Selection of productive hybrid oil flax plants by methods of multivariate analysis

Use of computer technology allows to quickly analyze and use subject, technological, analytical and other information. Biometric statistics in plant breeding is aimed at optimizing (increasing efficiency, reliability, acceleration and cheapening) the process of breeding varieties of agricultural crops. Therefore, creation and study of new varieties of oil flax requires widespread introduction of modern computer information technologies that provide information support of the breeding process at all its stages. Methods of multi-criteria mathematical statistics - factor and cluster analyses - were used in the studies for a comprehensive assessment of hybrid populations of oil flax by productivity elements (plant height, technical length, inflorescence length, number of pods per plant, number of seeds per plant, number of seeds in a box, weight of 100 seeds, and oil content in seeds). Effectiveness of selection of hybrids of the third cycle of breeding has been evaluated, and also the distinctive features of hybrid combinations in a number of generations have been established. As a result of selection and technological cycle of the analysis, 31 highly productive hybrids (or 6.9%) were identified for further reproduction. Despite the high level of the breeding differential determined in hybrid combinations during the F2-F3 generation change, their response to traits based selection according to “number of seeds in a box” and “weight of 100 seeds” was weak, and selection by the number of boxes and seeds from the plant turned out to be ineffective. The selection method used makes it possible to cull low-yielding plants that have fallen into the worst groups of clusters. Culling by the method of multidimensional analysis should be used in later generations (fourth-fifth cycle of selection) as homozygosity of traits is established.

1. Grebennikova I. G., Aleinikov A. F., Stepochkin P. I., Chanyshev D.I. Structure of the complex of information support of the selection process of triticale. Noveishie napravleniya razvitiya agrarnoi nauki v rabotakh molodykh uchenykh: trudy IV Mezhdunarodnoi nauchnoi konferentsii molodykh uchenykh, posvyashchennoi 40-letiyu SO Rossel’khozakademii (22-23 aprelya 2010 g., pos. Krasnoobsk) [The latest directions for the development of agrarian science in the works of young scientists: proceedings of the IV International scientific conference of young scientists dedicated to the 40th anniversary of the Siberian Branch of the Russian Agricultural Academy (April 22-23, 2010, Krasnoobsk village]. Novosibirsk, 2010, pt. 1, pp. 247-249 (in Russian).

2. Martynov S.P. Biometric approaches to optimizing the selection process. Molekulyarnaya i prikladnaya genetika: sbornik nauchnykh trudov [Molecular and applied genetics: a collection of scientific works]. Minsk, 2009, vol. 9, pp. 49-60 (in Russian).

3. Andronik E. L., Ivanova E.V. Creation of the «Flax Collection» database. Molodezh’ v nauke - 2007. Ch. 4. Seriya agrarnykh nauk [Youth in science - 2007. Part 4: Series of agrarian sciences]. Minsk, 2008, pp. 166-169 (in Russian).

4. Portyankin D. E., Khomutovskii P. R., Karochan V. N., Ivashko L.V. On the need to create a computer database of genealogy of flax varieties of Belarusian breeding. Zashchita rastenii: sbornik nauchnykh trudov [Plant protection: a collection of scientific works]. Minsk, 1998, iss. 22, pp. 150-158 (in Russian).

5. Yau S. K., Ortis-Ferrara G., Srivastava J.P. Claster analysis of bread wheat lines grown in diverse rain fed environment. Barley and Wheat Newsletter, 1992, vol. 8, no. 2, pp. 31-35.

6. Kovalenko N. N., Volchkov Yu. A. Chemical composition of fruits of species far eastern cherries as a classification criterion at the study of source material for breeding. Plodovodstvo i vinogradarstvo Yuga Rossii = Fruit Growing and Viticulture of South Russia, 2011, no. 12 (6), pp. 30-42 (in Russian).

7. Shcheglov S.N. Intervarietal variability of features of the root system of strawberries. Nauchnyi zhurnal KubGAU = Scientific Journal of KubSAU, 2005, no. 11, pp. 186-192 (in Russian).

8. Vlasov M.V. Internal heterogeneity of samples of the amaranth collection as a prerequisite for its selection. Ph.D. Thesis. Krasnodar, 2002. 136 p. (in Russian).

9. Mel’nichuk A.D. Determination of genetic heterogeneity of potato varieties and selection of parent pairs for hybridization based on the results of factor analysis. Kartofelevodstvo: sbornik nauchnykh trudov [Potato growing: collection of scientific papers]. Minsk, 2000, iss. 10, pp. 63-73 (in Russian).

10. Kilchevsky A. V., Mel’nichuk A. D., Kruk A.V. Discriminant analysis of accumulation 137Cs by vegetable plants. Agroekologiya: sbornik nauchnykh trudov. Vyp. 2. Ekologicheskie osnovy plodoovoshchevodstva [Agroecology: collection of scientific papers. Iss. 2. The ecological foundations of horticulture]. Gorki, 2005, pp. 124-129 (in Russian).

11. Rotaru L. I., Lupashku G.A. The use of factor and cluster analyses to create genotypes of tomato with stable productivity. Sovremennye tendentsii v selektsii i semenovodstve ovoshchnykh kul’tur. Traditsii i perspektivy: II Mezhdunarodnaya nauchno-prakticheskaya konferentsiya, 2-4 avgusta 2010 g. [Current trends in vegetable breeding and seed production. Traditions and perspectives: II International scientific-practical conference, August 2-4, 2010]. Moscow, 2010, vol. 1, rr. 462-

12. Brach N. B., Porokhovinova E. A., Sharov I. Ya. Factor analysis of donors of economically valuable features from the collection of flax of VIR. Sovremennye problemy l’novodstva na Severo-Zapade RF : sbornik materialov nauchno-prakticheskoi konferentsii, Pskov, 4-6 iyulya 2000 g. [Modern problems of flax growing in the North-West of the Russian Federation: collection of materials of the scientific-practical conference, Pskov, July 4-6, 2000]. Pskov, 2000, pp. 29-30 (in Russian).

13. Ivanova E. V., Andronik E.L. Informational content of indicators of quantitative signs in long-fiber flax and minimization of their estimation. Vestnik Belorusskoi gosudarstvennoi sel’skokhozyaistvennoi akademii = Bulletin of the Belarussian State Agricultural Academy, 2012, no. 2, pp. 40-45 (in Russian).

14. Ruanet V.V. Using artificial neural networks to solve private problems of genetics and selection. Ph.D. Thesis. Moscow, 2003. 108 p. (in Russian).

15. Hartigan J.A. Clustering algorithms. New York, Wiley, 1975. 351 p.

16. Martynov S.P. Clyster analysis of the Saratov spring wheat cultivars by the parentage coefficients. Tsitologiya i genetika = Cytology and Genetics, 1989, vol. 23, no. 4, pp. 37-43 (in Russian).

17. Griffing B. Concept of general and specific combining ability in relation to diallel crossing systems. Australian Journal of Biological Sciences, 1956, vol. 9, no. 4, pp. 463-493. https://doi.org/10.1071/bi9560463

18. Peruanskii Yu. V., Tazhibaeva T.L. Clustering by productivity elements of promising forms of winter wheat of various frost resistance. Selektsiya i urozhai: sbornik nauchnykh trudov [Selection and harvest: collection of scientific papers]. Alma-Ata, 1988, pp. 143-153 (in Russian).

19. Chebotar’ S. V., Sivolap Yu. M. Differentiation, identification and development of database of T. aestivum L. varieties of Ukrainian selection on the basis of STMS-analysis. Tsitologiya i genetika = Cytology and Genetics, 2001, vol. 35, no. 6, pp. 18-27 (in Russian).

20. Andronik E. L., Ivanova E.V. Selection of individual highly-productive plants of oil flax in hybrid population F2. Vestnik Belorusskoi gosudarstvennoi sel’skokhozyaistvennoi akademii = Bulletin of the Belarussian State Agricultural Academy, 2017, no. 4, pp. 114-117 (in Russian).