PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Choosing Important Traits for the Model of High-Yielding Winter Wheat Variety Based on the Results of Regional Ecological Varietal Testing

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Current study is devoted to the development of an ideotype of winter wheat variety for cultivation in the conditions of the South of Ukraine. The investigation is based on the results of regional ecological varietal testing, conducted in the Southern Steppe zone on the non-irrigated lands. Varietal traits, included in the study, embraced growing season duration, 1000 grains weight, plant height, and ear length. The results of the testing were further processed using statistical procedures of linear Pearson’s correlation analysis and multiple regression analysis. As a result, the model of a winter wheat variety for the non-irrigated lands of the South of Ukraine was developed. The developed model is characterized by very high fitting quality (R2 = 0.9476) and good prediction accuracy (MAPE = 23.27%). According to the model, the variety should be late ripening with moderate to high plant height to provide the highest grain yield. The trait of 1000 grains weight was found out to be unimportant. The main trait, providing for the grain yield increase, is growing season duration, which must be long enough. Further ecological varietal testing studies with inclusion of additional varietal traits, such as cold-resistance, drought-resistance, frost-resistance, tolerance to diseases, etc., are to be conducted to extend the ideotype of winter wheat.
Rocznik
Strony
8--12
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
  • Institute of Climate-Smart Agriculture of NAAS, Omelianovycha-Pavlenka Street, 9, 01010 Kyiv, Ukraine
Bibliografia
  • 1. Austin, R.B. 1988. A different ideotype for each environment. In Cereal Breeding Related to Integrated Cereal Production. Proceedings of the EUCARPIA conference. Wageningen, the Netherlands: Pudoc., 47–60.
  • 2. Berry, P.M., Sylvester-Bradley, R., Berry, S. 2007. Ideotype design for lodging-resistant wheat. Euphytica, 154(1–2), 165–179.
  • 3. Blasco, B.C., Moreno, J.J.M., Pol, A.P., Abad, A.S. 2013. Using the R-MAPE index as a resistant measure of forecast accuracy. Psicothema, 25(4), 500–506.
  • 4. Erenstein, O., Jaleta, M., Mottaleb, K.A., Sonder, K., Donovan, J., Braun, H.J. 2022. Global trends in wheat production, consumption and trade. In Wheat Improvement: Food Security in a Changing Climate Cham: Springer International Publishing, 47–66.
  • 5. Evans, J.D. 1996. Straightforward statistics for the behavioral sciences. Thomson Brooks/Cole Publishing Co.
  • 6. FAOStat. 2020. FAO Stat. Access: http://www.fao.org/faostat
  • 7. Kolomiets, L.A., Bulavka, N.V. 2015. Selecting frost resistant forms among hybrid populations in earlier generations of winter wheat. Myronivka Herald, 1, 46–53.
  • 8. Konvalina, P., Stehno, Z., Moudry, J. 2007. Testing of suitability of ideotype and varieties of wheat for organic and low input agriculture. Lucrări Ştiinţifice, Seria Agronomie, 50, 248–256.
  • 9. Lammerts van Bueren, E.T., Struik, P.C., Jacobsen, E. 2002. Ecological concepts in organic farming and their consequences for an organic crop ideotype. NJAS: Wageningen Journal of Life Sciences, 50(1), 1–26.
  • 10. Lavrenko, N., Lavrenko, S., Revto, O., Lykhovyd, P. 2018. Effect of tillage and humidification conditions on desalination properties of chickpea (Cicer arietinum L.). Journal of Ecological Engineering, 19(5), 70–75.
  • 11. Lavrenko, S.O., Lavrenko, N.M., Lykhovyd, P.V. 2019.Effect of degree of salinity on seed germination and initial growth of chickpea (Cicer arietinum). Biosystems Diversity, 27(2), 101–105.
  • 12. Lykhovyd, P. 2021. Irrigation needs in Ukraine according to current aridity level. Journal of Ecological Engineering, 22(8), 11–18.
  • 13. Pedhazur, E.J. 1997. Multiple regression in behavioral research (3rd ed.). Orlando, FL: Harcourt Brace.
  • 14. Semenov, M.A., Stratonovitch, P. 2013. Designing high‐yielding wheat ideotypes for a changing climate. Food and Energy Security, 2(3), 185–196.
  • 15. Semenov, M.A., Stratonovitch, P., Alghabari, F., Gooding, M.J. 2014. Adapting wheat in Europe for climate change. Journal of Cereal Science, 59(3), 245–256.
  • 16. Stolzenberg, R.M. 2004. Multiple regression analysis. Handbook of Data Analysis, 165(208), 175–198.
  • 17. Tyshchenko, O., Tyshchenko, A., Piliarska, O., Kuts, H., & Lykhovyd, P. 2020. Evaluation of drought tolerance in alfalfa (Medicago sativa) genotypes in the conditions of osmotic stress. AgroLife Scientific Journal, 9(2), 353–358.
  • 18. Tyshchenko, O., Tyshchenko, A., Piliarska, O., Kuts, H., Lykhovyd, P. 2020. Evaluation of drought tolerance in alfalfa (Medicago sativa) genotypes in the conditions of osmotic stress. AgroLife Scientific Journal, 9(2), 353–358.
  • 19. Ushkarenko, V.O., Vozhehova, R.A., Holoborodko, S.P., Kokovikhin, S.V. 2014. Field experiment methodology. Kherson: Hrin D.S.
  • 20. Vozhehova, R.A., Lykhovyd, P.V., Kokovikhin, S.V., Biliaieva, I.M., Markovska, O.Y., Rudik, O.L. 2019. Artificial neural network and their implementation in agricultural science and practice. Warsaw: Diamond Trading Tour.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2775addc-74f0-4ea6-a19d-fe45fff4bb24
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.