A comparison of seed germination coefficients using functional regression

• Published in: Applications in plant sciences Vol. 8; no. 8; pp. e11366 - n/a
• Main Authors: Talská, Renáta, Machalová, Jitka, Smýkal, Petr, Hron, Karel
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.08.2020; John Wiley and Sons Inc; Wiley
• Subjects: Application; continuous germination index; Data analysis; data collection; functional regression; germination curve; Measurement techniques; nondecreasing positive smoothing splines; peas; Plant sciences; regression analysis; Seed germination; Seeds; Velocity; nondecreasing positive smoothing splines; seed germination; functional regression; continuous germination index; germination curve
• ISSN: 2168-0450; 2168-0450
• DOI: 10.1002/aps3.11366

Premise Seed germination over time is characterized by a sigmoid curve, called a germination curve, in which the percentage (or absolute number) of seeds that have completed germination is plotted against time. A number of individual coefficients have been developed to characterize this germination curve. However, as germination is considered to be a qualitative developmental response of an individual seed that occurs at one time point, but individual seeds within a given treatment respond at different time points, it has proven difficult to develop a single index that satisfactorily incorporates both percentage and rate. The aim of this paper is to develop a new coefficient, the continuous germination index (CGI), which quantifies seed germination as a continuous process, and to compare the CGI with other commonly used indexes. Methods To create the new index, the germination curves were smoothed using nondecreasing splines and the CGI was derived as the area under the resulting spline. For the comparison of the CGI with other common indexes, a regression model with functional response was developed. Results Using both an experimentally obtained wild pea (Pisum sativum subsp. elatius) seed data set and a hypothetical data set, we showed that the CGI is able to characterize the germination process better than most other indices. The CGI captures the local behavior of the germination curves particularly well. Discussion The CGI can be used advantageously for the characterization of the germination process. Moreover, B‐spline coefficients extracted by its construction can be employed for the further statistical processing of germination curves using functional data analysis methods.