Analysis of Maize Value Chain

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Published on International Journal of Agriculture & Agribusiness
Publication Date: May 30, 2019

Nugusa Abajobir
Departement of Agribusiness and Value Chain Management, Wollega University
Shambu, Oromia Region, Ethiopia

Journal Full Text PDF: Analysis of Maize Value Chain (The Case of Guduru Woreda, Horro Guduru Wollega Zone of Oromia Regional State, Ethiopia).

Abstract
Lower production, marketing inefficiencies and low coordination of maize value chain is the main problems though Guduru woreda is potential in maize production. Therefore, this study investigated the value chain of maize in Guduru district to analyze marketing cost and margin, identify actors and their roles, identify the determinants of market participation decision and level of participation. Both primary and secondary data were utilized by using two-stage sampling procedure. The primary data was collected from 119 sampled households, 15 traders and 25 consumers. Value chain analysis, descriptive statistics and econometric models (i.e Double hurdle model) were used for data analysis. The first hurdle revealed that market participation was affected by extension contact, use of credit service, access to market information, education, membership to cooperatives, yield and market price. The truncated results indicated that market supply of maize was influenced by off-farm income, sex, yield, family size(man-equivalent) and experience. Producers captured high profit margin when they sold their product through channel IV(Producer to collector to wholesaler to Consumer(24.99), VI(Producer to Cooperatives to Consumer (7.84%) and VII(Producer to Cooperatives to Consumer (7.84%) which is 386.45birr, 383.9.15birr and 381.2birr per quintals, respectively. Absence of improved seed, absence of information on how to use credit, shortage of land, lack of transport facility, quality problem, poor actors linkage, lack of market information, late arrival and high cost of seed and fertilizers, and lack of modern storage were the major constraints of maize value chain. Providing credit, extension service, improved infrastructure, supplying production inputs timely, improving yield and volume of sales, knowledge, and creating and strengthening linkages between actors were forwarded by the study to improve maize value chain in the study area.

Keywords: Maize, Guduru, Performance, Value Chain & Double Hurdle Model.

1. INTRODUCTION
1.1 Background of the study
Agriculture continues to be at the forefront of Ethiopia’s rapid economic growth. It continues to play a critical role in Ethiopia’s development. It is also providing the output required to feed for growing population. Central to advancing the transformation of Ethiopia’s agriculture sector is the need to ensure that smallholder farmers and pastoralists are empowered with the tools, knowledge, and support needed to transition from a traditional subsistence orientation to one that is market focused(oriented) and more commercialized (ATA, 2016).
Agriculture contributes 41.4% of the country’s gross domestic product (GDP), 83.9% of the total exports, and 80% of all employment in the country (Matousa, Todob, & Mojoc, 2013). It also accounts 85% of the national export earnings (UNDP, 2016). Cereal production and marketing is the single largest sub-sector within Ethiopia’s agriculture. It dominates in terms of its share in rural employment, agricultural land use, and calorie intake, as well as its contribution to national income.
Ethiopia is the third largest producer of maize in Africa, next to South Africa and Nigeria (RATES, 2003). Maize is currently one of the most important cereal crops widely grown and consumed in Oromia region. Total land area of about 4,679,264.49 hectares are covered by cereal crops from private peasant holdings. Out of the total cereal crop area, from which (about 1,125,747.85hectares) was covered by maize. Average yield level for maize was 35.12quintals/hectare. Maize ranks first among cereals such as teff, wheat and sorghum in terms of yield in quintals per hectare in the total cereal production, respectively (CSA, 2016). The maize value chain in Ethiopia involves multiples actors, including: input suppliers, producers, traders (local assemblers and wholesalers), retailers, processors, and consumers.
Maize is the significant contributor to the economic and social development of Ethiopia. In Ethiopia maize is produced by 8 million stallholders which is greater than other crops like (teff 5.8 million and wheat 4.2 million smallholders).It is critical to smallholder livelihoods in Ethiopia. In addition, maize is the cereal crop with the greatest production at 4.2 million tons,
compared to teff at 3.0 million tons and sorghum at 2.7 million tons. Moreover, maize plays a central role in food security. It is the lowest cost source of cereal calories, providing one and 1.5 and 2 times the calories per dollar compared to wheat and teff respectively. An effective maize sector could propel Ethiopia`s food production to quickly reduce the national food deficit and keep pace with a fast growing population (FAO, 2012).
As indicated by Bonnard and Sheahhan (2009), markets play a vital role in the provision of goods and services critical to survival, promotion and protection of livelihoods. Markets determine food prices, and incomes that producer households receive from sale of own products and labor. Markets promote the stability of food supply and prices by ensuring food distribution from surplus to deficit areas, effective demand that promotes production.

1.2. Statement of the Problem
In Ethiopia, smallholders dominate the agricultural sector, accounting for 95 percent of the total production, whereas large farms contribute only 5 percent of the total and only 2.6 percent of cereal production in particular. The average farm size is less than one hectare, with 40 percent of farmers cultivating less than 0.52 hectares and small-scale subsistence farmers dependent on low input, rain-fed mixed farming agriculture dominated with traditional technologies (Hassen, 2006 and Alemayehu, 2011).
Productivity of maize in Ethiopia remains below potential due to low input usage, post-harvest loss of production, National maize commercialization rates are low, most marketable surplus is sold within three to four months during harvest when prices are low due to farmer`s cash needs and risks associated with pest infestation and other storage losses, aggregation and trading. There is lack of fully functioning maize market, price volatility, lack of year-round market, or sufficient supply, especially for quality maize. The smallholder producers are price takers since they have little participation in the value chain (IFPRI, 2010).
Maize production in the area is below potential due to lack of extension service, high cost of fertilizer, lack of improved varieties, high cost of production and delay in inputs arrival for purchase, lack of storage lead to: sharp seasonal fluctuations in maize prices (particularly in remote areas). Farmers also lack information about prices in nearby markets and lack cost-effective means of transporting individually. Farmers have low bargaining power to sell their products at appropriate price (GWANRO, 2016).
Even though some related studies on cereal crops like maize were carried out in different regions of the country, empirical evidence of improving production and marketing of maize has not been undertaken in the study area. Moreover, previous studies related with maize focused on production rather than giving attention on the marketing, quality, value addition, developed marketing system and linkages among actors which include input suppliers, producers, traders, and consumers in the study area. Hence, there is strong need to conduct value chain analysis to identify maize value chain actors, their respective roles and sketch value chain map of maize, assess maize value chain performance and governance, and identify determinants of maize market participation decision and level of participation and to identify constraints and opportunities especially at the production and marketing level in study area. This study was conducted to analyze maize value chain in Guduru Woreda by taking into consideration all the above mentioned problems.

1.3. Objective of the Study
The general objective of the study is analyzing maize value chain in the study area
The specific objectives are:
1. To map maize value chain and identify maize value chain actors, their linkage and respective roles
2. To analyze marketing margins of actors in the maize value chain
3. To identify the determinants of market participation and quantity of maize supplied to the market in the study area.
4. To identify constraints and opportunities at production and marketing level in the study area

1.4. Significance of the Study
The study analyzed the maize value chain starting from the input supply up to the end user. This study provides information on the determinants of maize market participation decision and level of participation, marketing margin, and benefit share of actors of maize value chain in the study area. The result of the study is helpful for maize growers and traders to make appropriate market decision in the study area. The information generated in this study could also help research institutions, development organizations, extension service providers, government and non-governmental organizations to formulate maize value chain development programs and guidelines for interventions that would improve efficiency of the maize value chain analysis in the study area. The other benefit that can be anticipated is its significance as a source for further studies.

1.5. Scope and Limitation of the Study
This study focused on the maize value chain from input supplier to the consumer in the study area. The study was conducted in one district and important information were collected from sampled households and value chain actors involved in the study area. However, there are spatial as well as temporal limitations to make the study more representatives in terms of wider range of area coverage and time horizon. Furthermore, since Ethiopia has wide range of institutional capacities and organizations, the result of the study may have limitations to make generalizations and make them applicable to the country as a whole.

1.6. Organization of the thesis
The paper contains five chapters and sub chapters under each chapter. Accordingly, chapter one is introductory chapter. Under introduction: background of the study, statement of the problem, objective of the study, significance of the study and scope and limitation of the study were discussed in detail. Similarly, chapter two clearly explained the literature review of the study. Both theoretical and empirical reviews were sub-titles under this chapter. Chapter three of the study discussed the methodology of the study. Sub-titles discussed under this chapter were: description of the study area, types, sources and methods of data collection and sampling design, methods of data analysis. Chapter four of the study presents results and discussions which include descriptive part, value chain analysis, and econometric result. Chapter five summarizes the main findings of the study and it draws conclusion of the study and forwarded appropriate recommendations to the concerned body/bodies mainly to the policy makers.

2. RESEARCH METHODOLOGY
2.1. Description of the Study Area
This survey were conducted at Guduru Woreda of Horro-Guduru Wollega Zone on maize value chain analysis, located in Horo-Guduru Wollega zone of Oromia National Regional state at about 372km west of Addis Ababa along Gedo-Fincha sugar factory main road. The area is located in between 09029’ North latitude 370 26’ East longitudes. The boundary of the study area are on the North of the study area, Abbay Coman and Hababo Guduru are found, to South of it Jima Rare Woreda is located, to the West of the study area Jima Genet Woreda is found and to the East of it Gindeberet Woreda , West Showa zone is located. This Woreda also found 67 kilometer from Zonal administrative town (Shambu) and 372 kilometer to the west from regional administrative town, Addis Ababa (GWANRO, 2016).
Figure 1. Map of study area
Source: Adopted and manipulated from Ethiopian map

2.2. Type, Sources and Method of Data Collection
Both primary and secondary data sources were utilized for this study. Primary data was collected from sampled producers, traders and consumers by using semi-structured questionnaire through interview method. Before embarking into data collection, the questionnaire was pre-tested to check its appropriateness for gathering the required information. Enumerators who speak the local language, Oromigna and Amarigna, were recruited based on prior experience in data collection. Enumerators are trained regarding the contents of the questionnaire and data collection procedure. Secondary data were gathered from various sources such as reports of MoA, CSA, GWANRO, NGOs, previous research findings, internet and other published and unpublished materials.

2.3. Sampling Technique and Sample Size
For this study, Two-stage sampling procedure was used to select sample size of smallholder maize producers for the interview. The Woreda has 20 total Kebeles . In the first stage, from Kebeles which produce maize, three Kebeles were randomly selected because all the kebeles have equal potential. In the second stage, the sampled households were drawn randomly from each of the three Kebeles based on probability proportional to total population size.
Where
n= sample size = 119
N= population size = 3265, e = sampling error/level of precisions =9% was used.
The minimum level of precision is acceptable at 10%. However, for this study 9% of precision level was used because if a precision level is less than 9% the sample size is large and expensive for data collection.
Sample size of traders was obtained from Guduru Woreda Trade and Industry office (GWTIO). Accordingly, 25 consumers and 15 traders were selected from the Woreda`s town like Baro, kombolcha, Gabate, and Ayele.

3.4. Method of Data Analysis
Three types of data analysis, namely descriptive statistics, value chain analysis and econometric analysis was used for analyzing the data collected from producers, traders and consumers.

2.4. Descriptive analysis
Descriptive statistics such as means, percentages, and standard deviations was computed for this study.

2.4.1 Marketing margin
Marketing margin analysis deals with comparison of price at different levels of marketing chain over the same period of time. It measures the share of the final selling price that is captured by a particular agent in the marketing chain and always related to the final price or the price paid by the end consumer, expressed in percentage (Mendoza, 1995).
Computing the Total Gross Marketing Margin (TGMM) is always related to the final price paid by the end buyer and is expressed as a percentage (Mendoza, 1995)
Where, TGMM = Total gross marketing margin.
To find the benefit share of each actor, the same concept was applied with some adjustments. In analyzing margins, first the TGMM was calculated. This is the difference between producer’s (farmer’s) price and consumer’s price (price paid by final consumer) i.e.
TGMM = Consumer’s price – Farmer’s price
Where: SPi is selling price at ith link and
PPi is purchase price at ith link.
The trade margins in this study were calculated with the average prices practiced at each level of the market chain and the various charges incurred by each actor.
Total gross profit margin also computed as:
(5)
Where:
TGPM: is total gross profit margin,
TGMM: is total gross marketing margin and
TOE: is total operating expense.

2.5. Value chain analysis
Value chain analysis is the process of breaking a chain into its constituent parts in order to better understand its structure and functioning. The analysis consists of identifying chain actors and discerning their functions and relationships; determining the chain governance, or leadership, to facilitate chain formation and strengthening; and identifying value adding activities in the chain and assigning costs and added value to each of those activities (UNIDO, 2009).
Value chain analysis was used to identify the actors, their linkage, roles and to sketch the value chain map of maize in the study area. Value chain analyses were also used to draw Value chain Governance and opportunities and constraints of value chain at production and marketing levels.

2.6. Econometric analysis
Econometric analysis was used to estimate relationship between the dependent variable and the explanatory variables. It is crucial to understand the effects of different factors on maize market participation decision and intensity of participation of producers.
Most participation studies have used the Tobit model to estimate market participation relationships with limited dependent variables. Tobit model is statistically restrictive because it assumes that the same set of variables determine both the probability of participation decision and level of participation. That is why recent empirical studies have shown the inadequacy of the Tobit model in cross-sectional analysis, stressing the relevance of alternative approaches. In this case, the appropriate approach is to use the double-hurdle model. This model assumes farmers faced with two hurdles in any agricultural decision making processes. Accordingly, participation decision was made first and the decision to intensity of sales made secondly. In this study, double hurdle model was chosen because it allows the distinction between market participation decision and level of participation on maize through two stages. This model involves running the probit model to identify the determinants of the market participation of maize and truncated model to identify the determinants of maize market supply. Accordingly, double hurdle model is useful. In the double hurdle model, there is no restriction regarding the explanatory variables i.e it is possible to analysis the determinants of participation and volume of sale separately by using probit for market participation decision and truncated for volume of sale.