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Opportunities and Challenges for Data-Driven Agricultural Innovation

By Guest Writer on June 14, 2017

Opportunities and challenges relevant to smallholder agriculture in emerging economies are increasing thanks to the continued proliferation of smartphones, sensors, and advanced analytics globally.

In the focus countries of the U.S. Government’s Feed the Future initiative, smartphone adoption increased an incredible 800% 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.

Recently, 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.

Last chance to join us at ICTforAg for the Data-Driven Agriculture Breakout Session

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.

Data Innovations

Through a series of presentations, panels, and workshop activities, the group identified promising examples of current data collection and analysis:

  • Stantec Consulting is using soil moisture sensors, combined with real-time weather data and on-demand water delivery, to provide optimal irrigation levels in water-scarce conditions in California. These systems facilitate up to 30% increases in crop yields while also reducing water and energy use by up to 30%.
  • The Geospatial and Farming Systems Research Consortium (GFSRC) is conducting quantitative monitoring of crop trial experiments through unmanned aerial vehicles (UAVs), or drones, equipped with sensors.
  • The Land Potential Knowledge System (LandPKS) is a mobile application that performs digital assessment of site vegetation and obtains on-farm location and soil data, cross-referenced with global soil databases, to produce a high resolution estimate of local soil characteristics.
  • DigitalGlobe is collecting and analyzing satellite imagery aided by spectral analysis. Their data offers insights into population distribution, land use, crop yields, crop health, and key vulnerabilities such as food security in areas otherwise inaccessible due to conflict or other crises. DigitalGlobe also employs crowdsourcing to identify items of interest within imagery. Crowdsourcing results are then used to train machine-learning algorithms to improve the accuracy of automated object identification.
  • aWhere is applying weather modeling and prediction, along with agronomic modeling, to offer recommendations on potential pest and disease crop stress, production forecasting, and more.

Data Challenges

For all the opportunities, challenges remain in data collection and sharing for agricultural development. Some of the challenges discussed include:

Sparse or low quality on-farm data.

Some data—such as observed weather data—is not available at the resolution needed. Other data—including on-farm management activities—is variable, inconsistent, or of poor quality in many locations. On-farm observations of crop selection, timing of planting, use of fertilizers, and other key decision points provide the most direct insight into the successes and failures of crops over time and across space.

Since technologies such as machine learning, simulation models, and predictive analytics require site-specific information to generate successful estimates and predictions, there remains a need to collect accurate on-site information to provide ground-truthing and locally-specific knowledge for farmers. This need will likely increase as new analytical capabilities come online.

Uncertain relevance or metadata

Collected data, once shared, often lacks relevance or metadata. Metadata provides the key information on how data is organized and its relevance. It is critical for easily finding and using data for analysis. Without it, shared data may ultimately not be of use to stakeholders or researchers.

Increasing weather variability

There are unique agricultural and developing economy risks that innovators must take into account. Due to planting and harvest cycles, farmers’ economic concerns change throughout the year and growing season, and are highly dependent on weather. And, as weather becomes more variable, farmers will experience extreme episodic events and periods of acute risk and vulnerability more frequently.

Data Relevance

Convening discussions also focused on the need to frame research, programs, and ventures within a firm understanding of smallholder needs and concerns. Not only will farmers in different contexts access data and technology differently, data and technology may be irrelevant if a farmer lacks reliable access to market and has no power to set prices for goods.

If you are interested in joining the effort to build a dynamic community of practice in data-driven agricultural development, please read the full Key Findings Report: Innovation for Data-Driven Agriculture Workshop.

This post was authored by Karina Lundahl, Facilitator for USAID’s Innovation for Data-Driven Agriculture Convening on April 27–28, 2017.

Digital Development for Feed the Future is a collaboration between USAID’s Global Development Lab and the Bureau for Food Security and is focused on integrating a suite of coordinated digital tools and technologies into Feed the Future activities to accelerate agriculture-led economic growth and improved nutrition.

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