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Sharing EO data with farmers and herders in the West African Sahel: Lessons from the GARBAL program

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Sharing EO data with farmers and herders in the West African Sahel: Lessons from the GARBAL program
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351
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CC-Namensnennung 3.0 Unported:
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Herausgeber
Erscheinungsjahr
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Produktionsjahr2022

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Abstract
Farmers and herders in the West African Sahel are critically vulnerable to climate shocks and need access to climate information to secure their livelihoods. Herders use data on pasture and water availability to move their livestock and farmers need weather predictions to plan their planting. While satellite imagery has made much of this information readily accessible to the spatial community, few channels exist to transmit this information to herding communities. As a result, climate data has become more powerful than ever before, yet mostly inaccessible to those who depend on this information for their livelihoods. This talk goes over the lessons of a programme that seeks to bridge this gap. GARBAL is a call center that uses Copernicus Earth Observation imagery and field data to provide farmers & herders with information on pasture, water and markets in Mali, Niger and Burkina Faso. GARBAL was first developed in 2015 and this talk will provide lessons from several years of practice. The GARBAL interface is built on mapserver and uses automated scripts to download and treat imagery from Sentinel 2 and Meteosat which then display information on pasture conditions and water availability. Field data is routed through a network of local data collectors who provide weekly updates on livestock conditions and market prices. In addition to an interactive map, the interface provides user-friendly textual outputs that summarize all the layers for any area of interest on the map, which allows call center agents to quickly provide data to callers. The talk will share lessons from the technical and programmatic aspects of the project. The technical side will go over the architecture of the data treatment, demo the interface, talk about successes and failures and show how you can play with the data yourself. The programmatic side focuses more on how the user needs evolved over the years, techniques for translating GIS data into information useful to farmers and herders, operating in areas of active conflict and how EO data fits into existing centuries-old traditional data collection systems in the Sahel.
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Transkript: Englisch(automatisch erzeugt)
All right, cool, thanks. Appreciate it. So I'm going to be talking briefly today about Garbale, which is a call center in West Africa that provides farmers and livestock herders with information on pasture, water, and weather, and which is, in many ways, built a lot on Map Server. This is going to be a lot more about a use case than
the technical side. But afterwards, I'm happy to talk if you have questions about the tech behind it. So a brief problem statement. Why does this system exist? You'll be unsurprised to know that in the Sahel, just like everywhere else, farmers and herders need access to weather forecasts.
They need to understand pasture availability. They need to know where surface water is. This is a given pretty much anywhere in the world. But unfortunately, in the Sahel, and specifically in the three countries we're talking about here, Mali, Niger, and Burkina Faso, this data is incredibly hard to find. Weather station coverage is poor, so most of the
forecasts that are available publicly cover major towns. And internet and smartphone access remains quite low for most of these rural areas. So then the proposed solution is, well, what if there was a hotline that could be called that could give this information in an easy-to-understand digestible format?
And this is where Garballe comes into play. So the project was launched in 2015 with a consortium from SNV, a Dutch NGO, a Dutch geospatial company called Hoofsland Spatial Solutions, the governments of all the three countries, and Orange, the telecom provider, as well as some local cooperatives.
And the idea was that basically you have this system that collects remote sensing information through Copernicus portals, NDVI, biomass, surface water, weather forecasts, and then rainfall, and then puts it into a dashboard. However, unlike most of the dashboards we are familiar with, the user of the dashboard is not the end user.
There's a layer of abstraction between the end user and the dashboard, and that's a call center agent. So the actual user, the decision maker of this information, is calling in, and the call center agent is providing a readout in the local language from that dashboard. In Burkina Faso in 2021 to 22, we received over
one million calls. So this is a system that is being actively used and is an active part of the information chain for a lot of people. So there are currently three dashboards, one for each country. They're all publicly accessible. I'll provide the links at the end. But if you want to explore the data, you can.
This is the one for Burkina Faso, modem.org. And then you see on the right, you see the map server side where we have a Sentinel-2 layers being served up to show NDVI. And on the right, we have a dialog box, which is not only providing the layers, but is also providing a textual readout
of the information. What's the weather forecast for the next seven days? Will it rain? How close is nearest surface water? How's the situation of biomass? So this is providing the information to pastoralists and to farmers, but done so in a textual way. So this really eases the burden on the call center agent who can, if they want to, read a table
or read a script rather than having to go and analyze a map. So a very, very, very broad overview of what the tools look like is we get basically three sources. Remote sensing data, which is mostly from Copernicus. Open weather map, which we get through their API.
And then field data, which provides information on prices, the concentration of animals, and all of that is pushed together through a machine, served onto map server as visual layers, then provided through open layers, and then brought to the call center.
So I don't think I have enough time to go through these limitations and lessons learned, but we can talk about them afterwards if you're curious. Basically, the main one is listen to your users. Yeah, that would be the one that I would say the most. Now, I'd also like to spend the last couple seconds providing the two links to the active interfaces
here at the bottom. We are also working on documentation. We're going to have manuals written in English and French very soon for basically how the whole thing works and how it functions. You can also contact me if you have any questions, but please check out the interface. I would love to hear your feedback. If you have any ideas on things we can improve or change,
you would certainly be welcome. Thank you.