Katarni Rice is the most prevalent, ceremonial and finest quality scented rice landrace of Bihar, India. Like Basmati, this aromatic rice is most preferred due to its flavour, palatability and popcorn like essence before and after cooking. However, it is low yielder (25-30 Q/ha) due to its tall and week stature and lodging tendency at the time of maturity. To overcome existing problems of Katarni rice, introgression of semi-dwarfing (sd1) gene from rice variety Rajendra Sweta was attempted with the help of marker assisted back crossing and present study is emphasizing on selection of dwarf and aromatic progenies in BC1F2 and F3 generation of Katarni x R. Sweta.51 plants in BC1F2 and 31 in F3 population were selected on the basis of 1.7% KOH sensory test for aroma. The segregation ratio of non-aromatic vs. aromatic plants in 325 BC1F2 plants was 3:1 confirming the monogenic inheritance of aroma. The selection of aromatic and semi-dwarf plants in both the population was further done through the trait specific markers for these traits in rice. The semi-dwarf plant height and early maturity of selected 49 aromatic plants in BC1F2 and 25 F3 plants of Katarni x R. Sweta advocated the utilization of markers in selection of desirable segregants.
Eight Vigna and one Phaseolus species were used in the present study to estimate the contribution of component traits to the total variation. The genotypes included nine each from black gram and mungbean, three wild relatives of black gram and one mungbean, three genotypes of rice bean, five genotypes of cowpea and one genotype of french bean. The contribution of different morphological traits has been evaluated by using D2 and principal component analysis, which has led to the recognition of significant phenotypic variability. The relative contribution of root dry weight (9.952), shoot to root dry weight ratio (6.817), P content in seed (6.320), 100 seed weight (5.695), total P uptake at maturity (5.382) and seed yield/plant (5.248) was maximum towards the genetic divergence by D2 method. The seven principal components PC1, PC2, PC3, PC4, PC5, PC6 and PC7 with eigen roots of 8.721, 5.048, 3.268, 1.941, 1.155, 1.005 and 0.812, respectively have accounted for 91.46% of total variation of which first three principal components accounted for 70.98 per cent variation. PCA analysis revealed the maximum contribution of root dry weight (0.269) followed by total biological yield/plant (0.253) in PC1, harvest index (0.369) followed by 100 seed weight (0.294) in PC2 and seed yield/plant (0.384) followed by plant height (0.268) in PC3. The eigen root of first principal component accounted for 36.338 per cent of total variation followed by second to seventh principal components, which accounted for 21.035, 13.615, 8.089, 4.813, 4.189 and 3.382 per cent of total variations present in the genotypes, respectively. These results confirmed the presence of considerable genetic diversity for use in Vigna and Phaseolus genotypes improvement program. The study revealed that principal component analysis was more effective in partitioning variation than D2 analysis.
Availability of superior restorer lines is prerequisite for hybrid rice development. The present study was conducted on half-sib recombinants in F5 generation derived from the scented rice CMS line Pusa 6A, in order to isolate promising restorer lines. Fifty-one such recombinants were evaluated along with 5 check varieties in an Alpha-lattice design during 2019 and observations were recorded for 13 agro-economically important traits. Results of the phenotypic assessment of the genotypes revealed that genotypic coefficient of variance and phenotypic coefficient of variance were moderate to high for different traits used in the study, whereas environmental coefficient of variance was found to be low. The study revealed high heritability coupled with high genetic advance as per cent of mean for important phenotypic traits. It could be effectively used for selection of promising genotypes for better genetic gain in the next generations.
The present study was undertaken during the Kharif season of 2017, using 73 diverse indigenous genotypes of forage sorghum. The observations were recorded for 10 morphological and eight quality parameters to assess the genetic diversity. Analysis of variance revealed sufficient variability for all the traits under study. The Mahalanobis D2 analysis was carried out for estimation of divergence between genotypes and Tocher method was used for grouping of genotypes into different clusters. Genotypes were grouped into seven clusters. Cluster I had the maximum number of genotypes i.e., 45 followed by cluster II (15) and Cluster IV (9). Cluster III, V, VI, VII had only single genotype each. Inter cluster distance was observed maximum between cluster IV and cluster VII.b Cluster means for the traits under investigation showed that the genotypes in first cluster are high yielding, where genotypes IC 436522 and IC 436598 present in cluster VI and VII respectively are good for quality traits and can be further used for enhancement of yield and quality of forage sorghum.
Estimation of genetic diversity present in gene pools is an important segment for breeding programs in crops. The present study was carried out to analyse genetic diversity based on morpho-physiological and seed vigour traits using 60 genotypes of bread wheat during Rabi 2016-17. The data were recorded for yield, days to 50% heading, days to anthesis, grain growth rate (at 14, 21, 28 days), plant height, number of effective tillers, flag leaf length, width and area, spike length, spikelets per spike, number of grains per spike, 1000 grain weight, harvest index, germination, seed density, seedling length and dry weight, vigour index-I, vigour index-II along with dehydrogenase activity in seeds. Based on the cluster analysis using Ward's Algorithm and Squared Euclidean Distances, genotypes were assigned into 8 clusters. The intra-cluster distance ranged from 4.942 (II) to 7.191 (VIII), and inter-cluster distance ranged from 6.035 (between II and VI) to 9.507 (between III and VIII). These values were higher than any corresponding intra-cluster values. The cluster V was the largest cluster consisting of 12 genotypes followed by cluster VI (10 genotypes), II (8 genotypes), cluster IV (8 genotypes), cluster VII (7 genotypes), cluster I (6 genotypes), cluster III (5 genotypes) and cluster VIII (4 genotypes). Cluster III showed maximum genetic divergence with cluster VIII. The cluster possessing the maximum genetic distance can be used in the hybridization program as it is expected that more heterotic F1 and more promising segregants will be produced in the segregating population. Therefore, more emphasis should be given on cluster III (P-13714, P-13717, P-13710, P-13718, and P-13743) followed by IV for selecting parents from available germplasm clusters to produce new recombinants with desired traits.
