Forecasting Sugar Beet Production In Syria Using Autoregressive And Moving Average Models (ARIMA)

Abstract

This research addressed the topic of forecasting sugar beet production in Syria using autoregressive and moving averages (ARIMA) models, as sugar beet is one of the vital agricultural crops that contributed to meeting the local market's needs for sugar and enhancing food security. Sugar beet cultivation in Syria has faced many challenges, including climate change, water scarcity, and price fluctuations, which negatively affected productivity.

The research aimed to analyze historical data on sugar beet production during the period from 1991 to 2023, and explore the prevailing trends in the cultivated area and production. The Box-Jenkins methodology was used to apply ARIMA models, which were considered effective tools in analyzing time series and providing accurate forecasts.

 The results showed a strong relationship between the cultivated area and production quantities, as the decrease in area was negatively reflected in production. Production has shown significant fluctuations over the years, peaking in some periods and deteriorating significantly in others, reflecting the fragility of the agricultural sector and its impact on environmental and economic factors. The ARIMA (1.1.0) model was used to forecast sugar beet production, with results showing that this model was able to provide accurate forecasts that reflected changes in production based on historical data.

The forecasts also indicated a continued decline in production during the period from 2024 to 2030, which necessitated taking strategic measures to ensure the sustainability of this sector.

In light of these results, the research recommended adopting effective support policies for the agricultural sector, focusing on improving water resource management and increasing investments in modern agricultural technologies. Research should also be enhanced to develop improved sugar beet strains that adapt to climate change, which contributes to increasing productivity and improving quality.