\documentclass[11pt,twoside]{article}\makeatletter

\IfFileExists{xcolor.sty}%
  {\RequirePackage{xcolor}}%
  {\RequirePackage{color}}
\usepackage{colortbl}
\usepackage{wrapfig}
\usepackage{ifxetex}
\ifxetex
  \usepackage{fontspec}
  \usepackage{xunicode}
  \catcode`⃥=\active \def⃥{\textbackslash}
  \catcode`❴=\active \def❴{\{}
  \catcode`❵=\active \def❵{\}}
  \def\textJapanese{\fontspec{Noto Sans CJK JP}}
  \def\textChinese{\fontspec{Noto Sans CJK SC}}
  \def\textKorean{\fontspec{Noto Sans CJK KR}}
  \setmonofont{DejaVu Sans Mono}
  
\else
  \IfFileExists{utf8x.def}%
   {\usepackage[utf8x]{inputenc}
      \PrerenderUnicode{–}
    }%
   {\usepackage[utf8]{inputenc}}
  \usepackage[english]{babel}
  \usepackage[T1]{fontenc}
  \usepackage{float}
  \usepackage[]{ucs}
  \uc@dclc{8421}{default}{\textbackslash }
  \uc@dclc{10100}{default}{\{}
  \uc@dclc{10101}{default}{\}}
  \uc@dclc{8491}{default}{\AA{}}
  \uc@dclc{8239}{default}{\,}
  \uc@dclc{20154}{default}{ }
  \uc@dclc{10148}{default}{>}
  \def\textschwa{\rotatebox{-90}{e}}
  \def\textJapanese{}
  \def\textChinese{}
  \IfFileExists{tipa.sty}{\usepackage{tipa}}{}
\fi
\def\exampleFont{\ttfamily\small}
\DeclareTextSymbol{\textpi}{OML}{25}
\usepackage{relsize}
\RequirePackage{array}
\def\@testpach{\@chclass
 \ifnum \@lastchclass=6 \@ne \@chnum \@ne \else
  \ifnum \@lastchclass=7 5 \else
   \ifnum \@lastchclass=8 \tw@ \else
    \ifnum \@lastchclass=9 \thr@@
   \else \z@
   \ifnum \@lastchclass = 10 \else
   \edef\@nextchar{\expandafter\string\@nextchar}%
   \@chnum
   \if \@nextchar c\z@ \else
    \if \@nextchar l\@ne \else
     \if \@nextchar r\tw@ \else
   \z@ \@chclass
   \if\@nextchar |\@ne \else
    \if \@nextchar !6 \else
     \if \@nextchar @7 \else
      \if \@nextchar (8 \else
       \if \@nextchar )9 \else
  10
  \@chnum
  \if \@nextchar m\thr@@\else
   \if \@nextchar p4 \else
    \if \@nextchar b5 \else
   \z@ \@chclass \z@ \@preamerr \z@ \fi \fi \fi \fi
   \fi \fi  \fi  \fi  \fi  \fi  \fi \fi \fi \fi \fi \fi}
\gdef\arraybackslash{\let\\=\@arraycr}
\def\@textsubscript#1{{\m@th\ensuremath{_{\mbox{\fontsize\sf@size\z@#1}}}}}
\def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}}
\def\abbr{}
\def\corr{}
\def\expan{}
\def\gap{}
\def\orig{}
\def\reg{}
\def\ref{}
\def\sic{}
\def\persName{}\def\name{}
\def\placeName{}
\def\orgName{}
\def\textcal#1{{\fontspec{Lucida Calligraphy}#1}}
\def\textgothic#1{{\fontspec{Lucida Blackletter}#1}}
\def\textlarge#1{{\large #1}}
\def\textoverbar#1{\ensuremath{\overline{#1}}}
\def\textquoted#1{‘#1’}
\def\textsmall#1{{\small #1}}
\def\textsubscript#1{\@textsubscript{\selectfont#1}}
\def\textxi{\ensuremath{\xi}}
\def\titlem{\itshape}
\newenvironment{biblfree}{}{\ifvmode\par\fi }
\newenvironment{bibl}{}{}
\newenvironment{byline}{\vskip6pt\itshape\fontsize{16pt}{18pt}\selectfont}{\par }
\newenvironment{citbibl}{}{\ifvmode\par\fi }
\newenvironment{docAuthor}{\ifvmode\vskip4pt\fontsize{16pt}{18pt}\selectfont\fi\itshape}{\ifvmode\par\fi }
\newenvironment{docDate}{}{\ifvmode\par\fi }
\newenvironment{docImprint}{\vskip 6pt}{\ifvmode\par\fi }
\newenvironment{docTitle}{\vskip6pt\bfseries\fontsize{22pt}{25pt}\selectfont}{\par }
\newenvironment{msHead}{\vskip 6pt}{\par}
\newenvironment{msItem}{\vskip 6pt}{\par}
\newenvironment{rubric}{}{}
\newenvironment{titlePart}{}{\par }

\newcolumntype{L}[1]{){\raggedright\arraybackslash}p{#1}}
\newcolumntype{C}[1]{){\centering\arraybackslash}p{#1}}
\newcolumntype{R}[1]{){\raggedleft\arraybackslash}p{#1}}
\newcolumntype{P}[1]{){\arraybackslash}p{#1}}
\newcolumntype{B}[1]{){\arraybackslash}b{#1}}
\newcolumntype{M}[1]{){\arraybackslash}m{#1}}
\definecolor{label}{gray}{0.75}
\def\unusedattribute#1{\sout{\textcolor{label}{#1}}}
\DeclareRobustCommand*{\xref}{\hyper@normalise\xref@}
\def\xref@#1#2{\hyper@linkurl{#2}{#1}}
\begingroup
\catcode`\_=\active
\gdef_#1{\ensuremath{\sb{\mathrm{#1}}}}
\endgroup
\mathcode`\_=\string"8000
\catcode`\_=12\relax

\usepackage[a4paper,twoside,lmargin=1in,rmargin=1in,tmargin=1in,bmargin=1in,marginparwidth=0.75in]{geometry}
\usepackage{framed}

\definecolor{shadecolor}{gray}{0.95}
\usepackage{longtable}
\usepackage[normalem]{ulem}
\usepackage{fancyvrb}
\usepackage{fancyhdr}
\usepackage{graphicx}
\usepackage{marginnote}

\renewcommand{\@cite}[1]{#1}


\renewcommand*{\marginfont}{\itshape\footnotesize}

\def\Gin@extensions{.pdf,.png,.jpg,.mps,.tif}

  \pagestyle{fancy}

\usepackage[pdftitle={Determinants of Farmers' Preference to Coffee Market Outlet in Jimma Zone: The Case of Coffee Potential Districts},
 pdfauthor={}]{hyperref}
\hyperbaseurl{}

	 \paperwidth210mm
	 \paperheight297mm
              
\def\@pnumwidth{1.55em}
\def\@tocrmarg {2.55em}
\def\@dotsep{4.5}
\setcounter{tocdepth}{3}
\clubpenalty=8000
\emergencystretch 3em
\hbadness=4000
\hyphenpenalty=400
\pretolerance=750
\tolerance=2000
\vbadness=4000
\widowpenalty=10000

\renewcommand\section{\@startsection {section}{1}{\z@}%
     {-1.75ex \@plus -0.5ex \@minus -.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\Large\bfseries}}
\renewcommand\subsection{\@startsection{subsection}{2}{\z@}%
     {-1.75ex\@plus -0.5ex \@minus- .2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\Large}}
\renewcommand\subsubsection{\@startsection{subsubsection}{3}{\z@}%
     {-1.5ex\@plus -0.35ex \@minus -.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\large}}
\renewcommand\paragraph{\@startsection{paragraph}{4}{\z@}%
     {-1ex \@plus-0.35ex \@minus -0.2ex}%
     {0.5ex \@plus .2ex}%
     {\reset@font\normalsize}}
\renewcommand\subparagraph{\@startsection{subparagraph}{5}{\parindent}%
     {1.5ex \@plus1ex \@minus .2ex}%
     {-1em}%
     {\reset@font\normalsize\bfseries}}


\def\l@section#1#2{\addpenalty{\@secpenalty} \addvspace{1.0em plus 1pt}
 \@tempdima 1.5em \begingroup
 \parindent \z@ \rightskip \@pnumwidth 
 \parfillskip -\@pnumwidth 
 \bfseries \leavevmode #1\hfil \hbox to\@pnumwidth{\hss #2}\par
 \endgroup}
\def\l@subsection{\@dottedtocline{2}{1.5em}{2.3em}}
\def\l@subsubsection{\@dottedtocline{3}{3.8em}{3.2em}}
\def\l@paragraph{\@dottedtocline{4}{7.0em}{4.1em}}
\def\l@subparagraph{\@dottedtocline{5}{10em}{5em}}
\@ifundefined{c@section}{\newcounter{section}}{}
\@ifundefined{c@chapter}{\newcounter{chapter}}{}
\newif\if@mainmatter 
\@mainmattertrue
\def\chaptername{Chapter}
\def\frontmatter{%
  \pagenumbering{roman}
  \def\thechapter{\@roman\c@chapter}
  \def\theHchapter{\roman{chapter}}
  \def\thesection{\@roman\c@section}
  \def\theHsection{\roman{section}}
  \def\@chapapp{}%
}
\def\mainmatter{%
  \cleardoublepage
  \def\thechapter{\@arabic\c@chapter}
  \setcounter{chapter}{0}
  \setcounter{section}{0}
  \pagenumbering{arabic}
  \setcounter{secnumdepth}{6}
  \def\@chapapp{\chaptername}%
  \def\theHchapter{\arabic{chapter}}
  \def\thesection{\@arabic\c@section}
  \def\theHsection{\arabic{section}}
}
\def\backmatter{%
  \cleardoublepage
  \setcounter{chapter}{0}
  \setcounter{section}{0}
  \setcounter{secnumdepth}{2}
  \def\@chapapp{\appendixname}%
  \def\thechapter{\@Alph\c@chapter}
  \def\theHchapter{\Alph{chapter}}
  \appendix
}
\newenvironment{bibitemlist}[1]{%
   \list{\@biblabel{\@arabic\c@enumiv}}%
       {\settowidth\labelwidth{\@biblabel{#1}}%
        \leftmargin\labelwidth
        \advance\leftmargin\labelsep
        \@openbib@code
        \usecounter{enumiv}%
        \let\p@enumiv\@empty
        \renewcommand\theenumiv{\@arabic\c@enumiv}%
	}%
  \sloppy
  \clubpenalty4000
  \@clubpenalty \clubpenalty
  \widowpenalty4000%
  \sfcode`\.\@m}%
  {\def\@noitemerr
    {\@latex@warning{Empty `bibitemlist' environment}}%
    \endlist}

\def\tableofcontents{\section*{\contentsname}\@starttoc{toc}}
\parskip0pt
\parindent1em
\def\Panel#1#2#3#4{\multicolumn{#3}{){\columncolor{#2}}#4}{#1}}
\newenvironment{reflist}{%
  \begin{raggedright}\begin{list}{}
  {%
   \setlength{\topsep}{0pt}%
   \setlength{\rightmargin}{0.25in}%
   \setlength{\itemsep}{0pt}%
   \setlength{\itemindent}{0pt}%
   \setlength{\parskip}{0pt}%
   \setlength{\parsep}{2pt}%
   \def\makelabel##1{\itshape ##1}}%
  }
  {\end{list}\end{raggedright}}
\newenvironment{sansreflist}{%
  \begin{raggedright}\begin{list}{}
  {%
   \setlength{\topsep}{0pt}%
   \setlength{\rightmargin}{0.25in}%
   \setlength{\itemindent}{0pt}%
   \setlength{\parskip}{0pt}%
   \setlength{\itemsep}{0pt}%
   \setlength{\parsep}{2pt}%
   \def\makelabel##1{\upshape ##1}}%
  }
  {\end{list}\end{raggedright}}
\newenvironment{specHead}[2]%
 {\vspace{20pt}\hrule\vspace{10pt}%
  \phantomsection\label{#1}\markright{#2}%

  \pdfbookmark[2]{#2}{#1}%
  \hspace{-0.75in}{\bfseries\fontsize{16pt}{18pt}\selectfont#2}%
  }{}
      \def\TheFullDate{2016-01-15 (revised: 15 January 2016)}
\def\TheID{\makeatother }
\def\TheDate{2016-01-15}
\title{Determinants of Farmers' Preference to Coffee Market Outlet in Jimma Zone: The Case of Coffee Potential Districts}
\author{}\makeatletter 
\makeatletter
\newcommand*{\cleartoleftpage}{%
  \clearpage
    \if@twoside
    \ifodd\c@page
      \hbox{}\newpage
      \if@twocolumn
        \hbox{}\newpage
      \fi
    \fi
  \fi
}
\makeatother
\makeatletter
\thispagestyle{empty}
\markright{\@title}\markboth{\@title}{\@author}
\renewcommand\small{\@setfontsize\small{9pt}{11pt}\abovedisplayskip 8.5\p@ plus3\p@ minus4\p@
\belowdisplayskip \abovedisplayskip
\abovedisplayshortskip \z@ plus2\p@
\belowdisplayshortskip 4\p@ plus2\p@ minus2\p@
\def\@listi{\leftmargin\leftmargini
               \topsep 2\p@ plus1\p@ minus1\p@
               \parsep 2\p@ plus\p@ minus\p@
               \itemsep 1pt}
}
\makeatother
\fvset{frame=single,numberblanklines=false,xleftmargin=5mm,xrightmargin=5mm}
\fancyhf{} 
\setlength{\headheight}{14pt}
\fancyhead[LE]{\bfseries\leftmark} 
\fancyhead[RO]{\bfseries\rightmark} 
\fancyfoot[RO]{}
\fancyfoot[CO]{\thepage}
\fancyfoot[LO]{\TheID}
\fancyfoot[LE]{}
\fancyfoot[CE]{\thepage}
\fancyfoot[RE]{\TheID}
\hypersetup{citebordercolor=0.75 0.75 0.75,linkbordercolor=0.75 0.75 0.75,urlbordercolor=0.75 0.75 0.75,bookmarksnumbered=true}
\fancypagestyle{plain}{\fancyhead{}\renewcommand{\headrulewidth}{0pt}}

\date{}
\usepackage{authblk}

\providecommand{\keywords}[1]
{
\footnotesize
  \textbf{\textit{Index terms---}} #1
}

\usepackage{graphicx,xcolor}
\definecolor{GJBlue}{HTML}{273B81}
\definecolor{GJLightBlue}{HTML}{0A9DD9}
\definecolor{GJMediumGrey}{HTML}{6D6E70}
\definecolor{GJLightGrey}{HTML}{929497} 

\renewenvironment{abstract}{%
   \setlength{\parindent}{0pt}\raggedright
   \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}}
   \vskip16pt
   \textcolor{GJBlue}{\large\bfseries\abstractname\space}
}{%   
   \vskip8pt
   \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}}
   \vskip16pt
}

\usepackage[absolute,overlay]{textpos}

\makeatother 
      \usepackage{lineno}
      \linenumbers
      
\begin{document}

             \author[1]{Wondaferahu  Mulugeta}

             \author[2]{Solomon  Assefa}

             \author[3]{Jibril  Haji}

             \affil[1]{  Jimma University}

\renewcommand\Authands{ and }

\date{\small \em Received: 15 December 2015 Accepted: 1 January 2016 Published: 15 January 2016}

\maketitle


\begin{abstract}
        


Background and objective: Choice of a marketing outlet is one of the key ingredients to successful marketing of both agricultural and non-agricultural products. The aim of the study was to show determinants of coffee farmers' preference of coffee market outlet in Southwest Ethiopia/Jimma zone.Methodology: Cross-sectional data was collected from 156 randomly selected rural households of three Districts. Structured questionnaire prepared for household heads were filled by the help of selected and well trained enumerators. The study used multinomial logistic regression model to determine factors determining coffee market outlet preference.

\end{abstract}


\keywords{marketing outlet, cross sectional, multinomial logistic, formal traders, informal buyers, brokers.}

\begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) 
\textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark}
\end{textblock*}

\begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) 
\textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}}
\end{textblock*}


\let\tabcellsep& 	 	 		 \par
also an important source of government revenue through various taxes levied on the crop (ICO/ \hyperref[b8]{CFC, 2000)}.\par
Market volatility and declining terms of trade, systemic poverty; and environmental degradation are threats to the sustainability of coffee sector. Emphasis given to efficient management of the markets and efficient management of supply chains are the sustainable remedy for the sustainability of the sector. The natural interdependence between market and supply chain efficiency suggests that systemic treatment of both aspects at a policy level is imperative to the effective implementation of sustainability in the sector \hyperref[b13]{(Potts, 2006)}.\par
Though efficient agricultural marketing is a tool to improve farmers income and livelihood, farmers faced barriers such as insufficient and inadequate physical infrastructure, lack of basic education and marketing knowledge, lack of organizational support and institutional barriers in marketing \hyperref[b9]{(Kherallah and Kirsten, 2001)}. This has an implication on the choice of marketing channels that farmers prefer in marketing their produce.\par
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's or a nation's economy.\par
The most frequently used coffee markets in Jimma zone include informal buyers, formal coffee traders, brokers and farmer groups/cooperatives. Informal coffee buyers include farm gate buyers and consumers while formal coffee traders include village and urban coffee traders who are licensed and officially known in coffee market chain. Although farmers of the zone is prominent coffee producers, literature regarding determinants of farmers' coffee market outlet preference for the study area even for the countries coffee producing zones very scant and limited.\par
The aim of the study was to show determinants of coffee farmers' preference of coffee market outlet in south west Ethiopia/Jimma zone and the specific objectives of the study are: outlet is one of the key ingredients to successful marketing of both agricultural and non-agricultural products. The aim of the study was to show determinants of coffee farmers' preference of coffee market outlet in Southwest Ethiopia/Jimma zone.\par
Methodology: Cross-sectional data was collected from 156 randomly selected rural households of three Districts. Structured questionnaire prepared for household heads were filled by the help of selected and well trained enumerators. The study used multinomial logistic regression model to determine factors determining coffee market outlet preference.\par
Results: Informal buyers, formal traders, brokers and cooperatives were four main coffee market outlet exist on the study area. Multinomial logistic regression result shows that number of extension visit has positive and significant effect on the preference of formal red coffee markets and transport cost to the main market has negative and significant impact on formal traders preference relative to informal buyers. Distance to cooperatives has negative and significant effect on the preference of farmers for cooperatives and number of visit by extensionists has negative and significant effect on formal dry coffee markets and brokers and positive and significant effect on cooperatives as compared to informal market.\par
Recommendations: Based on the main findings the study recommended extensionists to aware coffee producers to choose good market outlets such as cooperatives for efficient and profitable marketing of coffee and recommended the government to increase the access of cooperatives and the improvement of the infrastructure to enhance coffee marketing.\par
Keywords: marketing outlet, cross sectional, multinomial logistic, formal traders, informal buyers, brokers. 
\section[{I.}]{I.}\par
Background of the Study offee sub sector is continues to be the pillar for Ethiopian economic development as it accounts over 35\% of agricultural foreign exchange earnings and about 4\% of agricultural gross domestic product. It also provides income to over 15 million people in the country through provision of jobs for farmers, local traders, processors, transporters, exporters and bankers (Ministry of Trade, 2012). It is ? To identify coffee market outlets exist on the study area and proportion of coffee supplied to those market outlets. ? To analyze factors determines coffee market outlet preference among the small holder farmers. The rest of this study is organized in to four sections. Section two embraces some theoretical and empirical literature on marketing channels or outlets. Section three included data collection and data analysis methods and section four discussed the result and its interpretation and finally section five concluded and gave policy recommendation based on the core findings. 
\section[{II.}]{II.} 
\section[{Review of Related Literature}]{Review of Related Literature}\par
Market is a particular group of people, an institution, a mechanism for facilitating exchange (Solomon, 2002) and marketing is a societal process by which individuals and groups obtain what they need and want through creating, offering, and freely exchanging products and services and value with others \hyperref[b10]{(Kotler, 2003)}.\par
Marketing outlets or channels are sets of interdependent organizations involved in the process of making a product or services available for use or consumption. The sequence of intermediaries and markets through which goods pass from producer to consumer is known as marketing channel. Marketing channel decisions are among the most critical decisions facing management \hyperref[b10]{(Kotler, 2003)}.\par
The importance of the distribution function in marketing is apparent when one considers the magnitude of goods and services that are transported and sold at millions of locations throughout the world. Many experts believe that the distribution decision is the most important marketing decision a company can make. The design of an organization's distribution system is a key factor in creating customer value and in differentiating one company's offering from that of another \hyperref[b1]{(Anderson and Vincze, 2000)}. They noted that the field of distribution is made up of two distinct branches: channels of distribution and physical distribution. Channels of distribution consist of a network of intermediaries that manages a flow of goods and services from the producer to the final consumer. The success of this network depends on relationships among manufacturers or producers, wholesalers, retailers, sales representatives, and others.\par
As products move from one intermediary to the next, exchange takes place exchange of physical goods, intangible services, and value added dimensions. Physical distribution activities include the actual movement of goods and services, with a focus on transporting and warehousing them. Thus far, a number of channels were tested and used throughout generations by farmers, and the most important ones were selected from the point of view of their use for particular commodity and their individual advantages and disadvantages \hyperref[b3]{(Barker, 1989)}.\par
Limited empirical studies exist regarding factors affecting farmers channel choice decision. \hyperref[b0]{Agarwal and Ramaswami, (1992)} have identified factors related to price, production scale and size, farm household characteristic, behavioral aspects such as (trust, risk, and experience), and market context (distance and purchase condition) affect producer market outlet choice. Furthermore, Zuniga-Arias (2007) found out that factors such as price attributes, production system, farm household characteristic, and market context could affect market outlet decision of farmers in mango supply chain in Costarica. Hobbs, (1997) also found out that age, education, farm profit and transaction cost are some factors that influence farmers channel choice decision in livestock marketing. The study also indicated that the mode of payment, long standing relationship with the buyer, and the price received as the most important reasons for selling to a particular buyer in the livestock sector. \hyperref[b11]{Magogo et al., (2015)} on their study on the market outlet preference for African indigenous vegetables using multinomial logistic regression model found that education level negatively affects farmers preference to sell their the vegetables for farm gate buyers and quantity of the vegetable to be sold, marketing cost and level of value addition positively and significantly affects the preference of the farmers for farm gate market. On other hand, household size, extension visit and off farm income affects the preference of local open market negatively and market cost and level of value addition affects the preference of local open market positively. The finding also witnessed the positive relation between education level and marketing cost with the preference of farmers to sell for brokers and negative relation between agricultural market distance and the preference of brokers.\par
Multinomial regression model was also used by \hyperref[b4]{Berhanu et al., (2013)} on their study on the determinants of milk market outlet preference. The result indicated compared to accessing individual consumer milk market outlet, the likelihood of accessing cooperative milk market outlet was lower among households who owned large number of cows, who considered price offered by cooperative lower than other market outlets and who wanted payment other than cash mode. The likelihood of accessing cooperative milk market outlet was higher for households who were cooperative members, who owned large landholding size, who had been in dairy farming for many years and who received better dairy extension services. Compared to accessing individual consumer milk market outlet, the likelihood of accessing hotel/restaurant milk market outlet was lower among households who were at far away from the nearest distance to the nearest urban center and higher among Tobit model was used by Anteneh et al., (2011) to identify factors determine choice of coffee market channel. Accordingly, level of education, proportion off farm income to total income, proportion of land allocated to coffee cultivation, index of cooperative performance, amount of the second payment (dividend) and satisfaction on cooperatives performance had significantly influenced the market out-let choice of member coffee farmers in the study area. Except land allocated to coffee production, all other variables do have a negative relationship with the proportion of coffee sold to private traders by members. On other hands, they identified age of the respondent and proportion of off-farm income to total income have a negative relationship with the proportion of coffee sold to cooperatives by non-members, while access to training has a positive relationship. They also indicated that age of the household head, education, proportion of off-farm income to total income, and coffee productivity positively influence the proportion of coffee sold to private trader by members and respondents' age and proportion of off-farm income to total income negatively influence the proportion of coffee sold to cooperatives by members.\par
The base for market outlet choice is the theory of rational choice which assumes that farmers are rational and will rank alternative marketing outlets in order of utility. The choice of the marketing outlet was based on farmers' socio-economic characteristics and relevant factors influencing the choice entrenched in each outlet. A farmer's marketing outlet choice was conceptualized using the random utility model. Random utility model is particularly appropriate for modeling discrete choice decisions such as between marketing outlets because it is an indirect utility function where an individual with specific characteristics associates an average utility level with each alternative marketing outlet in a choice set. The smallholder farmers of Jimma zone used farm gate, brokers, formal local markets and cooperatives to sell their coffee. Therefore they was able to choose from a set of alternatives (j = 1, 2, 3, 4) which provided a certain level of utility U ij from each alternatives.\par
This model was based on the principle that the farmer will choose the outlet that will maximize his/her utility. The farmer will make a comparison on marginal benefit and cost based on the utility that will be gained by selling to a particular marketing outlet. However, it is not possible to directly observe the utilities but the choice made by the farmer revealed which marketing outlet provides the greater utility \hyperref[b5]{(Greene, 2002)}. Hence, the utility was decomposed into deterministic (V ij ) and random (? ij ) parts.\par
Since it was not possible to observe ?ij and predict exactly the choice of marketing outlet, the probability of any particular outlet choice was used in which a farmer selected a marketing outlet j = 1 if:\par
Where U ik represents a random utility associated with the market outlet j=k, V ij represents an index function denoting the decision maker's average utility associated with this alternative and ? ij represents the random error. 
\section[{III.}]{III.} 
\section[{Methodology of the Study a) Study area}]{Methodology of the Study a) Study area}\par
The study was conducted in Jimma zone which is located 335 km to the Southwest of Addis Ababa. The zone is characterized by a tropical highland climate with heavy rainfall, warm temperatures and a long wet period. The mean annual rainfall ranges between 1,200mm and 2,500mm. Coffee is produced in 13 of 18 districts of Jimma zone. Meaning coffee is the major contributor to the socio economic wellbeing of the zone as well as for Ethiopia.\par
Limu kosa, Gomma and Manna districts of Jimma zone are randomly selected from the coffee potential districts for this specific study. Gomma district is located 397 km to southwest of capital Addis Ababa and 50 km away from Jimma town. The annual rainfall varies between 800-2000 mm. The agro climate of the district is high land (highland), intermediate high land (88\%) and low land (4\%). Manna is one of the major coffee producing districts in Jimma zone, which is located at 368 km southwest of Addis Ababa and 20 km west of Jimma town. The district is constitutes 12\% is highland, 65\% intermediate highland and 23\% lowland with altitude ranges between 1470-2610 m.a.s.l. Limmu kosa is another major coffee generating districts in Jimma zone, which is located at 421 km from the capital Addis Ababa and 20 km from north of Jimma town. The agro climate of the district is intermediate highland (65\%), highland (25\%) and lowland (10\%) (Respective district agricultural office, 2014). 
\section[{b) Data type and method of data collection}]{b) Data type and method of data collection}\par
Cross-sectional data was collected from 156 randomly selected rural households of respective districts. All attitudinal, institutional, demographic, and socioeconomic factors related to the farmers were collected through personal interviews. Structured questionnaire prepared for household heads were filled by the help of selected and well trained enumerators. Some secondary data such as socio economic data of the study areas was also gathered from zonal and ... .......... district bureaus of rural development offices to supplement the primary data.ij ij ij V U ? + = ij ik U U > ....\par
(\par
(2) 
\section[{c) Data analysis technique}]{c) Data analysis technique}\par
The Statistical Package for the Social Sciences (SPSS version 20) was used for data entry while STATA 12.1 was used for data cleaning and analysis of factors influencing the choice of coffee marketing outlets. Descriptive statistics and econometric models were used to analyze the data collected from households. Multinomial logistic regression model was used to determine factors determining coffee market outlet used by coffee farmers.\par
Variables of physical capital (farm size, coffee land size and distance to market), human capital (sex, age, education level, farm experience, household size and extension visits) and financial capital (farm income, off-farm income, credit access and marketing costs) are expected to influence coffee market outlet choice of coffee producers. However, not all of those variables are included in the econometric model due to specification problems. The empirical model used to assess the significance of the independent variables is given as:\par
Where SEX is household head sex, AGE is household head age, FAMSIZE is family size of the head, FEXP is farming experience of the head, DFORMRT is the distance to formal market, DCOOP is distance to cooperatives, VEXT is the number of visit of the extensionists, OFFINC is off farm income of the head, TCOFLAND is total coffee land, TLAND is total land and TRCOSTFORMRT is the transport cost to the formal market. Market outlet preference: This is categorical dependent variable which represents the market channel preference of the farmer to sell his/her coffee (dry/red cherry). Four main coffee market outlets such as informal buyers, formal traders, cooperatives and brokers exist on the study area. Informal local coffee buyers include farm gate markets and consumers while formal local markets include urban and rural coffee traders. Thus, category (1) represents the base market outlet which is informal buyers, category (2) represents formal traders, category (3) represents brokers and (4) represents cooperative. Thus the model assessed the effects of various independent variables on the odds of three coffee market outlets versus informal local markets. Sex of the household (SEX): Sex of the household is dummy independent variable where (1) represent for male and (2) represents for female. Due to resource constraint and labor for transporting to the distant market like main market, female household heads do not prefer formal markets and cooperatives. Therefore being male headed household is hypothesized to affect formal market and cooperatives positively relative to the informal markets. Age of the household (AGE): This is a continuous independent variable that is measured in years. As age of the household increases, the likelihood of selling the product to the distant market such as formal markets decline. Therefore, being old aged household head is hypothesized to affect the preference of formal market outlet and cooperative negatively as compared to informal local markets.\par
Family size (FAMSIZE): This is a continuous independent variable that is measured in the number of members in the household. Household size increases the labor force to transport the coffee to the market. Therefore, family size is hypothesized to affect the preference of farmers for formal market and cooperatives positively relative to informal coffee market outlet. 
\section[{Farm experience (FEXP):}]{Farm experience (FEXP):}\par
This is a continuous independent variable measured in the number of years a household has been engaged in coffee farming. Households who have been in coffee production for many years are expected to have rich experiences regarding opportunities and challenges of coffee production and marketing. Therefore, the variable is hypothesized to affect the preference of formal market and cooperatives positively as compared informal local markets. 
\section[{Distance to formal market (DFORMRT):}]{Distance to formal market (DFORMRT):}\par
This is also continuous variables measured in the kilometer. The long the distance to formal markets, the less the preference of the farmers to sell to those markets. Thus, they opts to sell to markets such as brokers and informal local market. Hence, distance to formal markets are hypothesized to affect the preference of formal market negatively and cooperatives and brokers positively as compared informal markets. 
\section[{Distance to cooperatives (DCOOP):}]{Distance to cooperatives (DCOOP):}\par
Distance to cooperatives is also a continuous variable measured in the kilometer. It is hypothesized to affect the preference of brokers, informal market and formal market positively and affect the preference of farmers for cooperatives negatively as compared to informal local coffee markets. Off farm income of the household (OFFINC): Off farm income of the household is continuous variable measured in Ethiopian Birr. Farmers with large off farm income did not want to sell their coffee for brokers or informal buyers even for formal markets since the income might serve them as a source of livelihood. Thus, the variable is hypothesized to have negative effect on brokers and have positive effect on the preference of formal market and cooperatives as compared to informal local markets. 
\section[{Number of extension visit (VEXT):}]{Number of extension visit (VEXT):}\par
Total coffee landholding (TCOFLAND) and total land (TLAND): They are variables related to the wealth of the household and measured in hectares. Farmer with large coffee land is expected to produce more coffee and he/she prefer to sell this coffee to the cooperative or formal market. Thus, total coffee land size is expected to have positive effect on cooperatives and formal market as compared to informal markets which is also true for total land holding. 
\section[{Transport cost to formal market (TRCOSTFORMRT):}]{Transport cost to formal market (TRCOSTFORMRT):}\par
Transport cost is cost related to sell of coffee which is measured in single trip cost to the market in Ethiopian Birr. It is expected to affect the preference of farmers for brokers and cooperatives positively and affect formal market preference negatively as compared to informal markets since the farmer opts to sell at the market with low or nil transport cost.\par
IV. 
\section[{Result and Discussion}]{Result and Discussion} 
\section[{a) Characteristics of survey respondents}]{a) Characteristics of survey respondents}\par
The data was collected from three districts of Jimma Zone, Nine kebeles and sixty eight villages of the kebeles. A total of 156 farmers were randomly selected randomly from the three districts. The result shows that 144 (92.3\%) respondents were male headed respondent and only 12 (7.7\%) of respondents were female headed households.\par
The demographic and socioeconomic characteristics of the study area as shown in Institutional accessibility of respondents' shows average distance of respondents to formal village market is 3.19 km, which is high at Gomma district (5.26 km) and low at Manna district (1.6km). On the other hand, the average distance to formal main market is 53.95 km, which is high at Manna district and low at Gomma district. The average distance to cooperative and extension service is 5.5 km and 7.85 km respectively. See the detail on the table below (Table \hyperref[tab_4]{2}). 
\section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par
Volume XVI Issue IV Version I Year ( ) Regarding the opinion of farmers on the trustfulness of the markets, only 30\% of respondents trust buyers and 70\% of coffee suppliers did not trust the markets. Farmers of the study area sell their coffee in two different ways: Red cherry and Dry coffee. 
\section[{Red cherry coffee marketing:}]{Red cherry coffee marketing:}\par
The result shows only 4\% of red cherry coffee was sold by women and 22.5\% of coffee was sold by men. However, more than 73\% of the coffee was sold by men and women indifferently. October to December was peak period when marketing of red cherry coffee was undertaken which accounts 80\% of coffee sold. The rest 20\% of red cherry coffee is sold in January and February. Donkey is the main mode of transport for more than 74\% of respondents though back (head) load, public transport and cart are other mode of transportation used reach red cherry coffee to the market. Four main coffee market outlet exist in the study area for red cherry coffee. However, 48\% of red cherry coffee was sold to cooperatives and 30\%, 15\% and 5\% of red cherry coffee was sold to formal traders, informal buyers and brokers or assemblers respectively, which shows existence of non-formal coffee market. (See the summary below on figure \hyperref[fig_0]{1}). Dry coffee market: The result of the survey also shows only 3\% of dry coffee was sold by women; 51\% of dry coffee was sold by men and 46\% of the coffee was sold by both men and women. More than 69\% of dry coffee was sold from December to January and the rest 31\% is sold in all months through the year except on August, September and October depending on the economic status of the farmer. The mode of transport transport for dry coffee used on the study area are truck, public transport, donkey, cart and back (head) load. However, more than 70\% of respondents used donkey as a mode of transport for dry coffee. Regarding the market outlet preference, 73\% of respondents used formal coffee trader and 15\%, 9\% and 3\% of respondents used informal buyers, cooperatives and brokers respectively as summarized on (figure \hyperref[fig_1]{2}).  Market information is one of the main inputs for farmers to sell their produces for optimum price. The survey shows 98\% of farmers got market information and only 2\% of respondents sold their coffee without any information. Own observation, discussion with friends and neighbors, telephone and extensionists are the main source of market information for the study areas. However discussion was the main source of information for 48 percent of respondents. Telephone, observation and extensionists were also information mode for 30\%, 15\% and 7\% of respondents respectively. On the other hand, a negative value shows how less likely a household will consider the alternative \hyperref[b6]{(Gujarati, 1992;}\hyperref[b14]{Pundo and Fraser, 2006)}. The significance values (p-values) show whether a change in the independent variable significantly influences the Logit at a given level and the marginal effects are the probabilities of observing a particular outcome which indicates the extent of the effect on the dependent variable caused by the predictor variables. The value of the marginal effects is obtained by differentiating the coefficients at their mean. A marginal effects value greater than one implies greater probability of variable influence on the Logit and a value less than one indicates that the variable is less likely to influence the Logit. The results revealed that households had four coffee market outlets and combinations thereof. However, due to mutually inclusiveness of outlets, fewer representation and similar collection and operation practices, only households who had access to cooperative, formal market, informal local markets and brokers were considered in the regression. For estimation purpose, the base category used was informal market preference; thus the model assessed the effects of various independent variables on the odds of three coffee market outlets versus informal coffee market outlet.\par
Multinomial logistic regression model used sex of the household, age of the household, farm experience, family size, distance to formal coffee market, distance to cooperatives, total coffee land, total land, and transport cost to formal market as an independent variables of both red cherry and dry coffee market outlet preference.\par
i 
\section[{. Factors affect red cherry market outlet}]{. Factors affect red cherry market outlet}\par
The regression used sex of the household, age of the household, farm experience, family size, distance to formal market, distance to cooperatives, number of visit of extensionists, total coffee land, total land, total annual off farm income and transport cost to formal market as an independent variables.\par
Age of the household has negative and significant effect on the preference of farmers for formal markets and brokers as compared to informal local markets meaning increase in age of the household declines the preference of formal market and brokers as they opted to use informal markets which do corroborate with the hypothesized sign.\par
Farm experience of the household has positive and significant effect on the preference of the farmer for formal market and brokers as compared to informal markets, which is consistent with the hypothesized sign. As farm experience increases, the households opted to use formal markets as compared to the informal one.\par
Distance to formal coffee market has positive and significant effect on the preference of the farmer to cooperatives and brokers and has negative and significant effect on formal markets preference as compared to informal markets. The sign found on both formal market and cooperatives is consistent with the hypothesized sign.\par
The marginal effect of distance to cooperative on formal markets preference is negative and statistically significant and the result shows that a one 
\section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par
Volume XVI Issue IV Version I Year ( ) kilometer increase in distance to cooperatives decreases the preference of farmers to use formal markets by 2.9\%. Unlike to distance to cooperative, however, the number of extension visit the farmer received from the extensionists has positive and statistically significant effect on the preference of formal markets relative to informal markets, which is consistent with the hypothesized sign. The marginal effect shows that a single visit by extensionists will increase the preference of formal markets by 1.19\% relative to the informal one.\par
On the other hand, the regression results indicate that the effect of off farm income of the household on formal market preference is positive and statistically significant, which is inconsistent with the hypothesized expected sign. Total coffee land of the household has positive and significant effect on the preference of farmers for formal markets and brokers and has negative and significant effect on the preference farmers for cooperatives as compared to informal markets. The signs on broker and cooperative are not consistent with the hypothesized sign. Total land of the household has positive and significant effect on cooperatives as compared to that of the informal one which corroborates with the hypothesized sign. The implication is that farmers with large land size opt to use cooperative market to sell their red cherry coffee. The possible reason is farmers with large land size produces relatively large agricultural produces. Thus they are not obligated to sell their coffee to informal markets and choose to sell to cooperatives or formal markets.\par
Transport cost to the main market has negative and significant impact on formal traders preference of farmers relative to informal buyers which is consistent with the hypothesized sign. The marginal effect of transport cost to cooperatives was also positively and statistically significant; meaning as transport cost increases by one Birr, the preference for cooperative increases by 2.4 \% as compared to informal markets. ii. Factors affecting dry coffee market outlet The coefficient of age of the household has negative and significant effects on the preference for farmers for formal markets as compared to informal markets, which is consistent with the hypothesized sign. The possible reason could be inability of old household to reach his/her coffee to formal markets and cooperatives.\par
The coefficient of farming experience has positive and significant effect on the preference of farmers for formal markets as compared to informal markets which is consistent with the hypothesized sign. The marginal effect of farm experience was also positively and significantly related to formal market preference. This means an increase in one year farm sign. The reason could be farmers with large family size have large labor force to reach their coffee even if the cooperative is distant from their locality. Distance to cooperatives has negative and significant effect on the preference of farmers for cooperatives and has positive and significant impact on preference of farmers for brokers as compared to informal market which is also consistent with the hypothesized sign. The marginal effect of distance to cooperatives is also significant with positive sign on farmers preference for brokers.\par
Number of visit by extensionists has negative and significant effect on formal markets and brokers and positive and significant effect on cooperatives as compared to informal markets which all are consistent with the hypothesized sign. The reason behind this could be extensionists advice farmers to sell their coffee to cooperatives rather than local traders.\par
Annual off farm income the household has negative and significant effect on the preference of the farmers for brokers as compared to informal markets which is consistent with hypothesized result. The reason might be farmers with large off farm income are expected use attractive market outlet when the coffee price becomes attractive.\par
Total coffee land of the household has negative and significant effect on the preference of formal markets relative informal markets which is inconsistent with the hypothesized sign.\par
Total land holding of the household has positive and significant effect on the preference of formal market outlet as compared to informal markets which is consistent with the hypothesized sign.\par
The coefficient of transport cost to the formal market is not statistically significant. However the marginal effects of transport cost to formal market were statistically significant with negative sign. The implication is that and a one Birr increase in transport cost to the formal market decreases the likelihood to use formal market by 1\% as compared to informal markets. V. Conclusions and Recommendations 
\section[{a) Conclusions}]{a) Conclusions}\par
The study was undertaken with the objective of assessing factors affecting coffee market outlet choices on Jimma Zone, Ethiopia. The data was collected from 156 coffee smallholder farmers. Multinomial Logit model was used to analyze factors that determines dry and red cherry coffee market outlet.\par
The result shows that, 48\% of red cherry coffee was sold to cooperatives and 30\%, 15\% and 5\% of red cherry coffee was sold to formal traders, informal buyers and brokers or assemblers respectively and 73\% of respondents used formal coffee trader and 15\%, 9\% and 3\% of respondents used informal buyers, cooperatives and brokers respectively for dry coffee.\par
Farmers were raising different reasons of preference of market outlet they sold for. The criteria of The study identified factors that affects red coffee market outlet and resulted that age of the household has negative and significant effect on the preference of farmers for formal markets and brokers and farm experience of the household has positive and significant effect on the preference of the farmer for formal market and brokers as compared to informal markets. Distance to formal coffee market has positive and significant effect on the preference of the farmer to cooperatives and brokers and it has negative and significant effect on formal markets preference. The result also shows number of extension visit the farmer received from the extensionists has positive and significant effect on the preference of formal markets and transport cost to the main market has negative and significant impact on formal traders preference of farmers relative to informal buyers as compared to informal markets.\par
The dry coffee market preference result also shows that age of the household has negative and significant effects on the preference for farmers for formal markets and the coefficient of farming experience has positive and significant effect on the preference of farmers for formal markets as compared to informal markets. Family size of the household head is positively and significantly related to farmer's preference for cooperatives and distance to cooperatives has negative and significant effect on the preference of farmers for cooperatives and has positive and significant impact on preference of farmers for brokers. Number of visit by extensionists has negative and significant effect on formal markets and brokers and positive and significant effect on cooperatives and the marginal effects of transport cost to formal market were statistically significant with negative sign. 
\section[{b) Recommendations}]{b) Recommendations}\par
Finally the study recommends the government to increase the access of cooperatives among coffee producing areas which increases coffee farmers' income from fair market and coffee supplied to national and international market and reduces marketing cost and distribution of informal coffee sell, market and traders. The study also point to the need for improvement of the extension services to aware farmers to choose formal markets such as cooperatives for efficient and profitable marketing of coffee through redesigning or reforming implementation strategies or improving/strengthening existing policy. Long distance to the agricultural produce markets and poor infrastructure was also an hindrance in marketing of coffee and this study recommends the improvement of the infrastructure to enhance coffee marketing.\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}1 Global}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-3.png}
\caption{\label{fig_1}2 Global}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Figure 3 :}\end{figure}
  \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.04047619047619047\textwidth}P{0.12142857142857141\textwidth}P{0.04047619047619047\textwidth}P{0.4047619047619047\textwidth}P{0.24285714285714283\textwidth}}
?\tabcellsep j ?\tabcellsep k\tabcellsep ..........\tabcellsep ......\end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.012781954887218044\textwidth}P{0.04473684210526315\textwidth}P{0.06390977443609022\textwidth}P{0.08308270676691729\textwidth}P{0.006390977443609022\textwidth}P{0.006390977443609022\textwidth}P{0.006390977443609022\textwidth}P{0.025563909774436087\textwidth}P{0.006390977443609022\textwidth}P{0.019172932330827067\textwidth}P{0.051127819548872175\textwidth}P{0.006390977443609022\textwidth}P{0.006390977443609022\textwidth}P{0.006390977443609022\textwidth}P{0.019172932330827067\textwidth}P{0.006390977443609022\textwidth}P{0.006390977443609022\textwidth}P{0.07030075187969924\textwidth}P{0.006390977443609022\textwidth}P{0.006390977443609022\textwidth}P{0.04473684210526315\textwidth}P{0.006390977443609022\textwidth}P{0.006390977443609022\textwidth}P{0.06390977443609022\textwidth}P{0.006390977443609022\textwidth}P{0.006390977443609022\textwidth}P{0.025563909774436087\textwidth}P{0.006390977443609022\textwidth}P{0.006390977443609022\textwidth}P{0.006390977443609022\textwidth}P{0.10225563909774435\textwidth}P{0.0575187969924812\textwidth}P{0.006390977443609022\textwidth}P{0.006390977443609022\textwidth}P{0.006390977443609022\textwidth}P{0.03195488721804511\textwidth}}
ln\tabcellsep ? ? ? ?\tabcellsep p p ik ij\tabcellsep ? ? ? ?\tabcellsep =\tabcellsep ?\tabcellsep +\tabcellsep \multicolumn{2}{l}{1 ?}\tabcellsep \multicolumn{2}{l}{SEX}\tabcellsep +\tabcellsep ?\tabcellsep 2\tabcellsep \multicolumn{3}{l}{AGE}\tabcellsep +\tabcellsep ?\tabcellsep 3\tabcellsep \multicolumn{2}{l}{FAMSIZE}\tabcellsep \multicolumn{2}{l}{+}\tabcellsep ?\tabcellsep 4\tabcellsep FEXP\tabcellsep \multicolumn{2}{l}{+}\tabcellsep ?\tabcellsep 5\tabcellsep DFORMRT\tabcellsep +\tabcellsep ?\tabcellsep 6\tabcellsep DCOOP\\
\tabcellsep \tabcellsep ?\tabcellsep \multicolumn{4}{l}{VEXT 7}\tabcellsep +\tabcellsep \multicolumn{2}{l}{?}\tabcellsep \multicolumn{5}{l}{OFFINC 8}\tabcellsep +\tabcellsep ?\tabcellsep \multicolumn{4}{l}{TCOFLAND 9}\tabcellsep \multicolumn{2}{l}{+}\tabcellsep \multicolumn{5}{l}{TLAND 10 ?}\tabcellsep \multicolumn{2}{l}{+}\tabcellsep TRCOSTFORM 11 ?\tabcellsep RT\end{longtable} \par
 
\caption{\label{tab_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.85\textwidth}}
below indicates that among the three districts farmers in\\
Manna district have relatively high mean age (47 years),\\
large farming experience (26.1 years), and large\\
average family size (4.9) but low land ownership (2.24\\
hectare). On average farmers in Limu kosa district have\\
large land ownership (3.63 hectares) and coffee land\\
(2.07 hectare) compared to only 2.04 and 1.8 for\\
farmers in Manna district as well as 2.63 and 1.51\\
hectares for farmers in Gomma district. But farmers in\\
Limu kosa have small average family size (3.98) and\\
farming experience (23.09).\end{longtable} \par
 
\caption{\label{tab_2}Table 1}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.2395027624309392\textwidth}P{0.07983425414364641\textwidth}P{0.06339779005524862\textwidth}P{0.07279005524861878\textwidth}P{0.05870165745856354\textwidth}P{0.15027624309392265\textwidth}P{0.05400552486187846\textwidth}P{0.07748618784530387\textwidth}P{0.05400552486187846\textwidth}}
\tabcellsep \multicolumn{2}{l}{Manna}\tabcellsep \multicolumn{2}{l}{Gomma}\tabcellsep \multicolumn{2}{l}{Limu Kosa}\tabcellsep \multicolumn{2}{l}{Overall}\\
Variables\tabcellsep Mean\tabcellsep S.D\tabcellsep Mean\tabcellsep S.D\tabcellsep Mean\tabcellsep S.D\tabcellsep Mean\tabcellsep S.D\\
Age of respondent\tabcellsep 47.00\tabcellsep 9.33\tabcellsep 45.42\tabcellsep 12.45\tabcellsep 40.50\tabcellsep 7.58\tabcellsep 44.30\tabcellsep 9.78\\
Farming experience (years)\tabcellsep 26.10\tabcellsep 8.610\tabcellsep 23.19\tabcellsep 11.24\tabcellsep 23.09\tabcellsep 6.33\tabcellsep 24.12\tabcellsep 8.72\\
Family size\tabcellsep 4.900\tabcellsep 1.630\tabcellsep 4.61\tabcellsep 1.90\tabcellsep 3.98\tabcellsep 1.70\tabcellsep 4.49\tabcellsep 1.74\\
Coffee land (Hect)\tabcellsep 1.800\tabcellsep 1.490\tabcellsep 1.51\tabcellsep 0.88\tabcellsep 2.07\tabcellsep 1.34\tabcellsep 1.79\tabcellsep 1.23\\
Land ownership (Hect)\tabcellsep 2.240\tabcellsep 1.730\tabcellsep 2.63\tabcellsep 1.56\tabcellsep 3.63\tabcellsep 2.43\tabcellsep 2.83\tabcellsep 1.91\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{Source: Own computation, 2015}\end{longtable} \par
 
\caption{\label{tab_3}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.3149746192893401\textwidth}P{0.06256345177664974\textwidth}P{0.04314720812182741\textwidth}P{0.15748730964467006\textwidth}P{0.13375634517766496\textwidth}P{0.04530456852791878\textwidth}P{0.025888324873096447\textwidth}P{0.040989847715736036\textwidth}P{0.025888324873096447\textwidth}}
\tabcellsep \multicolumn{2}{l}{Manna}\tabcellsep \multicolumn{2}{l}{Gomma}\tabcellsep \multicolumn{2}{l}{Limu Kosa}\tabcellsep \multicolumn{2}{l}{Overall}\\
Variables\tabcellsep \multicolumn{2}{l}{Mean S.D}\tabcellsep Mean\tabcellsep \multicolumn{5}{l}{S.D Mean S.D Mean S.D}\\
Distance to formal village market (KM)\tabcellsep 1.60\tabcellsep 1.450\tabcellsep 5.26\tabcellsep 1.669\tabcellsep 2.73\tabcellsep 1.81\tabcellsep 3.19\tabcellsep 1.64\\
Distance to formal main market (KM)\tabcellsep 90.3\tabcellsep 80.84\tabcellsep 20.0\tabcellsep \multicolumn{5}{l}{13.57 51.55 20.6 53.95 38.33}\\
Distance to cooperatives (KM)\tabcellsep 3.20\tabcellsep 8.170\tabcellsep 8.96\tabcellsep 5.46\tabcellsep 4.35\tabcellsep 1.91\tabcellsep 5.50\tabcellsep 5.18\\
Distance to extension services (KM)\tabcellsep 5.60\tabcellsep 4.540\tabcellsep 10.68\tabcellsep 4.41\tabcellsep 7.28\tabcellsep 7.65\tabcellsep 7.85\tabcellsep 5.53\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{6}{l}{Source: Own computation from survey data (201)5}\end{longtable} \par
 
\caption{\label{tab_4}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.34375\textwidth}P{0.33749999999999997\textwidth}P{0.16874999999999998\textwidth}}
Multinomial logistic regression\tabcellsep \multicolumn{2}{l}{Number of obs = 156}\\
\tabcellsep LR chi 2 (36)\tabcellsep = 95.21\\
\tabcellsep Prob > chi 2\tabcellsep = 0.0000 ***\\
Log likelihood = -123.94\tabcellsep Pseudo R 2\tabcellsep = 0.3195\end{longtable} \par
 
\caption{\label{tab_5}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.16190476190476188\textwidth}P{0.023351648351648352\textwidth}P{0.0576007326007326\textwidth}P{0.10352564102564102\textwidth}P{0.2607600732600733\textwidth}P{0.08717948717948718\textwidth}P{0.08250915750915751\textwidth}P{0.07316849816849817\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 2016\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Year\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 9\\
Variables\tabcellsep \multicolumn{2}{l}{Cooperatives Coefficient dy/dx}\tabcellsep \multicolumn{2}{l}{Formal market Coefficient dy/dx}\tabcellsep \multicolumn{2}{l}{Brokers Coefficient dy/dx}\tabcellsep Volume XVI Issue IV Version I\\
\multicolumn{2}{l}{HHSEX HHAGE HHFEXP FAMSIZE DFOMRT DCOOP VEXT OFINC TCLAND TLAND TCOSTFMRT CONSTANT Base category = Cooperatives -1.604 0.052 -0.0082 0.2444 0.711 *** 0.002 -0.017 -0.000 -1.487 ** 0.824 * 0.076 4.190}\tabcellsep -0.269 0.0309 -0.036 0.022 -0.122 0.025 -0.011 -0.000 -0.216 0.144 0.024 *\tabcellsep 1.534 -0.135 *** 0.152 *** -0.0589 -0.435 *** -0.123 0.0514 * 0.000 *** 0.714 * -0.518 -0.105 ** 0.528\tabcellsep 0.394 -0.029 0.033 -0.0064 -0.0787 -0.029 * 0.0119 * 0.000 ** 0.1183 -0.095 -0.023 -\tabcellsep -15.64 -0.219 ** 0.284 ** -0.158 0.878 *** 0.103 -0.049 0.000 1.892 *** -1.02 ** -0.052\tabcellsep -0.205 -0.002 0.003 -0.002 0.008 * 0.002 -0.001 -0.000 0.0188 -0.01 -0.000\tabcellsep E ( ) Global Journal of Management and Business Research\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{experience increases the likelihood to use formal}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{markets by 1.6\% as compared to informal markets.}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{Family size of the household head is positively}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{and significantly related to farmer's preference for}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{cooperatives which is consistent with the hypothesized}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep © 2016 Global Journals Inc. (US)\end{longtable} \par
 
\caption{\label{tab_6}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.35038167938931297\textwidth}P{0.3374045801526717\textwidth}P{0.16221374045801526\textwidth}}
Multinomial logistic regression\tabcellsep \multicolumn{2}{l}{Number of obs = 156}\\
\tabcellsep LR chi2(30)\tabcellsep = 43.55\\
\tabcellsep Prob > chi 2\tabcellsep = 0.012 **\\
Log likelihood = -64.59\tabcellsep Pseudo R 2\tabcellsep = 0.2775\end{longtable} \par
 
\caption{\label{tab_7}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.14182320441988952\textwidth}P{0.21414364640883976\textwidth}P{0.006574585635359116\textwidth}P{0.016906077348066297\textwidth}P{0.09580110497237569\textwidth}P{0.10988950276243092\textwidth}P{0.08546961325966851\textwidth}P{0.09861878453038674\textwidth}P{0.08077348066298343\textwidth}}
the\tabcellsep respondents\tabcellsep include\tabcellsep market\tabcellsep \multicolumn{2}{l}{accessibility,}\tabcellsep \\
\multicolumn{2}{l}{trustfulness of the}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
2016\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Year\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
10\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Volume XVI Issue IV Version I\tabcellsep Variables\tabcellsep \tabcellsep \multicolumn{2}{l}{Cooperatives}\tabcellsep \multicolumn{2}{l}{Formal traders}\tabcellsep Brokers\\
E ( ) Global Journal of Management and Business Research\tabcellsep \multicolumn{3}{l}{Coefficient 1.802 0.076 -0.363 0.402 * 0.097 -1.541 ** 0.413 ** 0.000 -3.542 3.062 0.260 -5.384 Base category = Cooperatives HHSEX HHAGE HHFEXP FAMSIZE DFOMRT DCOOP VEXT OFFINC TCLAND TLAND TCOSTFMRT CONSTANT}\tabcellsep dy/dx -0.128 0.0008 -0.0012 -0.0006 0.0017 -0.013 * 0.0018 -0.000 0.0158 -0.012 0.0006 -\tabcellsep Coefficient 15.65 -0.108 ** 0.146 ** 0.0767 -0.207 0.158 -0.223 * 0.000 -1.933 ** 1.482 ** -0.085 -9.69\tabcellsep dy/dx 0.129 -0.0015 0.016 * -0.0004 -0.0023 0.0011 -0.0012 0.000 -0.0183 0.0138 -0.010 ** -\tabcellsep Coefficient 15.45 -0.012 0.084 0.247 -0.110 0.186 * -0.315 ** -0.0002 * -1.507 1.203 -0.059 -14.44\tabcellsep dy/dx -0.0004 0.0005 -0.0003 0.001 0.0006 0.002 * -0.0006 -0.000 0.003 -0.002 0.0002 -\\
\multicolumn{2}{l}{© 2016 Global Journals Inc. (US) 1}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_8}}\end{figure}
 			\footnote{Determinants of Farmers' Preference to Coffee Market Outlet in Jimma Zone: The Case of Coffee Potential Districts © 2016 Global Journals Inc. (US) 1} 			\footnote{© 2016 Global Journals Inc. (US)} 			\footnote{© 2016 Global Journals Inc. (US) 1} 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[Barker ()]{b3}\label{b3} 	 		 			J Barker 		.  		\textit{Agricultural Marketing},  				 (New York)  		1989. Oxford University Press.  	 
\bibitem[Agarwal and Ramaswami ()]{b0}\label{b0} 	 		‘Choice of foreign market entry mode: Impact of ownership, Location and international factors’.  		 			S Agarwal 		,  		 			S Ramaswami 		.  	 	 		\textit{Journal of International Business Studies}  		1992. 23  (1)  p. .  	 
\bibitem[Magogo et al. ()]{b11}\label{b11} 	 		‘Determinants of Choice of Marketing Outlets for African Indigenous Vegetables among the Agro-Pastoral Maasai of Narok and Kajiado Counties of Kenya’.  		 			J Magogo 		,  		 			P Mshenga 		,  		 			S Mlongo 		,  		 			N Mwanarusi 		,  		 			O Agnes 		,  		 			I Shem 		.  	 	 		\textit{Journal of Economics and Sustainable Development}  		2015. 2015. 6  (8) .  	 
\bibitem[Determinants of Farmers' Preference to Coffee Market Outlet in Jimma Zone: The Case of Coffee Potential Districts]{b17}\label{b17} 	 		\textit{Determinants of Farmers' Preference to Coffee Market Outlet in Jimma Zone: The Case of Coffee Potential Districts},  		 	 
\bibitem[Ministry Of Trade (2012)]{b12}\label{b12} 	 		‘Determinants of Farmers' Preference to Coffee Market Outlet in Jimma Zone: The Case of Coffee Potential Districts’.  		 			Ministry Of Trade 		.  	 	 		\textit{Ministry of Trade},  				 (Addis Ababa, Ethiopia)  		2012. February 2012.  	 	 (Federal Democratic Republic of Ethiopia) 
\bibitem[Gujarati ()]{b6}\label{b6} 	 		\textit{Essentials of econometrics},  		 			D N Gujarati 		.  		1992. New York: Mac Graw-Hill. p. 466.  	 	 (2nd Ed) 
\bibitem[Anteneh et al. ()]{b2}\label{b2} 	 		\textit{Factors Affecting Coffee Farmers Market Outlet Choice, The Case of Sidama Zone, Ethiopia},  		 			A Anteneh 		,  		 			R Muradian 		,  		 			R Ruben 		.  		2011.  		 			Centre for International Development Issues, Nijmegen, Radboud University, the Netherlands 		 	 
\bibitem[Berhanu et al. (2013)]{b4}\label{b4} 	 		‘Factors affecting milk market outlet choices in Wolaita zone’.  		 			K Berhanu 		,  		 			D Baker 		,  		 			G Kindie 		,  		 			K Belay 		.  	 	 		\textit{African Journal of Agricultural Marketing}  		2013. December, 2013. 1  (2)  p. .  	 
\bibitem[Greene ()]{b5}\label{b5} 	 		 			W H Greene 		.  		\textit{Econometric analysis},  				 (Upper Saddle river)  		2002. Prentice hall. 291 p. 304.  	 	 (5th ed.) 
\bibitem[Potts ()]{b13}\label{b13} 	 		\textit{International Institute for Sustainable Development, Leveraging the Market for a Sustainable Coffee Economy},  		 			J Potts 		.  		2006.  	 
\bibitem[Ico/Cfc ()]{b8}\label{b8} 	 		\textit{Marketing and trading policies and systems in selected coffee producing countries: Ethiopia Country Profile. International Coffee Organization/Common Fund for Commodities},  		 			Ico/Cfc 		.  		2000. New York: LMC International.  	 
\bibitem[Kotler ()]{b10}\label{b10} 	 		\textit{Marketing Management},  		 			P Kotler 		.  		2003. Delhi-India.  	 
\bibitem[Pundo and Fraser ()]{b14}\label{b14} 	 		‘Multinomial Logit analysis of household cooking fuel choice in rural Kenya: The case of Kisumu district’.  		 			M O Pundo 		,  		 			G C Fraser 		.  	 	 		\textit{Agrekon}  		2006. 45  (1)  p. .  	 
\bibitem[Tilahun ()]{b15}\label{b15} 	 		\textit{Performance of Cattle Marketing in Southern Ethiopia with Special Emphasis on Borena Zone},  		 			Solomon Tilahun 		.  		2002.  	 
\bibitem[Hobbs ()]{b7}\label{b7} 	 		\textit{Producers attitudes towards marketing channels for finished cattle},  		 			J E Hobbs 		.  		1997. p. .  	 
\bibitem[Zuniga-Arias ()]{b16}\label{b16} 	 		\textit{Quality Management and Strategic Alliances in the Mango Supply Chain from Costa Rica: An interdisplinary Approach for Analyzing Coordination, Incentive, and governance},  		 			G E Zuniga-Arias 		.  		2007. Wageningen University, The Netherlands.  	 
\bibitem[Anderson and Vincze ()]{b1}\label{b1} 	 		\textit{Strategic Marketing Management},  		 			C J Anderson 		,  		 			J W Vincze 		.  		2000. New York: Houghton Mifflin Company.  	 
\bibitem[Kherallah and Kirsten ()]{b9}\label{b9} 	 		\textit{The new institutional economic: applications for agricultural policy research in developing countries. Markets and Structural Studies Division},  		 			M Kherallah 		,  		 			J Kirsten 		.  		2001. Washington, D.C. U.S.A: International Food Policy Research Institute.  	 
\end{bibitemlist}
 			 		 	 
\end{document}
