Search Results (465)

Legume intercropping is a productive diversification strategy that can improve land-use efficiency and farm profitability, particularly for smallholders. However, its adoption remains limited in resource-intensive farming systems because crop-specific agronomic performance, input-use implications, and economic feasibility are not well documented under farmer-field conditions. This four-year field study (2021–2024) evaluated four sole cropping systems (sole cotton, sole maize, sole sugarcane, and sole soybean) and three additive soybean-based intercropping systems (cotton/soybean, maize/soybean, and sugarcane/soybean) under arid-irrigated conditions. Crop yield, dry matter accumulation, nutrient uptake, land equivalent ratio for land (LER_L), land equivalent ratio for nitrogen (LER_N), land equivalent ratio for phosphorus (LER_P), economic profitability, and labor requirement were assessed. On average, across the four study years, intercropped cotton, maize, and sugarcane produced 80%, 74%, and 88% of their respective sole-crop yields, while intercropped soybean produced 72%, 59%, and 83% of sole-soybean yield in cotton/soybean, maize/soybean, and sugarcane/soybean intercropping systems, respectively. At the system level, the total LER_L, LER_N, and LER_P values ranged from 1.33 to 1.71, 1.35–1.68, and 1.25–1.64, respectively, indicating resource-use (land and nutrients) advantages of intercropping compared with sole cropping. Based on these observed LER_N and LER_P values, soybean-based intercropping showed theoretical potential to reduce nitrogen and phosphorus fertilizer requirements by 26–40% and 20–39%, respectively; however, these estimates should be interpreted as potential input-economy indicators rather than experimentally validated fertilizer reductions. Economically, intercropping increased net income by ≈29–154% and generated 18–28% more labor demand than the corresponding sole systems, with sugarcane/soybean showing the highest net income (2937 USD ha⁻¹). Overall, additive soybean-based intercropping, particularly cotton/soybean and sugarcane/soybean systems with greater temporal niche differentiation, improved land productivity, nutrient-use efficiency indicators, and farm profitability under the tested arid-irrigated conditions. Further multi-location studies with actual reduced-fertilizer treatments are needed to validate fertilizer-saving potential and broader applicability. Full article

Winter cereals, which are vital for global food security in temperate regions, face severe challenges during overwintering due to the development of snow mold—a complex disease caused by different microorganisms that combine phytopathogenicity with cold tolerance. Even within a single field plot, individual plants exhibit significant variation in snow mold severity. This natural variation was exploited to achieve the aim of the present study—the comparison of microbiomes of healthy and diseased plants of winter cereal crops (rye, triticale, and wheat) at the peak of snow mold manifestation to interpret differential disease severity through differences in plant-associated microbial communities and to obtain information necessary for the biological control of snow mold. Fungi of the genus Herpotrichia were implicated as novel candidate causal agents of snow mold in winter cereals. Variations in snow mold severity defy simple explanations tied solely to pathogen abundance or broad changes in overall microbial community composition. Instead, the most striking contrast between healthy and diseased plants was observed in the inferred candidate hub taxa, accompanied by marked changes in exploratory co-occurrence networks involving the candidate snow mold pathogens. These network alterations were crop-specific. Several key taxa were implicated as probable influencers of snow mold dynamics. Full article

Reliable quantification of crop evapotranspiration (ET_c) at field resolution is a prerequisite for evidence-based irrigation scheduling in agricultural systems subject to nitrate leaching constraints. This study presents and evaluates a multi-sensor data fusion framework integrating MODIS Terra (500 m, daily) and Sentinel-2 (10–20 m, 5-day revisit) imagery to generate cloud-robust, daily ET_c maps for an 87.5 ha irrigated maize field in Nyírbátor, Hungary, during the 2020 and 2021 growing seasons. Three gap-filling strategies for missing Sentinel-2 NDVI observations were systematically compared: (i) co-regionalisation with cokriging, (ii) local time series interpolation of MODIS pixel centres using ordinary kriging, and (iii) a median time series of cotemporal MODIS pixels—a novel approach developed to suppress sub-pixel spectral contamination from roads and irrigation infrastructure. For field-mean temporal reconstruction, the median approach consistently outperformed the alternatives (adjusted R² = 0.81, NRMSE = 0.15–0.17; pixel-wise correlation 0.70–0.85), effectively filtering heterogeneous landscape artefacts. Daily crop coefficients (K_c) derived from fused NDVI time series via the FAO-56 framework yielded ET_c ranging from 0.99 mm day⁻¹ (initial stage) to 6.40 mm day⁻¹ (peak crop development). Seasonal precipitation–ET_c deficit analyses revealed contrasting patterns: near balance in 2020 versus an 85 mm mid-season deficit at critical nodes in 2021, demonstrating the potential utility of spatially explicit daily ET_c monitoring for irrigation scheduling. These deficit estimates represent irrigation demand indicators; a complete water balance would additionally require measured irrigation volumes, soil water storage changes, deep percolation, and surface runoff data. The methodology provides a proof-of-concept framework for EU Nitrates Directive compliance monitoring, relying solely on freely available satellite data. Independent ET_c validation is required before operational deployment, and transferability to other crops and regions requires validation across contrasting pedoclimatic conditions. Full article

Nitrogen (N) plays an important role in the growth and development of potatoes, but overapplication of the nutrient compromises environmental systems’ sustainability and limits tuber productivity and quality. A two-season study was carried out in 2022 and 2023 at Ofcolaco in the Mopane District of South Africa to determine the influence of N and nano micronutrients on tuber bulking rate (TBR), crop growth rate (CGR), and stomatal activities. A Randomized Complete Block Design (RCBD) fitted into a split-split plot arrangement with four replications was employed with the hypothesis that N and nano micronutrients applications will not have an effect on growth, stomatal activity, and bulking rate of potato cultivars. The main-plot factor was N rates (0, 80, 160, and 240) kg Nha⁻¹; the sub-plot factor was nano-zinc (Zn) and copper (Cu) micronutrients, while cultivar, Mondial, and Valor were the sub-sub-plot factors. The application of N and nano Zn and Cu significantly influenced dry matter accumulation, TBR, CGR, and stomatal activities of both Mondial and Valor cultivars. From our study, the application of 160 kg Nha⁻¹ in conjunction with nano micronutrients resulted in an increase in dry matter in the two cultivars, in comparison with the application of 240 kg Nha⁻¹ without nano micronutrients. This observation was consistent in TBR and CGR in Mondial during the 2023 season. In 2022, the CGR under 160 kg Nha⁻¹, along with nano micronutrients in Valor, achieved 90% of the CGC of sole 240 kg Nha⁻¹. The physiological and plant growth parameters’ response to treatment in the two cultivars were generally optimized, when nano micronutrients were applied in conjunction with higher N rates of 160 and 240 kg Nha⁻¹. Significant principal component factors influencing variability in growth and physiological parameters varied between seasons. The findings generally demonstrated that 160 kg Nha⁻¹, in conjunction with micronutrients, has the potential to downsize N application in potato growth and development. Full article

Background: Metastatic lymph node (MLN) detection remains a major clinical challenge in head and neck cancer, as nodal involvement is strongly associated with poor prognosis and directly affects treatment planning. Previous approaches typically rely on cropped lymph node (LN) regions or tumor contours for MLN identification, requiring substantial expert annotation during preprocessing and relying solely on imaging information. As a result, small or low-contrast metastatic nodes may be missed, while benign lymph nodes may be incorrectly identified as metastatic due to overlapping imaging characteristics. To address these limitations, we propose a multimodal learning framework that integrates anatomical and metabolic features from head and neck PET/CT images with biochemical features derived from FLIm for patient-level MLN prediction, without requiring manual lymph node cropping or tumor contouring during inference. Methods: To enable robust imaging representation learning, a region-aware PET/CT network based on a merging-diverging architecture was first pretrained on the HECKTOR 2022 dataset and then fine-tuned on the institutional cohort. In parallel, FLIm point-wise measurements with clinical variables were encoded using a multilayer perceptron (MLP) and aggregated into subject-level representations. To effectively combine these modalities, two multimodal fusion strategies were evaluated at the decoder stage, including cube-based fusion and squeeze-and-excitation (SE)-based fusion. The proposed strategies were evaluated on a cohort of 53 patients. Results: Compared with the single-modality baselines, both multimodal fusion strategies achieved better patient-level MLN prediction. The PET/CT-only segmentation-driven model and FLIm-only model reached balanced accuracies of 0.815 and 0.665, with AUCs of 0.828 and 0.614, respectively. Cube-based fusion improved balanced accuracy and AUC to 0.827 and 0.850, respectively, while channel-wise SE-based fusion achieved the best overall performance, with a balanced accuracy of 0.839 and an AUC of 0.872. Conclusions: These results suggest that multimodal integration may improve patient-level MLN prediction compared with single-modality approaches. Given the limited sample size, these findings should be interpreted as hypothesis-generating and require validation in larger, independent patient cohorts. Full article

The long-term effects of organic and inorganic fertilizer application on crop yield stability and grain quality were investigated in a maize–winter wheat rotation system in Tianjin, northern China, based on a continuous field experiment initiated in 1979. Forty-five years of data (1979–2023) were analyzed across six fertilizer treatments: an unfertilized control (CK); nitrogen only (N); nitrogen + phosphorus (NP); nitrogen + phosphorus + potassium (NPK); farmyard manure only (M); and nitrogen combined with high-rate manure (NM). The results indicated that the NM treatment yielded the highest crop productivity for both maize and winter wheat, with grain yields increasing by 81.6% and 162.6%, respectively, relative to the N treatment. Manure application significantly improved yield stability: the coefficient of variation (CV) of the winter wheat grain yield was the lowest under the M treatment, whereas maize grain yield exhibited the highest stability under the NM treatment. Grain quality analyses revealed that the N treatment significantly increased the wet gluten and protein content in winter wheat grain by 40.43% and 13.6%, respectively, relative to CK; the sedimentation value followed a similar trend. However, starch content remained statistically unchanged across all treatments. Collectively, the long-term combined application of nitrogen fertilizer and manure can steadily increase crop yield, mitigate inter-annual yield variability, and have no adverse effects on grain quality. These findings indicate that integrated N + manure fertilization is a more robust and sustainable alternative to sole chemical or sole organic fertilization for achieving high, stable yields and maintaining grain quality in intensive cereal production systems. Full article

Extracellular enzyme stoichiometry is a key indicator for assessing nutrient limitation experienced by soil microorganisms. Yet, the characteristics of enzyme-inferred microbial nutrient limitation in rhizosphere soil under the combined agricultural practices of intercropping and straw retention remain unclear. Here, we conducted a field experiment in the black soil region of Northeast China to quantify the effects of intercropping and straw retention on soil nutrients, microbial biomass, extracellular enzyme activities, and their C:N:P stoichiometry in the rhizosphere of maize and peanut. Our results showed that compared with sole cropping, intercropping increased soil organic carbon (SOC) by 6.21–13.57%, total nitrogen (TN) by 8.57–12.49%, and total phosphorus (TP) by 12.01–40.29% in the rhizosphere. The vector analysis revealed an average vector length (VL) of 1.68 and 1.57 for extracellular enzymes in the rhizosphere soil of maize and peanut, with a vector angle (VA) of 37.80° and 34.67°, respectively. These values suggest that soil microorganisms in the rhizosphere of both crops experienced C limitation, and that the degree of enzyme-inferred N limitation was modulated by microbial C acquisition strategies, with a dynamic trade-off between the two. This N limitation was more pronounced in the peanut rhizosphere. Notably, the combined treatment of intercropping and full straw retention increased the VA of peanut by 5.38%, corresponding to a partial alleviation of enzyme-inferred N limitation in the rhizosphere soil. The extracellular enzyme C:N:P stoichiometry in the rhizosphere soil of maize and peanut was 1.33:1.29:1.00 and 0.89:1.29:1.00, respectively. Microbial biomass nitrogen (MBN) was the primary factor affecting enzyme-inferred microbial nutrient limitation (explaining 54.6% of variation). The extracellular enzyme stoichiometric characteristics of rhizosphere soil differed significantly between the two crops. Intercropping had a stronger impact on rhizosphere microbial nutrient limitation than straw retention, and their synergistic effect was associated with a partial alleviation of rhizosphere enzyme-inferred N limitation by enhancing extracellular enzyme activity. These findings demonstrate that integrated intercropping and straw retention can support sustainable soil management in black soil agroecosystems. Full article

Glaucoma, the leading cause of irreversible vision loss, often goes undetected in early stages due to its asymptomatic behaviour. Early diagnosis typically involves visual analysis of the optic disc (OD) in eye fundus images. Machine and deep learning techniques have emerged as valuable tools for automating this process; however, their integration into clinical practice still faces limitations. These challenges include the presence of image regions that are not directly related to glaucoma assessment, such as retinal vasculature, the macula, and background structures, which may introduce irrelevant information and negatively affect classification performance, as well as a general lack of transparency in the decision-making process. This article proposes a methodology that enhances both the accuracy and interpretability of glaucoma detection by focusing solely on the OD region. First, a metaheuristic-based strategy is employed for precise OD detection and cropping, generating an OD-centric dataset with glaucoma-labeled images, which is composed of different public datasets. Four convolutional neural networks (CNNs), namely VGG-19, MobileNet-V2, ResNet-50, and DenseNet-161, are trained on this dataset using transfer learning. To address the need for model explainability, Grad-CAM, Score-CAM, and Eigen-CAM are applied to the trained models to generate post hoc visual explanations of their predictions. The experimental results showed that DenseNet-161 achieved the best overall performance on the assembled public dataset, using an 80%-10%-10% training, validation, and testing split, with a test accuracy of 0.9369 and an AUC of 0.9831. By isolating the OD region and incorporating explainability techniques, the methodology provides a robust and interpretable second opinion, supporting more accurate and efficient glaucoma screening. Full article

Globally, Trichogramma Westwood (Hymenoptera: Trichogrammatidae) is known as the most effective biological control agent due to its ability to parasitize insect pest eggs. However, identifying an appropriate host is vital for Trichogramma to prosper. Therefore, this study delves into the complex role of semiochemicals in shaping the host-seeking behavior of Trichogramma parasitoids, with a particular focus on their responses to both plant-derived and host-derived cues. The mechanism of semiochemical reception in Trichogramma wasps relies on a highly specialized, sensitive olfactory and gustatory system to locate host eggs and mates. Semiochemicals, which mediate ecological interactions, have been identified as pivotal in influencing the parasitic efficiency of Trichogramma species. Trichogramma’s host-seeking behavior is influenced not solely by ovipositional cues but also by the intrinsic physical attributes of Lepidopteran hosts, such as the scales on the wings and abdomen, which emit semiochemicals capable of eliciting positive chemotactic responses, thereby guiding parasitoids toward optimal sites for oviposition. Furthermore, the interplay between insect-derived and plant-derived chemical cues exhibits a synergistic effect, collectively enhancing the chemotactic attraction of Trichogramma, thereby fine-tuning its host-seeking behavior with greater precision and specificity. This study further underscores Trichogramma’s innate behavioral ability to discriminate between host eggs of varying developmental stages, facilitating the precise identification and selection of the most suitable host for parasitization. Age and experience both make Trichogramma more selective of hosts, but younger parasitoids may take a broader approach to host selection due to their greater life expectancy. Furthermore, the removal of these cues affects their host localization and learning abilities. Associative learning enables Trichogramma to exhibit flexible behaviors, providing them with a selective advantage; allows them to explore various hosts; and reduces environmental uncertainty. Plant structure, host density, and host age are the key factors that significantly influence the foraging and parasitism of Trichogramma. The searching speed of this parasitoid is significantly influenced by temperature. Heat stress increases VOC emissions in plants such as potato via stomatal opening, reducing herbivore attraction and enhancing parasitoid recruitment. Furthermore, air pollution, including CO₂, O₃, and NOx, impairs parasitoid efficiency by disrupting volatile-mediated host location and reducing biological control performance. Trichogramma wasps are generally effective biological control agents, but their success depends on the species used, target pest, crop, release density, and field conditions. Overall, species such as T. ostriniae, T. japonicum, and T. leucaniae show the strongest performance in several crops by increasing parasitism, reducing pest damage, and improving yield. This study highlights the successful integration of semiochemical cues in pest management programs and the effective utilization of Trichogramma in conjunction with entomopathogenic bacteria to control Lepidopteran pests. This approach contributes to the development of more effective pest management strategies, thereby promoting agricultural sustainability. Full article

Plant growth-promoting rhizobacteria (PGPR) foster sustainable and environmentally friendly agriculture by promoting plant growth and development. PGPR colonize the root rhizosphere, rhizoplane and root tissues, where they drive organic matter turnover and nutrient cycling, thereby increasing the (phyto)availability of essential macro- (P, N, K, S, Ca, Mg) and micronutrients (Fe, Zn, Mn, Mo, Co, Ni, Cu, B). This process relies on various mechanisms, including acid secretion (rhizospheric acidification and metal chelation), siderophore production (binding Fe, Zn, and other metals) and hydrolytic enzyme-mediated catalysis (phosphatases, phytases). Some of these microorganisms can also modulate the phytohormonal balance, reshaping root architecture and enhancing nutrient uptake, and even can alleviate abiotic stress or serve as biocontrol agents, contributing to pathogen resistance. Even though plant cultivation practices relying solely on synthetic fertilizers rapidly increase crop yield and productivity, they eventually result in crops poor in essential micronutrients and trace elements. This may contribute to micronutrient malnutrition in the human population. On the contrary, PGPR enhance both crop yield and nutritional quality. Therefore, in utilization with other nutrient sources, PGPR provide a promising and scalable approach towards advancing environmentally sustainable agriculture systems. Full article

Soybean (Glycine max L.) is a globally strategic crop valued for its high-quality protein and oil, yet its yield potential is frequently constrained by inconsistent seed germination and a heavy reliance on chemical treatments that carry environmental and health risks. Physical pre-sowing stimulation has emerged as an eco-friendly alternative, but the comparative efficacy of direct current (DC) versus alternating current (AC) high-voltage electric fields—and the mechanistic basis for their differential effects—has remained poorly understood. Here, we systematically compared DC and AC pre-sowing treatments across a comprehensive matrix of field intensities (0.5, 1.0, and 1.5 kV/cm) and exposure durations (30, 60, and 120 s) at a fixed electrode gap of 10 cm, using soybean seeds of the Volgogradka 1 cultivar. Germination energy (day 3) and total germination (day 7) were assessed under standardized laboratory conditions in triplicate, followed by a replicated field trial to evaluate plant height, bean yield, and disease incidence. DC treatment significantly outperformed both the untreated control and AC treatment: germination energy increased by up to 60%, and total germination reached 100% compared with 85% in the control. The optimal DC window was identified at 0.8–1.5 kV/cm with a 30 s exposure. In stark contrast, AC treatment at industrial frequency not only failed to enhance germination but also frequently suppressed it and markedly increased susceptibility to fungal crown rot. Field results corroborated these findings: DC-treated seeds produced the highest bean mass (85 g per five plants vs. 80 g in the control), while AC-treated seeds yielded the lowest (72 g). Backward elimination regression analysis revealed that field intensity alone was the sole significant predictor of treatment outcomes, whereas exposure time and interaction effects were non-significant. We conclude that short-duration DC pre-sowing stimulation (1.0 kV/cm, 30–60 s) is a robust, chemically safe, and readily scalable technique for enhancing soybean establishment and yield. Conversely, AC treatment at power frequency is not recommended due to its deleterious effects on plant health and productivity. These findings establish a clear, evidence-based framework for the rational design of electrical seed treatment protocols. Full article

The utilization of multi-source sensing data to achieve intelligent perception and refined management of farmland has become a vital research direction in modern agriculture. However, traditional inspection approaches based solely on visual information are highly susceptible to illumination variations, occlusion, and background interference, which makes stable pest detection and accurate crop growth assessment difficult to achieve. To address these problems, we propose a multimodal target perception network for intelligent farmland inspection. By integrating UAV imagery, ground environmental sensor data, and spatial location information, joint perception of farmland pests, diseases, and crop growth status is achieved. In the proposed framework, cross-modal alignment and collaborative encoding mechanisms, a multi-scale target perception structure, and a dynamic multimodal fusion strategy are introduced to collaboratively model information within a unified semantic space. Experimental results on a constructed multimodal farmland dataset demonstrate that the proposed method achieved 87.53% Precision and 89.16% mAP in the pest and disease detection task, and 88.04% Accuracy in the crop growth assessment task, significantly outperforming several mainstream visual detection models and multimodal fusion approaches. The results indicate that this intelligent perception framework can significantly improve the robustness of farmland inspection systems, providing an effective technical pathway for AI-driven precision agriculture decision-making. This technology breaks the barrier between production-side sensing data and e-commerce demand, providing a practical technical solution for agricultural production-marketing synergy, quality premium realization and digital rural revitalization. Full article

Herbicides considered selective to rice are generally evaluated based on their direct crop safety and weed suppression effects, yet it remains unclear whether they may also trigger indirect or context-dependent effects on rice under rice–barnyardgrass co-cultivation. To address this question, we compared rice performance and associated microbial dynamics under six conditions: rice–barnyardgrass co-cultivation and rice monoculture, each treated with a water spray control or sublethal doses of propanil (Pro, 66.7 mg a.i. L⁻¹) or cyhalofop-butyl (Cyh, 5.86 mg a.i. L⁻¹). Barnyardgrass exhibited visible injury and stronger leaf-level oxidative stress responses, whereas rice displayed no discernible phytotoxic symptoms. Nevertheless, under co-cultivation, herbicide treatment significantly suppressed rice growth, with up to 17.8% lower root lengths and 24.8% lower shoot fresh weights, with reductions varying by herbicide and trait. By contrast, comparable suppression was not observed under herbicide exposure or co-cultivation alone, identifying this response as an emergent, context-dependent negative effect. Microbiota reassembly emerged as an early and stage-specific component of the herbicide-associated response under co-cultivation, with the most pronounced changes detected on day 5 and occurring primarily in bacterial communities. Moreover, bacterial community variation was negatively correlated with root length (ρ = −0.664), and urease activity declined under herbicide treatment. Together, these findings indicate that in paddy fields, herbicides act not only on individual plants but also as an external disturbance to the coupled rice–barnyardgrass system, for which microbiota reorganization represents a key component of the ecological response. Our results suggest that herbicide selectivity should be interpreted within a crop–weed–microbiome context, rather than being inferred solely from their direct crop safety and weed suppression effects. Full article

Mechanized harvesting in the industrial tomato sector is currently bottlenecked by excessive mechanical injuries and elevated levels of foreign materials generated during electro-mechanical combine harvesting operations. To combat these limitations, this comprehensive review explores recent breakthroughs in harvester-mounted smart grading systems engineered specifically for complex, open-field conditions. Rather than relying solely on conventional optical inspection, the study examines the transition toward advanced, heterogeneous edge-computing frameworks—incorporating FPGAs and embedded GPUs—deployed within electro-mechanical harvesting platforms. This architectural evolution plays a crucial role in mitigating unpredictable processing delays caused by intense operational vibrations, although achieving absolute real-time stability under extreme field conditions remains an ongoing challenge. To minimize bruising and physical deterioration, our analysis synthesizes findings from multi-scale explicit dynamic finite element simulations, unpacking the underlying microstructural failure modes of the crop. We illustrate how regulating applied forces via soft robotic effectors can help approach a ‘damage-free’ handling threshold, though empirical results vary depending on fruit maturity and dynamic operational speeds. Furthermore, coupling multi-modal sensor fusion with Convolutional Neural Networks (CNNs) shows promising potential for non-destructive internal property evaluation under the vibration, dust, and throughput constraints of electro-mechanical harvesters, pending broader validation across diverse field datasets. Ultimately, by projecting future trends in onboard electro-mechanical harvester separation and advocating for a closer synergy between agronomic practices and machine engineering, this paper delivers a comprehensive blueprint for building next-generation, highly resilient, and gentle sorting machinery. Full article

Root-knot nematodes (RKNs) of the genus Meloidogyne are major plant pests causing severe crop losses. Microbial volatile organic compounds (VOCs) have emerged as promising biopesticides. In this study, we evaluated the effects of two fungal VOCs, 1-octen-3-ol and 3-octanone, on nematode survival in five Meloidogyne species (M. incognita, M. javanica, M. hapla, M. arenaria, and M. luci) in plate assays. Results showed near-complete (95–100%) J2 mortality at 500–1000 ppm within 24 h. At lower concentrations, mobility declined, and species-dependent differences were observed: 1-octen-3-ol was more effective against M. arenaria. Meanwhile, 3-octanone showed stronger effects only on M. hapla and moderate effects on M. incognita and M. javanica. Further experiments using solely M. javanica showed that egg differentiation was significantly inhibited at 7, 14, and 21 days, with up to an 80% reduction at 1000 ppm, and the effects persisted at 125 ppm. Egg hatching from egg masses was reduced by up to 95% in a concentration-dependent manner, irrespective of compound type. Soil-like microcosm assays resulted in substantial reductions in recovered juveniles, with over 90% reduction at 125 ppm after 24 h, suggesting sustained effects under the tested conditions. In more complex plant–soil greenhouse conditions, effects were reduced, although decreasing trends in nematode infection were observed. Overall, these results indicate that fungal VOCs exhibit strong effects on different nematode life stages under controlled conditions, highlighting 1-octen-3-ol and 3-octanone as promising candidates for further evaluation in nematode management strategies. Full article