Double sigmoidal models describing the growth of coffee berries

Fernandes, Tales Jesus; Pereira, Adriele Aparecida; Muniz, Joel Augusto

doi:10.1590/0103-8478cr20160646

Cited by 33 publications

(22 citation statements)

References 10 publications

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“…The emergence, physiological maturity and senescence phases are clearly observed in Fig 4 for irrigation treatments and planting date. These results are similar to those obtained in other studies, which shows that this type of adjustment is a good descriptor of the growth evolution of a plant [ 52 , 53 , 54 , 55 ].…”

Section: Discussionsupporting

confidence: 91%

“…The method used to estimate the dry matter accumulation or aerial biomass as functions of time and chia phenology is based on fitting the biomass accumulation data (BIOM) using a double logistic function [ 51 , 52 , 53 , 54 ]in the growth cycle. This method is very useful when one needs to represent the complete crop cycle quantitatively from emergence to senescence.…”

Section: Methodsmentioning

confidence: 99%

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Effect of sowing date and water availability on growth of plants of chia (Salvia hispanica L) established in Chile

Silva¹,

Arriagada²,

Campos-Saez³

et al. 2018

PLoS ONE

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From 2010 to 2014 two trials were performed to assess the effect of sowing date (SD1, SD2) and irrigation treatments (IT1, IT2) on the growth of chia in central Chile, measuring leaf area (LA) and dry matter (DM) as primary parameters and relative growth rate (RGR), net assimilation rate (NAR), leaf weight ratio (LWR), crop growth rate (CGR) and specific leaf weight (SLW) as secondary parameters. Both LA and DM reached maximum values between 640 and 1150 accumulated degree days (ADD). However, LA and DM were 25% greater for sowing dates than for available water. Flowering date was also not affected by sowing date or water availability; plants flowered at 1140 and 942 ADD in SD1 and SD2 respectively, and at 499 ADD in the water availability trial. Sowing date had a significant effect on RGR 0.15 g g-1 d-1 for SD1 and 0.12 g g-1 d-1 for SD2 at 410 ADD. Greater water availability increased RGR by 60% compared to stressed plants, however NAR was similar between sowing dates with a tendency to greater values in SD2 plants; maximum values were recorded at 514 ADD in IT1 and IT2, with a tendency toward higher values in IT1. Thus, the primary growth variables LA, DM and flowering are genetically determined. However, the derived growth variables RGR, LWR, NAR, CGR and SLW were affected by sowing date and water availability, with significant differences at p≤ 0.05. The results showed that the sowing date and water availability influence significantly the growth parameters. The physiological component (NAR) show a strong influence on the growth rate of the chia (RGR), above the morphological component (SLW and LAR).

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Section: Discussionsupporting

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Effect of sowing date and water availability on growth of plants of chia (Salvia hispanica L) established in Chile

Silva¹,

Arriagada²,

Campos-Saez³

et al. 2018

PLoS ONE

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show abstract

“…The efficiency of the models was observed by the Pearson's correlation coefficient (R) and the coefficient of determination (r 2 ) that reached values higher than 80%, as well as the Willmott concordance index (d) and the performance index proposed by Camargo and Sentelhas (c). For other species, adequate adjustments of Logistic and Gompertz models were observed (Batista et al, 2013;Fernandes et al, 2017). Moreover, a high accuracy of the Gaussian, Lorentzian, Log-Normal, Gompertz and Exponential models were classified as "Excellent" for the TAPDB of the species analyzed on different water regime replenish, where VI DAS and ADD (c>0.85) (Tables 3 and 4).…”

Section: T CMmentioning

confidence: 93%

Partitioning and Modeling of Biomass in Caatinga Legume Seedlings in Different Water Conditions

et al. 2019

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The accumulation of dry biomass, its partitioning and the adjustment of equations were assessed for the initial growth of Piptadenia stipulacea (Benth) Ducke and Anadenanthera colubrina (Vell.) Brenan under water availability variation. The experiment was conducted in pots arranged in a completely randomized design with three replicates. Water conditions were evaluated using the percentages of reference evapotranspiration. Samples of the biomass were taken at 10-day intervals and used for the achievement of partitioning data and model adjustments. The short period of water scarcity in the initial growth phase of P. stipulacea and A. colubrina does not affect the establishment of these species, but different water regimes alter their root growth dynamics. The A. colubrina allocates more dry matter to the roots, while P. stipulacea sends more dry matter to the aerial part. The Gaussian, Lorentzian, Log-Normal and Gompertz models had excellent adjustments for the biomass variables of the P. stipulacea species, whereas for A. colubrina, we recommend the Exponential model.

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“…The equation therefore basically deals with the flipping of a parameter from an original state (state of y i ) to a final state (state of y r ) through a transition x 0 . In some cases, the independent variable, y, reproduces the sigmoidal pattern with a time offset; a double sigmoidalequation (DSBE) takes the following form (Fernandes et al (2017)…”

Section: Sigmoidal-boltzmann Equationmentioning

confidence: 99%

Mathematical model of Boltzmann’s sigmoidal equation applicable to the spreading of the coronavirus (Covid-19) waves

Aferni¹,

Guettari²,

Tajouri³

2020

Environ Sci Pollut Res

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Currently, investigations are intensively conducted on modeling, forecasting, and studying the dynamic spread of coronavirus (Covid-19) new pandemic. In the present work, the sigmoidal-Boltzmann mathematical model was applied to study the Covid-19 spread in 15 different countries. The cumulative number of infected persons I has been accurately fitted by the sigmoidal-Boltzmann equation (SBE), giving rise to different epidemiological parameters such as the pandemic peak t p , the maximum number of infected persons I max , and the time of the epidemic stabilization t ∞. The time constant relative to the sigmoid Δt (called also the slope factor) was revealed to be the determining parameter which influences all the epidemiological parameters. Empirical laws between the different parameters allowed us to propose a modified sigmoidal-Boltzmann equation describing the spread of the pandemic. The expression of the spread speed V p was further determined as a function of the sigmoid parameters. This made it possible to assess the maximum speed of spread of the virus V pmax and to trace the speed profile in each country. In addition, for countries undergoing a second pandemic wave, the cumulative number of infected people I has been successfully adjusted by a double sigmoidal-Boltzmann equation (DSBE) allowing the comparison between the two waves. Finally, the comparison between the maximum virus spread of two waves V p max 1 and V p max 2 showed that the intensity of the second wave of Covid-19 is low compared to the first for all the countries studied.

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Double sigmoidal models describing the growth of coffee berries

Cited by 33 publications

References 10 publications

Effect of sowing date and water availability on growth of plants of chia (Salvia hispanica L) established in Chile

Effect of sowing date and water availability on growth of plants of chia (Salvia hispanica L) established in Chile

Partitioning and Modeling of Biomass in Caatinga Legume Seedlings in Different Water Conditions

Mathematical model of Boltzmann’s sigmoidal equation applicable to the spreading of the coronavirus (Covid-19) waves

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