Mediterranean Agronomic Institute of Chania

Greece exhibits a continuous and exceptionally well-documented tradition of water management extending from prehistoric times to the modern era. This review critically examines the evolution of Greek hydro-technologies within their historical, environmental, and socio-political contexts, highlighting their enduring relevance to contemporary water resource management. Beginning with the advanced hydraulic systems of the Minoan civilization, the study traces the development, transformation, decline, and rediscovery of water management practices across successive periods, including the Mycenaean, Classical, Hellenistic, Roman, Byzantine, Venetian, Ottoman, and modern Greek eras. The analysis is based on a synthesis of archeological evidence, historical sources, and technical studies from approximately 200 documented sites throughout Greece. Particular emphasis is placed on water supply, drainage, sanitation, irrigation, and governance systems, as well as on periods during which hydraulic knowledge was partially lost and later reintroduced or adapted. Representative case studies illustrate the spatial and technological diversity of Greek hydro-technological solutions, ranging from monumental aqueducts and dams to decentralized cisterns, wells, and communal irrigation systems. The review demonstrates that Greek hydro-technologies consistently integrated technical innovation with environmental adaptation, social organization, and institutional regulation. This diachronic synthesis provides transferable lessons for modern water governance, infrastructure resilience, and climate adaptation, offering a conceptual framework for addressing contemporary challenges related to water scarcity, climate variability, and sustainable water management in Mediterranean and semi-arid regions.

The objective of the present study was to apply supervised Machine Learning to predict severe complications after equine orchiectomy. A dataset of 612 cases of orchiectomies in stallions was used for the development of a computational model, among which in 8.5% of cases severe complications (colic, continued stallion-like behaviour, evisceration, funiculitis, haemorrhage, and scrotal infection) were diagnosed post-orchiectomy. Three supervised Machine Learning tools were employed: Logistic Regression (12 different models evaluated), Random Forest (64 models), and Gradient Boosting (8 models). For the prediction of the development of severe complications post-orchiectomy, Logistic Regression was the tool that produced the best discrimination measures, where accuracy, precision and recall were 0.9134, 0.8391, and 0.9133, respectively. The results of the analysis for SHapley Additive exPlanations values for the impact of the independent variables in the prediction of the development of complications indicated that (a) the age of the horse and (b) the surgical technique employed were the two variables that mostly influenced the prediction outcome, findings that were unambiguous in the models developed by any Machine Learning tool. The findings of this study indicate that computational models could be used as adjunct tools to support clinical decisions in the peri-operative management of horses.

Postoperative endophthalmitis is a rare but vision-threatening complication of cataract surgery. Multidrug-resistant organisms present additional therapeutic challenges. We report a 52-year-old man who underwent uncomplicated phacoemulsification for posterior subcapsular cataract. On the first postoperative day, visual acuity was 8/10, but inflammation gradually appeared without the classic signs of endophthalmitis. On the fifth postoperative day, the patient's vision deteriorated significantly, reaching 1/10, accompanied by a strong inflammatory reaction in the anterior chamber. A pars plana vitrectomy was performed and vancomycin and ceftazidime were administered intravitreally. Staphylococcus hominis, resistant to most common antibiotics but sensitive to gentamicin, was isolated from the vitreous culture. The medication was modified accordingly, with topical gentamicin and oral doxycycline, under daily close monitoring. One month later, cystoid macular edema appeared, which was successfully treated with acetazolamide, topical corticosteroids, and bromfenac. Three months after surgery, the patient's vision had returned to 9/10 without any permanent damage. Early surgical intervention, culture-guided therapy, and intensive follow-up can preserve vision even in postoperative endophthalmitis due to multidrug-resistant organisms.

The citrus processing industry is an economically important agro-industrial sector worldwide; however, it produces significant amounts of waste annually. The biorefinery concept and the recovery of bio-based materials from agro-industrial residues, including citrus processing waste, are emphasized in the European Green Deal, reflecting the EU's commitment to fostering circularity. Biotreatment of citrus processing waste, including bioconversion into biomethane, biohydrogen, bioethanol and biodiesel, has been applied to valorize biomass for energy recovery. It can also be composted into a valuable soil conditioners and fertilizers, while raw and fermented citrus residues may exhibit phytoprotective activity. Citrus-derived residues can be converted into materials such as nanoparticles with adsorptive capacity for heavy metals and recalcitrant organic pollutants, and materials with antimicrobial properties against various microbial pathogens, or the potential to remove antibiotic-resistance genes (ARGs) from wastewater. Indeed, citrus residues are an ideal source of industrial biomolecules, like pectin, and the recovery of bioactive compounds with added value in food processing industry. Citrus processing waste can also serve as a source for isolating specialized microbial starter cultures or as a substrate for the growth of bioplastic-producing microorganisms. Solid-state fermentation of citrus residues can enhance the production of hydrolytic enzymes, with applications in food and environmental technology, as well as in animal feed. Certain fermented products also exhibit antioxidant properties. Citrus processing waste may be used as alternative feedstuff that potentially improves the oxidative stability and quality of animal products.

During recent years, agriculture has been undergoing a significant transformation in response to contemporary economic pressures and the escalating impact of climate change [...].

Soil erosion poses a significant threat to natural resources and agricultural productivity in arid regions. This study applied the Revised Universal Soil Loss Equation (RUSLE) model to simulate rainfall erosivity and soil erosion risk in the Wadi Allith basin, Saudi Arabia, using rainfall data from 2016 to 2018. The results demonstrated that the basin experienced a predominant slight level of erosion risk, with around 5 tons/ha annually. This study revealed that a very slight erosion risk was predominant in 2016 (97% of the basin area), 2017 (96%), and 2018 (95%), while less than 1% of the study area was exposed to severe erosion risks across all three years. An increasing trend in erosion severity was observed between 2016 and 2018, correlating with rising average annual rainfall amounts of 120 mm, 145 mm, and 155 mm. This underscores the importance of understanding how climatic factors influence soil stability, particularly in arid regions where water scarcity is typically a limiting factor. The successful application of Geographic Information Systems (GISs) and remote sensing tools integrating the various components of the RUSLE model showcases the effectiveness of these technologies in environmental monitoring and risk assessment. These tools facilitate a comprehensive analysis of the factors contributing to soil erosion, enabling researchers and policymakers to visualize erosion risk across the basin and prioritize areas for intervention. This study highlights the importance of ongoing soil erosion monitoring in arid environments such as the Wadi Allith basin, Saudi Arabia.

Abstract Genetic improvement and the identification of drought-tolerant cultivars are crucial in perennial ryegrass (Lolium perenne L.) turfgrass to enhance germplasm reserves for molecular breeding and the development of sustainable landscapes in arid and semi-arid green spaces. Cytokinins (CKs) are plant hormones that regulate various physiological processes, including cell division, shoot growth, and leaf senescence, and also are known to regulate plant responses to drought stress. This study aimed to enhance drought tolerance in perennial ryegrass cultivars by utilizing the drought-inducible RD29A promoter to drive the expression of the IPT gene, which boosts cytokinin levels. The research also compared the performance of these transgenic plants with wild-type (WT) plants and local perennial ryegrass accessions under varying irrigation conditions. Results showed that certain transgenic plants and local accessions displayed higher drought tolerance based on turf quality, physiological, and biochemical characteristics. The expression of the IPT gene was confirmed in transgenic plants exposed to drought stress. Transgenic lines including GM24, GM12, GC8, GC6, NC12, NC14, NS14, and GC3 exhibited increased drought tolerance, maintaining higher levels of cytokinins in the leaves, improving water content, photosynthetic rate, and antioxidant activity while reducing damage indicators. Catalase and superoxide dismutase activities were more influential than peroxidase in drought adaptation and recovery. The efficiency of the RD29A promoter and the use of the UBQ10 intron in the IPT gene construct affected gene expression. Moreover, the significant genotypic variation among local accessions indicates opportunities for improving drought tolerance through targeted breeding in sensitive and moderately tolerant genotypes. Further research is recommended to investigate hormonal balance and osmoregulation in transgenic and wild-type plants under multiple stresses. Additionally, identifying candidate genes involved in drought tolerance, particularly in local ryegrass accessions, should be a focus for future studies.

PurposeHyperspectral imaging combined with machine learning offers a promising, cost-effective alternative to invasive chemical analysis for early plant disease detection. In this study, the use of 3D Convolutional Neural Networks (3D-CNNs) was explored to detect presymptomatic viral infections in the model plant Nicotiana benthamiana L. and assess the generalization of these models across different plant genotypes.MethodsFour genotypes of Nicotiana benthamiana L. (wild-type, DCL2/4, AGO2, and NahG) were inoculated with different potexviruses (PepMV mild or severe strain, PVX, BaMV). Viral infection was verified via northern blot analysis at 5 and 10 days post inoculation (DPI). Hyperspectral images were captured over 10 days following inoculation, focusing on the top 3 leaves where symptoms typically appear. The dataset was carefully processed to remove errors, and raster masks were generated to isolate only the leaf pixels. The Extremely Randomized Trees algorithm was used for Effective Wavelength selection, and a novel 3D-CNN architecture was developed to classify 16×16×16 nonoverlapping cubes extracted from the unmasked leaf surfaces. The aim was to classify each cube into healthy or diseased for each of the four viruses at different time points.ResultsAccuracies of 0.78–0.87 were achieved for AGO2 mutants at the cube level, and overall plant-level accuracies of 0.68–0.89. The model’s generalization capabilities were tested across other genotypes, yielding accuracies of up to 0.75 for DCL2/4, 0.83 for NahG, and 0.78 for the wild-type. The timing of disease detection was also assessed, finding that accuracies approached 0.8 as early as 6–8 DPI depending on the virus. The results were validated against northern blot analyses and benchmarked against another state-of-the-art methodology for Nicotiana benthamiana viral infections, achieving superior overall classification accuracies.ConclusionThe proposed patch-based method demonstrated key advantages: (a) exploiting both spectral and textural information, (b) deriving a large training dataset from few hyperspectral images, (c) providing localized classification explainability within leaf regions, and (d) achieving high accuracy for early detection of viral infections.