Register      Login
Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality
RESEARCH ARTICLE

Grain yield stability of spring safflower (Carthamus tinctorius L.)

Reza Mohammadi A C , Sayyed Saeid Pourdad A and Ahmed Amri B
+ Author Affiliations
- Author Affiliations

A Dryland Agricultural Research Institute, 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: rmohammadi1973@yahoo.com

Australian Journal of Agricultural Research 59(6) 546-553 https://doi.org/10.1071/AR07273
Submitted: 19 July 2007  Accepted: 22 February 2008   Published: 10 June 2008

Abstract

The additive main effect and multiplicative interaction (AMMI) model and the phenotypic stability parameters, ecovalence (W2), regression coefficient (b), coefficient of determination (R2), coefficient of variation (CV), stability variance (S2), AMMI stability value (ASV), and TOP (proportion of environments in which a genotype ranked in the top third), were used to evaluate simultaneously the yield performance and stability of 17 spring safflower genotypes and to evaluate 26 rainfed environments during 2003–05 in Iran. These parameters were designated as Type-A and Type-B for genotypes and environments, respectively. Among Type-B parameters, Spearman’s rank correlation showed that the AMMI stability value (ASVj), ecovalence (Wj2), genotypic variance (Sj2), and coefficient of variation (CVj) were significantly and positively associated (P < 0.01), indicating that one of these parameters can be used as an alternative to the others, but were significantly and negatively correlated with the genotypic selectivity (bj) parameter. The results showed that none of the Type-A statistics per se was useful for selecting high-yielding and stable genotypes. Based on these parameters, the genotypes G9, G10, and G11 combined high and stable yields while the highest yielding genotypes G1 and G17 were the most instable. Type-A and Type-B stability parameters are useful to identify genotypes with specific and large adaptations and the contrasting environments with high contribution to genotype × environment interaction.

Additional keywords: breeding methodology, phenotypic stability, genotypic selectivity, rank correlation.


Acknowledgments

Financial support from the Agricultural Research and Education Organization (AREO) of Iran is highly appreciated. We thank all members of this project for any contribution they have made towards this work. We are also grateful to respected reviewers for their valuable comments and discussions on the manuscript.


References


Adugna W, Labuschagnem MT (2003) Parametric and nonparametric measures of phenotypic stability in linseed (Linum usitatissimum L.). Euphytica 129, 211–218.
Crossref | GoogleScholarGoogle Scholar | open url image1

Becker HC (1981) Correlation among some statistical measures of phenotypic stability. Euphytica 30, 835–884.
Crossref | GoogleScholarGoogle Scholar | open url image1

Becker HC, Leon J (1988) Stability analysis in plant breeding. Plant Breeding 101, 1–23.
Crossref | GoogleScholarGoogle Scholar | open url image1

Burdon RD (1977) Genetic correlation as a concept for studying genotype-environment interaction in forest tree breeding. Silvae Genetica 26, 168–175. open url image1

Crossa J, Fox PN, Pfeifer WH, Rajaram S, Gauch HG (1990) AMMI adjustment for statistical analysis of an international wheat yield trial. Theoretical and Applied Genetics 81, 27–37. open url image1

Eberhart SA, Russell WA (1966) Stability parameters for comparing varieties. Crop Science 6, 36–40. open url image1

Falconer DS (1981) ‘Introduction to quantitative genetics.’ 2nd edn (Longman: London, New York)

FAO (2006) FAOSTAT. Available at: http://faostat.fao.org.

Finlay KW, Wilkinson GN (1963) The analysis of adaptation in a plant-breeding programme. Australian Journal of Agricultural Research 14, 742–754.
Crossref | GoogleScholarGoogle Scholar | open url image1

Fox PN, Skovmand B, Thompson BK, Braun HJ, Cormier R (1990) Yield and adaptation of hexaploid spring triticale. Euphytica 47, 57–64.
Crossref | GoogleScholarGoogle Scholar | open url image1

Francis TR, Kannenberg LW (1978) Yield stability studies in short-season maize. I. A descriptive method for grouping genotypes. Canadian Journal of Plant Science 58, 1029–1034. open url image1

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

Gauch HG, Zobel RW (1997) Identifying mega-environments and targeting genotypes. Crop Science 37, 311–326. open url image1

Isik K, Kleinschmit J (2003) Stability related parameters and their evaluation in a 17-year old Norway spruce clonal test series. Silvae Genetica 52, 133–138. open url image1

Isik K, Kleinschmit J (2005) Similarities and effectiveness of test environments in selecting and deploying desirable genotypes. Theoretical and Applied Genetics 110, 311–322.
Crossref | GoogleScholarGoogle Scholar | PubMed | open url image1

Karlsson B, Wellendorf H, Roulund H, Werner M (2001) Genotype × trial interaction and stability across sites in 11 combined provenance and clone experiments with Picea abies in Denmark and Sweden. Canadian Journal of Forest Research 31, 1826–1836.
Crossref | GoogleScholarGoogle Scholar | open url image1

Lin CS, Binns MR, Lefkovitch LP (1986) Stability analysis: where do we stand? Crop Science 26, 894–900. open url image1

McKeand SE, Li B, Hatcher AV, Weir RJ (1990) Stability parameter estimates for stem volume for loblolly pine families growing in different regions in the southeastern United States. Forest Science 26, 10–17. open url image1

Mohammadi R, Abdulahi A, Haghparast R, Armion M (2007) Interpreting genotype × environment interactions for durum wheat grain yields using non-parametric methods. Euphytica 157, 239–251.
Crossref | GoogleScholarGoogle Scholar | open url image1

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 | GoogleScholarGoogle Scholar | open url image1

Pinthus JM (1973) Estimate of genotype value: a proposed method. Euphytica 22, 121–123.
Crossref | GoogleScholarGoogle Scholar | open url image1

Purchase JL (1997) Parametric analysis to describe G × E interaction and yield stability in winter wheat. PhD Thesis, Department of Agronomy, Faculty of Agriculture, University of the Orange Free State, Bloemfontein, South Africa.

Roemer J (1917) Sinde die ertagdreichen sorten ertagissicherer? Mitt DLG 32, 87–89. open url image1

Simmonds NW (1991) Selection for local adaptation in a plant breeding programme. Theoretical and Applied Genetics 82, 363–367.
Crossref | GoogleScholarGoogle Scholar | open url image1

Wricke G (1962) Über eine Methode zur Erfassung der ökologischen Streubreite in Feldversuchen. Z. Pflanzenzüchtg. 47, 92–96. open url image1

Yan W (1999) A study on the methodology of yield trial data analysis—with special reference to winter wheat in Ontario. PhD Thesis, University of Guelph, Ontario, Canada.

Yan W, Hunt LA, Sheng Q, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science 40, 597–605. open url image1

Yan W, Rajcan I (2002) Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Science 42, 11–20.
PubMed |
open url image1

Zobel BJ , Talbert J (1984) ‘Applied forest tree improvement.’ (Wiley: New York)