AMMI AND GGE BIPLOT ANALYSIS OF LONG-TERM DATA ON WINTER BARLEY YIELDING CAPACITY IN THE CENTRAL FOREST STEPPE OF UKRAINE
DOI:
https://doi.org/10.31548/dopovidi2017.01.005Keywords:
barley, yielding capacity, stability, interaction "genotype-environment», AMMI, GGE biplotAbstract
The article covers the results of many years (2011/2012-2015 2016) research at the V.M. Remeslo Myronivka Institute of Wheat of NAAS on 29 winter barley varieties bred in different periods which vary in origin, systematic, biological, and morphological features. For the first time in the Central Forest-steppe of Ukraine when using AMMI and GGE biplot models, profound evaluation of “genotype-environment” interaction on yielding capacity of winter barley genotypes has been conducted. Advantages of modern varieties for productive and adaptive capacity as compared with those created in the 1980's and early 1990’s are demonstrated. There were identified the varieties with the most optimal combination of average yielding capacity and its manifestation by the years: six-row Cartel, Paladin Myronivs’kyi; two-row Atlant Myronivs’kyi. They are recommended to involve in breeding work as valuable genetic resources to create a new source material of winter barley with high adaptability to this ecological zone. The varieties Paladin Myronivs’kyi Atlant Myronivs’kyi are included in the State Register of Ukraine and should be introduced for growing in the Central Forest-steppe of Ukraine.
References
Gauch, H.G. (1988). Model selection and validation for yield trials with interaction. Biometrics, 44, 705-715.
https://doi.org/10.2307/2531585
Hongyu, K., Garcia-Pena, M., Borges de Araujo, L., Tadeu dos Santos Dias, C. (2014). Statistical analysis of yield trials by AMMI analysis of genotype x environment interaction. Biometrical letters, 51 (2), 89-102.
https://doi.org/10.2478/bile-2014-0007
Abtew, W.G., Lakew, B., Haussmann, B.I.G., Schmid, K.J. (2015). Ethiopian barley landraces show higher yield stability and comparable yield to improved varieties in multi-environment field trials. Journal of plant breeding and crop science, 7 (8), 275-291.
https://doi.org/10.5897/JPBCS2015.0524
Verma, R.P.S., Kharab, A.S., Singh, J., Kumar, V., Sharma, I., Verma, A. (2016). AMMI model to analyse GxE for dual purpose barley in multi-environment trials. Agric. Sci. Digest, 36 (1), 9-16.
https://doi.org/10.18805/asd.v35i1.9303
Kiliç, H. (2014). Additive main effects and multiplicative interactions (AMMI) analysis of grain yield in barley genotypes across environments. Journal of agricultural sciences, 20, 337-344.
https://doi.org/10.1501/Tarimbil_0000001292
Gebremedhin, W., Firew, M., Tesfye, B. (2014). Stability analysis of food barley genotypes in Northern Ethiopia. African crop science journal, 22 (2), 145-153.
Abdipur, M., Vaezi, B. (2014) Analysis of the genotype-by-environment interaction of winter barley tested in the rain-fed regions of Iran by AMMI adjustment / // Bulgarian journal of agricultural science, 20 (2), 421-427.
Mirosavljevic, M., Przulj, N., Bocanski, J., Stanisavljevic, D., Mitrovic, B. (2014). The application of AMMI model for barley cultivars evaluation in multi-year trials. Genetika, 46 (2), 445-454.
https://doi.org/10.2298/GENSR1402445M
Yan, W., Tinker, N.A. (2006). Biplot analysis of multi-environment trial data: Principles and applications. Canadian journal of plant science, 86 (3), 623-645.
https://doi.org/10.4141/P05-169
Jalata, Z. (2011) GGE-biplot analysis of multi-environment yield trials of barley (Hordeum vulgare L.) genotypes in Southeastern Ethiopia Highlands. International journal of plant breeding and genetics, 5 (1), 59-75.
https://doi.org/10.3923/ijpbg.2011.59.75
Sarkar, B., Sharma, R. C., Verma, R.P.S., Sarkar, A., Sharma, I. (2014). Identifying superior feed barley genotypes using GGE biplot for diverse environments in India. Indian J. Genet., 74 (1), 26-33.
https://doi.org/10.5958/j.0975-6906.74.1.004
Mohammadi, M., Noorinia, A.A., Khalilzadeh, G.R., Hosseinpoo T. (2015). Application of GGE biplot analysis to investigate GE interaction on barley grain yield. Current opinion in agriculture, 4 (1), 25-32.
Yan, W., Kang, M. S. , Ma, B., Woods, Sh., Cornelius, P. L. (2006). GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop science, 47 (2), 643-653.
https://doi.org/10.2135/cropsci2006.06.0374
Kendal, E., Doğan, Y. (2015). Stability of a candidate and cultivars (Hordeum vulgare L.) by GGE biplot analysis of multi-environment yield trial in spring barley / E. Kendal. Agriculture & forestry, 61 (4), 307-318.
https://doi.org/10.17707/AgricultForest.61.4.37
Mortazavian, S.M.M., Nikkhah, H.R., Hassani, F.A., Sharif-al-Hosseini, M., Taheri, M., Mahlooji, M. (2014). GGE biplot and AMMI analysis of yield performance of barley genotypes across different environments in Iran. Agr. Sci. Tech., 16, 609-622.
Ahmadi, J., Vaezi, B., Fotokian, M.H. (2012). Graphical analysis of multi-environment trials for barley yield using AMMI and GGE-biplot under rain-fed conditions / J. Ahmadi, // Journal of plant physiology and breeding, 2 (1), 43-54.
Volkodav, V. V. (Ed.). (2003). Method of examination and state testing of varieties of grain, cereal and leguminous crops. Okhorona prav na sorty roslyn [Plant variety rights protection] (Vol. 2, Part. 3). Kyiv: Alefa., 241 [in Ukrainian].
Dospekhov, B. A. (1985). Metodika polevogo opyta (s osnovami statisticheskoy obrabotki rezul'tatov issledovaniy) [Methods of field experiment (with the basics of statistical processing of research results)]. 5th ed., rev. Moscow: Agropromizdat, 351 [in Russian].
Frutos, E., Galindo, M.P., Leiva, V. (2014). An interactive biplot implementation in R for modeling genotype-by-environment interaction. Stoch. Environ. Res. Risk. Assess., 28, 1629-1641.
Downloads
Published
Issue
Section
License
Relationship between right holders and users shall be governed by the terms of the license Creative Commons Attribution – non-commercial – Distribution On Same Conditions 4.0 international (CC BY-NC-SA 4.0):https://creativecommons.org/licenses/by-nc-sa/4.0/deed.uk
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).