AMMI analysis of yield performance in canola (Brassica napus L.) across different environments

Paper Details

Research Paper 01/03/2015
Views (579)
current_issue_feature_image
publication_file

AMMI analysis of yield performance in canola (Brassica napus L.) across different environments

Sayed Saeid Rahnejat, Ezatollah Farshadfar, Ahmad Ali Mohammadi
J. Biodiv. & Environ. Sci. 6(3), 134-140, March 2015.
Copyright Statement: Copyright 2015; The Author(s).
License: CC BY-NC 4.0

Abstract

In order to explore the effect of genotype, environment and genotype × environment interaction (GEI) on grain yield of 15 canola genotypes in four different environments, an experiment was conducted in a randomized complete block design with 3 replications during 2013-2014 growing seasons. Combined analysis of variance exhibited that grain yield was significantly (p<0.01) affected by environments (E), genotypes (G) and genotype × environment interaction (GEI) indicating the presence of genetic variation and possible selection of stable entries. AMMI analysis revealed that the first and second interaction principal component (IPCA1 and IPC2) explained 65.11% and 19.64% of the G×E variation, respectively. According to AMMI1 biplot, G2, G3, G4, G5, G7, G8, G11, G12, G13, G15, and G14 with grain yield less than mean indicated specific adaptation for E1 and G1, G6 and G10 for E1, E2 and E3. Distribution of genotypes in the AMMI II biplot displayed that genotypes, G2, G5, G13 and G15 scattered close to the origin, indicating minimum G×E interaction and hence stability. The remaining 11 genotypes scattered away from the origin in the biplot indicating that the genotypes were more sensitive to environmental fluctuations.

Crossa J, Gauch H, Zobel RW. 1990. Additive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Science 30, 493-500.

Farshadfar E, Sabaghpour SH, Zali H. 2012. Comparison of parametric and non-parametric stability statistics for selecting stable chickpea (Cicer arietinum L.) genotypes under diverse environments. Australian Journal of Crop Science 6(3), 514-524.

Gauch HG, Zobel RW. 1996. AMMI analysis of yield trials. In: Kang MS, Gauch HG (eds) Genotype by environment interaction. CRCP Boca Raton, FL

Gauch HG. 1992. Statistical analysis of regional yield trials: AMMI analysis of factorial designs. Elsevier Science Publishers.

Gollob HF. 1968. A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33, 73-115.

Kang MS, Gauch HG. 1996. Genotype-by-environment interaction. CRC Press, Boca Raton, FL.

Kempton R. 1984. The use of biplots in interpreting variety by environment interactions. The Journal of Agricultural Science 103, 123-135.

Marjanović-Jeromela A, Nagl N, Gvozdanović-Varga J, Hristov N, Kondić-Špika A, Marinković MVR. 2011. Genotype by environment interaction for seed yield per plant in rapeseed using AMMI model. Pesquisa Agropecuária Brasileira 46, 174-181.

Meziani N, Bouzerzour H, Benmahammed A, Menad A, Benbelkacem A. 2011. Performance and adaptation of barley genotypes (Hordeum vulgare L.) to diverse locations. Advances in Environmental Biology 5(7), 1465-1472.

Mortazavian SMM, Azizinia SH. 2014. Nonparametric stability analysis in multi-environment trial of canola. Turkish Journal of Field Crops 19(1), 108-117.

Oliveira EJD, Freitas JPXD, Jesus OND. 2014. AMMI analysis of the adaptability and yield stability of yellow passion fruit varieties. Scientia Agricola 71, 139-145.

Pourdad SS, Mohammadi R. 2008. Use of stability parameters for comparing safflower genotypes in multi-environment trials. Asian Journal Plant Science 7, 100-104.

Rashidi M, Farshadfar E, Jowkar MM. 2013. AMMI analysis of phenotypic stability in chickpea genotypes over stress and non-stress environments. International Journal of Agriculture and Crop Sciences 5, 253-260.

Starner DE, Hamama AA, Bhardwaj HL. 2002. Prospects of canola as an alternative winter crop in Virginia, p. 127–130. In J. Janick and A. Whipkey (eds.). Trends in new crops and new uses. ASHS Press, Alexandria,VA.

Zobel RW, Wright MJ, Gauch HG. 1988. Statistical analysis of a yield trial. Agronomy journal 80, 388-393.

Related Articles

Using chitosan made from modified chitosan (Crab shells) for dye adsorption, equilibrium, kinetic, and response surface methods

M. Priyanga, V. Gomathi Priya, P. Bhuvaneswari, T. Shanmuga Vadivu, S. Viswanathan, G. Annadurai, R. Soranam*, J. Biodiv. & Environ. Sci. 28(2), 85-98, February 2026.

Effects of logging regimes on woody species diversity and stand structure in community forests adjacent to the Dja biosphere reserve, Cameroon

Nanga Charnelle Prudence*, Angoni Hyacinthe, Menyene Etoundi Laurent Florent, Ifo Averti Suspense, Nkemnkeng Francoline Jong, Mbolo Marie Marguerite, J. Biodiv. & Environ. Sci. 28(2), 76-84, February 2026.

Analysis of soil physicochemical characteristics and heavy metal concentrations in Lourdes, Alubijid, Misamis Oriental

Prosibeth G. Bacarrisas*, Romeo M. del Rosario, Angelo Mark P. Walag, J. Biodiv. & Environ. Sci. 28(2), 49-58, February 2026.

Tick-borne blood parasites in small ruminants: An epidemiological study of Anaplasma sp. and Babesia sp. in Cagayan, Philippines

Kathlyn B. Cruz*, Jhaysel G. Rumbaoa, Mary Ann M. Santos, Bryan Jerome R. Bassig, John Michael U. Tabil, J. Biodiv. & Environ. Sci. 28(2), 34-48, February 2026.

Diversity, spatial and seasonal distribution of gastropod molluscs in Taï national park (Côte d’Ivoire): Influence of environmental factors

Doue Obin*, Memel Jean-Didié, Kouadio Behegbin Habib Herbert, J. Biodiv. & Environ. Sci. 28(2), 20-33, February 2026.

Assessment of heavy metal levels in spring water of Dansolihon, Cagayan de Oro City

Faith M. Guimary*, Romeo M. Del Rosario, Angelo Mark P. Walag, J. Biodiv. & Environ. Sci. 28(2), 12-19, February 2026.

Evaluating curriculum alignment, accuracy, and readability of ‘environmental disaster, sanitation, and waste management

Analyn I. Diola*, Priscilla R. Castro, J. Biodiv. & Environ. Sci. 28(2), 1-11, February 2026.