Search Results (12,366)

Olive cultivation represents a key pillar of rural economies and cultural heritage in Mediterranean regions, including western Greece. Despite its socio-economic importance, the sector faces increasing pressures from climate change, market volatility, and technological transformation, while progress toward environmentally sustainable production remains uneven. This study investigates how olive farmers’ perceptions of carbon footprint and climate risks are influenced by their demographic characteristics. Primary data were collected through 402 structured questionnaires distributed to olive producers in the Aetolia–Acarnania region. The sample was designed to represent farmers directly engaged in olive production, ensuring the relevance and reliability of the collected data. The findings, based on descriptive statistics, reveal significant heterogeneity in producers’ perceptions of climate risks and their capacity to respond through sustainable practices. Demographic characteristics appear to play an important role in shaping awareness of carbon footprint and the potential adoption of environmentally responsible farming strategies. These results suggest that sustainability transitions in perennial cropping systems depend not only on technological availability but also on social, informational, and institutional capacities. Strengthening agricultural advisory services, farmer training, and climate adaptation strategies may therefore support the adoption of climate-smart practices in olive cultivation. Furthermore, cooperation and value-chain integration are identified as potentially important mechanisms for facilitating knowledge transfer and supporting the adoption of sustainable practices (e.g., efficient irrigation and optimized input use). However, their contribution to environmental performance and greenhouse gas mitigation cannot be directly inferred from the present perception-based analysis and should be examined in future research using appropriate quantitative or environmental assessment frameworks. Full article

A systematic elucidation of soil ammonia (NH₃) volatilization (SAV) and the underlying drivers is imperative for evaluating NH₃ pollution mitigation strategies and advancing sustainable agricultural practices. Currently, no scientific consensus has been established on the effects of maize–soybean intercropping on SAV across varying nitrogen (N) application rates. A consecutive field experiment was conducted over a 2-year period from 2024 to 2025 with a split-plot design. The experiment comprised three cropping systems (maize monocropping (MM), soybean monocropping (MS), and maize–soybean intercropping (IMS)) and three N application rates (no N application (NN), 20% reduced N application (20%RN), and conventional N application (ConN)). The results demonstrated that N application markedly increased SAV. Accumulative SAV was 4.94–6.01 kg ha⁻¹ under NN treatment, whereas it was 8.21–27.89 kg ha⁻¹ under ConN treatment, 7.25–21.52 kg ha⁻¹ under 20%RN treatment. Under ConN treatment, the accumulative SAV in IMS was 21.34 kg·ha⁻¹ and 27.89 kg·ha⁻¹ in 2024 and 2025, respectively, which were significantly higher than those in MM by 16.80% and 13.33%. Under 20% RN treatment, the accumulative SAV in IMS was 15.46 kg·ha⁻¹ and 19.24 kg·ha⁻¹ in 2024 and 2025, respectively, which were lower than those in MM by 3.07% and 10.59%. SAV was positively correlated with soil ammonium N concentration. Moreover, within an appropriate range, SAV increased in response to rising soil water content and temperature. Collectively, maize–soybean intercropping integrated with a 20% nitrogen reduction mitigated environmental risks associated with reactive nitrogen losses. This system constitutes a stable yield, resource-efficient, and ecologically sustainable cropping practice. Full article

Cotton, a globally significant crop grown in over 100 countries, sustains a $40 billion market and provides employment for over 350 million people worldwide. However, plastic contamination remains a persistent challenge within the industry, degrading cotton fiber quality and disrupting ginning. Manual inspection and optical machine-vision systems struggle when plastic fragments are concealed by fibers or lack sufficient color contrast. To address these challenges, we developed an ultrasonic phased-array imaging system operating at 40 kHz under field-programmable gate array (FPGA) control. Transmitter elements emit pulsed ultrasound along radial paths, separate reflection receivers record echo amplitudes to form acoustic images, and a set of transmission receivers captures signal attenuation, which is overlaid onto the reflection-based image to highlight potential contaminants. In preliminary laboratory-based tests on both seed cotton and lint samples, the system successfully detected visually obscured plastic fragments as small as $2\mathrm{cm}\times 2\mathrm{cm}$ with an angular resolution limit of $\pm 3^{\circ }$. Distinct reflection peaks and corresponding attenuation overlays were produced across the field of view, validating the system’s detection capabilities. These results demonstrate the feasibility of using ultrasonic imaging to reveal concealed plastics in cotton processing. Integrating this approach with existing optical methods could enhance contaminant-removal workflows and improve overall fiber quality and processing efficiency. Full article

Black pepper (Piper nigrum L.) is a crop of significant economic importance in the Amazon, especially in the state of Pará, where intensive production systems predominate. Understanding the spatial variability of soil attributes and their relationship with plant vigor is essential to optimize agricultural practices and input use. Geotechnology-based approaches enable the generation of more precise management zones, contributing to efficient resource use and increased profitability. This study aimed to delimit potential management zones in black pepper crops based on the spatial analysis of soil bulk density (BD) integrated with the NDVI (Normalized Difference Vegetation Index), evaluated using the Bivariate Moran’s Index. The research was conducted in a production area in the municipality of Baião, Pará, Brazil, using soil samples to determine bulk density and UAV images for NDVI calculation. Data were interpolated by kriging and analyzed to identify spatial associations between soil compaction and NDVI. Soil bulk density ranged from 1.14 to 1.80 Mg m⁻³, while NDVI values ranged from 0.07 to 0.91, revealing a clear inverse spatial relationship between soil compaction and vegetative vigor. The integration of BD and NDVI allowed the delineation of site-specific management zones, supporting more efficient decision-making in precision agriculture. Full article

Continuous monocropping and inappropriate fertilization have contributed to nutrient depletion and soil degradation, limiting peanut productivity in subtropical red soil agroecosystems. Although diversified cropping may help alleviate these constraints, the reasons why it improves peanut productivity remain unclear. In this study, we conducted a long-term field experiment in Jiangxi, China, to compare four cropping systems, assess soil nutrients, peanut productivity, and bacterial communities, and further evaluate the role of key taxa through inoculation assays and structural equation modeling. Results showed that diversified cropping improved peanut growth and yield, with the green manure integrated system performing best overall. Diversified cropping also increased soil organic carbon, total nitrogen, and available phosphorus, while reshaping bacterial communities. Several taxa, including Bradyrhizobium, Mycobacterium, Dormibacter, and Ardenticatena, were positively associated with soil nutrients. Inoculation assays further showed that a synthetic consortium assembled from representative strains affiliated with key taxa produced stronger effects on plant growth than a single-strain inoculation. Structural equation modeling identified key taxa as the factor most strongly associated with crop productivity. These findings suggest that higher peanut productivity under diversified cropping was closely associated with concurrent improvements in soil fertility and the enrichment of key taxa. Full article

We reported the first isolation and characterization of Neopestalotiopsis spp. from symptomatic strawberry plants in Nova Scotia, Canada. Morphological and multilocus sequence analyses confirmed that these isolates were closely related to previously identified aggressive Neopestalotiopsis spp. strains from strawberry and blueberry in the southeastern United States and other countries. Five representative isolates were evaluated for pathogenicity on detached leaves, whole plants, and fruits of multiple strawberry cultivars. The results revealed significant variation in virulence, with isolate NS-1 causing the most severe necrosis across all tissue types. Statistical analysis revealed significant effects of isolate, cultivar, and their interaction on disease severity, indicating differential cultivar responses to the tested isolates. Notably, tissue-specific differences were observed, with some isolates being aggressive on leaves but less virulent on fruit or whole plants, reinforcing the importance of multi-organ phenotyping in resistance screening. Phylogenetic analysis clustered the Nova Scotia isolates within the same clade as Neopestalotiopsis isolate 17–43 L from strawberry and isolates from blueberry, suggesting a potential epidemiological link. The shared nursery propagation system of strawberries and blueberries raises the risk of cross-infection, posing a substantial challenge to disease management strategies in both crops. Collectively, these findings underscore the urgent need for continued surveillance, population-level pathogen analysis, and the development of resistant cultivars to mitigate the spread of this emerging and rapidly evolving pathogen. Full article

The use of biostimulants in agriculture is increasing; however, their effects on raspberry remain insufficiently understood. The aim of this study was to evaluate the impact of foliar-applied biostimulants on yield and growth in three primocane raspberry cultivars grown under field conditions using multispectral imaging based on unmanned aerial vehicles. An experiment included a control and four foliar biostimulant treatments based on animal-derived amino acids, plant-derived amino acids, seaweed extract, and seaweed extract combined with animal-derived amino acids. Biostimulant effects on primocane raspberry were found to vary substantially depending on cultivar, environmental conditions, and formulation type, with measurable impacts on both yield formation and vegetative growth. These responses were further supported and characterized using multispectral UAV-based mutlispectral imaging, which enabled effective detection of treatment-related physiological changes. This approach was based on the analysis of relative percentage changes between consecutive measurements of selected vegetation indices, allowing the identification of dynamic physiological responses over time. These findings highlight the need for a more targeted approach to biostimulant use, taking into account cultivar-specific responses and environmental variability. Future research should extend this framework to a broader range of genotypes, cultivation systems, and biostimulant formulations, while integrating remote sensing with other analytical methods to better understand plant physiological responses. Such developments may support the transition toward data-driven and precision-guided biostimulant application strategies in sustainable crop production. Full article

Land cover (LC) change is reshaping terrestrial ecosystems and profoundly impacting sustainable development in Africa, yet the long-term, continental-scale spatiotemporal dynamics of these shifts remain obscured. To address the above issue, this study systematically explores the spatiotemporal dynamics of LC across Africa from 1985 to 2022 by leveraging the fine-resolution remote-sensing-derived GLC_FCS30D LC dataset within a stratified Intensity Analysis framework. To decompose landscape changes into interval, category, and transition levels across five climatic sub-regions of Africa, we systematically evaluate the temporal consistency of land systems. This hierarchical approach disentangles systematic transition pathways from random fluctuations, thereby revealing the distinct regional regimes governing continental transformation of LC. Our results ultimately show a strong LC change acceleration in Africa after 2010, mainly in Southern, Eastern, and Western Africa, which together made up 80 to 90% of the continent’s LC dynamics. During the whole study period, shrubland and grassland had the highest gross turnover due to their high bidirectional volatility. Intensity-wise, forest remained inactive even though it was a persistent net loser to crop in East Africa (2010–2020), to shrub in Southern Africa (1990–2022), and to wetland in West Africa during the post-2000 intervals. Wetland had a major change in dynamics from historical growth during 1985–1990 to systematic decline in 2015–2022. Cropland increased by systematically targeting shrubland and grassland, mainly in East Africa. Additionally, the Sahel contributed 40% of continental grassland to bare area transitions, despite some recovery of grassland in the region. These findings show that aggregate net-change metrics obscure the volatility in African LC; therefore, distinct regional regimes such as agricultural expansion and forest degradation necessitate spatially differentiated management strategies. Full article

Lettuce (Lactuca sativa L.) is an important leafy vegetable crop, yet the efficiency and reliability of genome editing platforms in lettuce remain limited, particularly for precision base editing applications. In this study, we established an optimized PEG-mediated protoplast transformation system for lettuce through systematic evaluation of key parameters, including protoplast density, incubation time, plasmid size, and transformation method. Under optimized conditions, a maximum transient transformation efficiency of up to 81% was achieved. Using this optimized protoplast platform, we comparatively evaluated the performance of three single-base editing systems—adenosine base editor (ABE), glycosylase-based guanine base editor (gGBE), and rice alkylpurine DNA glycosylase-mediated A-to-K base editor (rAKBE)—targeting the LsALS gene, encoding acetolactate synthetase as a herbicide target with great value in weed control. Among the tested editors, ABE exhibited the highest A-to-G editing efficiency, reaching 9.3%. In contrast, gGBE and rAKBE showed lower editing efficiencies. Together, this study established a robust and reproducible protoplast-based platform for transient genome editing in lettuce and provides a practical framework for the rapid evaluation of base editing tools and target sites, firstly for gGBE and rAKBE evaluation in lettuce. The optimized system facilitates functional genomics studies and supports the development of precision breeding strategies in lettuce. Full article

Cultivated lettuce (Lactuca sativa L.) is a major leafy crop and an emerging model for functional genomics within the Asteraceae family, supported by high-quality reference genomes and efficient transformation systems. Although CRISPR/Cas technology offers powerful opportunities for crop improvement, editing efficiency depends on optimized construct architecture and reliable guide RNA (gRNA) validation. However, a rapid platform for evaluating CRISPR reagents in lettuce is still lacking. Here, we developed an efficient hairyroot-based system to accelerate CRISPR/Cas genome editing optimization in L. sativa. Four Agrobacterium rhizogenes strains were compared for hairy root induction in two cultivars, ‘Saladin’ and ‘Osiride’, identifying strain ATCC15834 as the most effective based on transformation frequency and root production. Using this platform, we evaluated multiple CRISPR construct configurations, including alternative promoters for nuclease and gRNA expression. A plant-derived promoter combined with At-pU6-26 variant significantly improved editing efficiency. As a proof of concept, we targeted LsHB2, the putative ortholog of Arabidopsis thaliana ATHB2, a key regulator of the shade avoidance response using SpCas9, SaCas9, and LbCas12a nucleases. The system enabled rapid genotyping and quantitative indel profiling. Overall, this workflow provides a robust framework for efficient guide selection and construct optimization in lettuce genome editing. Full article

Soil salinity poses a major challenge to agricultural productivity, especially threatening food security in arid and semi-arid areas. Traditional soil reclamation methods, such as leaching, chemical amendments, and drainage engineering, usually need large amounts of water, involve high costs, and can lead to environmental problems. This review compiles existing knowledge on innovative strategies for managing saline soils, focusing on buried interlayer systems that use materials like straw, sand, gravel–sand mixtures, and biochar. These interlayers improve soil hydraulic properties by preventing capillary rise, encouraging salt leaching, and reducing surface salt buildup. Biochar stands out as a particularly useful material because of its stability, large surface area, porosity, and high cation exchange capacity. These features help improve soil structure, increase water retention, and effectively retain sodium. Evidence from lab and field tests shows that buried biochar layers can stop salt from moving upward, aid in desalinating the root zone, and boost crop yields. While straw and sand interlayers show potential in reducing salinity, biochar is noted for its multifunctionality and long-term effectiveness in addressing salinity problems. The success of buried biochar systems depends on several factors, including the properties of the biochar, how much is used, how deep it is buried, and the specific soil and climate conditions. This review highlights how these systems work, compares their performance, and points out research gaps, advocating for their potential as a sustainable, resource-efficient way to manage salinity and improve soil health over the long term. A substantial proportion of the existing evidence is derived from controlled laboratory studies, and the buried biochar layer approach remains an emerging technique that requires further validation under field conditions. Still, significant knowledge gaps persist regarding long-term performance and water-salt dynamics, while site-specific soil variability and scalability challenges may limit the effective implementation of biochar interlayer systems under field conditions. Full article

Pruning is a critical horticultural practice that requires continuous interpretation of plant structure to maintain crop health and prevent disease. Manual identification of pruning-relevant regions is labor-intensive and limits scalability in precision agriculture. This study presents a cognitive vision-based pruning region identification system using a You Only Look Once version 9 model to detect lateral branches, lower leaves, and diseased leaves in Solanum lycopersicum. A custom dataset of 4905 augmented images was used for training and evaluation. The model achieved 82.86% precision, 77.24% recall, 79.96% F1-score, and 83.21% mAP. Deployment on Raspberry Pi 5 demonstrated real-time, cloud-independent edge inference, indicating the feasibility of low-cost cognitive vision systems for smart agriculture. Full article

Soil salinization is a major threat to global crop production. Tartary buckwheat (Fagopyrum tataricum), valued for its hardiness in marginal environments, provides an excellent system for studying plant salt tolerance. Using an integrated multi-omics approach, we deciphered the physiological, metabolic, and transcriptional responses of Tartary buckwheat to prolonged NaCl stress. Physiological profiling confirmed membrane damage alongside osmotic adjustment via proline accumulation and a phased antioxidant response. Metabolomics revealed extensive reprogramming, with dynamic enrichment in pathways of flavonoid biosynthesis, lipid metabolism, and the TCA cycle. Transcriptomics delineated a time-specific cascade from early signaling to late defense activation. Critical regulators within ABA and MAPK signaling pathways showed fine-tuned, divergent expression; for instance, SnRK2.3 was suppressed while specific PP2Cs were induced, and FtMAPK10 was dramatically up-regulated. Integrated analysis demonstrated coordinated induction of osmoprotectant synthesis (e.g., galactinol and betaine pathways) and a rewiring of central carbon metabolism. Our findings reveal a sophisticated, multi-layered adaptation strategy in Tartary buckwheat, integrating enhanced osmolyte production, antioxidant defense, membrane remodeling, and metabolic reprogramming, orchestrated by key signaling networks. This study provides a comprehensive molecular framework for salt tolerance and identifies valuable genetic targets for improving crop resilience. Full article

Climate change is intensifying hydroclimatic extremes and agricultural water scarcity, sharpening trade-offs among yield stability, water saving, and farm incomes in major grain regions. Existing studies often optimise cropping patterns or irrigation schedules separately, seldom embedding yield robustness and policy instruments in one decision framework. We propose an integrated Machine-learning–System-dynamics–Non-dominated-sorting-genetic-algorithm-II (ML–SD–NSGA-II) framework linking long-horizon meteorological scenario generation, crop–water–economy feedback and multi-objective optimisation of crop areas and irrigation depths. ML models generate daily climate sequences to drive an SD model of soil moisture, yield formation, basin-scale allocable water, and farm returns; NSGA-II searches Pareto-optimal strategies that maximise profit and irrigation water productivity while minimising yield deviation. Applied to a rice–wheat irrigation system in the middle Yangtze River Basin, knee-point solutions lift irrigation water productivity by about 14%, maintain near-baseline profits, and reduce yield deviation. Scenario tests with block tariffs, quota-based subsidies, and extreme drought show pricing mainly curbs low-value water use in normal years, while under drought, physical scarcity dominates and economic tools offer limited buffering. This reveals the existence of a scarcity-regime threshold beyond which economic instruments become second-order relative to binding biophysical constraints. The framework supports transparent ex ante testing of tariff–subsidy packages for irrigation governance and adaptation. Full article

The increasing demand for innovative strategies in sustainable food production—driven by rapid global population growth, particularly in sub-Saharan Africa (SSA)—necessitates urgent attention to agricultural resilience. Recent technological advancements have enhanced crop productivity, post-harvest preservation, and environmentally sustainable farming practices. However, three critical bottlenecks remain: (i) the lack of accurate, maize-specific yield prediction methods tailored to SSA; (ii) limited multimodal modeling approaches capable of capturing complex, nonlinear interactions among heterogeneous data sources; and (iii) a lack of explainability mechanisms, which render high-performing models “black boxes” and hinder stakeholder trust. To address these gaps, this study presents an explainable machine learning framework for smart maize yield prediction. We integrate multimodal SSA-specific soil, crop, and weather data to capture the multi-dimensional drivers of maize productivity. Six diverse algorithms—including extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), categorical boosting (CatBoost), support vector machine (SVM), random forest (RF), and an artificial neural network (ANN) combined with a k-nearest neighbors (kNN)—were benchmarked to evaluate predictive performance. To ensure robustness against spatial heterogeneity, we employed a Leave-One-Plot-Out (LOPO) cross-validation strategy. Empirical results on unseen test data identify CatBoost as the best-performing model, achieving a coefficient of determination of ($R^2 =~76\%$), demonstrating its ability to capture complex, nonlinear relationships in agricultural data. To enhance transparency and stakeholder trust, we integrated Local Interpretable Model-agnostic Explanations (LIME), providing plot-level insights into the physiological and environmental drivers of maize yield. Together, these contributions establish a scalable and interpretable modeling framework capable of supporting data-driven agricultural decision-making in SSA. Full article