Indian Journal of Agricultural Research

  • Chief EditorT. Mohapatra

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Genetic Divergence Analysis in Rice (Oryza sativa L.) Germplasms under Sodic Soil

Shiv Prakash Shrivastav1,*, O.P. Verma1, Kanhaiya Lal2, Vishal Singh1, Kuldeep Srivastava3
1Department of Genetics and Plant Breeding, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya-224 229,  Uttar Pradesh, India.
2Department of Genetics and Plant Breeding, CSA University of Agriculture and Technology, Kanpur-208 002,  Uttar Pradesh, India.
3ICAR-Indian Institute of Vegetable Research, Varanasi-221 305, Uttar Pradesh, India.
Background: Salt affected areas have increased day by day because of excessive use of irrigation water with improper drainage coupled with the poor quality of irrigation water. The assessment of genetic divergence existing in the germplasm collections is very important for success of hybridization programme leading to development of high yielding varieties.

Methods: The experiment on 113 genotypes (aromatic and non-aromatic rice) including three checks viz., Sarjoo 52, FL 478 and CSR 10 (salt tolerant ) of rice (Oryza sativa L.) was conducted to work out the genetic divergence.

Result: The 113 genotypes were grouped in to eleven different non-overlapping clusters. Cluster II, having 20 genotypes, emerged with highest number of entries. The highest intra-cluster distance was found for cluster X. The maximum inter-cluster distance was recorded between cluster VII and XI. The highest cluster mean for grain yield per plant was observed in cluster III. Presence of substantial genetic diversity among the germplasm lines screened in the present study indicating that this material may serve as good source for selecting the diverse parents for further hybridization program aimed at isolating desirable segregants for grain yield and other important characters.
Rice (Oryza sativa L.) is the most important staple food crop (Wang et al. 2018). The demand for rice is expected to increase with continuous increase in global population. India has the largest area 43.78 million hectare constituting 28.26% of the land under rice in the world and rank second in total production 118.87 million tonnes next to China with an average productivity of 2705 Kg/hectare (DAC and FW, 2019-20).

The soil sodicity is a major factor that adversely affecting the growth and yield of crop plant (Wang, et al., 2012).  Approximately one-third of the land area on which rice is grown is affected by salinity (He et al., 2019). Current scenario, approximately 10% of the world’s total land area (950 million ha), 20% of the world’s arable land (300 million ha) and 50% of the total irrigated land (230 million ha) are affected by soil salinization (Kashyap et al., 2020). Further, it is expected to influence 50% of total cultivated land in 2050 at a dis-quieting rate (Munns and Tester 2008 and Ruan et al., 2010). Every year almost 12 billion US$ are globally lost due to salt stress that significantly affects the agricultural production (Qadir et al., 2008 and Flowers et al., 2010).

Most varieties are having only few major desirable significance, that are the subjects of intensive breeding efforts. The assessment of genetic divergence existing in the germplasm collections is very important for success of hybridization and  recombination breeding in an autogamous crop such as rice leading to development of high yielding, multiple resistance/tolerance coupled with high quality and wider adoptive superior hybrids varieties for commercial exploitation as well as recover transgressive segregants in segregating generations (Arunachalam, 1981). Thus, adoptions of high yielding rice varieties to various stress/underutilized land such as sodic soil would be an important strategy to meet this challenge.
The study was designed to work out the genetic divergence and cluster analyses of their various attributes on grain yield per plant among 113 genotypes (aromatic and non-aromatic rice) including three check varieties viz., Sarjoo 52, FL 478 and CSR 10. The experiment was conducted during Kharif, 2018 at the Main Experimental Station of A.N.D. University of Agriculture and Technology, Narendra Nagar (Kumarganj), Ayodhya under natural sodic soil with the pH, EC and ESP  were 9.5, 3.2 dSm-1 and 45% respectively. Geographically, experimental site is located between 24° 47' and 26° 56' N latitude, 82° 12¢ and 83° 98' E longitude and at an altitude of 113 m above mean sea level. This area falls in sub-tropical climatic zone. The experiment was laid out in 11 blocks of augmented design suggested by Federer, W.T. (1961). An examination of the clustering pattern of the mean was adjusted after augmented RBD of 113 genotypes. The observations were recorded on sixteen different grain yield and its contributing traits. Chlorophyll content, leaf nitrogen and leaf temperature data were recorded by Soil Plant Analysis Development (SPAD value). The certain selected statistical approaches were used for data analysis i.e. non-hierarchical euclidean cluster analysis by following Beale, 1969 and Spark, 1973; Mahalanobis (1936) D2 statistic. Tocher´s method as described by Rao (1952) was followed for cluster formation with the help of INDOSTAT and MS excel.
Genetic divergence analysis
 
The study of genetic divergence among 113 rice genotypes including checks were grouped in to eleven different non-overlapping clusters as presented in Table 1 according to non-hierarchical euclidean cluster analysis by following Beale 1969Spark 1973 and  by tocher method. Cluster II, having 20 genotypes, emerged with highest number of entries followed by cluster III with 15 genotypes, cluster VI and cluster XI with 12 genotypes, cluster I and cluster IX with 10 genotype, cluster V and VIII with 8 genotypes, cluster IV and VII with 7 genotypes and cluster X with only 4 genotypes. Cheema et al., (2004) advocated that the number of clusters formed, number of genotypes in the clusters and superposition of the genotypes within the clusters indicated the possibility of genetic improvement for yield and yield components.

Table 1: Clustering pattern of 113 rice genotypes on the basis of Non-hierarchical Euclidean Cluster analysis for 16 characters.



The estimates of intra and inter-cluster distances for eleven clusters are presented in Table 2 according to non-hierarchical euclidean cluster analysis by following Beale 1969, Spark 1973 and clustering and their interrelationships by Mahalnobis euclidean distance. The highest intra-cluster distance was found for cluster X (23.302) followed by cluster II (22.604), cluster I (22.451), cluster VII (19.536), cluster III (18.657), cluster VI (16.431), cluster VIII (16.016), cluster IX (15.933), cluster V (11.787), cluster XI (9.124) and cluster IV (7.859). The maximum inter-cluster distance was recorded between cluster VII and XI (82.207) followed by cluster I and VII (71.065), cluster VII and X (63.163), cluster VI and XI (61.996), cluster VII and VIII (58.872), cluster VII and IX (58.307), cluster III and VII (54.139), cluster IV and VII (52.302), cluster V and XI (50.554), cluster II and XI (46.673), cluster II and VII (46.384), cluster V and X (46.151), cluster I and XI (44.385), cluster VI and X (44.182), cluster IV and X (43.425),  cluster I and VIII (42.845), cluster I and X (41.655), cluster II and X (40.129) and cluster VI and VII (40.023). The discrimination of germplasm lines into so many discrete clusters indicating presence of high degree of genetic diversity in the evaluated materials. Earlier workers have also reported substantial genetic divergence in the rice materials (Nayak et al., 2004; Suman et al., 2005; Gahalain, 2006; Chandra et al., 2007; Singh et al., 2008; Dushyantha and Kantti 2010; Seetharaman et al., 2013; Supriya et al., 2017; Sarif et al., 2020).

Table 2: Estimates of average intra- and inter-cluster distances for 11 clusters in rice.



The highest intra-cluster distance was found for cluster X (23.302) has  symmetrical  genetic  dissimilarity  matrix  depicting  pairwise  comparisons  between  the  tested  genotypes  indicates  closer  genetic  similarity  within  USAR 1, NDRK 5049 NDRK 50056 and Pokkali, suggesting their common origin and geographical occurrence  (Table 3). Moreover,  the maximum inter-cluster distance was recorded between cluster VII  (NDRK 5007, NDRK 5014, NDRK 50033, NDRK 5011, IR 85920-11-2-1AJAY1-2-B, Kalanamak 3, Sundari)  and XI (NDRK 5062, NDRK 5099, NDR 2064, NDRK 5038, NDRK 5047, NDRK 5042, CSR 28, Pusa Basmati 1, Moti Gold, IR 11 T 183, CSR 43, Sarjoo 52). These genetically highly dissimilar genotypes belonged to different climatic  situations and may act as prospective parents for transgressive breeding and exploitation of heterosis in hybrid breeding programs.

Table 3: Clusters means for 16 characters in rice.



The intra-cluster group means for sixteen characters (Table 3) revealed marked differences between the clusters in respects of cluster means for different characters. The highest cluster mean for grain yield per plant was observed in cluster III (21.634 g) followed by cluster II (19.247 g), cluster VII (19.062 g) and cluster V (19.033 g), while XI possessed low cluster mean for grain yield per plant i.e., 10.484 g.

Presence of substantial genetic diversity among the germplasm lines screened in the present study indicated that this material may serve as good source for selecting the cluster VII  (NDRK 5007, NDRK 5014, NDRK 50033, NDRK 5011, IR 85920-11-2-1AJAY1-2-B, Kalanamak 3, Sundari)  and XI (NDRK 5062, NDRK 5099, NDR 2064, NDRK 5038, NDRK 5047, NDRK 5042, CSR 28, Pusa Basmati 1, Moti Gold, IR 11 T 183, CSR 43, Sarjoo 52) diverse parents for hybridization programme aimed at isolating desirable segregants for grain yield and other important characters.

The choice of suitable diverse parents based on genetic divergence analysis would be more fruitful than the choice made on the basis of geographical distances. This finding is in conformity with the previous reports advocating lack of parallelism between genetic and geographic diversity in rice (Nayak et al., 2004; Suman et al., 2005; Gahalain, 2006; Chandra et al., 2007; Singh et al., 2008; Dushyantha and Kantti 2010; Seetharaman et al., 2013; Supriya et al., 2017; Sarif et al., 2020).
 
Contribution of sixteen traits of rice toward divergence
 
The contribution of sixteen characters towards divergence in Table 4 showed the highest contribution by spikelets per panicle (55.31%) followed by grains per panicle (23.10%), while the grain yield per plant (0.00%) showed lowest contribution towards divergence.  

Table 4: Contribution of 16 traits of rice towards divergence.

The eleven clusters formed in divergence analysis contained genotypes of heterogeneous origin, thereby indicating non-parallelism between genetic and geographic diversity. Therefore, crosses between the members of diverse clusters separated by high inter-cluster distances are likely to throw desirable segregants. The highest intra-cluster distance was found for cluster X which has symmetrical genetic  dissimilarity matrix depicting pairwise  comparisons  between  the  tested  genotypes  indicating  closer  genetic  similarity  within cluster X genotypes i.e., USAR 1, NDRK 5049, NDRK 50056  and  Pokkali, suggesting their common origin and geographical occurrence. Moreover, the maximum inter-cluster distance was recorded between cluster VII  (NDRK 5007, NDRK 5014, NDRK 50033, NDRK 5011, IR 85920-11-2-1AJAY1-2-B, Kalanamak 3, Sundari)  and XI (NDRK 5062, NDRK 5099, NDR 2064, NDRK 5038, NDRK 5047, NDRK 5042, CSR 28, Pusa Basmati 1, Moti Gold, IR 11 T 183, CSR 43, Sarjoo 52). It is suggested that cluster VII and XI have  highly genetically dissimilar genotypes belonged to different climatic conditions and therefore, these tested materials expected to act as  prospective parents for transgressive breeding and  exploitation of  heterosis in hybrid breeding programs.
None

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