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RESEARCH ARTICLE

Grain yield stability of high-yielding barley genotypes under Egyptian conditions for enhancing resilience to climate change

Elsayed Mansour A D , Ehab S. A. Moustafa B , Nehal Z. A. El-Naggar A , Asmaa Abdelsalam A and Ernesto Igartua C
+ Author Affiliations
- Author Affiliations

A Crop Science Department, Faculty of Agriculture, Zagazig University, 44519 Zagazig, Egypt.

B Genetic Resources Department, Desert Research Center, Cairo 11753, Egypt.

C Aula Dei Experimental Station, EEAD-CSIC, Avda Montañana, 1005, 50059-Zaragoza, Spain.

D Corresponding author. Email: sayed_mansour_84@yahoo.es

Crop and Pasture Science 69(7) 681-690 https://doi.org/10.1071/CP18144
Submitted: 10 April 2018  Accepted: 2 June 2018   Published: 26 June 2018

Abstract

Identifying stable, high-yielding genotypes is essential for food security. This is particularly relevant in the current climate change scenario, which results in increasing occurrence of adverse conditions in the Mediterranean region. The objective of this study was to evaluate stability of barley (Hordeum vulgare L.) grain yield, and its relationship to the duration of the growth cycle and its stability under Mediterranean conditions in Egypt. Nineteen genotypes were evaluated during three growing seasons (2013–14 to 2015–16) at two locations (Elkhatara, Ghazala) and two growing seasons (2014–15 and 2015–16) at a third location (Ras-Sudr), i.e. eight environments (location–year combinations) in total. The linear regression explained a significant 48.2% and 22.8% of GEI variation for days to heading and grain yield, respectively, and the genotypic linear slopes were highly related to the first principal component of the AMMI model. Although all genotypes were well adapted to the region, there were different GEI responses, with changes in ranking across locations. Some stable and broadly adapted genotypes were identified, as well as unstable genotypes with specific adaptations. High yields across environments were attained by very stable (G4, G5), intermediate and stable (G1, G9) and highly responsive (G18, G19) genotypes. In general, responsiveness (b values) of yield and days to heading were negatively correlated, and high yielding genotypes showed different patterns of responses of days to heading. Genotypes G1, G4, G5 and G9 seemed best adapted overall, with longer season genotypes (e.g. G18 and G19) offering prospects to explore other formats of varieties in breeding, particularly for situations of climate instability.

Additional keywords: adaptation, biplot analysis, multi-environment trials, stability.


References

Aktaş H (2016) Tracing highly adapted stable yielding bread wheat (Triticum aestivum L.) genotypes for greatly variable South-Eastern Turkey. Applied Ecology and Environmental Research 14, 159–176.
Tracing highly adapted stable yielding bread wheat (Triticum aestivum L.) genotypes for greatly variable South-Eastern Turkey.Crossref | GoogleScholarGoogle Scholar |

Becker H, Leon J (1988) Stability analysis in plant breeding. Plant Breeding 101, 1–23.
Stability analysis in plant breeding.Crossref | GoogleScholarGoogle Scholar |

Bidinger F, Musgrave R, Fischer R (1977) Contribution of stored pre-anthesis assimilate to grain yield in wheat and barley. Nature 270, 431–433.
Contribution of stored pre-anthesis assimilate to grain yield in wheat and barley.Crossref | GoogleScholarGoogle Scholar |

Brancourt-Hulmel M, Lecomte C, Denis J-B (2001) Choosing probe genotypes for the analysis of genotype-environment interaction in winter wheat trials. Theoretical and Applied Genetics 103, 371–382.
Choosing probe genotypes for the analysis of genotype-environment interaction in winter wheat trials.Crossref | GoogleScholarGoogle Scholar |

Casao MC, Igartua E, Karsai I, Bhat PR, Cuadrado N, Gracia MP, Lasa JM, Casas AM (2011) Introgression of an intermediate VRNH1 allele in barley (Hordeum vulgare L.) leads to reduced vernalization requirement without affecting freezing tolerance. Molecular Breeding 28, 475–484.
Introgression of an intermediate VRNH1 allele in barley (Hordeum vulgare L.) leads to reduced vernalization requirement without affecting freezing tolerance.Crossref | GoogleScholarGoogle Scholar |

Ceretta S, van Eeuwijk F (2008) Grain yield variation in malting barley cultivars in Uruguay and its consequences for the design of a trials network. Crop Science 48, 167–180.
Grain yield variation in malting barley cultivars in Uruguay and its consequences for the design of a trials network.Crossref | GoogleScholarGoogle Scholar |

Comadran J, Russell J, Booth A, Pswarayi A, Ceccarelli S, Grando S, Stanca A, Pecchioni N, Akar T, Al-Yassin A (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. Theoretical and Applied Genetics 122, 1363–1373.
Mixed model association scans of multi-environmental trial data reveal major loci controlling yield and yield related traits in Hordeum vulgare in Mediterranean environments.Crossref | GoogleScholarGoogle Scholar |

Comadran J, Kilian B, Russell J, Ramsay L, Stein N, Ganal M, Shaw P, Bayer M, Thomas W, Marshall D (2012) Natural variation in a homolog of Antirrhinum CENTRORADIALIS contributed to spring growth habit and environmental adaptation in cultivated barley. Nature Genetics 44, 1388–1392.
Natural variation in a homolog of Antirrhinum CENTRORADIALIS contributed to spring growth habit and environmental adaptation in cultivated barley.Crossref | GoogleScholarGoogle Scholar |

Cuevas J, Crossa J, Montesinos-López OA, Burgueño J, Pérez-Rodríguez P, de los Campos G (2017) Bayesian genomic prediction with genotype× environment interaction kernel models. G3: Genes, Genomes, Genetics 7, 41–53.

de Leon N, Jannink J-L, Edwards JW, Kaeppler SM (2016) Introduction to a special issue on genotype by environment interaction. Crop Science 56, 2081–2089.
Introduction to a special issue on genotype by environment interaction.Crossref | GoogleScholarGoogle Scholar |

Dehghani H, Ebadi A, Yousefi A (2006) Biplot analysis of genotype by environment interaction for barley yield in Iran. Agronomy Journal 98, 388–393.
Biplot analysis of genotype by environment interaction for barley yield in Iran.Crossref | GoogleScholarGoogle Scholar |

Del Moral L, Miralles DJ, Slafer G (2002) Initiation and appearance of vegetative and reproductive structures throughout barley development. In ‘Barley science: recent advances from molecular biology to agronomy of yield and quality’. (Eds GA Slafer, JL Molina-Cano, R Savin, JL Araus, I Romagosa) pp. 243–267. (CRC Press: Boca Raton, FL, USA)

Eberhart S, Russell W (1966) Stability parameters for comparing varieties 1. Crop Science 6, 36–40.
Stability parameters for comparing varieties 1.Crossref | GoogleScholarGoogle Scholar |

Esuma W, Kawuki RS, Herselman L, Labuschagne MT (2016) Stability and genotype by environment interaction of provitamin A carotenoid and dry matter content in cassava in Uganda. Breeding Science 66, 434–443.
Stability and genotype by environment interaction of provitamin A carotenoid and dry matter content in cassava in Uganda.Crossref | GoogleScholarGoogle Scholar |

Fox P, Skovmand B, Thompson B, Braun H-J, Cormier R (1990) Yield and adaptation of hexaploid spring triticale. Euphytica 47, 57–64.
Yield and adaptation of hexaploid spring triticale.Crossref | GoogleScholarGoogle Scholar |

Gauch H (1992) ‘Statistical analysis of regional yield trials: AMMI analysis of factorial designs.’ (Elsevier Science Publishers: Amsterdam)

Gauch HG (2006) Statistical analysis of yield trials by AMMI and GGE. Crop Science 46, 1488–1500.
Statistical analysis of yield trials by AMMI and GGE.Crossref | GoogleScholarGoogle Scholar |

Hageman J, Malosetti M, Van Eeuwijk F (2012) Two-mode clustering of genotype by trait and genotype by environment data. Euphytica 183, 349–359.
Two-mode clustering of genotype by trait and genotype by environment data.Crossref | GoogleScholarGoogle Scholar |

Karsai I, Szűcs P, Mészáros K, Filichkina T, Hayes PM, Skinner JS, Láng L, Bedő Z (2005) The Vrn-H2 locus is a major determinant of flowering time in a facultative × winter growth habit barley (Hordeum vulgare L.) mapping population. Theoretical and Applied Genetics 110, 1458–1466.
The Vrn-H2 locus is a major determinant of flowering time in a facultative × winter growth habit barley (Hordeum vulgare L.) mapping population.Crossref | GoogleScholarGoogle Scholar |

Kebede G, Assefa G, Feyissa F, Alemayehu M, Mengistu A, Kehaliew A, Melese K, Mengistu S, Tadesse E, Wolde S, Abera M (2017) Genotype × environment interaction and stability analysis for dry matter yield of napier grass (Pennisetum purpureum (L.) Schumach) genotypes tested across diverse environments in Ethiopia. Omo International Journal of Science 1, 1–14.

Kole C, Muthamilarasan M, Henry R, Edwards D, Sharma R, Abberton M, Batley J, Bentley A, Blakeney M, Bryant J, Cai H, Cakir M, Cseke LJ, Cockram J, de Oliveira AC, De Pace C, Dempewolf H, Ellison S, Gepts P, Greenland A, Hall A, Hori K, Hughes S, Humphreys MW, Iorizzo M, Ismail AM, Marshall A, Mayes S, Nguyen HT, Ogbonnaya FC, Ortiz R, Paterson AH, Simon PW, Tohme J, Tuberosa R, Valliyodan B, Varshney RK, Wullschleger SD, Yano M, Prasad M (2015) Application of genomics-assisted breeding for generation of climate resilient crops: progress and prospects. Frontiers in Plant Science 6, 563
Application of genomics-assisted breeding for generation of climate resilient crops: progress and prospects.Crossref | GoogleScholarGoogle Scholar |

Lacaze X, Roumet P (2004) Environment characterisation for the interpretation of environmental effect and genotype × environment interaction. Theoretical and Applied Genetics 109, 1632–1640.
Environment characterisation for the interpretation of environmental effect and genotype × environment interaction.Crossref | GoogleScholarGoogle Scholar |

Li W, Yan ZH, Wei YM, Lan XJ, Zheng YL (2006) Evaluation of genotype × environment interactions in Chinese spring wheat by the AMMI model, correlation and path analysis. Journal of Agronomy & Crop Science 192, 221–227.
Evaluation of genotype × environment interactions in Chinese spring wheat by the AMMI model, correlation and path analysis.Crossref | GoogleScholarGoogle Scholar |

Lijalem GE (2014) Stability of barley genotypes for earliness, yield and resistance to leaf scald disease in Ethiopia. MSc Thesis, Makerere University, Kampala, Uganda.

Ludlow M, Muchow R (1990) A critical evaluation of traits for improving crop yields in water-limited environments1. Advances in Agronomy 43, 107–153.
A critical evaluation of traits for improving crop yields in water-limited environments1.Crossref | GoogleScholarGoogle Scholar |

Mansour E, Abdul-Hamid MI, Yasin MT, Qabil N, Attia A (2017) Identifying drought-tolerant genotypes of barley and their responses to various irrigation levels in a Mediterranean environment. Agricultural Water Management 194, 58–67.
Identifying drought-tolerant genotypes of barley and their responses to various irrigation levels in a Mediterranean environment.Crossref | GoogleScholarGoogle Scholar |

Mansour E, Moustafa EA, Qabil N, Abdelsalam A, Wafa HA, El Kenawy A, Casas AM, Igartua E (2018) Assessing different barley growth habits under Egyptian conditions for enhancing resilience to climate change. Field Crops Research 224, 67–75.
Assessing different barley growth habits under Egyptian conditions for enhancing resilience to climate change.Crossref | GoogleScholarGoogle Scholar |

Mohammadi R, Amri A (2013) Genotype× environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran. Euphytica 192, 227–249.
Genotype× environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran.Crossref | GoogleScholarGoogle Scholar |

Mohammadi R, Amri A, Ansari Y (2009) Biplot analysis of rainfed barley multienvironment trials in Iran. Agronomy Journal 101, 789–796.
Biplot analysis of rainfed barley multienvironment trials in Iran.Crossref | GoogleScholarGoogle Scholar |

Purchase JL (1997) Parametric analysis to describe genotype × environment interaction and yield stability in winter wheat. PhD Thesis, University of the Free State, Bloemfontein, South Africa.

Rharrabti Y, del Moral LG, Villegas D, Royo C (2003) Durum wheat quality in Mediterranean environments: III. Stability and comparative methods in analysing G × E interaction. Field Crops Research 80, 141–146.
Durum wheat quality in Mediterranean environments: III. Stability and comparative methods in analysing G × E interaction.Crossref | GoogleScholarGoogle Scholar |

Rodriguez M, Rau D, Papa R, Attene G (2008) Genotype by environment interactions in barley (Hordeum vulgare L.): different responses of landraces, recombinant inbred lines and varieties to Mediterranean environment. Euphytica 163, 231–247.
Genotype by environment interactions in barley (Hordeum vulgare L.): different responses of landraces, recombinant inbred lines and varieties to Mediterranean environment.Crossref | GoogleScholarGoogle Scholar |

Romagosa I, van Eeuwijk FA, Thomas WT (2009) Statistical analyses of genotype by environment data. In ‘Cereals’. pp. 291–331. (Springer: Dordrecht, The Netherlands)

Samarah N, Alqudah A, Amayreh J, McAndrews G (2009) The effect of late‐terminal drought stress on yield components of four barley cultivars. Journal of Agronomy & Crop Science 195, 427–441.
The effect of late‐terminal drought stress on yield components of four barley cultivars.Crossref | GoogleScholarGoogle Scholar |

Sanchez A, Subudhi P, Rosenow D, Nguyen H (2002) Mapping QTLs associated with drought resistance in sorghum (Sorghum bicolor L. Moench). Plant Molecular Biology 48, 713–726.
Mapping QTLs associated with drought resistance in sorghum (Sorghum bicolor L. Moench).Crossref | GoogleScholarGoogle Scholar |

Singh M, Ceccarelli S, Grando S (1999) Genotype × environment interaction of crossover type: detecting its presence and estimating the crossover point. Theoretical and Applied Genetics 99, 988–995.
Genotype × environment interaction of crossover type: detecting its presence and estimating the crossover point.Crossref | GoogleScholarGoogle Scholar |

St-Pierre C, Klinck H, Gauthier F (1967) Early generation selection under different environments as it influences adaptation of barley. Canadian Journal of Plant Science 47, 507–517.
Early generation selection under different environments as it influences adaptation of barley.Crossref | GoogleScholarGoogle Scholar |

Tewolde H, Fernandez C, Erickson C (2006) Wheat cultivars adapted to post‐heading high temperature stress. Journal of Agronomy & Crop Science 192, 111–120.
Wheat cultivars adapted to post‐heading high temperature stress.Crossref | GoogleScholarGoogle Scholar |

Trnka M, Rötter RP, Ruiz-Ramos M, Kersebaum KC, Olesen JE, Žalud Z, Semenov MA (2014) Adverse weather conditions for European wheat production will become more frequent with climate change. Nature Climate Change 4, 637–643.
Adverse weather conditions for European wheat production will become more frequent with climate change.Crossref | GoogleScholarGoogle Scholar |

Turner A, Beales J, Faure S, Dunford RP, Laurie DA (2005) The pseudo-response regulator Ppd-H1 provides adaptation to photoperiod in barley. Science 310, 1031–1034.
The pseudo-response regulator Ppd-H1 provides adaptation to photoperiod in barley.Crossref | GoogleScholarGoogle Scholar |

van Oosterom E, Kleijn D, Ceccarelli S, Nachit M (1993) Genotype-by-environment interactions of barley in the Mediterranean region. Crop Science 33, 669–674.
Genotype-by-environment interactions of barley in the Mediterranean region.Crossref | GoogleScholarGoogle Scholar |

Warzecha T, Adamski T, Kaczmarek Z, Surma M, Chełkowski J, Wiśniewska H, Krystkowiak K, Kuczyńska A (2011) Genotype-by-environment interaction of barley DH lines infected with Fusarium culmorum (WG Sm.) Sacc. Field Crops Research 120, 21–30.
Genotype-by-environment interaction of barley DH lines infected with Fusarium culmorum (WG Sm.) Sacc.Crossref | GoogleScholarGoogle Scholar |

Yan W, Hunt L (2001) Interpretation of genotype × environment interaction for winter wheat yield in Ontario. Crop Science 41, 19–25.
Interpretation of genotype × environment interaction for winter wheat yield in Ontario.Crossref | GoogleScholarGoogle Scholar |

Yan W, Kang MS (2002) ‘GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists.’ (CRC Press: Boca Raton, FL, USA)

Yan W, Hunt L, Sheng Q, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science 40, 597–605.
Cultivar evaluation and mega-environment investigation based on the GGE biplot.Crossref | GoogleScholarGoogle Scholar |

Yan W, Kang MS, Ma B, Woods S, Cornelius PL (2007) GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Science 47, 643–653.
GGE biplot vs. AMMI analysis of genotype-by-environment data.Crossref | GoogleScholarGoogle Scholar |

Yang R-C (2007) Mixed-model analysis of crossover genotype–environment interactions. Crop Science 47, 1051–1062.
Mixed-model analysis of crossover genotype–environment interactions.Crossref | GoogleScholarGoogle Scholar |

Yang R-C, Crossa J, Cornelius PL, Burgueño J (2009) Biplot analysis of genotype × environment interaction: Proceed with caution. Crop Science 49, 1564–1576.
Biplot analysis of genotype × environment interaction: Proceed with caution.Crossref | GoogleScholarGoogle Scholar |