1ICAR-Indian Institute of Maize Research, Office New Delhi, 110 012
2CSK HPKV, HAREC, Bajaura, Kullu, Himachal Pradesh, 175 125
3G. B. Pant University of Agriculture and Technology, Pantnagar, Uttrakhand, 263 140
4ICAR-Vivekananda Parvatiya Krishi Anusandhan Sansthan, Almora, Uttarakhand, 263 601
5CCS Haryana Agricultural University, RRS, Uchani, Karnal, Haryana, 132 001
6MP University of Agriculture and Technology, Udaipur, Rajasthan, 313 001
7ND University of Agriculture & Technology, CRS, Bahraich, Uttar Pradesh, 271 801
8Zonal Agricultural Research Station, V.C. Farm, Mandya, Karnataka, 571 405
9Orissa University of Agriculture and Technology, Bhubaneswar, Odisha, 751 003
10ICAR-National Bureau of Plant Genetic Resources, New Delhi, 110 012
ICAR-Indian Institute of Maize Research, Ludhiana, 141 004
*Corresponding author's e-mail: bhupender.iimr@gmail.com
Online published on 11 February, 2020.
Baby corn has emerged as one of the most important sources to augment the farmer's income in peri-urban areas. It has diverse uses as vegetables, snacks, value-added products and assured supply of green fodder for livestock. The multi-location varietal trials mainly emphasize on the identification of new superior cultivars over commercial checks, while genotype environment interaction (GEI) is ignored. In the current study, 13 baby corn hybrids were evaluated for green ear yield, baby corn yield and green fodder yield over eight locations (environments) in kharif seasons of 2015 and 2016 using GGE biplot analysis. The results revealed a higher proportion of the variation in the data is attributable to the environment (72.4–87.0%), while genotype contributed only 2.5–7.3% of the total variation. GEI contributed 10.5–24.1% of the total variation. Superior stable hybrids for green ear yield, baby corn yield and green fodder yield could be identified using a biplot graphical approach effectively. ‘Which won where ’plot for each of the traits partitioned testing locations into three megaenvironments with different winning genotypes for different traits in respective mega-environments. Thus it can be concluded that similar inferences can be drawn from one or two representatives of each mega-environment instead of using several locations. Hence, the presence of extensive crossover GEI in baby corn multi-location trials clearly suggests the need to emphasize on smaller zonation of testing locations and location-specific breeding. Particularly in baby corn, this is the first study on GGE biplot analysis to identify mega-environments for effective evaluation of baby corn trials.
Baby corn, stability analysis, GGE biplot, mega-environment, genotype-environment interaction
