Africa Archives Africa

Nov 12 2024

What Africa needs to do to become a major AI player

Kessel Okinga-Koumu paced around a crowded hallway. It was her first time presenting at the Deep Learning Indaba, she told the crowd gathered to hear her, filled with researchers from Africa’s machine-learning community. The annual weeklong conference (‘Indaba’ is a Zulu word for gathering), was held most recently in September at Amadou Mahtar Mbow University in Dakar, Senegal. It attracted over 700 attendees to hear about—and debate—the potential of Africa-centric AI and how it’s being deployed in agriculture, education, health care, and other critical sectors of the continent’s economy.

A 28-year-old computer science student at the University of the Western Cape in Cape Town, South Africa, Okinga-Koumu spoke about how she’s tackling a common problem: the lack of lab equipment at her university. Lecturers have long been forced to use chalkboards or printed 2D representations of equipment to simulate practical lessons that need microscopes, centrifuges, or other expensive tools. “In some cases, they even ask students to draw the equipment during practical lessons,” she lamented.

Okinga-Koumu pulled a phone from the pocket of her blue jeans and opened a prototype web app she’s built. Using VR and AI features, the app allows students to simulate using the necessary lab equipment—exploring 3D models of the tools in a real-world setting, like a classroom or lab. “Students could have detailed VR of lab equipment, making their hands-on experience more effective,” she said.

Established in 2017, the Deep Learning Indaba now has chapters in 47 of the 55 African nations and aims to boost AI development across the continent by providing training and resources to African AI researchers like Okinga-Koumu. Africa is still early in the process of adopting AI technologies, but organizers say the continent is uniquely hospitable to it for several reasons, including a relatively young and increasingly well-educated population, a rapidly growing ecosystem of AI startups, and lots of potential consumers.

“The building and ownership of AI solutions tailored to local contexts is crucial for equitable development,” says Shakir Mohamed, a senior research scientist at Google DeepMind and cofounder of the organization sponsoring the conference. Africa, more than other continents in the world, can address specific challenges with AI and will benefit immensely from its young talent, he says: “There is amazing expertise everywhere across the continent.”

However, researchers’ ambitious efforts to develop AI tools that answer the needs of Africans face numerous hurdles. The biggest are inadequate funding and poor infrastructure. Not only is it very expensive to build AI systems, but research to provide AI training data in original African languages has been hamstrung by poor financing of linguistics departments at many African universities and the fact that citizens increasingly don’t speak or write local languages themselves. Limited internet access and a scarcity of domestic data centers also mean that developers might not be able to deploy cutting-edge AI capabilities.

Attendees of Deep Learning Indaba 2024 in session hall on their computers

Complicating this further is a lack of overarching policies or strategies for harnessing AI’s immense benefits—and regulating its downsides. While there are various draft policy documents, researchers are in conflict over a continent-wide strategy. And they disagree about which policies would most benefit Africa, not the wealthy Western governments and corporations that have often funded technological innovation.

Taken together, researchers worry, these issues will hold Africa’s AI sector back and hamper its efforts to pave its own pathway in the global AI race.

On the cusp of change

Africa’s researchers are already making the most of generative AI’s impressive capabilities. In South Africa, for instance, to help address the HIV epidemic, scientists have designed an app called Your Choice, powered by an LLM-based chatbot that interacts with people to obtain their sexual history without stigma or discrimination. In Kenya, farmers are using AI apps to diagnose diseases in crops and increase productivity. And in Nigeria, Awarri, a newly minted AI startup, is trying to build the country’s first large language model, with the endorsement of the government, so that Nigerian languages can be integrated into AI tools.

The Deep Learning Indaba is another sign of how Africa’s AI research scene is starting to flourish. At the Dakar meeting, researchers presented 150 posters and 62 papers. Of those, 30 will be published in top-tier journals, according to Mohamed.

Meanwhile, an analysis of 1,646 publications in AI between 2013 and 2022 found “a significant increase in publications” from Africa. And Masakhane, a cousin organization to Deep Learning Indaba that pushes for natural-language-processing research in African languages, has released over 400 open-source models and 20 African-language data sets since it was founded in 2018.

“These metrics speak a lot to the capacity building that’s happening,” says Kathleen Siminyu, a computer scientist from Kenya, who researches NLP tools for her native Kiswahili. “We’re starting to see a critical mass of people having basic foundational skills. They then go on to specialize.”

She adds: “It’s like a wave that cannot be stopped.”

Khadija Ba, a Senegalese entrepreneur and investor at the pan-African VC fund P1 Ventures who was at this year’s conference, says that she sees African AI startups as particularly attractive because their local approaches have potential to be scaled for the global market. African startups often build solutions in the absence of robust infrastructure, yet “these innovations work efficiently, making them adaptable to other regions facing similar challenges,” she says.

In recent years, funding in Africa’s tech ecosystem has picked up: VC investment totaled $4.5 billion last year, more than double what it was just five years ago, according to a report by the African Private Capital Association. And this October, Google announced a $5.8 million commitment to support AI training initiatives in Kenya, Nigeria, and South Africa. But researchers say local funding remains sluggish. Take the Google-backed fund rolled out, also in October, in Nigeria, Africa’s most populous country. It will pay out $6,000 each to 10 AI startups—not even enough to purchase the equipment needed to power their systems.

Lilian Wanzare, a lecturer and NLP researcher at Maseno University in Kisumu, Kenya, bridles at African governments’ lackadaisical support for local AI initiatives and complains as well that the government charges exorbitant fees for access to publicly generated data, hindering data sharing and collaboration. “[We] researchers are just blocked,” she says. “The government is saying they’re willing to support us, but the structures have not been put in place for us.”

Language barriers

Researchers who want to make Africa-centric AI don’t face just insufficient local investment and inaccessible data. There are major linguistic challenges, too.

During one discussion at the Indaba, Ife Adebara, a Nigerian computational linguist, posed a question: “How many people can write a bachelor’s thesis in their native African language?”

Zero hands went up.

Then the audience disintegrated into laughter.

Africans want AI to speak their local languages, but many Africans cannot speak and write in these languages themselves, Adebara said.

Although Africa accounts for one-third of all languages in the world, many oral languages are slowly disappearing, their population of native speakers declining. And LLMs developed by Western-based tech companies fail to serve African languages; they don’t understand locally relevant context and culture.

For Adebara and others researching NLP tools, the lack of people who have the ability to read and write in African languages poses a major hurdle to development of bespoke AI-enabled technologies. “Without literacy in our local languages, the future of AI in Africa is not as bright as we think,” she says.

On top of all that, there’s little machine-readable data for African languages. One reason is that linguistic departments in public universities are poorly funded, Adebara says, limiting linguists’ participation in work that could create such data and benefit AI development.

This year, she and her colleagues established EqualyzAI, a for-profit company seeking to preserve African languages through digital technology. They have built voice tools and AI models, covering about 517 African languages.

Lelapa AI, a software company that’s building data sets and NLP tools for African languages, is also trying to address these language-specific challenges. Its cofounders met in 2017 at the first Deep Learning Indaba and launched the company in 2022. In 2023, it released its first AI tool, Vulavula, a speech-to-text program that recognizes several languages spoken in South Africa.

This year, Lelapa AI released InkubaLM, a first-of-its-kind small language model that currently supports a range of African languages: IsiXhosa, Yoruba, Swahili, IsiZulu, and Hausa. InkubaLM can answer questions and perform tasks like English translation and sentiment analysis. In tests, it performed as well as some larger models. But it’s still in early stages. The hope is that InkubaLM will someday power Vulavula, says Jade Abbott, cofounder and chief operating officer of Lelapa AI.

“It’s the first iteration of us really expressing our long-term vision of what we want, and where we see African AI in the future,” Abbott says. “What we’re really building is a small language model that punches above its weight.”

InkubaLM is trained on two open-source data sets with 1.9 billion tokens, built and curated by Masakhane and other African developers who worked with real people in local communities. They paid native speakers of languages to attend writing workshops to create data for their model.

Fundamentally, this approach will always be better, says Wanzare, because it’s informed by people who represent the language and culture.

A clash over strategy

Another issue that came up again and again at the Indaba was that Africa’s AI scene lacks the sort of regulation and support from governments that you find elsewhere in the world—in Europe, the US, China, and, increasingly, the Middle East.

Of the 55 African nations, only seven—Senegal, Egypt, Mauritius, Rwanda, Algeria, Nigeria, and Benin—have developed their own formal AI strategies. And many of those are still in the early stages.

A major point of tension at the Indaba, though, was the regulatory framework that will govern the approach to AI across the entire continent. In March, the African Union Development Agency published a white paper, developed over a three-year period, that lays out this strategy. The 200-page document includes recommendations for industry codes and practices, standards to assess and benchmark AI systems, and a blueprint of AI regulations for African nations to adopt. The hope is that it will be endorsed by the heads of African governments in February 2025 and eventually passed by the African Union.

But in July, the African Union Commission in Addis Ababa, Ethiopia, another African governing body that wields more power than the development agency, released a rival continental AI strategy—a 66-page document that diverges from the initial white paper.

It’s unclear what’s behind the second strategy, but Seydina Ndiaye, a program director at the Cheikh Hamidou Kane Digital University in Dakar who helped draft the development agency’s white paper, claims it was drafted by a tech lobbyist from Switzerland. The commission’s strategy calls for African Union member states to declare AI a national priority, promote AI startups, and develop regulatory frameworks to address safety and security challenges. But Ndiaye expressed concerns that the document does not reflect the perspectives, aspirations, knowledge, and work of grassroots African AI communities. “It’s a copy-paste of what’s going on outside the continent,” he says.

Vukosi Marivate, a computer scientist at the University of Pretoria in South Africa who helped found the Deep Learning Indaba and is known as an advocate for the African machine-learning movement, expressed fury over this turn of events at the conference. “These are things we shouldn’t accept,” he declared. The room full of data wonks, linguists, and international funders brimmed with frustration. But Marivate encouraged the group to forge ahead with building AI that benefits Africans: “We don’t have to wait for the rules to act right,” he said.

Barbara Glover, a program manager for the African Union Development Agency, acknowledges that AI researchers are angry and frustrated. There’s been a push to harmonize the two continental AI strategies, but she says the process has been fractious: “That engagement didn’t go as envisioned.” Her agency plans to keep its own version of the continental AI strategy, Glover says, adding that it was developed by African experts rather than outsiders. “We are capable, as Africans, of driving our own AI agenda,” she says.

crowd of attendees mingle around display booths at Deep Learning Indaba 2024. Booth signs for Mila, Meta and OpenAI can be seen in the frame.

This all speaks to a broader tension over foreign influence in the African AI scene, one that goes beyond any single strategic document. Mirroring the skepticism toward the African Union Commission strategy, critics say the Deep Learning Indaba is tainted by its reliance on funding from big foreign tech companies; roughly 50% of its $500,000 annual budget comes from international donors and the rest from corporations like Google DeepMind, Apple, Open AI, and Meta. They argue that this cash could pollute the Indaba’s activities and influence the topics and speakers chosen for discussion.

But Mohamed, the Indaba cofounder who is a researcher at Google DeepMind, says that “almost all that goes back to our beneficiaries across the continent,” and the organization helps connect them to training opportunities in tech companies. He says it benefits from some of its cofounders’ ties with these companies but that they do not set the agenda.

Ndiaye says that the funding is necessary to keep the conference going. “But we need to have more African governments involved,” he says.

To Timnit Gebru, founder and executive director at the nonprofit Distributed AI Research Institute (DAIR), which supports equitable AI research in Africa, the angst about foreign funding for AI development comes down to skepticism of exploitative, profit-driven international tech companies. “Africans [need] to do something different and not replicate the same issues we’re fighting against,” Gebru says. She warns about the pressure to adopt “AI for everything in Africa,” adding that there’s “a lot of push from international development organizations” to use AI as an “antidote” for all Africa’s challenges.

Siminyu, who is also a researcher at DAIR, agrees with that view. She hopes that African governments will fund and work with people in Africa to build AI tools that reach underrepresented communities—tools that can be used in positive ways and in a context that works for Africans. “We should be afforded the dignity of having AI tools in a way that others do,” she says.

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Mar 2 2024

The tech that helps these herders navigate drought, war, and extremists

Hainikoye hits Accept and a young woman greets him in Hausa, a gravelly language spoken across West Africa’s Sahel region. She has three new cows and wants to know: Does he have advice on getting them through the lean season?

Hainikoye—a twentysomething agronomist who has “followed animals,” as Sahelians refer to herding, since he first learned to walk—opens an interface on his laptop and clicks on her village in southern Niger, where humped zebu roam the dipping hills and dried-up valleys that demarcate the northern desert from the southern savanna. He tells her where the nearest full wells are and suggests feeding the animals peanuts and cowpea leaves—cheap food sources with high nutritional value that, his screen confirms, are currently plentiful. They hang up after a few minutes, and Hainikoye waits for the phone to ring again.

Seven days a week at the Garbal call center, agents like Hainikoye offer what seems like a simple service, treating people to a bespoke selection of location-specific data: satellite-fed weather forecasts and reports of water levels and vegetation conditions along various herding routes, as well as practical updates on brushfires, overgrazed areas, nearby market prices, and veterinary facilities. But it’s also surprisingly innovative—and is providing critical support for Sahelian herders reeling from the effects of interrelated challenges ranging from war to climate change. Over the long term, the project’s supporters, as well as the herders connecting with it, hope it could even safeguard an ancient culture that functions as an economic lifeline for the entire region.

The glossy red cubicles of Garbal’s office in Niamey, Niger’s capital, are tucked away in the second-floor space the call center shares with the local headquarters of Airtel, an Indian telecom. It had only been open for a few weeks when I visited early last year. Bursts of fuchsia bougainvillea garlanded the entryway to the building, a welcome respite from the sand-colored landscape and sewage-infused scent of the rotting industrial district around it. One lot over sat a former Total gas station that has remained unbranded since a drug cartel bought it to launder money and removed the sign. Running across the zone was a boulevard commemorating a 1974 coup d’état, which has been followed by four more over the ensuing five decades, the latest in July 2023. In the middle of the boulevard sat a few dozen miles of decomposing railway tracks that had been “inaugurated” by a right-wing French billionaire in 2016. For decades, postcolonial elites, promising development, have pillaged one of Africa’s poorest countries.

In more recent years, various Western players touting tech trends like artificial intelligence and predictive analysis have swooped in with promises to solve the region’s myriad problems. But Garbal—named after the word for a livestock market in the language of the Fulani, an ethnic group that makes up the majority of the Sahel’s herders—aims to do things differently. Building on an approach pioneered by a 37-year-old American data scientist named Alex Orenstein, Garbal is focused on how humbler technologies might effectively support the 80% of Nigeriens who live off livestock and the land.

“There’s still this idea of ‘How can we use new tech?’ But the tech is already there—we just need to be more intentional in applying it,” Orenstein says, arguing that donor enthusiasm for shiny, complex solutions is often misplaced. “All of our big wins have come from taking some basic-ass shit and making it work.”

Garbal call center workers in red cubicles — Workers in the Garbal call center in Niamey are able to review data to help herders.

Garbal’s work comes down to data and, critically, who should have access to it. Recent advances in data collection—both from geosatellites and from herders themselves—have generated an abundance of information on ground cover quantity and quality, water availability, rain forecasts, livestock concentrations, and more. The resulting breakthroughs in forecasting can, in theory, help people anticipate—and protect herds from—droughts and other crises. But Orenstein believes it is not enough to extract data from herders, as has been the focus of numerous efforts over the past decade. It must be distributed to them.

The work couldn’t be more urgent. The region’s herders face an existential crisis that has already started to shred the very fabric of society.

Herding—prestigious, high risk, and one of humanity’s most foundational ways of life—is a pillar of survival in the Sahel. In Niger, for instance, known across the continent for its succulent steak, animal production accounts for 40% of the agricultural GDP. Migratory herders usher between 70% and 90% of the cattle population between seasonal pastures, since they rarely own land. These pastoralists have historically relied on common resources, in coordination with local communities.

But the traditional ways are becoming next to impossible. The crisis stems, in part, from the changing climate: as the desert creeps south, and as the dry season stretches longer and the rains come in shorter and more volatile intervals, water, pasture, and other renewable resources are increasingly erratic. But the strain is also political: brutal fighting between pro-government forces and local groups with links to Boko Haram, Al-Qaeda, and the Islamic State has turned major transit hubs, cow superhighways, and wetlands into battlegrounds. Making matters worse, herders tend to be underrepresented within state institutions, whose land-use policies favor farmers, and overrepresented within jihadist groups, which appeal to this exclusion to draw recruits from herding communities. A common lack of schooling among children of herders further deepens this exclusion.

Herders driving cattle along Badagry-Mile 2 Express Road, Lagos Nigeria. — In their long journeys, herders sometimes drive cattle near or through urban land.

The result is that tens of millions of Sahelian herders who depend upon free movement are increasingly penned in. Things are especially dire for Fulani herders, who get scapegoated as troublemaking outsiders. So addressing the multidimensional crisis would not only help herders; it could remove an intractable driver of one of Africa’s worst wars.

“Ensuring that herders have land and water rights, and working out their access to these through dialogue, is an important part of the solution to conflict in the Sahel,” says Adam Higazi, a researcher at the University of Amsterdam and Nigeria’s Modibbo Adam University, whose 2018 report on pastoralism and conflict for the UN’s West Africa office remains a key reference in the field.

The question now is whether Garbal and a handful of other tech-driven projects can in fact deliver on promises to help stabilize herders experiencing rising precarity.

Aliou Samba Ba, who leads a regional pastoralist organization that has teamed up with Orenstein to get data to Senegalese herders, says he’s optimistic, largely because Orenstein is turning traditional interventions upside down: “We say he looks with the eye of the herder as well as with the eye of the satellite.”

When institutions fail

The Sahel stretches from Senegal’s Atlantic coastline across Africa to the Red Sea, bounded by the Sahara to the north and by verdant forests and savanna to the south. Much of the region has been ravaged by drought and insurgencies over the past few decades, but rural Senegal is still home to the types of spaces that herders elsewhere are fighting for: maintained, not overdetermined; protected, not overpoliced. There is climate change here, but no war.

Last September, I drove deep into the Ferlo, a pastoral reserve roughly the size of New Jersey, to meet with a Fulani herder named Salif Sow.

It was the height of the rainy season, and the Sahel was having a great one. The environment that greeted me was a miracle and a mirage—a desert burst into bloom. Tall, bony Fulani herders scrambled to keep up with throngs of lambs, goats, cows, and camels spread out over a seemingly infinite expanse of green grass and lushly foliated trees. The Ferlo was brimming with carefully maintained wells, abundantly filled seasonal ponds, and clearly marked pastoralist corridors, with the country’s biggest wholesale livestock market just a few hours’ ride by donkey cart. There were no paved roads, no commercial farmland, and no extremist recruiters for hundreds of miles in any direction.

A woman and two young boys astride cattle seen through the horns of a cow on the water to a watering hole — Herders have to make complex calculations when choosing where to take their cows to wait out the dry season.

Not that the herding was easy work. “A herder’s life is difficult,” Sow said, welcoming me to his compound with sweet tea and a calabash filled with fresh milk. “There is not one day of rest.”

In a few months’ time, the rains would stop, the herds would exhaust the pastures, and the grassland would revert back to desert. And Sow would again face the difficult decision he faces every year: whether to stay and buy livestock feed to tide his animals over until next year’s rains or to lead his cows on a journey, and if so, where.

A lot of complex spatial calculations go into choosing where to take hundreds of hungry cows to wait out the dry season on the edge of the world’s largest subtropical desert, while making sure they have enough to eat along the way. Observing these deliberations filled Orenstein with wonder more than a decade ago, when he started surveying herders in Chad for a food security project with the French NGO Action Against Hunger (ACF).

In 2014, Orenstein helped ACF develop an early-warning system, mining new data sources using remote sensing—observing the conditions of grazing pastures from space via satellite imagery and, in some cases, with the use of drones. He also worked with pastoralist organizations to gather information about diverse conditions on the ground, ranging from wildfire locations to the spread of animal disease. He then began making maps using open-access sources; passing the data through an algorithm that he developed to treat and filter imagery, he created detailed and accessible illustrations of rainfall levels and vegetation that became a rare reliable resource for herders and their allies. Aid workers in war zones would print out his maps and pass them around to herders.

It was part of a system designed to extract data, analyze it, and send it up the chain to institutions, including national ministries, UN agencies, and donors. Being able to see crises coming, the thinking went, would give institutional actors more time and power to prepare their response and assign their resources. Being able to deploy emergency programming earlier would in turn afford herders a bit more protection.

In practice, that’s not always how it worked.

At the start of the rainy season in the early summer of 2017, Orenstein was tracking rainfall patterns and felt a knot in his stomach. The first rains had hit too hard, washing the dormant seeds out of the soil; a dry spell followed that lasted for several weeks. When the rains did return, the grassland growth was stunted. Drought was coming.

By mid-August, Orenstein was scribbling reports and ringing journalists to warn that disaster was imminent. But when presented with this evidence, the regional body with the authority to declare an emergency did not act. By the time it finally did, in April 2018—eight months after initial warnings were sounded—it was far too late to respond effectively to what turned out to be the worst drought in 20 years.

Alex and three other men crowded around a table with a large map of Nigeria — Data scientist Alex Orenstein marks up areas during a field mapping exercise.

Two months after that, in June 2018, the United Nations Office for the Coordination of Humanitarian Affairs urgently warned that 1.6 million children faced severe acute malnutrition, up more than 50% from the previous year.

That blighted season was also brutal for Sow. In March, his entire village sent its animals south to escape the drought—the first time anyone could remember doing so that early in the dry season. But Sow lingered, unwilling to take his sons out of school to help him. Nonetheless, he also could not afford to stay and buy several tons of animal feed per month at inflated prices. By the time Sow finally hired a few assistants and headed south with his cattle, sands had engulfed the grasslands.

They marched across the desert like soldiers at war, covering 18 miles a day. On the 10th day, they reached the Tambacounda region by the Malian border, where the cows would spend the rest of the lean season grazing on savanna woodlands and lush forest. Not all the herd survived the trek, and the cows that did were emaciated and more prone to insect-borne tropical diseases. By season’s end, a quarter of the herd had dropped dead—a defeat from which Sow still hasn’t recovered.

Democratizing data

Driving through the Ferlo in 2018, Orenstein was distraught to see the rail-thin Fulani herders trailing behind their withering cows. Across the Sahel, anti-Fulani pogroms were on the rise; some West Africans were taking to Twitter to call for their extermination. As weather, food, and protection systems broke down, it was easier to scapegoat the drifting “foreigner” than to demand accountability from anyone responsible.

The combination of starvation and ethnic massacres reminded Orenstein of the stories his grandfather used to tell of surviving Auschwitz. What good were early warnings if institutions were not willing to act on them? Not that the drought could have been prevented. But declaring an emergency sooner would have facilitated measures to soften its impact on herders. For example, governments could have sent cash transfers and distributed food for both humans and livestock at strategic transit locations.

From that point on, Orenstein decided to do things differently. If institutions could not be trusted to make good use of new data, why not get it directly to herders?

But delivering data to herders would prove extremely challenging. The centralized, vertically oriented systems traditionally used for data collection and analysis are better adapted to those institutions, usually located in capital cities, than to herders dispersed across thousands of miles of desert. What’s more, Sahelian herders are some of the world’s least reachable, least connected people. Many of them don’t have cell phones or access to internet or strong cellular service.

Still, the timing was good—aid workers and donors were increasingly hopeful that technology could solve stubborn problems. In 2018, Orenstein secured a $250,000 grant for ACF to broadcast data reports to herders in northern Senegal via text message and community radio.

The project launched several months later, though by then Orenstein was already working on another one: the Garbal call centers. Even more than community radio, the call centers, which are a collaboration with the Netherlands Development Organization, could offer data tailored to individuals in very specific locations over a wider remit. The first center launched in Bamako, Mali, in 2018. Another, in Ouagadougou, Burkina Faso, followed in 2019.

Orenstein and the Garbal team—roughly a dozen local data analysts, project managers, digital finance experts, and tele-agents with degrees in livestock management and applied agriculture—have designed different tools for herders’ needs. For example, they’ve offered ways to connect with veterinarians, compare market prices for animal feed, and use satellite data to find seasonal migration corridors and track brushfires. Crucially, the team has also engaged directly with pastoralist organizations, training and equipping herders to send back field data about vegetation quality in different zones—a piece of critical information that is undetectable via satellite.

screenshot of the STAMP+ Interface showing a map of the area around Kokolorou. An info panel on the left shows other data about the area including a chart of current animal and cereal prices, vegetation levels and button for a 7 day weather forecast — A screenshot of a tool developed by Orenstein and others that call center agents use to provide herders with location-specific data.

Orenstein himself went into the field as often as he could to hold focus groups with herders and ensure that the way information was delivered would be adapted to their epistemic culture. “Instead of asking them, ‘Do you need rainfall information?’ I would say, ‘What kind of information do you need? And how do you measure it?’” he recalls. “Otherwise, the system would tell them to expect 25 millimeters of rain. Math is not how they measure. So instead, I would hold consultations on pond fullness, for example, and define rain strength in those terms—terms they can use.”

Samba Ba, the Senegalese herder, notes how effective this work has been in bridging the gulf between what tech had promised and what he and his peers actually needed. “Orenstein would help us forecast in September what the vegetation would be like the following year, so we could plan the next seasonal migration,” he says. “He came to us in the field, took into account our customs, habits, and knowledge, and used technology to give us a clearer idea of the grazing situation.”

Still, the most popular Garbal service has been its weather forecasting for rural zones. Previously, reliable information was severely lacking, in part because there were not enough ground stations and in part because satellite data was available only for urban areas. (Mali, for instance, has just 13 active weather stations, compared with 200 in Germany—a country one-third its size.)

Orenstein came up with a way to make rural forecasts more readily available. “We had the coordinates for every village in Burkina Faso. Why couldn’t we just plug those into an API?” he remembers thinking, referring to an application programming interface, a kind of intermediary that allows applications to interact with one another. “Suddenly, we were getting weather forecasts for places that weren’t listed anywhere.”

The API has enabled Garbal tele-agents to click on remote pastoral zones on a map and receive tables showing weekly, daily, and hourly forecasts that are updated with fresh satellite data every three hours. Honoré Zidouemba, the project manager for the Ouagadougou call center, estimates that during the rainy season, his center receives 2,000 to 3,000 calls a day about the weather. “Herders and farmers used to derive information from natural cues,” he says, “but with climate change, those are more and more perturbed.”

false color image of a 3 Period Timescan Cropland Monitor built with Earth Engine Apps — A tool created by Orenstein and collaborators allows a user to highlight the presence of active cropland across time.

It’s simple and inexpensive—costing under $100 a month to use—but of all the team’s technological innovations, the API has made the biggest impact. And it’s a far cry from the kinds of higher-tech applications NGOs and development organizations have been promoting.

Since 2015, the World Bank has committed half a billion dollars to a two-phase project to support Sahelian herders’ “resilience” through strategies that include developing technological tools to map pastoral infrastructure. A senior humanitarian-agency staffer working with herders and technology, who requested anonymity to speak frankly, says the resulting databases have not been shared with herders; he calls the approach, which is geared more toward informing institutions than informing herders, “very technocratic.” (The World Bank did not respond to a request for comment.)

Meanwhile, ACF, the French NGO Orenstein previously worked with, got international attention in 2020 for reportedly using AI to help herders, a claim several people involved in the project say was simply incorrect. (“ACF does not use self-learning for its Pastoral Early Warning System. Presently, the analysis is done ‘manually’ by human expertise,” says Erwann Fillol, a data analysis expert at the organization.)

drone shot of cattle immersed in brown muddy water — Climate change is making herding routes, like this one across the Niger River, increasingly volatile.

Other groups are experimenting with using predictive analytics to forecast displacements and herders’ movements. A pilot project from the Danish Refugee Council in Burkina Faso, for example, predicts subnational displacement three to four months into the future, allowing aid workers to pre-position relief. “Anticipatory action in response to climate hazards can be more timely, dignified, and cost effective than alternatives,” says Alexander Kjaerum, an expert on data and predictive analytics with the organization. “AI is a last option when other things fail. And then it does add value.”

Still, some argue these kinds of projects have missed the point. “How are high technology and AI going to address land access issues for pastoralists? It is questionable if there are technological fixes to what are political, socioeconomic, and ecological pressures,” says Higazi, the pastoralist expert.

Blama Jalloh, a herder from Burkina Faso who heads the influential regional pastoralist organization Billital Maroobé, echoes this broad sentiment, arguing that big-budget, high-tech efforts mainly just produce studies, not innovation.

Taking matters into its own hands, in 2022 Billital Maroobé organized the first hackathon designed by and for Sahelian herders. Jalloh says the hackathon aimed to narrow the gap between herders and tech developers who lack familiarity with herding lifestyles. It granted up to $8,000 to startups from Mauritania and Mali to track animals and introduce digital ID cards for herders, which could help them cross borders more seamlessly.

An uncertain future

With three call centers now open, and Orenstein serving as a remote technical advisor from the US, the Garbal team is striving to stay focused and make their work sustainable.

Nevertheless, the fate of the project is far beyond its supporters’ control. The region’s slide into violence shows no sign of stopping. As a result, even though more of the herders that Garbal set out to support have started carrying smartphones charged with battery packs, they are increasingly being pushed out of cell range.

drone view of a city block with people standing near multiple fires burning in the streets after a protest — Protesters fill the streets of Ouagadougou, the capital of Burkina Faso, where nearly 10% of the population has been displaced in recent years.

Between 2018 and 2022, Burkina Faso witnessed one of the world’s fastest-growing displacement crises, with the number of internally displaced people exploding from 50,000 to 1.8 million—almost 10% of the population. Fulanis in particular were targeted for killing by security forces and government-backed vigilantes, and in some areas that are home to significant Fulani herding communities, militants destroyed as many as half the mobile-phone antennas. One tele-agent says the herders who did manage to call in from war zones told her how happy they were to reach the center. When I visited the Ouagadougou call center last year, a tele-agent named Dousso, a 24-year-old with a livestock degree who speaks French, Gourmantche, Dioula, and Moré, told me that “all of the coups,” as well as incidents in which jihadists took over markets, were also making it increasingly difficult to get certain types of data.

This can make the service even more meaningful where it’s still available, says Catherine Le Come, a Garbal cofounder, pointing to Mali, where Garbal is still accessible in some parts of the country that are now cut off from the state.

Yet Garbal, just like other efforts to get data to herders, faces the always pressing issue of how to fund this work consistently over time.

Nonprofit projects like ACF’s community radio and SMS bulletin alerts are pegged to funding cycles that run out after a few years. In March 2021, for instance, as Sow marched his cows 140 miles east toward the Senegal River, he relied on geospatial data he received by community radio and text message from two different NGOs, informing him where pastures were plentiful. But just three months later, both projects ran out of money and stopped supplying information.

Fulani herder dtanding near a body of water with his cattle, using his cell phone — Traditionally, Sahelian herders have been some of the least-connected individuals. But now more are carrying smartphones charged by battery packs.

The Garbal call centers are trying to build a more sustainable model. The plan is to phase out NGO sponsorship by 2026 and operate as a public-private partnership between the state and telephone operators. Garbal charges callers a modest fee—the equivalent of five cents a minute—and has plans to roll out online marketplaces and financial products to generate revenue.

“Technology in itself has lots of potential,” says Le Come. “But it is the private sector that must believe and invest in innovation. And the risks it faces innovating in a context as fragile as the Sahel must be shared with a public sector that sees user impact.” (Cedric Bernard, a French agro-economist who has worked with ACF, firmly disagrees; he insists that the information should be free, and that trying to be profitable “is going the wrong way.”) Furthermore, the for-profit model means that Garbal—which set out to help vulnerable herders—is already pivoting toward providing services to farmers, who make more reliable customers because they are easier to reach and better connected. Zidouemba, the Ouagadougou project manager, says that its callers are now overwhelmingly farmers; herders, he estimates, account for just 20% of the calls to the Burkina Faso center.

Sow standing with his cattle in the Ferlo — In 2018, a quarter of Salif Sow’s herd dropped dead in a severe drought. But that season he made a sacrifice that is finally paying off: his son recently started studying abroad in Paris.

As the tides of data that reach them ebb and flow, the herders themselves are aware that the real work needed to keep their way of life going is a longer-term political effort. As I prepared to leave the Ferlo this fall, the landscape still resplendent from the rainy season, Sow pulled me aside. He was a modest man, but there was something he wanted me to know. That very night, he said shyly, his eldest son, Abdoulsalif, was leaving Dakar for Paris to begin graduate studies at the Sorbonne, where he had received a scholarship—a fruit of the sacrifice that Sow made during the year of the terrible drought.

I reached Abdoulsalif over WhatsApp a few weeks later, by which time he had learned that Sciences Po was more prestigious than the Sorbonne and enrolled there instead. He is studying public policy and plans to seek work on pastoralist policy in the Sahel after graduation.

“Herding is a beautiful way of life, a space where I feel very happy,” Abdoulsalif told me. “It is extraordinary to see, so far away, the animals in their vast spaces. Far more beautiful than to live in a place with four walls. Even in Paris, I feel nostalgic for this life, this space of herders.”

Hannah Rae Armstrong is a writer and policy adviser on the Sahel and North Africa. She lives in Dakar, Senegal.

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