June 21, 2017
Digital Development for Feed the Future: Building an Innovative Community of Practice to Respond to Smallholder Farmers’ Needs
Written by Karina Lundahl
This article originally appeared on Agrilinks.
This post is the first in a two-part series focusing on data-driven agricultural development and was authored by Karina Lundahl, Facilitator for USAID’s Innovation for Data-Driven Agriculture Convening on April 27–28, 2017.
With the continued global proliferation of smartphones, sensors and advanced analytics, opportunities and challenges relevant to smallholder agriculture in emerging economies are increasing. In the focus countries of the U.S. Government’s Feed the Future initiative, smartphone adoption increased an incredible 800 percent between 2010–2015 according to data from GSMA Intelligence. And in 2017, the combined processing power of global smartphones will surpass the processing capacity of all servers worldwide. To create an agile and informed response to technological opportunities addressing the context-specific problems faced in developing regions, a diverse group of thinkers and innovators is required.
Addressing this, USAID, in collaboration with the Sustainability Innovation Lab at the University of Colorado, Boulder (SILC), hosted its second convening focused on building a cross-industry community of practice in data-driven agricultural development. Representatives from the U.S. Global Development Lab and Bureau for Food Security at USAID joined a group of researchers, tech innovators, funders and development practitioners to discuss the state of the industry as well as paths forward for data-driven approaches to agricultural development. Through a series of presentations, panels and workshop activities, three major themes emerged:
1. Opportunities and challenges in the data landscape: collection, analysis, open sharing and distribution
Growing opportunities for data-driven agricultural innovation include the use of smartphones, low cost ground sensors, weather stations and remote sensing to gather on-farm data and landscape information. Ever-improving machine learning and predictive analytics, which capture existing information and generate predictions where gaps in site-specific information exist, can also be employed to model data for further insights.
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