Search Results (13,347)

Global food production systems are increasingly challenged by population growth, climate change, water scarcity, and environmental degradation, necessitating the adoption of sustainable, resource-efficient food production strategies. Aquaponic systems integrate recirculating aquaculture with hydroponic crop cultivation, enabling nutrient recycling and improved water-use efficiency. Simultaneously, CRISPR/Cas9 genome-editing technology has emerged as a powerful tool for precise genetic improvement of economically important aquaculture traits. This review critically evaluates current progress in CRISPR/Cas9 applications in aquaculture, with emphasis on Nile tilapia (Oreochromis niloticus). Evidence from peer-reviewed studies indicates that targeted modification of genes associated with growth regulation, disease resistance, nutrient metabolism, feed efficiency, and stress tolerance can significantly enhance fish productivity and physiological resilience. Genes involved in hypoxia adaptation and nitrogen metabolism may further improve environmental performance in intensive recirculating systems by reducing ammonia accumulation and enhancing nutrient utilization. However, most genome-editing studies have been conducted under laboratory or conventional aquaculture conditions, with limited information available regarding the long-term performance, ecological interactions, microbial dynamics, and biosafety of genome-edited fish in aquaponic environments. Technical limitations including off-target effects, mosaicism, delivery efficiency, regulatory uncertainty, and public acceptance continue to constrain large-scale implementation. In the short term, CRISPR/Cas9 applications are likely to focus on practical trait enhancement under controlled aquaculture systems, whereas longer-term research may explore fish lines specifically optimized for nutrient cycling, environmental resilience, and integrated aquaponic sustainability. Overall, CRISPR/Cas9-mediated genome editing represents a promising but still emerging strategy for improving sustainable aquaculture and aquaponic food production systems. Full article

Watermelons account for 7% of the world’s fruit vegetable production. In the European market, Spain contributes around 35% of total watermelon supply, with the majority grown in greenhouses in Almería, Southern Spain. This study presents experimental results from the first agrivoltaic watermelon trial conducted in a raspa-y-amagado greenhouse during the 2024 growing season in Almería, Spain. Watermelons were cultivated under two shading treatments with 30% and 50% of the roof area covered with PV modules and compared against an unshaded control group. Throughout the experiment, temperature values in the 30% and 50% zones were $2.2$${}^{\circ }\mathrm{C}$ and $4.3$${}^{\circ }\mathrm{C}$ lower than in the control zone, respectively. The unshaded control zone and the 30% shading treatment maintained DLI conditions within the optimal range between 21/$\mathrm{m}$/day and 32/$\mathrm{m}$/day for most of the crop cycle, while the 50% shading zone remained largely above the minimum threshold of 15/$\mathrm{m}$/day required for adequate crop growth. No statistically significant differences were observed in fruit weight, rind width, fruit firmness, or soluble solids content at harvest. In addition, the experimentally measured irradiance data from this study were compared with simulations from a previously established irradiance model. The model was applied to the raspa-y-amagado greenhouse, and the experimental data were used to perform a long-term comparison between simulated and measured irradiance for 265 days of data. The irradiance model accurately reproduced shading effects from both the PV modules and greenhouse structure, achieving nRMSE values of 0.09, 0.18, and 0.27 for the control, 30% shading, and 50% shading zones, respectively. Full article

Tomato crop residues (TCR) from soilless greenhouses are treated as waste, causing greenhouse gas emissions and biomass loss. Within a circular economy framework, gasification converts TCR into renewable energy and biochar; however, its high pH and electrical conductivity (EC) limit its use as a substrate. This study evaluated whether pre-treatment could enable TCR biochar to act as a substrate component and nutrient source in tomato and pepper seedlings. Biochar was produced by gasification and pre-treated by water incubation plus nitric acid, reducing EC from 27 to 8.7 dS m⁻¹ and pH from 10.4 to 8.2 while achieving nitrate loading without leaching. Pristine biochar severely restricted growth. Subsequent experiments evaluated pre-treated biochar mixed with perlite or cocopeat, with or without external N and K. The 15/85% (w/w) pre-treated biochar/cocopeat mixture (PTB/C) showed the best overall performance. In the absence of additional N/K, PTB/C produced shoot biomass and shoot N concentrations comparable to N-/K-supplemented cocopeat; shoot K was comparable in tomato and higher in pepper. With N and K supplementation, PTB/C exceeded supplemented cocopeat biomass by 1.41- and 1.95-fold in tomato and pepper, respectively. These results indicate that pre-treated TCR biochar can reduce dependence on imported cocopeat and external N/K supply. Full article

This study assesses the future availability of water resources in Lake Guiers by 2050, considering the combined impacts of climate change and human activities, using the Water Evaluation and Planning System. As Senegal’s main freshwater source, the lake faces growing pressure from agricultural expansion, aquatic plant overgrowth, competing stakeholder demands, and increasing water use. The study combines field data on hydrological flows and agricultural water use with climate projections under the Shared Socioeconomic Pathways 4.5 and 8.5 scenarios. Climate data were downscaled and bias-corrected using CMhyd, multiple linear regression, and the Mann–Kendall test. Model calibration showed strong performance (NSE = 0.95; R² = 0.96). Results reveal decreasing precipitation and rising temperatures under both scenarios. Agricultural withdrawals (79,331,457.14 m³/year) already exceed crop water needs (69,115,088.03 m³/year), resulting in significant water losses estimated at over 10 million m³ per year. Scenario analysis indicates that high water demand under Shared Socioeconomic Pathways SSP8.5 could lead to critical declines in lake volume as early as 2026 (550 million m³), while moderate demand growth under SSP4.5 could maintain water availability until 2050. The proposed PREFERLO-Grand Transfer project would add further stress to the lake’s capacity. These findings emphasize the urgent need for sustainable water management and policy actions. Full article

Rapid and non-destructive estimation of maize (Zea mays L.) leaf flavonoid (Flav) content is important for crop stress monitoring and precision agriculture. This study aimed to improve Flav estimation by integrating unmanned aerial vehicle (UAV)-based multispectral data, texture features, and phenological parameters across six key growth stages in the Guanzhong Plain, China. Maize Flav content was measured in situ using a Dualex Scientific+ meter, while canopy reflectance was acquired with a DJI M300 RTK UAV equipped with an MS600 Pro multispectral camera. A comprehensive feature set, including spectral bands, vegetation indices, texture features, texture indices, and logistic curve-derived phenological parameters, was constructed. Three feature selection methods, competitive adaptive reweighted sampling (CARS), the genetic algorithm (GA), and the successive projections algorithm (SPA), together with three regression models, partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and convolutional neural network (CNN), were evaluated for Flav estimation. The results showed that integrating spectral, texture, and phenological information significantly improved model performance compared with spectral variables alone. CNN and XGBoost generally outperformed PLSR. Across the six growth stages, the stage-specific optimal models achieved coefficient of determination (R²) values ranging from 0.7749 to 0.8686 and residual prediction deviation (RPD) values ranging from 2.0046 to 2.6019, indicating high to outstanding predictive ability. The highest accuracy was obtained at R3 using the CARS-X_II-CNN model, with R² = 0.8686, root mean square error of validation (RMSEV) = 0.0382, and RPD = 2.6019. Texture features and phenological metrics, especially the start of season derived from the normalized difference vegetation index (NDVI_SOS) and the rate of senescence derived from the enhanced vegetation index (EVI_ROS), contributed substantially to model accuracy. In addition, maize Flav showed a unimodal response to nitrogen supply, with moderate nitrogen levels associated with higher Flav content. This study demonstrates the potential of UAV-based multisource feature integration and machine learning for accurate maize Flav estimation, and provides a useful framework for digital crop phenotyping and stress diagnosis. Full article

Soil salinization severely constrains plant growth, yet the roles of ion composition and rhizosphere microbial communities in shaping plant performance remain poorly resolved. Here, we investigated multiple crop and wild plant species in saline–alkali soils and compared rhizosphere ion composition, microbial communities, and plant growth status. Restricted plant growth was consistently associated with elevated Na⁺ and Cl⁻ concentrations, while fungal diversity was significantly higher in well-growing plants. Ion composition (particularly Na⁺, Cl⁻, SO₄^2–, and Mg²⁺) was strongly correlated with microbial community structure, and a set of microbial taxa, including bacterial phyla such as Deinococcota and Gemmatimonadota and fungal phyla within Ascomycota and Basidiomycota, were repeatedly associated with plant growth status across species. Notably, plant species exhibited distinct apparent, threshold-like responses, and in several cases, plant growth differences were not fully explained by salinity levels alone, suggesting that rhizosphere microbial communities may buffer salt stress. Together, our results reveal that ion composition governs plant growth not only through direct ionic stress but also via microbially mediated pathways, highlighting an ion–microbe–plant interaction framework underlying growth variation in saline–alkali soils. Full article

Cirsium arvense (L.) Scop is a perennial plant of the family Asteraceae that is mainly distributed in the temperate regions of the Northern Hemisphere. Despite being widely recognized as an invasive weed in agriculture, most of the scientific evidence shows its significant phytochemical and pharmacological importance. In the present review article, a comprehensive summary of the available literature on C. arvense’s botanical properties, phytochemical composition, biological activities, standardization potential, and future therapeutic prospects has been carefully provided. This plant has been used traditionally for the treatment of inflammation, infections, bleeding disorders, and liver-related disorders. Phytochemical investigations showed the presence of many bioactive compounds such as flavonoids, phenolic acids, triterpenes, sterols, tannins, glycosides, and volatile compounds. Among the reported biological activities, antioxidants and antimicrobial properties are the most studied activities. In addition, anticancer, antidiabetic, neuroprotective, anti-inflammatory, and antiproliferative activities have also been investigated. The environmental adaptability, rapid growth, and extensive root system of C. arvense highlight its potential for development as a sustainable medicinal and industrial crop. However, there are critical research gaps present in phytochemical standardization, toxicity assessment, pharmacokinetics, and clinical validation, warranting further comprehensive studies. Full article

Soil pH constitutes a key factor in the nutrient availability and initial growth of maize (Zea mays L.). Inadequate management of soil pH can lead to problems in plant growth, which may result in reduced food production yields and agricultural investment. To evaluate the effects of pH dynamics on seedling development in soils, not only was a correlational and quantitative study conducted, which included a completely randomised laboratory experiment design with three treatments (pH < 6, pH > 7, and pH 6–7), each with five replicates, but a systemic analysis using a causal map also described the impacts of pH on plant growth. The initial pH was measured every four days, as were the germination rate, electrical conductivity, and final biomass. The results show that in alkaline soil, seedling germination is reduced by 87%, whilst in acidic soil it is reduced by 80% in comparison to the neutral scenario. pH values are therefore shown to affect early development due to reduced nutrient availability. These results reveal the need for the consideration of measures that influence management practices for the promotion of uniform and sustainable growth to favour the early establishment of crops such as native maize. Full article

Rhizoctonia solani is a necrotrophic phytopathogenic fungus that causes disease in various crops. In agriculture, many crops suffer from root or seedling rot caused by this soil-borne pathogen, whereas cabbage and rice develop lesion-like symptoms on aboveground tissues. Diseases caused by R. solani are generally controlled using chemical fungicides; however, environmentally friendly alternatives are needed for sustainable agriculture. In this study, we evaluated the efficacy of lecithin, a mixture of phospholipids previously registered in Japan as an agrochemical for controlling cucumber powdery mildew, against Rhizoctonia diseases. In cabbage, foliar spraying of 0.2–1.0% soybean lecithin effectively suppressed leaf symptoms caused by R. solani isolate RhiCa-2, which was identified as AG-1 IB. In rice and Brachypodium distachyon, 0.2–1.0% lecithin significantly suppressed leaf symptoms induced by R. solani AG-1 IA. Hyphal staining of inoculated leaves revealed reduced hyphal density on lecithin-treated leaves. Consistently, hyphal growth of R. solani on cellophane placed on water agar was retarded by lecithin treatment. However, 5.0% lecithin induced phytotoxicity in B. distachyon. Egg yolk-derived lecithin also exhibited disease-suppressive activity in cabbage and B. distachyon, with efficacy comparable to that of soybean lecithin under the conditions tested. These results suggest that lecithin suppresses foliar infection by R. solani, at least in part, through direct inhibitory effects on fungal hyphae, and may serve as a potential alternative material for disease control in sustainable crop production. Full article

Botrytis cinerea is a major phytopathogenic fungus responsible for substantial economic losses in horticultural crops, underscoring the need for sustainable alternatives to synthetic fungicides. This study investigated the influence of physical, chemical and biological culture parameters on the antifungal activity of culture filtrates produced by Trichoderma harzianum BOL-12QD. Culture conditions were sequentially optimised by evaluating light-filter exposure, carbon and nitrogen source composition, potato ecotype selection, co-cultivation with Botrytis cinerea, and volatile-mediated interactions. Antifungal activity was assessed using mycelial growth inhibition assays against Botrytis cinerea. Among the individual factors, violet-filter illumination, a medium containing 5 g L⁻¹ glucose and 250 g L⁻¹ potato extract, the Leke Pek’e potato ecotype, ammonium nitrate as nitrogen source, and co-cultivation with Botrytis cinerea at 10⁴ conidia mL⁻¹ produced the highest inhibitory effects. Sequential integration of these optimised conditions resulted in enhanced antifungal activity, reaching up to 62% inhibition. Volatile organic compounds produced by Trichoderma harzianum BOL-12QD exhibited only minimal antifungal activity under the conditions tested, suggesting that volatile-mediated antagonism plays a limited role in this system. In contrast, culture-dependent modulation of extracellular metabolite profiles was evidenced by comparative ¹H NMR fingerprinting, which revealed condition-specific spectral differences, with the optimised treatment displaying a distinct metabolic signature relative to all other conditions. Cytotoxicity assays in murine peritoneal macrophages showed no significant reduction in cell viability at concentrations up to 200 μg mL⁻¹. In vivo exposure to the optimised culture filtrate (250 mg kg⁻¹ d⁻¹ for 10 days) induced transient treatment-related clinical observations without mortality, indicating a need for further detailed toxicological characterisation. Overall, these findings demonstrate that the antifungal activity of Trichoderma harzianum BOL-12QD is strongly modulated by interacting environmental, nutritional and biological culture parameters. The results support the potential of optimised culture filtrates as a source of bioactive metabolites for biocontrol applications, while highlighting the importance of integrated biochemical and toxicological evaluation. Full article

Climate change and increasing water scarcity are intensifying pressure on irrigated agriculture, which currently represents 70% of global freshwater withdrawals. Remote sensing technologies have become essential tools for monitoring soil moisture, evapotranspiration, crop growth, and irrigation performance across multiple spatial and temporal levels. This review synthesizes 83 peer-reviewed studies published between 2002 and 2025, focusing on the use of optical, thermal, and microwave sensors to support irrigation water management under climate variability. The analysis highlights progress in multi-sensor integration, UAV-based monitoring, crop and agro-hydrological modeling, and emerging machine learning approaches that enhance irrigation scheduling, soil moisture estimation, and crop water stress detection. Despite these advancements, several methodological challenges persist, including data integration constraints, sensor-specific limitations, model transferability issues, insufficient ground validation, and difficulties in translating remote sensing outputs into operational decision support systems. In addition, structural gaps at the policy level restrict the evaluation of irrigation efficiency and climate resilience. This review aims to clarify current limitations and outline priority research directions to enhance the climate resilience and sustainability of irrigated agricultural systems. Full article

Soybean is a critical economic, oil and industrial raw material crop, yet its production is often hindered by pathogen infection and pesticide residues. This study explored the synergistic effects of Rhizophagus intraradices and mycorrhizal helper bacteria (MHB) on AMF colonization, AMF spore density, total number of bacterial colonies, soybean growth, root rot disease index, and carbendazim residues. Hydroponic and pot experiments were conducted using a completely randomized design (CRD) with five biological replicates per treatment; after 30 days of growth, three replicates were randomly selected for all measurements. Results showed that inoculation with microbial agents, particularly co-inoculation, increased soybean biomass, reduced disease index, and decreased carbendazim residues. In the hydroponic experiment, co-inoculation increased plant height, aboveground fresh weight, and underground dry weight by 64.28%, 78.13%, and 109.09%, respectively, and decreased carbendazim residues by 71.84% relative to the carbendazim-alone group. In the pot experiment, co-inoculation reduced carbendazim residues by 81.25% and root rot disease index by 45.56% compared with the carbendazim-alone group. Correlation analysis showed a strong positive correlation (p < 0.001) between carbendazim degradation in hydroponic and pot systems, indicating stable degradation function across environments. Co-inoculation of R. intraradices and MHB synergistically promotes soybean growth, suppresses root rot, and reduces carbendazim residues, providing a theoretical basis for developing functional microbial inoculants for safe and green soybean production. Full article

Sesame is a considerable oilseed crop, but its growth and production are restricted by drought. Potassium (K) is well known for its mitigating effects against drought. Here, two consecutive years of experiments were conducted with varying K fertilizer rates (0, 60, and 120 kg K₂O ha⁻¹) under well-watered and drought conditions to evaluate the impacts of K on sesame seed quality. The results demonstrated that, compared to well-watered conditions, drought caused a decline in seed oil content (5.9–8.6%) but inversely induced an increase in seed K (8.5–23.8%), lignans (10.2–21.6%), and essential amino acids over a period of 2 years. Potassic fertilizer significantly increased seed K, oil, and lignans contents, aligning with ameliorative oil and protein yield relative to K deficiency plants under drought. Moreover, K supply (especially 120 kg K₂O ha⁻¹) increased proline and tryptophan contents by 5.2% and 4.9% under drought compared to the plants without K application, which contributed to producing lignans and enhancing the capacity against oxidative changes. Under drought, 60 and 120 kg K₂O ha⁻¹ application significantly increased linoleic (5.5–9.3%), and stearic acids (7.1–13.7%) content while decreasing palmitic (5.3–14.7%), oleic (4.6–6.4%), and linolenic acids (4.8–11.9%) content, respectively, thereby increasing the ratio of unsaturated to saturated fatty acids and unsaturation index compared with control without K. Overall, K application at the rate of 120 kg K₂O ha⁻¹ could be considered as a practical and straightforward strategy to improve the quality of sesame seed products by increasing seed K, oil, lignans, linoleic acid, and unsaturated index for pharmaceutical and food purposes in areas encountering drought stress. Full article

In large-scale rice farming systems, the design of efficient cropping schedules is essential for improving labor management and operational efficiency. However, climate change, including rising temperatures and increased frequency of extreme weather events, has altered crop growth dynamics, making it difficult to achieve optimal management using conventional experience-based scheduling. In addition, the need to distribute operations across numerous fields and optimize labor allocation has increased the complexity of schedule design. In this study, we propose a decision-support method for designing rice cropping schedules using a heading date prediction model and climatological temperature data. The method adjusts transplanting dates based on predicted heading and maturity dates and determines operation periods through both forward and backward scheduling. A case study conducted on a large-scale farming system in Chiba Prefecture demonstrated that the proposed method effectively dispersed the distribution of heading and maturity dates, leading to improved temporal distribution of operations. The standard deviation of heading dates decreased from 11.7 to 8.7 days, indicating a reduction in peak labor demand. The novelty of this study lies in extending a heading date prediction model from growth prediction to practical applications in cropping schedule design and visualization. This approach enables a transition from experience-based planning to data-driven decision-making and contributes to labor distribution in large-scale farming under climate change conditions. Full article

Accurate wheat spike detection is essential for crop phenotyping and yield estimation, but real-world field conditions—such as dense spike overlap, environmental domain shifts, and degradation-induced failures like motion blur—pose significant challenges. Achieving robust perception under these circumstances while maintaining a strict accuracy-efficiency trade-off for edge devices remains a pressing research problem. To overcome these limitations, we propose GG-YOLO, a unified lightweight detection framework specifically tailored for complex agricultural environments. Rather than a simple recombination of existing lightweight modules, GG-YOLO integrates three original structural adaptations: First, a Dual-path Attentive Ghost Mechanism (DAGM) introduces gradient-guided attention modulation to enhance feature discrimination and explicitly resolve feature confusion in dense, overlapping regions. Second, a C3Ghost module combines multi-branch aggregation with linear feature generation, mitigating parameter redundancy in the prediction head by approximately 31% compared to the standard YOLOv8s without sacrificing semantic capacity. Third, DSample, a dynamic upsampling operator featuring an original dual-mode adaptive mechanism, robustly recovers fine-grained spatial details during multi-scale feature pyramid fusion. Extensive cross-dataset experiments on the GlobalWheat2020 and HNKJXYwheat datasets validate the model’s exceptional resilience to domain shifts and varying growth stages. GG-YOLO achieves a precision of 94.35%, a recall of 91.93%, and a state-of-the-art mAP@50 of 96.47%. Furthermore, the model contains only 7.89 M parameters and requires 20.4 GFLOPs, reaching an inference speed of 165 FPS on a desktop GPU and a validated real-time speed of 64 FPS on an NVIDIA Jetson edge computing platform. These results demonstrate that GG-YOLO establishes a superior accuracy-efficiency frontier, making it highly reliable for real-time field deployment in precision agriculture. Full article