Assessing genotype-by-environment interactions and trait associations in forage sorghum using GGE biplot analysis

Abstract

Forage sorghum is an important component of the fodder supply chain in the arid and semi-arid regions of the world because of its high productivity, ability to utilize water efficiently and adaptability to a wide range of climatic conditions. Identification of high-yielding stable genotypes (G) across environments (E) is challenging because of the complex G × E interactions (GEI). In the present study, the performance of 16 forage sorghum genotypes over seven locations across the rainy seasons of 2010 and 2011 was investigated using GGE biplot analysis. Analysis of variance revealed the existence of significant GEI for fodder yield and all eight associated phenotypic traits. Location accounted for a higher proportion of the variation (0·72-0·91), while genotype contributed only 0·06-0·21 of total variation in different traits. Genotype-by-location interactions contributed 0·02-0·13 of total variation. Promising genotypes for fodder yield and each of the associated traits could be identified effectively using a graphical biplot approach. The majority of test locations were highly correlated. A 'Which-won-where' study partitioned the test locations into two mega-environments (MEs): ME1 was represented by five locations with COFS 29 as the best genotype, while ME2 had two locations with S 541 as the best genotype. The existence of two MEs suggested a need for location-specific breeding. Genotype-by-trait biplots indicated that improvement for forage yield could be achieved through indirect selection for plant height, leaf number and early vigour.

Harnessing Sorghum Landraces to Breed High-Yielding, Grain Mold-Tolerant Cultivars With High Protein for Drought-Prone Environments

“…The environments E5 and E12 and the environments E3 and E7 fall under the latter. The present study indicated a mixture of crossover and non-crossover types of G × E interaction, which has been reported in various studies (Rakshit et al, 2012 ; Naroui et al, 2013 ; Aruna et al, 2015 ). Furthermore, eliminating similar environments from multilocation trials of sorghum will help in the optimal utilization of resources.…”

“…These environments are in complete contrast to the best yielding environments E3, E8, E12, and E13 for high fodder yield. Several authors identified high-yielding genotypes for grain sorghum (Rakshit et al, 2012;Al-Naggar et al, 2018), forage sorghum (Aruna et al, 2015), and sweet sorghum (Rono et al, 2016).…”

Development of Sorghum Genotypes for Improved Yield and Resistance to Grain Mold Using Population Breeding Approach

“…In the present study, 33 derivatives from the population breeding program are evaluated in the four testing locations. The differential ranking of genotypes across locations (hereafter referred to as environments) is caused due to a crossover GEI (Mohammadi and Amri, 2013 ), and a significant GEI for yield and grain mold in sorghum has been reported earlier (Rakshit et al, 2012 ; Aruna et al, 2016 ; Diatta et al, 2019 ).…”

“…In addition, the potential known genotypes that are good performers for specific traits give insights into a parental line with trait combinations for enhancing yield. The GGE biplot technique was used to understand the stability of genotypes and also to interpret the complexity of GEI in grain sorghum (Rakshit et al, 2012 ), forage sorghum (Aruna et al, 2016 ), and sweet sorghum (Rao et al, 2011 ).…”

Trait association and stability of virus resistance among cowpea genotypes in Uganda

“…This indicates that any of these traits can be used to indirectly select for maturity and virus resistance. The use of GT biplots to describe interrelationships among traits has been reported previously for other crops such as soybean (Yan and Rajcan, 2002), barley (Yan and Tinker, 2005), wheat (Yan and Tinker, 2006) and forage sorghum (Aruna et al, 2016).…”