2012
DOI: 10.1590/s0103-90162012000500004
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Critical points in logistic growth curves and treatment comparisons

Abstract: Several biological phenomena have a behavior over time mathematically characterized by a strong increasing function in the early stages of development, then by a less pronounced growth, sometimes showing stability. The separation between these phases is very important to the researcher, since the maintenance of a less productive phase results in uneconomical activity. In this report we present methods of determining critical points in logistic functions that separate the early stages of growth from the asympto… Show more

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Cited by 17 publications
(10 citation statements)
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“…This point can be interpreted as an indication of maximum growth and therefore a plateau. 14 Finally, we calculated the number of days from the start of the epidemic to each peak and plateau point. The empirical means and confidence intervals (eCI) for each duration were estimated by Monte Carlo simulation.…”
Section: Methodsmentioning
confidence: 99%
“…This point can be interpreted as an indication of maximum growth and therefore a plateau. 14 Finally, we calculated the number of days from the start of the epidemic to each peak and plateau point. The empirical means and confidence intervals (eCI) for each duration were estimated by Monte Carlo simulation.…”
Section: Methodsmentioning
confidence: 99%
“…Although there are reports in the literature of tilapia with up to 5 kg mass (FAO, 2012), this value is far from the national average, which is 600g to 1 kg (NOGUEIRA, 2007). Growth stages have different biological significance, being their analysis recommended separately (PASSOS et al, 2012), as the case of tilapia mass above the average. In such cases, should take into account the growing point can be considered almost constant, in other words, when the curve is sufficiently close to the asymptote, indicating no significant difference (MISCHAN et al, 2011).…”
Section: Resultsmentioning
confidence: 99%
“…According to the analysis results, a linear-plateau regression model was used to fit the relationships between ISP and yield, and between ISP and N partial productivity, and a parabolic model was selected to fit the relationship between ISP and NUE. These analyses were performed in accordance with the NLIN procedure in SPSS 17.0 (SPSS Institute Inc., Chicago, IL, United States), and the corresponding critical inflection point of the model was determined by following the modified piecewise regression method ( Passos et al, 2012 ). To examine the key determinants for ISP, the relationship between ISP and soil physico-chemical parameters was analyzed using the linear regression procedure of SPSS 17.0 software, and the coefficient of determination of linear regression ( R 2 ) was compared to quantitatively reflect the contribution of soil physico-chemical parameters to the change of ISP.…”
Section: Methodsmentioning
confidence: 99%