Disclaimer:
Please be aware that the content herein has not been peer reviewed. It consists of personal reflections, insights, and learnings of the contributor(s). It may not be exhaustive, nor does it aim to be authoritative knowledge.
Learnings on your challenge
What are the top key insights you generated about your learning challenge during this Action Learning Plan? (Please list a maximum of 5 key insights)
Direct engagement with the community is foundational. It's not just about providing a solution; it's about understanding the community's needs and involving them in the process. Aligning stakeholders (ICAT, farmers, weather agency) into a cohesive system enhances the agrometeorological process.
Unforeseen internal challenges and delays are inevitable. Being flexible in our approach and adapting to unexpected obstacles is crucial for the success of the initiative.
Applying diverse innovation methods, leveraging new data types, and engaging unusual partners generated unique insights unattainable by traditional means.
Real-life machine learning dynamics emerged; potential for AI/ML to automate data flow and enhance advisory systems.
Considering the outcomes of this learning challenge, which of the following best describe the handover process? (Please select all that apply)
Our work has not yet scaled, Other
Can you provide more detail on your handover process?
Our work has gained the buy-in of the environment unit in our country office and has been integrated in their annual workplan so we can further pursue the development of the digital solution.
Please paste any link(s) to blog(s) or publication(s) that articulate the learnings on your frontier challenge.
Data and Methods
Relating to your types of data, why did you chose these? What gaps in available data were these addressing?
We opted for real-time sensor data to provide accurate and timely agrometeorological insights. This choice aimed to fill gaps in traditional data sources, offering more granular and current information for better decision-making in agriculture.
Direct interviews and focus groups were chosen to complement quantitative data, providing qualitative context and bridging potential gaps in understanding user needs and preferences.
Why was it necessary to apply the above innovation method on your frontier challenge? How did these help you to unpack the system?
Collective Intelligence allowed us to tap into the diverse perspectives of stakeholders, aggregating insights crucial for understanding the complex agrometeorological system.
Human-Centered Design ensured that our solution addressed real user needs, fostering better engagement and usability. This approach helped unpack the system by placing the end-users at the core of the solution development process.
Partners
Please indicate what partners you have actually worked with for this learning challenge.
Please state the name of the partner:
Institut de Conseil et d'Appui Technique (ICAT)
What sector does your partner belong to?
Government (&related)
Please provide a brief description of the partnership.
Collaborated to leverage their agrometeorological expertise and insights to inform the digital solution's development. In return they will profit from the not only the digital platform but also the data collection station prototyped.
Is this a new and unusual partner for UNDP?
Yes
Please indicate what partners you have actually worked with for this learning challenge.
Please state the name of the partner:
Agence Nationale de la Météorologie
What sector does your partner belong to?
Government (&related)
Please provide a brief description of the partnership.
Provided domain expertise with the chance to gain access to sensor data and an data analysis platform with the potential to automate the improvement of meteorological insight accuracy
Is this a new and unusual partner for UNDP?
Yes
Please indicate what partners you have actually worked with for this learning challenge.
Please state the name of the partner:
Farmers of Yotokope
What sector does your partner belong to?
Private Sector
Please provide a brief description of the partnership.
Provide insights about typical farmers' needs and wants and guided the design of the prototype giving valuable feedback at each step
Is this a new and unusual partner for UNDP?
No
End
Bonus question: How did the interplay of innovation methods, new forms of data and unusual partners enable you to learn & generate insights, that otherwise you would have not been able to achieve?
The interplay of innovative methods, new data sources, and unconventional partnerships facilitated a holistic understanding of the agricultural landscape.
Co-creation with farmers through participatory design and citizen science unveiled nuanced insights, while diverse data types enriched our perspective.
Collaboration with ICAT and other partners brought domain expertise, uncovering context-specific challenges.
This synergy allowed us to unearth deeper insights, fostering a more comprehensive and contextually relevant approach that traditional methods alone couldn't achieve.
Please upload any further supporting evidence / documents / data you have produced on your frontier challenge that showcase your learnings.
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