Since the 1990s exports of fresh agricultural products by air from Uganda have been increasing and making a significant contribution to her International trade. Products include mostly fish, flowers, papain, and vanilla constituting over 95% of all air exports. Farming of the items is mainly by small scale farmers who depend on the natural climate of the country. Consequently, monthly yields are also climate dependent making individual export volumes unpredictable. In spite of these uncertainties, this study was intended to investigate possible existence of a model in the trends. Monthly data were collected from Uganda Civil Aviation Authority from 2009 to 2012. Analysis was by using ARIMA Approach with the help of Eviews 8. Visually the data exhibited irregular patterns and without a trend or seasonality. First order differencing stationarised the data and the residuals had a random non-significant noise suggesting a Random Walk Model expressed as ARIMA (0, 1, 0) and a negative drift. The model shows a link between current and one lag export volumes and the negative drift is a convergence of successive differences in export volumes. These findings have policy implications in expansion and forecasting of the exports potential of applicability of Random Walk Theory in practice.