Due to the pressures imposed by climate change, the European Union (EU) has been forced to design several initiatives (the Common Agricultural Policy, the European Green Deal, Farm to Fork) to tackle the climate crisis and ensure food security. Through these initiatives, the EU aspires to mitigate the adverse effects of the climate crisis and achieve collective prosperity for humans, animals, and the environment. The adoption or promotion of crops that would facilitate the attaining of these objectives is naturally of high importance. Flax (Linum usitatissimum L.) is a multipurpose crop with many applications in the industrial, health, and agri-food sectors. This crop is mainly grown for its fibers or its seed and has recently gained increasing attention. The literature suggests that flax can be grown in several parts of the EU, and potentially has a relatively low environmental impact. The aim of the present review is to: (i) briefly present the uses, needs, and utility of this crop and, (ii) assess its potential within the EU by taking into account the sustainability goals the EU has set via its current policies.

Microbial oil production from fatty materials of animal or plant origin has been an object of research and industrial interest for many years. During the process of microbial growth/accumulation of fat reserves, the dominating phenomena that define the composition of endocellular fat are, first, the specific process of incorporation of substrate fatty acids into the microbial cell and, second, the endocellular changes of fatty acids defined by the enzymic capabilities of the microorganism. The fatty acids will either be degraded for growth needs or act as substrate of endocellular biotransformation processes, leading to concentration changes and production of "new" fatty acids which did not previously exist in the substrate. The purpose of the present work is to study the endocellular lipids of Mucor circinelloidesCBS 172-27 grown on sunflower oil. The mathematical model, described in part I, was applied in order to investigate the following: a. Microorganism specificity in the incorporation of substrate fatty acids. b. Microorganism specificity in the degradation of fatty acids present in the reserve fat. c. Possibilities of endocellular biotransformations during the microbial growth. In conclusion, this work is aimed at developing a quantitative expression of parameters defining the lipid composition of fat reserves. The proposed mathematical model can be used not only for selection of microbial strains having specific enzymic potential but also for substrate selection.

Addressing the urgent sustainability challenges in the wine industry, this study explores the efficacy of sustainability-oriented innovations (SOIs) and smart farming technologies (SFTs) across wine value chains in Cyprus and Italy. Utilising a mixed-methods approach that includes quantitative analysis through Key Performance Indicators (KPIs) and qualitative assessments to understand stakeholders’ perspectives, this research delves into the environmental, economic, and social impacts of these technologies. In Cyprus, the integration of digital labelling and smart farming solutions led to a substantial reduction in pesticide usage by up to 75% and enhanced the perceived quality of wine by an average of 8%. A pilot study in Italy witnessed a 33.4% decrease in greenhouse gas emissions, with the additional benefit of a 5.3% improvement in intrinsic product quality. The pilot introduced a carbon credit system, potentially generating an average annual revenue of EUR 4140 per farm. These findings highlight the transformative potential of SOIs and SFTs in promoting sustainable practices within the wine industry, demonstrating significant advancements in reducing environmental impact, improving product quality, and enhancing economic viability. This study underscores the critical role of innovative technologies in achieving sustainability goals and provides a compelling case for their wider adoption within the agricultural sector.

Modeling cotton plant growth is an important aspect of improving cotton yields and fiber quality and optimizing land management strategies. High-throughput phenotyping (HTP) systems, including those using high-resolution imagery from unmanned aerial systems (UAS) combined with sensor technologies, can accurately measure and characterize phenotypic traits such as plant height, canopy cover, and vegetation indices. However, manual assessment of plant characteristics is still widely used in practice. It is time-consuming, labor-intensive, and prone to human error. In this study, we investigated the use of a data-processing pipeline to estimate cotton plant height using UAS-derived visible-spectrum vegetation indices and photogrammetric products. Experiments were conducted at an experimental cotton field in Aliartos, Greece, using a DJI Phantom 4 UAS in five different stages of the 2022 summer cultivation season. Ground Control Points (GCPs) were marked in the field and used for georeferencing and model optimization. The imagery was used to generate dense point clouds, which were then used to create Digital Surface Models (DSMs), while specific Digital Elevation Models (DEMs) were interpolated from RTK GPS measurements. Three (3) vegetation indices were calculated using visible spectrum reflectance data from the generated orthomosaic maps, and ground coverage from the cotton canopy was also calculated by using binary masks. Finally, the correlations between the indices and crop height were examined. The results showed that vegetation indices, especially Green Chromatic Coordinate (GCC) and Normalized Excessive Green (NExG) indices, had high correlations with cotton height in the earlier growth stages and exceeded 0.70, while vegetation cover showed a more consistent trend throughout the season and exceeded 0.90 at the beginning of the season.

This review paper provides an in-depth analysis of × Tritordeum [(Ascherson et Graebner)], a novel cereal crop with promising agronomic, nutritional, and economic potential. Through a comprehensive examination of the crop's agronomic characteristics and management requirements, we highlight its adaptability to Mediterranean climates, resilience under changing environmental conditions, and potential applications in the food industry, such as breadmaking and pasta production. We also discuss the role of digital agriculture technologies in optimizing tritordeum cultivation and their potential impact on agricultural practices. Furthermore, we assess the opportunities and challenges associated with tritordeum's integration into the agricultural economy and production systems, emphasizing its potential to contribute to a more sustainable and health-conscious food system. By offering a holistic understanding of tritordeum's attributes, this review serves as a valuable resource for researchers, agronomists, food manufacturers, and policymakers interested in exploring the potential of this innovative cereal grain.