00074918.2015.1108389
Bulletin of Indonesian Economic Studies
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Market-Channel Choices of Indonesian Potato
Farmers: A Best–Worst Scaling Experiment
Wendy J. Umberger, Thomas Reardon, Randy Stringer & Simone Mueller
Loose
To cite this article: Wendy J. Umberger, Thomas Reardon, Randy Stringer & Simone
Mueller Loose (2015) Market-Channel Choices of Indonesian Potato Farmers: A Best–Worst
Scaling Experiment, Bulletin of Indonesian Economic Studies, 51:3, 461-477, DOI:
10.1080/00074918.2015.1108389
To link to this article: http://dx.doi.org/10.1080/00074918.2015.1108389
Published online: 29 Nov 2015.
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Date: 17 January 2016, At: 23:18
Bulletin of Indonesian Economic Studies, Vol. 51, No. 3, 2015: 461–77
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MARKET-CHANNEL CHOICES OF INDONESIAN POTATO
FARMERS: A BEST–WORST SCALING EXPERIMENT
Wendy J. Umberger*
University of Adelaide
Thomas Reardon*
Michigan State University; University of Adelaide
Randy Stringer*
University of Adelaide
Simone Mueller Loose*
Geisenheim University; University of South Australia
We present an empirical analysis of how buyer (and trader) attributes inluence
decisions of market participation and channel choice among smallholder potato
farmers in West Java, Indonesia. We use a best–worst scaling experiment to evaluate
the determinants of these decisions and gauge the inluence of buyer attributes. Our
latent-class cluster analysis reveals that producers have heterogeneous preferences
for buyer attributes, which address classic smallholder constraints such as access to
inputs, credit, and information. This heterogeneity can be somewhat explained by
household characteristics and assets. The broad mass of our sample sought buyers
whose attributes imply lower market risk for farmers. Yet roughly a quarter of our
sample, a portion that included farmers with large, specialised farms, sought buyers who could provide inputs such as high-quality seeds.
Keywords: best–worst scaling, latent-class cluster analysis, marketing channel, smallholders
JEL classiication: O13, Q12, Q13
INTRODUCTION
Food markets in Indonesia are increasingly complex, with a widening array of
market channels. As in other Southeast Asian economies, the demand-side inluences of income, urbanisation, and demographics are reshaping food value
chains (Pingali 2007; Reardon et al. 2003; Reardon and Timmer 2014). As food
value chains evolve, expand, and reorganise, smallholders face a greater number
of choices and more daunting decisions about which crops to produce, how to
produce, and to whom to sell.
* We wish to thank Nick Minot, from the International Food Policy Research Institute, and
David Shearer, from the Australian Centre for International Agricultural Research, for their
helpful comments and suggestions during our research. We acknowledge and thank our
research partner, the International Potato Center, as well as Eka Puspitawati and Wahida
for survey and research assistance. This article was made possible by inancial support
from the Australian Centre for International Agricultural Research. All views, interpretations, and conclusions expressed are those of the authors and not necessarily those of the
supporting or cooperating institutions.
ISSN 0007-4918 print/ISSN 1472-7234 online/15/000461-17
http://dx.doi.org/10.1080/00074918.2015.1108389
© 2015 Indonesia Project ANU
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462 Wendy J. Umberger, Thomas Reardon, Randy Stringer, and Simone C. Mueller Loose
In a broad sense, the existing literature on the transformation of modern food
value chains addresses how market liberalisation, trade and foreign-investment
policies, rapid income growth, and urbanisation affect food producers, buyers and traders (inside and outside wholesale markets), processors, and retailers—both modern and traditional. Previous research has explored the impacts
of change on poverty incidence and net-income distribution, and on the production eficiency of smallholder participation in modern food chains (Reardon et al.
2009). A research question that remains unanswered, and on which this article
focuses, is whether smallholder farmers are choosing not to participate in modern
value chains or whether they are merely being excluded from them. If smallholders are indeed choosing not to participate, what are the consequences for income
improvement and thus, indirectly, rural economic development?
This article builds on several strands of literature on market participation.
The irst focuses on the relation between transaction costs and commercialisation decisions—how, when, and why smallholders market their produce. This
strand explores why smallholders either sell or do not sell their produce, and
assumes that transaction costs are determined exogenously. It focuses on the possibility that imperfect, underdeveloped, or absent markets push transaction costs
so high that smallholders are unable to participate as buyers or sellers (Benjamin
1992; Henning and Henningsen 2007; De Janvry, Fafchamps, and Sadoulet 1991;
Finkelshtain and Chalfant 1991; Fafchamps 1992; Kranton 1996; Lopez 1986).
Goetz (1992) and Key, Sadoulet, and De Janvry (2000), for example, highlight how
distance to market and poor infrastructure keep producers from selling.
The second strand identiies ways in which producers innovate to reduce or
avoid transaction costs associated with searching for and screening trading partners, learning about new technologies, acquiring credit, meeting product standards, and bargaining. Poole (2000) analysed factors affecting producers’ marketing
decisions in the Spanish citrus industry, exploring how contracts facilitate producers’ marketing choices, reduce uncertainty, or lower other related transaction costs. Henson, Masakure, and Boselie (2005) showed how smallholders in
Zimbabwe enter market contracts for vegetables because, inter alia, the contracts
alleviate uncertainties associated with input supply, market demand, and market
prices, and help farmers get extensions from vegetable companies in situations
where there are limited public extension services.
The third strand explores the market-channel choices of farmers who once sold
only to traditional markets but are now able to sell to new market channels such as
supermarkets, large processors, or their dedicated agents. Examples include the
articles by Rao and Qaim (2011) and Hernandez, Reardon, and Berdegué (2007).
This strand tends to model the context and assets of the suppliers as determinants
of their selling to one market channel rather than another. It does not model the
attributes of the buyers as we do here.
This article ills a gap in and complements the literature by examining how
buyer attributes related to transaction costs and market risk inluence the marketchannel participation decisions of smallholder potato producers in West Java. We
posit that these farmers use buyer attributes to overcome cost barriers to market
participation. The attributes that we consider in this article address classic smallholder constraints, including access to inputs, credit, and information. Prior studies have not examined these buyer attributes collectively.
Market-Channel Choices of Indonesian Potato Farmers
463
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We use a best–worst (BW) scaling experiment (Finn and Louviere 1992) to
understand the relative importance of different buyer attributes to potato producers choosing market channels. BW scaling allows for discrimination in the importance of each attribute and avoids potential differences related to how respondents
use rating scales. Combining BW scaling with a latent class (LC) cluster analysis
allows us to explore how the assets and individual, heterogeneous preferences of
producers inluence their choice of whom to sell to and how much to sell. As far
as we are aware, this is the irst study that uses BW scaling to explore producer
utilities for buyer attributes.
Indonesian Potato Marketing Systems
We focus on potato producers for several reasons. Potatoes are widely produced
by smallholders in West Java, and are one of the top ive vegetables produced
in the country (in terms of acreage, value, and volume). They are also a priority
crop in government horticultural research and in declared strategies of horticultural development. Potato markets are evolving; demand for table potatoes, for
example, is moving away from those sold through wholesale markets and traditional retailers, and towards those sold through supermarkets and restaurants.
Specialised potato wholesalers dedicated to supermarkets and restaurants have
arisen beside traditional traders, and potato agribusinesses-cum-traders have
entered the market as producers and wholesalers (Campilan and Asmunati 2007;
World Bank 2007; Puspitawati et al. 2013).
Most potatoes marketed in West Java are produced by smallholders and sold
into traditional supply chains. Smallholder potato farmers marketing through
traditional supply chains rely on traders, central wholesale markets, and wet
markets. Smallholder farmers are connected to the main marketing participants
through numerous intermediaries. This long chain hinders farmers’ abilities to
establish relationships with other supply-chain partners on the basis of open
communication, a high degree of trust, and a better understanding of consumer
needs.
Other, non-traditional supply chains also exist in Indonesia, including specialised wholesalers distributing to supermarkets, large-scale agribusiness companies
engaging in potato processing through contract arrangements with individual
farmers, and home-based enterprises producing potato-based snack foods. These
chains can increase or stabilise producers’ incomes by offering greater market
opportunities than traditional channels, including credit; price premiums for
higher-quality products; improved access to certain inputs (such as seed); and
technology, training, and technical advice (Hernández, Reardon, and Berdegué
2007; Neven et al. 2009; Morrison Paul, Nehring, and Banker 2004).
The existing literature on potato value chains in West Java, together with the
ieldwork for this study, suggests that the ability of Indonesian potato farmers
to participate in non-traditional channels is limited owing to many challenges.
Long and disjointed supply chains, a lack of available and timely market-related
information, weak farmers’ organisations, and the limited marketing capacity
and generally poor market orientation of smallholders all contribute to a mismatch between what is produced and the variety, quality, and quantity required
by retailers and processors (Ameriana 2010; Campilan and Asmunati 2007; Fuglie
et al. 2006; World Bank 2007; Puspitawati et al. 2013).
464 Wendy J. Umberger, Thomas Reardon, Randy Stringer, and Simone C. Mueller Loose
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The different types of buyers and supply chains present producers with a range
of market channels, each with its own buyer attributes. The demand for speciic
attributes varies by producer, but certain attributes may reduce risk, lower speciic
transaction costs, offer market incentives, or loosen household inancial constraints.
Some buyers, for example, pay cash immediately, at the point of sale, while others
offer premium prices for graded and sorted produce. Other buyers provide input
credits, certiied seeds, technical guidance for adopting new technologies, or information on how to meet the standards preferred by supermarkets and processors.
Some buyers are recognised for their integrity, reliability, and consistency.
RESEARCH METHODOLOGY AND ANALYSIS
Sampling Methods
We analysed data from 307 household survey questionnaires collected in 2009 in
Bandung and Garut, two regencies in West Java that together account for nearly
all of the province’s potato production. The sample included potato producers
from three groups: the general potato population; producers supplying Indofood,
one of Indonesia’s largest food processors; and growers participating in a farmer
ield school (FFS), a program of practical education.
Our sample of the general potato population (64% of the total sample) comprised 197 households, or an average of 12 randomly chosen potato-producing
households from each of 16 villages. We selected these villages by stratifying
regency subdistricts (kecamatan) into major and minor potato-production areas on
the basis of oficial published production data and interviews with key informants.
We then randomly selected two subdistricts from each of these areas, resulting in
eight subdistricts, and we conducted site visits and more interviews in order to
develop a list of potato-producing villages in each subdistrict. We obtained names
and contact information for potato producers in each village from land-registry
and tax-ofice iles.
The Indofood sample (20% of the total) included 60 households drawn randomly from a list of more than 400 producers provided by Indofood. We randomly
selected 50 FFS producers (16% of the total sample) from lists of more than 200
households provided by extension oficers from two government departments—
Balitsa (Research Institute for Vegetable Crops) and Dinas Pertanian (Agricultural
Extension Ofice). This particular FFS introduced participants to new varieties of
potatoes and new farming techniques (Puspitawati et al. 2013).
The questionnaires collected information on household characteristics, assets,
potato production and marketing history, other sources of income, contractual
details, and perceptions of changes over time. Information was also collected
on producer preferences for buyer attributes by using a BW-scaling experiment
(tables 1a and 1b), as explained in the following section. To examine if producer
preferences are gender-speciic, we invited female and male respondents in each
household to participate in the experiment.
Best–Worst Scaling
BW scaling measures the relative value or importance that individuals place on
attributes. Based on Finn and Louviere’s (1992) random utility theory for paired
comparisons, it uses a balanced incomplete block design to derive subsets of the
Market-Channel Choices of Indonesian Potato Farmers
465
TABLE 1a Summary of Respondent Characteristics Used
as Active Covariates in Latent Class Analysis
Characteristic
Description
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Gender
Children
1= female; 0 = male
No. of dependent children
(< 18 years) living at home
Education of household head Years of education of
household head
Potato-farming experience
Years of producing potatoes
Owns mobile phone
1 = yes, owns at least 1
mobile phone
Owns motorbike
1 = yes, owns at least 1
motorbike
Potatoes main income
1 = potatoes main source of
farm income
Non-farm income
Non-farm income as a
share of total income (%)
Payment on child education 1 = potato income used to
pay for children’s
education
Production
Total potatoes produced (t)
Productivity
Potatoes (t) produced per
hectare
Expanded production
1 = expanded the potatoplanting area in the past
3 years
Both seasons
1 = produced in both wet &
dry seasons
One variety
1 = planted only one variety
Distance to plots
Average distance (km) to
plots from home
FFS
1 = belongs to FFS
Indofood
1 = has relationship with
Indofood
Cooperative involvement
1 = obtained production
assistance and guidance
for growing potatoes from
cooperative
One buyer
1 = sells all potatoes to one
buyer
Observations
Mean
SD
Min
Max
0.50
1.69
0.50
1.31
0
0
1
7
7.28
3.26
0
18
18.85
0.74
12.51
0.44
0
0
58
1
0.69
0.46
0
1
0.56
0.50
0
1
18.42
30.20
0
100
0.59
0.49
0
1
23.47
15.19
44.18
7.71
0.28
0.27
414
54
0.31
0.46
0
1
0.51
0.50
0
1
0.93
1.98
0.25
2.37
0
0
1
16
0.16
0.20
0.37
0.40
0
0
1
1
0.08
0.28
0
1
0.86
0.35
0
1
614
Note: SD = standard deviation. FFS = farmer ield school.
attributes being assessed. Respondents choose both the best (most important) and
worst (least important) attributes, making trade-offs among subsets and giving
the researcher more discriminating estimates of relative importance. BW scaling
is relatively easy for respondents to perform; it also allows eficient elicitation of
attribute importance, particularly when comparing many attributes (see Auger,
466 Wendy J. Umberger, Thomas Reardon, Randy Stringer, and Simone C. Mueller Loose
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TABLE 1b Summary of Respondent Characteristics Used
as ‘Passive’ Variables in Ex-post Characterisation
Variable
Description
Age of household head
Size of household
Production area
Grade
Years
45.30
Number living in the home
4.46
Area of production (hectares)
1.91
1 = sold only one grade of
0.29
potatoes
Potato income as a share of
40.60
total farm income (%)
1 = obtained production
0.25
assistance from buyer
1 = obtained production
0.16
assistance from processor
1 = buyer provides incentives
0.35
to grade & sort
1 = buyer provides incentives
0.46
to produce certain quality
Contacts others for price
0.82
information
0.34
1 = opportunity to contract
potatoes with Indofood
1 = contracted to grow
0.23
potatoes
1 = land owned &
0.75
farmed by respondent
1 = land owned & rented out
0.03
1 = land owned & sharecropped 0.02
1 = land rented
0.36
1= land sharecropped
0.04
1 = land leased from
0.08
government
1= land borrowed from
0.12
own family
Potato income
Trader assistance
Processor assistance
Grading incentive
Quality incentive
Search price information
Contract opportunity
Contracted
Owns & farm
Own & rent
Own & sharecrop
Rent only
Sharecrop only
Goverment lease
Borrowed from family
Observations
Mean
SD
Min
Max
11.40
1.72
2.56
0.46
22
2
0.10
0
80
10
25.08
1
30.28
0
100
0.43
0
1
0.37
0
1
0.48
0
1
0.50
0
1
0.38
0
1
0.47
0
1
0.42
0
1
0.43
0
1
0.17
0.13
0.48
0.19
0.27
0
0
0
0
0
1
1
1
1
1
0.33
0
1
614
Note: SD = standard deviation.
Devinney, and Louviere 2007; Cohen 2009; Cohen and Orme 2004; Jaeger et al.
2008; Mueller and Rungie 2009; and Mueller, Lockshin, and Louviere 2010).
We conducted in-depth interviews and focus groups with producers and buyers to ensure that our selected buyer attributes covered characteristics of importance to producers. The inal 11 attributes were as follows: improved access to
inputs (‘access to certiied potato seed’, ‘credit or access for input purchases’,
and ‘provides money for loan’); access to information (‘technical assistance’ and
‘shares information about market conditions’); market and price risk (‘pays cash
immediately’, ‘established relationship’, and ‘always follows through on commitments to buy my products’); negotiation aspects (‘willing to negotiate or match
Market-Channel Choices of Indonesian Potato Farmers
467
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another buyer’s price’); and economic attributes (‘provides price premiums’ and
‘price per kilogram’).
We used a balanced incomplete block design to assign attributes to 11 subsets
(tasks) with ive attributes each. For each task, respondents were asked to consider which attribute was the most important to them and which was the least
important.1 Back translation conirmed that the meaning of the attributes had
been preserved after translation into Bahasa Indonesia.
Aggregate Analysis
To determine the relative importance of buyer attributes for all producers, we calculated an aggregate BW score for each attribute by summing the number of times
each attribute was selected as being most important or least important. We subtracted the aggregate ‘least important’ value from the aggregate ‘most important’
value. The 11 aggregate BW scores are crude measures of relative importance. To
make these results easier to interpret, we calculated a standardised interval scale
(SIS) (Marley and Louviere 2005; Mueller and Rungie 2009), taking the square root
of the frequency of the best, or most important, attribute, divided by the frequency
of worst, or least important, attribute: SQRT(B/W). We gave the attribute with the
highest SQRT(B/W) a score of 100 and scaled all other attributes accordingly.
Modelling Heterogeneity
To explore heterogeneity among producers, we calculated the relative importance
of each of the 11 attributes by summing the number of times a respondent, i, indicated that an attribute, j (j = 1–11), was most important or least important. We
then subtracted the sum of ‘least important’ for attribute j from the sum of ‘most
important’ for attribute j, to calculate BWij. The larger the BWij score, the more
important the attribute to respondent i.
We used an LC cluster analysis to determine if unique classes of producers exist
that can be grouped by household, farm, and marketing characteristics. This clustering technique is useful for explaining unobserved differences in preferences or
utilities (Meghani et al. 2009). LC cluster analysis assumes that individuals belong
to one of k LCs, of which the size and number are unknown a priori (Vermunt and
Magidson 2008; Haughton, Legrand, and Woolford 2009).
LC analysis uses the covariation among observed preference scores (BWij) as
a measure of utility to predict membership in a speciic LC. Observed preference
scores for individuals in a class are assumed to have come from the same probability distribution. Unobserved utility is heterogeneous across classes but homogeneous within a class. Therefore, although we cannot directly observe a potato
producer’s utility for a marketing channel, we can measure the importance of
buyer attributes to producers by using BW scaling. The 11 individual BW scores
(BWij) for the attributes were the dependent variables in the LC cluster analysis.
If producers are heterogeneous in their ratings of buyer attributes then unique
LC clusters or segments should exist (see, for example, Haughton, Legrand, and
Woolford 2009; Meghani et al. 2009; and Mueller and Rungie 2009).
1. An example of one of the 11 BW tasks and descriptions of the inal 11 attributes are available from the authors on request.
468 Wendy J. Umberger, Thomas Reardon, Randy Stringer, and Simone C. Mueller Loose
TABLE 2 Measures of Attribute Importance Using Best–Worst Scaling
Aggregrates
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Attributes (overall)
Pays cash immediately
Provides price premiums
Always follows through on commitments
to buy my product
Price per kilogram
Established relationship
Access to certiied potato seed
Willing to negotiate or match another
buyer’s price
Provides money for loan
Shares information about market conditions
Credit or access for input purchases
Technical assistance
Most
(B)
Least
(W)
BW
SQRT
(B/W)
SIS
values
1,497
993
202
162
1,295
831
2.72
2.48
100.0
90.9
788
809
497
517
267
305
310
687
521
504
187
–170
1.72
1.63
1.27
0.87
63.1
59.8
46.5
31.9
421
445
368
319
100
675
868
775
1,029
1,474
–254
–423
–407
–710
–1,374
0.79
0.72
0.69
0.56
0.26
29.0
26.3
25.3
20.5
9.6
Note: B = best. W = worst. SQRT = square root. SIS = standardised interval scale.
LC cluster analysis can use active covariates to further explain classes and
improve the classiication by controlling for other variables that may also help to
explain market-channel choice (Haughton, Legrand, and Woolford 2009). These
covariates are selected on the basis of previous empirical studies exploring factors
that explain market participation, market-channel choice, or contract adoption
(Wollni and Zeller 2007; Fafchamps and Hill 2005; Shilpi and Umali-Deininger
2007; Abdulai and Birachi 2009).
We characterised the resulting clusters from the LC analysis ex post, using
passive variables (table 1b). These variables will be useful for further explorative
analysis. We can evaluate, for example, whether respondents indicated attribute
importance is consistent with their marketing behaviour, their relationships with
buyers, or both (by comparing the BWij scores with passive variables such as
‘trader assistance’, ‘processor assistance’, and ‘contracted’).
RESULTS AND CHARACTERISATION OF CLUSTERS
Aggregate Importance of Buyer Attributes
We determined the relative importance of each buyer attribute to the entire sample
by evaluating the SIS values of each attribute (table 2). The attribute ‘pays cash
immediately’ was the most important (standardised to 100%), across all respondents, while ‘technical assistance’ was the least important (about 10% as important).
‘Provides price premiums’ was very important. ‘Always follows through on commitments to buy’ was 63% as important to respondents as ‘pays cash immediately’.
Providing access to certiied potato seed was 32% as important as paying cash immediately, while the attributes ‘provides money for loan’, ‘credit or access for input
purchases’, and ‘technical assistance’ were less important than other attributes.
Market-Channel Choices of Indonesian Potato Farmers
469
TABLE 3a Mean Best–Worst Scores for Buyer Attributes, by Latent Class Cluster
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Buyer attributes
Pays cash immediately
Provides price premiums
Always follows through on commitments
to buy my product
Price per kilogram
Established relationship
Access to certiied potato seed
Willing to negotiate or match another
buyer’s price
Provides money for loan
Shares information about market conditions
Credit or access for input purchases
Technical assistance
Log likelihood
R2
Classiication error
Number of parameters
Degrees of freedom
Cluster 1
(44%)
Cluster 2
(16%)
Cluster 3
(24%)
Cluster 4
(16%)
3.02a
1.58a
3.30a
1.19a
0.83b
1.59a
0.35b
0.58
0.79
–0.04a
–0.04a
3.14
1.48
0.22
–1.25a
–0.81
–0.27a
0.23
a
0.65
–0.23a
2.13
0.88a
1.86
–1.62a
0.96
–0.87
–1.92a
–1.50a
–1.93
0.06a
–2.38
–2.56
1.02
–2.65
0.39
–1.47a
–0.19a
–1.06
–0.38a
–1.36a
0.26a
–0.10a
–0.48a
–3.36
–13,249.57
0.83
0.08
203
411
Note: Means with the same superscript letters in the same row are not statistically different (α = 0.05,
post-hoc Tukey test).
In the key informant interviews we conducted during the development phase
of the study, farmers, traders, and government extension specialists (particularly
those involved with Indofood) indicated that farmers struggle to gain access to
potato seed, other inputs, credit, and technical assistance. Our study results, however, suggest that these attributes are less important for most potato producers
than the four attributes related to guaranteed inancial returns (‘pays cash immediately’, ‘always follows through on commitments to buy my product’, ‘provides
price premium’, and ‘price per kilogram’). This result is not surprising, considering that 56% of respondents relied on potatoes as their main source of farm income
(table 1a). The results in table 2 suggest that long-term buyer–seller relationships
are more likely to develop if potato buyers guarantee producers that they will
pay cash upon receipt of potatoes and follow through on any commitments to
buy more potatoes—that is, if they play a role in reducing market and price risk.
Producer Heterogeneity
Our LC cluster analysis revealed four distinct clusters of potato producers, each
with different demographic characteristics, attitudes, and preferences (tables 3–5).
The mean BW scores and the SIS values for each attribute and each LC cluster are
provided in tables 3a and 3b. As expected, the relative importance of buyer attributes differed across clusters.
Statistically signiicant active covariates in the LC analysis include the number
of dependent children living at home, the number of years of education of the
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TABLE 3b Standardised-Interval-Scale Values and Ranks for Buyer Attributes, by Latent Class Cluster
Cluster characterisation
Cluster 1 (44%):
Financial returns
Buyer attributes
Pays cash immediately
Provides price premiums
Always follows through on their commitments to
buy my product
Price per kilogram
Established relationship
Access to certiied potato seed
Willing to negotiate or match another buyer’s price
Provides money for loan
Shares information about market conditions
Credit or access for input purchases
Technical assistance
Note: SIS = standardised interval scale.
Cluster 2 (16%):
Cash & credit access
Cluster 3 (24%):
Input access
Cluster 4 (16%):
Commitment
& relationship
SIS
value
SIS
rank
SIS
value
SIS
rank
SIS
value
SIS
rank
SIS
value
SIS
rank
100.0
58.7
1
2
100.0
38.7
1
2
40.0
59.7
4
2
13.3
20.1
6
4
36.4
51.3
23.8
8.5
41.5
6.5
19.6
3.1
3.1
5
3
6
8
4
9
7
10
11
17.1
10.0
14.1
20.5
9.7
31.6
3.6
21.6
7.4
6
8
7
5
9
3
11
4
10
25.3
40.7
22.1
100.0
8.3
24.5
15.5
23.0
12.2
5
3
8
1
11
6
9
7
10
100.0
23.9
54.8
3.6
4.0
13.7
11.1
8.7
0.8
1
3
2
10
9
5
7
8
11
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TABLE 4 Signiicant Covariates and Top Standardised-Interval-Scale Characteristics for Latent Class Clusters
Covariates
Gender (1 = female)
Children
Education of household head
Potato-farming experience
Owns mobile phone
Owns motorbike
Potatoes main income
Non-farm income
Payment on child education
Production
Productivity
Expanded production
Both seasons
One variety
Distance to plots
Farmer ield school
Indofood
Cooperative involvement
One buyer
Cluster 1:
Pays cash
immediately
0.50
1.59
7.38
19.01
0.71
0.70
0.55
20.68
0.58
24.44
14.85
0.26
0.51
0.91
1.79
0.14
0.05
0.04
0.85
Cluster 2:
Pays cash
immediately
0.56
2.16
6.31
14.85
0.66
0.55
0.40
13.84
0.68
7.90
14.38
0.28
0.21
1.00
1.33
0.25
0.05
0.09
0.80
Cluster 3:
Has access to
certiied seed
0.47
1.34
8.13
21.98
0.85
0.74
0.68
15.80
0.67
31.99
16.89
0.37
0.74
0.89
2.50
0.16
0.50
0.23
0.81
Cluster 4:
Meets
commitments
0.48
2.00
6.75
17.81
0.72
0.71
0.59
20.55
0.44
23.79
14.42
0.40
0.46
0.99
2.35
0.13
0.27
0.00
1.00
Wald
p-value
2.41
19.50
20.41
18.53
4.20
7.99
3.80
5.73
15.93
9.06
6.05
2.19
20.61
9.36
16.09
15.65
53.68
8.71
2.42
0.49
0.00
0.00
0.00
0.24
0.05
0.28
0.13
0.00
0.03
0.11
0.53
0.00
0.03
0.00
0.00
0.00
0.03
0.49
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472 Wendy J. Umberger, Thomas Reardon, Randy Stringer, and Simone C. Mueller Loose
household head, the number of years of potato-farming experience, motorbike
ownership, whether potato income pays for children’s education, total production, planting in both wet and dry seasons, the number of varieties planted, the
average distance to potato plots, contracts with Indofood, and participation in
FFSs or cooperatives (table 4). Contrary to our expectations, the following covariates were not signiicant in the LC analysis: gender, mobile-phone ownership,
potatoes as the main income source, the percentage of non-farm income, productivity, expanded production, and the existence of only one buyer. Accordingly,
these characteristics are not suficiently related to differences in attribute importance between producers.
We used an ex-post analysis of variance to consider passive variables (that is,
those not used as active covariates in the LC analysis), inding that additional
signiicant differences in household, farm, and marketing characteristics existed
across the four clusters (table 5). Farmers in cluster 1, which accounted for the
largest share of the sample (44%), had a higher utility than those in other clusters
for buyers who would negotiate on price and follow through on commitments to
buy their products. These producers were less likely to be engaged in relationships, including contracting, with supply-chain partners. They had a lower utility
for buyer attributes related to reducing search or information costs through the
provision of credit or technical assistance.
Relative to clusters 2 and 3, a higher share of respondents in cluster 1 received
incentives for grading and for producing higher-quality potatoes (table 5), while
93% of producers in this cluster said they contacted others to obtain price information. Thus, it is not surprising that ‘willingness to negotiate on price’ was
one of the most important attributes; its importance points to a strong degree of
consistency between estimated preferences for buyer attributes and marketing
behaviour.
The importance placed on price-related attributes, including incentives for
grading and quality, by farmers in cluster 1 suggests that they sought marketrelated incentives to encourage them to coordinate more closely with supplychain partners. Given the large size of this cluster, additional work is needed
to explore what other factors motivated these producers to choose a marketing
channel and to understand why more producers in the general potato population
are not involved in vertical partnerships.
Cluster 2 (16% of the sample) is somewhat similar to cluster 1 in the importance
placed on certain buyer attributes. ‘Pays cash immediately’ was by far the most
important attribute (table 3b) for cluster 2. Two of the top four attributes for these
farmers suggest that this cluster was the most inancially constrained. Farmers in
this cluster preferred buyers who could help them reduce their search, travel, and
negotiation costs for obtaining loans. Farmers in this cluster placed little importance on attributes related to buyers’ willingness to negotiate on selling prices or
provide technical assistance and market information.
In general, farmers in cluster 2 had less land allocated to potatoes, lower levels
of education, less potato-farming experience, lower yields, lower incomes, and
fewer assets than farmers in other clusters. This cluster had the highest proportion
of farmers participating in FFSs (25%) and the smallest proportions receiving production assistance from processors (5%) and receiving grading or quality incentives from their buyers (10% and 26%, respectively). Off-farm income accounted
Market-Channel Choices of Indonesian Potato Farmers
473
TABLE 5 Means of Respondents’ Household, Farm, and
Marketing Characteristics for Latent Class Clusters
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Variable
Age of household head
Size of household
Production area
Grade
Potato income
Trader assistance
Processor assistance
Grading incentive
Quality incentive
Search price information
Contract opportunity
Contracted
Own & farm
Own & rent
Own & sharecrop
Rent only
Sharecrop only
Government lease
Borrowed from family
Cluster 1
45.52
4.38a b
2.06a
0.27
39.93a b
0.18a
0.10a
0.49a
0.59a
0.93a
0.21a
0.09a
0.76
0.02
0.02
0.31a
0.03
0.08a b
0.14
Cluster 2
Cluster 3
44.71
4.85b
0.89
0.37
36.99b
0.21a
0.05a
0.10b
0.26b
0.86a
0.13a
0.12a
0.70
0.05
0.02
0.20
0.06
0.12a
0.12
46.62
4.09a
2.25a
0.26
47.49a
0.38b
0.39
0.19b
0.31b
0.66b
0.73
0.55
0.76
0.05
0.01
0.51b
0.04
0.02b
0.11
Cluster 4
43.34
4.85b
2.02a
0.33
36.47b
0.30a b
0.13a
0.43a
0.50a
0.71b
0.36
0.27
0.75
0.01
0.02
0.43a b
0.02
0.12a
0.07
Note: Means within a row with the same superscript letters are not statistically different (α = 0.05,
post-hoc Tukey test). Means in rows with no superscript are not statistically different across clusters.
for only 14% of their household income. It is not surprising that buyer attributes
related to cash low, inancing, and input access were more important to this cluster than to others.
Farmers in cluster 3 (24% of the sample) valued buyers who provided access
to certiied seed, suggesting that they valued production stability and lower costs
for inding and buying quality inputs. Compared with farmers in other clusters,
they had completed more education and had more farming experience, and had
the highest potato production and yields. They also travelled farthest to reach
their potato plots (2.5 kilometres). This cluster had the largest share of producers
involved in contractual agreements with Indofood (50%) or a cooperative (23%).
Cluster 3 represents a relatively large segment of experienced farmers who
relied on potatoes as a main income source. Many of these farmers were involved
with supply-chain partners (50% with Indofood). The importance that the farmers placed on having access to certiied potato seed might have been why they
were contracting with Indofood and other processors. In other words, their experience in producing potatoes may have taught them that their potato production
and revenue relied on having good-quality, certiied seed, and thus they contract
with buyers. This would support Henson, Masakure, and Boselie’s (2005) inding that producers seek contractual relationships with buyers to reduce certain
transaction costs related to accessing inputs and technology. Cluster 3 also has the
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474 Wendy J. Umberger, Thomas Reardon, Randy Stringer, and Simone C. Mueller Loose
highest share (nearly 40%) of producers who obtained production assistance for
growing potatoes from traders or processors, even though ‘technical assistance’
was of little importance to respondents.
Farmers in cluster 4 (16% of the sample) valued the beneits of long-term relationships and placed less value on having access to technical assistance or certiied seed. Farmers in this cluster looked for opportunities to build relationships
with buyers, and it seems that these producers sought price premiums for valueadding (for example, by grading and sorting) when trying to get contracts with
buyers who rewarded quality. Several characteristics help to explain why commitment and long-term relationships were important to farmers in this cluster.
The education level and age of the household head, as well as the share of total
farm income from potatoes, were lower than for farmers in other clusters. It is
therefore not surprising that all respondents in this cluster each sold all of their
potatoes to one buyer or that 27% were in contracts with Indofood. A relatively
high share of farmers in this cluster received incentives from buyers for grading
(43%) and for producing higher-quality potatoes (50%).
SUMMARY AND IMPLICATIONS
The indings of our BW scaling experiment for the aggregate sample revealed
that the three most important buyer attributes for potato farmers in West Java
were immediate cash payments, price premiums for sorting for quality, and buyers following through on their commitment to buy potatoes as agreed. This result
suggests that long-term buyer–seller relationships will develop only if buyers
are able to offer farmers inancial incentives and consistently follow through on
their commitments. Sellers will otherwise market their products through other
channels.
Our joint LC cluster analysis of the respondents’ individual BW values and
selected respondent characteristics (household, socio-demographic, and farm and
social assets) allowed us to explore the heterogeneity of farmers’ utilities for buyer
attributes and to better understand farmers’ marketing choices. The indings of
this research support earlier work by the World Bank (2007) and Puspitawati et al.
(2013) that suggests potato farmers have unique needs when deciding where to
market their potatoes. Taking into account growers’ attitudes and characteristics,
we discovered four distinct and heterogeneous clusters of potato producers. Each
cluster had unique utilities for market-channel attributes.
The largest group of farmers, cluster 1, placed relatively greater value on
attributes related to reduced market and inancial risk, whereas farmers in cluster
3, which included the most experienced farmers, who owned relatively larger,
irrigated farms, tended to be more interested in the buyer’s ability to provide
access to certiied seed. Farmers in cluster 3 were therefore more likely to supply
Indofood, which gives farmers access to high-quality potato seed; the domestic
seed market appears to fall short of being able to do this. Farmers in cluster 2, on
the other hand, placed relatively greater importance on attributes related to the
provision of credit for accessing inputs (such as fertiliser or pesticides) or loans to
cover other operating expenses or the purchase of capital assets. Farmers in cluster 4 placed the highest value on buyers following through on their commitments,
including paying on time.
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Market-Channel Choices of Indonesian Potato Farmers
475
Our clusters are not necessarily representative of the different segments that
may exist in the Indonesian potato-farming population, or of the characteristics
of the farmers within them. We purposely sampled farmers who were known
to be working with Indofood (20% of the sample), yet it is unlikely that 20% of
potato farmers are selling to Indofood. Our sampling bias may have overinlated,
for example, the size of certain clusters—particularly cluster 3. This bias should
be taken into account when considering the following policy implications of this
study.
This research demonstrates that reining and targeting smallholder assistance
schemes (including those offered by the public and private sectors as well as nongovernment organisations) to account for farmers’ unique needs, preferences,
and characteristics may create more effective programs. In turn, this could lead
to more eficient potato value chains and possibly beneit both buyers and sellers.
The generally high degree of importance placed on market issues related to price
suggests that farmers may beneit from having improved access to general market information (for example, through mobile-phone applications and improved
access to the Internet). Farmers may also be interested in undertaking training
programs to improve their understanding of market dynamics and to build capacity in skills such as negotiating with buyers and understanding their demands.
Furthermore, some potato farmers—particularly those in cluster 3—are likely
to welcome programs and policies that increase supply and access to high-quality
potato seed. There may be some beneit from introducing development initiatives
that encourage both public- and private-sector investment in increasing smallholders’ access to quality seed, including by increasing the domestic production
of potato seed, or from enacting more liberal trade policies to increase supplies of
high-quality imported seeds.
Farmers in clusters 1 and 4 are likely to beneit from targeted agricultural insurance schemes, which allow them to better manage production, price, and market
risks, while those in cluster 2 may beneit from programs that increase smallholder access to affordable inancing options for purchasing inputs and working
capital (such as micro-credit schemes). These programs could allow them to capitalise on marketing possibilities and emerging market niches.
Recognising, understanding, and accounting for heterogeneity in the attitudes
and needs of smallholder farmers with respect to market channels and related
capacity building are central to improving the effectiveness of public- and privatesector agricultural development programs and investment schemes. Ultimately,
we hope that consideration of our indings will lead to changes that improve
Indonesian smallholder potato farmers’ access to and participation in modern
food market channels.
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ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Market-Channel Choices of Indonesian Potato
Farmers: A Best–Worst Scaling Experiment
Wendy J. Umberger, Thomas Reardon, Randy Stringer & Simone Mueller
Loose
To cite this article: Wendy J. Umberger, Thomas Reardon, Randy Stringer & Simone
Mueller Loose (2015) Market-Channel Choices of Indonesian Potato Farmers: A Best–Worst
Scaling Experiment, Bulletin of Indonesian Economic Studies, 51:3, 461-477, DOI:
10.1080/00074918.2015.1108389
To link to this article: http://dx.doi.org/10.1080/00074918.2015.1108389
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Bulletin of Indonesian Economic Studies, Vol. 51, No. 3, 2015: 461–77
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MARKET-CHANNEL CHOICES OF INDONESIAN POTATO
FARMERS: A BEST–WORST SCALING EXPERIMENT
Wendy J. Umberger*
University of Adelaide
Thomas Reardon*
Michigan State University; University of Adelaide
Randy Stringer*
University of Adelaide
Simone Mueller Loose*
Geisenheim University; University of South Australia
We present an empirical analysis of how buyer (and trader) attributes inluence
decisions of market participation and channel choice among smallholder potato
farmers in West Java, Indonesia. We use a best–worst scaling experiment to evaluate
the determinants of these decisions and gauge the inluence of buyer attributes. Our
latent-class cluster analysis reveals that producers have heterogeneous preferences
for buyer attributes, which address classic smallholder constraints such as access to
inputs, credit, and information. This heterogeneity can be somewhat explained by
household characteristics and assets. The broad mass of our sample sought buyers
whose attributes imply lower market risk for farmers. Yet roughly a quarter of our
sample, a portion that included farmers with large, specialised farms, sought buyers who could provide inputs such as high-quality seeds.
Keywords: best–worst scaling, latent-class cluster analysis, marketing channel, smallholders
JEL classiication: O13, Q12, Q13
INTRODUCTION
Food markets in Indonesia are increasingly complex, with a widening array of
market channels. As in other Southeast Asian economies, the demand-side inluences of income, urbanisation, and demographics are reshaping food value
chains (Pingali 2007; Reardon et al. 2003; Reardon and Timmer 2014). As food
value chains evolve, expand, and reorganise, smallholders face a greater number
of choices and more daunting decisions about which crops to produce, how to
produce, and to whom to sell.
* We wish to thank Nick Minot, from the International Food Policy Research Institute, and
David Shearer, from the Australian Centre for International Agricultural Research, for their
helpful comments and suggestions during our research. We acknowledge and thank our
research partner, the International Potato Center, as well as Eka Puspitawati and Wahida
for survey and research assistance. This article was made possible by inancial support
from the Australian Centre for International Agricultural Research. All views, interpretations, and conclusions expressed are those of the authors and not necessarily those of the
supporting or cooperating institutions.
ISSN 0007-4918 print/ISSN 1472-7234 online/15/000461-17
http://dx.doi.org/10.1080/00074918.2015.1108389
© 2015 Indonesia Project ANU
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462 Wendy J. Umberger, Thomas Reardon, Randy Stringer, and Simone C. Mueller Loose
In a broad sense, the existing literature on the transformation of modern food
value chains addresses how market liberalisation, trade and foreign-investment
policies, rapid income growth, and urbanisation affect food producers, buyers and traders (inside and outside wholesale markets), processors, and retailers—both modern and traditional. Previous research has explored the impacts
of change on poverty incidence and net-income distribution, and on the production eficiency of smallholder participation in modern food chains (Reardon et al.
2009). A research question that remains unanswered, and on which this article
focuses, is whether smallholder farmers are choosing not to participate in modern
value chains or whether they are merely being excluded from them. If smallholders are indeed choosing not to participate, what are the consequences for income
improvement and thus, indirectly, rural economic development?
This article builds on several strands of literature on market participation.
The irst focuses on the relation between transaction costs and commercialisation decisions—how, when, and why smallholders market their produce. This
strand explores why smallholders either sell or do not sell their produce, and
assumes that transaction costs are determined exogenously. It focuses on the possibility that imperfect, underdeveloped, or absent markets push transaction costs
so high that smallholders are unable to participate as buyers or sellers (Benjamin
1992; Henning and Henningsen 2007; De Janvry, Fafchamps, and Sadoulet 1991;
Finkelshtain and Chalfant 1991; Fafchamps 1992; Kranton 1996; Lopez 1986).
Goetz (1992) and Key, Sadoulet, and De Janvry (2000), for example, highlight how
distance to market and poor infrastructure keep producers from selling.
The second strand identiies ways in which producers innovate to reduce or
avoid transaction costs associated with searching for and screening trading partners, learning about new technologies, acquiring credit, meeting product standards, and bargaining. Poole (2000) analysed factors affecting producers’ marketing
decisions in the Spanish citrus industry, exploring how contracts facilitate producers’ marketing choices, reduce uncertainty, or lower other related transaction costs. Henson, Masakure, and Boselie (2005) showed how smallholders in
Zimbabwe enter market contracts for vegetables because, inter alia, the contracts
alleviate uncertainties associated with input supply, market demand, and market
prices, and help farmers get extensions from vegetable companies in situations
where there are limited public extension services.
The third strand explores the market-channel choices of farmers who once sold
only to traditional markets but are now able to sell to new market channels such as
supermarkets, large processors, or their dedicated agents. Examples include the
articles by Rao and Qaim (2011) and Hernandez, Reardon, and Berdegué (2007).
This strand tends to model the context and assets of the suppliers as determinants
of their selling to one market channel rather than another. It does not model the
attributes of the buyers as we do here.
This article ills a gap in and complements the literature by examining how
buyer attributes related to transaction costs and market risk inluence the marketchannel participation decisions of smallholder potato producers in West Java. We
posit that these farmers use buyer attributes to overcome cost barriers to market
participation. The attributes that we consider in this article address classic smallholder constraints, including access to inputs, credit, and information. Prior studies have not examined these buyer attributes collectively.
Market-Channel Choices of Indonesian Potato Farmers
463
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We use a best–worst (BW) scaling experiment (Finn and Louviere 1992) to
understand the relative importance of different buyer attributes to potato producers choosing market channels. BW scaling allows for discrimination in the importance of each attribute and avoids potential differences related to how respondents
use rating scales. Combining BW scaling with a latent class (LC) cluster analysis
allows us to explore how the assets and individual, heterogeneous preferences of
producers inluence their choice of whom to sell to and how much to sell. As far
as we are aware, this is the irst study that uses BW scaling to explore producer
utilities for buyer attributes.
Indonesian Potato Marketing Systems
We focus on potato producers for several reasons. Potatoes are widely produced
by smallholders in West Java, and are one of the top ive vegetables produced
in the country (in terms of acreage, value, and volume). They are also a priority
crop in government horticultural research and in declared strategies of horticultural development. Potato markets are evolving; demand for table potatoes, for
example, is moving away from those sold through wholesale markets and traditional retailers, and towards those sold through supermarkets and restaurants.
Specialised potato wholesalers dedicated to supermarkets and restaurants have
arisen beside traditional traders, and potato agribusinesses-cum-traders have
entered the market as producers and wholesalers (Campilan and Asmunati 2007;
World Bank 2007; Puspitawati et al. 2013).
Most potatoes marketed in West Java are produced by smallholders and sold
into traditional supply chains. Smallholder potato farmers marketing through
traditional supply chains rely on traders, central wholesale markets, and wet
markets. Smallholder farmers are connected to the main marketing participants
through numerous intermediaries. This long chain hinders farmers’ abilities to
establish relationships with other supply-chain partners on the basis of open
communication, a high degree of trust, and a better understanding of consumer
needs.
Other, non-traditional supply chains also exist in Indonesia, including specialised wholesalers distributing to supermarkets, large-scale agribusiness companies
engaging in potato processing through contract arrangements with individual
farmers, and home-based enterprises producing potato-based snack foods. These
chains can increase or stabilise producers’ incomes by offering greater market
opportunities than traditional channels, including credit; price premiums for
higher-quality products; improved access to certain inputs (such as seed); and
technology, training, and technical advice (Hernández, Reardon, and Berdegué
2007; Neven et al. 2009; Morrison Paul, Nehring, and Banker 2004).
The existing literature on potato value chains in West Java, together with the
ieldwork for this study, suggests that the ability of Indonesian potato farmers
to participate in non-traditional channels is limited owing to many challenges.
Long and disjointed supply chains, a lack of available and timely market-related
information, weak farmers’ organisations, and the limited marketing capacity
and generally poor market orientation of smallholders all contribute to a mismatch between what is produced and the variety, quality, and quantity required
by retailers and processors (Ameriana 2010; Campilan and Asmunati 2007; Fuglie
et al. 2006; World Bank 2007; Puspitawati et al. 2013).
464 Wendy J. Umberger, Thomas Reardon, Randy Stringer, and Simone C. Mueller Loose
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The different types of buyers and supply chains present producers with a range
of market channels, each with its own buyer attributes. The demand for speciic
attributes varies by producer, but certain attributes may reduce risk, lower speciic
transaction costs, offer market incentives, or loosen household inancial constraints.
Some buyers, for example, pay cash immediately, at the point of sale, while others
offer premium prices for graded and sorted produce. Other buyers provide input
credits, certiied seeds, technical guidance for adopting new technologies, or information on how to meet the standards preferred by supermarkets and processors.
Some buyers are recognised for their integrity, reliability, and consistency.
RESEARCH METHODOLOGY AND ANALYSIS
Sampling Methods
We analysed data from 307 household survey questionnaires collected in 2009 in
Bandung and Garut, two regencies in West Java that together account for nearly
all of the province’s potato production. The sample included potato producers
from three groups: the general potato population; producers supplying Indofood,
one of Indonesia’s largest food processors; and growers participating in a farmer
ield school (FFS), a program of practical education.
Our sample of the general potato population (64% of the total sample) comprised 197 households, or an average of 12 randomly chosen potato-producing
households from each of 16 villages. We selected these villages by stratifying
regency subdistricts (kecamatan) into major and minor potato-production areas on
the basis of oficial published production data and interviews with key informants.
We then randomly selected two subdistricts from each of these areas, resulting in
eight subdistricts, and we conducted site visits and more interviews in order to
develop a list of potato-producing villages in each subdistrict. We obtained names
and contact information for potato producers in each village from land-registry
and tax-ofice iles.
The Indofood sample (20% of the total) included 60 households drawn randomly from a list of more than 400 producers provided by Indofood. We randomly
selected 50 FFS producers (16% of the total sample) from lists of more than 200
households provided by extension oficers from two government departments—
Balitsa (Research Institute for Vegetable Crops) and Dinas Pertanian (Agricultural
Extension Ofice). This particular FFS introduced participants to new varieties of
potatoes and new farming techniques (Puspitawati et al. 2013).
The questionnaires collected information on household characteristics, assets,
potato production and marketing history, other sources of income, contractual
details, and perceptions of changes over time. Information was also collected
on producer preferences for buyer attributes by using a BW-scaling experiment
(tables 1a and 1b), as explained in the following section. To examine if producer
preferences are gender-speciic, we invited female and male respondents in each
household to participate in the experiment.
Best–Worst Scaling
BW scaling measures the relative value or importance that individuals place on
attributes. Based on Finn and Louviere’s (1992) random utility theory for paired
comparisons, it uses a balanced incomplete block design to derive subsets of the
Market-Channel Choices of Indonesian Potato Farmers
465
TABLE 1a Summary of Respondent Characteristics Used
as Active Covariates in Latent Class Analysis
Characteristic
Description
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Gender
Children
1= female; 0 = male
No. of dependent children
(< 18 years) living at home
Education of household head Years of education of
household head
Potato-farming experience
Years of producing potatoes
Owns mobile phone
1 = yes, owns at least 1
mobile phone
Owns motorbike
1 = yes, owns at least 1
motorbike
Potatoes main income
1 = potatoes main source of
farm income
Non-farm income
Non-farm income as a
share of total income (%)
Payment on child education 1 = potato income used to
pay for children’s
education
Production
Total potatoes produced (t)
Productivity
Potatoes (t) produced per
hectare
Expanded production
1 = expanded the potatoplanting area in the past
3 years
Both seasons
1 = produced in both wet &
dry seasons
One variety
1 = planted only one variety
Distance to plots
Average distance (km) to
plots from home
FFS
1 = belongs to FFS
Indofood
1 = has relationship with
Indofood
Cooperative involvement
1 = obtained production
assistance and guidance
for growing potatoes from
cooperative
One buyer
1 = sells all potatoes to one
buyer
Observations
Mean
SD
Min
Max
0.50
1.69
0.50
1.31
0
0
1
7
7.28
3.26
0
18
18.85
0.74
12.51
0.44
0
0
58
1
0.69
0.46
0
1
0.56
0.50
0
1
18.42
30.20
0
100
0.59
0.49
0
1
23.47
15.19
44.18
7.71
0.28
0.27
414
54
0.31
0.46
0
1
0.51
0.50
0
1
0.93
1.98
0.25
2.37
0
0
1
16
0.16
0.20
0.37
0.40
0
0
1
1
0.08
0.28
0
1
0.86
0.35
0
1
614
Note: SD = standard deviation. FFS = farmer ield school.
attributes being assessed. Respondents choose both the best (most important) and
worst (least important) attributes, making trade-offs among subsets and giving
the researcher more discriminating estimates of relative importance. BW scaling
is relatively easy for respondents to perform; it also allows eficient elicitation of
attribute importance, particularly when comparing many attributes (see Auger,
466 Wendy J. Umberger, Thomas Reardon, Randy Stringer, and Simone C. Mueller Loose
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TABLE 1b Summary of Respondent Characteristics Used
as ‘Passive’ Variables in Ex-post Characterisation
Variable
Description
Age of household head
Size of household
Production area
Grade
Years
45.30
Number living in the home
4.46
Area of production (hectares)
1.91
1 = sold only one grade of
0.29
potatoes
Potato income as a share of
40.60
total farm income (%)
1 = obtained production
0.25
assistance from buyer
1 = obtained production
0.16
assistance from processor
1 = buyer provides incentives
0.35
to grade & sort
1 = buyer provides incentives
0.46
to produce certain quality
Contacts others for price
0.82
information
0.34
1 = opportunity to contract
potatoes with Indofood
1 = contracted to grow
0.23
potatoes
1 = land owned &
0.75
farmed by respondent
1 = land owned & rented out
0.03
1 = land owned & sharecropped 0.02
1 = land rented
0.36
1= land sharecropped
0.04
1 = land leased from
0.08
government
1= land borrowed from
0.12
own family
Potato income
Trader assistance
Processor assistance
Grading incentive
Quality incentive
Search price information
Contract opportunity
Contracted
Owns & farm
Own & rent
Own & sharecrop
Rent only
Sharecrop only
Goverment lease
Borrowed from family
Observations
Mean
SD
Min
Max
11.40
1.72
2.56
0.46
22
2
0.10
0
80
10
25.08
1
30.28
0
100
0.43
0
1
0.37
0
1
0.48
0
1
0.50
0
1
0.38
0
1
0.47
0
1
0.42
0
1
0.43
0
1
0.17
0.13
0.48
0.19
0.27
0
0
0
0
0
1
1
1
1
1
0.33
0
1
614
Note: SD = standard deviation.
Devinney, and Louviere 2007; Cohen 2009; Cohen and Orme 2004; Jaeger et al.
2008; Mueller and Rungie 2009; and Mueller, Lockshin, and Louviere 2010).
We conducted in-depth interviews and focus groups with producers and buyers to ensure that our selected buyer attributes covered characteristics of importance to producers. The inal 11 attributes were as follows: improved access to
inputs (‘access to certiied potato seed’, ‘credit or access for input purchases’,
and ‘provides money for loan’); access to information (‘technical assistance’ and
‘shares information about market conditions’); market and price risk (‘pays cash
immediately’, ‘established relationship’, and ‘always follows through on commitments to buy my products’); negotiation aspects (‘willing to negotiate or match
Market-Channel Choices of Indonesian Potato Farmers
467
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another buyer’s price’); and economic attributes (‘provides price premiums’ and
‘price per kilogram’).
We used a balanced incomplete block design to assign attributes to 11 subsets
(tasks) with ive attributes each. For each task, respondents were asked to consider which attribute was the most important to them and which was the least
important.1 Back translation conirmed that the meaning of the attributes had
been preserved after translation into Bahasa Indonesia.
Aggregate Analysis
To determine the relative importance of buyer attributes for all producers, we calculated an aggregate BW score for each attribute by summing the number of times
each attribute was selected as being most important or least important. We subtracted the aggregate ‘least important’ value from the aggregate ‘most important’
value. The 11 aggregate BW scores are crude measures of relative importance. To
make these results easier to interpret, we calculated a standardised interval scale
(SIS) (Marley and Louviere 2005; Mueller and Rungie 2009), taking the square root
of the frequency of the best, or most important, attribute, divided by the frequency
of worst, or least important, attribute: SQRT(B/W). We gave the attribute with the
highest SQRT(B/W) a score of 100 and scaled all other attributes accordingly.
Modelling Heterogeneity
To explore heterogeneity among producers, we calculated the relative importance
of each of the 11 attributes by summing the number of times a respondent, i, indicated that an attribute, j (j = 1–11), was most important or least important. We
then subtracted the sum of ‘least important’ for attribute j from the sum of ‘most
important’ for attribute j, to calculate BWij. The larger the BWij score, the more
important the attribute to respondent i.
We used an LC cluster analysis to determine if unique classes of producers exist
that can be grouped by household, farm, and marketing characteristics. This clustering technique is useful for explaining unobserved differences in preferences or
utilities (Meghani et al. 2009). LC cluster analysis assumes that individuals belong
to one of k LCs, of which the size and number are unknown a priori (Vermunt and
Magidson 2008; Haughton, Legrand, and Woolford 2009).
LC analysis uses the covariation among observed preference scores (BWij) as
a measure of utility to predict membership in a speciic LC. Observed preference
scores for individuals in a class are assumed to have come from the same probability distribution. Unobserved utility is heterogeneous across classes but homogeneous within a class. Therefore, although we cannot directly observe a potato
producer’s utility for a marketing channel, we can measure the importance of
buyer attributes to producers by using BW scaling. The 11 individual BW scores
(BWij) for the attributes were the dependent variables in the LC cluster analysis.
If producers are heterogeneous in their ratings of buyer attributes then unique
LC clusters or segments should exist (see, for example, Haughton, Legrand, and
Woolford 2009; Meghani et al. 2009; and Mueller and Rungie 2009).
1. An example of one of the 11 BW tasks and descriptions of the inal 11 attributes are available from the authors on request.
468 Wendy J. Umberger, Thomas Reardon, Randy Stringer, and Simone C. Mueller Loose
TABLE 2 Measures of Attribute Importance Using Best–Worst Scaling
Aggregrates
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Attributes (overall)
Pays cash immediately
Provides price premiums
Always follows through on commitments
to buy my product
Price per kilogram
Established relationship
Access to certiied potato seed
Willing to negotiate or match another
buyer’s price
Provides money for loan
Shares information about market conditions
Credit or access for input purchases
Technical assistance
Most
(B)
Least
(W)
BW
SQRT
(B/W)
SIS
values
1,497
993
202
162
1,295
831
2.72
2.48
100.0
90.9
788
809
497
517
267
305
310
687
521
504
187
–170
1.72
1.63
1.27
0.87
63.1
59.8
46.5
31.9
421
445
368
319
100
675
868
775
1,029
1,474
–254
–423
–407
–710
–1,374
0.79
0.72
0.69
0.56
0.26
29.0
26.3
25.3
20.5
9.6
Note: B = best. W = worst. SQRT = square root. SIS = standardised interval scale.
LC cluster analysis can use active covariates to further explain classes and
improve the classiication by controlling for other variables that may also help to
explain market-channel choice (Haughton, Legrand, and Woolford 2009). These
covariates are selected on the basis of previous empirical studies exploring factors
that explain market participation, market-channel choice, or contract adoption
(Wollni and Zeller 2007; Fafchamps and Hill 2005; Shilpi and Umali-Deininger
2007; Abdulai and Birachi 2009).
We characterised the resulting clusters from the LC analysis ex post, using
passive variables (table 1b). These variables will be useful for further explorative
analysis. We can evaluate, for example, whether respondents indicated attribute
importance is consistent with their marketing behaviour, their relationships with
buyers, or both (by comparing the BWij scores with passive variables such as
‘trader assistance’, ‘processor assistance’, and ‘contracted’).
RESULTS AND CHARACTERISATION OF CLUSTERS
Aggregate Importance of Buyer Attributes
We determined the relative importance of each buyer attribute to the entire sample
by evaluating the SIS values of each attribute (table 2). The attribute ‘pays cash
immediately’ was the most important (standardised to 100%), across all respondents, while ‘technical assistance’ was the least important (about 10% as important).
‘Provides price premiums’ was very important. ‘Always follows through on commitments to buy’ was 63% as important to respondents as ‘pays cash immediately’.
Providing access to certiied potato seed was 32% as important as paying cash immediately, while the attributes ‘provides money for loan’, ‘credit or access for input
purchases’, and ‘technical assistance’ were less important than other attributes.
Market-Channel Choices of Indonesian Potato Farmers
469
TABLE 3a Mean Best–Worst Scores for Buyer Attributes, by Latent Class Cluster
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Buyer attributes
Pays cash immediately
Provides price premiums
Always follows through on commitments
to buy my product
Price per kilogram
Established relationship
Access to certiied potato seed
Willing to negotiate or match another
buyer’s price
Provides money for loan
Shares information about market conditions
Credit or access for input purchases
Technical assistance
Log likelihood
R2
Classiication error
Number of parameters
Degrees of freedom
Cluster 1
(44%)
Cluster 2
(16%)
Cluster 3
(24%)
Cluster 4
(16%)
3.02a
1.58a
3.30a
1.19a
0.83b
1.59a
0.35b
0.58
0.79
–0.04a
–0.04a
3.14
1.48
0.22
–1.25a
–0.81
–0.27a
0.23
a
0.65
–0.23a
2.13
0.88a
1.86
–1.62a
0.96
–0.87
–1.92a
–1.50a
–1.93
0.06a
–2.38
–2.56
1.02
–2.65
0.39
–1.47a
–0.19a
–1.06
–0.38a
–1.36a
0.26a
–0.10a
–0.48a
–3.36
–13,249.57
0.83
0.08
203
411
Note: Means with the same superscript letters in the same row are not statistically different (α = 0.05,
post-hoc Tukey test).
In the key informant interviews we conducted during the development phase
of the study, farmers, traders, and government extension specialists (particularly
those involved with Indofood) indicated that farmers struggle to gain access to
potato seed, other inputs, credit, and technical assistance. Our study results, however, suggest that these attributes are less important for most potato producers
than the four attributes related to guaranteed inancial returns (‘pays cash immediately’, ‘always follows through on commitments to buy my product’, ‘provides
price premium’, and ‘price per kilogram’). This result is not surprising, considering that 56% of respondents relied on potatoes as their main source of farm income
(table 1a). The results in table 2 suggest that long-term buyer–seller relationships
are more likely to develop if potato buyers guarantee producers that they will
pay cash upon receipt of potatoes and follow through on any commitments to
buy more potatoes—that is, if they play a role in reducing market and price risk.
Producer Heterogeneity
Our LC cluster analysis revealed four distinct clusters of potato producers, each
with different demographic characteristics, attitudes, and preferences (tables 3–5).
The mean BW scores and the SIS values for each attribute and each LC cluster are
provided in tables 3a and 3b. As expected, the relative importance of buyer attributes differed across clusters.
Statistically signiicant active covariates in the LC analysis include the number
of dependent children living at home, the number of years of education of the
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TABLE 3b Standardised-Interval-Scale Values and Ranks for Buyer Attributes, by Latent Class Cluster
Cluster characterisation
Cluster 1 (44%):
Financial returns
Buyer attributes
Pays cash immediately
Provides price premiums
Always follows through on their commitments to
buy my product
Price per kilogram
Established relationship
Access to certiied potato seed
Willing to negotiate or match another buyer’s price
Provides money for loan
Shares information about market conditions
Credit or access for input purchases
Technical assistance
Note: SIS = standardised interval scale.
Cluster 2 (16%):
Cash & credit access
Cluster 3 (24%):
Input access
Cluster 4 (16%):
Commitment
& relationship
SIS
value
SIS
rank
SIS
value
SIS
rank
SIS
value
SIS
rank
SIS
value
SIS
rank
100.0
58.7
1
2
100.0
38.7
1
2
40.0
59.7
4
2
13.3
20.1
6
4
36.4
51.3
23.8
8.5
41.5
6.5
19.6
3.1
3.1
5
3
6
8
4
9
7
10
11
17.1
10.0
14.1
20.5
9.7
31.6
3.6
21.6
7.4
6
8
7
5
9
3
11
4
10
25.3
40.7
22.1
100.0
8.3
24.5
15.5
23.0
12.2
5
3
8
1
11
6
9
7
10
100.0
23.9
54.8
3.6
4.0
13.7
11.1
8.7
0.8
1
3
2
10
9
5
7
8
11
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TABLE 4 Signiicant Covariates and Top Standardised-Interval-Scale Characteristics for Latent Class Clusters
Covariates
Gender (1 = female)
Children
Education of household head
Potato-farming experience
Owns mobile phone
Owns motorbike
Potatoes main income
Non-farm income
Payment on child education
Production
Productivity
Expanded production
Both seasons
One variety
Distance to plots
Farmer ield school
Indofood
Cooperative involvement
One buyer
Cluster 1:
Pays cash
immediately
0.50
1.59
7.38
19.01
0.71
0.70
0.55
20.68
0.58
24.44
14.85
0.26
0.51
0.91
1.79
0.14
0.05
0.04
0.85
Cluster 2:
Pays cash
immediately
0.56
2.16
6.31
14.85
0.66
0.55
0.40
13.84
0.68
7.90
14.38
0.28
0.21
1.00
1.33
0.25
0.05
0.09
0.80
Cluster 3:
Has access to
certiied seed
0.47
1.34
8.13
21.98
0.85
0.74
0.68
15.80
0.67
31.99
16.89
0.37
0.74
0.89
2.50
0.16
0.50
0.23
0.81
Cluster 4:
Meets
commitments
0.48
2.00
6.75
17.81
0.72
0.71
0.59
20.55
0.44
23.79
14.42
0.40
0.46
0.99
2.35
0.13
0.27
0.00
1.00
Wald
p-value
2.41
19.50
20.41
18.53
4.20
7.99
3.80
5.73
15.93
9.06
6.05
2.19
20.61
9.36
16.09
15.65
53.68
8.71
2.42
0.49
0.00
0.00
0.00
0.24
0.05
0.28
0.13
0.00
0.03
0.11
0.53
0.00
0.03
0.00
0.00
0.00
0.03
0.49
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472 Wendy J. Umberger, Thomas Reardon, Randy Stringer, and Simone C. Mueller Loose
household head, the number of years of potato-farming experience, motorbike
ownership, whether potato income pays for children’s education, total production, planting in both wet and dry seasons, the number of varieties planted, the
average distance to potato plots, contracts with Indofood, and participation in
FFSs or cooperatives (table 4). Contrary to our expectations, the following covariates were not signiicant in the LC analysis: gender, mobile-phone ownership,
potatoes as the main income source, the percentage of non-farm income, productivity, expanded production, and the existence of only one buyer. Accordingly,
these characteristics are not suficiently related to differences in attribute importance between producers.
We used an ex-post analysis of variance to consider passive variables (that is,
those not used as active covariates in the LC analysis), inding that additional
signiicant differences in household, farm, and marketing characteristics existed
across the four clusters (table 5). Farmers in cluster 1, which accounted for the
largest share of the sample (44%), had a higher utility than those in other clusters
for buyers who would negotiate on price and follow through on commitments to
buy their products. These producers were less likely to be engaged in relationships, including contracting, with supply-chain partners. They had a lower utility
for buyer attributes related to reducing search or information costs through the
provision of credit or technical assistance.
Relative to clusters 2 and 3, a higher share of respondents in cluster 1 received
incentives for grading and for producing higher-quality potatoes (table 5), while
93% of producers in this cluster said they contacted others to obtain price information. Thus, it is not surprising that ‘willingness to negotiate on price’ was
one of the most important attributes; its importance points to a strong degree of
consistency between estimated preferences for buyer attributes and marketing
behaviour.
The importance placed on price-related attributes, including incentives for
grading and quality, by farmers in cluster 1 suggests that they sought marketrelated incentives to encourage them to coordinate more closely with supplychain partners. Given the large size of this cluster, additional work is needed
to explore what other factors motivated these producers to choose a marketing
channel and to understand why more producers in the general potato population
are not involved in vertical partnerships.
Cluster 2 (16% of the sample) is somewhat similar to cluster 1 in the importance
placed on certain buyer attributes. ‘Pays cash immediately’ was by far the most
important attribute (table 3b) for cluster 2. Two of the top four attributes for these
farmers suggest that this cluster was the most inancially constrained. Farmers in
this cluster preferred buyers who could help them reduce their search, travel, and
negotiation costs for obtaining loans. Farmers in this cluster placed little importance on attributes related to buyers’ willingness to negotiate on selling prices or
provide technical assistance and market information.
In general, farmers in cluster 2 had less land allocated to potatoes, lower levels
of education, less potato-farming experience, lower yields, lower incomes, and
fewer assets than farmers in other clusters. This cluster had the highest proportion
of farmers participating in FFSs (25%) and the smallest proportions receiving production assistance from processors (5%) and receiving grading or quality incentives from their buyers (10% and 26%, respectively). Off-farm income accounted
Market-Channel Choices of Indonesian Potato Farmers
473
TABLE 5 Means of Respondents’ Household, Farm, and
Marketing Characteristics for Latent Class Clusters
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Variable
Age of household head
Size of household
Production area
Grade
Potato income
Trader assistance
Processor assistance
Grading incentive
Quality incentive
Search price information
Contract opportunity
Contracted
Own & farm
Own & rent
Own & sharecrop
Rent only
Sharecrop only
Government lease
Borrowed from family
Cluster 1
45.52
4.38a b
2.06a
0.27
39.93a b
0.18a
0.10a
0.49a
0.59a
0.93a
0.21a
0.09a
0.76
0.02
0.02
0.31a
0.03
0.08a b
0.14
Cluster 2
Cluster 3
44.71
4.85b
0.89
0.37
36.99b
0.21a
0.05a
0.10b
0.26b
0.86a
0.13a
0.12a
0.70
0.05
0.02
0.20
0.06
0.12a
0.12
46.62
4.09a
2.25a
0.26
47.49a
0.38b
0.39
0.19b
0.31b
0.66b
0.73
0.55
0.76
0.05
0.01
0.51b
0.04
0.02b
0.11
Cluster 4
43.34
4.85b
2.02a
0.33
36.47b
0.30a b
0.13a
0.43a
0.50a
0.71b
0.36
0.27
0.75
0.01
0.02
0.43a b
0.02
0.12a
0.07
Note: Means within a row with the same superscript letters are not statistically different (α = 0.05,
post-hoc Tukey test). Means in rows with no superscript are not statistically different across clusters.
for only 14% of their household income. It is not surprising that buyer attributes
related to cash low, inancing, and input access were more important to this cluster than to others.
Farmers in cluster 3 (24% of the sample) valued buyers who provided access
to certiied seed, suggesting that they valued production stability and lower costs
for inding and buying quality inputs. Compared with farmers in other clusters,
they had completed more education and had more farming experience, and had
the highest potato production and yields. They also travelled farthest to reach
their potato plots (2.5 kilometres). This cluster had the largest share of producers
involved in contractual agreements with Indofood (50%) or a cooperative (23%).
Cluster 3 represents a relatively large segment of experienced farmers who
relied on potatoes as a main income source. Many of these farmers were involved
with supply-chain partners (50% with Indofood). The importance that the farmers placed on having access to certiied potato seed might have been why they
were contracting with Indofood and other processors. In other words, their experience in producing potatoes may have taught them that their potato production
and revenue relied on having good-quality, certiied seed, and thus they contract
with buyers. This would support Henson, Masakure, and Boselie’s (2005) inding that producers seek contractual relationships with buyers to reduce certain
transaction costs related to accessing inputs and technology. Cluster 3 also has the
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474 Wendy J. Umberger, Thomas Reardon, Randy Stringer, and Simone C. Mueller Loose
highest share (nearly 40%) of producers who obtained production assistance for
growing potatoes from traders or processors, even though ‘technical assistance’
was of little importance to respondents.
Farmers in cluster 4 (16% of the sample) valued the beneits of long-term relationships and placed less value on having access to technical assistance or certiied seed. Farmers in this cluster looked for opportunities to build relationships
with buyers, and it seems that these producers sought price premiums for valueadding (for example, by grading and sorting) when trying to get contracts with
buyers who rewarded quality. Several characteristics help to explain why commitment and long-term relationships were important to farmers in this cluster.
The education level and age of the household head, as well as the share of total
farm income from potatoes, were lower than for farmers in other clusters. It is
therefore not surprising that all respondents in this cluster each sold all of their
potatoes to one buyer or that 27% were in contracts with Indofood. A relatively
high share of farmers in this cluster received incentives from buyers for grading
(43%) and for producing higher-quality potatoes (50%).
SUMMARY AND IMPLICATIONS
The indings of our BW scaling experiment for the aggregate sample revealed
that the three most important buyer attributes for potato farmers in West Java
were immediate cash payments, price premiums for sorting for quality, and buyers following through on their commitment to buy potatoes as agreed. This result
suggests that long-term buyer–seller relationships will develop only if buyers
are able to offer farmers inancial incentives and consistently follow through on
their commitments. Sellers will otherwise market their products through other
channels.
Our joint LC cluster analysis of the respondents’ individual BW values and
selected respondent characteristics (household, socio-demographic, and farm and
social assets) allowed us to explore the heterogeneity of farmers’ utilities for buyer
attributes and to better understand farmers’ marketing choices. The indings of
this research support earlier work by the World Bank (2007) and Puspitawati et al.
(2013) that suggests potato farmers have unique needs when deciding where to
market their potatoes. Taking into account growers’ attitudes and characteristics,
we discovered four distinct and heterogeneous clusters of potato producers. Each
cluster had unique utilities for market-channel attributes.
The largest group of farmers, cluster 1, placed relatively greater value on
attributes related to reduced market and inancial risk, whereas farmers in cluster
3, which included the most experienced farmers, who owned relatively larger,
irrigated farms, tended to be more interested in the buyer’s ability to provide
access to certiied seed. Farmers in cluster 3 were therefore more likely to supply
Indofood, which gives farmers access to high-quality potato seed; the domestic
seed market appears to fall short of being able to do this. Farmers in cluster 2, on
the other hand, placed relatively greater importance on attributes related to the
provision of credit for accessing inputs (such as fertiliser or pesticides) or loans to
cover other operating expenses or the purchase of capital assets. Farmers in cluster 4 placed the highest value on buyers following through on their commitments,
including paying on time.
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Market-Channel Choices of Indonesian Potato Farmers
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Our clusters are not necessarily representative of the different segments that
may exist in the Indonesian potato-farming population, or of the characteristics
of the farmers within them. We purposely sampled farmers who were known
to be working with Indofood (20% of the sample), yet it is unlikely that 20% of
potato farmers are selling to Indofood. Our sampling bias may have overinlated,
for example, the size of certain clusters—particularly cluster 3. This bias should
be taken into account when considering the following policy implications of this
study.
This research demonstrates that reining and targeting smallholder assistance
schemes (including those offered by the public and private sectors as well as nongovernment organisations) to account for farmers’ unique needs, preferences,
and characteristics may create more effective programs. In turn, this could lead
to more eficient potato value chains and possibly beneit both buyers and sellers.
The generally high degree of importance placed on market issues related to price
suggests that farmers may beneit from having improved access to general market information (for example, through mobile-phone applications and improved
access to the Internet). Farmers may also be interested in undertaking training
programs to improve their understanding of market dynamics and to build capacity in skills such as negotiating with buyers and understanding their demands.
Furthermore, some potato farmers—particularly those in cluster 3—are likely
to welcome programs and policies that increase supply and access to high-quality
potato seed. There may be some beneit from introducing development initiatives
that encourage both public- and private-sector investment in increasing smallholders’ access to quality seed, including by increasing the domestic production
of potato seed, or from enacting more liberal trade policies to increase supplies of
high-quality imported seeds.
Farmers in clusters 1 and 4 are likely to beneit from targeted agricultural insurance schemes, which allow them to better manage production, price, and market
risks, while those in cluster 2 may beneit from programs that increase smallholder access to affordable inancing options for purchasing inputs and working
capital (such as micro-credit schemes). These programs could allow them to capitalise on marketing possibilities and emerging market niches.
Recognising, understanding, and accounting for heterogeneity in the attitudes
and needs of smallholder farmers with respect to market channels and related
capacity building are central to improving the effectiveness of public- and privatesector agricultural development programs and investment schemes. Ultimately,
we hope that consideration of our indings will lead to changes that improve
Indonesian smallholder potato farmers’ access to and participation in modern
food market channels.
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