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=12 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 mold in rainy season grown sorghum deteriorates the physical and chemical quality of the grain, which causes a reduction in grain size, blackening, and making them unfit for human consumption. Therefore, the breeding for grain mold resistance has become a necessity. Pedigree breeding has been widely used across the globe to tackle the problem of grain mold. In the present study, a population breeding approach was employed to develop genotypes resistant to grain mold. The complex genotype × environment interactions (GEIs) make the task of identifying stable grain mold-resistant lines with good grain yield (GY) challenging. In this study, the performance of the 33 population breeding derivatives selected from the four-location evaluation of 150 genotypes in 2017 was in turn evaluated over four locations during the rainy season of 2018. The Genotype plus genotype-by-environment interaction (GGE) biplot analysis was used to analyze a significant GEI observed for GY, grain mold resistance, and all other associated traits. For GY, the location explained a higher proportion of variation (51.7%) while genotype (G) × location (L) contributed to 21.9% and the genotype contributed to 11.2% of the total variation. For grain mold resistance, G × L contributed to a higher proportion of variation (30.7%). A graphical biplot approach helped in identifying promising genotypes for GY and grain mold resistance. Among the test locations, Dharwad was an ideal location for both GY and grain mold resistance. The test locations were partitioned into three clusters for GY and two clusters for grain mold resistance through a “which-won-where” study. Best genotypes in each of these clusters were selected. The breeding for a specific cluster is suggested. Genotype-by-trait biplots indicated that GY is influenced by flowering time, 100-grain weight (HGW), and plant height (PH), whereas grain mold resistance is influenced by glume coverage and PH. Because GY and grain mold score were independent of each other, there is a scope to improve both yield and resistance together.