Search Results (456)

Acid soils are often a limiting factor in the production of most cultivated plants. In practice, the application of inadequate, physiologically acidic fertilizers, urea and NPK, is often encountered, which further worsens the already poor physicochemical properties of such soils. In this study, the influence of different amounts of NPK and urea fertilizers and methods of their application on the chemical properties of a very acidic soil and the accumulation of essential biogenic elements (N, P, K, Ca, Mg, and Al) in raspberry plants (leaves and fruits) was evaluated. The field trial with the raspberry plants was set up on a very acidic soil (pH in KCl 3.6), type Dystric Cambisol, and was monitored for 2 years. The application of NPK and urea mainly increased soil acidity in the second year in all treatments (for 0.10–0.18 pH unit) (except for urea applied in rows). The application of higher amounts of NPK increased the content of available forms of P (for 9.3–30.8 mg/kg) and K (for 57–95 mg/kg) in soil in both years, as well as exchangeable Ca (for 200–510 mg/kg) and Mg in the first year (15–165 mg/kg). The introduction of fertilizers in rows, compared to fertilization of the entire surface, influenced the reduction in mobile Al (especially when applying NPK, from 5.89 to 7.13 mg/100 g), the increase in mineral N and K content in the soil, and the increase in Ca and Mg only when applying urea, i.e., P when applying NPK in rows. In the leaves, the application of fertilizers in rows increased the content of Ca and Mg in the first year and P and K in the second year. In the fruits, the content of all estimated elements was not in correlation with their content in leaves and the fertilizer application, which indicates the influence of other ecological and biological factors on plant nutrition. Full article

Indoor horticulture requires a substantial quantity of electricity to meet crops extended photoperiodic requirements for optimal photosynthetic rate. Simultaneously, global electricity costs have grown dramatically in recent years, endangering the sustainability and profitability of indoor vertical farms and/or modern greenhouses that use artificial lighting systems to accelerate crop development and growth. This study investigates the growth rate and physiological development of cherry tomato plants cultivated in a pilot indoor vertical farm at the Agricultural University of Athens’ Laboratory of Farm Structures (AUA) under continuous and disruptive lighting. The leaf physiological traits from multiple photoperiodic stress treatments were analyzed and utilized to estimate the plant’s tolerance rate under varied illumination conditions. Four different photoperiodic treatments were examined and compared, firstly plants grew under 14 h of continuous light (C-14L10D/control), secondly plants grew under a normalized photoperiod of 14 h with intermittent light intervals of 10 min of light followed by 50 min of dark (NI-14L10D/stress), the third treatment where plants grew under 14 h of a load-shifted energy demand response intermittent lighting schedule (LSI-14L10D/stress) and finally plants grew under 13 h photoperiod following of a load-shifted energy demand response intermittent lighting schedule (LSI-13L11D/stress). Plants were subjected also under two different light spectra for all the treatments, specifically WHITE and Blue/Red/Far-red light composition. The aim was to develop flexible, energy-efficient lighting protocols that maintain crop productivity while reducing electricity consumption in indoor settings. Results indicated that short periods of disruptive light did not negatively impact physiological responses, and plants exhibited tolerance to abiotic stress induced by intermittent lighting. Post-harvest data indicated that intermittent lighting regimes maintained or enhanced growth compared to continuous lighting, with spectral composition further influencing productivity. Plants under LSI-14L10D and B/R/FR spectra produced up to 93 g fresh fruit per plant and 30.4 g dry mass, while consuming up to 16 kWh less energy than continuous lighting—highlighting the potential of flexible lighting strategies for improved energy-use efficiency. Full article

Early crop yield prediction is a major challenge in precision agriculture, and efficient and rapid yield prediction is highly important for sustainable fruit production. The accurate detection of major fruit characteristics, including flowering, green fruiting, and ripening stages, is crucial for early yield estimation. Currently, most crop yield estimation studies based on the YOLO model are only conducted during a single stage of maturity. Combining multi-growth period data for crop analysis is of great significance for crop growth detection and early yield estimation. In this study, a new network model, YOLOv8-RL, was proposed using citrus multigrowth period characteristics as a data source. A citrus yield estimation model was constructed and validated by combining network identification counts with manual field counts. Compared with YOLOv8, the number of parameters of the improved network is reduced by 50.7%, the number of floating-point operations is decreased by 49.4%, and the size of the model is only 3.2 MB. In the test set, the average recognition rate of citrus flowers, green fruits, and orange fruits was 95.6%, the mAP@.5 was 94.6%, the FPS value was 123.1, and the inference time was only 2.3 milliseconds. This provides a reference for the design of lightweight networks and offers the possibility of deployment on embedded devices with limited computational resources. The two estimation models constructed on the basis of the new network had coefficients of determination R² values of 0.91992 and 0.95639, respectively, with a prediction error rate of 6.96% for citrus green fruits and an average error rate of 3.71% for orange fruits. Compared with network counting, the yield estimation model had a low error rate and high accuracy, which provided a theoretical basis and technical support for the early prediction of fruit yield in complex environments. Full article

Food safety concerns related to pesticide residues in fruits and vegetables have increased globally, particularly in regions where monitoring programs are scarce or inconsistent. This study provides the first multi-year evaluation of pesticide contamination and associated dietary risks in Cape Verde, an African island nation increasingly reliant on imported produce. A total of 570 samples of fruits and vegetables—both locally produced and imported—were collected from major markets across the country between 2017 and 2020 and analyzed using validated multiresidue methods based on gas chromatography coupled to Ion Trap mass spectrometry (GC-IT-MS/MS), and both gas and liquid chromatography coupled to triple quadrupole tandem mass spectrometry (GC-QqQ-MS/MS and LC-QqQ-MS/MS). Residues were detected in 63.9% of fruits and 13.2% of vegetables, with imported fruits showing the highest contamination levels and diversity of compounds. Although only one sample exceeded the maximum residue limits (MRLs) set by the European Union, 80 different active substances were quantified—many of them not authorized under the current EU pesticide residue legislation. Dietary exposure was estimated using median residue levels and real consumption data from the national nutrition survey (ENCAVE 2019), enabling a refined risk assessment based on actual consumption patterns. The cumulative hazard index for the adult population was 0.416, below the toxicological threshold of concern. However, when adjusted for children aged 6–11 years—taking into account body weight and relative consumption—the cumulative index approached 1.0, suggesting a potential health risk for this vulnerable group. A limited number of compounds, including omethoate, oxamyl, imazalil, and dithiocarbamates, accounted for most of the risk. Many are banned or heavily restricted in the EU, highlighting regulatory asymmetries in global food trade. These findings underscore the urgent need for strengthened residue monitoring in Cape Verde, particularly for imported products, and support the adoption of risk-based food safety policies that consider population-specific vulnerabilities and mixture effects. The methodological framework used here can serve as a model for other low-resource countries seeking to integrate analytical data with dietary exposure in a One Health context. Full article

This study proposes an integrated framework for sustainable tropical agriculture by combining biochemical waste valorization with spatial carbon footprint estimation in ‘Phulae’ pineapple production. Peel and eye residues from fresh-cut processing were enzymatically converted into rare sugar, achieving average conversion efficiencies of 35.28% for peel and 37.51% for eyes, with a benefit–cost ratio of 1.56 and an estimated unit cost of USD 0.17 per gram. A complementary zero-waste pathway produced functional gummy products using vinegar fermented from pineapple eye waste, with the preferred formulation scoring a mean of 4.32 out of 5 on a sensory scale with 158 untrained panelists. For spatial carbon modeling, the Bare Land Referenced Algorithm (BRAH) and Otsu thresholding were applied to multi-temporal Sentinel-2 and THEOS imagery to estimate plantation age, which strongly correlated with field-measured emissions (r = 0.996). This enabled scalable mapping of plot-level greenhouse gas emissions, yielding an average footprint of 0.2304 kg CO₂ eq. per kilogram of fresh pineapple at the plantation gate. Together, these innovations form a replicable model that aligns tropical fruit supply chains with circular economy goals and carbon-related trade standards. The framework supports waste traceability, resource efficiency, and climate accountability using accessible, data-driven tools suitable for smallholder contexts. By demonstrating practical value addition and spatially explicit carbon monitoring, this study shows how integrated circular and geospatial strategies can advance sustainability and market competitiveness for the ‘Phulae’ pineapple industry and similar perennial crop systems. Full article

The S100B protein, known for its role in the central and enteric nervous systems, has recently been identified in dietary sources such as milk, dairy products, fruits, and vegetables. Given its potential interaction with the gut microbiota, this study explores the relationship between dietary intake of S100B and microbiota biodiversity across different diets. A comprehensive study was conducted, estimating S100B concentrations in 13 dietary patterns recommended in different countries. This is the first study to provide a comparative estimation of S100B exposure from the diet and to explore its potential ecological and epidemiological relevance. The association between S100B levels and microbiota biodiversity was statistically analyzed, showing a direct correlation. Microbial diversity was assessed using the Shannon index, based on data extracted from studies reporting microbiota composition across dietary patterns. Additionally, the relative risk of Crohn’s disease was assessed in different populations to examine potential links between dietary patterns, S100B, and chronic disease prevention. A moderate positive correlation (R² = 0.537) was found between S100B concentration and Shannon index, suggesting that diets higher in S100B (e.g., Mediterranean diet) were associated with higher microbial alpha-diversity. Furthermore, Western-style diets, with the lowest S100B levels, exhibited a higher relative risk for Crohn’s disease (R² = 0.780). These findings highlight the potential role of dietary S100B content in modulating gut microbiota diversity and reducing chronic disease risk. Full article

Passiflora cincinnata (Mast), native to the Brazilian semi-arid region, produces exotic fruits even under low water availability. However, its green coloration at ripening complicates optimal harvesting, impacting post-harvest fruit quality. Therefore, this study aimed to evaluate the influence of cultivation systems (irrigated and rainfed) and different ripening stages on the physical and post-harvest characteristics of wild passion fruit during the second production cycle. The experiment was conducted using a randomized block design in a 2 × 4 factorial scheme, corresponding to two cultivation systems (irrigated and rainfed) and four fruit ripening stages (60, 80, 100, and 120 days after anthesis—DAA), with five replications. The fruit pulps were analyzed for physicochemical characterization and bioactive compounds. The physical and chemical characteristics of wild passion fruit were influenced by ripening stages and the irrigation system. The rainfed system decreased the total fruit mass by 15.50% compared to the irrigated cultivation. Additionally, the rainfed cultivation reduced the fruit color index by 14.82% and altered the respiratory pattern, causing a linear decrease of 73.37% in the respiration rate during ripening, in contrast to the behavior observed in the irrigated system, which reached an estimated minimum rate of 33.74 mg CO₂ kg⁻¹ h⁻¹ at 110 days after anthesis. Full article

Silver nanoparticles, with various uses in pharmacy, cosmetics, sanitation, textiles, optoelectronics, photovoltaics, etc., that are provided by worldwide industrial production, estimated to hundreds of tons annually, are finally released in the environment impacting randomly the biosphere. An alternative synthesis approach could be implemented by replacing chemical reductants of silver with natural antioxidants ensuring production and utilization sustainability with focus on environmental pollution diminishing. We synthesized silver nanoparticles by using plant extracts, aiming to offer antimicrobial products with reduced impact on the environment through sustainable green-chemistry. Fresh extracts of lemon pulp, blueberry and blackberry fruits as well as of green tea dry leaves were the sources of the natural antioxidants able to ensure ionic silver reduction and silver nanoparticle formation in the form of colloidal suspensions. The four samples were characterized by UV–Vis spectrophotometry, scanning electron microscopy, dark field optical microscopy, X-ray diffractometry, dynamic light scattering, which evidenced specific fine granularity, plasmonic features, standard crystallinity, and good stability in water suspension. Antimicrobial activity was assayed using the agar diffusion method and the bacteria kill-time technique against Staphylococcus aureus and Escherichia coli. In both cases, all silver nanoparticles revealed their adequacy for the aimed purposes, the sample synthesized with green tea showing the best efficiency, which is in concordance with its highest contents of polyphenols, flavones and best total antioxidant activity. Various applications could be safely designed based on such silver nanoparticles for sustainable chemistry development. Full article

Background/Objectives: Gestational diabetes mellitus (GDM) is a major pregnancy complication with rising global prevalence. The Mediterranean Diet (MD) has shown metabolic benefits, but total adherence scores may obscure meaningful variation in dietary quality. This study aimed to investigate whether specific dietary patterns, identified within the MD framework, and their glycemic load (GL) are associated with GDM risk. Methods: This prospective cohort is part of the BORN2020 longitudinal study on pregnant women in Greece; dietary intake was assessed using a validated food frequency questionnaire (FFQ) at two time points (pre-pregnancy and during pregnancy). MD adherence was categorized by Trichopoulou score tertiles. GL was calculated for food groups using glycemic index (GI) reference values and carbohydrate content. Dietary patterns were identified using factor analysis. Logistic regression models estimated adjusted odds ratios (aORs) for GDM risk, stratified by MD adherence and time period, controlling for maternal, lifestyle, and clinical confounders. Results: In total, 797 pregnant women were included. Total MD adherence was not significantly associated with GDM risk. However, both food-specific GLs and dietary patterns with distinct dominant foods were predictive. GL from boiled greens/salads was consistently protective (aOR range: 0.09–0.19, p < 0.05). Patterns high in tea, coffee, and herbal infusions before pregnancy were linked to increased GDM risk (aOR = 1.96, 95% CI: 1.31–3.02, p = 0.001), as were patterns rich in fresh juice, vegetables, fruits, legumes, and olive oil during pregnancy (aOR = 2.91, 95% CI: 1.50–6.24, p = 0.003). A pattern dominated by sugary sweets, cold cuts, animal fats, and refined products was inversely associated with GDM (aOR = 0.34, 95% CI: 0.17–0.64, p = 0.001). A pattern characterized by sugar alternatives was associated with higher risk for GDM (aOR = 4.94, 95% CI: 1.48–19.36, p = 0.014). These associations were supported by high statistical power (power = 1). Conclusions: Within the context of the MD, evaluating both the glycemic impact of specific food groups and identifying risk-associated dietary patterns provides greater insight into GDM risk than overall MD adherence scores alone. Full article

Okra (Abelmoschus esculentus) is a tropical vegetable with high nutritional and economic value. Rich in fiber, vitamins (C, K, and B9), and minerals (magnesium, potassium, calcium, and iron), it contributes to food security in many tropical regions. Global production is estimated at 11.5 million tons in 2023, 62% of which will come from India. Nigeria, Mali, Sudan, Pakistan, and Côte d’Ivoire are also among the major producers. Given its economic importance, optimizing its growth through controlled methods such as greenhouse cultivation and light-emitting diode (LED) lighting is a strategic challenge. Energy-efficient LED horticultural lighting offers promising prospects, but each plant variety reacts differently depending on the light spectrum, intensity, and duration of exposure (photoperiod). This study evaluated the effects of different LED spectra on okra’s flowering after 30 days of growth using B (blue, 445 nm) and R (red, 660 nm) LED lights and red-blue alternating in a three-day cycle (R3B3) by alternating the photoperiod from 14 to 10 h. Outdoor and greenhouse conditions served as controls. The results show that the R3B3 treatment improves germination in terms of both speed and percentage. However, plant growth (height, stem diameter, and leaf area) remains higher in the control group. R3B3 and red light stimulate leaf and node development. Flowering occurs earlier in the control group (51 days) and later under LED, particularly blue (73 days). Fruit diameter after petal fall was also larger in the control group. These results confirm the sensitivity of okra to photoperiod and light quality, and highlight the potential of spectral and photoperiod manipulation to regulate flowering in controlled-environment agriculture. Full article

Litter and pruning biomass are integral to nutrient cycling in the plant–soil ecosystem, contributing significantly to organic matter formation and humus development through decomposition and nutrient mineralization, which ultimately influence soil fertility and health. However, the litterfall dynamics in mango orchards are not well understood, and its contribution to nutrient cycling has seldom been measured. This study aimed to estimate litterfall and pruning biomass in mango orchards and assess the nutrient contents of various biomass components. Litter and pruning biomass samples were collected from four commercial mango orchards planted with Kensington Pride (‘KP’) and ‘B74’ (‘Calypso^®’) cultivars in the Darwin and Katherine regions, using litter traps placed on the orchard floors. Samples were sorted (leaves, flowers, panicles, fruits, and branches) and analyzed for nutrient contents. Results showed that most biomass abscissions occurred between late June and August, spanning approximately 100 days involving floral induction phase, fruit set, and maturity. Leaves made up most of the abscised litter biomass, while branches were the primary component of pruning biomass. The overall ranking of biomass across both regions and orchards is as follows: leaves > branches > panicles > flowers > fruits. The carbon–nitrogen (C:N) ratio of litter pruning material ranged from 30 (flowers) to 139 (branches). On a hectare basis, litter and biomass inputs contained 1.2 t carbon (C), 21.2 kg nitrogen (N), 0.80 kg phosphorus (P), 4.9 kg potassium (K), 8.7 kg calcium (Ca), 2.0 kg magnesium (Mg), 1.1 kg sulfur (S), 15 g boron (B), 13.6 g copper (Cu), 99.3 g iron (Fe), 78.6 g manganese (Mn), and 28.6 g zinc (Zn). The results indicate that annual litterfall may contribute substantially to plant nutrient supply and soil health when incorporated into the soil to undergo decomposition. This study contributes to a better understanding of litter biomass, nutrient sources, and nutrient cycling in tropical mango production systems, offering insights that support accurate nutrient budgeting and help prevent over-fertilization. However, further research is needed to examine biomass accumulation under different pruning regimes, decomposition dynamics, microbial interactions, and broader ecological effects to understand litterfall’s role in promoting plant growth, enhancing soil health, and supporting sustainable mango production. Full article

Among the numerous diseases that affect papaya (Carica papaya L.) cultivation, anthracnose, caused by a complex of fungi from the genus Colletotrichum spp., stands out, primarily due to its damage to the commercial part of the papaya, the fruit, specifically the pulp. Although chemical control with synthetic molecules is the most commonly used method to combat anthracnose, it is not the most appropriate solution. The indiscriminate use of synthetic chemical products results in numerous harmful effects on the environment, the health of farmers, and the final consumers. Given these circumstances, the objective of this study was to analyze the efficacy of essential oils (EOs) from Citrus aurantium var. dulcis L., known as sweet orange, Citrus limon (L.), known as Sicilian lemon, and the major compound present in these oils, limonene, against the pathogens Colletotrichum okinawense, which cause anthracnose in papaya fruits. The percentage inhibition of mycelial growth was evaluated on the seventh day, with estimates of 50% and 90% inhibition, to compare the inhibitory effect among the fungal isolates. Chromatographic analysis revealed that sweet orange EO contains myrcene and limonene. Sicilian lemon essential oil includes myrcene, limonene, α- and β-pinene, and γ-terpinene. Both EOs and limonene exhibited activity against C. okinawense. The 50 µL/mL concentration was the most effective in inhibiting growth. The EOs and limonene showed similar IC₅₀ values, with limonene at 48 µL/mL, Sicilian lemon EO at 51 µL/mL, and sweet orange EO at 57 µL/mL. Full article

This study presents the development of a system based on convolutional neural networks for the automated detection and counting of Gossypium barbadense fruits, specifically the IPA cotton variety, during its maturation stage, known as “mota”, in crops located in the Lambayeque region of northern Peru. To achieve this, a dataset was created using images captured with a mobile device. After applying data augmentation techniques, the dataset consisted of 2186 images with 70,348 labeled fruits. Five deep learning models were trained: two variants of YOLO version 8 (nano and extra-large), two of YOLO version 11, and one based on the Faster R-CNN architecture. The dataset was split into 70% for training, 15% for validation, and 15% for testing, and all models were trained over 100 epochs with a batch size of 8. The extra-large YOLO models achieved the highest performance, with precision scores of 99.81% and 99.78%, respectively, and strong recall and F1-score values. In contrast, the nano models and Faster R-CNN showed slightly lower effectiveness. Additionally, the best-performing model was integrated into a web application developed in Python, enabling automated fruit counting from field images. The YOLO architecture emerged as an efficient and robust alternative for the automated detection of cotton fruits and stood out for its capability to process images in real time with high precision. Furthermore, its implementation in crop monitoring facilitates production estimation and decision-making in precision agriculture. Full article

The dragon fruit industry holds significant market potential and is crucial for rural economic development. However, a comprehensive understanding and precise technical approach for analyzing the spatiotemporal dynamics of dragon fruit agriculture remain lacking. This study utilizes Nighttime Light (NTL) remote sensing data and proposes the Vegetation and Impervious area Adjusted Nighttime light Dragon fruit Index (VIANDI) to extract artificial light sources associated with dragon fruit cultivation. Furthermore, a regression model is constructed to estimate production based on light intensity. By integrating geospatial analysis methods, this study reveals the spatiotemporal evolution of dragon fruit cultivation area and production in Guangxi, China, from 2017 to 2022. The results demonstrate that the proposed method effectively monitors the dynamics of dragon fruit agriculture, achieving a Kappa Coefficient of 0.72 for area extraction and a Mean Relative Error (MRE) of 8.90% for production estimation. The spatial pattern of dragon fruit production follows a northwest–southeast distribution, with its centroid located in Nanning. The spatial expansion of cultivation areas exhibited an initial growth phase followed by stabilization, whereas production distribution transitioned from expansion to aggregation, maintaining an overall upward trend. Notably, 2019 marks a key turning point in these trends. Additionally, the rapid increase in light pollution intensity within cultivation areas warrants further attention. The study results have advanced precise monitoring of dragon fruit agriculture and enhanced understanding of its spatiotemporal evolution patterns. Full article

Background/Objectives: Although a healthy dietary pattern is a modifiable lifestyle factor in the prevention of chronic kidney disease (CKD), studies that investigate the association between a healthy diet and prevalent CKD in patients with diabetes, using the Korean Healthy Eating Index (KHEI), are lacking. Methods: This cross-sectional study included 1991 patients with diabetes from the eighth Korean National Health and Nutrition Examination Survey 2019–2021. A higher KHEI indicated healthier eating habits. CKD was defined as an estimated glomerular filtration rate < 60 mL/min/1.73 m² or urine albumin–creatinine ratio ≥ 30 mg/g. The risk of prevalent CKD was evaluated according to the median KHEI value using logistic regression analysis adjusted for various clinicodemographic characteristics. Each KHEI component score was compared between those with and those without CKD, using the Student’s t-test. Results: Participants with a higher KHEI were older, with higher proportions of women, non-smokers, and non-alcoholics. A higher KHEI was significantly associated with a lower risk of prevalent CKD (adjusted odds ratio [aOR], 0.73 [0.58–0.93]). Subgroup analysis revealed stronger associations in those without hypertension status (aOR, 0.57 [0.37–0.87]) with at least high school education (aOR, 0.56 [0.38–0.81]). Moreover, patients with diabetes and CKD had significantly lower KHEI, particularly in the adequacy category components, including breakfast consumption, total fruit intake, and dairy product intake. Conclusions: A healthier dietary pattern was associated with a lower risk of prevalent CKD in patients with diabetes. Dietary intervention, which recommends the intake of breakfast, fruits, and dairy products, may be an effective strategy for CKD prevention. Full article