Equacionamento da qualidade inicial de sementes de milho e soja para aplicação no modelo de probit

Sinicio, R.; Vidigal, Déborah de Souza; Dias, Denise Cunha Fernandes dos Santos

doi:10.1590/s0101-31222009000100014

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Many models used to describe the longevity of seed lots have been based on Probit. For example, Gianinetti and Cohn (2007), Sinicio et al (2009), Pires et al (2009), and Probert et al (2009) studied the germination of seed lots using Probit analysis.…”

Section: Introductionmentioning

confidence: 99%

Probit regression to estimate the physiological potential of hybrid maize seed

et al. 2015

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This work was carried out to study the physiological potential of artificially aged seed lots of maize. The specific aim of this study was to fit a simplified equation from Andreoli, , and present a methodology using probit regression analysis, given by the equation . We used seeds from three lots of the maize hybrid OC 705 which were submitted to the accelerated aging test, at the temperature of 43 ºC, every 24 hours. The simplified equation did not provide a good fit to the data, with r2 of at most 92%. Pearson's Chi-square test and the log-likelihood ratio Chi-square test indicated that probit regression had a good fit to the data, providing estimated values with high accuracy. It was observed that lot three maintained the highest vigor throughout the storage period.

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Section: Introductionmentioning

confidence: 99%

Probit regression to estimate the physiological potential of hybrid maize seed

et al. 2015

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Cauchy, Cauchy–Santos–Sartori–Faria, Logit, and Probit Functions for Estimating Seed Longevity in Soybean

et al. 2019

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Seed longevity is characterized as the time for which seed remains viable during storage. Seed longevity can be estimated by a Probit model that determines the period in which 50% of seeds have lost viability (P 50 ). The transformed data are binary and when they are not normally distributed, it is necessary to modify the Probit model or apply other functions to estimate longevity. This work aimed studied the use of the Logit, Cauchy, and Cauchy-Santos-Sartori-Faria (Cauchy-SSF) functions to estimate the longevity of soybean seed [Glycine max (L.) Merr.] and compared Probit longevity models for the ordinary least squares (OLS) adjustment method and the generalized linear model (GLM). Ten seed lots were used to estimate water content, germination, and longevity. The P 50 data were transformed via the Probit, Logit, Cauchy, and Cauchy-SSF functions to estimate the coefficients of determination, the Akaike information criterion, deviance, dispersion, and the regression residuals. The effect on the results was observed, depending on the link function. The Cauchy-SSF function as part of the OLS method estimated longevity in eight seed lots within the interval of interest (II), and the Cauchy function as part of the GLM estimated longevity in nine seed lots. The Cauchy, Cauchy-SSF, and Logit models were capable of estimating the longevity of soybean seeds (P 50 ) slightly better than the Probit model. We suggest the Cauchy-SSF function for the OLS method and the Cauchy function for the GLM method to estimate soybean seed longevity when the data are not normally distributed. core Ideas• The Cauchy, Cauchy-Santos-Sartori-Faria Cauchy-SSF, and Logit functions estimated longevity in soybean seeds more robustly than the Probit function. • The ordinary least squares method combined with the Cauchy-SSF function is as good as the generalized linear model method with the Cauchy function. • The selection of the function changes the estimated time when 50% of seeds have lost viability, emphasizing the importance of the correct choice.Abbreviations: Cauchy-SSF, Cauchy-Santos-Sartori-Faria; GLM, generalized linear model; II, interval of interest; OLS, ordinary least squares; P 50, period in which 50% of seeds have lost viability, σ, standard deviation. bIoMetrY, ModeLInG, And stAtIstIcsPublished in Agron.

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Equacionamento da qualidade inicial de sementes de milho e soja para aplicação no modelo de probit

Cited by 2 publications

References 6 publications

Probit regression to estimate the physiological potential of hybrid maize seed

Probit regression to estimate the physiological potential of hybrid maize seed

Cauchy, Cauchy–Santos–Sartori–Faria, Logit, and Probit Functions for Estimating Seed Longevity in Soybean

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