Abstract: (12 Views)
Introduction and Objective: In many countries of the world, due to biological and non-biological challenges and the growth of the world population, food supply and especially the food security of the countries is a priority. Plants including rice have turned it into a strategic plant due to its daily consumption and food needs of many countries. One of the most important ways to increase the potential of rice production is to use the crossing system. Crossbreeding in rice is a very widely used method to increase yield due to the phenomenon of heterosis and leads to the production of superior offspring from the parents or the commercial variety of the region. Multivariate statistics is a suitable method for analyzing different relationships between traits, identifying existing diversity and accurate evaluation in choosing the superior genotype to improve grain yield in rice. The purpose of this research is to use classical breeding and multivariate statistics among the F2 rice population to select lines with high yield potential for the next generation..
Materials and methods: In this research, the number of 2000 F2 seeds obtained from the crossing of Nemat as the maternal base with the local variety Seng Tarem as the paternal base in the rice research farm of the Research Institute of Agricultural Genetics and Biotechnology in Tabaristan, Sari University of Agricultural Sciences and Natural Resources, in the number of 50 Rows were planted with a length of 10 meters and a planting distance of 25x25 in an area of about 125 square meters. The top 18 plants were selected in terms of morphological traits and analyzed using multivariate statistics. Cluster analysis of traits with the Euclidean distance scale was used to group the plants and the detection function was used for the correctness of the grouping. Correlation and regression analysis were used to investigate the relationship between traits.
Results: The result of the cluster analysis divided the genotypes into three groups. The third group as the yield group had the highest weight of 100 seeds, the number of claws and the height of the plant. The correctness of the grouping of the plants belonging to the groups was confirmed by the detection function. The average traits of the number of claws, the number of kernels, the total number of seeds, the weight of 100 seeds and the yield of a single plant had a significant difference between the groups and were the main factors that differentiated the plants. The traits of plant height and the number of claws showed a positive and significant correlation with the yield at the 1% probability level. In the interpretation of yield changes, using regression analysis using the combined method of plant height and number of claws, they showed positive and very significant effects on yield variability.
Conclusion: The result of the analysis showed that multivariate statistics is a suitable tool in identifying plants with high yield potential, so that plants number 5 (87.83 g/plant), 10 (80.45), 13 (93.93) 78), 4(77/69), 15(72/24) and 12(70/75) with desirable morphological traits were selected as superior genotypes for producing promising lines.
Materials and methods: In this research, the number of 2000 F2 seeds obtained from the crossing of Nemat as the maternal base with the local variety Seng Tarem as the paternal base in the rice research farm of the Research Institute of Agricultural Genetics and Biotechnology in Tabaristan, Sari University of Agricultural Sciences and Natural Resources, in the number of 50 Rows were planted with a length of 10 meters and a planting distance of 25x25 in an area of about 125 square meters. The top 18 plants were selected in terms of morphological traits and analyzed using multivariate statistics. Cluster analysis of traits with the Euclidean distance scale was used to group the plants and the detection function was used for the correctness of the grouping. Correlation and regression analysis were used to investigate the relationship between traits.
Results: The result of the cluster analysis divided the genotypes into three groups. The third group as the yield group had the highest weight of 100 seeds, the number of claws and the height of the plant. The correctness of the grouping of the plants belonging to the groups was confirmed by the detection function. The average traits of the number of claws, the number of kernels, the total number of seeds, the weight of 100 seeds and the yield of a single plant had a significant difference between the groups and were the main factors that differentiated the plants. The traits of plant height and the number of claws showed a positive and significant correlation with the yield at the 1% probability level. In the interpretation of yield changes, using regression analysis using the combined method of plant height and number of claws, they showed positive and very significant effects on yield variability.
Conclusion: The result of the analysis showed that multivariate statistics is a suitable tool in identifying plants with high yield potential, so that plants number 5 (87.83 g/plant), 10 (80.45), 13 (93.93) 78), 4(77/69), 15(72/24) and 12(70/75) with desirable morphological traits were selected as superior genotypes for producing promising lines.
Keywords: rice, Segregation generation of f2 population, multivariate statistics, yield and selection
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