Evaluation of seed yield stability of barley promising genotypes using principal coordinates analysis

Document Type : Research Paper

Authors

1 Department of Agronomy and Plant Breeding, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.

2 Moghan Agricultural Research Center, Parsabad, Iran.

Abstract

Analysis of the structure of genotype by environment (GE) interaction is essential in crop stability programs. To study the effects of GE interaction on the seed yield and identify stable genotypes of barley for warm and humid regions, 16 barley genotypes with two check cultivars were assayed in a randomized complete block design with four replications in Gachsaran, Moghan, Khorramabad and Gonbad regions for three years (2017-2019). Combined analysis of variance for yield data of 12 environments (year/location combined) showed significant differences among environments and genotypes and significant GE interaction. The GE interaction was examined using principal coordinates analysis (PCoA). Based on the deviation from the grand mean, 12 environments were divided into two main groups: five environments with higher mean yield and seven environments with lower mean yield. The most stable genotypes based on the minimum spanning tree and distance from the center of plots were G13 (2.43 kg/ha), G2 (2.38 kg/ha), G14 (2.29 kg/ha), which could be recommended for environments with a yield lower than the average mean of all studied environments. The results of the PCoA showed that the genotype G18 (2.32 kg/ha) was also located five times in the vertex positions of high cycles and so it can be recommended for favorable or high yielding environments.
 

Keywords

References

Ahmadi J, Vaezi B and FotokianMH, 2012. Graphical analysis of multi-environment trials for barley yield using AMMI and GGE-biplot under rain-fed onditions, Journal of Plant Physiology and Breeding, 2(1): 43-54.
Akcura M, Kaya Y and Taner S, 2009. Evaluation of durum wheat genotypes using parametric and nonparametric stability statistics. Turkish Journal of Field Crops 14(2): 111-122.
Crossa J, 1988. A comparison of results obtained with two methods for assessing yield stability. Theoretical and Applied Genetics 75: 460-467.
Elias AA, Robbins KR, Doerge RW and Tuinstra MR, 2016. Half a century of studying genotype × environment interactions in plant breeding experiments. Crop Science 56: 2090-2105.
Flores F, Moreno MT, Martinez A and Cubero JI, 1996. Genotype × environment interaction in faba bean: comparison of AMMI and principal coordinate models. Field Crops Research 47: 117-127.
GENSTAT Committee, 2009. GENSTAT 12 release 1, Reference Manual. Clarendon Press, Oxford, UK.
Ibanmez MA, Direnzo MA, Samame SS, Bonamico MM and Poverene NC, 2001. Genotype × environment interaction of lovegrass forage yield in the semi-arid region of Argentina. Journal of Agricultural Science 137: 329-336.
Karimizadeh R, Asghari A, Chinipardaz R, Sofalian O and Ghaffari A, 2016. Determining yield stability and model selection by AMMI method in rain-fed durum wheat genotypes. Turkish Journal of Field Crops 21(2): 174-183.
Karimizadeh R, Asghari A, Chinipardaz R, Sofalian O, Ghaffari A, Shahbazi K, Hosseinpour T, Ghojog H and Armion M, 2019. Use of principal coordinate analysis for measuring GE interactions in rain-fed durum wheat genotypes. Agricultural Sciences 25: 38-46.
Karimizadeh R, Mohammadi, M and Sabbagnia, N, 2013. Site regression biplot analysis for matching new improved lentil genotypes into target environments. Journal of Plant Physiology and Breeding, 3(2): 51-65.
Medina JL, Moore PP, Shanks JR, Gil FF and Chandler CK, 1999. Genotype × environment interaction for resistance to spider mites in Fragaria. Journal of American Society of Horticultural Science 124: 353-357.
Mohebodini M, Karimizadeh R, Mohammadi M and Sabaghnia N, 2012. Principal coordinates analysis of genotype × environment interaction in grain yield of lentil genotypes. Agriculture and Forestry 57: 93-107.
Reynolds MP, Quilligan E, Aggarwal PK, Bansal KC, Cavalieri AJ, Chapman, SC, Chapotin SM, Datta SK, Duveiller E, Gill KS, Jagadish KSV, Joshi AK, Koehler AK, Srivalli PK, Lafitte K.R, Mahala R.S, Muthurajan R, Paterson AH, Prasanna B.M, Rakshit S, Rosegrant MW, Sharma I, Singh RP, Sivasankar S, Vadez V, Valluru R, Prasad PVV and Yadav OM, 2016. An integrated approach to maintaining cereal productivity under climate change. Global Food Security 8: 9-18
Sabaghnia N, Mohammadi M and Karimizadeh R, 2013. Principal coordinate analysis of genotype × environment interaction for grain yield of bread wheat in the semi-arid regions. Genetika 45: 691-701.
Tabachnick GB and Fidell LS, 2012. Using Multivariate Statistics. Pearson Publications, USA, 1024 pp.
Westcott B, 1986. Some methods of analyzing genotype-environment interaction. Heredity 56: 243-253.
Westcott B, 1987. A method of assessing the yield stability of crop genotypes. Journal of Agricultural Science 108: 267-274.
Yan W, 2012. Biplot analysis of incomplete two-way data. Crop Science, 53: 48-57.
Yan W, Kang MS, Ma B, Woods S and Cornelius PL, 2007. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Science 47: 643-655.
Zuur AF, Leno EN and Smith GM, 2007. Statistics for Biology and Health - Analyzing Ecological Data. Springer, New York.
 
Journal of Plant Physiology and Breeding
Volume 10, Issue 2
December 2020
Pages 59-68

Files

Share

How to cite

Statistics