Artificial Intelligence (AI) and machine learning could help African farmers anticipate climate shocks, make timely decisions and reduce losses, agriculture and climate experts have said, as the continent seeks to build more resilient food systems.

AI-powered weather forecasting can provide more accurate, localised and actionable climate information, allowing farmers, technicians and policymakers to act before extreme weather events strike rather than respond after losses occur.

The experts made the observations during a side event on "A Pan-African Agenda for AI-Powered Climate Intelligence for Climate-Resilient Food Systems, Early Warning Systems, and Community Resilience,” held on September 3 during the Africa Food Systems Forum 2026 in Kigali.

The session, led by the Ministry of Environment and Rwanda Meteorology Agency in partnership with UNDP, called for greater investment and partnerships to integrate AI and machine learning into weather forecasting and early warning systems across Africa.

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Building the data foundation

Africa remains one of the least observed continents from a meteorological perspective, with sparse weather stations, inadequate observation infrastructure, limited computing capacity, fragmented climate data systems and shortages of specialised technical expertise.

Aimable Gahigi, Director General of Rwanda Meteorology Agency, said effective AI-powered forecasting depends first on a strong data foundation.

"AI is a good cook, and a good cook still needs ingredients. Without data there is nothing to forecast with,” he noted.

Rwanda, he said, has invested in observation equipment and systems that support national forecasting while enabling the country to exchange meteorological information with regional and global institutions.

Gahigi identified three key lessons from Rwanda’s experience: building a reliable data foundation, having a shared national plan and investing in local expertise.

He said government institutions, UNDP and other partners need to work around common priorities rather than implementing isolated projects that may not leave sustainable systems behind.

He also stressed the importance of developing local technicians and climate experts capable of implementing, monitoring and controlling new technologies.

"Rwanda builds its own capacity to implement, monitor and control the systems rather than waiting on foreign expertise, which is what makes the products last,” he said.

Gahigi said AI should complement, rather than replace, traditional meteorological forecasting and human expertise.

"AI works with the traditional forecasting models that have already been tested, not against them, and it will not replace the human forecaster,” he said.

For farmers, the objective is to turn sophisticated technology into simple information that can support practical decisions, such as when to plant, apply fertiliser, control pests and diseases or harvest crops.

Turning forecasts into action

Fatmata Lovetta Sesay, UNDP Resident Representative, said resilience requires more than simply producing forecasts.

"Resilience needs more than a forecast: sensors in the field, early warning that reaches people, and information local enough to be acted on,” she said.

Sesay put the investment requirement at about $3.5 million, arguing that resources should first improve forecasting, then enable earlier action and ultimately strengthen resilience, with farmers remaining at the centre of the process.

"If you want to go fast, go alone. If you want to go far, go together,” she said.

Solange Uwituze, Minister of State in the Ministry of Agriculture and Animal Resources (MINAGRI), said climate forecasting should no longer be viewed as merely a technical exercise because its consequences directly affect livelihoods and national economies.

"Climate forecasting is not an ordinary technical job. It is a livelihood issue, an economic issue and a moral issue,” she said.

She said Africa needs to move from reacting to climate disasters after they occur to anticipating risks and preparing communities in advance.

Uwituze cited Rwanda’s coffee value chain as an example of the potential economic benefits, saying improved forecasting and better-timed disease control could generate about $1.4 million in annual savings, while avoiding quality downgrading could generate an additional $2 million to $3 million in annual revenue.

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Uwituze called for greater investment in observation networks, digital infrastructure and institutional capacity, while urging innovators and researchers to develop AI solutions tailored to farmers and easy to use.

"The future of a resilient Africa lies in the ability to anticipate, prepare and innovate, and to stop being reactive to shocks that are costly by the time they are visible,” she said.

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Making climate intelligence useful to farmers

The Kigali dialogue also presented findings from a feasibility study jointly developed by the Government of Rwanda, through Rwanda Meteorology Agency and UNDP, on integrating AI and machine learning into weather forecasting and climate information services.

Participants examined the modernisation of weather observation, including the operationalisation of Rwanda’s first automatic upper-air station, described as the second automated station of its kind in Africa.

The initiative is intended to provide a replicable model for other African countries seeking to strengthen weather observation and forecasting.

Experts said AI-enhanced systems could improve forecast accuracy, detect extreme weather events earlier and provide hyper-local advisories to climate-sensitive sectors, particularly agriculture.

Bernadette Arakwiye, the Minister of Environment, challenged stakeholders to consider the value of a forecast if the information arrives too late for people to act.

"A useful forecast tells a farmer where and when to cultivate,” she said.

She underlined that climate risks cross national borders, requiring African countries to connect their science, data, AI systems, institutions and communities.

"Disasters do not wait for our systems to be ready,” she said.

Participants noted that warnings can sometimes arrive after critical farming decisions have already been made, while information may be too broad or too late to be useful at community level. They therefore called for systems that remain operational beyond individual projects.

They argued that Africa now has growing evidence, technical expertise and partnerships to advance AI-powered climate intelligence, but that investment must move from dialogue to implementation.