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- Table 4.3 Marketing activities of processors in the States and North Central Nigeria
- Available market GNO
- Distribution of GNC customers
- 4.3. Input Use Efficiency in Traditional and Modern Groundnut Oil Processing in North Central Nigeria
- Table 4.4 Generalized log likelihood-ratio tests of the complete technical efficiency of groundnut oil processors (γ=0) in North Central Nigeria
- 4.3.1Technical efficiency estimates for groundnut oil processors in North Central Nigeria
- Table 4.5 Maximum likelihood estimates (MLE) of the stochastic frontier production (processing) function for traditional GNO processors in Nasarawa and Benue States
- Technical inefficiency model
- Table 4.6 Maximum likelihood estimates (MLE) of the stochastic frontier production (processing) function for GNO processing in Niger state and North Central Nigeria
- Technical inefficiency mode
- Table 4.7 Maximum likelihood estimation (MLE) of the stochastic frontier production (processing) function for modern GNO processors in North Central Nigeria
- Table 4.8 Distribution of technical efficiency estimates for traditional and modern small - scale GNO processors in the states and the North Central zone
4.2.4 Marketing Product marketing commenced immediately after processing activities. The products sold were the groundnut oil (GNO) and groundnut cake (GNC). For traditional processors in Nasarawa State, 57% of them sold their products both at the processing sites and the markets, 30% sold in the markets and 13% sold only at the sites, (Table 4.3). In Benue State, 33% sold at the sites and 42% sold both in the markets and the sites while 25% sold only at the sites. In Niger State, 30% sold in the markets and 32% at the processing sites. For the North Central zone, 47% sold at sites and the markets, 29% sold in the markets only, while 24% sold at the processing sites. For modern processors, 76% of the firms supplied to buyers at their respective locations, and only 12% sold at the sites. Pertaining to unit of sales, Table 4.3 also shows that traditional processors sold their products in small quantities probably to meet the needs of small buyers. Ninety percent in Niger State, 66% in Nasarawa State, 34% in Benue State sold in small units. In the entire North Central Zone 60% sold in small units. Some traditional processors also sold in larger units of 25litres containers; 65% of them were in Nasarawa State; 44% in Benue State and only 10% in Niger State. All the modern processors (100%) sold their products in larger quantities in 25litres cans, drums and tankers. Concerning availability of markets for processed products in the study area (Table 4.3), 98% of the traditional processors in Niger State, 94% in Nasarawa State and 58% in Benue State agreed that there was adequate market for groundnut oil. In the North central zone it was 86%. All modern processors (100%) agreed that there was good market for GNO, and they received bookings for products ahead of production. The problem of low market for GNO faced by traditional processors in Benue State, particularly Makurdi, was attributed to inadequate market information resulting in large inventory of unsold products. This resulted in alternate day marketing of products. That is processors, sold only on market days allocated to them by the market association; a kind of quota sales. In the market for groundnut cake, 98% in Niger State; 96% in Nasarawa State and 76% in Benue State agreed that there was good market for groundnut cake. In the North Central Zone, 91% said the market was good. For modern processors, the market was favourable because all their cake was sold even ahead of production. Only a few traditional processors, 4% in Nasarawa State, 24% in Benue State and 2% in Niger State said there was no market. Also a few, 9% for North Central Zone said there was no market. Customers’ patronage of the products of traditional and modern processed products is also shown in Table 4.3. It was observed that purchases of GNO by consumers, from traditional processors were highest in Benue State with 96%; 93% in Niger State and lowest in Nasarawa State with 27%. It was 67% in the entire zone. Only 18% of the consumers bought GNO from modern processors. Wholesalers’ purchase of GNO produced by traditional processors in Nasarawa State was 71%; 27% and 20% in Benue and Niger States, respectively. The result for the central zone showed that wholesalers’ purchase was 42%. For the modern processors, 94% of their sales were to wholesalers. Retailers were the highest (94%) customers of GNO from traditional processors in Nasarawa State; 100% in Benue State and 95% in Niger State. In the entire zone, it was 96%, and 88% from modern processors. Fewer manufacturers and processors (packaging firms) bought GNO in the study area, but usually in larger quantities, see also table 4.3. These processors included an oil processing and packaging company with a factory in Jos. It was observed here that major buyers of GNO from traditional processors were the retailers, consumers and followed by wholesalers (Table 4.3). The retailers bought and sold within their communities or nearby markets; while the wholesalers came from distant markets in the eastern and northern states to buy the products for sell in their home markets. With respect to groundnut cake, consumers were the highest buyers of GNC produced by traditional processors. This was seen in 66% of them in Nasarawa State; 100% in Benue State and 94% in Niger State and 86% for the North Central Zone. No consumer bought GNC from modern processors. It is also noted that wholesalers patronized 76% of the processors in Nasarawa State, and less in other states. No wholesalers patronized modern processors’ cake. Ninety seven percent (97%) of the retailers in Nasarawa State; 96% in Benue State and 95% in Niger State bought cake from traditional processors. For the zone it was 96% of the retailers that bought GNC from traditional processors and no retailer bought GNC from modern processors. Very few manufacturers and processors bought GNC from traditional processors. It was noted that retailers and consumers were the major buyers of GNC from traditional processors, followed by wholesalers. Retailers bought and sold within their communities. Consumers bought to eat, drink with ‘gari,’ make local salad, while manufacturers used it in food processing e.g. steak meat and/or mixed with vegetables among others. All the cake (100%) processed by modern processors were sold to manufacturers; animal feeds makers in farms and feed companies, who normally placed order ahead of production. The cake from modern processors was not consumed by humans because of the method of processing, but used for animal feeds. Table 4.3 Marketing activities of processors in the States and North Central Nigeria
4.3. Input Use Efficiency in Traditional and Modern Groundnut Oil Processing in North Central Nigeria The result of the analysis of technical efficiencies of traditional and modern groundnut oil processors is presented in this section. The parameters of maximum likelihood estimation (MLE) of the stochastic frontier function adopted is discussed and presented in Tables 4.5, 4.6 and 4.7. This is for traditional processors in Nasarwa, Benue and Niger States; as well as for the zone and modern processors in the selected states. To validate the results from the stochastic frontier analysis (SFA) models for purpose of analysis, test of hypotheses was done to show the presence of inefficiency in the models, else the model could be analysed with the ordinary least square (OLS) method (Coelli 1996; Saris & Kariagianis, 2006). This was achieved with the likelihood ratio test and the result is presented in Table 4.4. The null hypothesis (H0 :) that γ=δ0=δm=0 indicating that technical inefficiency was not present in the models was rejected at various levels of significance of alpha. This implied that there existed some level of inefficiency in the processing activities of processors, hence the models were appropriate for analysis (Ajibefun & Daramola, 2003; Osborne & Trueblood, 2006; Bamire, Oluwasola & Adesiyan, 2007). Table 4.4 Generalized log likelihood-ratio tests of the complete technical efficiency of groundnut oil processors (γ=0) in North Central Nigeria
* Critical values (8 degrees of freedom) obtained from table D.4 pp 988-989 in Gujarathi (2007), the abridged table from table of percentage points χ2 (at α) by E. S. Pearson & H. O. Hartley eds. Biometrika table for statisticians Vol. 1,3d., table 8, Cambridge University Press, New York, 1966 Source: Computed from field survey data, 2010/2011 The maximum likelihood estimates of the parameters for the models estimated for traditional processors in the States and the zone are presented in Tables 4.5, 4.6 and 4.7. The results from technical efficiency aspect indicated that raw groundnut (X1), was significant at 1% level of probability in Nasarawa and Niger States, but not significant in Benue State (Tables 4.5 and 4.7). It had positive coefficient in all the States. This explains the fact that quantity or quality of raw groundnuts for processing determined, to a very large extent the yield of groundnut oil (GNO) and groundnut cake (GNC) obtained. Labour (X2) was also significant in Nasarawa State at 1% level of significance (LOS), but not significant in Benue and Niger States. Labour coefficient was negative in all the States, except in Benue State. This suggests caution in labour use, so as not to exceed its marginal productivity. Fuel-wood (X3) was significant at 5% in Benue State with positive coefficient. It was not significant in Nasarawa and Niger States. Salt (X4) was significant in Nasarawa State at 1% LOS and positive. In the inefficiency model, age (Z1) was significant in Nasarawa and Niger States at 1% LOS. Age coefficient was negatively signed in all the States, implying that increase in age reduced inefficiency of the processors. This is plausible because age goes with accumulation of experiences, knowledge and human capital development capable of reducing inefficiencies. Level of education (Z2) was significant only in Nasarawa State at 10% LOS with negative coefficients in all the States. This also implied that the level of education reduced inefficiencies in traditional processing. Years of experience (Z3) was significant at 1% LOS in Benue and Niger States, and significant at 5% LOS in Nasarawa State. The years of experience coefficient in all the States except Niger State were negative. Gender (Z4) was significant at 1% in Niger State, and 5% in Nasarawa State. Marital status (Z5) was only significant in Nasarawa State at 5% LOS and negative. Household size (Z6) was also significant at 5% LOS and negative only in Nasarawa State. Cooperative participation (Z7) was significant at 1% LOS in Benue and Niger States. The results for the North Central Zone for both traditional and small-scale modern processors are presented in Tables 4.6 and 4.7. In the pooled data for the zone, labour and salt were significant at 1% level of significance. Fuel-wood was significant at 5% while raw groundnut was significant at 10% LOS. In the inefficiency model, household size was significant at 5% LOS while level of education and years of experience were significant at 10% level of probability. In small-scale modern processing, raw groundnut and labour were significant at 1% level of probability (Table 4.7). This underscores the critical nature of these variables. This is in line with the fact that raw groundnut constitute over 80% of variable input in groundnut oil processing. Labour marks its importance as procurement, processing and marketing of products required labour. Maintenance and quality of groundnut were not significant but contributed positively to efficiency attainment in the processing. Level of education and years of experience were significant at 1%. Gender had little or no effect in efficiency of modern groundnut processing. Efficiency estimates from the model in the various states for traditional processors indicated that the gamma, γ, statistic was 0.5501 and significant at 1% level of probability in Nasarawa State, but not significant in Benue State. In Niger State γ was 000.184 and significant at 1%. For modern processors, the γ statistic was estimated at 0.89 and significant at 5% level of probability. It is to be noted that γ ϵ (0, 1), therefore the closer γ is to one, and the more error variance is attributable to inefficiency. If γ is 0 and statistically insignificant then the ordinary least squares (OLS) method becomes more appropriate tool for this analysis. In this study, inefficiency in the production (processing) activities has been identified, which is akin to findings of Karagianis & Saris (2006); Ogundele & Okuruwa (2006) and Bamire et al (2007).
Frequency distributions of technical efficiency scores as well as the means are reported in Table 4.8 for traditional processors in the states and the region as well as for small-scale modern processors. Majority of traditional processors had efficiency scores above 0.80 in all the states and the zone, except for the modern processors whose efficiency scores were fairly distributed from 0.56 – 1.0. The mean efficiency in Nasarawa State was 0.880; Benue State, 0.851; Niger State, 0.979 and for the zone 0.907. For modern processors the mean efficiency was 0.741. The minimum efficiency score for traditional processors in Benue State was 0.32 and a maximum of one. In Nasarawa State, the minimum was 0.461and a maximum of 0.999; a minimum of 0.913 and a maximum of 0.998 for Niger State. For North Central zone, the minimum score was 0.32 and a maximum of one; in the small-scale modern processing the minimum efficiency score was 0.473 and the maximum of 0.99 with a mean of 0.804. The high technical efficiency recorded in this study implied that processors attained efficiency even though some level of inefficiency was still present. That is, there existed little chance to improve technical efficiency of the processors given their present state of technology, if they were operating with such high efficiencies. Ogundelele & Okoruwa (2006) and Amaza et al (2007) also reported such high efficiencies among rice farmers. Arising from this result, it is implied that any desire to increase output required change in technology type and hence the production function of the processors. On the structure of technical efficiency, some firms were distributed below 0.8 (80%) implying that there was still room for some improvements based on the technology currently practiced by the processors. Also agro-processing best approximates to the industrial production than crop and animal production, hence the precision in measurement and the resultant high efficiency scores.
***, **, * = 1%, 5% and 10% levels of significance respectively. Values in parenthesis are standard errors Source: Computed from field survey data 2010/2011 Table 4.6 Maximum likelihood estimates (MLE) of the stochastic frontier production (processing) function for GNO processing in Niger state and North Central Nigeria
***, **, * = 1%, 5% and 10% level of significance in that order. Values in parenthesis are standard errors. Source: Computed from field data, 2010 – 2011 Table 4.7 Maximum likelihood estimation (MLE) of the stochastic frontier production (processing) function for modern GNO processors in North Central Nigeria
***, **,* = 1%, 5% and 10% levels of significance respectively. N=17 Source: Computed from field survey data, 2010/2011 Table 4.8 Distribution of technical efficiency estimates for traditional and modern small - scale GNO processors in the states and the North Central zone
Source: Computed from field survey data, 2010/2011 Yüklə 0,67 Mb. Dostları ilə paylaş:
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