AUTHOR=Aruna C. , Das I. K. , Reddy P. Sanjana , Ghorade R. B. , Gulhane A. R. , Kalpande V. V. , Kajjidoni S. T. , Hanamaratti N. G. , Chattannavar S. N. , Mehtre Shivaji , Gholve Vikram , Kamble K. R. , Deepika C. , Kannababu N. , Bahadure D. M. , Govindaraj Mahalingam , Tonapi V. A. 

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

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.687332

DOI=10.3389/fpls.2021.687332

ISSN=1664-462X

ABSTRACT=The infection caused by grain molds in rainy season sorghum, deteriorates the physical and chemical quality of the grain causing reduction in grain size, blackening and making them unfit for human consumption. Therefore, breeding for grain mold resistance has become a necessity. Pedigree breeding has been widely used across the globe to tackle the problem of grain molds. In the present study, a population breeding approach was employed to develop genotypes resistant to grain molds. The complex G×E interactions make the task of identifying stable grain mold resistant lines with good grain yield challenging. In this study, the performance of 33 population breeding derivatives selected from four-location evaluation of 150 genotypes during 2017, were in turn evaluated over four locations during the rainy season of 2018. The  GGE biplot analysis was used to analyzse significant G×E interaction (GEI) observed for  grain yield, grain mold resistance and all other associated traits. For grain yield, location explained a higher proportion of variation (51.7%), while genotype × location contributed to 21.9% and genotype for 11.2% of the total variation. For grain mold resistance, G×L contributed for higher proportion of variation (30.7%). Graphical biplot approach helped in identifying promising genotypes for grain yield and grain mold resistance. Among the test locations, Dharwad was the ideal location for both grain yield and grain mold resistance. The test locations were partitioned into three mega-environmentsclusters (MEs) for grain yield and two MEsclusters for grain mold resistance through a ‘which-won-where’ study. Best genotypes in each of these MEsclusters  were identified. Breeding for a specific MEs cluster is suggested. Genotype-by-trait biplots explained that grain yield is influenced by flowering time, hundred grain weight and plant height, while grain mold resistance is influenced withby glume coverage and plant height. Since grain yield and grain mold score were independent of each other, there is a scope to improve both yield and resistance together.