Describing tomato plant production using growth models

Sari, Bruno Giacomini; Lúcio, Alessandro Dal’Col; Santana, Cinthya Souza; Savian, Taciana Villela

doi:10.1016/j.scienta.2018.10.044

Cited by 22 publications

(18 citation statements)

References 15 publications

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“…Gonçalves et al (2016) when studying the drying of green banana pulp found R² values above 0.95 for the Lewis, Page, Henderson and Midilli models. Sari et al (2019), in studies with tomato growth, found R 2 values above 0.96 for nonlinear regression models, and considered adjustment quality.…”

Section: Resultsmentioning

confidence: 99%

Natural convection drying kinetics of ‘Prata’ and ‘D’água’ banana cultivars (Musa ssp) by nonlinear regression models

et al. 2019

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Banana is among fruits most planted in tropical countries and belongs to the fruit group most consumed in the world; however, banana needs proper conservation techniques. The aim of this study was to describe the drying kinetics of ‘Prata’ and ‘D’água’ banana cultivars at temperatures of 40 and 70°C, comparing the Henderson, STPE, Lewis, Page and Fick regression models, estimating the Absolute Drying Rate (ADR). Parameters were estimated with R and SAS Studio softwares, using for comparison and selection models of the R²aj, RSD and corrected Akaike Information Criteria. The Page and Fick models did not adjust, and the others presented good adjustment to data. The Henderson model was the most suitable to describe data of ‘Prata’ banana at both temperatures and ‘D’água’ banana at 70°C and Lewis at 40°C for this cultivar. The drying rate of ‘Prata’ banana at temperatures of 40 and 70°C were 0.00079 g of water/ min and 0.00400 g of water/min respectively and for ‘D’água’ banana, drying rates were 0.00111 g of water/min. and 0.00495 g of water/min., respectively. Using ADR, it was observed that in one third of the drying period, there was 70% of moisture loss at 70°C.

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Section: Resultsmentioning

confidence: 99%

Natural convection drying kinetics of ‘Prata’ and ‘D’água’ banana cultivars (Musa ssp) by nonlinear regression models

et al. 2019

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“…The logistic model requires specific models for each trait and cultivar, because the differences were predominant. Different models were also needed to estimate the production of salad-type tomato in two years of cultivation (Sari et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

“…of coffee trees (Pereira, Fernandes, Scalco, & Morais, 2016), and the modeling of yield traits of crotalaria (Bem et al, 2018). In addition, they were used to describe the production of zucchini and pepper (Lúcio, Nunes, & Rego, 2015), strawberry (Diel et al, 2018), and salad-type tomato (Sari, Lúcio, Santana, & Savian, 2019).…”

Section: Introductionmentioning

confidence: 99%

Growth Models for Lettuce Cultivars Growing in Spring

Carini¹,

Filho²,

Bandeira³

et al. 2019

JAS

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The objectives of this study were to adjust the Gompertz and logistic models to fit the fresh and dry matters of leaves and fresh and dry matters of shoots of four lettuce cultivars and indicate the model that best describes the growth in spring. Cultivars Ceres, Gloriosa, Grandes Lagos, and Rubinela were grown in protected environment and in soilless system, in the spring of 2016 and 2017. Seven days after transplantation, fresh and dry leaf matters and fresh and dry shoot matters were weighed every four days until beginning of flowering. The Gompertz and logistic models were adjusted as a function of accumulated thermal sum. The parameters of the Gompertz and logistic models and their confidence intervals were estimated, the assumptions of the models were verified, the goodness-of-fit measures and critical points were calculated, and the parametric and intrinsic nonlinearities quantified. The logistic and Gompertz growth models fitted well to fresh and dry leaf and shoot matters of cultivars Ceres, Gloriosa, Grandes Lagos, and Rubinela, under spring conditions. The logistic model is the most suitable to describe the growth of lettuce cultivars.

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“…The locations of these points on the CMY curve are shown in Figure . Researchers have previously used these points to describe experimental data of the imbibition of araribá seeds, Centrolobium tomentosum , eucalyptus growth data, and cattle growth data, to describe the productive behaviour of salad tomato genotypes and to predict the number and fresh mass of fruit per plant . However, although the PAA point has been defined in the literature, to our knowledge it has not been used in any study.…”

Section: Introductionmentioning

confidence: 99%

Identification of new critical points for logistics model in cumulative methane yield curves after co‐digestion of apple pulp and chicken manure with sulphuric acid pretreatment and a new modelling study

Şenol

2020

Int J Energy Res

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In this study, anaerobic co-digestion of apple pulp (AP) with high carbon content and chicken manure (CM) with high nitrogen content was evaluated with regard to sulphuric acid pretreatment conditions in the concentration range of 1%-6% v/v. The best mixing ratio of CM:AP was determined as 3:1 w/w under untreated conditions for which the methane yield was 255.88 ± 11.55 mL/g VS (volatile solid). Sulphuric acid pretreatments were applied to this mixture ratio.The highest methane yield after pretreatment was 466.01 ± 10.85 mL/g VS in the reactor, where the sulphuric acid pretreatment concentration was 3.0% v/v. Control of acid pretreatment results were achieved by analysis of lignocellulosic degradation and increase in soluble chemical oxygen demand. The highest cellulose, hemicellulose and lignin degradation after pretreatment were 27.5% ± 1.4%, 35.9% ± 2.9% and 14% ± 1.5% w/w, respectively. The cumulative methane yields (CMY) were analysed by the modified logistic model, modified Bertalanffy model (MBM), modified Gompertz model (MGM) and modified Holling model (MHM). The MBM and MGM were found to fit the experimental data better than other models. The MHM has not previously been applied to CMY. In addition to the kinetic studies, new critical points for CMYs were identified in the logistics model (lm). The importance and critical properties of these points with regard to CMY are introduced. The differences and superior properties of these critical points compared to other modelling methods are discussed. K E Y W O R D S apple pulp, chicken manure, logistic model, methane yield, new critical points, sulphuric acid pretreatment

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Describing tomato plant production using growth models

Cited by 22 publications

References 15 publications

Natural convection drying kinetics of ‘Prata’ and ‘D’água’ banana cultivars (Musa ssp) by nonlinear regression models

Natural convection drying kinetics of ‘Prata’ and ‘D’água’ banana cultivars (Musa ssp) by nonlinear regression models

Growth Models for Lettuce Cultivars Growing in Spring

Identification of new critical points for logistics model in cumulative methane yield curves after co‐digestion of apple pulp and chicken manure with sulphuric acid pretreatment and a new modelling study

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