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African Crop Science Journal
African Crop Science Society
ISSN: 1021-9730 EISSN: 2072-6589
Vol. 15, Num. 2, 2007, pp. 67-72

African Crop Science Journal, Vol. 15, No. 2, June, 2007, pp. 67-72

Genetic Divergence in Selected Durum Wheat Genotypes of Ethiopian Plasm

Arega Gashaw, Hussein Mohammed1 and Harjit Singh2

Sirinka Agricultural Research Center, Woldia, Ethiopia
1Debub University, College of Agriculture, Awassa, Ethiopia
2Alemaya University, College of Agriculture, Harar, Ethiopia

Received 29 January, 2007; accepted 29 May, 2007

Code Number: cs07008

ABSTRACT

Wheat of both the tetraploid (Triticum durum Desf.) and hexaploid (Triticum aestivum L.), is the most important cereal crop in Ethiopia, ranking third in total production (17%) next to maize (Zea mays L.) and tef (CSA, 2002). Wheat covers a total arable land of 110,434 ha with average productivity of about 8.4 qt ha-1, which is below the national average (14.4 qt ha-1). A field experiment was conducted at the Sirinka Agricultural Research Centre, northeastern Ethiopia, to estimate the genetic divergence among indigenous durum wheat (Triticum durum Desf.) genotypes of diverse origin, and clustering them into homogenous groups for the hybridisation programme. Genetic divergence analysis was done based on multivariate analysis using Mahalanobis’s D2 statistic, which grouped the durum wheat genotypes into ten clusters. The highest inter-cluster distance was between clusterll and cluster-lll (D2 = 57.15). There was no correspondence between geographic and genetic distances, i.e., germplasms, collected from the same geographic area were placed into different cluster groups and those collected from different geographic regions were placed into the same cluster. The presence of significant genetic variability among the evaluated durum wheat genotypes suggests an opportunity for improvement of grain yield through hybridisation of genotypes from different clusters and subsequent selection from the segregating generations.

Key Words: Clustering, Ethiopia, hybridisation, Triticum

RÉSUMÉ

Le Blé, à la fois du type tetra- (Triticum durum Desf.) et hexaploïde (Triticum aestivum L.) constitue la culture céréalière la plus importante en Ethiopie; classée 3e par sa production totale (17%) après le maïs (Zea Mays L.) et le tef (CSA, 2002). Le blé couvre une totalité de terres arable de 110.434 ha avec une productivité d’environ 8, 4 qt ha-1, qui se trouve en-dessous de la moyenne nationale (14, 4 qt ha-1). Une expérience de terrain a été menée au Centre de Recherche Agricole de Sirinka dans le nord-est éthiopien en vue de pouvoir estimer les divergences génétiques au sein de génotypes de diverses origines, blé durum indigène (Tricicum durum Desf.) et en vue de pouvoir les conglomérer ensemble en groups homogènes pour le programme d’hybridation. L’analyse de la divergence génétique était faite en se basant sur l’analyse multi variable, utilisant les statistiques D2 de Mahalanobis, qui a groupé les génotypes de blé durum en 10 groupes. La plus grande distance inter-groupe était notée entre le conglomérat II et III (D2=57,15). Il n’y avait pas de correspondance entre distance géographiques et distance génétiques c1-à-d, que les germoplasmes collectionnés à partir de mêmes zones géographiques étaient placés dans différents groupes et ceux collectés à partir de régions géographiques différents étaient placés dans le même conglomérat. La présence d’une variabilité génétique significative au sein des génotypes de blé durum évalués suggère une opportunité pour l’amélioration du rendement de grain, à travers l’hybridation de génotypes des différents conglomérats et sélection subséquente à partir de générations à ségrégation.

Mots Clés: Conglomération, Ethiopie, hybridation, Triticum

INTRODUCTION

Wheat of both the tetraploid (Triticum durum Desf.) and hexaploid (Triticum aestivum L.), is the most important cereal crop in Ethiopia, ranking third in total production (17%) next to maize (Zea mays L.) and tef (CSA, 2002). Wheat covers a total arable land of 110,434 ha with an average productivity of about 8.4 qt ha-1, which is below the national average (14.4 qt ha-1).

Most of the tetraploid wheat varieties, grown in Ethiopia are landraces consisting of a large number of different genetic lines. Purseglove (1975) reported the presence of genetic diversity of durum wheat in Ethiopia and Zohary (1970) identified Ethiopia as the centre of origin for tetraploid wheat. However, the absence of ancestral forms and wild relatives ruled-out Ethiopia as the centre of origin of cultivated wheat (Pecetti et al., 1992).

The major breeding objective in durum wheat is to create new improved genotypes with features that contribute to greater yield potential, increased yield stability and improved product quality (Poehlman and Sleper, 1995). To make the crossing programme effective, parents should belong to different genetic clusters. The more distant the parents within overall limits of fitness are the greater the chances of obtaining higher amount of heterotic expression in F1’s and broad spectrum of variability in segregating populations (Reddy, 1988). However, crossing of genotypes belonging to the same genetic cluster would not be expected to yield desirable recombinants.

The use of D2 statistic (Mahalanobis, 1936) is one of the most important biometrical techniques for estimating genetic divergence present in a population. Selection of parents based on the extent of genetic divergence has been successfully utilised in different crop species (Singh and Gupta, 1979; Jain et al., 1981; Ghaderi et al., 1984; Jatasra and Paroda, 1978; Shoran and Tandom, 1995).

This experiment was aimed at identifying genetically divergent durum wheat parents with desirable traits for hybridisation particularly for moisture stressed areas.

MATERIALS AND METHODS

The experiment was conducted at Geregara and Kone testing sites of Sirinka Agricultural Research Centre, northeastern Ethiopia. The trial was evaluated for two cropping seasons at Geregera located at an altitude of 2650 masl; and for one cropping season at Kone located at 2800 masl. Rainfall in both locations is erratic in distribution and less predictable with uni-modal pattern having a mean annual rainfall of 1105 and 1054 millimeter, respectively.

The experiment consisted of 64 durum wheat genotypes of which 20 are exotic (received from CIMMYT) and the remaining 44 were randomly taken from the indigenous germplasm collections. The indigenous durum wheat germplasm was collected from the central highlands and western part of the country, where durum wheat is widely cultivated.

The trial was laid-down using 8 x 8 triple lattice design. Each genotype was planted in four rows of 2.5 m by 0.2 m. Seed rate was adjusted based on the kernel size where seed rates of 125 and 150 kg ha-1 were used for small kernelled and large kernelled genotypes, respectively (Tanner et al., 1991). Urea and diammonium phosphate (DAP) fertilisers were applied at the rate of 50 and 100 kg ha-1, respectively. The whole of the DAP was applied at sowing, while urea was applied in splits with the first half applied at sowing and the second top-dressed at full tillering stage. The trial was hand-weeded at 20 and 45 days after emergence.

Data on plant height, number of spikeletes spike-1, number of kernels spike-1 and number of tillers plant-1 were recorded from five randomly taken plants from the central two rows, which were tagged ahead of heading. Data for the rest of the characters were recorded from the whole plots.

The data were subjected to Analysis of Variance (ANOVA) using MSTAT-C computer software (Michigan State University, 1988). Genetic divergence analysis was computed based on multivariate analysis using Mahalanobis’s D2 statistic (Mahalanbis, 1936) using Statistical Package for Agricultural Research (SPAR-1) software developed by IASRA, New Delhi.

Squared distances (D2) for each pair of genotype combinations were computed using the following formula:

D2 ij= (X i – X j) S-1 (X i – Xj),

where:

D2ij = the square distance between any two genotypes i and j,

Xi and Xj = the vectors for the values for ith and jth genotypes, and

S-1 = the inverse of pooled variance covariance matrix

Clustering of genotypes was done based on the squared distance (D2) values using Tocher’s method as described by Singh and Chaudhary (1985). Average intra- and inter-cluster D2 values were estimated using the formula:

is the sum of distances between all possible combinations (n) of durum wheat genotypes included in a cluster. Significance of the squared distances for each cluster was tested against the tabulated X2 values at P degree of freedom at 5% probability level, where P represents the number of characters used for clustering genotypes.

RESULTS AND DISCUSSION

There were highly significant (P<0.02) genotypic differences for all characters considered, revealing the existence of substantial amount of variation among the genotypes. Likewise, Wilk’s test of aggregate variation showed significant differences among genotypes, when all the characters were pooled except for the grain filling period. This justified the need to estimate squared distance values for the genotype combinations using these characters.

Based on D2 value estimates of genetic divergence, the 64 durum wheat genotypes were grouped into ten distinct clusters (Table 1). Cluster-1 and cluster-VII consisted of a maximum of nine genotypes each. This was followed by cluster –VI, cluster-VIII and cluster-X with eight genotypes each. Cluster-III and IX had the least number of genotypes (three each).

Within the indigenous germplasms, there was no correspondence between geographic and genetic distances. This suggested that germplasm collected from the same geographic area were not necessarily closely related and different regions did not necessarily have different genetic background. Such occurrences could be due to the same genetic background (base population) of the germplasm, being spread in the country. This suggests that germplasm from the same region might have the same genetic background and those collected from the same region might have different genetic background. Similar findings appeared in earlier reports (Garg and Gautam, 1988; Walia and Garg, 1996; Singh et al., 2003). Most reports attributed this to lack of parallelism between genetic and geographic diversity due to movement of germplasm.

The exotic and indigenous germplasm were grouped into different clusters. The only exceptions were cluster-VI and cluster-X, where the former consisted of one exotic and seven indigenous genotypes, while cluster-X consisted of one indigenous and seven exotic genotypes. This clearly showed that wider geographic distances for indigenous and exotic germplasm created wider genetic variability because of adaptation to different environmental conditions. The present finding is in agreement with Adary (1978) who reported relationships between genetic divergence and geographical distance among countries of origin and to environmental differences among sites of collection.

Overall, cluster-III (Table 2) possessed desirable combinations of characters; namely, high number of kernels plant-1 (47.83), high grain yield (4343 kg ha-1), high harvest index (43.5%), high biological yield (10 t ha-1) and high thousandkernel weight (39.2 g). This cluster could serve as valuable parent for future crossing programme. Similarly, cluster-IX comprised genotypes having important traits and could also be used as parents to develop superior cultivars for dry-land areas, where terminal moisture-stress is a major problem.

Plant height accounted the highest contribution to total genetic divergence (14.0%) followed by grain yield (10.3%) and number of kernels spike-1 (10.2%), while days to maturity had the least contribution (5.7%) (Table 1). Similar findings were reported by Das and Brothakur (1973) for days to heading, thousand seeds weight and plant height contribution to genetic divergence in rice.

Estimates of intra- and inter-cluster squared distance (D2). Intra-and inter-cluster D2 values among the ten clusters are presented in Table 3. The magnitude of intra-cluster distances indicates the extent of genetic diversity among genotypes of the same cluster. The intra-cluster distance varied from 1.66 to 5.06, with the maximum distance in cluster-VII and the minimum in cluster-IX suggests close relationships of individual genotypes.

The highest inter-cluster distance was exhibited between clusters II and III (D2 = 42.90), indicating wider genetic divergence among the clusters. The lowest D2 value was observed between clusters II and VI (D2 = 4.20), indicating that these clusters were genetically close. Thus, crossing of genotypes from these two clusters may not produce a high amount of heterotic expression in the F1’s and broad-spectrum of variability in segregating (F2) populations. Parents for hybridisation could be selected on the basis of large inter-cluster distance for isolating useful recombinants in the segregating generations. Increasing parental distance implies a greater number of constraining alleles at the desired loci, and then to the extent that these loci recombine in the F2 and F3 generations following a cross of distantly related parents, the greater will be the opportunities for successful selection for any character of yield interest (Ghaderi et al., 1984).

CONCLUSION

From this study, genotypes in clusters III and IX possess desirable combinations of traits and thus; the genotypes of these two clusters hold great promise as parents to obtain promising heterotic expression in F1’s and may create considerable variability in the segregating populations.

ACKNOWLEDGEMENTS

The first author is very grateful to Dr. Wondimu Bayu for his unreserved technical comments during the write up of this paper. The research was funded by Ethiopian Agricultural Research Organization Agricultural Research Training Project (ARTP) and Amhara Agricultural Research Institute (ARARI).

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©2007, African Crop Science Society


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