By
Chinedum Nwajiuba
Department of Agribusiness Finance and
Management, Michael Okpara University of
Agriculture Umudike, Nigeria.
Lead paper presented at the 2nd International Conference of the College of Agricultural Economics, Extension and Rural Sociology (CAERSE), Michael Okpara University of Agriculture, Umudike (MOUAU), Centre for Entrepreneurship Development, 14-15 July 2026
Conference theme: Smart Agricultural Production, Green Growth and Sustainable Development in Nigeria.
Abstract
Climate-smart agriculture (CSA), digital agriculture, green growth and sustainable development now occupy a prominent place in Nigeria’s agrarian-development discourse. Yet these concepts are frequently collapsed into a technology-centred narrative in which sensors, drones, artificial intelligence and precision equipment are treated as self-evidently ‘smart’. This lead paper separates CSA from digital and precision agriculture and asks a more consequential policy question: smart for whom, for which farming system, at what cost and under what institutional conditions? Drawing on Nigerian policy documents, empirical adoption studies and an indicative reading of agricultural research-grant titles, the paper argues that technological sophistication is not equivalent to agricultural intelligence. In Nigeria, smartness must be judged by locally demonstrated gains in productivity and income, climate resilience, resource efficiency, inclusion, affordability and capacity for sustained use. The paper proposes a framework of contextual smartness built on farmer co-design, strong extension and climate services, frugal and interoperable technologies, rural infrastructure, responsible data governance and outcome-based public research funding. The central policy message is that Nigeria should not reject advanced technology; it should sequence and govern it so that innovation solves binding farm and value-chain constraints rather than merely displaying technical novelty.
Keywords: climate-smart agriculture; digital agriculture; agricultural innovation systems; smallholder farmers; extension; Nigeria.
Appreciation
I begin by thanking my colleagues in the College of Agricultural Economics, Extension and Rural Sociology, Michael Okpara University of Agriculture, Umudike, for the opportunity to present this lead paper. I am particularly grateful to the Dean, the heads and staff of the College’s departments, and the Local Organising Committee.
I also acknowledge the Vice-Chancellor and University Management for sustaining this annual conference as a forum for scholarly exchange and policy engagement.
The College’s ability to convene a second conference is commendable. Conferences advance the university’s core responsibilities by enabling peer learning, improving teaching and research, and connecting scholarship to community service and public policy.
I hope this paper stimulates a deeper examination of the roles and constraints of Nigeria’s agricultural sector and, especially, of what climate-smart and digitally enabled agriculture should mean in Nigerian contexts.
Purpose and scope of the lead paper
A lead paper should do more than report a narrow experiment. It should clarify concepts, synthesise evidence, identify policy tensions and frame questions for the conference. That responsibility is particularly important where familiar terms acquire the status of slogans. Academic confidence must therefore be accompanied by conceptual humility: repeated use does not guarantee shared understanding.
Accordingly, this paper treats the conference theme as an invitation to interrogate assumptions and establish an agenda for research, policy and practice.
Three features guide the discussion:
-It provides a broad conceptual framework and reviews the state of knowledge.
-It synthesises evidence and evaluates policy directions rather than presenting a single experiment.
-It sets an agenda for discussion, research and action within the conference theme.
These functions informed the title and the paper’s critical engagement with the conference theme, ‘Smart Agricultural Production, Green Growth and Sustainable Development in Nigeria’.
The phrase ‘smart this and smart that’ is deliberately provocative. It asks whether smartness is being defined by the sophistication of a device or by the quality, distribution and durability of agricultural outcomes.
Why interrogate ‘smart’?
The conference theme brings together three widely used but distinct concepts:
-smart agricultural production, which may include but is not synonymous with climate-smart agriculture (CSA);
-green growth;
-sustainable development.
The adjective ‘smart’ now appears across sectors – in education, energy, transport, health and agriculture. Its value depends on whether it adds analytical precision or merely confers an aura of modernity.
-smart schools;
-smart energy;
-smart transport;
-smart health services; and, in agriculture,
-smart agriculture and climate-smart agriculture.
The core dilemma: where is the ‘smart’ in practice?
Nigeria expects agriculture to supply food, generate livelihoods and employment, provide industrial raw materials, earn foreign exchange and sustain rural economies. These functions are constrained by low and variable productivity, post-harvest losses, weak logistics and market coordination, limited irrigation and extension coverage, insecurity, land-tenure constraints and climate shocks. CSA is therefore relevant, but it cannot be reduced to a catalogue of technologies. The policy question is whether an intervention addresses a binding constraint in a
specific agro-ecology and value chain, and whether farmers can adopt, maintain and benefit from it over time.
Conceptual clarity: CSA is not a synonym for digital agriculture
For analytical clarity, three overlapping but non-identical domains should be distinguished:
Climate-smart agriculture (CSA). CSA is an approach for pursuing three objectives: sustainably increasing productivity and incomes; strengthening adaptation and resilience; and, where feasible, reducing or removing greenhouse-gas emissions. FAO stresses that CSA is context-specific and does not require every practice to deliver an equal ‘triple win’ in every location (FAO, 2013).
Digital agriculture. Digital agriculture uses digital data, connectivity and software to support decisions, transactions and service delivery. Examples include weather and market information, digital farmer registries, mobile payments, remote sensing and digitally enabled extension. A digital service may support CSA, but it is not climate-smart merely because it is digital.
Precision and controlled-environment agriculture. These approaches use measurement and control to apply inputs more accurately or modify production conditions. Sensors, variable-rate application, drones, hydroponics and automated greenhouses fall here. Their value is enterprise- and scale-specific; they require reliable energy, skills, maintenance, finance and markets.
The appropriate questions are therefore not whether a practice is old or new, but:
-What problem does the intervention solve, and for which farmers or value-chain actors?
-What evidence shows gains in productivity, income, resilience or resource efficiency relative to available alternatives?
-What complementary infrastructure, finance, skills, extension and institutions are required?
-Who bears the cost and risk, who controls the data, and who captures the benefits?
Many familiar practices – adjusted planting dates, mixed cropping, improved and stress-tolerant varieties, soil and water conservation, agroforestry, composting, integrated pest management and water harvesting – can be climate-smart when they are selected for a defined risk and evaluated against the CSA objectives. Their historical familiarity does not make them obsolete; equally, relabelling them does not remove the need for evidence.
Advanced digital and engineering applications can also be valuable. Automated machinery, satellite imagery, algorithmic diagnostics and networked sensors can improve timeliness and precision where their total cost, reliability and institutional fit are favourable. Illustrative applications include:
– controlled-environment and soilless systems for high-value production under carefully managed water, nutrient and energy regimes;
– precision agriculture, including soil-moisture sensing, decision-support
software, geospatial scouting and targeted input application; and
– digital advisory, market, finance and traceability services delivered through channels ranging from radio, voice and USSD (Unstructured Supplementary Service Data) to smartphones and web platforms.
The language of high technology is increasingly visible in Nigeria’s research portfolio. An indicative examination of project titles associated with the Tertiary Education Tetfund (TETFund), National Research Fund (NRF), 2025, provides a useful, although limited, illustration.
Nigeria’s research portfolio: an indicative TETFund case (2025 NRF Agriculture category success cases)
The following titles, reproduced as portfolio evidence rather than evaluated research results, illustrate the prominence of AI, IoT, digital platforms and climate-resilience language:
Development of a cocoa quality-assurance and trading system using multimodal artificial-intelligence techniques for sustainable cocoa trading
Design, development and IoT-enabled assessment of solar-powered forced-ventilation mesh storage systems for selected onion and garlic varieties in semi-arid Northern Nigeria
Enhancing food security and wealth creation through intelligent irrigation systems and a digital agro-hub in Niger State, Nigeria
AI-enhanced precision agriculture through an IoT- and blockchain-integrated smart mixed-cropping system for optimised soil health and sustainable agricultural productivity
Development of a Long-Range Wide Area Network (LoRaWAN)-based Internet of Things system for post-harvest management of yam
Leveraging big-data analytics and artificial intelligence for climate-smart tuber-crop disease management and post-harvest loss reduction in Nigeria
Developing a climate-resilient integrated agriculture-aquaculture farming model for increased food production and livelihoods among smallholder farmers in North-East Nigeria
Design and development of a mobile-based, optimised soil-fertility testing system with AI-driven analysis to enhance agricultural productivity in Northern Nigeria
Design and implementation of an Internet of Things-based greenhouse for smart-irrigation monitoring and control
Development of a smart AI surveillance system for monitoring artificial-ripening agents and preservatives in fruits and vegetables
AI-driven climate-smart agriculture for smallholder farmers in Nigeria: harnessing precision tools for sustainable food security and community resilience
Developing a resource-efficient, on-device mobile-vision platform for automated catfish-fingerling counting in Nigerian aquaculture facilities
Indicative portfolio observations
The listed agriculture-related projects span approximately 11 universities and polytechnics, suggesting geographically dispersed institutional interest.
Using the paper’s title-based coding, 12 of 26 agriculture projects (46.2%) explicitly invoke climate-smart, digital, AI, IoT or closely related approaches.
Agriculture accounts for 26 of 109 projects in the relevant science-and-technology grouping (23.9%). These figures describe titles, not adoption, impact or value for money; they should therefore motivate portfolio evaluation rather than conclusions about research quality.
The Nigerian adoption and scaling challenge
The central issue is not whether Nigerian researchers should pursue frontier technologies, but how research ambition connects to the structure and needs of the agricultural sector. Six constraints deserve attention:
Infrastructure and service reliability. Rural connectivity, electricity, roads, irrigation, repair services and last-mile logistics shape the feasible technology frontier. Digital tools cannot compensate for every physical bottleneck; in some settings, investment in feeder roads, storage or water control will produce a larger and more inclusive return.
Affordability and business-model fit. Adoption depends on total cost of ownership, expected returns, cash-flow timing and risk. Subscription fees, device replacement, data charges, calibration and downtime must be counted alongside the purchase price. Shared-service, pay-per-use and cooperative models may be more viable than individual ownership.
Human capability and intermediation. Extension agents, producer organisations, agro-dealers and local service providers translate information into action. Evidence from Southeast Nigeria identifies extension exposure, credit, education and cooperative membership among important correlates of CSA adoption (Mbanasor et al., 2024). Digital extension should strengthen, not displace, trusted human support.
Agro-ecological and enterprise specificity. Nigeria’s production systems differ across the humid forest, derived savannah, Guinea and Sudan savannah, Sahel, floodplains and coastal zones. A practice that is smart for irrigated rice, peri-urban horticulture or commercial poultry may be unsuitable for rainfed cassava, pastoral systems or artisanal fisheries. Recommendations must be location-, commodity- and risk-specific.
Inclusion and distribution. Women, young people, older farmers, tenants, pastoralists, persons with limited literacy and remote communities face different constraints. Smartphone-only delivery, English-only interfaces and collateral-based finance can widen existing inequalities. Voice, radio, USSD, local languages, assisted access and gender-responsive design should be treated as core system requirements.
Data governance, interoperability and accountability. Digital agriculture raises questions of consent, privacy, data ownership, cybersecurity, vendor lock-in and algorithmic bias. Publicly funded platforms should adopt proportionate data-protection safeguards, interoperable standards and transparent rules for access and reuse. Farmers should know what data are collected, for what purpose and with what benefit.
A practical contextual-smartness test should therefore ask eight questions before public support is committed: Is the intervention relevant to a diagnosed constraint? Does it generate measurable net benefits? Is it affordable across the intended adoption horizon? Is it inclusive by design? Is it robust to local infrastructure and climate conditions? Can it be operated, repaired and governed locally? Does it protect natural resources and farmer data? Can it scale without permanent subsidy or loss of service quality?
From buzzword to contextual smartness: a policy agenda
At a time of acute food-system pressure, smart agriculture must be assessed by the public value it creates. Nigeria needs both frontier research and widespread, low-cost improvements; the policy task is to sequence them and connect them through institutions capable of learning and scaling.
The National Agricultural Technology and Innovation Policy (2022-2027), Nigeria’s NDC 3.0 and emerging agrifood investments already recognise digital and climate-smart agriculture. Implementation should now be disciplined by contextual smartness: relevance, demonstrated benefit, affordability, inclusion, resilience, environmental integrity, maintainability and scalability.
Eight priorities follow:
Fund binding constraints and complete systems. Combine innovation grants with the complementary public goods required for impact – rural roads, reliable energy, water management, connectivity, storage, extension and repair ecosystems. Funding appraisals should state the constraint addressed and the full delivery chain.
Institutionalise farmer co-design. Require problem diagnosis and iterative testing with diverse producer groups, extension personnel and value-chain actors before large-scale procurement or rollout. Participation should influence design choices, not merely validate a finished prototype.
Build hybrid extension and climate services. Equip and retrain extension agents to combine field knowledge with geospatial, weather and market information. Deliver actionable advisories through multiple channels and local languages, with feedback and escalation mechanisms.
Adopt an evidence-to-scale pathway. Move innovations through problem validation, controlled testing, independent outcome evaluation, adaptation and phased scaling. Report productivity, income, resilience, labour, gender, environmental and distributional outcomes – not only devices deployed or users registered.
Differentiate by agro-ecology and enterprise. States and research institutions should develop location-specific CSA menus and
investment plans based on climate risk, soils, water, market opportunity and farmers’ resources, with clear treatment of trade-offs.
Promote frugal and inclusive digital design. Prioritise voice, USSD, SMS, radio and assisted-service options where smartphones and data are limiting. Apply open standards, modular architecture and ofline functionality to reduce exclusion and vendor dependence.
Use finance and risk instruments strategically. Align credit, insurance, guarantees and results-based support with proven practices and service models. Protect farmers from being made residual bearers of technology and climate risk.
Strengthen governance and learning. Establish safeguards for agricultural data, transparent procurement, interoperability and independent evaluation. Track cost per sustained adopter and cost per unit of outcome, publish failure as well as success, and use evidence to discontinue ineffective pilots.
These priorities do not oppose technology. They place technology within an agricultural innovation system in which infrastructure, institutions, ecological knowledge, markets and human capability jointly determine performance.
Frugal digital innovation remains especially important: low-cost tools should work on available devices, minimise data and energy demands, and connect farmers to credible advice, services and markets.
Voice, USSD, SMS and radio can deliver weather, market and agronomic information in accessible formats and languages.
Pay-per-use mechanisation and irrigation services can spread fixed costs where scheduling, maintenance and operator quality are effectively managed.
Solar-powered micro-irrigation can reduce dependence on the grid, but must be paired with water-resource assessment, efficient application and maintenance arrangements.
Indigenous and experiential knowledge should be documented and tested alongside scientific evidence. Its value lies neither in romanticising tradition nor in displacing science, but in improving relevance, trust and local adaptation.
Hybrid climate services can combine meteorological forecasts with local observation and trusted intermediaries while clearly communicating uncertainty.
Agroforestry, mixed cropping, soil-cover management and integrated pest management should be evaluated and optimised for productivity, labour, resilience and environmental outcomes in specific systems.
Nigeria will locate the genuine ‘smart’ in agriculture when public policy and research reward problem-solving, learning and sustained farmer benefit rather than technological novelty alone. The measure of success is not how advanced an intervention appears, but whether it enables diverse producers and value-chain actors to achieve better, more resilient and environmentally responsible outcomes at a cost they and the country can sustain.
References
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