Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-25T04:24:27.638Z Has data issue: false hasContentIssue false
  • English
  • Français

Comparison of three different statistical approaches (non-linear least-squares regression, survival analysis and Bayesian inference) in their usefulness for estimating hydrothermal time models of seed germination

Published online by Cambridge University Press:  01 May 2020

Elena Moltchanova Open the ORCID record for Elena Moltchanova [Opens in a new window] ,
Shirin Sharifiamina ,
Derrick J. Moot ,
Ali Shayanfar  and
Mark Bloomberg
Elena Moltchanova*
Affiliation:
School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
Shirin Sharifiamina
Affiliation:
Field Research Centre, Lincoln University, Canterbury, New Zealand
Derrick J. Moot
Affiliation:
Field Research Centre, Lincoln University, Canterbury, New Zealand
Ali Shayanfar
Affiliation:
Seed and Plant Certification and Registration Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
Mark Bloomberg
Affiliation:
Kura Ngahere - New Zealand School of Forestry, University of Canterbury, Christchurch, New Zealand
*
Correspondence: Elena Moltchanova, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Hydrothermal time (HTT) models describe the time course of seed germination for a population of seeds under specific temperature and water potential conditions. The parameters of the HTT model are usually estimated using either a linear regression, non-linear least squares estimation or a generalized linear regression model. There are problems with these approaches, including loss of information, and censoring and lack of independence in the germination data. Model estimation may require optimization, and this can have a heavy computational burden. Here, we compare non-linear regression with survival and Bayesian methods, to estimate HTT models for germination of two clover species. All three methods estimated similar HTT model parameters with similar root mean squared errors. However, the Bayesian approach allowed (1) efficient estimation of model parameters without the need for computation-intensive methods and (2) easy comparison of HTT parameters for the two clover species. HTT models that accounted for a species effect were superior to those that did not. Inspection of credibility intervals and estimated posterior distributions for the Bayesian HTT model shows that it is credible that most HTT model parameters were different for the two clover species, and these differences were consistent with known biological differences between species in their germination behaviour.

Keywords

Bayesian inferencehydrothermal time model (HTT)seed germinationsubterranean clover (Trifolium subterraneum L.)survival analysiswhite clover (Trifolium repens L.)
Type
Technical Update
Information
Seed Science Research , Volume 30 , Issue 1 , March 2020 , pp. 64 - 72
Check for updates
Copyright
Copyright © The Author(s), 2020. 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.)

References

Allen, PS, Meyer, SE and Khan, MA (2000) Hydrothermal time as a tool in comparative germination studies, pp. 401–410in Black, M; Bradford, KJ and Vázquez-Ramos, J (Eds) Seed biology, advances and applications. Proceedings of the Sixth International Workshop on Seeds, Merida, Mexico 1999. Wallingford, Oxon, UK, CAB International.Google Scholar
Bradford, KJ (2002) Applications of hydrothermal time to quantifying and modelling seed germination and dormancy. Weed Science 50, 248–260.CrossRefGoogle Scholar
Burdon, JJ (1983) Trifolium repens L. Journal of Ecology 71, 307–330.CrossRefGoogle Scholar
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
Finch-Savage, WE (2004) The use of population-based threshold models to describe and predict the effects of seedbed environment on germination and seedling emergence of crops, pp. 51–95in Benech-Arnold, RLand Sanchez, RA (Eds) Handbook of seed physiology, applications to agriculture. Binghamton, NY, Haworth Reference Press.Google Scholar
Frame, J and Laidlaw, AS (2005) Prospects for temperate forage legumes, pp. 4–28in Reynolds, SG and Frame, J (Eds) Grasslands, developments opportunities perspectives. Enfield, Food and Agriculture Organization of the United Nations Science Publishers.Google Scholar
Gibson, PB and Cope, WA (1985) White clover, pp. 471–490in Taylor, NL (Ed.) Clover science and technology, vol. 25. Agronomical Monographies. Madison WI, ASA CSSA SSSA.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, Walter, CT, Boehm, AR, Olsoy, PJ, Clark, PE and Pierson, FB (2015) Hydrothermal germination models, comparison of two data-fitting approaches with probit optimization. Crop Science Society of America 55, 2276–2290.CrossRefGoogle Scholar
Hardegree, SP, Moffet, CA, Walters, CT, Sheley, RL and Flerchinger, GN (2017) Hydrothermal germination models: improving experimental efficiency by limiting data collection to the relevant hydrothermal range. Crop Science Society of America 57, 2753–2760.CrossRefGoogle Scholar
Hay, FR, Mead, A and Bloomberg, M (2014) Modelling seed germination in response to continuous variables, use and limitations of probit analysis and alternative approaches. Seed Science Research 24, 165–186.CrossRefGoogle Scholar
Hills, PN and van Staden, J (2003) Thermo-inhibition of seed germination. South African Journal of Botany 69, 455–461.CrossRefGoogle Scholar
Horowitz, M and Taylorson, RB (1983) Effect of high temperatures on imbibition germination and thermal death of velvetleaf (Abutilon theophrasti) seeds. Canadian Journal of Botany 61, 2269–2276.CrossRefGoogle Scholar
Humplík, JF, Dostál, J, Ugena, L, Spíchal, L, De Diego, N, Vencálek, O and Fürst, T (2020) Bayesian approach for analysis of time-to-event data in plant biology. Plant Methods 16, 14.CrossRefGoogle ScholarPubMed
Jensen, S, Andreasen, C, Streibig, J, Keshtkar, E and Ritz, C (2017) A note on the analysis of germination data from complex experimental designs. Seed Science Research 27, 321–327.CrossRefGoogle Scholar
Köchy, M and Tielbörger, K (2007) Hydrothermal time model of germination, parameters for 36 Mediterranean annual species based on a simplified approach. Basic and Applied Ecology 8, 171–182.CrossRefGoogle Scholar
Kos, M and Poschlod, P (2008) Correlates of inter-specific variation in germination response to water stress in a semi-arid savannah. Basic and Applied Ecology 9, 645–652.CrossRefGoogle Scholar
McGuire, WS (1985) Subterranean clover. pp. 515–534in Taylor, NL (Ed.) Clover science and technology, vol. 25. Agronomical Monographies, Madison WI, ASA CSSA SSSA.Google Scholar
McNair, JN, Sunkara, A and Frobish, D (2012) How to analyse seed germination data using statistical time-to-event analysis: non-parametric and semi-parametric methods. Seed Science Research 22, 77–95.CrossRefGoogle Scholar
Mesgaran, MB, Mashhadi, HR, Alizade, H, Hunt, J, Young, KR and Cousens, RD (2013) Importance of distribution function selection for hydrothermal time models of seed germination. Weed Research 53, 89–101.CrossRefGoogle Scholar
Onofri, A, Mesgaran, MB, Neve, P and Cousens, RD (2014) Experimental design and parameter estimation for threshold models in seed germination. Weed Research 54, 425–435.CrossRefGoogle Scholar
Onofri, A, Benincasa, P, Mesgaran, MB and Ritz, C (2018) Hydrothermal-time-to-event models for seed germination. European Journal of Agronomy 101, 129–139.CrossRefGoogle Scholar
Probert, RJ (2000) The role of temperature in the regulation of seed dormancy and germination, pp. 261–292in Fenner, M (Ed.) Seeds, the ecology of regeneration in plant communities (2nd edn). Wallingford, UK, CAB International.CrossRefGoogle Scholar
R Core Team (2018) R, A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna Austria. Available at: https//www.R-projectorg/.Google Scholar
Ritz, C, Pipper, CB and Streibig, JC (2013) Analysis of germination data from agricultural experiments. European Journal of Agronomy 45, 1–6.CrossRefGoogle Scholar
Roberts, EH (1988) Temperature and seed germination. Symposia of the Society for Experimental Biology 42, 109–132.Google ScholarPubMed
Rowse, HR and Finch-Savage, WE (2003) Hydrothermal threshold models can describe the germination response of carrot (Daucus carota) and onion (Allium cepa) seed populations across both sub-and supra-optimal temperatures. New Phytologist 158, 101–108.CrossRefGoogle Scholar
Shayanfar, A, Sharifiamina, S, Moot, DJ, Moltchanova, E and Bloomberg, M (2019) Quantifying perennial ryegrass (Lolium perenne L) and white clover (Trifolium repens L) seed germination responses to water potential and temperature with a hydrothermal time model. New Zealand Journal of Agricultural Research https://doi.org/10.1080/00288233.2019.1622571CrossRefGoogle Scholar
Shor, NZ (1985) Minimization methods for non-differentiable functions. Berlin, Springer-Verlag.CrossRefGoogle Scholar
Smetham, ML (2003) A review of subterranean clover (Trifolium subterraneum L), its ecology and use as a pasture legume in Australasia. Advances in Agronomy 79, 303–350.CrossRefGoogle Scholar
Spiegelhalter, DJ, Best, NG, Carlin, BP and Van Der Linde, A (2014) The deviance information criterion, 12 years on. Journal of the Royal Statistical Society, Series B (Statistical Methodology) 76, 485–493.CrossRefGoogle Scholar
Teixeira, CS, Lucas, D and Moot, DJ (2015) Subterranean clover–literature review. SFF Project Number, 408090 Lincoln University November 2015.Google Scholar
Tribouillois, H, Dürr, C, Demilly, D, Wagner, MH and Justes, E (2016) Determination of germination response to temperature and water potential for a wide range of cover crop species and related functional groups. PloS One 11, e0161185. .CrossRefGoogle ScholarPubMed
Watt, MS and Bloomberg, M (2012) Key features of the seed germination response to high temperatures. New Phytologist 196, 332–336.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Supplementary material: File

Moltchanova et al. supplementary material

Moltchanova et al. supplementary material

Download Moltchanova et al. supplementary material(File)
File 18.5 KB