Search Results (269)

Diploid Vaccinium fuscatum is a wild blueberry species with a low chilling requirement, an evergreen growth habit, and soil adaptability to southeast US growing regions. Regardless of its potential to improve the abiotic and biotic resilience of cultivated blueberries, this species has rarely been used for blueberry breeding. One hurdle is the ploidy barrier between diploid V. fuscatum and tetraploid cultivated highbush blueberries. To overcome the ploidy barrier, vegetative shoots micro-propagated from one genotype of V. fuscatum, selected because it grew vigorously in vitro and two southern highbush cultivars, ‘Emerald’ and ‘Rebel,’ were treated with colchicine. While shoot regeneration was severely repressed in ‘Emerald’ and ‘Rebel,’ shoot production from the V. fuscatum clone was not compromised at either 500 µM or 5000 µM colchicine concentrations. Due to the high number of shoots produced in vitro via the V. fuscatum clone shoots of this clone that had an enlarged stem diameter in vitro were subjected to flow cytometer analysis to screen for induced polyploidy. Sixteen synthetic tetraploid V. fuscatum, one synthetic octoploid ‘Emerald,’ and three synthetic octoploid ‘Rebel’ were identified. Growth rates of the polyploid-induced mutants were reduced compared to their respective wildtype controls. The leaf width and length of synthetic tetraploid V. fuscatum and synthetic octoploid ‘Emerald’ was increased compared to the wildtypes, whereas the leaf width and length of synthetic octoploid ‘Rebel’ were reduced compared to the wildtype controls. Significant increases in stem thickness and stomata guard cell length were found in the polyploidy-induced mutant lines compared to the wildtypes. In the meantime, stomata density was reduced in the mutant lines. These morphological changes may improve drought tolerance and photosynthesis in these mutant lines. Synthetic tetraploid V. fuscatum can be used for interspecific hybridization with highbush blueberries to expand the genetic base of cultivated blueberries. Full article

Monitoring crop damage caused by wildlife remains a significant challenge in agricultural management, particularly in the case of large-scale monocultures such as maize. The given study presents a semi-automated process for detecting wildlife-induced damage using RGB imagery acquired from unmanned aerial vehicles (UAVs). The method is designed for non-specialist users and is fully integrated within the QGIS platform. The proposed approach involves calculating three vegetation indices—Excess Green (ExG), Green Leaf Index (GLI), and Modified Green-Red Vegetation Index (MGRVI)—based on a standardized orthomosaic generated from RGB images collected via UAV. Subsequently, an unsupervised k-means clustering algorithm was applied to divide the field into five vegetation vigor classes. Within each class, 25% of the pixels with the lowest average index values were preliminarily classified as damaged. A dedicated QGIS plugin enables drone data analysts (Drone Data Analysts—DDAs) to adjust index thresholds, based on visual interpretation, interactively. The method was validated on a 50-hectare maize field, where 7 hectares of damage (15% of the area) were identified. The results indicate a high level of agreement between the automated and manual classifications, with an overall accuracy of 81%. The highest concentration of damage occurred in the “moderate” and “low” vigor zones. Final products included vigor classification maps, binary damage masks, and summary reports in HTML and DOCX formats with visualizations and statistical data. The results confirm the effectiveness and scalability of the proposed RGB-based procedure for crop damage assessment. The method offers a repeatable, cost-effective, and field-operable alternative to multispectral or AI-based approaches, making it suitable for integration with precision agriculture practices and wildlife population management. Full article

This study aimed to predict the potential habitats of Luciola unmunsana using a species distribution model (SDM). Luciola unmunsana is an endemic species that lives only in South Korea, and because its females do not have genus wings and are less fluid, it is difficult to collect, so research related to its distribution and restoration is relatively understudied. Therefore, this study predicted the potential habitats of Luciola unmunsana across South Korea using the single model Maximum Entropy (MaxEnt) and a multi-model ensemble model to prepare basic data necessary for a conservation and habitat restoration plan for the species. A total of 39 points of occurrence were built based on public data and prior research from the Jeonbuk Green Environment Support Center (JGESC), the Global Biodiversity Information Facility (GBIF), and the National Institute of Biological Resources (NIBR). Among the input variables, climate variables were based on the shared socioeconomic pathway (SSP) scenario-based ecological climate index, while nonclimate variables were based on topography, land cover maps, and the Enhanced Vegetation Index (EVI). The main findings of this study are summarized below. First, in predicting Luciola unmunsana potential habitats, the EVI, water network analysis, land cover, and annual precipitation (Bio12) were identified as good predictors in both models. Accordingly, areas with high vegetation activity in their forests, adjacent to water resources, and stable humidity were predicted as potential habitats. Second, by overlaying the predicted potential habitats and highly significant variables, we found that areas with high vegetation vigor within their forests, proximity to water systems, and relatively high annual precipitation, which can maintain stable humidity, are potential habitats for Luciola unmunsana. Third, literature surveys used to predict potential habitat sites, including Geumsan-gun, Chungcheongnam-do, Yeongam-gun, Jeollabuk-do, Mudeungsan Mountain, Gwangju-si, Korea, and Gijang-gun, Busan-si, Korea, confirmed the occurrence of Luciola unmunsana. This study is significant in that it is the first to develop a regional SDM for Luciola unmunsana, whose population is declining due to urbanization. In addition, by applying various environmental variables that reflect ecological characteristics, it contributes to more accurate predictions of the potential habitats of this species. The predicted results can be used as basic data for the future conservation of Luciola unmunsana and the establishment of habitat restoration strategies. Full article

This study investigated the influence of cane diameter on vegetative, productive, and physiological behaviors in Actinidia chinensis, cv. Gold 3. Conducted over two years (2021–2022), the experiment compared canes with larger (HD) and smaller (LD) proximal diameters. This research focused on parameters such as shoot morphology, leaf gas exchange, fruit quality, and hydraulic resistance. The results revealed that HD canes promoted more vigorous growth, with a higher proportion of long and medium shoots, whereas LD canes resulted in shorter shoots. Additionally, the HD canes demonstrated a higher leaf area and more extensive leaf coverage, contributing to enhanced photosynthetic activity, as evidenced by enhanced gas exchange, stomatal conductance, and transpiration rates. This higher photosynthetic efficiency in HD canes resulted in more rapid fruit growth, with a larger fruit size and weight, particularly in fruits from non-terminate shoots. By contrast, fruits on LD canes exhibited slower growth, particularly in terms of fresh weight and dry matter accumulation. Despite these differences, maturation indices, including soluble solids and acidity levels, were not significantly affected by cane type. The findings suggest that selecting HD canes during winter pruning could lead to earlier harvests, with improved fruit quality and productivity, making this practice beneficial for optimizing vineyard management in Actinidia chinensis. Full article

Recognizing the interrelationship among variables becomes critical in genetic breeding programs, where the goal is often to optimize selection for multiple traits. Conventional multi-trait models face challenges such as convergence issues, and they fail to account for cause-and-effect relationships. To address these challenges, we conducted a comprehensive analysis involving confirmatory factor analysis (CFA), Bayesian networks (BN), structural equation modeling (SEM), and genome-wide selection (GWS) using data from 195 arabica coffee plants. These plants were genotyped with 21,211 single nucleotide polymorphism markers as part of the Coffea arabica breeding program at UFV/EPAMIG/EMBRAPA. Traits included vegetative vigor (VV), canopy diameter (CD), number of vegetative nodes (NVN), number of reproductive nodes (NRN), leaf length (LL), and yield (Y). CFA established the following latent variables: vigor latent (VL) explaining VV and CD; nodes latent (NL) explaining NVN and NRN; leaf length latent (LLL) explaining LL; and yield latent (YL) explaining Y. These were integrated into the BN model, revealing the following key interrelationships: LLL → VL, LLL → NL, LLL → YL, VL → NL, and NL → YL. SEM estimated structural coefficients, highlighting the biological importance of VL → NL and NL → YL connections. Genomic predictions based on observed and latent variables showed that using VL to predict NVN and NRN traits resulted in similar gains to using NL. Predicting gains in Y using NL increased selection gains by 66.35% compared to YL. The SEM-GWS approach provided insights into selection strategies for traits linked with vegetative vigor, nodes, leaf length, and coffee yield, offering valuable guidance for advancing Arabica coffee breeding programs. Full article

Germination ability and seedling development of grape (Vitis vinifera L.) seeds show significant differences depending on cultivar characteristics and germination conditions, and this situation is known to create significant difficulties in grape breeding programs and vegetative propagation. In this study, we explored the effects of different concentrations of sodium nitroprusside (SNP; 500–3000 ppm) and gibberellic acid (GA₃) on seed germination and seedling growth in several grape cultivars. Our findings show that cultivar, treatment type, and their interaction had significant effects on both germination and growth. The 5 BB rootstock stood out with consistently high germination rates, reaching up to 95% with 1500 ppm SNP. Overall, SNP treatments outperformed both the control and GA₃ applications, although the most effective concentration differed by cultivar. The most beneficial SNP doses ranged between 1000 and 3000 ppm, with 1500 ppm yielding the highest improvement, up to a 21.6% increase compared to the control. Notably, the ‘Çeliksu’ cultivar responded strongly to SNP, while ‘Rizpem’ showed weak germination, regardless of treatment. Seedling growth, as measured by plant height and node number, was also influenced by both treatment and cultivar, with 5 BB again showing the most robust development. Multivariate analyses revealed strong correlations across germination dates and growth traits. Higher SNP concentrations (1500–3000 ppm) consistently promoted better germination and seedling vigor than GA₃ and untreated controls. These results highlight the importance of considering cultivar-specific responses and suggest that well-calibrated SNP applications could be a valuable tool for improving seed-based propagation in grape breeding programs. Full article

Asparagus decline syndrome (ADS) poses a significant threat to asparagus cultivation worldwide. To address this challenge, a two-year investigation was carried out in Spain to assess the impacts of three soil disinfection strategies on asparagus crops. These included biofumigation with Brassica carinata seed pellets, biofumigation using poultry manure pellets, and chemical disinfection with dazomet. In addition to evaluating the potential of these treatments to alleviate ADS, the research also focused on identifying the physiological changes linked to the syndrome by examining indicators of oxidative metabolism, hormonal equilibrium, and phenolic compound profiles. Among the treatments evaluated, biofumigation with B. carinata pellets enhanced vegetative growth, photosynthetic pigment accumulation, antioxidant capacity, and hormonal homeostasis, with these improvements becoming more pronounced in the second year. This approach appeared to promote a healthier physiological status in asparagus plants, likely through improved soil health and reduced biotic and abiotic stress perception. In contrast, chemical disinfection with dazomet, despite initially stimulating some physiological responses, was associated with elevated oxidative stress. Overall, the findings suggest that organic-based soil treatments, particularly B. carinata biofumigation, represent a promising strategy to strengthen asparagus vigor and resilience against ADS. Further studies are needed to assess their long-term effects in perennial cultivation systems. Full article

The increasing global demand for resilient, sustainable agricultural systems has intensified the need for advanced monitoring strategies, particularly for climate-adaptive crops such as Moringa oleifera Lam. This study presents an integrated approach using Unmanned Aerial Vehicles (UAVs) equipped with multispectral and thermal cameras to monitor the vegetative performance and determine the optimal harvest period of four M. oleifera genotypes in a Mediterranean environment. High-resolution data were collected and processed to generate the NDVI, canopy temperature, and height maps, enabling the assessment of plant vigor, stress conditions, and spatial canopy structure. NDVI analysis revealed robust vegetative growth (0.7–0.9), with optimal harvest timing identified on 30 October 2024, when the mean NDVI exceeded 0.85. Thermal imaging effectively discriminated plant crowns from surrounding weeds by capturing cooler canopy zones due to active transpiration. A clear inverse correlation between NDVI and Land Surface Temperature (LST) was observed, reinforcing its relevance for stress diagnostics and environmental monitoring. The results underscore the value of UAV-based multi-sensor systems for precision agriculture, offering scalable tools for phenotyping, harvest optimization, and sustainable management of medicinal and aromatic crops in semiarid regions. Moreover, in this study, to produce M. oleifera leaf powder intended for use as a food ingredient, the leaves of four M. oleifera genotypes were dried, milled, and evaluated for their hygiene and safety characteristics. Plate count analyses confirmed the absence of pathogenic bacterial colonies in the M. oleifera leaf powders, highlighting their potential application as natural and functional additives in food production. Full article

Accurate crop yield prediction is vital towards optimizing agricultural productivity. Machine Learning (ML) has shown promise in this field; however, its application to legume crops, especially to lupin, remains limited, while many models lack interpretability, hindering real-world adoption. To bridge this literature gap, an interpretable ML framework was developed for predicting lupin yield using Sentinel-2 remote sensing data integrated with georeferenced yield measurements. Data preprocessing involved computing vegetation indices, removing outliers, addressing multicollinearity, normalizing feature scales, and applying data augmentation techniques to correct target imbalance. Subsequently, six ML models were evaluated representing different algorithmic strategies. Among them, XGBoost showed the best performance ($R^2$ = 0.8756) and low error values across $MAE$, $MSE$, and $RMSE$ metrics. To enhance model transparency, SHapley Additive exPlanations (SHAP) values were applied to interpret the feature contributions of the XGBoost model. The Enhanced Vegetation Index ($EVI$) and Normalized Difference Vegetation Index ($NDVI$) were found to be key predictors of crop yield, both showing a positive correlation with higher values reflecting greater vegetation vigor and corresponding to increased yield. These were followed by $B03$ (green) and $B12$ (short-wave infrared), which captured key reflectance properties associated with chlorophyll activity and water content, respectively. Both of them substantially influence photosynthetic efficiency and plant health, ultimately affecting yield potential. Full article

Precision agriculture facilitates improved management by studying the spatial and temporal variability of soil attributes. Soybean (Glycine max (L.) Merrill) seeds may exhibit distinct quality when produced in different management zones. This study aimed to validate management zones during seed production and identify the variability and spatial distribution of soybean seed physiological quality using geostatistical tools. Management zones were defined based on interpolated maps of soil and vegetation attributes using the Smart Map Plugin (SMP) within the QGIS environment. Post-harvest, the variability of physiological seed quality across different management zones was assessed. Germination, accelerated aging, dry weight, emergence, electrical conductivity, and tetrazolium tests were conducted in a completely randomized design. Soil attributes, initial plant stand, and soybean seed productivity validated the management zones. Physiological seed quality varies across the production field, particularly in terms of vigor, thereby enhancing diagnostics through map interpolation. Geostatistics enable determination of the spatial distribution of soybean seed physiological quality in seed production areas, facilitating decision-making regarding harvest zones. Full article

Bermudagrass is a warm-season turfgrass commonly grown in drought-prone areas. Harnessing natural genetic variation available in germplasm is a principal strategy to enhance its resilience to drought stress. This study was carried out to assess the comparative performance of bermudagrass hybrids under drought conditions and their subsequent recovery following the drought period. A total of 48 hybrids, including 2 commercial cultivars, ‘Tifway’ and ‘TifTuf’, were established under optimum growth conditions in the greenhouse and then subjected to drought stress by withholding irrigation for four weeks. The dry-down experiment was laid out in a randomized complete block design with four replications. Turf color, visual quality, and active spectral reflectance data were collected weekly and used to assess the health and vigor of the hybrids during progression of the drought stress for four weeks and through recovery after rewatering. Analysis of variance revealed significant differences among the hybrids for color, visual quality, and spectral vegetation indices. A multivariate analysis grouped the hybrids into drought-tolerant with full recovery after rewatering, moderately tolerant, and susceptible to extended drought stress without recovery. These results showed the prevalence of genetic variation for drought tolerance and proved instrumental in the development of bermudagrass cultivars resilient to drought stress and improved water use efficiency. Full article

The genus Musa L. is one of the most important genera worldwide due to its use in food as a source of carbohydrates. A morphological characterization was performed to evaluate the potential of 100 accessions of Musa spp. from the Amazon region of Ecuador, applying 73 qualitative and quantitative descriptors in addition to the ecogeographic characterization. The multivariate analyses identified four large groups: The first is composed of the Musa AAB Simmonds ecotype “Hartón Plantain” and the “Cuerno Clone”. The second group is composed of the Musa acuminata Colla ecotype “Orito”. The third group is composed of the Musa acuminata ecotype “Malay plantain or red plantain”; and the fourth group is composed of the Musa × paradisiaca L. AAB ecotype “Barraganete” and banana or banana materials and the Musa AAB Simmonds ecotype “Plátano Dominico”. The qualitative descriptors with the highest discriminant value were the shape of the ♂ floret bud, the appearance of the rachis, and the pigmentation of the compound tepal, and the quantitative discriminant characters were the height of the pseudostem, the length of the leaf blade, the width of the leaf blade, and the weight of the raceme. The analysis with CAPFITOGEN of these 100 accessions through the ecogeographic characterization map identified 23 categories, highlighting category 20 with a coverage of 40.35%, which mainly includes the provinces of Orellana, Sucumbíos, part of Napo, Pastaza, and Morona Santiago. This category occurs within an annual temperature range between 21.6 °C and 27 °C, an apparent density of 1.25 to 1.44 g cm⁻³, and a cation exchange capacity (CEC) of 4 to 29 Cmol kg⁻¹. The morphological characterization of 100 Musa accessions revealed significant phenotypic variability, with four distinct morphological groups identified through cluster analysis. Key differences were observed in traits such as bunch weight, fruit length, and vegetative vigor. This variability highlights the potential of certain accessions for use in genetic improvement programs. The findings contribute valuable information for the efficient conservation, selection, and utilization of the Musa germplasm in Ecuadorian agroecosystems. The results demonstrate the existence of an important genetic variability in the INIAP Musa Germplasm Bank in the Ecuadorian Amazon region. Full article

Sowing date significantly affects plant growth, development, and yield, holding a crucial role in soybean cultivation. This study was conducted in the southern coastal region of Korea under recent climate change conditions to investigate the effects of five different sowing dates on climatic characteristics, growth, and yield. Compared to historical data, the southern coastal region has experienced a consistent increase in average temperature during the soybean cultivation period, along with frequent abnormal summer climate events such as concentrated heavy rainfall and monsoons. These climate changes prolonged the vegetative growth period in earlier sowings, leading to an increased risk of lodging at maturity due to vigorous vegetative growth. Furthermore, earlier sowing delayed flowering and exposed plants to longer post-flowering photoperiods, consequently reducing the number of pods. Therefore, in the southern coastal region of Korea, it is crucial to re-evaluate conventional sowing practices and establish region-specific optimal dates, with careful consideration given to postponing the soybean sowing date to late June in order to enhance yield stability and improve the feasibility of double-cropping systems by shortening the growing period. Full article

Watermelon is a widely cultivated fruit and vegetable that is native to Africa and has become one of the world’s important summer fruits. Watermelon seed vigor has a critical impact on watermelon planting and yield, and seed water content is a key factor in maintaining vigor during seed storage and germination. In this study, reflectance and transmittance spectral data from hyperspectral imaging were fused to improve the detection accuracy of moisture content in watermelon seeds. First, watermelon seed samples with different water content gradients were prepared by dividing all 456 selected watermelon seeds into 10 groups and drying them in a drying oven at 60 °C for 0, 3, 5, 10, 15, 20, 25, 30, 40, and 50 min. Reflectance and transmission spectra of 456 watermelon seeds were collected by a hyperspectral imaging system, and the single spectral data were subsequently used to build PLSR and LSSVR models for quantitative analysis of watermelon seed moisture content. Model performance is enhanced by Competitive Adaptive Reweighted Sampling (CARS), Unrelated Variable Elimination (UVE), and primary and intermediate data fusion methods. Primary data fusion improves model predictions compared to single models based on reflectance and transmission spectra. The intermediate data fusion of the feature spectral data of reflectance and transmittance selected by the CARS algorithm improves the prediction effect of the model more obviously, in which the model with the best prediction accuracy is Raw-CRAS-LSSVR, whose $R_P^2$ and RMSEP are 0.9149 and 0.0144, respectively, which improves the prediction effect of the model built by a single full-spectrum datum by 5.72%. This study demonstrates that hyperspectral reflectance and transmission imaging techniques combined with data fusion can effectively detect watermelon seed moisture content quickly and with high accuracy. Full article

Wet deposition of atmospheric polycyclic aromatic hydrocarbons (PAHs) is considered an important source of these potentially toxic compounds in soils. In addition to affecting soil quality, they might be taken up by higher plants, potentially causing phytotoxicity or being accumulated in various organs. Plants are exposed to atmospheric PAHs via the aerial parts and via the soil-root system. The primary aim of this study was to present an experimental setup which can be properly used to quantify PAH accumulation investigating both potential pathways. Rocket (Eruca sativa Mill.) was selected as the model species. The test was conducted following the No. 227 OECD Vegetative Vigor Test. Plants were sprayed with the extract of particles generated during the operation of a diesel-powered vehicle simulating the air–aerial parts–root pathway, while the same extract was used to treat the soil simulating the soil–root–aerial parts pathway. In the soil–root–stem–leaf pathway, the total PAH concentration was 108 μg/kg in the soil, 143 μg/kg in the roots, 92.3 μg/kg in the stems, and 62.5 μg/kg in the leaves. Results showed that higher molecular weight PAHs were mostly accumulated in the roots, but their transfer to above-ground parts cannot be excluded. This study supports the importance of wet deposition in transferring atmospheric PAHs to soils. Full article