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Emergence speed comparison by non-linear regression and approached by time-to-event models for censored data

Published online by Cambridge University Press:  31 January 2022

Thomas B. Michelon*
Affiliation:
Department of Plant Science, Federal University of Paraná – R, dos Funcionários, 1540, Curitiba, PR CEP 80035-050, Brazil
Andreza C. Belniaki
Affiliation:
Department of Plant Science, Federal University of Paraná – R, dos Funcionários, 1540, Curitiba, PR CEP 80035-050, Brazil
Cesar A. Taconeli
Affiliation:
Departament of Statistics, Federal University of Paraná – R, Evaristo F, Ferreira da Costa, 408, Curitiba, PR CEP 81530-015, Brazil
Elisa S. N. Vieira
Affiliation:
Embrapa Forestry, Estrada da Ribeira, km 111, Colombo, PR CEP 83411-000, Brazil
Maristela Panobianco
Affiliation:
Department of Plant Science, Federal University of Paraná – R, dos Funcionários, 1540, Curitiba, PR CEP 80035-050, Brazil
*
Author for Correspondence: Thomas B. Michelon, E-mail: thomasnbrunomichelon@gmail.com

Abstract

Determining the germination speed is essential in experiments in the field of seed technology, as it allows the performance evaluation of a seed lot and the creation of predictive models. To this end, the literature addresses several methods and indexes. The objective of this study was to compare the main methods of emergence speed analysis in seeds, namely the non-linear regression models and the Emergence Speed Index (ESI), with the time-to-event models. The research was conducted with peach palm seeds (Bactris gasipaes) that were measured for viability and vigour through daily evaluations for 4 months. Vigour was evaluated by the quantification of the seed emergence speed, which was performed in three ways: ESI, non-linear regression and non-linear regression considering germination as a time-to-event event. From the results obtained, we conclude that the ESI is not a good indicator to evaluate the emergence speed; the non-linear regression model underestimates the errors and, thus, increases the probability of misclassifying treatments; the time-to-event model is more reliable in classifying treatments according to the emergence speed.

Type
Research Paper
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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References

Akaike, H (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716723.Google Scholar
Bates, D and Watts, D (1988) Nonlinear regression and its applications. New York, NY, USA, John Wiley and Sons Inc.CrossRefGoogle Scholar
Benjamini, Y and Yekutieli, D (2001) The control of the false discovery rate in multiple testing under dependency. The Annals of Statistics 29, 11651188.CrossRefGoogle Scholar
Bradburn, MJ, Clark, TG, Love, SB and Altman, DG (2003) Survival analysis part III: multivariate data analysis – choosing a model and assessing its adequacy and fit. British Journal of Cancer 89, 605611.CrossRefGoogle Scholar
Bradford, K (2002) Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Science 50, 248260.CrossRefGoogle Scholar
Brasil (2013) Instruções para Análise de Sementes de Espécies Florestais. Brasília, DF, Brazil, Ministério da Agricultura Pecuária e Abastecimento (in Portuguese).Google Scholar
Brown, RF and Mayer, DG (1986) A critical analysis of Maguire's germination rate index. Journal of Seed Technology 10, 101110.Google Scholar
Brown, RF and Mayer, DG (1988a) Representing cumulative germination. 1. A critical analysis of single-value germination indices. Annals of Botany 61, 117125.CrossRefGoogle Scholar
Brown, RF and Mayer, DG (1988b) Representing cumulative germination. 2. The use of the Weibull function and other empirically derived curves. Annals of Botany 61, 127138.CrossRefGoogle Scholar
Carroll, RJ, Wang, S, Simpson, DG, Stromberg, AJ and Ruppert, D (1998) The sandwich (robust covariance matrix) estimator. Technical Report, Department of Statistics. A & M, pp. 116.Google Scholar
Crane, M, Newman, MC, Chapman, FP and Felon, J (2002) Risk assessment with time to event models. New York, USA, Lewis Publishers.Google Scholar
Dey, AK and Kundu, D (2010) Discriminating between the log-normal and log-logistic distributions. Communications in Statistics – Theory and Methods 39, 280292.Google Scholar
Gardarin, A, Dürr, C and Colbach, N (2011) Prediction of germination rates of weed species: relationships between germination speed parameters and species traits. Ecological Modelling 222, 626636.Google Scholar
ISTA – The International Seed Testing Association (2020) International rules for seed testing. Bassersdorf, CH, International Rules for Seed Testing.Google Scholar
Kader, MA (2005) A comparison of seed germination calculation formulae and the associated interpretation of resulting data. Journal & Proceeding of the Royal Society of New South Wales 138, 6575.Google Scholar
Maguire, JD (1962) Speed of germination – aid in selection and evolution for seedling emergence and vigor. Crop Science 2, 176177.CrossRefGoogle Scholar
Mccullagh, P and Nelder, JA (1989) Generalized linear models (2nd edn). Florida, USA, Chapman & Hall.CrossRefGoogle Scholar
Mcnair, JN, Sunkara, A and Frobish, D (2012) How to analyze seed germination data using statistical time-to-event analysis: non-parametric and semi-parametric methods. Seed Science Research 22, 7795.CrossRefGoogle Scholar
Meerow, AW and Broschat, TK (1991) Palm seed germination. University of Florida, Bulletin 274, 19.Google Scholar
Olsson, U (2002) Generalized linear models: an applied approach. Lund, Sweden, Studentlitteratur.Google Scholar
O'Neill, ME, Thomson, PC, Jacobs, BC, Brain, P, Butler, RC, Turner, H and Mitaka, B (2004) Fitting and comparing seed germination models with a focus on the inverse normal distribution. Australian and New Zealand Journal of Statistics 46, 349366.CrossRefGoogle Scholar
Onofri, A, Benicasa, P, Mesgaran, MB and Ritz, C (2018) Hydrothermal-time-to-event models for seed germination. European Journal of Agronomy 101, 129139.CrossRefGoogle Scholar
Onofri, A, Gresta, F and Tei, F (2010) A new method for the analysis of germination and emergence data of weed species. Weed Research 50, 187198.CrossRefGoogle Scholar
Onofri, A, Mesgaran, MB, Tei, F and Cousens, RD (2011) The cure model: an improved way to describe seed germination? Weed Research 51, 516524.CrossRefGoogle Scholar
Onofri, A, Piepho, HP and Kozak, M (2019) Analyzing censored data in agricultural research: a review with examples and software tips. Annals of Applied Biology 174, 313.CrossRefGoogle Scholar
Pire, R and Vargas-Simón, G (2019) Recurrent inconsistencies in publications that involve Maguire's germination rate formula. Forest Systems 28, 15.CrossRefGoogle Scholar
Ranal, MA and Santana, DG (2006) How and why to measure the germination process? Revista Brasileira de Botanica 29, 111.Google Scholar
Ribeiro-Oliveira, JP and Ranal, MA (2016) Sample size in studies on the germination process. Botany 94, 103115.CrossRefGoogle Scholar
Ritz, C, Baty, F, Streibig, JC and Gerhard, D (2015) Dose-response analysis using R. PLoS ONE 10, 113.CrossRefGoogle ScholarPubMed
Ritz, C, Pipper, CB and Streibig, JC (2013) Analysis of germination data from agricultural experiments. European Journal of Agronomy 45, 16.Google Scholar
Romano, A and Stevanato, P (2020) Germination data analysis by time-to-event approaches. Plants 9, 25.CrossRefGoogle ScholarPubMed
Shafii, B and Price, WJ (2001) Estimation of cardinal temperatures in germination data analysis. Journal of Agricultural, Biological, and Environmental Statistics 6, 356366.CrossRefGoogle Scholar
Shafii, B, Price, WJ, Swensen, JB and Murray, GA (1991) Nonlinear estimation of growth curve models for germination data analysis. Conference on Applied Statistics in Agriculture 3, 1936.Google Scholar
Soltani, E, Ghaderi-Far, F, Baskin, CC and Baskin, JM (2015) Problems with using mean germination time to calculate rate of seed germination. Australian Journal of Botany 63, 631635.CrossRefGoogle Scholar
Throneberry, GO and Smith, FG (1955) Relation of respiratory and enzymatic activity to corn seed viability. Plant Physiology 30, 337344.CrossRefGoogle ScholarPubMed
Yu, B and Peng, Y (2008) Mixture cure models for multivariate survival data. Computational Statistics & Data Analysis 52, 15241532.Google Scholar