Gompertz and Logistic Models to the Productive Traits of Sunn Hemp

Abstract

Studies on growth models for productive character of sunn hemp are important to know the behavior of the culture. Therefore, the objective of this research was to adjust non-linear models, Gompertz and Logistic, in the description of productive traits of sunn hemp in two sowing periods. Two uniformity trials were performed. The evaluations began on October the 29 th 2014 and December the 16 th 2014, totaling 94 and 76 evaluation days for periods 1 and 2, respectively. After the emergence of the seeds of sunn hemp, for first period from 7 days after sowing, and from 2 to 13 days after sowing, on each day, they were collected randomly four plants. The traits: fresh matter leaf, stem, root, shoot, and total, and dry matter leaf, stem, root, shoot, and total. For both models the confidence interval was calculated of parameters a, b and c. The adjustment quality of the Gompertz and Logistic models was verified by the determination coefficient, the Akaike information criteria, residual standard deviation, mean absolute deviation, mean absolute percentage error and mean prediction error. The Gompertz model when compared between the sowing periods through the confidence interval of the parameters, for the productive traits, differs. The same result was found for the Logistic model. The growth models of Gompertz and Logistic presented good adjustment quality.

Nonlinear Models for Plant Height of Rye Cultivars at Sowing Times

“…Comparing the estimates of the parameters of the nonlinear models for plant height, between the sowing times in each cultivar, it was verified was no significant effect in the estimates of the parameters in 75.83% of the comparisons (Table 4). This comparison between the estimates of the parameters was found in a study comparing the sowing times of Crotalaria juncea in the Gompertz and Logistic models (Bem et al, 2018). However, in this study a smaller non-significant effect was observed in the estimates of the parameters in the comparisons between the sowing times.…”

“…He might also predict the moment in which the crop will start to decline in growth, until it reaches the maximum peak, resembling the asymptote of the chosen model. Therefore, according to Bem et al (2018), the period between the maximum point of acceleration and the end of the point of inflection is the best time to infer about management procedures such as fertilization, pest and disease control, and herbicide application, since in this interval the plant will respond efficiently. However, crop management is necessary along the whole period in which the crop is in the field.…”

“…Among the mathematical models that describe plant growth, the nonlinear Gompertz and Logistic models stand out, for being one of the most employed to describe plant behavior based in the observation of the crop itself. The adjustment of these models has been made to evaluate the triticale plants (Karadavut, 2009), the height of maize (Mangueira, Savian, Muniz, Sermarini, & Crosariol Neto, 2016), the development of cashew (Muianga, Muniz, Nascimento, Fernandes, & Savian, 2016), the morphological characters of Crotalaria juncea (Bem et al, 2017), the growth of cocoa fruits (Muniz, Nascimento, & Fernandes, 2017), and the productive features of Crotalaria juncea (Bem et al, 2018). The evaluations were made in the times between the appearance of the first expanded leaf, stage 1 (Large, 1954) until November 3, 2016 (time 1), October 27, 2016 (time 2), November 10, 2016 (time 3), and November 18, 2016 (times 4 and 5).…”

Modeling the Growth of Sudangrass Cultivars at Sowing Times

“…It is possible to observe that the fresh shoot mass (FSM) and the dry shoot mass (DSM) as a function of accumulated solar radiation (ASR), showed dispersion in the sigmoidal shape, justifying the fit of the Gompertz and Logistic models. Bem et al (2018) to compare sowing dates of crotalária juncea.…”

“…The nonlinear Gompertz and Logistic models are used to describe plant growth, as they have the same sigmoidal shape of the growth curves. These two models were studied to describe the growth of sunflower (Bem et al, 2018), cocoa (Muniz, Nascimento, & Fernandes, 2017), and coffee cultivar Rubi MG 1192 (Pereira et al, 2014) and were considered adequate.…”