Register      Login
The Rangeland Journal The Rangeland Journal Society
Journal of the Australian Rangeland Society
Shopping Cart: ( empty )
You are here: Home > Journals > RJ > RJ23015
RESEARCH ARTICLE
Contents Vol 45(3)

Herbage yield stability of cocksfoot (Dactylis glomerata L.) genotypes across rain-fed environments

Ali Vosough A , Ali Ashraf Jafari https://orcid.org/0000-0002-1211-3796 B * , Ezzat Karami https://orcid.org/0000-0001-5130-2541 A , Hooshmand Safari C and Reza Talebi A
+ Author Affiliations
- Author Affiliations

A Department of Agronomy and Plant Breeding, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran.

B Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.

C Agricultural Research and Education Center and Natural Resources of Kermanshah Province Agricultural Research, Education and Extension Organization (AREEO), Kermanshah, Iran.

* Correspondence to: aajafari@rifr-ac.ir

The Rangeland Journal 45(3) 109-122 https://doi.org/10.1071/RJ23015
Submitted: 21 April 2023  Accepted: 23 October 2023  Published: 28 November 2023

© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of the Australian Rangeland Society.

Abstract

Cocksfoot (Dactylis glomerata L.) is a cool-season perennial grass that naturally grows in semi-steppe rangelands in Iran. In recent years, as a result of climate change, coupled with high livestock grazing, rangeland productivity has decreased. This study aimed to analyse the stability of forage dry matter (DM) production of 36 cocksfoot genotypes across four rain-fed environments (Ardebil, Zanjan, Hamadan, and Kermanshah) in Iran. At each location, an experiment was conducted using a randomised complete-block design (RCBD) with three replications over 2 years. The result of combined analysis of variance (ANOVA) across locations showed significant effects of environment (E), genotype (G), and GE interaction (P < 0.01) for DM yield. The E, G and GE interaction effects accounted for 33.19%, 17.98% and 40.52% of the total variance respectively. The GE interaction was subsequently investigated using regression stability, Additive Main effects and Multiplicative Interaction (AMMI), and genotype main effect (G) plus genotype–environment (GE) interaction (GGE) biplot analysis. According to the regression method, genotypes G2, G3, G11 and G12 with a slope close to unity, coupled with high production, had good general stability in all locations. In addition, some genotypes were specifically identified for poor- and high-performing environments. According to the AMMI-2, IPC1 vs IPC2 biplot, genotypes G2, G10, G11, G14 and G15, placed close to the origin of the biplot coupled with higher production, showed general stability in all environments. The best genotypes for the respective environments were also determined. Using the GGE biplot, genotypes G2, G3, G11 and G12 were more stable in all environments. Therefore, on the basis of all analytical methods, three local genotypes, G2 (Karaj), G3 (Marand) and G11 (Qazvin), and a foreign genotype, G14 (from, USA), were identified as most suitable genotypes for breeding improved varieties and cultivation in the study locations and similar areas.

Keywords: AMMI, cool season grasses, drought stress, dryland farming, forage production, G × E interaction, GGE biplot, regression, Shukla’s stability.

References

Bartlett MS (1937) Properties of sufficiency and statistical tests. Proceedings of the Royal Society. Series A, Mathematical and Physical Sciences 160, 268-282.
| Crossref | Google Scholar |

Dabkevičienė G, Kemešytė V, Lemežienė N, Butkutė B (2013) Production of slender cocksfoot (Dactylis polygama H.) tetraploid populations and their assessment for agro-morphological characteristics. Zemdirbyste-Agriculture 100(3), 303-310.
| Crossref | Google Scholar |

Daneshian J, Ahmadi M, Kalantarahmadi SA (2022) Stability evaluation of advanced soybean lines (Glycine max L.) in drought conditions using GGE-biplot analysis and AMMI. Crop Production Journal 15(4), 119-138 [In Persian].
| Google Scholar |

Eberhart SA, Russell WA (1966) Stability parameters for comparing varieties. Crop Science 6, 36-40.
| Crossref | Google Scholar |

Falconer DS, Mackay TFC (1996) ‘Introduction to quantitative genetics.’ 4th edn. (Longman Inc.: London, UK)

Finlay KW, Wilkinson GN (1963) The analysis of adaptation in a plant-breeding program. Australian Journal of Agricultural Research 14(6), 742-754.
| Crossref | Google Scholar |

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

Gauch HG, Zobel RW (1997) Identifying mega environments and targeting genotypes. Crop Science 37(2), 311-326.
| Crossref | Google Scholar |

Gauch HG, Piepho HP, Annicchiarico P (2008) Statistical analysis of yield trials by AMMI and GGE: further considerations. Crop Science 48(3), 866-889.
| Crossref | Google Scholar |

Hassani M, Heidari B, Dadkhodaie A, Stevanato P (2018) Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.). Euphytica 214, 79.
| Crossref | Google Scholar |

Hayward MD, Bosemark NO, Romagosa T (1993) ‘Plant breeding principles and prospects.’ (Chapman and Hall: London, UK)

Jafari A (2016) Challenges of grass seed production for rehabilitation of rangelands and cultivation in low efficient dryland farming of Iran. Iranian Journal of Seed Science and Research 3(3), 117-132 [In Persian].
| Google Scholar |

Jafari A, Bafandeh Rouzbahani A, Armani E (2007a) Seed and herbage yield in Bromes tomentellus Boss. grown in optimum and drought stress conditions. In ‘Seed production in the northern light. Proceedingsof the Sixth International Herbage Seed Conference’. 18-20 June 2007. (Eds TS Aamlid, LT Havstad, B Boelt) pp. 33–38. (Gjennestad, Norway)

Jafari A, Bafandeh Rouzbahani A, Rahmani E, Panahpour H (2007b) Evaluation for seed yield and seed components among Iranian accessions of Boiss. ex Steud. In ‘Seed production in the northern light. Proceedings of the Sixth International Herbage Seed Conference’. 18-20 June 2007. (Eds TS Aamlid, LT Havstad, B Boelt) (Gjennestad, Norway)

Jafari A, Saidmohamadi A, Abdi N (2007c) Variation of seed yield and yield components in 31 genotypes of wheatgrass (Agropyron desertorum) through factor analysis. Iranian Journal of Rangelands and Forests Plant Breeding and Genetic Research 15(3), 211-221 [In Persian].
| Google Scholar |

Jocković M, Cvejić S, Jocić S, Marjanović-Jeromela A, Miladinović D, Jocković B, Miklič V, Radić V (2019) Evaluation of sunflower hybrids in multi-environment trial (MET). Turkish Journal of Field Crops 24, 202-210.
| Crossref | Google Scholar |

Levene H (1960) Robust tests for equality of variances. In ‘Contributions to probability and statistics: essays in honor of Harold Hotelling’. (Ed. I Olkin) pp. 205–207. (Stanford University Press: Palo Alto, CA, USA)

Mansouri Daneshvar MR, Ebrahimi M, Nejadsoleymani H (2019) An overview of climate change in Iran: facts and statistics. Environmental Systems Research 8, 7 7.
| Crossref | Google Scholar |

Moradi P, Jafari A (2006) Determination of important traits affecting yield of seven species Poa. Iranian Journal of Rangelands and Forests Plant Breeding and Genetic Research 14, 25-31 [In Persian].
| Google Scholar |

Naseri S, Arastoo B, Parvaneh T (2021) Influence of climatic factors on forage production and vegetation cover of Iran’s upland rangeland (Jashloobar Rangeland, Semnan Province). Journal of Rangeland Science 11(4), 386-401.
| Google Scholar |

Niaky S (1995) ‘Land grass cover of Iran.’ (Chmran University Press: Ahwaz, Iran) [In Persian]

Oliveira RLd, Von Pinho RG, Balestre M, Ferreira DV (2010) Evaluation of maize hybrids and environmental stratification by the methods AMMI and GGE biplot. Crop Breeding and Applied Biotechnology 10(3), 247-253.
| Crossref | Google Scholar |

Pazooki D (2001) ‘Rangeland.’ (Tehran University Press: Tehran, Iran) [In Persian]

Rahmani A, Jafari A, Turkmen M (2006a) Study of yield and quality traits of 18 ecotypes of Agropyron cristatum species for cultivation in rangeland of semi-steppe regions of Iran. Iranian Journal of Range and Desert Research 13(1), 53-61 [In Persian].
| Google Scholar |

Rahmani A, Jafari A, Hedaiati P (2006b) Comparison of seed and forage yield in Mountain rye (Secale montanum) in the moderate cold climate of northern Lorestan, Iran under dry and irrigated conditions. Iranian Journal of Range and Desert Research 13(3), 172-185 [In Persian].
| Google Scholar |

Rasoli M, Jafari AA, Tabaei-Aghdaei SR, Shanjani PS (2014) Herbage yield stability of 38 genotypes of sainfoin (Onobrychis sativa) across five environments of Iran. Legume Research 37(3), 245-252.
| Crossref | Google Scholar |

Rechinger KH (1970) ‘Flora Iranica.’ No. 70. (Akademische Druck-u. Verlagsanstal: Graz, Austria)

Sanderson MA, Skinner RH, Elwinger GF (2002) Seedling development and field performance of prairiegrass, grazing bromegrass, and orchardgrass. Crop Science 42(1), 224-230.
| Google Scholar | PubMed |

Shahriari Z, Heidari B, Dadkhodaie A (2018) Dissection of genotype × environment interactions for mucilage and seed yield in Plantago species: application of AMMI and GGE biplot analyses. PLoS One 13(5), e0196095.
| Crossref | Google Scholar | PubMed |

Shukla GK (1972) Some statistical aspects of partitioning genotype environmental components of variability. Heredity 29(2), 237-245.
| Crossref | Google Scholar | PubMed |

Skroppa T (1984) A critical evaluation of methods available to estimate the genotype x environment interaction. Studia Forestalia Suecica 166, 3-14.
| Google Scholar |

Vega DJ, Di Santo HE, Ferreira VA, Castillo EA, Bonamico NC, Grassi EM (2021) Phenotypic evaluation of Festuca arundinacea Schreber populations naturalized in sub-humid semi-arid environments. Cienciay Tecnología Agropecuaria 22(2), e1814.
| Google Scholar |

Wrick G (1962) Über eine methode zür erfassung der okologischen streubreite, in feldresuchen. Z. Pflanzuecht 47(1), 92-96 [In German].
| Google Scholar |

Yan W (2001) GGE Biplot - A Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agronomy Journal 93, 1111-1118.
| Crossref | Google Scholar |

Yan W (2002) Singular value partitioning in biplot analysis of multi-environment trial data. Agronomy Journal 94, 990-996.
| Crossref | Google Scholar |

Yan W, Kang MS (2003) ‘GGE biplot analysis, a graphical tool for breeders, geneticists, and agronomists.’ (CRC Press: Boca Raton, FL, USA)

Yan W, Tinker NA (2006) Biplot analysis of multi-environment trial data: principles and applications. Canadian Journal of Plant Science 86(3), 623-45.
| Crossref | Google Scholar |

Yan W, Hunt LA, Sheng Q, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science 40(3), 597-605.
| Crossref | Google 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.
| Crossref | Google Scholar |

Zobel RW, Wright MJ, Gauch HG (1988) Statistical analysis of a yield trial. Agronomy Journal 80(3), 388-393.
| Crossref | Google Scholar |