What Synthetic Agricultural Data (SAGDA) Means for Africa’s Agricultural Policymakers

12

Data has been a major bottleneck in Africa’s agriculture, and policymakers have had to make big decisions with limited, outdated, or inconsistent numbers. And in a continent where food systems are complex and rapidly changing, poor data can mislead policies and delay progress.

Synthetic agricultural data (SAGDA) has emerged and could be a massive game-changer that fills the void where surveys can’t reach, where records don’t exist, and where cost or logistics make traditional data collection nearly impossible.

SAGDA is an open-source Python library and initiative aimed at tackling data scarcity in African agriculture. It enables the generation, augmentation, validation, and visualisation of synthetic datasets for key agricultural variables, such as climate records, soil nutrient profiles, crop yield series, and fertiliser usage.

In simpler terms, synthetic data allows us to simulate reality. Like being able to test how a fertiliser subsidy would affect smallholder maize yields in Kano or how climate shocks might shift rice production zones in Sierra Leone.

This idea gained structure in mid-2025, and it gives Africa’s agricultural ecosystem a way to simulate realistic, region-specific data — the kind needed to train AI models for precision farming or to forecast food production patterns — without being constrained by the limitations of real-world data collection.

The initiative was publicly released on June 16, 2025, via an arXiv preprint and a GitHub repository, marking a pivotal step in open-source synthetic data tools for African contexts. Although synthetic data generation in agriculture had been explored globally since the early 2020s, SAGDA was the first tailored, comprehensive framework built with African conditions in mind.

It’s not the usual conversation we hear in African agriculture. But it’s one we can’t afford to ignore if we want to get our policies right. 

Why Policymakers Should Care

Every few years in Africa, we launch agricultural plans like food security roadmaps, mechanisation blueprints, or subsidy reforms. Yet, when it comes to evaluating results or predicting outcomes, many of these plans falter at the same place: data.

Most countries still depend on manual surveys and fragmented administrative data. Some rely heavily on field enumerators using paper-based or semi-digital methods in a process that’s time-consuming and vulnerable to errors. While in other regions, agricultural censuses are conducted only once every decade, if at all.

This means that by the time a new policy is launched, the data guiding it is already stale. And when new issues arise, the reaction is blind and uninformed.

Synthetic data offers several advantages here:

• Scenario Testing: One of synthetic data’s most compelling strengths lies in its ability to simulate plausible futures. Policymakers can model droughts, pest outbreaks, or price shocks without waiting for these crises to unfold in real life. By stress-testing agricultural systems in a virtual environment, governments and research institutions can anticipate vulnerabilities in input supply chains or irrigation planning.

• Bridging Data Gaps: Africa’s agricultural data landscape is uneven. Large-scale commercial farms are often well-documented, while smallholder systems (where most food is produced) remain statistically invisible. Synthetic data can bridge this gap by generating realistic, representative datasets that fill in where surveys and censuses stop.

So when we train generative models on limited real-world samples, we can expand datasets that better reflect diverse farming systems to improve the accuracy of policy models and support local innovation. That means agritech startups, financial institutions, and cooperatives can build better without waiting for a national data overhaul. In effect, synthetic data turns data scarcity into an innovation advantage.

• Cost Efficiency: Since agricultural data collection, cleaning, and maintenance are expensive, synthetic data offers an alternative that’s cost-efficient and democratises access at the same time. Once a base model is trained, generating new data for research, training, or product development costs a fraction of what traditional fieldwork requires. This efficiency can unlock new value chains because universities can expand research without high budgets; startups can train AI models without multimillion-dollar data licenses; and government agencies can conduct rapid assessments without waiting for annual surveys.
   
• Privacy Protection: Synthetic data introduces a way to share insights without exposing individuals. Because it mimics patterns rather than reproduces identities, it safeguards the privacy of farmers while still allowing for collaborative analysis. For us in Africa, this also ties into data sovereignty and addresses the idea of neocolonialism in African agriculture because synthetic datasets can reduce dependence on foreign cloud providers or third-party platforms holding sensitive agricultural data. This empowers governments to maintain control over local data ecosystems while promoting open research and cross-border collaboration.

Say, for example, a ministry wants to predict the impact of fertiliser distribution in northern Nigeria. Real data on soil fertility, rainfall, and adoption rates might be patchy. But synthetic models can simulate thousands of virtual farms based on available samples, giving planners a sandbox to test different policy mixes before rollout.

What Do We Need?

To make synthetic data a credible policy tool, Africa needs three things:

1. Our institutions must be ready.

National bureaus of statistics, research institutes, and ministries of agriculture need teams trained in AI-driven modelling and simulation. Partnerships with universities and private firms can help incubate these skills.

2. Public and private sectors must collaborate

Synthetic data sits at the intersection of technology and governance. Startups, agritech firms, and AI labs already working on data generation (like Kenya’s iShamba or Nigeria’s Crop2Cash) could collaborate with public agencies to develop local models tailored to African contexts.

3. Regulatory frameworks must be put in place.

Bodies like the African Union or AUDA-NEPAD could play a key role in developing ethical and technical standards to define how synthetic agricultural data should be validated, shared, and governed.

The long-term goal should be a continental approach where synthetic data augments traditional statistics to support the African Continental Free Trade Area (AfCFTA), climate resilience planning, and national food strategies.

Source

Risks and Ethical Considerations

Of course, synthetic data isn’t magic. It depends entirely on the quality of the original data and the integrity of the modelling process.

If the base data is biased, for instance, over-representing large farms or missing women farmers, the synthetic data will reproduce those biases. And because it looks real, policymakers might be tempted to trust it too much.

There’s also the issue of data governance and sovereignty. Who owns synthetic data generated from African datasets? If private AI firms create them, do governments retain the right to use or modify them? These are uncharted questions, and without clear governance, synthetic data could create new dependencies instead of solving old ones.

That’s why building local capacity is crucial. African institutions must be able to use synthetic data as well as generate and validate it. Otherwise, the continent risks importing both technology and bias.

In Conclusion

Synthetic data won’t fix every agricultural data problem. But it offers something African policymakers have long lacked.

Instead of waiting years to understand whether a subsidy worked or a crop insurance scheme failed, they can now simulate, test, and refine interventions before committing billions in real-world costs.

This is a massive shift, and the countries that embrace it early will be the ones shaping the future of agricultural policy.