Modeling the germination curves for magneto-priming of sorghum (Sorghum bicolor L. Moench) seeds using the Logistic and Gompertz models - Marwan; Samir (The Indian Journal of Agricultural Sciences, 2026)
Data analysis of the traditional germination percentages may provide the least amount of information. Therefore, germination behavior can be best described based on a regression model for the time to germination. In this study, the effects of magneto priming on sorghum seed germination were analysed and compared using Logistic and Gompertz models, LM and GM, respectively. Sorghum seeds were primed with a static magnetic field for four periods of time (0, 1,3, and 5 h). Seed germination collected for 10 days for four magneto priming durations was modelled by fitting the LM and GM growth curves. Growth curve selection was based on the coefficient of determination (R2) and mean square error (MSE). Following the growth curve selection, the results of these fittings and their parameter estimates were compared in terms of graphical representation and statistical criteria. The results showed that the GM model fit better than the LM model in describing the germination process of sorghum in the magneto-priming test, as it yielded the lowest MSE and highest R2 values. Overall, these findings suggest that the Gompertz model can represent the best shape of sorghum germination curves under magneto-primed conditions.