Volume 4 Issue 1, June 2011

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Journal of the

Drylands

ISSN 1817 - 3322

PUBLISHED BY The College of Dryland Agriculture and Natural Resources (CoDANR) Mekelle University P.O.Box 231, Mekelle, Ethiopia Tel +251-348-400610, +251-344-409015; Fax 251-04-409304 www.mu.edu.et Email: [email protected]

Volume 4 Issue 1, June 2011

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JOURNAL OF THE DRYLANDS 4(1): 259-266, 2011

Additive Main Effects and Multiplicative Interactions Analysis of Yield Performance of Sesame Genotypes across Environments in Northern Ethiopia Hagos Tadesse1 and Fetien Abay2 Hagos Tadesse and Fetien Abay. 2011. Additive Main Effects and Multiplicative Interactions Analysis of Yield Performance of Sesame Genotypes across Environments in Northern Ethiopia. Journal of the Drylands 4(1): 259-266. Thirteen sesame genotypes were tested across three years and locations (2006-2008) in Western and North Western lowlands of Tigray, Ethiopia. The experiment was laid down in Randomized Complete Block Design (RCBD) with three replications. The objectives were to understand the influence of Genotype by Environment Interaction (GEI) on grain yield, examine the performance of promising sesame genotypes and to determine the stability of yield. The combined analysis of variance showed that genotype, environment, and their interaction (GEI) were significant (p150 cm) clay textured. North Western Tigray (Sheraro) is located 259

at geographical coordinates 14o 23' latitude and 37o 46' longitudes and at altitude of 1086 m.a.s.l. The

average temperature ranges from 27oC to 30oC and it has euthric Vertisols soil type.

Figure 1: Map of Tigray and location of the test sites Experimental Materials The test materials used in this experiment were obtained from Werer Agricultural Research Center. One nationally released variety ‘Adi’ and a local variety ‘Hirhir’ were used as standard and local

checks, respectively. Eleven test genotypes were selected from preliminary variety trials on the basis of their promising results under the Western lowlands of Tigray region.

Table 1: Description of sesame genotypes used for the study Genotype name Genotype Status Seed color Code Col.sel.p#1 01 Advanced line White Acc.038.sel-2 02 Advanced line White Acc.038.sel-3 03 Advanced line White Acc.041-sel-2 04 Advanced line White Cone-27 05 Advanced line White Acc-111-105 06 Advanced line White ABXC.50-02 07 Advanced line White Acc-031-sel-7 08 Advanced line White Acc-038-sel-1 09 Advanced line White Serkamo White 10 Advanced line White Acc.019-sel-2 11 Advanced line White Adi 12 released White Hirhir 13 local White WARC= werer agricultural research center Experimental Design The experimental design was Randomized Complete Block Design (RCBD) with three replications at all locations and years. Genotype by environment interaction was quantified using the most common procedure; i.e. pooled analysis of variance, which partitions the total variance into its component parts (genotype, environment, GEI and pooled error). The statistical significance of these components was determined using the F-test. AMMI analysis was performed using IRISTAT software.

Copyright © Journal of the Drylands 2011 ISSN 1817-3322

Source WARC WARC WARC WARC WARC WARC WARC WARC WARC WARC WARC WARC Farmers variety

In this analysis, the model for phenotypic performance of genotype i tested in environment j can be expressed as:  n Y

ij

   G

j

 E

j





k  1



k



i k



j k

 e

ij

Where, Yij is the yield of the ith genotype in the jth environment;  is the grand mean; Gi and Ej are the genotype and environment deviations from the grand mean, respectively; λk is the eigen value of the PCA analysis axis k; αik and γjk are the genotype and environment principal component scores for axis k; n is the number of principal components retained in the model and eij is the error term. 260

AMMI Stability Value of the ith genotype (ASVi) was calculated for each genotype and each environment according to the relative contribution of IPCA1 to IPCA2 to the interaction SS as follows (Purchase et al., 2000): ASVi   SS IPCA1 

Where,

SS IPCA2

SS IPCA1

IPCA1SCORE  

SS IPCA 2

2

 IPCA2 SCORE 

2

is the weight given

to the IPCA1-value by dividing the IPCA1 sum of squares by the IPCA2 sum of squares. Based on the rank of mean grain yield of genotypes (RYi) across environments and rank of AMMI stability value (RASVi) a selection index called Genotype Selection Index (GSI) was calculated for each genotype, which incorporates both mean grain yield (RYi) and stability index in single criteria (GSIi) as (Farshadfar, 2008):

GSI i  RASVi  RYi

Environmental index (Ij) was obtained by the difference among the mean of each environment and the general mean. RESULTS AND DISCUSSION Combined Analysis of Variance The combined analysis of variance is shown in Table 2. The partitioning of sum of squares for the grain yield indicated that environments (year, location and environment), genotypes and GEI (Year*Genotype, Location* Genotype and Year*Location*Genotype) contributes to 73.1%, 8.3% and 16%, respectively (Table 2). This indicates the big influence of environment on yield performance of sesame genotypes. Main effects due to environment, genotype and GEI had significant effect (p