scholarly journals Growth Curves of Texel Male Lambs

2018 ◽  
Vol 44 (1) ◽  
pp. 6
Author(s):  
Michelli De Fátima Sieklicki ◽  
Victor Breno Pedrosa ◽  
Caroline Gomes Rocha ◽  
Raphael Patrick Moreira ◽  
Paula Roberta Falcão ◽  
...  
Keyword(s):  
Growth Rate ◽  
Mean Square Error ◽  
Nonlinear Models ◽  
Growth Curves ◽  
Coefficient Of Determination ◽  
Good Growth ◽  
Von Bertalanffy ◽  
Mean Square ◽  
The Mean ◽  
Best Fit

Background: The consumption of lamb meat is growing due to improved farming methods. However, to be economically feasible, the animal should stand out for its precocity, fast finishing and muscular force, such as seen in Texel breed. Besides, knowledge about weight gain and development can facilitate the selection of the best animals, and allow a better fitting to farming systems. Growth curves are an effective method that describes animal development, modeling the relationship between weight and age and help to predict the growth rate. Thus, this study aimed to analyze which nonlinear model, including Brody, Gompertz, Von Bertalanffy and Logistic best describe the growth curve of Texel sheep.Materials, Methods & Results: In this experiment, the lambs were kept in confined system while the ewes, in a semi-extensive system. This study followed 42 Texel male lambs, which were confined from birth to slaughter, and fed concentrated feed (3% of body weight) and corn silage (average 1.5 kg/animal/day), 4 times a day. The lambs were weighed fortnightly, in different classes considered as follows, weight at birth (BW), 15 days (P15), 30 days (P30), 45 days (P45), 60 days (P60), 75 days (P75), 90 days (P90), 105 days (P105), and 120 days (P120), which was defined as the slaughtering weight. The growth curves were determined using the nonlinear models of Brody, Von Bertalanffy, Gompertz and Logistic. The following parameters were used in the curves, Y, slaughtering weight; A, asymptotic weight; k, growth rate, t, animal age; B, constant related to the initial weight; and, m, constant of the curve shape. The criteria used for selecting the model that best described the curve were the mean square error (MSE), which was calculated by dividing the sum of squared error by the number of observations, and also the coefficient of determination (R²), calculated as the square of the correlation between the observed and estimated weights. The average weights observed were as follows, 4.02 kg at birth, 21.68 kg at weaning (P60) and 32.55 kg at slaughtering (P120). The solution of the nonlinear models allows, thru the parameters, establish specific feeding programs and define the optimal slaughtering age. Furthermore, the coefficients of determination, with values close to 97.3%, showed good fits for all models. Still, considering the mean square error, where the lower value indicates the best fit to the data evaluated, the results were 13.1564 (Brody), 13.3421 (Von Bertalanffy), 13.4876 (Gompertz) and 13.6717 (Logistic). The results showed that Brody could be considered the model that best describes the growth rate up to 120 days old of Texel lambs.Discussion: Compared to other studies, the average weights obtained in the experiment varied widely. This large variation can be explained by the used rearing system that might favor or not the performance of lambs. However, the average weaning weight obtained was similar to several studies in the literature, confirming the potential of Texel breed. This breed demonstrated to be capable to provide a precocious animal, with good growth results from the early developmental stage until the slaughtering age. Regarding the growth curves, the Brody model was the best fit for the estimated and observed weights. Moreover, the coefficient of determination indicated good fits for all models. However, an important aspect is the negative correlation between the A and k parameters, demonstrating that the higher the animal growth rate, the lower its asymptotic size.

scholarly journals Growth curves on females of the Caracu breed

2016 ◽  
Vol 37 (4Supl1) ◽  
pp. 2749 ◽  
Author(s):  
Raphael Patrick Moreira ◽  
Maria Eugênia Zerlotti Mercadante ◽  
Victor Breno Pedrosa ◽  
Joslaine Noely dos Santos Gonçalves Cyrillo ◽  
Wignez Henrique
Keyword(s):  
Growth Traits ◽  
Nonlinear Models ◽  
Average Daily Gain ◽  
Growth Curves ◽  
Coefficient Of Determination ◽  
Von Bertalanffy ◽  
Mean Square ◽  
The Mean ◽  
Best Fit ◽  
Daily Gain

The objective of the study was to analyze nonlinear models that best fit the growth of Caracu cows. The experiment was conducted at the Instituto de Zootecnia, Centro APTA Bovinos de Corte, Sertãozinho, SP. Data of weight at birth to 63 months of age, from 500 females of the Caracu breed were used. The mean weight at birth (BW), weaning weight (W7), weight at 26 months (W26) and weight at 63 months (W63) were, respectively, 32 kg, 198 kg, 354 kg and 488 kg, providing an average daily gain (ADG) of 0.241 kg/day. The nonlinear models used were: Brody, Von Bertalanffy, Logistic and Gompertz. All of the models tended to describe accordingly the growth curve of these animals, but, according to the mean square residual and coefficient of determination adopted to select the most appropriate model, Brody showed the best fit. All models presented a high and negative correlation between the A and k parameters, indicating that the most precocious animals are less likely to reach elevated weights at 63 months of age. The effect of year of birth significantly affected (P < 0.01) the parameters A and k, concluding that the animal selection based on growth traits favored the increase of mature weight and growth precocity over the generations.

scholarly journals IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1631
Author(s):  
Bruno Guilherme Martini ◽  
Gilson Augusto Helfer ◽  
Jorge Luis Victória Barbosa ◽  
Regina Célia Espinosa Modolo ◽  
Marcio Rosa da Silva ◽  
...  
Keyword(s):  
Technology Acceptance ◽  
Mean Square Error ◽  
Ease Of Use ◽  
Coefficient Of Determination ◽  
Square Root ◽  
Mean Square ◽  
Use Context ◽  
The Mean ◽  
Acceptance Model ◽  
The Internet Of Things

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

scholarly journals Sinusoidal function to describe the growth curve of dairy heifers

2019 ◽  
Vol 59 (6) ◽  
pp. 1039
Author(s):  
H. Darmani Kuhi ◽  
N. Ghavi Hossein-Zadeh ◽  
S. López ◽  
S. Falahi ◽  
J. France
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Dairy Heifers ◽  
Growth Functions ◽  
Best Fit ◽  
Sinusoidal Function

The objective of the present study is to introduce a sinusoidal function into dairy research and production by applying it to bodyweight records (from 1 to 24 months) from six dairy cow breeds reported by the Dairy Heifer Evaluation Project of Penn State Extension (USA) from 1991 to 1992. The function was evaluated with regard to its ability to describe the relationship between bodyweight and age in dairy heifers, and then compared with seven standard growth functions, namely monomolecular, logistic, Gompertz, von Bertalanffy, Richards, Schumacher and Morgan. The models were fitted to monthly bodyweight records of dairy heifers using non-linear regression to derive estimates of the parameters of each function. The models were tested for goodness of fit by using adjusted coefficient of determination, root mean square error, Akaike’s information criterion and Bayesian information criterion. Values of adjusted coefficient of determination were generally high for all models, suggesting the generally appropriate fit of the models to the data. The sinusoidal function provided the best fit of the growth curves for Brown Swiss, Guernsey and Milking Shorthorn breeds due to the lowest values of root mean square error, Akaike’s information criterion and Bayesian information criterion. According to the chosen statistical criteria, the Richards function provided the best fit for Ayrshire heifers, and the monomolecular the best for Holstein and Jersey. The least accurate estimates were obtained with the logistic. In conclusion, the sinusoidal function introduced here can be considered as an appropriate alternative to standard growth functions when modelling growth patterns in dairy heifers.

scholarly journals NONLINEAR MODELS BASED ON QUANTILES IN THE FITTING OF GROWTH CURVES OF PEPPER GENOTYPES

2021 ◽  
Vol 39 (3) ◽  
Author(s):  
Ana Carolina Ribeiro de OLIVEIRA ◽  
Paulo Roberto CECON ◽  
Guilherme Alves PUIATTI ◽  
Maria Eduarda da Silva GUIMARÃES ◽  
Cosme Damião CRUZ ◽  
...  
Keyword(s):  
Regression Models ◽  
Nonlinear Models ◽  
Growth Curves ◽  
Information Criterion ◽  
Ordinary Least Squares ◽  
Von Bertalanffy ◽  
Absolute Deviation ◽  
Nonlinear Regression Models ◽  
Curvature Measurements ◽  
Best Fit

This study aimed to fit nonlinear regression models to model the growth of the characters fruit length (FL) and fruit width (FW) of pepper genotypes (Capsicum annuum L.) over time using the method of ordinary least squares (OLS); and identify the model with the best fit and compare it to the model obtained via nonlinear quantile regression (QR) in the 0.25, 0.5, and 0.75 quantiles. Three regression models (Logistic, Gompertz, and von Bertalanffy) and four fit quality evaluators were adopted: Akaike information criterion, residual mean absolute deviation, and parametric and intrinsic curvature measurements. Five commercial genotypes of pepper were evaluated. Characters FL and FW were evaluated weekly from seven days after flowering, totaling ten measurements. In the estimation by OLS, the Logistic and von Bertalanffy models were considered adequate according to the quality evaluators. In the comparison between the models above by OLS and QR, the superiority of models obtained by QR was verified for the character FL. For the character FW, QR was efficient in three out of the five genotypes, being a valuable alternative in the study of fruit growth.

scholarly journals An automatic and non-intrusive hybrid computer vision system for the estimation of peel thickness in Thomson orange

2019 ◽  
Vol 16 (4) ◽  
pp. e0204
Author(s):  
Hossein Javadikia ◽  
Sajad Sabzi ◽  
Juan I. Arribas
Keyword(s):  
Computer Vision ◽  
Mean Square Error ◽  
Food Industry ◽  
Vision System ◽  
Optimization Technique ◽  
Orange Peel ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Hybrid Particle Swarm Optimization ◽  
The Mean

Orange peel has important flavor and nutrition properties and is often used for making jam and oil in the food industry. For previous reasons, oranges with high peel thickness are valuable. In order to properly estimate peel thickness in Thomson orange fruit, based on a number of relevant image features (area, eccentricity, perimeter, length/area, blue component, green component, red component, width, contrast, texture, width/area, width/length, roughness, and length) a novel automatic and non-intrusive approach based on computer vision with a hybrid particle swarm optimization (PSO), genetic algorithm (GA) and artificial neural network (ANN) system is proposed. Three features (width/area, width/length and length/area ratios) were selected as inputs to the system. A total of 100 oranges were used, performing cross validation with 100 repeated experiments with uniform random samples test sets. Taguchi’s robust optimization technique was applied to determine the optimal set of parameters. Prediction results for orange peel thickness (mm) based on the levels that were achieved by Taguchi’s method were evaluated in several ways, including orange peel thickness true-estimated boxplots for the 100 orange database and various error parameters: the sum square error (SSE), the mean absolute error (MAE), the coefficient of determination (R2), the root mean square error (RMSE), and the mean square error (MSE), resulting in mean error parameter values of R2=0.854±0.052, MSE=0.038±0.010, and MAE=0.159±0.023, over the test set, which to our best knowledge are remarkable numbers for an automatic and non-intrusive approach with potential application to real-time orange peel thickness estimation in the food industry.

scholarly journals Growth curves for Ile de France female sheep raised in feedlot

2016 ◽  
Vol 37 (1) ◽  
pp. 303 ◽  
Author(s):  
Raphael Patrick Moreira ◽  
Victor Breno Pedrosa ◽  
Paula Roberta Falcão ◽  
Michelli De Fátima Sieklicki ◽  
Caroline Gomes Rocha ◽  
...  
Keyword(s):  
Weight Gain ◽  
Negative Correlation ◽  
Linear Models ◽  
Growth Curves ◽  
Daily Weight Gain ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Non Linear ◽  
Best Fit ◽  
Female Sheep

The objective of the present study was to analize the non-linear models that best fit the growth of ewes Ile de France. The experiment was conducted in school farm Capão da Onça, located in city of Ponta Grossa - PR and so, were used data on weight at birth to 210 days of age of 34 females of the breed Ile de France. The animals showed mean weight at birth (PN) of 4,58 kilograms, weaning weight (P60) of 19,58 kilograms and weight at 210 days (P210) equal to 43,18 kilograms, providing daily weight gain (GMD) equal to 0,183 kg/day. The non-linear models used were: Brody, Von Bentarlanffy, Logístic and Gompertz, presenting results, respectively, of 33.5453; 33.7120; 33.6714 and 33.8836 for Error Mean Square (EMS) and 0.9650; 0.8302; 0.9649 and 0.9647 for coefficient of determination (R2). All models tended to describe accordingly the curve of animals growth, but, according to the methods adopted to select the most appropriate model, Von Bertarlanffy showed the best fit. All models presented high and negative correlation between the A and k parameters, indicating that the most precocious animals are less likely to reach elevated weights for 210 days of age

scholarly journals Classical nonlinear models to describe the growth curve for Murrah buffalo breed

2012 ◽  
Vol 42 (3) ◽  
pp. 520-525 ◽  
Author(s):  
Ronyere Olegário de Araújo ◽  
Cintia Righetti Marcondes ◽  
Maria Cecília Florisbal Damé ◽  
Analía del Valle Garnero ◽  
Ricardo José Gunski ◽  
...  
Keyword(s):  
Logistic Model ◽  
Nonlinear Models ◽  
Logistic Models ◽  
Growth Curves ◽  
Coefficient Of Determination ◽  
Von Bertalanffy ◽  
Average Deviation ◽  
Absolute Deviation ◽  
Murrah Buffalo ◽  
Deviation Coefficient

With the objective of to adjust nonlinear models for the growth curves for a buffaloes herd raised in floodable lands in Rio Grande do Sul state, monthly records measured from birth to two years-old of 64 males and 63 females born between 1982 and 1989 were used. The models used were: Von Bertalanffy, Brody, Gompertz and Logistic. The parameters were estimated by NLIN procedure and the criteria used to evaluate the adjustment given by the models were: asymptotic standard deviation; coefficient of determination; average absolute deviation of residues and asymptotic index. Von Bertalanffy and Brody models overestimated the male asymptotic weight (A) in 15.9 and 171.3kg, respectively, and the Gompertz and Logistic models underestimated it in 4.5 and 13.4kg, respectively. For females, the Logistic model underestimated the asymptotic weight (-2.09kg), and Gompertz, Von Bertalanffy and Brody overestimated this parameter in 8.04, 17.7, and 280.33kg, respectively. The biggest average deviation was estimated by Brody model for both sexes, characterizing the biggest index. Considering the criteria, it is recommended the Gompertz and Logistic models for adjust females and males Murrah buffaloes breed growth curves.

The Bordeaux Automatic Meridian Circle

1978 ◽  
Vol 48 ◽  
pp. 227-228
Author(s):  
Y. Requième
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Meridian Circle ◽  
The Mean ◽  
Full Automation

In spite of important delays in the initial planning, the full automation of the Bordeaux meridian circle is progressing well and will be ready for regular observations by the middle of the next year. It is expected that the mean square error for one observation will be about ±0.”10 in the two coordinates for declinations up to 87°.

Limit tolerances for sums of measurement errors

2018 ◽  
Vol 934 (4) ◽  
pp. 59-62
Author(s):  
V.I. Salnikov
Keyword(s):  
Normal Distribution ◽  
Mean Square Error ◽  
Gaussian Distribution ◽  
Measurement Errors ◽  
Theory And Practice ◽  
Mean Square ◽  
The Mean ◽  
Limiting Values ◽  
Limiting Value

The question of calculating the limiting values of residuals in geodesic constructions is considered in the case when the limiting value for measurement errors is assumed equal to 3m, ie ∆рred = 3m, where m is the mean square error of the measurement. Larger errors are rejected. At present, the limiting value for the residual is calculated by the formula 3m√n, where n is the number of measurements. The article draws attention to two contradictions between theory and practice arising from the use of this formula. First, the formula is derived from the classical law of the normal Gaussian distribution, and it is applied to the truncated law of the normal distribution. And, secondly, as shown in [1], when ∆рred = 2m, the sums of errors naturally take the value equal to ?pred, after which the number of errors in the sum starts anew. This article establishes its validity for ∆рred = 3m. A table of comparative values of the tolerances valid and recommended for more stringent ones is given. The article gives a graph of applied and recommended tolerances for ∆рred = 3m.

Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques

2021 ◽  
Vol 25 (2) ◽  
pp. 227-234
Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Water Table ◽  
Environmental Sustainability ◽  
Model Development ◽  
Water Table Depth ◽  
Water Levels ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Inference System

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.

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