Journal of
Dryland Agriculture

OFFICIAL PUBLICATION OF THE CENTRE FOR DRYLAND AGRICULTURE, BAYERO UNIVERSITY, KANO
  • Abbreviation: J. Dryland Agric.
  • Language: English
  • ISSN: 2476-8650
  • DOI: 10.5897/JODA
  • Start Year: 2015
  • Published Articles: 22

Full Length Research Paper

Phenotyping and Prediction of Maize (Zea mays L.) Yield using Physiological Traits

Shaibu A. S.
  • Shaibu A. S.
  • Department of Agronomy, Bayero University, Kano, Kano, Nigeria
  • Google Scholar
Yusuf S. I.
  • Yusuf S. I.
  • Department of Agronomy, Bayero University, Kano, Kano, Nigeria
  • Google Scholar
Adnan A. A.
  • Adnan A. A.
  • Department of Agronomy, Bayero University, Kano, Kano, Nigeria
  • Google Scholar


  • Article Number - 94C94DE58298
  • Vol.3(1), pp. 28-35, December 2017
  •  Received: 01 November 2017
  •  Published: 01 December 2017

Abstract

The use of physiological traits as an indirect selection is important in augmenting yield – based selection procedures. Field experiment was conducted at the Research and Teaching Farm of Faculty of Agriculture, Bayero University, Kano to study physiological responses of different maturity groups of maize in Sudan savanna, determine the association between physiological traits and grain yield as well as predict grain yield of maize using physiological traits. The genetic materials used were twenty two maize genotypes laid out in a randomized incomplete block design (RCBD) with three replications. The results obtained revealed no significant difference between the genotypes. However the genotypes showed a good response to some physiological traits that can be used to improve maize response in developing tolerant genotypes. Differences were also observed in anthesis-silking interval, plant height, days to tasseling and days to silking respectively. Significant correlation was observed between days to tasseling with days to silking and plant height, days to silking with plant height, anthesis-silking intervals with harvest index. There was a lack of fit in prediction of grain yield using physiological traits because of low R² (0.19) and high RMSE (480.871kg yield/ha).

Keywords: Correlation, Maize, Physiological trait, PLSR, Prediction.