A major component of the AGCommons program from the beginning has been to find a way to make spatial information more relevant and useful to small-holder farmers in the field. The idea that we can bring the same level of location intelligence used by large scale farming operations and agribusinesses in North America to small-holder farmers in Africa is not far fetched. This was validated during the AGCommons outreach trip(s) last April and has been proven by the Quick Win projects started last year but now wrapping up.
But how do we actually DO that, what data is involved? What are the delivery mechanisms to needed to reach those most difficult to reach? And how do we put together a stack of technology to do this and have it be sustainable over time?
The team early on realized that as technology and agriculture experts, we are too far removed from African farmers to hope to interface directly with them. And given the large number of NGOs, government and private run extension programs, rural community radio, and mobile providers who are already talking directly to farmers on a daily basis that it makes the most sense to work through these groups. To provide the intermediaries with more relevant and timely information, better tools, faster access to spatial data.
For data we are relying on the global community of data providers including the CGIAR-CSI for some of the most sophisticated and up-to-date agricultural modeling information, CIESIN for population, demographics and soils information, government run programs including NASA, UN sources including FAO and WFP, and other on-going projects funded by the Gates Foundation and other donor organizations. But we’re also looking at local programs that collect information from farmers themselves. The Quick Win projects including the Grameen App Lab CKW program and the Nodes For Growth project have proven the methodology and applications such as Ushahidi, FrontlineSMS and RapidSMS have overcome the technical barriers. After all, the farmers themselves have the most accurate information available from local weather, soil type and condition, crop pest and disease and local market prices.
By combining these unique sets of spatial information together on a single platform we will be able to answer a number of questions. The platform will allow us to validate agricultural models with field validated information. We will be able to augment field collected data with globally relevant information. Over time this will include providing more accurate information for researchers trying to grow drought tolerant maize varieties in Mali and messages directly to farmers about where and when to get particular fertilizers in Malawi.
In the next post I’ll cover the technical aspects of a platform to deliver this.
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