Targeting test environments and rust-resistant genotypes in lentils (Lens culinaris) by using heritability-adjusted biplot analysis

Abstract

Lentil rust incited by the fungus Uromyces viciae-fabae is a major impedance to lentil (Lens culinaris Medik.) production globally. Host-plant resistance is the most reliable, efficient and viable strategy among the various approaches to control this disease. In this study, 26 lentil genotypes comprising advanced breeding lines and released varieties along with a susceptible check were evaluated consecutively for rust resistance under natural incidence for two years and at five test locations in India. A heritability-adjusted genotype main effect plus genotype × environment interaction (HA-GGE) biplot program was used to analyse disease-severity data. The results revealed that, among the interactive factors, the GE interaction had the greatest impact (27.81%), whereas environment and genotype showed lower effects of 17.2% and 20.98%, respectively. The high GE variation made possible the evaluation of the genotypes at different test locations. The HA-GGE biplot method identified two sites (Gurdaspur and Pantnagar) as the ideal test environments in this study, with high efficiency for selection of durable and rust-resistant genotypes, whereas two other sites (Kanpur and Faizabad) were the least desirable test environments. In addition, the HA-GGE biplot analysis identified three distinct mega-environments for rust severity in India. Furthermore, the analysis identified three genotypes, DPL 62, PL 165 and PL 157, as best performing and durable for rust resistance in this study. The HA-GGE biplot analysis recognised the best test environments, restructured the ecological zones for lentil-rust testing, and identified stable sources of resistance for lentil rust disease, under multi-location and multi-year trials.

Deciphering Genotype-by- Environment Interaction for Targeting Test Environments and Rust Resistant Genotypes in Field Pea (Pisum sativum L.)

“…Testing locations with discrete agro-ecologies generated a differential response of the field pea genotypes and changed genotype ranking. Previous reports affirmed the role of environment and GEI, mystifying selection of stable genotypes with durable resistance against various pathogens (Pande et al, 2013; Alam et al, 2014; Sharma et al, 2015, 2016, Funga et al, 2017; Parihar et al, 2017a, 2017b, 2018).…”

“…The presence of COI in different environments switches over the genotype ranking and reduces the correlation between phenotypic and genotypic values, thus advocating multi-environment screening of genotypes for drawing conclusions regarding genotypic superiority. Unfortunately, screening of foliar disease like rust is a kind of tedious and costly affair, particularly when natural screening is the only option where unpredictable weather parameters may change the disease spectrum (Sharma et al, 2016; Parihar et al, 2018). Multi-location testing creates a burden on resource poor states and, therefore, seeks attention for identification of “hot spot” or ideal testing locations as well as “mega environment” delineation considering multi-year data for disease resistance screening.…”

“…IPF-2014-16 (8) was identified as the most “stable” and “ideal” genotype with lowest projection onto the “AEC abscissa.” Additionally, in the present study, KPMR-936 (12) and IPF-2014-13 (7) were identified as “desirable” genotypes amid others and were positioned closer to the ideal genotype, IPF-2014-16 (8). Similarly to the “ideal” genotype, these two “desirable” genotypes also have the resistance response i.e., higher negative projection on the ATC abscissa with less projection on AEC ordinates i.e., high stability (Yan et al, 2007; Parihar et al, 2018). These strategies have been successfully deployed for identifying stable and resistant genotypes in different crops (Beyene et al, 2011; Sharma et al, 2015, 2017; Parihar et al, 2017a, 2017b; Sillero et al, 2017).…”

“…Recently, GGE biplot has been deployed to appraise genotypes with wide or specific adaptation related with resistance to different pathogens viz . in faba bean for Ascochyta blight and chocolate spot (Rubiales et al, 2012; Tekalign et al, 2017), in chickpea for fusarium wilt and ascochyta blight (Sharma et al, 2012; Pande et al, 2013), in pigeonpea against sterility mosaic disease (Sharma et al, 2015), in lentil for fusarium wilt and rust (Parihar et al, 2017a, 2018) and in mungbean against MYMV (Alam et al, 2014; Parihar et al, 2017b). Although, in the previous studies, during the assessment of test locations, “repeatability” and “desirability index” were not lucidly addressed for proper delineation of “mega environment.” Moreover, in the previous reports, genotypes and environments recommendation was based on only graphical biplot approaches without involving sound statistical assumptions, thus created perplexity toward the validity of the recommendations.…”

Delineating Genotype × Environment interactions towards durable resistance in mungbean against Cercospora leaf spot (Cercospora canescens) using GGE biplot

“…Thus, it is imperative to identify “ideal testing locations” which have “repeatability” for screening the genotypes with coherent response as well as determining the real picture concerning the respective disease in such hot spots. During testing of genotypes towards disease resistance, it is necessary to scale down the environmental variation for explicating actual genotypic response (Parihar et al, ). Moreover, due to complexity in field testing against CLS, grouping of environments with similar reaction (“mega environment”) towards host genotype interaction is necessary which can further curtail down cost of the screening and generate meaningful information without compromising the repeatability of the trials (Krishnamurthy et al, ).…”