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Probit or Logit? Which is the better model to predict the longevity of seeds?

Published online by Cambridge University Press:  10 July 2020

Rute Q. de Faria Open the ORCID record for Rute Q. de Faria [Opens in a new window] ,
Amanda R. P. dos Santos ,
Deoclecio J. Amorim ,
Renato F. Cantão ,
Edvaldo A. A. da Silva  and
Maria M. P. Sartori
Rute Q. de Faria*
Affiliation:
Department of Agricultural Engineering, Instituto Federal de Educação Ciência e Tecnologia Goiano, Campus Urutaí, Rod. Geraldo Silva Nascimento, Km-2,5, Zona Rural, Urutaí - GO75790-000, Brazil
Amanda R. P. dos Santos
Affiliation:
Department of Production and Plant Breeding, School of Agriculture, Universidade Estadual Paulista (UNESP), Botucatu. Av. Universitária, n° 3780 - Altos do Paraíso, Botucatu, São Paulo18610-034, Brazil
Deoclecio J. Amorim
Affiliation:
Department of Production and Plant Breeding, School of Agriculture, Universidade Estadual Paulista (UNESP), Botucatu. Av. Universitária, n° 3780 - Altos do Paraíso, Botucatu, São Paulo18610-034, Brazil
Renato F. Cantão
Affiliation:
Universidade Federal de São Carlos, Campus de Sorocaba (UFSCar), Rodovia João Leme dos Santos (SP-264), Km 110, Bairro do Itinga, Sorocaba, São Paulo18052-780, Brazil
Edvaldo A. A. da Silva
Affiliation:
Department of Production and Plant Breeding, School of Agriculture, Universidade Estadual Paulista (UNESP), Botucatu. Av. Universitária, n° 3780 - Altos do Paraíso, Botucatu, São Paulo18610-034, Brazil
Maria M. P. Sartori
Affiliation:
Department of Production and Plant Breeding, School of Agriculture, Universidade Estadual Paulista (UNESP), Botucatu. Av. Universitária, n° 3780 - Altos do Paraíso, Botucatu, São Paulo18610-034, Brazil
*
*Author for Correspondence: Rute Q. de Faria, E-mail: rute.faria@ifgoiano.edu.br
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Abstract

The prediction of seed longevity (P50) is traditionally performed by the use of the Probit model. However, due to the fact that the survival data are of binary origin (0,1), the fit of the model can be compromised by the non-normality of the residues. Consequently, this leads to prediction losses, despite the data being partially smoothed by Probit and Logit models. A possibility to reduce the effect of non-normality of the data would be to apply the principles of the central limit theorem, which states that non-normal residues tend to be normal as the n sample is increased. The Logit and Probit models differ in their normal and logistic distribution. Therefore, we developed a new estimation procedure by using a small increase of the n sample and tested it in the Probit and Logit functions to improve the prediction of P50. The results showed that the calculation of P50 by increasing the n samples from 4 to 6 replicates improved the index of correctness of the prediction. The Logit model presented better performance when compared with the Probit model, indicating that the estimation of P50 is more adequate when the adjustment of the data is performed by the Logit function.

Keywords

central limit theoremlink functionsresidual normalityseed conservationseed viabilitysoybean
Type
Technical Update
Information
Seed Science Research , Volume 30 , Issue 1 , March 2020 , pp. 49 - 58
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Agresti, A (2013) Categorical data analysis. Hoboken, NJ, USA, John Wiley & Sons.Google Scholar
Bobkov, SG, Chistyakov, GP, Götze, F (2014) Probability theory and related fields, vol. 159, pp. 1–59. Springer-Verlag Berlin Heidelberg.Google Scholar
Chen, G and Tsurumi, H (2011) Probit and Logit model selection. Communications in Statistics – Theory and Methods 40, 159–175.CrossRefGoogle Scholar
Demir, I and Mavi, K (2003) Effect of controlled hydration treatments on storage longevity of aubergine seeds during development. Israel Journal of Plant Sciences 51, 291–295.CrossRefGoogle Scholar
Ellis, RH and Roberts, EH (1981) The quantification of aging and survival in orthodox seeds. Seed Science and Technology 9, 373–409.Google Scholar
Ellis, RH, Hong, TD, Roberts, EH and Tao, K-L (1990) Low moisture content limits to relations between seed longevity and moisture. Annals of Botany 65, 493–504.CrossRefGoogle Scholar
Fischer, H (2011) History of the central limit theorem: from classical to modern probability theory. New York, Springer.CrossRefGoogle Scholar
International Seed Testing Association (ISTA) (1985) International rules for seed testing. Seed Science & Technology 13, 300–520.Google Scholar
Marcondes, MC, Andreoli, C and Miglioranza, E´ (2011) Viability equation to determine the longevity of fungicide-treated seeds of wheat stored in a conventional warehouse. Acta Scientiarum 33, 539–544.Google Scholar
McDonald, MB, Evans, AF, Bennett, MA, Fujimura, K, Sako, Y, Xu, L and Hoffmaster, A (2008) Using the seed vigor imaging system for improving stand establishment. Acta Horticulturae 782, 83–91.CrossRefGoogle Scholar
Obunyali, CO, Muasya, RM, Nyamongo, DO and Van Rheenen, HA (2008) Study on comparative longevity of banked and freshly collected seeds of two wild sesame species. South African Journal of Botany 74, 764–767.CrossRefGoogle Scholar
Oral, E (2006) Binary Regression with Stochastic Covariates, Communications in Statistics - Theory and Methods 35(8), 1429–1447.CrossRefGoogle Scholar
Pereira Lima, JJ, Buitink, J, Lalanne, D, Rossi, RF, Pelletier, S, da Silva, EAA and Leprince, O (2017) Molecular characterization of the acquisition of longevity during seed maturation in soybean. PLoS ONE 12, e0180282.CrossRefGoogle Scholar
Schützenmeister, A, Jensen, U and Piepho, H-P (2012) Checking normality and homoscedasticity in the general linear model using diagnostic plots. Communications in Statistics: Simulation and Computation 41, 141–154.CrossRefGoogle Scholar
Sinício, R (2004) Generalised longevity model for orthodox seeds. Biosystems Engineering 89, 85–92.CrossRefGoogle Scholar
Thode, HC (2002) Testing for normality. New York: Marcel Dekker.CrossRefGoogle Scholar
Walters, C (2015) Genebanking seeds from natural populations. Natural Areas Journal 35, 98–105.CrossRefGoogle Scholar