Published on International Journal of Agriculture & Agribusiness
Publication Date: May 13, 2019
Regasa Dibaba Wake, Afework Hagos Mesfin, Adam Bekele & Dawit Alemu
Ethiopian Institute of Agricultural Research, Assosa Agricultural Research Center, Assosa
Ethiopian Institute of Agricultural Research, Addis Ababa
Mango is one of the main fruit crop produced and exported in Ethiopia. In support of stimulating growth, economic development, food security and alleviating poverty, the analysis of determinants in mango market supply and participation of fruits plays an important role in an ongoing fruit development in the country. In spite of the policy options provided by the Ethiopian government, there is very little empirical evidence on the mango marketing system to design appropriate policies for its improvement of marketing in the study area. Therefore, this study was aimed at analysing the market participation of small-holders with the specific objectives of identifying the major mango marketing and identifying factors influencing mango market supply in the study area. In order to attain the objective the study made use of primary and secondary data. The study used data collected in 2012 from a sample of 150 small-holder farmers using multistage and random sampling techniques. The data were generated by individual interview schedules and focus group discussions using pretested semi structured questionnaires and checklists respectively. This was supplemented by secondary data collected from different published and unpublished sources. Based on regression model, the study has identified the main determinants of mango market supply. OLS regression model was used to analyze the factors affecting smallholder market supply of mango fruits in the study area. Therefore, the most important variables influencing the market supply of mango fruits in the study area were family size, equine and oxen ownerships, market price of mango, income generated from mango, distance to market and main market, number brokers in the market system, relatives for critical support, quantity of mango produced and index of market orientation of mango. The findings suggests that effective market information service has to be established to provide accurate and timely market information to farmers and traders on current supply of mango output, infrastructure development, market center establishments and strengthened, improve mango production through providing improved seedlings, enhance mango market participation and avoid brokers and middle men. In this arena, emphasis should be given to infrastructural development to support the sub-sector and transportation system, target improving resource ownerships to improve effective production and marketing of mango.
Keywords: Mango marketing, Small-holder farmers, Commercialization, Assosa Zone, OLS.
Fruit crops play an important role in the national food security of people around the world. They are generally delicious and highly nutritious, mainly of vitamins and minerals that can balance cereal based diets. Fruits supply raw materials for local industries and could be sources of foreign currency. Moreover, the development of fruit industry will create employment opportunities, particularly for farming communities. In general, Ethiopia has great potential and encouraging policy to expand fruit production for fresh market and processing both for domestic and export markets. Besides, fruit crops are friendly to nature, sustain the environment, provide shade, and can easily be incorporated in any agro-forestry programs (MoARD, 2009). Mango is one of the second potential fruit crop product in Ethiopia next to banana, which is the first fruit crop produced in large quantity most Western and Southern parts of the country. Currently, mango sub-sector is a good entry point for tackling poverty and that the market for mangoes in Ethiopia is significant and growing and mango value chains is spurring development, introduces technologies, create employment and reduces poverty among the communities (Takele, 2014).
Ethiopia is agro-ecologically diverse and has a total area of 1.13 million km2. Many parts of the country are suitable for growing temperate, sub-tropical or tropical fruits. For instance, substantial areas in the western parts of the country have ideal agro-ecology that receiving sufficient rainfall to support fruits adapted to the climatic conditions. In addition, there are many rivers which could be used to grow various tropical fruits crops. Despite this potential, however, production-market chore of fruits has remained immature in Ethiopia (Joosten, 2007) mainly due to traditional focus which was in favour of cereals. Serious lack of information and ‘on and off’ productions have also played their deterring role (Naamani, 2007). Realizing these gaps, lately however, the government of Ethiopia has launched enabling environment to encourage actors.
According to Yilma (2009), the production potential of fruits is not widely and evenly distributed across the various regions of the country. The cultivation is also seasonal and the supply is scanty and volatile even in areas where irrigation is possible. The knowledge gap on fruit production techniques and processing technologies is wide. Also, knowledge of domestic consumers of the benefits of fruits is confined to very few varieties of fruits. Hence, domestic demand, with the exception of few widely known tropical fruits, is generally small and, various studies show that people generally consume fruits and vegetables on a daily basis, without considering them as basic. These factors have adversely affected the growth and expansion of the fruit sub-sector in Ethiopia.
Additionally Bezabih and Hadera (2007) stated that a production of horticultural product is seasonal and price is inversely related to supply. During the peak supply period, the prices decline. The situation is worsened by the perishability of the products and poor storage facilities. Along the market channel, 25 percent of the product is spoiled. Development needs of fruit in general and that of mango in particular is poorly addressed in Ethiopia.
Fruit production in Assosa Zone is mainly constrained by seasonality where surplus at harvest is the main characteristics of the product (mainly mango). The nature of the product on one hand and lack of organized marketing system on the other often resulted in low producers’ price. The study has been implemented at the major mango growing Zone of Benishangul Gumuz regional state of Ethiopia with target of generating information relevant for improved agricultural performance in the area of smallholder commercialization. According to James, et al., (2008) mango farmsteads in Assosa have on average about 17 mango trees, producing and average of 13,500 mangoes per farmstead. Average five pieces per kg and the farmer’s grow 2.7 tons of mangoes in a year and this yield is certainly impressive and conditions are well suited for mango production to become a formidable industry- particularly as a large stock of mature mango trees already exists in the region. Moreover, it was estimated that 28% of the mangoes sold in the capital Addis Ababa, were grown in the Assosa and the mango industry in this region is serving larger markets well, however the reality is that farmers derive very little income from their mango farming activities, and the industry faces many issues that hinder the development of a competitive agricultural market.
Even though mango is economically and socially important, marketing supply and its characteristics have not yet been studied and analyzed for the study area where great potential of tropical fruit production (particularly mango) exists. Therefore, this study has the purpose of investigating mango marketing and factors affecting mango supply to the market in the area, which will narrow the information gap on the subject and will contribute to better understand on improved strategies for reorienting marketing system for the benefit of small farmers and traders.
Therefore the study was conducted to generate information for better understanding of the system and the options available to promote market oriented agricultural production through efficient smallholder commercialization of mango in general and to assess the trends in mango smallholder commercialization and production systems ,to identify the level and determinants of smallholder mango market participation and to identify challenges and opportunities on mango smallholders commercialization in Assosa zone of Ethiopia.
2. MATERIALS AND METHODS
A cross-sectional survey approach was used to collect data from 150 farm households located at Assosa Zone of Benishangul Gumuz Regional State in Ethiopia. Assosa Zone is is dominantly inhabited by Berta (the indigenous) and the settlers farming communities. These communities have their own long developed farming practices and livestock keeping, natural resource management (forestry and soil). Again, socio-economic resources like land holding and farming implements are different among the two communities. Depending on such criteria six kebeles, three from indigenous and three from settlers were selected. The indigenous kebeles are URA, Baro and Kushmengel which are inhabited by Berta ethnic groups and the settlers are Amba_2, Amba_10 and Selga _23 which is inhabited by settlers since 1980.
Results were based on a survey of 150 households and six Kebele Associations (KAs) at Assosa district in 2011/12. Farming systems were stratified in to KAs and households were selected randomly based on the proportional to size sampling. Data related to all-weather road and nearest market from the settlement center were collected at community level. Indices of land fragmentation, market orientation and crop output market participation were computed using the Households quantity of mango sold to the market at the cropping season.
The research reviewed and analyzed existing secondary data with emphasis on commercialization, commercial farming, trends in agricultural production, Mango marketing, Mango value chain analysis and other secondary data relevant for data analysis and gap identification. The secondary data is collected from all relevant organizations like mango cooperatives, published and unpublished regional and district level documents.
Moreover, primary data were collected and generated using focus group discussion, key informant discussion, discussion with experts, and field observation methods and questionnaire based formal surveys with key informants and farmers so as to investigate and cross check the data collected from formal survey. To cross check the collected information is correct semi-structured checklists were prepared well-designed and pre-tested questionnaire is prepared and data about In addition household characteristics, social networks, household land ownerships and allocation, mango production trends, crop utilization and production, livestock ownership and utilization, crop sales (Marketing), Market access, off farm incomes, Institutions(Access to credit and saving, access to training, technology and information), risks and shocks, mango production constraints, mango marketing constraints, management practices and access to agricultural inputs and supply providers etc. was collected using household survey.
Berhanu and Moti (2010) define that a smallholder is market oriented if its production plan follows markets signals and produce commodities that are more marketable. Under a semi- commercial system, where both market and home consumption are playing a central role in production decision, all crops produced by household may not be marketable in the same proportion. Thus, households cold differ in their market orientation depending on their resource allocation (Land, labour and capital) to the more marketable commodities.
Therefore, based on the proportion of the total amount sold to total production at farming system level, a crop specific marketability index ( ) is computed for each crop produced at farming system level as follows.
Where is the proportion of crop k sold ( ) to the total amount produced (Qki) aggregated over the total sample households in a farming system. takes a value between 0 and 1, inclusive . Crops mainly produced for markets usually have values closer to 1.
Once the crop specific marketability index is computed, household’s market orientation index in land allocation (MOI is computed from the land allocation pattern of the household weighted by the marketability index of each crop ( ) derived from equation as follows.
Where is market Orientation n index of household i, and is amount of land allocated to crop k, and is the total crop land operated by household i. The higher the proportion of land a household allocates to the more marketable crops, the more the household is market oriented.
To achieve the stated objective, OLS has been used for the reason that least squared regression provides a best method for measuring “accuracy” (i.e. the sum of squared errors) for continuous dependent variable. Among the alternative Least-squares estimation and related techniques such as OLS, GLS and WLS; OLS was employed in the present study to take its advantage of being BLUE. Sure, it is clear that, in the presence of heteroscedasticity it is advisable to use GLS. However, because of the impossibility to know σ2 as well as the true variances and covariance of the model using this estimator, in practice it is not always easy to apply GLS. Also, unless heteroscedasticity is very severe, one should not abandon OLS in favor of GLS or WLS. According to Samuel and Sharp (2007), the degree of farmers’ participation in output markets could be measured either in terms of the proportion of output sold or the total value of output sold. As a result, in our study, we took the volume of output sold to measure the degree of households’ participation in output markets. Further we assumed that the level of total volume of output sold is determined by a host of household level demographic and socioeconomic factors. The multiple linear regression analysis/Ordinary Least Square estimation (OLS) is used to capture the cause and effect relationship between the dependent variable total volume of output sold and the independent variables that are expected to affect the quantity sold. Hence, the OLS regression estimator or the functional relationship between the dependent and independent variables is given by:
Where: Y represents the total volume of mango output sold
X represents a vector of household characteristics, efficiency parameters, asset (resource endowment), market/price characteristics, physical and institutional characteristics that affect the volume of output sold, and are estimated parameters, and U is the error term.
Before fitting the function, it is necessary to test multicollinearity problem among continuous variables and check associations among discrete variables, which seriously affects the parameter estimates. Multicollinearity refers to a situation where it becomes difficult to identify the separate effect of independent variables on the dependent variable because of the existing strong relationship among them. In other words, multicollinearity is a situation where explanatory variables are highly correlated. As a rule of thumb if the VIF is greater than 10 (this will happen if R2 is greater than 0.80) the variable is said to be highly collinear (Gujarati, 2003). Variance Inflation Factor (VIF) is a measure that is often suggested to test the existence of multicollinearity. Thus, in the present study, variance inflation factor (VIF) was used to check multicollinearity of explanatory variables. As R2 increase towards 1, it is a collinearity of explanatory variables. The larger the value of VIF, the more troublesome or collinear is the variable Xi. Multicollinearity of continuous variables can also be tested through Tolerance. Tolerance is 1 if Xi is not correlated with the other explanatory variable, whereas it is zero if it is perfectly related to other explanatory variables. A popular measure of multicollinearity associated with the VIF is defined as
Where: is explanatory variable and model coefficient of determination. The larger the value of is, the higher the value of causing higher collinearity in the variable (Xj).
In case a problem of endogeneity is detected, using OLS to estimate the structural equations results in biased and inconsistent parameter estimates. To take care of this, in the present study out of the explanatory variables of the estimated multiple linear regression model, the variable suspected of having reversible effect or being endogenous (total income obtained from mango), it is therefore, we used the previous year income obtained from mango than that of the current income which is exogenous with respect to the main equation.
Moreover, if the variance of the error term is not constant for all observations, the estimates of the coefficients becomes inefficient (i.e., larger than minimum variance), as well as the estimates of the standard errors becomes biased which leads to incorrect statistical tests and confidence intervals. Thus to assure the assumption of constant variance of the error term, Breusch-Pagan / Cook-Weisberg test for heteroskedasticity and robust standard errors could be employed. As a result we used robust standard errors of the estimates. Furthermore, the other assumptions of a classical regression model such as correct model specification, linearity and normality an appropriate test was employed (see appendix 1).
Information and data, which were collected were compiled, and analyzed using appropriate statistical methods. The quantitative and qualitative data was analyzed based on descriptive and narrative analysis technique, respectively. Interpretation of qualitative data and information was done by sorting out, ranking, grouping and triangulation.