Seeds of Change

Artificial Intelligence will integrate and analyze diverse data and models to make farming recommendations for more bountiful harvests in Ethiopia.

BY MARC BALLON ILLUSTRATIONS BY RAYMOND BIESINGER

An Ethiopian farmer plants teff, a grain used in traditional flatbread, as she has done for years. However, a short rainy season wipes out most of her crop, threatening her and other farmers’ livelihoods and food security.

Now, imagine that same farmer discovers beforehand that this season will be particularly dry. She learns how long crops will take to grow given the anticipated weather trends and which ones might thrive in such conditions. Instead of teff, she grows fast-maturing mung beans and has a decent harvest.

Thanks to Yolanda Gil, a USC Viterbi School of Engineering professor and one of the world’s foremost artificial intelligence (AI) experts, such a scenario could one day become a reality.

Three years ago, Gil and her collaborators visited Ethiopia intending to develop an AI platform that could make recommendations to policymakers to increase food production and reduce the likelihood of famine.

“If you have to manage extreme droughts and floods and other climate effects, you might not have a good harvest without good models,” says Gil, director of New Initiatives in AI and Data Science at USC Viterbi and former president of the Association for the Advancement of Artificial Intelligence (AAAI).

Gil knew she was on to something when she met with a high-level official from the Ministry of Water and Energy. He pointed out that the agricultural, water and other reports covering his desk were each narrowly focused. Worse, some of the recommendations contradicted each other. What they needed was an integrated view of the effects of climate on agriculture.

By contrast, Gil is developing a novel system that uses AI to integrate models and provide a comprehensive roadmap for decision-makers to help significantly boost Ethiopia’s agricultural output.

The Model INTegration framework, or MINT, employs AI to integrate heterogeneous models from separate disciplines efficiently and quickly. MINT currently includes climate, hydrology and agriculture models for different areas of Ethiopia to understand how government interventions (such as subsidies) can mitigate the effects of droughts and floods.

“We have become really good at running single models to generate reports with recommendations looking at only a single slice of the problem,” Gil says. “But it’s very challenging to put together comprehensive models across disciplines, across processes. That’s what my AI research is about.”

Feeding Ethiopia

Agriculture is the backbone of the Ethiopian economy. Mixed farming, which includes growing crops and raising livestock, accounts for nearly 40% of the country’s gross domestic product and more than 70% of the workforce, according to the United States Agency for International Development. Teff, wheat, maize, sorghum and barley, among other crops, make up the core of the food economy.

Even in the best of times, feeding the country’s roughly 115 million people can be challenging.

Bordering two distinctly different climate regimes — one wet and one dry — Ethiopia and other countries in sub-Saharan Africa experience periods of exceptionally low rainfall, says Lowell Stott, a professor of earth sciences at the USC Dornsife College of Letters, Arts and Sciences.

“The eastern part of the Nile Basin is highly vulnerable to droughts,” adds Essam Heggy, a USC Viterbi expert on water evolution in Earth’s arid environments.

Indeed, Ethiopia’s lowland areas are enduring one of the most severe dry periods in 40 years. “The prolonged drought is compromising fragile livelihoods heavily reliant data and analyzes various possible future scenarios. “With MINT, the AI could do most of the work so that we can come up with answers in a couple of weeks instead of a couple of years,” she says.

Gil says a rudimentary version of MINT could demonstrate how AI integrates models to make more effective recommendations for decision-makers.

With collaborator Belete Berhanu, a hydrologist at Addis Ababa University, Gil co-authored a paper last year that appeared at the First International Workshop on Social Impact of AI for Africa held at the annual AAAI