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\author{}
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\begin{document}

\textbf{Factors Influencing Coffee Marketing Channel Choices by
Smallholder Farmers in Jimma Zone, Southwestern Ethiopia}

By

Zewdu Getachew\textsuperscript{1}, Fekadu Beyene\textsuperscript{2},
Jema Haji\textsuperscript{3} and Tesfaye Lemma\textsuperscript{2}

\hypertarget{section}{%
\section{}\label{section}}

\hypertarget{abstract}{%
\section{Abstract}\label{abstract}}

\emph{Coffee is one of the most important agricultural commodities with
significant contributions to the growth and well-functioning of
Ethiopia's economy, and the social stability of the country (Alemseged,
2012).} \emph{Despite its importance, performance of the sector has been
unsatisfactory due to various production and marketing related factors.
This study thus was conducted with the objective of identifying factors
influencing the choices of marketing channel choices for selling
dry-processed or red coffee cherries. A cross-sectional survey data were
collected from 377 sample farmers selected from Gomma and Limmu Kossa
districts of Jimma zone southwestern Ethiopia selected through a
multistage stratified simple random sampling technique. The data were
collected from October 2019 to January 2020 after pretesting the
questionnaire. Descriptive statistics and multivariate probit (MVP)
econometric model were used to analyze the data. The results obtained
from separate MVP regression model analyses show that sex, coffee land
size and productivity, average dry-processed coffee selling price,
frequency of visit by DA, cooperative membership, access to credit,
training, off- and non-farm income sources, distance to cooperative's
and private traders' marketing centers influenced the choice of local
consumers, traders or cooperatives for selling dry-processed or red
coffee cherries. The findings suggest that farmers should be provided
with coffee production and productivity enhancing technologies that
could increase yield and productivity and marketed supply of coffee.
Meanwhile, farmers should be provided with information regarding the
importance of cooperatives and training on ways of improving coffee
production and productivity. Last but not the least, cooperatives should
incorporate credit scheme at peak coffee production and marketing season
in particular so that members prefer to supply their coffee to the
cooperatives and earn better coffee income at the end.}

\emph{\textbf{Key words}: Smallholder farmers, sample survey, coffee
marketing channel, cooperatives, multivariate probit model.}

1= Department of Rural Development and Agricultural Extension, College
of Agriculture and Natural Resources, Madda Walabu University, Bale
Robe, Ethiopia.

2= School of Rural Development and Agricultural Innovation, College of
Agriculture and Environmental Sciences, Haramaya University, Dire Dawa,
Ethiopia.

3= School of Agricultural Economics and Agribusiness Management, College
of Agriculture and Environmental Sciences, Haramaya University, Dire
Dawa, Ethiopia.

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\item ~
  \hypertarget{introduction}{%
  \section{Introduction}\label{introduction}}

  \begin{enumerate}
  \def\labelenumii{\arabic{enumii}.}
  \item ~
    \hypertarget{background}{%
    \subsection{\texorpdfstring{Background
    }{Background }}\label{background}}
  \end{enumerate}
\end{enumerate}

Coffee is one of the most important agricultural commodities with
significant contributions to the growth and well-functioning of
Ethiopia's economy, and the social stability of the country (Alemseged,
2012). Through various taxes levied on the crop, it also serves as an
important source of government revenue (Boansi and Crentsil, 2013). The
crop for example generated 909.4 million of USD) accounting for 25.14\%
of the total value of exports (f.o.b) in 2020/21 (NBE, 2022). Coffee
also provides income to over 15 million people in the country through
provision of jobs for farmers, local traders, processors, transporters,
exporters and bankers (MoT, 2012).

Despite its importance, coffee production and marketing performance of
Ethiopia has been unsatisfactory due to various reasons. All coffee
production is rain fed; thus, precipitation is the most important
production factor. Smallholder farmers produce about 95\% of Ethiopia's
coffee on a farm land averaging less than 2 hectares in varied
environments, including forest, semi-forest, garden, and plantation
coffee (Minten, 2017b; GAIN, 2019). Lack of improved seed, proper tree
management, and price incentives for coffee producers and promotion of
irrigation system in area where irrigation is possible are for example
the major bottlenecks to coffee production and productivity (Abu, 2016;
GAIN, 2019). The coffee sector also suffers from product assembling,
storing, handling, processing, quality inspection and grading, and
transparent grading system (Bastin and Matteucci, 2007; ECEA, 2008;
Minten et al., 2015). High transaction costs, lack of factor and weak
capital markets, market information and poor infrastructure add to the
domestic marketing problems.

Consequently, small-scale farmers in Ethiopia receive minimal benefits
from the efforts they put into farming (Tium, 2013). The efffects of
these problems are manifested in increasing poverty among coffee growers
(Samuel and Ludi, 2008; Minten \emph{et al}., 2014, 2015).

Choice of a marketing channel is one of the key ingredients to
successful marketing of both agricultural and non-agricultural products
as different channels are characterized by different magnitude of profit
and costs. Market development commonly parallels the development of a
region\textquotesingle s or a nation\textquotesingle s economy (Solomon
\emph{et al}., 2016). Therefore, understanding marketing behaviors of
smallholder farmers including their market outlet choices, and the
variables affecting the decisions in particular can be of a great
importance in the development of sound policies with respect to coffee
marketing, prices, exports, and in meeting the overall rural and
national development objectives of the country.

\hypertarget{statement-of-the-problem}{%
\subsection{Statement of the Problem}\label{statement-of-the-problem}}

Though coffee is produced in different parts of Ethiopia, the largest
volume of coffee is produced in the Oromia and Southern Nations,
Nationalities and Peoples (SNNPs) regions mainly by smallholder farmers
who practice mainly mixed crop-livestock farming systems (Sentayhu,
2011; Abu, 2012; Minten \emph{et al}., 2014). As such, 64\% and 35\% of
coffee production come from Oromia and SNNP regions, respectively. The
remaining 1\% comes from Gambela regional state of the country (MoT,
2012). In Oromia region, Guji, West Guji, Jimma, Bunno Bedelle, Ilu Aba
Bor, Kellam, West and East Wellega and Horro Guduru Wellega zones are
known as major coffee growing zones (ECCSA, 2020).

Jimma zone is one of the major coffee growing zones not only in Oromia
region but also in the country with about 105,140 hectares of land
covered with coffee, which includes small-scale farmers' holdings as
well as state and private owned plantations (Berhanu \emph{et al}.,
2015; Dagne \emph{et al}., 2015a; ECCSA, 2020). Out of the 40-55
thousand tons of coffee annually produced in the zone, about 28-35
thousand tons is sent to the central market, while the remaining is
locally consumed (Alemayehu \emph{et al.,} 2008). Jimma Zone covers a
total of 21\% of the export share of the country and 43\% of the export
share of the Oromia Region (Nasir, 2020).

Despite its importance to the livelihoods of a significant number of
rural households in Jimma zone and economic development of the country
at large, coffee production and marketing performance has been
unsatisfactory due to various reasons. Lack of sufficient and weakly
coordinated provision of institutional support services, and poor coffee
production management and post-harvest handling practices among
smallholder farmers are the major bottlenecks hindering production and
market supply of quality coffee from such high-potential and major
coffee growing areas of the country in particular (ECEA, 2008; Babur,
2009, Anwar, 2010; Amsaya, 2015).

A better understanding of farmers selling decision therefore is
important to produce empirical evidence for cooperative leaders and for
policy makers to design appropriate policies and strategies that can
contribute to increased income of coffee farmers (Mekonin, 2017; Dejen,
2018; Nasir, 2020; Alemayehu and Alemu, 2022). There are no or limited
empirical studies that investigated determinants of the choices of
marketing channels separately for selling dry-processed and red coffee
cherries from the same sample population in Jimma zone in particular and
in the country at large (Dejen, 2018). Such a study is relevant as it
produces evidence on whether determinants of choices of marketing
channels for selling dry-processed coffee beans and red coffee cherries
are similar or varied. This study thus was conducted with the aim of
understanding observed marketing channels, the choices of observed
marketing channels and the determinants of such choices for selling both
dry-processed and red coffee cherries by smallholder farmers in Jimma
zone of Oromia Region, southwestern Ethiopia.

\hypertarget{objectives-of-the-study}{%
\subsection{Objectives of the Study}\label{objectives-of-the-study}}

The main objectives of this study therefore were to identify observed
choices of marketing channels and the determinants of the choices for
selling dry-processed coffee beans or red coffee cherries by smallholder
coffee farmers in Jimma zone of Oromia region, southwestern Ethiopia.

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\setcounter{enumi}{1}
\item ~
  \hypertarget{research-methodology}{%
  \section{RESEARCH MethodOLOGY}\label{research-methodology}}

  \begin{enumerate}
  \def\labelenumii{\arabic{enumii}.}
  \item ~
    \hypertarget{description-of-the-study-area}{%
    \subsection{\texorpdfstring{Description of the Study Area
    }{Description of the Study Area }}\label{description-of-the-study-area}}
  \end{enumerate}
\end{enumerate}

Ethiopia is a federal country divided into 11 regional states and 2 city
administrations. Each region is subdivided into zones, and the zones
into \emph{woredas (districts),} which are roughly equivalent to a
county in the United States or UK. Woredas, in turn, are divided into
peasant associations (PAs), or \emph{kebeles}, an administrative unit
consisting of a number of villages (Dercon \emph{et al}., 2008). This
study was conducted in Gomma and Limmu Kossa districts of Jimma zone,
Oromia National Regional State of the country (see Figure 1). The zone
is located in the southwestern part of the country about 360km away from
Addis Ababa, the capital city of the country. The zone extends between
7\textsuperscript{0}13'- 8\textsuperscript{o}56' North latitudes and
35\textsuperscript{0}49'-38\textsuperscript{0}38' east longitudes. Jimma
zone is one of the major coffee producing areas with about 105,140
hectares of land covered with coffee, which includes small-scale
farmers' holdings as well as state- and privately-owned plantations
(Berhanu \emph{et al}., 2015; Dagne \emph{et al}., 2015). About 30-45\%
of the people in Jimma Zone directly or indirectly benefit from the
coffee industry (Anwar, 2010). However, coffee is widely produced in
eight districts, namely, Gomma, Manna, Gera, Limmu Kossa, Limmu Seka,
Seka Chokorsa, Kersa and Dedo districts. Gomma and Limmu Kossa districts
are among top coffee producing districts in Jimma zone. The majority of
smallholder farmers in the districts are engaged in engaged in coffee
production and marketing as their main livelihood acclivity and means of
cash income (BoCPA, 2018).

Figure 1. Map of the study area.

\includegraphics[width=6.34097in,height=2.43264in]{vertopal_879c92df90cc47c2806e23938bb1ab26/media/image1.png}

\hypertarget{data-and-methods-of-data-collection}{%
\subsection{Data and Methods of Data
Collection}\label{data-and-methods-of-data-collection}}

Secondary and primary sources of data were used to undertake the study.
However, primary data collected through semi-structured cross-sectional
sample survey was the main source of data for this research undertaking.

\hypertarget{sample-size-and-sampling-technique}{%
\subsection{Sample Size and Sampling
Technique}\label{sample-size-and-sampling-technique}}

The question of how large a sample to take arises early in the planning
of any survey or experiment. This is an important question that should
not be treated lightly. Sample size determination however can be done
based on whether the samples are needed for estimating population mean
or percentage or proportion under study (Kothari, 2004; Daniel and
Cross, 2013\textbf{)}. Since the aim of this study was to identify
factors influencing the choices of coffee marketing channels by
smallholder farmers portion of member farmers, the initial sample size
(n\textsubscript{0}) was determined by using the formula for estimating
the population proportion or percentage as given by the above-mentioned
authors as follows:

\begin{quote}
\includegraphics[width=0.7in,height=0.42986in]{vertopal_879c92df90cc47c2806e23938bb1ab26/media/image2.wmf}
(1)

n\textsubscript{0} = 384.
\end{quote}

where, n\textsubscript{0} is the initial desired sample size; Z = 1.96
for a 95\% desired confidence level, p is the estimated population
percentage or proportion (which is usually set at 0.5 for a population
proportion which is unknown \emph{a priori}), q = 1-q = 1-0.50 = 0.50,
and d = ±0.05 for a 95\% desired precision level, respectively.

Then, 10\% of sample size was added to the original sample size
(\emph{i.e}., 384), making the final sample size 423 in compensation for
possible drop out of respondents and/or incomplete survey questionnaires
(See Table 1 for details).

Table 1. Distribution of population and sample size of the study by
district and peasant association

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 6\tabcolsep) * \real{0.2080}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 6\tabcolsep) * \real{0.3196}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 6\tabcolsep) * \real{0.2640}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 6\tabcolsep) * \real{0.2084}}@{}}
\caption{Table 2. Econometric model variables hypothesized to influence
the choices coffee marketing channels by the sample
farmers}\tabularnewline
\toprule()
\endhead
\textbf{District} & \textbf{Kebele/PA} & \textbf{Population size} &
\textbf{Sample size} \\
\multirow{6}{*}{Gomma} & Bulbulo & 851 & 39 \\
& Choche Lemi & 901 & 41 \\
& Ilbu & 1061 & 48 \\
& Omo Beko & 1159 & 52 \\
& Omo Guride & 1330 & 60 \\
& \textbf{Sub-total} & \textbf{5302} & \textbf{240} \\
\multirow{10}{*}{Limmu Kossa} & Tencho & 444 & 20 \\
& Gena Denbi & 412 & 19 \\
& Chime & 330 & 15 \\
& Mito Gundub & 421 & 19 \\
& Chefe Ilfeta & 510 & 23 \\
& Babu & 493 & 22 \\
& Debello & 404 & 18 \\
& Harewa Jimate & 345 & 16 \\
& Kacho Tirtira & 670 & 31 \\
& \textbf{Sub-total} & \textbf{4029} & \textbf{183} \\
\multicolumn{2}{@{}>{\raggedright\arraybackslash}p{(\columnwidth - 6\tabcolsep) * \real{0.5276} + 2\tabcolsep}}{%
\textbf{Grand total}} & \textbf{9331} & \textbf{423} \\
\bottomrule()
\end{longtable}

Source: Cooperative promotion agencies of Jimma zone, Gomma and Limmu
Kossa districts (2019)

\hypertarget{methods-of-data-analysis}{%
\subsection{\texorpdfstring{Methods of Data Analysis
}{Methods of Data Analysis }}\label{methods-of-data-analysis}}

Both descriptive statistics and multivariate probit (MVP) econometric
model were used to analyze the data collected. As such frequency,
percentage and chi\textsuperscript{2} and t tests were used to describe
the socio-economic characteristics, access to institutional support
services, and infrastructure of the sample farmers against the marketing
channels they chose for selling dry-processed or red coffee cherries in
2017/18 coffee season. In order to analyze factors influencing the
choice of observed local marketing channel (\emph{i.e.}, consumers,
private traders or cooperatives) for selling dry-processed coffee beans
or red coffee cherries, two separate MVP model were run. STATA version
14.2 statistical package was used to analyze the data.

The MVP econometric model was used to identify factors influencing the
choice of one, two or three local coffee marketing channels (CMC),
\emph{viz}, consumers, private traders or cooperatives. The selection of
a local coffee market channel i by farmer j is defined as the choice of
farmer j to transact in coffee market channel i or not is expressed as
follows:

\includegraphics[width=4.07014in,height=0.47986in]{vertopal_879c92df90cc47c2806e23938bb1ab26/media/image3.png}(1)

where
\includegraphics[width=0.35556in,height=0.37222in]{vertopal_879c92df90cc47c2806e23938bb1ab26/media/image4.png}
is a vector of estimators and εa is a vector of errors terms under the
assumption of normal distribution,
\includegraphics[width=0.1875in,height=0.1875in]{vertopal_879c92df90cc47c2806e23938bb1ab26/media/image5.png}
is dependent variable for local coffee market channel choice of
consumers, private traders or cooperatives, and is
\includegraphics[width=0.20833in,height=0.20833in]{vertopal_879c92df90cc47c2806e23938bb1ab26/media/image6.png}
combined effect of the explanatory variables.

Then, the econometric model specification for choosing an observed local
coffee mdarket channel (\emph{i.e}., consumers, private traders or
cooperatives) is given as follows:

\begin{quote}
\includegraphics[width=1.69792in,height=0.74722in]{vertopal_879c92df90cc47c2806e23938bb1ab26/media/image7.wmf}
(2)

\includegraphics[width=5.36181in,height=0.86389in]{vertopal_879c92df90cc47c2806e23938bb1ab26/media/image8.png}

where,

each Consumersj, Prvtradersj and LCoopj local marketing channel is a
binary response variable and takes a value 1 if a sample farmer j
selects consumers, local private traders or local cooperatives,
respectively, or 0 otherwise;

X\textsubscript{1} to X\textsubscript{3} are vectors of independent
variables determining the respective marketing channel choices
variables;

β\textquotesingle s are vectors of simulated maximum likelihood (SML)
parameters to be estimated;

ɛA to ɛ\textsuperscript{C} are correlated disturbances in a seemingly
unrelated multivariate probit model; and

ρ's are tetrachoric correlations between endogenous variables.
\end{quote}

In the trivariate case, however, there are eight joint probabilities
corresponding to the eight possible combinations of successes (a value
of 1) and failures (a value of 0). As such, if we focus on the
significance that every outcome is a success for instance, the
probabilities that enter the likelihood function of the market channel
choices simulation are explained as:

\begin{quote}
PrConsumrsj = 1, Prvtradersj =1, LCoopj = 1

= ϕ3(β'\textsubscript{1}X\textsubscript{1},
β\textsubscript{2}'X\textsubscript{2},
β\textsubscript{3}'X\textsubscript{3}', ϼ)

Pr(ɛA≤ βX\textsubscript{1}, ɛB ≤ βX\textsubscript{2}, ɛC ≤
βX\textsubscript{3}) (4)

where ϕ\textsubscript{3} is the multivariate normal density function.

Table 2 below presents socio-economic, access to institutional support
services and infrastructure related variables hypothesized to influence
the choice of an observed local consumers, private traders or
cooperatives for selling dry-processed coffee beans or red coffee
cherries by the sample farmers in 2017/18 coffee season.
\end{quote}

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 4\tabcolsep) * \real{0.3567}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 4\tabcolsep) * \real{0.4958}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 4\tabcolsep) * \real{0.1476}}@{}}
\caption{Table 8. Multivariate regression model results of factors
influencing marketing channel chosen for selling red coffee cherries,
2017/18}\tabularnewline
\toprule()
\endhead
\textbf{Variable name} & \textbf{Description} & \textbf{Hypothesized
influence} \\
\textbf{Dependent variable: Coop\_}(1= Yes; 0 otherwise) &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 4\tabcolsep) * \real{0.6433} + 2\tabcolsep}@{}}{%
Membership status of a household to (Fairtrade, Organic or dual
Fairtrade and Organic) certified coffee marketing cooperative (1= Yes; 0
otherwise)} \\
\multicolumn{2}{@{}>{\raggedright\arraybackslash}p{(\columnwidth - 4\tabcolsep) * \real{0.8524} + 2\tabcolsep}}{%
\textbf{Independent variable:}} & \\
Sex (Sex) & Sex of household head (1 = male, 0= female) & - \\
Age (Age) & Age of the household head in years. & + \\
Marital status (Marstat) & Marital status of the household head (1=
Married, 0 = Single, divorced or Separated). & - \\
Literacy level (Literacy) & Literacy level of the household head (1
Literate= reads and/or writes, 0 Illiterate (cannot read and/or write at
all) & + \\
Family size (FamS\_ME) & Total number of family members (in adult
equivalent). & + \\
Total livestock holding size & Total livestock holding in TLU. & - \\
Total land size under coffee production (LANDCP) & Land size land under
coffee production in ha. & + \\
Coffee farming experience (CFarming\_EXP) & Full years of experience in
coffee farming. & + \\
Access to extension services (Access\_extension) & It refers to whether
the farmer had access to extension services (1= Yes, 0 otherwise). &
+ \\
Social capital (SCapital) & Membership to traditional rural
organizations (e.g., \emph{idir}\footnote{Is a traditional association
  which provides insurance for members during death and other accidents
  (Degnet and Mekbib, 2013).}or \emph{ekub} (rotating saving and credit
association) (1= Yes, 0 otherwise). & + \\
Access to credit (Access\_Credit) & It refers to whether the household
head had access to credit services (1= Yes, 0 otherwise) & + \\
Off-farm income sources (Off\_farm\_Income) & Participation in off-farm
income earning activities (such as serving as daily laborers on others
farms) (1= Yes, 0 otherwise). & - \\
Non-farm income sources (Nonfrm\_Income) & Participation in non-farm
income earning activities (such as carpentry and non-farm labour
markets) (1= Yes, 0 otherwise). & - \\
Distance to development agents' offices (DISTDAWM) & Walking distance
traveled to reach to development agents' offices in minutes. & + \\
Distance to coffee marketing centers (DISTTRCMC) & Walking distance
traveled to reach to coffee marketing centers in minutes . & - \\
Distance to cooperative's office (DISTCOOPWM) & Walking distance
traveled to reach to cooperative's office in minutes. & + \\
Distance to all-weather road (DISTTRAWR) & Walking distance traveled to
reach to the nearest all-weather road in minutes. & - \\
\bottomrule()
\end{longtable}

\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\setcounter{enumi}{2}
\item ~
  \hypertarget{results-and-discussion}{%
  \section{Results and Discussion}\label{results-and-discussion}}

  \begin{enumerate}
  \def\labelenumii{\arabic{enumii}.}
  \item ~
    \hypertarget{descriptive-statistics}{%
    \subsection{Descriptive Statistics}\label{descriptive-statistics}}

    \begin{enumerate}
    \def\labelenumiii{\arabic{enumiii}.}
    \item ~
      \hypertarget{socioeconomic-characteristics-by-marketing-channels-chosen-for-selling-dry-processed-coffee}{%
      \subsubsection{\texorpdfstring{Socioeconomic characteristics by
      marketing channels chosen for selling dry-processed coffee
      }{Socioeconomic characteristics by marketing channels chosen for selling dry-processed coffee }}\label{socioeconomic-characteristics-by-marketing-channels-chosen-for-selling-dry-processed-coffee}}
    \end{enumerate}
  \end{enumerate}
\end{enumerate}

Tables 3 below presents descriptive statistics of socio-economic
characteristics (categorical variables) of the sample farmers by
marketing channels chosen for selling dry-processed coffee in 2017/18
coffee season. The results depict that consumers, private traders
(collectors) were the observed local marketing channels chosen for
selling dry-processed coffee by smallholder farmers. However, there were
significant differences in terms of marital status, literacy status,
cooperative membership status, access to extension services, training,
and off-farm income sources between sample households (HHs) who chose
and did not choose local consumers for selling dry-processed coffee
beans. The sample HHs also significantly differed in terms of their
average family size, quantity of coffee harvested and dry-processed
coffee sold and price received (ETB/Kg), respectively.

On the other hand, the sample HHs who chose local private traders
significantly differed in terms of their literacy status, social
capital, access to extension services and off-farm income sources,
average family size, coffee land size, coffee farming experience and
total quantity of harvested coffee. Regarding the choice of
cooperatives, there were significant differences between those who chose
and did not choose cooperatives in terms of their marital status,
literacy status, social capital, access to extension services and
off-farm income sources. Also, these sample HHs significantly differed
in their average age, coffee land size, coffee farming experience and
total quantity of coffee harvested, respectively.

Table 3. Descriptive statistics of categorical variables by marketing
channel chosen for selling dry-processed coffee beans, 2017/18

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.1331}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0629}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0654}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0643}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0074}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0120}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0621}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0667}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0815}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0593}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0741}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0815}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0815}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0667}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0817}}@{}}
\caption{Table 8. Multivariate regression model results of factors
influencing marketing channel chosen for selling red coffee cherries,
2017/18 (Continued)}\tabularnewline
\toprule()
\endhead
\multicolumn{2}{@{}>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.1960} + 2\tabcolsep}}{%
\multirow{3}{*}{\emph{\textbf{Continuous variable}}}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.1371} + 4\tabcolsep}}{%
\multirow{2}{*}{\emph{\textbf{Combined sample}}}} &
\multicolumn{7}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.4370} + 12\tabcolsep}}{%
\emph{\textbf{Local marketing channel chosen}}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.2299} + 4\tabcolsep}@{}}{%
} \\
& & & & &
\multicolumn{4}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.2222} + 6\tabcolsep}}{%
\textbf{Consumers}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.2148} + 4\tabcolsep}}{%
\textbf{Private traders}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.2299} + 4\tabcolsep}@{}}{%
\textbf{Cooperatives}} \\
& & \textbf{Freq.} & \textbf{\%} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{Freq.} & \textbf{\%} & \emph{\textbf{Chi\textsuperscript{2}
test}} & \textbf{Freq.} & \textbf{\%} &
\emph{\textbf{Chi\textsuperscript{2} test}} & \textbf{Freq.} &
\textbf{\%} & \emph{\textbf{Chi\textsuperscript{2} test}} \\
\multirow{3}{*}{Sex

(Yes = Male)} & No & 21 & 5.57 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
\multirow{2}{*}{}} & 10 & 6.33 & \multirow{3}{*}{0.585} & 13 & 4.56 &
\multirow{3}{*}{0.133} & 5 & 4.63 & \multirow{3}{*}{0.614} \\
& Yes & 356 & 94.43 & & & 148 & 93.67 & & 272 & 9544 & & 103 & 95.37 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{158} & \textbf{100.00} & & \textbf{285} & \textbf{100.00} &
& \textbf{108} & \textbf{100.00} \\
\multirow{3}{*}{Marital status

(1= Yes)} & No & 219 & 58.09 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 7 & 4.43 & \multirow{3}{*}{\textbf{0.004**}} & 30 & 10.53 &
\multirow{3}{*}{0.256} & 3 & 2.78 & \multirow{3}{*}{\textbf{0.005**}} \\
& Yes & 158 & 41.91 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 151 & 95.57 & & 255 & 89.47 & & 105 & 97.22 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{158} & \textbf{100.00} & & \textbf{285} & \textbf{100.00} &
& \textbf{108} & \textbf{100.00} \\
\multirow{3}{*}{Literacy

(1= Yes)} & No & 219 & 58.09 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 17 & 10.76 & \multirow{3}{*}{\textbf{0.035**}} & 50 & 17.54 &
\multirow{3}{*}{\textbf{0.041**}} & 11 & 10.12 &
\multirow{3}{*}{\textbf{0.076*}} \\
& Yes & 158 & 41.91 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 141 & 89.24 & & 235 & 82.46 & & 97 & 89.84 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{158} & \textbf{100.00} & & \textbf{285} & \textbf{100.00} &
& \textbf{108} & \\
\multirow{3}{*}{Coop Membership (1=Yes)} & No & 143 & 37.93 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 76 & 48.10 & \multirow{3}{*}{\textbf{0.001**}} & 108 & 37.89 &
\multirow{3}{*}{0.980} & 40 & 37.04 & \multirow{3}{*}{0.821} \\
& Yes & 234 & 62.07 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 82 & 51.89 & & 177 & 62.11 & & 68 & 62.96 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{158} & \textbf{100.00} & & \textbf{285} & \textbf{100.00} &
& \textbf{108} & \textbf{100.00} \\
\multirow{3}{*}{Social capital (1=Yes)} & No & 219 & 58.09 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 138 & 87.34 & \multirow{3}{*}{\textbf{0.003**}} & 219 & 76.84 &
\multirow{3}{*}{\textbf{0.005**}} & 102 & 94.44 &
\multirow{3}{*}{\textbf{0.000***}} \\
& Yes & 158 & 41.91 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 20 & 12.66 & & 66 & 23.16 & & 6 & 5.64 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{158} & \textbf{100.00} & & \textbf{285} & \textbf{100.00} &
& \textbf{108} & \textbf{100.00} \\
\multirow{3}{*}{Contact with DA

(1= Yes)} & No & 219 & 58.09 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 10 & 6.33 & \multirow{3}{*}{0.122} & 29 & 10.18 &
\multirow{3}{*}{0.168} & 7 & 6.09 & \multirow{3}{*}{0.276} \\
& Yes & 158 & 41.91 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 148 & 93.67 & & 256 & 89.82 & & 108 & 93.91 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{158} & \textbf{100.00} & & \textbf{285} & \textbf{100.00} &
& \textbf{115} & \textbf{100.00} \\
\multirow{3}{*}{Extension access (1= Yes)} & No & 17 & 4.51 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 2 & 1.27 & \multirow{3}{*}{\textbf{0.010*}} & 15 & 5.26 &
\multirow{3}{*}{0.214} & 0 & 0.00 & \multirow{3}{*}{\textbf{0.008*}} \\
& Yes & 360 & 95.49 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 156 & 98.73 & & 270 & 94.74 & & 108 & 100.00 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{158} & \textbf{100.00} & & \textbf{285} & \textbf{100.00} &
& \textbf{108} & \textbf{100.00} \\
\multirow{3}{*}{Training access

(1= Yes)} & No & 48 & 12.73 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 6 & 3.78 & \multirow{3}{*}{\textbf{0.000}} & 42 & 14.74 &
\multirow{3}{*}{\textbf{0.040**}} & 4 & 3.70 &
\multirow{3}{*}{\textbf{0.001**}} \\
& Yes & 329 & 87.27 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 152 & 96.20 & & 243 & 85.26 & & 104 & 96.30 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{158} & \textbf{100.00} & & \textbf{285} & \textbf{100.00} &
& \textbf{108} & \textbf{100.00} \\
\multirow{3}{*}{Off-farm income access

(Yes = 1)} & No & 238 & 63.13 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 99 & 62.66 & \multirow{3}{*}{0.872} & 157 & 55.09 &
\multirow{3}{*}{\textbf{0.000***}} & 77 & 71.30 &
\multirow{3}{*}{\textbf{0.037**}} \\
& Yes & 139 & 36.87 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 59 & 37.34 & & 128 & 44.91 & & 31 & 28.70 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{158} & \textbf{100.00} & & \textbf{285} & \textbf{100.00} &
& \textbf{108} & \textbf{100.00} \\
\multirow{3}{*}{Non-farm income access

(Yes = 1)} & No & 325 & 86.21 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 123 & 77.85 & \multirow{3}{*}{\textbf{0.000***}} & 248 & 87.02 &
\multirow{3}{*}{0.422} & 94 & 87.04 & \multirow{3}{*}{0.767} \\
& Yes & 52 & 13.79 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 35 & 22.15 & & 37 & 12.98 & & 14 & 12.96 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{158} & \textbf{100.00} & & \textbf{285} & \textbf{100.00} &
& \textbf{108} & \textbf{100.00} \\
\bottomrule()
\end{longtable}

Source: Own survey data (2019).

***, ** and * stand for probability at the 1\%, 5\% and 10\%
significance levels, respectively.

On the other hand, the results in Table 4 below show descriptive
statistics of the relevant continuous socioeconomic and other proximity
to offices and infrastructure related characteristics of the sample
farmers. As such the results show that, the age of the total sample
respondents ranged from 19 to 100 years with a mean age of 43.17 years.
The average family size was 3.63 in adult equivalent. On average, a
sample farmer had 4.77 livestock holding size in tropical livestock unit
and 2.18 ha of agricultural land, respectively. Cereals, flowering and
oil crops, coffee, khat (Katha edulis), vegetables and sugar cane were
the main crops cultivated by the sample farmers. However, fruit crops
such as orange, mango, avocado, papaya and sugar apple) were also grown
on mini plots of land. Regarding coffee production, the sample farmers
on average had 1.15 ha of land under coffee production in 2017/18 G.C.
coffee season, with the average 18.86 years of coffee farming
experience. However, a sample farmer needed to walk on average for
29.40, 36.15 and 55.14 minutes to reach to his/her nearest coffee plots,
development agent (DA) office and nearest coffee marketing center,
respectively. There is significant difference in the means of the
walking distances traveled in minutes to reach to the DA's office
between coop member and non-coop member households at 5\% probability
level. The implication is that the non-cooperative member farmers needed
to walk for longer time than their counter but cooperative member
farmers needed to reach to DA's office.

The sample farmers on average needed to walk for 36.32 minutes to reach
to all-weather road. However, there is significant difference in the
walking time needed to reach to all-weather road between non-coop member
and coop member farmers at 5\% probability level. The implication is
that coop member farmers are relatively closer to all-weather roads than
the non-coop member farmers. The results on income earned indicate (in
Table 3) that the sample farmers earned incomes from three income
sources, \emph{viz.,} on-farm, off-farm and non-farm income sources),
respectively. They earned on average ETB 13253.20, 6784.79 and 5440.10
gross annual income from on-farm, off-farm and non-farm income earning
sources, respectively. The total average gross annual income earned by
the sample farmers was ETB 41745.64. However, the gross annual incomes
earned by the coop member and non-coop member farmers were ETB 48358.36
and 30924.81, respectively. But there is no significant difference
between the average gross annual incomes earned between the two groups.

Table 4. Descriptive statistics of continuous variables by marketing
channel chosen for selling dry-processed coffee beans, 2017/18

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.3387}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.1749}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.1651}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.1688}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.1525}}@{}}
\toprule()
\endhead
\multirow{2}{*}{\textbf{Variable}} & \multirow{2}{*}{\textbf{Combined}

\textbf{(N= 377)}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.4864} + 4\tabcolsep}@{}}{%
\textbf{Local marketing channel chosen}} \\
& & \textbf{Consumers}

\textbf{(n\textsubscript{1}=varies)} & \textbf{Private traders
(n\textsubscript{2}= varies)} & \textbf{Cooperatives}

\textbf{(n\textsubscript{3}= varies)} \\
Age, year & 43.17 (11.19) & 41.49 (10.99)\textbf{**} & 43.74
(11.16)\textbf{*} & 41.37 (10.39)\textbf{**} \\
Family size, AE & 3.60 (1.60) & 3.81 (1.43)\textbf{**} & 3.69
(1.70)\textbf{**} & 3.62 (1.39) \\
Livestock holding, TLU & 4.80 (2.40) & 4.76 (2.17) & 5.05 (2.46) & 4.24
(2.32) \\
Total land size, ha & 2.18 (2.02) & 1.87 (1.28) \textbf{**} & 2.42
(2.22) & 1.62 (0.95) \\
Total coffee land size, ha & 1.15 (1.24) & 0.93 (0.65)\textbf{**} & 1.29
(1.37) ** & 0.78 (0.44) ** \\
Coffee farming experience, year & 18.86 (8.61) & 17.31 (8.51)\textbf{**}
& 19.47 (8.57)\textbf{**} & 16.31 (7.89)\textbf{**} \\
Total quantity of coffee harvested, kg & 1586.67 (1609.00) & 1729.76

(1673.10) & 1690.33

(1769.30) \textbf{**} & 1132.93

(817.98)\textbf{**} \\
Total quantity of dry-processed coffee sold, kg &
\begin{minipage}[t]{\linewidth}\raggedright
\begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\setcounter{enumi}{191}
\item
  62 (335.55)
\end{enumerate}
\end{minipage} & 195.14 (338.23) & 192.62 (401.35) & 97.52 (65.00) \\
\bottomrule()
\end{longtable}

Source: Own survey data (2019).

***, ** and * stand for probability at the 1\%, 5\% and 10\%
significance levels, respectively.

\hypertarget{socioeconomic-characteristics-by-marketing-channel-chosen-for-selling-red-coffee-cherries}{%
\subsubsection{Socioeconomic characteristics by marketing channel chosen
for selling red coffee
cherries}\label{socioeconomic-characteristics-by-marketing-channel-chosen-for-selling-red-coffee-cherries}}

This sub-section presents results in Tables 5 and 6 on descriptive
statistics of socio-economic characteristics (both categorical and
continuous explanatory variables) of the sample farmers against their
choices of observed marketing channels chosen for selling red coffee
cherries in 2017/18 coffee season.

As such, the Chi\textsuperscript{2} results in Table 5 below show that
the were significant differences between the sample farmers who chose
and did not choose local consumers, private traders or cooperatives for
selling red coffee cherries in terms of their sex, literacy status,
cooperative membership, social capital, contact with DA, access to
off-farm and non-farm income sources, respectively.

Table 5. Descriptive statistics of categorical variables by marketing
channel chosen for selling red coffee cherries, 2017/18

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.1391}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0553}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0653}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0641}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0074}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0120}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0619}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0665}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0813}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0591}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0739}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0813}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0813}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0665}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0851}}@{}}
\toprule()
\endhead
\multicolumn{2}{@{}>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.1944} + 2\tabcolsep}}{%
\multirow{3}{*}{\emph{\textbf{Continuous variable}}}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.1368} + 4\tabcolsep}}{%
\multirow{2}{*}{\emph{\textbf{Combined sample}}}} &
\multicolumn{7}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.4359} + 12\tabcolsep}}{%
\emph{\textbf{Local marketing channel chosen}}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.2329} + 4\tabcolsep}@{}}{%
} \\
& & & & &
\multicolumn{4}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.2217} + 6\tabcolsep}}{%
\textbf{Consumers}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.2143} + 4\tabcolsep}}{%
\textbf{Private traders}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.2329} + 4\tabcolsep}@{}}{%
\textbf{Cooperatives}} \\
& & \textbf{Freq.} & \textbf{\%} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{Freq.} & \textbf{\%} & \emph{\textbf{Chi\textsuperscript{2}
test}} & \textbf{Freq.} & \textbf{\%} &
\emph{\textbf{Chi\textsuperscript{2} test}} & \textbf{Freq.} &
\textbf{\%} & \emph{\textbf{Chi\textsuperscript{2} test}} \\
\multirow{3}{*}{Sex

(Yes = Male)} & No & 21 & 5.57 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
\multirow{2}{*}{}} & 4 & 13.79 & \multirow{3}{*}{\textbf{0.044**}} & 6 &
3.75 & \multirow{3}{*}{0.186} & 14 & 94.51 & \multirow{3}{*}{0.922} \\
& Yes & 356 & 94.43 & & & 25 & 86.21 & & 154 & 96.25 & & 241 & 5.49 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{29} & \textbf{100.00} & & \textbf{160} & \textbf{100.00} & &
\textbf{255} & \textbf{100.00} \\
\multirow{3}{*}{Marital status

(1= Yes)} & No & 36 & 9.55 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 2 & 6.90 & \multirow{3}{*}{0.613} & 11 & 6.87 &
\multirow{3}{*}{0.129} & 28 & 10.98 & \multirow{3}{*}{0.172} \\
& Yes & 341 & 90.45 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 27 & 93.10 & & 149 & 93.13 & & 227 & 89.02 \\
& \textbf{Total} & \textbf{344} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{29} & \textbf{100.00} & & \textbf{160} & \textbf{100.00} & &
\textbf{255} & \textbf{100.00} \\
\multirow{3}{*}{Literacy

(1= Yes)} & No & 58 & 15.38 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 0 & 0 & \multirow{3}{*}{\textbf{0.017**}} & 28 & 17.50 &
\multirow{3}{*}{0.328} & 36 & 14.12 & \multirow{3}{*}{0.324} \\
& Yes & 319 & 84.62 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 29 & 100.00 & & 132 & 82.5 & & 219 & 85.88 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{29} & \textbf{100.00} & & \textbf{160} & \textbf{100.00} & &
\textbf{255} & \textbf{100.00} \\
\multirow{3}{*}{Coop Membership (1=Yes)} & No & 143 & 37.93 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 8 & 27.59 & \multirow{3}{*}{0.232} & 63 & 39.38 &
\multirow{3}{*}{0.620} & 66 & 25.88 &
\multirow{3}{*}{\textbf{0.000***}} \\
& Yes & 234 & 62.07 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 21 & 72.41 & & 97 & 60.63 & & 189 & 74.12 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{29} & \textbf{100.00} & & \textbf{160} & \textbf{100.00} & &
\textbf{255} & \textbf{100.00} \\
\multirow{3}{*}{Social capital (1=Yes)} & No & 302 & 80.11 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 21 & 72.41 & \multirow{3}{*}{0.280} & 137 & 85.63 &
\multirow{3}{*}{0.\textbf{021**}} & 206 & 80.78 &
\multirow{3}{*}{0.633} \\
& Yes & 75 & 19.89 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 8 & 27.59 & & 23 & 14.37 & & 49 & 19.22 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{29} & \textbf{100.00} & & \textbf{160} & \textbf{100.00} & &
\textbf{255} & \textbf{100.00} \\
\multirow{3}{*}{Contact with DA

(1= Yes)} & No & 34 & 9.02 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 5 & 17.24 & \multirow{3}{*}{0.108} & 19 & 11.88 &
\multirow{3}{*}{\textbf{0.096*}} & 22 & 8.63 & \multirow{3}{*}{0.702} \\
& Yes & 343 & 90.98 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 24 & 82.76 & & 141 & 88.12 & & 233 & 91.37 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{29} & \textbf{100.00} & & \textbf{160} & \textbf{100.00} & &
\textbf{255} & \textbf{100.00} \\
\multirow{3}{*}{Extension access (1= Yes)} & No & 17 & 4.51 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 0 & 0.00 & \multirow{3}{*}{0.223} & 8 & 5.00 &
\multirow{3}{*}{0.693} & 13 & 5.10 & \multirow{3}{*}{0.426} \\
& Yes & 360 & 95.49 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 29 & 100.00 & & 152 & 95.00 & & 242 & 94.90 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{29} & \textbf{100.00} & & \textbf{160} & \textbf{100.00} & &
\textbf{255} & \textbf{100.00} \\
\multirow{3}{*}{Training access

(1= Yes)} & No & 49 & 13.00 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 1 & 3.45 & \multirow{3}{*}{0.119} & 22 & 13.75 &
\multirow{3}{*}{0.611} & 30 & 11.76 & \multirow{3}{*}{0.415} \\
& Yes & 329 & 87.00 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 28 & 96.55 & & 138 & 86.25 & & 225 & 88.24 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{29} & \textbf{100.00} & & \textbf{160} & \textbf{100.00} & &
\textbf{255} & \textbf{100.00} \\
\multirow{3}{*}{Off-farm income access

(Yes = 1)} & No & 238 & 63.13 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 28 & 96.55 & \multirow{3}{*}{\textbf{0.000***}} & 107 & 66.88 &
\multirow{3}{*}{0.196} & 154 & 60.39 & \multirow{3}{*}{0.111} \\
& Yes & 139 & 36.87 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 1 & 3.45 & & 53 & 33.12 & & 101 & 39.61 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{29} & \textbf{100.00} & & \textbf{160} & \textbf{100.00} & &
\textbf{255} & \textbf{100.00} \\
\multirow{3}{*}{Non-farm income access

(Yes = 1)} & No & 325 & 86.21 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 29 & 100.00 & \multirow{3}{*}{\textbf{0.025**}} & 129 & 80.63 &
\multirow{3}{*}{\textbf{0.007**}} & 216 & 84.71 &
\multirow{3}{*}{0.222} \\
& Yes & 52 & 13.79 &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & 0 & 0.00 & & 31 & 19.37 & & 39 & 15.29 \\
& \textbf{Total} & \textbf{377} & \textbf{100.00} &
\multicolumn{2}{>{\raggedright\arraybackslash}p{(\columnwidth - 28\tabcolsep) * \real{0.0194} + 2\tabcolsep}}{%
} & \textbf{29} & \textbf{100.00} & & \textbf{160} & \textbf{100.00} & &
\textbf{255} & \textbf{100.00} \\
\bottomrule()
\end{longtable}

Source: Own survey data (2019).

***, ** and * stand for probability at the 1\%, 5\% and 10\%
significance levels, respectively.

On the other hand, the t-test results in Table 6 below indicate that
average family size, total land size, coffee land size, coffee farming
experience, quantity of coffee harvested and red coffee cherries sold
were the main continuous explanatory various that turned out to be
significantly different between the sample farmers who chose and did not
choose local consumers, private traders and cooperatives for selling red
coffee cherries.

Table 6. Descriptive statistics of continuous variables by marketing
channel chosen for selling red coffee cherries, 2017/18

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.2549}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.1567}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.2033}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.1622}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.2229}}@{}}
\toprule()
\endhead
\multirow{2}{*}{\textbf{Variable}} & \multirow{2}{*}{\textbf{Combined}

\textbf{(N= 311)}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.5884} + 4\tabcolsep}@{}}{%
\textbf{Local marketing channel chosen}} \\
& & \textbf{Consumers}

\textbf{(n\textsubscript{1}= varies)} & \textbf{Private traders
(n\textsubscript{2}= varies)} & \textbf{Cooperatives
(n\textsubscript{3}= varies)} \\
Age, year & 43.17 (11.19) & 44.55 (11.09) & 42.79 (11.45 & 42.96
(10.89) \\
Family size, AE & 3.60 (1.60) & 4.08 (1.29)\textbf{*} & 3.58 (1.46) &
3.64 (1.58) \\
Livestock holding, TLU & 4.80 (2.40) & 4.72 (2.02) & 4.75 (2.45) & 4.93
(2.40) \\
Total land size, ha & 2.18 (2.02) & 1.75 (0.95) & 1.99 (1.39) & 2.49
(2.18)\textbf{***} \\
Total coffee land size, ha & 1.15 (1.24) & 0.95 (0.77) & 1.07 (0.77) &
1.29 (1.22)\textbf{**} \\
Coffee farming experience, year & 18.86 (8.61 & 21.79 (11.96)
\textbf{**} & 18.86 (8.61) & 17.76 (16.87)\textbf{**} \\
Total quantity of coffee harvested, kg & 1586.67 (1609.00) & 2263.48
(2611.53)\textbf{**} & 1787.13 (1494.48)\textbf{**} & 1865.76
(1669.40)\textbf{***} \\
Total quantity of red coffee cherries sold, kg & 894.72 (843.81 & 1413
(1197.78) \textbf{**} & 962.70 (864.45) & 931.01 (849.71) \\
\bottomrule()
\end{longtable}

Source: Own survey data (2019).

***, ** and * stand for probability at the 1\%, 5\% and 10\%
significance levels, respectively.

\hypertarget{econometric-model-results}{%
\subsection{Econometric Model Results}\label{econometric-model-results}}

\hypertarget{section-1}{%
\section{}\label{section-1}}

\hypertarget{factors-influencing-dry-processed-coffee-marketing-channel-choices}{%
\subsubsection{Factors influencing dry-processed coffee marketing
channel
choices}\label{factors-influencing-dry-processed-coffee-marketing-channel-choices}}

The expected multivariate interdependence of selection of particular
marketing channel of local consumers, private traders, and certified
coffee marketing cooperatives was accounted for by employing the
multivariate probit (MVP) model (Table 6). The model fitness for the
data was checked by the Wald test result (200.887), which is significant
at 1\% significance level, showing that the subsets of the coefficients
are jointly significant and the independent variables inserted in the
model are acceptable. Moreover, the likelihood ratio test in the model
(ρ21 =ρ31 = ρ32 = 0) is significant at 1\% significance level.
Therefore, the null hypothesis that all the ρ (Rho) values are jointly
equal to 0 is rejected, indicating the goodness-of-fit of the model or
implying that the decisions to choose these market channels are
interdependent. Hence, the use of multivariate probit model is justified
to determine factors influencing choice of market channels. Further,
there are differences in market channel choice behaviors among farmers,
which are reflected in the likelihood ratio statistics (Abebe \emph{et
al}., 2018; Yadeta and Temesgen, 2018; Taye \emph{et al}, 2018).

The ρ values (ρij) indicate the degree of correlation between market
channel choices. The ρ21 (correlation between the choice for assembler
and wholesaler market outlet) and ρ32 (correlation between the choice
for retailer and wholesaler market outlet) are both negative and
statistically significant at the 1\% significance level (Table 6).

The MVP regression model results in Table 6 show that the choice of
local consumers (spot markets) for selling dry-processed was
significantly influenced by land size under coffee production (ha),
coffee productivity (yield in kg/ha), average dry-coffee selling price
(ETB/kg), frequency of visits made by DA, cooperative membership, access
to credit, access to non-farm income earning sources and walking
distance to cooperative's coffee marketing center, respectively.

On the other hand, the choice of local private traders (collectors) for
selling dry-processed coffee beans was significantly influenced by sex,
land size under coffee production (ha), coffee productivity, average
selling price of dry-processed coffee (ETB/kg), frequency of visits made
by development agent (DA), access to credit, off-farm and non-farm
income earning sources, walking distance to cooperative's and private
coffee marketing centers, respectively.

The choice of local cooperatives for selling dry-processed coffee
however was significantly influenced by land size under coffee
production, average selling price of dry-processed coffee, cooperative
membership status, access to credit, off-farm and non-farm income
earning sources, walking distance to the nearest cooperative and private
coffee marketing centers, respectively.

Table 7. Multivariate regression results of factors influencing the
choices of marketing channels for dry-processed coffee

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.1875}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0981}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0981}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0770}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0981}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0981}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0770}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0981}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0911}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0770}}@{}}
\toprule()
\endhead
\multirow{3}{*}{\textbf{Variable}} &
\multicolumn{9}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.8125} + 16\tabcolsep}@{}}{%
\textbf{Marketing channel chosen}} \\
&
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.2732} + 4\tabcolsep}}{%
\textbf{Local consumers}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.2732} + 4\tabcolsep}}{%
\textbf{Local private traders}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.2662} + 4\tabcolsep}@{}}{%
\textbf{Local coops}} \\
& \textbf{Coef.} & \emph{\textbf{Std. Err.}} &
\emph{\textbf{P\textgreater\textbar t\textbar{}}} & \textbf{Coef.} &
\emph{\textbf{Std. Err.}} &
\emph{\textbf{P\textgreater\textbar t\textbar{}}} & \textbf{Coef.} &
\emph{\textbf{Std. Err.}} &
\emph{\textbf{P\textgreater\textbar t\textbar{}}} \\
\multicolumn{10}{@{}>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{1.0000} + 18\tabcolsep}@{}}{%
\textbf{Dependent variable} (1= Local consumers; 2 = Local private
traders; or 3= Local coops)} \\
\multicolumn{10}{@{}>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{1.0000} + 18\tabcolsep}@{}}{%
\textbf{Independent variables:}} \\
Coop membership (1=Yes, 0=No) & -0.1791802 & 0.0451939 &
\textbf{0.000\textsuperscript{***}} & -0.1465969 & 0. 0409572 &
\textbf{0.026\textsuperscript{**}} & 0.0754683 & 0.0436035 &
\textbf{0.085\textsuperscript{*}} \\
Age, years & -0.0146112 & 0.008626 & 0.074\textsuperscript{*} &
0.0055036 & 0 .0073974 & 0.457 & -0.0114345 & 0.0078753 & 0.148 \\
Religion (1= Muslim; 0= (Orthodox or Protestant) & 0.0474315 & 0.0558021
& 0.396 & -0.2041995 & 0.050571 & \textbf{0.000\textsuperscript{***}} &
0.1267572 & 0.0538385 & \textbf{0.019\textsuperscript{**}} \\
Marital status (1= Married; 0 =(single, separated or divorced)) &
0.1203395 & 0.0828308 & 0.147 & -0.2404023 & 0.075066 &
\textbf{0.002\textsuperscript{**}} & 0.0961985 & 0.0799161 & 0.230 \\
Literacy (1= Literate; 0= Illiterate) & -0.0178215 & 0.0276771 & 0.520 &
-0.0417868 & 0.0250571 & \textbf{0.097\textsuperscript{*}} & -0.0325796
& 0.0267032 & 0.223 \\
Family size in adult man equivalent & 0.0471508 & 0.0159676 &
\textbf{0.003\textsuperscript{**}} & 0.0112828 & 0.014438 & 0.436 &
0.00702 & 0.0154058 & 0.649 \\
Land size under coffee production, ha & -0.1850711 & 0.0566419 &
\textbf{0.001\textsuperscript{***}} & 0.1143742 & 0.0513321 &
\textbf{0.027\textsuperscript{**}} & -0.1591333 & 0.0546487 &
\textbf{0.004\textsuperscript{**}} \\
Total quantity of harvested coffee, kg & 0.0001459 & 0.0000322 &
\textbf{0.000\textsuperscript{***}} & -0.0000567 & 0.0000291 &
\textbf{0.052\textsuperscript{*}} & 0.0000304 & 0.000031 & 0.329 \\
Contact with DA, 0=No, 1=Yes & 0.1011005 & 0.083861 & 0.229 & -0.1569615
& 0.0759996 & 0.040\textsuperscript{**} & -0.0667708 & 0.08091 &
0.410 \\
Social capital, 0=No, 1=Yes & -0.0366366 & 0.057324 & 0.524 & 0.0964461
& 0.0520485 & \textbf{0.065\textsuperscript{*}} & -0.2328132 & 0.05562 &
\textbf{0.000\textsuperscript{***}} \\
Access to credit, 0=No, 1=Yes & 0.396616 & 0.0586366 &
\textbf{0.000\textsuperscript{***}} & -0.1972303 & 0.0531398 &
\textbf{0.000\textsuperscript{***}} & 0.494943 & 0.0565732 &
\textbf{0.000\textsuperscript{***}} \\
Saving account, 0=No, 1=Yes & 0.1166762 & 0.050659 &
\textbf{0.022\textsuperscript{**}} & 0.0412917 & 0.04591 & 0.369 &
0.0032634 & 0.0488763 & 0.947 \\
Off farm income, 0=No, 1=Yes & -0.0674628 & 0.0479687 & 0.161 &
0.1797504 & 0.0434719 & \textbf{0.000\textsuperscript{***}} & -0.0425879
& 0.0462807 & 0.358 \\
Non-farm income, 0=No, 1=Yes & 0.3468971 & 0.0649766 &
\textbf{0.000\textsuperscript{***}} & -0.1123793 & 0.0588855 &
\textbf{0.057\textsuperscript{*}} & 0.0566306 & 0.0626901 & 0.367 \\
Distance to DA's office, walking minutes & 0.0035244 & 0.001277 &
\textbf{0.006\textsuperscript{*}} & -0.0007021 & 0.0011573 & 0.544 &
0.0012297 & 0.001232 & 0.319 \\
\bottomrule()
\end{longtable}

Source: Results obtained from computation of own sample survey data,
2017/18.

Key: ***, **, and * refer to significance at 1\%, 5\% and 10\%
probability levels, respectively.

Table 7. Multivariate regression results of factors influencing the
choices of marketing channels for dry-processed coffee (Continued)

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.1875}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0981}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0981}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0770}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0981}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0981}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0770}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0981}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0911}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0770}}@{}}
\toprule()
\endhead
\multirow{3}{*}{\textbf{Variable}} &
\multicolumn{9}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.8125} + 16\tabcolsep}@{}}{%
\textbf{Marketing channel chosen}} \\
&
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.2732} + 4\tabcolsep}}{%
\textbf{Local consumers}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.2732} + 4\tabcolsep}}{%
\textbf{Local private traders}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.2662} + 4\tabcolsep}@{}}{%
\textbf{Local coops}} \\
& \textbf{Coef.} & \emph{\textbf{Std. Err.}} &
\emph{\textbf{P\textgreater\textbar t\textbar{}}} & \textbf{Coef.} &
\emph{\textbf{Std. Err.}} &
\emph{\textbf{P\textgreater\textbar t\textbar{}}} & \textbf{Coef.} &
\emph{\textbf{Std. Err.}} &
\emph{\textbf{P\textgreater\textbar t\textbar{}}} \\
\multicolumn{10}{@{}>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{1.0000} + 18\tabcolsep}@{}}{%
\textbf{Dependent variable} (1= Local consumers; 2 = Local private
traders; or 3= Local coops)} \\
\multicolumn{10}{@{}>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{1.0000} + 18\tabcolsep}@{}}{%
\textbf{Independent variables:}} \\
Distance to cooperative's office, walking minutes & -0.0040004 &
0.0012135 & \textbf{0.001\textsuperscript{***}} & 0.0039214 & 0.0010997
& \textbf{0.000\textsuperscript{***}} & -0.0034262 & 0.0011708 &
\textbf{0.004\textsuperscript{**}} \\
Distance to all-weather road, minutes & -0.0019524 & 0.0009512 &
\textbf{0.041\textsuperscript{**}} & -0.000457 & 0.000862 & 0.596 &
-0.0006632 & .0009177 & 0.470 \\
\textbf{Constant} & 0.1060566 & 0.3443833 & 0.758 & 1.623182 & 0.3120998
& \textbf{0.000\textsuperscript{***}} & 0.0505457 & 0.3322648 & 0.879 \\
\textbf{Number of obs.} & \textbf{329} & & & \textbf{329} & & &
\textbf{329} & & \\
\textbf{Parms} & \textbf{30} & & & \textbf{30} & & & \textbf{30} & & \\
\textbf{RMSE} & \textbf{0.3591037} & & & \textbf{0.3254402} & & &
\textbf{0.3464672} & & \\
\textbf{"R-sq"} & \textbf{0.5214} & & & \textbf{0.4425} & & &
\textbf{0.4688} & & \\
\textbf{F} & \textbf{11.2345} & & & \textbf{8.183475} & & &
\textbf{9.099471} & & \\
\textbf{P} & \textbf{0.0000\textsuperscript{***}} & & &
\textbf{0.0000\textsuperscript{***}} & & &
\textbf{0.0000\textsuperscript{***}} & & \\
\bottomrule()
\end{longtable}

Source: Results obtained from computation of own sample survey data,
2017/18.

Key: ***, **, and * refer to significance at 1\%, 5\% and 10\%
probability levels, respectively.

\hypertarget{factors-influencing-red-coffee-cherries-marketing-channel-choices}{%
\subsubsection{Factors influencing red coffee cherries marketing channel
choices}\label{factors-influencing-red-coffee-cherries-marketing-channel-choices}}

The expected multivariate interdependence of selection of particular
marketing channel of local consumers, private traders, and certified
coffee marketing cooperatives was accounted for by employing the
multivariate Probit regression model (Table 7). The model fitness for
the data was checked by the Wald test result (130.24) which is
significant at 1\% significance level, showing that the subsets of the
coefficients are jointly significant and the independent variables
inserted in the model are acceptable. Moreover, the likelihood ratio
test in the model (ρ21 =ρ31 = ρ32 = 0) is significant at 1\%
significance level. Therefore, the null hypothesis that all the ρ (Rho)
values are jointly equal to 0 is rejected, indicating the
goodness-of-fit of the model or implying that the decisions to choose
these market channels are interdependent. Hence, the use of multivariate
probit (MVP) regression model is justified to identify factors
influencing the choice of local consumers (LC), private traders, (PT) or
cooperatives (C) for selling red coffee cherries by the sample farmers
in 2017/18. Further, there are differences in market channel choice
behaviors among farmers, which are reflected in the likelihood ratio
statistics (Abebe \emph{et al}., 2018; Yadeta and Temesgen, 2018; Taye
\emph{et al}, 2018).

The MVP regression model results (see Table 8) show that the decision to
choose local consumers (LC) for selling red coffee cherries was
significantly influenced by land sex, coffee farming experience,
frequency of visits by DA and access to credit, respectively. However,
the choice of local private traders was significantly influenced by sex,
coffee yield (productivity) (Kg/ha), average red coffee cherries price
(ET/kg), cooperative membership status, access to training and non-farm
income earning sources, respectively. On the other hand, the choice of
local cooperatives for selling red coffee cherries was significantly
influenced by coffee farming experience, land size under coffee
production, cooperative membership status, access to credit, access to
non-farm income earning sources, and walking distance to cooperative's
and private coffee marketing center, respectively.

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.1543}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.1022}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0947}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0704}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.1054}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0934}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.1030}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.1034}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0967}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0766}}@{}}
\toprule()
\endhead
\multirow{2}{*}{\textbf{Variable}} &
\multicolumn{9}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.8457} + 16\tabcolsep}@{}}{%
\textbf{Marketing channel chosen}} \\
&
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.2673} + 4\tabcolsep}}{%
\textbf{Local consumers}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.3018} + 4\tabcolsep}}{%
\textbf{Local private traders}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.2766} + 4\tabcolsep}@{}}{%
\textbf{Local cooperatives}} \\
& \textbf{Coef.} & \emph{\textbf{Std. Err.}} &
\emph{\textbf{P\textgreater\textbar t\textbar{}}} & \textbf{Coef.} &
\emph{\textbf{Std. Err.}} &
\emph{\textbf{P\textgreater\textbar t\textbar{}}} & \textbf{Coef.} &
\emph{\textbf{Std. Err.}} &
\emph{\textbf{P\textgreater\textbar t\textbar{}}} \\
\multicolumn{10}{@{}>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{1.0000} + 18\tabcolsep}@{}}{%
\textbf{Dependent variable} (1= Local consumers; 2 = Local private
traders; or 3= Local cooperatives)} \\
\multicolumn{10}{@{}>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{1.0000} + 18\tabcolsep}@{}}{%
\textbf{Independent variables:}} \\
Coop membership (0=No, 1=Yes) & 0.0909423 & 0.0388546 &
\textbf{0.020\textsuperscript{**}} & -0.2588235 & 0.066046 &
\textbf{0.000\textsuperscript{***}} & 0.2372921 & 0.0464043 &
\textbf{0.000\textsuperscript{***}} \\
Sex (1= male; 0= female) & -0 2009958 & 0.094133 &
\textbf{0.034\textsuperscript{**}} & -0.3079094 & 0.1600096 &
\textbf{0.055\textsuperscript{*}} & 0.0644772 & 0.1124235 & 0.567 \\
Religion (1= Muslim; 0 otherwise (Orthodox or Protestant)) & 0.1363208 &
0.0432622 & \textbf{0.002\textsuperscript{**}} & -0.0633922 & 0.07205 &
0.389 & 0.0442288 & 0.0516683 & 0.393 \\
Literacy (1= Literate; 0= Illiterate) & 0.0258391 & 0.022375 & 0.249 &
-0.1109712 & 0.0380335 & \textbf{0.004\textsuperscript{**}} & 0.0408357
& 0.0267225 & 0.128 \\
Coffee farming experience, years & 0.0069131 & 0.0029599 &
\textbf{0.020\textsuperscript{**}} & -0.0024096 & 0.0050314 & 0.632 &
-0.0104154 & 0.0035351 & \textbf{0.004\textsuperscript{**}} \\
Coffee productivity, Kg/ha & -7.00e-06 & 0.0000134 & 0.601 & 0.0000381 &
0.0000227 & 0.095\textsuperscript{*} & -8.31e-06 & 0.000016 & 0.603 \\
Social capital,, 0=No, 1=Yes & 0.0044895 & 0.0456717 & 0.922 &
-0.1740289 & 0.0776338 & \textbf{0.026\textsuperscript{**}} & -0.0175213
& 0.0545459 & 0.748 \\
Saving account, (1=Yes; 0=No) & 0.0632655 & 0.0407608 & 0.122 &
-0.033382 & 0.0692861 & 0.630 & 0.1000176 & 0.0486808 &
\textbf{0.041\textsuperscript{**}} \\
Off farm income, (1=Yes; 0=No) & -0.1028855 & 0.0408758 &
\textbf{0.012\textsuperscript{**}} & -0.0479637 & 0.0694816 & 0.491 &
0.1473849 & 0.0488182 & \textbf{0.003\textsuperscript{***}} \\
\bottomrule()
\end{longtable}

Source: Results obtained from computation of own sample survey data,
2017/18.

Key: ***, **, and * refer to significance at 1\%, 5\% and 10\%
probability levels, respectively.

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.1549}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0982}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0951}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0707}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.1058}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0938}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.1034}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.1038}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0972}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.0769}}@{}}
\toprule()
\endhead
\multirow{2}{*}{\textbf{Variable}} &
\multicolumn{9}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.8451} + 16\tabcolsep}@{}}{%
\textbf{Marketing channel chosen}} \\
&
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.2641} + 4\tabcolsep}}{%
\textbf{Local consumers}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.3031} + 4\tabcolsep}}{%
\textbf{Local private traders}} &
\multicolumn{3}{>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{0.2779} + 4\tabcolsep}@{}}{%
\textbf{Local cooperatives}} \\
& \textbf{Coef.} & \emph{\textbf{Std. Err.}} &
\emph{\textbf{P\textgreater\textbar t\textbar{}}} & \textbf{Coef.} &
\emph{\textbf{Std. Err.}} &
\emph{\textbf{P\textgreater\textbar t\textbar{}}} & \textbf{Coef.} &
\emph{\textbf{Std. Err.}} &
\emph{\textbf{P\textgreater\textbar t\textbar{}}} \\
\multicolumn{10}{@{}>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{1.0000} + 18\tabcolsep}@{}}{%
\textbf{Dependent variable} (1= Local consumers; 2 = Local private
traders; or 3= Local cooperatives)} \\
\multicolumn{10}{@{}>{\raggedright\arraybackslash}p{(\columnwidth - 18\tabcolsep) * \real{1.0000} + 18\tabcolsep}@{}}{%
\textbf{Independent variables:}} \\
Walking distance to to the nearest coffee marketing center, minutes &
-0.0011381 & 0.0006094 & \textbf{0.063\textsuperscript{*}} & 0.0008008 &
0.0010358 & 0.440 & 0.0009011 & 0.0007278 & 0.217 \\
Walking distance to cooperative's office, minutes & 0.000433 & 0.0009937
& 0.663 & -0.0042747 & 0.0016891 & \textbf{0.012\textsuperscript{**}} &
-0.0025847 & 0.0011868 & \textbf{0.030\textsuperscript{**}} \\
Walking distance to all-weather road, minutes & 0.0007132 & 0.0008137 &
0.382 & -0.0047612 & 0.0013831 & \textbf{0.001\textsuperscript{**}} &
0.0016358 & 0.0009718 & \textbf{0.094\textsuperscript{*}} \\
\textbf{Constant} & \textbf{-0.0086118} & \textbf{0.288588} &
\textbf{0.976} & \textbf{0.7018469} & \textbf{0.4905488} &
\textbf{0.154} & \textbf{0.070906} & \textbf{0.3446621} &
\textbf{0.837} \\
\textbf{Number of obs.} & \textbf{286} & & & \textbf{286} & & &
\textbf{286} & & \\
\textbf{Parms} & \textbf{31} & & & \textbf{31} & & & \textbf{31} & & \\
\textbf{RMSE} & \textbf{0.2688634} & & & \textbf{0.4570205} & & &
\textbf{0.321105} & & \\
\textbf{"R-sq"} & \textbf{0.2926} & & & \textbf{0.2551} & & &
\textbf{0.3418} & & \\
\textbf{F} & \textbf{3.516565} & & & \textbf{2.910728} & & &
\textbf{4.413284} & & \\
\textbf{P} & \textbf{0.0000} & & & \textbf{0.0000} & & & \textbf{0.0000}
& & \\
\bottomrule()
\end{longtable}

Source: Results obtained from computation of own sample survey data,
2017/18.

Key: ***, **, and * refer to significance at 1\%, 5\% and 10\%
probability levels, respectively.

\hypertarget{conclusions-and-recommendations}{%
\section{Conclusions and
Recommendations}\label{conclusions-and-recommendations}}

The findings of the study show that local consumers, private traders or
cooperatives were the marketing channels available for the sample
farmers for selling dry-processed coffee beans or red coffee cherries
they harvested in 2017/18 coffee season. The MVP regression model
analyses done separately for dry-processed coffee beans and red coffee
cherries show that the choices of the aforementioned channels for
selling dry-processed coffee beans or red coffee cherries were
significantly influenced by sex, coffee land size and productivity,
average dry-processed coffee selling price, frequency of visit by DA,
cooperative membership, access to credit, training, off- and non-farm
income earning sources, distance to cooperative's and private traders'
coffee collection centers, respectively.

As such, various policy measures should be taken regarding factors that
significantly influenced the choices of the observed marketing channels.
Given the intention of the government is to ensure supply of higher
quantity and better quality coffee to central markets through
cooperatives, there is a need to work on factors that positively and
significantly influenced the choices of cooperatives for selling coffee
by smallholder farmers. Moreover, appropriate measures should be taken
against the factors that positively and significantly influenced the
choices of local consumers and private traders in order to encourage
farmers prefer cooperatives to these marketing channels as the former
providers better benefits for both the farmer and the government.

On the other hand, farmers should be provided with coffee production and
productivity enhancing technologies that could increase yield and
productivity and marketed supply of coffee by smallholder farmers.
Meanwhile, provision of extension services and conducting training
programs aimed at providing information on the importance of joining
cooperatives and knowledge and skills on ways of improving coffee
production and productivity. Moreover, cooperatives should incorporate
credit scheme at peak coffee production and marketing season in
particular so that members prefer to supply their coffee to the
cooperatives and earn better coffee income at the end.

Last but not the least, cooperatives are advised to establish nearby
farmers' vicinity so that not only members but also non-members will
supply their coffee more likely to the cooperatives and earn better
coffee income.

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\end{document}
