Combining ability analysis in bread wheat (Triticum aestivum L.) for yield and its associated traits over the environments

Introduction

Wheat stands equally with Maize ensuring global food security. India achieved the feat of "deficit to surplus" status with the introduction of Mexican dwarf wheat that brought the great green revolution in India during the sixties. One third of the Indian population depends on wheat as their main food. India is the second largest producer of wheat after China claiming the position of a significant stakeholder in Global wheat production. Bread wheat (Triticum aestivum L.) is the most important food grain crop among the cereals and stands next to rice in India. It is one of the ancient grain crops consumed as primary food by human beings since the dawn of civilization and is known as the protector of man from hunger. It is a self-pollinated crop belonging to the family Poaceae. It is a hexaploid species (2n=6x=42: AABBDD genomes) with 21 pairs of chromosomes. It significantly contributed towards the success of the "Green Revolution" and has greatly helped to transform our country from a situation of ship to mouth to become a self-sufficient. In India, it is the second most important crop after rice and occupies 318.33 lakh hectares with a total production of 1132.92 lakh tonnes and productivity of 3560 kg/ha during 2024-25 crop season (USDA Report, 2024-25). It is third ranking cereal crop in the world in terms of area and production followed by maize and rice.

Major wheat growing states in India are Uttar Pradesh, Punjab, Haryana, Madhya Pradesh, Rajasthan, Bihar and Gujarat. The concept of combining ability introduced by Sprague and Tatum (1942) [15] assumes greater importance in crop improvement program. The knowledge of combining ability is essential in selection of the appropriate parents in breeding program. The combining ability estimates helps in the identification of promising parents and desirable combinations for the improvement of plant characters through the process of selection and hybridization. The estimates of general combining ability and specific combining ability helps to identify the relative importance of additive and non-additive actions. The GCA effects are the sum of the total effects of additive and additive x additive variances whereas, the SCA effects are the total effects of the dominance and additive x dominance variances. With the advancement in biometrical genetics, several techniques are now available, which permit quantitative genetic analysis and the selection of promising parents and hybrids for further exploitation. The parents who produce promising progenies on crossing are of immense value to the plant breeder. In a crop improvement program the success depends upon the isolation of the valuable cross combinations. This emphasizes the importance of testing the parents for their combining ability because many times the high yielding parents may not combine well to give good segregants.

Materials and Methods

The present investigation was carried out at two locations viz, Wheat Research Unit, Dr PDKV, Akola (M.S.) and Agriculture Research Station, Niphad under MPKV, Rahuri (M.S.). The experimental material used was ten varieties of bread wheat (Triticum aestivum L.) viz, AKAW-3717, AKAW-2862-1, AKAW-2344, AKAW-1071, HD-3086, HI-1633, GW-2010-288, SSN-2021-41, AKW-381 and MACS-6478. These parental lines were crossed in all possible combinations including reciprocals during rabi-2023-24 at Wheat Research Unit, Dr PDKV, Akola. Total 90 crosses comprising 45 direct crosses and 45 reciprocal crosses were attempted in full diallel fashion. These 90 crosses along with ten parents and a check variety MACS-6478 were evaluated in an experiment with full diallel fashion using Randomized Block Design with three replications at two locations viz, Wheat Research Unit, Dr PDKV, Akola (M.S.) and at Agriculture Research Station, Niphad, Dist-Nashik (M.S.) during rabi-2024-25. All recommended package of practices were followed at both the locations to raise the healthy crop. Ten observations were recorded viz, days to heading, days to maturity, plant height (cm), number of effective tillers plant -1 , spike length (cm), number of spikelets spike -1 , number of grains spike -1 , grain weight spike -1 (g), 1000 grain weight (g) and grain yield plant -1 (g). The Analysis of Variance and the combining ability analysis for individual location and pooled analysis were carried out as per standard procedure (Singh and Chaudhary, 1977) [14] for all the characters under investigation. The combing ability estimation was done by the method given by Griffing (1956b) [4] utilizing experimental method I and model. In this model, experimental material was regarded as population about which inference was to be drawn and combining ability effects of parents could be compared when these parents themselves are used as tester to identify the good combiner. It was assumed that variety and block effects were constant, but error (environment and other uncontrollable components) was variable and normally and independently distributed with mean zero and variance.

Results and Discussion

The Analysis of Variance for combining ability (Table 1) revealed that the mean sum of squares due to GCA of the parents were found significant for all the characters exhibiting additive gene effects. The mean sum of squares due to the SCA of the cross combinations were significant for all the characters except for the trait days to maturity at Niphad location exhibiting non-additive gene effects in determining the traits. The mean sum of squares due to reciprocal crosses was also significant for all the reciprocal crosses exhibiting non-additive genetic effects in determining the traits. The Pooled Analysis of Variance over the two locations Akola and Niphad for analysis of combining ability revealed that the mean sum of squares due to GCA, and SCA due to direct and reciprocal crosses were highly significant for all important characters. The variances due to GCA x Environment interaction were found significant for the characters viz., days to heading, days to maturity, plant height (cm), grain weight plant -1, grain yield plot -1 and membrane thermostability index at 60 DAS. While the variances due to SCA crosses x environment was found significant for the characters viz., grain weight plant -1 , grain yield plot -1 and membrane thermostability index at 60 DAS.

The variances due to SCA reciprocals x Environment interaction was found significant for grain weight spike -1 , grain weight plot -1 , grain yield plot -1 , membrane thermostability index at 60 DAS and membrane thermostability index at 75 DAS.

The similar results which revealed that mean sum of squares due to GCA as well as SCA were exhibited significant results for all the characters, indicating the importance of both additive and non-additive gene actions in the inheritance of characters were reported by Kumar and Kerkhi (2015) [6] , Patel (2017) [9] , Gommaal A. A. (2018) [3] , Ranjitha et al. (2018) [11] , Sharma et al. (2019 a ) [13] , Tayade et al. (2019) [16] , Saini et al. (2020) [2] , Chaudhary et al.

(2022) [2] and Kajla et al. (2022) [5] . At Akola, among the parents, AKAW-2862-1 (1.750) revealed significantly highest desirable GCA effects and was found as best general combiner for grain yield plant -1 (Table 2), followed by HD-3086 (0.571). Whereas the parent AKAW-3717 (-1.198) exhibited significantly lowest negative GCA effect. At Niphad, among the parents, AKAW-2862-1 (1.457) revealed significantly highest desirable GCA effects and was found as best general combiner for grain yield plant -1 , followed by SSN-2021-41.

Parents

revealed that none of the parent was consistently good general combiner for all the traits under study. The similar results were also recorded by Kumar and Kerkhi (2015) [6] , Ahmad et al. (2017) [1] , Patel (2017) [9] , Gommaal (2018) [3] , Ranjitha et al. (2018) [11] , Nagar et al. (2019) [8] , Saini et al. (2020) [12] , Kajla et al. (2022) [5] , Chaudhary et al. (2022) [2] , Tiwari et al. (2024) [17] and Puri et al. (2025) [10] . Among the total 90 crosses including both direct and reciprocal crosses, the direct cross AKAW-2862-1 x GW-2010-288 recorded highest significant SCA effect for grain yield plant -1 (3.436) and other yield contributing traits viz., number of effective tillers plant -1 (0.717), spike length (0.457), number of spikelets spike -1 (0.889), number of grains spike -1 (3.1698), grain weight spike -1 (0.260) and 1000 grain weight (2.583), followed by another direct crosses HD-3086 x MACS-6478 and AKAW-3717 x AKW-381, which were exhibited the significant SCA effects for grain yield plant -1 (3.272 and 3.237 respectively) and for other component traits. Among the reciprocal crosses, the cross MACS-6478 x GW-2010-288 reported the highest significant SCA effect for grain yield plant -1 (4.615) and other important yield contributing traits viz., days to heading (-1.750), number of effective tillers plant -1 (0.513), spike length (1.124), number of spikelets spike -1 (0.850), number of grains spike -1 (7.950), grain weight spike -1 (0.679) and 1000 grain weight (5.537).

While the cross MACS-6478 x AKAW-1071 is at second position and reported significant SCA effect for grain yield plant -1 (4.397) and for other traits. Similar results were also recorded by Kumar and kerkhi (2015) [6] , Ahmad et al. (2017) [1] , Patel (2017) [9] , Gammaal (2018), Ranjitha et al. (2018) [11] , Nagar et al. (2018) [8] , Saini et al. (2020) [12] , Kajla et al. (2022) [5] , Chaudhary et al. (2022), Tiwari et al. (2024) [17] and Puri et al. (2025) [10] . The Analysis of Variance for combining ability (Table 1) for different characters over the environments on pooled basis showed that the variance due to specific combining ability was higher in magnitude than variance due to general combining ability for the characters viz., days to heading, plant height, number of effective tillers plant -1 , spike length, number of spikelets spike -1 , number of grains spike -1 , grain weight spike -1 , 1000 grain weight and grain yield plant -1 which indicates the predominance of non-additive gene action in controlling the expression of these traits. The ratio of GCA variance to the SCA variance was found lower than unity for these characters indicating the predominance of non-additive gene action in controlling the expression of these traits. The similar results were reported by Ahmad et al. (2017) [1] , Patel (2017) [9] , Kumar et al. (2017 a ), Ranjitha et al. (2018) [11] , Tayade et al. (2019) [16] , Saini et al. (2020) [12] , Chaudhary et al. (2022) [2] and Tiwari et al. (2024) [17] .

Conclusion

Estimates of GCA effects of the parents revealed that none of the parent was consistently good general combiner for all the traits under study. Among the crosses two direct crosses AKAW-2862-1 x SSN-2021-41 and AKAW-2862-1 x HD-3086 were identified as best combinations since they exhibited significant SCA effects and both the parents involved in these crosses expressed high x high gca effects. This explains the important role of additive x additive type of gene interaction in high-ranking performance of these hybrids. The four direct crosses viz, AKAW-1071 x MACS-6478, GW-2010-288 x MACS-6478, AKAW-2862-1 x GW-2010-288 and AKAW-2862-1 x MACS-6478 and three reciprocal crosses viz., MACS-6478 x HD-3086, GW-2010-288 x AKAW-2862-1 and GW-2010-288 x HI-1633 had high x low, low x high and low x low gca effects for grain yield over the environments. This explains the important role of additive x dominant, dominant x additive and dominant x dominant type of gene interaction in highranking performance of these hybrids.

Acknowledgement

Acknowledgements

References

1. Ahmad E, Akhtar M, Badoni S, Jaiswal JP. Combining ability studies for seed yield related attributes and quality parameters in bread wheat (Triticum aestivum L.). Journal of Genetics, Genomics & Plant Breeding. 2017;1(1):21-27.
2. Chaudhary NK, Gangwar LK, Chand P, Singh SK, Kumar M, Mohan S, Kushawaha P. Analysis of combining ability for yield and its contributing traits in bread wheat (Triticum aestivum L.). The Pharma Innovation Journal. 2022;11(5):2500-2504.
3. Gommaal AA. Combining ability analysis of drought tolerance screening techniques among wheat genotypes (Triticum aestivum L.). J Plant Production, Mansoura Univ. 2018;9(11):875-885.
4. Griffing B. Concept of general and specific combining ability in relation to diallel crossing systems. Australian Journal of Biological Sciences. 1956;9:463-493.
5. Kajla SL, Sharma AK, Singh H, Bhakal M, Neelu Kumari, Natwaria MR. Combining ability analysis for grain yield and its contributing traits in bread wheat (Triticum aestivum) over environments. The Pharma Innovation Journal. 2022;11(1):1797-1800.
6. Kumar D, Kerkhi SA. Combining ability analysis for yield and some quality traits in spring wheat (Triticum aestivum L.). Electronic Journal of Plant Breeding. 2015;6(1):26-29.
7. Kumar B, Bharadwaj DN, Singh L, Gupta SK. Estimation of combining ability in F1 and F2 generations of diallel crosses in winter wheat (Triticum aestivum L. Em. Thell). International Journal of Current Microbiology and Applied Sciences. 2017;6(12):14321444.
8. Nagar SS, Kumar P, Vishwakarma SR, Gupta V. Genetic analysis of grain yield and its component traits using diallel analysis in bread wheat. Wheat and Barley Research. 2019;10(1):45-51. doi:10.25174/22494065/2018/77261. International Journal of Advanced Biochemistry Research https://www.biochemjournal.com
9. Patel HN. Combining ability analysis for yield and its components in bread wheat. Electronic Journal of Plant Breeding. 2017;8(2):404-408.
10. Puri P, Ramgiry SR, Sikrwar RS, Tiwari S, Solanki RS. Analysis of heterosis and combining ability for seed yield and its components in bread wheat (Triticum aestivum L.). International Journal of Plant & Soil Science. 2025;37(1):194-207.
11. Ranjitha KM, Biradar SS, Desai SA, Naik VR, Singh SK, Satisha T, Hiremath G, Chetana CK, Kumar Y, Sudha T, Moger N, Uday G. Combining ability of six wheat genotypes and their F1 diallel crosses for nitrogen use efficiency (NUE) and related traits under 50 per cent nitrogen condition. Int J Curr Microbiol Appl Sci. 2018;7(1):1237-1243.
12. Saini PK, Kumar S, Singh SV. Combining ability analysis of F1 generation in bread wheat (Triticum aestivum L.). Journal of Pharmacognosy and Phytochemistry. 2020;9(6):1717-1722.
13. Sharma V, Dodiya NS, Dubey RB, Khandagale SG, Khan R. Combining ability analysis over environments in bread wheat. Electronic Journal of Plant Breeding. 2019;10(4):1397-1404.
14. Singh RK, Chaudhary BD. Biometrical methods in quantitative genetic analysis. New Delhi: Kalyani Publishers; 1977. p. 127-140.
15. Sprague GF, Tatum LA. General vs. specific combining ability in single crosses of corn. J Am Soc Agron. 1942;34:923-932.
16. Tayade SD, Potdukhe NR, Das BK, Gahukar SJ, Bharad SG, Phuke RM. Combining ability analysis in direct crosses for yield and yield related traits among bread wheat (Triticum aestivum L.). Journal of Pharmacognosy and Phytochemistry. 2019;8(6):17721777.
17. Tiwari U, Singh SV, Nageshwar, Tripathi S, Shukla MK, Awasthi P, et al. Identification of good combiner with heterotic group in bread wheat (Triticum aestivum L.). Journal of Food Chemistry & Nanotechnology. 2024;10(1):1-10.