An Evaluation of Groundnut Processing by Women in a Rural Area of North Central Nigeria

This study evaluated the economic empowerment potentials of groundnut processing by women in rural areas of North central Nigeria state using a sample of 100 women processors randomly selected from the study area. Data analysis was done using Descriptive statistics, Net Farm Income Model and Data Envelopment Analysis (D.E.A). An average net returns of N10, 586.6 was obtainable within a processing cycle. The average pure technical and scale efficiency scores were 80 and 83 percent respectively. The major constraints confronting the processing of groundnut include inadequate capital for expansion and lack of processing machines. A significant opportunity exists for empowering rural women through groundnut processing.


Introduction
Groundnut (Arachis hypogea L.) otherwise called peanut, monkey nut, gobber pea and arachide belongs to the family leguminosea.It originated from Latin America and the Portuguese who were responsible for its introduction into West Africa from Brazil in the 16 th Century (Gibbon and Pain, 1985;Abalu and Etuk, 1986).Peanut is one of the most popular commercial crops in Nigeria.Nigeria produces 41% of the total groundnut production in West Africa (Echekwu and Emeka, 2005).It is cultivated for its kernels, the oil and hay for livestock.Groundnut cake is often deep fired or dried to make a snack locally called kuli-kuli.Groundnut flour is used as an ingredient in soups, sweet, confectionaries and puddings.Groundnut especially those produced in developing countries has been used traditionally since the origin of humanity.It is rich in oil and protein and has a high energy value.
Developing countries account for nearly 95 percent of world production (Echekwu and Emeka, 2005).Asia accounts for about 70% of this amount while the major producers, India and China together represent over two-thirds of global output.Other important producers of groundnut are: Nigeria, Senegal, Sudan and Argentina.Groundnut with 25% protein and more than 40% oil, is an important food crop in many areas of semi-arid tropics (Food and Agriculture Organisation, 1994).
In Nigeria, the processing of groundnut into various products is mostly done by women either for home consumption or for commercial purposes (Ibrahim et al., 2005).The most common commercial products of groundnut are: groundnut oil, groundnut cake and fried peanuts which are sold at market places or hawked on the streets, (Ihekoronye and Ngoddy, 1985).The processing of groundnut is both the source of income and employment to a large proportion of rural women in northern Nigeria.Thus, the achievement of the Millennium Development Goal number three (promotion of gender equality and women empowerment) in northern Nigeria, requires that a study be conducted to assess the economic empowerment potentials of this very important economic activity.In addition, the technical and scale efficiency in groundnut processing were also determined alongside the constraints affecting the processing of groundnut by rural women.

Issues in literature on Data Envelopment Analysis
Data Envelopment Analysis (DEA) is a non-parametric, linear programming based frontier analysis method that was originally developed to analyze the performance of organizations whose goals are not limited to profit maximization (Charnes et al., 1978).Data Envelopment Analysis (DEA) uses a non-parametric non stochastic piecewise linear production frontier in estimating technical efficiency.The DEA frontier estimates efficiency relative to the Pareto-efficient frontier which estimates best performance.Furthermore, it can obtain target values based on the best practices units (peers) for each inefficient firm that can be used to provide guidelines for improved performance (Abdulwadud, 2000).
The technique (DEA) is flexible in that it does not require specification of an underlying production relationship between inputs and outputs.It is able to incorporate inputs and outputs that are measured in different units and at different scales, and can accommodate multiple inputs and multiple outputs with minimal value judgments placed on the relative "worth" or "cost" of these inputs and outputs (Frija et al., 2008).According to Diaz et al (2004), a DEA model may be either input-oriented or output-oriented.As such, in deciding on the orientation of a DEA model one should also consider over which variables decision making units (DMUs) have most control (a sample of producers are referred to as decision making units (DMU) in DEA terminology).If DMUs have more control over output variables than input variables, the DEA model should be output-oriented; otherwise, the model should be input-oriented.
Furthermore, both output-oriented and input-oriented DEA models produce the same technical efficiency estimate for a farm under the assumption of constant returns to scale in production (Lovell, 1993).Under the assumption of variable returns to scale, the estimates of technical efficiency will differ.However, Coelli (1995) claims that since linear programming does not suffer from statistical problems such as simultaneous equation bias, the choice of a measure does not affect the efficiency estimates significantly.
Since none of the production frontier models used in empirical analyses of production efficiency is without its limitations, it is very important to make a careful choice of model.Coelli (1995) identified some weaknesses of the technique as follows.
(1) Since DEA is an extreme point technique, noise (even symmetrical noise with zero mean) such as measurement error can cause significant problems.
(2) A standard formulation of DEA creates a separate linear program for each DMU, thus large problems can be computationally intensive.
(3) DEA is good at estimating "relative" efficiency of a DMU but it converges very slowly to "absolute" efficiency.In other words, it can tell you how well you are doing compared to your peers but not compared to a "theoretical maximum." (4) The main criticism of deterministic frontiers is that they rule out the possibility of a deviation from the frontier being caused by measurement error or other noise (such as bad weather).Therefore, any deviations from the estimated frontier are attributed to inefficiency.
Econometric stochastic production frontiers, however, obviate these criticisms.Furthermore, they provide a measure of the reliability of the technical efficiency estimates by means of the standard errors of the model parameters.However, this benefit comes at the cost of imposing assumptions about the functional form of the production technology and the distribution of the inefficiency term.These assumptions affect the analysis and distort efficiency scores (Fraser and Cordina, 1999).Avoiding such assumptions is an advantage of the DEA approach (Jafarullah and Premachandra, 2003).The minimum assumption DEA requires is the monotonicity and convexity of the efficient frontier (Abdulwadud, 2000).
The major weakness of DEA relates to its inability to account for measurement error (Kalyan, 2002).However, Banker (1996) and Fare and Grosskopf (1995) proposed several statistical tests which have subsequently made DEA a powerful tool for efficiency analysis Despite its limitations, DEA is surely a competitor with the stochastic production frontier in efficiency analysis.Several researchers such as Dalton (2004), Reig-Martinez and Picazo-Tadeo (2004), Abdulwadud (2000), Ogunyinka et al (2004) and Helfand (2003) have used DEA for estimating technical efficiency in agriculture.

Materials and methods
The study was conducted in five rural areas spread across the north central zone Nigeria.The locations are largely agrarian with the majority of the people as subsistence farmers who cultivate crops such as groundnut, yam, maize, sesame, cassava, cowpea, millet, and sorghum.Twenty women groundnut processors were randomly selected from each location to give a total of 100 respondents for the study.Data was collected with the aid of an interview schedule.Data was collected on the socio-economic characteristics of groundnut processors as well as input such as; raw groundnut, capital, machines, labour, and the outputs.The data collected were analyzed using simple descriptive statistics, Net farm income model and Data Envelopment Analysis The variable cost items considered include capital (cost of transportation, firewood, and packaging), labour, cost of grinding, water, salt and raw groundnut.The fixed cost items include; drums, basin, processing machine, frying pan and mortar.

Data Envelopment Analysis (DEA)
Data Envelopment Analysis is a non-parametric, linear programming based frontier analysis method that was originally developed to analyze the performance of organizations whose goals are not limited to profit maximization (Charnes et al., 1978).Data Envelopment Analysis (DEA) uses a non-parametric non stochastic piecewise linear production frontier in estimating technical efficiency.The DEA frontier estimates efficiency relative to the pareto-efficiency frontier which estimates best performance.An output-oriented variable returns to scale DEA model was used to calculate technical, and scale efficiency in groundnut processing.The output oriented model estimates the proportional increase in outputs as inputs remains the unchanged.Assuming that there is data available on K inputs and M outputs in each of the N decision making units (i.e.processing) and input and output vectors are represented by the vectors x and y, respectively for the ith processor.The data for all processors may be denoted by the K N input matrix (X) and M N output matrix (Y).The envelope form of input-oriented VRS DEA model which is the most widely used is then specified according to Coelli, et al (1998) and Sharma et al (1999) as follows: Min θλ θ Where λ is scalar and is a N x 1 vector of constraints, the value of θ obtained signifies the efficiency score for the ith DMU.It will satisfy θ ≤ 1with a value of 1 indicating a point on the frontier hence a technically efficient DMU according to Farrell (1957) definition.Thus, the linear programming problem needs to be solved N times and a value of θ is provided for each the processor (DMU) in the sample.Both CRS and the VRS DEA are conducted on the same data set and the ratio between the CRS and the VRS technical efficiency scores (CRS T.E /VRS T.E ) is called scale efficiency (Latruffe et al, 2005).Efficiency scores in the study were estimated using the computer program, DEAP version 2.1 described in Coelli (1996).The inputs considered include: Raw groundnut (kg), Water (litres), Labour (man/days), Salt (g), Capital (firewood, packaging and transportation).The outputs considered include: Oil (litres) and Cake (kg).

Inputs and outputs in groundnut processing
The result shows inputs used and outputs obtained in groundnut processing.The inputs used include raw groundnut, water, salt and firewood.Others include fuel (kerosene) and labour.In a processing cycle of about 4 days, the total quantity of groundnut processed was 3862.80kg with an average of 154.5120kg.The total quantity of water used was 1160.00 litres with an average of 46.400 litres per processor while the total quantity of fuel (kerosene) used was 44.00 litres.Furthermore, Table 1 shows the total of groundnut cake obtained was 2236.80kg with an average of 89.4720kg per processor while the total quantity of groundnut oil obtained was 1520.00 litres with an average of 60.800 litres.

Costs and returns analysis in groundnut processing
The result for the cost and returns analysis is presented in Table 2.The average total cost of processing was N20,250.9, which was dominated by the variable cost of processing which accounted for 90.7% of the average total cost.The fixed cost component on the other hand accounts for 9.3% of the average total cost of processing.The cost of raw groundnut dominated the variable cost by accounting for 79.59 of the total variable cost.
In terms of returns, an average gross returns of N30,817.6 per processing cycle was obtained from groundnut processing.The average gross returns was dominated by the return from groundnut oil which accounted for 56.3% of the average gross returns while the groundnut cake (kuli-kuli) accounts for 43.7% of the average gross returns.The revenue from groundnut oil also accounts for 85.5% of the total average cost of processing.This implies that for the processors to make sufficient profit, they have to sell both groundnut cake and groundnut oil.A similar finding was made by Hamidu, et al (2007).The result further shows that the average net return of N10,586.6 per processing cycle of about four days was obtained in groundnut processing by rural women in the study area.This means that in a month, net revenue of about N74106.20 was obtainable.

Pure Technical Efficiency in Groundnut Processing
An improvement in technical efficiency is essential for enhancing the profitability of any enterprise.An assessment of the level of technical efficiency in groundnut processing was done to provide further insights into the nature and causes of inefficiency in groundnut processing.The technical efficiency in groundnut processing in the study area varies from 0.07% for the 'least' practice processors and 100% for the 'best' practice processors with a mean value of 0.802.Thus, in the short run, there is scope for increasing the outputs of groundnut oil/cake by about 20 percent through improvement in technical efficiency.

Scale Efficiency in Groundnut Processing
The scale Efficiency in groundnut processing in the study area varies from 12.4% to 100% with a mean of 83%.This implies that, the groundnut processors in the study area need to increase their scale of operation by 17% to attain full scale efficiency.If the average scale efficiency score is less than the average pure technical efficiency score, then scale inefficiency is the cause of overall inefficiency (Krasachat, 2003).Otherwise, it is attributed to inefficient management practices (Latruffe et al., 2005).Hence, the low average pure technical efficiency (80 percent) in comparison to the average scale efficiency (83 percent) as shown in Table 3 and 4 respectively.Implies that pure Technical Efficiency in the cause of overall inefficiency.This implies that inefficiency in groundnut processing is due to managerial factors and not the scale of operation.

The constraints faced by rural women in groundnut processing
The constraints militating against groundnut processing in the study area varies from one respondent to another.However, ten constraints were identified as shown in Table 5.The processors pointed out that inadequate capital for expansion, unstable price of inputs and inadequate processing machines are the three major constraints hindering the processing of groundnut.A similar finding was also made by Haruna et al., 2006).The respondents also pointed out that their profit will increase if the constraints can be overcome.

Conclusion
Inefficiency in groundnut processing is due to managerial factors and not the scale of operation.Furthermore, a significant opportunity exists for empowering rural women and alleviating poverty in north central Nigeria through groundnut processing.This opportunity can be exploited through improvement in managerial ability and provision of advisory services.
. The Net farm income model is expressed as: NFI = TR (Qc x Pc + Qo x Pou) -TC (TVC + TFC) Where: -NFI = Net farm income TR = Total revenue (from cake and oil)

Table 1 .
Inputs and outputs in groundnut processing

Table 3 .
Pure technical efficiency estimates in groundnut processing

Table 5 .
Constraints faced by women groundnut processors *Multiple responses allowed.