Improving a nonlinear Gompertz growth model using bird-specific random coefficients in two heritage chicken lines

Review: When worlds collide - poultry modeling in the 'Big Data' era

Within poultry production systems, models have provided vital decision support, opportunity analysis, and performance optimization capabilities to nutritionists and producers for decades. In recent years, due to the advancement of digital and sensor technologies, 'Big Data' streams have emerged, optimally positioned to be analyzed by machine-learning (ML) modeling approaches, with strengths in forecasting and prediction. This review explores the evolution of empirical and mechanistic models in poultry production systems, and how these models may interact with new digital tools and technologies. This review will also examine the emergence of ML and Big Data in the poultry production sector, and the emergence of precision feeding and automation of poultry production systems. There are several promising directions for the field, including: (1) application of Big Data analytics (e.g., sensor-based technologies, precision feeding systems) and ML methodologies (e.g., unsupervised and supervised learning algorithms) to feed more precisely to production targets given a 'known' individual animal, and (2) combination and hybridization of data-driven and mechanistic modeling approaches to bridge decision support with improved forecasting capabilities.

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.

Crossbreeding to improve local chicken breeds: predicting growth performance of the crosses using the Gompertz model and estimated heterosis

The present study aims to validate the Gompertz model to predict the growth performance of chicken crosses according to growth curve parameters of the parental lines and the estimated heterosis for each curve parameter. A total of 252 one-day-old chicks of both sexes belonging to 6 genotypes, including Ross 308, Sassò (SA), Bionda Piemontese (BP), and Robusta Maculata (RM), and the crosses between these local breeds and SA (BP × SA and RM × SA) were randomly allocated in 18 pens (3 pens/genotype) in mixed-sex groups (14 animals/pen; 7 females and 7 males). The individual body weight (BW) of all birds was recorded once a week from hatching until slaughtering (81 d for Ross 308; 112 d for SA, 140 d for the other genotypes). We drew up our final dataset with 240 birds (40 birds/genotype; 20 females and 20 males). The growth curve of each genotype was described using the Gompertz model, and the heterosis for each growth curve parameter was calculated as the difference between F₁ crosses and the average of parental breeds. The predicted growth curve parameters were evaluated by cross-validation. The Gompertz model accurately estimated the growth curves of all the genotypes (R² > 0.90). Heterosis was significant for almost all growth curve parameters in both crosses (P < 0.05). Heterosis ranged from -13.0 to +11.5%, depending on parameters, but varied slightly between the crossbreeds (BP × SA and RM × SA). The predicted values of adult BW, weight at the inflection point, and maximum growth rate were overestimated for BP × SA and underestimated for RM × SA, with a mean error between observed and predicted values <│2.7│% for all the curve parameters. In conclusion, the growth performance of chicken crosses between local breeds and commercial strains can be accurately predicted with Gompertz parameters of the parental lines adjusting for heterosis.

Modelling nitrogen-corrected apparent metabolisable energy requirement for egg production of 3 BW types of Yellow Broiler breeder hens during the egg-laying period

Accurate prediction of energy requirement is important in formulating diets, but an energy model for Yellow Broiler breeder hens is publicly unavailable. The objective of this study was to establish energy prediction models for the nitrogen-corrected apparent metabolisable energy (AMEn) requirement of different categories of Yellow Broiler breeder hens during the egg-laying period. Data for modelling were collected from research papers, public databases and production data from companies. Breeder hens were generally categorised into three BW types: heavy, medium and light (HBWT, MBWT and LBWT). Published articles were cited for providing coefficients of AMEn maintenance requirement (AMEnm, 101 kcal/kg BW^0.75, 423 KJ/kg BW^0.75) and growth requirement (AMEng, 5.33 kcal/g, 22.3 KJ/g), respectively. Models of AMEn for egg production (AMEnp) were established from the known daily intake of AMEn (AMEni) and those of maintenance and growth by the factorial approach: AMEnp = AMEni - AMEnm - AMEng. For the three types of hens, AMEnp HBWT (kcal, KJ) = 2.55 kcal (10.7 KJ) × egg mass (EM, g); AMEnp MBWT (kcal, KJ) = 2.70 kcal (11.3 KJ) × EM (g), and AMEnp LBWT (kcal, KJ) = 2.94 kcal (12.3 KJ) × EM (g) were determined. The total AMEni requirements, depending on Gompertz models, were HBWT: BW (g) = 3 144 × e^{-EXP(-0.162×(week of age (wk)-15.6))}; MBWT: BW (g) = 2 526 × e^{-EXP(-0.333×(wk-19.1))}; LBWT: BW (g) = 1 612 × e^{-EXP(-0.242×(wk-16.5))}. Models of egg production, HBWT: egg production (%) = 124 × e^-0.017×wk/(1 + e^{-0.870×(wk-26.2)}); MBWT: egg production (%) = 144 × e^-0.020×wk/(1 + e^{-0.751×(wk-24.9)}); LBWT: egg production (%) = 163 × e^-0.024×wk/(1 + e^{-0.476×(wk-26.5)})) and egg weight for each wk of the three types of hens during the egg-laying period were all established. These models showed good applicability in simulating and predicting the literature or production data.

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.