Indian Journal of Genetics and Plant Breeding (The)
SCOPUSWeb of Science
  • Year: 2023
  • Volume: 83
  • Issue: 4

Stability of maize hybrids under drought, rainfed and optimum field conditions revealed through GGE analysis

  • Author:
  • Ramesh Kumar*, Yashmeet Kaur, Abhijit K. Das, Shyam B. Singh, Bhupender Kumar1, Manish B. Patel2, Jai P. Shahi3, Pervez H. Zaidi4
  • Total Page Count: 9
  • Page Number: 499 to 507

1ICAR-Indian Institute of Maize Research, Unit office, New Delhi, India

2Maize Research Station, Godhra, 389001, Gujarat, India

3Institute of Agriculture Sciences, Banaras Hindu University, Varanasi, India

4International Maize and Wheat Improvement Center (CIMMYT), Hyderabad, India

ICAR-Indian Institute of Maize Research, Ludhiana, India

*Corresponding Author: Ramesh Kumar, ICAR-Indian Institute of Maize Research, Ludhiana, India, E-Mail: rk.phagna@gmail.com

Online Published on 01 February, 2024.

Abstract

Identification of high-yielding and stable cultivars across different environments through multi-location trials are very important in maize breeding. A study was conducted to evaluate 30 maize hybrids in three diverse environments, viz., drought, rainfed and optimal conditions during the years, 2016 and 2017. Environments, genotypes and Genotype × Environment interactions (G × E) were found to be highly significant in both the years. The biplot explained 69.49% of total variation which was partitioned into 53.61 and 15.88% relative to genotype and genotype by environment interaction. Genotype, ZH15449 performed considerably well in 2016 under optimum (113.41 q/ha) and drought (54.19 q/ha) while in 2017, under optimum (82.28 q/ha) and rainfed (65.37 q/ha) conditions. ZH 161285 gave considerable grain yield at all three ecologies (108.70, 74.29, 60.60 q/ha) in year 2016, whereas genotype, ZH 161330 performed well under rainfed (67.76 q/ha) and drought (52.87q/ha) conditions in year 2017.

Keywords

Maize, Genotype and environment interaction, Additive main effects and multiplicative interaction, GGE biplot, Principal component analysis