INT L JOURNAL OF AGRIC. AND RURAL DEV. SAAT FUTO 2015 ECONOMIC EFFICIENCY OF SHEA BUTTER PRODUCTION IN OYO STATE, NIGERIA.

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1 ECONOMIC EFFICIENCY OF SHEA BUTTER PRODUCTION IN OYO STATE, NIGERIA. R. Adeyemo 1 J. T. Oke 1 P. T. Owombo 2* and O. Lanlokun 1 1 Department of Agricultural Economics, Obafemi Awolowo University, Ile-Ife, Nigeria 2 Moist Forest Research Station, Forestry Research Institute of Nigeria, Ibadan *Corresponding author: owombopaul@gmail.com Abstract This study investigated the economic efficiency of Shea butter production in Oyo state, Nigeria. A multi-stage sampling procedure was used to select 120 respondents for the study. The data collected were subjected to descriptive statistics, budgetary techniques, and stochastic production frontier analysis. Results indicated that most of the farmers were female; and traditional method of production was prevalent. The mean age and farming experience of the producers were 56.4±13.7 and 11.4±4.3, respectively. An average gross margin of N159,233 and net returns of N154,636 were recorded which implied that Shea butter production is profitable in the area. The results from the stochastic frontier analysis revealed that the efficiency of Shea butter production was influenced by labour, unit price, family size and level of education all of which were significant at 10 percent except for level of education which was significant at 5 percent level of probability. The mean technical efficiency was 67.6% in the study area. Therefore, producers should be encouraged to produce more and government should put in place policy thrust that will educate farmers as well as make labour available and affordable. Keywords: Shea butter, economic efficiency, production, stochastic production frontier Introduction Shea butter is a slightly yellowish or ivorycolored fat extracted from the nut of African Shea tree, Vitellaria paradoxa (Aboyella, 2002). Shea nut grows in the wild across the Sudan-Sahelian region of Africa. The major producing countries are Nigeria; which is currently the leading producer, Togo, Ghana, Mali, Uganda, Burkina Faso and Central Africa Republic (FAO, 2002). Shea is popularly called tree of life because of the numerous healing characteristics it possesses. Shea butter is mainly used as cooking oil, in the making of chocolate (Fold, 2000) candle and moisturizers in cosmetic industry. The healing qualities of Shea butter are due to the presence of several fatty acids and plant sterols, namely oleic, Stearic, Palmitic and Linolenic acids. These oil-soluble components are non-saponifiable, meaning they do not undergo saponification, or convert to soap, when introduced to an alkali. Shea butter possesses a significantly greater non-saponifiable fraction than most other nut oils and fats, which lends the substance greater healing potential for the skin (Fold, 2000).The healing qualities of Shea butter are due to the presence of several fatty acids and plant sterols, namely oleic, Stearic, Palmitic and Linolenic acids. These oil-soluble components are nonsaponifiable, meaning they do not undergo saponification, or convert to soap, when introduced to an alkali. Shea butter possesses a significantly greater nonsaponifiable fraction than most other nut oils and fats, which lends the substance greater healing potential for the skin. Boffa (1999) showed that the Shea belt comprised more than 500 million fruiting tree. FAO (2002) estimated approximately the total African production as 1,760,000 MT of Shea nuts per year. From this potential yield, only 35 % of the nuts are gathered and 85 % of this harvest is locally processed, to make 100,000 MT of local butter. The remaining portions, approximately 65,000 MT are exported, mostly to the food industry. Less than 5 % of the exported butter is used in the international cosmetics industry; a ball park figure would be 3000 MT per annum. Large quantities of Shea nuts are produced in West Africa though the exact production figures are not known (Mensah, 2010). FAO (1997) posited that the bulk of Shea butter produced in developing countries are from the local producers. The production and processing of Shea butter is still rudimentary in nature. The output is mostly less than the expected due to poor resources use among producers (Issahaku et al., 2011). They posited that that efficiency of production constitutes a major impediment to it production. Industrially, a mechanical Sheller such as the Universal nut Sheller may be used, although controversy over its usability with Shea nut has not being reported. The refined butter may be extracted with chemicals such as hexane (Addaquay, 2004) or clay filtering. Issahaku et al (2011) estimated and compared the allocative efficiencies of the traditional, Improved Shea Butter Processing Technology (ISBPT) and the Bridge Press (BP) methods of Shea butter processing. Marginal product approach was used to obtain the allocative efficiency estimates. Paschal (1988) investigated Shea butter production in the then Dagomba district now Tamale Metropolis (Ghana) using cost - benefit analysis to establish the profitability of traditional Shea butter production. Rarely, however had known studies been conducted to estimate determinants of economic efficiency of the products in the study area. The broad objective of the study was therefore to investigate the economic efficiency of Shea butter Volume 18 (1): ,

2 processing in the study area. The specific objectives are to examine the socio-economic characteristics of producers in the study area; estimate costs and return on Shea butter production and assess the economic efficiency of Shea butter production in the area. frequency distribution, tables and percentages to analyze the socio-economic characteristics of the respondents. Budgetary Technique Analysis This technique was used to evaluate the level of profitability of Shea butter production. Revenue was computed as monetary value of the total output. The total revenue is equal to the quantity of output multiplied by the price per unit of the produce. The technique is expressed as GM= TR-TVC Π= GM-TFC Where π= profit (N) TR= Total revenue (N) TVC= Total variable cost (N) GM= Gross margin (N) TFC= total fixed cost (N) METHODOLOGY Area of Study The study was conducted in Oyo state which is the leading producer of Shea butter in Nigeria. The state is located in the Southwestern part of Nigeria which lies between Latitudes and east of Greenwich meridian. The State covers 28,454 square kilometers and has a total population of 5,591,589 (NPC, 2006). There are 33 local government areas in the state. It is bordered in the North by Kwara, east by Osun State and south by Ogun states. In the west, Production Frontier Models it is bothered by Ogun state and partly by the Production frontier function shows the Republic of Benin. Oyo state is characterized by two maximum output obtainable from each level of input climatic seasons and they are the dry season that or input combination. Generally, there are four occurs between November and March and the rainy methods that can be used to estimate the production season that occur between April and October. The frontier: these are state has an annual rainfall range of 1000 mm-1400 Non- parametric frontier as stated by mm coupled with vast area of fertile land that is Hughes (1988) suitable for the production of crops such as the Deterministic parametric frontier applied by vegetables, yam, cassava, cowpea, tomatoes, maize Angner and Chu (1969) and perennial crops such as Shea nut, Cashew etc. Deterministic parametric frontier applied by Farming in the state is largely traditional and small Schmidt (1976) using maximum likelihood scale relying on manual labor equipped with and corrected ordinary least squares method, cutlasses and hoes except some emerging private respectively. agricultural enterprises. Stochastic parametric frontier popularized Sampling procedure and data by Jondrow et al. (1982). A multi-stage sampling technique was used in Although a variety of methods are used to selecting respondents for this study. The first stage measure production frontier, each of these involved a purposive selection of two Local methods has advantages and disadvantages Government Areas (LGAs) that are known primarily and there is no obvious superior approach for Shea butter production in the state. In the second among these methods (Forsund, 1980; Aly stage, four communities were randomly selected et al., 1987; Afolayan et al., 2012; Ayodele from each of the two selected LGAs to arrive at a et al., 2012). This position was supported by total of 8 communities. In the final stage, 15 Idiong (2007). The most common method of respondents were selected randomly from each estimating frontier function is the stochastic community. A total of 120 respondents were selected parametric frontier which this study in all for the study. Data were collected on the socioeconomic employs. characteristics of the producers, inputs Y 1 = f(x 1,b) exp (V 1 -U 1 )...(1) accessibility for Shea butter, constraints faced by Where y is the output of the i-th term producer among others. X 1 is a vector of inputs Analytical Technique b is a vector of parameter to be estimated The data collected were analyzed with the aid of f(x) is suitable functional form, descriptive statistical analysis, budgetary techniques V 1 is a symmetric random error that is and stochastic frontier analysis. assumed to account for measurement error Descriptive statistics provides a quantitative and other factors not under the control of the description of a set of data in a meaningful form farmer especially in a research study like this where there U 1 accounts for the technical inefficiency in are many measures; therefore, descriptive statistics is production exp stands for the exponential best fitted to simplify all the enormous data involved function and i= 1, 2.N in this study in a sensible way. This study employed Volume 18 (1): ,

3 This model is such that the possible production Y 1 is bounded by the stochastic quantity f(x 1,b) exp (v 1 ) hence the term stochastic frontier. The random error v1 are assumed to be independently and identically distributed as N(0,S 2 v) random variables independent of the u1 are half normal, truncated normal exponential and the two parametric estimates are obtained using maximum or a variant of corrected ordinary least squares. Stochastic frontier production model for Sheabutter enterprise Prior to the estimation of a frontier function, the form of production function has to be specified. Two different functional forms for the stochastic frontier were considered, namely the translogrithm and the Cobb-Douglas production functions. These are the two most common functional forms which have been used in empirical studies on production including frontier analysis. The interest was in testing if the Cobb- Douglas production function is an adequate representation of the data given the specification of the translogrithm stochastic frontier in choosing the function that better describe the data between the two functions. However, it should be noted that the Cobb-Douglas frontier is the restricted form of the translogrithm frontier, in which the coefficient of the second order terms are zero. In this study, a typical one output-five input Shea-butter production function was specified. If the result of the estimated model indicated Cobb- Douglas as a better fit than the translogrithm and was chosen as the lead equation, the non- stochastic production function would be specified as follows: Y=X O X 1 Q1, X 2 Q2,X 3 Q3,X 4 Q4,X 5 Q5,.. (2) Where Y is the dependent variable and it represents output of Shea-butter produced in kg per annum. X 1 = labour measure in man- days X 2 = quantity of Shea nut processed daily(kg) X 3 = distance of source of shea nut(km) X 4 =average price of Shea butter produced X 5 =distance from processing centre to home(km) Management plays an important role in any production system. A frontier with a good management method is at a vantage position to bring production to its frontier parameter. X o denotes the technical efficiency level and β 1, β 2 β 3 β 4 and β 5 are partial elasticities of the various inputs with respect to output. Since the Cobb-Doug stochastic function states as Y= 0, X 1 β1 X 2 β2 X 3 β3 X 4 β4 X 5 β5 e v1-u1 Which when linearized by finding both sides becomes U 1 LnY= ln 0 + β lnx 1 + β2 lnx 2 + β3 lnx 3 + β4 lnx 4 + β5 lnx 5 +V 1 - U 1 Where ln represents the logarithm to base e. V 1 are assumed to be independent and identically distributed random error having W(0,σ 2 ) distribution are non-negative random variables called technical inefficiency effects which are assumed to be independently distributed such that U 1 is defined by the truncation which mean U 1 and variance, σ 2 and U1 is defined by U 1 =δ 0 X 0 +δ 1 X 1 +δ 2 X 2 +δ 3 X 3 +δ 4 X 4 Where X 1 = age X 2 = family size X 3 = Level of education X 4 =years of experience RESULTS AND DISCUSSION Table 1 reveals the summary statistics of the socioeconomic characteristics of the respondents. The results in the table show that 83.3 percent and 16.7 percent of the respondents were female and male, respectively. This implies that Shea butter production in the area is female dominant. The results also show that while 2.5 percent of the respondents were less than or equal to 30 years of age, 3.3 percent, 30 percent and 63 percent of the producers fell within the age brackets of years, years and years, respectively. The mean age of the respondents was 56.4±13.7. Results further reveal that 13.2 percent, 72.4 percent, 1.4 percent and 13 percent of the respondents were single, married, divorced and widowed, respectively. It could be inferred from the above that most the respondents were married. Also, while 42.1 percent of the respondents had no education at all, 27.3 percent, 28.2 percent and 2.4 percent of the respondents had primary, secondary and tertiary education, respectively. This implies that a good number of the respondents do not have education at all. While 52.3 percent of the respondents had Shea butter production as full time occupation, 47.7 percent of them had it as part time occupation. The mean farming experience of the respondents was 11.4±4.3. The distribution of the experience reveals that 11.4 percent, 33.1 percent, 32.7 percent, 18.1 percent and 4.7 percent of the respondents had year of experience range 1-5 years, 6-10 years, years, years and greater than or equal to 21 years, respectively. Traditional method of processing is still the prominent processing method in the area as 58.3 percent of them used traditional method while 41.7 percent employed modern method. Majority (70.7%) of the respondents buy Shea nut while 29.3 percent of them obtained Shea nut from their plot. This implies that most of the producers do not own Shea nut tree in the area. Volume 18 (1): ,

4 Table1: Summary statistics of socio-economic characteristics of respondents Variables Percentage Mean Sex Male 83.3 Female 16.7 Age of respondents ± Marital status Single 13.2 Married 72.4 Divorced 1.4 Widowed 13 Level of education No education 42.1 Primary 27.3 Secondary 28.2 Tertiary 2.4 Mode of operation Part time 47.7 Full time 52.3 Year of experience ± Method of processing Traditional method 58.3 Mechanical method 41.7 Source of Shea nut Personal 29.3 Buy from farmers 70.7 Source: Field survey, 2012 Table 2 reveals the costs and returns on Shea butter production in the area. The results in the table revealed that gross revenue was N 294, The total variable and fixed costs were N135,110 and N4,597.00, respectively. Also, while the total cost was N139, 707, gross margin and net revenue were N159,233 and N154,636, respectively. It could be inferred from the above that return on expenses incurred is high. Table2: Costs and returns on Shea butter production Item Amount (N) Gross margin 294,343 Firewood 18,066 Shea butter 77, 970 Mortar and pestle 8,040 Transport 4,189 Water 1,930 Packing materials 1,881 Labour 19,656 Miscellaneous 3,378 Total variable cost (TVC) 135,110 Fixed cost (FC) Depreciation on fixed items 3,260 Rent 1,337 Volume 18 (1): ,

5 Total fixed cost (TFC) 4,597 Total cost (TVC+TFC) 139,707 Gross margin 159,233 Net revenue 154,636 Source: Field survey, $1= N Table 3 shows the maximum likelihood estimates of the stochastic frontier analysis. The general model included labour, quantity of Shea nut, distance from Shea nut, price and distance from home. The results of the general model in the table reveal that while labour and price were significant determinants of efficiency of Shea butter production in the area. While labour was positively significantly influenced efficiency of Shea butter production in the area, unit price of Shea nut was negatively significantly influenced efficiency of Shea butter production in the area. The variables were significant at 10 percent each. A unit increase in man-day of labour will increase output by 28 percent while a unit decrease in the price of Shea nut will increase efficiency by 24 percent. The inefficiency factors considered were age, family size, level of education and years of experience. From the table, only family size and level of education were significant. While family size was negatively significant, level of education was positively significant. They were significant at 10 percent and 5 percent levels of probability, respectively. This agrees with Afolayan et al. (2012) that level of education is a significant determinant of output level. The implication of the result was that these factors actually affected the efficiency of Shea butter. The negative sign of the family size implies that the added family member(s) are not participating in the Shea butter production. This might be because the added members are dependents. Table3: Maximum Likelihood Estimates of the Stochastic Production Frontier for Shea butter production Independent Variable Parameter Co-efficient T-ratio General model Constant Β * ln(labour) β * ln (quantity) β ln (distance from Shea nut) β ln(price) β * ln(distance from home) β Inefficiency model Age δ Family size δ * Level of education δ ** Years of experience δ * Sigma-squared Gamma ** Source: Data analysis, *Significant at 10%, **= significant at 5% Table 4 reveals the technical efficiency of the producers. The results from the table show that majority of the producers (38.4%) had technical efficiency of between 51-60%, while just 0.8% had Volume 18 (1): ,

6 efficiency of 30. Also, 27.5 percent had technical efficiency of 70%. The table further reveals that 7.5 percent and 25.8 percent had technical efficiency range of 31-40% and 41-50%, respectively. The mean technical efficiency of the farmers was 67.6%. This implied that the producers in the area still have opportunity for improvement. Shea butter producers in the area could improve their technical efficiency by 42.4% through appropriate use of their input. It shows that Shea butter producers in the area are technically inefficient. Table 4: Technical efficiency of the producers Technical efficiency percent Total 100 Mean technical efficiency (%) = 67.6 Source: Data analysis, 2012 Table 5 reveals the constraints faced by Shea butter producers. The table shows that credit unavailability is the most important constraint faced by Shea butter producers in the area. This agrees with studies such as Akinola and Owombo (2012) that smallholder farmers have extremely low access to credit which impedes their productivity. Inadequate supply of water is the least important constraint to Shea butter producers. The table reveals that unfavourable weather condition is the second most important constraint while poor storage and preservation method is the third rated constraint. It can be concluded from the above that constraints faced by the respondents could influence their performance. Table5: Constraints that Militate against Profitable Shea butter Production Constraint Mean Rank Credit unavailability st Unfavourable weather condition nd Poor storage and preservation methods rd Unavailability of packaging materials th Inadequate supply of skilled labour th Inadequate supply of Shea nut th Inadequate supply of firewood th Inadequate supply of water th Total Source: Data analysis, 2012 SUMMARY AND CONCLUSION put in place policy thrust that would educate the Shea butter production in the area was female producers, make labour available and affordable as dominant and were still in their active ages. Majority well as enhance credit access in the area were married. A good proportion of the respondents do not have education at all. Majority f the REFERENCES respondents engage in the production on full time Aboyella, G. E. (2002): Economic Analysis of Shea basis and they are well experienced. Traditional nut and Cashew Production in Bakwu East method of processing is the most popular in the area. District. International Journal of Social Most of the respondents buy Shea nut from other Sciences, Vol. 6, Issue 4, pg , farmers. The net return on Shea butter was N Addaquay J.A (2004). The Shea Butter Value Chain: 154,636. The producers efficiency was affected by Refining in West Africa, West Africa labour, price, family size and level of education, Trade Hub November, Ghana, respectively. The mean technical efficiency of the Afolayan, A. F., Tijani, A. A. and Sofoluwe, N. A. respondents was 67.6 percent. The major constraint (2012). Socio-economic factors affecting rated by the respondents was credit unavailability. It Nerica and Asian Rice production in Ekiti could be inferred from the above that Shea butter State, Nigeria. Nigerian Association of production is profitable. Therefore, farmers should Agricultural Economic conference be encouraged to go into Shea butter production and Proceedings, Pg government at the three levels of governance should Volume 18 (1): ,

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