Agronomy (Jul 2023)

Exploring the Agronomic Performance and Molecular Characterization of Diverse Spring Durum Wheat Germplasm in Kazakhstan

  • Daniyar Tajibayev,
  • Kadyrzhan Mukin,
  • Adylkhan Babkenov,
  • Vladimir Chudinov,
  • Abdelfattah A. Dababat,
  • Karlyga Jiyenbayeva,
  • Serik Kenenbayev,
  • Timur Savin,
  • Vladimir Shamanin,
  • Kuttymurat Tagayev,
  • Askhat Rsymbetov,
  • Minura Yessimbekova,
  • Vadim Yusov,
  • Ruslan Zhylkybaev,
  • Alexey Morgounov,
  • Muhammad Tanveer Altaf,
  • Muhammad Azhar Nadeem,
  • Faheem Shehzad Baloch

DOI
https://doi.org/10.3390/agronomy13071955
Journal volume & issue
Vol. 13, no. 7
p. 1955

Abstract

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Spring durum wheat occupies over 0.5 M ha in Kazakhstan and represents an important domestic and export commodity. This study aimed to characterize 151 durum wheat cultivars and advanced lines originating from eight breeding programs of the Kazakhstan–Siberia Spring Wheat Improvement Network (KASIB) between 2003 and 2018. The phenotypic characterization was performed in two contracting evaluation sites more than 1000 km apart (Almaty in the Southeast and Shortandy in the North) for two years and a total of 11 agronomic traits were recorded. Field trials at both locations followed regional agronomy practices, including sowing, harvesting, and genotype evaluation using a randomized complete block design (RCBD). The growing season was longer in Almaty, resulting in a higher number of grains per spike. Though grains are smaller in size with an overall higher yield, 243 g/m2 versus 170 g/m2, there was no correlation between germplasm performance at the two sites. Molecular characterization was performed with 10 iPBS-retrotransposons primers that resulted in a total of 345 bands and showed a mean polymorphism of 91.9%. Mean values of gene diversity (0.251), Shannon’s information index (0.388), and expected heterozygosity (0.233) revealed a relatively high level of genetic diversity in the KASIB set. AMOVA revealed higher genetic variations due to differences within the populations. Marker-based cluster analysis, including STRUCTURE and neighbor-joining algorithms, divided the material into two populations with clear differences in geographic origin. Superiors and diverse germplasm identified in the study are recommended for marker assisted selection and breeding.

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