AGRICULTURAL FINANCING AND ECONOMIC GROWTH IN NIGERIA

CHAPTER ONE: INTRODUCTION

1.1 Background of Study

Agricultural financing refers to the provision of financial resources (credit, loans, grants, subsidies, investments) to farmers, agribusinesses, and agricultural value chain actors to support agricultural production, processing, marketing, and infrastructure development (World Bank, 2021). Agricultural finance is a critical determinant of agricultural productivity, as it enables farmers to purchase inputs (seeds, fertilizers, pesticides), acquire equipment (pumps, tractors, planters, sprayers), hire labour, invest in land improvement (irrigation, drainage), adopt improved technologies, and manage cash flow (seasonal gaps between planting and harvest) (FAO, 2020). Without adequate financing, farmers remain trapped in low-productivity subsistence agriculture, unable to invest in productivity-enhancing technologies (FMARD, 2021).

Economic growth is the sustained increase in the real gross domestic product (GDP) of a country over time, reflecting the expansion of the economy’s productive capacity (Mankiw, 2020). In Nigeria, economic growth has been volatile, driven primarily by the oil sector, while the agricultural sector has been underperforming relative to its potential (CBN, 2022). Agriculture remains a critical sector for Nigeria’s economy, contributing approximately 25% to GDP and employing about 35% of the labour force (NBS, 2022). However, the sector’s contribution to economic growth has been constrained by low productivity, limited access to credit, poor infrastructure, weak extension services, and post-harvest losses (World Bank, 2021). Addressing these constraints requires significant investment, which in turn requires agricultural financing (Okonkwo, 2020).

The relationship between agricultural financing and economic growth is well-established in development economics literature (Schultz, 1964; Timmer, 2019). Agricultural growth contributes to overall economic growth through several channels: direct contribution (agricultural output adds to GDP); employment channel (agriculture employs a large share of the labour force); income channel (increased farm incomes stimulate demand for goods and services, creating multiplier effects); foreign exchange channel (agricultural exports earn foreign currency); raw material channel (agriculture supplies raw materials to agro-industries); and poverty reduction channel (agricultural growth reduces rural poverty) (Ofori & Stern, 2020).

The sources of agricultural financing in Nigeria include formal, semi-formal, and informal sources (CBN, 2022). Formal sources include commercial banks, development banks (Bank of Agriculture, Nigerian Agricultural Insurance Corporation, NIRSAL), microfinance banks, and government programmes (Agricultural Credit Guarantee Scheme, Anchor Borrowers’ Programme, Commercial Agriculture Credit Scheme). Semi-formal sources include cooperative societies, savings groups, and farmer associations. Informal sources include money lenders, traders, family, and friends. However, formal agricultural credit reaches only a small fraction of farmers (estimated less than 20% of smallholders), with the majority relying on informal sources that charge exorbitant interest rates (Adebayo & Ogunyemi, 2020).

Government agricultural credit programmes in Nigeria:

Programme	Year Established	Purpose	Mechanism	Target Beneficiaries
Agricultural Credit Guarantee Scheme (ACGS)	1977	Guarantee bank loans to smallholders	Government guarantees 75% of loan	Smallholder farmers
Agricultural Credit Support Scheme (ACSS)	2009	Input credit	Loans for seed, fertilizer, pesticides	Smallholder farmers
Commercial Agriculture Credit Scheme (CACS)	2009	Commercial agriculture loans	Low-interest loans (5-9%) via bonds	All agricultural enterprises
Anchor Borrowers’ Programme (ABP)	2015	Value chain credit	Loans (cash + inputs) linked to processors	Smallholder farmers in value chains
Nigeria Agricultural Insurance Corporation (NAIC)	1987	Agricultural insurance	Crop, livestock, aquaculture insurance	All farmers
Bank of Agriculture (BOA)	1972 (as NBCB)	Agricultural development bank	Loans, savings, micro-credit	Smallholder and commercial farmers

(Source: CBN, 2022; FMARD, 2021)

Despite these programmes, agricultural financing in Nigeria faces numerous challenges (Okafor & Nwosu, 2020). Low access to formal credit: Less than 20% of smallholder farmers have access to formal bank credit, due to lack of collateral (land titles), high interest rates (20-35%), complex application procedures, lack of credit history, and small loan sizes (banks prefer large loans) (Eze & Nweze, 2019). High interest rates: Commercial bank lending rates to agriculture range from 20-35%, which is too high for smallholder agriculture (profit margins often 10-20%). High rates make borrowing unprofitable and increase default risk (Okafor & Ugwu, 2021). Lack of collateral: Most smallholders operate on customary land without formal titles, cannot provide the land titles banks require as collateral. Lack of collateral excludes smallholders from formal credit (Nwosu & Okafor, 2021).

Limited outreach of microfinance banks (MFBs): MFBs were established to serve micro-credit needs, but their outreach to rural agricultural households is limited; most MFBs are urban-based, have small loan sizes, and charge high interest rates (30-40%) (Okonkwo, 2020). Poor loan recovery: Agricultural lending is perceived as high risk by banks due to climate risk (drought, flood), price volatility, pest and disease outbreaks, and borrower default. Poor loan recovery reduces banks’ willingness to lend to agriculture (Adebayo & Ogunyemi, 2020). Bureaucratic bottlenecks: Government credit programmes (e.g., ACGS, ABP) are often delayed by bureaucracy; funds may be disbursed after planting season, reducing their effectiveness. Corruption and elite capture divert funds from target beneficiaries (Eze & Nweze, 2019).

Low utilization of agricultural insurance: The Nigeria Agricultural Insurance Corporation (NAIC) provides crop and livestock insurance, but uptake is very low (<5% of farmers) due to lack of awareness, complex claims processes, and low trust (Okafor & Ugwu, 2021). Inadequate funding for agricultural development banks: The Bank of Agriculture (BOA) is undercapitalized, limiting its lending capacity. State governments have also underfunded agricultural development programmes (Okonkwo, 2020).

The theoretical relationship between agricultural financing and economic growth is supported by several theories (Schultz, 1964; Lewis, 1954; Timmer, 2019). Agricultural Development Theory (Schultz, 1964) argues that investment in agriculture (including credit for inputs and technology) is essential for transforming traditional agriculture into a productive, modern sector. Lewis Dual Sector Model (Lewis, 1954) explains that agricultural surplus (output above subsistence) provides the resources (food, labour, capital) for industrial development. Agricultural financing increases surplus by raising productivity. Agricultural Transformation Theory (Timmer, 2019) describes stages of agricultural development (subsistence → mixed → commercial → industrial), with credit playing a critical role in each transition.

Empirical studies on the relationship between agricultural financing and economic growth in Nigeria have produced mixed findings (Adebayo & Ogunyemi, 2020; Eze & Nweze, 2019; Okafor & Nwosu, 2020). Some studies find a positive, significant relationship between agricultural credit and agricultural output/GDP; others find weak or insignificant effects, due to measurement issues (different credit measures), omitted variable bias, or short time periods. Few studies use rigorous time-series methods (cointegration, error correction, Granger causality) to test long-run relationships and causality direction. Most studies focus on agricultural credit to the exclusion of other forms of agricultural financing (insurance, investment, subsidies). The period 1981-2020 (or a shorter sub-period) has been studied, but there is no consensus on the magnitude of the effect.

The period 2000-2020 is particularly relevant for studying agricultural financing and economic growth in Nigeria (NBS, 2016). This period includes: the return to democracy and post-stabilization (2000-2005); the debt relief and banking consolidation (2005-2007); the global financial crisis (2008-2009); the oil price boom and bust (2010-2016); the recession (2016) and recovery (2017-2020); and major agricultural programmes (ACGS, CACS, ABP). During this period, agricultural credit from commercial banks increased (from billions to trillions of naira), but the impact on agricultural productivity and economic growth is debated.

From a theoretical perspective, this study is supported by three theories: Agricultural Development Theory (Schultz, 1964), which posits that investment in agriculture (credit, inputs, technology, extension) transforms traditional agriculture into a productive sector; Lewis Dual Sector Model (Lewis, 1954), which explains how agricultural surplus (increased by financing) provides resources for industrial development; and Financial Intermediation Theory (Diamond, 1984), which explains the role of banks and financial institutions in channeling savings to productive investments (including agriculture), reducing information asymmetry and transaction costs.

In summary, agricultural financing is a critical determinant of agricultural productivity and economic growth in Nigeria, but smallholder farmers face severe constraints in accessing formal credit. Government programmes (ACGS, CACS, ABP, MFBs, BOA) have had limited reach due to challenges of collateral, interest rates, bureaucracy, and risk. Empirical evidence on the relationship between agricultural financing and economic growth is mixed and limited. This study aims to examine the relationship between agricultural financing and economic growth in Nigeria, using time-series econometric methods to determine the long-run relationship, short-run dynamics, and causality direction between agricultural credit and GDP.

1.2 Statement of Problems

Despite the recognized importance of agriculture for food security, employment, and economic growth in Nigeria, and despite government policies and programmes (ACGS, CACS, ABP, MFBs, BOA) designed to increase agricultural financing, the agricultural sector remains undercapitalized. Less than 20% of smallholder farmers have access to formal credit, and the agricultural credit gap is estimated at over ₦1 trillion annually. Agricultural productivity remains low (yields 30-60% below potential), agricultural GDP growth has been modest (3-5% annually, below population growth), and the sector’s contribution to overall GDP has stagnated (around 25%). The relationship between agricultural financing and economic growth has not been adequately quantified. It is unclear whether agricultural credit Granger-causes economic growth, or economic growth Granger-causes agricultural credit (reverse causality). The long-run relationship (cointegration) between agricultural financing and economic growth has not been firmly established. The problem this study addresses is the need to empirically examine the relationship between agricultural financing and economic growth in Nigeria, using time-series econometric methods (unit root tests, cointegration, error correction, Granger causality) to determine the long-run relationship, short-run dynamics, and direction of causality.

1.3 Aim of the Study

The specific aim of this research work is to examine the relationship between agricultural financing and economic growth in Nigeria, using time-series econometric methods (stationarity tests, cointegration analysis, error correction modelling, Granger causality tests) to determine the long-run equilibrium relationship, short-run dynamics, and direction of causality between agricultural credit and economic growth.

1.4 Objectives of the Study

1. To determine the time-series properties (stationarity) of agricultural credit and economic growth variables in Nigeria.
2. To examine the long-run relationship (cointegration) between agricultural financing (agricultural credit, agricultural credit guarantee scheme loans, agricultural insurance) and economic growth (GDP, agricultural GDP).
3. To estimate the short-run dynamics (error correction mechanism) of the relationship between agricultural financing and economic growth.
4. To determine the direction of causality (Granger causality) between agricultural financing and economic growth.
5. To quantify the magnitude of the effect of changes in agricultural financing on economic growth.

1.5 Research Questions

1. What are the time-series properties (stationarity) of agricultural credit and economic growth variables in Nigeria?
2. Is there a long-run relationship (cointegration) between agricultural financing (agricultural credit, agricultural credit guarantee scheme loans, agricultural insurance) and economic growth (GDP, agricultural GDP) in Nigeria?
3. What are the short-run dynamics (error correction mechanism) of the relationship between agricultural financing and economic growth?
4. What is the direction of causality (Granger causality) between agricultural financing and economic growth?
5. What is the magnitude of the effect of changes in agricultural financing on economic growth?

1.6 Research Hypotheses

Hypothesis One

• H₀ (Null): Agricultural credit has no significant effect on economic growth (GDP) in Nigeria.
• H₁ (Alternative): Agricultural credit has a significant effect on economic growth in Nigeria.

Hypothesis Two

• H₀ (Null): There is no long-run relationship (cointegration) between agricultural financing and economic growth.
• H₁ (Alternative): There is a long-run relationship (cointegration) between agricultural financing and economic growth.

Hypothesis Three

• H₀ (Null): There are no significant short-run dynamics (error correction) between changes in agricultural financing and changes in economic growth.
• H₁ (Alternative): There are significant short-run dynamics between changes in agricultural financing and changes in economic growth.

Hypothesis Four

• H₀ (Null): Agricultural financing does not Granger-cause economic growth.
• H₁ (Alternative): Agricultural financing Granger-causes economic growth.

Hypothesis Five

• H₀ (Null): Economic growth does not Granger-cause agricultural financing.
• H₁ (Alternative): Economic growth Granger-causes agricultural financing.

1.7 Justification of the Study

This study is justified on several grounds. First, despite the importance of agriculture for Nigeria’s economy and the government’s stated commitment to agricultural transformation, there is limited empirical evidence quantifying the relationship between agricultural financing and economic growth. Second, understanding whether the relationship is long-run (cointegrated) or only short-run has different policy implications: cointegration suggests a stable equilibrium relationship that policies can target; absence of cointegration suggests that shocks have permanent effects. Third, determining the direction of causality (Granger causality) is essential for policy: if agricultural credit Granger-causes growth, then increasing agricultural credit will increase growth; if growth Granger-causes agricultural credit, then growth will increase credit availability (reverse causality). Fourth, quantifying the magnitude of effects will inform policy on agricultural credit targets, interest rates, and government programmes. Fifth, the findings will inform agricultural credit policy (CBN, FMARD), development banks (BOA), and government agricultural programmes (ACGS, ABP, CACS).

1.8 Significance of the Study

The findings of this research will be significant to several stakeholders. To the Central Bank of Nigeria (CBN) , the study will provide evidence on the effectiveness of agricultural credit policies (credit to agriculture targets, interest rate subsidies) in promoting economic growth. To the Federal Ministry of Agriculture and Rural Development (FMARD) , the findings will inform agricultural credit programme design (ACGS, ABP, CACS) and resource allocation. To the Bank of Agriculture (BOA) , the study will inform lending strategy and capital requirements. To commercial banks and microfinance banks, the findings will inform agricultural lending strategies and risk assessment. To development partners (World Bank, IFAD, FAO, AfDB) , the findings will inform project design for agricultural finance programmes. To academic researchers, the study will contribute empirical evidence on agricultural finance-growth linkages, testing and extending agricultural development theory, Lewis dual sector model, and financial intermediation theory.

1.9 Scope of the Study

The scope of this study is delimited to the relationship between agricultural financing and economic growth in Nigeria. The study uses annual time-series data from 1981 to 2020 (40 observations) or 2000-2020 (21 observations) depending on data availability. Variables include: agricultural financing measures (agricultural credit from commercial banks (₦ billion), Agricultural Credit Guarantee Scheme (ACGS) loans (₦ million, number of loans), Bank of Agriculture (BOA) loans (₦ million), agricultural insurance premiums (₦ million), government agricultural expenditure (₦ billion). Economic growth measures: real GDP (₦ billion, constant prices), real agricultural GDP (₦ billion, constant prices), GDP growth rate (%), agricultural GDP growth rate (%). Control variables: interest rate (lending rate, %), inflation rate (CPI %), exchange rate (₦/USD), population growth rate (%). The study employs time-series econometric methods: unit root tests (ADF, PP, KPSS), cointegration tests (Engle-Granger, Johansen), error correction model (ECM), Granger causality tests within VECM/VAR framework. The study does not extend to agricultural financing from other sources (informal credit, cooperative credit, donor grants), nor to micro-level analysis (household/farm level), nor to other sectors of the economy (manufacturing, services, oil).

1.10 Definition of Terms

Agricultural Financing: The provision of financial resources (credit, loans, grants, subsidies, insurance, investments) to farmers, agribusinesses, and agricultural value chain actors to support agricultural production, processing, marketing, and infrastructure development.

Agricultural Credit: Loans provided to farmers and agribusinesses by formal financial institutions (commercial banks, microfinance banks, development banks) for agricultural purposes (input purchase, equipment acquisition, land improvement, labour hire).

Agricultural Credit Guarantee Scheme (ACGS): A Nigerian government programme (established 1977) that guarantees bank loans to smallholder farmers (up to 75% of loan amount), reducing bank risk and encouraging lending to agriculture.

Anchor Borrowers’ Programme (ABP): A Nigerian government programme (launched 2015) that provides loans (cash and inputs) to smallholder farmers linked to processors (anchors); farmers repay loans with harvest purchased by the anchor processor.

Commercial Agriculture Credit Scheme (CACS): A Nigerian government programme providing loans to agricultural enterprises at single-digit interest rates (5-9%), funded through bonds issued by the CBN.

Bank of Agriculture (BOA): A Nigerian development bank established to provide agricultural credit, savings, and micro-credit services to smallholder and commercial farmers.

Economic Growth: The sustained increase in the real gross domestic product (GDP) of Nigeria over time, measured as the annual percentage change in real GDP (constant 2010 prices).

Agricultural GDP: The value added of the agricultural sector (crops, livestock, forestry, fisheries) as a percentage of total GDP, measured in constant prices (real agricultural GDP).

Gross Domestic Product (GDP): The total market value of all final goods and services produced within Nigeria in a given year, measured in constant prices (real GDP) to remove the effect of inflation.

Cointegration: A statistical property of two or more non-stationary time series that move together over the long run such that a linear combination of them is stationary; cointegration indicates a long-run equilibrium relationship.

Error Correction Model (ECM): A time-series model that captures the short-run dynamics of how variables adjust to deviations from long-run equilibrium; the error correction term (ECT) measures the speed of adjustment.

Granger Causality: A statistical concept of predictive causality (not necessarily true causal mechanism) where one time series (X) is said to “Granger-cause” another (Y) if past values of X help predict current Y better than past values of Y alone, controlling for other variables.

Unit Root Test: A statistical test (Augmented Dickey-Fuller ADF, Phillips-Perron PP, Kwiatkowski-Phillips-Schmidt-Shin KPSS) to determine whether a time series is stationary (no unit root) or non-stationary (has a unit root).

Financial Intermediation Theory: A theory explaining the role of financial institutions (banks, microfinance banks) as intermediaries between savers (surplus units) and borrowers (deficit units), reducing information asymmetry, transaction costs, and risk through diversification, monitoring, and screening.

Agricultural Development Theory: A theory (Schultz, 1964) arguing that investment in agriculture (credit, inputs, technology, extension, research, infrastructure) is essential for transforming traditional agriculture into a productive, modern sector.

Lewis Dual Sector Model: A theory (Lewis, 1954) explaining how agricultural surplus (output above subsistence) provides the resources (food, labour, capital) for industrial development; increased agricultural productivity (enabled by credit) increases surplus, accelerating structural transformation.

CHAPTER TWO: LITERATURE REVIEW

2.1 Conceptual Framework

The conceptual framework for this study is organized around the key concepts of agricultural financing, economic growth, the channels through which agricultural financing affects economic growth, and the sources of agricultural financing. These concepts are defined, operationalized, and related to one another below.

2.1.1 Concept of Agricultural Financing

Agricultural financing refers to the provision of financial resources (credit, loans, grants, subsidies, insurance, investments) to farmers, agribusinesses, and agricultural value chain actors to support agricultural production, processing, marketing, and infrastructure development (World Bank, 2021). Agricultural financing can be categorized by source, purpose, and instrument.

Sources of Agricultural Financing in Nigeria:

Source	Type	Examples	Characteristics
Formal	Commercial banks	First Bank, UBA, Access, GTBank, Zenith	Collateral required; high interest (20-35%); urban bias
Formal	Development banks	Bank of Agriculture (BOA), NIRSAL	Agricultural focus; lower interest; rural branches
Formal	Microfinance banks	LAPO, Fortis, Accion	Small loans (micro-credit); high interest (30-40%)
Formal	Government programmes	ACGS, CACS, ABP	Guarantees, subsidies, low interest (5-9%)
Semi-formal	Cooperatives	Farmer cooperatives, savings groups	Group lending; peer monitoring; lower interest
Informal	Money lenders	Individual lenders	Very high interest (50-200%); no collateral
Informal	Traders	Input suppliers, produce buyers	Input credit (deducted from harvest); exploitative
Informal	Family/friends	Relatives, neighbours	Zero or low interest; informal agreements

Instruments of Agricultural Financing:

Instrument	Description	Examples
Short-term credit	Repaid within 1 year (seasonal)	Input credit, labour hire
Medium-term credit	Repaid 1-5 years	Equipment purchase, land improvement
Long-term credit	Repaid >5 years	Irrigation, tree crops (cocoa, oil palm, rubber)
Leasing	Equipment rental	Tractor hire, processing equipment
Insurance	Risk transfer	Crop insurance, livestock insurance
Grants/subsidies	Non-repayable funds	Fertilizer subsidy, interest subsidy
Equity investment	Ownership stake	Agribusiness investment, venture capital

2.1.2 Concept of Economic Growth

Economic growth is the sustained increase in the real gross domestic product (GDP) of a country over time, reflecting the expansion of the economy’s productive capacity (Mankiw, 2020). In Nigeria, economic growth is measured by the National Bureau of Statistics (NBS) as the annual percentage change in real GDP (constant 2010 prices).

Measures of Economic Growth:

Measure	Definition	Unit	Relevance to Agriculture
Real GDP	Total output at constant prices	₦ billion	Overall economic growth
Real GDP growth rate	Annual % change in real GDP	%	Pace of economic expansion
Agricultural GDP	Agricultural value added at constant prices	₦ billion	Agricultural sector growth
Agricultural GDP growth rate	Annual % change in agricultural GDP	%	Agricultural productivity
GDP per capita	Real GDP / population	₦/person	Living standards

Stages of Economic Growth (Rostow, 1960; Timmer, 2019):

Stage	Characteristics	Role of Agriculture
1. Traditional society	Subsistence agriculture, low productivity	Dominant sector
2. Preconditions for take-off	Agricultural surplus, infrastructure investment	Surplus provides resources for industry
3. Take-off	Industrialization, structural transformation	Labour shifts from agriculture to industry
4. Drive to maturity	Diversification, technology adoption	Agriculture becomes commercial, mechanized
5. High mass consumption	Service sector dominant	Agriculture is highly productive, small labour share

2.1.3 Channels Through Which Agricultural Financing Affects Economic Growth

Agricultural financing affects economic growth through multiple interconnected channels (Schultz, 1964; Lewis, 1954; Timmer, 2019).

Channel 1: Input Use Channel

Financing Enables	Effect on Agriculture	Effect on Economic Growth
Purchase of improved seeds	Higher yields (30-100%)	Higher agricultural GDP
Purchase of fertilizers	Higher yields (40-60%)	Higher agricultural GDP
Purchase of pesticides	Reduced losses (20-50%)	Higher effective yield
Purchase of equipment (pumps, sprayers)	Labour saved, timeliness	Higher productivity

Channel 2: Investment Channel

Financing Enables	Effect on Agriculture	Effect on Economic Growth
Land improvement (irrigation, drainage)	Higher yields, dry season cultivation	Higher agricultural GDP
Storage facilities (silos, warehouses)	Reduced post-harvest losses (20-50%)	Higher marketable surplus
Processing equipment (mills, dryers)	Value addition (100-500%)	Higher agricultural GDP, agribusiness
Tree crop establishment (cocoa, oil palm)	Long-term income stream	Export earnings

Channel 3: Technology Adoption Channel

Financing Enables	Effect on Agriculture	Effect on Economic Growth
Mechanization (tractors, planters)	Labour productivity increased	Labour released to industry
Improved varieties	Higher yields, disease resistance	Higher agricultural GDP
Modern irrigation systems	Year-round production	Higher agricultural GDP
ICT/digital agriculture	Information access, price discovery	Improved market efficiency

Channel 4: Labour Productivity Channel

Financing Enables	Effect on Agriculture	Effect on Economic Growth
Hired labour during peak seasons	More area cultivated, timely operations	Higher output
Labour-saving technology	Reduced labour demand	Labour shifts to industry/services
Off-farm employment	Farm family income diversification	Structural transformation

Channel 5: Risk Management Channel

Financing Enables	Effect on Agriculture	Effect on Economic Growth
Agricultural insurance	Reduced risk of loss	Farmers invest more (less risk averse)
Emergency loans (post-disaster)	Recovery after crop failure	Stability of agricultural output
Crop diversification	Reduced risk	Stable farm income

Channel 6: Multiplier and Agribusiness Channel

Financing Enables	Effect on Agriculture	Effect on Economic Growth
Increased farm income	Farmers spend on goods, services	Multiplier effect (1.5-2.5x)
Demand for inputs	Growth of input supply businesses	Employment, tax revenue
Demand for processing	Growth of agro-industries	Value addition, employment
Demand for transport	Growth of rural transport services	Employment, connectivity

2.1.4 Government Agricultural Credit Programmes in Nigeria

Programme	Year	Mechanism	Target	Achievements	Challenges
ACGS	1977	75% loan guarantee	Smallholders	Increased bank lending to agriculture	Low awareness, banks add collateral
CACS	2009	Low-interest (5-9%) bonds	All agricultural enterprises	Large-scale lending	Limited reach to smallholders
ABP	2015	Input loans + off-taker guarantee	Smallholders in value chains	Rice, maize, cotton, cassava	Late disbursement, elite capture
BOA	1972	Agricultural development bank	All farmers	Rural branches, micro-credit	Under-capitalized, loan recovery
NAIC	1987	Agricultural insurance	All farmers	Crop, livestock insurance	Low uptake (<5%)

(Source: CBN, 2022; FMARD, 2021; Okonkwo, 2020)

2.1.5 Constraints to Agricultural Financing in Nigeria

Constraint	Description	Impact
Lack of collateral	Customary land tenure, no formal titles	Excludes smallholders from formal credit
High interest rates	20-35% from commercial banks	Borrowing unprofitable (margins 10-20%)
Complex procedures	Lengthy applications, multiple documents	Farmers unable to complete applications
No credit history	No record of past borrowing	Banks cannot assess creditworthiness
Small loan sizes	Amount needed too small for banks	Banks prefer large loans
Perceived high risk	Climate, price, pest, disease risk	Banks ration credit, charge risk premium
No insurance	Agricultural insurance underdeveloped	Banks bear full default risk
Poor loan recovery	Default rates high in some areas	Reduces banks’ willingness to lend
Bureaucracy	Delays in government programmes	Funds arrive after planting season
Elite capture	Subsidies captured by large farmers, politicians	Target beneficiaries excluded

(Source: Adebayo & Ogunyemi, 2020; Eze & Nweze, 2019; Okafor & Nwosu, 2020)

2.1.6 Conceptual Framework Diagram (Described in Text)

The conceptual framework can be visualized as follows:

Agricultural Financing (Independent Variable) → Channels → Economic Growth (Dependent Variable)

Independent Variables (Agricultural Financing Measures):

• Agricultural credit from commercial banks (₦ billion)
• Agricultural Credit Guarantee Scheme (ACGS) loans (₦ million)
• Bank of Agriculture (BOA) loans (₦ million)
• Agricultural insurance premiums (₦ million)
• Government agricultural expenditure (₦ billion)

↓ Channels (Mediating Variables):

• Input use channel (fertilizer, seeds, pesticides)
• Investment channel (irrigation, storage, processing)
• Technology adoption channel (mechanization, improved varieties)
• Labour productivity channel (hired labour, labour-saving tech)
• Risk management channel (insurance, emergency loans)
• Multiplier/agribusiness channel (input supply, transport, processing)

↓ Dependent Variables (Economic Growth):

• Real GDP (₦ billion, constant prices)
• Real agricultural GDP (₦ billion, constant prices)
• GDP growth rate (%)
• Agricultural GDP growth rate (%)

Control Variables:

• Interest rate (lending rate, %)
• Inflation rate (CPI %)
• Exchange rate (₦/USD)
• Government expenditure (₦ billion)
• Rainfall (mm)

The framework posits that agricultural financing (independent variables) affects economic growth (dependent variables) through six channels: input use, investment, technology adoption, labour productivity, risk management, and multiplier effects. The magnitude of the effect depends on the effectiveness of these channels, which is moderated by control variables (interest rates, inflation, exchange rates, government spending, rainfall).

2.2 Theoretical Framework

This study is anchored on three supporting theories that provide a comprehensive theoretical foundation for understanding the relationship between agricultural financing and economic growth. These theories are Agricultural Development Theory, Lewis Dual Sector Model, and Financial Intermediation Theory.

2.2.1 Agricultural Development Theory

Agricultural Development Theory, associated with Nobel laureate Theodore Schultz (1964), argues that investment in agriculture (credit, inputs, technology, extension, research, infrastructure) is essential for transforming traditional agriculture into a productive, modern sector (Schultz, 1964).

Core Propositions (Schultz, 1964):

1. Traditional agriculture is poor but efficient: Farmers in traditional agriculture allocate resources efficiently given the constraints they face (limited technology, no credit, poor infrastructure). However, traditional agriculture is “poor” (low output, low income) because of limited investment.
2. Low productivity is not due to farmer irrationality: Farmers are rational but constrained. They do not adopt improved practices because they lack credit to purchase inputs, lack information (extension), or face high risk.
3. Investment in agriculture yields high returns: Investment in agricultural research (improved seeds), human capital (farmer education, extension), credit (inputs), and infrastructure (roads, irrigation) generates high economic returns.
4. Transforming traditional agriculture requires: (a) new technology (high-yielding varieties, fertilizers), (b) incentives (profitable prices for outputs), (c) credit (to purchase inputs), (d) education (extension to teach practices), and (e) infrastructure (roads, storage, markets).

Application to Agricultural Financing

Agricultural Development Theory predicts (Schultz, 1964; Timmer, 2019):

• Agricultural credit (financing) is a critical input for transforming traditional agriculture. Without credit, farmers cannot purchase improved seeds, fertilizers, or irrigation equipment.
• The returns to agricultural credit are high (increased yields, increased farm income, increased agricultural GDP).
• Credit constraints (lack of access to affordable credit) keep farmers trapped in low-productivity traditional agriculture.
• Policy should remove credit constraints through subsidized credit, credit guarantees, or microfinance.

Limitations: Schultz’s theory was developed before the widespread availability of microfinance and mobile banking. It focuses on formal credit and does not fully address informal credit markets (money lenders, traders) (Schultz, 1964).

2.2.2 Lewis Dual Sector Model

The Lewis Dual Sector Model, developed by Nobel laureate Arthur Lewis (1954), explains how agricultural surplus (output above subsistence) provides the resources (food, labour, capital) for industrial development (Lewis, 1954).

Core Propositions (Lewis, 1954):

1. Dual economy: The economy is divided into a traditional agricultural sector (low productivity, subsistence wages, surplus labour) and a modern industrial sector (higher productivity, higher wages).
2. Unlimited supply of labour: The agricultural sector has surplus labour (disguised unemployment) where marginal product of labour is zero or below subsistence wage. This surplus labour can be withdrawn for industrial employment without reducing agricultural output.
3. Capital accumulation in industry: Industrial capitalists reinvest profits to expand production, creating more industrial jobs, drawing more labour from agriculture.
4. Turning point: Once surplus labour is exhausted, agricultural wages rise, and both sectors share in productivity gains.

Role of Agricultural Financing in the Lewis Model

Agricultural financing (credit) can increase agricultural productivity, generating surplus (output above subsistence) that can be used to: (a) feed the industrial workforce (food surplus), (b) provide labour (workers released from agriculture as productivity increases), (c) provide capital (savings from agriculture can be invested in industry), and (d) provide foreign exchange (agricultural exports earn currency to import industrial machinery) (Lewis, 1954; Timmer, 2019).

Application to Nigeria

Indicator	Current Status	Lewis Model Implication
Agricultural employment share	~35%	Still high; surplus labour exists
Agricultural productivity	Low (hand hoe)	Low surplus for industry
Agricultural credit access	<20% of smallholders	Credit constraints limit productivity
Industrial employment share	~10%	Low; limited absorption of surplus labour

Limitations: The Lewis model assumes that industrial sector can absorb unlimited labour without raising wages (due to unlimited supply). In reality, absorptive capacity may be limited (unemployment in cities). Also, the model does not fully account for rural-urban migration costs or urban informal sector (Todaro & Smith, 2020).

2.2.3 Financial Intermediation Theory

Financial Intermediation Theory, developed by Diamond (1984) and extended by Freixas and Rochet (2019), explains the role of financial institutions (banks, microfinance banks) as intermediaries between savers (surplus units) and borrowers (deficit units), reducing information asymmetry and transaction costs (Diamond, 1984).

Core Propositions (Diamond, 1984; Freixas & Rochet, 2019):

1. Information asymmetry: Lenders (savers) cannot easily assess the creditworthiness of borrowers (farmers) or monitor their use of funds. Borrowers have private information about their risk and effort (adverse selection, moral hazard).
2. Transaction costs: Direct lending between savers and borrowers is costly (search costs, contracting costs, monitoring costs, enforcement costs).
3. Financial intermediaries reduce information asymmetry and transaction costs: Banks specialize in screening borrowers (reducing adverse selection), monitoring borrowers (reducing moral hazard), diversifying risk (lending to many borrowers), and achieving economies of scale (reducing transaction costs per loan).
4. Credit rationing: Even with financial intermediation, some borrowers (especially smallholders) may be rationed (denied credit) because screening and monitoring costs are high relative to loan size, or because they lack collateral.

Application to Agricultural Financing

Financial Intermediation Theory explains several features of agricultural credit markets in Nigeria (Diamond, 1984; Freixas & Rochet, 2019):

• Why commercial banks are reluctant to lend to agriculture: Information asymmetry is severe (farmers’ risk difficult to assess); transaction costs are high (small loan sizes, remote rural locations); collateral is lacking.
• Why microfinance banks (MFBs) exist: MFBs specialize in small loans (micro-credit), use group lending (peer monitoring to reduce information asymmetry), and accept alternative collateral (group guarantee).
• Why government credit programmes (ACGS, ABP, CACS) are needed: Government guarantees (ACGS) reduce bank risk; interest subsidies (CACS) reduce cost; off-taker guarantees (ABP) reduce risk of default.
• Why credit constraints persist: Even with financial intermediaries, smallholder farmers may still be rationed due to high transaction costs relative to loan size.

Limitations: Financial Intermediation Theory focuses on formal financial intermediaries and does not fully explain informal credit markets (money lenders, traders) that dominate agricultural finance in Nigeria (Freixas & Rochet, 2019).

Integration of the Three Theories

The three theories are complementary and collectively provide a robust theoretical framework for this study:

Theory	Focus	Contribution to Study
Agricultural Development Theory	Investment in agriculture for transformation	Explains why agricultural credit is essential for productivity growth
Lewis Dual Sector Model	Surplus labour and structural transformation	Explains how agricultural growth (enabled by credit) supports industrial development
Financial Intermediation Theory	Role of banks and microfinance in reducing information asymmetry	Explains why credit markets fail smallholders and why government programmes are needed

Together, these theories support the study’s examination of the relationship between agricultural financing and economic growth, recognizing that: (1) agricultural credit is a critical input for transforming traditional agriculture (Agricultural Development); (2) agricultural growth (enabled by credit) generates surplus for industrial development (Lewis); and (3) financial intermediaries (banks, MFBs) and government programmes are needed to overcome credit constraints (Financial Intermediation).

2.3 Review of Related Empirical Studies

This section reviews empirical studies relevant to the relationship between agricultural financing and economic growth, organized by geographic focus and key findings.

2.3.1 Studies on Agricultural Financing and Economic Growth (Nigeria)

Adebayo and Ogunyemi (2020) conducted a study on the effect of agricultural credit on economic growth in Nigeria (1981-2018). Using a Vector Error Correction Model (VECM), they found that agricultural credit had a positive and significant effect on agricultural GDP in the long run (coefficient 0.32, p<0.05). A 1% increase in agricultural credit increased agricultural GDP by 0.32% in the long run. In the short run, the effect was positive but not significant. The study recommended increasing agricultural credit to smallholders.

Eze and Nweze (2019) studied the relationship between Agricultural Credit Guarantee Scheme (ACGS) loans and agricultural output in Nigeria (1990-2018). Using Ordinary Least Squares (OLS) regression, they found a positive and significant relationship (R² = 0.67, p<0.01). However, they did not test for stationarity or cointegration; OLS on non-stationary data may produce spurious results. The study recommended expanding ACGS coverage.

Okafor and Nwosu (2020) studied the effect of commercial bank credit to agriculture on agricultural GDP in Nigeria (2000-2019). Using Autoregressive Distributed Lag (ARDL) bounds testing, they found a long-run relationship (cointegration) between agricultural credit and agricultural GDP. The long-run elasticity was 0.28 (p<0.05). The study concluded that agricultural credit significantly affects agricultural output.

Okonkwo (2020) studied the effect of agricultural financing (credit, insurance, government expenditure) on agricultural productivity in Nigeria (1990-2018). Using a VECM, he found that: agricultural credit had a positive effect (0.25 elasticity), agricultural insurance had a positive but smaller effect (0.08 elasticity), and government agricultural expenditure had a negative effect (attributed to corruption and inefficiency). The study recommended increasing agricultural credit and insurance while reforming government expenditure.

2.3.2 Studies on Agricultural Financing and Economic Growth (Other Countries)

Study	Country	Period	Key Findings
Ibrahim & Aliero (2012)	Nigeria (Northern)	1975-2010	Positive relationship between ACGS loans and agricultural output
Akinyemi et al. (2019)	Nigeria	1981-2015	Agricultural credit Granger-causes agricultural GDP (unidirectional)
Chisasa & Makina (2013)	South Africa	1970-2010	Agricultural credit positively affects agricultural output
Abate (2019)	Ethiopia	2000-2015	Microfinance credit increases agricultural productivity (household-level)
Khan & Bashir (2019)	Pakistan	1980-2015	Positive long-run relationship between agricultural credit and GDP

2.3.3 Studies on Government Agricultural Credit Programmes in Nigeria

Okafor and Ugwu (2021) evaluated the effectiveness of the Anchor Borrowers’ Programme (ABP) in Anambra State (2015-2020). Using a survey of 200 rice farmers (100 ABP beneficiaries, 100 non-beneficiaries), they compared outcomes. ABP beneficiaries had: higher fertilizer use (+65%), higher yields (+55%), higher net income (+60%). However, only 15% of rice farmers in the study area had accessed ABP, with problems including: late disbursement (45% of beneficiaries reported), insufficient loan amounts (38%), and bureaucratic selection (25% reported favouritism).

2.3.4 Summary of Empirical Findings

The empirical literature reveals consistent findings: (1) agricultural credit has a positive effect on agricultural GDP and overall GDP in Nigeria; (2) the effect is stronger in the long run than the short run; (3) ACGS loans and ABP loans positively affect agricultural output; (4) formal credit reaches only a small fraction of farmers (<20%); (5) constraints include lack of collateral, high interest rates, bureaucracy, and risk; (6) most Nigeria studies use time-series methods (VECM, ARDL) but some use OLS without testing for stationarity (spurious regression risk); (7) few studies test for Granger causality; (8) few studies include multiple financing measures (credit, insurance, government expenditure) simultaneously; (9) period coverage varies (1981-2018, 1990-2018, 2000-2019). This study addresses these gaps.

2.4 Summary of Literature Review

The table below summarizes key theoretical and empirical literature relevant to agricultural financing and economic growth in Nigeria.

Author(s) & Year	Focus of Study	Strength	Weakness	Limitation	Gap Identified
Schultz (1964)	Agricultural Development Theory	Seminal theory; investment in agriculture	Pre-microfinance era	Not Nigeria-specific	Application to Nigeria needed
Lewis (1954)	Lewis Dual Sector Model	Explains structural transformation	Assumes unlimited labour absorption	General theory	Application to Nigeria needed
Diamond (1984); Freixas & Rochet (2019)	Financial Intermediation Theory	Explains role of banks in reducing information asymmetry	Focuses on formal finance; less on informal	General theory	Application to agricultural finance needed
Adebayo & Ogunyemi (2020)	Agricultural credit and growth (Nigeria 1981-2018)	VECM; long-run positive effect	No causality test	Causality gap	Granger causality needed
Eze & Nweze (2019)	ACGS loans and agricultural output (1990-2018)	Positive relationship	OLS on non-stationary data (spurious)	Methodological gap	Cointegration test needed
Okafor & Nwosu (2020)	Commercial bank credit and agricultural GDP (2000-2019)	ARDL; long-run elasticity 0.28	Short period (20 years)	Period gap	Longer period needed
Okonkwo (2020)	Agricultural financing and productivity (1990-2018)	Includes credit, insurance, government expenditure	Negative effect for government expenditure	Efficiency gap	Why government expenditure is negative?
Okafor & Ugwu (2021)	ABP evaluation (Anambra)	Positive impact on beneficiaries	Single state; small sample	Geographic gap	National evaluation needed
CBN (2022)	Statistical bulletin	Official data	Not research; descriptive	No analysis	Analytical study needed
FMARD (2021)	Agricultural sector report	Official data	Not research; descriptive	No analysis	Analytical study needed
NBS (2022)	Agricultural survey report	Official data	Not research; descriptive	No analysis	Analytical study needed
World Bank (2021)	Nigeria agricultural sector review	Comprehensive overview	Not primary research; descriptive	No primary data	Primary research needed
FAO (2020)	State of food and agriculture	Global overview	Not Nigeria-specific	Not primary research	Nigeria primary research needed
Timmer (2019)	Agricultural transformation	Theory	Not empirical	Not Nigeria-specific	Nigeria empirical needed
Todaro & Smith (2020)	Economic development (textbook)	Comprehensive theory	Not empirical	Not Nigeria-specific	Nigeria empirical needed
Mankiw (2020)	Macroeconomics (textbook)	Comprehensive theory	Not empirical	Not Nigeria-specific	Nigeria empirical needed
Ibrahim & Aliero (2012)	ACGS and agricultural output (Nigeria)	Positive relationship	Older study (pre-2010)	Temporal gap	Updated study needed
Akinyemi et al. (2019)	Granger causality (Nigeria 1981-2015)	Credit Granger-causes GDP	Short period (35 years)	Period gap	Longer period?
Chisasa & Makina (2013)	Agricultural credit and output (South Africa)	Positive relationship	South Africa, not Nigeria	Geographic gap	Nigeria replication needed
Abate (2019)	Microfinance and productivity (Ethiopia)	Positive effect (household-level)	Ethiopia, not Nigeria	Geographic gap	Nigeria replication needed