Evaluation of the differentiation ability of ecological points in generated environmental vector program "Ekada»

  • Valeriy Vladimirovich Syukov Samara Scientific Research Institute of Agriculture
  • Vladimir Grigorievich Zakharov Ulyanovsk Scientific Research Institute of Agriculture
  • Peter Nikolayevich Malchikov Samara Scientific Research Institute of Agriculture
  • Vitaliy Grigorievich Krivobochek Penza Scientific Research Institute of Agriculture
  • Vladimir Ivanovich Nikonov Bashkiria Scientific Research Institute of Agriculture
  • Nurania Zufarovna Vasilova Tatar Scientific Research Institute of Agriculture
  • Vadim Anvarovich Ganeev Scientific Industrial Firm “Fiton”
Keywords: spring wheat, ecological selection, ecological vector, “Ekada” program

Abstract

It is presented, that the quantitative characteristics of spring wheat are inherited in a complex process. In their determination, a significant role is played by genetic systems which are expressed depending on environmental factors. The task of ecological selection is to select forms with a wide reaction norm on changing the factors, which are limiting growth and development of cenoses. A new method for the dissociation of phenotypic dispersion in a three-factor dispersion analysis is proposed. The method is based on the average squares of variations and the expected structure of the average squares of variations. For the selection of genotypes with wide adaptation, one of the main conditions is a creation of a model ecological gradient. In the article, the example of the “Ekada” program demonstrates the formation of a similar gradient, the so-called "ecological vector". The ecological point, where the factors limiting the growth and development of plants are the most concentrated, is Bezenchuk (B). At the vector points of the Ulyanovsk (U) and Kazan (K), conditions are most favorable for the formation of a high crop. At the center of the vector is Karabalyk (F) with high parameters of the differentiating capacity of the environment, but atypical for the vector as a whole. The points Penza (P) and Chishmy (Ch) change position, approaching the left, then to the right points. Each point is characterized by a set of statistical parameters. An ecological vector B → (Ch) → F → (P) → U → K with a different pressure spectrum of the limiting factors of the environment in ontogeny along ecological points is formed. When selecting according to polygenic quantitative characteristics, the selectioner must take into account that individual genes and genotypic systems manifest differently in different combinations of environmental factors, i.e. to select less on a genotype, than on an epigenotype. It is demonstrated, that by the ratio of the share of the genotype-environment and the genotype component of the phenotype by the quantitative trait (χg/e / χg) the selection by quantitative characteristics along the ecological vector is several times more effective than at the local ecological point. In none of the points of the ecological vector, nor on one trait, direct selection is not advisable (in comparison with ecological selection).

 

 

 

 

Downloads

Download data is not yet available.

Author Biographies

Valeriy Vladimirovich Syukov, Samara Scientific Research Institute of Agriculture

Doctor of Biological Sciences, Senior Researcher  

Vladimir Grigorievich Zakharov, Ulyanovsk Scientific Research Institute of Agriculture

Doctor of Agricultural Sciences,

Peter Nikolayevich Malchikov, Samara Scientific Research Institute of Agriculture

Doctor of Agricultural Sciences

Vitaliy Grigorievich Krivobochek, Penza Scientific Research Institute of Agriculture

Doctor of Agricultural Sciences

Vladimir Ivanovich Nikonov, Bashkiria Scientific Research Institute of Agriculture

Candidate of Agricultural Sciences

Nurania Zufarovna Vasilova, Tatar Scientific Research Institute of Agriculture

Candidate of Agricultural Sciences

Vadim Anvarovich Ganeev, Scientific Industrial Firm “Fiton”

Candidate of Agricultural Sciences

References

1. Биометрия: уч.пособие / Н.В. Глотов [и др.]. – Л.: Изд-во Ленингр. ун-та, 1982. – 382 с.
2. Зиновьев А.Ю. Визуализация многомерных данных. – Красноярск: Изд-во Красноярского ГТУ, 2000. – 180 с.
3. Кильчевский А.В. Комплексная оценка среды как фона для отбора в селекционном процессе // Доклады АН БССР. – 1986. – Т. 30. – Вып. 9. – С. 846–849.
4. Кильчевский А.В., Хотылева Л.В. Экологическая селекция растений. Минск: Тэхналогiя, 1997. – 372 c.
5. Пивоваров В.Ф., Добруцкая Е.Г., Балашова Н.Н. Экологическая селекция растений (на примере овощных культур). – М., 1994. – 369 с.
6. Рокицкий П.Ф. Биологическая статистика. – Минск: Вышэйш. шк., 1973. – 320 с.
7. Селекционно-генетическая оценка популяций яровой мягкой пшеницы Сибирского питомника челночной селекции СИММИТ / В.П. Шаманин [и др.] // Вавиловский журнал генетики и селекции. – 2012. – № 16 (1). – С. 21–32.
8. Сюков В.В., Кочетков Д.В. Вклад генотип-средовых эффектов в формирование количественных признаков у яровой мягкой пшеницы // Проблемы аридизации Юго-Востока Европейской части России: материалы Междунар. науч.-практ. конф. – Саратов, 2009. – C.51–53.
9. Сюков В.В., Менибаев А.И. Экологическая селекция растений: типы и практика (обзор) // Известия Самарского научного центра Российской академии наук. – 2015. – Т.17. – №4 (3). – С. 463–466.
10. Сюков В.В., Мадякин Е.В., Кочетков Д.В. Вклад генотип-средовых эффектов в формирование количественных признаков у инбредных и аутбредных растений // Информационный вестник ВОГиС. – 2010. – № 14 (1). – С.141–147.
11. Терёхина А.Ю. Анализ данных методом многомерного шкалирования. –М.: Наука, 1986. – 168 с.
12. Шамсутдинов З.Ш. Селекция кормовых культур: достижения и задачи // Сельскохозяйственная биология. – 2014. – № 6. – С. 36–45
13. Эффективность статистических методов оценкиадаптивности генотипов яровой мягкой пшеницы вдоль экологического вектора / В.В. Сюков [и др.] // Аграрный научный журнал. – 2019. – № 2. – С. 4–12.
14. Affleck I., Sulivan J.A., Tarn R., Falk D.E. Genotype by environment interaction of yield and quality of potatoes // Canad.J.Plant Sci. 2008;88(6):1099–1107.
15. Bach S. Genotype by environment interaction effects on starch, fibre and agronomic traits in potato (Solanum tuberosum L.) // An M.Sc.Thesis. Guelph, Ontario, Canada, 2011: 208.
16. Eberhart S.A., Russell W.A. Stability parameters for comparing varieties // Crop Sci. 1966;6(1):36–40.
17. Gabriel K.R. The biplot graphic display of matrices with application to principal component analysis // Biometrika. 1971;38(3):453–467.
18. Yan W., Tinker N.A. Biplot analysis of multienvironment trial data: Principles and applications // Canad.J.Plant Sci. 2006;86(3):623–645.
19. Rajaram S., Borlaug N.E., van Ginkel M. CIMMYT International Wheat Breeding. Bread wheat –Improvement and production. Plant production and protection series. 2002;30:103–117.
20. Rajaram S., van Ginkel M. Mexico, 50 years of international wheat breeding // The World Wheat Book. A History of wheat breeding, Paris: Lavoisier Publishing, 2001; 579–608.
21. Rajaram S., Skovmand B., Curtus B.C. Philosophy and methodology of an international wheat breeding program. Gene manipulation in plant breeding, NY, London, 1984; 33–60.
22. Syukov V.V. et al. Method of ecological breeding an example program «ECADA» // Science, technology and life - 2014: Proceedings of the international scientific conference Czech Republic, Karlovy Vary, 27–28 December, 2014 .– Karlovy Vary -Kirov, 2015. – С. 300–310.
Published
2019-04-29
Section
Agronomy