Production of biquinho pepper in different growing seasons characterized by the logistic model and its critical points

Abstract

The objective of this study was to characterize the production of biquinho pepper through the interpretation of parameter estimates from the logistic model and its critical points obtained by the partial derivatives of the function, and to indicate the best cultivar and growing season for subtropical climate sites. For this, a 2x3 factorial experiment was conducted with two cultivars of biquinho pepper (BRS Moema and Airetama biquinho) in three growing seasons (E1: October 2015, E2: November 2015, E3: January 2016). The logistic non-linear model for fruit mass was specified as a function of the accumulated thermal sum, and the critical points were calculated through the partial derivatives of the model, in order to characterize the productive performance of the crop by the biological interpretation of the estimates of the three set parameters. In E3, temperatures close to 0 ºC during the experiment were lethal to the plants, and a linear regression model was used in this case. The production of the cultivars in E1 and E2 were well characterized by the estimated logistic models, and the most productive cultivar was Airetama biquinho in all evaluated seasons. This cultivar also presented higher concentration of production. The two cultivars did not differ significantly with regards to productive precocity. For E3, it was not possible to interpret the parameters in the same way as for E1 and E2, since the use of the linear model did not allow the same interpretations performed for the nonlinear model, reaffirming its applicability horticultural crops of multiple harvests.

Using nonlinear models to define production, production rate, and precocity of strawberry cultivars

“…For non-linear regression models, the quality of the fit must be defined from the results of the linearity measures proposed by Bates and Watts. The use of bootstrap confidence intervals (CI) to estimate the parameters circumvents the non-compliance with the assumptions of the regression models, normality, heterogeneity and autocorrelation of errors, and also allows comparisons to be made between the different treatments (Sari et al 2019;Diel et al 2020).…”

“…These results can be interpreted thanks to the use of the non-linear Logistic regression model, which has critical points with biological interpretation. With the evaluation of only one variable, it is possible to obtain several relevant information for the production and for the decision making on which cultivar to choose according to the interests of the producer (Sari et al, 2018;Diel et al, 2019;Diel et al, 2020), whereas otherwise, this information would not be discovered.…”

“…When assessing the production of Capsicum chinense variety, Diel et al (2020) adjusted a linear model and were able to determine only which cultivar was more productive, but failed to show the productive response of the crop throughout the cycle, unlike when they adjusted the Logistic model, characterized the entire crop production cycle.…”

“…Nonlinear growth models can be used to extract as much information as possible from a data set. They provide the reality of the production cycle in each experimental treatment, allowing inferences and interpretations not obtained in analyzes of variances or complementary statistical tests such as comparisons of treatment averages or analyzes of linear regressions (Sari et al, 2018;Diel et al, 2020).…”

Promising antifungal activity of two varieties of Capsicum chinense against Sclerotinia sclerotiorum, Rhizopus stolonifer and Colletotrichum goleosporoides

“…Little beak pepper (Capsicum chinense Jacq.) is a species that has small round fruit -with tails that resemble a bird's beakwhich have low pungency and are characterized as sweet fruit that may be consumed fresh or processed (Diel et al, 2020). Another Capsicum variety that grows in Brazil was evaluated by this study: bode pepper (pimenta-bode in Brazilian Portuguese).…”