Potential and Challenges of Smart Agriculture: A Critical Narrative Review of Evidence, Uneven Outcomes and Structural Constraints
A. Ayandiji *
Department of Agricultural Economics and Extension, Faculty of Agriculture, Federal University, Lokoja, Kogi State, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Smart agriculture, understood as the integration of sensing, connectivity, data analytics and automated actuation into agricultural decision-making, has been promoted as a route to simultaneous gains in productivity, resource-use efficiency, environmental performance and resilience. The scale of investment and policy attention now attached to the field has outpaced the maturity of the evidence base that supports it. This critical narrative review examines what the accessible peer-reviewed literature actually establishes about the potential of smart agriculture, where claims remain conditional, and which constraints are technical rather than structural. Literature was identified through open scholarly databases and indexes, supplemented by citation searching and authoritative institutional sources, with a final search date of 14 June 2026. Evidence was appraised for design adequacy, measurement validity, external validity, geographical representation and the correspondence between the claim made and the data supporting it.
Four findings emerge. Capability at the sensing and analytics layers has advanced considerably faster than demonstrated capability at the decision and outcome layers, so that reported technical performance frequently fails to translate into measurable agronomic or economic gain. Input savings are the most consistently reported benefit, whereas yield effects are small and inconsistent, and profitability evidence remains weak, largely modelled and concentrated in mechanised, high-income systems. Adoption is highly technology-specific rather than uniform, and the technologies with the strongest evidence of uptake are not those with the strongest evidence of environmental benefit. Distributional outcomes are shaped less by technical performance than by connectivity, farm scale, data governance and market structure, which together determine who can use these systems at all.
The synthesis indicates that the principal constraints on smart agriculture are no longer predominantly sensor or algorithm limitations but the absence of independent outcome evaluation, the weakness of interoperability and data-governance arrangements, and the structural exclusion of small-scale producers. Research priorities are identified accordingly, with emphasis on multi-season field evaluation against realistic counterfactuals, transparent reporting of model transferability, and the design of governance instruments that distribute value rather than merely enable data flow.
Keywords: Smart agriculture, precision agriculture, digital agriculture, technology adoption, agricultural data governance, agricultural robotics, evidence appraisal