Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality

Yield stability of rainfed durum wheat and GGE biplot analysis of multi-environment trials

Reza Mohammadi A C , Reza Haghparast A , Ahmed Amri B and Salvatore Ceccarelli B
+ Author Affiliations
- Author Affiliations

A Dryland Agricultural Research Institute (DARI), PO Box 67145-1164, Kermanshah, Iran.

B International Centre for Agricultural Research in the Dry Areas (ICARDA), PO Box 5466, Aleppo, Syria.

C Corresponding author. Email:

Crop and Pasture Science 61(1) 92-101
Submitted: 25 May 2009  Accepted: 24 September 2009   Published: 17 December 2009


Integrating yield and stability of genotypes tested in unpredictable environments is a common breeding objective. The main goals of this research were to identify superior durum wheat genotypes for the rainfed areas of Iran and to determine the existence of different mega-environments in the growing areas of Iran by testing 20 genotypes in 4 locations for 3 years via GGE (genotype + genotype-by-environment) biplot analysis. Stability of performance was assessed by the Kang’s yield-stability statistic (YSi) and 2 new methods of yield-regression statistic (Ybi) and yield-distance statistic (Ydi).The combined analysis of variance showed that environments were the most important source of yield variability, and accounted for 76% of total variation. The magnitude of the GE interaction was ~10 times the magnitude of the G effect. The GGE biplot suggested the existence of 2 durum wheat mega-environments in Iran. The first mega-environment consisted of environments corresponding to ‘cold’ locations (Maragheh and Shirvan) and a moderately cold location (Kermanshah), where ‘Sardari’ was the best adapted cultivar; the second mega-environment comprised ‘warm’ environments, including the Ilam and Kermanshah locations, where the recommended breeding lines G16 (Gcn//Stj/Mrb3), G17 (Ch1/Brach//Mra-i), and G18 (Lgt3/4/Bcr/3/Ch1//Gta/Stk) produced the highest yields. Ranking of genotypes based on GGE was found to be highly correlated with that based on the statistics YSi and Ybi. The discriminating power v. the representative view of the GGE biplot identified Kermanshah as the location with the least discriminating ability but greater representation, suggesting the possible of testing genotypes adapted to both warm and cold locations at the Kermanshah site. The results verified that the statistics YSi and Ybi were highly correlated (r = 0.94**) and could be a good alternative for GGE biplot analysis for selecting superior genotypes with high-yielding and stable performance.

Additional keywords: mega-environment, discriminating ability, representativeness, yield-stability statistic.


This work was part of the durum wheat research project of the Dryland Agricultural Research Institute (DARI) of Iran, supported by the Agricultural Research and Education Organization (AREO). We thank all members of the project who contributed to the implementation of the field work. The authors acknowledge and appreciate the comments of two anonymous reviewers, and associate and technical editors of the journal of Crop and Pasture Science.


Barah BC, Binswanger HP, Rana BS, Rao NGP (1981) The use of risk aversion in plant breeding; concept and application. Euphytica 30, 451–458.
CrossRef |

Bhan MK, Pal S, Rao BL, Dhar AK, Kang MS (2005) GGE biplot analysis of oil yield in lemongrass. Journal of New Seeds 7, 127–139.
CrossRef |

Blanche SB, Myers GO (2006) Identifying discriminating locations for cultivar selection in Louisiana. Crop Science 46, 946–949.
CrossRef |

Brancourt-Hulmel M, Lecomte C (2003) Effect of environmental variates on genotype 3 environment interaction of winter wheat: A comparison of biadditive factorial regression to AMMI. Crop Science 43, 608–617.

Casanoves F, Baldessari J, Balzarini M (2005) Evaluation of multienvironment trials of peanut cultivars. Crop Science 45, 18–26.
CrossRef |

Ceccarelli S (1996) Positive interpretation of genotype by environment interactions in relation to sustainability and biodiversity. In ‘Plant adaptation and crop improvement’. (Eds M Cooper, GL Hammer) pp. 467–486. (CAB International: Wallingford, UK; ICRISAT: Patancheru, India; and IRRI: Manila, The Philippines)

Cooper M , Hammer GL (1996) ‘Plant adaptation and crop improvement.’ (CAB International: Wallingford, UK; ICRISAT: Patancheru, India; and IRRI: Manila, The Philippines)

Dardanelli JL, Balzarinic M, Martneza MJ, Cunibertib M, Resnikd S, Ramundaa SF, Herrerob R, Baigorrib H (2006) Soybean maturity groups, environments, and their interaction define mega-environments for seed composition in Argentina. Crop Science 46, 1939–1947.
CrossRef |

Eberhart SA, Russell WA (1966) Stability parameters for comparing varieties. Crop Science 6, 36–40.

Eskridge KM (1990) Selection of stable cultivars using a safety-first rule. Crop Science 30, 369–374.

Fan LJ, Hu BM, Shi CH, Wu JG (2001) A method of choosing locations based on genotype × environment interaction for regional trials of rice. Plant Breeding 120, 139–142.
CrossRef |

Fan XM, Kang MS, Chen H, Zhang Y, Tan J, Xu C (2007) Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agronomy Journal 99, 220–228.

Fox PN, Rosielle AA (1982) Reducing the environmental main effects on pattern analysis of plant breeding environments. Euphytica 31, 645–656.
CrossRef |

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

Gauch HG , Zobel RW (1996) AMMI analysis of yield trials. In ‘Genotype-by-environment interaction’. (Eds MS Kang, HG Gauch, Jr) pp. 85–122. (CRC Press: Boca Raton, FL)

Gauch HG, Zobel RW (1997) Identifying mega-environments and targeting genotypes. Crop Science 37, 311–326.

Gravois KA, Bernhardt JL (2000) Heritability and genotype × environment interactions for discolored rice kernels. Crop Science 40, 314–318.

Huhen M (1996) Nonparametric analysis of genotype × environment interactions by ranks. In ‘Genotype-by-environment interaction’. (Eds MS Kang, HG Gauch, Jr) pp. 235–271. (CRC Press: Boca Raton, FL)

Kang MS (1988) A rank-sum method for selecting high-yielding, stable corn genotypes. Cereal Research Communications 16, 113–115.

Kang MS (1993) Simultaneous selection for yield and stability in crop performance trials: Consequences for growers. Agronomy Journal 85, 754–757.

Kang MS (1998) Using genotype-by-environment interaction for crop cultivar development. Advances in Agronomy 62, 199–252.
CrossRef |

Kang MS, Aggarwal VD, Chirwa RM (2006) Adaptability and stability of bean cultivars as determined via yield-stability statistic and GGE biplot analysis. Journal of Crop Improvement 15, 97–120.
CrossRef |

Kang MS, Pham HN (1991) Simultaneous selection for high yielding and stable crop genotypes. Agronomy Journal 83, 161–165.

Lin CS, Binns MR, Lefkovitch LP (1986) Stability analysis: Where do we stand? Crop Science 26, 894–900.

Magari R, Kang MS (1993) Genotype selection via a new yield stability statistic in maize yield trials. Euphytica 70, 105–111.
CrossRef |

Malvar RA, Revillaa P, Butrona A, Gouesnardc B, Boyatc A, Soengasa P, Alvarezb A, Ordas A (2005) Performance of crosses among French and Spanish maize populations across environments. Crop Science 45, 1052–1057.
CrossRef |

McKnight TL , Darrel H (2000) Climate zones and types: The Koppen System. In ‘Physical geography: A landscape appreciation’. pp. 1–200. (Prentice Hall: Upper Saddle River, NJ)

Mohammadi R, Amri A (2008) Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica 159, 419–432.
CrossRef |

Navabi A, Yang RC, Helm J, Spaner DM (2006) Can spring wheat-growing mega-environments in the Northern Great Plains be dissected for representative locations or niche adapted genotypes? Crop Science 46, 1107–1116.
CrossRef |

Pazdernik DL, Hardman LL, Orf JH (1997) Agronomic performance and stability of soybean varieties grown in three maturity zones in Minnesota. Journal of Production Agriculture 10, 425–430.

Robins JG, Waldron BL, Vogel KP, Berdahl JD, Haferkamp MR, Jensen KB, Jones TA, Mitchell R, Kindiger BK (2007) Characterization of testing locations for developing cool-season grass species. Crop Science 47, 1004–1012.
CrossRef |

Samonte SOPB, Wilson LT, McClung AM, Medley JC (2005) Targeting cultivars onto rice growing environments using AMMI and SREG GGE biplot analyses. Crop Science 45, 2414–2424.
CrossRef |

Shukla GK (1972) Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29, 237–245.
CrossRef | CAS | PubMed |

Thomason WE, Phillips SB (2006) Methods to evaluate wheat cultivar testing environments and improve cultivar selection protocols. Field Crops Research 99, 87–95.
CrossRef |

Trethowan RM, van Ginkel M, Ammar K, Crossa J, Payne TS, Cukadar B, Rajaram S, Hernandez E (2003) Associations among twenty years of international bread wheat yield evaluation environments. Crop Science 43, 1698–1711.

Upadhya MD , Cabello R (2000a) Selection of parental lines using stability analysis of hybrid true potato seed families produced through line 3 tester method. CIP Program Report 1999–2000. CIP, Lima, Peru. pp. 197–206.

Upadhya MD , Cabello R (2000b) Influence of seed size and density on the performance of direct seedling transplants from hybrid true potato seed. CIP Program Report 1999–2000. CIP, Lima, Peru. pp. 207–210.

Voltas J, Lopez-Corcoles H, Borras G (2005) Use of biplot analysis and factorial regression for the investigation of superior genotypes in multi-environment trials. European Journal of Agronomy 22, 309–324.
CrossRef |

Waldron BL, Asay KH, Jensen KB (2002) Stability and yield of cool-season pasture grass species grown at five irrigation levels. Crop Science 42, 890–896.

Yan W (2001) GGEBiplot—A Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agronomy Journal 93, 1111–1118.

Yan W (2002) Singular-value partition for biplot analysis of multienvironment trial data. Agronomy Journal 94, 990–996.

Yan W, Hunt LA, Sheng Q, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Science 40, 596–605.

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

Yan W, Kang MS, Ma B, Woods S, Cornelius PL (2007) GGE Biplot vs. AMMI analysis of genotype-by-environment data. Crop Science 47, 643–655.

Yan W, Rajcan I (2002) Biplot evaluation of test sites and trait relations of soybean in Ontario. Crop Science 42, 11–20.
PubMed |

Yan W, Tinker NA (2006) Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86, 623–645.

Zhang Z, Lu C, Xiang ZH (1998) Stability analysis for varieties by AMMI model. Acta Agronomica Sinica 24, 304–309.

Rent Article (via Deepdyve) Export Citation Cited By (18)