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3007-3015, 2012 Issn 1995-0756 3007 Advances in Environmental Biology, 6(11): 3007-3015, 2012 ISSN 1995-0756 This is a refereed journal and all articles are professionally screened and reviewed ORIGINAL ARTICLE Genotype xEnvironment Interaction Assessment in Durum Wheat (Triticum durumDesf) using AMMI and GGE Models 1Hind Nouar, 2Hamenna Bouzerzour, 1LeilaHaddad, 3Abdelkader Menad, 4Tahar Hazmoune, and 5Hocine Zerargui 1Agronomy Department, Ferhat Abbas University, Setif, Algeria 2Ecology and Plant Biology Department, Faculty of Life and Natural Sciences, Ferhat Abbas University, Setif, Algeria. 3Biology Department, Tahar Moulay University, Saida, Algeria. 4Agronomy Department, University of Skikda, Algeria 5Field Crop Institute, Agricultural Research Station, Setif, Algeria. Hind Nouar, Hamenna Bouzerzour, LeilaHaddad, Abdelkader Menad, Tahar Hazmoune and Hocine Zerargui; Genotype xEnvironment Interaction Assessment in Durum Wheat (Triticum durumDesf) using AMMI and GGE Models ABSTRACT Development of varieties with high yield potential coupled with wide adaptability is an important plant breedingobjective. The presence of genotype by environment interaction plays a crucial role in determining theperformance of genetic materials, tested at different locations and in different years. This study was undertaken toassess yield performance, stability and adaptability of twelve durum wheat genotypes evaluated over five environments. There was highly significant genotype-environment interaction. The use of the additive model as well as the joint regression proved to be inefficient in the interpretation of results compared to AMMI analysis of variance which explained90.80% of the sum of square of the interaction. This analysis indicated that cultivar Waha exhibited a wide adaptation across all test-sites, while the GGE biplot analysis indicated that the five test-locations represented 3sub-regions where Mrb3, Boussalem and Badre were the winners. Selection for specific adaptation allowed 10.51% genetic gain over selection for large adaptation. Grain yield stability was approached using the genotypic superiority index, the across locations variance, the AMMI stability variance and the genotypes rank on both mean and stability, using the GGE biplot analysis. Rank correlation coefficients among stability parameters indicated their dissimilar. Pi indexcombinesyield performance and stabilityclassified Boussalem and Mrb3 were high yielding and stable.Similar conclusion was reached using the genotype rank method of the GGE biplot analysis. Key words: Triticumdurum, adaptation, G x L interaction, stability, yield, AMMI, GGE. Introduction exploited through appropriate selection strategies[2]. In fact, the objective of durum wheat breeding In the Nord Africa and West Asia region,durum program is to develop new cultivars showing either wheat grain yield is severely limited by biotic and wide adaptation to overall growing area or specific abiotic stresses with a strong effect of rainfall adaptation to sub regions[2, 24].The adaptation amount and distribution pattern [8]. This resulted in pattern of each tested cultivar is described by its grain yield variation, ranging from crop failureto 4.0 response across a range of environments t ha-1at different sites,and cropping seasons[4, 13]. representative of the target production area [2, 17, 3, Low yields resulted from the combined effects of 21]. In this context the joint regression and the winter low temperatures, spring frost hazards, additive main effect and multiplicative interaction terminal high temperatures and water shortage [2, 20, (AMMI) models help understanding the magnitude 17]. Franciaet al.,[14] noted that grain yield response of the interaction to be able to exploit it[12, 29, to environmental changes wasrelated to the allelic 11].Yan et al.,[26]developed the GGE biplot analysis variation of the Vrn-H1, Ppd-H2 and Eam6genes which combines the genotype (G) effect and the which control growth habit. Even though genotype x interaction (GE) to address the issue of cultivar environments interaction, when large enough, recommendation in multi-location trials. The hinders selection progress, it can be properly GGEbiplot analysis identifies which genotype won in Corresponding Author Hind Nouar, Agronomy Department, Ferhat Abbas University, Setif, Algeria E-mail: [email protected] 3008 Adv. Environ. Biol., 6(11): 3007-3015, 2012 which environments, facilitatingsub-regions Sites, genotypes and experimental design: identification. Grain yield stability is a desirable characteristic when selecting genotypes for wide Field trials were carried out during the 2009-10 adaptation while reliability allowscombining cropping season at five locations, namely Harrouch, performance and stability into a unique index Khroub, Setif, Sidi Bel Abbes and Saïda (Algeria, representing the genotype merit [1]. Several Table 1). The experiment was conducted under biometrical methods have been developed to assess rainfed conditions. Twelve durum wheat genotypes stability [6]. The deviation from linear regression is were tested, including Algerian commercial varieties widely adopted by breedersdue to its simplicity, but and advanced breeding lines from the national and it is unrelated to genotypic grain yield performances Cimmyt- Icarda durum wheat breeding programs [13, 17].AMMI stability value (ASV) statistic, based (Table 2). Plant material was sown in a randomised on the first two principal components scores,ranks complete block design with four replications. Sowing the genotypes according to their yield stability[23]. was done in November with an experimental drill in The objective of this study was to assess the plots 1.2 m wide x 5 m long, at a seeding rate of 300 magnitude of the genotype x locations interaction for seeds m-2. Nutrient deficiencies were prevented with grain yield and to identify stable durum wheat fertilization at sowing, by an application of 100 kg (Triticum durumDesf.) varieties within a set of ha-1 of super phosphate 46% and at jointing, by an twelveadvanced breeding lines evaluated across five application of 75 kg ha-1 of urea 46%. Weeds were diverse locationsduring the 2009/2010 cropping controlled chemically with GranStar [Methyl season. Tribenuron] at 12 g ha-1 rate. Grain yield was determined by mechanical harvesting all 6 rows per Materials and methods plot. Accumulated rainfall during the course of the crop cycle at the five locations is given in Table 1. Table 1: Characteristics of the experimental locations. Ordre Site name Abrev. Latitude (°) longitude (°) Altitude (m) Rain(mm) 1 Harrouch HA 37°09’N 8°67’E 132 674.0 2 Khroub KH 36°55’N 6°65’E 640 520.0 3 Setif SE 36°12’N 5°24’E 1081 417.0 4 Sidi Bel Abbes SB 35°02’N 0°3’W 554 418.1 5 Saïda SA 34°03’N 0°8’W 1013 467.8 Table 2: Pedigree of the durum wheat breeding lines and cultivars evaluated at the five experimental sites during the 2009/2010. Genotype Pedigree Cross origin Badre Boussalem/Ofanto ITGC (Setif) Boussalem Heider/Martes//Huevos de Oro Cimmyt-Icarda Essalem Ofanto/*2Waha ITGC- Setif Gaviota durum Crane/4/Polonicum185309//T. glutinosumenano/2*Teh60/3/Grulla Cimmyt-Icarda Korifla DS15/Gieger Cimmyt-Icarda MBB Genealogical selection from a landrace population ITGC (Setif) Mrb3 JoriC69/Haurani Cimmyt-Icarda Mrb5 JoriC69/Haurani Cimmyt-Icarda Ofanto Appulo/Valnova IAO (Italy) Setifis Ofanto/Waha//MBB. ITGC (Setif) Vitron Turkey77/3/Jori/Anhinga//Flamingo Cimmyt-Icarda Waha Pelicano/Ruffino//Gaviota/Rolette Cimmyt-Icarda Data analysis: To describe the genotype x locations interaction of grain yield and to identify stable genotypes, joint Single analysis of variance of grain yield was regression and additive main effects and performed per site to test the genotypic effect. multiplicative interaction (AMMI) analyses were Homogeneity of the residuals was tested and was performed on the (G x L)ij term. The joint regression found not significant. The combined analysis of analysis was performed according to Finlay and variance was, then, performed according to the Wilkinson [12] where the (G x L)ij effect was following model: partitioned into components biLj and dij, which th accounted for the linear regression of the i genotype th Yijk =µ + Gi + Lj + (G x L)ij + eijk, on the j location yield index and the deviation from regression, respectively. Slope value (b) was th where Yijkis the yield of the i genotype in the determined for each genotype, as well as the th th j location and k block, μis the grand mean, Giis the genotype contribution to the G X L interaction and th main effect of the i genotype, ljis the main effect of deviation from regression. The (G x L)ij effect was th the j location, (G x L)ij is the interaction term of the also partitioned according to the AMMI model th th i genotype in the j location, and eijk is the error proposed by Gauch[15] as: term[15]. 3009 Adv. Environ. Biol., 6(11): 3007-3015, 2012 newlyselected varieties as reported by Nouar et (G x L)ij = ∑ιnｕniｖnj+rij, al., [22].The fact that each site identified a specific set of top yielding genotypes is suggestive of the where ∑ is the sum of the n =1, 2… n PC axes presence of genotype x locations interaction (G x L) included in the model, ι is the eigenvalue of the nth n which was confirmed by the combined analysis of PC axis, u is the scaled eigenvector of the ith ni grain yield of all locations (Table 5). The location genotype for the nth axis, v is the scaled eigenvector nj was the most important source of variation, of the jth location for the nth axis, and r is the
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