ECONOMIC ANALYSIS OF OIL PALM PROCESSING IN OVIA NORTH EAST AND IKPOBA-OKHA LOCAL GOVERNMENT AREAS OF EDO STATE, NIGERIA

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1 ECONOMIC ANALYSIS OF OIL PALM PROCESSING IN OVIA NORTH EAST AND IKPOBA-OKHA LOCAL GOVERNMENT AREAS OF EDO STATE, NIGERIA ABSTRACT Emokaro, C. O. and Ugbekile, P. C. Department of Agricultural Economics and Extension Services, Faculty of Agriculture, University of Benin, P.M.B. 1154, Benin City, Edo State, Nigeria. or The fortunes of Nigeria s palm oil production plunged as a result of the discovery of crude oil and this has caused a major decline in the processing of palm oil in Nigeria, with attendant negative implications on the nation s micro and macro economics. Hence, this study focused on the economics of oil palm processing in Ovia North East and Ikpoba-Okha Local Government Areas of Edo State, Nigeria, in order to identify gaps that could be exploited to ameliorate this negative economic trend. Primary data used in the study were collected through well-structured questionnaire, administered on 120 randomly selected oil palm processors in the study area. Descriptive statistics, budgetary analysis and the Stochastic Frontier Production Function were used in analyzing the data. Results of the data analysis showed that majority of the respondents were; males (81.7%), in their most active years (66.7%) and married (90%). The average household size was seven persons. Oil palm processing was shown to be profitable in the study area, with a net farm income of N1, 006, per annum and a return per naira invested of 0.86 (86%). Also, results of the Stochastic Frontier Production Function analysis indicated that the major factors that influenced the output of oil palm processing enterprise in the study area were; palm fruits, water and labour. From the result of the inefficiency model, the major factors which influenced the technical efficiency of the respondents were; processing experience and membership of cooperative society. The technical efficiency of oil palm processors range from , with an average efficiency of , with majority of the respondents (63.3%) having technical efficiency exceeding The major constraints faced by the processors were inadequate finance, labour shortage, unavailability of land and high maintenance cost. In conclusion, it was shown that enough gaps exist in the oil palm processing business in the study area that could be exploited for better results. Keywords: Budgetary Analysis, Constraints, Oil palm, Stochastic Frontier, Technical Efficiency INTRODUCTION Nigeria used to be the world s largest producer of oil palm (Elaeis guineensis), before the crude oil boom era and now Malaysia has taken the leading position (Onwubuya et al., 2012). The crop remains one of the most important economic crops in the tropics. Processing Fresh Fruit Bunch (FFB) to extract the oil is labour intensive and involves the following stages threshing, picking, parboiling, digestion, extraction and separation (Chinedum et al., 2002). Nigeria is the third largest producer of palm oil in the world after Malaysia and Indonesia (Omoti, 2003). It also accounts for about 72% (1.3 million tonnes per annum) of Nigeria s total vegetable oil production and contributes to the country s foreign exchange earned yearly (Omoti, 2003). Oil palm is appreciated by most people in the Southern part of Nigeria because of its level of utilization with respect to the various products and by-products that can be obtained from it; such as; palm oil, palm kernel oil and palm kernel cake. Oil palm gives the highest yield of oil per unit area, compared to any other oil producing plant when processed, and it produces two distinct oils; Palm oil and Palm Kernel Oil which are of great importance in the industrial market (FAO, 2002). Palm oil and palm kernel oil were once very vital to Nigeria s export trade, as Nigeria was a leading producer of oil palm products in the world (Ibitoye et al., 2011). There are different techniques used in processing palm oil and these range from modern methods to traditional methods. However, the traditional method of processing is more prevalent among small scale processors and these small scale processors are responsible for the bulk of palm oil processed in Nigeria (Olagunju, 2008). Palm oil is used as an energy source in livestock feed. Industrially, it is used in the manufacturing of detergents, cosmetics, shoe polish, magazine and candle sticks. Generally, the oil palm tree is considered a Complete plant because all the products and by-products derived from the tree possess commercial importance. Hence, No part of the tree is wasted. Nigeria used to produce a large proportion of palm oil sold in the world market (Hartley, 1998). It was a dominant source of foreign exchange for Nigeria before Indonesia and Malaysia took over (Omoti, 2003). The fortunes of Nigeria s palm oil production plunged as a result of the discovery of crude oil and this has caused a major decline in the processing of palm oil in Nigeria. There are indications that promotion of private sector participation in oil palm plantation holds the ace in effective revival of the produce business in the country. Irrespective of all the research efforts on improved oil palm processing methods over the years by research institutes, Oil Palm processing is still faced with a lot of problems including; inadequate finance, shortage of labour, scarcity of water and firewood and lack of effective processing techniques. Recently, production has failed NJAFE VOL. 10 No. 2,

2 to meet the domestic demand for palm products; as a result, Nigeria has become a net importer of palm products (Olagunju, 2008). However, several questions need to be answered with respect to Oil Palm processing in Nigeria. Some of these include: How feasible and profitable is the oil palm processing business? What are the challenges faced by oil palm processors? Is the traditional method of processing efficient enough to ensure sustainability? The main objective of this study was thus to analyze the economics of oil palm processing in the study area. In order to achieve this broad objective, the following specific objectives were achieved: estimation of the profitability of oil palm processing, the efficiency of resources used in oil palm processing were examined, the constraints faced by oil palm processors were identified. The interest in this study stems from the fact that oil palm processing in Nigeria is dwindling and Nigeria is experiencing difficulties meeting local demand for palm oil. This however, creates an opportunity for increased production. It therefore becomes necessary to offer information on this sub-sector of the agricultural sector in order to showcase the potentials of oil palm production and also to highlight the potential for boosting the Nigerian economy by concentrating on the enterprise dynamics. In view of the above, this study becomes expedient as it would provide the information needed for a successful oil palm processing enterprise and also could serve as a guide for investors in the venture as well as for policy makers. METHODOLOGY Study area This study was carried out in Ovia North East and Ikpoba-Okha Local Government Areas (LGAs) of Edo State. Ovia North East has a land area of 2,301 km 2, while Ikpoba-Okha has a land area of 862 km 2. Edo State is an inland State in Central Southern Nigeria, with Benin-city as its capital. It is bounded in the North and East by Kogi State, in the South by Delta State, and in the West by Ondo State. It is located between Longitudes 05 o 04 East and 06 o 43 East and Latitudes 05 o 44 North and 07 o 34 of the Greenwich. The State has a tropical climate characterized by two distinct conditions of wet and dry seasons. The wet season ranges from April to October with a brief fall in August. While the dry season ranges from November to March. The annual rainfall averages 250 cm near the coastal areas and 150 cm in the extreme northern part of the State. Temperature ranges from C. The State covers an area of 17,802 sq km and it is divided into three agro-ecological zones with a total of eighteen local government areas. The major ethnic groups found in the State include; Benin, Esan, Akoko-Edo, Igbanke, Emai and Ijaw. Edo state is noted for the both crop and livestock production. The following are some of its crop products; oil palm, rubber, cashew, cocoa, citrus, plantain, banana, cassava, rice, maize, melon, leafy and fruit vegetables among others. The State is also blessed with precious stones like; quartz, amethyst, mica, dolomite, granite stone and lime stone used in cement production. Sampling procedures and Sampling Size: The simple random sampling procedure was used in selecting respondents for this study. Firstly, five (5) and seven (7) communities were selected from Ikpoba-Okha and Ovia North East LGAs (known for their high production of oil palm) respectively, using random sampling techniques. This gave a total of 12 communities. Then, 10 respondents were randomly selected from each of the 12 communities, giving a total sample size of 120 oil palm processors. Measurement of variables Data collected were converted to appropriate scale for analysis, these are; Output (palm oil): Measured in tones; Seed/Fruit: Measured in bunches; Water: Measured in litres; Fuel: Measured in litres; Labour: Measured in mandays; Transport: Measured in Naira. Analytical techniques Descriptive analysis Descriptive statistics employed in the study include; simple frequency, percentage and averages. These were used to describe the socio-economic characteristics of the processors and the factors militating against processing. Budgetary analysis: This was used to analyze the cost and returns in the oil palm processing as well as to estimate profit. The mathematical form of budgetary analysis is represented as, Gross margin (N) = Gross revenue (N) Total Variable Cost (N) Net return = Gross margin (N) Total Fixed Cost; i.e. = Z Y. The Fixed Costs were depreciated using the straight line method as given by Olukosi and Erhabor (2005). That is; Depreciation = Where; C = Cost of Procuring the fixed input N = Lifespan of the asset Stochastic frontier analysis Stochastic Frontier Production Function (SFPF), using Cobb Douglas functional form was used to estimate the technical efficiency of oil palm processors in the study area (Ojo, 2003 and Ashagidigbi et al., 2011). NJAFE VOL. 10 No. 2,

3 The function is specified as; LnY i = β 0 + β 1 Ln (X 1) + β 2 Ln (X 2) + β 3 Ln (X 2) + β 4 Ln (X 4)+ β 5 Ln (X 5) V i - U i Where, Y i = Quantity of Palm Oil (measured in tonnes); X i = Seed/Fruit (measured in bunches); X 2 = Water (measured in litres); X 3 = Fuel (measured in litres); X 4= Labour (measured in man-days); X 5 = Transport (measured in naira); V i = A random error term with normal distribution N (O, S 2 ) ; and μ I = A non-negative random variable called technical inefficiency effects associated with the technical inefficiency production of processors involved. Ln = natural logarithm β 0 β 5 = Parameters to be estimated Technical efficiency is defined in terms of the observed output (Y i) to the frontier output (Y * * i ). The Y i is the maximum output achievable given existing technology and assuming 100% efficiency. It is denoted as; Y = f (X i β) + e * That is, TE = Y i /Y i Where, 0 TE 1 The technical inefficiency model is specified as: μ it = R 1it + 2 R 2it + 3 R 3it + 4 R 4it + 5 R 5it + V i Where; μ = Inefficiency R 1 = Age, R 2 = Sex, R 3 = Co-operative Society membership, R 4 = Years of experience, R 5 = Household size, V i = Error term, 0-5 = Parameters to be estimated. RESULT AND DISCUSSION Socioeconomic characteristics of respondents Sex Distribution of Respondents: The sex distribution of palm oil processors in the study area showed that out of the 120 respondents, 98 (81.7%) were males, while 22 (18.3%) were females. This could be attributed to the fact that majority of the respondents used the traditional method of processing which is more strenuous than the modern method. This result contradicts most findings that food processing is the responsibility of women (Azam- Ali et al., 2003). Age Distribution of Respondents: The distribution of the respondents based on their age group indicates that, 0.8% fell within the age of years, 5.8% fell within years, and 26.7% fell within years, while 66.7% fell within 40 years and above. This implied that majority (66.7%) of the processors were in their most active years or most productive years as opined by Anzanku et al.(2006), that oil palm processors were within the age bracket of 30 and 50 years. Educational Status of Respondents: Of the 120 respondents selected, 24 (20%) had no formal education, 18 (15%) had only primary education, 64 (53.3%) had only secondary education and 14 (11.7%) had tertiary education. The high level of literacy among the respondents should have a positive effect on their productivity and hence, have an impact on their standard of living. This is so because majority of the respondents (80%) were literate with one form of formal education or the other and this implies that they would be highly receptive to technological changes that could improve production and better adoption. This is in consonance with the view of Erhabor and Emokaro (2007) who opined, in line with World Bank reports that, the output of an educated farmer is about 13% higher than that of the uneducated. The obvious reason being that, the educated and literate farmer has the added advantage of learning with little promptings, the rules and application of production inputs in order to achieve optimum result. Marital status of respondents The result showed that five (4.2%) were single, 108 (90%) were married, seven (5.8%) were widowed and none of them were divorced. Majority of the respondents were married. This implies that a sense of responsibility of married people is capable of prompting them to put more commitment to the business and consequently enhance productivity towards meeting their family needs. Household size distribution of respondents The distribution of respondents according to their household size showed that, 11 (9.2%) had household sizes of between 1 4 persons, 89 (74.2%) had between 5 8 persons, and 20 (16.6%) had household sizes larger than eight persons. The mean household size was seven persons. The implication is that the business must be profitable enough in order to meet their needs and also, there is a tendency for cost of production to be reduced, since large family size translates to ready supply of family labour. This result agrees with the findings of Agwu (2006), who opined that majority of the oil palm processors have a household size ranging from 5-9 persons with an average household size of six persons. Distribution of respondents according to cooperative society membership NJAFE VOL. 10 No. 2,

4 The result indicated that while 29 (24.2%) respondents were members of cooperative societies, 91 (75.8%) were non-members. The implication of this is that the high rate of non-membership might restrict accessibility to improved technology as well as finance which are essential for expansion purpose. These organizations could also serve as channels for extension contact with large number of the processors, as well as, offer opportunities for participatory interaction with extension organizations Occupational distribution of respondents The result of the distribution of respondents according to whether oil palm processing was their major occupation or not showed that 76 (63.4%) majored in oil palm processing, while 30 (25%), seven (5.8%), six (5.0%) and one (0.8%) respondents were involved in farming, trading, combination of both, as well as any other business respectively, as their primary sources of income, making oil palm processing their secondary source of income. The implication of this result is that the respondents still engaged in other income generating activities; and this could be attributed to the seasonal nature of the processing business (Yusuf, 2007). Sources of funds It is shown in the result that personal savings was the most common source of fund as claimed by 88 (73.3%) respondents. 26 (21.7%) respondents sourced funds from both personal savings and cooperative societies while six (5%) obtained funds from other sources. The implication of this is that majority of the processors relied on their personal savings for financing the business. However, this may not be enough for expansion purpose. Besides, only 24.2% were members of cooperative societies which is a reflection of how accessible the processors were to improved processing technology and procurement of loan. Processing experience of the respondents Result of analysis showed that 11 (9.2%) respondents had less than 5 years experience, 30 (25%) had between 5 10 years experience in oil palm processing, 37 (30.8%) had between years experience while 42 (35%) had above 15 years experience in oil palm processing. The respondents had a mean processing experience of 13 years. This implied that majority of the respondents (65.8%) had more experience in oil palm processing and hence the greater tendency to be technically efficient, as indicated by Karki, (2004), Onyenweaku and Nwosu (2005), that there is a positive correlation between experience and efficiency in business. Constraints faced by oil palm processors The distribution of respondents responses according to the constraints faced in oil palm processing is presented in Table 1. The result showed inadequate finance as the major constraint faced by oil palm processors with a mean value of Shortage of firewood was considered the least constraint faced by oil palm processors with a mean value This is because majority of the processors were accustomed to using the pressed cake obtained after extracting the oil from the Fresh Fruit Bunches (FFB), making it a very good substitute for firewood. Other constraints like; low product prices, shortage of labour, slow season harvest, unavailability of land as well as their mean values are presented in the Table. This result compares favorably with the findings of Ekunwe et al. (2009), who reported inadequate finance as the most serious constraint facing palm oil processors in Ovia North East area of Edo State. Table 1: Distribution of Constraints Experienced in Oil Palm Processing Constraint Mean Standard deviation Inadequate finance Labour shortage Unavailability of land High maintenance cost High cost of processing equipment Scarcity of water Low product prices Slow season harvest Shortage of firewood Budgetary analysis of oil palm processing The budgetary analysis shows the difference between total revenue and total cost of production. This difference is the profit. This was used to determine the profitability of investing in the oil palm processing business. Fixed Cost: This is the cost that remains unchanged irrespective of the level of production in the short run. It refers to expenditure on durable assets such as; processing shed, storage containers, boiling drums, press, etc. The fixed cost of oil palm processing per annum of the processors in the study area is presented in Table 2. NJAFE VOL. 10 No. 2,

5 Table 2: Fixed cost of oil palm processing per annum Cost items Depreciation Value (N) Percentage Processing shed 74, Storage container 3, Boiling drum 31, Others (press, digester etc) 22, Total 130, As shown in Table 2, processing shed accounted for most of the fixed costs with 56.8%. Storage container and boiling drum accounted for 26.3%. Variable cost This refers to cost which changes with the level of output. It is the cost of non-durable capital asset of the processors. It is the expenditure on palm fruit, water, fuel, labour and transportation. The variable cost of oil palm processing per annum is presented in Table 3. Table 3: Variable Cost of Oil Palm Processing per annum Cost items Unit Cost/unit (N) Value (N) Percentage Palm fruits (tonnes) , , Water (jerricans) 1, , Diesel (litres) 1, , Transport (naira) 144, Family labour (man-day) , Hired labour (man-day) , Others (firewood, electricity) Total 1,040, From the Table above, expenditure on palm fruits contributed the most to variable cost with a percentage of This is a reflection of the processor s desire to increase the quantity of palm oil produced which is a function of the quantity and quality of palm fruits processed. This also confirms the findings of Ogbonna and Ezedinma (2005), who opined that the cost of palm fruits was the highest cost factor in oil palm processing. Total Cost: This is the sum of the fixed cost and variable cost of production. It is shown in Table 4. Table 4: Total cost per annum of oil palm processing Cost items Value (N) Percentage Fixed 130, Variable 1,040, Total 1,170, As shown in Table 4, the variable cost constituted 88.85% of the total cost. This is in accordance with expectation due to the flexibility characteristic of oil palm processing; while the fixed cost constituted 11.15% of the total production cost. Total revenue This refers to the value of the processed products. The total revenue generated from each product by the processors in the study areas is presented in Table 5. Table 5: Revenue generated from each product Product Unit Price / unit (N) Value (N) Percentage Palm oil (drums) , ,654, Uncracked kernel (head pans) 1, , Other (Cracked kernel, pressed cake etc) 147, Total 2,177, NJAFE VOL. 10 No. 2,

6 That palm oil generated the highest revenue which contributed 75.98% of the total revenue (Table 5) implied that palm oil is the major product. Uncracked kernel generated 17.25%, while cracked kernel, pressed cake as well as other by-products generated 6.77%. Gross margin This is the total revenue less the total variable cost of production. It is presented in Table 6. Net income This is the gross income less the total cost of production. The net income per annum of oil palm processing business in the study areas is presented in Table 7. Table 6: Gross margin per annum of oil palm processing Items Value (N) Total Revenue 2,177,652 Variable cost 1,040,216 Total 1,137,436 Table 7: Net income of oil palm processors per annum Items Value (N) Total Revenue 2,177, Total Variable cost 1,040, Gross Margin 1,137, Total Fixed Cost 130, Net income 1,006, Return per naira (R/N) 0.86 The result for profitability analysis is presented in Table 7. The net income per annum of oil palm processing was N1, 006, with a return per naira of 0.86.This shows that for every one naira invested, a return of 86 kobo was obtained; an indication that the oil palm processing business is profitable. This result agrees with Ogbonna and Ezedinma (2005), that palm oil processing is profitable. The result also compares favorably with the findings of Ekunwe, et al. (2009), who reported a mean net income per annum and return per naira invested of N841, and N0.84 respectively. Efficiency analysis This section presents the result of the analysis of the factors that determine technical efficiency of resource use of the respondents. The estimated results of the Maximum Likelihood Estimates (MLE) of the parameters of the Cobb Douglas Stochastic Frontier Production Function (SFPF) and the inefficiency model are presented in Table 8. From the Table, the sigma squared was statistically significant (p > 0.01), which indicates the correctness of the specified assumption of the distribution of the composite error term. Also, the major factors that influenced the output of an oil palm processing enterprise in the study areas were; palm fruits, water, and labour. These also contributed significantly to the technical efficiency of the respondents. The co-efficient of the oil palm fruit was significant (p< 0.01) and positive, which implies that increase in output of palm oil can be achieved by increasing the quantity of oil palm fruits processed. However, the importance of quality should not be neglected. Similarly, the co-efficient of water was positive and significant (p< 0.05), which indicates the relevance of water to output as it is an important input for processing the palm fruits. Therefore, as quantity of fruits required for processing increased, water needed for processing increased which ultimately increased the output of palm oil obtained. Furthermore, the co-efficient of labour was positive and significant (p<0.01). Thus, an increase in labour input results in an increase in output. However, the coefficients of fuel and transport were not significant, with fuel having a negative co-efficient. From the result of the inefficiency model, the major factors which influenced the technical efficiency of the respondents were; processing experience and cooperative society membership. However, age and sex were found to have the expected negative but insignificant co-efficient. The implication of this is that these factors do not contribute to processors inefficiency. The years of processing experience had a negative co-efficient which was significant (p<0.05). This implies that processors with more years of experience were likely to be more efficient. The longer a person stays in a particular business, the more productive and experienced the person becomes. This is in agreement with Adeoti (2004), who opined that years of experience reduce inefficiency. The coefficient of cooperative society membership was found to be negative and significant (p<0.01). This implied that, processors who were members of co-operative societies were more likely to be technically efficient. This is because of the merits attached to being a co-operative society member and the benefits accruing to its members. Such benefits could include access to credit facilities as well as improved technology, all of which promotes technical efficiency. As shown in Table 9, the technical efficiency of oil palm processors range from with an average efficiency of Majority of the respondents (63.3%) had technical efficiency exceeding 0.94 while 36.7% had technical efficiency ranging from This implied that production capacity could still be expanded at this level of processing technology. This can be achieved through intensification of enlightenment or cultivation and processing of improved fruits, provision of infrastructural facilities among others. NJAFE VOL. 10 No. 2,

7 Table 8: Estimates of the stochastic production functions and inefficiency parameters of oil palm processing business Variables Co-efficient Standard error t-ratio General model Constant ** Fruits ** Water * Fuel Labour ** Transport Inefficiency model Constant * Age Sex Co-operative Membership ** Experience * Household size Variance parameters Sigma-squared ** Gamma Log likelihood Mean Technical Efficiency Number of observation (N) 120 *(p< 0.05), **(p<0.01) Table 9: Distribution of technical efficiency among oil palm processors in Edo State Range of Technical efficiency Frequency Percentage < Total Statistics Minimum Maximum Mean Range Elasticity of production and returns to scale The return to scale analysis which is a measure of total resources productivity is shown in Table 10. This is obtained by the summation of the co-efficient of the various inputs used in oil palm processing. A value of was obtained which indicated decreasing returns to scale; a feature characteristic of the stage II production level, where an increase in input would result in an increase in output but at a decreasing rate until optimum level is attained. At this stage, resources and production are said to be efficient. Hence, processors are advised to maintain production at this level where maximum output is obtained from a certain level of input utilization. Table 10: Elasticity of oil palm processing and returns to scale Variables Elasticity Fruit Water Fuel Labour Transport Return to Scale (RTS) RTS = 1: Constant return to scale RTS > 1: Increasing return to scale RTS < 1: Decreasing return to scale NJAFE VOL. 10 No. 2,

8 CONCLUSION AND RECOMMENDATIONS Findings from this study showed that oil palm processing was a profitable business in the study areas, irrespective of the problems militating against it. This study also showed that most of the processors were technically efficient with respect to usage of most of their inputs, although none of the processors operated on the frontier. This shows that the technical efficiency of the oil palm processors can be improved upon through appropriate enlightenment programmes. Based on these findings recommendations are made; (i) financial assistance should be given to processors through loans from agricultural banks or any other related financial institution. This should be done at low interest rates to encourage borrowing, which would lead to increase in production and hence, improve the standard of living of processors; (ii) co-operative societies should be formed by processors and the Government should make extension services available to processors in order to encourage them to join these societies and enlighten them on the benefits of these societies, since it was found that belonging to co-operative society significantly influenced the technical efficiency of the processors. Also, co-operative societies would facilitate the actualization of the first recommendation through loans and other means of assistance; (iii) females should be encouraged to go into oil palm processing by awareness creation through various media (seminars, workshop, television, radio, newspaper). This will ensure a balance in gender participation; (iv) processors should be encouraged to own their own palm plantations instead of purchasing palm fruits from outsiders. This would go a long way in reducing cost of transportation and cost of purchasing fruits which will in turn reduce cost of production and hence increase returns and efficiency; (v) furthermore, appropriate authorities should ensure the availability of basic infrastructure such as, adequate water supply, regular power supply, and accessible feeder roads among others to facilitate the marketing of the palm products; (vi) in order to tackle the problem of labour shortage, concrete effort should be made to derive mechanical means of harvesting the palm fruits. Also, government and private bodies should encourage researchers through regular funding at both universities and research institution in order to improve the breeding, cultivation, harvesting, processing and marketing of oil palm. REFERENCES Adeoti, A. I Impact of HIV/AIDS and Related Sickness on the Technical Efficiency of Farmers in Benue State, Nigeria. Issues in African Rural Development Monograph Series. Monograph (34) Agwu, A. E Adaptation of Improved Oil Palm Production and Processing Technologies in Arochukwu Local Government Area of Abia State, Nigeria. Journal of Agriculture, Food, Environment and Extension. 5(1): Anzanku, H., Abiniku, O. E., Azayaku, E. O. and Yohana, J. K Socioeconomic Analysis of Small Scale Oil Palm Fruit Processor in Nasarawa State.Proceedings of the 46 th Annual Conference of Agricultural Society of Nigeria. Pp Ashagidigbi, W. M., Suleiman, S. A. and Adesiyan, A Technical and Allocative Efficiency of Poultry Egg Producers in Nigeria. Agricultural Journal.6 (4): Azam Ali, S. E., Judge, P. F. and Battcock, M Small Scale Food Processing: A Directory of Equipment and Methods, 2 nd Edition, Intermediate Technology Development Group (ITDG) Publishing, London, pp.1 6 Chinedum, N., and Adeola, O. A Implications of Improved Oil Palm (Elaeis guineensis) Fruit Processing Technologies for Labour and Income among Rural Households in Imo State, Nigeria. Department of Agricultural Economics, Imo State University, Owerri.University of Hohenheim. Institute of Agricultural Economics and Sciences Economics in the Tropics and Sub-Tropics, Stuttgart, Germany. Pp 1 Ekunwe, P. A., Emokaro, C. O. and Obayuwana, I Economics of Palm Oil Processing in Ovia North East Local Government Area of Edo State, Nigeria. Journal of Sustainable Tropical Agricultural Research. 31: Erhabor, P. O. and Emokaro, C. O Economic Importance of Cassava. In: Cassava the White Gold. Erhabor, P. O., Azaiki, S. S. And Ingawet, S. A. (Eds). Initiative Publication Company. Pp. 8. FAO Small Scale Palm Oil Processing in Africa. FAO Agricultural Services Bulletin, 148. ISSN Rome, Italy. Hartley, C. W. S The Oil Palm. Third Edition. Harlow, England: Longman. Ibitoye, O. O., Akinsorotan, A. O., Meludu, N. T. and Ibitoye, B. O Factors Affecting Oil Palm Production in Ondo State of Nigeria. Journal of Agriculture and Social Research. 2 (1) Karki, I. B The Impact of Project Intervention on Rural Household in Nepal.Assessment of Socioeconomic and Environmental Implication. A PhD Thesis Submitted To The University Of Gussen. Institute of Project and Regional Planning, Gussen, Germany. NJAFE VOL. 10 No. 2,

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