A Comparative Forecasting Approach to Forecast Animal Production: A Case of Turkey
2020
Ordu, Muhammed | Zengi̇n, Yusuf
A number of reasons such as the increase in the world population, changes in the climate due to global warming and pandemic diseases affecting many regions have brought the importance of vegetative and animal production to the agenda, which is necessary for the healthy and balanced nutrition of the societies. Due to the global changes occurring for many years, researchers and policy makers have carried out studies on sustainable agriculture and livestock policies at the national and international level of food supply. In the literature, a limited number of forecasting studies on animal production have been carried out. The aim of our study is to develop a comparative forecasting approach and determine the best forecasting methods and models for each type of red meat (i.e. goat, seep, buffalo carcass, and cattle and calf carcass). Accordingly, we used ARIMA, exponential smoothing and STLF forecasting methods. Quarterly data between 2010 and 2018 published by Turkish Statistical Institute were used. As a result, ARIMA method was successful in forecasting amount of red meat production of cattle and calf carcass, and goat; exponential smoothing method was the best for other red meat resources. On the other hand, STLF method performed better than ARIMA and exponential smoothing methods in the training process of all forecasting models. The results of the study showed that comparing more than one forecasting method rather than using a single method in estimating the amount of red meat production will produce more reliable and accurate results.
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