Determinants of Maize Market Participation among Smallholder Farmers in the Damot Pulassa District, Ethiopia

Table of Contents

1. INTRODUCTION
1.1. Background of the Study
1.2. Statement of the Problem
1.3. Objectives of the Study
1.3.1. General Objective
1.3.2. Specific Objectives
1.4. Research Questions
1.5. Significance of the Study
1.6. Scope and Limitations of the Study

2. MATERIALS AND METHODS
2.1. Description of the Study Area
2.2. Research Design, Data Type, Sources and Collection Method
2.2.1. Research Design
2.2.2. Data Type, Source and Collection Method
2.3. Sample Size Determination and Sampling Techniques
2.3.1. Sample Size Determination
2.3.2. Sampling Techniques
2.4. Methods of Data Analysis
2.4.1. Descriptive Statistical Analysis
2.4.2. Econometric Analysis

3. RESULTS AND DISCUSSION
3.2. Statistical Results
3.2.1. Socio-Economic Characteristics of Sampled Households
3.3. Econometric Result
3.3.1. Determinants of Market Participation and Extent of Market Participation

4. SUMMARY, CONCLUSSIONS AND RECOMMENDATIONS
4.2. Summary
4.3. Conclusion
4.4. Recommendations

ABSTRACT

Market participation in rural households is a vital strategy in assuring better income and a key factor to lifting rural households from poverty. The overall objective of the study was to investigate determinants of maize market participation decision of smallholder farmers in the maize market. The specific objectives of the study were: to investigate the factors that influence maize market participation decision, to assess factors that determine extent of participation and to identify the constraints faced by farmers in maize marketing in the study area. Multistage sampling procedure was employed to contact 360 households. Semi-structured questionnaires were used to collect data from smallholder maize producing farmers through face to face interview. The data was analyzed using descriptive, inferential statistics and Heckman two-stage selection model. STATA version 14 statistical package was used to process the data. The analyzed results showed that age, sex, price of maize, family size, farming experience, proximity of market, ownership of means of transportation, frequency of extension contact, accessibility of credit service and off-farm income had significant influence on the decision to participate in maize marketing. Sex, family size, proximity of market, land size, ownership of means of transportation, membership of cooperative, accessibility of market information, accessibility of credit and Invers Mills Ratio (lambda)had significant influence on the extent of market participation..As result of this study the accessibility of market information has positive influence on both maize market participation and level of supply of maize marketing. Therefore, government should develop ways that farmers can easily access the market information.Government should further invest on road connection to ease transportation challenge of the maize from the area of production to marketing point so that farmers can easily participate in maize marketing.

Key words: Damot Pulassa, Maize, Market Participation, Heckman two-stage model, STATA

1. INTRODUCTION

1.1. Background of the Study

Market participation in rural households is an important strategy for poverty alleviation and food security (Mathengeet al., 2010). It refers to the markets actors’ decision on whether to be involved or not in the flow of products from producers to end users (Yaynabeba and Tefera, 2013). Majority of smallholder farmers in rural areas are trapped in a vicious circle of poverty characterized, inter alia, by low economic returns due to low market participation. Poverty reduction and improving the livelihood of the rural smallholders has strong relationships with their market participation (Mathengeet al.,2010). Increased market participation by the poor has been found to be vital as a means of breaking from the traditional semi-subsistence farming and a key factor to lifting rural households from poverty. However, smallholders do not often participate much in food crops markets due to subsistence production and also higher costs associated with searching for markets (World Bank, 2011).

1.2. Statement of the Problem

In Ethiopia 90 percent of agricultural product comes from smallholder farmers that are not producing and selling their produce and agricultural inputs in an organized manner where part of their benefit may transfer to the middlemen. However, similar to other agricultural product the maize market in Ethiopia was fragmented as a result the low volumes are supplied by millions of smallholders and handled by small traders at different levels. Agricultural farmers face difficulties in accessing markets where they can obtain agricultural inputs, consumer goods and sell their produce. These difficulties include bad feeder roads, poor storage facilities, poor packaging of farm produce, high transaction costs, and lack of access to market information among others, shortage of credit access, shortage of means of product transport, distance to the nearest markets, market price and number of extension visit. As a result, farmers suffer from market failure in which case, they do not get economically optimal price for their produce (Aleneet al.,2014).

A major reason why some farmers who produce surplus remain trapped in the poverty cycle is the lack of access to profitable markets. Also, farmers are forced to sell to the buyers of convenience at the buyer’s price. Because they often lack business and negotiating experience and collective bargaining skill to give them the power needed to interact on equal terms with strong market intermediaries thus resulting in poor terms of exchange. In these reasons, maize market participation in sub-Saharan Africa is very low for smallholder farmers (Adeoti, et al.,2014).

Maize production in the study area is below potential due to shortage of extension service, high cost of fertilizer and seed varieties, lack of different improved seed varieties, high cost of production and delay in inputs arrival for purchase, shortage of farming land and 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 (DPDANRO, 2019).

Previous studies on maize in Ethiopia have concentrated on analysis of maize value chain (Nugusa, 2018 and Dagmawit, 2016), determinants of adoption of improved maize technology (Yishak and Punjabi, 2011) and factors affecting economic efficiency in maize production (Bealuet.al.2013). To date, information on factors influencing the participation of producers in the maize market in Ethiopia is lacking. Particularly inDamot Pulassadistrict, the determinants that affect farmers to participate on maize market while supplying the produce in both fresh and dry stage to the market have not been elucidated. Understanding farmers’ market participation inDamotPulassadistrict is important as an opportunity to increase smallholder farmer incomes. In addition, the study areas also did not have any research conducted in determinants of maize market participation of smallholder farmers. Knowing basic factors that determine maize market participation of smallholder farmers is important to understand the challenges that face farmers. Therefore, this study has attempted to fill the gap of information by identifying factors affecting the smallholder farmers in maize market participation decision and its utilization in maize production and marketing.

1.3. Objectives of the Study

1.3.1. General Objective

The general objective of this study is to investigate determinants of maize market participation decision among smallholder farmers in study area.

1.3.2. Specific Objectives

The specific objectives of the study are:

1. To investigate the factors that influence maize market participation decision of smallholder farmers in the study area,
2. To assess factors that determine extent of participation among smallholder farmers in the district and
3. To identify constraints faced by farmers in maize marketing inDamot Pulassadistrict.

1.4. Research Questions

1. What are the factors that influence maize market participation decision of smallholder farmers?
2. What are the factors influencing extent of participation in marketing of maize in the study area?
3. What are the constraints of farmers in supplying maize into the market inDamot Pulassadistrict?

1.5. Significance of the Study

The study analyzed the determinants of maize market participation inDamot Pulassadistrict. It also provides a holistic picture of existing constraints of entry points in the maize market participation. Moreover, this study provides information on maize output market participation decision to farm households in study area.

Therefore, it could give light on required efforts to enhance the production and utilization of maize at larger scale to bring about economic development in the area. The information that has generated can also help a number of organizations including: research and development organizations, traders, producers, policy makers, extension service providers, government and non-governmental organizations, to assess their activities and redesign their mode of operations and ultimately influence the design and implementation of policies and strategies. It is also helpful for different actors to identify and analyze new ways of stimulating innovation.

1.6. Scope and Limitations of the Study

This study focused on assessing the determinants of maize market participation in Damot Pulassa district. It also focused on identifying the determinants the extent of maize marketing among smallholder farmers. The study was conducted in one district and important information was collected from sampled households involved in the study area. However, due to time and resources limitations, the study didn’t represent the entire determinants of maize market participation in the country. 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.

2. MATERIALS AND METHODS

2.1. Description of the Study Area

Damot Pulasa is one of 22 districts which are found in Wolaita zone of SNNPRS. It is divided into 23 rural kebeles. It is located at 360 km to the Southwest from the capital city of the country, Addis Ababa along the main road that passes through Shashemene to WolaitaSodo. It is bordered on the east and south by Damot Gale district of Wolaita zone, on the west by the Boloso Sore district of Wolaita zone, and on the north by the Misrak Badawacho and Mirab Badawacho districts of Hadiya Zone. Based on the district finance and economic development office, 2019, this district as a total population of 130,398, of which 64,209 are male and 66,189 female; 5.08% of its population is urban dweller. In the district 15,225 household head are permanent residents. The total area of district is 15,998.50 ha out of 12,798.80 ha used for cultivation and 3,199.70 ha used for grazing. Agro-ecologically the district is categorized into weyna-dega with two sub-zones; dry and humid weyna-dega. Geographically, it is located between 6°95’-7°11’N latitude and 37°96’-38°46’E longitude. The area receives mean annual rainfall of 1450mm and daily average temperature of 22.5 °C. Its altitude ranges from 1200m-1500m above sea level.

Editorial note: This image was removed due to copyright issues.

Figure 1: Map of Study Area

2.2. Research Design, Data Type, Sources and Collection Method

2.2.1. Research Design

This study employed descriptive and empirical method of research analysis to conduct this research. Moreover, the study used cross-sectional survey data which is a type of data collected at the single point of time.The reason for preferring a cross-sectional study is because of difficulties to get organized long year data in the study area.

2.2.2. Data Type, Source and Collection Method

The study used both primary and secondary data to attain the stated objectives. The secondary data were collected from different sources including research papers, booklets, and internet, CSA, ATA, from Zone and district sector offices and unpublished materials. Moreover different published sources including journals were used to collect some secondary data. The primary data were collected through household’s survey and key informant interviews with zonal and district agricultural officersand the primary data were collected from sample households by using structured questionnaires. Moreover FGDs was held during the survey with 10-15 famers, local administrators and DAs. During the survey, information was gathered on issues related to factors that affect maize market participation among smallholder farmers in the study area. The questionnaire in this study was also structured to elicit responses from the selected farmers on their households’ farming and marketing activities.

2.3. Sample Size Determination and Sampling Techniques

2.3.1. Sample Size Determination

An important decision was taken while adopting a sampling technique and determining the size of the sample. Hence, the sample size of the study was determined based on the scientific formula that is designed to find out the appropriate size of the survey research. In this study, the total sample size was determined by the formula of (Yamane, 1967) as cited by (Walelign and Lemma, 2019).

Accordingly, to calculate the required size of sample farmers by assuming 95% level of confidence at 5% level of precision or significance level (e). So, to obtain a sample size required to represent the true population of the district is calculated as.

Abbildung in dieser Leseprobe nicht enthalten

Where:

- n is the sample size,
- N is total number of household heads in fourkebeles= 3,598 households,
- e is degree of precision at 95% confidence interval in this study i.e. = 5%.

The distribution of the total sample in sampledkebeleswas based on the probability of proportional to the number of population of maize producers in eachkebele.Therefore, the minimum sample size of this study was estimated below.

Abbildung in dieser Leseprobe nicht enthalten

2.3.2. Sampling Techniques

A multi-stage sampling technique was used to select sample farmers in the study area. In the first stage, the study district was selected purposively based on the extent and potentiality of maize production (WZANRO, 2019). Then the district was divided into two zones: humidweina-degawhich includes 13kebelesand dryweina-degawhich includes 10kebeles. From humidweina-dega2kebeles Lera and Tontome Mentaand from dryweina-dega2kebeles GudichoandPullassa Bakalawere selected randomly by using simple random sampling technique. Finally, in the third stage using the population list of maize producer farmers from sampledkebeles,the intended sample size was determined proportionally to population size of maize producer farmers. Then, 360 representative households were randomly selected by using simple random sampling technique and considering probability proportional.It was represented in (Table 1) as follows.

Table 1: Sample Size and Sample Distribution of Respondents

Abbildung in dieser Leseprobe nicht enthalten

Source: Own computation based on data from DPDANRO, 2019.

2.4. Methods of Data Analysis

Data from the field was edited, coded, and cleaned to ensure consistency, uniformity, and accuracy. Data were entered into computer software (SPSS version 20) for analysis. STATA version 14 statistical package was used to process the data. Statistical data analysis, namely: descriptive, inferential and econometric methods were used for analyzing the collected data.

2.4.1. Descriptive Statistical Analysis

The descriptive statistical method including mean, percentages, frequencies, standard deviations, etc… were applied for this study. The qualitative and quantitative data were tabulated in the way that can enable to understand or compute the view of factors that affect maize market participation and level of participation. In addition, chi-square test for dummy variable and t-test for continuous variable were used as inferential statistics for the study.

2.4.2. Econometric Analysis

The econometric model which was used for analyzing the collected datawasHeckman two-stage model.

Heckman two-stage model

The standard approach to modeling the determining factors that affect the market participation decision and volume/extent of participation in the maize market could beTobit, double-hurdle (DH) and Heckman two-stage. For this study Heckman two-stage selection model was used because there is incidental truncation in survey data, and sample selection bias. The model also allows for separation between the initial decision to participate and the decision of how much to participate. In this case, it is assumed that a different set of explanatory variables influences participation and the extent of participation differently (Mathengeet al.,2010).

Tobitmodel is a statistical model proposed by James Tobin to describe the relationship between non-negative dependent variable and independent variable (Tobin, 1958). The modeling approach assumes that the participation and sales volume decisions are made simultaneously and hence the same set of parameter and variables determine both the probability of market participation and the extent of participation. In addition to this, the partial effects of a particular variable, Xj, on the probability that the farmer wasselling and the expected value of the quantity traded conditional on participation, have the same signs (Wooldridge, 2002). So, this model structure cannot handle the situation in which participation and quantity sold may be a separate decisions and possibly influenced by different variables or by the same variables but in different ways.

Double hurdle model was first introduced as a class of models by Cragg (1971) as a more flexible alternative toTobitmodel. The modeling approach assumes a two-step decision process. This is based on the assumption that household makes two separate decisions; the first step involves the decision whether to participate in the market or not and secondly the extent of participation. The model estimation involves aProbitregression to identify factors affecting the decision to participate in market by using all sample households in the first stage, and a truncated regression model on the participating households to analyze the extent of participation, in the second stage. The limitation of the model is, it does not capture sample selection bias when the error of the selection and outcome equation are correlated i.e. in the case of incidental selection, some part of the dependent variable is not observed because of the outcome of another variable (Hoffman and Kassouf, 2005).

There are multiple constraints of marketing in agricultural products that can affect smallholder farmers are not equally and likely to supply their produce to the output markets. Such populations from which samples drown are expected to be distorted where samples lack representativeness result in sample selection bias. Heckman two-stage procedure was identified as an appropriate model for such independent estimation, because it corrects sample selectivity bias problem (Gujarati, 2004). Heckman procedure is also a relatively simple procedure for correcting sample selection bias. The residuals of the selection equation are used to construct a selection bias control factor, which is called Lambda and which is equivalent to the Inverse Mill's Ratio. Since the explanatory variables that determine household’s maize market participation decision was likely determine the volume of maize sale and it is reasonable to assume that there might be some unobserved characteristics that influence both decisions and these variables are omitted by the model, and the missing causes incidental truncation (Greene, 2003).

In the Heckman two-stage model, the decision either to participates in the market or not and extent of participation is dependent variables and was estimated sequentially by including inverse mills ratio because of the Inverse Mills ratio (IMR) is statistically significant that implies two decisions are interdependent. In the first stage, maize producing farmers make a discrete decision whether to sale or not sale maize whereas in the second stage, conditional on their participation decision in maize market; farmers make continuous decision on marketed surplus.

The model was consisting of two steps; firstly, selection equation was estimated by using aProbitmodel and secondly, an outcome equation was estimated using OLS regression. AProbitmodel predicts the probability of whether an individual household participated in the maize market or not as shown in the formula below.

Pr(zi = 1/wi.α) = Փ(h(wi.α)) + εi(2)

Where:

- zi is an indicator variable equal to unity for smallholder maize farmers who were participate in the marketing,
- Փ is the standard normal cumulative distribution function,
- wi is the vector of factors affecting the decision to participate in maize market, α is the vector of coefficients to be estimated and
- εi is the error term assumed to be distributed normally with a mean of zero and a variance σ[2]. The variable takes the value of 1 if the marginal utility the household i get from participating in marketing of maize is greater than zero, and zero otherwise.

This is shown as follows,

zi * = α wi + ui(3)

Where:

- ziis the latent level of utility the small holder maize farmers get from participating in the market, ui ~ N (0, 1) and,

Abbildung in dieser Leseprobe nicht enthalten

In the second step, an additional regressor in the sales equation was included to correct for potential selection bias. This regressor is Inverse Mills Ratio (IMR). The IMR is computed as:

Abbildung in dieser Leseprobe nicht enthalten

Where:

- φis the normal probability density function.

The second-stage equation is given by:

Abbildung in dieser Leseprobe nicht enthalten

Where:

-Eis the expectation operator,
-Yis the volume of maize sold,
- λi(lamda)is the inverse mills ratio that corrects sample selection bias,
-xiis a vector of independent variables affecting the quantity of maize sold, and
- β is the vector of the corresponding coefficients to be estimated. Therefore,Yi can be expressed as follows:

Yi* = β’xi +γ λi+ ui(8)

Where:

- Yi * is only observed for those maize farmers who participates in the marketing.
- ui ~ N (0, σu). (zi= 1), in which case Yi= Yi *.

The model was estimated as follows; in the first step of deciding whether to participate in maize marketing or not. This can be specified as:

Abbildung in dieser Leseprobe nicht enthalten

Where:

- Participation is denoted by 1,
- Non- participation is denoted by 0,
- β0 is a constant,
- β1…..n are parameters would be estimated,
- Xns are vector of explanatory variables.

The Second step which was involved on the extent of maize marketing was estimated by use of an OLS as follows;

Abbildung in dieser Leseprobe nicht enthalten

Where:

- Y denotes the extent of maize sales,
- β0 is a constant,
- β1…..n are parameters estimated
- Xnsare vector of explanatory variables.

Marginal effects of the attributes on participation are determined by getting the differential of probability of participation and it is given by:

Abbildung in dieser Leseprobe nicht enthalten

3. RESULTS AND DISCUSSION

3.2. Statistical Results

3.2.1. Socio-Economic Characteristics of Sampled Households

Age of Household Head:as shown in (Table 2) the youngest market participant was 27 years old while the oldest was 75 years old. On the other hand, the youngest non-market participant was 25 years old while the oldest was 79 years old. The mean age of market participants was about 44.31 years while that for non-market participants was about 43.42 years. The overall mean age of the maize farmers was found to be 43.82 years old. The result of the two tailed tests showed that the age was statistically significant insignificant in maize market participation and extent of participation.

Family Size of Household:based in(Table 2), the smallest household size among market participants was found to be 2 members while the highest was found to be 11 members. Among non-market participants, the smallest household size was found to be 3 members while the highest was found to be 13 members. The mean of the household size for the market participants was found to be 6.60 members, while that for non-market participants was found to be 6.77 members. The overall household size mean was found to be 6.69 members. The two tailed test results showed that household size was statistically significant at less than 5%.

Quantity of Maize produced:as shown in (Table 2), total production of maize yield, the smallest amount produces by market participant on a per year basis was found to be 8 quintals while the highest amount was found to be 31 quintals. Among non-market participants the smallest amount produces on yearly basis was found to be 3 quintals and the highest amount was found to be 15 quintals. The means of maize yields for market participants per year was found to be 21.44 quintals while that for non-market participant was found to be 7.92 quintals. The mean of overall maize yield was found to be 14.095 quintals. The result of the two tailed tests showed that the maize yield was statistically significant at less than 1% indicating that the market participants had more maize yields than non-market participants.

Education Year of Household heads:as revealed in (Table 2), indicated that the least year of education among maize market participants was 0 and the highest year of education among maize market participants was 16 while the least year of education among non-participants was 0 and the highest year of education was 14 year. In addition, the average year of education for market participants was found to be 2.61 years while that for non-market participant was found to be 2.02. The overall year of maize market participants was found to be 2.290. The result of the two tailed tests showed that the year of education was statistically significant at less than 10% indicating that the market participants had more educated than non-market participants.

Maize Farming Experience:as per in (Table 2), the least year of maize farming experience among market participants was 3 years and the highest year of maize farming experience was 55 while the least year of maize farming experience among non-market participants was 2 years and the highest year of maize farming experience was 48 years. In addition the average year of maize farming experience among maize market participants was found to be 16.34 year while average maize farming experience of non-market participants was 15.21 years and the overall average maize farming experience among two groups was 15.723 years. The result of the two tailed tests showed that there existed a significant difference at less than 5% in terms of farm experience between the two groups. So, experienced households know how to prepare the land, period of plantation and harvesting, and how to appropriately use the input to increase the level of participation of households.

Land Size Allocated to Maize Farming:The results showed that in (Table 3) the mean land allocated for maize farming of participant and non-participant farmers were 0.72331 and 0.26179 hectares respectively. The overall average land size allocated for maize farming was found to be 0.47244 hectares. The result of the two tailed tests showed that the land size allocated for maize farming was statistically significant at less than 1%, meaning that total land size allocated for maize farming of market participants was greater than that of non-market participants.

Number of Oxen Ownership:as shown in (Table 3), the average number of ownership of oxen to plough farm land is 1.66 for participant farmers and 1.62 for non-participant. The overall average number of oxen ownership was found to be 1.64. The two tailed test showed that there was statistically insignificant among participant and nonparticipant households.

Annual Amount of Off-Farm Income: as revealed in (Table 3), mean annual amount of off-farm income among participants of maize market and non-market participants was 1030.34birr and 1005.66 birr respectively and the overall average amount of off-farm income was 1016.92 birr. The result of the two tailed tests showed that the annual amount of off-farm income was statistically significant at less than 10% indicating that the market participants had more off-farm income than that of non-market participants.

3.3. Econometric Result

Maize products are produced for both market and household consumption in the study area. Socio-economic, demographic characteristics, institutional and market variables were assumed to determine maize market participation decision and level of participation by sampled households.

3.3.1. Determinants of Market Participation and Extent of Market Participation

Maize is produced to supply in to market and household consumption in the study areas. Various variables were assumed to determine the market participation decision and its marketed surplus by sampled households. Under this section, the result of the Heckman two-stage model is given for maize. The Heckman selection model was employed in order to control the selectivity bias andheteroscedasticityproblem; and obtain consistent and unbiased parameter estimates. It is important to checkmulticollinearityandheteroscedasticityproblem before running the model for both the continuous as well as the dummy variables. So, before running the model for both the continuous as well as the dummy variablesmulticollinearityandheteroscedasticityproblem were checked. The usual measure ofmulticollinearityamong continuous and dummy variables is Variance Inflation Factor (VIF). As a result, the values of variance inflation factor of the variables were in the ranges of 1.04 and 5.40. Therefore, depending on the results of variance inflation factor there was nomulticollinearityproblem among the hypothesized continuous and dummy variables (Appendix Table 1).

3.3.1.1. Determinants of Maize Market Participation Decision

To find the factors influencing market participation of maize inDamot Pulassadistrict, the first step of the Heckman sample selection equation,Probitmodel estimated results are discussed below. The results presented in (Table 4) show that ten variables: annual price of maize sold per quintal, age of household head, sex of household head, family size of household, maize farming experience of household head, proximity of district market, ownership of means of transportation, frequency of extension contact, accessibility of credit service, and annual off-farm income per year were significantly found to influence the farmers’ decision to participate in the maize market. The coefficient results of variables were used to interpret the results.

Market Price of Maize:market price affects maize market participation positively as expected. As indicated in (Table 4), the market price affects the market participation at less than 1% significant level. This isdue to if the market price of produce is fair, maize producing farmers could be initiated to participate in maize market. The positive effect of market price on maize market participation implies that as the market price increase, the probability of maize market participation is also increase while keeping all others variables constant. The marginal effect for price of maize confirms that as market price of maize increases in one birr, the probability of maize market participation also increases by 0.29% keeping all other variables constant. This is in line with Dagmawit (2016) and Nugusa (2018) who found out that there is positive relationship between market price and market participation of maize. In addition, it is similar with the finding of Agete (2014) who discovered that there was positive impact between price of red bean and market participation of red bean and Geoffrey (2014) who revealed that there was positive impact between price of pineapple and pineapple market participation.

Age of Household Head:age of households affects maize market participation positively in different of expected hypothesis. As specified in (Table 4), an age of households was positively affecting the market participation at less than 1% significant level. This is due to matured maize producing farmers can produce high quantity of product as result they could have better experience about their farming rather than immature farmers. The marginal effect of the result approves that when the household head age increases by one year, the probability of participating in the maize market also increases by 2.66% while all other variables held constant. This is consistent with the findings of the study of Dagmawit (2016) and Nugusa (2018) who found that an increase in the age of household head by one year also increases the probability of maize market participation. In addition to this, the study of Walelgn and Lemma (2019) agrees that an age of sample household heads got old; the likelihood of maize market participation tends to be increased.

Sex of Household Heads:sex affects the market participation of maize positively at 10% significance level as expected hypothesis (Table 4). As shown in (Table 4), the econometric result of the study revealed that being male household head increases the probability of maize market participation. The positive effects of sex as expected implies that female headed households have less access to adopt new technologies and extension contacts in case of different burdens as compared to male headed households. This is in line with Haymanot (2014) and Nugusa (2018) indicated positive relationship between maize market participation and male-headed household. In addition to this, the study of Dagmawit (2016) approves that sex of the household head is one of the variables that affect the market participation of maize in positively at 1% level of significance meaning that the econometric result of the study showed that being male household head increases the probability of market participation of the sample participant.

Family Size of Households:previously it was hypothesized that family size affects participation of maize market positively. Similarly, as specified in (Table 4), the model result confirmed that the market participation of maize was positively influenced at less than 1% significance level by family size of the households due to high number of family size can produce in greater amount by substituting labor and could be participated in maize market. The marginal effect for family size ratifies that as family size increases by one; the probability of maize market participation also increases by 0.719% while keeping all other variables constant. This result is consistent with the study of Dagmawit(2016) and Nugusa (2018) who indicated that there was positive relationship between maize market participation and male-headed household. Similarly, Agete (2014) confirms that family size had a positive and significant relationship with market participation decision in red bean marketing. In addition to this, the study of Geoffrey (2014) justifies that family size had a positive and significant relationship with market participation decision in pineapple marketing.

Maize Farming Experience of Household:a household with better experience in maize farming is assumed to produce more amounts of maize as a result, assumed to supply more amounts of maize to the market. This variable was expected positively to influence the maize market participation of farmers. Likewise, as shown in (Table 4), the econometric result shows that maize farming experience of the households positively influenced the market participation at 5% significance level. As result a household with better experience in maize farming can produce more amounts of maize and can participate in maize market rather than less experienced farmers. The marginal effect of the result reveals that as experience of households’ increases by one year, the probability of maize market participation also increases by 0.22%; all other variables held constant. This finding is in line with the study of Adeotiet al.(2014) who discovered that the increase in the maize farming experience increases the probability to engage in market participation. In addition to, this was consistent with the study of Olusola and Gregory (2014) who studied that an experience of the household head and farm practice had significant influence on the maize market participation.

Proximity of District Market:thedistance to the nearest markets was hypothesized to be negatively related to market participation likewise, the survey result confirmed that it had negative influence on the market participation at 1% significance level. The model result confirms that when distance to the market increases by one kilometer,but the probability of maize market participation decreases by 1.68%; all other factors thought constant (Table 4). This is due to farmers who are far away from market has less participation in maize marketing than farmers who are nearest to the market. Similarly, it was reported that smallholder households who were away from market centers had lower market participation (Gebremedhin and Jaleta, 2012). Distance to the nearest markets was associated to be negatively related to red bean market participation (Agete, 2014.) Also the distance to the market has been found to have a negative impact on pineapple market participation in Kenya (Geoffrey, 2014). In contrast, the study of Dagmawit, (2016) and Nugusa (2018) stated that there was negative relationship among maize market participation and the market distance.

Ownership of Means of Transportation:as previously hypothesized, ownership to a means of transportation was positively and significantly influences maize farmers’ market participation decision at 1%. As specified in (Table 4), the marginal effect of the result reveals that ownership of means transportation increases by one, likewise the probability of maize market participation increases by 4.54% while keeping all other variables constant. This suggests that when farmers own transportation means, it motivates them to produce surplus and participate in the market because they own the means of transport to bring production inputs from the source and convey outputs to the market. This finding is consistent with the findings of Agete (2014) who specified that the ownership of a means of transport had positive and significant influence for red bean market participation and the study of Geoffrey (2014) revealed that vehicle ownership was positively and significantly influences the market participation of pineapple. This is due to its crucial role in lowering the transport cost as well as boosting the volume of transport and this increases the proportion of pineapple sales to the market.

Frequency of Extension Contact:as presented in (Table 4) frequency of extension contact affects the market participation of maize as expected and significant at 10%. The marginal effect confirms as extension contact increased by one week, the probability of maize market participation also increases by 0.47%; keeping all other variables constant.As result, households who have extension contact at required time on input use, price, and about demand have more participation in the market. This result is in line with the study of Pilile Hamlet (2015) who found that found that there is positive and significant relationship among extension contact and market participation decision. In addition, Nugusa (2018) found that there was positive and significant relationship between number of extension contact per year and market participation decision.

Use of Credit service: access to credit was hypothesized positively to influence maize market participation of smallholder farmers. Likewise, it was positively and significantly influences the likelihood of farmers in maize market participation inDamot Pulassadistrict. As presented in (Table 4), the model result shown that use of credit service was significantly and positively influence maize market participation at less than 1%. As compared to rather than non-credit user households, credit user households’ increases in the probability maize market participation decision by 3.57% because credit users might have better ability to hire labors and to purchase improved seed and fertilizers on required time rather than farmers who are non-credit users. This result is in line with the findings of Nugusa (2018) and Walelign and Lemma (2019) the variable access to and use of credit had positive and significant influence on the likelihood of participation of maize marketing among smallholder farmers. Additionally, the result is likewise with the finding of Agete (2014) revealed that access to credit positively and significantly influenced the likelihood of farmers inHalabaSpecial District participating in red bean market

Annual Off-farm income per Year:this study was hypothesized off-farm income negatively meaning that if the earning from the non/off-farm income is higher than the maize production, mostly the farmer’s shift towards the non-farm income activities. In contrast, as specified in (Table 4), the model result revealed that increase of off-farm income had significant and positive influence on maize market participation at less than 1% significant level. This is due to farmers who have off-farm income can participate better than farmers who have no off-farm income because they can easily purchase inputs and hire labors. The marginal effect confirms as annual off-farm income increases by one birr, the probability of maize market participation also increases by 0.94%; keeping all other variables constant.This finding is in line with the study of Dagmawit (2016) who discovered that the increase in off-farm income increases the probability to engage in market participation. In addition, this was in different with study of Geoffrey (2014) stated that there was positive and significant influence among the market participation of pineapple and off-farm income.

Table 4: Selection Equation/ProbitModel Result

Abbildung in dieser Leseprobe nicht enthalten

Note:***, ** and * = significant at 1, 5 and 10 percent respectively

Source: Own computation from survey result, 2020.

3.3.1.2. Factors Influencing the Extent of Market Participation

To determine the factors influencing the extent of maize market participation, the second step of the Heckman outcome equation, OLS regression estimated outputs are discussed in this part. The results are presented in (Table 5), nine variables (sex of household, family size of household, proximity of district market, land size allocated for maize farming, ownership of means of transportation, membership of cooperative, accessibility of maize market information, accessibility of credit service and lambda) were significantly found to influence the extent of maize market participation.

Sex of Household Head:sex was expected positively to influence maize market participation and level of participation. But the model result revealed that sex of the household head significantly and negatively influences the extent of market participation at less than1 %in (Table 5). The negative sign implies that if the household is female headed the probability of maize to be marketed was decreased which is the variation in maize market participation due to this variable. This is consistent with the findings of Walelgn and Lemma (2019) who revealed that sex of the household head significantly and negatively influences the extent of maize market participation. In contrast to the findings of Haymanot (2014) who specified there was positive relationship between sale volume of durum wheat and male-headed household. In addition, it was different with the finding of Nugusa (2018) who stated there was positive relationship between sale volume of durum wheat and male-headed household.

Family Size of Household:previously it was hypothesized that family size affect volume of maize marketed positively. But, as indicated in (Table 5), the model result confirmed that family size of the households had negative influence on the market supply at less than 10% significance level. The negative effects of family size on market supply may imply that, households who have large family size allocated more quintals of products for consumption purpose and supply less to the market. This is due to for households who have high number of family member could consume high amount of maize and might be less participated in the market rather than households who have less number of family size.The coefficient confirms that as the family size of the households increase by one number, the proportion of maize market supply decreases by 1.046 quintals while keeping all other variables constant. This result is similar with the study of Nugusa (2018) who found that as the family size of the households increase by one, the market supply was decreases by 0.379 quintals. In addition to this, the study of Agete (2014) revealed that as a unit increase in family size would reduce the amount of red beans to be sold.

Land Size Allocated for Maize Farming:the total size of farm land owned by a farmer is among the variables that could influence both participation and supply of maize into market as expected, it was found household heads with large land size allocated more land to maize cultivation.As presented in (Table 5), theresult confirmed that land size allocated for maize farming had positive influence on maize market supply of at 1% significance level because farmers who have large size of farming land could produce maize product in high quantity and might participate in high volume than that of farmers who have less farming land. It was found that a positive and significant relationship which indicated that as market land size of the household increases by 1 hectare, the proportion of volume of market supply increases by 10.783 quintals while all other variables held constant. Likewise the study of Dagmawit (2016) was confirmed that an increase in land size of maize farming increases the extent of maize supply in market. In addition, Adeneganet al.(2012) confirmed that, households who have large size for maize farming can be participated in maize market in high level rather households who have small size farm land.

Proximity of District Market:the distance to the nearest market did affect market supply as expected. It was hypothesized that, proximity of district market negatively affects the maize market supply that implies if the distance from the farm gate high, an extent of the maize supply to the market decrease. Likewise, in (Table 5) the survey result confirmed that it had negative influence on the extent of market participation at 5% significance level. Meaning that, when distance to the market increases by one kilometer, but the level of maize market participation decreases by 0.22 quintals while keeping all other variables constant. Similarly, it was reported that smallholder households who were away from market centers had lower extent of maize market participation. Distance to the nearest markets was related to be negatively related to the extent of red bean market participation (Agete, 2014). Also the distance to the market has been found to have a negative impact on level of pineapple market in Kenya (Geoffrey, 2014).

Ownership of Means of Transportation:asexpected hypothesis ownership to means of transportation was positively and significantly influences the extent of market participation at 10%. As indicated in (Table 5), the result shows that an increase in ownership to means of transportation by one increases the proportion of maize supply in the market increases by 0.577 quintals while holding all other variables constant. Because, ownership to means of transportation plays crucial role in lowering the transport cost as well as boosting the volume of transport and this increases the proportion of maize sales to the market.Likewise, the study of Geoffrey (2014) revealed that an increase in ownership of vehicle increases the volume of supply of pineapple to the market. Additionally, the discovery of Agete (2014) confirmed that ownership of means of transport had positive and significant influence on the extent of red bean farmers’ market participation. In his study Nugusa (2018) also found that ownership of means of transport had positive and significant influence on market supply of maize

Membership of Cooperative:This variable was expected to affect the household supply of maize positively. Similarly it affects extent of maize market participation positively and significantly at 1%. This implies that, households who are the member of the cooperatives obtain inputs like fertilizers, seeds, pesticides, credits and others which boosts the famer’s production and influences the maize households to participate in the market. As indicated in (Table 5), the coefficient of the result confirms that households who are the member of the cooperatives increasesby 0.934 quintals in the extent of maize market participation while keeping all other variables constant as compared to households who are not the member of the cooperatives. In similar manner, in the study of A. Benjamin M.et al.(2014) membership of cooperative organization was positively related to maize production and extent of maize marketing. In addition, this was in line with Walelgn and Lemma (2019) who stated that there was significant and positive relationship at 5% level between being member of a cooperative society and extent of maize market participation.

Accessibility of Maize Market Information:as hypothesized market information affects supply of maize positively and significantly at 1% significant level. It was found that a positive and significant relationship which indicated that as the household have access for the market information, volume of market supply also increased (Table 5). The result ratifies that households who have access to market information increases by 1.907 quintals of level of maize market participation rather than non-accessible households while keeping all other variables constant. This suggests that access to market information reduces farmers risk aversion behavior of getting a market and decreases marketing costs of farmers that affects the marketable surplus. This is in line with the study of Walelgn and Lemma (2019) who indicated that those farmers who had access to market information were more likely to participate in maize marketing than those who had no access to market information of maize and the finding of Dagmawit (2016) agreed that accessibility of maize market information had positive and significant relationship in extent of maize marketing. In alike, Muhammed (2011) who illustrated access to market information by farming households increase marketable supply ofteffsignificantly inHalabaSpecial district.In addition, the result agrees with the finding of Geoffrey (2014) who found that price information significantly and positively influences the extent of market participation in pineapple sales.

Use of Credit service:credit service is very important instruments for households to purchase inputs, materials, pesticides, hire labors on time at required time and boosts her/his production as compared to non-credit users. This variable was expected to influence the level of participation in maize market of producers positively. Likewise, as shown in (Table 5), the result confirms that there was positive and significant influence on extent of maize marketing at less than 1% significant level. This means, the increase of credit use in one birr, increases the extent/level of maize market participation by 1.445 quintals rather than non-credit users while holding all other variables constant. This is in line with the findings of Negassa (2012) found credit to have a positive relation with likelihood of selling raw milk in Ethiopia, indicating access to credit increased extent of milk market participation.Based on his finding Agete (2014) discovered that accessibility and use of credit influences positively the extent of marketing participation of smallholder farmers in red bean marketing in Halaba Special District. In addition, to the study of Walelgn and Lemma (2019) it was positive and significant influence at 10% among extent of maize market participation and the access to and use of credit meaning access to and use of formal credit were more likely to supply than those who had no access to and use of formal credit.

Table 52: Outcome Equation/ OLS Regression Result

Abbildung in dieser Leseprobe nicht enthalten

Note:***, ** and * = significant at 1, 5 and 10 percent respectively

Source: Own computation from survey result, 2020.

4. SUMMARY, CONCLUSSIONS AND RECOMMENDATIONS

4.2. Summary

The study was focused on determinants of maize market participation inDamotPulassadistrict,WolaitaZone, Ethiopia. The specific objectives include investigating the factors that influence maize market participation decision of smallholder farmers in the study area, assessing factors that determine extent of participation among smallholder farmers in the district and identifying the constraints faced by farmers in maize marketing inDamotPulassadistrict.

The data were collected from both primary and secondary sources. The primary data were collected from individual and FGD interviews using pre-tested semi-structured questionnaire and checklist. Primary data were collected from 360 sampled households. Secondary data was collected from CSA, agricultural and natural resource office of the district, and DAs atKebeleslevel. The analysis was undertaken by descriptive and econometric analysis.

The descriptive method includes percentages, frequencies, mean, chi-square test for dummy variable and t-test for continuous variable were utilized. All households were maize producers but not all households participate in maize market. From 360 sampled households 162(45%) of the total households were participate in maize market and the rest 198(55%) were not participate in maize market. From male headed households 91.36% and 54.55% were participants and nonparticipants in maize marketing respectively and from female headed maize producers 8.64% and 45.45% were participants and non-participants, respectively. The average age of maize participant households were 44.31 years while that of non-participants was 43.42 years. As result sex, family size, total quantity of maize produced, year of education, maize farming experience, farm land allocated for maize farming, off-farm income, use of credit service, frequency of extension service, market price of maize and proximity of market were significant factors that influence maize market participation and level of participation among smallholder farmers.

The Heckman selection model was used to identify factors affecting maize market participation and level of participation. The model result revealed that market participation of maize was affected by annual price of maize per quintal, age of household, sex of household, family size of household, maize farming experience of household, proximity of district market, ownership of means of transportation, frequency of extension contact, use of credit service, and annual off-farm income per year. The truncated part of the Heckman model revealed that market supply of maize was affected by sex of household, family size of household, proximity of district market,land size allocated for maize farming, ownership of means of transportation, membership of cooperative, accessibility of maize market information and use of credit service.

The major constraints identified at market and production level in the study area were influence of brokers, poor access to an important information, price fluctuation of maize, poor road and transportation facility, shortage of farm land, poor accessibility of improved seed variety, weak coordination with marketing actors, poor storage facilities of maize and problems related with of credit service.

4.3. Conclusion

In Ethiopia, there is huge potential for maize production and marketing. InDamot PulassaDistrict farmers maize production and marketing constraints were identified to be brokers, poor accessibility of improved seed varieties, poor accessibility of market information, price fluctuation of maize, poor road and transportation facility, difficulty of finding buyers and shortage of land.

The important role of maize quantity produced was explained through its significant influence on market participation decision indicating the need for enabling environment for increasing smallholders’ ability to produce quality maize product in better amount. Use of credit was a crucial factor that had positive influence on maize farmers’ market participation decision and extent of participation in the market. This implies that availability of credit especially during planting could encourage farmers to produce surplus and participate in the market. Improving access to maize market information appears important in market participation decision as well as the extent of market participation. Reliable and consistent information and information presented in an understandable manner is most valuable.

4.4. Recommendations

It is indispensable to forward policy directions based on the findings of the study to formulate strategies. Based on this understanding the following recommendations have been made.

As result of this study the accessibility of market information has positive influence on both maize market participation and level of supply of maize marketing. Therefore, government should develop/expand the ways that farmers can easily access the market information through ease technologies.

Based on statistical result year of education and farming experience had positive influence on maize market participation decision. Therefore, government should invest in awareness creation and training; especially on the making farmers to adopt new technology and developing skill and knowledge of stallholder farmers to boost their produce so that farmers can easily participate in maize marketing.

Market price also affects the market participation decision of a household hence increasing the bargaining power of farmers regarding to setting of market prices for their products will positively play a role in increasing market participation decision of a household. As a result, policy should be made regarding to formation of fair and motivating price of farmers’ produces.

Proximity of market had negatively influence of the maize market participation. So, government should invest on improvement of rural infrastructures especially, on road connection to ease transportation of the maize from the area of production to marketing point so that farmers can easily participate in maize marketing.

According to the model result, sex of the household head had negative and significant influence on the supply of maize to the market. Therefore, Affirmative action should be considered for gender awareness; this is done by empowering more women to engage in maize marketing.

According to the model result use of credit service had significant and positive influence on extent of maize market participation. Improving access to credit for farmers should therefore be a priority for improving maize market performance, in turn, increasing efficiency and improving consumers’ welfare. Therefore, government should invest and encourage on satisfactory of credit service providence in the way of solving the problems like high interest rate, inadequate supply and bureaucracy which are related with the use of credit service.

Based on econometric model result, membership to the cooperatives affects volume of maize market participation. Households need motivation to be member of cooperatives. Education had also very important role in increasing the level of market participation and to identify the perfect information. Therefore, government and concerned bodies should create awareness to smallholder farmers to be member of cooperative organization since it has positive influence on the volume of maize market participation.

REFERENCES

AbuBenjamin Musah., Ose-Asarei Yaw Bonsu and WoyoSeini, 2014. Market Participation of Small Househlod Maize Farmers in Upper West Region of Ghana

Abraham Tegegn, 2013. Value Chain Analysis of Vegetables in Habro and KombolchaWoredas in Oromiya region, Ethiopia: M.Sc Thesis Presented to the School of Graduate Studies of Haramaya University, Ethiopia.

Adenegan, K.O., Adepoju, A. and Nwauwa, O.E. 2012.Determinants of Market Participation of Maize Farmers in Rural Osun State of Nigeria.Department of Agricultural Economics, University of Ibadan, Ibadan, Nigeria.International Journal of Agricultural Economics and Rural Development, 5 (1): 1-12.

AdetolaAdeoti, Isaac B. Oluwatayo&Raheem O. Soliu. 2014. Determinants of market participation among maize producers in Oyo State, Nigeria.British Journal of Economics, Mg t & Trade, 4: 1115-1127.

AddisuHailu. 2016. Value chain analysis of vegetables: the case of ejere district, west shoa zone, oromia national regional state Ethiopia. MSc. Thesis.Haramaya University, Haramaya, Ethiopia.

AdmasuShibru. 1998. Performance evaluation of coffee marketing in Sidama Zone. M.Sc Thesis Presented to the School of Graduate Studies of Alemaya University.

Agete J., 2014. An Analysis Of Factors Influencing Participation Of SmallholderFarmers In Red Bean Marketing In Halaba Special District, Ethiopia

Alene, A.D., Manyong, V.M., Omanya, G., Mignouna, H.D., Bokanga, M., and Odhiambo, G. 2014. Smallholder market participation under transactions costs: Maize supply and fertilizer demand in Kenya. Food Policy, 33: 318–328.

Arega D. Alene,, V.M. Manyong, G. Omanya, H.D. Mignouna, M. Bokanga, G. Odhiambo 2007. Smallholder market participation under transactions costs: Maize supply and fertilizer demand in Kenya

Asfaw, S.,Shiferaw, B., Simtowe, F., Muricho, G., Abate, T. and Ferede, S. 2010. Socioeconomic Assessment of Legume Production, Farmer Technology Choice, Market Linkages, Institutions and Poverty in Rural Ethiopia. Nairobi: ICRISAT

Asogwa, B, C. &Okwoche, V.A. 2012. Marketing of agricultural produce among rural farm households in Nigeria: the case of sorghum marketing in Benue State. Inter J of Bus and SocSci, 3: 269-277.

Astewel, T. (2010). Analysis of Rice Profitability and Market chain: The Case of FogeraWoreda, South Gondar Zone, Amhara National Regional State, Ethiopia. AnMsc. Thesis Presented to School of Graduate Studies of Haramaya University.

ATA /Agricultural Transformation Agency. 2016. Annual Report in Ethiopia. Innovations to help our country grow

Barret, B.C, 2012). Smallholder market participation: Concepts and evidence from Eastern and Southern Africa. Food Policy, 33:299–317.

Bealu T. Endrias G. and Tadesse A., 2013. Factors Affecting Economic Efficiency In Maize Production: The Case Of BorichaWoreda In Sidama Zone, Southern Ethiopia

Caitlin Heather 2011. Lowe Determinants of Maize Marketing Decisions for Smallholder Households in Tanzania

Cragg, J. 1971. Some Statistical Models for Limited Dependent Variables with Application to the Demand for Durable Goods. Econometric a, 39 (5), 829–844.retrivedfrom https://www.jstor.org/stable/1909582

CIA/Central Intelligence Agency. 2017. World fact book on overview of Ethiopian economy. USA.

CSA/Central Statistical Agency. 2017. The federal democratic republic of Ethiopia central statistical agency. Agricultural sample survey. 2017. Ethiopia

Cuddeback, G. Elizabeth Wilson. John, G. Orme. and Terri Combs-Orme. 2004. Detecting and Statistically Correcting Sample Selection Bias.Journal of Social Service Research, Vol. 30(3).The Haworth Press, Inc.

DagmawitGirefSahile 2016. Maize Value Chain Analysis In South Achefer and Jabitehnan Districts Of Amhara Region, Ethiopia

DPDANRO, (DamotPulassaDistrict Agricultural and Natural Resource Office), 2019.Unpublished document

ECX/Ethiopian Commodity Exchange Authority, 2014. Annual Report on overview of Ethiopian Commodity Exchange

Ehui, S., Benin, S. and Paulos, Z. 2013. Policy Options for Improving Market Participation and Sales of Smallholder Livestock Producers: A Case Study of Ethiopia. Draft Prepared for Presentation at the 27th Conference of the International Association of Agricultural Economists (IAAE), 16-22 August 2009, Beijing, China.

EleniGebre-Medhin 2013. Market institutions, transaction costs, and social capital in the Ethiopian grain market. International Food Policy Research Institute. USA.

FAOSTAT 2015.Food and Agricultural Organization of the United Nations.FAO Statistical Database 2015. Retrieved 29/01/2016 from World Wide Web http://faostat3.fao.org/download/FB/CC/E.

Gani, B.S., and Adeoti, A.I, 2011. Analysis of Market Participation and Rural Poverty among Farmers in Northern Part of Taraba State, Nigeria.Department of Agricultural Economics, University of Ibadan, Ibadan.OyoState.Nigerian Journal of Economics, 2(1):23-36

Gebremedhin, B., and Jalata, M, 2012. Market Orientation and Market Participation of Smallholders in Ethiopia: Implications for Commercial Transformation. Paper presented at International Association of Agricultural Economists (IAAE) Triennial Conference, Foz do Lguacu, Brazil.

Geoffrey M, Menale K. and Gideon O. 2015. Determinants of Market Participation Regimes among Smallholder Maize Producers in Kenya

Geoffrey KiprotichSigei 2014. Determinants of Market Participation among Small-Scale Pineapple Farmers in Kericho County, Kenya

Greene, W.H. 2003.LIMDEP Version 8.0 Econometric Modeling Guide, Vol.2.Econometric Software, Inc., New York.

Gujarati, D.N. 200. Basics Econometrics 4th edition. McGraw-Hill, New York. pp. 563636.Gujarati DN. 2004. Basic Econometrics 4th ed, McGraw-Hill Companies

Haddad, L. J. and Bouis, H.E, 2013. Agricultural commercialization, nutrition and the rural poor: A study of Philippine farm households. International Food Policy Research Institute (IFPRI), Washington, DC.

Haymanot Asfaw.2014. Value chain analysis of durum wheat in Southern Ethiopia.MSc Thesis, Haramaya University, Haramaya, Ethiopia.

Hoffmann, R. &Kassouf, A.L.2007.Deriving conditional and unconditional marginal effects in log earnings equations estimated by Heckman’s procedure. Applied Economics 37(11):1303-1311.

IFPRI 2010 International Food Policy Research Institute. Sustainable solution for ending hanger and poverty. Maize Value Chain Potential in Ethiopia, Constraints and opportunities for enhancing the system. II, Ethiopia. ESSP II Working Paper No. 21.May 2011. inHalaba Special Woreda , Southern Ethiopia. MSc Thesis. Haramaya University.Haramaya, Ethiopia.

International Grains Council and IGC Grains Conference, London, 13-14 June 2016

Jagwe, J., Machethe, C. and Ouma, E. 2010. Impact of Transaction Cost on the Participation of Smallholder Farmers and Intermediaries in the Banana Market in Burundi, Democratic Republic of Congo and RwandaAfrican Journal on Agricultural and Resource Economics, 6:1.

Jamal Hashim. 2013. Coffee Value Chain Analysis the Case of Meta District, East Hararghe Zone of Oromia Regional State. MSc Thesis. Haramaya University.Haramaya, Ethiopia.

Jari, B. 2015. “Institutional and Technical Factors Influencing Agricultural Marketing Channel Choices amongst Smallholder and Emerging Farmers in the Kat River Valley” Master of Science in Agriculture (Agricultural Economics) Faculty of Science and Agriculture University of Fort Hare.

Karim, M. R. and Rahman, M. A. 2015.Drought Risk Management for Increased Cereal Production in Asian Least Developed Countries.Weather and Climate Extremes Journal.Vol. 7. pp 24–35.

Key, N., Sadoulet, E. and Dejanvry, A. 2012. Transactions Costs and Agricultural Household Supply Response.American Journal of Agricultural Economics, 88 (2): 245-259

Maddala, G.S, (1983). Limited Dependent and Qualitative Variables in Econometrics.Cambridge University Press.

MahiletMokennon 2013. Value Chain Analysis of Malt Barley: The Case of Tiyo and Lemu-Bilbilo Districts in Arsi Zone, Oromia National Regional State. MSc Thesis. Haramaya University.Haramaya, Ethiopia.

Mathenge, M., Frank, P., Olwande, J., and Dagmar, M, 2010.Market Participation among Poor Rural Households in Kenya.World Agroforestry Center, Tegemeo Institute of Agricultural Policy and Development, Egerton University and World Agroforestry Center, Study report.

Martey, E., Ramatu, M.A., and John, K.M.K, 2012. Commercialization of smallholder agriculture in Ghana: A Tobit regression analysis.African Journal of Agricultural Research, 7(14):2131-2141.

Mendoza M, Rosegant M. 1995.Pricing conduct of spatially differentiated markets.Prices, Products, and People.International Potato Center, Lima, Peru. Pp.343-356

Minot, N. and Ngigi, M. 2012. Are Horticultural Exports a Replicable Success Story? Successes in African Agriculture: Building for the Future, IFPRI Paper 7, International Food Policy Research Institute, Washington DC.

Moyo, S. 2010. Peanut research and poverty reduction: impacts of variety improvement to control peanut viruses in Uganda. Am J Agr Econ, 89(2): 448-60.

Muhammed Urgessa.2011. Market Chain Analysis of Teff and Wheat Production.Thesis, Haramaya University, Haramaya, Ethiopia.

Mussema, M., and Dawit, A, 2012. Red pepper marketing in Siltie and Alaba in SNNPRS of Ethiopia: factors affecting households' marketed pepper.International Research Journal of Agricultural Science and Soil Science, 2(6):261-266.

Negassa, A, 2012.Improving smallholder farmers’ marketed supply and market access for dairy products in Arsi Zone, Ethiopia.Research Report 21. ILRI (International Livestock Research Institute), Nairobi, Kenya. 107 pp

NugusaAbajobir, 2018 Analysis Of Maize Value Chain: The Case Of GuduruWoreda, HorroGuduruWollega Zone Of Oromia Regional State, Ethiopia

Nurilefebo 2016.Value Chain Analysis of Enset (Enseteventricosum).Thesis, Haramaya University, Haramaya, Ethiopia.

Egbetokun, Shittu,Ayoade, 2017. Determinants of Market Participation Among Maize Farmers In Ogbomoso Zone, Oyo State, Nigeria

Ogunlege, K.Y. and Oladeji, J.O. 2013.Choice of Cocoa Market Channels among Cocoa Farmers in ILA Local Government Area of Osun State, Nigeria’ Middle-East.Journal of Scientific Research, 2:14-20.

Olusola O. and Gregory D., 2014. Determinants of Market Participation by Maize Farmers in Ondo State, Nigeria

Olwande, J. and Mathenge, M. 2012. Market Participation among the Poor Rural household in Kenya.Tegemeo Institute, Egerton University, Kenya. Selected Paper Prepared for Presentation at the International Association of Agricultural Economists (IAAE) Triennial Conference, Foz do Iguaçu, Brazil, 18-24 August.

Omiti, J.M., Otieno, D.J., Nyanamba, T.O. and McCullough, E. 2016. Factors Influencing the Intensity of Market Participation by Smallholder Farmers; A Case Study of Rural and Peri-Urban Areas of Kenya,African Journal of Agricultural and Resource Economics, 3:71.

Pilile Hamlet Hlomendlini. 2015. Key Factors Influencing Smallholder Market Participation in the Former Homelands of South Africa: Case Study of the Eastern Cape.

Rashid and Negassa. 2011. Policies and Performance of Ethiopian Cereal Markets Development.

Rashid, S. K., Getnet and S, Lemma. 2010. Maize value chain potential in Ethiopia: Constraints and opportunities for enhancing the system, IFPRI, Working Paper, July 2010.

Reardon, T, and Timmer, C.P, (2012). Transformation of Markets for Agricultural Output in Developing Countries Since 1950: How Has Thinking Changed? Handbook of Agricultural Economics. Amsterdam, the Netherlands.

Rios, A. R., William, A. M and Gerald, E, 2012. Linkages between Market Participation and Productivity: Results from a Multi-Country Farm Household Sample Prepared for presentation at the American Agricultural Economics Association, Annual Meeting, Orlando, Florida, July 27-29, 2008.

Shahidur Rashid, KindieGetnet, and Solomon Lemma. 2010. Maize Value Chain Potential in Ethiopia Constraints and Opportunities for Enhancing the System International Food Policy Research Institute, Ambo College, Ethiopia

Shiferaw, B. And Teklewold, H. 2014. Structure and Functioning of Chickpea Markets in Ethiopia: Evidence Bases on Analyses of Value Chains Linking Smallholders and markets. Improving Productivity and Market Success (IPMS) of Ethiopian Farmers Project Working 6. ILRI (International Livestock Research Institute), Nairobi, Kenya. pp. 63.

Storck, H., Bezabih E., Berhanu A., Borowiccki and Shimeles W., 1991. Farming system and farm management practices of small holders in the Hararghe highland. Farming systems and resource economics in the tropics, II, WissehschaftsverlagVauk, and Kiel, Germany.

Thakur, D.S., D.R. Harbans Lal, K.D. Sharmaand A.S.Saini. 2012. Market supply response and marketing problems of farmers in the Hills.Indian Journal of Agricultural Economics, 52(1): 139-150

Tobin, J. (1958). Estimation of Relationships for Limited Dependent Variable.Econometrica, 46:24-36.

Toit, D. C., Ramonyai, M. D., Lubbe, P. A. and Ntushelo, V. 2011.Food Security.Republic of South Africa.Department of Agriculture, Forestry and Fisheries.

Toyiba. 2013. Market chain analysis of papaya: The case of Dugdaworeda, eastern Shewa of Oromia regional state. MSc thesis presented to School of graduate students of Haramaya, Ethiopia.

UNDP /United Nations Development Program, 2016.Strengthening national capacities to enhance agricultural production and productivity.Agricultural Growth and Transformation, Addis Abeba, Ethiopia.

WalelgnYalewBeadgie, Lemma Zemedu 2019. Analysis of Maize Marketing; The Case of FartaWoreda, South Gondar Zone, Ethiopia.International Journal of Agricultural Economics. Vol. 4, No. 4, , pp. 169-180. doi: 10.11648/j.ijae.20190404.15

Wooldridge, J, 2002. Econometric Analysis of Cross-Section and Panel Data. Massachusetts: The MIT Press. World Bank, (2008).World Bank Development Report.

World Bank, 2011.“Agriculture for Development”.World Development Report 2011. Washington: World Bank.

WZANRO (Wolaita Zone Agricultural and Natural Resource Office), 2019.Unpublished document

Yamane T 1967. Statistics: An Introductory Analysis, 2nd Ed., New York: Harper and Row.

Yaynabeba, A., and Tefera, T, 2013. Factors influencing market participation decision and extent of participation of haricot bean farmers in Meskan District.International Journal of Management and Development Studies, 2(6): 17-26.

Yishak G. and N. K. Punjabi, 2011. Determinants of Adoption of Improved Maize Technology in Damot Gale, Wolaita, Ethiopia

Zeberga, A, 2010. Analysis of poultry market chain: the case of Dale and Alaba “Special” Woredas of SNNPRS, Ethiopia. Unpublished M.Sc. Thesis, Haramaya University, Ethiopia.

Zegeye, T., Tadesse, B., Tesfaye, S., Nigussie, M., Tanner, D. and Twumasi. A. S. 2011. Determinants of Adoption of Improved Maize Technologies in Major Growing Region of Ethiopia. Second National Maize Workshop of Ethiopia, 12-16 November, 2011.

[...]