Analysis of Maize Value Addition among Entrepreneurs in Taraba State, Nigeria

This study analyzed maize value addition among maize entrepreneurs in Taraba State, Nigeria. A multi-stage sampling procedure was used to collect primary data from two hundred and twelve respondents (212), using structured questionnaire. Data were analyzed using descriptive statistics, Value addition model, ANOVA, multinomial logistic regression, log likelihood test ratio and factor analysis. The result of the study revealed that most (62.3%) respondents were males. 61.3% were within the productive age of 21-40 years. Majority (71.1%) were married with household sizes of 6-10. The total of (32.8%) respondents of them had secondary school education, (46.6%) had processing experience of between 6-10 years in processing as their major occupation. proportionate (49.2%) of total respondents had non-farm income of N150,001 per annum. Results of the value addition analysis showed that maize processed as boiled maize is more profitable with a mean of N130, 900 per annum. The result from the multinomial logistic regression on choice of maize processing enterprises revealed that sex had negative coefficient, which implied that male respondents preferred grain production enterprise rather than processing into akamu, corn flour, massa and boiled maize. Also age had negative coefficient, implying that age increase tends to favour grain production than processing. In relation to processing constraints, the maize processing value chain was hampered by the following: inadequate processing facilities, inadequate credit/funds, high cost of transport and inadequate access to inputs. The study concluded that maize value addition is a profitable enterprise and entrepreneurs should be encouraged to venture into it. Also the Agricultural Development Project Programme should send extension agents to processors to encourage processing diversification especially into poultry feeds. Keyword— Value addition, entrepreneurs, maize, processing, enterprise.

product to a certain destination. Value chain studies are important because the results yield interested stakeholders and company's ability to understand and optimize the activities that lead to its competitiveness and high profit levels (Keyser, 2006). Maize is a multipurpose crop because every part of its plant has economic value. The stems and leaves are used for feeding cattle and the seeds are used for food, livestock feeding and pharmaceuticals.

Problem Statement
Over the years in Taraba State precisely entrepreneurs of maize production enterprise and other stakeholders in maize industry have continued to be poor with low income, as observed in the works of Gani and Adeoti (2011). These authors identified the cause of these low level of income and poverty on government negligence of the industry as well as the extreme poverty of the stakeholders who could not participate in effective marketing to turn around economic fortune to their favour. Thus leading to consumption of almost all of the produce year round with little or no processing activities. Where related relevant local research could have provided an insight into the situation, there scarcely exist any. Although a lot of research has been conducted on maize production and marketing in some States of the Federation, however little work has been done on maize value addition in Nigeria. For instance, adoption of cassava value added innovation and its implication in rural livelihood in Abia State (Chidozie, 2014). Capacity building on Cocoyam value addition training for rural women in Abia State (Onuekwusi et al., (2016). Yam value chain, constraints and opportunities for small scale farmers in the middle belt Nigeria (Damulak, 2012). This and many related studies have been conducted in Nigeria; however, there is a gap of information on analysis of maize value addition among maize entrepreneurs in Taraba State. The need for the conduct of this research becomes an obvious necessity in filling the existing gap.

Aim and Objectives of the Study
The main objective of this study is to analyze maize value addition among maize entrepreneurs in Taraba

Sampling and Sample Size Selection
The state comprises three agricultural zones, the Northern, Central and Southern zones. A multi-stage sampling procedure was adopted. Producers, marketers, processors and transporters enterprises were the target for this study. Firstly, purposive sampling was adopted, to select two agricultural zones, the Central and Northern zones, noted for high level of maize production. The second stage involved purposive selection of five Local Governments Areas from the two zones , involved in high level of maize production. These were Gashaka, Gassol and Bali from the Central zone, Jalingo and Ardo-kola from the Northern zone. Thirdly communities from each Local Government Area were selected by simple random sampling technique. Fourthly, a sampling frame was developed for each of the rural communities using proportional allocation of 5% (0.05) across board, a total of 212 respondents were selected as a sample size. Primary data were collected for the study. Data were obtained through administration of a well structured questionnaire and administered by the researcher and trained enume rators to maize producers, transporters, processors and marketers. Descriptive statistics which involves the use of frequencies, percentages and means were used to analyze objectives i and ii, Value addition model was used to analyze objective iii, Multinomial logistic regression was used to analyze objective iv, Factor analysis was used to achieve objective v, ANOVA was used to test hypotheses one (i), Log Likelihood test ratio was used to test hypotheses two (ii).

Model Specification:
The following models were adopted for data analysis Multinomial Logistic Regression model The Multinomial logistic regression model is generally specified in equation 1

Value Addition model
Value Addition model was used to examine the most profitable maize processing value chain, which is simply the difference between the total revenue receipts and the total variable cost as expressed in equation 2, i.e VA=TRR-TVC … 2 Where, VA=Value Added TRR=Total Revenue Receipts and TVC=Total Variable Cost

Analysis of variance (ANOVA)
The analysis of variance technique enables the determination of the number of relevant factors (or causes) of variation and the logical significance of each one of them. This is specified in equation 3. F ratio = …3 Where Vb = the difference between the variance Vw = the mean value of the variance

Factor Analysis
Factor analysis is a method for investigating whether a number of variables of interest Y1, Y2 ……., Yl, are linearly related to a smaller number of unobservable factors F1, F2,…….., Fk . The factor analysis model expresses the variation and covariation in a set of observed continuous variables y (j = 1 to p) As a function of factors η (k = 1 to m) and residuals ε (j = 1 to p).
νj are intercepts λjk are factor loadings ηik are factor values εij are residuals with zero means and correlations of zero with the factors Table 1 shows that majority (61.3%) of the respondents fell within the age bracket of 21-40 years while (36.8%) of the respondents fell within the age bracket of 41-60 years. The mean age was 37.78 years. This indicates that most of the respondents were young, active and of productive age. Table 1 also indicated that 62.3% of the respondents were males, while 37.7% of the respondents were females. This implies that majority of the respondents were males. This agrees with the findings of Ogunniyi and Omotesho (2011) and Osondu et al., (2014) who reported that most of the respondents were males. Majority (71.1%) of the respondents were married, 20.6% of the respondents were single, 6.4% of the respondents were widowed and 2.0% of the respondents were divorced. This implies that the area under study was dominated by married people. It was also found that 32.8% of the respondents had secondary school education, 20.6% of the respondents had primary sch ool education. This implies that majority of respondents were   Table 2 shows that maize processed into akamu has a mean of N127,714.63, maize processed into corn flour has an annual mean of N112,609.677, maize processed into massa has an annual mean of N119,283.333, maize processed into boiled corn annually has a mean of N130,900.00, maize processed into roasted maize annually has a mean of N121,160.00. This indicates that boiled maize is more profitable compared to the other products annually in the study area. However, these annual means are generally low monetarily. This agrees with the findings of Umeh et al.,  Table 3 indicates that 32.8% of the respondent process their maize into corn flour, 19.1% process their maize into massa, 18.1% process their maize into akamu, 18.7% process their maize into roasted corn, 11.3% of the respondent process their maize into grains and 5.9% process their maize into boiled maize. This shows that most of the maize is processed into corn flour in making tuwo because majority of the households consume tuwo as their major meal in the study area. This study agrees with the finding of Thomas (2010) who reported that maize are mostly used for processing into flour, maize are roasted or boiled and eaten as a snack. This implies that male participation in processing unfavored maize processing like akamu, corn flour, massa and boiled maize. It means that female processors are involved in processing these products more than their male counterparts. This agrees with the findings of FAO. (2000) that women are basically responsible for processing most food for storage and it also agrees with the programme Strategies for increasing food production and food security in Nigeria that traditionally women have been the custodian of most primary on farm processing operation and p ost harvest operations. The marginal effects of these products were -0.276, -0.09, -0.163 and -0.008 respectively. This implies that a 1% increase in male participation reduced their choice of processing akamu, corn flour, massa and boiled maize by 0.276%, 0.09%, 0.163 and 0.008% respectively. The coefficient of age was negative and statistically significant with akamu (-0.09) and massa (-0.122). This implies that increase in age of processors favored grain production as against processing it into akamu and massa. The marginal effects for these variables were (-0.09) and (-0.018). This means that at 1% increase in age of processors reduced their choice of processing maize into akamu and massa by 0.09 and 0.018% respectively.The coefficient for level of educational level is positive and statistically significant with akamu (0.48) at 5% level. This implies that the level of education favored processing into akamu as against grain production. The marginal effect for the variable was 0.071. This means that a 1% increase in level of education increase their choice of processing maize into akamu by 0.07%. The coefficient of household size is positive and statistically significant with akamu (0.15) and (0.192) corn flour against grain production. The marginal effects for these variables were (0.023) and (0.427) respectively. This means 1% increase in household size increase their choice of processing maize into akamu and corn flour by 0.023% and 0.42% respectively. This is because increased household size provides cheap family labour for the intensive tasks needed for akamu and corn flour processing at small scale level. The coefficient of net farm income is negative and statistically significant with akamu (-6.39) and corn flour (-8.45). This implies that increase in Net farm income favored grain production as against processing maize into akamu and corn flour. Following that sequence, the marginal effects for these variables were (-9.34) and (-5.07). This implied that increase in Net farm income of processors reduced their choice of processing akamu and corn flour, by 9.34% and 5.07% respectively. This conforms to the findings of Jay.

Forms in Which Maize is Processed into in the Study Area
(2018) that if an enterprise is continually unprofitable, then get rid of it and carefully consider options.   Table 6 presents factor analysis of constraints to maize value addition in the study area. The analysis reveals two major constraints namely: 1, socio-economic infrastructural constraints) and 2 (Marketing constraints). These were derived from loadings of 0.400 and above, while those with loadings less than 0.400 were ignored. This agrees with the findings of Abiyong (2017), who derived variable loadings of 0.400 and above, while those with loading less than 0.400 were not considered. The variables that (loaded high under factor 1) socio-economic infrastructural constraints were dominated by inadequate provision of processing equipment (0.419), inadequate availability of funds (0.669), high cost of products (0.661), poor transportation system (0.409), high cost of transportation (0.455). This was revealed by Olayemi (1982) that government places emphasis on increased food production without commensurate attention to food distribution in regards to funding. The variables that loaded high under factor 2 (marketing constraints) were high cost of inputs (-578), high cost of onloading/offloading (0.640) and access to input (0.714). The result agrees with Ajala and Adesehinwa (2007) who also reported high cost of transportation as constraints that may lead to market inefficiency.

IV. CONCLUSION AND RECOMMENDATIONS
The study examined analysis of maize value addition among entrepreneurs in Taraba State and concluded that maize value addition is a profitable enterprise which entrepreneurs are encouraged to venture into and it is revealed that value addition is essential to maize entrepreneurs in the study area. On the basis of results of the study, it was concluded that maize value addition among entrepreneurs in the study area was hampered by constraints. This can be improved when infrastructure are put in place. Based on the results of the study and conclusion drawn, the following recommendations were made:

1.
The result revealed that processors have low level of educational qualification. Education is very important in achieving any development; to achieve this there should be effective educational training to strengthen the processing enterprise.

2.
Extension agents to be sent to processors to encourage processing into more products, like processing into animal feed. It is important that processing should follow modern trends revealed from research.

3.
Maize processors should be guided by Extension agents to register themselves into cooperative societies in order to harness the benefit of such group, one of which is the ease of getting access to credit facilities from both formal and informal financial institutions.

4.
Government should provide infrastructure such as roads, transport and Storage facilities for processors to have ease of transportation, low cost of products and also getting access to the products