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Please be aware that the content herein has not been peer reviewed.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.
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.
Collaborating with stakeholders, especially key entities like the Weather Agency, is crucial. Their involvement ensures not only data accuracy but also a smoother integration of the solution into existing structures.
The validation of collected data by governmental weather stations is paramount for scalability. While innovation is encouraged, aligning with existing systems ensures long-term viability and acceptance.
The initiative revealed the intricate interdependence of stakeholders - farmers relying on ICAT, which, in turn, depends on accurate data from the Weather Agency in a system where insights, data collection, and feedback loop seamlessly work together and that could be improved with AI/ML techniques to significantly enhance efficiency and accuracy.
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 the solution came from the users for the users, and addressed real user needs, fostering better engagement and usability.
Both 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.
Contributed domain expertise and shared contact list in exchange for a digital solution that would facilitate their community outreach and help collect feedback on the agricultural insight provided by the organization to the farmers
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.
They would use the collected agrometeorological data from farmers to see if it can be used to improve the accuracy insights provided to ICAT to be forwarded to farmers. They would contribute domain expertise and feedback for the solution development
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
What sector does your partner belong to?
Private Sector
Please provide a brief description of the partnership.
Their active involvement ensures the solution is tailored to address their specific needs and challenges.
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 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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2Zero hunger
13Climate action
15Life on land
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