A total number of 300 females and 30 males of two experimental broiler lines, TT (male) and PP (female), with 39 and 36 weeks of age, respectively. Experiment 1 (TT) evaluated the effect of storage time (ST) on hatchability and embryo mortality (EM). Eggs were stored for 2 (ST1), 4 (ST2), 6 (ST3), 8 (ST4), 10 (ST5), 12 (ST6), and 24 (ST7) days and EM was assessed in the periods of 0 to 6 (EM1), 7 a 13 (EM2), 14 to 17 (EM3), 18 to 21 (EM4), and 0 to 21 (TEM) days. Experiment 2 (PP) evaluated the effect of egg weight (EW) on embryo development. Eggs were individually collected and weighed. Embryos were collected and weighed on days 9 (EW9), 11 (EW11), 13 (EW13), EW15, 17 (EW17) days of incubation and at hatching (EW21). ST linearly influenced hatchability and EM, with an estimated 1.17% reduction and a 1.15% increase, respectively for each 1.0 day of storage. Hatchability was reduced in 21% betweeb ST2 and ST7, resulting from a 462% increase in EM. The correlation between egg weight and embryo weight (EW) was significant after EW3 (0.25), reaching 0.72 for EW21, which corresponded to 70.92% of egg weight. The estimated increase in embryo weight for each 1.0g increase in egg weight was 0.71g for EW21

Neural networks are capable of modeling any complex function and can be used in the poultry and animal production areas. The aim of this study was to investigate the possibility of using neural networks on an egg production data set and fitting models to the egg production curve by applying 2 approaches, one using a nonlinear logistic model and the other using 2 artificial neural network models [multilayer perceptron (MLP) and radial basis function]. Two data sets from 2 generations of a White Leghorn strain that had been selected mainly for egg production were used. In the first data set, the mean weekly egg-laying rate was ascertained over a 54-wk egg production period. This data set was used to adjust and test the logistic model and to train and test the neural networks. The second data set, covering 52 wk of egg production, was used to validate the models. The mean absolute deviation, mean square error, and R(2) were used to evaluate the fit of the models. The MLP neural network had the best fit in the test and validation phases. The advantage of using neural networks is that they can be fitted to any kind of data set and do not require model assumptions such as those required in the nonlinear methodology. The results confirm that MLP neural networks can be used as an alternative tool to fit to egg production. The benefits of the MLP are the great flexibility and their lack of a priori assumptions when estimating a noisy nonlinear model.

The objectives of this paper were to identify the phenotypic egg-laying patterns in a White Leghorn line mainly selected for egg production, to estimate genetic parameters of traits related to egg production and to evaluate the genetic association between these by principal components analysis to identify trait(s) that could be used as selection criteria to improve egg production. Records of 54 wk of egg production from a White Leghorn population were used. The data set contained records of the length:width ratio of eggs at 32, 37, and 40 wk of age; egg weight at 32, 37, and 40 wk of age; BW at 54 and 62 wk of age; age at first egg; early partial egg production rate from 17 to 30 wk and from 17 to 40 wk of age; late partial egg production rate from 30 to 70 wk and from 40 to 70 wk of age; and total egg production rate (TEP). The estimates of genetic parameters between these traits were estimated by the restricted maximum likelihood method. Multivariate analyses were performed: a hierarchical cluster analysis, a nonhierarchical clustering analysis by the k-means method of weekly egg production rate to describe the egg-laying patterns of hens, and a principal components analysis using the breeding values of all traits. The highest heritability estimates were obtained for BW at 54 wk of age (0.68 ± 0.07) and age at first egg (0.53 ± 0.07). It is recommended that a preliminary clustering analysis be performed to obtain the population structure that takes into account the pattern of egg production, rather than the TEP, because hens may have the same final egg production with different patterns of egg laying. Early partial production periods were not good indicators for use in improving total egg production because these traits presented an overestimated genetic correlation with TEP because of the part-whole genetic correlation component. Egg production might be improved by selecting individuals based on TEP.

Egg production curves describe the laying patterns of hen populations over time. The objectives of this study were to fit the weekly egg production rate of selected and nonselected lines of a White Leghorn hen population, using nonlinear and segmented polynomial models, and to study how the selection process changed the egg-laying patterns between these 2 lines. Weekly egg production rates over 54 wk of egg production (from 17 to 70 wk of age) were measured from 1,693 and 282 laying hens from one selected and one nonselected (control) genetic line, respectively. Six nonlinear and one segmented polynomial models were gathered from the literature to investigate whether they could be used to fit curves for the weekly egg production rate. The goodness of fit of the models was measured using Akaike's information criterion, mean square error, coefficient of determination, graphical analysis of the fitted curves, and the deviations of the fitted curves. The Logistic, Yang, Segmented Polynomial, and Grossman models presented the best goodness of fit. In this population, there were significant differences between the parameter estimates of the curves fitted for the selected and nonselected lines, thus indicating that the effect of selection changed the shape of the egg production curves. The selection for egg production was efficient in modifying the birds' egg production curve in this population, thus resulting in genetic gain from the 5th to the 54th week of egg laying and improved the peak egg production and the persistence of egg laying.

In the present study, 35 farmers contracted by an integration company were selected. Each farmer owned an average of seven poultry houses, and housed six flocks per year, with a total of 4.0 million housed broilers. Birds were grouped into 5 market ages (MA1=<43, 4346 days), and the following parameters were measured: average flock body weight (AFW), feed conversion ratio (FCR), livability (L), production efficiency index (PEI), production cost, and farmer's gross margin. MA significantly influenced all parameters, except production cost/kg broiler. The effects of farm and farm*MA interaction were not significant. Each day of MA increase resulted in increases of 68.43g and 0.039 units in AFW and FCR, respectively. PEI was 4.0% lower in MA5 as compared to MA1, thereby reducing farmer's compensation in 11.89% per reared broilers. Production costs were not different among market ages, partially due to a reduction from 16.86 (MA1) to 14.62% (MA5) in the farmer's participation in the total cost. The results show that a new farmer's compensation index that included MA is necessary to calculate farmer's margin.