Genetic Divergence Analysis over Seasons in Single Cross Hybrids of Maize (Zea mays L.)

N. Sabitha^* , D. Mohan Reddy , D. Lokanadha Reddy , M. Hemanth Kumar , P. Sudhakar , B. Ravindra Reddy , S. J. Mallikarjuna

Acharya N.G. Agricultural University, Department of Genetics and Plant Breeding, Agricultural College, Mahanandi- 518502, A.P, India

Corresponding Author Email: nsabitha84@gmail.com

DOI : http://dx.doi.org/10.5281/zenodo.7135093

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INTRODUCTION

Maize (Zea mays L.) is one of the most important staple food crop among all the cereals in the present world agriculture scenario due to its several uses and wider adaptability to different environments. It is used as food grain, animal feed, fodder and in industries for various purposes, chiefly for starch. It plays a significant role in human and livestock nutrition worldwide and has greater nutritional value, as it contains about 72% starch, 10% proteins, 8.5% fibre, 4.8% oil, 3% sugar and 1.7% ash, [1]. Maize occupies the third place at the global level after wheat and rice due its high production potential and broad resilience [3]. It contributes maximum among the cereal food crops (38%) annually to the global food production as compared to wheat (30%) and rice (20%) [3]. Maize accounts 21.5 million tonnes of annual production in India. It is greatly preferred by farmers for its versatility and good yield potential. Maize is predominantly cultivated as a kharif crop in India. The use of genetically diverse parents is essential to generate a genetic variation for the successful selection of genotypes in a breeding program [4]. An assessment of the nature and magnitude of diversity between genotypes will help to choose better ones to produce superior genotypes. The determination of genetic diversity in crop plant is either from pedigree data [5] or morphological characters [6]. Multivariate analysis (D² statistics) developed by [7] is the most powerful tool for quantifying genetic diversity among the specified set of genotypes [8]. Genetic divergence for metric traits to a great extent is subjective to an environment. The present study was aimed at determining the genetic diversity among single cross hybrids of maize over three environments (seasons) based on morpho-physiological and yield contributing components.

Materials and Methods

The 45 single cross hybrids made involving 10 promising inbred lines in a half diallel fashion excluding reciprocals were evaluated in three seasons viz., rabi, summer and kharif from2016-17 to 2017-18 in a randomized block design with three replications at the experimental farm of Agricultural Research Station, Perumallapalli, Chittoor, A.P. Seeds of each hybrid were sown in five meters long one row plot with a spacing of 75 × 20 cm in kharif and 60 × 20 cm in rabi and summer between rows and hills, respectively. One plant per hill was kept after proper thinning. Recommended doses of fertilizers were applied. Necessary intercultural operations and irrigations were accorded during the crop growth period to ensure normal growth, and development of the plants and to raise a uniform crop. Fifteen morpho-physiological and yield contributing characters were recorded in all three seasons. Data for days to 50% tasseling, days to 50% silking and days to maturity were recorded on a whole plot basis. Five randomly selected plants were used for recording observations on plant height, leaf area, leaf weight, SPAD meter readings, cob length, cob girth, number of kernel rows cob^-1, number of kernels row^-1, 100 kernel weight and kernel yield plant^-1. Anthesis-silking interval was determined as the deviation of days to 50% silking from days to 50% tasseling, whereas specific leaf area and specific leaf weight were worked out as ratios of leaf area and leaf weight and vice versa. Harvest index was calculated as the ratio of kernel yield to total dry matter/plant. Analysis of variance was carried out following standard statistical techniques to establish significance levels among the hybrids [9-10]. Data collected on 15 characters were subjected to [11]. D² statistic to calculate intra and inter-cluster genetic distance values and clustering was carried out adopting Tocher’s method [12] using INDOSTAT version 8.1.

Results and Discussion

Analysis of variance revealed that the mean sum of squares  of genotypes for all the 15 characters studied were significant in all three seasons viz., rabi, summer, and kharif indicating the existence of considerable variability among the single cross hybrids for all the characters under investigation. Based on the results of the analysis of variance, D² analysis was carried out for each season. The clustering pattern, average intra and inter cluster distances, cluster means for different characters and number of hybrids included in different clusters determined separately for each season were presented in Fig. 1 to 6 and Table .1 to 6. The results obtained are presented on season- wise hereunder.

The cluster analysis grouped all the 45 single cross hybrids of maize into five major clusters in rabi based on the quantitative characters indicating the importance of phenotypic descriptors to differentiate between them (Fig. 1). Cob girth (36.77%), days to 50% silking (19.20%), days to maturity (11.21%), 100 kernel weight (11.11%), SPAD meter reading (8.38), days to 50% tasseling (6.97%), kernel yield (6.26%) and harvest index (5.25%) in that order contributed much to the clustering pattern. Cluster II and cluster I were the largest having 22 and 20 hybrids indicating genetic similarity among them, whereas clusters III, IV and V had one hybrid each (Fig. 2). All the 11 top high yielding hybrids viz., BML 2 × Heypool, BML 6 × PDM 1474, BML 7 × DFTY, BML 15 × Heypool, BML 15 × PDM 1474, Heypool × PDM 1574, BML 6 ×DFTY, BML 15 × PDM 1452, DFTY × Heypool, DFTY × PDM 1452 and PDM 1452 × PDM 1474 were included in cluster I, while only one hybrid BML 2 × BML 7 was included in the cluster III.

The intra and inter-cluster distances among the five clusters were presented in Table 1. The intra-cluster distances were lower than the inter-cluster distances. The inter-cluster distance varied from 22.23 (cluster I and III) to 131.88 (cluster V and IV), while the intra cluster distance varied from 0.00 (cluster III and V) to 11.18 (cluster IV and V). The low intra cluster distance indicates that these are solitary clusters with single genotypes in them. Cluster means for various characters studied indicate that Clusters III and I possessed higher values for SPAD meter readings, specific leaf area, cob length, cob girth, number of kernels cob^-1, number of kernels row^-1, 100 kernel weight and kernel yield and moderate values for days to 50% tasseling, days to % silking, anthesis to silking interval, days to maturity and plant height. Clusters V recorded lower mean values for kernel yield and yield components and higher means for days to 50% tasseling, days to % silking, anthesis to silking interval, days to maturity and plant height (Table 2).

During summer, anthesis–silking interval (53.43%), days to 50% tasseling (13.43%), cob girth (6.67%), 100 kernel weight (6.46%), plant height (6.36%) contributed maximum to the clustering pattern compared to other characters. Maximum inter-cluster distance (96.66) was noted between clusters I and VI and minimum distance between clusters (IV and V). Intra cluster distances ranged from 0.00 (Cluster III, IV and VI) to 9.83 (cluster V) (Fig. 3 and Table 3). Cluster I contained a maximum number of hybrids (25) followed by cluster II (11) and cluster V (6). Clusters III, IV, and VI were the minor clusters and together consisted three hybrids (Fig. 4). Single cross hybrids viz., DFTY × Heypool, DFTY × PDM 1452, Heypool × PDM 1474, BML 2 × PDM 1474, BML 6 × DFTY, BML 7 × DFTY and BML 15 × PDM 1474 in cluster I and BML 6 × PDM 1474 and BML 2 × Heypool were included in cluster V. Cluster mean values were found higher in cluster V and cluster I for most of the characters along with low mean values for developmental characters. All other clusters registered lower means for kernel yield and yield contributing characters (Table 4).

All the 45 hybrids were grouped in to seven clusters during kharif season (Fig.6). Anthesis–silking interval (43.23%) followed by cob girth (20.51%), 100 kernel weight (9.09), days to maturity (6.87%), days to 50 %tasseling (6.06 %) and kernel yield (5.05%) contributed maximum to the differentiation of hybrids and inclusion in different clusters.

The highest inter-cluster distance of 132.16 was observed between clusters III and VII followed by 116.13 between clusters III and VI and 100.53 between clusters II and VI suggesting more genetic diversity among the hybrids included in different clusters (Table 5). The largest major cluster I consisted 20 hybrids followed by cluster III (13) and cluster II (8). Clusters IV, V, VI and VII contained one hybrid each (Fig.5). Top yielding hybrids viz., BML 2 × BML 6, BML 6 × PDM 1416, BML 7 × Heypool, BML 15 × DFTY, DFTY × PDM 1428 in cluster I; BML 7 × DFTY, BML 15 × PDM 1452, BML 15 × PDM 1474, DFTY × Heypool, DFTY × PDM 1452, Heypool × PDM 1452 and Heypool × PDM 1474 in cluster III and BML 6 × DFTY in cluster II were included. Cluster mean values were higher in cluster III followed by cluster I and II for most of the yield components and yield and lower mean values for maturity (Table 6). Next best cluster was cluster I for yield and yield components. The clusters viz., IV, V, VI and VII registered low values for all the characters studied. The hybrids belonging to the clusters separated by high statistical distance could be selected for further improvement. Similar results on the clustering pattern of maize  genotypes based on quantitative characters through multivariate analysis such as D² analysis and cluster analysis  were reported in maize [13-17]

The hybrids viz., BML 7 × DFTY, BML 15 × PDM 1474, DFTY × Hey pool and DFTY × PDM 1452 were included in the same cluster in all the seasons revealing that they are more similar and stable over seasons. The clusters having the largest number of hybrids indicate genetic similarity among the hybrids. The highest inter-cluster distances also suggest more variability in the genetic makeup of the hybrids included in the clusters and the genotypes from those clusters may be utilized for advanced crop development programme. Out of 15 characters studied, the number of kernels per row, test weight, days to 50% tasseling and yield per plant contributed to high genetic divergence.

CONCLUSIONS

Cluster analyses based on the characters studied indicated the presence of enough variability among the hybrids considered under this investigation. Cluster analysis assigned the 45 hybrids of maize single cross hybrids grouped into five, six and seven major clusters in rabi, summer, and kharif, respectively based on the quantitative characters clearly indicating the impact of seasons on clustering pattern and the importance of phenotypic descriptors to differentiate between them. High levels of intra and inter-cluster distances indicate the presence of broad genetic variation between and within a cluster. Therefore, the present investigation could be helpful in a reliable selection of single cross hybrids as well as for the development of high-yielding varieties for further breeding programs. The characters viz., cob girth, kernel weight and days to 50% tasseling contributed much to the clustering pattern of hybrids in all three seasons, while the number of kernel rows cob^-1, number of kernels row^-1, cob length, SPAD meter reading, days to 50% silking and specific leaf weight contributed much less to the genetic diversity. Among the top high-yielding hybrids viz., BML 7 × DFTY, BML 15 × PDM 1474, DFTY × Heypool and DFTY × PDM 145 consistently were included in the same clusters over seasons indicating their genetic similarities and stability in performance.

References

• Chaudhary, A. 1983. Maize in Pakistan, Punjab Agri. Research Coordination Board Univ of Agri Faisalabad.
• Datta, D and Mukherjee, B.K. 2004. Genetic divergence among maize (Zea mays L.) inbreds and restricting traits for group constellation. Indian J. Genet. 64(3): 201-207.
• FAO STAT. 2012. htt:/faostat.fao.org/site/291/default.aspx.
• Habiburahman Ayoubi, Shailesh Marker, Gideon J. Synrem and Naresh Kumar, L. 2016. Genetic diversity analysis in sub-tropical maize (Zea mays L.) germplasm. IOSR Journal of Agriculture and Veterinary Science. 9(8): 01-05.
• Jaishreepriyanka, R., Sweta Sinha, Shyam Sundar Manda, Birender Singh and Kumari Rashmi. 2020. Diversity analysis of maize inbred lines. International Journal of Current Microbiology and Applied Sciences.9(10): 2765-2773.
• Lubberstedt, T., Melchinger, A.E., Duble, C., Vuylsteke, M. and Kuiper, M. 2000. Relationships among early European maize inbreds: IV Genetic diversity revealed with AFLP markers and comparison with RFLP, RAPD, and pedigree data. Crop Sci. 40: 783-791.
• Mahalanobis, P.C. 1936. On the generalized distance in statistics. Proc Nat Ins Sci India.2(1): 49-55.
• Marker, S and Krupakar, A. 2009. Genetic Divergence in Exotic Maize Germplasm (Zea mays L.) ARPN J. Agrl. Biol. Sci. 4: 44-47.
• Murthy, B.R and Arunachalam, V. 1966. The nature of divergence in relation to breeding system in some crop plants. Indian J Genet. 26(1): 188-198.
• Nelson Mubai, Julia Sibiya, James Mwololo, Cousin Musvosvi, Harvey Charlie, Wills Munthali, Lizzie Kachulu and Patrick Okori. 2020. Phenotypic correlation, path coefficient and multivariate analysis for yield and yield-associated traits in groundnut accessions. Cogent Food & Agriculture.6: 1-22.
• Panse, V.G and Sukhatme. 1978. Statistical methods for Agricultural workers. Published by ICAR, New Delhi.
• Rao, C.R. 1952. Advanced statistical methods in Biometrical Research. Ed, John Wiley and Sons Inc, New York, pp. 357-363.
• Reddy, V.R., Farzana Jabeen, Sudarshan, M.R and Seshagiri Rao, A. 2013. Studies on genetic variability, heritability, correlation and path analysis in maize (Zea mays L.) over locations. Int J Appl Bio Pharma Tech. 1: 195-199.
• Singh, P.K and Choudhary, L.B. 2001. Genetic divergence in maize (Zea mays L.). J. of Res. Birsa Agricultural University. 13(2): 193-195.
• Singh, R.K and Choudhury, B.D. 1985. Biometrical method in quantitative genetic analysis. Kalyani Publishers, Ludhiana, New Delhi, pp. 54-57.
• Varaprasad, B.V and Shivani, D. 2017. Genotype clustering of maize (zea mays l.) germplasm using mahalanobis D² statistic. Journal of Global Biosciences. 6(2): 4776-478.
• Yee, E.K., Kidwell, K., Sills, G.R. and Lumpin, T.A. 1999. Diversity among selected Vigna angularis (Azuki) accessions based on RAPD and AFLP markers. Crop Sci. 39: 191-197.

Table 1. Average intra and inter cluster D² and D (in bracket) values of five clusters formed from 45 single cross hybrids of maize in rabi season

Clusters	I	II	III	IV	V
I	15.29 (3.91)	38.29 (6.19)	22.23 (4.71)	58.68 (7.66)	91.70 (9.58)
II	38.29 (6.19)	15.55 (3.94)	44.91 (6.70)	38.06 (6.17)	48.80 (6.99)
III	22.23 (4.71)	44.91 (6.70)	0.00 (0.00)	78.19 (8.84)	82.26 (9.07)
IV	58.68 (7.66)	38.06 (6.17)	78.19 (8.84)	0.00 (0.00)	131.88 (11.48)
V	91.70 (9.58)	48.80 (6.99)	82.26 (9.07)	131.88 (11.48)	0.00 (0.00)

Table 3. Average intra and inter cluster D² and D (in bracket) values of six clusters formed from 45 single cross hybrids of maize in summer season

Clusters	I	II	III	IV	V	VI
I	22.53 (4.75)	35.19 (5.93)	45.57 (6.75)	41.83 (6.47)	41.83 (6.47)	96.66 (9.83)
II	35.19 (5.93)	30.98 (5.57)	36.90 (6.07)	43.51 (6.60)	47.30 (6.88)	68.62 (8.28)
III	45.57 (6.75)	36.90 (6.07)	0.00 (0.00)	23.14 (4.81)	31.63 (5.62)	71.61 (8.46)
IV	41.83 (6.47)	43.51 (6.60)	23.14 (4.81)	0.00 (0.00)	18.99 (4.36)	43.39 (6.59)
V	41.83 (6.47)	47.30 (6.88)	31.63 (5.62)	18.99 (4.36)	24.59 (4.96)	62.42 (7.90)
VI	96.66 (9.83)	68.62 (8.28)	71.61 (8.46)	43.39 (6.59)	62.42 (7.90)	0.00 (0.00)

Table 5. Average intra and inter cluster D2 and D (in bracket) values of seven clusters formed from 45 single cross hybrids of maize in kharif season

Clusters	I	II	III	IV	V	VI	VII
I	16.93 (4.11)	41.38  (6.43)	44.85 (6.70)	29.37  (5.42)	30.29 (5.50)	52.76 (7.26)	49.38 (7.03)
II	41.38 (6.43)	18.37 (4.29)	78.11 (8.84)	72.50 (8.51)	32.75 (5.72)	52.23 (7.23)	100.53 (10.03)
III	44.85 (6.70)	78.11 (8.84)	23.79 (4.88)	80.67 (8.98)	72.92 (8.54)	132.16 (11.50)	116.13 (10.78)
IV	29.37 (5.42)	72.50 (8.51)	80.67 (8.98)	0.00 (0.00)	44.06 (6.64)	39.38 (6.28)	24.71 (4.97)
V	30.29 (5.50)	32.75 (5.72)	72.92 (8.54)	44.06 (6.64)	0.00 (0.00)	43.11 (6.57)	65.31 (8.08)
VI	52.76 (7.26)	52.23 (7.23)	132.16 (11.50)	39.38 (6.28)	43.11 (6.57)	0.00 (0.00)	48.22 (6.94)
VII	49.38 (7.03)	100.53 (10.03)	116.13 (10.78)	24.71 (4.97)	65.31 (8.08)	48.22 (6.94)	0.00 (0.00)

Table 2 . Cluster means for kernel yield and yield contributing characters in rabi season

Clusters	DT	DS	ASI	DM	PH	SPAD	SLA	SLW	CL	CG	NKRC	NKPR	KW	HI	KY
I	61.28	64.68	3.47	101.17	191.86	52.91	89.24	1.13	18.63	15.57	15.19	40.52	35.90	38.27	138.55
II	62.32	65.70	3.38	102.58	191.24	50.17	86.46	1.15	17.06	14.52	14.11	37.95	33.79	37.62	122.29
III	60.33	63.67	3.44	102.67	183.53	53.45	89.46	1.12	18.40	16.00	14.37	41.00	35.53	36.31	142.73
IV	61.33	62.33	1.00	98.00	195.93	49.95	85.88	1.17	17.18	14.50	14.27	38.67	33.20	37.43	137.53
V	65.33	69.67	4.78	106.67	176.30	51.27	84.55	1.18	17.30	13.97	13.70	37.67	31.93	37.20	103.93

Table 4. Cluster means for kernel yield and yield contributing characters in summer season

Clusters	DT	DS	ASI	DM	PH	SPAD	SLA	SLW	CL	CG	NKRC	NKPR	KW	HI	KY
I	60.73	63.32	2.59	98.57	170.23	51.30	81.98	1.22	17.49	13.55	14.40	35.76	30.73	37.65	108.70
II	61.52	64.12	2.61	99.82	180.26	49.68	80.23	1.25	16.41	12.76	13.71	33.00	28.49	37.22	98.75
III	64.33	67.00	2.67	102.00	153.67	49.60	81.34	1.23	16.10	12.97	13.23	32.67	27.63	37.22	90.07
IV	60.33	62.33	2.00	98.00	158.33	51.77	81.74	1.22	17.53	13.35	14.00	35.67	29.67	36.45	92.47
V	61.78	64.44	2.67	98.35	177.92	51.28	82.26	1.22	17.80	13.63	14.45	36.20	30.78	37.15	108.86
VI	58.33	60.67	2.33	98.00	202.15	50.47	81.17	1.23	16.60	12.34	13.00	30.67	24.73	37.26	93.63

Table 6. Cluster means for kernel yield and yield contributing characters in kharif season

Clusters	DT	DS	ASI	DM	PH	SPAD	SLA	SLW	CL	CG	NKRC	NKPR	KW	HI	KY
I	64.40	68.12	3.72	105.22	197.55	51.22	84.96	1.17	17.76	14.55	14.03	38.40	34.52	37.55	127.26
II	64.87	68.75	3.88	105.65	203.55	51.51	84.40	1.19	17.77	14.19	13.83	37.67	33.79	37.30	120.32
III	62.82	66.41	3.59	103.36	189.64	52.92	88.13	1.14	19.12	15.77	15.04	40.79	36.06	37.95	136.27
IV	64.33	68.33	4.00	105.33	210.00	51.07	81.79	1.22	17.17	14.38	13.92	37.67	33.83	37.01	96.27
V	64.67	68.33	3.67	105.00	174.53	50.80	86.78	1.20	17.97	13.83	13.45	36.00	35.60	38.01	105.87
VI	66.67	71.67	5.00	108.33	214.20	50.27	87.79	1.14	17.17	14.50	13.48	32.67	31.93	37.73	102.30
VII	66.33	70.67	4.33	104.00	192.07	51.33	85.74	1.17	17.43	13.80	13.65	38.33	31.57	37.95	113.43

DT=Days to tasseling; DS=Days to silking; ASI=Anthesis-silking interval; DM=Days to maturity; PH=Plant height; SPAD=SPAD meter readings; SLA=Specific leaf area; SLW=Specific leaf weight; CL=Cob length; CG=Cob girth; NKRC=Number of kernel rows cob-1; NKPR=Number of kernels row-1; KW=100 kernel weight; HI=Harvest index; KY=Kernel yield plant-1

Fig. 1. Clustering of 45 single cross hybrids of maize by Tocher method for rabi season

Fig.3. Clustering of 45 single cross hybrids of maize by Tocher method for summer season

Fig. 5 Clustering of 45 single cross hybrids of maize by Tocher method for kharif season

Fig 2. Distribution of 45 single cross hybrids of maize in to different clusters for rabi season

Fig 4. Distribution of 45 single cross hybrids of maize in to different clusters for summer season

Fig 6. Distribution of 45 single cross hybrids of maize in to different clusters for kharif season