Background

Vegetable farming especially Corchorus olitorius (L.) required little capital investment which is of utmost importance to resource poor farmers. However, potential for increasing the production of the vegetable faces a significant threat from limiting resources. Given economic barrier and the fact that smallholder vegetable farmers who produce the bulk of Corchorus olitorius in Nigeria are resource poor and lack access to credit facilities, improving farmer’s efficiency without enhancing their resource base may be a viable option.

With the population growth rate of 5.5 percent as against food production growth rate of 3.2 percent annually in the country, the need for an improved agricultural production system in Nigeria is paramount (Okunlola, 2009). Therefore sustainability of vegetables production to meet the increasing demands in the country calls for attention (Oladoja et al., 2006). One of the ways to increase vegetable output is improvement in efficiency of vegetable production.

Earlier studies on efficiency of vegetable production (Emokaro et al., 2007; Onyango et al., 2008) worked on efficiency using deterministic production function with mathematical programming techniques to compute the parameters. However, there is an inherent limitation of the statistical inference on the parameters and resulting efficiency estimates. To overcome this deficiency, Aigner et al. (1977) and Meeusen and Van den Broeck (1977) independently developed the stochastic frontier production function which was used in this study. In estimating the efficiency of vegetable production, this work employs statistical model which avoids the parametric specification of technology and distribution assumption of inefficiency term which gives it an edge over past works.

On theoretical basis, existing studies suggested that low productivity could be attributed to inefficiency in resource use (Abang and Agom, 2008). Thus, this study describes the socio-economic characteristics of Corchorus olitorius vegetable producers; estimate the costs and returns to vegetable production and examine the level of resource productivity and efficiency of the vegetable farms in the study area. An efficient production system is necessary to ensure increased production through efficient allocation of productive resources and thereby improve the producer’s income.

1 Materials and Methods

The study area is Oyo State in Southwestern Nigeria. The state is located between latitudes 2° 38¹ and 4° 35¹ east of the Greenwich meridian. The state has two distinct ecological zones- the western rain forest to the south and the intermediate savannah to the north with an area of 28,454 square kilometres. The climate is equatorial, notably with dry and wet seasons with relatively high humidity. The dry season lasts from November to March while the wet season starts from April and ends in October. Average daily temperature ranges between 25°C and 35°C almost throughout the year.

A three stage sampling technique was used in selecting four Local Government Areas (LGAs) in the study area. Ten villages were chosen to be included in the study; five farmers were selected per village. A total of 200 vegetable farmers were sampled using simple random selection at each sampling stage. Data collection occurred between October and December 2016 and involved the use of structured questionnaire and personal interviews. The questionnaire was covered information on socio-economic characteristics of the vegetable farmers, quantity and prices of inputs and output of the vegetable. Data collected were analyzed using descriptive analysis, budgetary analysis and stochastic frontier analysis.

1.1 Budgetary analysis

An enterprise budget approach was used to estimate the costs and return of the vegetable production enterprise so as to determine the farmer’s income/profitability. Production costs/ total costs refer to the total expenditure or expenses incurred during a given period on a specified enterprise by the firm. The components of the enterprise budget are:

(1) Total revenue (TR) = Output (Q) x Unit price (P)

(2) Total cost (TC) = Total variable cost (TVC) + Total fixed cost (TFC)

(3) Gross Margin (GM) = Total income (TI) – Total variable cost (TVC)

(4) Profit (π) = Gross margin (GM) – Total fixed cost (depreciated value)

(5) Benefit Cost Ratio (BCR) = Total income (TI) / Total cost (TC)

Depreciation was calculated by the straight line method as follows:

(6) Depreciation = (Cost of purchase – Salvage value) / Useful life

Other Profitability ratios include the following:

(7) Profitability Index (PI) or Return on sale = NI / TR

(8) Rate of return on Investment (RRI) = NI / TC * 100

(9) Rate of return on variable cost (RRVC) = TR – TFC / TVC *100

(10) Operating ratio (OR) = TVC / TR

Where NI = Net Income.

(11) Efficiency level = π/TC

1.2 Stochastic frontier analysis

The stochastic frontier production function with assumed presence of technical inefficiency of production can be written as:

(12) Y = f (X_i; β) exp (V_i- U_i), i= 1, 2 ….n

Where Y is the quantity of vegetable output, X_i is input quantities used by i^th farm. β is vector parameters. V_i is a symmetric error, which accounts for random variations in output due to factors beyond the control of the farmers while U_i is representing inefficiency in production. Empirically, the stochastic production function frontier for the analysis of technical efficiency of the vegetable production is specified as:

(13) InY_ij= ln βo + β₁lnX_1ij+ β₂lnX_2ij+ β₃X_3ij+ β₄X_4ij+ β₅X_5ij+∈     

Where, Y = total quantity of output (Naira); X₁ = farm size (acres); X₂ = quantity of seed (kg); X₃ = farm labour (man/days); X₄ = fertilizer (kg); X₅ = pesticides (litres)

β_o, β_{1, ……}β ₅= regression parameters.

Subscript i and j refer to the i^th vegetable produce and the j^th input respectively and ∈=V_ij–U_ij is the error term (Aigner et al., 1977, Meeusen and Van den Broeck, 1977). V is the symmetric (two-sided) component, which capture variations in output due to factors outside the control of the farmer and U is the one-sided efficiency component called technical inefficiency effect associated with the technical efficiency of vegetable production and it capture the variation in output due socio-economic characteristics.

1.3 Resource use efficiency

To determine the extent of efficiency in the use of the variable inputs expressed in physical quantities, the Marginal Value Product (MVP) of land, seed, family labour, hired labour, fertilizer and pesticide were computed and then compared with their input unit prices.

MVP_xi= dy/dx X P_y

Where: dy/dx= Marginal Physical Product (MPP), Py= Farm gate price

2 Results and Discussion

2.1 Socio-economic characteristics of the vegetable producers

The mean age of sampled vegetable farmers was 49.43 years (Table 1) which is consistent with findings of Tsoho (2004). This implies that the farmers involved in vegetable production were adults and still within active age bracket. This agrees with the findings of Usman and Bakar (2013) who found that vegetable production was dominated by adults who were between the age ranges of 40-60 years of age and attributed it to labour requirement in vegetable production. This shows that vegetable production is a business that has a future in Oyo State. Majority (67.5%) of the respondents were married. Marital status could increase consumption pressure on the household head and the marketable surplus of vegetable may reduce due to increase in consumption level. The mean household size was about 7 members per household which serve as source of labour on the farm. This corroborates Subba – Reddy, Ram, Sastry and Devi (2004) who stated that household sizes have been noted to make family labour available for farm work and other household activities. This was affirmed also by Effiong (2005) that large family size is the most important input for unpaid labour, especially in the rural areas. The need for more persons to make work easier on the farm could be the reason for the high household size of the respondents. This corroborates the findings of Obinaju and Asa, (2013) who opined that children in sub-Saharan Africa tend to be of economic value and are desirable assets for struggling parents.

The percentage of farmers with the highest years of education was 49% suggesting that most of the farmers had low level of education implying that vegetable farmers in Oyo State were more of illiterates than literates unlike some other enterprises. This corroborates the findings of Baruwa (2013) who opined that years of formal education by pineapple farmers was six years. This level of education will affect vegetable producers in computation of loss and gain, efficient and utilization of existing inputs and their readiness to adopt improved technologies. The primary occupation of the farmers was farming which make up a percentage of 74.8% meaning that the farmers are more involved in farming than any other occupation. This is an indication that most of the farmers depend on vegetable farming as their source of livelihood. Years of experience of the respondents varies. Almost thirty-four per cent (33.5%) of the farmers had experience in the range of 6 and 15years, 32.5% of them had 16-25 years of experience, 17.5% had 26-35%, 6%  had 35 and above experience in farming occupation. This suggests that more people tend to stay longer on vegetable production possibly due to its rewarding economic returns. This finding is in line with Baruwa (2013) assertions. Most (69.9%) of the vegetable farmers were male while 30.1% were female.

2.2 Costs and returns to vegetable production

The results of the budgetary analysis revealed that the total revenue of an average vegetable farmer was ₦60,017.63 (Table 2). The total variable cost incurred by an average vegetable farmer was ₦36,980.92. Labour took the highest share of total variable costs. This agrees with other studies which indicate that vegetable production is labour intensive, and also need equipment and other inputs such as seed, fertilizer for maximum production (Nurah, 1999; Ramaila et al., 2011; Ash, 2011). The gross margin fixed inputs used for vegetable production which included land rent, cutlass, hoe, rake, sprayer, watering can, wheel barrow, knife, basket, sack, bucket, spade, pumping machine and file, was ₦18,207.16 and total cost which include cost of fixed inputs and that of variable inputs was ₦55,188.08. The net income, also known as profit of an average vegetable farmer was ₦4,829.55.

From the above calculation, other profitable ratios were calculated and they include: Benefit Cost Ratio (BCR) was 1.09; Expense Structure Ratio (ESR) is 0.49, Rate of Returns which is 0.09; Gross Margin Ratio is 0.92. The rate of return which is also known as efficiency level is 0.09. This is known as the enterprise economic efficiency and it implies that ₦1 spent by the farmer on vegetable production, ₦0.09 is realized as profit. Improved management can increase profitability (Afolami and Ayinde, 2001)

Also, labour efficiency was calculated as Net farm Income per man day which gave ₦1,802.10. Crop Efficiency was also calculated as crop value per acre which gave ₦44,401.61 and Net Crop Income per acre gave ₦14,995.26.

2.3 Resource use efficiency

The marginal value products of all the resources were less than their prices (MVP < MFC). For labour, farm size and fertilizer, seed and even pesticides had ratios of MVP to MFC greater than one, showing that they were underutilized (Table 3). Increasing farm sizes will lead to improvement in use of labour, fertilizer used, seed and even pesticide and encourage optimal utilization of resources. However, to attain optimal allocation of the resources in vegetable production there is need to reduce the level of resource use until the marginal value product and the marginal factor cost of each resource are at equilibrium (i.e. MVP=MFC).

2.4 Efficiency of resource use using stochastic frontier analysis

The sigma squared (σ²= 0.0026) and the gamma (ɣ = 0.99) were quite high and significant at 1.0% level of probability (Table 4). This indicates the goodness of fit and the correctness of the specified assumption of the composite error term distribution (Okoye and Onyenweaku, 2007). The gamma (γ = 0.99) shows that 99 percent variation in the total production cost is due to differences in their technical efficiency among the production units considered in the study. By implication, about 1% of the variation in output among producers is due to random factors such as unfavorable weather, effect of pest and diseases, errors in data collection and aggregation etc.

The coefficients of all the variables have desired positive sign, which agrees with a priori expectations. Quantity of seed, farm labour, fertilizer’s coefficients were highly significant at 99% confidence level while farm size and pesticides’ coefficients were significant at 5% level of probability. This implies that increasing quantity of seed, fertilizer and farm labour by 1.0% would increase the economic efficiency by 0.067, 0.035 and 0.114 respectively. This is in line with the findings of other studies (Umoh, 2006; Okezie and Okoye, 2006; Udoh and Etim, 2008; Okon et al., 2010) which indicates the importance of labour, seed, fertilizer in vegetable production.   Since, the increase in the quantity of seed, fertilizer and farm labour is less than the proportionate increase in efficiency, the output of vegetable is said to be inelastic to the inputs used in the area. Also, 5 per cent increase in farmland area cultivated given the input; pesticide will correspond to an increase in economic efficiency of vegetable with 0.190 and 0.076 respectively.

Results in Table 5 show the elasticities of factors of production (factor substitution) and the return to scale. The returns to scale (RTS) computed as the sum of the elasticities was found to be 0.4826. This was less than one but greater than zero (Stage II), suggesting positive decreasing returns to scale (rational zone of production). Thus 1% joint increased in inputs decreases the output by 0.48%.

2.5 Maximum likelihood estimates of the determinants of efficiency in vegetable production

Most of the coefficients were not significant except for age and years of education which are significant at 10% level of significance (Table 6). This implies that older farmers were more efficient as against the findings of Idiong (2007). Also, farmers with higher years of education are more technically efficient, as corroborated by Obwona (2000) in his findings. The other coefficients were not significant, although years of experience was positive, which implies that the years of experience of a farmer and number of visit by an extension agent does not determine efficiency among farmers sampled. However, extension awareness of the farmers had a negative relationship with the economic efficiency. This result is in line with the findings of Alam (2012) and Vanisaveth (2012), that extension contact received by farmers negatively affect technical inefficiency. This implies that there is a negative relationship between extension contact and inefficiency among the respondents.

2.6 Conclusion and recommendations

The paper focuses on the estimates of stochastic frontier production for vegetable farmers in Oyo State. The age and education are the major factors contributing to the efficient production of vegetable in the study areas. Other variables such as seed (kg), farm labour (man-days), fertilizer (kg) and pesticides (litres) also showed positive effect on the production of vegetable. It is therefore concluded that farmers with higher age and increase plot of the land used for vegetable with adequate level of education, there would be enhanced productivity level of the farmers. The differences in production of efficiency of farmers are a sign that there is ample opportunity for the enterprise to improve upon their operations. The years of experience of farmer and their access to extension service does not have a significant level. Therefore government policies should not embark fully on the programmes that encourage years of experience and access to extension service. Government policies should rather focus more on education of the farmers and also the age of the farmers as criteria through which programmes and resources can be channeled to those who are engaged in vegetable or are willing to go into vegetable farming.

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