Articles publicats (Agrotecnio Center)
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Agrotecnio aims to become a reference in Europe addressing all the key elements of the food production chain in an integrated way focusing on target crops and animals of commercial importance, rather than model systems. This later aspect sets our centre apart from other centers which focus on fundamental science and/or model plant and animal systems. As a result we should be able to address fundamental and important questions in the crop/animal of interest and results from our research will be directly and immediately applicable to our target organism. [Més informació]
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- ItemOpen AccessPost-Tsunami forest resilience in a coastal forest ecosystem after the mega-tsunami of 2011, Japan(MDPI, 2026) Trigubenko, Anna; Lopez Caceres, Maximo Larry; Ferrio, Juan Pedro; Shestakova, Tatiana A.; Bukin, Vladislav; Garcia Riera, SergiThe Mega-Tsunami of March 2011 in eastern Japan caused severe damage in the coastal black pine (Pinus thunbergii) forests along the Pacific coast. To evaluate post-disturbance forest recovery, tree-ring samples from 30 trees at Ishinomaki coastal forest were analyzed for the period 2006–2020 using tree-ring indices and stable carbon isotope discrimination (Δ13C). The results revealed a strong decline in radial growth immediately after the tsunami, indicating severe growth suppression during the years 2011–2014. Simultaneously, Δ13C values decreased, suggesting reduced stomatal conductance and acute physiological stress associated with the initial salinity effect at the root zone. Although isotopic signals indicated gradual physiological adjustment in subsequent years, radial growth recovery occurred more slowly. Most trees returned to pre-disturbance growth levels within approximately 3–5 years and later exceeded pre-disturbance growth levels, likely due to reduced competition following the mortality of nearly 40% of trees after the tsunami. However, recovery trajectories differed markedly among individual trees, with some trees showing prolonged growth suppression beyond 6 years. This variability may reflect highly localized or tree-level factors, including intrinsic differences in individual resilience, while spatial autocorrelation analysis did not indicate significant clustering of recovery time across the stand. We conclude that black pine coastal forests show a high degree of resilience, showing physiological recovery in a short period (3–4 years). Although growth recovery took longer, initial tree mortality promoted the growth of the surviving trees beyond pre-disturbance values.
- ItemOpen AccessExploring the biochemical basis of brown rot tolerance in interspecific Prunus spp. populations(Elsevier, 2026) Verde-Yáñez, Lucía; Vall-llaura, Núria; Lara Ayala, Isabel; Zaracho, Nathalia; Eduardo, Iban; Usall, Josep; Torres, RosarioBrown rot, caused by Monilinia spp., is one of the most economically important postharvest diseases affecting peaches and nectarines (Prunus persica L. Batsch). This study investigates the relationship between some biochemical traits (including the antioxidant profile and cuticular wax composition) and brown rot susceptibility. Fruit quality and biochemical traits were evaluated in two interspecific BC1 populations called NT1Ba and NT1Ca, derived from the peach and Prunus davidiana hybrids ‘Barrier’ and ‘Cadaman’, respectively, while cuticular wax composition analyses were performed in the NT1Ba population uniquely. While oxidative stress indicators, including antioxidant capacity (AC), total phenolic content (TPC) and hydrogen peroxide (H₂O₂), did not correlate with disease tolerance, significant differences were detected in cuticle yield and wax composition. Triterpenoids were the most abundant wax compounds, followed by fatty acids, alkanes, alcohols, aldehydes, and sterols. Partial Least Squares Regression (PLSR) analysis revealed that genotypes less susceptible to brown rot had higher cuticle yields and greater levels of acyclic compounds in the wax fraction, suggesting a role in enhancing physical barriers to fungal penetration. In contrast, more susceptible genotypes exhibited higher levels of triterpenes and sterols, together with higher fruit weight and pH, which may have facilitated fungal infection. Although these results require validation across additional genetic backgrounds, they suggest that cuticle traits, particularly the abundance of acyclic wax compounds, may play a key role in brown rot resistance in the NT1Ba population and could represent valuable targets for breeding programs.
- ItemOpen AccessModelling of codling moth-canopy interactions as a new approach to variable-rate pesticide application in apple orchards(Elsevier, 2026) Hajjaj, Ouijdan; Bruin, Sytze de; Martínez Casasnovas, José Antonio; Llorens Calveras, Jordi; Bosch Serra, Dolors; Plata Moreno, José Manuel; Torrent Martí, Xavier; Arnó Satorra, JaumeVariable-rate pesticide application (VRA) technologies have advanced considerably in recent years; however, current approaches continue to overlook plot-scale pest distribution, which may compromise control efficacy. This study assesses the relationship between Cydia pomonella spatial patterns and canopy variability in an apple orchard to evaluate the feasibility of prescribed VRA based on pest-canopy interactions. A total of 18 georeferenced traps were deployed across a 1.40 ha plot, and tree canopies were scanned over two consecutive years (2024–2025) using ground-based LiDAR. Treatment-threshold exceedance was modelled via standard and mixed-effects logistic regression, incorporating LiDAR-derived canopy features as predictors, with model selection guided by residual spatial autocorrelation (Moran's I) and predictive performance. Three VRA scenarios were evaluated: (i) canopy-driven, (ii) canopy- and pest-driven, and (iii) model-driven selective VRA. Scenarios (i) and (ii) used block kriging and k-means clustering to delineate management zones, while scenario (iii) generated prescription maps directly from model outputs. Canopy cross-sectional area yielded very satisfactory prediction results, indicating that pest incidence was predominantly associated with smaller trees. These results indicated that pest–canopy interaction must be properly quantified and spatially modelled to enhance decision-making when opting for VRA. More targeted approaches may further restrict treatments to only those areas with a high likelihood of exceeding treatment thresholds.
- ItemOpen AccessBarley extrudates modulate the gut microbiome– metabolome axis in vitro through β-glucan fermentation and polyphenol biotransformation(The Royal Society of Chemistry, 2026-05-12) Martínez Subirà, Mariona; Cortijo-Alfonso, Maria Engracia; Friero Moreno, Iván; Macià i Puig, Ma Alba; Pena i Subirà, Ramona Natacha; Molinero, Natalia; Moreno-Arribas, M. Victoria; Rubió Piqué, Laura; Moralejo Vidal, Mª AngelesBarley is rich in fermentable dietary fiber and phenolic compounds, both of which have recognized benefits for gut health and whose functionality is influenced by processing. Here, four barley genotypes differing in beta-glucan content, type of starch, and phenolic profiles were extruded to obtain ready-to-eat products, which were then evaluated using a combined in vitro digestion-colonic fermentation model. The gastrointestinal fate of beta-glucans and phenolics, short-chain fatty acids production, phenolic metabolite formation, and gut microbiota composition were assessed. After digestion, substantial amounts of beta-glucans and phenolics remained in the non-bioaccessible fraction, supporting their relevance as substrates for colonic fermentation. During fermentation, the beta-glucan-rich genotypes Annapurna (R) and Hilose (R) showed the strongest butyrogenic response, while the purple-grain genotype DHL-151340, characterized by a flavone- and anthocyanin-rich profile, showed an earlier and more pronounced accumulation of low-molecular-weight phenolic catabolites. Compared with the control, barley extrudates induced time-dependent shifts in microbiota composition, although community profiles tended to converge at later fermentation stages. Overall, genotype- and processing-driven differences translated into distinct fermentation and phenolic biotransformation footprints, highlighting the relevance of barley matrix composition in shaping the colonic fate of cereal bioactive compounds.
- ItemOpen AccessMethodology for the assessment of leaf area in fruit tree orchards using a terrestrial LiDAR-based system(Springer Nature, 2025-12-01) Lavaquiol Colell, Bernat; Llorens Calveras, Jordi; Sanz Cortiella, Ricardo; Torrent Martí, Xavier; Plata Moreno, José Manuel; Escolà i Agustí, AlexandreAccurate estimation of canopy geometric and structural characteristics, such as leaf area (LA), is essential for improving resource efficiency in fruit tree crop management. LA is a key biophysical parameter, influencing physiological processes like carbon fixation, evapotranspiration, and light interception, as well as fruit quality and yield. However, its measurement is complex due to the substantial number of leaves and the three-dimensional nature of tree canopies.An alternative approach, the Projected Tree Row Surface (PTRS), has shown a strong correlation with LA and has been recognized by the scientific community. Despite its robustness, the original PTRS method requires time-consuming manual data collection, which limits its practical application in the field.This study introduces a novel automated methodology for calculating the PTRS, validated using high-resolution ground-truth data providing LA values at 0.1-m intervals along the tree rows. When evaluated on almond, pear, and apple trees as well as vineyards, the method achieved remarkably high correlations between PTRS and LA, with coefficients up to r = 0.97 and r = 0.99 at optimal resolutions (0.1 –0.2 m PTRS per 1 m row section). These results demonstrate that the approach delivers consistent and reliable measurements of LA under diverse field conditions, enabling real-time, high-resolution assessment of tree-row canopies.The automated PTRSn approach enables fast and efficient LA estimation and can be adapted to any point cloud dataset. It supports flexible resolution to balance accuracy and processing time and can be applied to full rows, individual trees, or canopy segments. This methodology represents a step forward in automating LA assessment and supports the development of real-time applications in precision agriculture.