Submitted to: Annals Of Botany
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 8, 2006
Publication Date: July 25, 2006
Citation: Hardegree, S.P. 2006. Predicting germination response to temperature. iii. model validation under field-variable temperature conditions. Annals Of Botany 98:827-834 Interpretive Summary: Native grass species must compete with introduced annual weeds on western US rangelands. Seedlots can be screened for relative germination response in the spring by evaluating their behavior under constant-temperature in the laboratory. Models can then be used to simulate potential response under a wide range of field conditions and planting dates. In this study, we tested previous indices that have been used to evaluate seedlots and compared these indices to historical model simulations of potential seedlot response. We found that indices based on model simulations provide a more realistic basis for comparison of what is most likely to happen in the field. Use of these models will help us design planting and weed control strategies to optimize establishment success in rangeland seedings in the Intermountain western United States.
Technical Abstract: Two previous papers in this series evaluated model fit of eight thermal-germination models parameterized from constant-temperature germination data. The previous studies determined that model formulations with the fewest shape assumptions provided the best estimates of both germination rate and germination time. The purpose of this study was to evaluate the accuracy and efficiency of these same models in predicting germination time and relative seedlot performance under field-variable temperature scenarios. The seeds of four rangeland grass species were germinated under 104 variable-temperature treatments simulating six planting dates at three field sites in southwestern Idaho. Measured and estimated germination times for all subpopulations were compared for all models, species and temperature treatments. All models showed similar, and relatively high, predictive accuracy for field-temperature simulations except for the Iterative-Probit-Optimization (IPO) model which exhibited systematic errors as a function of subpopulation. Highest model efficiency was obtained by the Statistical-Gridding (SG) model which could be directly parameterized by measured-subpopulation-rate data. Relative seedlot response predicted by thermal-time coefficients was somewhat different from that estimated from mean field-variable temperature response as a function of subpopulation. All germination response models tested in this study performed relatively well in estimating field-variable temperature response. Iterative-probit-optimization caused systematic errors in predictions of germination time, and may have degraded the physiological relevance of resultant cardinal-temperature parameters. Comparative indices based on expected field performance may be more ecologically relevant than indices derived from a broader range of potential-thermal conditions.