Jiri Calls For AI Farming Skills Surge As Drought Cycles Shrink
In a small boardroom at the Ministry of Lands, Agriculture, Fisheries, Water and Rural Development in Harare, the talk was not about fertiliser shortages or maize prices. It was about algorithms, satellites, and the 14,000 people who will have to learn to use them.
Permanent Secretary for Agriculture, Prof. dr. O. Jiri, used a courtesy call by the Director General of the Eastern and Southern African Management Institute (ESAMI), Prof. P. Kiuluku, to sound an alarm: Zimbabwe’s agriculture workforce must rapidly upskill for data-driven, AI-led farming as drought cycles compress to just three or four years.
“We can’t keep planning with yesterday’s tools when drought is coming every three or four years,” Jiri said, underscoring the urgency for the Ministry’s roughly 14,000 staff to be retooled for a new era of climate risk and digital agriculture.
AI In Farming As A Survival Tool, Not A Luxury
For Jiri, AI in farming is no longer a distant concept reserved for research stations and big estates. It is a survival tool for a sector that still feeds the nation and anchors the rural economy, yet now faces shorter and harsher dry spells driven by climate change.
FFA Explainer:
Artificial intelligence (AI) in agriculture refers to computer systems that can process huge amounts of data to recognise patterns, make predictions, or support decisions. In farming, this can include yield forecasting, weather and drought prediction, pest detection from images, and optimising planting dates or fertiliser use.
New satellite and digital tools, Jiri noted, are already changing how the Ministry monitors crops, water, and rangelands. But those same tools have exposed deep data gaps and skills shortages that make it hard to respond quickly and precisely to climate shocks.
“Satellites are telling us stories about our soils, crops, and water that we never had before,” he said. “The danger is that the data is there, but we don’t have enough people who can read it and turn it into action in Gokwe, Tsholotsho, or Chiredzi.”
Why The Ministry’s 14,000 Staff Are At The Centre Of Change
The Ministry’s workforce – from Agritex officers in remote wards to senior managers in Harare – is the backbone of public agricultural support. Jiri argued that without equipping these officers to use digital tools, climate-smart ideas will remain PowerPoint slides, not practice.
He called for tailor-made training programmes for Agricultural Business Advisory Officers and senior managers, with three clear priorities:
- Supervising and deploying modern technologies such as satellite-based monitoring, mobile data collection, and AI-supported decision tools.
- Understanding and managing global shocks – from climate extremes to commodity price swings – using timely, quality data.
- Responding to changing food preferences of Zimbabwe’s growing youth population.
“Our officers must be able to open a dashboard, interpret rainfall scenarios, understand a youth market for small grains or horticulture, and then advise a farmer with confidence,” Jiri said. “That is a very different skill set from twenty years ago.”
The push aligns with national ambitions under Zimbabwe’s National Development Strategy 1 (NDS1) and broader goals under the African Union’s Agenda 2063 and the UN Sustainable Development Goals, which all place data, technology, and resilience at the centre of food systems.
ESAMI Pledges To Help Build An AI-Ready Agriculture Service
Responding to Jiri’s call, ESAMI Director General Prof. P. Kiuluku reaffirmed the institute’s commitment to Zimbabwe and other member states, and proposed a strategic partnership focused squarely on institutional capacity and agribusiness management in an age of climate shocks.
“Our mandate is to strengthen institutions so they can deliver,” Kiuluku said. “If agriculture is to withstand climate change, then ministries like this one must have managers, planners, and field officers who are fluent in data and comfortable with digital tools.”
According to Kiuluku, ESAMI is ready to collaborate with the Ministry to design and roll out targeted training programmes that combine management skills with practical exposure to digital agriculture tools, including AI-enabled systems where feasible.
The proposed collaboration would likely touch on:
- Institutional capacity building – helping departments reorganise around data flows and evidence-based planning.
- Agribusiness management – sharpening skills in value chain analysis, markets, and finance, so officers can advise farmers as entrepreneurs.
- Food security and rural modernisation – integrating digital tools into extension, irrigation schemes, and climate adaptation projects.
From Satellite Images To Ward Meetings
The promise of AI in farming often arrives as glossy maps and dashboards. Jiri and Kiuluku both stressed that the real test will be whether those tools change decisions at ward and village level.
Take Gokwe South in the Midlands Province. In recent seasons, farmers in wards bordering Chireya and Njelele have faced erratic rains, with some seasons marked by mid-season dry spells that wipe out rainfed maize just as it tassels. Agritex officers there already use mobile phones to capture crop data, yet they rarely receive timely, analysed feedback that can guide farmers on switching to drought-tolerant crops or adjusting planting windows.
If the Ministry succeeds in its AI and data upskilling agenda, a different pattern could emerge: satellite and weather data feeding into AI-assisted seasonal forecasts, translated into simple advisories on crop choices, planting dates, and water-saving techniques, and shared through extension meetings, WhatsApp groups, and community radio.
That kind of loop – from satellite to spreadsheet to ward meeting – is what Jiri wants to normalise.
What Data Gaps Are Slowing Climate Response?
Jiri’s reference to “data gaps” goes beyond missing numbers in reports. It speaks to structural weaknesses in how information flows across Zimbabwe’s agriculture sector and many other African systems.
Common gaps include:
- Fragmented data collection – different departments and projects collect similar information using different tools, without standard formats.
- Limited analysis capacity – even where data exists, there are too few analysts who can clean, interpret, and visualise it for decision-makers.
- Slow feedback loops – information gathered in the field may take months to shape planning, by which time droughts or pests have already done their damage.
- Weak local access – ward-level officers and farmers rarely see the processed data that could guide them.
AI systems, especially those using satellite imagery and automated modelling, can help fill some of these gaps by generating frequent, consistent information on crops, water, and vegetation. But they still require human expertise – in ministries, agribusinesses, and farmer organisations – to choose the right models, verify outputs, and turn them into clear advice.
FFA Explainer:
Data-driven farming uses information from field measurements, satellites, weather stations, and markets to guide decisions. Instead of relying only on experience or tradition, farmers and planners use up-to-date data to choose crops, schedule irrigation, apply fertiliser efficiently, or target relief support to the most affected areas.
Young, Urban, And Hungry For Different Food
Beyond climate, Jiri framed AI and data as tools to understand something more subtle: changing food preferences, especially among young people.
Zimbabwe, like many African countries, has a rapidly urbanising, youthful population. That brings shifts in diet – from sadza and dried vegetables to demand for poultry, convenience foods, fresh horticulture, and processed snacks. It also brings new expectations on quality, safety, and branding.
“We must track what our young people are eating and aspiring to eat, not what we assume they should eat,” Jiri said. “Data can tell us which crops and livestock products have a future market, so we guide farmers correctly.”
Using mobile surveys, social media analysis, and market data, AI tools can help ministries and farmers’ unions spot emerging demand – for example, a spike in interest in orange-fleshed sweet potatoes in Harare’s high-density suburbs, or rising orders for small grains from health-conscious consumers.
For Zimbabwe’s youth who are trying to build agribusinesses, that kind of information can be the difference between a thriving enterprise and a failed venture. It also opens doors for women entrepreneurs in agro-processing, packaging, and retail, where up-to-date market intelligence is critical.
Makonde’s Lesson: When Data Meets Extension
Makonde District in Mashonaland West offers a glimpse of what AI-ready extension could look like. Over the past few seasons, some farmers there have worked with local agribusinesses and development partners to pilot basic digital tools for crop planning and weather alerts. Officers share simple SMS-based advisories on expected rainfall, and farmers respond with updates on crop conditions.
While not fully AI-driven, these efforts already show how better information can shift behaviour – from spreading planting dates to reduce risk, to testing early-maturing maize varieties, to trialling small grains in known dry pockets.
If the kind of AI and data training Jiri is demanding becomes standard for Agricultural Business Advisory Officers across the country, districts like Makonde could go further, drawing on satellite-based yield estimates and predictive models to manage input distribution, irrigation schedules, and even local grain storage and marketing.
For women farmers in communal areas – who often have less access to extension officers and market news – such systems could be a lifeline, especially if they are designed to work through community groups, mobile phones, and trusted local leaders.
Aligning Policy, Training, And Rural Modernisation
Jiri’s call lands at a time when Zimbabwe is investing heavily in rural infrastructure through programmes such as smallholder irrigation rehabilitation, dam construction, and agro-processing support. Rural modernisation is a core promise of government strategy and a central pillar of NDS1.
The missing layer, he argues, is human capacity that matches those investments.
“We are building dams, irrigation schemes, dip tanks, and roads,” he said. “If the officers managing these assets are not trained in modern data and AI tools, we will not get full value from them.”
ESAMI’s proposed partnership with the Ministry speaks directly to that challenge. By integrating management training with digital skills, the collaboration could help Zimbabwe and other member states move from hardware-only development – tractors, dams, roads – to smarter systems where data and AI guide how those assets are used.
Digital tools for smallholder farmers, from simple weather apps to more complex decision-support systems, will be central to this shift. So will partnerships with local universities, technical colleges, and private agritech firms, which can help develop tools that speak local languages, run on basic phones, and answer real questions farmers face.
Risks, Limits, And The Human Factor
The enthusiasm around AI in farming comes with warnings. Poor connectivity, expensive data, unreliable power, and limited hardware can all slow adoption in rural areas. There is also the risk that shiny tools benefit only large farms or urban agribusinesses, leaving smallholders and communal farmers further behind.
Jiri and Kiuluku both stressed that technology must serve people, not replace them.
“AI will not walk the field for us,” Kiuluku said. “Farmers will still plant, weed, and harvest. Extension officers will still listen, negotiate, and advise. What changes is that they do so with better information and stronger analysis.”
That human factor is why upskilling, not just procurement of tools, sits at the centre of the Ministry’s vision. Training Agricultural Business Advisory Officers to understand both data and people – the spreadsheet and the soil – could determine whether AI becomes a practical ally or just another buzzword.
Climate-smart agriculture programmes across the region already show that when farmers co-design solutions, adoption rises. Bringing AI into that conversation requires the same respect for local knowledge and context.
A New Kind Of Harvest
As the meeting in Harare drew to a close, the hashtags being used by observers – #AIinFarming, #ClimateChange, #FoodSecurity, #ESAMI, #Upskilling – captured a sense that something more than a courtesy call was underway. It felt like the framing of a new kind of harvest, one measured not just in tonnes of grain but in terabytes of useful, timely data.
For the maize farmer in Makonde, the cattle keeper in Matabeleland South, the horticulture youth in Mutare, and the extension officer in Gokwe, the impact of Jiri’s call will be judged by whether advisory visits, input programmes, and drought plans actually change – and whether those changes keep food on the table when the next three- or four-year drought cycle arrives.
If ESAMI and the Ministry follow through, the next time satellites pass over Zimbabwe’s fields, the data they send down may land on the desks of officers who know exactly what to do with it. As Jiri put it, that is how technology starts to taste like sadza: when it feeds people.
?? Subscribe to our WhatsApp Channel to Get Our Updates On Your Mobile
About The Author
