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ARS Home » Pacific West Area » Boise, Idaho » Northwest Watershed Research Center » Research » Publications at this Location » Publication #188724

Title: Predicting germination response to temperature. I. Cardinal-temperature models and subpopulation-specific regression

item Hardegree, Stuart

Submitted to: Annals of Botany
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/10/2006
Publication Date: 4/19/2006
Citation: Hardegree, S.P., 2006. Predicting germination response to temperature. I. Cardinal-termperature models and subpopulation-specific regression, Annals of Botany 97:1115-1125, 2006.

Interpretive Summary: Seed germination response to temperature is an important indicator of seed vigor and quality. Relative temperature response is also important when assessing potential competition with weed species. In this study, new methods were developed to predict the amount of time it takes for a seed population to germinate at different temperatures. The new methods significantly improved the accuracy of these predictions at low temperatures, which are common in the early spring when rangeland grass species typically emerge in the field. These predictions can be used to develop an index for seed vigor that is based on what the seed may actually experience in the field, rather than just an artificial comparison of behavior under non-stress conditions in the laboratory.

Technical Abstract: The purpose of this study was to compare the relative accuracy of different thermal-germination models in predicting germination-time under constant-temperature conditions. We were specifically interested in assessing shape assumptions associated with the cardinal-temperature germination model and probit distribution often used to distribute thermal coefficients among seed subpopulations. We germinated seeds of four rangeland grass species over the constant-temperature range of 3 to 38 'C and monitored subpopulation variability in germination-rate response. We estimated subpopulation-specific germination rate as a function of temperature and residual model error for several 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. 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 days for the cardinal-temperature models. We recommend that 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.