Comprehensive evaluation of spring barley breeding lines in yield, stability and tolerance to biotic and abiotic factors under condition of the central part of the Ukrainian Forest-Steppe
DOI:
https://doi.org/10.21498/2518-1017.17.1.2021.228206Keywords:
Hordeum vulgare L., genotype by environment interaction, complex of traits, AMMI, GGE biplot, GYT biplotAbstract
Purpose. Identification of spring barley promising breeding lines with combination of adaptive traits under conditions of the central part of the Ukrainian Forest-Steppe.
Methods. Field trial, laboratory-field analysis of drought tolerance, statistical and graphical analysis of experimental data.
Results. The analysis of variance of the AMMI model showed that the largest contribution to the general variation (85.78%) had environmental conditions (years of research). The value of the genotype was 8.21%, and the genotype by environment interaction was 6.01%. The first and second principal components of both AMMI and GGE biplot explained more than 85% of the genotype-environment interaction. Spring barley breeding lines ‘Deficiens 5162’, ‘Nutans 5073’ and ‘Deficiens 5161’ had the superior combination of yield performance and relative stability through the years according to GGE biplot. With GYT biplot analysis it has been determined that the breeding lines ‘Deficiens 5162’ and ‘Nutans 5073’ also significantly predominated over the other genotypes in terms of combination of yield performance and a number of other traits – 1000 kernels weight, drought tolerance, resistance to pathogens. Breeding lines ‘Deficiens 5161’, ‘Nutans 4966’, ‘Nutans 4705’, ‘Nutans 4816’, ‘Nutans 5184’, ‘Nutans 5193’, which exceeded the mean value in the trial in terms of combination of yield performance and a number of adaptive traits may have practical significance in the breeding process for creation of new initial material.
Conclusions. As a result of the complex evaluation when using AMMI, GGE biplot and GYT biplot graphical models the breeding lines ‘Deficiens 5162’ and ‘Nutans 5073’ with the optimal combination of yield, stability, thousand kernel weight and tolerance to abiotic and biotic environmental factors have been identified
Downloads
References
Psota, V., Hartmann, J., Sejkorova, S., Louckova, T., & Vejrazka, K. (2009). 50 Years of progress in quality of malting barley grown in the Czech Republic. J. Inst. Brew., 115(4), 279–291. doi: 10.1002/j.2050-0416.2009.tb00382.x
Mackay, I. J., Horwell, A., Garner, J., White, J., McKee, J., & Philpott, H. R. (2011). Reanalysis of the historical series of UK variety trials to quantify the contributions of genetic and environmental factors to trends and variability in yield over time. Theor. Appl. Genet., 122(1), 225–238. doi: 10.1007/s00122-010-1438-y
Laidig, F., Piepho, H.-P., Rentel, D., Drobek, T., & Meyer, U. (2017). Breeding progress, genotypic and environmental variation and correlation of quality traits in malting barley in German official variety trials between 1983 and 2015. Theor. Appl. Genet., 130(11), 2411–2429. doi: 10.1007/s00122-017-2967-4
Smith, P., & Gregory, P. J. (2013). Climate change and sustainable food production. Proc. Nutr. Soc., 72(1), 21–28. doi: 10.1017/S0029665112002832
Moore, F. C., & Lobell, D. B. (2015). The fingerprint of climate trends on European crop yields. Proc. Natl. Acad. Sci. USA., 112(9), 2670–2675. doi: 10.1073/pnas.1409606112
Hakala, K., Jauhiainen, L., Rajala, A. A., Jalli, M., Kujala, M., & Laine, A. (2020). Different responses to weather events may change the cultivation balance of spring barley and oats in the future. Field Crops Res., 259, 107956. doi: 10.1016/j.fcr.2020.107956
Rötter, R. P., Palosuo, T., Kersebaum, K. C., Angulo, C., Bindi, M., Ewert, F., … Trnka, M. (2012). Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models. Field Crops Res., 133, 23–36. doi: 10.1016/j.fcr.2012.03.016
Alemayehu, F. R., Frenck, G., van der Linden, L., Mikkelsen, T. N., & Jørgensen, R. B. (2014). Can barley (Hordeum vulgare L. s.l.) adapt to fast climate changes? A controlled selection experiment. Genet. Resour. Crop. Evol., 61(1), 151–161. doi: 10.1007/s10722-013-0021-1
Ingvordsen, C. H., Backes, G. Ø., Lyngkjær, M. F., Peltonen-Sainio, P., Jensen, J. D., Jalli, M., … Jørgensen, R. B. (2015). Significant decrease in yield under future climate conditions: stability and production of 138 spring barley accessions. Eur. J. Agron., 63, 105–113. doi: 10.1016/j.eja.2014.12.003
Yawson, D. O., Ball, T., Adu, M.O., Mohan, S., Mulhollan, B. J., & White, P. J. (2016). Simulated regional yields of spring barley in the United Kingdom under projected climate change. Climate, 4, 54. doi: 10.3390/cli4040054
Tambussi, E.A., Nogués, S., Ferrio, P., Voltas, J., & Araus, J. L. (2005). Does higher yield potential improve barley performance in Mediterranean conditions? A case study. Field Crops Res., 91(2–3), 149–160. doi: 10.1016/j.fcr.2004.06.002
Araus, J. L., Slafer, G. A., Royo, C., & Serret, M. D. (2008). Breeding for yield potential and stress adaptation in cereals. Crit. Rev. Plant Sci., 27(6), 377–412. doi: 10.1080/07352680802467736
Dawson, I. K., Russell, J., Powell, W., Steffenson, B., Thomas, W. T. B., & Waugh, R. (2015). Barley: a translational model for adaptation to climate change. New Phytol., 206(3), 913–931. doi: 10.1111/nph.13266
Macholdt, J., & Honermeier, B. (2016). Impact of climate change on cultivar choice: adaptation strategies of farmers and advisors in German cereal production. Agronomy, 6(3), 40. doi: 10.3390/agronomy6030040
Gilliham, M., Able, J. A., & Roy, S. J. (2017). Translating knowledge about abiotic stress tolerance to breeding programmes. Plant J., 90(5), 898–917. doi: 10.1111/tpj.13456
Mühleisen, J., Piepho, H.-P., Maurer, H. P., Zhao, Y. S., & Reif, J. C. (2014). Exploitation of yield stability in barley. Theor. Appl. Genet., 127(9), 1949–1962. doi: 10.1007/s00122-014-2351-6
Pržulj, N., Mirosavljević, M., Čanak, P., Zorić, M., & 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
Solonechnyi, P., Vasko, N., Naumov, A., Solonechnaya, O., Vazhenina, O., Bondareva, O., & 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
Vaezi, B., Pour-Aboughadareh, A., Mohammadi, R., Armion, M., Mehraban, A., Hossein-Pour, T., & 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
Solonechnyi, P., Kozachenko, M., Vasko, N., Gudzenko, V., Ishenko, V., Kozelets, G., … 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. doi: 10.17707/AgricultForest.64.1.15
Al-Ghzawi, A. L. A., Al-Ajlouni, Z. I., Al Sane, K. O., Bsoul, E. Y., Musallam, I., Khalaf, Y. B., … Al-Saqqar, H. (2019). Yield stability and adaptation of four spring barley (Hordeum vulgare L.) cultivars under rainfed conditions. Res. Crops, 20(1), 10–18. doi: 10.31830/2348-7542.2019.002
Kendal, E., Karaman, M., Tekdal, S., & Doğan, S. (2019). Analysis of promising barley (Hordeum vulgare L.) lines performance by AMMI and GGE biplot in multiple traits and environment. Appl. Ecol. Environ. Res., 17(2), 5219–5233. doi: 10.15666/aeer/1702_52195233
Verma, A., Kumar, V., Kharab, A. S., & Singh, G. P. (2019). AMMI model to estimate G×E for grain yield of dual purpose barley genotypes. Int. J. Curr. Microbiol. Appl. Sci., 8(5), 1–7. doi: 10.20546/ijcmas.2019.805.001
Al-Sayaydeh, R., Al-Bawalize, A., Al-Ajlouni, Z., Akash, M. W., Abu-Elenein, J., & Al-Abdallat, A. M. (2019). Agronomic evaluation and yield performance of selected barley (Hordeum vulgare L.) landraces from Jordan. Int. J. Agron., 219, 9575081. doi: 10.1155/2019/9575081
Ceccarelli, S. (1989). Wide adaptation. How wide? Euphytica, 40(3), 197–205. doi: 10.1007/BF00024512
Chenu, K. (2015). Characterizing the crop environment – nature, significance and applications. In V. O. Sadras, & D. F. Calderini (Eds), Crop Physiology: Applications for Genetic Improvement and Agronomy (pp. 321–348). London, UK: Academic Press. doi: 10.1016/B978-0-12-417104-6.00013-3
Bustos-Korts, D., Malosetti, M., Chenu, K., Chapman, S., Boer, M. P., Zheng, B., & Eeuwijk, F. A. van (2019). From QTLs to adaptation landscapes: using genotype-to-phenotype models to characterize G×E over time. Front. Plant Sci., 10, 1540. doi: 10.3389/fpls.2019.01540
Chenu, K., Deihimfard, R., & Chapman, S. C. (2013). Large-scale characterization of drought pattern: a continent-wide modeling approach applied to the Australian wheatbelt – spatial and temporal trends. New Phytol., 198(3), 801–820. doi: 10.1111/nph.12192
Ceccarelli, S., & Grando, S. (1991). Selection environment and environmental sensitivity in barley. Euphytica, 57(2), 157–167. doi: 10.1007/BF00023074
Ceccarelli, S., & Grando, S. (1991). Environment of selection and type of germplasm in barley breeding for stress conditions. Euphytica, 57(3), 207–219. doi: 10.1007/BF00039667
Ceccarelli, S., Grando, S., & Hamblin, J. (1992). Relationship between barley grain yield measured in low- and high-yielding environments. Euphytica, 64(1–2), 49–58. doi: 10.1007/BF00023537
Oosterom, E. J. van, & 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
Ceccarelli, S. (1994). Specific adaptation and breeding for marginal conditions. Euphytica, 77(3), 205–219. doi: 10.1007/BF02262633
Ceccarelli, S. (1996). Adaptation to low/high input cultivation. Euphytica, 92(1–2), 203–214. doi: 10.1007/BF00022846
Ceccarelli, S., Grando, S., & Impiglia, A. (1998). Choice of selection strategy in breeding barley for stress environments. Euphytica, 103(3), 307–318. doi: 10.1023/A:1018647001429
Teulat, B., Merah, O., Souyris, I., & This, D. (2001). QTLs for agronomic traits from a Mediterranean barley progeny grown in several environments. Theor. Appl. Genet., 103(5), 774–787. doi: 10.1007/s001220100619
Ceccarelli, S., Grando, S., & Baum, M. (2007). Participatory plant breeding in water-limited environment. Exp. Agric., 43(4), 411–435. doi: 10.1017/S0014479707005327
Teulat, B., Zoumarou-Wallis, N., Rötter, B., Salem, M. B., Bahri, H., & This, D. (2003). QTL for relative water content in field-grown barley and their stability across Mediterranean environments. Theor. Appl. Genet., 108(1), 181–188. doi: 10.1007/s00122-003-1417-7
Garcia del Moral, L. F., Garcia del Moral, M. B., Molina-Cano, J. L., & Slafer, G. A. (2003). Yield stability and development in two- and six-rowed winter barleys under Mediterranean conditions. Field Crops Res., 81(2–3), 109–119. doi: 10.1016/S0378-4290(02)00215-0
Baum, M., Grando, S., Backes, G., Jahoor, A., Sabbagh, A., & Ceccarelli, S. (2003). QTLs for agronomic traits in the Mediterranean environment identified in recombinant inbred lines of the cross ‘Arta’ × H. spontaneum 41-1. Theor. Appl. Genet., 107(7), 1215–1225. doi: 10.1007/s00122-003-1357-2
Korff, M. von, Grando, S., Del Greco, A., This, D., Baum, M., & 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
Comadran, J., Russell, J. R., Eeuwijk, F. A. van, Ceccarelli, S., Grando, S., Baum, M., … Thomas, W. T. B. (2008). Mapping adaptation of barley to droughted environments. Euphytica, 161(1–2), 35–45. doi: 10.1007/s10681-007-9508-1
Comadran, J., Russell, J. R., Booth, A., Pswarayi, A., Ceccarelli, S., Grando, S., … Thomas, W. T. B., Romagosa, I. (2011). Mixed model association scans of multi-environmental trial data reveal major loci controlling yield and yield related traits in Hordeum vulgare in Mediterranean environments. Theor. Appl. Genet., 122(7), 1363–1373. doi: 10.1007/s00122-011-1537-4
Mohammadi, R., Mahmoodi, K. N., Haghparast, R., Grando, S., Rahmanian, M., & Ceccarelli, S. (2011). Identifying superior rainfed barley genotypes in farmers’ fields using participatory varietal selection. J. Crop Sci. Biotech., 14(4), 281–288. doi: 10.1007/s12892-010-0106-8
Tondelli, A., Francia, E., Visioni, A., Comadran, J., Mastrangelo, A. M., Akar, T., … Pecchioni, N. (2014). QTLs for barley yield adaptation to Mediterranean environments in the ‘Nure’ × ‘Tremois’ biparental population. Euphytica, 197(1), 73–86. doi: 10.1007/s10681-013-1053-5
Yan, W., & Frégeau-Reid, J. (2018). Genotype by yield*trait (GYT) biplot: a novel approach for genotype selection based on multiple traits. Sci. Rep., 8, 8242. doi: 10.1038/s41598-018-26688-8
Babayants, L. T., Mesterhazy, A, Wachter, F., Neklesa, N., Dubinina, L., Omel’chenko, L., Klechkovskaya, E., Slyusarenko, A., & Bartosh, P. (1988). Metody selektsii i otsenki ustoychivosti pshenitsy i yachmenya k boleznyam v stranakh-chlenakh SEV [Methods of breeding and evaluation of wheat and barley resistance to diseases in the CMEA member countries]. Prague: N.p. [in Russian]
Dorofeev, V. F., Rudenko, M. I., & Udachin, R. A. (1974). Zasukhoustoychivyye pshenitsy (metodicheskiye ukazaniya) [Drought-tolerant wheat (guidelines)]. Leningrad: VIR. [in Russian]
Yan, W., & Tinker, N. A. (2006). Biplot analysis of multi-environment trial data: principles and applications. Can. J. Plant Sci., 86(3), 623–645. doi: 10.4141/P05-169
Yan, W., Kang, M. S., Ma, B., Woods, S., & Cornelius, P. L. (2007). GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci., 47(2), 643–653. doi: 10.2135/cropsci2006.06.0374
Gauch, H. G., Piepo, H.-P., & Annicchiarico, P. (2008). Statistical analysis of yield trials by AMMI and GGE: further consideration. Crop Sci., 48(3), 866–889. doi: 10.2135/cropsci2007.09.0513
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(7), 1629–1641. doi: 10.1007/s00477-013-0821-z
Hongyu, K., Garcia-Pena, M., Araujo, L. B. de, & Santos Dias, C. T. dos. (2014). Statistical analysis of yield trials by AMMI analysis of genotype × environment interaction. Biometrical Letters, 51(2), 89–102. doi: 10.2478/bile-2014-0007
Kalenska, S. M., & Ryzenko, A. S. (2020). Evaluation of weather conditions for growing sunflower (Heliantus annuus L.) in the northern part of the Left-bank Forest Steppe of Ukraine. Plant Var. Stud. Prot., 16(2), 162–172. doi: 10.21498/2518-1017.16.2.2020.209229
Downloads
Accepted by editor
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 В. М Гудзенко
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Our journal abides by the CREATIVE COMMONS copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons Attribution License, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.