Pigeonpea (Cajanus cajan (L.) Millsp.) is an important leguminous short lived perennial cultivated as annual crop in semi-arid tropical and subtropical regions of the world. It is generally cultivated as a sole crop or as a mixed crop with short duration cereals or legumes as well as with other crops like cotton and groundnut. Across the globe, pigeonpea is cultivated on 4.86/million ha, with an annual production of 4.1 million tons and productivity of 844 kg/ha. India is the leading producer of pigeonpea in the world accounting for 4.09/million ha area, 3.27 million tons of production and productivity of 800 kg/ha (DCA 2011). Pigeonpea is a hardy and drought tolerant crop assuring sustainable returns from marginal lands with minimum input, hence it is considered as very suitable crop for sustainable agriculture. Pigeonpea though predominantly a self-pollinated crop has cross pollination ranging from 5 to 70% (Saxena et al., 1990). The choice of an appropriate selection/breeding method and its success for improvement of quantitative traits largely depends on the extent of genetic variability present in segregating material and gene action. Knowledge on genetic architecture of yield and related traits plays an important role in deciding breeding strategies and methodologies for crop improvement. In comparison to other economically important crops, relatively less effort has been made to understand the genetics of important quantitative traits in pigeonpea. Additive and dominant/ non-additive effects have been reported to be important in determining yield, plant height, and protein content (Saxena and Sharma, 1990). Pleiotropic effects of gene, physiological changes, and highly sensitive nature of pigeonpea towards the environmental changes make it difficult to interpret the inheritance of yield and associated traits (Byth et al., 1981). Information about nature and magnitude of gene action can be useful for breeding program (Shashikumar et al., 2010). Yield and its component characters that are quantitative in nature exhibit all the three types of gene action (Saxena, 2008). The correlation among the yield and yield contributing characters provides reliable information on nature and direction of selection and also useful as a basis for selecting desirable plant type. Correlation coefficient enables to identify characters or combination of characters, which might be useful as indications of high yield by way of evaluating the relative influence of various characters as well. It provides reliable information on the consequence of selection for simultaneous improvement of desirable yield component characters. Path coefficient analysis is the best method to evaluate the cause and effect relationship between yield and its contributing traits. The present study was undertaken to derive information on genotypic correlation, direct and indirect effect of various traits

Table 1 Genotypic correlation coefficients of seed yield and component trats among recombinant lines (F3) of Gullyal white×Maruti cross in pigeonpea

Table 2 Genotypic correlation coefficients of seed yield and component trats among recombinant lines (F4) of Gullyal white×Maruti cross in pigeonpea

Figure 1 Correlation of Seed yield/plantin F3 and F4 generation

Path coefficient is an excellent means of studying direct and indirect effect of interrelated studying complex traits path coefficient measures the direct influence of one variable on another variable. The path coefficient analysis of seed yield per plant and it’s another component are presented in table (Table 3; Table 4; Figure 2). The path coefficient reveled that number of pod per plant gives the highest positive direct effect toward seed yield per plant (0.413 in F₃ and F₄) followed by test weight (0.396 in F₃ and 0.408 in F₄) , number of seeds per plant ( 0.379 in F₃) and number of secondary branches (0.264 in F₃). The same result raveled earlier by Marekar and Nerkar (1987), Salunke et al (1995), H.P.Thanki and S.L.Sawargaonkar (2010). Further days to 50% flowering was found to contribute seed yield per plant indirectly through number of pod pre plant (0.255) and test weight was found indirectly via number of seeds per plant (0.255) and number of pod per plant (0.265) in only F₃ and number of seeds per pod wad found indirectly effect to seed yield via number of seed per plant (0.174) and number of pod per plant (0.169) and negatively indirectly effect via number of primary branches similarly number of seeds per plant indirectly effecting via secondary branches per plant (0.166) and number of pods per plant (0.166) and negatively affect via days to 50% flowering and primary branches in F₄ generation. Direct and indirect effects were worked out as per Dewey and Lu (1959). The residual effect on seed yield per plant was moderate indicating that the most contributing characters were including in path analysis, thus from correlation and path analysis it can be concluded that the number of seeds per pod, number of pod per plant and test weight plays major role as major yield component in both the early and late generation, therefore selection of many characters would be offer the scope for equally improvement of all the traits which directly providing seed yield and it is similarly reported by Firoz Mahomad (2006) and D.Badru (2010). Effectiveness of single plant selection was observed by reveling intergeneration correlation coefficients and it gives estimation of gene action at some extent. If the correlation coefficient is high, it would mean high heritable portion and probably the additive component (Table 5; Figure 3).

Table 3 Genotypic path matrix of seed yield in recombinant lines (F3) of Gulyal white×Maruti cross in pegionpea

Table 4 Genotypic path matrix of seed yield in recombinant lines (F4) of Gulyal white×Maruti cross in pigeonpea

Figure 2 Direct effect of path analtsis in F3 and F4 generation with all yield component

Table 5 Analysis of intergeneration correlation 12 quantitative traits in recombinant line (F3) and (F4) of Gullya white×Maruti cross generation

Figure 3 Intergeneration correlation in F3 and F4 of all the characters

 

V.G. Panse., and P.V. Sukhatme., 1967, Statistical Methods for Agricultural Workers, ICAR, New Delhi