Deciphering Genotype-by- Environment Interaction for Targeting Test Environments and Rust Resistant Genotypes in Field Pea (Pisum sativum L.) - Das, Arpita; Parihar, Ashok K.; Saxena, Deepa; Singh, Deepak; Singha, K. D.; Kushwaha, K. P. S.; Chand, Ramesh; Bal, R. S.; Chandra, Subhash; Gupta, Sanjeev (Frontiers in Plant Science, 2019)
Rust caused by Uromyces viciae-fabae is a major biotic constraint to field pea (Pisum sativum L.) cultivation worldwide. Deployment of host-pathogen interaction and resistant phenotype is the modest strategy for controlling this intricate disease. However, resistance against this pathogen is partial and influenced by environmental factors. Therefore, the magnitude of environment and genotype-by-environment interaction was assessed to understand the dynamism of resistance and identification of durable resistant genotypes as well as ideal testing locations for rust screening through multi-location and multi-year evaluation. Initial screening was conducted with 250 diverse genotypes at rust hot spots. A panel of 23 promising field pea genotypes extracted from initial evaluation was further assessed under inoculated conditions for rust disease for consecutive two years at six locations in India. Integration of GGE biplot analysis and multiple comparisons tests detected higher proportion of variation in rust reaction due to environment (56.94 %) as an interactive factor followed by genotype x environment interaction (35.02 %), justified the requisite of multi-year and multi-location testing. Environment component for disease reaction and dominance of cross over interaction were asserted by the inconsistent and non-repeatable genotypic response. Present study effectively allocated the testing locations into various categories considering their ‘repeatability’ and “desirability index” over the years along with “discrimination power” and ‘representativeness’. “Mega environment” identification helped in restructuring the ecological zonation and location specific breeding. Detection of non-redundant testing locations would expedite optimal resource utilization in future. The computation of confidence limit at 95% level through bootstrapping strengthened the accuracy of the GGE biplot and legitimated the precision of genotypes recommendation. Genotype, IPF-2014-16, KPMR-936 and IPF-2014-13 identified as ‘ideal’ genotypes which can be recommended for release and exploited in resistance breeding programme for the region confronting field pea rust.