Quantifying Cardinal Temperatures of Chia (Salvia hispanica L.) Using Non-Linear Regression Models

Abstract

Temperature is the main factor that impacts germination and therefore the success of annual crops, such as chia (Salvia hispanica L.), whose seeds are known for their high nutritional value related to its oil. The effect of temperature on germination is related to cardinal-temperature concepts that describe the range of temperature over which seeds of a particular species can germinate. Therefore, in this study, in addition to calculated germinative parameters such as total germination and germination rate of S. hispanica seeds, the effectiveness of non-linear models for estimating the cardinal temperatures of chia seeds was also determined. We observed that germination of S. hispanica occurred in cold to moderate-high temperatures (10–35 °C), having an optimal range between 25 and 35 °C, with the highest GR and t50 at 30 °C. Temperatures higher than 35 °C significantly reduced germination. Output parameters of the different non-linear models showed that the response of chia germination to temperature was best explained by beta models (B). Cardinal temperatures calculated by the B1 model for chia germination were: 2.52 ± 6.82 °C for the base, 30.45 ± 0.32 °C for the optimum, and 48.58 ± 2.93 °C for the ceiling temperature.

Comparison and Analysis of Several Quantitative Identification Models of Pesticide Residues Based on Quick Detection Paperboard

“…The concept of a nonlinear regression model resembles the approach of linear regression. The regression equation of a nonlinear regression model is frequently a complicated nonlinear function, and the regression law is visually represented by different-shaped curves; such models are referred to as nonlinear regression models [38][39][40]. For model estimates, the modeling tool utilized SPSS 26.0 statistical analysis software.…”