Open AccessForecasting Goat Milk Production in Turkey Using Artificial Neural Networks and Box-Jenkins Models
Author(s) -
Ferhan Kaygısız,
Funda Hatice Sezgin
Publication year - 2017
Publication title -
animal review
Language(s) - English
Resource type - Journals
eISSN - 2412-3382
pISSN - 2409-6490
DOI - 10.18488/journal.ar.2017.43.45.52
Subject(s) - production (economics) , milk production , artificial neural network , agriculture , box–jenkins , agricultural science , investment (military) , cow milk , agricultural economics , statistics , microbiology and biotechnology , mathematics , geography , zoology , economics , computer science , biology , autoregressive integrated moving average , food science , artificial intelligence , time series , macroeconomics , archaeology , politics , political science , law
The demand for goat milk has gradually increased in Turkey in recent years and dairy goat breeding began to be seen as an alternative investment area. The aim of the study is to create the data that will contribute to policy formulation in the stockbreeding industry by making a 10-year forecast of output pertaining to the goat milk production in Turkey. In the study, the annual data of the goat milk production in Turkey during the time period 1961 and 2016 obtained from Turkish Statistical Institute and Food and Agriculture Organization was utilized. Box-Jenkins estimation models and artificial neural networks model were used to forecast the production of goat milk. It was identified that artificial neural networks model gave the best result and prospective estimations were made through this model. As a result of the study, the projected value of milk production for 2026 was found to be 495,536.1 tons. Following the forecasts, it was calculated that the average rate of increase in the goat milk production will be 0.12%.