Phenotypic characterization and genetic diversity of sugarcane varieties cultivated in Northern Karnataka of India based on principal component and cluster analyses

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Abstract

Sugarcane is a major commercial crop grown in India and across the world. Hence, several elite varieties have been developed now-a-days to overcome many obstacles including abiotic stresses and diseases. The present study was undertaken to screen genetic variation among twenty four sugarcane varieties that are commonly cultivated across Northern Karnataka, India with reference to physicochemical characters. Experiment was conducted in triplicate following randomized complete block design (RCBD) at S. Nijalingappa Sugar Institute, Belagavi, Karnataka, India during February 2016-17. Physiological parameters such as internode length, stalk height, plant height, stalk girth, number of internodes, single cane weight, single cane volume of juice, cane yield and recovery were investigated. Further, statistical techniques such as principal component analysis and agglomerative hierarchical clustering were performed to characterize the twenty four varieties. Among twenty four sugarcane varieties studied, Co 86032 and CoC 671 were found to be elite varieties with respect to sugar recovery and cane yield, whereas varieties such as Co 86032 and Com 0265 were found to be best with respect to cane yield only. Based on the results obtained, eight varieties, viz., Co SNK 09232, Com 0265, Co 86032, Co SNK 09293, Co SNK 07680, CoC 671, Co 13006 and Co 2001-15 were found to be good with respect to overall qualities. Further studies need to be involved with molecular marker that would help in identification of elite varieties which could substantially contribute to construction of genetic resources library that may in turn find maximum use in molecular breeding.

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Tawadare, R., Thangadurai, D., Khandagave, R. B., Mundaragi, A., & Sangeetha, J. (2019). Phenotypic characterization and genetic diversity of sugarcane varieties cultivated in Northern Karnataka of India based on principal component and cluster analyses. Brazilian Archives of Biology and Technology, 62. https://doi.org/10.1590/1678-4324-2019180376

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