Abstract. At the final stage of the breeding process, an urgent task is to identify the adaptive response of promising breeding material for making a decision on the targeting of state testing. The purpose of the study was to study the adaptive reaction of the selective breeding number of barley 3856h-6-18. Methods. The specified number was compared with the standard of Pamyati Chepeleva. In the conditions of 2023, the environmental variability of quantitative values was ensured by three sowing periods and four seeding rates. Statistical methods and mathematical models were used: analysis of variance, additive mathematical model, analysis of adaptive characteristics. Results. Based on the obtained variable values, additive mathematical models of the relationship between biological yield and its structural elements were constructed and statistically justified, and point forecasts of biological yield were made. The ki coefficient proved the accuracy of the forecasts, which varied in the range of 2.6...4.1 % and shows that the effective feature is predicted quite accurately. Further, the obtained results were differentiated into forecasts of biological yield depending on the effects of variable values of quantitative characteristics (Xri). The results became a source for calculating indicators of adaptive ability, environmental stability and breeding value, which are further shown in the dynamics, according to the periods of plant growth and development. The analysis of biological yield forecasts showed the advantage of number 3856h-6-18 in terms of breeding value (BVT2i = 1.70) during the formation of ear lake, which was ensured by the predominance of both general adaptive capacity (WIA2i = 0.34 t/ha) and environmental stability (Sg2i = 9.4 %). At the end of the growing season (formation, filling and maturation of grain), the advantage belongs to the promising number (BVT3i = 1.28), associated with environmental stability – Sg3i = 7.1 %. Scientific novelty. According to the prospective number 3856n-6-18 in a fixed range of yield variability (7.05…8.11 t/ha) revealed the dynamics of changes in adaptive characteristics in the process of plant growth and development.
barley (Hordeum vulgare L.), quantitative characteristics, yield, genotype, regression
1. Faye B., Webber H., Gaiser T., Müller C., Zhang Y., Stella T., Latka C., Reckling M., Heckelei T., Helming K., Ewert F. Climate change impacts on European arable crop yields: Sensitivity to assumptions about rotations and residue management // European Journal of Agronomy. 2023. No. 142. DOI:https://doi.org/10.1016/j.eja.2022.126670. EDN: https://elibrary.ru/VRGIZC
2. Goncharov N. P., Kosolapov V. M. Selekciya rasteniy – osnova prodovol'stvennoy bezopasnosti Rossii // Vavilovskiy zhurnal genetiki i selekcii. 2021. T. 25, № 4. S. 361–366. DOI:https://doi.org/10.18699/VJ21.039. EDN: https://elibrary.ru/MAZDRX
3. Semenov T. E. Bar'ery i perspektivy primeneniya novyh geneticheskih tehnologiy dlya proizvodstva produktov pitaniya: varianty regulirovaniya v interesah Rossiyskoy ekonomiki // Strategicheskie resheniya i risk-menedzhment. 2021. T. 12, № 4. S. 344–353. DOI:https://doi.org/10.17747/2618-947X-2021-4-344-353. EDN: https://elibrary.ru/ORKPYU
4. Anisimova I. N., Dubovskaya A. G. Sistemy CMS u rapsa i ih ispol'zovanie v selekcii otechestvennyh gibridov // Trudy po prikladnoy botanike, genetike i selekcii. 2020. T. 181, № 3. S. 171–180. DOI:https://doi.org/10.30901/2227-8834-2020-3-171-180. EDN: https://elibrary.ru/NSDDRT
5. Galgovskaya L. A, Terkina O. V., Romanova A. N. Kombinacionnaya sposobnost' novyh inbrednyh liniy kukuruzy selekcii VNIIK // Izvestiya Kabardino-Balkarskogo nauchnogo centra RAN. 2023. № 6 (116). S. 263–269. DOI:https://doi.org/10.35330/1991-6639-2023-6-116-264-269. EDN: https://elibrary.ru/TEGVMU
6. Yangyang Gu, Hongxu Ai, Tai Guo, Peng Liu, Yongqing Wang, Hengbiao Zheng, Tao Cheng, Yan Zhu, Weixing Cao and Xia Yao. Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR // Plant Methods. 2023. Vol. 19. Article number 134. DOI:https://doi.org/10.1186/s13007-023-01093-z. EDN: https://elibrary.ru/IEIFXQ
7. Erunova M. G., Simakina A. S., Yakubaylik O. E. Sozdanie bazy dannyh dlya tochnogo zemledeliya OPH «Kuraginskoe» // Vestnik KrasGAU. 2022. № 1 (178). S. 13–20. DOI:https://doi.org/10.36718/1819-4036-2022-1-13-20. EDN: https://elibrary.ru/UXHCYQ
8. Lihodeevskiy G. A., Shanina E. P. The use of long-read sequencing to study the phylogenetic diversity of the potato varieties plastome of the Ural selection // Agronomy. 2022. Vol. 12, No. 4. Article number 846. DOI:https://doi.org/10.3390/agronomy12040846. EDN: https://elibrary.ru/ULYMDU
9. Maksimov R. A. Adaptivnaya reakciya kollekcionnyh sortoobrazcov yarovogo yachmenya (Hordeum vulgare L.) v usloviyah Srednego Urala // Dostizheniya nauki i tehniki APK. 2022. T. 36, № 4. S. 35–40. DOI:https://doi.org/10.53859/02352451_2022_36_4_35. EDN: https://elibrary.ru/FBQGKO
10. Dragavcev V. A., Kardashina V. E., Kovtunovskaya E.S. Ocenka sortov i liniy yarovogo ovsa s pomosch'yu principa ortogonal'noy identifikacii genetiko-fiziologicheskih sistem, opredelyayuschih urozhai // Dostizheniya nauki i tehniki APK. 2022. T. 36, № 7. S. 19–24. DOI:https://doi.org/10.53859/02352451_2022_36_7_19. EDN: https://elibrary.ru/QPYAVR
11. Maksimov R. A. Mnozhestvennyy regressionnyy analiz kak sposob differenciacii urozhaynosti po fazam rosta i razvitiya genotipov yachmenya (Hordeum vulgare L.) // Dostizheniya nauki i tehniki APK. 2021. № 4. S. 29–34. DOI:https://doi.org/10.24411/0235-2451-2021-10404. EDN: https://elibrary.ru/KDHOBW
12. Filippov E. G., Bragin R. N., Dontsov D. P. Analysis of adaptability indicators of spring barley varieties and lines in the ecological variety testing // Taurida Herald of the Agrarian Sciences. 2022. No. 4 (32). Pp. 221–230.
13. Chashkova A. F., Stepochkin P. I., Aleynikov A. F., Grebennikova I. G., Ponomorenko V. I. Sravnenie statisticheskih metodov ocenki stabil'nosti urozhaynosti ozimoy pshenicy // Vavilovskiy zhurnal genetiki i selekcii. 2020. T. 24, № 3. S. 267–275. DOI:https://doi.org/10.18699/VJ20.619. EDN: https://elibrary.ru/HKAOWK
14. Maksimov R. A. Metod opredeleniya parametrov adaptivnoy sposobnosti s ispol'zovaniem mnozhestvennogo regressionnogo analiza vzaimosvyazi urozhaynosti i ee elementov struktury // Dostizheniya nauki i tehniki APK. 2021. № 6. S. 4–10. DOI:https://doi.org/10.24411/0235-2451-2021-10601. EDN: https://elibrary.ru/NXPAWF
15. Reyting 10 sortov liderov s/h kul'tur po ob'emam vyseva v RF v 2023 g. [Elektronnyy resurs]. URL: https://rosselhoscenter.ru.nformaczionnyj_listok_№_4_ot_rshcz_rejting_sortov.pdf (data obrascheniya: 10.03.2024).
16. Dospehov B. A. Metodika polevogo opyta. Moskva: Agropromizdat, 1985. 336 s. EDN: https://elibrary.ru/ZJQBUD
17. Kil'chevskiy A. V., Hotyleva L. V. Metody ocenki adaptivnoy sposobnosti i stabil'nosti genotipov, differenciruyuschey sposobnosti sredy. Soobschenie 1. Obosnovanie metoda // Genetika. 1985. T. 21, № 9. S. 1481–1498. EDN: https://elibrary.ru/XIWYIJ