AOBPreview originally published online on April 19, 2006
Annals of Botany 2006 97(6):1115-1125; doi:10.1093/aob/mcl071
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Published by Oxford University Press on behalf of the Annals of Botany Company 2006
Predicting Germination Response to Temperature. I. Cardinal-temperature Models and Subpopulation-specific Regression
USDA Agricultural Research Service, Northwest Watershed Research Center, 800 Park Blvd, Suite 105, Boise, ID 83712, USA
* E-mail shardegr{at}nwrc.ars.usda.gov
Received: 25 November 2005 Returned for revision: 17 January 2006 Accepted: 10 February 2006 Published electronically: 19 April 2006
• Background and Aims The purpose of this study was to compare the relative accuracy of different thermal-germination models in predicting germination-time under constant-temperature conditions. Of specific interest was the assessment of shape assumptions associated with the cardinal-temperature germination model and probit distribution often used to distribute thermal coefficients among seed subpopulations.
• Methods The seeds of four rangeland grass species were germinated over the constant-temperature range of 3–38 °C and monitored for subpopulation variability in germination-rate response. Subpopulation-specific germination rate was estimated as a function of temperature and residual model error for three variations of the cardinal-temperature model, non-linear regression and piece-wise linear regression. The data were used to test relative model fit under alternative assumptions regarding model shape.
• Key Results In general, optimal model fit was obtained by limiting model-shape assumptions. All models were relatively accurate in the sub-optimal temperature range except in the 3 °C treatment where predicted germination times were in error by as much as 70 d for the cardinal-temperature models.
• Conclusions Germination model selection should be driven by research objectives. Cardinal-temperature models yield coefficients that can be directly compared for purposes of screening germplasm. Other model formulations, however, may be more accurate in predicting germination-time, especially at low temperatures where small errors in predicted rate can result in relatively large errors in germination time.
Key words: Thermal, germination, model, Elymus elymoides, Elymus lanceolatus, Poa secunda, Pseudoroegneria spicata
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