Cropping and operational weather forecast information system in Ghana with the FarmerSupport App
Objective: Develop a tool that facilitate farmers’ access timely and location-specific water, weather and climate information services for farm decision making
|Site: Nakpazoo and Yapalsi, in Tamale, Ghana
|Partners involved: WUR, UDS
|Other stakeholders: Farmers, extension officers, experts informants from government, NGO, and private business organizations
The dynamics of urbanization and climate change have made water availability (too much, not enough, too late or early rain) very erratic for local farmers. Small scale farmers, who are the backbone of the rural economy, have limited access to weather and climate information operational farm decision making. Access to reliable weather and climate information by smallholders’ farmers is important for improved crop production.
A baseline study was first conducted to co-select sites and understand the needs of the smallholder farmers concerning weather and climate information for crop decision-making. This included meeting with stakeholders and farmers in Tamale, northern Ghana, to select the hub sites and performed field baseline research to understand farmers‘ needs in terms of climate information and the requirements of the supporting web-based based tools to develop.
Before testing the FarmerSupport app that was developed within the WATERAPPs project and is now being tested within the WATERAPPscale project in Bangladesh, we engaged with farmers to learn from their local knowledge and integrate it with scientific knowledge on weather and climate. Interviews and training workshops were conducted with keys experts informants and/or farmers themselves in the co-selected sites. Information acquired from these interactions allowed to improve the design of the farmers support tool.
The tool was (iteratively) co-designed to provide both local and scientific knowledge on rainfall and additionally the soil moisture component is envisaged to be further researched. The scientific forecasts are acquired from weather and climate models outputs for the sites while local rainfall forecasts are acquired from local farmers following training and capacity building on data collection. This is possible by engaging farmers to collect biophysical indicators (e.g. sun, cloud, insects, etc.) which were then translated into weather forecasts. The two local and scientific forecasts are combined to produce an added-value hybrid forecast, in terms of accuracy, usability, and acceptance by local farmers.
The FarmerSupport app was used during on-site training sessions and for real-time testing with farmers. After the testing phase, an evaluation was carried out to help understand the benefit of the tool and forecast information as well as translocation principles of the App to other regions. Feedbacks from farmers, stakeholders and analyses show the existence of an added-value for improving crop production in the LLV-GH.