Biomedical subjects

Stuart P Hardegree

Publications and source records attributed to Stuart P Hardegree.

4 recordsLinked to original sources

PubMed2006

Predicting germination response to temperature. III. Model validation under field-variable temperature conditions.

BACKGROUND AND AIMS: 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 latest 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. METHODS: The seeds of four rangeland grass species were germinated under 104 variable-temperature treatments simulating six planting dates at three field sites in south-western Idaho. Measured and estimated germination times for all subpopulations were compared for all models, species and temperature treatments. KEY RESULTS: 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 efficiency was obtained with 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. CONCLUSIONS: All germination response models tested performed relatively well in estimating field-variable temperature response. IPO 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.

Germination↗

PubMed2006

Predicting germination response to temperature. II. Three-dimensional regression, statistical gridding and iterative-probit optimization using measured and interpolated-subpopulation data.

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 degrees 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.

Elymus↗

PubMed2006

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

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 degrees 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 degrees 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.

Germination↗

PubMed2002

Variability in thermal response of primed and non-primed seeds of squirreltail [Elymus elymoides (Raf.) Swezey and Elymus multisetus (J. G. Smith) M. E. Jones].

Bottlebrush squirreltail [Elymus elymoides (Raf.) Swezey = Sitanion hystrix (Nutt.) J. G. Smith] and big squirrel-tail [Elymus multisetus (J. G. Smith) M. E. Jones = Sitanion jubatum (J. G. Smith)] have a broad geographical distribution and have been identified as high priority species for restoration of degraded rangelands in the western United States. These rangelands exhibit high annual and seasonal variability in seedbed microclimate. The objective of this study was to examine variability in thermal response of both primed and non-primed seeds of these species in the context of field-variable temperature regimes. Seed priming treatments were selected to optimize germination rate in a low-temperature test environment. Primed and non-primed seeds were evaluated for laboratory germination response under 12 constant temperature treatments between 3 and 36 degrees C. Thermal time and base temperature were estimated by regression analysis of germination rate as a function of temperature in the sub-optimal temperature range. The thermal germination model and 6 years of field temperature data were used to simulate the potential germination response under different field planting scenarios. Seed priming reduced the total germination percentage of some seedlots, especially at higher germination temperatures. Seed priming increased the germination rate (reduced the number of days to 50 % germination) by 3.8-8.4 d at 6 degrees C with a mean germination advancement of 6.9 +/- 0.6 d. Maximum germination advancement in the model simulations was 5-10 d for planting dates between I March and 15 May. Model simulations can be used to expand germination analysis beyond simple treatment comparisons, to include a probabilistic description of potential germination response under historical or potential future conditions of seedbed microclimate.

Germination↗