Many West African farmers are struggling to cope with changing weather and climatic conditions. This situation limits farmers’ ability to make optimal decisions for food and income security. Developing more useful and accessible weather and climate information services (WCIS) can help small-scale farmers improve their adaptive capacity. The literature suggests that such WCIS can be achieved if forecast information is produced jointly by farmers and scientists. To test this hypothesis and derive design requirements for effective WCIS, we evaluated the outcomes of an experimental coproduction of weather forecasts in Ada, Ghana. The experiment involved a user-driven design and testing of information and communications technology (ICT)-based digital (smartphones and apps) and rainfall monitoring tools by 22 farmers. They collected data and received weather forecasts during the 2018/2019 study period. The results showed a positive evaluation of the intervention, expressed by the level of engagement, the increase in usability of the tools and understanding of forecast uncertainty, outreach capacity with other farmers, and improved daily farming decisions. The success of the intervention was attributed to the iterative design process, as well as the training, monitoring, and technical support provided. We conclude that the application of modern technology in a coproduction process with targeted training and monitoring can improve smallholder farmers’ access to and use of weather and climate forecast information.

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 farmers’ decision-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 were derived over the coastal zone of Ghana. LFK indicators were documented, and farmers were 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 time-scale 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 the Ada District. The results of the documentation and skills indicate that approaches and methods developed for integrating LFK and SFK can contribute to increasing forecast resolution and skills and reducing recurring tensions between the two knowledge systems. Future research and application of these methods can help improve weather and climate information services in Ghana.

Improved information on the distribution of seasonal rainfall is important for crop production in Ghana. The predictability of key agro-meteorological indices, namely, seasonal rainfall, maximum dry spell length (MDSL) and dry spell frequency (DSF) was investigated across Ghana (with an interest on the coastal savannah agro-ecological zone). These three variables are relevant for local agricultural water management. A dynamical model (i.e. European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 seasonal forecasts) and a statistical model (i.e. response to sea surface temperatures (SSTs)) were used and analysed using correlation and other discrimination skill metrics. ECMWF-System 4 was bias-corrected and verified with 14 local stations’ observations. Results show that differences in variability and skills of the agro-meteorological indices are small between agro-ecological zones as compared to the differences between stations. The dynamic model System 4 explains up to 31% of the variability of the MDSL and seasonal rainfall indices. Coastal savannah exhibits the highest level of discrimination skills. However, these skills are generally higher for the below and above normal MDSL and seasonal rainfall categories at lead time 0. Similarity in skills for the agro-meteorological indices over the same zones and stations is found both for the dynamical and statistical models. Although System 4 performs slightly better than the statistical model, especially, for dry spell length and seasonal rainfall. For dry spell frequency and longer lead time dry spell length, the statistical model tends to perform better. These results suggest that the agro-meteorological indices derived from System 4′ updated versions, corrected with local observations, together with the response to SST information, can potentially support decision-making of local smallholder farmers in Ghana.

Skillful weather and seasonal predictions have considerable socio-economic potential and could provide meaningful information to farmers and decision-makers towards agricultural planning and decision-making. Peri-urban farmers in the Lower Ganges Delta need skillful forecast information to deal with increased hydroclimatic variability. In the current study, verification of European Centre for Medium-Range Weather Forecasts’ System 5 (ECMWF SEAS5) seasonal prediction system is performed against ground observations for the Lower Ganges Delta using three skills assessment metrics. Additionally, meteoblue hindcasts are verified for Khulna station according to the peri-urban farmers’ needs and an assessment of onset/offset dates of rainy season is also conducted using the same ground observations. The results indicated that the skill of both examined products is limited during the pre-monsoon and monsoon periods, especially in the west side of the Bay of Bengal. However, during the dry winter season, skill is high, which could lead to potential agricultural benefits concerning irrigation planning. Interannual variability and trend indicated that onset dates have become later and that the length of the rainy season reduced. This could increase the pressure on the already challenging situation the farmers are experiencing, in relation to hydro-climatic variability.

This paper examines changes in rainfall effectiveness indices of the Awun basin in Nigeria during the late twenty-first century for agricultural applications with outputs from high-resolution regional climate model (RCM) simulations. The RCM simulations are driven by two global climate models for a reference period (1985–2004) and a future period (2080–2099) and for RCP4.5 (a scenario with some mitigation) and RCP8.5 (a business as usual scenario) forcings. Simulations are provided for the control (1985–2004) and scenario (2080–2099) periods. Observations from synoptic station are used for bias-correction. Three indices being local onset date, seasonality index (SI), and hydrologic ratio (HR) are analyzed. Onset and HR are tested with two evapotranspiration (ET_p) models. Farmers’ perceptions are also collected to validate trends of rainfall indices for the present-day climate. We found that onset dates do not depend much on the ET_p models used, and farmers’ perceptions are consistent with predicted rainfall patterns. Present-day climate trend shows an early onset. However, onset is projected to be late in future and the delay will be magnified under the business as usual scenario. Indeed, average onset date is found on the 5th May for present-day while in the future, a delay about 4 and 8 weeks is projected under RCP4.5 and RCP8.5 scenarios respectively. SI is between 0.80 and 0.99, and HR is less than 0.75 for all scenarios, meaning respectively that (i) the rainy season will get shorter and (ii) the area will get drier in the future compared to the present-day. Local stakeholders are forewarned to prepare for potential response strategies. A continuous provision of forecast-based rainfall indices to support farmer’s decision making is also recommended.