Search Results (9,010)

The intensification of anthropogenic pressures, particularly those related to agriculture driven by increasing demands for food and cash crops, generates negative environmental externalities. Assessing these externalities is essential to better identify and implement measures that promote the environmental sustainability of rural landscapes. This study aims to develop a multi-criteria assessment method of the negative environmental externalities of rural landscapes in the northern Benin agricultural basin, based on satellite-derived data. Starting from a 12-class land cover map produced through satellite image classification, the evaluation was conducted in three steps. First, the 12 land cover classes were reclassified into Human Disturbance Coefficients (HDCs) via a weighted sum model multi-criteria analysis based on nine criteria related to the negative environmental externalities of anthropogenic activities. Second, the HDC classes were spatially aggregated using a regular grid of 1 km² landscape cells to produce the Landscape Environmental Sustainability Index (LESI). Finally, various discretization methods were applied to the LESI for cartographic representation, enhancing spatial interpretation. Results indicate that most areas exhibit moderate environmental externalities (HDC and LESI values between 2.5 and 3.5), covering 63–75% (HDC) and 83–94% (LESI) of the respective sites. Areas of low environmental externalities (values between 1.5 and 2.5) account for 20–24% (HDC) and 5–13% (LESI). The LESI, derived from accessible and cost-effective satellite data, offers a scalable, reproducible, and spatially explicit tool for monitoring landscape sustainability. It holds potential for guiding territorial governance and supporting transitions towards more sustainable land management practices. Future improvements may include, among others, refining the evaluation criteria and introducing variable criteria weighting schemes depending on land cover or region. Full article

Weed management remains a critical challenge in the U.S. container ornamental production industry, where weeds not only compete with crops for limited resources but also harbor pests and pathogens, thereby diminishing plant quality and marketability. The paper explores the economic impact of weed infestations, herbicide resistance development, and the limited availability of selective herbicides for ornamental crops in the United States. This review synthesizes current chemical weed control tactics, focusing not only on both preemergence and postemergence herbicides commonly used in ornamental nurseries, but also organic alternatives and integrated weed management (IWM) approaches as complementary strategies by evaluating their effectiveness, crop safety, and usage. There is a critical need for research in the areas of alternative chemical options such as insecticides, miticides (e.g., Zerotol and Tetra Curb Max), and organic products for liverwort control in greenhouses. Although essential oils and plant-based extracts show some potential, their effectiveness and practical use remain largely unexplored. Full article

This study, conducted in Southern Italy in 2023, investigated the effects of a dynamic agrivoltaics (AV) system on microclimate, water consumption, plant growth, and essential oil yield in six medicinal species: lavender (Lavandula angustifolia L. ‘Royal purple’), lemmon thyme (Thymus citriodorus (Pers.) Schreb. ar. ‘Aureus’), common thyme (Thymus vulgaris L.), rosemary (Salvia rosmarinus Spenn. ‘Severn seas’), mint (Mentha spicata L. ‘Moroccan’), and sage (Salvia officinalis L. subsp. Officinalis). Due to the rotating solar panels, two distinct ground zones were identified: a consistently shaded area under the panels (UP), and a partially shaded area between the panels (BP). These were compared to an adjacent full-sun control area (T). Microclimate parameters, including solar radiation, air and leaf infrared temperature, and soil temperature, were recorded throughout the cultivation season. Reference evapotranspiration (ET_O) was calculated using Turc’s method, and crop evapotranspiration (ET_C) was estimated with species-specific crop coefficients (K_C). Results showed significantly lower microclimatic values in the UP plot compared to both BP and especially T, resulting in ET_C reductions of 81.1% in UP and 13.1% in BP relative to T, an advantage in water-scarce environments. Growth and yield responses varied among species and treatment plots. Except for mint, all species showed a significant reduction in fresh biomass (40.1% to 48.8%) under the high shading of UP compared to T. However, no biomass reductions were observed in BP. Notably, essential oil yields were higher in both UP and BP plots (0.60–2.63%) compared to the T plot (0.51–1.90%). These findings demonstrate that dynamic AV systems can enhance water use efficiency and essential oil yield, offering promising opportunities for sustainable, high-quality medicinal crop production in arid and semi-arid regions. Full article

In the past three decades, localized research has highlighted shifts in rainfall patterns and temperature trends in central Ethiopia, a region vital for agriculture and economic activities and heavily dependent on climate conditions to sustain livelihoods and ensure food security. However, comprehensive analyses of long-term climate data remain limited for this area. Understanding local climate trends is essential for enhancing agricultural resilience in the study area, a region heavily dependent on rainfall for crop production. This study analyzes historical rainfall and temperature patterns over the past 30 years and projects future climate conditions using downscaled CMIP6 models under SSP4.5 and SSP8.5 scenarios. Results indicate spatial variability in rainfall trends, with certain areas showing increasing rainfall while others experience declines. Temperature has shown a consistent upward trend across all seasons, with more pronounced warming during the short rainy season (Belg). Climate projections suggest continued warming and moderate increases in annual rainfall, particularly under SSP8.5 by the end of the 21st century. It is concluded that both temperature and rainfall are projected to increase in magnitude by 2080, with higher Sen’s slope values compared to earlier periods, indicating a continued upward trend. These findings highlight potential breaks in agricultural calendars, such as shifts in rainfall onset and cessation, shortened or extended growing seasons, and increased risk of temperature-induced stress. This study highlights the need for localized adaptation strategies to safeguard agriculture production and enhance resilience in the face of future climate variability. Full article

In an increasingly challenging agricultural environment, the identification of novel tools for protecting crops from stress agents while securing marketable production is a key objective. Here we investigated the effects of three previously characterized Prosystemin-derived functional peptide fragments as protective agents against salt stress and as biostimulants modulating tomato yield and quality traits. The treatments of tomato plants with femtomolar amounts of the peptides alleviated salt stress symptoms, likely due to an increase in root biomass up to 18% and the upregulation of key antioxidant genes such as APX2 and HSP90. In addition, the peptides exhibited biostimulant activity, significantly improving root area (up to 10%) and shoot growth (up to 9%). We validated such activities through two-year field trials carried out on industrial tomato crops. Peptide treatments confirmed their biostimulant effects, leading to a nearly 50% increase in marketable production compared to a commonly used commercial product and consistently enhancing fruit °Brix values. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

The objective of this study was to evaluate the physical-hydric properties of a Planosol under an Integrated Crop–Livestock–Forest (ICLF) system in the Agreste region of Paraíba, Brazil, after eight years of implementation, and to compare them with areas under a conventional cropping system and secondary native vegetation. The experiment was conducted at the experimental station located in Alagoinha, in the Agreste mesoregion of the State of Paraíba, Brazil. The experimental design adopted was a randomized block design (RBD) with five treatments and four replications (5 × 4 + 2). The treatments consisted of: (1) Gliricidia (Gliricidia sepium (Jacq.) Steud) + Signal grass (Urochloa decumbens) (GL+SG); (2) Sabiá (Mimosa caesalpiniaefolia Benth) + Signal grass (SB+SG); (3) Purple Ipê (Handroanthus avellanedae (Lorentz ex Griseb.) Mattos) + SG (I+SG); (4) annual crop + SG (C+SG); and (5) Signal grass (SG). Two additional treatments were included for statistical comparison: a conventional cropping system (CC) and a secondary native vegetation area (NV), both located near the experimental site. The CC treatment showed the lowest bulk density (1.23 g cm⁻³) and the lowest degree of compaction (66.3%) among the evaluated treatments, as well as a total porosity (TP) higher than 75% (0.75 m³ m⁻³). In the soil under the integration system, the lowest bulk density (1.38 g cm⁻³) and the highest total porosity (0.48 m³ m⁻³) were observed in the SG treatment at the 0.0–0.10 m depth. High S-index values (>0.035) and a low relative field capacity (RFc < 0.50) and K_θ indicate high structural quality and low soil water storage capacity. It was concluded that the SG, I+SG, SB+SG, and CC treatments presented the highest values of soil bulk and degree of compaction in the layers below 0.10 m. The I+SG and C+SG treatments showed the lowest hydraulic conductivities and macroaggregation. The SG and C+SG treatments had the lowest available water content and available water capacity across the three analyzed soil layers. Full article

Agriculture is the backbone of Punjab’s economy, and with much of India’s population dependent on agriculture, the requirement for accurate and timely monitoring of land has become even more crucial. Blending remote sensing with state-of-the-art machine learning algorithms enables the detailed classification of agricultural lands through thematic mapping, which is critical for crop monitoring, land management, and sustainable development. Here, a Hyper-tuned Deep Neural Network (Hy-DNN) model was created and used for land use and land cover (LULC) classification into four classes: agricultural land, vegetation, water bodies, and built-up areas. The technique made use of multispectral data from Sentinel-2 and Landsat-8, processed on the Google Earth Engine (GEE) platform. To measure classification performance, Hy-DNN was contrasted with traditional classifiers—Convolutional Neural Network (CNN), Random Forest (RF), Classification and Regression Tree (CART), Minimum Distance Classifier (MDC), and Naive Bayes (NB)—using performance metrics including producer’s and consumer’s accuracy, Kappa coefficient, and overall accuracy. Hy-DNN performed the best, with overall accuracy being 97.60% using Sentinel-2 and 91.10% using Landsat-8, outperforming all base models. These results further highlight the superiority of the optimised Hy-DNN in agricultural land mapping and its potential use in crop health monitoring, disease diagnosis, and strategic agricultural planning. Full article

The Fusarium genus includes some of the most economically and ecologically impactful fungal pathogens affecting global agriculture and human health. Over the past 15 years, rapid advances in molecular biology, genomics, and diagnostic technologies have reshaped our understanding of Fusarium taxonomy, host–pathogen dynamics, mycotoxin biosynthesis, and disease management. This review synthesizes key developments in these areas, focusing on agriculturally important Fusarium species complexes such as the Fusarium oxysporum species complex (FOSC), Fusarium graminearum species complex (FGSC), and a discussion on emerging lineages such as Neocosmospora. We explore recent shifts in species delimitation, functional genomics, and the molecular architecture of pathogenicity. In addition, we examine the global burden of Fusarium-induced mycotoxins by examining their prevalence in three of the world’s most widely consumed staple crops: maize, wheat, and rice. Last, we also evaluate contemporary management strategies, including molecular diagnostics, host resistance, and integrated disease control, positioning this review as a roadmap for future research and practical solutions in Fusarium-related disease and mycotoxin management. By weaving together morphological insights and cutting-edge multi-omics tools, this review captures the transition into a new era of Fusarium research where integrated, high-resolution approaches are transforming diagnosis, classification, and management. Full article

Potato (Solanum tuberosum L.) crop yields may be reduced by nitrogen deficiency stress tolerance. An evaluation of 144 tetraploid potato genotypes was carried out during two consecutive seasons (2019 and 2020), with the objective of characterizing their variability in key physiological and agronomic parameters. Physiological parameters included chlorophyll content and fluorescence, stomatal conductance, NDVI, leaf area, and perimeter, while agronomic characteristics such as yield, tuber fresh weight, tuber number, starch content, dry matter, and reducing sugars were evaluated. To genotype the population, the GGP V3 Potato array was used, generating 18,259 high-quality SNP markers. Marker–trait association analysis was conducted using the GWASpoly package in R, applying Q + K linear mixed models to enhance precision. This methodology enabled the identification of 18 SNP markers that exhibited statistically significant associations with the traits analyzed in both trials and periods, relating them to genes whose functional implication has already been described. Genetic loci associated with chlorophyll content and tuber number were detected across non-stress and stress treatments, while markers linked to leaf area and leaf perimeter were identified specifically under nitrogen deficiency stress. The genomic distribution of these markers revealed that genetic markers or single-nucleotide polymorphisms (SNPs) correlated with phenotypic traits under non-stress conditions were predominantly located on chromosome 11, whereas SNPs linked to stress responses were mainly identified on chromosomes 2 and 3. These findings contribute to understanding the genetic mechanisms underlying potato tolerance to nitrogen deficiency stress, offering valuable insights for the development of future marker-assisted selection programs aimed at improving nitrogen use efficiency and stress resilience in potato breeding. Full article

To address the problems of traditional methods that rely on destructive sampling, the poor adaptability of fixed equipment, and the susceptibility of single-view angle measurements to occlusions, a non-destructive and portable device for three-dimensional phenotyping and biomass detection in lettuce was developed. Based on the Structure-from-Motion Multi-View Stereo (SFM-MVS) algorithms, a high-precision three-dimensional point cloud model was reconstructed from multi-view RGB image sequences, and 12 phenotypic parameters, such as plant height, crown width, were accurately extracted. Through regression analyses of plant height, crown width, and crown height, and the R² values were 0.98, 0.99, and 0.99, respectively, the RMSE values were 2.26 mm, 1.74 mm, and 1.69 mm, respectively. On this basis, four biomass prediction models were developed using Adaptive Boosting (AdaBoost), Support Vector Regression (SVR), Gradient Boosting Decision Tree (GBDT), and Random Forest Regression (RFR). The results indicated that the RFR model based on the projected convex hull area, point cloud convex hull surface area, and projected convex hull perimeter performed the best, with an R² of 0.90, an RMSE of 2.63 g, and an RMSEn of 9.53%, indicating that the RFR was able to accurately simulate lettuce biomass. This research achieves three-dimensional reconstruction and accurate biomass prediction of facility lettuce, and provides a portable and lightweight solution for facility crop growth detection. Full article

This study investigates the delineation of management zones (MZs) to support site-specific crop management by simplifying within-field variability in irrigated (54.6 ha) and rainfed (7.9 ha) sorghum and soybean fields in Brazil. Historical yield, apparent soil electrical conductivity (ECa) at 0.75 m and 1.50 m, and terrain data were analyzed using multivariate statistics to define MZs. Two clustering methods—fuzzy c-means (FCM) and hierarchical clustering—were compared for variance reduction effectiveness. Rainfed areas showed greater spatial variability (yield CV 9–12%; ECa CV 20–27%) than irrigated fields (yield CV < 7%; ECa CV ~5%). Principal component analysis (PCA) identified subsoil ECa and elevation as key variables in irrigated fields, while surface ECa and topography influenced rainfed variability. FCM produced more homogeneous zones with fewer classes, especially in irrigated fields, whereas hierarchical clustering better detected outliers but required more zones for similar variance reduction. Yield correlated strongly with slope and moisture in rainfed systems. These results emphasize aligning MZ delineation with production system characteristics—enabling variable rate irrigation in irrigated fields and promoting moisture conservation in rainfed systems. FCM is recommended for operational efficiency, while hierarchical clustering offers higher precision in complex contexts. Full article

Aiming at the problems of the high multiple- and missed-seeding index and low operation efficiency of current mechanical potato seed-meters in minituber sowing, this study designed an air-suction roller-type minituber seed-metering device for minitubers (mass between 2 and 4 g) in accordance with the agronomic standards for potato cultivation in the single-cropping area of northern China. An account of the device’s structure and operational principle was made, its working process was theoretically analysed, and the three main factors affecting the airflow suction were determined: the seed roller speed, the suction seeding hole diameter, and the air inlet negative pressure. This study adopted the fluid dynamics simulation method and determined that the ideal location of the air inlet was 30° for horizontal inclination and 60° for vertical inclination. Then, based on the CFD-DEM fluid-structure coupling simulation method, the impact of a range of factors on the functionality of the seed-metering device under different conditions was studied and verification tests were carried out. Design-Expert was used to analyse test results. The results showed that when the pressure at the air inlet was −7000 Pa, the speed of the seeding roller was 40.2 r·min⁻¹, the suction seeding hole diameter was 10.37 mm, and the performance was optimal: the qualified index was 92.95%, the multiple-seeding index was 4.16%, and the missed-seeding index was 2.89%. The research results show that the seed-metering device developed under this scheme exhibited satisfactory seeding performance during operation and was able to meet the demands of minituber sowing. 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

Drosophila suzukii is an invasive pest of many fruit crops worldwide. Employing the Sterile Insect Technique (SIT) could mitigate D. suzukii population growth and crop damage. This study evaluated the efficacy of SIT on commercial fruit, by (1) validating the quality of irradiated sterile males (male mating competitiveness, courtship, and flight performance) in the laboratory, and (2) assessing population suppression and fruit damage reduction in commercial raspberry fields. Treatment with SIT was compared to the grower’s standard chemical insecticide program throughout the season. The principal metrics of efficacy were trap counts of wild adult female D. suzukii in crops and larvae per fruit during harvesting. These metrics together with monitoring of border areas allowed targeting of high-pressure areas with higher releases of sterile males, to maximise efficacy for a given release number. The sterile male D. suzukii were as competitive as their fertile non-irradiated counterparts in laboratory mating competitiveness and flight performance studies while fertility egg-to-pupae recovery was reduced by 99%. In commercial raspberry crops, season-long releases of sterile males significantly suppressed the wild D. suzukii population, compared to the grower standard control strategy; with up to 89% reduction in wild female D. suzukii and 80% decrease in numbers of larvae per harvested fruit. Additionally, relative fruit waste (i.e., percentage of harvested fruits rejected for sale) at harvest was reduced for early, mid and late harvest crops, by up to 58% compared to the grower standard control. SIT has the potential to provide an effective and sustainable strategy for managing D. suzukii in raspberries, increasing marketable yield by reducing adult populations, fruit damage and waste fruit. SIT could therefore serve as a valuable tool for integrated pest management practices in berry production systems. Full article