Modelling Growth Curves in a Nondescript Italian Chicken Breed: an Opportunity to Improve Genetic and Feeding Strategies

Financial impacts of a housing order on commercial free range egg layers in response to highly pathogenic avian influenza

Recent annual outbreaks of Highly Pathogenic Avian Influenza (HPAI) have led to mandatory housing orders on commercial free-range flocks. Indefinite periods of housing, after poultry have had access to range, could have production and financial consequences for free range egg producers. The impact of these housing orders on the performance of commercial flocks is seldom explored at a business level, predominantly due to the paucity of commercially sensitive data. The aim of this paper is to assess the financial and production impacts of a housing order on commercial free-range egg layers. We use a unique data set showing week by week performance of layers gathered from 9 UK based farms over the period 2020-2022. These data cover an average of 100,000 laying hens and include two imposed housing orders, in 2020/2021 and in 2021/22. We applied a random intercept linear regression to assess impacts on physical outputs and inputs, bird mortality and the impacts on revenue, feed costs and margin over feed cost. Feed use and feed costs per bird increased during the housing order which is a consequence of increased control over diet intake in housed compared to ranged birds. An increase in revenue was also found, ostensibly due to a higher proportion of large eggs produced, leading to a higher margin over feed cost. Overall, these large commercial poultry sheds were able to mitigate some of the potential adverse economic effects of housing orders. Potential negative impacts may occur dependant on the duration of the housing order and those farms with less control over their input costs.

Growth curve mixed nonlinear models in quails

Our aim was to evaluate the use and application of different nonlinear mixed models, as well as to compare them with approach in nonlinear fixed models, for describing the growth curve of meat-type quails according to gender. A total of 15,002 and 15,408 records of males and females were used, respectively. The body weights were regressed on age of the animals using nonlinear models (Brody; Gompertz; Logistic, Morgan-Mercer-Flodin, Richards and Von Bertalanffy). All model parameters were considered fixed, whereas parameters related to asymptotic weight and maturity rate were fitted as random effects. The Bayesian Information Criterion was used to find the model of best fit. For both genders, the model that used the Morgan-Mercer-Flodin function with the inclusion of asymptotic weight as a random effect was considered the best-fitting model because it reduced the residual variance and increased the accuracy. Based on the lower absolute growth rate and growth velocity of male quails compared to that of females, it can be inferred that males should be slaughtered later. Given the results of this study, it can contribute to the current knowledge about animal yield, specifically at the best moment to slaughter and, this sense, improv the quality genetic of the populations in time.

Estimation of genetic parameters for production, reproduction, and growth curve of Fars indigenous chicken

Iranian native chicken, including Fars indigenous chicken, is an important genetic resource due to its adaptation to stressful environmental conditions, good endurance and resistance to disease. The aim of this research was to determine the genetic infrastructure of Fars indigenous chicken using several nonlinear functions. The dataset included body weight at hatch (BW1), body weight at the 8th week (BW8), body weight at the 12th week (BW12), weight at sexual maturity (WSM), age at sexual maturity (ASM), number of eggs in the first 12 weeks of laying period (EN), egg weight at the first day of laying (EW1), average egg weight at the 28thday of laying (EW28), and average egg weight at weeks 28, 30, and 32 of the laying period (AEW). Growth models were fitted using the NLIN procedure and WOMBAT software was used to predict variance components for the best fit model parameters. Results suggested three-parameter models, for example, Gompertz, fitted better to the data than others. The maturity weight (A), initial weight (B), and maturity rate (K) parameters in the Gompertz model were 1996.8 ± 6.63, 4.11 ± 0.03, and 0.021 ± 0.0001, respectively. The heritability of A, B, and K parameters were 0.03, 0.05, and 0.12, respectively.

Mexican Creole chickens: effect of data collection periods on goodness-of-fit and parameter precision of growth models

The objective of this study was to estimate the good-of-fitness and precision of parameters of the Gompertz-Laird, Logistic, Richards, and Von Bertalanffy growth models, using different data collection periods (DCP). Two hundred and sixty-two Mexican Creole chicks (116 females and 146 males), were individually weighed to form the following sets of data for each sex: DCP₁ (weights recorded weekly from hatching to 63 d, and every 2 wk, from 63 to 133 d of age), DCP₂ (weights recorded weekly from hatching to 133 d of age), DCP₃ (weights recorded every third day, from hatching to 63 d, and every 14 d, from 63 to 133 d of age), and DCP₄ (weights recorded every third day, from hatching to 63 d, and weekly, from 63 to 133 d of age). Data were analyzed using the NLIN procedure of SAS (Marquardt algorithm). For all growth models, the width of confidence interval (CI) of each parameter, was estimated (α = 0.05). The adjusted coefficient of determination (AR²), as well as the Akaike (AIC) and Bayesian information criteria (BIC) were used to select the best model. The higher the AR², and the lower the width of CI, as well as the AIC and BIC values, the better the model. The Gompertz-Laird model, more frequently showed the highest AR², and the lowest AIC and BIC values compared to the other models. Moreover, for all models, both sexes and all parameters, most confidence interval widths (all with the Gompertz-Laird model) were the lowest with DCP₃ when compared to the other sets of data. In conclusion, the Gompertz-Laird model was the best provided that the chickens are weighed every third day from hatching until 63 d of age, and every 2 wk thereafter.

Genetic Effect and Growth Curve Parameter Estimation under Heat Stress in Slow-Growing Thai Native Chickens

Heat stress is becoming a major problem because it limits growth in poultry production, especially in tropical areas. The development of genetic lines of Thai native chickens (TNC) which can tolerate the tropical climate with the least compromise on growth performance is therefore necessary. This research aims to analyze the appropriate growth curve function and to estimate the effect of heat stress on the genetic absolute growth rate (AGR) in TNC and Thai synthetic chickens (TSC). The data comprised 35,355 records for body weight from hatching to slaughtering weight of 7241 TNC and 10,220 records of 2022 TSC. The best-fitting growth curve was investigated from three nonlinear regression models (von Bertalanffy, Gompertz, and logistic) and used to analyze the individual AGR. In addition, a repeatability test-day model on the temperature-humidity index (THI) function was used to estimate the genetic parameters for heat stress. The Gompertz function produced the lowest mean squared error (MSE) and Akaike information criterion (AIC) and highest the pseudo-coefficient of determination (Pseudo-R²) in both chicken breeds. The growth rates in TSC were higher than TNC; the growth rates of males were greater than females, but the age at inflection point in females was lower than in males in both chicken breeds. The THI threshold started at 76. The heritability of the AGR was 0.23 and 0.18 in TNC and TSC, respectively. The additive variance and permanent environmental variance of the heat stress effect increased sharply after the THI of 76. The growth rate decreased more severely in TSC than TNC. In conclusion, the Gompertz function can be applied with the THI to evaluate genetic performance for heat tolerance and increase growth performance in slow-growing chicken.

Mathematical models to describe the growth curves of Vietnamese Ri chicken

Ri chicken is the most popular backyard chicken breed in Vietnam, but little is known about the growth curve of this breed. This study compared the performances of models with three parameters (Gompertz, Brody, and Logistic) and models containing four parameters (Richards, Bridges, and Janoschek) for describing the growth of Ri chicken. The bodyweight of Ri chicken was recorded weekly from week 1 to week 19. Growth models were fitted using minpack.lm package in R software and Akaike's information criterion (AIC), Bayesian information criterion (BIC), and root mean square error (RMSE) were used for model comparison. Based on these criteria, the models having four parameters showed better performance than the ones with three parameters, and the Richards model was the best one for males and females. The lowest and highest value of asymmetric weights (α) were obtained by Bridges and Brody models for each of sexes, respectively. Age and weight estimated by the Richard model were 8.46 and 7.51 weeks and 696.88 and 487.58 g for males and for females, respectively. Differences in the growth curves were observed between males and female chicken. Overall, the results suggested using the Richards model for describing the growth curve of Ri chickens. Further studies on the genetics and genomics of the obtained growth parameters are required before using them for the genetic improvement of Ri chickens.

The Study of Growth and Performance in Local Chicken Breeds and Varieties: A Review of Methods and Scientific Transference

A review of the scientific advances in the study of the growth and performance in native chicken breeds and varieties over the past 20 years was performed. Understanding the growth patterns of native breeds can only be achieved if the constraints characterizing these populations are considered and treated accordingly. Contextually, the determination of researchers to use the same research methods and study designs applied in international commercial poultry populations conditions the accuracy of the model, variability capturing ability, and the observational or predictive performance when the data of the local population are fitted. Highly skewed sex ratios favouring females, an inappropriate census imbalance compensation and a lack of population structure render models that are regularly deemed effective as invalid to issue solid and sound conclusions. The wider the breed diversity is in a country, the higher the scientific attention paid to these populations. A detailed discussion of the most appropriate models and underlying reasons for their suitability and the reasons preventing the use of others in these populations is provided. Furthermore, the factors conditioning the scientific reception and impact of related publications used to transfer these results to the broad scientific public were evaluated to serve as guidance for the maximization of the success and dissemination of local breed information.

Evaluation of non-linear growth curve models in the Vietnamese indigenous Mia chicken

Understanding of animal growth is important for the improvement of management and feeding practices; however, little is known about the growth curve in Vietnamese indigenous chicken. This study was performed to determine the most appropriate models for describing the growth curve of Vietnamese Mia chicken. The study evaluated the performances of the Logistic, Gompertz, Richards, and Bridges models of body weights in 224 Mia chickens. Models were fitted using minpack.lm package in R software and Akaike's information criterion and Bayesian information criterion were used for model comparison. Based on these criteria, the Gompertz and Bridges were the best models for males and females, respectively. Estimated asymmetric weights (α) were ranged from 2,241.91 ± 14.74 (g) (Logistic) to 2,623.86 ± 30.23 (g) (Gompertz) for males and from 1,537.36 ± 10.97 (g) (Logistic) and 1,958.36 ± 72.92 (g) (Bridges) for females, respectively. The age at the inflection point was estimated from 9.32 to 10.5 weeks and from 8.51 to 9.86 weeks for males and females, respectively. In conclusion, the Gompertz model is the most suitable model for describing the growth curve of Mia chicken. The parameters obtained from growth models could help define feeding programs to meet nutritional needs from hatching to the age of maximum growth, reproduction programs, and marketing strategies.

Comparison on accuracy of different nonlinear models in predicting growth of Podolica bulls

Animal growth does not follow a linear pattern, being explained mathematically by functions that have parameters with biological meaning. These parameters are used to estimate the expected weight of animals at specific ages. Several nonlinear models have been used to describe growth. This study was carried out to estimate the parameters of logistic, Gompertz, Richards and von Bertalanffy growth curve models in a sample of Podolica young bulls to determine the goodness of fit. Animals were weighed every 3 months from birth to 810 days of age. The results indicate that all the growth models used were easily fitted to the observed data with Gompertz and logistic functions presenting less computational difficulty in terms of number of iterations to achieve convergence. Moreover, logistic and Richards equations provided the best overall fit being useful to describe the growth of Podolica bulls. Considering that the literature lacks information on growth curves in Podolica breed, the study of a mathematical model for growth describing the developmental pattern of a specific population within a peculiar environment is a useful tool to improve Podolica breed production.