Online Reporter
Obert Chifamba
Agri-Insight
IN recent farming seasons, smallholder farmers across the globe, have had to contend with a mix of climate risks – droughts, rainfall variability, soil fertility constraints, pest/disease pressure, limited access to timely agronomic advice, and volatility in input costs and markets.
The impact from this onslaught of adverse situations has had a telling effect on yields and subsequently on food security.
And, thanks to the concerted global efforts to come up with mitigation measures, artificial intelligence (AI) has extended its functions to cover agriculture too.
AI has, overnight, become a very useful tool the farming world is now using to turn messy, sparse, locally generated information (weather forecasts, satellite imagery, phone photos of crop problems, soil/management notes, market prices) into actionable decisions—often faster than what traditional extension used to deliver.
In short, it has come in as that virtual extension agent that is readily available to help farmers decide better, faster, and more precisely than they would have done ordinarily using advice from extension officers.
AI has simplified the exact things that are hardest to solve under resource constrained circumstances.
It is exciting to observe that many farmers know what is happening on their land, but not what it means for the next two weeks or so.
AI has come in to bridge that gap through its capacity to covert field visuals and environmental data into decisions, hence the current noises to have many farmers using it to boost production.
It does not require rocket science for anyone to appreciate the fact that most yield losses often come from timing errors — planting too early/late, responding to pests too late, or misjudging nutrient needs.
This is one area that AI is taking care of, thanks to its power to help farmers forecast risk and target actions when they matter most.
At this point, it is important to dispel the common myth that technology and other seemingly sophisticated improvisations such as AI are only for big farms.
They are meant for all farmers regardless of their production categories. With the right design—low-data tools, cooperative access, and ‘decision-support’ guidance—resource-poor farmers can reduce costly mistakes and improve harvest outcomes too.
The use of AI is not restricted to particular segments of agricultural production.
It can assist with planting windows, early pest detection, stress monitoring, and nutrient timing.
These are exactly the constraints that keep many farmers, for instance, Zimbabweans from reaching their potential. In difficult seasons, farmers need timely, localised guidance for them to get an easy appreciation of their situation and spur them to act.
AI can help interpret changing conditions and translate them into step-by-step advice that protects both yields and income.
The other good thing about AI is that it can combine forecasts, historical climate patterns, and crop-stage signals to recommend planting and management actions—so farmers spend less time reacting and more time preparing.
This means that farmers will now spend less time searching for solutions to challenges but focus on implementing them to save crops from any clear and present dangers.
The beauty about AI is that where traditional extension services would not be able to readily reach every farm in time, it is doing so at the blink of an eye.
AI can scale agronomic support—especially when it is localised, trustworthy, and easy to use.
And given AI’s ability to improve decision-making, farmers need to play their part by adopting it earnestly and select proffered solutions that suit their local conditions.
In other words, delivery must match rural constraints, and farmers must retain ground-truth judgment.
This will permit the farmers to boost yields without ballooning costs of production.
To achieve this, they have to use AI for targeted farming ventures and when money is tight, such things as wasting fertiliser or mistiming control measures can wipe out gains.
AI can address this because it naturally supports targeted, risk-based interventions that maximise impact per dollar.
When all is said and done, it is fast becoming an obvious and inevitable reality that AI will help farmers boost yields through a process, which starts even before the planting stage where the farmer is expected to make smart decisions and choices.
AI can help farmers decide things like the best planting window based on the forecast weather pattern and historical patterns.
It will also assist in the crop and variety selection in which the farmer needs to pay close attention to matching his choices to local weather risk and maturity windows.
The use of AI can also give the farmer guidance on field zoning – respecting the stark reality that different parts of the same farm may need different management styles for productive results.
In Zimbabwe, this is important because planting at the wrong time or choosing poorly matched varieties can cause yield collapse even when inputs are adequate.
In some cases, smallholder farmers need someone to hold their hand and help them make precision nutrient and soil management decisions (targeted use of fertiliser and other inputs).
Many smallholder farmers’ biggest undoing is that they over-apply (wasting money) or under-apply (limiting yield) because they do not have access to soil tests.
Since its adoption for farming purposes, AI has proven its potential to guide the farmer in the fertiliser requirements of their soils through its soil-aware recommendations by combining limited soil data with satellite-derived vegetation signals and management history.
Better timing of fertiliser application helps reduce losses during heavy rains.
One important observation is that even those that are semi or AI-illiterate can use it productively if they are part of a cooperative or extension hubs.
There are some farmers who cannot afford smart phones but still need the help of AI.
They can share with those that have the gadgets while local agents or non-governmental organisations (NGOs) can use AI to prepare recommendations for groups.
This will enable farmers to receive simple outputs in the form of field-specific notes, calendars, and prioritised tasks.
AI has the capacity to forecast likely yield outcomes mid-season using satellite vegetation indices, crop stage tracking, rainfall/temperature signals and management actions taken, which allows the farmer to prepare in advance on how he will handle the predicted output and even seek markets that can absorb the projected quantities.
But, as the current AI adoption craze sweeps across the country, farmers must remember that AI is not a magic yield button. Its strongest value is that it can help them overcome information scarcity and timing risk—two of the biggest threats to yields in rain-dependent, resource-constrained settings.
The good thing is that even if AI does not directly ‘grow’ yields, it can protect farmers’ income through recommending optimal seed/fertiliser choices under budget constraints, estimate harvest timing and expected volumes and use market price forecasts to guide selling decisions (when available).
