Germination of Aristida armata Under Constant and Alternating Temperatures and Its Analysis With the Cumulative Weibull Distribution as a Model

Brown, RF

doi:10.1071/bt9870581

Cited by 23 publications

(21 citation statements)

References 26 publications

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“…The equation is called monomolecular reaction function with a lag phase and is easily fitted to the rate of seed germination (Brown and Mayer, 1986). Several functions have been applied to such time variation, namely, autocatalytic reaction rate equation (Hageseth and Joyner, 1975), Gompertz function (Lapp and Skoropad, 1976), Weibull function (Bonner and Dell, 1976; Brown, 1987), logistic function (Schimpf et al. , 1977), incomplete gamma function (Thornley, 1977; Shibuya and Hayashi, 1984), monomolecular reaction function (Bierhuizen et al.…”

Section: Discussionmentioning

confidence: 99%

Thermal time approach to predicting seedling emergence dates of red clover, white clover and lucerne in farm fields

Sakanoue

2010

Grass and Forage Science

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A practical method for predicting seedling emergence dates of red and white clovers, and lucerne is proposed. The germination response at seven different constant temperatures ranging from 5 to 35°C was examined. Germination rate, which is a reciprocal of the duration for 50% germination of seeds, was linearly regressed against temperature to calculate the base temperature and thermal constant of seed germination. The calculated values were 3AE9°C and 15AE8 degree days for red clover, 4AE2°C and 13AE6 degree days for white clover, and 2AE9°C and 17AE7 degree days for lucerne. Using the base temperatures, thermal constants, and the daily mean air temperatures at the study site, the seedling emergence dates of the three forage legumes were predicted. At the same time, in an outdoor pot experiment, seeds of these legumes were sown approximately every 3 weeks and seedling emergence dates were determined. The predicted dates of seedling emergence generally fitted the observed dates. Another prediction was attempted using the base temperatures, thermal constants, and normal daily mean air temperatures recorded over more than 30 years in the study site. This prediction showed that the seedlings of the three forage legumes began to emerge at the beginning of April and could continue to emerge until the beginning of November when their non-dormant seeds were present in the site, and that when the seeds were sown from mid-November of one year to late March of the next year, the emergence of seedlings was delayed until the beginning of April.

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Section: Discussionmentioning

confidence: 99%

Thermal time approach to predicting seedling emergence dates of red clover, white clover and lucerne in farm fields

Sakanoue

2010

Grass and Forage Science

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“…The data set with cumulative frequencies of seed germination were combined with the distribution of Weibull (Weibull, 1951) using the following parameterization: Y = M(1 -exp (-(t/b) c )) (Carneiro, 1994a). The additional parameter M indicates the maximum percentage of seed germination and is necessary due to the aspects related to the censored cases (Brown, 1987;Brown & Mayer, 1988b;Carneiro 1994aCarneiro , 1996. The parameter b is the time to reach 63.21% of the maximum (M), and the c is a shape parameter which reflects skewness and accounts for the greater versatility of the distribution (Brown & Mayer, 1988b).…”

Section: Discussionmentioning

confidence: 99%

Effects of different temperatures on the performance of seeds germination of Cecropia pachystachya Trec. (Cecropiaceae)

Pilati¹,

Andrian

Carneiro

1999

Braz. arch. biol. technol.

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Seed germination of Cecropia pachystachya Trec. was investigated at 25, 30, and 35ºC using fluorescent day lights. Ten replicates each of 200 seeds were used in each thermal treatment. Germitest® paper, CEL-065®, and 11x11x5 cm Gerbox® were also used. The frequencies of radicle protrusions were counted at consecutive two hour intervals. The cumulative frequencies were combined with the distribution of Weibull using the following model: Y = M (1 - exp (-(t/b)c)). The best performance was achieved with the temperature of 30ºC ( M, 95.29%). Time to achieve 60.23% of seed germination (0.6321 x 95.29%) was 99.35 h with a spread of 7.19. All model/data set combinations had close to linear.
Com o objetivo de avaliar o desempenho germinativo de sementes de Cecropia pachystachya Trec., sob diferentes níveis de temperatura, foi realizado um experimento com sementes coletadas em fragmentos da Floresta Estacional Semidecidual Submontana e Aluvial, localizados na planície de inundação do alto rio Paraná. As sementes foram submetidas a temperaturas constantes de 25ºC, 30ºC e 35ºC. Luzes fluorescentes do tipo "luz do dia" foram mantidas acesas durante todo o período experimental. Cada tratamento consistiu de 10 repetições de 200 sementes distribuídas de maneira uniforme sobre papel especial de germinação, CEL-065, em caixas de plástico transparente do tipo gerbox medindo 11x11x5 cm. As freqüências germinativas foram avaliadas em intervalos consecutivos de duas horas. As freqüências acumuladas foram combinadas com o seguinte modelo da função de Weibull: Y = M ( 1- exp (-(t/b)c)). Os resultados indicaram que o melhor desempenho germinativo foi alcançado com a temperatura de 30ºC. A porcentagem máxima de germinação (M) foi igual a 95,29%, e o tempo (b) para a ocorrência de uma porcentagem de germinação igual a 60,23% (0,6321 x 95,29%) foi igual a 99,35 horas. A dispersão das germinações (c) durante o tempo de avaliação foi igual a 7,19, indicando que a distribuição não se afastou muito da normal com pequeno desvio padrão. Ficou evidenciado também, que as estimativas dos parâmetros tiveram um desempenho próximo à linearidade

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“…where: M is the maximum of seed germination (BROWN, 1987;BROWN;MAYER, 1988a and b;CARNEIRO, 1994CARNEIRO, , 1996CARNEIRO;GUEDES, 1995), b is time to 63.21% of M and c is the spread over the time t (CARNEIRO, 1994(CARNEIRO, , 1996CARNEIRO;GUEDES, 1995).…”

Section: Methodsmentioning

confidence: 99%

Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)

et al. 2014

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ABSTRACT. The objective of this analysis was to fit germination data of Rhipsalis cereuscula Haw seeds to the Weibull model with three parameters using Frequentist and Bayesian methods. Five parameterizations were compared using the Bayesian analysis to fit a prior distribution. The parameter estimates from the Frequentist method were similar to the Bayesian responses considering the following non-informative a priori distribution for the parameter vectors: gamma (10³, 10³) in the model M 1 , normal (0, 10 6 ) in the model M 2 , uniform (0, L sup ) in the model M 3 , exp (μ) in the model M 4 and Lnormal (μ, 10 6 ) in the model M 5 . However, to achieve the convergence in the models M 4 and M 5 , we applied the μ from the estimates of the Frequentist approach. The best models fitted by the Bayesian method were the M 1 and M 3 . The adequacy of these models was based on the advantages over the Frequentist method such as the reduced computational efforts and the possibility of comparison.Keywords: Bayesian inference, growth curve, modeling. Modelos de regressão não-linear aplicados à germinação de sementes de Rhipsalis cereuscula Haw (Cactaceae)RESUMO. Neste estudo, foi proposto o ajuste de germinação de sementes pelo modelo Weibull com três parâmetros por meio da metodologia frequentista e da Bayesiana. Na análise Bayesiana foram utilizadas cinco parametrizações para as distribuições a prior não-informativas e foram comparadas quanto ao ajuste. As estimativas dos parâmetros obtidas pela metodologia frequentista foram similares aos da metodologia Bayesiana quando considerado distribuições a priori não-informativas para o vetor de parâmetros: gama (10³, 10³) no modelo M 1 , normal (0, 10 6 ) no modelo M 2 , uniforme (0, L sup ) no modelo M 3 , exp (μ) no modelo M 4 e lognormal (μ, 10 6 ) no modelo M 5 . No entanto, para a convergência nos modelos M 4 e M 5 , foi utilizado para μ os valores obtidos pela metodologia frequentista. Os melhores modelos para a modelagem Bayesiana foram os modelos M 1 e M 3 . Estes modelos foram considerados adequados, tendo como vantagem sobre a metodologia frequentista o menor esforço computacional e a possibilidade de comparação.Palavras-chaves: inferência Bayesiana, curva de crescimento, modelagem.

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Germination of Aristida armata Under Constant and Alternating Temperatures and Its Analysis With the Cumulative Weibull Distribution as a Model

Cited by 23 publications

References 26 publications

Thermal time approach to predicting seedling emergence dates of red clover, white clover and lucerne in farm fields

Thermal time approach to predicting seedling emergence dates of red clover, white clover and lucerne in farm fields

Effects of different temperatures on the performance of seeds germination of Cecropia pachystachya Trec. (Cecropiaceae)

Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)

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