Prediction of Marketing Live Weights in Hair Goat Kids Using Artificial Neural Network

In this study, marketing live weights (120th day) were predicted using artificial neural network model according to the herd, gender, birth type, maternal age, birth weight, body weight at 60th day and weaning weight (90th day) measurements of 12983 hair goat kids born between 2018-2021 years. Artificial neural networks (ANN) have been frequently used as an alternative to classical regression analysis in recent years, especially in future estimation studies in the field of livestock, and also in many different fields. In this study, it was aimed to predict the marketing weights of hair goats according to the holding, gender, birth type, maternal age, birth, 60th day and weaning weights with the ANN model. For this purpose, the multi-layer feed-forward backpropagation algorithm the ANN model, in which the number of hidden layers is one and the numbers of hidden neurons are three, was used. This model performance metrics were obtained for training set as 0.98, 0.62 and 0.55; for validation set as 0.97, 0.62 and 0.55, respectively. According to these results, it was determined that ANN can be used successfully in terms of estimation of marketing live weight in Hair goat kids. Estimating the marketing weight will enable the economic cost calculations to be obtained from kids to be evaluated both based on Turkey and on the farm basis, and to reveal future projections.Turkish Ministry of Agriculture and Forestry, General Directorate of Agricultural Research and Policies [TAGEM/35KIL2013-01]The data used in this study were obtained from the farms in Hair Goat Breeding Project in Izmir People in the Turkish National Sheep and Goat Breeding Project under the coordination of The Turkish Ministry of Agriculture and Forestry, General Directorate of Agricultural Research and Policies (Project Code: TAGEM/35KIL2013-01), from the kids, between the birth and weaning periods. In this context, I would like to thank TAGEM and The Turkish Ministry of Agriculture and Forestry for the opportunity, and the business owners whom I have reviewed