Genetic diversity of bread wheat genotypes and relationship between agronomic traits using multivariate analysis

Document Type : Research Paper

Authors

1 Department of Genetics and Plant Production, Faculty of Agriculture, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran.

2 Department of Plant Protection, Faculty of Agriculture, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran,

3 Department of Agronomy & Plant Breeding, Faculty of Agronomy Sciences, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran

10.22059/ijfcs.2024.370255.655054

Abstract

Germplasm improvement and genetic diversity is a fundamental solution for reliable and sustainable production of field crops. In this research, to study the genetic diversity and the relationship between agronomic traits, 800 winter bread wheat genotypes along with five varieties of Falat, Sardari, Roshan, Zare, and Pishgam in the farm of the Faculty of Agriculture of Vali –e- Asr University in Rafsanjan were evaluated in an augmented design. Cluster analysis was conducted by two methods k-Means and fuzzy clustering. The genotypes under study divided into 4 groups in both methods. The traits: Biological weight, seed yield, harvest index, height, spike length and seed weight per spike were respectively the most important in grouping the genotypes in both clustering methods. In the regression analysis, the traits of days to flowering and days to ripening, spike length, biological yield, number of seeds and weight of seeds per spike, 1000 kernel weight, and harvest index accounted for 85.89% of the total variation in grain yield. The traits of Biological yield and harvest index had the most direct and positive effect on grain yield. Based on the results, some genotypes were superior to control cultivars based on a number of traits, especially seed yield and its components. These genotypes can be considered as candidate genotypes in breeding programs in Rafsanjan climate conditions.

Key Words: Genetic diversity, yield, path analysis, k-means, fuzzy clustering

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Volume 55, Issue 3
October 2024
  • Receive Date: 01 January 2024
  • Revise Date: 24 March 2024
  • Accept Date: 01 April 2024
  • Publish Date: 22 September 2024