Stochastic Model of Egg Production in Broiler Breeders

Investigation of egg production curve in ostrich using nonlinear functions

In most countries, ostrich farming is considered a developing branch of the efficient poultry industry. The profitability of ostrich farm requires specific consideration of productions features such as the female fertility, egg production, hatchability, and growth performance. Hence, this study aimed to fit nonlinear functions to describe the ostrich egg production pattern to achieve the most appropriate and recommendable mathematical function for future studies. For this purpose, 14,507 daily records of 184 female ostriches in 5 production seasons (periods) during 2016 to 2021 were used. Five nonlinear functions including Incomplete gamma (Wood function), Corrected gamma (McNally), nonlinear Logistic (Yang), Logistic (Nelder), and Lokhorst were fitted for modeling the egg production curve in ostrich. The goodness of fit criteria's including Mean Square Error (MSE), Likelihood Ratio Test (LRT), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to evaluate and selection of the best function. The results indicated that the Wood and the McNally functions with a slight difference in all fitting criteria were the best-fitted functions and the Yang function with the highest values of MSE, LRT, AIC, BIC, were the most inappropriate function to describe the ostrich egg production curve. The McNally and the Wood can be recommended as appropriate functions to describe egg production during 5 production seasons in the studied ostrich flock.

Analyses of mathematical models for Yangzhou geese egg-laying curves

Mathematical models of the egg-laying curves for Yangzhou geese exposed to both natural and artificial photoperiods were established to optimise the parameters for maximising geese reproductive performance and for the development of precision feeding methods. With the natural photoperiod, egg-laying starts in autumn when daily photoperiod decreases, but accelerates after the winter solstice, and reaches the peak in spring when photoperiod increases. An accumulating model was constructed based on the hypothesis that the egg-laying capacity of geese was determined by two components of the photoperiod: photo-stimulation and photo-inhibition. In addition, a second segmented model was constructed based on the hypothesis that the photo-stimulation only occurred with lengthening photoperiods after the winter solstice, and the lesser laying rate in autumn could be attributed to the non-photo-dependent animal-husbandry technologies. This model consists of a logistic model before the winter solstice, and an accumulating model after this solstice. The use of the logistic and accumulating resulted in more precise predictions that occurred with use of Model 1 with a greater R² and lesser RMSE, AIC and BIC. Likewise, the egg-laying curves when there was consideration of artificial photoperiods could also be constructed with consideration of stimulatory and inhibitory photoperiodic effects. The model consists of an initial logistic and subsequently a quadratic polynomial model. With use of this model, there is consideration of changes in egg-laying patterns when there is a fixed photoperiod, with the model parameters reflecting the effects by photoperiod control-programs and age of the geese. In conclusion, new mathematical models have been developed to best fit egg-laying curves when there are both natural and artificial photoperiods. These models can contribute to development of precision-feeding technologies for breeding geese in future.

Modelling the egg components and internal cycle length of laying hens

A model that can estimate the changes that occur to the composition of egg components over time is an important tool for the nutritionists, since it can provide information about the nutrients required by a laying hen to achieve her potential egg output. In this context, the present study was aimed to model the potential egg production of laying hens during the egg-production period. One hundred and twenty Hy-Line W36 and ISA-Brown layers were used from 18 to 60 weeks of age, with each bird being an experimental unit. The birds were housed in individual cages during the experimental period. Egg production (%), egg weight (g) and the weight of egg components were recorded for each bird. The data were used to calculate the parameters of equations for predicting the weights of yolk, albumen and shell, and for predicting internal cycle length. The predicted results were evaluated by regressing residual (observed minus predicted) values of the predicted values centred of their average value. The equations for predicting mean yolk weight with age are for Hy-Line W36 (y1) and ISA-Brown (y2) respectively. Albumen and shell weights for Hy-Line W36 were described by the equations 15.07 × (yolk weight)0.37 and 0.70 × (yolk + albumen weight)0.50 respectively, and for ISA-Brown, 21.99 × (yolk weight)0.24 and 1.60 × (yolk + albumen weight)0.34 respectively. The average internal cycle length over time for Hy-Line W36 (ICL1) is described by the model 22.95 + 5.24 × (0.962t) + 0.02 × t and for ISA-Brown by 24.01 + 10.29 × (0.94t) + 0.004 × t, where t is the age at first egg (days). The assessment of the results indicated that the equations for predicting egg weight were more accurate for Hy-Line W36 but less precise for both strains, whereas the equation models for predicting the internal cycle lengths were more accurate and precise for ISA-Browns. The models could predict the potential weight of egg components and the rate of laying associated with the internal cycle lengths, and, on the basis of this information, it is possible to improve the nutrient requirement estimated.

Unexpected consequences of genetic selection in broilers and turkeys: problems and solutions

1. Genetic theory leads to the expectation that unexpected consequences of genetic selection for production traits will inevitably occur and that these changes are likely to be undesirable. 2. Both artificial selection for production efficiency and "natural" selection for adaptation to the production environment result in selection sweeps that increase the frequencies of rare recessive alleles that have a negative effect on fitness. 3. Fitness is broadly defined as any trait that affects the ability to survive, reproduce and contribute to the next generation, such as musculoskeletal disease in growing broiler chickens and multiple ovulation in adult broiler parents. 4. Welfare concerns about the negative effects of genetic selection on bird welfare are sometimes exaggerated but are nevertheless real. Breeders have paid increasing attention to these traits over several decades and have demonstrated improvement in pedigree flocks. There is an urgent need to monitor changes in commercial flocks to ensure that genetic change is accompanied by improvements in that target population. 5. New technologies for trait measurement, whole genome selection and targeted genetic modification hold out the promise of efficient and rapid improvement of welfare traits in future breeding of broiler chickens and turkeys. The potential of targeted genetic modification for enhancing welfare traits is considerable, but the goal of achieving public acceptability for the progeny of transgenic poultry will be politically challenging.

Egg production forecasting: Determining efficient modeling approaches

Several mathematical or statistical and artificial intelligence models were developed to compare egg production forecasts in commercial layers. Initial data for these models were collected from a comparative layer trial on commercial strains conducted at the Poultry Research Farms, Auburn University. Simulated data were produced to represent new scenarios by using means and SD of egg production of the 22 commercial strains. From the simulated data, random examples were generated for neural network training and testing for the weekly egg production prediction from wk 22 to 36. Three neural network architectures-back-propagation-3, Ward-5, and the general regression neural network-were compared for their efficiency to forecast egg production, along with other traditional models. The general regression neural network gave the best-fitting line, which almost overlapped with the commercial egg production data, with an R(2) of 0.71. The general regression neural network-predicted curve was compared with original egg production data, the average curves of white-shelled and brown-shelled strains, linear regression predictions, and the Gompertz nonlinear model. The general regression neural network was superior in all these comparisons and may be the model of choice if the initial overprediction is managed efficiently. In general, neural network models are efficient, are easy to use, require fewer data, and are practical under farm management conditions to forecast egg production.

Successful modification of a stochastic model of egg production in broiler breeders housed in temperate climates to predict flock productivity in tropical farms in Venezuela

1. The production of hatching eggs in tropical flocks of broiler breeders was estimated from records of initial body weight and subsequent body weight gains from 6 flocks containing over 140,000 birds in Venezuela using a stochastic model developed in temperate climates. The model was then modified to account for the greater persistency of egg production in the tropical environment of Venezuela. 2. The tropical model overestimated total egg production by 1.8 eggs. Lin's concordance correlation coefficient averaged 0.99 compared with 0.85 for the temperate model of egg production. 3. It was concluded that the modified model simulated egg production sufficiently well in the tropical environment to make it a useful management tool. Breeding company recommended target rates of lay for broiler breeders subjected to a long photoperiod during rearing such as in the tropics should be modified to account for their greater persistency of lay.

Stochastic modelling of optimum initial body weight, daily weight gain and effect of genetic changes in ovulation rate and age at sexual maturity on total egg production of broiler breeders

1. A model that simulates the total production of eggs (TEP) in broiler breeders was used to predict the optimum initial (20 week) body weight (IBWexp), daily weight gains from 20 to 30 (DWGexp(20-30)) and 31 to 62 weeks of age (DWGexp(31-62)), age at photostimulation (affecting age at first egg, AFEexp), coefficients of variation of initial body weight (CV-IBWexp) and age at first egg (CV-AFEexp), and the effect of genetically increasing the numbers of yellow follicles at the onset of lay. 2. The results suggest that TEP in broiler breeders is very sensitive to changes in body weight gain during the first 10 weeks of the production period and body weight at the start of egg production, whereas changes in body weight gain after peak rate of lay showed only minor effects on TEP. Increasing CV-IBWexp was associated with a linear decrease in the mean and increased variability of TEP. 3. Decreasing AFEexp was negatively associated with TEP, whereas higher CV-AFEexp increased variability of TEP and had a trivial affect on the mean. 4. Results of the simulation suggested that reducing ovarian yellow follicle numbers by means of genetic selection could reduce the degree of feed restriction currently used in broiler breeder commercial stocks while maintaining total egg production. Higher numbers of yellow follicles associated with selection for higher growth rate would not result in lower egg production if the body weight target was maintained at the currently recommended commercial level and the effect on TEP of increasing the target in proportion to potential body weight may be relatively small.