scholarly journals Adjusting the growth curve of sugarcane varieties using nonlinear models

2020 ◽  
Vol 50 (3) ◽  
Author(s):  
Sérgio Alberto Jane ◽  
Felipe Augusto Fernandes ◽  
Edilson Marcelino Silva ◽  
Joel Augusto Muniz ◽  
Tales Jesus Fernandes ◽  
...  
Keyword(s):  
Growth Curve ◽  
Least Squares Method ◽  
Nonlinear Models ◽  
Height Growth ◽  
Coefficient Of Determination ◽  
Model Parameters ◽  
Suitable Model ◽  
Software Environment ◽  
Phenological Stages ◽  
Sugarcane Varieties

ABSTRACT: Assessing sugarcane (Saccharum spp.) stalk growth helps to adequately manage the phenological stages of the crop. The aim of this study was to describe the height-growth curve of four sugarcane varieties (RB92579, RB93509, RB931530 and SP79-1011), in irrigated plant-cane and ratoon cane plantations, using the Logistic and Gompertz nonlinear models, while considering all deviations from assumptions. The model parameters were estimated based on the least squares method using the Gauss-Newton algorithm. To select the most suitable model, nonlinear measures, adjusted coefficient of determination (R2 adj), residual standard deviation (RSD), and corrected Akaike information criterion (AICc) were used. Based on the best models, stalk height growth rates and crop phenological stages were determined using critical points. All tests were performed in the free software environment for statistical computing and graphics, R. In general, the Logistic and Gompertz models without AR(1) better described the plant-cane and ratoon cane stalk height, respectively. All varieties showed early growth, and the RB92579 variety presented higher rates in both cycles.

scholarly journals The use of the nonlinear models in the growth of pears of ‘Shinseiki’ cultivar

2017 ◽  
Vol 48 (1) ◽  
Author(s):  
Thais Destefani Ribeiro ◽  
Taciana Villela Savian ◽  
Tales Jesus Fernandes ◽  
Joel Augusto Muniz
Keyword(s):  
Logistic Model ◽  
Least Squares Method ◽  
Nonlinear Models ◽  
Logistic Models ◽  
Information Criterion ◽  
Fruit Weight ◽  
Coefficient Of Determination ◽  
Pear Fruit ◽  
Asian Pear ◽  
Residual Correlations

ABSTRACT: The goal of this study was to elucidate the growth and development of the Asian pear fruit, on the grounds of length, diameter and fresh weight determined over time, using the non-linear Gompertz and Logistic models. The specifications of the models were assessed utilizing the R statistical software, via the least squares method and iterative Gauss-Newton process (DRAPER & SMITH, 2014). The residual standard deviation, adjusted coefficient of determination and the Akaike information criterion were used to compare the models. The residual correlations, observed in the data for length and diameter, were modeled using the second-order regression process to render the residuals independent. The logistic model was highly suitable in demonstrating the data, revealing the Asian pear fruit growth to be sigmoid in shape, showing remarkable development for three variables. It showed an average of up to 125 days for length and diameter and 140 days for fresh fruit weight, with values of 72mm length, 80mm diameter and 224g heavy fat.

scholarly journals Nonlinear models in the height description of the Rhino sunflower cultivar

2022 ◽  
Vol 52 (3) ◽  
Author(s):  
Anderson Chuquel Mello ◽  
Marcos Toebe ◽  
Rafael Rodrigues de Souza ◽  
João Antônio Paraginski ◽  
Junior Carvalho Somavilla ◽  
...  
Keyword(s):  
Growth Curve ◽  
Critical Points ◽  
Nonlinear Models ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Maximum Acceleration ◽  
Inflection Points ◽  
Best Fitting ◽  
Microsoft Office

ABSTRACT: Sunflower produces achenes and oil of good quality, besides serving for production of silage, forage and biodiesel. Growth modeling allows knowing the growth pattern of the crop and optimizing the management. The research characterized the growth of the Rhino sunflower cultivar using the Logistic and Gompertz models and to make considerations regarding management based on critical points. The data used come from three uniformity trials with the Rhino confectionery sunflower cultivar carried out in the experimental area of the Federal University of Santa Maria - Campus Frederico Westphalen in the 2019/2020 agricultural harvest. In the first, second and third trials 14, 12 and 10 weekly height evaluations were performed on 10 plants, respectively. The data were adjusted for the thermal time accumulated. The parameters were estimated by ordinary least square’s method using the Gauss-Newton algorithm. The fitting quality of the models to the data was measured by the adjusted coefficient of determination, Akaike information criterion, Bayesian information criterion, and through intrinsic and parametric nonlinearity. The inflection points (IP), maximum acceleration (MAP), maximum deceleration (MDP) and asymptotic deceleration (ADP) were determined. Statistical analyses were performed with Microsoft Office Excel® and R software. The models satisfactorily described the height growth curve of sunflower, providing parameters with practical interpretations. The Logistics model has the best fitting quality, being the most suitable for characterizing the growth curve. The estimated critical points provide important information for crop management. Weeds must be controlled until the MAP. Covered fertilizer applications must be carried out between the MAP and IP range. ADP is an indicator of maturity, after reaching this point, the plants can be harvested for the production of silage without loss of volume and quality.

scholarly journals Sigmoid growth curves, a new approach to study the dynamics of the epicotyl emergence of oak

2019 ◽  
Vol 61 (1) ◽  
pp. 30-41
Author(s):  
Joanna Ukalska ◽  
Szymon Jastrzębowski
Keyword(s):  
Growth Curve ◽  
Goodness Of Fit ◽  
Nonlinear Models ◽  
Germination Rate ◽  
Growth Curves ◽  
Model Parameters ◽  
Residual Variance ◽  
Nonlinear Behaviour ◽  
Pedunculate Oak ◽  
Growth Phenomenon

Abstract Three of the most frequently used sigmoidal growth curves from the Richards family are the logistic model, Gompertz model and Richards model. They are used in the analysis of organismal growth over time in many disciplines/studies and were proposed in many parameterisations. Choosing the right parameterisation is not easy. The correct parameterisation of the model should take into account such parameters that are useful to describe the analysed growth phenomenon and are biologically relevant without additional calculations. In addition, each parameter of the model only affects one shape characteristic of each growth curve, which makes it possible to determine standard errors and confidence intervals using statistical software. Growth curves in germination dynamics studies should provide information on topics such as the length of the lag in onset of germination, the maximum germination rate and, when it occurs, the time at which 50% of seeds will germinate and the final germination proportion. In this article, we present three parameterisations of the logistic, Gompertz and Richards models and indicate two parameterisations for each model, corresponding to the above-mentioned issues. Our proposition is parameterisation by taking into account the maximum absolute growth rate. Parameterisations indicated as useful for germination dynamics are characterised by the fact that each parameter has the same meaning in every model, so its estimates can be compared directly amongst the models. We also discussed the goodness-of-fit measures for nonlinear models and in particular measures of nonlinear behaviour of a model’s individual parameters as well as overall measures of nonlinearity. All described models were used to study the dynamics of the epicotyl emergence of pedunculate oak. After checking the close-to-linear behaviour of the studied model parameters and by taking into account the criteria of model selection (AICc of each growth curve and the residual variance [RV]), the best model describing the dynamics of epicotyl appearance of pedunculate oak was the Richards curve.

scholarly journals  Estimation of genetic parameters and evaluation of test-day milk production in sheep

2012 ◽  
Vol 57 (No. 11) ◽  
pp. 522-528 ◽  
Author(s):  
J. Bauer ◽  
M. Milerski ◽  
J. Přibyl ◽  
L. Vostrý
Keyword(s):  
Milk Production ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Least Squares Method ◽  
Coefficient Of Determination ◽  
Suitable Model ◽  
Breeding Values ◽  
Parity Number ◽  
Daily Milk Production ◽  
Daily Milk

 Genetic parameters and breeding values were estimated based on 11 708 daily milk yields from 2255 lactations (1351 sheep, 19 different flocks) between the years 2004 and 2010. The pedigree covered 2334 individuals, including both the recorded animals and their known ancestors. The fixed effects were estimated by the least-squares method, while the genetic parameters were estimated by the REML method. The data were tested by 49 models, but on the basis of the coefficient-of-determination value and the significance of the effects, only 10 models were used for REML analysis. The most suitable model was chosen on the basis of the breeding values distribution and the heritability of daily milk production, which was estimated at 0.28. The fixed effects of the flock parity number, the flock test day, and the linear and quadratic coefficients of flock’s days-in-milk in the chosen model were all highly significant (P < 0.0001) for the test-day milk yield. The breeding values had a normal distribution and a standard deviation of 0.46 kg.  

scholarly journals Predicting of the Coronavirus Disease 2019 (COVID-19) Epidemic Using Estimation of Parameters in the Logistic Growth Model

2021 ◽  
Vol 13 (2) ◽  
pp. 465-485
Author(s):  
Agus Kartono ◽  
Setyanto Tri Wahyudi ◽  
Ardian Arif Setiawan ◽  
Irmansyah Sofian
Keyword(s):  
Growth Model ◽  
Least Squares Method ◽  
Time Dependent ◽  
Actual Data ◽  
Coefficient Of Determination ◽  
Logistic Growth ◽  
Model Parameters ◽  
Time Interval ◽  
Non Linear ◽  
Logistic Growth Model

The COVID-19 pandemic was impacting the health and economy around the world. All countries have taken measures to control the spread of the epidemic. Because it is not known when the epidemic will end in several countries, then the prediction of the COVID-19 pandemic is a very important challenge. This study has predicted the temporal evolution of the COVID-19 pandemic in several countries using the logistic growth model. This model has analyzed several countries to describe the epidemic situation of these countries. The time interval of the actual data used as a comparison with the prediction results of this model was starting in the firstly confirmed COVID-19 cases to December 2020. This study examined an approach to the complexity spread of the COVID-19 pandemic using the logistic growth model formed from an ordinary differential equation. This model described the time-dependent population growth rate characterized by the three parameters of the analytical solution. The non-linear least-squares method was used to estimate the three parameters. These parameters described the rate growth constant of infected cases and the total number of confirmed cases in the final phase of the epidemic. This model is applied to the spread of the COVID-19 pandemic in several countries. The prediction results show the spread dynamics of COVID-19 infected cases which are characterized by time-dependent dynamics. In this study, the proposed model provides estimates for the model parameters that are good for predicting the COVID-19 pandemic because they correspond to actual data for all analyzed countries. It is based on the coefficient of determination, R2, and the R2 value of more than 95% which is obtained from the non-linear curves for all analyzed countries. It shows that this model has the potential to contribute to better public health policy-making in the prevention of the COVID-19 pandemic.

Constructing and model-fitting receiver operator characteristics using continuous data.

2018 ◽  
Author(s):  
Josephine Ann Urquhart ◽  
Akira O'Connor
Keyword(s):  
Signal Detection ◽  
Least Squares Method ◽  
Model Fitting ◽  
Simulated Data ◽  
Continuous Variable ◽  
Continuous Data ◽  
Model Parameters ◽  
Operating Characteristics ◽  
Unequal Variance ◽  
Discrete Discrimination

Receiver operating characteristics (ROCs) are plots which provide a visual summary of a classifier’s decision response accuracy at varying discrimination thresholds. Typical practice, particularly within psychological studies, involves plotting an ROC from a limited number of discrete thresholds before fitting signal detection parameters to the plot. We propose that additional insight into decision-making could be gained through increasing ROC resolution, using trial-by-trial measurements derived from a continuous variable, in place of discrete discrimination thresholds. Such continuous ROCs are not yet routinely used in behavioural research, which we attribute to issues of practicality (i.e. the difficulty of applying standard ROC model-fitting methodologies to continuous data). Consequently, the purpose of the current article is to provide a documented method of fitting signal detection parameters to continuous ROCs. This method reliably produces model fits equivalent to the unequal variance least squares method of model-fitting (Yonelinas et al., 1998), irrespective of the number of data points used in ROC construction. We present the suggested method in three main stages: I) building continuous ROCs, II) model-fitting to continuous ROCs and III) extracting model parameters from continuous ROCs. Throughout the article, procedures are demonstrated in Microsoft Excel, using an example continuous variable: reaction time, taken from a single-item recognition memory. Supplementary MATLAB code used for automating our procedures is also presented in Appendix B, with a validation of the procedure using simulated data shown in Appendix C.

Nonlinear models to describe the testicular size growth curve of Mangalarga Marchador stallions

2021 ◽  
pp. 103422
Author(s):  
Danielle Estanislau Coelho Silva ◽  
Jurandy Mauro Penitente-Filho ◽  
Domingos Lollobrigida Souza Neto ◽  
Bruna Waddington ◽  
Renan Reis de Oliveira ◽  
...  
Keyword(s):  
Growth Curve ◽  
Nonlinear Models ◽  
Size Growth ◽  
Testicular Size

scholarly journals Genome-Wide Association Analysis of Growth Curve Parameters in Chinese Simmental Beef Cattle

Animals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 192
Author(s):  
Xinghai Duan ◽  
Bingxing An ◽  
Lili Du ◽  
Tianpeng Chang ◽  
Mang Liang ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Candidate Genes ◽  
Growth Curve ◽  
Growth And Development ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Coefficient Of Determination ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
A Genome

The objective of the present study was to perform a genome-wide association study (GWAS) for growth curve parameters using nonlinear models that fit original weight–age records. In this study, data from 808 Chinese Simmental beef cattle that were weighed at 0, 6, 12, and 18 months of age were used to fit the growth curve. The Gompertz model showed the highest coefficient of determination (R2 = 0.954). The parameters’ mature body weight (A), time-scale parameter (b), and maturity rate (K) were treated as phenotypes for single-trait GWAS and multi-trait GWAS. In total, 9, 49, and 7 significant SNPs associated with A, b, and K were identified by single-trait GWAS; 22 significant single nucleotide polymorphisms (SNPs) were identified by multi-trait GWAS. Among them, we observed several candidate genes, including PLIN3, KCNS3, TMCO1, PRKAG3, ANGPTL2, IGF-1, SHISA9, and STK3, which were previously reported to associate with growth and development. Further research for these candidate genes may be useful for exploring the full genetic architecture underlying growth and development traits in livestock.

scholarly journals Determination of synchronous generator nonlinear model parameters based on power rejection tests using a gradient optimization algorithm

2017 ◽  
Vol 65 (4) ◽  
pp. 479-488 ◽  
Author(s):  
A. Boboń ◽  
A. Nocoń ◽  
S. Paszek ◽  
P. Pruski
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Least Squares Method ◽  
Synchronous Generator ◽  
Operating Conditions ◽  
Model Parameters ◽  
State Variables ◽  
Starting Point ◽  
Gradient Optimization

AbstractThe paper presents a method for determining electromagnetic parameters of different synchronous generator models based on dynamic waveforms measured at power rejection. Such a test can be performed safely under normal operating conditions of a generator working in a power plant. A generator model was investigated, expressed by reactances and time constants of steady, transient, and subtransient state in the d and q axes, as well as the circuit models (type (3,3) and (2,2)) expressed by resistances and inductances of stator, excitation, and equivalent rotor damping circuits windings. All these models approximately take into account the influence of magnetic core saturation. The least squares method was used for parameter estimation. There was minimized the objective function defined as the mean square error between the measured waveforms and the waveforms calculated based on the mathematical models. A method of determining the initial values of those state variables which also depend on the searched parameters is presented. To minimize the objective function, a gradient optimization algorithm finding local minima for a selected starting point was used. To get closer to the global minimum, calculations were repeated many times, taking into account the inequality constraints for the searched parameters. The paper presents the parameter estimation results and a comparison of the waveforms measured and calculated based on the final parameters for 200 MW and 50 MW turbogenerators.

scholarly journals Growth in achondroplasia, from birth to adulthood, analysed by the JPA-2 model

2020 ◽  
Vol 33 (12) ◽  
pp. 1589-1595
Author(s):  
Mariana del Pino ◽  
Virginia Fano ◽  
Paula Adamo
Keyword(s):  
General Population ◽  
Growth Curve ◽  
Growth Velocity ◽  
Adult Height ◽  
Growth Curves ◽  
Peak Height ◽  
Height Growth ◽  
Individual Growth ◽  
Height Velocity ◽  
Peak Height Velocity

AbstractObjectivesIn general population, there are three phases in the human growth curve: infancy, childhood and puberty, with different main factors involved in their regulation and mathematical models to fit them. Achondroplasia children experience a fast decreasing growth during infancy and an “adolescent growth spurt”; however, there are no longitudinal studies that cover the analysis of the whole post-natal growth. Here we analyse the whole growth curve from infancy to adulthood applying the JPA-2 mathematical model.MethodsTwenty-seven patients, 17 girls and 10 boys with achondroplasia, who reached adult size, were included. Height growth data was collected from birth until adulthood. Individual growth curves were estimated by fitting the JPA-2 model to each individual’s height for age data.ResultsHeight growth velocity curves show that after a period of fast decreasing growth velocity since birth, with a mean of 9.7 cm/year at 1 year old, the growth velocity is stable in late preschool years, with a mean of 4.2 cm/year. In boys, age and peak height velocity in puberty were 13.75 years and 5.08 cm/year and reach a mean adult height of 130.52 cm. In girls, the age and peak height velocity in puberty were 11.1 years and 4.32 cm/year and reach a mean adult height of 119.2 cm.ConclusionsThe study of individual growth curves in achondroplasia children by the JPA-2 model shows the three periods, infancy, childhood and puberty, with a similar shape but lesser in magnitude than general population.

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