African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6688

Full Length Research Paper

Evaluation of drought tolerance from a wheat recombination inbred line population at the early seedling growth stage

Hong Zhang1,2 and Honggang Wang1*
1State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, Taian Subcenter of National Wheat Improvement Center, College of Agronomy, Shandong Agricultural University, Taian 271018, China. 2Department of Agronomy, Dezhou University, Dezhou 253023, China.
Email: [email protected]

  •  Accepted: 16 October 2012
  •  Published: 05 December 2012

Abstract

The drought tolerance of wheat seedlings from a recombination inbred line (RIL) population derived from crosses between Weimai 8 and Luohan2 and its parents was evaluated according to the drought tolerance indices of 11 early seedling traits and using principal component analysis and K-means clustering methods to select materials with good germplasm. Results indicated that drought could promote the increase of coleoptile length (CL) and inhibit seedling height (SH), longest root length (RL), seedling fresh weight (SFW), shoots fresh weight (STFW), root fresh weight (RFW), seedling dry weight (SDW), shoots dry weight (STDW), root dry weight (RDW), root-to-shoot fresh weight ratio (RSFWR), and root-to-shoot dry weight ratio (RSDWR). Two hundred forty-five lines from a RIL population and two parents were divided into three clusters. The parent Weimai 8 and 101 lines were attributed to drought-sensitive types; the parent Luohan 2 and 102 lines mediated drought-resistant types, and 42 lines were highly drought-resistant types. Whether or not a relatively strong root system could be formed was the most important condition in evaluating the drought tolerance of wheat at the seedling stage.

 

Key words: Recombination inbred line, evaluation of drought tolerance, seedling growth stage, principal component analysis and cluster analysis.