 Forecasting Goat Milk Production in Turkey Using Artificial Neural Networks and Box-Jenkins ModelsFerhan Kaygisiz () and Funda Hatice Sezgi̇n () Animal Review, 2017, vol. 4, issue 3, 45-52 Abstract: 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%. Keywords: Artificial neural networks model; Box - Jenkins model; Forecasting; Goat milk.; Time series; Milk production (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:pkp:anirew:v:4:y:2017:i:3:p:45-52:id:24 Access Statistics for this article More articles in Animal Review from Conscientia Beam |
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