Grain number estimation, regression model and grain distribution pattern in sorghum (Sorghum bicolor) genotypes

Abstract

Crop yields in physiological models are determined as a product of their yield components like the grain numbers/plant times the average kernel weight at maturity. The grain numbers are calculated as a function of the above ground biomass growth during the panicle initiation phase. Biomass production of sorghum [Sorghum bicolor (L.) Moench] is influenced by genotype and management interaction, while transformation from vegetative to reproductive phase is influenced by genotype by environment, i.e. photoperiod of a given season. The grain weight in all the models is calculated as a function of the cultivar specific optimum growth rate multiplied by the duration of grain filling. Grain growth dynamics is thus a function of management, environment and genotype. Plant breeder’s objective of enhancing the grain yield potential was achieved through improved translocation of dry matter produced into grain, that varies from 30 - 40%, in the newly developed hybrids. Increase in productivity brought about by genetic improvement is related to grain number and kernel weight. Grain number estimation is a tedious process and this paper attempts a simplified application of a regression model that relates grain number (dependent variable) with the spikelet weight (independent variable). A regression equation, Y = 24.626 X + 9.7136 derived using pooled dataset was used to predict the grain number. The rigor of the regression fit is validated using both predicted and observed grain number, based on the derived regression coefficient, which ranged from 0.97 to 0.99.