Hostname: page-component-7f64f4797f-tldsr Total loading time: 0.001 Render date: 2025-11-09T11:40:07.613Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  02 March 2021

Dali Chen ,
Xianglai Chen ,
Jingjing Wang ,
Zuxin Zhang ,
Yan Wang ,
Cunzhi Jia  and
Xiaowen Hu Open the ORCID record for Xiaowen Hu [Opens in a new window]
Dali Chen
Affiliation:
State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China
Xianglai Chen
Affiliation:
State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China
Jingjing Wang
Affiliation:
State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China
Zuxin Zhang
Affiliation:
State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China
Yan Wang
Affiliation:
State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China
Cunzhi Jia
Affiliation:
State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China
Xiaowen Hu*
Affiliation:
State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China
*
Author for Correspondence: Xiaowen Hu, E-mail: huxw@lzu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Thermal time models have been widely applied to predict temperature requirements for seed germination. Generally, a log-normal distribution for thermal time [θT(g)] is used in such models at suboptimal temperatures to examine the variation in time to germination arising from variation in θT(g) within a seed population. Recently, additional distribution functions have been used in thermal time models to predict seed germination dynamics. However, the most suitable kind of the distribution function to use in thermal time models, especially at suboptimal temperatures, has not been determined. Five distributions (log-normal, Gumbel, logistic, Weibull and log-logistic) were used in thermal time models over a range of temperatures to fit the germination data for 15 species. The results showed that a more flexible model with the log-logistic distribution, rather than the log-normal distribution, provided the best explanation of θT(g) variation in 13 species at suboptimal temperatures. Thus, at least at suboptimal temperatures, the log-logistic distribution is an appropriate candidate among the five distributions used in this study. Therefore, the distribution of parameters [θT(g)] should be considered when using thermal time models to prevent large deviations; furthermore, an appropriate equation should be selected before using such a model to make predictions.

Keywords

distribution functionlog-logistic distributionseed germinationtemperaturethermal time model

Information

Type
Research Paper
Information
Seed Science Research , Volume 31 , Issue 2 , June 2021 , pp. 83 - 90
Check for updates
Copyright
Copyright © The Author(s) 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Allen, PS, Meyer, SE and Khan, MA (2000) Hydrothermal time as a tool in comparative germination studies. pp. 401–410 in 6th international workshop on seed, January 1999, Merida, Mexico.CrossRefGoogle Scholar
Alvarado, V and Bradford, KJ (2002) A hydrothermal time model explains the cardinal temperatures for seed germination. Plant Cell and Environment 25, 1061–1069.CrossRefGoogle Scholar
Baskin, CC and Baskin, JM (2014) Seeds: ecology, biogeography, and evolution dormancy and germination (2nd edn). San Diego, CA, Academic Press.Google Scholar
Bewley, JD, Bradford, KJ, Hilhorst, HWM and Nonogaki, H (2013) Seeds: physiology of development, germination and dormancy (3rd edn). New York, NY, Springer-Verlag.CrossRefGoogle Scholar
Bradford, KJ (2002) Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Science 50, 248–260.10.1614/0043-1745(2002)050[0248:AOHTTQ]2.0.CO;2CrossRefGoogle Scholar
Carhuancho León, FM, Aguado Cortijo, PL, Morato Izquierdo, M and Castellanos Moncho, MT (2020) Application of the thermal time model for different Typha domingensis populations. BMC Plant Biology 20, 377–397.CrossRefGoogle ScholarPubMed
Cave, RL, Birch, CJ, Hammer, GL, Erwin, JE and Johnston, ME (2011) Cardinal temperatures and thermal time for seed germination of Brunonia australis (Goodeniaceae) and Calandrinia sp. (Portulacaceae). Hort Science 46, 753–758.Google Scholar
Cheng, ZY and Bradford, KJ (1999) Hydrothermal time analysis of tomato seed germination responses to priming treatments. Journal of Experimental Botany 50, 89–99.CrossRefGoogle Scholar
Covell, S, Ellis, RH, Roberts, EH and Summerfield, RJ (1986) The influence of temperature on seed-germination rate in grain legumes. 1. A comparison of chickpea, lentil, soybean and cowpea at constant temperatures. Journal of Experimental Botany 37, 705–715.CrossRefGoogle Scholar
Daibes, LF and Cardoso, VJM (2018) Seed germination of a South American forest tree described by linear thermal time models. Journal of Thermal Biology 76, 156–164.CrossRefGoogle ScholarPubMed
Dürr, C, Dickie, JB, Yang, XY and Pritchard, HW (2015) Ranges of critical temperature and water potential values for the germination of species worldwide: contribution to a seed trait database. Agricultural and Forest Meteorology 200, 222–232.CrossRefGoogle Scholar
Ellis, RH, Covell, S, Roberts, EH and Summerfield, RJ (1986) The influence of temperature on seed-germination rate in grain legumes. 2. Intraspecific variation in chickpea (Cicer arietinum L.) at constant temperatures. Journal of Experimental Botany 37, 1503–1515.CrossRefGoogle Scholar
Felipe Daibes, L and Cardoso, VJM (2018) Seed germination of a South American forest tree described by linear thermal time models. Journal of Thermal Biology 76, 156–164.CrossRefGoogle Scholar
Fenner, MK, Fenner, M and Thompson, K (2005) The ecology of seeds. Cambridge, Cambridge University Press.CrossRefGoogle Scholar
Finney, DJ (1971) Probit analysis (3rd edn). Cambridge, Cambridge University Press.Google Scholar
Gummerson, RJ (1986) The effect of constant temperatures and osmotic potentials on the germination of sugar-beet. Journal of Experimental Botany 37, 729–741.CrossRefGoogle Scholar
Hardegree, SP (2006) Predicting germination response to temperature. I. Cardinal-temperature models and subpopulation-specific regression. Annals of Botany 97, 1115–1125.CrossRefGoogle ScholarPubMed
Hu, XW, Zhou, ZQ, Li, TS, Wu, YP and Wang, YR (2013) Environmental factors controlling seed germination and seedling recruitment of Stipa bungeana on the Loess Plateau of northwestern China. Ecological Research 28, 801–809.10.1007/s11284-013-1063-8CrossRefGoogle Scholar
Hu, XW, Fan, Y, Baskin, CC, Baskin, JM and Wang, YR (2015) Comparison of the effects of temperature and water potential on seed germination of Fabaceae species from desert and subalpine grassland. American Journal of Botany 102, 649–660.CrossRefGoogle ScholarPubMed
ISTA (2014) International rules for seed testing (Edition 2014). Switzerland, International Seed Testing Association.Google Scholar
Mesgaran, MB, Mashhadi, HR, Alizadeh, H, Hunt, J, Young, KR, Cousens, RD and Andersson, L (2013) Importance of distribution function selection for hydrothermal time models of seed germination. Weed Research 53, 89–101.CrossRefGoogle Scholar
Ostadian Bidgoly, R, Balouchi, H, Soltani, E and Moradi, A (2018) Effect of temperature and water potential on Carthamus tinctorius L. seed germination: quantification of the cardinal temperatures and modeling using hydrothermal time. Industrial Crops and Products 113, 121–127.CrossRefGoogle Scholar
Parmoon, G, Moosavi, SA, Akbari, H and Ebadi, A (2015) Quantifying cardinal temperatures and thermal time required for germination of Silybum marianum seed. The Crop Journal 3, 145–151.CrossRefGoogle Scholar
Peng, MW, Wang, M, Jiang, P, Chang, YL and Chu, GM (2018) The impact of low temperature on seed germination of two desert species in Junggar Basin of China. Applied Ecology and Environmental Research 16, 5771–5780.CrossRefGoogle Scholar
Rong, YP, Li, HX and Johnson, DA (2015) Germination response of Apocynum venetum seeds to temperature and water potential. Journal of Applied Botany and Food Quality 88, 202–208.Google Scholar
Rosbakh, S, Poschlod, P and Anten, N (2015) Initial temperature of seed germination as related to species occurrence along a temperature gradient. Functional Ecology 29, 5–14.10.1111/1365-2435.12304CrossRefGoogle Scholar
Saberali, SF and Shirmohamadi-Aliakbarkhani, Z (2020) Quantifying seed germination response of melon (Cucumis melo L.) to temperature and water potential: thermal time, hydrotime and hydrothermal time models. South African Journal of Botany 130, 240–249.CrossRefGoogle Scholar
Sakanoue, S (2010) Use of a simple distribution function to estimate germination rates and thermal time requirements for seed germination in cool-season herbage species. Science and Technology 38, 612–623.Google Scholar
Sugiura, N (1978) Further analysts of the data by akaike's information criterion and the finite corrections: further analysts of the data by akaike's. Communications in Statistics – Theory and Methods 7, 13–26.CrossRefGoogle Scholar
Walck, JL, Hidayati, SN, Dixon, KW, Thompson, K and Poschlod, P (2011) Climate change and plant regeneration from seed. Global Change Biology 17, 2145–2161.CrossRefGoogle Scholar
Watt, MS, Xu, V and Bloomberg, M (2010) Development of a hydrothermal time seed germination model which uses the Weibull distribution to describe base water potential. Ecological Modelling 221, 1267–1272.10.1016/j.ecolmodel.2010.01.017CrossRefGoogle Scholar
Zhang, R, Luo, K, Chen, DL, Baskin, JM, Baskin, CC, Wang, YR and Hu, XW (2020) Comparison of thermal and hydrotime requirements for seed germination of seven stipa species from cool and warm habitats. Frontiers in Plant Science 11, 560714.CrossRefGoogle ScholarPubMed
Supplementary material: File

Chen et al. supplementary material

Appendices A-C

Download Chen et al. supplementary material(File)
File 941.3 KB