Nonlinear models to describe production of fruit in Cucurbita pepo and Capiscum annuum

Lúcio, Alessandro Dal’Col; Nunes, Luís; Rego, Francisco

doi:10.1016/j.scienta.2015.07.021

Cited by 23 publications

(16 citation statements)

References 18 publications

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“…Several studies have focused on the use of these evaluation criteria, namely Dionello et al (2009) who studied models describing pineapple drying curves using R², SDR, and MAPE as selection criteria; Terra et al (2010) who applied R², SDR, and AIC as evaluation criteria to analyze the fitting of Gompertz and Logistic models to data of pygmy date palm fruits; Reis et al (2014) who studied clusters of garlic accessions, and concluded that, based on R², MSE, and MAD, the best-fitting model was the Logistic model; Lúcio et al (2015) who analyzed nonlinear models in the production of pumpkin and bell pepper using R²aj and standard error as evaluation criteria; and Deprá et al (2016) who evaluated logistic models for the growth of creole corn cultivars and of maternal half-sib progeny using R² and MAD to assess goodness of fit. Silveira et al (2012) reported that the greater the number of evaluation criteria applied, the more adequate is the indication of which are the bestfitting models.…”

Section: Traitsmentioning

confidence: 99%

Growth models for morphological traits of sunn hemp

Filho

Facco

et al. 2017

SCA

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The objective of the present study was to fit Gompertz and Logistic nonlinear to descriptions of morphological traits of sunn hemp. Two uniformity trials were conducted and the crops received identical treatment in all experimental area. Sunn hemp seeds were sown in rows 0.5 m apart with a plant density of 20 plants per row meter in a usable area of 52 m × 50 m. The following morphological traits were evaluated: plant height (PH), number of leaves (NL), stem diameter (SD), and root length (RL). These traits were assessed daily during two sowing periods—seeds were sown on October 22, 2014 (first period) and December 3, 2014 (second period). Four plants were randomly collected daily, beginning 7 days after first period and 13 days after for second period, totaling 94 and 76 evaluation days, respectively. For Gompertz models the equation was used y=a*e^((?-e?^((b-c*xi))and Logistic models the equation was used yi= a/(1+e^((-b-c*xi)). The inflection points of the Gompertz and Logistic models were calculated and the goodness of fit was quantified using the adjusted coefficient of determination, Akaike information criterion, standard deviation of residuals, mean absolute deviation, mean absolute percentage error, and mean prediction error. Differences were observed between the Gompertz and Logistic models and between the experimental periods in the parameter estimate for all morphological traits measured. Satisfactory growth curve fittings were achieved for plant height, number of leaves, and stem diameter in both models using the evaluation criteria: coefficient of determination (R²), Akaike information criterion (AIC), standard deviation of residuals (SDR), mean absolute deviation (MAD), mean absolute percentage error (MAPE), and mean prediction error (MPE).

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Section: Traitsmentioning

confidence: 99%

Growth models for morphological traits of sunn hemp

Filho

Facco

et al. 2017

SCA

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“…The logistic model was selected a priori since it presents lower intrinsic and parametric nonlinearity values when compared with other nonlinear growth models. In addition, the logistic model was selected in other researches with multipleharvested crops (LÚCIO et al, 2015;DIEL et al, 2019;SARI et al, 2018). The logistic model for the cultivars in the E1 and E2 seasons was specified as…”

Section: Statistical Analyzesmentioning

confidence: 99%

“…For multiple-harvest crops, logistic regression models can efficiently describe fruit production which is the appropriate for crops such as Capsicum annuum, Cucurbita pepo, Solanum melongena, Phaseolus vulgaris and Fragaria ananassa (DIEL et al, 2019;LUCIO et al, 2016;LÚCIO;NUNES;REGO, 2015;SARI, et al, 2018;. For Fragaria ananassa, DIEL et al (2019) modeled the fruit production as a function of STa (accumulated thermal sum) for the logistic, Gompertz and von Bertalanffy models in different parameterizations and concluded that the Logisitic model described fruit production best while the models of Gompertz and von Bertalanffy overestimate the parameter that represent the production.…”

Section: Introductionmentioning

confidence: 99%

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

Diel

Lúcio

Valera³

et al. 2020

Cienc. Rural

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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.

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“…These models have the advantage of having smaller number of parameters, generally with biological interpretation, besides facilitating the estimation of daily growth rates, with the maximum rate occurring on the day of the model curve inflection (Gurgel et al, 2011). According to Lúcio et al (2015) and Sari et al (2019), through the inflection point (IP) it is possible to determine the phenological phases of the crop and its duration.…”

Section: Introductionmentioning

confidence: 99%

Comparison of polynomial and nonlinear models on description of pepper growth

Jane

Fernandes

Silva

et al. 2019

Agraria

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Pepper (Capsicum sp.) is important for the Brazilian agribusiness, serving as raw material for the food, pharmaceutical and cosmetic industries. The adequate evaluation of its plants growth may help in understanding the causes of crops yield variation, with it being able to be studied by regression models, which help to adequate the management with the different phenological phases. This study aimed to compare the fit of linear Polynomial model and the Logistic and Gompertz nonlinear models in the description of pepper plants growth from the Doce cultivar. Estimates were obtained by the Gauss-Newton method, with the quality of fitted models compared by graphical analysis and evaluators: adjusted coefficient of determination (R 2 adj), Residual Standard Deviation (RSD) and the corrected Akaike Information Criterion (AICc). Residues were normal, independent and homocedastic at 5% level of significance. All models properly described the height of the Doce cultivar. The Logistic model was the most adequate according to the fitting evaluators, having higher value of R 2 adj , and lower RSD and AICc values.

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Nonlinear models to describe production of fruit in Cucurbita pepo and Capiscum annuum

Cited by 23 publications

References 18 publications

Growth models for morphological traits of sunn hemp

Growth models for morphological traits of sunn hemp

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

Comparison of polynomial and nonlinear models on description of pepper growth

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