Field research stations play a crucial role in agricultural development, particularly in resource-limited settings such as Ethiopia. However, their efficiency and impact are often subject to variability and require detailed evaluation. The methodology involves collecting and analysing data from multiple research stations over a period, employing time-series forecasting models such as ARIMA (AutoRegressive Integrated Moving Average) to estimate efficiency gains. A significant proportion of the forecasted improvements in crop yields can be attributed to targeted interventions implemented by the research stations, indicating potential for enhancing agricultural productivity. The results suggest that time-series forecasting models provide valuable insights into the operational efficiency of field research stations, offering a robust framework for future evaluations and policy development. Further studies should consider incorporating additional variables to refine the model and ensure more accurate predictions of station performance. Field Research Stations, Efficiency Measurement, Time-Series Forecasting, ARIMA Model The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.