Identification of Effective Traits on Sugar Beet (Beta vulgaris L.) Root Yield under Natural Infection Conditions to Rhizomania Virus Disease

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

In order to identification of effective traits on root yield and also determination of cause-and-effect relationships between in 20 different sugar beet cultivars, an experiment was carried out in West Azarbaijan Agricultural and Natural Resources Research and Education Center, Miandoab Research Station based on RCB design with four replications in 2017 crop season. The assayed traits included root yield, sugar yield, white sugar yield, sugar content, white sugar content, extraction coefficient of sugar, root α-amino nitrogen, sodium and potassium, alkalinity coefficient and molasses sugar. The results of analysis of variance showed that difference between studied cultivars for amino nitrogen (α-N) all traits was significant at the 0.05 probability level and for other traits was significant at the 0.01 probability level. Phenotypic and genotypic coefficients of variation for most traits were high, indicating relatively high variability in studied cultivars for evaluated traits. Correlation coefficients between traits showed that there is the most negative and significant correlation between root yield trait with sugar content (-0.62), white sugar content (-0.53), alkalinity coefficient (-0.52), potassium (-0.49), sodium (-0.45) and also molasses sugar content (-049) traits, and the most positive and significant correlation with sugar yield (0.95), white sugar yield (0.90) and extraction coefficient of sugar (0.58) traits, respectively. In multiple regression analysis by stepwise method, sugar content and white sugar content, amino nitrogen and alkalinity coefficient traits were entered into model, respectively, that explained 82 percent of root yield variations. Based on correlation, stepwise regression and path analysis results, among the studied traits, amino nitrogen (0.35) and white sugar content
(-0.31) traits with most direct effect were effective on root yield and will be useful in identifying genotypes.

Language:
Persian
Published:
Journal of Crop Breeding, Volume:13 Issue: 37, 2021
Pages:
197 to 204
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