For comparing reproductive performance of sexed versus conventional semen a total of 3573 heifer insemination records collected from five herds in four provinces of Iran were investigated. The studied provinces were classified into three regions: hot semiarid (Tehran and Alborz provinces), temperate semiarid (Khorasan Razavi province) and cold semiarid (Zanjan province). Various parameters including the conception rate, number of services per conception, calf sex ratio, calf birth weight, gestation length, calving ease score, abortion and stillbirth as well as twining rate were investigated. The logistic regression method was deployed for the analysis of categorical variables while the GLM procedure was applied for the analysis of continuous variables. The average conception rate of sex sorted and conventional semen was 48.3 and 63.8%, respectively. The utilisation of sex sorted semen resulted in 91.1% female calves. The conception rate, number of services per conception, gestation length, calf birth weight and the calf sex ratio were among the reproductive variables that were significantly influenced by the semen sexing. A greater number of services per conception and calving ease score were observed in hot and temperate semiarid areas. These climatic regions also provided a lower incidence of female calf births compared to the cold semiarid regions. Our results highly reaffirm the previous findings on the reproductive performance of sex sorted semen. Yet, climatic and management practices of a herd within a special climate have to be considered when deciding for utilisation of sex sorted semen in a dairy farm.

Carcass yield of meat-type quails is strongly correlated with the weight of the birds at slaughter (slaughter weight [SW]; body weight at 45 D of age). Moreover, prediction of superior animals for SW at the earlier stages of the rearing period is favorable for producers. Therefore, the aim of the present study was to predict and optimize SW of Japanese quails based on their early growth performances, sex, and egg weight as predictors through artificial neural network (ANN) modeling. To construct the ANN model a feed-forward multilayer perceptron neural network structure was used. Moreover, sensitivity analysis was used to arrange the predictors in the ANN model(s) according to their predictive importance too. In addition, the optimization process was conducted to determine the optimum values for the input variables to yield maximum SW. The best-fitted network on input data to predict SW in Japanese quails was determined with 7 neurons in the input layer, 11 neurons in the hidden layer, and one neuron in the output layer. The coefficient of determination (R2) was 0.9404, 0.9359, and 0.9223 for training, validation, and testing phases, respectively. For the corresponding phases, SEM were also 51.8854, 52.2764, and 55.2572, respectively. According to sensitivity analysis, the most important input variable for prediction of SW was body weight at 20 D of age (BW20), whereas the less important input variables were weight of the birds at hatch and body weight at 5 D of age. The results of the neural network optimization indicated that all the input variables, except for BW20, were very similar but slightly higher than mean values (μ for each input variable). The results of this study suggest that the ANN provides a practical approach to predict the final body weight (SW) of Japanese quails based on early performances. Moreover, phenotypic selection for higher values of early growth traits did not ensure the achievement of maximum SW, except for BW20.