In the dashboards webpage, it is possible to select the country of interest for which to access the data visualization dashboards from your web browser. Note that the number of dashboards and level of detail and quantity of information provided by the dashboards varies across countries. If you are interested in generating information that is currently unavailable for your area of interest, please reach out to us, consider joining a modelling platform capacity development event, or following the RE4AFAGRI modelling platform official user guide.

Dashboards summarize information at the level of national administrative units to ensure a smooth website navigation experience. However, as the analysis is carried out at the settlement or crop field level, the user is enabled to access the original output data within a specific administrative unit of interest. To do so, it is sufficient to click above a given administrative unit and follow the "Download local data" link in the popup that show. This action will trigger a file download to your local computer, which can then be browsed with any GIS software, such as QGIS or ArcGIS, For more details on how to open and browse the downloaded file in QGIS, follow this guide.

The first dashboard covers the current status of agriculture, presenting the distribution of cultivated land by crop and irrigation status. The data are derived from the SPAM 2017 v2.1 Sub-Saharan Africa database. The dashboard also provides WaterCROP model-based estimates of the necessary water input from irrigation (namely, in addition to rainfall) that would need to be provided each month of the year to avoid crop water stress in cropland that currently is rainfed only. These estimates are specific to each crop, and their value depends on the local climate, growing season, and soil characteristics. In addition, a second dashboard reports current yields and WaterCROP model-based estimates of the additional yield that this irrigation input could potentially generate.

The electricity requirements dashboard – reporting M-LED model outputs – displays information on the potential (also called latent) electricity demand to enable energy services in different sectors. These include both agricultural electricity uses (water pumping for irrigation to close the water gap, crop processing and storage), residential household demand to ensure certain appliances can be used (compatibly with the local income level), micro SMEs, as well as healthcare facilities and schools. This information might be useful to understand where the hotspots of potential electricity demand in areas are, both in a specific sector and in terms of total demand.

The electricity access infrastructure planning dashboard visualizes results of the OnSSET model, a tool that can support electrification planning and decision making for the achievement of energy access goals in currently unserved locations. The model takes as inputs the electricity requirements seen in the electricity requirements dashboards and based on available infrastructure, resource potentials, and cost parameters, estimates the local cost-optimal technological solution to supply each settlement or community with access to sufficient electricity to meet those needs. It also reports estimates for the corresponding investment requirements.

Finally, the multi-sectoral policy analysis dashboard presents selected output from the NEST integrated assessment models, allowing for an understanding of water-energy-land interdependencies in terms of resources and investment requirements, inclusive of their seasonality across months of the years.

The business model research webpage introduces the user to a detailed report on the concept of user-led electrification interventions using renewable energy and offers approaches for how these can be designed and implemented in the context of the agri-energy nexus. In addition, it introduces a ready-to-use techno-economic model that can identify agricultural value chain activities that are financially viable for electrification from the perspective of the smallholder farmer under a broad range of contexts and cost and revenue assumptions.