AOBPreview originally published online on May 30, 2006
Annals of Botany 2006 98(2):403-410; doi:10.1093/aob/mcl112
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Published by Oxford University Press on behalf of the Annals of Botany Company 2006
Predicting Germination Response to Temperature. II. Three-dimensional Regression, Statistical Gridding and Iterative-probit Optimization Using Measured and Interpolated-subpopulation Data
USDA Agricultural Research Service, Northwest Watershed Research Center, 800 Park Blvd, Suite 105, Boise, ID 83712, USA
* For correspondence. E-mail shardegr{at}nwrc.ars.usda.gov
Received: 28 December 2005 Returned for revision: 16 February 2006 Accepted: 27 March 2006 Published electronically: 30 May 2006
• Background and Aims Most current thermal-germination models are parameterized with subpopulation-specific rate data, interpolated from cumulative-germination-response curves. The purpose of this study was to evaluate the relative accuracy of three-dimensional models for predicting cumulative germination response to temperature. Three-dimensional models are relatively more efficient to implement than two-dimensional models and can be parameterized directly with measured data.
• Methods Seeds of four rangeland grass species were germinated over the constant-temperature range of 3 to 38 °C and monitored for subpopulation variability in germination-rate response. Models for estimating subpopulation germination rate were generated as a function of temperature using three-dimensional regression, statistical gridding and iterative-probit optimization using both measured and interpolated-subpopulation data as model inputs.
• Key Results Statistical gridding is more accurate than three-dimensional regression and iterative-probit optimization for modelling germination rate and germination time as a function of temperature and subpopulation. Optimization of the iterative-probit model lowers base-temperature estimates, relative to two-dimensional cardinal-temperature models, and results in an inability to resolve optimal-temperature coefficients as a function of subpopulation. Residual model error for the three-dimensional model was extremely high when parameterized with measured-subpopulation data. Use of measured data for model evaluation provided a more realistic estimate of predictive error than did evaluation of the larger set of interpolated-subpopulation data.
• Conclusions Statistical-gridding techniques may provide a relatively efficient method for estimating germination response in situations where the primary objective is to estimate germination time. This methodology allows for direct use of germination data for model parameterization and automates the significant computational requirements of a two-dimensional piece-wise-linear model, previously shown to produce the most accurate estimates of germination time.
Key words: Thermal, germination, model, Elymus elymoides, Elymus lanceolatus, Poa secunda, Pseudoroegneria spicata
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