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American Journal of  Economics and 
Business Innovation (AJEBI)

Analysis of  Rice Marketing Channels Choices in Mwea 
Irrigation Scheme, Kirinyaga County, Kenya

Murage Boniface Wachira1*, Eric Kiprotich Bett1, Bernard Njehia1, Charles Karani2

Volume 2 Issue 1, Year 2023
ISSN: 2831-5588 (Online), 2832-4862 (Print)

DOI: https://doi.org/10.54536/ajebi.v2i1.1238
https://journals.e-palli.com/home/index.php/ajebi

Article Information ABSTRACT

Received: January 25, 2023

Accepted: February 11, 2023

Published: February 22, 2023

The National Rice Development Strategy in (Kenya vision 2030) aims to improve food 
security, rural employment, and income for smallholder farmers in the Mwea irrigation 
scheme (MIS) through planned and intentional efforts. A study was conducted to identify 
major influences in choosing market avenues among rice farmers. The study used a sample 
size of  384 small-scale rice farmers to analyze the different rice marketing channels used 
in MIS, and to understand the factors that influence farmers’ choices of  marketing channels. 
A pretested semi-structured questionnaire was used for data collection. Descriptive statistics 
were used to characterize the marketing channels, multinomial logit model was used to 
evaluate channel choices. The marketing channels were Channel 1: Farmer-Consumer 
(0.3%); Channel 2: Farmer-Brokers-Millers-Consumer (51.1%); Channel 3: Farmer-Bro-
kers-Millers- Retailer-Consumer (42.3%); Channel 4: Farmer- Millers-Retailer-Consumer 
(62.72%) and Channel 5: Farmer- Millers- Wholesaler-Retailer-Consumer (37.28%).  The 
most profitable channel was direct sales to consumers, which accounted for only 0.3% 
of  total rice production in MIS. The study also found that factors such as education level, 
participation in marketing groups, distance to market, and age of  the household head 
significantly influenced farmers’ choice of  marketing channels. The study is critical in enriching 
literature on rice agricultural supply chains in Kenya. The study recommends policy 
considerations to protect small-scale farmers from exploitative brokers and suggests registering 
brokers to monitor their activities and set standards for fair practices, with the goal of  regulating 
their activities to prevent exploitation of  small-scale farmers in rice marketing.

Keywords
Brokers, Traders, Farmers’ 
Preferences, Marketing Channels, 
Multinomial Logit Model

1 School of  Agriculture and Environmental Sciences, Department of  Agricultural Economics, Kenyatta University, Kenya
2 School of  Agricultural Sciences and Agribusiness, Department of  Crop Sciences, Pwani University, Kenya
* Corresponding author’s e-mail: murageb2022@gmail.com

INTRODUCTION
Rice has been harvested, consumed, and produced by 
many people around the world for over 10,000 years, 
making it the oldest crop on the planet (Talhelm & Oishi, 
2018). For more than half  of  the world’s population, 
rice is the most important staple food (Tanaka et al., 
2017). Rice agriculture covers 150 million hectares (ha) 
globally, with annual paddy production averaging 759.6 
million metric tons (MMT), or 503.9 million metric tons 
milled basis (MMT), with over 85 percent (408 MMT) for 
human use. This is equivalent to 29% of  total grain crop 
output worldwide (FAOSTAT, 2017).
Rice growing is a significant concern in Kenya because it 
plays a crucial role in household food security and farmer 
income. The Mwea Irrigation Scheme (MIS) accounts 
for 80% of  Kenya’s rice output, which is a vital part 
of  the grain supply in a country that relies on imports 
to meet its rice needs (Ndegwa, 2018). The fact that 
farmers in Kenya sell about 95 percent of  the rice they 
produce locally, highlights the importance of  rice in the 
country’s economy. However, due to changing market 
conditions and consumer demand, several rice sellers in 
the country are taking advantage of  small-scale farmers. 
The government-owned National Cereals and Produce 
Board (NCPB) is one of  the main dealers, it buys paddy 
from farmers, processes it in state-owned mills, and sells 
milled rice to retailers (Serade et al., 2015). This shows 
that the market is not favorable for small scale farmers 
and government’s policies are not favorable for farmers.

Rice production in Kenya is estimated at 150,000 metric 
tons from 25,000 hectares of  land (GoK, 2017). Rice 
production barely fulfils around 20% of  overall demand, 
with rice consumption expected to climb as the world’s 
population grows and eating patterns change (Atera et 
al., 2018). Rice consumption in Kenya is estimated to be 
550,000 metric tons per year (Kenya Bureau of  Statistics, 
2017), and it is growing at a an anual rate  of  12% , 
compared to 4% for wheat and 1% for maize, the leading 
basic food (Ministry of  Agriculture, 2018). Per capita, rice 
consumption in Kenya is estimated to be 10-18 kilograms 
per year (Ministry of  Agriculture, 2017). Rice demand 
surpasses supply, and the vast gap between supply and 
demand is covered at a high cost by importing to meet 
domestic needs. By 2030, the rice market is expected to 
grow to 517.5 million tons (Musila et al., 2018). For Kenya 
to achieve rice self-sufficiency by 2030, total domestic 
rice output must increase at a rate of  9.3 percent each 
year (Ministry of  Agriculture, 2018).
The Mwea Irrigation Scheme was established in 1958 as 
a resettlement scheme with the primary goal of  resettling 
landless and ex-detainees. The program covers 30,350 
acres, with 16,000 acres dedicated to rice production and 
the rest used for settlement, public amenities, subsistence 
farming, and horticultural crop farming. The Nyamindi 
and Thiba rivers provide water to the project. Around 
7,500 households are working on an average of  4 acres 
of  land (NIA, 2020). Until 1998, the government oversaw 
the plan through the National Irrigation Board (NIB). The 

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NIB was the landlord, and the farmers were the tenants 
regarding land tenure. The tenant provided labour services 
at a cost established by the landlord, while the landlord 
provided inputs, infrastructure, machinery, and extension 
services.The government had an elaborate structure and 
mechanisms, farming operations management, water 
management, funding arrangements, storage, processing, 
and marketing (Mati et al., 2016).
Farming is still a significant concern in Kenya because of  
its good impact on household food security and farmer 
revenue. Kenya’s rice output is 80% based on the Mwea 
Irrigation Scheme. Because of  its outstanding quality, MIS 
rice is frequently blended with low-quality imported rice 
in the region. Rice traders are increasingly capitalizing on 
changing market conditions and consumer preferences. In 
the recent decade, smallholder rice farmers have been less 
involved in rice marketing. Many new competitors have 
entered the rice marketing scene. NIB, Mwea Rice Millers, 
cooperative millers, distributors, merchants, brokers, 
and importers are among the marketing chain’s actors 
(Ndegwa, 2018). Farmers in the Mwea Irrigation Scheme 
have numerous avenues to dispose their rice surplus. 
In the Mwea Irrigation Scheme, there is inadequate 
information on the specific avenues farmers may opt for 
and farmers’ choices of  rice marketing channels. This 
study identified the rice marketing channels and channel 
choices among rice farmers to bridge this knowledge gap. 

LITERATURE REVIEW
Theoretical Review
Adam Smith’s (1770) Rational Choice Theory (RCT) 
frequently assumes that the individual decision-making 
unit in question is typical or representative of  a broader 
group, such as buyers or sellers in a given market. After 
determining individual behaviour, the research usually 
looks at how individual decisions interact to produce 
results. Theorists of  Rational Choice Theory think that 
most human actions are made to maximize a person’s 
benefits while minimizing those that may harm them.
The assumption behind the rational choice analysis is that 
some agent, or set of  agents, maximizes utility by selecting 
the preferred option. The presence of  restrictions is 
another crucial aspect of  the decision-making process. 
Limitations necessitate the choice, and one of  the benefits 
of  rational choice theory is that it makes the trade-offs 
between alternative options very obvious. The budget 
restriction, which states that the customer cannot spend 
more than her income in a simple one-period consumer 
choice problem, is a common constraint. Rational Choice 
Theory is relevant because it assumes that rice agents, 
large-scale private millers, small private millers, NIB, 
Mwea Rice Millers, cooperative millers, wholesalers, 
retailers, brokers, and importers, among others, make 
rational choices with the farmers’ choice in mind.

Empirical Evidence on Farmer’s Choice in Rice 
Marketing Channels
Using a logit model, Mburu et al. (2017) evaluated 

farmers’ milk marketing channel choices in central 
Kenya. According to the study, the land negatively 
influenced farmers’ adoption of  milk selling through 
the dairy cooperative channel, average milk price, the 
total number of  cows milked, and farm acreage. Hired 
permanent labour, whether or not a household head 
worked off-farm, average milk output per cow (kg per 
day), dairy cooperative as a source of  animal production 
information, and the availability of  financial services 
all had a beneficial impact. The traders were divided 
into two groups in the study: cooperatives and itinerant 
traders (hawkers, neighbours, and hotels). The difference 
between Mburu et al. (2017) and this current study is 
that the study collapsed all market alternatives into a 
binary outcome while the current study does not. The 
current study built on Mburu’s research by evaluating rice 
marketing channels using the multinomial logit (MNL), 
which captures each marketing channel separately.
Nyaga et al. (2016) investigated the factors influencing 
fish producers’ marketing channel selection in Kirinyaga 
County. The data was analyzed using a multinomial logit 
model after a descriptive research design comprising 147 
fish farmers in Kirinyaga’s five sub-counties. It defined 
market channels for fish farmers and investigated the 
factors influencing their marketing channel selection. 
According to the survey, there are three main channels: 
neighbours, direct market, and traders, which account 
for 49 percent, 29 percent, and 22 percent of  the total. 
Farmers’ choice of  the market channel was influenced by 
the gender of  the household head, distance to market; 
marketing cost, land tenure, number of  fish ponds 
owned, access to extension services, cost of  marketing, 
membership in a fish farming group, access to inputs, 
household income, price of  fish, and type of  fish reared. 
Despite government assistance, farmers faced predators, 
insufficient extension services, and limited access to 
information. The conclusion is that county governments 
and other stakeholders must make it easier for practising 
farmers in their communities to access vibrant extension 
programs and market support. 
Chalwe (2015) evaluated the factors influencing bean 
producers’ decision to sell and their choice of  marketing 
channel in Zambia using the multinomial probit. The 
results showed that farmers’ choice of  marketing 
channels was positively influenced by the price of  beans, 
scale of  operation (as measured by the number of  beans 
harvested and quantity sold), distance to the market, 
farming mechanization used, and livestock ownership.
According to Ogunleye and Oladeji (2017), cocoa farmers 
in the research area chose choice market channels for their 
output based on time of  payment, the form of  payment, 
product pricing, distance from the farm, transportation 
cost, and product grading. In that order, the majority 
of  the farmers in the study patronized itinerant buyers, 
cocoa merchants, fellow farmers, and cooperative society 
stores. They conclude that the time between when goods 
are sold and when they are paid is a high negotiation cost 
that determines cocoa growers’ market choice. Farmers 

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were hesitant to choose an outlet because of  payment 
delays. Transportation costs rise as the distance between 
the farm and the road deteriorates. Farmers will prefer a 
low transportation cost if  they cannot entirely avoid it due 
to bad roads. Uncertainties are associated with produce 
grading because farmers risk having their produce 
rejected or reduced prices. As a result, farmers will seek to 
avoid any of  the two repercussions associated with food 
grading.
According to a study by Wojciech et al. (2013) titled 
“Marketing Portfolio Choices by Independent Peach 
Growers,” Georgia commercial peach growers select a 
marketing channel for fresh peach sales after considering 
consumer preferences for quality qualities. Through the 
use of  a polychotomous selection model and survey data, 
it was discovered that both external and internal quality 
attributes influenced the choice of  marketing channel 
chosen and the percentage of  the crop sold. Other 
influencing factors included orchard characteristics and 
the variety-determined fruit maturity.
Inadequate market channels and a lack of  price knowledge 
were two problems Jaleta (2007) cited as limiting 
agricultural commercialization. Furthermore, a study by 
Emana and Gebremedhin (2007) claimed that insufficient 
local markets, a flawed pricing system, a lack of  local 
markets to absorb supply, low produce prices, an excess 
of  intermediaries, and poor marketing institutions and 
farmer coordination affect the marketing of  horticultural 
crops in their study on market chain analysis. Inadequate 
product handling and packing, poor pricing mechanisms, 
and information asymmetry, according to Emana and 
Gebremedhin (2007), all have an impact on farm produce 
marketing.

Summary of  Research Gaps
The literature review suggests a gap in research on the 
factors that influence farmers’ choice of  marketing 
channels for their products in different geographical 
locations and types of  products. It would be valuable 
to investigate the impact of  government policies and 
programs on farmers’ decision-making process and their 
choice of  marketing channels. 

MATERIALS & METHODS
The research was carried out in the Mwea Irrigation 
Scheme (MIS), a National Irrigation Board (NIB) scheme 
located in Kirinyaga County, Central Kenya, near the 
base of  Mount Kenya. The MIS is located 112 kilometres 
northeast of  Nairobi. On the dry plains of  Mt. Kenya’s 
southeast, it is located at latitude 0° 37’ S and longitude 
37° 20’ E, at an elevation of  1159 meters above sea level. 
The Nyamindi and Thiba rivers provide water to the 
project. MIS is situated on top of  a worn trachyte bed 
and is surrounded by impenetrable, heavy black cotton 
soil (NIB, 2017). 
The MIS covers 30,350 acres (Ministry of  Agriculture, 
2017). Paddy production is spread across 16,000 acres 
(Ministry of  Agriculture, 2017).

Research Design
A cross-sectional survey research design was used in 
this study. Cross-sectional studies are studies in which 
data is gathered once, during a period of  days, weeks, or 
months. Many cross-sectional studies are exploratory or 
descriptive in purpose.

Target Population
The study population consisted of  all rice farmers, 
wholesalers, retailers, and rice millers in Mwea Irrigation 
Scheme. According to the records from the Ministry of  
Agriculture County Director of  Agriculture (CDA) Mwea 
Irrigation Scheme, has seven units with 5,576 households 
(Ministry of  Agriculture Kirinyaga County Office, 2017). 
The target population for this study was 5,576 rice 
farmers. The choice of  the respondents was purposive 
since they are aware of  the rice production and marketing 
channels. Table 1 shows the total population.

Table 1: Target Population
No Section Farmers
1 Mwea 867
2 Thiba 776
3 Wamumu 794
4 Karaba 709
5 Ndekia 724
6 Tebere 950
7 Curukia 756

Total 5,576

Sample Size and Sampling Techniques
In determining sample size, the researcher used a formula 
adopted by Yamane (1963)

 

Where n – Sample size, N – Population size, e – (the 
acceptable margin error)
Thus

A total of  384 farmers was sampled from the target 
population. Table 2 shows the distribution of  the sampled 
respondents.

Table 2: Sample Size
No Section Farmers
1 Mwea 60
2 Thiba 53
3 Wamumu 55
4 Karaba 49
5 Ndekia 50
6 Tebere 65
7 Curukia 52
 Total 384

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Piloting and data collection
Data was collected by interviewing farmers using a semi-
structured questionnaire. The researcher administered 
the questionnaires and waited to be filled out. This not 
only improved the reliability of  given information but 
also assured a high return rate. Before collecting actual 
data, the research questionnaire was tested. This was aimed 
at identifying weaknesses and making requisite instrument 
adjustments before commencing data collection. Piloting 
involved collecting data from a sample of  20 farmers from 
the Karaba section, representing 5% of  the study sample 
size of  384 respondents (Mugenda & Mugenda, 2003).  
The data was collected over two months, between 
August 2018 and December 2018. The researcher sought 
permission from the County Administrator in Mwea 
County before commencing data collection. A research 
permit was also sought and granted from the National 
Commission of  Science, Technology, and Innovation 
(NACOSTI). Five researcher assistants were recruited 
and trained on field data quantification, research ethics, 
triangulation, and probing to assist in data collection.
The study used primary data that was quantitative and 
descriptive. Primary data was obtained from the rice 
farmers, wholesalers, retailers, and rice millers using 
structured questionnaires. 

Data Analysis
Raw data was entered into a Microsoft Excel spreadsheet 
after the questionnaires were filtered to ensure the 
completeness and consistency of  responses. Following 
that, the replies were coded for a statistical package of  

social sciences (SPSS) analysis. The study’s first objective 
identified rice marketing channels in the Mwea Irrigation 
Scheme. To attain this objective, descriptive statistics 
were employed in the analysis, which included means, 
frequencies, percentages, and standard deviations.
The study’s second objective evaluated factors influencing 
farmers’ choice of  rice marketing channels. The 
multinomial Logit model analysis was utilized to assess the 
factors influencing the choice of  rice marketing channel 
in MIS. Because it is chosen among other marketing 
channels, a specific marketing channel is distinct. If  P 
is the likelihood of  rice farmers choosing a particular 
marketing channel, then the equation to represent this is,

Where i takes values (1, 2, 3 and 4), each representing the 
choice of  marketing channel (X1…..k= farmers choices), β 
are parameters to be estimated and e is randomized error.
The multinomial Logit model is given below:

The choice of  marketing channel is given by the variables 
of  measurement for multinomial logit in Table 3.3 and 
the empirical equation:
Pij=β0+β1Age+β2Ged+β3Edc+β4Hhse+β5HseY+
β6Ocpt+β7Dmkt+β8Ricot+β9Mrktin+β10Vop+
β11Mgrp+β12 Mrktac +  εi………(3)

Pij=Mktcho dependent variable, β parameter to be 
estimated and εi is the error term, and the other variables 
are specified in Table 3.

Table 3: Variables used in Multinomial logit
Variable code Variable Measurement of  the variables Expected sign 
Dependent variable
Mktcho Rice marketing channel 

choice
Consumer=1,Retailer=2,
Wholesaler=3,Processor=4,Broker=5

N/a

Independent variables
Age
Ged
Edc

Age in years
Gender
Education

In years Dummy (Male=1,Female=0)
No education=1,primary=2,
secondary=3,Tertiary=4

+
-
-

Hhse
Ocpt

Household Size
Occupation

Size of  Household ( continuous)
Farmer=1,Businessman=2, Employed=3

+
-

HseY Household Income In shillings (Ksh) continuous +
Dmkt Distance to market In Kilometres ( Km) ( continuous) +
Ricot Rice output In Kilograms +
Mrktin Market information Dummy ( yes=1, No=0) -
Mrktac Market access Dummy ( yes=1, No=0) -
Mgrp Marketing in groups Dummy ( yes=1, No=0) -

RESULTS & DISCUSSION
This chapter presents the patterns of  the results of  the 
study. The findings are presented in tables and narrations 
as per the specific objectives. The chapter presents 
descriptive and inferential statistics, the pre-estimation 
and post-estimation tests.

Response Rate
Table 4 shows the summary for the response rate. 
The sample for the study was 384, although 372 were 
filled and returned, representing approximately 97% 
of  the response. This response rate was appropriate 
since Kothari (2011) argued that a 50% response rate is 

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adequate, 60% good and above 70% rated as appropriate 
for analysis. Thus, the response rate was adequate for the 
study.

Table 4: Response Rate
Response Rate Frequency Percentage
Response 372 96.87%
Non-Response 12 3.13%
Total 384 100

Demographics Characteristics
Descriptive analysis was conducted for demographics 
to show the frequencies for gender, level of  education, 
access to rice market information, marketing channel and 
rice type, access to credit and finance and choice of  rice 
marketing channel. The results are shown in Table 5.
From the results in Table 5, the number of  female 
farmers composition was 27.42%, whereas 72.58% were 
males, an indication of  male domination in rice farming. 
On education level, the results show that 29.84% had no 
formal education, 50.81% were of  primary level, while 
only 7.53 % and 11.83% had secondary level and tertiary 

Table 5: Demographics
Gender Frequency Percent
Female 102 27.42
Male 270 72.58
Total 372 100
Level of  Education
No formal education 111 29.84
Primary education 189 50.81
Secondary education 28 7.53
Tertiary education 44 11.83
Total 372 100

level, respectively. This indicated that there was a low 
level of  education in the rice farming sector and could 
influence the understanding of  the questions. However, 
the study used simplified questions that allowed all 
respondents to understand.
Respondent’s access to rice market information, rice type 
produced, access to credit and finance, and difficulties in 
accessing credit responses are shown in Table 6.
Results in Table 6 showed that the majority, 56.99%, could 
not access rice market information, and only 43.01% 
were able to. This indicates that there could be a lack 
of  meaningful information on the rice farming sector, 
which could have a negative effect. On the Rice Type, 
the majority of  the respondents, 77.42%, indicated that 
they grow Basmati followed by BW 196 with 12.37%, and 
lastly, Nerica 1 having the least with 10.22%. On Access 
to Credit and Finance, the majority, 88.8% of  farmers, do 
not have access to credit, while only 11.2% have access 
to credit. This could indicate that many farmers could be 
restrained in their farming activities due to a shortage in 
accessing credit and finance. 

Table 6: Market Information
Access to Rice Market 
Information

Frequency Percent

No 212 56.99
Yes 160 43.01
Rice Type
Basmati 288 77.42
BW 196 46 12.37
Nerica 1 38 10.22
Access to Credit and Finance
No 330 88.8
Yes 42 11.2
Difficulties in Accessing Credit
Delays in payment 71 19
High interest rates 156 42
Lack of  land title deed 60 16
Low yields 82 22
Not applicable 4 1
Reason for Choice of  Channel
Availability 1 0.27
Better prices 129 34.68
Credibility of  weighing scale 3 0.81
Ease of  cash payment/
prompt cash

118 31.72

Inputs for rice farming and 
services (loans)

26 7.26

Lack of  security and stores 4 1.08
Nearer to market 90 24.19
Prompt payment, profitability, 
cost of  water payment

1 0.27

Total 372 100

On the difficulties in accessing credit, the majority, 42%, 
indicated that they had difficulties accessing credit due 
to high-interest rates, followed by low yields with 22%. 
19% of  the respondents indicated difficulty accessing 
credit due to payment delays, while 16% cited a lack of  
land title deeds for acting as collateral. Lastly, regarding 
the choice of  rice marketing channel, the majority of  
respondents, 129(34.68%), chose channels that provided 
better prices for their produce. 118(31.72%) acclaimed 
ease of  cash payment as the reason for their choice of  
marketing channel. 90(24.19%) participants preferred 
market channels due to their nearness to the market. 
Other factors that the farmers looked at were access to 
credit and farm inputs (7.26%), the credibility of  weighing 
scales (0.81%), provision of  stores (1.08%), and low cost 
of  water payment at 0.27%.

Marketing Channels of  Rice
Marketing channels link the market actors in the exchange 
process by indicating how the marketable and marketed 

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surplus is transferred from farms to the final consumer. 
They are routes through which agricultural commodities 
pass from different market actors to the final consumer. 
In evaluating a marketing system, marketing channels are 
very important as they indicate how market actors are 
organized to accomplish the product movement from the 
producer to the ultimate consumer. Marketing channels 
are also crucial in that they show a snapshot of  the various 
agencies and thus help assess market performance. The 
complexity of  the channel is governed by the distance 
from the production area to the terminal market. The 
purpose of  marketing channels analysis was to provide 

Figure 1: Flow Diagram Showing Percentage Marketed Volumes of  Rice across actors

systematic knowledge of  the flow of  rice produce from 
the farmer to the final consumer. Data collected from 
the survey was used to construct the marketing channels. 
Based on the rice marketing pathways identified in Table 
7, the typical marketing channels which were observed in 
the rice market are presented in Table 7 and Figure 1. Five 
main marketing channels were identified.
Channel 1: Farmer-Consumer
Channel 2: Farmer- Brokers-Millers-Consumer
Channel 3: Farmer- Brokers-Millers- Retailer-Consumer
Channel 4: Farmer- Millers- Retailer-Consumer
Channel 5:  Farmer- Millers- Wholesaler-Retailer-Consumer 

Table 7: Rice Marketing Channels Participation by Farmers
Marketing 
channel

Farmers 
cumulative frequency

Percent of  farmers 
who sold through the channel

1. Farmer-Consumer 102 27.42
2. Farmer- Brokers-Millers-Consumer 270 72.58
3. Farmer- Brokers-Millers- Retailer-Consumer 372 100
4. Farmer- Millers- Retailer-Consumer 111 29.84
5. Farmer- Millers- wholesaler-retailer-Consumer 189 50.81

In the figure 1, the first and shortest channel involved 
producers selling directly to consumers at the farm 
level. This accounted for 0.3% of  total produce. The 
second channel involved farmers, brokers, millers and 
consumers where 51.1%, 42.3%, and 0.3% of  the total 
sold rice by the farmer was taken up by brokers, millers 
and consumers respectively. The third channel entailed 
farmers, brokers, millers, retailers then consumer. Under 
this channel, 6.3% of  the farmers’ sales volume was sold 
to the retailers, 100% of  the brokers’ sales volume was 
sold to the millers, and 62.72% of  the millers’ sales volume 
was sold to retailers whereas 100% of  retailers’ marketed 
volume was sold to the consumers. The fourth channel 
involved farmers, millers, retailers and consumers. The 

final channel consisted of  farmers, millers, wholesalers, 
retailers and consumers. For the fifth channel, 37.28% 
of  the millers’ marketed volume was sold to wholesalers, 
while 100% of  the wholesalers’ marketed volume was 
sold to retailers.

Factors Influencing Farmer’s Choice in Rice 
Marketing Channels
The study’s second objective was to evaluate factors 
influencing farmers’ choice in rice marketing channels. 
To evaluate the factors influencing the choice of  rice 
marketing channel in MIS, the multinomial Logit model 
analysis was used, as shown in Table 8.
The results show that household level of  education has 

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Table 8: Choice of  Channel Multinomial logit
Choice of  channel multinomial logit Coef.   Std. Err. P>Z
Broker Base outcome
Consumer
Age of  household -.0454955   .0789512 0.564
Gender 13.73346   1153.884 0.991
Household education level .693347   .7755638 0.037
Distance to Market -.0761318   .1197213 0.525
Marketing Groups -13.98628   2292.413 0.010
Cons -16.67165   1153.888 0.988
Retailer
Age of  household .0299817   .0184035 0.103
Gender -1.177961   .4718407 0.113
Household education level -.3518268   .2908893 0.226
Distance to Market -.0037031   .0242566 0.020
Marketing Groups -.060495   .8250111 0.942
Cons -1.937592   1.047563 0.064
Wholesaler
Age of  household -.0021384   .0254887 0.933
Gender -1.679302   .5986321 0.405
Household education level -.221291   .3740105 0.554
Distance to Market -.0449224   .0433417 0.030
Marketing Groups .3090625   .8519942 0.717
Cons -.6575785   1.326909 0.620
Mrgmcs Millers
Age of  household .0479347   .0115128 0.000
Gender -.0604444   .3448411 0.861
Household education level .1855477   .1511891 0.220
Distance to Market -.0003128   .0142207 0.982
Marketing Groups -.2606187   .5374264 0.628
Cons -3.356687   .6668942 0.000
Private Millers
Age of  household .0229527   .0128331 0.074
Gender .2735538   .3755243 0.466
Household education level .0538184    .166681 0.747
Distance to Market -.2136234   .0535911 0.000
Marketing Groups -.5826178   .5254568 0.268
Cons -.8896918    .731307 0.224
Mwea Rice Millers
Age of  household .0591975   .0276663 0.032
Gender .7640662   1.130448 0.499
Household education level .1093976   .3586288 0.760
Distance to Market -.4959636   .2714533 0.068
Marketing Groups 1.117868   .8165309 0.171
Cons -4.790973   2.066583 0.020

a positive and significant relationship with the choice 
of  marketing channels (β=0.693347, p=0.037<0.05). 
This implies that the odds of  a consumer as a marketing 

channel over a broker is 0.693347 times higher given 
their household level of  education. The results further 
show that household marketing groups have a positive 

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and significant relationship with the choice of  marketing 
channels (β=-13.98628, p=0.010<0.05). This implies that 
the odds of  a consumer as a marketing channel over a 
broker is 13.98628 times lower given their marketing 
groups. Distance to market has a negative and significant 
relationship with the choice of  marketing channels (β=-
0.0037031, p=0.020<0.05). This implies that the odds of  
a retailer as a marketing channel over a broker is 0.0037031 
times lower given their distance to market.
The results show that distance to the market has a positive 
and significant relationship with the choice of  marketing 
channels (β=-0.0449224, p=0.030<0.05). This implies 
that the odds of  a wholesaler as a marketing channel over 
a broker is 0.0449224 times higher given their household 
level of  education. The results show that the age of  the 
household has a positive and significant relationship 
with the choice of  marketing channels (β=0.0479347, 
p=0.000<0.05). This implies that the odds of  MRGMCS 
millers as a marketing channel over a broker is 0.0479347 
times higher, given their household age. Distance to 
market has a negative and significant relationship with 
the choice of  marketing channels (β=-0.2136234, 
p=0.020<0.05). This implies that the odds of  a private 
miller as a marketing channel over a broker is 0.2136234 
times lower, given their distance to the market. Lastly, the 
results show that the age of  the household has a positive 
and significant relationship with the choice of  marketing 
channels (β=0.0591975, p=0.032<0.05). This implies that 
the odds of  a consumer as a marketing channel over a 
broker is 0.0591975 times higher given their household age.

DISCUSSION 
The socio-demographic characteristics showed that on 
gender, females were 23.9% and 76.1% for males and 
thus indicated that the majority of  the respondents were 
males involved in rice farming and marketing processes. 
On education, 29.8% had no formal education, 50.8% 
were of  primary level, while only 7.5 % and 11.8% had 
secondary and tertiary levels respectively. This indicated 
that there was a low level of  education in the rice farming 
sector and could influence the understanding of  the 
questions. However, the study used simplified questions 
that allowed all respondents to understand. Under access 
to rice market information, the majority, 58.1%, are not 
able to access rice market information, and only 41.0% 
were able to. This indicates that there could be a lack of  
significant information on the rice farming sector that 
could influence farmers. On access to marketing channels, 
the majority of  the respondents, with, 95.3%, indicated 
that they receive access to marketing channels, while only 
4.7% indicated they do not receive market information.
Under rice type, the majority of  the respondents 83.2%, 
indicated that they grow Basmati followed by BW 196 at 
9.6%, and lastly, Nerica 1 having the least at 6.8%. The 
majority of  the farmers 88.8%, did not have access to 
credit, while only 11.2% had access to credit and finance. 
This could indicate that many of  the farmers could be 
restrained in their farming activities due to a shortage in 

accessing credit and finance. Further, the majority with, 
42% indicated that they had difficulties accessing credit 
due to high-interest rates, followed by low yields 22%. 
19% of  the respondents indicated they had difficulties 
accessing credit due to delays in payment, while 16% 
cited a lack of  title deeds for acting as collateral. Lastly, 
the majority of  respondents, 113(35.1%), chose channels 
that provided better prices for their produce. 107(33.2%) 
acclaimed ease of  cash payment as the reason for their 
choice of  marketing channel. 78(24.2%) participants 
showed a preference for market channels due to nearness 
to the market. The farmers looked at other factors: 
access to credit and farm inputs (3.1%), the credibility of  
weighing scales (0.3%), provision of  stores and low cost 
of  water payment at 0.3% each.
In line with the study of  Akwaa-Sekyi (2013), there is an 
increasing need to invest in agriculture worldwide due to the 
rising populations and to curb draughts and malnutrition. 
Access to finance and credit from microfinance would 
have a significant impact on the welfare of  farmers and 
their farming activities like working capital, labour, farm 
input and output, among others.
The study sought to identify the rice marketing channels 
in Mwea Irrigation Scheme. There were seven rice 
marketing outlets identified. Of  the rice marketing outlets, 
farmer to consumer represented 0.6%, farmer to retailer, 
5%, farmer to broker, 46.0%, farmer to wholesaler, 2.8%, 
farmer to MRGMCS millers 23.9%, farmer to private 
millers 19.6% and farmer to Mwea rice millers, 2.2%. 
This implied that the most common channel was farmer 
to broker, and the least was farmer to consumer. Most 
respondents indicated they used brokers at 45% and 
MRGMCS millers at 23%. Private millers had 19%, while 
retailers had only 7%. The least were wholesalers, with 
4%, Mwea rice millers, with 2%, and consumers, with 1%.
Out of  the rice marketing outlets, there were five rice 
marketing channels identified based on percentage 
margins across actors, and the first channel was the 
shortest. It involved producers selling directly to 
consumers at the farm level. This accounted for 1.78% 
of  total sales. The second channel involved farmers, 
retailers and consumers, mainly at farms, accounting for 
5.33% of  sales volume. These were local retailers who 
bought direct from the farmers and sold to consumers 
in the neighboring market centers in the rice farming in 
Mwea Irrigation Scheme. The third channel consisted of  
farmers, brokers, millers, and consumers, and it handled 
3.47% of  the sales volume. While the fourth channel 
involved farmers, millers, retailers and consumers, and it 
handled 62.72% of  the volume bought by millers. The final 
channel consisted of  farmers, millers, wholesalers, retailers 
and consumers. It accounted for 37.28% of  the volume 
handled by millers. The wholesaler sold its entire share to 
the retailer, who finally sold 100% to the consumer.
The study’s second objective was to evaluate factors 
influencing farmers’ choice of  rice marketing channels. 
The results show that household level of  education has 
a positive and significant relationship with the choice 

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Am. J. Econ. Bus. Innov. 2(1) 29-38, 2023

of  marketing channels (β=0.693347, p=0.037<0.05). 
This implies that the odds of  a consumer as a marketing 
channel over a broker is 0.693347 times higher given 
their household level of  education. The results further 
show that household marketing groups have a positive 
and significant relationship with the choice of  marketing 
channels (β=-13.98628, p=0.010<0.05). This implies that 
the odds of  a consumer as a marketing channel over a 
broker is 13.98628 times lower given their marketing 
groups. Distance to market has a negative and significant 
relationship with the choice of  marketing channels (β=-
0.0037031, p=0.020<0.05). This implies that the odds of  
a retailer as a marketing channel over a broker is 0.0037031 
times lower, given their distance to the market.
The results show that distance to the market has a positive 
and significant relationship with the choice of  marketing 
channels (β=-0.0449224, p=0.030<0.05). This implies 
that the odds of  a wholesaler as a marketing channel over 
a broker is 0.0449224 times higher given their household 
level of  education. The results show that the age of  the 
household has a positive and significant relationship 
with the choice of  marketing channels (β=0.0479347, 
p=0.000<0.05). This implies that the odds of  MRGMCS 
millers as a marketing channel over a broker is 0.0479347 
times higher, given their household age. Distance to 
market has a negative and significant relationship with 
the choice of  marketing channels (β=-0.2136234, 
p=0.020<0.05). This implies that the odds of  a private 
miller as a marketing channel over a broker is 0.2136234 
times lower, given their distance to the market. Lastly, the 
results show that the age of  the household has a positive 
and significant relationship with the choice of  marketing 
channels (β=0.0591975, p=0.032<0.05). This implies 
that the odds of  a consumer as a marketing channel over a 
broker is 0.0591975 times higher given their household age.
The finding is consistent with Nyaga et al. (2016), who 
conducted a study on factors influencing fish farmers’ 
choice of  marketing channel in Kirinyaga County. The 
study identified three main channels; neighbours, direct 
market, traders and accounting for 49 percent, 29 percent, 
and 22 percent respectively. Gender of  the household 
head, distance to market; marketing cost, land tenure, 
number of  fishponds owned, access to extension services, 
cost of  marketing, membership to a fish farming group, 
access to inputs, household income, price of  fish and type 
of  fish reared had significant influence on farmers’ choice 
of  market channel.

CONCLUSION
The study found that brokers had the most significant 
impact on rice marketing channels in the Mwea 
Irrigation Scheme, and that rice supply chains can 
be improved through digital agriculture platforms. 
Education, marketing groups, distance to market, and 
age of  household were found to affect farmers’ choice 
in rice marketing channels. The study also noted that rice 
marketing relies on private and cooperative services for 
storage and milling due to farmers’ limited assets, and that 

the private sector improves production and marketing 
through horizontal or vertical integration.

Recommendations
The study recommends that the management of  rice 
farming in Mwea Irrigation Scheme take control of  the 
marketing channels to protect farmers from brokers 
who take advantage of  them. It suggests regulation to 
control the operations of  brokers, who play a crucial role 
in the rice marketing channel. The study recommends 
better storage/warehouse plans to reduce marketing 
costs, and more research on the use of  husks and straws 
for bioenergy and carbon sequestration. Additionally, 
it suggests that research on water availability in other 
rice-growing areas in Kenya is important for improving 
marketing channel choices and overall rice farming quality 
and profitability.

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