Genetic Divergence Study in Sorghum (Sorghum bicolor L.) using D2 Analysis

Genetic Spectrum of Variation and Diversity for Yield and Yield Attributing Traits in Mutagenized Population of Sorghum (Sorghum bicolor L. Moench)

“…The pattern of distribution of genotypes into various clusters was at random suggesting that the genetic diversity was not related to geographic diversity. The results are in consistent with the Shinde et al [15] and Kavya et al [16].…”

Ethanol from maize hybrids in Brazil

“…Only in Assis Chateaubriand there was a difference between the hybrids, in which hybrid 30F53 YH showed lower ºBrix after fermentation (2.03); the hybrid with the lowest sugar consumption was Defender, with 3.28 ºBrix (Figure 4A). The higher the ºBrix, the lower the consumption of sugars from the medium; consequently, the lower the ethanol production (Santos et al 2018, Kavya et al 2020. However, the values only consider the final value obtained after fermentation, and not the consumption between the initial and final ºBrix in each hybrid, because when this difference is taken into account, other hybrids show higher and lower consumption of sugars.…”

Estimates of Variability, Correlation and DirectIndirect effects among Yield and its Allied Attributes in Sorghum (Sorghum bicolor (L.) Moench)

“…Transpiration rate was observed to bear the maximum positive indirect effect was via days to 50% flowering (1.349); leaf temperature recorded the maximum positive indirect effect was via days to maturity (0.740); number of leaves per plant showed the maximum positive indirect effect via days to flowering (1.601); panicle length registered the highest positive indirect effect via days to flowering (1.078); test weightregistered the highest positive indirect effect via days to maturity (0.421); green fodder yield per plantshowed the maximum positive indirect effect via days to maturity (1.497); dry fodder yield per plant registered the highest positive indirect effect via days to maturity (1.527). Residual effect refers to the effect of other possible independent variables not included in the study on dependent variables (Kavya et al, 2020). Residual effect was found to be 0.3797 for phenotypic path coefficient while for genotypic path coefficient it was 0.346.If the residual effect is high, some other factors which have not been considered here need to be included in this analysis to account fully for the variation in yield (Kavya et al, 2020).…”

“…Residual effect refers to the effect of other possible independent variables not included in the study on dependent variables (Kavya et al, 2020). Residual effect was found to be 0.3797 for phenotypic path coefficient while for genotypic path coefficient it was 0.346.If the residual effect is high, some other factors which have not been considered here need to be included in this analysis to account fully for the variation in yield (Kavya et al, 2020). Results obtained in the study were in accordance with the findings of Ezeaku and Mohammed (2006) for test weight, Dhutmal et al (2015) for Total biomass, Khandelwal et al (2015) for test weight, panicle length and leaf area, Amare et al (2015) for plant height, Vendruscolo et al (2016) for green fodder yield and dry fodder yield, Hundekar et al (2016) for test weight, panicle length, days to 50%flowering, days to maturity and plant height, Goswami et al (2020).…”