Lupine publishers | Scholarly Journal of Food and Nutrition\
Abstract
The study is on net revenue
determination of beans marketers in Imo state, Nigeria. Specifically, it
examines the socioeconomic characteristics of the respondents, determines the
factors influencing the selling price and marketing margin of beans and
identifies the marketing constraint for beans in the study area. Purposive and
simple random sampling techniques were employed to select eighteen (18)
wholesalers and forty-eight (48) retailers. Descriptive statistics and multiple
regression models were used to analyze the data collected. Results showed that
majority of the beans traders were males, within the age range of 41-50 years, well
educated, have high marketing experience, married and belong to
trade/cooperative association. The regression analysis shows that the purchase
price of beans favored the selling price of wholesale and retail marketing
however it has negative effect on variables such on their marketing margins and
seasonality. The constraints to beans marketing in the study area are problem
of insurgency (91.94%), high transportation cost (95.16%) and high purchase
price (87.10%).The results suggest that policies that could impact on
transportation and insurgencies in the north should be adopted to ameliorate
the challenges of Bean marketers.
Keywords: Net Revenue; Determination; Beans (Cowpea); Marketers;
Nigeria
JEL Classification: D41, L10, Q13
Introduction
Beans are third most popular food
after rice and cassava in Nigeria, grown throughout sub-Saharan Africa commonly
in the dry savanna regions of West Africa. A warm climate and drought tolerant
crop grown throughout the tropics particularly in the semi-arid and low rainfall
regions [1], it is a pulse with high protein content, affordable and also a
staple food crop. Its forage is used in animal feed especially during dry
season when animal feed is scarce and demand for feed is very high. Beans play
a key role in the diet of rural and urban dwellers especially the poor and
vulnerable group as an affordable source of protein and a substitute for animal
protein. This is in line with Ayinde and Adejobi [2], who reported that beans
provide the most affordable protein supplement to the urban and rural poor.
This is as a result of its ability to serve in different forms such as Moimoi,
Akara (bean cake) etc. it can also be cooked alongside with other cereals and
tubers such as rice, maize, yam, plantain etc. which is very affordable and
helps to reduce high rate of starchy food consumption in the developing
countries. In Nigeria Beans production is appreciating, having its major
producing states in the north. Singh et al. [3] as reported by [4] revealed
that Nigeria is the major producer and consumer of beans in the world with
production estimate of 1.7million metric tons from about 4million hectares.
Futuless et al. [5] also reported that about 14million hectares are under beans
cultivation with about 5million of the total being in Nigeria. In 2013 it was
revealed that Nigeria produces an average of 2.5 million metric tons of beans
[6]. The high production rate of beans in Nigeria can be attributed to high
population growth, poverty and the demand for low-cost food. According to
Nathan et al. [7] the economic importance of beans in Nigeria is on the
increase particularly in the southern states where its demand is increasingly
high due to population growth. This is as a result of its aptitude in boosting
their protein intake thereby covering the gap created by insufficient supply of
animal protein in the diets of many households [8]. However, beans production
in Nigeria has declined in the past few years as a result of insurgency in the
northern region of the country which is the main producer of beans, it is
reported that most of the beans consumed in Nigeria is imported from
neighboring countries such as Niger republic and Burkina Faso (African
agricultural technology foundation [9]. Beans have emerged with the
potentiality of bridging the protein-carbohydrate imbalance prevailing in
Nigeria through its wide range of acceptability and its nutritional value which
aims towards sustainability and food and nutrition security Thus, improving the
revenue generation of both the producers and the marketers. However, Gieri et
al. [4] reported that some of the major problems associated with beans
production and marketing is inadequate capital, pest and disease, poor
logistics marketing outlets and management difficulties which have adverse effect
on their net revenue [10]. Also, according to Bakoji [11] farmers socioeconomic
factors such as farm size, level of education, years of farming experience,
institutional and technological factors influence their net revenue on beans
production and marketing activities [12,13]. Despite this limitations on beans
production, there is a rapid population growth with increasing demand for
staple food crops therefore there is a need to make advancements in coping with
the challenges surrounding beans production and marketing in order to meet the
demand for food to achieve food sufficiency and eradicate malnutrition in the
study area. This study aims to examine net revenue determination of beans
marketers in Imo state, Nigeria. Specifically; it intends to achieve the
following
a)
Examine the socioeconomic characteristics of beans marketers
b)
Determine the factors influencing the net revenue of beans marketers in the
study area.
c)
Identify the marketing constraints for beans marketers in the study area.
Methodology
This study was conducted in Imo
State. The state lies in the south east geopolitical zone of Nigeria with
Owerri as its capital and largest city. The State also lies within latitudes
40451N and 70151N, and longitude 60501E and 70251E with an area of around
5,100sq.km. Imo State is composed of three Agricultural zones, namely; Owerri,
Okigwe and Orlu and it is subdivided into 27 Local Government Areas (LGAs). The
State has a total population of 3,934,899 persons with a population density
that varies from 230 persons per square kilometer in the densely populated
areas [14]. Agriculture is assumed to be one of the major sources of income of
most occupants of the area though they are largely civil servants. Purposive
and random sampling techniques were used in selecting the respondents. A main
market from the three agricultural zones namely owerri, okigwe and orlu was
purposively selected due to high concentration of marketing activities in these
markets. One rural local government area was randomly selected from each of the
three agricultural zones. A major market was selected from the three rural
LGAs. Thus, three urban and three rural markets making a total of six markets
were randomly selected for the study. Six beans wholesalers and six retailers
were selected from the three urban markets and from the rural market; ten beans
retailers were randomly selected. This gives 18 wholesalers and 48 retailers
(18 urban and 30 rural retailers) and a total of 66 respondents for the study.
Data for this study were collected from primary source and was obtained using a
well-structured questionnaire. Data collected were analyzed using descriptive
statistics and multiple regression techniques.
Results and Conclusion
Source: Field survey data, 2019
The result of the socioeconomics of
beans marketers as shown in the Table 1 above discloses that majority of beans
marketers were male (Tables 2 & 3). This points to the fact that it is a
strenuous business, which often requires activities such loading and
off-loading, movement of product from the long distance of production to areas
of sale may be a challenge to the female counterparts. It further shows that
the marketers have a mean age of 46 which indicates that majority of them are
active and energetic enough to carry out the work associated with transferring
beans from one place to another. The educational attainment of the marketers
shows that majority of the marketers spent 13-18 years in school, which implies
that the marketers has gained a high level of education and are innovative
enough to maximize the limited resources and generate profit. The table further
highlights that majority of the bean’s marketers have marketing experience
range from 21-30 years, this implies that the marketers have been in the
marketing business for a reasonable number of years and have acquired
experience and skills needed to cope with the complexities of marketing.
Majority of the marketers were married and belongs to trade/cooperative
association, this implies that they may have availability of labor and access
to credit, extension/business strategist which will boost their marketing
activities. The estimated functions were evaluated in terms of the statistical
significance and coefficient of magnitude. Using the coefficient of multiple
determinations (R~) and (F) value coefficient which defines the magnitude of
t-values and based on a priori expectation. The regression result of factors
influencing wholesaler's selling price of Beans as presented. Based on these
statistical and economic criteria, the Linear functional form was selected as
the lead equation since it has the highest significant of coefficient of R at
0.7737 indicated by F-value of 5.8594.
***Sig@ 1%; **sig@ 5%, *sig@ 10%, +
Lead equation. Source: Computed Results 2019
This implies that 77.37% variability
in selling price was explained by the variables considered in the model, while
the remaining 22.63% was unexplained which could be attributed to errors and
omitted variables. The F-statistics value of 5.8594 was significant which
implies that the model is the best fit for the expression and an indication of
overall significance of the regression. Out of the seven independent variables,
three variables namely purchase price (X1), marketing cost (X2)
and seasonality (X3) were statistically significant at 5% level of
probability. The regression result of the factors influencing the retailer's
selling price for beans in the Table 3 above shows the four functional forms,
and based on the values of R~, F-statistics and the a priori expectation, the
exponential function was chosen as the lead equation as it has the highest
significant R2 as indicated by F-statistics (9.6281). Results show that the
coefficient of multiple determinations (R~) was 0.6647.This implies that 68.78%
of the variability in retailer's selling price was explained by the exogenous
variables included in the model while the remaining 33.53% was unexplained
variables attributed to omitted variables captured by the error term. The
F-statistics value of 5.8594 (p<0.00) was significant implied the model is
best fit for the expression and an indication of overall significance of the
regression. Results show that the purchase price (X1), number of
buyers (X4) and market location (7) were statistically significant
at 5% respectively. This implies that purchase price, number of buyers and
market location significantly affects the retailers selling price of beans.
***sig at 1%; ** sig at 5%, * sig at
10% + Lead Equation Source: Computed Results, 2019
Factors
Influencing the Marketing Margins for Beans
Table 4 shows the regression result
of the factors influencing the wholesaler's marketing margins for beans in the
study area. It shows the four functional forms, and based on the values of R~,
l: -statistics and the a priori expectation, the double log function was chosen
as the lead equation. Results showed that the coefficient of multiple
determinations (R~) was 0.7441. This implies that 74.41% of the variability in
wholesaler's marketing margins was explained by the independent variables
included in the regression model, while the remaining 25.59% was unexplained
due to omitted variables in the model as captured by the errors term of the
regression model. The F-statistics value of 2.6166 (p<0.01) was significant
implied the model is best fit for the expression and an indication of overall
significance of the regression. Results show that the purchase price (X1),
transportation cost (X3), handling cost (X5) and
seasonality (X10) were statistically significant at 5% which implies
that the marketing margin of the beans marketers are greatly influenced by the
impact of purchase price, transportation cost, handling cost and seasonality
nature of beans. The above results in Table 5 shows the regression result of
the factors influencing the retailer's marketing margins for beans in the study
area. It shows the four functional forms and based on the values of R and
F-statistics the linear function was chosen as the lead equation. Results
showed that the coefficient of multiple determinations (R) was 0.8401.
*** Significant at 1%; **
significant at 5%, ^significant at 10% + Lead equation Source: Computed
Results, 2019.
***significant at 1%; ** significant
at 5%, *significant at 10% + = Lead equation Source: Computed Results, 2019.
Results of the Factor Influencing
Retailer’s Marketing Margins for Beans
This implies that 84.01% of the
variability in retailer's marketing margins was explained by the included
variables in the model, while the remaining 15.99% was unexplained and could be
attributed to omitted variables in the regression which are captured as error
term. The F-statistics value of 1.6282 (p<0.00) was significant implied the
model is best fit for the expression and an indication of overall significance
of the regression results show that the (X1), (X7), (X8)
and (X9) were significant at 5% which means that purchase price,
market charges, level of education and household size have tremendous impact on
the retailers marketing margins. Results in Table 6, indicates that the
insurgencies in the northern part of country, high transportation cost to and
from the major purchase sources and high purchase price are the constraints
militating against efficient marketing of beans in the area. The high prices of
beans may be due the unrest experienced from point of production. High price
could equally be as resulting in low yields and consequently scarcity. NPC [15]
linked the high prices to the sale of beans to urban markets thus decreasing
supply in source markets, and hence results in high prices. Abate also
confirmed that prices for beans are generally higher in a season especially in
the months between January and April [16,17].
*Major constraints. Source: Field
Survey Data, 2019.
Conclusion
The study shows that the beans marketers were mostly men with majority on wholesaling than in retailing, still in their youthful age and are energetic to carry out their marketing activities efficiently. Purchase price of beans favored the selling price of wholesalers and retail marketing however; it is a major disincentive to their marketing margins and marketing costs particularly during bumper period when the selling price is lower than purchase price. Seasonality of beans has a negative effect on wholesalers selling price and marketing margins during harvest periods when sellers’ price is at the lowest and distinctive to wholesale marketing. High marketing charges and handling costs have negative impact on the marketing margins on both traders. Problem of insurgencies from the north is the major constraint faced by beans marketers in the study area
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