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Introduction: Digital Agriculture Landscape Assessment Toolkit
Understanding a digital ecosystem is crucial to understanding the gaps and challenges administrators, stakeholders, users, and others experience. This toolkit was created to support IFAD-financed partners to better understand the digital landscapes in which they operate.
Desk Review: Digital Agriculture Landscape Assessment Toolkit
This tool offers tips and tricks for conducting an efficient desk review so that the team can be sufficiently prepared when it comes time to kickoff interviews; and so that you and your assessment team don’t have to spend hours reading and referencing the same lengthy documents repeatedly.
Interview Guides: Digital Agriculture Landscape Assessment Toolkit
This tool provides guidance on how to develop interview methodology to garner the information you need to understand the stakeholders’ decision making and develop user stories, use cases, and stakeholder maps.
Analytical Framework: Digital Agriculture Landscape Assessment Toolkit
This tool provides guidance on how to develop an analytical framework – the model used by a team to synthesize all the key information gathered during the data collection phase. The main goal of the analytical framework is to gather, organize, and summarize the critical information and evidence that will underpin the outputs of your assessment.
User Analysis: Digital Agriculture Landscape Assessment Toolkit
This tool explains the importance of user-centered design to digital ecosystem assessments. This tool examines how user stories and user journeys can help develop user-centered assessment methodologies, report outputs, recommendations, and technical requirements.
Stakeholder Map: Digital Agriculture Landscape Assessment Toolkit
This tool provides guidance on stakeholder mapping, which can be used to clarify the team and team members’ individual relationships with each stakeholder in an ecosystem. These maps can be used to inform a strategic communications or dissemination plan for the assessment outputs, or identify potential blockers, bottlenecks, or risks to data use or assessment usability.
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