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The antifungal effect of aromatic plants (oregano, thyme, and Satureja) in dry form and as essential oils was evaluated in vitro (in potato dextrose agar (PDA)) and in bread against two phytopathogenic fungi found in food (Aspergillusniger and Penicillium). Gas and liquid chromatography were used to analyze essential oils attained by hydrodistillation of the aerial parts of the aromatic plants and of the dried plant aqueous solutions that were autoclaved for 20 min at 121 °C before analysis. Carvacrol, α-pinene, p-cymene, and γ-terpinene were the main components of the essential oils, whereas carvacrol, rosmarinic and caffeic acids were the main components of the water extracts. In vitro antifungal test results showed that the addition of plants in dry form had great antifungal potential against both fungal strains studied. Penicillium was more sensitive to the presence of aromatic plants than Aspergillus. Among the three plant species tested, thyme was the most potent antifungal against both fungi. For the bread product, all three aromatic plants studied showed inhibitory effects against both fungi. Results presented here suggest that oregano, thyme and Satureja incorporated in a bread recipe possess antimicrobial properties and are a potential source of antimicrobial ingredients for the food industry.

Background In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and Water Accounting Rice Model (WARM)) and two approaches based on machine learning algorithms (M5Rules and Recurrent Neural Networks (RNN)), the former inducing a rule-based model and the latter building a neural network. In situ telemetry is important to obtain quality in-field data for predictive models and this was a key aspect of the RICE-GUARD project on which this study is based. According to the authors, this is the first time process-based and machine learning modelling approaches for supporting plant disease management are compared. Results Results clearly showed that the models succeeded in providing a warning of rice blast onset and presence, thus representing suitable solutions for preventive remedial actions targeting the mitigation of yield losses and the reduction of fungicide use. All methods gave significant “signals” during the “early warning” period, with a similar level of performance. M5Rules and WARM gave the maximum average normalized scores of 0.80 and 0.77, respectively, whereas Yoshino gave the best score for one site (Kalochori 2015). The best average values of r and r 2 and %MAE (Mean Absolute Error) for the machine learning models were 0.70, 0.50 and 0.75, respectively and for the process-based models the corresponding values were 0.59, 0.40 and 0.82. Thus it has been found that the ML models are competitive with the process-based models. This result has relevant implications for the operational use of the models, since most of the available studies are limited to the analysis of the relationship between the model outputs and the incidence of rice blast. Results also showed that machine learning methods approximated the performances of two process-based models used for years in operational contexts. Conclusions Process-based and data-driven models can be used to provide early warnings to anticipate rice blast and detect its presence, thus supporting fungicide applications. Data-driven models derived from machine learning methods are a viable alternative to process-based approaches and – in cases when training datasets are available – offer a potentially greater adaptability to new contexts.

Rice by-products are extensively abundant agricultural wastes from the rice industry. This study was designed to optimize experimental conditions for maximum recovery of free and bound phenolic compounds from rice by-products. Optimized conditions were determined using response surface methodology based on total phenolic content (TPC), ABTS radical scavenging activity and ferric reducing power (FRAP). A Box-Behnken design was used to investigate the effects of ethanol concentration, extraction time and temperature, and NaOH concentration, hydrolysis time and temperature for free and bound fractions, respectively. The optimal conditions for the free phenolics were 41–56%, 40 °C, 10 min, whereas for bound phenolics were 2.5–3.6 M, 80 °C, 120 min. Under these conditions free TPC, ABTS and FRAP values in the bran were approximately 2-times higher than in the husk. However, bound TPC and FRAP values in the husk were 1.9- and 1.2-times higher than those in the bran, respectively, while bran fraction observed the highest ABTS value. Ferulic acid was most evident in the bran, whereas p-coumaric acid was mostly found in the husk. Findings from this study demonstrates that rice by-products could be exploited as valuable sources of bioactive components that could be used as ingredients of functional food and nutraceuticals.

Bread wheat variety development suited to organic farming conditions remains a major challenge mainly because of the high breeding costs involved and the few cultivars adapted to low-input systems. The present work explores whether early generation selection needs to take place under organic conditions for subsequent adaptation or whether conventional testing at an early stage could be adequate. A diverse set of crosses involving Greek landraces and commercial cultivars were developed and advanced by honeycomb pedigree selection under both organic and conventional environments. Subsequently, F4 progenies and an upgraded landrace were evaluated over two years in neighboring organic and conventional trials. Both statistical and GGE biplot analyses revealed significant genotype × environment interactions. The results clearly indicate that early selection under organic conditions did not provide a consistent advantage for subsequent performance under organic management compared with conventional early selection. Genotypes derived from the Africa × Atheras cross consistently showed the highest and most stable yields across the two environments, irrespective of the early selection environment. These results indicate that genetic background and landrace-derived diversity are more important than the early selection environment for the expression of performance. A staged breeding strategy involving initial selection in conventional management followed by multi-environment testing in organic conditions can provide a cost-effective approach to developing resilient, high-yielding wheat cultivars suitable for organic farming systems, which are typically characterized by low-input management practices, and in tune with the EU targets for expanded organic farming.

Rice, after wheat, is the second largest cereal crop, and is the most consumed major staple food for more people than any other crop. Rice blast (caused by Pyricularia oryzae, teleomorph Magnaporthe grisea) is the most destructive of all rice diseases, causing multi-million dollar losses every year. Chemical control of this disease remains the most effective rice blast management method. Many attempts have been made to develop models to forecast rice blast. A review of literature of the rice blast forecasting models revealed that 52 studies have been published, with the majority capable of predicting only leaf blast. The most frequent input variable has been air temperature, followed by relative humidity and rainfall. Critical factors for the pathogenesis, such as leaf wetness, nitrogen fertilization and variety resistance have had limited integration in the development of these models. This review reveals low rates of model application due to inaccuracies and uncertainties in the predictions. Five models are part of current operational forecasting systems in Japan, Korea and India. Development of in-field rice-specific weather stations, along with integration of leaf wetness and end-user interactive inputs should be considered. This review will be useful for modelers, users and stakeholders, to assist model development and selection of the most suitable models for the effective rice blast forecasting.

The knowledge of rice nitrogen (N) requirements and uptake capacity are fundamental for the development of improved N management. This paper presents empirical models for predicting agronomic traits that are relevant to yield and N requirements of rice (Oryza sativa L.) through remotely sensed data. Multiple linear regression models were constructed at key growth stages (at tillering and at booting), using as input reflectance values and vegetation indices obtained from a compact multispectral sensor (green, red, red-edge, and near-infrared channels) onboard an unmanned aerial vehicle (UAV). The models were constructed using field data and images from two consecutive years in a number of experimental rice plots in Greece (Thessaloniki Regional Unit), by applying four different N treatments (C0: 0 N kg∙ha−1, C1: 80 N kg∙ha−1, C2: 160 N kg∙ha−1, and C4: 320 N kg∙ha−1). Models for estimating the current crop status (e.g., N uptake at the time of image acquisition) and predicting the future one (e.g., N uptake of grains at maturity) were developed and evaluated. At the tillering stage, high accuracies (R2 ≥ 0.8) were achieved for N uptake and biomass. At the booting stage, similarly high accuracies were achieved for yield, N concentration, N uptake, biomass, and plant height, using inputs from either two or three images. The results of the present study can be useful for providing N recommendations for the two top-dressing fertilizations in rice cultivation, through a cost-efficient workflow.

Optimizing rice productivity is crucial for global food security, especially in Mediterranean environments. This study investigated the influence of tillering capacity on yield and other agronomic traits in nine European rice cultivars over two seasons (2021–2022). A split-plot design was used with cultivars as the main factor and five tillering levels: main stems (Mn), primary (T1), secondary (T2), tertiary (T3), and quaternary (T4) as sub-factors. The grain yield, total dry matter, harvest index, 1000-grain weight, and number of stems were measured. Significant differences were revealed among cultivars, tillering levels, and their interaction for all traits. Mn and T1 consistently outyielded later tillers, with Ronaldo’s Mn achieving 4.71 t ha−1. Mare and Olympiada displayed the highest average yields (1.52 t ha−1) through balanced resource allocation across tillers. Strong correlations between tillering levels and yield (R2 = 0.73) demonstrate that early tillers significantly enhance productivity. We conclude that optimizing early tiller productivity—rather than maximizing tiller numbers—should be prioritized in breeding programs. Cultivars combining vigorous Mn and T1 development with efficient resource partitioning offer the most promising approach for improving Mediterranean rice productivity.

Urban air pollution, especially from heavy metal (HM) contamination, poses significant risks to human health and environmental sustainability. This study investigates the spatial and temporal distribution of HM contamination in Thessaloniki, Greece, using Syntrichia moss as a bioindicator to inform urban environmental management strategies. Moss samples were collected from 16 locations representing diverse urban activity zones (motorway, industrial, city center, airport) in March, May, and July 2024. The concentrations of 12 HMs (Al, Sb, As, Ba, Cd, Cr, Co, Cu, Pb, Ni, V, and Zn) were analyzed using ICP-MS, and the contamination factors were calculated relative to controlled moss samples. The results revealed significant spatial variation, with elevated levels of As, Cd, Cr, Pb, and Zn, particularly in high-traffic and industrial zones, exceeding the background levels by up to severe and extreme contamination categories. Temporal trends showed decreases in Al, Ba, and Ni from March to July 2024, while Cr and Cu increased, suggesting seasonally varying sources. Multivariate analyses further distinguished the contamination patterns, implicating traffic and industrial activities as key contributors. Syntrichia effectively captures HM contamination variability, demonstrating its value as a cost-effective bioindicator. These findings provide critical data that can guide urban planners in developing targeted pollution mitigation strategies, ensuring compliance with the European Green Deal’s Zero Pollution Action Plan.

The narrow margin for irrigation error in aeroponics necessitates advanced control strategies beyond fixed timer-based approaches. This study evaluates a plant-driven irrigation method based on real-time leaf turgor feedback in aeroponic romaine lettuce (Lactuca sativa L. var. longifolia) cultivation. A leaf thickness–turgor sensor was interfaced with an Arduino Mega 2560 to activate misting events dynamically. Two identical aeroponic systems were operated in a fully controlled environment: a conventional timer-based control (TC) system applying mist every 10 min and an Arduino-controlled (AC) system triggered by turgor changes. Over two independent 37-day cultivation cycles, the AC strategy reduced total water use by an average of 15.9% and pump activations by 17.2% while improving water use efficiency by 17.8% and nutrient use efficiency for N, P, and K by an average of 17.8%, with no statistically significant differences in shoot biomass, height, or yield. Although root dry weight was significantly higher under TC, the AC treatment led to a 45.0% reduction in leaf nitrate accumulation and non-significant increases in phenolic content. These findings demonstrate the potential of turgor-responsive irrigation for enhancing sustainability, resource use efficiency, and the quality of produce in aeroponic systems, thereby supporting its broader integration into controlled-environment agriculture (CEA).

Rice is the major staple crop worldwide, whereas fertilization practices include mainly the application of synthetic fertilizers. A novel compost was developed using 74% of rice industrial by-products (rice bran and husks) and tested in rice cultivation in Greece’s main rice producing area. Field experimentation was conducted in two consecutive growing seasons (2017 and 2018) and comprised six fertilization treatments, including four compost rates (C1: 80, C2: 160, C3: 320 kg ha−1 of nitrogen all in split application, C4: 160 kg ha−1 of nitrogen in single application), a conventional treatment, as well as an untreated control. A total of 21 morpho-physiological and quality traits were evaluated during the experimentation. The results indicated that rice plants in all compost treatments had greater height (8%–64%) and biomass (32%–113%) compared to the untreated control. In most cases, chlorophyll content index (CCI) and quantum yield (QY) were similar or higher in C3 compared to the conventional treatment. C2 and C3 exhibited similar or greater yields, 7.5–8.7 Mg ha−1 in 2017 and 6.3–6.9 Mg ha−1 in 2018, whereas the conventional treatment resulted in 7.3 Mg ha−1 and 6.8 Mg ha−1 in the two years, respectively. No differences were observed in most quality traits that affect the rice commodity. The current study reveals that in sustainable farming systems based on circular economy, such as organic ones, the application of the proposed compost at the rate of 6 Mg ha−1 can be considered sufficient for the rice crop nutrient requirements.