Assessment of Selection Criteria in Sesame by Using Correlation Coefficients, Path and Factor Analyses (Report‪)‬

Introduction Sesame (Sesamum indicum L.) has lots of demand owing to high and quality oil, protein and antioxidants (Arslan et al., 2007; Erbas et al., 2009; Uzun et al., 2002; 2007; 2008). Seeds are used as raw food as well as in confectionery, sweets, bakery products and also oil is used for industry in preparation of soap, perfume, and carbon papers as well as in vegetable oil (Khan et al. 2001). Beside this consumption benefits, sesame has also many agricultural attributes: It grows well tropical and subtropical climates, it can grow on only soil moisture without rainfall or irrigation, and be grown in mixed stands with diverse crops (Ashri, 2007). Although it is highly favorable advantages, sesame production is insufficient compared to the other oilseed crops like that soybean, sunflower and peanut. Comparatively, low seed yield is one of the most important reasons that sesame needs breeding to provide more yield (Furat and Uzun, 2010). Especially in Mediterranean and Mediterranean type environments, sesame is an important option for the second crop production. Nevertheless, sesame growing areas are decreasing due to low seed yield compared to the other second crops such as corn. Selection for good yield types should be very useful and contribute to breeding programs in this area. Breeding process in sesame is not very easy because its seed yield is a complex phenomenon entailing several contributing factors which are highly correlatively with environmental interactions and thus these factors influence seed production both directly and indirectly (Rauf et al., 2004). Yield components also have complex features and they are affected together strongly (Bidgoli et al., 2006). The understanding of the relationship between yield and its components is crucial for selection process and this relationship can be explained by means of correlation, factor and path coefficient analyses. Factor analysis provides more information than a simple correlation matrix because it discriminates between groups of variables (factors) and indicates percentage contribution of variables (Biabani and Pakniyat, 2008). Path coefficient analysis permits the separation of direct effects from indirect effects and gives more realistic relationship of the characters and helps in effective selection (Sumathi et al., 2007). Some researchers (Subramanian and Subramanian 1994; Shim et al. 2001; Yingzhong and Yishou, 2002; Biabani and Pakniyat, 2008) have worked out character associations to create proper database for breeding practices. Their results differ widely for trait to trait which could be attributed due to differences in genetic material used for their studies (Gnanasekaran et al., 2008). Therefore, assessment of character interactions in a wide range of sesame accessions is very important and can be generalized for the sesame breeding programs. With this view, we evaluated seed yield, yield components and their associations in 345 sesame genotypes originated from all around the world and produced in the true Mediterranean type environment.