Using Nonlinear Regression Model for Estimation of Cardinal Temperatures in Three Medicinal Plants

Abstract

Medicinal plants have been used as a source of remedies since ancient times. Most medicinal plants in Iran are herbs and they have dormancy and a special biological cycle. Knowledge of this cycle is required to grow these plants. Understanding the response of seed germination in medicinal plants to temperature involves selecting the best nonlinear regression models for the prediction of their seed germination, the characterization of their germination pattern and the prediction of the cardinal temperatures of medicinal plants. Thus, to understand the medicinal seed germination response to temperature, an experiment was conducted at the University of Tehran, Iran in 2011. The germination rate of three medicinal plants—wild oat (Avena fatua L.), wild mustard (Sinapis arvensis L.) and Descurania Sophia (L.)—was calculated at different temperatures (0, 5, 10, 15, 20, 25, 30, 35, 40 and 45 °C) based on a completely randomized design with three replications. Three nonlinear regression (segmented, dent-like, beta) approaches were applied to model the germination rate. The analysis of variance showed that temperature had a significant effect on the seed germination rate. Among models, the segmented model was the best for the three plants and the cardinal temperatures were estimated by this model. The base, optimum and ceiling temperatures for wild oat, wild mustard and D. Sophia were estimated as1.6, 10.2 and 29.3 °C and 2.01, 15 and 30.6 °C and 1.2, 29.6 and 35 °C, respectively. The germination models based on temperature can used for the prediction of cardinal temperatures.