Search Results (2,935)

Viticulture is highly vulnerable to weather variability and climate change. Growers increasingly face risks associated with extreme weather events, water scarcity, and emerging pests and diseases. To address these challenges, this study presents the development and implementation of the first operational digital decision support platform (DSP) tailored to Montenegrin vineyards within the MONTEVITIS project. The platform integrates IoT sensor data, national meteorological records and high-resolution global climate datasets to provide real-time monitoring and climate projections for vineyard management. The system was piloted in four vineyards representing diverse microclimatic and soil conditions of Montenegro. Key functionalities include phenology, irrigation and disease alerts supported by a user-friendly dashboard, map-based visualisation tools and data export functions. The pilot deployment demonstrated that combining heterogeneous data streams increases the reliability of outputs and enables timely, site-specific recommendations. Challenges identified during implementation include connectivity limitations, gaps in data and variable levels of digital expertise among growers; however, lessons learned point to the importance of continuous stakeholder engagement and institutional support for sustained use. The MONTEVITIS experience demonstrates how digital agriculture tools can bridge tradition and innovation in viticulture. By fostering collaboration between growers, researchers and policy makers, the platform enables adaptive strategies for climate resilience and sustainable vineyard management. Although the platform has been successfully deployed and tested under pilot conditions, a comprehensive long-term validation of its performance and impact on vineyard decision-making remains part of ongoing future work. Full article

Global population growth has increased the demand for food production and, therefore, for higher crop yields, especially for soybean, which is one of the most cost-effective and affordable sources of high-quality protein for animal and human nutrition. This frequently leads to an overuse of traditional chemical insecticides to maximize yields, thereby triggering negative side effects. However, both consumers and governments around the world demand a reduction in chemical insecticides in agriculture. To address this challenge, pest control must be guided by proper adoption of economic thresholds (ETs), which indicate the most appropriate time to initiate control in the crop. Despite the well-documented science behind ETs, farmers have questioned the adoption and reliability in a search for higher production, highlighting the importance of reviewing this topic. Thus, based on the available literature, the role of ETs in optimizing insecticide application in soybean production around the world is herein discussed, highlighting the importance of their adoption to mitigate the overuse of chemicals by emphasizing examples from the major world soybean producers (Brazil, the United States of America, Argentina, and China). In conclusion, this review highlights the importance of changing farmers’ perception that using more insecticide is necessary to guarantee higher yields. On the contrary, only by the adoption of ETs within Integrated Pest Management is it possible to obtain better pest management and, consequently, reduce yield loss. Despite some limitations related to their simplicity and risks of adoption, the use of ETs improves soybean sustainability and farmers’ profits while benefiting the agroecosystem. For future directions, their complexity should increase to more realistically represent agroecosystems; in addition, tools should be developed (computer programs and smartphone apps using, for instance, artificial intelligence) to translate this complexity into easy-to-adopt ETs. Full article

Alkaloids are structurally diverse, nitrogen-containing plant secondary metabolites with well-documented insecticidal activity. This review examines alkaloid-based insecticides, focusing on their chemical diversity, biosynthetic origins, plant distribution, and physicochemical properties relevant to pest control on farms. The principal molecular targets and modes of action are discussed, including interactions with nicotinic acetylcholine receptors, acetylcholinesterase, ryanodine receptors, and GABAergic signaling. Another focus is key metabolic enzymes, together with their activity spectra against major agricultural pests. Recent advances in rational structural modification, supported by crystallographic data, computational modeling, and structure–activity relationship studies, are highlighted as strategies to enhance the potency, selectivity, and stability of these compounds. Toxicological profiles, food residue behavior, analytical challenges, and regulatory considerations are critically assessed, emphasizing that natural origin does not equate to inherent safety. The review further evaluates the role of alkaloid-based insecticides within integrated pest management systems and identifies key research gaps related to environmental safety, non-target effects, and regulatory development and harmonization. It concludes that alkaloids are positioned as potentially valuable tools for sustainable agriculture when deployed within science-based regulatory frameworks and integrated control strategies. Full article

Artificial intelligence (AI) is transforming modern agriculture from experience-driven practices to data-driven production paradigms. To provide an in-depth analysis of AI technologies in intelligent agriculture, we retrieved literature from Web of Science, IEEE Xplore, Google Scholar and Scopus, covering publications from 2015 to 2025, and 85 articles remained after screening 1867 relevant publications. These articles are grouped into three stages from perception, to decision making, to execution (PDE) in a closed-loop framework. At the perception level, we highlight progress in intelligent sensing systems, such as unmanned aerial vehicle (UAV) and multi-modal monitoring platforms, for crop disease and pest detection, growth monitoring and abiotic stress assessment. At the decision making level, integration of heterogeneous data sources, including meteorological records, soil measurements, remote sensing (RS) imagery and market information, supports advanced analytics, such as yield prediction, pest and disease warning, irrigation and fertilization planning, and crop management optimization. At the execution level, agricultural robots equipped with simultaneous localization and mapping (SLAM) and deep reinforcement learning (RL) facilitate precision spraying, autonomous harvesting, and unmanned field operations. Overall, AI technologies demonstrate substantial potential in the PDE pipeline of agricultural production. However, several challenges remain, including heterogeneous data fusion, limited generalization across diverse environments, complex system integration, and high hardware and deployment costs. Future directions are discussed from the perspectives of lightweight model design, cross-platform standardization, enhanced human–machine collaboration, and a deeper integration of emerging AI paradigms to support scalable, robust, and autonomous agricultural intelligence systems. Full article

Weed infestation poses a significant threat to bok choy (Brassica rapa subsp. chinensis) cultivation, reducing crop yield and quality through resource competition and pest facilitation. Traditional weed detection methods face two major bottlenecks: one is data annotation, arising from the need for extensive, species-diverse datasets, and the other is visual discrimination, due to the high morphological similarity between crops and weeds at certain growth stages. To address these challenges, this study proposed an indirect weed detection framework that combines an optimized You Only Look Once version 10 (YOLOv10) model for crop detection with Excess Green ExG-based segmentation of residual vegetation. The model incorporates RFD and C2f-WDBB modules to improve feature preservation and multi-scale fusion. Compared with baseline YOLOv10, the final proposed RCW-YOLOv10 reduced the number of parameters by 1.04 million and improved detection performance, achieving increases of 3.5%, 1.5%, and 1.1% percentage points in Precision, Recall, and mAP50, respectively, under field conditions. The system initially detected bok choy plants, subsequently localizing weeds by masking crop regions and thresholding residual ExG signals in the uncovered areas. The detected weed coordinates were used to construct a distribution map that may support targeted control in precision agriculture. This approach simplifies weed identification under the tested bok choy field conditions and may be adaptable to other crops after further validation. Full article

Crop losses driven by fungal pathogens remain a major constraint to global food production, reinforcing agriculture’s dependence on fungicide-based disease control. Soil acts as a long-term reservoir and key hotspot for the evolution and persistence of antifungal-resistant Fusarium. The intensive, prolonged use of overlapping single-site fungicides in agriculture strongly selects for both intrinsic and acquired resistance in soilborne Fusarium populations, contributing to major crop losses, food insecurity, and One Health concerns. This review synthesizes current knowledge on (i) target-site (CYP51, β-tubulin, cytochrome b, SDH, myosin-5) and non-target-site (ABC/MFS efflux, multidrug resistance, epigenetic regulation) resistance mechanisms across the genus Fusarium; (ii) the influence of management practices and fungicide characteristics and behaviour in soil in reshaping microbial communities and selecting for resistant Fusarium; (iii) the consequences for plant disease management and the limitations of practices like cultural and biological control; and (iv) innovative strategies for plant disease management, as well as the monitoring and detection of antifungal resistance in soils. These aspects show that soil reservoirs of antifungal-resistant Fusarium are compromising fungicide-based control and increasing risks across sectors, highlighting the urgent need for sustainable, multi-layered, integrated pest management strategies combined with robust, molecularly informed resistance monitoring. Full article

Zeugodacus tau Walker (Diptera: Tephritidae) is a quarantine pest of cucurbit crops in China. In recent years, the damage it has caused to Trichosanthes kirilowii (Cucurbitales: Cucurbitaceae) has increased, but evidence for host adaptation and population growth on this plant remains limited. We investigated the suitability of T. kirilowii as a host plant for Z. tau, compared with the known suitable host Cucurbita moschata, by examining adult olfactory preference, oviposition choice, and demographic performance using an age–stage, two-sex life table. Females preferred T. kirilowii significantly more than C. moschata in cases of both peeled and unpeeled fruits, whereas males showed no significant difference. Oviposition was also higher on T. kirilowii and further increased on peeled fruit. Developmental duration, total longevity, ovipositing days, oviposition period, and hatching rate did not differ between the two host species. However, Z. tau reared on T. kirilowii exhibited significantly shorter APOP and TPOP, along with higher fecundity and increased pupal weight. Life table parameters indicated higher intrinsic (r = 0.1028 ± 0.0040 d⁻¹) and finite rates of increase (λ = 1.1083 ± 0.0044 d⁻¹) on T. kirilowii, whereas R₀ was similar and mean generation time was shortened by 11.61 d. A 100-day projection predicted 11,980 adults from T. kirilowii compared to 1231 from C. moschata. Overall, T. kirilowii is a highly suitable host that supports the rapid population growth of Z. tau. Full article

The coffee berry borer (Hypothenemus hampei) is the primary pest of coffee crops worldwide. Sustainable management strategies increasingly rely on the integration of biological control and interventions activated by population thresholds. In this work, a comparative framework based on dynamical systems is presented, integrating three complementary mathematical models to analyze different management strategies for the coffee berry borer. First, a biologically structured three-dimensional model describes the interaction between adult and immature borers and predatory ants. Second, a two-dimensional formulation allows the maximum per capita consumption rate of the predator to be studied as a bifurcation parameter, identifying critical parameter values that delimit regions of coexistence or effective pest control. Finally, a piecewise-smooth dynamical system incorporates ethological control activated when infestation exceeds a predefined threshold, whose effectiveness depends on the capture intensity associated with the traps. Using stability theory, bifurcation analysis, and techniques from piecewise-smooth dynamical systems, parametric regions associated with persistence, coexistence, or significant pest reduction are characterized. The results show that biological control alone may be insufficient if a predation threshold is not exceeded, whereas its combination with early threshold-based interventions considerably enlarges the dynamical regions favorable to producers. This study provides a dynamical interpretation of the agricultural concept of intervention threshold and offers a quantitative framework to strengthen integrated management and the sustainability of coffee production. Full article

Parasitic plants represent a unique group of angiosperms that extract nutrients from host plants through specialized structures called haustoria. With over 4750 recognized species, these plants vary in their dependence on hosts, classified as holoparasites (completely non-photosynthetic) or hemiparasites (partially photosynthetic). Despite their parasitic lifestyle, these plants contribute significantly to ecological stability by regulating plant communities. Some parasitic species, such as Striga and Orobanche, are major agricultural pests, while others, including Cistanche and Cynomorium, are valued for their medicinal properties. Parasitic plants in general are rich in secondary metabolites with potential pharmacological significance. These compounds, including alkaloids, phenolics, and terpenoids, display antimicrobial, anticancer, and immunomodulatory effects. Mistletoe (Viscum album L.) produces lectins and viscotoxins, which exhibit cytotoxic and immune-stimulating properties. Traditional medicine has long utilized parasitic plants, and modern pharmacological research continues to uncover their potential in drug development. However, an intriguing question arises: whether they are superior in any way to their non-parasitic counterparts, or just received more attention due to their unique appearance. Understanding the unique chemistry of parasitic plants provides insights into their ecological role and offers opportunities for advancements in medicine and agriculture. Full article

The immature stages and biology of Neoplinthus tigratus porculus (Fabricius, 1801) (Coleoptera: Curculionidae: Molytinae) associated with common hop (Humulus lupulus L.) are described for the first time. Biological observations show that the species develops mainly within the root collar and roots of Humulus lupulus, where larvae feed internally and older instars overwinter. Infested plants are characterized by swollen and weakened roots, often containing multiple larvae. The species should be considered a potential pest of common hop, an economically important crop; however, the current observations indicate that its populations are generally very low, consistent with the status of several related Molytinae and Cleonini taxa, which are predominantly regarded as rare or locally occurring under contemporary agricultural conditions. Nevertheless, changes in agroecosystem management may significantly alter its abundance, as documented in other weevil taxa, where reductions in plant protection measures have led to local pest outbreak. The morphology and diagnostic characters of mature larvae and pupae are documented and compared with related Molytinae and selected Cleonini (Lixinae). The mature larva generally fits the diagnostic characters of Molytinae larvae but differs in several traits, particularly the very short endocranial line and the relative length of frontal setae (fs_1–5), with fs₄ distinctly shorter than fs₅. Full article

Agricultural pests can rapidly adapt to chemical pressures, and expression-based surveys of chemosensory genes may not fully capture the associated genomic variation. We hypothesized that the molecular profiles of chemosensory and detoxification genes in the mulberry thrips Pseudodendrothrips mori Niwa (Thysanoptera: Thripidae) are associated with local genomic variability and methylation context alongside transcript abundance. To explore this, we integrated PacBio HiFi-derived single-nucleotide polymorphisms (SNPs), structural variants (SVs), DNA methylation, and RNA-seq data on a chromosome-level reference genome. We analyzed 179 focal genes from six families, applying a consensus prioritization framework—based on weighted percentiles of feature values, principal component distances, and anomaly-detection scores—to rank the candidates. The integrated priority score correlated positively with SNP (r = 0.603) and SV burden (r = 0.632) and negatively with local methylation (r = −0.524), whereas its correlation with expression was weaker (r = 0.427). Three OBPs—PSMOgene01223, PSMOgene012530, and PSMOgene012982—emerged among the highest-priority candidates, exhibiting favorable in silico docking scores (−5.038 to −6.792 kcal/mol) with (Z)-octadec-11-enyl acetate and a long-chain oxygenated acetate. These findings indicate potential linkages between multi-omics plasticity and chemosensory gene variation. Furthermore, these computationally prioritized OBPs suggest potential targets for exploring semiochemical-based management tools. Full article

Plants in agricultural systems rarely face single stressors; instead, they encounter concurrent biotic (pathogen, pests) and abiotic (drought, salinity, heavy metals) stresses that causes severely reduce crop yields and endanger food security. The traditional methods of breeding, genetic engineering, and agrochemicals tend to target individual stresses and still do not suffice in the complex field conditions. Compared to these approaches, nanotechnology offers distinct advantages: nanoparticles (NPs) can be applied as foliar sprays or seed treatments without lengthy breeding cycles or regulatory hurdles associated with genetically modified organisms. However, nanotechnology is not inherently “better” but rather complementary to crop engineering; each approach has specific strengths. Breeding and genetic engineering provide heritable, long-term solutions, while nanotechnology offers immediate, season-specific, and reversible interventions. Cross-tolerance, the phenomenon whereby exposure to one stress enhances tolerance to another, offers a promising alternative. This review critically examines how NPs act as stress-priming agents that induce cross-tolerance by activating overlapping defense networks, including antioxidant systems (SOD, CAT, APX), phytohormonal crosstalk (ABA, SA, JA), osmolyte homeostasis, and stress-responsive gene expression. We synthesize current evidence on NP uptake, translocation, and cellular interactions, and evaluate their dual role in directly suppressing pathogens while simultaneously enhancing plant immune responses and physiological resilience. However, efficacy is highly dose-dependent: low, subtoxic doses prime defense through hermetic ROS signaling, whereas supraoptimal doses cause phytotoxicity. The current challenges in nano-mediated stress alleviation include: (i) a persistent laboratory-to-field translation gap, with field outcomes averaging only 60–70% of greenhouse efficacy; (ii) dose-dependent phytotoxicity; (iii) poor reproducibility across studies; (iv) scalability and formulation stability issues; and (v) insufficient understanding of long-term environmental fate, including soil accumulation, non-target organism effects, and food chain safety. Future research should consider field-validated formulations (e.g., SiNPs, ZnONPs, Fe₃O₄NPs) across major staple crops); integrating nanotechnology with precision agriculture through nanosensors, remote sensing, and artificial intelligence for site-specific, dose-optimized applications;developing smart, biodegradable nanoparticles with stimuli-responsive release; and establishing harmonized regulatory frameworks for nano-agrochemical approval. When deployed responsibly, nanoparticle-induced cross-tolerance represents a sustainable approach to improve crop resistance against multifactorial stress, with significant implications for climate-resilient agriculture and global food security. Full article

Effective weed control is essential for sustainable and safe rice production, particularly under the long-term and widespread use of chemical herbicides. Oilseed rape (Brassica napus L.) is one of the most important oil crops worldwide, and the oilseed rape–rice rotation system is widely practiced in China. It has been reported to exhibit strong allelopathy on various plants, but the feasibility of using its straw incorporation for weed control in transplanted rice fields remains unclear. In this study, a natural weed management strategy based on shallow tillage of oilseed rape straw (ORS) was evaluated through laboratory bioassays, greenhouse experiments, and field trials. The results indicated that soil decomposition liquids (SDLs) of ORS exhibited strong dose- and decomposition time-dependent allelopathic effects on seven paddy weed species, while rice showed markedly lower sensitivity. ORS incorporated at 700–1100 g/m² generally exhibited high integrated allelopathic inhibition (in lab) and population control effects (in greenhouse) on paddy weeds, especially Leptochloa chinensis (L.) Nees, Cyperus iria L., and Cyperus difformis L. Among the growth parameters of ORS allelopathic stress, root growth was the most sensitive indicator, followed by shoot growth and seed germination. Greenhouse experiments displayed variety-dependent impact on the transplanted rice seedlings, with Xiushui134 and Yongyou1540 showing relatively high tolerance. Field trials revealed that ORS incorporation at 1100 g/m² for 10 d achieved a satisfactory control of population (77.7–84.9%) and fresh weight (80.7–95.6%) across Gramineae, Cyperaceae and Broadleaf weeds, without adverse impact on the growth of transplanted rice seedlings (Yongyou1540). This treatment also significantly promoted theoretical grain yield by 13.4–19.4%. Overall, shallow tillage of oilseed rape straw provides a feasible and environmentally friendly weed control strategy for transplanted rice systems. Full article

Silicon is increasingly applied in agriculture to improve plant productivity under both abiotic and biotic stress constraints. Nevertheless, its mechanisms of action are often studied separately at the soil, plant, or microbiome levels, limiting a comprehensive understanding of its overall impact on agroecosystem functioning. This review proposes an integrated perspective of the soil–plant–microbiome continuum, linking silicon chemistry in soil solutions with the effects of silicon amendments on soil properties and the processes of uptake, transport, and deposition in the plants. We show that silicon bioavailability depends on maintaining a pool of dissolved silicon dominated by orthosilicic acid, regulated by mineral weathering, adsorption–desorption dynamics, polymerization, pH, iron and aluminum oxides, and organic matter. In soils, silicon inputs can improve structure, modulate acidity and cation exchange balances, influence nutrient availability, and reduce the mobility of certain metals. They may also affect enzymatic activities and microbial community composition. In plants, silicon uptake and transport, mediated by specific transporters, contribute to tissue silicification, the maintenance of leaf architecture, and the regulation of water, ionic, and redox homeostasis. These processes provide a basis for enhanced tolerance to drought, salinity, and metal toxicity, as well as biotic stress caused by pathogens and pests. Finally, we discuss key limitations to the agronomic application of silicon, including the diagnosis of the silicic status of soils, the choice of source and mode of application, and the genotypic variability of acquisition, as well as the need for multi-site tests and more robust mechanistic validations. This synthesis provides a coherent mechanistic framework to better define the conditions under which silicon can serve as a reliable tool for sustainable crop management under climate change. Full article

Peste des Petits Ruminants (PPR) is a highly contagious viral disease of small ruminants and remains a major threat to food security and rural livelihoods across Africa, the Middle East, and Asia. In the Mediterranean, uneven outbreak reporting and intense spatial clustering hinder the identification of regions where environmental and anthropogenic conditions favor disease occurrence. This study applied an interpretable machine-learning ecological niche modeling framework to characterize PPR spatial suitability in North Africa. A merged outbreak dataset (n = 744) was compiled from the Food and Agriculture Organization (FAO) EMPRES-i and the World Animal Health Information System (WAHIS) databases for 2005–2026. Outbreak locations were linked to environmental and anthropogenic predictors, spatially thinned, and paired with randomly sampled pseudo-absences at a 1:1 ratio. After correlation-based screening and Boruta feature selection, four classifiers were compared under five-fold spatial block cross-validation: a generalized linear model (GLM), a support vector machine (SVM), Random Forest (RF), and extreme gradient boosting (XGBoost). All models showed good discriminatory performance. Random Forest (RF) and extreme gradient boosting (XGBoost) yielded the highest area under the receiver operating characteristic curve value (AUC = 0.94). Random Forest achieved the highest specificity, XGBoost achieved the highest sensitivity, and the support vector machine showed the most even sensitivity–specificity tradeoff among the machine-learning classifiers. Sheep density, mean diurnal temperature range, temperature seasonality, and human population density were consistently the dominant drivers. Predicted PPR suitability based on reported outbreaks was concentrated along the North African coastal belt and low across most arid inland regions. These findings suggest that passive surveillance is likely to be most informative in coastal production systems where host density, environmental suitability, and reporting opportunity overlap. At the same time, areas of lower reported-outbreak suitability should not be interpreted as disease-free and may require complementary active surveillance approaches. Full article