Assessing sugarcane (Saccharum spp.) stalk growth helps to adequately manage the phenological stages of the crop. The aim of this study was to describe the height-growth curve of four sugarcane varieties (RB92579, RB93509, RB931530 and SP79-1011), in irrigated plant-cane and ratoon cane plantations, using the Logistic and Gompertz nonlinear models, while considering all deviations from assumptions. The model parameters were estimated based on the least squares method using the Gauss-Newton algorithm. To select the most suitable model, nonlinear measures, adjusted coefficient of determination (R2 adj), residual standard deviation (RSD), and corrected Akaike information criterion (AICc) were used. Based on the best models, stalk height growth rates and crop phenological stages were determined using critical points. All tests were performed in the free software environment for statistical computing and graphics, R. In general, the Logistic and Gompertz models without AR(1) better described the plant-cane and ratoon cane stalk height, respectively. All varieties showed early growth, and the RB92579 variety presented higher rates in both cycles.

The objective of this work was to evaluate how the parameterization and the application of different allometric values affect the obtention of the most adequate fit of von Bertalanffy’s model, in the description of the growth curve of meat-producing mammals (bovine, pigs, rabbits, and sheep). Among the nonlinear models, von Bertalanffy’s has been very often applied in several areas, with different parameterizations. This model has been commonly used with an allometric value of m = 2/3; however, for mammals, it is believed that this value can be m = 3/4. The analyzed data referring to the mass of meat-producing mammals according to their age were obtained from research institutions and from the literature. The results showed that von Bertalanffy’s model, with the allometric value of m = 3/4 and the used parameterization, provided better adjustments to quality evaluators. Besides, the model softened the overestimation of parameter a, giving a direct interpretation of parameter b, with the lowest values for curvature measurements, mainly for the parametric ones, and provided more reliable adjustments. Von Bertalanffy’s model can be used in the description of the growth curves of meat-producing mammals.

The growth curves of animals, in general, have an “S” shape, also known as sigmoidal curves. This type of curve is well fitted by nonlinear regression models, including von Bertalanffy’s model, which has been widely applied in several areas, being presented in literature through different parameterizations, which in practice, can complicate its understanding, affect nonlinearity measures and inferences about parameters. To quantify the nonlinearity present in a Bates and Watts model, a geometric concept of curvature has been used. The aim of this work was to analytically develop three parameterizations of the von Bertalanffy’s nonlinear model referring to its nonlinearity, implications for inferences and to establish relationships between parameters in the different ways of expressing the models. These parameterizations were adjusted to the growth data of sheep. For each parameterization, the intrinsic and parametric curvature measurements described by Bates and Watts were calculated. The parameterization choice affects nonlinearity measures, consequently, influences the reliability and inferences about estimated parameters. The forms most used in literature showed the greatest deviations from linearity, showing the importance of analyzing these measures in any growth curve study. Parameterization should be used in which the b estimate represents the abscissa of the inflection point, as it presents minor linearity deviations and direct biological interpretation for all parameters.

Comparação dos modelos polinomial e não lineares na descrição do crescimento de pimenta - DOI:10.5039/agraria.v14i4a7180

The objective of this work was to study the growth curves of total dry matter (TDM) accumulation of the P30F33 and GNZ2004 maize hybrids using nonlinear models. The used models were: Brody, Gompertz, logistic, Meloun I, Meloun II, Michaelis-Menten, modified Michaelis-Menten, Mitscherlich, Richards, Schnute, von Bertalanffy, and Weibull. To estimate the parameters, the least squares method and the Gauss-Newton convergence algorithm were used. The adjusted coefficient of determination, the residual standard deviation, and the Akaike information criterion were used as criteria to evaluate the goodness of fit of the models. The Gauss-Newton method did not converge for 8 out of the 12 models studied. The Gompertz, logistic, von Bertalanffy, and Weibull models were considered appropriate for fitting the dry matter accumulation of the evaluated maize hybrids. The estimated TDM was 34,700 and 31,980 kg ha⁻¹ for GNZ2004 and P30F33, respectively. The maximum daily gain in TDM was 483 and 381 kg ha⁻¹, respectively, reached at 83 days after emergence, with TDM stabilization at 121 and 129 days after emergence. The logistic model is the best one to describe the TDM accumulation of the GNZ2004 and P30F33 maize hybrids.

The dynamics of organic waste decomposition in the soil can be described by nonlinear regression models, however, the theory for regression models is valid for sufficiently large samples, and in general, in small samples, these properties are unknown. One of the methods for data analysis that has been widely used to overcome this problem is the bayesian inference, as it has the advantage of being able to work with small samples, in addition to allowing the incorporation of information from previous studies, and even having a probability distribution for the parameters, consequently, to present a direct interpretation for the credibility interval. However, criticism has been made because of the effect that a prior subjective distribution can have on posterior distribution. One way of determining objective prior is through of maximum entropy prior distributions. For data of organic waste decomposition in the soil, little is known about the probability distributions of the parameters. The present study aimed to use of maximum entropy prior distributions to the parameters of the Stanford & Smith nonlinear model. In addition, using simulated data, to understand the effect that hyperparameters of prior distribution has on the posterior curve, and also to apply the methodology in the description of CO2 mineralization data from swine manure applied to the soil surface. Data analyzed came from an experiment conducted in a laboratory that evaluated the carbon mineralization of swine manure on the soil surface over time. The posterior distributions were obtained, so the bayesian methodology with maximum entropy prior was efficient in the study of the Stanford & Smith nonlinear model to the data of carbon mineralization of swine manure on the soil surface.