Germination of Warm-Season Grasses under Constant and Dynamic Temperatures

Roundy, Bruce A.; Biedenbender, Sharon

doi:10.2307/4002924

Cited by 48 publications

(38 citation statements)

References 18 publications

(14 reference statements)

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“…Andrews et al (1997) and Roundy and Biedenbender (1996) also found a rapid germination rate for the warm-season grasses at higher temperatures. The ability of Coolatai grass seed to rapidly germinate over a wide range of temperatures provides a partial explanation for the establishment of this species throughout much of Australia.…”

Section: Resultsmentioning

confidence: 80%

Factors Affecting Germination of Coolatai Grass (Hyparrhenia hirta)

et al. 2008

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Coolatai grass is an invasive, perennial grass from Africa and the Middle East that has infested large areas of northern New South Wales, Australia, and also occurs in other Australian states. An understanding of the seed germination ecology of Coolatai grass can assist in predicting its potential distribution and developing effective management strategies. The effects of various environmental factors on Coolatai grass seed germination were investigated in a series of laboratory experiments. The effect of diurnal alternating temperatures (5–45 C) with a 12-h photoperiod were examined on a thermogradient plate. Seed germination occurred at almost all temperature combinations from 5 to 45 C. At moderate temperatures the speed of seed germination was very high; at 30/20 C more than 80% of seeds were germinated within 12 h. Germination was slightly enhanced by the presence of light, but length of photoperiod did not affect germination. Germination at neutral pH was over 90%, and declined to 65% at acidic (pH 4) and alkaline (pH 10) pH levels. Germination was completely inhibited at an osmotic stress of −0.55 MPa or greater and was reduced by 50% at −0.37 MPa. Greater germination in relatively dry soil conditions compared with native species may contribute to the establishment of this species in the field. Results indicate that Coolatai grass seeds can germinate over a wide range of diurnal temperatures, light regimes, pH levels, and under marginal water stress. These characteristics help explain the successful invasion of Coolatai grass and provide evidence that this species is capable of establishing in many parts of Australia.

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Section: Resultsmentioning

confidence: 80%

Factors Affecting Germination of Coolatai Grass (Hyparrhenia hirta)

et al. 2008

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“…Model output consisted of hourly temperature and water potential at 2-cm depth for the period October 1, 1961, through September 30, 2005. Germination time for a given subpopulation can be estimated when the sum of hourly post-planting germination rates becomes equal to 1 (Roundy and Biedenbender, 1996). Hourly T and y estimates from the field simulation were used to estimate R for each combination of seedlot, subpopulation, and hydrothermal model.…”

Section: Evaluation Of Model Residual Errormentioning

confidence: 99%

Hydrothermal Germination Models: Comparison of Two Data‐Fitting Approaches with Probit Optimization

Hardegree

Walters²,

Boehm

et al. 2015

Crop Science

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Probit models for estimating hydrothermal germination rate yield model parameters that have been associated with specific physiological processes. The desirability of linking germination response to seed physiology must be weighed against expectations of model fit and the relative accuracy of predicted germination response. Computationally efficient empirical models have been proposed that do not require a priori assumptions about model shape parameters, but the accuracy of these models has not been compared to the more common probit‐optimization procedure. Thirteen seedlots, representing six native perennial rangeland grasses and an invasive annual weed, were germinated over the constant temperature range of 3 to 36°C and water potential range of 0 to ‐2.5 MPa. Hydrothermal germination models were generated using probit optimization, optimized regression, and statistical gridding. These models were evaluated for the pattern and magnitude of residual model error and the relative magnitude of predictive errors under field‐simulated temperature and moisture conditions. Residual model errors in predictions of germination rate were greatest for the probit optimization procedure. Statistical gridding and optimized regression produced lower predictive model error, but the latter procedure could not resolve germination response for slower‐germinating seed populations. The more computationally efficient and accurate regression and statistical‐gridding procedures may be desirable for identifying germination strategies and syndromes that are based on predicted response to simulated conditions of field temperature and moisture.

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“…Each box, containing 100 randomly selected whole seeds or caryopsis, was considered as an experimental unit and 3 replications per treatment were used in the study. Germination conditions were 12-hour dark 208C and 12-hour light 308C based on results reported by Fulbright et al (1983), Schrauf et al (1995), and Roundy and Biedenbender (1996). Germination counts were made every day for 28 days.…”

Section: Methodsmentioning

confidence: 99%