Influence of weather conditions of the Kirov region on the economically valuable traits of winter rye

As a result of five-year research (2019–2023), 12 breeding valuable traits of winter rye were analyzed. The object of the study was 11 released and promising winter rye varieties of the FARC NorthEast selection. The assessment of hydrothermal conditions was carried out, during the study period 3 years with favorable conditions of spring-summer vegetation (2019–2021) and 2 years with excessive moisture (2022, 2023) were identified. The influence of agrometeorological factors of separate months of spring-summer vegetation on the formation of winter rye yield was considered using correlation analysis. A high negative dependence of yield on July temperature regime: average monthly air temperature and the sum of effective temperatures (>5 ℃) (r = –0.94) was revealed. The amount of precipitation made a significant negative contribution to the formation of yields in the phases of spring tillering and booting (April–May) (r = –0.93 and –0.89, respectively). The variability of the studied characteristics under different weather and climatic conditions was assessed. Highly stable characteristics included grain unit (CV = 1%) and spikelet productivity indices: length, number of spikelets, grains, spikelet density (CV = 3–4%). The highest variability was observed in the number of spikelets (CV = 43%). The principal component analysis (PCA) method was used to analyze the results. Different factor loadings of yield-forming indicators in contrasting years were observed. Grain coarseness in years with excessive moisture had a greater influence on yield dispersion than in the years with optimal conditions. It was revealed that the yield was more dependent on the number of spikelets, ear grain content, grain weight per spikelet, plant height and lodging resistance. The factor loadings of the above-mentioned indices determined most of the yield dispersion: 45.09% in a favorable year and 48.25%, hence, breeding work on these characteristics will be more effective.

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