Improved weather and climate forecast information services are important to sustain small-scale crop production in many developing countries. Previous studies recognized the value of integrating local forecasting knowledge (LFK) with scientific forecasting knowledge (SFK) to support farming’s decisions making. Yet, little work has focused on proper documentation, quality verification, and integration techniques. The skills of local and scientific forecasts were compared and new integration approaches derived over the coastal zone of Ghana. LFK-indicators were documented and farmers trained to collect indicators’ observations and record rainfall in real-time using digital tools and rain gauges respectively in 2019. Dichotomous forecasts verification metrics were then used to verify the skills of both local and scientific forecasts against rainfall records.
Farmers use a diverse set of LKF-indicators for both weather and seasonal climate timescale predictions. LFK-indicators are mainly used to predict rainfall occurrence, amount of seasonal rainfall, dry spell occurrence, and onset and cessation of the rainy season. The average skill of a set of LFK-indicators in predicting one-day rainfall is higher than individual LFK-indicators. Also, the skills of a set of LFK-indicators can potentially be higher than the forecasts given by the Ghana Meteorological Agency for Ada district. The results of the documentation and skills indicate that approaches and methods developed for integrating LFK and SFK can contribute to increase forecast resolution, skills, and reduce recurring tensions between the two knowledge systems. Future research and applications on these methods can help improve weather and climate information services in Ghana.