AI Shows a Modest but Real Boost to South African Food Production, Study Finds

Artificial intelligence has become one of the most talked-about forces in modern agriculture, promising everything from precision irrigation to predictive pest control. But does the hype hold up when tested against three decades of national data? A new study by economist Fiyinfoluwa Giwa of the Department of Economics at the University of South Africa (UNISA), published in the journal Discover Sustainability, set out to answer that question quantitatively. Using annual data for South Africa spanning 1995 to 2024, the research finds that artificial intelligence has a positive and statistically significant association with domestic food production, both in the short run and in the long run. Crucially, however, the study also shows that this contribution, while real, is modest in magnitude, a nuance that carries important implications for how governments should think about technology-driven agricultural policy.

The research stands out because it moves beyond case studies and anecdotal accounts of smart farming, which dominate much of the popular discussion about AI in agriculture. Instead, it treats artificial intelligence as a measurable economic input, proxied by AI patent activity, and asks whether innovation in this domain is statistically linked to the output of the food production sector over time. By framing the question this way, the study connects two literatures that rarely speak to each other: the economics of technological innovation and the empirical analysis of food systems. The result is one of the first time-series investigations to quantify the AI-food production relationship for an African economy using a nationally representative, multi-decade dataset.

Methodologically, the study employs the autoregressive distributed lag bounds-testing approach, a widely used econometric technique for detecting long-run relationships between variables that may behave quite differently in the short term. The bounds test confirmed that a long-run equilibrium relationship exists among food production, AI patents, inflation, population growth, and household income. This is a critical first step in such analyses: without evidence of a stable long-run relationship, any estimated coefficients could be statistical artifacts rather than meaningful economic signals. The confirmation of cointegration among these variables means that, despite year-to-year fluctuations driven by droughts, policy shifts, and global market shocks, food production in South Africa moves together with these determinants over the long haul.

Once the long-run relationship was established, the study estimated how each factor contributes to food production. The headline finding is that artificial intelligence carries a positive and statistically significant coefficient in both the short-run and long-run models. In practical terms, periods of heightened AI innovation in the dataset are associated with subsequent increases in food production, and this association persists when the analysis is extended over longer horizons. Yet the author is careful to note that the magnitude of the effect is modest. AI is not a substitute for the fundamental drivers of agricultural output; rather, it appears to act as a supporting factor whose benefits depend on the surrounding economic and institutional environment.

The inclusion of control variables sharpens the picture. Inflation and population growth, both of which shape the demand and cost conditions facing the agricultural sector, were incorporated into the models alongside household income, which captures the purchasing power of consumers. Their presence in the confirmed long-run relationship underscores that food production is embedded in a broader macroeconomic system. Rising incomes can shift demand toward more resource-intensive foods, population growth expands the consumer base, and inflation affects input costs such as fertilizer, fuel, and machinery. Against this backdrop, the persistent positive AI signal suggests that technological innovation exerts an influence that is distinguishable from these conventional economic forces.

One of the most timely aspects of the study is its treatment of the COVID-19 pandemic. The author re-estimated the models with a dummy variable capturing the pandemic period, a standard robustness technique for checking whether an extraordinary shock is distorting the results. The findings held: the AI coefficient remained positive even after accounting for the pandemic, while the dummy itself revealed that COVID-19 had a negative effect on food production. This dual result is reassuring for the credibility of the AI-food production link and simultaneously documents the disruptive footprint of the pandemic on South Africa’s food system, likely reflecting lockdown-related labor disruptions, supply chain bottlenecks, and reduced economic activity.

The modest size of the AI effect is arguably the study’s most policy-relevant insight. The author interprets it as evidence that AI’s contribution to food production is conditional: it depends on complementary infrastructure and adoption capacity. In other words, patents and algorithms do not raise yields by themselves. Their value is realized only when farms have access to reliable digital connectivity and electricity, when agricultural data systems exist to feed models with usable information, and when farmers possess the technical skills to deploy AI-enabled tools effectively. Where these complements are missing, as is often the case for smaller producers, the gap between innovation on paper and impact in the field can be wide.

This conditionality points directly to a policy agenda. The study recommends that policymakers prioritize reliable digital and energy infrastructure, the development of agricultural data systems, investment in technical skills, and affordable access to AI-enabled technologies, with particular attention to smaller producers who are least likely to capture new technologies on their own. South Africa’s agricultural sector is characterized by a dual structure in which large commercial operations coexist with smallholder farmers, and the benefits of any general-purpose technology tend to accrue first to those already well resourced. Deliberate policies to lower the cost and skill barriers facing smaller producers could therefore determine whether AI narrows or widens existing productivity gaps in the food system.

The research also carries implications for how innovation is measured and encouraged. By using AI patents as the indicator of artificial intelligence activity, the study links food production outcomes to formal inventive output, suggesting that intellectual property generation in AI is a meaningful signal of technological capacity relevant to agriculture. For a middle-income economy such as South Africa, this raises questions about how to stimulate domestic AI innovation and ensure it is directed toward sectoral priorities like food security. It also highlights the value of long-run empirical evidence in an area where enthusiasm often outpaces rigorous measurement; the study’s finding that AI matters, but modestly, offers a calibrated baseline against which future adoption trends can be judged.

As climate variability, population pressure, and economic shocks continue to test food systems across the African continent, evidence of this kind is likely to become increasingly valuable. The study’s central message is one of grounded optimism: artificial intelligence is not a silver bullet for food production challenges, but it is a genuine and statistically detectable contributor that can support domestic output when the enabling conditions are in place. For South Africa, the task ahead is less about proving that AI can help agriculture and more about building the infrastructure, data ecosystems, and human capital that allow a modest statistical association to grow into a transformative one. The research, published open access in Discover Sustainability, was conducted without external financial support, and the author declares no competing interests.

Subject of Research: The econometric relationship between artificial intelligence and food production in South Africa

Article Title: The relationship between artificial intelligence and food production in South Africa

Article References: Giwa, F. (2026). The relationship between artificial intelligence and food production in South Africa. Discover Sustainability. https://doi.org/10.1007/s43621-026-04990-0

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04990-0

Keywords: artificial intelligence, food production, South Africa, agriculture, food security, autoregressive distributed lag, AI patents, agricultural economics, Discover Sustainability, COVID-19, digital infrastructure, smallholder farmers