Monitoring networks have been established in Senegal to track adoption rates of environmental technologies among rural communities. A comprehensive analysis of existing monitoring data from various regions was conducted, employing advanced statistical techniques including ARIMA models for forecasting future trends in adoption rates. The application of ARIMA models revealed significant predictive power with an ARIMA (1, 1, 0) model achieving a forecast accuracy within 5% confidence intervals, indicating reliable trend predictions. This study validates the use of time-series forecasting as a robust method for monitoring and predicting adoption rates in Senegal's rural areas. The findings suggest integrating ARIMA models into ongoing regional monitoring efforts to enhance the accuracy and reliability of adoption rate measurements.
Diallo et al. (Mon,) studied this question.