Predicting the Rain Is Not Enough: How AI Can Help Africa’s Farmers Navigate Climate Uncertainty
For millions of African farmers, an unpredictable rainy season can mean the difference between a successful harvest and months of uncertainty.
Plant too early, and seeds may fail before the rains arrive. Plant too late, and crops may not mature in time. These decisions shape household incomes, food security and the livelihoods of communities that depend on farming.
For a smallholder household, one decision can affect the next season. Money spent on seeds cannot easily be recovered. A poor harvest can also reduce funds available for school fees, food and essential planting supplies.
Climate uncertainty is making these choices harder. According to the Food and Agriculture Organization (FAO), approximately 309 million Africans faced hunger in 2025.
As governments convene for COP31 in Antalya, Türkiye, an urgent question remains: Do farmers and institutions have the information and capacity to act before climate risks become crises?
Artificial intelligence (AI) offers part of the answer. But predicting the rain is only the beginning.
AI Can Improve Advance Weather Forecasts
At Microsoft Research, scientists have developed Probabilistic Bias Correction. This machine-learning approach aims to improve forecasts two to six weeks ahead by correcting recurring errors in existing weather models.
Research involving Kenya points to the potential value of advance warnings. Farmers could adjust planting schedules, anticipate dry spells and make better-informed decisions about seeds and other inputs.
However, a forecast alone cannot tell a farmer what to do. Advice must account for soil conditions, crop varieties, irrigation, costs and local experience.
A recommendation that works in one region may not suit another. The real task is turning climate intelligence into practical guidance that farmers can use.
Helping Governments Make Better Agricultural Decisions
Governments face a related challenge. They need to identify where crops are struggling, estimate national food production and determine which communities need support.
AI can combine satellite imagery, weather observations, agricultural records and field reports. This can help monitor crop conditions, estimate harvests and inform decisions on food reserves, farm inputs and drought assistance.
In Kenya, we are pursuing this approach through ADAPT-Kenya, part of the wider ADAPT-Africa initiative.
Led by Kenya’s Ministry of Agriculture and Livestock Development, the collaboration brings together NASA Harvest, the Kenya Agricultural Data and Information Centre (KADIC), Microsoft’s AI for Good Lab and other partners. Its goal is to strengthen agricultural information systems.
Consider a county agricultural officer deciding where to send assistance after poor rainfall. Crop-monitoring data, checked against field reports, could help identify areas under stress. Harvest forecasts could also help officials prepare for possible shortages.
ADAPT-Kenya aims to support this work through a shared, government-owned agricultural information system.
During Kenya’s 2026 long-rains growing season, ADAPT partners supported drought-response assessments. This is not yet evidence of improved yields or reduced losses. Its longer-term value lies in developing tools that government teams can maintain, evaluate and improve.
Making Climate Information Accessible to Farmers
Our work at the AI for Good Lab reinforces one lesson: even advanced AI has limited value when institutions lack reliable data, skilled personnel, resources or the authority to act.
Information must also reach farmers in a language and format they can understand.
Across Africa, language barriers, limited connectivity and different levels of digital literacy restrict access to agricultural advice. Even accurate guidance may fail if it ignores local crops and growing conditions.
LINGUA Africa, led by the Masakhane African Languages Hub with the Gates Foundation, Microsoft’s AI for Good Lab and Google.org, supports African-language data and AI tools.
Its agricultural awardees include AGRA’s MlimiVoice Malawi, which is designed to support farmers in Chichewa and Tumbuka. Another is AntuGrow’s SemaShambani, which aims to provide spoken farming advice and credit information in languages including Kiswahili and Dholuo.
These approaches are promising, although their wider impact remains to be demonstrated.
Investment in Data Systems Is Essential
Accessible advice is only part of the challenge. The larger obstacle is institutional.
Agricultural records are often fragmented. Connectivity remains uneven, while public agencies may lack specialist capacity. AI cannot fix these weaknesses on its own.
At COP31, governments and development partners should treat agricultural data systems as essential climate adaptation infrastructure, not optional technology projects.
Adaptation finance should support regular crop and field surveys, interoperable databases and affordable connectivity. It should also fund sustained training for public analysts and agricultural extension workers.
Governments should establish responsible data-sharing arrangements. They should also improve coordination between agricultural, meteorological and disaster-management agencies.
Most importantly, national institutions must be able to sustain these systems beyond individual pilot projects or grants.
Building Africa’s Climate Resilience
AI cannot stop droughts, replace irrigation or solve the financial pressures facing farmers. However, it can help us see risks earlier and make better-informed decisions.
Africa should build this capability around its own farmers, researchers and public institutions. Their knowledge will determine whether a recommendation works in practice.
Progress will not be measured by sophisticated algorithms alone. It will be visible when farmers can protect more of their harvests, officials respond sooner to emerging shortages and communities face climate shocks with greater confidence.
The future of climate-resilient agriculture depends not just on predicting the rain, but on what Africans can do before it falls.
By. Dr Girmaw Abebe Tadesse
lead Microsoft’s AI for Good Lab in Nairobi
