Search for a command to run...
Smallholder farmers in the global south predominantly rely on rainfed agriculture, making accurate precipitation forecasts crucial for agricultural decision-making. However, the low reliability, limited skills, and accessibility of scientific forecasts (SF) derived from Numerical Weather Prediction models in rural communities hinder the use of SF. Instead, smallholders often rely on indigenous knowledge to predict the rainfall based on observed local indicators, e.g., meteorology, animals’ behavior, and astronomy, hereafter we call it local forecasts (LF). However, the use of LF also faces challenges, including the loss of LF knowledge since it is communicated orally, not documented, not always observable, or not deemed useful. In addition, the use of local forecast also faces challenges by increasing climate variability, which undermines farmers’ confidence in their forecast. Studies conducted in Africa evaluating SF and LF skills have demonstrated that LF’s performance is comparable to or even outperforms the SF. Furthermore, these studies highlight that integrating SF and LF, known as hybrid forecast (HF), results in higher forecast performance than either SF or LF alone. In this study, we aim to develop an HF system that combines SF and LF using machine learning approaches to improve precipitation predictions in northern Ghana. Four rain gauges were installed at the field and used to evaluate the performance of SF, LF, and HF to predict precipitation events based on the Hanssen-Kuipers discriminant (HK) and accuracy skill assessment metrics. Results show that the HF achieved a HK value of 0.79, outperforming the scientific forecast (HK = 0.50), and local forecasts (HK = 0.37). In terms of accuracy, the HF also led with a score of 0.92, followed by the SF at 0.69. Similar to its HK, LF has the lowest accuracy of 0.65. Our study proved that ML approaches can be highly effective in developing a seamless forecasting system, specifically the HF, which outperforms the accuracy of individual forecasts alone. Such enhanced precipitation forecasts could enable smallholder farmers in the Global South to make better-informed agricultural decisions, ultimately enhancing their livelihoods.