DROP app: A hydroclimate information service to deliver scientific rainfall, local rainfall, and soil moisture forecasts for agricultural decision-making

Weather and Climate Information Services developed for agriculture often only provide scientific weather and climate forecasts on various timescales. Yet, local forecasts derived from indigenous knowledge and soil moisture information are still missing. In this study, we evaluate the implementation of the DROP app, a hydroclimate information service, offering both local (LF) and scientific rainfall forecasts (SF) and soil moisture forecasts, that was designed with and for smallholder farmers working on rainfed agriculture in northern Ghana. Results of the forecast assessment show that the LF generates a high probability of rain detection (POD), with a minimum value of 0.7. The hybrid forecast (HF) that integrates the SF and LF yields the highest POD value of 0.9 compared to others. However, the hybrid system also has a high number of false alarms which results in an overall lower forecast performance of HF compared to SF. Using forecasts obtained from the app, farmers adjusted their farming activities, such as time of sowing, planting and weeding dates, fertilizer and herbicide application, and harvesting. Although some limitations exist, the DROP app has potential to deliver actionable knowledge for climate-smart farm decision-making and thus, facilitate effective agriculture management.