scholarly journals Adjustment of growth models in broiler chickens

2017 ◽  
Vol 52 (12) ◽  
pp. 1241-1252 ◽  
Author(s):  
Leandro Félix Demuner ◽  
Diana Suckeveris ◽  
Julian Andrés Muñoz ◽  
Vinicius Camargo Caetano ◽  
Cesar Gonçalves de Lima ◽  
...  
Keyword(s):  
Body Weight ◽  
Growth Models ◽  
Ordinary Least Squares ◽  
Information Criteria ◽  
The Body ◽  
Coefficient Of Determination ◽  
Parameter Estimates ◽  
Model Parameters ◽  
First Order ◽  
Autocorrelated Error

Abstract: The objective of this work was to investigate adjustments of the Gompertz, Logistic, von Bertalanffy, and Richards growth models, in male and female chickens of the Cobb 500, Ross 308, and Hubbard Flex lines. Initially, 1,800 chickens were randomly housed in 36 pens, with six replicates per lineage and sex, fed ad libitum with feed according to gender, and bred until 56 days of age. Average weekly body weight for each line and sex was used to estimate model parameters using the ordinary least squares, weighted by the inverse variance of the body weight and weighted with a first-order autocorrelated error structure. Weighted models and weighted autocorrelated error models showed different parameter values when compared with the unweighted models, modifying the inflection point of the curve and according to the adjusted coefficient of determination, and the standard deviation of the residue and Akaike information criteria exhibited optimal adjustments. Among the models studied, the Richards and the Gompertz models had the best adjustments in all situations, with more realistic parameter estimates. However, the weighted Richards model, with or without ponderation with the autoregressive first order model AR (1), exhibited the best adjustments in females and males, respectively.

Effects of some factors on growth of lambs and the determination of growth curve models

2018 ◽  
Author(s):  
Ayhan Yilmaz ◽  
Ferda Karakus ◽  
Mehmet Bingöl ◽  
Baris Kaki ◽  
Gazel Ser
Keyword(s):  
Body Weight ◽  
Growth Curve ◽  
Growth Models ◽  
Age Groups ◽  
Growth Curves ◽  
The Body ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Male And Female ◽  
Growth Curve Models

he aims were to identify the body weight of the several age groups in Norduz lambs and its correlations between these traits were to determine the best non-linear growth curve models for the growth performance of the Norduz sheep breed. A total of 91 male and female of Norduz lambs were evaluated under extensive system conditions. The least square means for weights at birth and at 15, 30, 45, 60, 75, 90, 105, 120, 135, 150, 165, 180, 195 and 210 days of age periods were 4.51±0.56, 9.28±0.25, 11.14±0.29, 14.99±0.37, 18.21±0.43, 22.54±0.54, 22.33±0.25, 23.59±0.54, 25.58±0.55, 28.07±0.58, 29.45±0.60, 29.98±0.84, 32.44±0.61, 32.03±0.59 and 31.45±0.57 kg, respectively. There were differences in favor of lambs of four-year old dams at 15 days of age and also lambs born single at 90 days of age for the body weight. The effect of weight of dam at birth, 30, 45, 60 days of age was significant (P less than 0.05-P less than 0.01) and the birth weight in lambs importantly effected the weights at 15, 30, and 45 days of age. All correlations between the body weights of several age periods were significant as statistical (P less than 0.01). As for the growth models, distinguished models were compared using the coefficient of determination and mean square error for both sexes. As a result, we concluded that von Bertalanffy model were the best model in comparison with the other models for biological growth curves in Norduz male and female lambs.

Growth performance, correlation and regression estimates of seven-chicken strains in South-Western Nigeria

2020 ◽  
Vol 45 (2) ◽  
pp. 40-48
Author(s):  
A. J. Atansuyi ◽  
U. C. Ihendu ◽  
C. A. Chineke
Keyword(s):  
Body Weight ◽  
Growth Performance ◽  
Correlation Coefficients ◽  
Tail Length ◽  
Experimental Period ◽  
The Body ◽  
Coefficient Of Determination ◽  
Indigenous Chicken ◽  
Randomized Design ◽  
Study Results

This study was conducted to determine the growth performance, correlation and regression estimates of seven-chicken strains in South-western Nigeria using a total of 300 day-old chicks. The birds were divided into seven groups based on their strain. The seven strains are Normal feather (NF), Fulani ecotype (FE), Frizzle feather (FF), naked neck (NN) and Transylvania indigenous strains while Hubbard and Marshal were meat-type exotic chickens. There were forty- five (45) unsexed day-old chicks in each strain except the Frizzle feather that were 30 in number. Completely randomized design (CRD) was used for the trial that lasted for 8 weeks. The birds were fed experimental diets ad libitum throughout the period of the study. Results showed that there were significant differences (p<0.05) in the initial and final weights of the birds. It was observed that exotic strains weighed heavier (3569.73gHB) than their indigenous counterparts (1391.11gNF). However, the Fulani ecotype weighed heaviest (1840.99g) among Nigeria indigenous strains during the experimental period. This showed that FE strains are generally heavy breed chicken and could be incorporated into a meat producing indigenous chicken if improved upon. The result of the correlation coefficients showed that a very strong, positive and highly significant (P<0.001) relationship existed between body weights and linear body measurements as most of the values are (>0.40). All the body parameter examined had significant (p<0.01) and direct relationship with the body weight. Shoulder-to-tail length (STL) had the highest coefficient of 0.98.The high correlation estimates obtained in this study could be as a result of pleiotropy, heterozygosity or linkage of genes in the birds. The three functions were highly significant (p<0.05) for all the parameters studied. This shows that the functions well described the parameters. On the basis of coefficient of determination (R ), the body weight of poultry birds at any age can be predicted most accurately with BRG using cubic function.

Prediction of body weights of Nellore sheep under field condition

2015 ◽  
Author(s):  
M. Rani ◽  
B. Ekambaram ◽  
B. Punya Kumari
Keyword(s):  
Body Weight ◽  
Body Length ◽  
Age Groups ◽  
The Body ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Body Weights ◽  
Chest Girth ◽  
Males And Females ◽  
Nellore Sheep

Data on 1350 Nellore sheep of 2, 4, 6 and 8-teeth age, reared under field conditions in 12 mandals of Chittoor district of Andhra Pradesh were utilized for development of prediction equations and study the phenotypic association among body measurements and body weights. The coefficients of correlation between body weight with the height at withers, chest girth, paunch girth, hip width and body length were positive and high in magnitude in both males and females in majority of the age groups studied. Step-down regression equations were fitted to predict the body weight based on biometrical measurements at different ages. The height at withers, chest girth, paunch girth, hip width and body length have contributed significantly to the expression of body weights at the majority of the ages studied. High coefficient of determination (R2) value was observed in males at 6 and 8-teeth age as 88 per cent, while in females 50 per cent at 2-teeth age.

A general method for predicting the weight of water in the empty bodies of pigs

1995 ◽  
Vol 61 (1) ◽  
pp. 103-108 ◽  
Author(s):  
G. C. Emmans ◽  
I. Kyriazakis
Keyword(s):  
Body Composition ◽  
Body Weight ◽  
Food Intake ◽  
General Equation ◽  
Growth Models ◽  
The Body ◽  
Protein Ratio ◽  
A Value ◽  
Mature Size ◽  
General Method

AbstractAs water is the major component of the pig body its accurate prediction is of importance in pig growth models. It has become conventional to predict the weight of water, WA kg, from the weight of protein, P kg. The purpose of this paper is to find how this can be done across pig genotypes of different mature size. The widely used equation to relate WA to P is of the form: WA = a.Pb. This equation is examined theoretically. It is concluded that the form of the equation is reasonable and, that while the value of the exponent b is likely to be constant across genotypes, the value of the scalar a is not. It is proposed that the value of the scalar a is best estimated as a = WAPRm Pm1·b where WAPRm is the water: protein ratio in the body at maturity and Pm is the weight of protein in the body at maturity. The value of the parameter WAPRm is assumed to be constant across genotypes with a value in the range of 3·04 to 3·20, depending on the methods used for measuring body composition. The general value of b = 0·855, taken from published work, is confirmed. A consequence of the argument quantified in the paper is that the value of a is predicted to vary from a = 4·69 for a pig with Pm = 20 kg to a = 5·36 for a pig with Pm = 50 kg. The general equation is expected to give more accurate predictions of the weight of water and, hence, of body weight, in models intended to predict pig growth, food intake, body composition and efficiency.

scholarly journals A Comparative Study of the Nonlinear Methods for Estimate Body Weight by Body Measurements on Different Sample Sizes in Morkaraman Sheeps

2021 ◽  
Author(s):  
Mutlu YAGANOGLU
Keyword(s):  
Body Weight ◽  
Correlation Analysis ◽  
Body Length ◽  
Growth Models ◽  
Nonlinear Models ◽  
Small Sample ◽  
The Body ◽  
Body Measurements ◽  
Sample Sizes ◽  
Small Sample Sizes

Abstract The objective of this study was to estimate body weight of Morkaraman sheeps from body measurements with nonlinear models. Selected 110 sheeps 3-5 years were scored for body weight, body length, height at wither, chest width and pump width. For determine relationships with body weight between body measurements, correlation analysis was performed. The results of the correlation analysis indicated that the highest relationship according to the all sample sizes were body weight between body length (0.95, 0.90, 0.83, 0.81). Considering all parameters included in the model, the parameter showing the highest correlation with body weight was determined as body length according to all sample sizes. the highest correlation was found in 50 sample sizes (r:0.95). According to the small sample sizes (10-20), Logistic and Saturation growth models can be used to determine the body weight by using body length, on the other hand, Incomplete gamma model is more succesful to estimate body weight when sample size is nearly 30 and 50.

scholarly journals Modelling growth in seven chicken genotypes

2021 ◽  
Vol 32 (1) ◽  
pp. 28-38
Author(s):  
S. O. Peters ◽  
C. O. N. Ikeobi ◽  
M. O. Ozoje ◽  
O. A. Adebambo
Keyword(s):  
Body Weight ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Growth Models ◽  
The Body ◽  
Richards Model ◽  
Non Linear ◽  
Chest Girth ◽  
Mature Size ◽  
Early Phases

Three non-linear growth models were used to fit weight-age data for seven chicken genotypes: Comparisons were made among these models for goodness of fit, biological interpretability and computational case. Monomolecular and Richards Models overestimated body weight at the early phases of growth. All the three models underestimated the asymptotic mature weight but Gumpertz function gave a better estimate than the other two. Maturing rates were also variable and Richards Model gave the best estimate of K. Using these three non-linear models to describe growth rate of chest girth of the seven chicken genotypes yields a different result from that of the body weight. The point of inflection ranged from - 3 56 for FINA (F/Na) genotype to 28.26 for frizzled (Frx Fr) genotype. Genetic variations in rates of gain, maluring rute und mature size were observed. 

scholarly journals Towards the quantitative characterization of piglets’ robustness to weaning: A modelling approach

2018 ◽  
Author(s):  
M. Revilla ◽  
N.C. Friggens ◽  
L.-P. Broudiscou ◽  
G. Lemonnier ◽  
F. Blanc ◽  
...  
Keyword(s):  
Body Weight ◽  
Time Windows ◽  
Live Weight ◽  
The Body ◽  
Model Parameters ◽  
Antibiotic Administration ◽  
Quantitative Characterization ◽  
Weight Trajectories ◽  
Modelling Approach

AbstractWeaning is a critical transition phase in swine production in which piglets must cope with different stressors that may affect their health. During this period, the prophylactic use of antibiotics is still frequent to limit piglet morbidity, which raises both economic and public health concerns such as the appearance of antimicrobial-resistant microbes. With the interest of developing tools for assisting health and management decisions around weaning, it is key to provide robustness indexes that inform on the animals capacity to endure the challenges associated to weaning. This work aimed at developing a modelling approach for facilitating the quantification of piglet resilience to weaning. We monitored 325 Large White pigs weaned at 28 days of age and further housed and fed conventionally during the post-weaning period without antibiotic administration. Body weight and diarrhoea scores were recorded before and after weaning, and blood was sampled at weaning and one week later for collecting haematological data. We constructed a dynamic model based on the Gompertz-Makeham law to describe live weight trajectories during the first 75 days after weaning following the rationale that the animal response is partitioned in two time windows (a perturbation and a recovery window). Model calibration was performed for each animal. Our results show that the transition time between the two time windows, as well as the weight trajectories are characteristic for each individual. The model captured the weight dynamics of animals at different degrees of perturbation, with an average coefficient of determination of 0.99, and a concordance correlation coefficient of 0.99. The utility of the model is that it provides biological parameters that inform on the amplitude and length of perturbation, and the rate of animal recovery. Our rationale is that the dynamics of weight inform on the capability of the animal to cope with the weaning disturbance. Indeed, there were significant correlations between model parameters and individual diarrhoea scores and haematological traits. Overall, the parameters of our model can be useful for constructing weaning robustness indexes by using exclusively the growth curves. We foresee that this modelling approach will provide a step forward in the quantitative characterization of robustness.ImplicationsThe quantitative characterization of animal robustness at weaning is a key step for management strategies to improve health and welfare. This characterization is also instrumental for the further design of selection strategies for productivity and robustness. Within a precision livestock farming optic, this study develops a mathematical modelling approach to describe the body weight of piglets from weaning with the rationale that weight trajectories provide central information to quantify the capability of the animal to cope with the weaning disturbance.

scholarly journals Application of path analysis methodology in assessing the relationship between body weight and biometric traits of Red Sokoto goats in northern Nigeria

2012 ◽  
Vol 28 (1) ◽  
pp. 107-117 ◽  
Author(s):  
A. Yakubu ◽  
G.L. Mohammed
Keyword(s):  
Body Weight ◽  
Path Analysis ◽  
Body Length ◽  
Neck Circumference ◽  
The Body ◽  
Coefficient Of Determination ◽  
Path Coefficient ◽  
Northern Nigeria ◽  
Linear Type ◽  
Chest Girth

Relationship between body weight (BW) and seven morphobiometrical traits [withers height (WH), body length (BL), chest girth (CG), shoulder width (SW), ear length (EL), cannon circumference (CC) and neck circumference (NC)] were studied in 142 Red Sokoto goats aged 19.3-30.6 months old using path analysis. The animals were randomly selected in certain smallholders? farms located in northern Nigeria. Pair-wise correlations among body weights and linear type traits were positive and highly significant (r = 0.74 - 0.92; P< 0.01). The path analysis revealed that body length had the highest direct effect on body weight, closely followed by chest girth and shoulder width, respectively (path coefficient = 0.354, 0.253 and 0.214 for BL, CG and SW, respectively). The optimum linear regression model with a coefficient of determination ( R2) value of 0.934 included forecast indices, such as body length, chest girth, shoulder width, cannon circumference and neck circumference. This regression equation could be used to predict the body weight of Red Sokoto goats in the field and for selection purposes.

scholarly journals Body composition predicted with a Bayesian network from simple variables

2010 ◽  
Vol 105 (8) ◽  
pp. 1265-1271 ◽  
Author(s):  
Laurence Mioche ◽  
Caroline Bidot ◽  
Jean-Baptiste Denis
Keyword(s):  
Public Health ◽  
Body Composition ◽  
Body Weight ◽  
Bayesian Network ◽  
Linear Models ◽  
The Body ◽  
Fat Free Mass ◽  
Composition Analysis ◽  
Model Parameters ◽  
Health Issues

The relative contributions of fat-free mass (FFM) and fat mass (FM) to body weight are key indicators for several major public health issues. Predictive models could offer new insights into body composition analysis. A non-parametric equation derived from a probabilistic Bayesian network (BN) was established by including sex, age, body weight and height. We hypothesised that it would be possible to assess the body composition of any subject from easily accessible covariables by selecting an adjusted FFM value within a reference dual-energy X-ray absorptiometry (DXA) measurement database (1999–2004 National Health and Nutrition Examination Survey (NHANES),n10 402). FM was directly calculated as body weight minus FFM. A French DXA database (n1140) was used (1) to adjust the model parameters (n380) and (2) to cross-validate the model responses (n760). French subjects were significantly different from American NHANES subjects with respect to age, weight and FM. Despite this different population context, BN prediction was highly reliable. Correlations between BN predictions and DXA measurements were significant for FFM (R20·94,P < 0·001, standard error of prediction (SEP) 2·82 kg) and the percentage of FM (FM%) (R20·81,P < 0·001, SEP 3·73 %). Two previously published linear models were applied to the subjects of the French database and compared with BN predictions. BN predictions were more accurate for both FFM and FM than those obtained from linear models. In addition, BN prediction generated stochastic variability in the FM% expressed in terms of BMI. The use of such predictions in large populations could be of interest for many public health issues.

scholarly journals Predicting the Digestive Tract Development and Growth Performance of Goat Kids Using Sigmoidal Models

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 757
Author(s):  
Mahmoud Abdelsattar ◽  
Yimin Zhuang ◽  
Kai Cui ◽  
Yanliang Bi ◽  
Naifeng Zhang
Keyword(s):  
Body Weight ◽  
Digestive Tract ◽  
Goodness Of Fit ◽  
Pearson Correlation ◽  
Body Height ◽  
Growth Curves ◽  
The Body ◽  
Quadratic Model ◽  
Model Parameters ◽  
Poor Growth

The transition from monogastric to rumination stage is crucial in ruminants’ growth to avoid stressors—weaning and neonatal mortalities. Poor growth of the digestive tract could adversely affect the performance of the animal. Modeling informative growth curves is of great importance for a better understanding of the effective development pattern, in order to optimize feeding management system, and to achieve more production efficiency. However, little is known about the digestive tract growth curves. For this reason, one big goat farm of Laiwu black breed was chosen as a basis of this study. Forty-eight kids belonging to eight-time points (1, 7, 14, 28, 42, 56, 70, and 84 d; 6 kids for each) were selected and slaughtered. The body weight, body size indices, rumen pH, and stomach parts were determined and fitted to the polynomial and sigmoidal models. In terms of goodness of fit criteria, the Gompertz model was the best model for body weight, body oblique length, tube, and rumen weight. Moreover, the Logistic model was the best model for carcass weight, body height, and chest circumference. In addition, the Quadratic model showed the best fit for dressing percentage, omasum weight, abomasum weight, and rumen volume. Moreover, the cubic model best fitted the ruminal pH and reticulum percentage. The Weibull model was the best model for the reticulum weight and omasum percentage, while the MMF model was the best model describing the growth of chest depth, rumen percentage, and abomasum percentage. The model parameters, R squared, inflection points, area under curve varied among the different dependent variables. The Pearson correlation showed that the digestive tract development was more correlated with age than body weight, but the other variables were more correlated with body weight than age. The study demonstrated the use of empirical sigmoidal and polynomial models to predict growth rates of the digestive tract at relevant age efficiently.

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