Product → Insights / Assumptions → Background→ Next Steps

Background:

Pivot and where we are now →Teddy

• Go through iteration of ideas
• Started in climate change → moved into agriculture → decided to pursue irrigation → wanted to improve water efficiency → shifted
• Started to realize physical limitations of water and constraints on our target farms
  • Water itself is very unchanging, some improving the efficiency would not be related to changing its physical characteristics
  • Instead it would need to come in the form of changing the way in which the water interacts with the system, whether that be the irrigation method, soil, or plants themselves
  • While this sounds good on paper, it effectively upends the farm and procedure, which would not be possible for small scale farms
  • We then changed course to focus on helping farmers maximize their water usage and yield by working within the confines of their farm and providing them with a product that only provides information which they can use to make changes as they see fit.

Product:

The Product → Ethan

• Data is one of the most important insights
• Pivot based on initial interviews - understanding the impact of water
• Moving towards data-driven solution → mobile sensor payload with sampling capabilities to provide critical information
  • allows for data analytics and providing guidance based on individualized data
  • goal: low cost, high scalability
  • audience: small, local farmers that 1) make decisions based on qualitative data or 2) don't currently have irrigation or water efficiency implementations

Interview Insights →Pedro

• From our conversations with experts we have had three main breakthroughs:
  • Water is essential for improving agricultural resiliency in a climate change affected world
  • There is a big information gap between small farmers and farming corporations
  • Current sensor technologies and use methods that are impractical and create a high entry point for small farmers into data collection
• Interviews have further demonstrated that:
  • There will be many different cases that we will have to think through based on location, crop, time of year, weather patterns
    • So we decided to compliment our sensor payload with existing weather pattern prediction algorithms like: Decagon Probe System, Watermark
  • Being aware of that, we will try to create a final product that is non-intrusive and user friendly.
    • One of our interviewers suggested workshops at the beginning to garner local farmer support and understanding of the product. This would be an important experiment once we have our MVP.

Assumptions →Manav

• People in this industry are interested in getting data driven insights on this problem space.
• The technology that is necessary is present today.