New technology predicts yields for African smallholder farmers

American researchers successfully developed a new way to measure African farm yields, using high-resolution photos snapped by a wave of recently developed compact satellites.

The Stanford University researchers now plan to scale up their project from a small Kenyan trial across more of Africa.

“Our aspiration is to make accurate seasonal predictions of agricultural productivity for every corner of Sub-Saharan Africa,” says Marshall Burke, an assistant professor in the department of Earth System Science.

“Our hope is that this approach we’ve developed using satellites could allow a huge leap in our ability to understand and improve agricultural productivity in poor parts of the world.”

New technology, using earth-observing satellites, provides high-enough resolution to visualize the smaller agricultural fields of smallholder farmers in developing countries.
These new satellites are smaller and more cost effective.

Burke and David Lobell, also an associate professor in the Department of Earth System Science, tested this new technology by focusing on Western Kenya, on an area with several smallholder farmers planting maize on areas varying between 0.2 ha en 0.4 ha.


They compared two methods for estimating agricultural productivity yields using satellite imagery. The first approach involved “ground truthing,” or conducting ground surveys to check the accuracy of yield estimates calculated using the satellite data.

Burke and his field team spent weeks conducting house-to-house surveys with his staff, talking to farmers and gathering information about individual farms.

“We get a lot of great data, but it’s incredibly time consuming and fairly expensive, meaning we can only survey at most a thousand or so farmers during one campaign” he says.

The team then tested an alternative “uncalibrated” approach that did not depend on ground survey data to make predictions. Instead, it uses a computer model of how crops grow, along with information on local weather conditions, to help interpret the satellite imagery and predict yields.

Burke says this method allowed them to make “surprisingly” accurate predictions.

Burke says the work has important implications. He says improving agricultural productivity is one of the main ways to reduce hunger and improve livelihoods in poor parts of the world.

“But to improve agricultural productivity, we first have to measure it.”

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