Economics of Resource Utilisation Efficiency and Constraints Related to Maize Production in North West Nigeria

Maize (Zea mays) is one of the most commonly grown cereals in the world. It comes after wheat and rice in terms of world importance and is the most significant staple cereal crop with great potential to address the problem of food insecurity in Africa (Abdoulaye et al., 2018). Nigeria is the second-highest producer of maize in Africa, coming after South Africa. The three highest maize-producing countries in Africa (i.e. South Africa, Nigeria, and Ethiopia) accounted for 39 % of the continent’s total maize output in 2019 (PWC, 2021). In Nigeria, the top 10 maize-producing states include Plateau, Borno, Katsina, Niger, Bauchi, Gombe, Oyo, Kogi, Kaduna, and Taraba and they accounted for 64 % of maize produced in the country. Maize industries provide employment opportunities for many producers and serve as a source of income and foreign exchange earnings for Nigeria (Abdulaleem et al., 2019; Onuk et al., 2010). Maize is mainly used as animal feed, food, and for industrial purposes such as the production of starch and ethanol. Approximately 45.5% and 6.5% of maize produced in Nigeria is used to manufacture animal feeds and is utilised by brewing companies, respectively. Similarly, 13% of maize produced in Nigeria is used for the manufacture of corn flakes, industrial flour, and other confectionery. The share of household maize consumption is approximately 10–15%. It is crucial for the nation to adopt policies that address the challenges faced by maize farmers to increase productivity. These policies should include promoting high-yield and disease-resistant maize varieties, reviewing non-tariff and tariff barriers affecting maize, encouraging producer aggregation and supporting both forward and backward integration in the supply chain. Additionally, the policies should focus on supporting agricultural research and development, strengthening the value chains of maize-based products, enhancing the role of commodity exchanges, and promoting agricultural mechanization.

Maize is significant to the international food market. In 2019, the USA, China, and Brazil produced the majority of the world’s maize, accounting for 62% of global maize production. Table 1 shows the maize output in Nigeria and the world for 2021 and 2022. The Nigerian maize output accounted for 1.07% and 1.09% of the world output of maize in 2021 and 2022, respectively (Table 1).

In 2019, Africa exported approximately 1.8 million metric tons (MMT) of maize, valued at around 464.9 million USD. Conversely, the continent imported about 12.5 MMT of maize, with a total value exceeding 4 billion USD. This disparity is largely driven by the rapidly growing population, which intensified the demand for maize in Africa for purposes such as industrial processing, human consumption, and manufacturing. The major suppliers of maize to African nations include Ukraine, Argentina, Brazil, the United States, and Romania. Furthermore, African countries continue to import significant quantities of processed maize products, including cornstarch and cornmeal, valued at approximately 82 million USD and 125 million USD, respectively.

The maize yield is generally low in Nigeria, having one of the lowest yields in Africa, with widening gaps in Africa compared to the rest of the world (Olaseinde et al., 2023). In 2021, the average maize yield in Nigeria was 2.05 tons ha⁻¹ (FAO, 2024). Resource utilisation efficiency plays a significant role in agriculture, and helps to identify opportunities for increasing productivity with the available resources. Inefficiencies in resource utilisation have a significant effect on the output (Adeyemo and Kuhlmann, 2009). Low agricultural productivity in Nigeria arises from inefficient utilisation of available resources such as fertilisers, pesticides, herbicides, and improved seeds, along with a scarcity of essential technologies. When these resources are available, resource-poor producers often cannot afford to purchase the required quantities (Alabi and Safugha, 2022).

Efficiency with available technology and a resource base can sustain and increase farm productivity (Alabi et al., 2021). One way to increase productivity among farmers is by efficiently utilising all available resources in the production process (Mesike et al., 2009). Due to low productivity of maize, Nigeria could not meet the large quantity of maize demand, which is approximately 12 to 15 million metric tons annually, bringing a demand-supply gap of close to 4 million metric tons of maize annually. The smallholder maize producers in Nigeria and sub-Saharan Africa are experiencing significant yield gaps due to inefficient resource utilisation, and sub-optimal agricultural practices (Bucagu et al., 2020). Development economists, food analysts, researchers, and policymakers are debating the continued lags in Sub-Saharan African maize yields, and the design of appropriate measures to overcome these yield gaps (Beza et al., 2017; Olasehinde et al., 2023). It is forecasted that there will be a 60% rise in the demand for agricultural output by 2050 (FAO, 2012).

There is a significant yield gap in maize production, and closing this gap can be achieved not only by increasing productivity but also by improving the efficiency of resource utilisation. Closing the yield gaps and improving resource utilisation efficiency are necessary strategies to meet these challenges. Small but productive resource-poor farmers need timely access to the required quantities of inputs to achieve high yields. Although empirical studies have demonstrated the positive effect of resources, such as improved varieties on maize output, none have demonstrated whether this was due to an increase in resource utilisation efficiency or technological change. This study aims to overcome the limitations of previous research and shed light on the dynamics and sustainability of the benefits of resource utilisation efficiency.

This research study provided answers to the following research questions:

• What are the factors influencing maize output?

• What is the resource productivity of inputs and input elasticity among maize producers?

• Are maize producers efficient in resource utilisation?

• What are the challenges facing maize producers in the study area?

This study investigated the economics of resource utilisation efficiency and constraints related to maize production in North West Nigeria. Specifically, the objectives were:

• To evaluate the predictors affecting maize output

• To estimate the resource productivity of inputs and input elasticity, and input elasticity among maize producers

• To assess resource utilisation efficiency among maize producers, and

• To describe the challenges faced by maize producers in the study area.

According to Alabi et al. (2022), the SPM is stated thus: (2) Yi=fXi,βievi_ui {Y_i} = f\left( {{X_i},{\beta _i}} \right){e^{vi\_ui}} (3) Yi=β0X1β1X2β2X3β3X4β4X5β5evi−μi {Y_i} = {\beta _0}{X_1}^{\beta 1}{X_2}^{\beta 2}{X_3}^{\beta 3}{X_4}^{\beta 4}{X_5}^{\beta 5}{e^{vi - \mu i}} (4) LnYi=β0+∑j=15βiLnXi+vi−ui {\rm{Ln}}{Y_i} = {\beta _0} + \sum\nolimits_{j = 1}^5 {{\beta _i}{\rm{Ln}}{X_i} + \left( {{v_i} - {u_i}} \right)} where:

• Y_i – output of maize (kg)

• X_i – predictors

• β_i – vectors of evaluated parameters

• β₀ – constant term

• V_i – noise term

• U_i – noise term due to TIE (Technical Inefficiency)

• X₁ – land (ha)

• X₂ – agrochemicals (litres)

• X₃ – labour (mandays)

• X₄ – fertiliser (kg)

• X₅ – seed (kg).

This is given as: (5) MPx=PxPy {MP_x} = {{{P_x}} \over {{P_y}}} (6) MPx×Py=MVPx {MP_x} \times {P_y} = {MVP_x} (7) βij=Y¯iX¯ij=PxPy {\beta _{ij}} = \left[ {{{{{\bar Y}_i}} \over {{{\bar X}_{ij}}}}} \right] = {{{P_x}} \over {{P_y}}} (8) r=MVPxPx r = {{{MVP_x}} \over {{P_x}}}

• If r > 1, the resource is underutilised

• If r < 1, the resource is overused

• MP_x – marginal product of the input

• β_ij – elasticities of input

• P_x – price of the factor input

• P_y – price of output

• Ȳ_i – average output of maize

• X̄_ij – average of stimulus inputs

• MVP_x – marginal value product of each resource employed

• MVP_x – P_x optimum resource utilisation

• MVP_x >

• (<) P_x – there is disequilibrium in the use of input that is underutilisation (overutilisation)

• r – resource utilisation efficiency

• MFC – marginal factor cost = P_x.

The equation is stated as follows: (9) RTS=Σεp {\rm{RTS}} = \Sigma {\varepsilon _p} where:

• RTS – return to scale

• ε_p – input elasticities (scale elasticities)

• Σ – summation symbol.

The challenges faced by maize producers were submitted to PCM. The model reduces many interrelated challenges to a few unrelated ones.

The summary information of maize producers is presented in Table 2. The average age, farming experience, and educational status are approximately 46 years, 9 years, and 11 years, respectively. Similarly, gender, household size, and farm size are approximately 0.85, 7 persons, and 1.75 ha, respectively. In addition, the maize output and number of members of the cooperative are approximately 1, 500 kg/ha and 0.75, respectively. The maize producers were literate, had formal education, and were young, energetic, and resourceful. They are smallholder producers who cultivate less than 5 ha of farmland. Approximately 15% of maize producers are male, and 25% are not members of a cooperative association. This outcome agrees with Onuk et al. (2010), who reported that 73.3% of maize producers in Plateau State, Nigeria, are male and that 82% are less than 50 years of age.

Table 2.

Variables	Unit of measurement	X¯i {{{\rm{\bar X}}}_i}	SD
Age	Years	46	11.25
Gender	1 = male; 0 = otherwise	0.85	0.39
Farming experience	years	9	3.71
Household size	number	7	2.72
Educational status	years	11	5.6
Farm size	ha	1.75	0.97
Maize output	kg/ha	1,500	203.01
Member of cooperatives	1 = yes; 0 = otherwise	0.75	0.57

Source: field survey, 2024.

The estimates of the stochastic production model are presented in Table 3. The predictors included in the Cobb-Douglas production model include land, agrochemicals, labour, fertiliser, and seed. Land and fertiliser are significantly different from zero at a 1% probability level. Furthermore, agrochemicals and seeds are significantly different from zero at a 5% probability level. In addition, labour is significantly different from zero at a 10% probability level. A 1% increase in fertiliser, keeping all other predictors fixed, will result in an 18.18% increase in maize output. Similarly, a 1% increase in improved seeds, keeping all other predictors fixed, will result in a 14.61% increase in maize output.

The mean Technical Efficiency (TE) score of 72% (0.72) indicates that the average smallholder maize producer in the sample requires approximately 28% (0.28) more inputs to reach optimal production levels. This implies that, on average, a smallholder maize producer loses about 28% of their potential output due to technical inefficiency (TIE).

In the component of diagnostic statistics, the coefficient of variance ratio (γ), also called gamma, is 0.8139. This reveals that 81.39% of variations in maize output are due to differences in TE. Furthermore, this indicates that 81.39% of random fluctuations in the maize producers’ output are due to their inefficiency. Therefore, reducing the effect of the gamma or variance ratio will increase maize output and greatly enhance the TE of the producers. The estimate of total variance (σ²), also called sigma squared, is 4.2574, which is significantly different from zero at a 1% probability level. This signifies that the model used and data obtained are well-fitted. The log-likelihood is −728.65. This work agrees with the findings of Alabi and Abdulazeez (2018), who reported that land, hired labour, family labour, chemicals, insecticides, fertiliser, and income from other enterprises are significantly different from zero in influencing the output of maize producers in Kaduna State, Nigeria.

Table 4 presents the returns to scale (RTS) and resource productivity of inputs. The estimates from the Cobb-Douglas production function are reported as partial elasticities, providing insight into how relative changes in the predictors affect maize output. These estimates also indicate the resource productivity of the inputs. The estimated coefficients fall between zero and one, thus all marginal products (MPs) are positive and diminishing at the mean of factors. This agrees with a priori expectations and is in line with estimates obtained by Abdulai and Abdulahi (2016), who documented the significant and positive effect of frontier factors on the output of maize producers in Zambia.

A 1% increase in land, agrochemicals, labour, fertilisers, and seeds will result in an increase in cassava output of 0.17%, 0.11%,0.29%, 0.18%, and 0.15% respectively, with labour and agrochemicals having the highest and lowest elasticities respectively. The inclusion of the first-order derivatives of the output predictors, referred to as scale elasticities, indicates decreasing returns to scale in the frontier model, totalling 0.90. This means that a proportional increase in all predictors will lead to a less-than-proportional increase in the output of smallholder maize producers.

Specifically, the sum of the partial elasticities (Σε_p) of inputs is approximately 0.90, indicating that a 1% increase in all predictors at the sample mean will result in a 0.90 increase in maize output, which is significantly different from zero. This finding aligns with the results of Ogunniyi et al. (2013), who reported an estimated return to scale of 0.54 for cassava farmers in Oyo state, Nigeria.

The resources utilised by maize producers under consideration are land, agrochemicals, labour, fertilisers, and seeds (Table 5). The MPP (Marginal Physical Product) of land is approximately 145.71, and the MPP of agrochemicals is approximately 75. Similarly, the MPP of labour is approximately 27.19, the MPP of fertiliser is approximately 0.9, and the MPP of seeds is approximately 9.

The resource utilisation efficiency (r) which is calculated based on the ratio of MVP (Marginal Value Product) to MFC (Marginal Factor Cost), shows that all resources utilised by maize producers are underutilised. This signifies that an increase in resource utilisation will lead to greater efficiency in maize production.

This outcome is in line with the results of Ahmed et al. (2011), who, in their analysis of resource-use efficiency of garlic production in Kano State, Nigeria approximated that organic manure and farm labour were under-utilised, while seeds were over-utilised.

The challenges faced by maize producers are displayed in Table 6. Approximately six (6) challenges facing maize producers were retained by the principal component model (PCM). Lack of access to land was ranked first, with an Eigenvalue of 4.3603, accounting for 30.04% of the challenges faced by maize producers. The high cost of fertiliser, with an Eigenvalue of approximately 3.6033, was ranked second, accounting for 42.20% of the challenges encountered by maize producers. The lack of farm technologies, with an Eigenvalue of approximately 2.8162, was ranked third, accounting for 11.09% of all challenges encountered by maize producers. The challenges identified by the PCM accounted for 80.42% of the total challenges reported by the maize producers. The chi-square value of 3692.78 is significantly different from zero at a 1% probability level, demonstrating that the PCM is well-fitted.