Our findings reveal projected increases in runoff and SY of 14.69% and 49.23%, respectively, between 2000 and 2030. Notably, the sub-decadal acceleration from 2023 to 2030 (17.32% for runoff and 18.51% for SY) underscores a shifting dominance where LULC-driven surface modifications now outweigh climatic variance in forcing hydrologic change. Furthermore, the study quantifies how anthropogenic interventions such as strategic crop selection, tillage intensity, and irrigation regimes act as critical determinants of topsoil preservation. These results provide a scalable, economically feasible framework for precision land stewardship and sustainable watershed management in rapidly developing tropical landscapes. Full article

Broccoli (Brassica oleracea var. italica) is an important crop valued for its nutritional and health-promoting properties, yet its biotechnological improvement is limited by low effectivity and genotype-dependent transformation protocols. The absence of reliable transient expression systems further constrains functional genomics and genome-editing applications. Here, we optimized regeneration and transformation protocols for different broccoli genotypes. Endoreduplication patterns in young tissues were analyzed by flow cytometry to identify suitable explants, and combinations of plant growth regulators were tested to develop an efficient organogenic medium. Stable transformation was achieved via Agrobacterium tumefaciens using nptII and eGFP markers. Cotyledons and hypocotyls up to day 7 showed similar endoreduplication patterns, with abundant 2n cells, but hypocotyls exhibited higher regeneration capacity. The optimized medium supported efficient organogenesis while maintaining diploidy. Transformation efficiency reached 10.4% in ‘S1’ and 2.8% in ‘Naxos’, highlighting genotype dependence. In parallel, a transient expression system was established using cotyledon-derived protoplasts and electroporation-mediated DNA delivery. GFP expression was confirmed through fluorescence microscopy, confocal imaging, and Western blotting. These protocols provide a robust toolkit for broccoli genetic manipulation, facilitating molecular biology studies in the native plant, functional genomics and genome-editing strategies, including CRISPR-based approaches. Full article

The leg structure of swine is closely related to their robustness and longevity. Animals with sound legs generally have longer productive lifespans and higher reproductive efficiency, whereas leg defects can markedly impair performance and shorten service life. To address the high subjectivity, low efficiency, and poor consistency of traditional leg-structure evaluation by humans, this study developed an automatic scoring system for swine leg structure based on 3D point clouds. The hardware components of the system include the acquisition channel, a multi-view time-of-flight (ToF) depth camera array, an industrial computer, and a star-type synchronization hub. The core algorithm modules include point cloud preprocessing, leg segmentation, geometric feature extraction, and structure-based scoring. Body orientation was corrected using principal component analysis (PCA). An adaptive limb region segmentation method was proposed that combines iterative cropping with geometric verification. Two point cloud tasks were performed: key structural points were extracted via multi-scale curvature analysis, and angular and symmetry parameters of the fore- and hindlimbs were computed in the sagittal and coronal planes. Following a “classify first, then score” strategy, a nine-level linear scoring model was constructed. Field validation showed that the classification accuracy exceeded 90%, the scores were significantly negatively correlated with the degree of structural deviation, and multi-frame resampling yielded good repeatability. The processing time per animal ranged from 1.6 s to 3.0 s, which met the requirements for real-time applications. These results demonstrated that the proposed method could automatically identify and quantitatively evaluate swine leg structure, providing efficient and reliable technical support for objective selection and smart pig farming. Full article

Spring frosts pose a major threat to tea production, causing severe damage to tender spring buds and substantial economic losses. To support timely frost protection measures, this study develops an interpretable machine learning framework for next-day frost forecasting in a tea plantation in Danyang, eastern China. Leveraging nine years (2008–2016) of multi-source data—including high-resolution on-site meteorological observations and daily records from surrounding regional stations—we engineered a comprehensive set of predictive features capturing local microclimatic, regional synoptic, and short-term temporal dynamics. A two-stage feature selection approach, combining Spearman correlation screening with SHAP-based importance ranking, identified an optimal subset of 14 robust predictors. Among eight benchmarked models, XGBoost achieved the best performance on a chronologically held-out test set, yielding a CSI of 0.736, accuracy of 91.0%, F1-Score of 0.848 and AUC-ROC of 0.968. Ablation experiments demonstrated the added value of data integration: model performance improved from a CSI of 0.617 (using only local data) to 0.736 (with full multi-source inputs). SHAP interpretability analysis further revealed that the model’s predictions align with established frost formation physics, highlighting key drivers such as nocturnal cooling rate and regional humidity. This work demonstrates that integrating multi-scale meteorological data with interpretable machine learning offers a reliable, transparent, and operationally viable tool for frost risk management—providing actionable insights to enhance resilience in precision horticulture for perennial crops like tea. Full article

Cotton above-ground biomass (AGB) is a key indicator of crop growth and yield potential. Traditional monitoring methods are labor-intensive and destructive, limiting their suitability for precision agriculture. This study developed a high-precision, non-destructive model for estimating cotton AGB by integrating spectral and texture features derived from UAV multispectral and RGB images. UAV data were collected at major growth stages in 2024. Eight vegetation indices (VIs) and eight texture features (TFs) were extracted. Four machine learning algorithms—support vector regression (SVR), random forest regression (RFR), partial least squares regression (PLSR), and extreme gradient boosting (XGB)—were evaluated using independent validation data. Models based on fused spectral and texture features outperformed single-feature models. RFR achieved the best performance (R² = 0.811; RMSE = 2.931 t ha⁻¹). Texture features alone also showed strong predictive capability (R² = 0.789), highlighting their value in capturing canopy structural information. These results demonstrate that spectral–texture fusion significantly improves cotton AGB estimation and that RFR provides a robust modeling framework for UAV-based crop monitoring. Full article

Background/Objectives: Downy mildew, caused by Peronosclerospora and Sclerophthora species, is a major constraint to maize production in tropical and subtropical regions, with yield losses of 30–100%. This systematic review synthesised evidence on methods used to screen maize for downy mildew resistance and assessed their effectiveness, reliability, and associated markers. Methods: PubMed, Google Scholar, ScienceDirect, and CAB Abstracts were searched (last searched 22 October 2025) for English-language studies (1990–2025) evaluating phenotypic or molecular screening methods. Risk of bias was assessed using the RoB 2 framework. Narrative synthesis was conducted following a protocol registered on the Open Science Framework. Results: Twelve studies met the inclusion criteria, predominantly from India and Cambodia. Spreader row systems (seven studies) and conidial spray inoculation (six studies) were the most common field methods, while the glasshouse sandwich technique generated the highest disease pressure. Cross-method correlations were strong (r = 0.92–0.99), and heritability estimates ranged from 0.50 to 0.97. QTL mapping identified resistance loci on chromosomes 2, 3, and 6, with chromosome 6 stable across multiple pathogen species. Evidence certainty was moderate for method effectiveness and low for molecular markers. Conclusions: Established phenotypic screening methods reliably discriminate resistant germplasm; however, standardised protocols, broader geographic validation, and independent molecular marker confirmation are needed. Full article

Cassava brown streak disease (CBSD), caused by cassava brown streak virus (CBSV; Ipomovirus brunusmanihotis) and Ugandan cassava brown streak virus (UCBSV; Ipomovirus manihotis) (family Potyviridae, genus Ipomovirus), is increasingly becoming a threat to cassava production in several parts of Africa, especially in Eastern, Central and Southern Africa. In Kenya, the disease continues to wreak havoc on cassava production leading to a significant reduction in crop yields and economic losses of up to USD 1 billion. Variation in virus populations make the control of CBSD challenging as virus genomic variation can affect the accuracy of diagnostic tests, lead to resistance breaking isolates and jeopardize strategies of breeding for resistance. CBSV and UCBSV populations obtained from cassava fields in Kenya were characterized. In total, 44 new complete sequences of CBSV and UCBSV were assembled and 40 sequences successfully submitted to GenBank. Single Nucleotide Polymorphism (SNP) analysis revealed that the cylindrical inclusion protein (CI) is the most stable region across the genome of CBSV and UCBSV. In contrast, protein 1 (PI) and the coat protein (CP) were the most hypervariable regions. Phylogenetic analysis showed three major geographical groupings for both UCBSV and CBSV isolates, suggesting a continued spread of the viruses through human-mediated movement of infected planting materials. The data obtained in this study can support the development of disease management strategies through improved molecular diagnostic tests and targets for breeding for resistance against CBSD. Full article

Combine harvesters are core modern grain production equipment with high reliability, critical for food security. Yet their metal parts suffer severe grain-induced wear during operation, directly reducing efficiency, increasing grain loss, and raising maintenance costs and environmental burdens. This paper clarifies the grain-induced wear source characteristics and the dominant mechanisms and hazards for combine harvester metal surfaces, as well as summarizes the research progress of four key protection strategies: wear-resistant materials, surface engineering, structural and parameter optimization, and maintenance and remanufacturing. Based on the latest research data, the working principles, performance advantages and application scenarios of various protective technologies were analyzed. Current research faces several challenges: insufficient systematic wear data for multiple crops, unclear multi-factor coupled wear mechanisms, limited low-cost and long-lasting protective technologies, and the absence of online wear monitoring techniques. Finally, the directions for future research focus, such as the systematic research on the wear characteristics of multiple crops, the deepening of the wear mechanism of multi-factor coupling, the development of green, low-cost and long-term protection technologies, and the development of online wear monitoring and active control systems, are explored, providing theoretical support and technical reference for the transformation of wear control in combine harvesters, from passive maintenance to active protection throughout the entire life cycle. Such future work supports the high-quality development of agricultural mechanization and ensures food security. Full article

Field breeding trial-plot harvesting is one of the key processes in crop breeding, as any mixing between varieties during harvest directly leads to the invalidation of breeding data. Therefore, achieving zero-residue self-cleaning inside the machine during harvesting is essential. Existing studies have largely relied on simulations to optimize cleaning parameters. However, research specifically targeting the synergistic design of the mechanical and pneumatic components of the cleaning device to achieve efficient and thorough self-cleaning in complex real-world conditions remains lacking. To address this issue, this paper presents a novel cleaning system specifically designed for efficient self-cleaning and optimizes its key parameters. Key structural parameters of the straw walker, vibrating sieve, and cleaning fan were analyzed, establishing preliminary ranges for crank speed, sieve-airflow angle, and fan speed. A test bench was developed, and single-factor experiments were conducted to investigate the effects of these parameters on core self-cleaning indicators, including the self-cleaning rate and self-cleaning time. The optimal parameter combination was obtained using the Box–Behnken design (BBD) response surface methodology: a crank speed of 390.80 r/min, a sieve-airflow angle of 29.88°, and a fan speed of 1995 r/min. Bench tests validated that the system achieved excellent cleaning performance while ensuring a self-cleaning rate of 100% and a reduced self-cleaning time of 20 s. The system’s effectiveness was further validated through field experiments using a 4LX1 prototype harvester on three wheat varieties. Results demonstrated zero grain mixing between plots, with self-cleaning times of 9–12 s. Both bench and field test results exceeded the relevant standards, effectively resolving the long-standing issue of grain residue in trial plot harvesting. Through dual validation, this study provides a referential solution for addressing grain residue and establishes a theoretical foundation for the synergistic design of efficient and precision breeding harvest technologies. Full article

China is one of the world’s leading producers of celery. In recent years, the market price of celery has often experienced rollercoaster-like fluctuations. Such volatility has become a significant factor affecting the income of vegetable farmers, market stability, and household consumption. The key to addressing this issue lies in understanding the threshold of the celery planting area at which supply and demand are balanced. However, relevant research has been rarely conducted on this topic to date. Shandong Province is a major vegetable-producing region in China, and its celery output and pricing have a crucial impact on the national market. Therefore, this study takes Yucheng City, Shandong Province, as a case study. By leveraging the land vacancy characteristics before the celery planting period, the NDVI data was calculated, and the object-based supervised classification was used to extract the celery planting area from remote sensing imagery. Based on a comprehensive statistical analysis of collected annual celery wholesale prices and break-even prices over the past decade, it was found that when the autumn celery planting area in the study region exceeds 12,000 hectares, oversupply occurs, leading to losses for celery farmers. Moreover, this situation recurs approximately every four years. To prevent celery oversupply, the government should estimate the prospective celery planting area using remote sensing imagery during the one-month land vacancy period before celery transplantation. Once the estimated data reach or exceed the supply–demand balance threshold, proactive guidance should be provided to encourage celery farmers to switch to other vegetables, thereby reducing potential losses for farmers. This study provides an effective method for the government to intervene in the cultivation of crops with highly volatile prices. This study could also maintain the vegetable production at a constant level and make the celery plantation sustainable in the future. This study provides an effective method for the government to intervene in the cultivation of crops with highly volatile prices and could enable farmers to achieve sustained profitability. The sustainable profit could maintain the vegetable production at a constant level and make the celery plantation sustainable in the future. Full article

The yellow peach moth, Conogethes punctiferalis, is a destructive polyphagous pest and poses a severe threat to the fruit industry and field crops worldwide with its continuously increasing population and expanding host range in recent years. Despite the severe damage caused by C. punctiferalis larvae, their antennae and mouthparts, equipped with abundant sensilla responsible for feeding behavior, have not been investigated in detail. In our study, the antennae, mouthparts, and associated sensilla of first-instar and mature larvae of C. punctiferalis were examined with light and scanning electron microscopy. Our results revealed no obvious morphological differences between the two instars in the basic composition of the antennae and mouthparts, or in the types, distribution, and numbers of sensilla. The antenna is three-segmented, with no sensilla on the scape, three sensilla basiconica and two sensilla chaetica on the pedicel, and three sensilla basiconica and one sensillum styloconicum on the flagellum. The mouthparts of C. punctiferalis are typically mandibulate and consist of a labrum-epipharynx, paired mandibles, a pair of maxillae, a labium, and a hypopharynx. Six types of sensilla were primarily concentrated on the labrum-epipharynx, maxilla, and labial palp, including sensilla chaetica, sensilla basiconica, sensilla styloconica, sensilla digitiformia, sensilla epipharyngea, and sensilla placodea. We conducted a systematic analysis of the characteristics of sensilla and discussed their variation in the context of Lepidoptera phylogeny. The potential functions of the sensilla have also been inferred. The study could advance our understanding of the behavioral ecology of C. punctiferalis and provide potentially useful information on the development of pest control technologies. Full article

Effective integrated pest management (IPM) relies on precise knowledge of pest developmental biology, particularly the identification of larval instars, which is fundamental for predicting population dynamics and timing control interventions. This study established a morphometric framework for the larval staging of a sap beetle pest infesting pear orchards. Specimens were collected and reared under laboratory conditions, with their identity confirmed as Urophorus humeralis through integrated morphological and molecular (COI barcoding) analysis. To determine the number of larval instars, head capsule width (HCW), inter-antennal distance (IAD), and inter-caudal distance (ICD) were measured. Frequency distribution analysis and validation using Dyar’s rule via linear regression revealed three distinct larval instars. Head capsule width was identified as the most reliable and consistent morphological character for instar discrimination. This study reports for the first time the infestation of pear fruits by U. humeralis and provides detailed morphometric criteria for larval staging, delivering essential baseline data for the biology of Nitidulidae and a scientific basis for developing stage-specific pest management strategies. Full article

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