Acta Univ. Agric. Silvic. Mendelianae Brun. 2020, 68(6), 947-958 | DOI: 10.11118/actaun202068060947

Identification of Spring Barley Breeding Lines With Superior Yield Performance and Stability

Volodymyr Hudzenko1, Tetiana Polishchuk1, Oleksandr Demydov1, Mykola Sardak2, Nataliia Buniak2, Vitalii Ishchenko3
1 Barley Breeding Laboratory, the V. M. Remeslo Myronivka Institute of Wheat (MIW) of the National Academy of Agrarian Sciences of Ukraine (NAAS), Tsentralne village, Myronivka district, Kyiv region, Ukraine
2 Crop Breeding Laboratory, Nosivka Plant Breeding and Experimental Station of the V. M. Remeslo MIW of NAAS (NPBES), Doslidne village, Nosivka district, Chernihiv region, Ukraine
3 Crop Breeding Department, Institute of Agriculture of Steppe of NAAS (IAS), Sozonivka village, Kirovohrad district, Kirovohrad region, Ukraine

The aim of the present study was to substantiate theoretically and to test in practice scheme of multi-environment trials at the final stage of spring barley breeding process and to distinguish the genotypes which combine superior yield performance and stability. In the first year of competitive testing (2015) nine promising spring barley breeding lines have been selected under condition of the Central part of Forest-Steppe of Ukraine (latitude 49°64', longitude 31°08', altitude 153 m). In 2016 and 2017, the genotypes were additionally tested in two other different agro-climatic zones of Ukraine: Polissia (latitude 50°93', longitude 31°69', altitude 126 m) and Northern Steppe (latitude 48°56', longitude 32°32', altitude 171 m). In addition to the standard variety Vzirets, the breeding lines were compared with ten widespread spring barley varieties in agricultural production. Significant total yield variability of the genotypes and cross-over genotype by environment interaction has been revealed. It confirmed the validity of proposed combination of spatial (zones) and temporal (years) gradients for more efficient evaluation of the genotype by environment interaction and differentiation of genotypes in terms of yield performance and stability. As a practical result, using additive main effects and multiplicative interaction (AMMI) and genotype main effects plus genotype by environment interaction (GGE) models, four spring barley breeding lines with combination of superior yield performance and high stability have been identified.

Keywords: spring barley, yield, stability, genotype by environment interaction, AMMI, GGE biplot

Received: April 15, 2020; Accepted: December 4, 2020; Published: December 17, 2020  Show citation

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Hudzenko, V., Polishchuk, T., Demydov, O., Sardak, M., Buniak, N., & Ishchenko, V. (2020). Identification of Spring Barley Breeding Lines With Superior Yield Performance and Stability. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 68(6), 947-958. doi: 10.11118/actaun202068060947
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    References

    1. ABTEW, W. G., LAKEW, B., HAUSSMANN, B. I. G. and SCHMID, K. J. 2015. Ethiopian barley landraces show higher yield stability and comparable yield to improved varieties in multi-environment field trials. J. Plant Breed. Crop Sci., 7(8): 275-291. Go to original source...
    2. AHMADI, J., VAEZI, B. and FOTOKIAN, M. H. 2012. Graphical analysis of multi-environment trials for barley yield using AMMI and GGE-biplot under rain-fed conditions. J. Plant Physiol. Breed., 2(1): 43-54.
    3. AL-GHZAWI, A. L. A., AL-AJLOUNI, Z. I., AL SANE, K. O., BSOUL, E. Y., MUSALLAM, I., KHALAF, Y. B., AL- HAJAJ, N., AL-TAWAHA, A. R., ALDWAIRI, Y. and AL-SAQQAR, H. 2019. Yield stability and adaptation of four spring barley (Hordeum vulgare L.) cultivars under rainfed conditions. Res. on Crops, 20(1): 10-18. Go to original source...
    4. AL-SAYAYDEH, R., AL-BAWALIZE, A., AL-AJLOUNI, Z., AKASH, M. W., ABU-ELENEIN, J. and AL- ABDALLAT, A. M. 2019. Agronomic evaluation and yield performance of selected barley (Hordeum vulgare L.) landraces from Jordan. Int. J. Agron., 2019: ID9575081. DOI: 10.1155/2019/9575081 Go to original source...
    5. ANBESSA, Y., JUSKIW, P., GOOD, A., NYACHIRO, J. and HELM, J. 2010. Selection efficiency across environments in improvement of barley yield for moderately low nitrogen environments. Crop Sci., 50(2): 451-457. DOI: 10.2135/cropsci2009.02.0058 Go to original source...
    6. BILGIN, O., BALKAN, A., KORKUT, Z. K. and BAŞER, İ. 2018. Multi-environmental evaluation of triticale, wheat and barley genotypes by GGE biplot analysis. J. Life Sci., 12: 13-23. Go to original source...
    7. BOCIANOWSKI, J., WARZECHA, T., NOWOSAD, K. and BATHELT, R. 2019. Genotype by environment interaction using AMMI model and estimation of additive and epistasis gene effects for 1000-kernel weight in spring barley (Hordeum vulgare L.). J. Appl. Genetics, 60: 127-135. DOI: 10.1007/s13353-019-00490-2 Go to original source...
    8. CECCARELLI, S. 1996. Adaptation to low/high input cultivation. Euphytica, 92(1-2): 203-214. DOI: 10.1007/BF00022846 Go to original source...
    9. DOĞAN, Y., KENDAL, E. and ORAL, E. 2016. Identifying of relationship between traits and grain yield in spring barley by GGE biplot analysis. Agriculture and Forestry, 62(4): 239-252. Go to original source...
    10. DYULGEROVA, B. and DYULGEROV, N. 2019. Genotype by environment interaction for grain yield of barley mutant lines. Agriculture (Polnohospodárstvo), 65(2): 51-58. Go to original source...
    11. FANA, G., TADESE, D., SEBSIBE, H. and VERMA, R. P. S. 2018. Multi-environment trial analysis of food barley in Ethiopia using AMMI and GGE biplot methods. J. Plant Breed. Genet., 6(3): 75-85. DOI: 10.33687/pbg.006.03.2579 Go to original source...
    12. FERIANI, W., REZGUI, S. and CHERIF, M. 2016. Grain yield assessment of genotype by environment interaction of Tunisian doubled-haploid barley lines. J. New Sci., Agricul. Biotechnol., 27(7): 1507-1512.
    13. FRUTOS, E., GALINDO, M. P. and LEIVA, V. 2014. An interactive biplot implementation in R for modeling genotype-by-environment interaction. Stoch. Environ. Res. Risk. Assess., 28: 1629-1641. DOI: 10.1007/s00477-013-0821-z Go to original source...
    14. GABRIEL, K. R. 1971. The biplot graphic display of matrices with application to principal component analysis. Biometrica, 58: 453-467. DOI: 10.1093/biomet/58.3.453 Go to original source...
    15. GAUCH, H. G., PIEPO, H.-P. and ANNICCHIARICO, P. 2008. Statistical Analysis of Yield Trials by AMMI and GGE: Further Consideration. Crop Sci., 48: 866-889. DOI: 10.2135/cropsci2007.09.0513 Go to original source...
    16. GUDZENKO, V. N. 2019. Statistical and graphical (GGE biplot) evaluation of the adaptive ability and stability of winter barley breeding lines. Vavilov J. Genet. Breed., 23(1): 110-118. DOI: 10.18699/VJ19.469 Go to original source...
    17. HILL, J. 1975. Genotype-environment interaction - a challenge for plant breeding. J. Agric. Sci., 85(3): 477-493. DOI: 10.1017/S0021859600062365 Go to original source...
    18. HONGYU, K., GARCIA-PENA, M., DE ARAUJO, L. B. and DOS SANTOS DIAS, C. T. 2014. Statistical analysis of yield trials by AMMI analysis of genotype x environment interaction. Biomet. let., 51(2): 89-102. DOI: 10.2478/bile-2014-0007 Go to original source...
    19. KENDAL, E. and DOĞAN, Y. 2015. Stability of a candidate and cultivars (Hordeum vulgare L.) by GGE biplot analysis of multi-environment yield trial in spring barley. Agriculture and Forestry, 61(4): 307-318. Go to original source...
    20. KENDAL, E., KARAMAN, M., TEKDAL, S. and DOĞAN, S. 2019. Analysis of promising barley (Hordeum vulgare L.) lines performance by AMMI and GGE biplot in multiple traits and environment. Appl. Ecol. Env. Res., 17(2): 5219-5233. DOI: 10.15666/aeer/1702_52195233 Go to original source...
    21. MALOSETTI, M., RIBAUT, J.-M. and VAN EEUWIJK, F. A. 2013. The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Front. Physiol., 4: Article 44. DOI: 10.3389/fphys.2013.00044 Go to original source...
    22. MANIRUZZAMAN, I., ISLAM, M. Z., BEGUM, F., KHAN, M. A. A., AMIRUZZAMAN, M. and HOSSAIN, A. 2019. Evaluation of yield stability of seven barley (Hordeum vulgare L.) genotypes in multiple environments using GGE biplot and AMMI model. Open Agriculture, 4(1): 284-293. DOI: 10.1515/opag-2019-0027 Go to original source...
    23. MEHARI, M., ALAMEREW, S. and LAKEW, B. 2014. Genotype × environment interaction and yield stability of malt barley genotypes evaluated in Tigray, Ethiopia using the AMMI analysis. Asian J. Plant Sci., 13(2): 73-79. DOI: 10.3923/ajps.2014.73.79 Go to original source...
    24. MENG, Y., REN, P., MA, X., LI, B., BAO, Q., ZHANG, H., WANG, J., BAI, J. and WANG, H. 2016. GGE biplot-based evaluation of yield performance of barley genotypes across different environments in China. J. Agr. Sci. Tech., 18: 533-543.
    25. MIROSAVLJEVIĆ, M., ČANAK, P., MOMČILOVIĆ, V., JOCKOVIĆ, B., ZORIĆ, M., AĆIN, V., DENČIĆ, S. and PRŽULJ, N. 2018. Identification of favorable testing location for barley breeding in South Pannonian Plain. J. Agri. Sci., 24: 303-311. Go to original source...
    26. MIROSAVLJEVIĆ, M., PRŽULJ, N., BOĆANSKI, J., STANISAVLJEVIĆ, D. and MITROVIĆ, B. 2014. The application of AMMI model for barley cultivars evaluation in multi-year trials. Genetika, 46: 445-454. DOI: 10.2298/GENSR1402445M Go to original source...
    27. MOHAMMADI, M., SHARIFI, P. and KARIMIZADEH, R. 2014. Stability analysis of durum wheat genotypes by regression parameteres in dryland conditions. Acta Univ. Agric. Silvic. Mendelianae Brun., 62(5): 1049-1056. DOI: 10.11118/actaun201462051049 Go to original source...
    28. ORAL, E., KENDAL, E. and DOĞAN, Y. 2018. Influence of nitrogen fertilization levels on grain yield and its components in barley (Hordeum vulgare L.). Agriculture and Forestry, 64(2): 43-63. Go to original source...
    29. PRŽULJ, N., MIROSAVLJEVIĆ, M., ČANAK, P., ZORIĆ, M. and BOĆANSKI, J. 2015. Evaluation of spring barley performance by biplot analysis. Cereal Res. Commun., 43(4): 692-703. DOI: 10.1556/0806.43.2015.018 Go to original source...
    30. PSWARAYI, A., VAN EEUWIJK, F. A., CECCARELLI, S., GRANDO, S., COMADRAN, J., RUSSELL, J. R., PECCHIONI, N., TONDELLI, A., AKAR, T., AL-YASSIN, A., BENBELCACEM, A., OUABBOU, H., THOMAS, W. T. B. and ROMAGOSA, I. 2008. Changes in allele frequencies in landraces, old and modern barley cultivars of marker loci close to QTL for grain yield under high and low input conditions. Euphytica, 163(3): 435-447. DOI: 10.1007/s10681-008-9726-1 Go to original source...
    31. SABAGHNIA, N., MOHAMMADI, M. and KARIMIZADEH, R. 2013. Yield stability of performance in multi-environment trials of barley (Hordeum vulgare L.) genotypes. Acta Univ. Agric. Silvic. Mendelianae Brun., 61(3): 787-793. DOI: 10.11118/actaun201361030787 Go to original source...
    32. SOLONECHNYI, P., KOZACHENKO, M., VASKO, N., GUDZENKO, V., ISHENKO, V., KOZELETS, G., USOVA, N., LOGVINENKO, Y. and VINUKOV, A. 2018. AMMI and GGE biplot analysis of yield performance of spring barley (Hordeum vulgare L.) varieties in multi environment trials. Agriculture and Forestry, 64(1): 121-132. Go to original source...
    33. SOLONECHNYI, P., VASKO, N., NAUMOV, A., SOLONECHNAYA, O., VAZHENINA, O., BONDAREVA, O. and LOGVINENKO, Y. 2015. GGE biplot analysis of genotype by environment interaction of spring barley varieties. Zemdirbyste-Agriculture, 102(4): 431-436. DOI: 10.13080/z-a.2015.102.055 Go to original source...
    34. TAHERIPOURFARD, Z., IZADI-DARBANDI, A., GHAZVINI, H., EBRAHIMI, M., MORTAZAVIAN, S. M. M. and ABDIPOUR, M. 2017. Identifying superior barley (Hordeum vulgare L.) genotypes using GGE-biplot across warm and moderate environments under irrigated conditions in Iran. Crop Breed. J., 7(1, 2): 23-35.
    35. ULLRICH, S. E. (ed.). 2011. Barley: production, improvement, and uses. Chichester, UK: Wiley-Blackwell, 637 p.
    36. VAEZI, B., POUR-ABOUGHADAREH, A., MOHAMMADI, R., ARMION, M., MEHRABAN, A., HOSSEIN-POUR, T. and DORII, M. 2017. GGE biplot and AMMI analysis of barley yield performance in Iran. Cereal Res. Commun., 45(3): 500-511. DOI: 10.1556/0806.45.2017.019 Go to original source...
    37. VAN EEUWIJK, F. A., BUSTOS-KORTS, D. V. and MALOSETTI, M. 2016. What should students in plant breeding know about the statistical aspects of genotype × environment interactions? Crop Sci., 56(5): 2119-2140. DOI: 10.2135/cropsci2015.06.0375 Go to original source...
    38. VAN OOSTEROM, E. J. and ACEVEDO, E. 1992. Adaptation of barley (Hordeum vulgare L.) to harsh Mediterranean environments III. Plant ideotype and grain yield. Euphytica, 62(1): 29-38. DOI: 10.1007/BF00036084 Go to original source...
    39. VASYLKIVSKYI, S. and GUDZENKO, V. 2017. Winter barley selection in steady grain production provision in the Central Forest-steppe of Ukraine. Agrobiology, 1: 25-33.
    40. VERMA, A., KUMAR, V., KHARAB, A. S. and SINGH, G. P. 2019. AMMI model to estimate G×E for grain yield of dual purpose barley genotypes. Int. J. Curr. Microbiol. App. Sci., 8(5): 1-7. DOI: 10.20546/ijcmas.2019.805.001 Go to original source...
    41. VON KORFF, M., GRANDO, S., DEL GRECO, A., THIS, D., BAUM, M. and CECCARELLI, S. 2008. Quantitative trait loci associated with adaptation to Mediterranean dryland conditions in barley. Theor. Appl. Genet., 117(5): 653-669. DOI: 10.1007/s00122-008-0787-2 Go to original source...
    42. YAN, W., KANG, M. S., MA, B., WOODS, S. and CORNELIUS, P. L. 2007. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci., 47: 641-653. Go to original source...
    43. YAN, W. and TINKER, N. A. 2006. Biplot analysis of multi-environment trial data: principles and applications. Can. J. Plant Sci., 86: 623-645. DOI: 10.4141/P05-169 Go to original source...
    44. YAN, W. 2018. LG biplot: a graphical method for mega-environment investigation using existing crop variety trial data. Sci. Rep., 9: 7130. DOI: 10.1038/s41598-019-43683-9 Go to original source...
    45. ZHAO, F. and XU, S. 2012. Genotype by environment interaction of quantitative traits: a case study in barley. G3. Genes. Genomes. Genetics, 2(7): 779-788. Go to original source...

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