Estimation of thermal time model parameters for seed germination in 15 species: the importance of distribution function

Chen, Dali; Chen, Xianglai; Wang, Jingjing; Zhang, Zuxin; Wang, Yan; Jia, Cunzhi; Hu, Xiao Wen

doi:10.1017/s0960258521000040

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…A linear regression equation was derived to relate germination rate with temperature in the sub-optimal temperature range. The thermal time constant (θ, • C•d) was estimated as the inverse slope of the regression line, and the base temperature (T b ) was calculated by extrapolation to the point where the germination rate was zero [33].…”

Section: Thermal Evaluationmentioning

confidence: 99%

A Thermal Time Basis for Comparing the Germination Requirements of Alfalfa Cultivars with Different Fall Dormancy Ratings

Wu,

Zhang,

Tian

et al. 2023

Agronomy

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Fall dormancy plays important roles in the evaluation of alfalfa’s winter hardiness and in the selection of alfalfa breeding. A rapid and effective method to estimate the fall dormancy rating of alfalfa will shorten the breeding cycle. The purpose of this study is to test the correlations between the germination thermal time model parameters and the fall dormancy ratings and to evaluate the potential of the thermal-based fall dormancy methodology. Alfalfa cultivars with a series of fall dormancy ratings were used to study the responses of seed germination at six constant temperatures (5, 10, 15, 20, 25, 30 °C). The results showed that all cultivars had a relatively high germination percentage at all temperatures and the optimal temperature is 25 or 30 °C. Germination rate and base temperature significantly increased with the fall dormancy rating of alfalfa cultivars while thermal time (θT) decreased with the fall dormancy rating. The extremely significant linear regression relationships between the germination rate, base temperature (Tb), θT, and fall dormancy rating indicated that it is convenient and straightforward to predict the fall dormancy rating of unknown cultivars or lines using thermal time model parameters. This method can significantly shorten the selection and breeding cycles in alfalfa cultivation.

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Section: Thermal Evaluationmentioning

confidence: 99%

A Thermal Time Basis for Comparing the Germination Requirements of Alfalfa Cultivars with Different Fall Dormancy Ratings

Wu,

Zhang,

Tian

et al. 2023

Agronomy

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“…Thus, for understanding the biological meaning of such patterns, it is more appropriate to assume that they are caused by multiple normally distributed subpopulations than to seek a specific function to empirically fit each novel pattern. The latter may fit the observed data well, but the biological meaning of the additional parameters required for such models can be obscure (Watt et al, 2010;Mesgaran et al, 2013;Chen et al, 2021). In contrast, the subpopulation approach is directly amenable to practical use, for example, if there is a less vigorous minor subpopulation in the seed lot (e.g., Fig.…”

Section: Subpopulations (All Models)mentioning

confidence: 99%

Applying population-based threshold models to quantify and improve seed quality attributes

Bradford¹,

Bello²

2022

Burleigh Dodds Series in Agricultural Science

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Achieving rapid and uniform stand establishment in crops requires a combination of high-quality seeds and appropriate environmental conditions. In particular, temperature and soil moisture (or water potential) are the major factors influencing germination in the field. In this chapter, we focus on the application of population-based threshold (PBT) models to characterize seed germination time courses and how environmental and technological inputs influence them. Viewing seed quality as a product of the behavior of populations of individual seeds is critical for understanding the causes and consequences of poor performance. Quantitatively characterizing seed population features enables their use in seed sorting and seed enhancement, and provides phenotypes for use in research, breeding, conservation and restoration. We believe that PBT models are essential tools to enable full utilization of new advances in seed technology to improve seed quality and enable successful stand establishment in agriculture or in natural settings.

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“…Models that simplify the base temperature as constant need three to four parameters to characterize the random base water potential to give the sigmoid time course of germination (Huang et al 2016;Liu et al 2020). For models that take the base temperature as a random number, depending on the types of the distribution functions, they need six parameters when both the base temperature and matric potential are approximated as normal distributions, or eight parameters when they are described by Weibull or loglogistic function (Chen et al 2021;Mesgaran et al 2013). As the normal distribution is least accurate to describe the time course of germination (Mesgaran et al 2013), the proposed model is more parameter-efficient than the hydrothermal time models.…”

Section: Comparison With the Hydrothermal Time Modelmentioning

confidence: 99%

A New Framework for Modelling Seed Germination and Seedling Tillering of Winter Wheat in the Field

Chen¹,

Whalley²,

Li³

et al. 2022

Preprint

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Aims Seed germination is regulated by multiple environmental cues in the field and understanding their relationship is critical to planning seed drilling and subsequent seedling management but challenging due to the trade-off impact of some environmental stimuli on metabolic reactions involved in the germination. Methods We develop a new framework by introducing a physiological dimension over which the metabolic reactions associated with seed germination can be modelled as a moving dynamic event. The influence of environmental cues and the heterogeneity of seeds is described by an average germination rate and a dispersion coefficient. Winter wheat seeds were drilled at different dates in field plots under different soil water contents, and the time course of germination in all treatments and the subsequent tillering development are used to validate the model. Results The model accurately reproduces the time course of germination in all treatments. We find that the average germination rate increases asymptotically rather than linearly as the temperature increases within the base-suboptimal temperature range, and that there is an optimal soil water content at which the germination rate peaks due to the trade-off impact of soil water on seed imbibition and soil anaerobicity. The average germination rate and the dispersion coefficient are closely related. Conclusions Using soil water content instead of matric potential in our model can naturally describe the simultaneous impact of soil water on soil anaerobicity and seed imbibition, and the physiological dimension can be extended to combine seed germination and seedling tillering development as a continued physiological process.

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Estimation of thermal time model parameters for seed germination in 15 species: the importance of distribution function

Cited by 10 publications

References 28 publications

A Thermal Time Basis for Comparing the Germination Requirements of Alfalfa Cultivars with Different Fall Dormancy Ratings

A Thermal Time Basis for Comparing the Germination Requirements of Alfalfa Cultivars with Different Fall Dormancy Ratings

Applying population-based threshold models to quantify and improve seed quality attributes

A New Framework for Modelling Seed Germination and Seedling Tillering of Winter Wheat in the Field

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