Evaluation of the performance of sorghum genotypes using GGE biplot

Abstract

Gasura, E., Setimela, P. S. and Souta, C. M. 2015. Evaluation of the performance of sorghum genotypes using GGE biplot. Can. J. Plant Sci. 95: 1205Á1214. In spite of sorghum's drought tolerance, it is largely affected by genotype)environment interaction (GE), making it difficult and expensive to select and recommend new sorghum genotypes for different environments. The objectives of this study were to examine the nature of GE for sorghum grain yield, to identify superior sorghum genotypes for sorghum production environments and determine ideal testing locations for future breeding activities in Zimbabwe. The grain yield of 20 sorghum genotypes from Seed Co. Pvt. Ltd. were evaluated for 2 yr (2011/2012 and 2012/ 2013 cropping seasons) at five locations in different agro-ecological zones of Zimbabwe. Combined analyses of variance showed significant differences for genotypes (PB0.01), environments (PB0.001) and genotype )location (PB0.01). Genotype )environment variance component was seven times greater than that of genotypes. Genotype)environment interaction was attributed to the variability in the predictable biotic and abiotic factors associated with the different locations. The genotype main effect plus GE biplot showed that the experimental sorghum genotypes W07, W09, W05, G06 and OP46 were high yielding and stable, and possessed other desirable agronomic traits. The most discriminating and representative location was Rattray Arnold Research Station.

GGE biplot analysis of genotypes by environment interaction on Sorghum bicolor L. (Moench) in Zimbabwe

“…This is similar to the study which was done by Gasura et al (2015), where they tested 20 sorghum varieties and there was a large effect of GEI about seven times larger than the effect of genotypes. The GGE biplot analysis showed that IPCA1 accounted for 50.72% and IPCA2 accounted for 25.82%, both accounting for a sum of 76.59% (Figure 1) and this showed similarity with study of Gasura et al (2015) where PC1 and 2 explained 36.8 and 29.5%, respectively.…”

“…This is similar to the study which was done by Gasura et al (2015), where they tested 20 sorghum varieties and there was a large effect of GEI about seven times larger than the effect of genotypes. The GGE biplot analysis showed that IPCA1 accounted for 50.72% and IPCA2 accounted for 25.82%, both accounting for a sum of 76.59% (Figure 1) and this showed similarity with study of Gasura et al (2015) where PC1 and 2 explained 36.8 and 29.5%, respectively. The biplot analysis identified the discriminating ability and representativeness as well as the correlation of environments (Sujay et al, 2014) and genotype average performance and the results showed the importance of testing and comparing genotypes so as to select the ones with specific and wide adaptation accordingly and environments which are representativeness to reduce experimenting costs by discarding unrepresentative locations and those with poor discriminating abilities.…”

“…Which-won-where (Yan et al, 2007) identified best winners for the megaenvironment or sector. This enables the researcher to have specific and valid justification to recommend genotypes which are good for that particular environment (Gasura et al, 2015). This also means the genotypes can be tested in those few mega-environments and still good yield data results can be obtained.…”

Genotype × Environment Interaction of Quality Protein Maize Hybrids under Contrasting Management Conditions in Eastern and Southern Africa

“…In this study, MPOP, BKLN, and CHDS were identified as highly discriminating environments for the QPM hybrids tested in their respective group of environments, so these test environments were considered as “ideal environments” for selecting superior hybrids and for the provision of information that is important for the identification of desirable varieties (Tukamuhabwa et al, 2012). Early‐generation testing in such environments will minimize germplasm development expenses by decreasing the number of varieties to be tested in METs, and inherently minimizing the number of test environments (Gasura et al, 2015).…”

Implications of correlations and genotype by environment interactions among cotton traits

“…A GGE biplot generated based on the SREG model has proved to be useful in grouping similar environments, identifying ideal testing sites, understanding the correlation of traits with either locations or genotypes and in identifying stable genotypes with high yield (Yan and Kang, 2002;Tinker, 2005, 2006). In Zimbabwe, applications of this technique have been reported in maize (Setimela et al, 2007;Setimela et al, 2010;Kamutando et al, 2013), and recently in sorghum (Gasura et al, 2015), but not yet extended to other crops including cotton. The objective of this study was to determine the importance and magnitude of GE and correlation among cotton traits, and their implications in future cotton breeding and variety recommendation.…”