NDLI: Estimation of cardinal temperatures in germination data analysis

Content Provider	Springer Nature Link
Author	Shafii, Bahman Price, William J.
Copyright Year	2001
Abstract	Seed germination is a complex biological process that is influenced by various environmental and genetic factors. The effects of temperature on plant development are the basis for models used to predict the timing of germination. Estimation of the cardinal temperatures, including base, optimum, and maximum, is essential because rate of development increases between base and optimum, decreases between optimum and maximum, and ceases above the maximum and below the base temperatures. Nonlinear growth curves can be specified to model the time course of germination at various temperatures. Quantiles of such models are regressed on temperature to estimate cardinal quantities. Bootstrap simulation techniques may then be employed to assure the statistical accuracy of these estimates and to provide approximate non parametric confidence intervals. A statistical approach to modeling germination and estimation of cardinal temperatures is presented with reference to replicated experiments designed to determine the effect of temperature gradient on germination of three populations of an introduced weed species, common crupina (Crupina vulgaris Per.).
Starting Page	356
Ending Page	366
Page Count	11
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	6
Issue Number	3
e-ISSN	15372693
Language	English
Publisher	Springer-Verlag
Publisher Date	2001-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bootstrap simulation Nonlinear estimation Seed germination Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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