1Dr. Y.S.R. Horticultural University, Horticultural Research Station, AICRPS, Guntur, Andhra Pradesh (522 034), India
2AICRP on Spices, Indian Institute of Spices Research, Kozhikode, Kerala (673 012), India
3Dept. of Plant Breeding and Genetics, Sri Karan Narendra Agricultural University, AICRPS, Jobner, Rajasthan (303 329), India
4National Research Center on Seed Spcies, Ajmer, Rajasthan (305 206), India
5Rajendra Agricultural University, Dept. of Horticulture, AICRPS, Tirhut College of Agriculture, Dholi, Bihar (843 105), India
6Dept. of Vegetable Science, Chaudhary Charan Singh Haryana Agricultural University, AICRPS, Hisar, Haryana (125 004), India
7Dept. of Vegetable Science, Narendra Deva University of Agriculture & Technology, AICRPS, Kumarganj, Uttar Pradesh (224 229), India
8Govind Ballabh Pant University of Agriculture and Technology, AICRPS, Pantnagar, Uttarakhand (263 145), India
9Dept. of Horticulture, Indira Gandhi Krishi Vishwavidyalaya, AICRPS, Raigarh, Chattisgarh (496 001), India
10Sardarkrushinagar Dantiwada Agricultural University, AICRPS, Jagudan, Gujarat (382 710), India
11Agricultural University, Agricultural Research Station, AICRPS, Kota, Rajasthan (324 001), India
12Dept. of Plant Breeding and Genetics, Navsari Agricultural University, AICRPS, Navsari, Gujarat (396 450), India
*Correspondence to E-mail: gkalidasu@yahoo.com
Online published on 26 November, 2018.
Coriander crop improvement is vested with several issues like lack of unified protocol, delineation of suitable test environments and difficulties in the selection of a genotype over a wide range of environments. A study was taken up with three fold objectives viz. genotype evaluation in a mega environment consisting of diverse sub-environments, test environment discrimination and selection, suitability and superiority of superior genotype in a specific set of environments. Thirteen genotypes were evaluated in eleven diverse agro-climatic zones for three years (2009−2012). The graphical tool GGE (genotype main effect [G] and genotype and environment interaction [GE]) biplot was used to analyze the multi-environment data obtained. The biplot explained the 75% (53 and 22% by PC1 and PC2, respectively) of the total G+GE and adequately represented the environment-centered data. The evaluation of test environments revealed the nature of mega-environment and ideal test environments among the environments evaluated. Based on this, the test sites could be grouped into two diverse sub-mega-environments. The best performing and candidate genotypes were identified for each sub-mega-environment. Among the eleven test environments, three sites were the most discriminative of the genotypes, hence to evaluate a large number of genotypes in India these three ideal test environments can be deployed thus saving time, resources and energy. The results revealed that GGEbiplot is very useful in discriminating coriander genotypes and test environments in traditional coriander growing areas of India.
GGEbiplot, multi environment data, stability, coriander
