Department of Plant Breeding and Genetics, Agricultural College and Research Institute, Maduraia, 625104, India;
Author
Correspondence author
Plant Gene and Trait, 2013, Vol. 4, No. 13 doi: 10.5376/pgt.2013.04.0013
Received: 31 May, 2013 Accepted: 12 Jul., 2013 Published: 20 Aug., 2013
This is an open access article published under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Sathya and Jebaraj., 2013, Inter-Relationship and Cause Effect Analysis among Drought and Physiological Traits in Three Line Aerobic Rice Hybrids, Plant Gene and Trait, Vol.4, No.13 70-73 (doi: 10.5376/pgt.2013.04.0013)
Path analysis helps to study the association between drought tolerant and yield related traits by partitioning of genotypic correlation coefficients of different characters on single plant yield to direct and indirect effects. The trait, proline content expressed high direct effect and harvest index had moderate direct effect on single plant yield. The traits panicle length and harvest index had high indirect effects on single plant yield through proline content. Likewise, filled grains per panicle, productive tillers per plant, 100 grain weight and harvest index showed moderate indirect effects through proline content. The traits panicle length, proline content expressed low indirect effects through harvest index. These characters have to be given importance for enhancing yield under drought condition.
Rice (Oryza sativa L.) is the major food crop of more than half of the global population and will continue to occupy the pivotal place in global food and livelihood security systems. But much of this important crop yield is devastated by drought or diseases (Reddy, 2005). Land races are one of the important components of the germplasm and serve as the donors for the drought tolerance. Local land races are naturally adapted to utilize the natural resource-base better than the introduced modern cultivars (Bhattacharya and Ghosh, 2004). Moreover, the land races have broad genetic base which provides them wider adaptability and protection from various stresses. Hence, we could develop the high yielding hybrids with an added advantage of drought tolerance by crossing the drought tolerant land races with high yielding varieties which are susceptible to drought.
Path analysis has been used to organize the relationship between predictor variables and response variables. The advantage of path analysis is that it permits the partitioning of the correlation coefficient into its components-one component being the path coefficient (or standardized partial regression coefficient) that measures the direct effect of a predictor variable upon its response variables, the second component being the indirect effect(s) of a predictor variable on the response variable through other predictor variables (Deway and Lu, 1959). Path coefficient analysis assists plant breeders in identifying traits on which selection pressure should be given for improving yield. With these points in view, the present investigation was framed to study the relationship between drought and yield related traits under drought stress environment.
Results and Discussion
Analysis of variance showed significant differences among the parents and hybrids for all the traits studied (Table 1; Table 2). Rapid improvement in yield is expected to result if selection is practiced for component characters. Rate of improvement is expected to be rapid if differential emphasis is laid on the component characters during selection. The basis of differential emphasis could be the degree of influence of component characters on the character of interest. Path analysis gives an idea about how a trait influences grain yield directly and indirectly via other traits. This is very important in giving due weightage to major yield contributing traits while selection.
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Table 1 Analysis of variance for combining ability for different biometrical traits in parents and hybrids
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Table 2 Analysis of variance for combining ability for different physiological traits in parents and hybrids
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The trait, proline content expressed high direct effect and harvest index had moderate direct effect on single plant yield (Table 3). This was in conformity with the findings of Anbumalarmathi and Nadarajan (2008). Similarly, chlorophyll stability index, plant height and biomass yield had positively low direct effects on single plant yield. Similar results were reported by Malarvizhi et al (2010). The remaining traits mostly showed either low or negligible direct effects on single plant yield.
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Table 3 Direct and indirect effects of different characters on single plant yield
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The traits panicle length and harvest index had high indirect effects on single plant yield through proline content. Likewise, filled grains per panicle, productive tillers per plant, 100 grain weight and harvest index showed moderate indirect effects through proline content. This was in accordance with the earlier findings of Anbumalarmathi (2005). The traits panicle length, proline content expressed low indirect effects through harvest index. Also, root length and productive tillers per plant showed low indirect effects through proline content and root: shoot ratio respectively. This is in conformity with the reports of Michael Gomez and Rangasamy (2002).
Materials and Methods
Six Lines and 15 Testers were subjected to crossing by ‘Line×Tester’ mating design (Kempthorne, 1957). Ninety hybrids along with six lines, 15 testers and one check were evaluated under non-puddled and non flooded aerobic soil, during Rabi, 2010. Observations were recorded for the drought tolerant, yield and its component traits viz., Days to 50 per cent flowering (DF), Plant height (PH), Number of Productive tillers per plant (PT), Number of panicles per plant (PP), Panicle length (PL), Filled grains per panicle (FG), Spikelet fertility (SF), Hundred grain weight (HGW), Proline content (PC), SPAD chlorophyll meter reading (SCMR), Chlorophyll stability index (CSI), Relative water content (RWC), Biomass yield (BMY), Dry shoot weight (DSW), Dry root weight (DRW), Root / shoot ratio (RS), Root length (RL), Harvest index (HI), Single plant yield (YLD) under water stress and fully irrigated (control) conditions as per the Standard Evaluation System (1996). The analysis of variance of RBD and their significance for all the characters were worked out as suggested by Panse and Sukhatme (1964). The relative influence of 19components on yield by themselves (direct effects) and through other traits (indirect effects) was evaluated by the method of path coefficient analysis as suggested by Dewey and Lu (1959). The simple correlation coefficients already estimated at genotypic level were utilized for this purpose. By keeping yield as dependent variable and other 19 traits as independent variables, simultaneous equations which express the basic relationship between path coefficients were solved to estimate the direct and indirect effects. The direct and indirect effects were classified based on the scale given by Lenka and Mishra (1973). More than 1.0: Very high; 0.30 to 0.99: High; 0.20 to 0.29: Moderate; 0.10 to 0.19: Low; 0.00 to 0.09: Negligible.
Anbumalarmathi J., and N. Nadarajan N., 2008, Studies on root characters for drought resistant in rice (Oryza sativa L.), Indian J. Agri. Res., 42: 71–74
Anbumalarmathi J., Sheeba A., and Deepasankar P., 2005, Genetic variability and interrelationship studies in cowpea [Vigna unguiculata (L.) Walp.], Res. Crops, 6(3): 517-519.
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Lenka D., and Misra B., 1973, Path co-efficient analysis of yield in rice varieties, Indian Journal of Agricultural Sciences, 43: 376-379
Malarvizhi D., K. Thiyagarajan, C. Vijayalakshmi, and S. Manonmani, 2010, Genetic analysis to assess the physiological efficiency of parental lines in rice (Oryza sativa L.), Electr. J. of Plant Breeding, 1(2):100-113
Michael-Gomez S., and Rangasamy P., 2002, Correlation and path analysis of yield and physiological characters in drought resistant rice (Oryza sativa L.). International Journal of Mendel 19(1/2): 33-34
Panse V.G. and P.V. Sukhatme, 1964, Statistical methods for agricultural research workers, ICAR, New Delhi, Pp. 287
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