Coffee, Climate, & Data Science: FTW!

This hackathon tackled the real-world problem of climate change impacts on coffee farming using real-world data sets from NOAA & NASA weather, and demographics from Population Explorer.
Hosted by the Collider (an Asheville-based hub for climate change solutions) and Counter Culture Coffee, participants were asked to imagine themselves as Guatemalan farmers, with limited means to technology and limited access to accurate forecasts. The farmers’ entire livelihood is built around productive coffee harvests. Too little precipitation in a given year and coffee yields will be low, too much precipitation can introduce threats like erosion, nutrient leaching, and mold growth. Knowing the forecast is a critical tool for agricultural success.
The goal of the 8-hour hack was to develop a short-term (weeks) and long-term (months) precipitation and temperature forecasts for Guatemalan coffee farmers using massive data sets from NOAA, NASA, and demographics sources. 

An Interdisciplinary Team

2 devs, 1 designer (me), 2 climate change adaptation experts

An approach toward resilience
All teams were handed the same huge climate datasets and most groups started honing in on building models to predict future weather patterns. However, we focused on predicting precipitation in a wider lens of building resilience. 
Building resilience of what?
Coffee yields
Building for resilience to what?
Extreme weather events heavy rain and drought. Wet harvest seasons.
Building resilience for what? (Why?)
To help small scale coffee farmers in Guatemala sustain their livelihoods in the face of increasing climate challenges.

Building resilience through what?
A communications system that provides users with actionable information and allows them to share their own experiences, tips, and best practices with other users.

Create a communications system to benefit small scale coffee farmers in the face of climatic shocks and stressors by collecting and sourcing data and disseminating it to users in the form of actionable information. 
Utilizing SMAP & NOAA Seasonal climate forecast data we can share soil moisture trends, one-month, & three-month forecasts.

Everyone with a phone can participate in being “sensors” on the ground and share climate and weather observations with one another. Lo-tech collateral will also be distributed & broadcast.
Broadcast & NArrowCast OPTIONS

SMS - Chat Bot
     Send actionable information and collect on-the-ground observations
     Collect and disseminate on-the-ground observations 
Printed 3-Month Weather Calendars
     Display in co-ops and deliver to farmers
Radio Announcements

I was tasked with creating a flow for the chatbot. For the quick hackathon demo we created a simple flow for forecasting. The "future flow" shows what could happen in a slightly more complex system where users can request specific info, make reports, and ask questions.
The Value of UX

I found this hackathon particularly interesting because there were only a few UX designers participating in a sea of data scientists. I arrived in the morning without a team and really felt a bit out of place. However, by the end of the day, I found that my UX skills helped to bridge the knowledge of my teammates, adding depth to the project, and my cobbled-together team actually won the hackathon!

The team that came in second also had a UX designer on-board. It seems that the addition of having human-centered approaches to both our projects really brought together the science and applicable solutions. Many of the data science-focused teams spent the day sorting through endless weather and climate data without being able to come up with a meaningful deliverable. I wonder if they could have all had more interdisciplinary teams, including UX pros if the competition would’ve been leveled?

Another factor that added to the unique challenge of this hackathon was the limited access to technology of the target audience of Guatemalan farmers: namely shared desktops at co-ops and cellular phones restricted to SMS communications. This really pushed our team to dig for low-tech solutions to a massive problem.

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