AS climate shocks intensify and input costs continue to rise, Zimbabwe’s agricultural sector is under growing pressure to produce more with fewer resources. From recurrent droughts in Matabeleland and parts of Mashonaland to fertiliser shortages and post-harvest losses, the need for smarter, technology-driven farming has never been more urgent. Emerging global examples now point to how artificial intelligence (AI) could offer a practical pathway for transforming Zimbabwean agriculture.
A recent case from India, where Microsoft’s AI-powered Farm Vibes platform has helped farmers in the drought-prone Baramati region dramatically improve productivity, provides a compelling blueprint for Zimbabwe. By integrating satellite imagery, drones, soil sensors and AI analytics, farmers in Baramati achieved yield increases of up to 40 percent, cut fertiliser use by 25 percent and reduced water consumption by as much as half. Similar outcomes could be transformative for Zimbabwe’s largely climate-vulnerable farming systems.
Precision farming for a climate-stressed economy
Zimbabwe’s agriculture remains the backbone of the economy, employing the majority of the population and contributing significantly to exports through tobacco, maize, cotton and horticulture. Yet productivity remains highly uneven, with smallholder farmers particularly exposed to erratic rainfall and poor access to agronomic information.
AI-driven precision farming could change this equation. By combining real-time data on soil moisture, temperature, humidity and nutrient levels, AI systems can generate tailored recommendations on when to plant, irrigate and apply fertiliser. For Zimbabwean farmers, this would mean more efficient use of scarce water resources, especially in drought-prone regions, while avoiding the blanket application of fertilisers that degrade soils and inflate costs.
With Zimbabwe already using satellite data for weather forecasting and early warning systems, the leap toward AI-enhanced decision-making is less radical than it may appear. What is required is integration, bringing together meteorological data, on-farm sensors and agronomic models into farmer-friendly platforms.
Cutting costs and improving sustainability
Rising input prices have eroded profitability across Zimbabwe’s farming sector. Fertiliser, seed and fuel costs remain a major constraint, particularly for A1 and communal farmers. AI-enabled spot fertilisation — where nutrients are applied only where needed — offers a way to reduce chemical use while improving soil health. Evidence from India suggests fertiliser savings of around 25 percent are achievable without sacrificing yields.
For Zimbabwe, where soil degradation and declining fertility are long-term structural challenges, such an approach could support sustainable intensification rather than expansion into marginal lands. Reduced chemical runoff would also align with environmental goals, particularly in sensitive catchment areas.
Water efficiency is another critical gain. AI systems that monitor weather patterns and field conditions can help farmers irrigate more precisely, reducing wastage. In a country where irrigation schemes are under strain and power supply is unreliable, cutting water use by even a fraction could significantly extend the viability of existing infrastructure.
Empowering farmers through local-language AI
One of the key lessons from the Baramati experience is accessibility. Farmers were able to interact with AI tools through vernacular language assistance, making complex data actionable at the farm level. For Zimbabwe, this is especially relevant given linguistic diversity and varying literacy levels.
AI platforms that deliver advice in Shona, Ndebele and other local languages — via mobile phones — could democratise access to advanced agronomic knowledge. With mobile penetration already high, such tools could reach thousands of farmers who currently rely on informal advice or delayed extension services.
This would not replace agricultural extension officers but complement them, allowing limited public resources to be used more strategically while farmers receive timely, data-driven guidance.
Reducing post-harvest losses and strengthening value chains
Post-harvest losses remain a silent drain on Zimbabwe’s agricultural output, particularly in maize, horticulture and small grains. AI-enabled logistics and storage planning, informed by crop forecasts and market data, could help reduce losses by improving timing, aggregation and storage decisions.
Even modest reductions in post-harvest losses would have macroeconomic benefits, improving food security, stabilising prices and increasing export competitiveness.
Building partnerships for local capacity
For Zimbabwe to realise these gains, collaboration will be essential. The Baramati project brought together technology firms, farmer organisations, research institutions and universities. A similar model could involve partnerships between government, local universities, agritech startups, mobile network operators and global technology firms.
Crucially, AI adoption must be adapted to Zimbabwe’s realities — small plot sizes, limited capital and infrastructure constraints — rather than imported wholesale. Pilot projects, starting with irrigation schemes or high-value crops, could demonstrate impact before scaling up.
As climate change reshapes agriculture across Southern Africa, AI is no longer a futuristic concept but a practical tool for resilience and growth. For Zimbabwe, embracing data-driven farming could mark a decisive shift from survival agriculture to a more productive, sustainable and competitive sector.
