Pregnancy Rate of Bunaji Cows in a Fixed Time AI Synchronization Protocol Using Ovatide

This study accessed the reproductive performance of Bunaji cows in an Ovsynch protocol involving ovatide.Bunaji cows(n=16)aged 4-6 years and weighing between 250-350 kg with body condition scores(BCS)of 2.5-3.5 were used.There were two treatment groups for synchronization of ovulation.Treatment group 1 comprising Bunaji(n=8)received 50μg of gonadotropin releasing hormone(GnRH)(Cystorelin)and 25 mg of PGF2α.While,treatment group 2 comprising Bunaji(n=8)received 50μg of ovatide and 25 mg of PGF2α.All cows from both treatment groups were inseminated at 16 h after each second GnRH or ovatide injections.Cows that showed mucus discharge from the vagina on the day of artificial insemination(AI)were recorded as well as those that had patent cervix.Cervical dilation was measured by taking note of the cows that had mid cervix insemination due to non-passage of the AI gun through the cervix.Transrectal palpation was conducted twice at a month interval to select cycling cows within 5-12 d of the estrous cycle before initiating Ovsynch protocol.It was repeated on day 45 post AI to confirm pregnancies in animals.Results showed that the rate of mucus discharge from the vagina was 37.5%and 87.5%for ovatide and Cystorelin,respectively;while the rate of cervical dilation were 75%and 87.5%for ovatide and Cystorelin,respectively(p>0.05),and pregnancy rates were 0%and 12.5%for ovatide and Cystorelin,respectively.There were no significant differences between the two groups.It was concluded that treatment of Bunaji cows with 50μg ovatide in Ovsynh protocol has heat and ovulation synchronization potentials and zero pregnancy rate.It was recommended that further studies be carried out using graded doses of 50,100 and 150μg of ovatide in a fixed time AI synchronization protocol in Bunaji cows,to tap the potentials of the hormone in manipulation of bovine reproduction.

Using Artificial Neural Network to Predict Body Weights of Rabbits

In this (modest) study, we developed artificial neural network (ANN) models for predicting body weight using various independent (input) variables in eight-week old New Zealand white purebred and crossbred rabbits. From the whole data sets of similar age groups, 75 percent were used to train the neural network model and 25 percent were used to test the effectiveness of the model. Five predictor variables were used viz, breed, sex, heart girth, body length and height at wither as input variables and body weight was considered as dependent variable from the model. The ANN used was multilayer feed forward network with back propagation of error for efficient learning. Our ANN models (with R2 = 0.68 at ten thousand iterations, and R2 = 0.71 one million iterations) performed better than traditional multivariate linear regression (MLR) models (R2 = 0.66) indicating that the ANN models were able to more accurately capture how the variations in input variables explained the variations in body weight. It is concluded that ANN models are more powerful than MLR models in predicting animals’ body weight. Nonetheless, we recognize that fitting an ANN model requires more computation resources than fitting a tradition MLR model but the benefits of its accuracy outweigh any demerit from the associated computation overhead.