Search Results (48)

Ants are widely used as bioindicators because of their sensitivity to environmental change and their functional roles in ecosystems. This study presents the first comparative analysis of ant communities in two habitats, an agricultural system and a semi-natural forest, within the Natural Park of Montesinho (northeastern Portugal). From May to October 2022, four plots were sampled per habitat: (i) semi-natural oak forest and (ii) chestnut orchard under human management, using five pitfall traps in each plot. A total of 1969 ants were captured, representing 32 species and 15 genera. Traditional chestnut orchards supported more exclusive species and greater functional diversity, dominated by generalist and thermophilic taxa. In contrast, oak forests hosted more specialist and cold-adapted species, which may reflect a higher structural stability. Seasonal variation was more pronounced in chestnut orchards, consistent with disturbance-driven dynamics. The functional composition also differed: chestnut orchards favoured granivores and scavengers, while oak forests supported predators and mutualists. These findings highlight the value of ant communities as sensitive indicators of land use and ecosystem condition in Mediterranean mountain systems. Full article

Abandoned and semi-abandoned chestnut (Castanea sativa Mill.) orchards can be restored to production by removing invasive vegetation and pruning overgrown crowns. Both interventions stimulate a strong reaction from the old trees, which sprout abundant suckers at the root collar and along the stem. Suckers must be removed promptly to boost fruit-bearing branches. Sucker removal can be achieved with traditional manual tools (e.g., pruning saws or pole saws) or with more modern semi-mechanized methods relying on battery-powered saws. The latter are much more expensive than the former and questions arise regarding the minimum amount of work necessary to justify their purchase. This study compared the two methods, showing that the introduction of a battery-powered saw would boost work productivity by 67%, that is, from 18 to 31 trees per day. At current cost levels, that productivity margin would justify investment in a semi-mechanized system when treating at least 100 trees per year. In that case, the de-suckering cost would amount to 3.8 and 3.9 € tree⁻¹ respectively for semi-mechanized and manual systems. Shifting from manual to semi-mechanized operation also resulted in a significant reduction in the physiological workload imposed on the workers, which would decrease by −4% to −71% depending on the circumstances. Productivity and workload variations followed the same trend, but their magnitude was highly dependent on the individual worker. Full article

This study aimed to detect growth stress at the individual-tree level in chestnut (Castanea crenata Sieb. et Zucc.) plantations using UAV-based RGB orthomosaic and multispectral imagery and to determine an optimal NDVI threshold for stress classification. UAV surveys were conducted over a 21 ha chestnut orchard located in Gongju, Chungcheongnam-do, Republic of Korea. NDVI was calculated and analyzed at the individual-tree level using multispectral imagery. Based on field observations, 100 healthy trees and 23 stressed trees were selected for statistical analysis. The mean NDVI value was 0.900 ± 0.012 for healthy trees and 0.816 ± 0.013 for stressed trees, showing a highly significant difference (p < 0.001). ROC analysis showed excellent classification performance with an AUC of 1.00. The optimal NDVI threshold determined using Youden’s index was 0.855. Independent validation in another chestnut plantation approximately 1 km away achieved high classification accuracy using the same threshold. These results indicate that UAV-based multispectral imagery combined with NDVI analysis provides an effective approach for early detection of growth stress and precision monitoring at the individual-tree level in chestnut plantations. This study provides a practical and efficient approach for the early detection of growth stress at the individual-tree level, enabling early intervention against potential declines in tree vitality and proactive management in chestnut orchards. The proposed NDVI threshold-based method offers a simple yet robust tool that can be readily applied in precision forestry and smart agriculture to support large-scale monitoring and informed management decisions for maintaining orchard productivity, enabling cost-effective early intervention at the individual-tree level, which is difficult to achieve using conventional ground-based surveys in complex mountainous orchards. Full article

Traditional mechanized chestnut harvesting is too costly for small producers, non-selective, and prone to damaging nuts. Accurate, reliable detection of chestnuts on the orchard floor is crucial for developing low-cost, vision-guided automated harvesting technology. However, developing a reliable chestnut detection system faces challenges in complex environments with shading, varying natural light conditions, and interference from weeds, fallen leaves, stones, and other foreign on-ground objects, which have remained unaddressed. This study collected 319 images of chestnuts on the orchard floor, containing 6524 annotated chestnuts. A comprehensive set of 29 state-of-the-art real-time object detectors, including 14 in the YOLO (v11–v13) and 15 in the RT-DETR (v1–v4) families at various model scales, was systematically evaluated through replicated modeling experiments for chestnut detection. Experimental results show that the YOLOv12m model achieved the best mAP@0.5 of 95.1% among all the evaluated models, while RT-DETRv2-R101 was the most accurate variant among the RT-DETR models, with mAP@0.5 of 91.1%. In terms of mAP@[0.5:0.95], the YOLOv11x model achieved the best accuracy of 80.1%. All models demonstrated significant potential for real-time chestnut detection, and YOLO models outperformed RT-DETR models in terms of both detection accuracy and inference, making them better suited for on-board deployment. This work lays a foundation for developing AI-based, vision-guided intelligent chestnut harvest systems. Full article

Visual harvest maturity is a key visual phenotype for orchard management and harvesting decisions, yet chestnut fruits in natural orchards often exhibit weak color contrast, subtle texture variation, blurred boundaries, and frequent occlusion under complex illumination. This study addresses RGB-based visual harvest maturity recognition and proposes AHM-YOLO, an improved instance segmentation model built upon YOLOv11n-seg. The proposed model enhances maturity-related feature representation by strengthening color- and edge-sensitive cues, stabilizing spatial dependencies under occlusion and illumination variation, and improving cross-scale semantic consistency in dense orchard scenes. A chestnut dataset collected from a typical orchard in Shandong Province is annotated into three visual harvest maturity stages (unripe, semi-ripe, and ripe). To ensure reliable evaluation, the dataset is partitioned at the acquisition unit level, and all experiments are conducted using multi-seed repeated runs. Experimental results show that AHM-YOLO achieves 84.3% Mask mAP50 and 72.2% Mask mAP50–95, demonstrating consistent improvements over the baseline model in complex orchard environments. Full article

Climate change is increasingly exposing sweet chestnut (Castanea sativa Mill.) to more frequent and prolonged drought events, which can compromise growth and nut production, particularly in Mediterranean environments. Understanding how trees respond physiologically to ecological and environmental constraints requires a detailed analysis of their architectures. The aim of this study was to investigate how the shoot vigour and leaf water status of mature chestnut trees vary with height within the canopy. Three mature chestnut trees with distinct crown architectures were selected in a traditional chestnut orchard in Central Italy; the differences in crown structure reflected individual tree development under comparable pruning practices. Morphological traits, leaf water status, and physiological parameters related to chlorophyll were measured directly within the canopy by professional tree climbers, allowing access to both lower and upper shoots during the growing season of 2020. One tree, called “Tree 1,” characterised by low bifurcation, with all epicormic shoot cluster (complexes) located on the two main branches and none on the main stem, showed partial vertical differences, mainly in water status and chlorophyll traits. “Tree 2”, characterised by high bifurcation and shoots running along the main stem, exhibited clear vertical gradients: lower-canopy shoots had larger leaf areas and more dry mass, higher relative water content, and better photosynthetic performance index e values than upper shoots. At the end, “Tree 3”, with the same architecture as Tree 1, displayed no consistent vertical trends. These findings indicate that individual tree architecture modulates hydraulic constraints and shoot vigour, even in hydraulically efficient epicormic branches. Although canopy access constraints limited the number of trees and measurements, this study—among the few to conduct in-canopy measurements on large, mature trees—provides valuable guidance for pruning and crown management, suggesting that lowering and simplifying the crown can enhance water-use efficiency, shoot vigour, and drought resilience in traditional and low-input chestnut orchards. Full article

The successful establishment of young chestnut orchards is increasingly challenged by drought stress and limited irrigation availability, especially in areas with limited water access. This study evaluated the effects of ridge planting and plastic foil mulching, individually and in combination, on the early growth and stress physiology of vegetatively propagated Castanea sativa × C. crenata ‘Marsol’ trees under rainfed conditions. Over a two-year field trial, vegetative traits, photosynthetic pigments, and leaf phenolic profiles were assessed to determine treatment effects. Ridge planting combined with foil mulching significantly improved tree growth, leading to a 2.6-fold increase in leaf number and 1.6-fold increase in height compared to control (flat planting without foil). This treatment also minimized stress indicators, such as chlorosis and elevated phenolic content. Notably, the ellagitannin chestanin emerged as a dominant stress-related metabolite in the first year, suggesting its potential as an early biochemical marker of transplantation stress. Over time, a compositional shift in phenolic groups, from hydroxycinnamic acids and flavanols to flavonols and hydroxybenzoic acids, was observed, reflecting the plant’s transition from acute stress response to developmental acclimation. These results support ridge planting with foil as a practical, climate-adaptive solution for chestnut orchard establishment and highlight chestanin as a candidate marker for stress monitoring in young trees. Full article

Chestnut (Castanea sativa Mill.) cultivation holds significant ecological and economic importance in Greece and other Mediterranean regions, where it represents a traditional crop with growing commercial demand in mountainous areas. Irrigation is critical for maintaining orchard productivity, especially under Mediterranean conditions where present climate conditions intensify heat stress and late-summer drought. In this study, the effects of different irrigation regimes—full irrigation (FI), deficit irrigation (DI), and no irrigation (NI)—were evaluated over two consecutive years (2017–2018) in an intensively managed chestnut orchard in Greece. FI enhanced fruit yield, nut size, and edible fraction, whereas DI and NI significantly reduced production and fruit set, while increasing nut dry matter and perisperm proportion of chestnuts. Plant physiological parameters, including midday stem water potential and chlorophyll fluorescence, confirmed the strong sensitivity of chestnut trees to water stress. Leaf dry matter, specific leaf weight, and total leaf chlorophyll content demonstrated either steady trends or slight reductions across years and treatments. Year-to-year variation was considerable, driven primarily by different summer temperatures, June to September rainfall, and the number of nuts per tree. Supplemental irrigation during nut development is essential for commercial chestnut production in the Mediterranean increasingly affected by climate. Full article

Chestnut (Castanea sativa Mill.) is a key crop in Mediterranean regions increasingly threatened by recurrent drought stress. To investigate cultivar-specific tolerance mechanisms, we evaluated four Portuguese cultivars (Longal, Judia, Martaínha, and ColUTAD^®) across four orchards with contrasting water regimes. Physiological (midday stem water potential—Ψ_wmid, soluble sugars, electrolyte leakage and proline) and biochemical traits (phenolics, flavonoids, catalase, peroxidase, ascorbate peroxidase and ferric reducing antioxidant power) were quantified under a natural drought gradient. Results revealed that environmental factors had a stronger influence than genetic background. Longal showed robust osmotic adjustment with high proline and soluble sugar levels, alongside stable starch reserves; Judia relied on inducible antioxidant activity, particularly peroxidase and ascorbate peroxidase; and Martaínha exhibited intermediate plasticity, whereas ColUTAD^® was consistently stress-sensitive, with weaker defences and greater membrane damage. Clustering analysis confirmed that location effects outweighed cultivar differences, separating orchards into conservative strategies (better water balance, higher starch, stronger peroxidase activity) and stress-adaptive strategies (enhanced enzymatic antioxidants). Overall, resilience in chestnut is not determined by a single trait but by a synergistic network of osmotic regulation, membrane protection, and antioxidant activity. Traits such as proline accumulation, starch stability, and inducible enzyme activation emerged as reliable biochemical indicators of tolerance. These findings provide a physiological basis for selecting climate-resilient cultivars and designing site-specific management strategies, thereby supporting the sustainability of chestnut production under Mediterranean climate change scenarios. Full article

The chestnut gall wasp (Dryocosmus kuriphilus Yasumatsu) is an invasive pest that attacks species of the genus Castanea, inducing gall formation on buds and leaves, which can significantly reduce tree growth, fruiting, and overall chestnut production. Native to China, D. kuriphilus has become a serious threat to chestnut orchards worldwide. Torymus sinensis Kamijo, a parasitoid also originating from China, is highly specific to D. kuriphilus and is currently considered the most effective biological control agent against this pest. This study aimed to evaluate the establishment of T. sinensis as well as its effectiveness in controlling D. kuriphilus at release sites between 2020 and 2023. Releases of T. sinensis were conducted in the municipality of Bragança with a sex ratio of 120 females to 70 males. The parasitoids were randomly released across three chestnut trees with infestation levels ranging from moderate (26–50% of the canopy affected by galls) to very severe (>80% of the canopy affected). At each release site, 250 galls were collected annually, and 10% of these galls were dissected to calculate parasitism rates by T. sinensis. Results revealed a positive correlation between the monitoring year and the parasitism rate. Following the releases, parasitism rates increased gradually, reaching values between 15% and 40%. T. sinensis successfully established itself in chestnut orchards and parasitized D. kuriphilus, despite normal population fluctuations being observed across years and orchards. Full article

Ink disease, primarily caused by the pathogen Phytophthora xcambivora, significantly threatens the health and productivity of sweet chestnut (Castanea sativa Mill.) orchards, highlighting the need for accurate detection methods. This study investigates the efficacy of machine learning (ML) classifiers combined with high-resolution multispectral imagery acquired via unmanned aerial vehicles (UAVs) to assess chestnut tree health at a site in Tuscany, Italy. Three machine learning algorithms—support vector machines (SVMs), Gaussian Naive Bayes (GNB), and logistic regression (Log)—were evaluated against eight vegetation indices (VIs), including NDVI, GnDVI, and RdNDVI, to classify chestnut tree crowns as either symptomatic or asymptomatic. High-resolution multispectral images were processed to derive vegetation indices that effectively captured subtle spectral variations indicative of disease presence. Ground-truthing involved visual tree health assessments performed by expert forest pathologists, subsequently validated through leaf area index (LAI) measurements. Correlation analysis confirmed significant associations between LAI and most VIs, supporting LAI as a robust physiological metric for validating visual health assessments. GnDVI and RdNDVI combined with SVM and GNB classifiers achieved the highest classification accuracy (95.2%), demonstrating their superior sensitivity in discriminating symptomatic from asymptomatic trees. Indices such as MCARI and SAVI showed limited discriminative power, underscoring the importance of selecting appropriate VIs that are tailored to specific disease symptoms. This study highlights the potential of integrating UAV-derived multispectral imagery and machine learning techniques, validated by LAI, as an effective approach for the detection of ink disease, enabling precision forestry practices and informed orchard management strategies. Full article

The sustainability of agriculture is seriously threatened by climate change. In Europe, chestnut ecosystems, which are growing mainly in Mediterranean climate, are facing during summertime increasing of heat and drought stresses. These induce fragilities on trees, leading to a reduction in productivity and predisposing them to pest and disease attacks. The plasticity of chestnut species under contrasting climate is known. Understanding the specific adaptation of cultivars to different climate features is now important to anticipating climate changes. Caucasian Region is considered the origin center of chestnut (Castanea sativa), which is characterized by climatic transition from the Mediterranean to the Euro-Siberian area. Mostly, areas of chestnut are concentrated in the countries around the Mediterranean Basin, thriving in regions with humid and Pré-Atlantic bioclimates. In Portugal, more than 95% of the chestnut area is located in the Center and North side of Portugal. This is an anisohydry species, characterized by good hydroplasticity: 90% reduction in A occurs when Ψ_wstem drops to −1.25 MPa, and a 50% reduction in A occurs at values of −1.7 MPa. The highest fatty acid contents in chestnut chloroplasts are a-linolenic acid (18:3), ranging between 40 and 50% of the total amount and being the unsaturated/saturated 2.27 for Longal. New strategies are being investigated in order to increase tolerance against those abiotic factors in chestnut species. They include the use of innovative irrigation techniques, which can increase production 22–37%. Fertilization with silicone (Si) has been investigated to promote the tolerance of plants against heat and drought stresses. Breeding programs, mostly (in Europe) against ink disease, have been performed since the middle of the XX century to create new genotypes (such the Portuguese ColUTAD^®). ClimCast, a network of orchards, was created in Portugal with the aim of responding to the new challenges facing orchards in the context of climate change. Full article

Chestnut orchards are a multifunctional resource, providing not only fruit or wood but also playing a role in the conservation of mountain and hillside landscapes. In the Piemonte Region, Italy, a rich genetic heritage of chestnut genotypes has contributed to considerable biodiversity and environmental value. The study aimed to valorize an important example of the chestnut agrobiodiversity in the Piemonte Region by focusing on the ‘Marrone di Chiusa Pesio’ (MCP) cultivar (cv). A multidisciplinary approach was applied, involving genetic and morphological analyses, Visual Tree Assessment (VTA), and phytochemical and nutritional profiling. The plant census provided identification and geolocation of 187 MCP specimens; the 20 most representative trees were genetically analyzed, and then, through the VTA, their morpho-functional status was evaluated. The nutraceutical properties and phytochemical composition were assessed by measuring the total polyphenol content (TPC), antioxidant capacity (AOC), and other phytochemical classes through spectrophotometric and chromatographic methods. The results showed significantly higher TPC values (ranged from 36.51 ± 1.60 mgGAE/100 g of dried weight—DW to 103.14 ± 1.24 mgGAE/100 g DW) compared to other ‘Marrone-type’ cultivars, along with high levels of key phenolic markers, bioactive compounds, and nutritional substances. These included tannins (about 22–28 mg/100 g DW) and cinnamic acids (about 23–25 mg/100 g DW), followed by flavonols, benzoic acids, organic acids, monoterpenes, vitamin C, and catechins, listed in order of predominance. A Principal Component Analysis (PCA) was performed to observe the distribution of the samples and their correlations based on the chemical composition. The results confirmed the interesting phytochemical properties of the ‘Marrone di Chiusa Pesio’ nuts, together with their good morphological and functional properties. Given the ongoing genetic erosion of Castanea sativa cultivars, due to cultivation abandonment and climate change, the main factors contributing to the progressive loss of biodiversity worldwide, the presented approach aimed to provide an overview of the conservation status of the local agrobiodiversity. This study highlighted the value of a local chestnut cultivar, presenting the low conservation status of the few remaining specimens. The goal was to define the significant phenotypic variation regarding MCP in the considered area due to environmental variations, which may be of interest in its genetic adaptation to climate change. The study may potentially encourage the development of strategies for actively conserving the forest agrobiodiversity and hillside ecosystem services in the highly diverse landscapes of the Alpine valleys. Full article

The chestnut tree (Castanea sativa Mill.) is gaining importance in the mountainous regions of southern Europe due to the high value of its fruits. It is essential to establish effective cultivation protocols, considering that this species is still relatively understudied. In this study, we present the outcomes of the initial establishment of a chestnut orchard conducted through a nutrient omission trial for four years. The treatments included a fertilization plan with nitrogen, phosphorus, potassium, and boron (NPKB), the control, and four other treatments corresponding to the omission of each nutrient (-NPKB, N-PKB, NP-KB, NPK-B). The -NPKB and NP-KB treatments showed significantly lower trunk circumferences and canopy volumes compared to the other treatments. The NPK-B treatment resulted in the lowest fruit production, with a total accumulated yield (2020–2022) of 0.56 kg tree^–1, a value significantly lower than that of NPKB (1.12 kg tree^–1) and N-PKB (1.19 kg tree^–1). The assessment of nutrient concentrations in the leaves revealed plants with deficient levels of B and K in treatments that did not receive these nutrients. Conversely, N levels in the leaves in the -NPKB treatment fell within the sufficiency range (20 to 28 g kg^–1). This suggests that the sufficiency range should be adjusted to higher values, given the treatment’s effect on tree growth. It was also observed that the -NPKB treatment led to lower soil organic matter compared to the other treatments, likely due to reduced herbaceous vegetation development under the canopy, leading to decreased organic substrate deposition in the soil. The main findings of this study are that N and K were crucial elements for the optimal growth of chestnut trees, while B played a significant role in fruit production. Full article

Phytophthora cinnamomi is a devastating soil-borne plant pathogen. The primary source of P. cinnamomi infection is the soil, where the pathogen can persist for long periods. Effective prevention and management of this pathogen in tree crops requires an early and reliable detection method. In this study, we developed a simple, fast, reliable, and sensitive method based on real-time quantitative PCR (qPCR) for P. cinnamomi detection and quantification directly in plant or soil samples. Primers were developed targeting the nuclear single-copy ras-related protein gene Ypt1, suitable for Phytophthora-specific PCR. The specificity of the assay was confirmed by testing it against genomic DNA from 50 isolates across eight different Phytophthora clades, including the very similar P. parvispora. The efficiency and reliability of the qPCR protocol were evaluated in challenging environmental samples, such as plant tissue of different host trees (walnut, chestnut, oak) and naturally infected soils in walnut orchards. The main outcome was the development of a qPCR method for the specific identification and quantification of P. cinnamomi in natural soil samples. Additionally, this study established a systematic and repeatable soil sampling method and developed an efficient soil DNA extraction technique to apply the developed qPCR in naturally infested soils of walnut orchards. Full article