Harnessing Local Forecasting Knowledge on Weather and Climate in Ghana: Documentation, Skills, and Integration with Scientific Forecasting Knowledge

Abstract

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.

Designing agrometeorological information services in Ghana by combining local and scientific forecasting knowledge

“…Similarly, before the design and testing phase, farmers' agrometeorological information needs and local forecasting indicators were assessed under the Waterapps project. Results of those presurveys were discussed in Reference (Gbangou et al, 2020b).…”

“…Farmers collected these on a real-time basis, sharing them with the research team via the weather app. These data were then processed into daily forecasts (for details see Gbangou et al (2020b)) and shared with all participants via the WhatsApp group. Similarly, daily scientific model-based weather forecasts (from www.meteoblue.com) were simplified and shared via the WhatsApp group.…”

“…This sharing was done in real time and on a daily basis from April to July 2019. (Gbangou et al, 2020b) Daily local forecasts derived from scientific sources (e.g., meteoblue) (Gbangou et al, 2020b)…”

“…Figure 5.4 summarizes the chronology, activities, and methods used. Activities carried out in the rainy season of 2017 on local agrometeorological information needs and local forecasting indicators are outside the scope of the present study (details on these can be found in Reference (Gbangou et al, 2020b). Nonetheless, this step is included in…”

“…The coproduction of forecast knowledge with and for farmers can help foster trust and increase the local uptake of scientific model-based forecasting knowledge (Ingram et al, 2002). Furthermore, collecting and integrating local or traditional knowledge with scientific data can help increase credibility and improve access (Nyadzi, 2020, Gbangou et al, 2020b. Good local information can help to enhance usefulness and skills of model-based forecasts (Nyadzi, 2020).…”