Beyond the Drone: Engineering Resilient Food Systems for a Hotter Future

A conversation about drones can quickly become a conversation about something much bigger.

Recently, MoloMolo Tech (MMT) discussed the potential for capacity building around drones, precision agriculture and food security in Zimbabwe. The immediate attraction is obvious: drones can survey crops, identify stressed areas, support precision spraying and provide farmers with information that would be difficult or expensive to obtain from the ground.

But the more important question is not “How do we get more drones onto African farms?”

It is: How do we build the local engineering capability to design agricultural systems that remain productive as the climate, technology and operating environment change?

That question is becoming increasingly urgent.

The operating environment is changing

The World Meteorological Organization's latest Global Annual-to-Decadal Climate Update makes uncomfortable reading.

Global mean temperatures between 2026 and 2030 are expected to remain around record levels, ranging between approximately 1.3°C and 1.9°C above the 1850–1900 baseline. There is a 91% probability that at least one year will temporarily exceed 1.5°C, and an 86% probability that at least one year between 2026 and 2030 will exceed 2024 as the warmest year on record.

The WMO also identifies a tendency towards El Niño conditions, particularly in 2027 and 2028, which could increase the likelihood of 2027 becoming another record-breaking year.

The consequences will not be uniform.

Europe, where MMT is headquartered, is already the world's fastest-warming continent. Copernicus reports warming at more than twice the global average, accompanied by increasingly significant heatwaves, drought, wildfire risk and changes in water availability.

In Africa, the consequences intersect with a different challenge: food security and the resilience of agricultural systems.

Zimbabwe provides a particularly interesting example.

Agriculture is a system, not simply a field

Approximately 90% of Zimbabwe's smallholder agriculture is rainfed, making production highly exposed to climate variability (Ref). Yet recent research also warns against assuming that technology by itself will solve the problem.

A 2026 United Nations University policy brief on digital agriculture in Zimbabwe found that digital tools can strengthen climate resilience, but their impact depends on the surrounding system: connectivity, affordability, digital literacy, institutions, agricultural extension services and collaboration between government and private organisations. Isolated technology deployments can have limited impact (Ref).

This distinction matters.

A drone equipped with sophisticated sensors may produce excellent imagery.

But imagery alone does not improve a harvest.

Someone must determine:

• What needs to be measured?

• When should it be measured?

• How accurately?

• Who receives the information?

• What decision follows?

• What intervention is triggered?

• How do we know that intervention worked?

Suddenly, what appeared to be a drone problem becomes a systems engineering problem.

And that is where MMT sees an opportunity.

Model. Validate. Digitise.

MMT's Model–Validate–Digitise (MVD) approach was developed around a simple principle: before digitising a process or investing heavily in technology, understand the system that technology is supposed to improve.

Applied to precision agriculture, the starting point therefore does not have to be a drone.

It could be a mission: Detect crop stress early enough to enable an intervention that protects yield.

We can then Model the operational system: farmer, crop, weather, water, sensors, drone or satellite, communications, agronomist, logistics and decision-making processes.

We can Validate whether the proposed solution actually delivers the required capability. Simulation can test questions such as coverage, fleet size, battery requirements, operating time and bottlenecks before expensive deployment. Field trials can subsequently compare the model against real evidence.

Finally, we can Digitise the validated workflow so that observations become traceable information supporting decisions rather than simply another collection of data.

This is where several existing MMT competencies converge.

Our experience in Model-Based Systems Engineering (MBSE) can help universities and engineering teams translate agricultural challenges into operational architectures, capabilities, requirements and system designs.

Our simulation and workflow intelligence capabilities can examine whether proposed operations are viable before scaling them.

Our work in digital workflows and evidence management can connect field observations to dashboards, decisions and continuous improvement.

And our training and capacity-building programmes can help develop engineers who understand not only individual technologies, but how technologies interact with people, processes and their operating environment.

Drones are one part of a much larger toolbox

The resulting agricultural system might use drones. But it might also combine satellite imagery, weather data, soil sensors, smartphones, AI, irrigation systems and traditional field observations.

FAO is already demonstrating how data analytics, machine learning and digital tools can support climate prediction, pest surveillance, irrigation management, advisory services and food-safety systems in Zimbabwe.

The engineering challenge is therefore increasingly about integration and decision-making.

1. Can a drone detect water stress earlier than a farmer?

2. Can satellite data perform the same task more economically?

3. Could inexpensive ground sensors improve the reliability of both?

4. Can AI distinguish between water stress, disease and nutrient deficiency?

5. Can the resulting recommendation reach the farmer in time to act?

And ultimately:

Did the intervention improve yield, reduce water consumption, lower cost or increase resilience?

These are questions that can be modelled, tested and progressively validated.

Building capability, not dependence

This also changes what international technology partnerships with African universities could look like.

Rather than transferring a finished technology and training people to operate it, universities, industry and organisations such as MMT can work together around real agricultural missions.

Students and researchers can model the problem, develop architectures, evaluate alternative technologies, simulate operations, build prototypes and validate them in the field.

Zimbabwe's current policy direction makes such collaboration particularly relevant. Its Agriculture, Food Systems and Rural Transformation Strategy for 2026–2030 places emphasis on food security, competitiveness, climate resilience and institutional transformation (Ref).

A recent Harare science-policy dialogue consequently identified digital agriculture, irrigation and stronger collaboration between policymakers, academia, industry and development organisations as important areas for action.

That creates an opportunity not simply to deploy technology, but to develop a generation of engineers capable of asking:

What system should we build for our particular environment?

Engineering for a changing operating environment

For MMT, precision agriculture is also part of a broader challenge. Climate change is altering assumptions across industries.

Higher temperatures affect people, machinery and production facilities. Water scarcity affects agriculture and industrial processes (Ref). Extreme weather affects logistics and infrastructure. In aerospace, temperature changes aircraft, propulsion and thermal-management performance. In tourism, changing weather patterns can shift seasonal demand and infrastructure requirements.

Different industries. Different technologies.

But fundamentally the same systems question:

If the environment in which our system operates changes, will the system still achieve its mission?

That is the capability MMT wants to help organisations develop.

Not technology for technology's sake.

Not drones for the sake of drones.

But the ability to model changing environments, validate possible responses, digitise the resulting evidence and continuously improve the system.

Because in a hotter and increasingly uncertain world, resilience will depend not simply on having access to better technology.

It will depend on our ability to engineer better systems.