Search Results (203)

Optimizing harvest time and oil production requires accurate olive fruit quality characterization. Traditional chemical methods are costly and tedious, leading to poor monitoring resolution and reliance on subjective visual assessments. While spectroscopy offers a non-destructive alternative, standard equipment remains complex and prohibitively expensive for smallholder farmers. To address this, we propose a methodology using a custom-made, low-cost multispectral device. Built upon the AS7265x board, the system acquires 18 spectral bands in the visible and near-infrared range (410–940 nm). We used these spectral data to feed artificial neural network (ANN) models for estimating the quality of intact olives. During a two-season field experiment, we monitored ripening to acquire spectral signatures and ground-truth values for oil content per fresh weight (OCFW), oil content per dry matter (OCDM), moisture (M), and titratable acidity (TA). External validation showed high accuracy for OCFW (R²p = 0.86), OCDM (R²p = 0.86), and M (R²p = 0.89), proving the system’s reliability. However, TA estimation showed lower performance (R²p = 0.21), indicating limited spectral correlation. These findings pave the way for affordable, real-time smart farming tools for olive quality monitoring. Full article

This bibliometric study maps olive (Olea europaea L.) yield prediction research as a coherent scientific domain for the first time. A Scopus query (27 February 2026) yielded 84 peer-reviewed articles (2002–2025), from which co-authorship network analysis, Bradford’s and Lotka’s Laws, Latent Dirichlet Allocation topic modelling (LDA), and OLS regression on citation counts were applied. Publication output increased nearly fourfold across three periods: 1.7 articles yr⁻¹ (2002–2014), 4.4 yr⁻¹ (2015–2019), and 6.7 yr⁻¹ (2020–2025). The 84 articles involve 382 authors, 61 journals, and 1551 citations (H-index = 22). Network analysis reveals a concentrated Spanish–Italian co-authorship axis. OLS regression (adj. R² = 0.267) identifies article age and abstract length as the only significant citation predictors, consistent with cumulative exposure time and study scope as structural drivers. Term-frequency screening against 18 a priori concepts finds that transfer learning, federated learning, hyperspectral imaging, digital twins, and SHAP-based explainability are absent or marginal. The field is producing more papers than ever on a narrowing methodological base geographically concentrated in the Mediterranean basin. Priority gaps—explainable AI, multi-region datasets, sensor-fusion pipelines, and federated data infrastructure—align directly with European Farm to Fork and Horizon Europe objectives. Full article

Optimizing olive orchard management requires timely, per-tree data to enhance productivity and sustainability. Unoccupied aerial vehicle (UAV)-based red, green, and blue (RGB) imagery offers a low-cost solution for acquiring high-resolution spatiotemporal insights for orchard management, which are not yet common in Tunisia. This study monitored tree structural parameters, leaf area index (LAI), and leaf nitrogen content (%N DW) in two Tunisian olive orchards during 2022 and 2023. UAV-derived imagery was photogrammetrically processed into 3D point clouds and analyzed using an automated approach. Target variables of the automated approach included tree-wise estimates of height, projected crown area, and crown volume, as well as raster cell counts of the canopy cloud and spectral indices such as the normalized green-red difference index (NGRDI) and green leaf index (GLI). In addition, the estimated parameters per tree were used to model LAI and leaf nitrogen content. Analyses were conducted separately for trees represented by a high and a low number of points in the dense point cloud. Outcomes were compared to reference data collected in the field on dates close to the UAV flights. The findings showed strong relationships for the projected crown area (R² = 0.82 and 0.91) and tree height (R² = 0.89 and 0.88) when compared to reference values. Linear regression models for LAI (R² = 0.73 and 0.68) and crown volume (R² = 0.85 and 0.91) estimation also show strong relationships. However, leaf nitrogen estimation was not feasible from RGB spectral index values, as it showed a weak relationship (R² = 0.34). A dataset with multispectral imagery could overcome this limitation but would increase costs, making it less suitable for the low-budget approach required in price-sensitive farming contexts, particularly in low-income regions. Full article

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

This article analyzes the scale, fluctuations and geographical distribution of olive (Olea europaea) cultivation in Palestine over 550 years, from the Late Mamluk period (1300–1517), through the Ottoman era (1517–1917), until the end of the British Mandate in 1947. Although olive oil played a dominant role in the diet and the local economy, there is currently no research that measures and quantifies the number of olive trees or the number of villages and towns that cultivated olive trees and produced olive oil. We reconstruct the agricultural landscape with its vast olive groves and examine the cultural history of olive tree farming, the growth of the olive oil industries and their economic role and importance. The earliest figures we have, that are from the year 1596, show that 400 villages cultivated 1,400,794 olive trees. By 1943, there were 6,053,367 olive trees that were cultivated by 644 villages. We found a strong correlation (R² = 0.96, p < 0.01) between the number of olive trees and the number of villages, indicating that olive oil demand and the olive oil industry align with population size. The research data derives from a variety of medieval local chroniclers, as well as diaries by European, North African and Middle Eastern travelers who provide descriptions of olive groves and the olive oil industry. Among the most important sources are the 1596 Ottoman tax registers. The tax registers are the first document that present clear-cut figures on the numbers of olive trees, olive presses and the names of the villages that cultivated olive groves. The main sources for the last period dealt with in this study are the British Mandate maps (1943), which display the acreage of the different crops across Palestine. The data from the maps is supplemented by two modern works on olive cultivation written by agronomists Assaf Goor (b. 1894) and Ali Nasouh (b. 1906) who were born in Palestine and employed by the British department of agriculture. The analysis of data shows that demands of local and oversea markets; the olive oil soap industry, which was based on the local olive oil; as well as competing agricultural crops like sugarcane, cotton and citrus, contributed to a complex economic structure. Olive tree cultivation did not depend on government investment. Olive groves in Palestine were rain fed, and, except for the harvest, they required relatively few working days a year. Hence, moderate policies (low taxation during periods of drought and low yields) adopted by enterprising local rulers and the central British government created a unique and relatively balanced relationship between rulers and farmers, which encouraged olive cultivation and led to a constant increase in the number of olive trees and the development of the olive oil industry. Full article

To develop carbon farming practices, decision-makers need detailed spatial data on the soil carbon sequestration (SCS) opportunities that conventional crop and soil management creates. This study exploratorily assessed SCS capacity across agricultural land in the Valencian Community using a simple carbon balance model within a GIS framework. Within this modelling approach, maps of net primary production (NPP), land-use-derived crop harvest indices, current soil organic carbon (SOC) stocks, and NPP and SOC mineralization coefficients were combined. Results show that while NPP across Valencian croplands and grasslands ranges from 0.64 to 6.43 Mg C ha⁻¹ yr⁻¹ (mean 2.42 Mg C ha⁻¹ yr⁻¹), the actual SCS capacity is much lower, ranging from −0.04 to 1.31 Mg C ha⁻¹ yr⁻¹ (mean 0.25 Mg C ha⁻¹ yr⁻¹). Significant variation exists among land uses: rice paddies exhibit the highest SCS capacity, while olive groves present the lowest. Between 2017 and 2021, SCS in Valencian agroecosystems may have offset the sector’s greenhouse gas (GHG) emissions, primarily driven by pasture and citrus because of their large extent and moderate SCS capacity, making agriculture a net-zero emitter. However, helping achieve cross-sectoral mitigation targets will depend in part on the widespread deployment of regenerative soil management (RSM) practices. While this study identifies priority areas for RSM implementation, further research is needed to determine which specific practices are most suitable for each location to maximize SCS. Full article

Olive groves in Mediterranean regions are being increasingly exposed to drought and heat extremes, intensifying the interannual yield variability. This study presents an integrated smart-farming framework that links soil context, climate forcing and satellite-observed canopy dynamics to enhance the interpretability and transferability of yield indicators at the parcel scale in southern Spain. Using SoilGrids root-zone properties and the Sentinel-2 time series of the normalized difference vegetation index (NDVI), we first classified parcels into three edaphic clusters. The canopy development was then expressed in thermal time using growing degree days (GDD), enabling phenology-aligned comparisons across campaigns. Two robust patterns emerged: (i) the cumulative NDVI up to 520 GDD showed a consistent negative association with both the biomass and the oil yield, suggesting an early-season vegetation trade-off and carry-over effects typical of perennial systems, and (ii) the rainfall accumulated during a thermally defined window (120–480 GDD) strongly estimated the yield in the subsequent year ($R^2=0.83$–0.97 across soil clusters). By anchoring both vegetation and precipitation indicators to physiologically meaningful thermal milestones, the proposed framework avoids arbitrary calendar windows and enhances the interpretability, cross-year comparability, and scalability. Under projected increases in drought frequency and heat extremes, such hydro-thermal scaling approaches offer a robust basis for early yield forecasting, cooperative-level production planning, and adaptive management in Mediterranean olive systems. Full article

The use of olive by-products in livestock farming is a valuable resource, given their high levels of bioactive compounds with antioxidant and health-promoting properties. This preliminary study adopted an integrated approach to evaluate the influence of dietary Olea europaea L. polyphenols on animal welfare, physiological stress response, intestinal morphofunctional traits, and meat quality in Neroametà finishing pigs, a novel Casertana × Large White genetic line (Neroametà). Thirty pigs reared under extensive farming conditions were randomly allocated to two groups (n = 15): a control group fed a standard diet (C) and a treatment group (OL) supplemented with 300 mg/head/day of olive polyphenolic extract for 90 days. The study focused on the systemic correlation between host health and product quality. Meat composition, rheological properties, meat antioxidant activity, stress parameters, and fatty acid profiles of the longissimus lumborum and psoas major muscles were analyzed. Results showed that the OL diet significantly modulated the HPA axis, as evidenced by a marked reduction in plasma ACTH and cortisol levels, alongside improved antioxidant status. These physiological changes were positively associated with a trophic effect on the intestinal mucosa, characterized by increased villus height and a more favorable villus/crypt ratio. Regarding meat quality, the OL group exhibited superior oxidative stability, optimized pH decline, and an improved intramuscular fatty acid profile (increased MUFA and n-3 PUFA, reduced SFA). Despite the pilot scale of 30 animals, these findings provide a solid foundation for characterizing the Neroametà breed. In conclusion, Olea europaea L. polyphenols act as a multi-level modulator, enhancing physiological resilience and meat quality, offering a sustainable strategy for high-quality pork production in line with circular economy and One Health principles. Full article

In water-limited areas, scientific irrigation scheduling is suggested as a valuable tool to optimize the amount and frequency of water required by crops. MOPECO, based on local data including soil texture, crop growth stages, climatic conditions, weather forecast and irrigation scheme characteristics, can be employed to define the optimal irrigation strategy. This tool was implemented within the SUPROMED project and tested in real farms managed by progressive farmers (leader farmers) who had been advised by the research team to monitor irrigation for seven major water-demanding crops (wheat, oat, onion, maize, olive, almond and pistachio). The obtained results were compared with conventional irrigation management as usually practiced by farmers (average farmers), based on their local experiences and knowledge, for the same crops growing in very similar conditions. Water use and energy efficiency use as well as irrigation cost and economic profitability were compared. The results showed that the advised irrigation scheduling provided an effective way to improve water and energy efficiency and increase yields and economic profitability with respect to current farm management. On average, the scientific method (MOPECO) reduced water consumption and energy use by 25.5% and 22%, respectively, achieving a 29% increase in yield and a reduction of 18% in water irrigation cost. The gross margin per hectare was also higher, increasing by 26%. The results also showed that, under advised management, the farmers’ income became more resilient to market price variability, allowing the farmers to have better economic viability. Based on these results, our study suggested that the adaptation of scientific models such as MOPECO to farmers’ requirements and their implementation through training activities could provide end users with a significant opportunity to improve the agronomic and economic efficiency of water and energy in arid regions. Full article

This study examines the economic feasibility of adopting digital technologies for the early detection of Verticillium wilt in olive cultivation in Northern Evia, Greece. A Net Present Value (NPV) framework with different scenarios was employed to derive three adoption thresholds: the minimum effectiveness required to break even, the maximum tolerable cost at a target effectiveness, and the break-even olive-oil price. The results reveal substantial variability across scenarios, reflecting uncertainty in both disease dynamics and market conditions. Key determinants of feasibility include detection effectiveness, adoption costs, olive oil prices, and disease incidence. Larger holdings consistently face more favourable thresholds due to economies of scale, while smaller farms remain constrained unless collective actions or policy support reduces costs. The preliminary evidence indicates that early detection technologies can strengthen the resilience of olive farms, especially in high-incidence areas, though feasibility remains highly sensitive to costs, prices, and pathogen pressure. Finally, the findings underscore the need for targeted policy interventions to facilitate broader adoption. Full article

Women play a key role in Greek olive cultivation, a sector at the heart of local economies currently facing increasing pressures from climate change. This study explores gender roles, responsibilities, and access to resources shaping the adoption of sustainable agriculture practices. Through in-depth interviews with female farmers on the island of Crete, the results show that women face limited access to training and financial services as well as gender-based discrimination and the hidden caregiving and house working tasks. However, they manifest a strong openness towards sustainable practices, driven by environmental values, even among those lacking decision-making authority. Despite these challenges, women show a positive attitude toward learning and innovation, calling for more institutional support and training opportunities. This study sheds light on the need for recognition of women’s roles in agriculture, particularly in the context of climate adaptation, and offers practical recommendations to improve gender roles within the olive sector. Full article

The olive psyllid (Euphyllura olivina), previously considered a secondary pest in Spain, is becoming more prevalent due to climate change and rising average temperatures. Its cottony wax secretions can cause substantial damage to olive crops under certain climatic conditions. Traditional monitoring methods for this pest are often labor-intensive, subjective, and impractical for large-scale surveillance. This study presents an automatic image segmentation approach based on deep learning to detect and quantify the cottony masses produced by E. olivina in olive trees. A well-annotated image dataset is developed and published, and a thorough evaluation of current camouflaged object detection (COD) methods is carried out for this task. Our results show that deep learning-based segmentation enables accurate and non-invasive assessment of pest symptoms, even in challenging visual conditions. However, further calibration and field validation are required before these methods can be deployed for operational integrated pest management. This work establishes a public dataset and a baseline benchmark, providing a foundation for future research and decision-support tools in precision agriculture. Full article

This study aims to examine the factors influencing the acceptance of extruded pellet (EP) usage among Korea’s olive flounder farming households by analyzing their acceptance factors to provide recommendations for its wider adoption. A survey was conducted among olive flounder farming households, and 188 valid questionnaires were collected. To examine the factors influencing EP acceptance intention, the UTAUT2 (extended unified theory of acceptance and use of technology) model was used. The independent variables were categorized into performance expectancy, effort expectancy, facilitating conditions, social influence, price value, and reliability as independent variables, while acceptance intention was considered as the dependent variable, to derive measurement items. In addition, the differences between the two groups were analyzed by using the aquaculture region and the manager’s experience as moderating variables. The hypothesis testing showed that performance expectancy, effort expectancy, social influence, price value, and reliability factors had a positive effect on acceptance intention, while facilitating conditions did not show a significant effect. The analysis of the moderating effect of the aquaculture region indicated a significant difference between the Jeju-do and Jeollanam-do groups. Conversely, the moderating effect of experience showed no significant difference between those with more experience (≥10 years) and those with less (<10 years). Full article

The latest climate change predictions indicate that the sea level will accelerate in the coming decades as a direct consequence of global warming. This is expected to seriously threaten low-lying coastal areas worldwide, resulting in severe coastal flooding with significant socio-economic impacts, leading to the loss of coastal settlements, exploitable land, and natural ecosystems. The main objective of this study is to provide a first-order preliminary estimation of potential inundation extents along the northern coastline of the Amvrakikos Gulf, a deltaic complex formed by the Arachthos, Louros, and Vouvos rivers in Western Greece, resulting from long-term sea-level rise induced by climate change, using the integrated Bathtub and Hydraulic Connectivity (HC) inundation method. A 2 m resolution Digital Elevation Model (DEM) was used, along with local long-term sea-level projections, for the years 2050 and 2100. Additionally, subsidence rates due to the compaction of deltaic sediments were taken into account. To assess the area’s proneness to inundation caused or enhanced by sea-level rise, the extent of each land cover type, the Natura 2000 Network protected area, the settlements, the total length of the road network, and the cultural assets located within the inundation zones under each climate change scenario were considered. The analysis revealed that under the optimistic SSP1-1.9 scenario of the Intergovernmental Panel on Climate Change (IPCC), areas of 40.81 km² (min 20.34 km², max 63.55 km²) and 69.10 km² (min 41.75 km², max 88.02 km²) could potentially be inundated by 2050 and 2100, respectively. Under the pessimistic SSP5-8.5 scenario, the inundation zone expands to 42.56 km² (min 37.05 km², max 66.31 km²) by 2050 and 84.55 km² (min 67.54 km², max 116.86 km²) by 2100, affecting a significant portion of ecologically valuable wetlands and water bodies within the Natura 2000 protected area. Specifically, the inundated Natura 2000 area is projected to range from 37.77 km² (min 20.30 km², max 46.82 km²) by 2050 to 50.74 km² (min 38.71 km², max 62.84 km²) by 2100 under the SSP1-1.9 scenario, and from 39.34 km² (min 34.53 km², max 49.09 km²) by 2050 to 60.48 km² (min 49.73 km², max 82.5 km²) by 2100 under the SSP5-8.5 scenario. Four settlements with a total population of approximately 800 people, as well as 32 economic facilities most of which operate in the secondary and tertiary sectors and are small to medium-sized economic units, such as olive mills, farms, gas stations, spare parts stores, construction companies, and food service establishments, are expected to experience significant exposure to coastal flooding and operational disruptions in the near future due to sea-level rise. Full article

In light of the exponential rise in feed costs within the livestock sector, the scientific research and valorization of novel agro-industrial by-products have essential strategies in animal nutrition. The overall objective of this study was to characterize and evaluate the inclusion of a novel olive pulp included at 12% of the concentrate on a dry matter basis in the diet of Murciano–Granadina goats to assess its effects on ruminal fermentation, nutrient digestibility, energy and nitrogen metabolism, and milk yield and composition. Two experiments were conducted, taking into account two groups (control group, CTL, and an experimental group) with the inclusion of 12% olive pulp in the concentrate (OPD): one in vivo trial in metabolic cages (n = 10 nulliparous female goats (34.1 ± 0.70 kg) per treatment) was conducted to evaluate digestibility, nitrogen balance, and energetic utilization; and a second on-farm production trial (n = 24 adult dairy goats (53.6 ± 1.14 kg) per treatment). The results showed no significant differences in energy balance or microbial protein synthesis between CTL and OPD (p > 0.05). However, the OPD exhibited higher digestibility of dry matter (71.2 vs. 68.8%; p = 0.028), organic matter (70.8 vs. 68.4%; p = 0.026), and crude fat (85.9 vs. 83.4%; p = 0.024), but lower crude protein digestibility (70.7 vs. 73.4%; p = 0.012) and nitrogen excretion (1.24 vs. 1.44 g/kg^0.75; p < 0.001). Additionally, ruminal butyrate concentrations were higher in OPD goats (13.5 vs. 11.3 mol/100 mol of total short-chain fatty acids; p = 0.020). Although milk yield remained unaffected, the OPD exhibited higher milk protein (4.17 vs. 3.79%; p = 0.036) and conjugated linoleic acid (0.620 vs. 0.400%; p < 0.001) concentrations compared to CTL. These findings demonstrate that the inclusion of 12% of the novel olive pulp in goat concentrate is a viable feeding strategy that maintains productive performance while enhancing the nutritional quality of milk. Full article