segmented function

In order to evaluate different nonlinear regression models for estimating cardinal temperatures of Lepyrodiclis holosteoides as an invasive weed, an experiment was carried out at Biodiversity Laboratory of Environmental Sciences Research Institute at Shahid Beheshti University. 8 germination temperatures (0, 5, 10, 15, 20, 25, 30 and 35 °C) and humidity potential (0, -0.2, -0.4, -0.6 and -0.8 MPa) with four replications. Were arranged in a completely randomized design with for replications. Then, germination rate and percentage of seeds were measured. To predict the response of germination rate to temperature in Lepyrodiclis holosteoides, some regression models including dent-like, segmented, beta (four and five parameters) were applied. Some statistical estimators like Root Mean Squared of Error (RMSE), Akaike Information Criterion (AIC) and coefficient of determination (R2) were used to evaluate goodness of fit for different regression models. Results showed that the highest germination percentage (72%) was obtained at 20 °C. Results also indicated that beta four and five-parameter models were amongst the superior functions in describing the response of germination rate to temperature in Lepyrodiclis holosteoides largely due to their higher R2 (0.99 for both beta models), lower AIC (-73.16 and -73.27) and RMSE (0.0092 and 0.0091). Generally, base, optimum and ceiling cardinal temperatures for Lepyrodiclis holosteoides were estimated 4.29, 19.76 and 37.55 °C for beta four-parameter models and 4.22, 19.72 and 37.83 °C for beta five-parameter model. The findings of the current study (i.e. cardinal temperatures) could be used in prediction of Lepyrodiclis holosteoides germination and emergenceunder various temperatures.