Information and technical features of the test for distinctness of new varieties of Lactuca sativa L. var. capitata

Authors

DOI:

https://doi.org/10.21498/2518-1017.15.3.2019.181081

Keywords:

variety, distinctness, uniformity, stability, lettuce, Lactuca sativa L. var. сapitata, sign, development code, IBM SPSS Statistics

Abstract

Purpose. To substantiate the information and technical features of the use of IBM SPSS Statistics tools in determ­ining the distinctiveness criterion for new varieties of lettuce Lactuca sativa L. var. capitata based on the morphological code formulas of phenotypes of well-known varieties using the nearest neighbor algorithm in a group of similar varieties.

Methods. Analytical, which is based on a comparison of the methods and means of data mining obtained by identification – a morphological description of the variety with the subsequent use of descriptive and multidimensional statistics. In accordance with the «Method for the examination of varieties of lettuce Lactuca sativa L. for distinctness, uniformity and stability» signs that do not vary or very poorly vary were used to group varieties. These signs are used seperately or in combination with others.

Results. As a result of modeling using the SPPS package, several mo­dels of similar varieties of Lactuca sativa L. var. сapitata were formed. The total number of varieties in the sample is distributed as follows: 71.4% represented the training sample, and 28.6% represented the control one. The resulting data is visualized on the diagrams of the model of the largest similarity. It is necessary to take into account the type of expression of the studied characteristic (sign): in a qualitative, quantitative, pseudo-qualitative way. A simulation experiment with a model of similar varieties of head-lettuce showed the dependence of the result on the selected target variable. The target variables were signs «lant: head formation», «head by density», «seed: coloration», «head size».

Conclusions. The effectiveness of technological tools for analyzing the examination data on distinctness, uniformity and stabili­ty (DUS) has been revealed, which greatly facilitates the search for patterns among a large data set. Differentiation of data into training and control allows you to «train» the model on a data set of well-known varieties. When building a model, it is important to correctly determine the target and focal variables. IBM SPSS Statistics software package is recommended as a tool.

Author Biographies

Н. В. Лещук, Ukrainian Institute for Plant Variety Examination

Leshchuk, N. V.

Н. С. Орленко, Ukrainian Institute for Plant Variety Examination

Orlenko, N. S.

О. В. Хареба, National University of Life and Environmental Sciences of Ukraine

Khareba, O. V.

References

Kalloo, & Krug, H. (1980). Sortendifferenzierung bei Kopfsalat (Lactuca sativa var. capitata) – Vorläufige Mitteilung. Die Gartenbauwissenschaft, 451(3), 101–103.

Helm, J. (1954). Lactuca sativa L. in morphologisch-systematischer Sicht. Die Kulturpflanze, 2(1), 72–129. doi: 10.1007/BF02095730

Dufault, R. J., Ward, B., & Hassell, R. L. (2006). Planting date and romaine lettuce cultivar affect quality and productivity. HortScience, 41(3), 640–645. doi: 10.21273/HORTSCI.41.3.640

Сraker, L. E., & Seibert, M. (1982). Light energy requirements for controlled environment growth of lettuce and radish. Transact. ASAE, 25(1), 214–216. doi: 10.13031/2013.33506

Grin’ko, N. N. (2011). Susceptibility to the yellow mosaic virus of head lettuce varieties. Zaŝita i karantin rastenij [Plant Protection and Quarantine], 4, 33–34. [in Russian]

Osipova, G. S., Kondrat’ev, V. M., & Yakovleva, M. G. (2015). Agrobiological assessment of head lettuce and semi-heading lettuce varieties in the autumn turnover of film greenhouses in Leningrad Region. In Nauchnyy vklad molodykh issledovateley v sokhranenie traditsiy i razvitie APK: sb. Mezhdunar. nauchno-prakt. konf. molodykh uchenykh i studentov [The scientific contribution of young researchers to preservation of traditions and development of agribusiness: a collection of the International Scientific and Practical Conference of Young Scientists and Students] (Part 3, pp. 32–34 ). March 26–27, 2015, St. Petersburg, Russia. [in Russian]

Derzhavnyi reiestr sortiv roslyn, prydatnykh dlia poshyrennia v Ukraini na 2019 rik [State register of plant varieties suitable for dissemination in Ukraine in 2019]. (2019). Retrieved from https://sops.gov.ua/reestr-sortiv-roslin. [inUkrainian]

Tyshchenko, V. M. (2005). Cluster analysis as a method of individual selection of high-yield winter wheat plants in F2. Selekciâ i nasìnnictvo [Plant Breeding and Seed Production], 89, 125–137. [in Ukrainian]

Tishchenko, V. N., Chekalin, N. M., & Zyukov, M. E. (2004). Using cluster analysis to identify and select highly productive winter wheat genotypes in the early stages of selection. Fakt. Eksp. Evol. Org. [Factors in Experimental Evolution of Organisms], 2, 270–278. [in Russian]

Tishchenko, V. N. (2004). Efficiency of using a new selection index in winter wheat breeding. Fakt. Eksp. Evol. Org. [Factors in Experimental Evolution of Organisms], 2, 266–270. [in Russian]

Orlenko, N. S., Mazhuha, K. M., Dushar, M. B., & Maslechkin, V. V. (2019). Comparative analysis of clustering methods sui­table for plant varieties morphological characteristics data processing. Vìsn. Poltav. derž. agrar. akad. [News of Poltava State Agrarian Academy], 2, 261–269. doi: 10.31210/visnyk2019.02.35 [in Ukrainian]

Leskovets, Yu., Radzharaman, Yu., & Ulman, D. (2016). Analiz bol’shikh naborov dannykh [Analysis of large data sets]. Moscow: DMK. [in Russian]

Marmanis, Kh., & Babenko, D. (2011). Algoritmy intellektual’nogo Interneta. Peredovye metodiki sbora, analiza i obrabotki dannykh [Algorithms of the intellectual Internet. Advanced techniques for data collecting, analyzing and processing]. Moscow: Simvol. [in Russian]

Lantz, B. (2013). Machine Learning with R. Learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications. Birmingham: Packt Publishing Ltd.

Compton, M. E. (1994). Statistical methods suitable for the analysis of plant tissue culture data. Plant Cell Tiss. Organ Cult., 37 (3), 217–242. doi: 10.1007/BF00042336

Nasledov, A. D. (2013). IMB SPSS Statistics 20 i AMOS: professional’nyy statisticheskiy analiz dannykh [IMB SPSS Statistics 20 and AMOS: professional statistical data analysis]. St. Petersburg: Piter. [in Russian]

UPOV. (2017). Lettuce Lactuca sativa. Guidelines for the conduct of tests for distinctness, uniformity and stability (TG/13/11TG/13/11). Retrieved from https://www.upov.int/edocs/tgdocs/en/tg013.pdf

Leshchuk, N. V. (2007). The technique of examination of varie­ties of lettuce (Lactuca sativa L.) for distinctness, uniformi­ty and stability. Okhorona prav na sorty roslyn [Protection of Rights to Plant Varieties], 3(2), 366–379. [in Ukrainian]

Published

2019-10-17

How to Cite

Лещук, Н. В., Орленко, Н. С., & Хареба, О. В. (2019). Information and technical features of the test for distinctness of new varieties of Lactuca sativa L. var. capitata. Plant Varieties Studying and Protection, 15(3), 241–248. https://doi.org/10.21498/2518-1017.15.3.2019.181081

Issue

Section

VARIETY STUDYING AND VARIETY SCIENCE

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