employee from 01.01.2020 to 01.01.2024
Federal State Budgetary Scientific Organization «Federal Center of Agriculture Research of the South- East Region». (oilseeds laboratory)
employee from 01.01.2020 to 01.01.2024
Saratov, Saratov, Russian Federation
Abstract. The creation of highly productive sunflower varieties and hybrids adapted to the conditions of cultivation regions depends on the use of source material with high genetic diversity. The purpose of the study is to study the variability of agronomic characters in sunflower varieties and hybrids created in various breeding centers, their correlation, and to group genotypes according to a set of economically valuable indicators, using multivariate statistics methods. Methods. The research was carried out on the Right bank of the Saratov region on the experimental field of the Federal Center of Agriculture Research of the South-East Region in 2021–2023. The objects of research were varieties and new experimental hybrids bred by epy Federal Center of Agriculture Research of the South-East Region (31 varieties and hybrids) and V. S. Pustovoyt All-Russian Research Institute of Oil Crops (12 hybrids). To evaluate the breeding material, generally accepted methodological recommendations were used. Results. 43 varieties and hybrids of sunflower were assessed according to 8 agronomic characters. It was established a low degree of variability in yield (V=9.4%); as well as of plant height (V = 7.8 %); anthode diameter (V = 5.2 %); in unit (V = 5.3 %); and of oil content (V = 5.7 %). Average variability was revealed in the weight of 1000 seeds (V = 16.0 %); anthode area (V = 11.1 %); oil crop (V = 15.8 %). Correlation analysis made it possible to identify 8 significant connections at the 5.0 % level. High correlation coefficients were established between oilseed yield and unit (r = 0.7), as well as between anthode diameter and anthode area (r = 0.83). As a result of factor analysis, 8 characteristics were reduced to 5 factors (loading above 5.0 %) with a total variance of 90.95 %. Clustering by minimum Euclidean distances allowed the studied genotypes to be grouped into 10 clusters, including from 1 to 16 genotypes at the 33rd step. The identified clusters can be considered as independent groups for inclusion in the selection process. Scientific novelty. An attempt was made to group varieties and new experimental sunflower hybrids created in various breeding centers according to the similarities and differences of the main agronomic characters in the arid conditions of the Saratov region.
sunflower, variety, hybrid, yield, oil content, unit, mass of 1000 seeds, coefficient of variation, correlation coefficient, cluster, factor loading
1. Gryazeva V. I., Koryagin V. Yu., Koryagina N. V., Sigov N. I. Ocenka gibridov podsolnechnika po hozyaystvenno-cennym priznakam i ekologicheskoy plastichnosti // Niva Povolzh'ya. 2020. № 3(56). S. 37–40. DOI:https://doi.org/10.36461/NP.2020.56.3.012.
2. Kolodyazhnyy S. V. Sravnitel'naya ocenka razlichnyh elementov tehnologii vozdelyvaniya podsolnechnika v Yuzhnoy Lesostepi CChR: dis. … kand. s.-h. nauk: 06.01.01. Voronezh, 2021. 221 s.
3. Kamenev R. A., Solodovnikov A. P., Letuchiy A. V., Gudova L. A., Lekarev A. V. Efficiency of application of compost of liquid poultry manure under sunflower in southern chernozem // IOP Conference Series: Earth and Environmental Science. 2022. Vol. 1045, № 1. Article number 012084. DOI:https://doi.org/10.1088/1755-1315/1045/1/012084.
4. Guchetl' S. Z., Golovatskaya A. V., Ramazanov S. A., Voloshko A. A. Geneticheskoe raznoobrazie liniy podsolnechnika rossiyskoy selekcii vyyavlennoe s pomosch'yu mikrosatellitnyh lokusov // Agrarnaya nauka Evro-Severo-Vostoka. 2023. № 24 (2). S. 173–184. DOI:https://doi.org/10.30766/2072-9081.2023.24.2.173-186.
5. Fokina E. M., Belyaeva G. N., Razancvey D. R. Ispol'zovanie zarodyshevoy plazmy netipichnyh form soi v selekcii // Dostizheniya nauki i tehniki APK. 2020. T. 34, № 8. S. 8–15. DOI:https://doi.org/10.24411/0235-2451-2020-10801.
6. Duktova N. A., Grib S. I., Ivanova E. V., Andronik E. L., Vinogradov D.V. Primenenie mnogomernogo analiza dlya ocenki selekcionnoy cennosti ishodnogo materiala tverdoy yarovoy pshenicy // AgroEkoInfo: Elektronnyy nauchno-proizvodstvennyy zhurnal. 2023. № 1. DOI:https://doi.org/10.51419/202131125.
7. Petrova L. V., Platonova A. Z. Klasternyy analiz sortov ovsa posevnogo (Avena Sativa L.) po elementam struktury urozhaya v usloviyah Central'noy Yakutii // Izvestiya TSHA. 2019. Vyp. 1. S. 56–78.
8. Stepanova N. A., Sidorenko V. S., Yandubaykin E. E. Klasternyy analiz sortov i selekcionnyh liniy yarovoy myagkoy pshenicy po pokazatelyam strukturnogo analiza i kachestva zerna // Zernovye i krupyanye kul'tury. 2023. № 2 (46). S. 45–51. DOI:https://doi.org/10.24412/2309-348H-2023-2-107-116.
9. Starchak V. I., Kibal'nik O. P., Stepanchenko D. A. Ispol'zovanie klasternogo analiza v selekcii zernovogo sorgo v Rossii // Journal of Agriculture and Environment. 2022. № 2 (22). DOI:https://doi.org/10.23649/jae.2022.2.22.10.
10. Vus N. A., Kobyzeva L. N., Bezuglaya O. N. Opredelenie selekcionnoy cennosti kollekcionnyh obrazcov nuta (Cicer arietinum L.) metodom klasternogo analiza // Vavilovskiy zhurnal genetiki i selekcii. 2020. T. 24, № 3. S. 244–251. DOI:https://doi.org/10.18699/VJ20.617.
11. Nizhimbere Zh., Suprunov A. I., Petryakov A. P. Klasternyy analiz novyh avtodiploidnyh srednespelyh liniy kukuruzy // Collected Papers XLV International Scientific-Practical conference “Russian Science in the Modern World”: sbornik statey. Moskva, 2022. S. 7–8.
12. Kodirova G. A., Kubankova G. V., Litvinenko O. V. Klassifikaciya sortov soi amurskoy selekcii po biohimicheskim pokazatelyam metodom klasterizacii // Vestnik KrasGAU. 2022. № 11. S. 54–61. DOI:https://doi.org/10.36718/1819-4036-2022-11-54-61.
13. Gudova L. A., Zaycev S. A., Zhuzhukin V. I., Kurasova L. G., Lekarev A. V. Ispol'zovanie metodov mnogomernoy statistiki dlya ocenki model'noy populyacii kukuruzy // Agrarnyy nauchnyy zhurnal. 2021. № 7. S. 9–15. DOI:https://doi.org/10.28983/asj.y2021i7pp9-15.
14. Tkachev Yu. A. Osnovaniya mnogomernogo klasternogo analiza v geologii // Vestnik IGKimiNC UroRAN. 2019. № 2. S. 44–52.
15. Sidorenko V. S., Tugareva F. V., Starikova Zh. V. Experimental verification of cluster analysis to identify valuable breeding samples of spring wheat // IOP Conference Series: Earth and Environmental Science. 2021. Vol. 650. Article number 012105. DOI:https://doi.org/10.1088/1755-1315/650/1/012105.
16. Zaitsev S. A., Lyovkina A. Yu., Volkov D. P., Kameneva O. B., Bychkova V. V., Gudova L. A. Biometric methods in studying the gene pool indian pea // BIO Web of Conferences. 2022. Vol. 47. Article number 02005. DOI:https://doi.org/10.1051/bioconf/20224702005.
17. Demina I. F. Rezul'taty izucheniya kollekcionnyh obrazcov pshenicy myagkoy yarovoy v usloviyah Srednego Povolzh'ya // Agrarnaya nauka Evro-Severo-Vostoka. 2020. T. 21, № 6. S. 653–659. DOI:https://doi.org/10.30766/2072-9081.2020.21.6.653-659.
18. Starchak V. I., Kibal'nik O. P., Larina T. V., Semin D. S. Metod «glavnyh komponent» v selekcii zernovogo sorgo dlya zasushlivyh usloviy Nizhnego Povolzh'ya // Zernovoe hozyaystvo Rossii. 2021. № 3 (75). S. 22–26. DOI:https://doi.org/10.31367/2079-8725-2021-75-3-22-26.
19. Zhuzhukin V. I., Muhatova Zh. N., Subbotin A. G., Sugrobov A. F., Druzhkin A. F. Ispol'zovanie metodov mnogomernoy statistiki v izuchenii sortoobrazcov nuta kak ishodnogo materiala dlya selekcii v Nizhnem Povolzh'e // Niva Povolzh'ya. 2022. № 2 (62). Article number 1005. DOI:https://doi.org/10.36461/NP.2022.62.2.011.
20. Metodika gosudarstvennogo sortoispytaniya sel'skohozyaystvennyh kul'tur. Moskva, 2019. 320 s.
21. Dospehov B. A. Metodika polevogo opyta (s osnovami statisticheskoy obrabotki rezul'tatov issledovaniy). Moskva: Agropromizdat, 1985. 351 s.
