Abstract The dairy sector is one of the most important and complexly regulated agricultural sectors, making a significant contribution to the national economy, public employment, and rural development. At the same time, it is a strategically important part of the food supply system, which is influenced by political, economic, technological, social, and environmental factors. Due to these circumstances, it is essential to develop and analyze scenarios for the development of the sector, which allow for the prediction of possible future situations and the assessment of the impact of policy measures or market changes on the sustainability of the sector. This is made possible by various forecasting models that are widely used in the agricultural sector. One such model is AGMEMOD, which was developed to forecast the agricultural and food market in the European Union (EU). In this study, using the AGMEMOD, scenarios for the development of the Latvian dairy sector were analyzed based on current sector trends and expert opinions obtained during interviews. Two scenarios were developed and the results show that gradual and structured integration of technology into milk production processes can promote productivity growth and ensure more stable adaptation of the sector to political and economic challenges without increasing herd size. Overall, the introduction of technology is the most promising direction for the development of the sector, allowing the potential for innovation to be balanced with practical adaptation processes. The aim of this study is using the AGMEMOD model to assess how different assumptions regarding technological progress and policy uncertainty may influence future milk production, herd size, and productivity.
Abstract Pea hulls, a by-product of pea protein production, represent a valuable source of dietary fibre, and its utilisation supports the implementation of zero-waste approaches in the development of new products. The incorporation of pea starch and extruded pea hulls as fibre sources in high-moisture extruded meat analogues represents a promising approach to obtaining products with desirable textural properties while increasing dietary fibre content. In contrast, the incorporation of fibre may disrupt the homogeneous protein–starch matrix, resulting in a less fibrous structure with shorter fibres. The aim of this study was to investigate the effect of extruded pea hull fibre and starch on structure formation, texture, colour, and sensory properties of high-moisture extrudates formulated with pea protein and pea starch. The formulations were based on 80% pea protein isolate (P) and 20% pea starch (S), with extruded pea hulls (F) added at levels of 5%, 10%, and 15% by partially replacing starch. High-moisture extrusion was performed using a twin-screw extruder. Texture profile analysis showed that, compared with extruded pea protein isolate, the hardness and chewiness of samples containing starch and extruded fibre were increase. Similar trends were observed during sensory evaluation, where experts noted that the addition of extruded pea hulls reduced perceived fibrousness, resulting in softer samples that were easier to chew and more closely resembled meat texture. Moderate replacement of starch with extruded pea hulls (10%) in high-moisture extruded products may improve textural properties and provide a basis for further research into meat analogue development.
Abstract Black elderberry juice is rich in polyphenols but tends to form a visually unappealing, cloudy precipitate, which is unappealing to consumers. This study aimed to evaluate how high-intensity ultrasound combined with lower-temperature pasteurisation affects the particle size distribution, turbidity, centrifugal sedimentation rate, viscosity, and colour of black elderberry juice. Ultrasound alone reduced the particle mean diameter (D₄,₃) values, indicating the deagglomeration of cell clusters and the formation of finer particles, while slightly decreasing centrifugal sedimentation. After ultrasound treatment (UT) and pasteurisation at 60–70 °C, particle size and the centrifugal sedimentation rate increased. Turbidity decreased to approximately 40 NTU, yielding clarified juice with large, dense aggregates that settled. In contrast, prolonged UT combined with pasteurisation at 60–70 °C significantly reduced D₄,₃ and increased turbidity to 425 NTU, indicating a fine, stable colloidal cloud. Colour analysis showed that UT followed by pasteurisation at 60 °C enhanced redness and chromatic hue. In contrast, prolonged UT and 70 °C temperature pasteurisation produced darker, less vivid red tones, consistent with partial pigment degradation and polymerisation. The results revealed two technological pathways within the same juice matrix: a clarification pathway (ultrasound + pasteurisation) and a cloud stabilisation pathway (extended ultrasound + pasteurisation), offering a tool for developing elderberry juices with the desired visual outcome.
Abstract Long-term cultivation of fruit trees leads to soil fatigue. It is a condition caused by the combined effects of adverse physical, chemical, and biological soil factors. In the experiment, the removal of an old apple orchard during its amortization phase was studied using stump grinding with a stump grinder. In the old apple orchard, intercrops such as cereals, white sweet clover ( Melilotus albus ), and many-leaved lupine ( Lupinus polyphyllus ) were grown. The intercrops were cultivated for four years. The selected apple cultivars for the experiment were ‘Antei’, ‘Auksis’, ‘Lobo’, ‘Tiina’, and ‘Vahur’, all grafted onto the dwarfing rootstock B9. In the old orchard, stump grinding combined with the use of green manure over four years improved tree height and trunk thickness in the new planting. Flower bud formation and fruit development depended strongly on the cultivar-rootstock combination. Fruit set and yield formation in apple orchards are determined primarily by the genetic characteristics of each cultivar. Tree height and trunk diameter continue to reflect the effects of soil preparation carried out up to a decade earlier. In a fully productive orchard, the average total yield per tree is higher in the variant with grinded stumps, although this had no effect on the average fruit weight.
Abstract Beekeeping plays a vital role in maintaining biodiversity, ecosystem balance, and global food security through pollination and the production of honey and other bee products. However, apiculture faces increasing threats from disease outbreaks, colony collapse disorder, and environmental stressors. In recent years, the convergence of artificial intelligence (AI), bio-inspired computation, and precision agriculture has introduced new tools for sustainable and intelligent beekeeping. This review aims to provide a comprehensive overview of AI applications in beekeeping, with an emphasis on both theoretical approaches and existing tools. It further explores the role of bio-inspired algorithms, such as the Artificial Bee Colony (ABC), in linking natural swarm behaviour to computational optimisation problems. The most prominent AI implementations are found in disease diagnostics, using Computer Vision (CV) and Deep Learning (DL) for the detection of pathogens such as Varroa destructor and Nosema . Behavioural tracking through video analytics and acoustic monitoring is gaining momentum, offering early warnings for colony stress and swarming. While commercial tools demonstrate practical feasibility, adoption remains uneven due to cost, infrastructure needs, and limited access among small-scale beekeepers. However, data scarcity and model generalisability remain significant limitations. The integration of AI and evolutionary computation into precision beekeeping presents a transformative opportunity to enhance sustainability, productivity, and resilience in apiculture. The interdisciplinary nature of intelligent apiculture opens up opportunities for collaboration across fields such as computer science, agriculture, ecology, and ethics. This study establishes a foundation for future research in AI-driven beekeeping, aligned with the goals of sustainable agriculture and environmental stewardship.
