Andijan Institute of Agricultural

Due to the complexity of the vineyard ecosystem, a growing number of eco-certified inputs has been integrated into a framework of sustainable agricultural practices. This research is aimed at developing a hybrid framework that can be used to predict the eco-label readiness of a certain vineyard and provide guidance about the ecological indicators that should be prioritized in it as well as to find ways to increase the marketing effectiveness. Secondly, the sample of vineyards (n = 50) was divided into three groups—early, mid-season and late ripening varieties—for the analysis of a significant difference observed between and within groups, in terms of the effects of sensor-calibrated inputs on eco-performance indicators. In the second phase, the structural analysis focused on the relationship between eco-performance indicators and its outcomes (eco-label readiness, both direct and indirect effects). Then, the regression analysis and structural equation modeling showed that out of all examined eco-performance results, the timing of the chlorophyll decline, the behaviour of the sugar accumulation under controlled application, and the stability of the eco-readiness over time contributed to the total variance of eco-performance. Yet, the results confirm our assumption that an improvement in sensor calibration and nutrient scheduling is also necessary, particularly in the eco-label certification process of vineyards. The methodology can be used to make informed decisions both about the operational strategies for increasing the eco-marketing performance and the selection of inputs that should be applied in the vineyard.

Organic farming plays an important role alongside conventional farming in the global food supply. Organic systems offer a sustainable alternative in agriculture. However, maintaining the nutritional consistency and build consumer trust of organic products remains a challenge. Metabolomics can help distinguish organic products from conventional products by analysing the biochemical composition of plants. This study used a simple systematic approach involving a search for scientific articles on Google Scholar over the past ten years (2014-2024). This review aims to examine the extent of metabolomics' role in contributing to organic farming, focusing on its comparison with nutritional studies and consumer perceptions. The results show that publications on metabolomics related to nutrition average 19,457 per year, metabolomics related to consumer perceptions average around 5,445 per year, and metabolomics related to organic farming average around 457 per year. Compared to metabolomics research related to nutrition, the contribution of metabolomics research to organic farming studies is only about 2.35%. Compared to metabolomics research related to consumer perceptions is only about 8.39%. This confirms that the use of metabolomics in the context of organic farming is still largely unexplored. In fact, organic farming is in dire need of scientific evidence regarding nutritional quality as well as building a more positive consumer perception. This study brings a new perspective for organic agricultural farmers to also focus on elaborating a metabolomics approach to improve the quality aspects that complement crop production quantity. The integration of metabolomics will strengthen nutritional claims, scientific transparency, credibility, and consumer trust.

Cotton ( Gossypium spp.) is a globally cultivated climate resilience fiber crop known for its potential to produce substantial biomass and yield in stress environments. However, its production requires a huge amount of chemicals, bringing harm to the environment and ecosystem services. Field studies during the 2023–2024 growing seasons were focused on agronomic parameters of cotton in response to nano (Si and Uzbi) and three chemical fertilizer regimes (N 150 P 105 K 75 ; N 200 P 140 K 100 ; N 250 P 175 K 125 ) under arid condition of Uzbekistan. The experiment was arranged in a split-plot design with three replications. The application of nano Si improved nutrients uptake and yield parameters of cotton under open-field arid environment due to a positive synergism between nano and chemical fertilization. The effect of Si nano compound was more pronounced at the lower fertilizer regime (N 150 P 105 K 75 ), exhibiting the increase of cotton yield by 31.3% and 1000 seed weight by 5.32% than those in the control. On the other hand, the Uzbi treatment exhibited high efficacy at the intermediate fertilizer level (N 200 P 140 K 100 ), enhancing total cotton biomass by 12.9% and total yield by 26.3% relative to the control. The Si nano was found to be relatively efficient in terms of NUE, crop yield and may help reduce the reliance on excessive chemicals in current climate changing scenario. This study explored the cost-effective pathways toward more sustainable cotton production through the synergistic combination of nano and chemical fertilizers in irrigated arid agricultural regions.

The article provides information on the participation of crustaceans (Crustacea) in the life cycles of helminths of domestic and wild (game and commercial) animals in Uzbekistan. To date, 730 species of parasitic worms have been registered, 114 of which parasitise fish, 376 – birds, and 240 – mammals. The most common of them represent the class Cestoda: Bothriocephalus opsariichth y dis , Schistocephalus pungitii , Ligula intestinalis , L. colymbi , Digramma interrupta , Gryporhynchus pusillus , Dicranotaenia coronula , Diorchis brevis , Diorchis ransomi , Diploposthe laevis , Drepanidotaenia lanceolata , Fimbraria fasciolaris , Microsomacanthus microsoma , M. compressa , Myxolepis collaris , Sobolevicanthus gracillus , and Diphyliobothrium ditremum . The most epizo o tologically significant representatives of Nematoda included Raphidascaris acus , Contracaecum microcephalum , Camallanus lacustris , Ph i lometra ovata , Ph i lometra rischta , Phylometroides sanguine us , Dracunculus medine n sis , Avioserpens mosgovoyi , Ascaridia galli , Heterakis gallinarum , Tetrameres fissispina , Streptocara crassica u da , Echinuria uncinate , and Gnathostoma hispidum . Parasitological studies of the Cyclops community (more than 30 species) in various bodies of water in Central , North - Eastern , and North - Western Uzbekistan show infe c tion of 11 species of Cyclopidae with larval stages of cestodes and nematodes. Naturally, infected Cyclops widely inhabit diverse bodies of water and include the following species: Macrocyclops albidus , M. fuscus , Eucyclops macrurus , E. serrulatus , Cyclops strenuus , Cyclops vicinus , C. heberti , Acanthocyclops trajani , Thermocyclops crassus and Mesocyclops leuckarti . All of them are intermediate hosts to the helminths. The total prevalence of the larval stages of cestodes and nematodes in the studied Cyclops was 6.9%, with Cestoda larvae accounting for 5.7% and Nematoda – 1.2%. All cestode larvae found in Cyclops are of two types – procercoids and cysticerc o ids. The nematode larvae found in 7 crustacean species turned out to be representatives of the following families: Anisakidae, Camallanidae, Ph i lometridae, Dracunculidae and Gnathostomatidae. We identified a total of 20 species of helminth larvae, whose mature forms parasitise fish, birds and mammals. Crustaceans play a signif i cant role in the life cycles and circulation of pathogens and groups of parasites in bodies of water in Uzbekistan.

Statistical Machine Translation (SMT), as part of an overall human-centered machine translation process, provides a significant opportunity for multilingual access to digital content, such as specialized educational content in medical artificial intelligence. As medical knowledge is a continually evolving field of study and is critical for timely and accurate access by global learners and health professionals, providing accurate and accessible learning materials across many languages makes sense. The traditional translation solutions including online machine translation utilize outdated methods of translation that do not account for specific or context-relevant solutions, and domain-specific languages, terminologies, and linguistic affordances or preferences. Rule-based machine and general-purpose machine translation solutions will be limited in reflecting medical terminology and will ultimately adversely affect the translation quality and educational access provided. Therefore, here suggested a new framework called MED-SMT (Medical Educational Domain Statistical Machine Translation) to deal with these problems. MED-SMT is a domain adapted statistical translation model, with an established medical terminology database and a set of contextually grounded alignment processes that will translate medical common language. MED-SMT supports the translation of various medical specific language patterns and provides accurate multi-linguistic rendition of the original medical course content. The MED-SMT framework aims to allow the worldwide access to medical Artificial Intelligence (AI) education through the automatic translation of course materials (e.g. lecture notes, research papers, interactive tutorials) into a variety of languages at a minimal cost in human operator time. This will make it easier for medical students, professionals, and researchers who do not speak English to understand advanced AI content. Experimental results show that MED-SMT provides a notable improvement in translation quality and terminology consistency over the advanced methods achieving better understanding and knowledge transfer of medical concepts in medical AI education.

Contextual Text Analytics for Knowledge Extraction in Biomedical Device Design Education addresses the growing need to efficiently process vast amounts of unstructured textual data generated during education and research. Traditional manual methods struggle to extract meaningful information from complex biomedical design documents, student feedback, and literature, causing delays in curriculum improvement and innovation. To tackle this, the paper proposes a novel framework termed Contextual Biomedical Text Analytics (CBTA) that integrates natural language processing (NLP) techniques with domain-specific ontologies to enhance contextawareness in text processing. CBTA leverages semantic embedding, named entity recognition, and topic modeling to extract relevant knowledge, trends, and competency indicators from diverse textual datasets. Experimental validation on biomedical device design course materials demonstrates CBTA's enhanced ability to identify key concepts, design challenges, and learning gaps, outperforming baseline text mining techniques by 18% in precision and 15% in recall. The results indicate that CBTA facilitates actionable insights for educators, enabling targeted curriculum updates and personalized learning paths. In conclusion, CBTA presents an intelligent, scalable solution for advancing biomedical device design education through data-driven knowledge extraction, supporting both academic excellence and industry readiness.

Photosynthesis represents one of the most essential physiological processes sustaining life on Earth. It provides plants with the energy and organic matter necessary for growth, development, and reproduction, while also maintaining the balance of gases in the atmosphere. This paper explores the physiological and biochemical foundations of photosynthesis and its integral role in plant metabolism. It also analyzes how internal and external factors—such as light intensity, temperature, water availability, and carbon dioxide concentration—affect the efficiency of this process. Moreover, it discusses recent advances in understanding the molecular regulation of photosynthesis and their implications for increasing agricultural productivity. In the broader context, photosynthesis is examined as both a biochemical mechanism and a key determinant of ecosystem stability and global food security. The article emphasizes that improving photosynthetic efficiency through physiological optimization and biotechnological innovation is vital for sustainable development in the face of climate change.

The article reveals the essence of the concepts of “sustainable development”, “sustainable development of agriculture”, “sustainable investment”. The current state of agriculture in the Andijan region, investments in fixed capital of the industry are considered. Proposals for sustainable development of agriculture are given.