There is often a disconnect between science and decision-making, as the data needed to support planning -- particularly local project and monitoring data -- are either scattered, inaccessible or not adapted to policy needs. At the Spatial Data Science and Applied Learning Lab (SPACIAL) at World AgroForestry, we have been advancing the concept of decision dashboards to address this challenge.

Decision dashboards are open access online interfaces that use data visualization to present important metrics and key performance indicators. Consolidating and presenting data in this way offers decision-makers a practical resource they can use to obtain a quick overview of a situation and respond with appropriate and effective strategies.

The dashboards are co-designed with stakeholders using the Stakeholder Approach to Risk Informed and Evidence-based Decision-making (SHARED) process -- a decision-support methodology that aims to encourage more inclusive, inter-sectoral and inter-institutional thinking. With the user in mind, the SHARED process promotes human and user-centered design to ensure ownership, usability and sustainability of the dashboards.

Decisions on land management are already benefiting from such information systems. To monitor land health in the Rift Valley in Kenya, for example, the Laikipia Resilience Diagnostics Tool brings together data via the Ecosystem Health Surveillance System. This builds on the Land Degradation Surveillance Framework (LDSF) while also integrating indicators from other information sources, including local partners and accessible databases on climate, education, population and human health.

Dashboards have been developed at national and county level for Kenya, Malawi, Zambia, Ethiopia and Tanzania and can be accessed through the Landscape Portal. Work on additional information systems is ongoing and reflects the needs of stakeholders. For instance, in line with its strategic agenda, Kenya's Makueni County has requested specific functionality and indicator sets to support its decision-making across the sectors of agriculture, water, livestock and education. Ultimately, the goal is to improve integration of data across sectors that are often extremely compartmentalized.

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