Forecasting Harvest Area and Production of Strawberry Using Time Series Analyses

Year 2017, , 18 - 26, 29.12.2017

Melekşen Akın Sadiye Peral Eyduran

https://doi.org/10.13002/jafag4298

Cited By: 1

Abstract

This study was conducted to model the harvest area and production of strawberry in Turkey using FAOSTAT data from period of 1965 - 2015 to forecast strawberry harvest area and production for 2016-2025 period. Non-stationary time series of strawberry harvest area and production for 1965-2015 period were transformed into stationary time series after taking the first difference of the time series. Three Autoregressive Integrated Moving Average (ARIMA (1,1,0), ARIMA (1,1,1) and ARIMA (0,1,1)) and three Exponential Smoothing (Holt, Brown and Damped) models were used comparatively for time series data sets on strawberry harvest area and production. Holt exponential smoothing model showed the best forecasting and Brown exponential smoothing model was the most appropriate forecasting model for strawberry harvest area and production from the tested six models. We forecasted that the strawberry harvest area is going to be 14 385 ha in 2016 and will increase to 16 591 ha in 2025. The strawberry production forecasted significant increase for the 2016-2025 period, from 396 341 tons to 519 816 tons. Briefly, the present forecasting results might help policy makers to develop macro-level policies for food security and more effective strategies for better planning strawberry production in Turkey.

Keywords

Strawberry, production, harvested area, exponential smoothing, time series

References

• Amin M, Amanullah M, Akbar A (2014). Time series modeling for forecasting wheat production of Pakistan, The Journal of Animal Plant Science, 24(5):1444-1451
• Borkar P (2016). Modeling of groundnut production in India using ARIMA model. International Journal of Research in IT & Management. 6(3): 36-43.
• Celik S (2013). Modelling of production amount of nuts fruit by using Box-Jenkins technique. Yuzuncu Yil J. Agr. Sci. 23(1):18-30.
• Celik S, Karadas K, Eyduran E (2017). Forecasting groundnut production of Turkey via ARIMA models. The Journal of Animal and Plant Science.
• FAOSTAT (2017). Statistical database of the food and agriculture organization of the United Nations. http://faostat.fao.org/.
• Karadas K, Celik S, Eyduran E, Hopoglu S (2017a). Forecasting production of some oil seed crops in Turkey using exponential smoothing methods. The Journal of Animal and Plant Science (in press).
• Karadas K, Celik S, Hopoglu S, Eyduran E, Iqbal F (2017b). A survey of the relationship between production amount, cultivation area and yield of cotton lint in Turkey using time series analysis. The Journal of Animal and Plant Science (in press).
• Masuda T, Goldsmith PD (2009). World soybean production: area harvested, yield, and long-term projections. International Food and Agribusiness Management Association. 12(4): 143-161.
• Pektas A (2013). SPSS ile veri madenciligi. Dikeyeksen Yayın Dagitim, Yazilim ve Egitim Hizmetleri San. ve Tic. Ltd. Sti.; Istanbul.
• Semerci A, Ozer S (2011). Turkiye’de aycicegi ekim alanı, üretim miktarı ve verim değerinde olası değişimler. Journal of Tekirdag Agricultural Faculty, 8(3): 46-52.
• Suresh K, Kiran R, Giridhar K, Sampath K (2012). Modelling and forecasting livestock feed resources in India using climate variables. Asian-Australasian Journal of Animal Science, 25(4): 462-470.
• Wang SY, Lin HS (2000). Antioxidant activity in fruits and leaves of blackberry, raspberry and strawberry varies with cultivar and developmental stage. J. Agr. Food.