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The number of eggs produced in a given period (egg production rate) is an important trait in layers that change over time and can be presented as a curve. This study aimed to fit the weekly egg production data of Khazak indigenous hens using non-linear regression models and to select an appropriate model for describing the egg production curve for this bird. Biweekly egg production of 144 laying hens over 52 weeks of egg production was used to evaluate the egg production curve. Seven non-linear models (Gamma, McNally, Compartmental II, Nelder, Yang, Lokhorst, and Narushin-Takma) were fitted to egg production data. The four goodness fit criteria (Akaike' s information criterion, Mean square error, Log Likelihood, and Bayesian information criterion) were used to compare the models. The results of the goodness of fit criteria showed that the Narushin-Takma and Yang models were the best and worst models, respectively, for describing the egg production curve of Khazak hens. The time and egg production at the peak with the Narushin-Takma model was similar to the actual values, and this model was significantly better than other studied models. The correlation between actual and predicted egg production indicated that the Narushin-Takma model could accurately predict the egg production of this breed. As a result, the Narushin-Takma model can be used to predict the egg production curve of Khazak hens in breeding programs and nutritional management. [ABSTRACT FROM AUTHOR]

Lysozyme activity is one of the nonspecific immunity parameters measured by changing the amount of adsorption at different times. The objective of the present study was to compare five non-linear models including Gompertz, Richards, Logistic, Lopez, and Weilbull to describe the cumulative plasma lysozyme activity in quails. In total 1364 plasma samples (1004 females and 360 males) were collected and the cumulative lysozyme activity was calculated by turbidimetric method assay in Micrococcus luteus. The goodness-of-fit of models was compared according to different criteria of Maximum loglikelihood, Akaike information criterion, Mean square error, and Bayesian information criterion. The results showed that the Gompertz model was the best model for describing of decreasing cumulative pattern of lysozyme activity in female and male quails and provided satisfactory predictions of lysozyme activity at different times (30, 60, 90, 120, 150, 180, 210, 240, 270, 300 seconds). The parameters of all models were higher in females than males except for the k parameter which was greater in the males. Male quails had higher values for time and lysozyme activity than females at inflection points, whereas the absolute growth rate in 30, 150, and 300 seconds was predicted higher in female quails. In conclusion, the Gompertz model can be used accurately to evaluate cumulative lysozyme activity patterns in both sexes of quails. [ABSTRACT FROM AUTHOR]

Non-additive genetic effects are important to increase the accuracy of estimating genetic parameters for growth traits. The aim of this study was to estimate genetic parameters and variance components, specially dominance and epistasis genetic effects, for growth traits (birth weight (BW), weaning weight (WW), 3 (W3), 6 (W6), 9 (W9), and 12 (W12) month weight) in Adani goats. Analyses were carried out using Bayesian method via Gibbs sampler animal model by fitting of 18 different models. All fixed effects (sex, type of birth, age of dam, and year) showed significant effects on BW, WW, W3, and W6, whereas the type of birth and age of dam were not significant on W9 and W12. With the best model, direct heritability estimates were 0.347, 0.178, 0.158, 0.359, 0.278, and 0.281 for BW, WW, W3, W6, W9, and W12 traits, respectively. Maternal permanent environmental effect was significant for BW and WW, but maternal genetic effect was significant only for W3. Dominance and epitasis effects were significant almost for all traits and as a proportion of phenotypic variance were ranged from 0.115 to 0.258 and 0.107 to 0.218, respectively. The range of accuracy of breeding values estimated for growth traits with appropriate evaluation models was from 0.521 to 0.652, 0.616 to 0.694, and 0.548 to 0.684 for the all animals, 10% of the best males and 50% of the best females, respectively. When dominance and epistasis effects added to models, the error variance was reduced and the accuracy of estimated breeding values increased. The accuracy of the best model showed a significant difference with the accuracy of other models (p < 0.01). The result of the present study suggests that non-additive genetic effects should be in genetic evaluation models for goat growth traits because of its effect on accuracy of estimated breeding values. [ABSTRACT FROM AUTHOR]