Growth performance in contrasting sets of mulberry (Morus Spp.) genotypes explained by logistic and linear regression models using morphological and gas exchange parameters

Plant growth is key determinant for leaf yield and biomass accumulation in mulberry. Accurate prediction of yield ensures brushing of right quantum of silkworms for silk production. Repeated harvesting of leaves/shoots leads to stress with physiological and biochemical changes. This necessitates in-depth analysis of growth attributes leading to rejuvenation and biomass production. A total of 22 contrasting genotypes for growth (CSG) were shortlisted from a diverse set of germplasm based on Number of Days required for Bud Sprouting (NDBS), Shoot Elongation Rate (SER) and Number of Branches (NB) in two distinct seasons. The logistic regression analysis of CSG expressed in odds ratio showed positive regression coefficients for important traits viz., inter-nodal distance (2.71), NB (1.31), Total Shoot Length (TSL, 1.38), Length of the Longest Shoot (LLS, 1.17) and Number of Leaves on the Longest Shoot (NLLS, 1.18) for high growth in August 2016. The growth curve estimates for SER was significantly (P < 0.0001) higher among High Growth Genotypes (HGG; 4-6.2 cm/day) compared to Low Growth Genotypes (LGG; 2.6-4.1 cm/day). Repeated measure ANOVA showed significance A (P < 0.0001), gs (P < 0.01), IWUE (P < 0.01) between three growth periods. A, Tr, and gs were higher among HGG compared to LGG and opposite was true in case of IWUE. Similarly, Amax and AQL were higher in HGG whereas Vcmax and Jmax were higher in LGG. Significant linear relationship of Tr, gs and IUWE was observed with NB, TSL and LLS. The study concludes that mulberry growth can be predicted using important morpho-metric traits and gas exchange parameters as supported by logistic and linear regression analysis.