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Identifying superior genotypes of soft winter wheat with high ecological plasticity to soil and climatic conditions, combined with tolerance to common diseases caused by fungal pathogens, including snow mould, is becoming increasingly important for both production and breeding. This is driven by the global demand for environmentally safe products. The preliminary selection of the most valuable initial material with high-stress resistance to periodic climate changes and deteriorating phytosanitary conditions is essential for the success of the breeding process in addressing current challenges. This study aimed to determine the level of ecological plasticity in soft winter wheat varieties, differentiated by their resistance to the snow mould pathogen, through the analysis of genotype effect ranks and yield regression coefficients. Additionally, high-yielding sources were identified for use in breeding for adaptability. The research was conducted according to commonly accepted methodologies for evaluating materials based on disease resistance and adaptive traits, ensuring a highly objective interpretation of the results obtained. It was determined that the highest ecological plasticity among the studied material was differentiated by resistance to Microdochium nivale (Fr.) Samuels & I.C. Hallett, was exhibited by five varieties (16.7%) that achieved yields ranging from 118% to 129% of the standard. These include the varieties Muza Bilotserkivska, Askaniiska Berehynia, Hetmanska, Malovanka (UKR), and Nordika (CZE), as evidenced by the lowest sum of ranks (sum of ranks 2) for the genotypic effect (ei) and regression coefficient (Ri). New sources of high and stable resistance in soft winter wheat to the snow mould pathogen were identified, specifically: Sanzhara, Malovanka, Svitiaz, Muza Bilotserkivska (UKR); Smuga (POL), and Nordika (CZE). It was found that, in soft winter wheat varieties, resistance to Microdochium nivale (Fr.) Samuels & I.C. Hallett significantly negatively correlates with the sum of ranks for genotypic effect and yield plasticity, with r =-0.69, P < 0.01. The systematisation of the results obtained and the identification of varieties with the highest ecological plasticity under variable weather conditions within the agroecological zone allows for the identification of the best gene pool for adaptive breeding. [ABSTRACT FROM AUTHOR]

The object of research is the process of automating systems for structuring data from several sources. The subject of the research is methods and algorithms for implementing a complete system for automated and parallel processing, validation and structuring of data. One of the most problematic areas is the merging of databases with different structures and several common fields into a generalized structure. The research was aimed at developing a system to increase the efficiency of automation of big data processing. As a result of the work, optimization methods were studied, the influence of their internal parameters on the operation of algorithms was analyzed, their main advantages and disadvantages were determined, and software was developed in which the corresponding methods were implemented. An algorithm for structuring data before processing has been obtained. Data structuring is achieved by performing the «mapping» operation. Mapping can take place by indexes of already cleaned data or using a defined dictionary with a given set of keys, which allows not to care about the sequence of storing values and their possible shift. The practical significance of the developed system lies in the improvement of methods of collecting and processing information for the purpose of its further validation, cleaning and accumulation in the following categories: geographic addresses and geo-coordinates, validation and automated addition of a mobile phone number to the international format, processing of car numbers (in modern and outdated format), VIN code of the engine and car brand, validation of urls of social networks, passport data and processing of personal data. Compared to similar methods for processing large volumes of data, the possibility of splitting the input file or stream into separate parts was used, the cleaned data from which is combined at the end of the system operation. Thanks to this, it is possible to process data whose size exceeds the available volume of the device’s RAM, and the method of working with loosely structured text files in CSV format has been improved. [ABSTRACT FROM AUTHOR]