Renewable Agriculture and Food Systems: Page 1 of 15
doi:10.1017/S1742170516000193
Factors affecting farmers’ decisions to
participate in direct marketing: A case study
of cherry growers in the Kemalpasa District
of Izmir, Turkey
Hakan Adanacioglu*
Department of Agricultural Economics, Faculty of Agriculture, Ege University, Bornova, Izmir, Turkey.
*Corresponding author: hakan.adanacioglu@ege.edu.tr
Accepted 18 April 2016
Research Paper
Abstract
The objective of this study is to explore the main factors that drive decisions of farmers to sell their products directly to
consumers through farm direct marketing channels. A case study on cherry growers on the subject of direct marketing,
which is one of the alternative marketing options in agricultural products marketing for farmers, is examined in this
study. In addition, further suggestions are put forward on how to improve the use of direct marketing strategies by
farmers in Turkey. An ordinal logistic regression analysis model was used to analyze the effects of agricultural businesses
and demographic features on the tendency of growers to choose direct marketing channels in cherry selling. According to
these model results, the cherry farming experience of the growers, the size of the cherry orchard, the level of specialization in cherry production, and the gross margin per hectare for cherry production have a statistically significant impact
on the tendency of the growers to choose direct marketing channels in cherry selling. In particular, the growers whose
experience is more than 20 yr, the farms that are semi-specialized, the farms providing a gross margin of more than
US$5506 ha−1, and the farms having a cherry orchard between 1 and 2 ha in size were determined to have more of a
tendency for direct marketing. These results show that owners of medium-sized farms are more interested in direct marketing. According to the interviewed cherry growers, the most important factor limiting their participation in direct marketing is that there are no organizations that will help them meet their direct marketing goals and build direct marketing
arrangements between themselves and their consumers.
Key words: direct marketing, farmers’ decisions, farmers’ participation, cherry growers
Introduction
In recent years, farm prices and income levels, financial
problems and uncertainty of government programs have
encouraged farmers to seek alternative marketing strategies (Cottingham et al., 1994). Pressures on farming in
the form of collapsing prices, overproduction, market saturation, internal and global competition, concentration
of powerful buyers and agricultural policy reform are
pushing interest in alternative marketing into the mainstream (IATP, 1998). One popular marketing option
that allows farmers to receive a higher return for their
crops is direct marketing (Young, 1995).
Direct marketing includes any marketing method
whereby farmers sell their products directly to consumers
(Bruch and Ernst, 2010). Direct marketing eliminates the
‘middle man’ functions of food exchange, transportation,
storage and processing (Cottingham et al., 1994). Several
reasons account for the increased interest in farm direct
marketing. One is dissatisfaction with farm commodity
prices. The farm price is often only a fraction of the
retail food price. Prices for produce sold directly to consumers can be substantially higher than typical wholesale
prices. Another reason is that producers value the relationships they form with the consumers as well as the
opportunity to receive immediate feedback on their
products. Consumers value the fresh, quality products
along with the opportunity to support local producers
(Alberta Ag-Info Centre, 2013). The direct sale of agricultural products plays an important role in the diversification of the activities of the rural population and helps to
develop rural areas. Not only does this form ensure the
© Cambridge University Press 2016
2
provision of healthy food for consumers, but it also provides increased income to farmers, allowing farmers to
remain in the countryside (Fehér, 2012).
Many direct marketing options are available. Selecting
one depends on the individual, the farm location, the
volume of products to sell and other factors. Major
direct marketing options include roadside stands, Upick operations, roadside markets and farmers’ markets.
Other options include ‘honor selling,’ gift baskets and
mail-order or Internet sales, community-supported
agriculture, selling direct to restaurants and other food
institutions, agritourism, ‘peddling,’ and rent-a-tree or
rent-a-plot arrangements (Burt and Wolfley, 2009).
Some statistical data reveal that the importance of
direct marketing for farm products has increased in
recent years. For example, throughout the USA,
144,530 farms sold US$1.3 billion in fresh, edible agricultural products directly to consumers in 2012. This was a
6% increase in farms and an 8% increase in sales over
2007, the last time the agriculture census was conducted.
Farms with direct sales to consumers made up 6.9% of
the nation’s 2.1 million farms in 2012 (USDA, 2014).
According to a study carried out by the UK’s
University of Essex for the Soil Association, organic
farms in the UK and Ireland involved in ‘on-farm processing’ and direct marketing enterprises employed 64%
more people than organic farms without such activities,
with a significant proportion (39%) of organic farms
engaged in this type of business innovation (Hird et al.,
2010). In Austria, family farmers in particular are
engaged in direct marketing and have built direct contacts
with other stakeholders to get information and networks
(Fujisawa et al., 2015). In 2007, for less than 0.2% of
farms in France, Germany and the UK did direct sales
account for more than half of the value of their output.
Yet at the same time, there were 384,995 Polish farms
(16% of the total) for which direct sales accounted for
over half of the total sales value. Direct marketing of
farm products is most developed in Italy, where the
number of farms involved in such activities rose by 64%
between 2001 and 2009 (Gorton et al., 2014).
This paper attempts to answer the basic question: What
are the factors that drive farmers’ decisions to sell their
produce in direct marketing? To answer this question, a
range of variables, including demographic, socio-economic,
market, and farm specific features, were jointly estimated
in order to be able to evaluate the influence of each of
them. As described in the literature review section, even
though there are some studies done to explore factors
that may be related to a farmer’s decision to participate
in direct marketing, further research is needed concerning
regional differences affecting farmer decisions to participate in these activities. So far, no similar analysis has
been carried out for Turkey. This paper aims to fill this
gap in the existing literature. The study was based on
primary data obtained in a cross-section survey of 102
cherry growers in order to determine the factors that
H. Adanacioglu
affect farmers’ decisions to participate in direct marketing
in Turkey. In addition, this study also explores what
options of direct farm marketing are being used by
farmers and their future plans for direct marketing strategies (DMSs). Lastly, in this study, further suggestions
are put forward on how to improve the use of DMSs by
farmers in Turkey.
Literature Review
There is considerable literature that analyzes characteristics and attributes of consumers who purchase products
through direct-to-consumer (DTC) outlets. On the other
hand, there are relatively few studies that focus on producer
behavior and characteristics regarding DMSs (Monson
et al., 2008; Wolanin, 2013; Park et al., 2014). There is no
study that has actually been carried out to determine the
factors that influence farmers’ decisions to participate in
direct marketing in Turkey. However, few studies that
have been carried out to date have analyzed the factors
that influence farmers’ decisions to participate in direct
marketing (Gilg and Battershill, 1999; Monson et al.,
2008; Aguglia et al., 2009; Auld et al., 2009; Corsi et al.,
2009, 2014; Nyaupane and Gillespie, 2010; Timmons and
Wang, 2010; Detre et al., 2011; Park et al., 2011, 2014;
Uematsu and Mishra, 2011; Sage and Goldberger,
2012; Wolanin, 2013; Benedek et al., 2014; Park, 2015;
Muthini, 2015).
Gilg and Battershill (1999) examined the household
factors influencing the participation of French farms in
direct selling of farm produce as an alternative farming
strategy. While age, family life cycle, succession and
labor relations were not found to be significant in understanding farm decision making, in this study farming
background, education and attitudes toward profit were
found to significantly influence decisions about direct
marketing.
Monson et al. (2008) analyzed how farm size, the importance of high-value crops, organic production, experience and demographic factors affect a producer’s reliance
on direct markets. They found that farm size, high-value
crop production, noncertified organic production methods
and household size are determinants of the share of
total farm output sold through direct marketing outlets.
Results from this study suggest that producers who
operate smaller farms, are less reliant on small fruit, implement organic production without USDA certification
and live in smaller households are more likely to sell
their products through direct outlets.
Aguglia et al. (2009) focused on the determinants of the
adoption of direct selling in Italian farms. They indicated
that the probability of uptake of this marketing strategy is
higher among multifunctional farms, for example, among
organic farms. They then tested on a smaller regional
sample the influence of proximity to urban areas on the
adoption of direct selling. The results show that the
Factors affecting farmers’ decisions to participate in direct marketing
probability of uptake is higher when the farm is close to
big urban markets.
Corsi et al. (2009) analyzed the choices of marketing
chains by organic farmers and identified the determinants
of their choices. They found that farmers’ personal characteristics influenced their choice, and that more educated
and skilled farmers were less likely to choose traditional
marketing chains and more likely to engage in the new
marketing chains. They pointed out that large farms
choose traditional chains rather than the direct and the
short chains. According to them, the other main determinant of the choice was the type of farming, with
some types more suitable for the traditional chains, and
other for the direct and short ones.
Auld et al. (2009) identified production characteristics
that affect the relative propensity to sell locally and
explore opportunities to support more direct and local
marketing by producers based on these characteristics.
The results of this study revealed that producer size,
product perishability, proximity to a population center,
consumer perceptions and producer philosophy influenced
where Colorado growers sell. They concluded that small
farmers sold the majority of their crops locally, the
produce grown by small farmers in Colorado had significantly lower food miles than conventional sources.
Finally, direct sales almost always result in local sales
and, subsequently, lower food miles. The authors also
pointed out that product characteristics also affected
where produce was sold. Most growers believed that perishability was advantageous to market ‘local’ to maintain
freshness, flavor and quality and reduce the need for
additives.
Timmons and Wang (2010) identified major factors
associated with direct food sales across states and counties
in the USA using 2007 USDA Census of Agriculture data.
They used regression analysis to identify major factors
that may explain variations in direct food sales. They
found that four variables, average farm size, population
density, region and available farmland, together explained
most of the variation in direct food sales across states.
Average farm size was negatively associated with direct
sales, i.e., the presence of smaller farms was correlated
to higher direct sales. Population density and farmland
were reported to be positively correlated with direct
food sales. The authors also revealed the wide regional
differences in direct food sales.
Detre et al. (2011) investigated the factors affecting the
adoption of a DMS by farmers and its impact on the gross
sales of farm operations in the USA. They found that the
production of organic crops and the regional location of
the farm positively affect adoption of a DMS. However,
they highlighted that adoption of a DMS has a negative
relation to large farms, farms with production contracts
and farms specializing in cash grains.
Uematsu and Mishra (2011) identified factors affecting
the total number of DMSs adopted by farmers. Then they
estimated a quantile regression model to assess the impact
3
of the intensity of adoption of DMSs on gross cash farm
income. They found that farming experience in years,
total acres operated, direct payments received by the
farm in dollars and farm location were all found to negatively affect adoption intensity of DMSs. They also found
that farmers with livestock, high value and other field
crops farms are likely to more intensively adopt DMSs
than cotton and cash grain farmers. According to the
authors, greater educational attainment, farming as
primary occupation, farming as a primary occupation
for the respondent’s spouse, seeking advice from
Natural Resource Conservation Service agents, receiving
payments from the Conservation Reserve Program and
having internet access at their farm were all positively
associated with the adoption of DMSs.
Nyaupane and Gillespie (2010) used the results of a
2008 survey data from the Louisiana crawfish industry
to investigate the factors influencing farmer selection of
a crawfish marketing outlet. Results from this study
show that farm size, farm income, household income,
age, education and pre-market grading and washing
operations significantly affect farmer selection of marketing outlet. The authors pointed out that farmers who
washed crawfish before selling and had higher percentages
of their farm income coming from crawfish were the more
likely farmers to market direct to consumers. Older, less
highly educated farmers were more likely to market
direct to processors. Scale of operation was the major determinant of whether farmers would sell directly to retailers, as larger farmers were the ones who had the volume
required to sell directly to the retail market.
Sage and Goldberger (2012) investigated the effects of
producers’ ‘worlds of justification’ on willingness to participate in DTC markets. They found that a producer who
had always farmed organically was nearly three times
more likely to participate in direct marketing compared
with a producer who switched from conventional production. They revealed that dairy/livestock producers were
significantly less likely to participate in a direct-to-consumer marketing strategy compared with other major
producer types. The results also indicated that both the
MI (Market Performance and Industrial Efficiency) and
CG (Civic Equality and Green) factors were demonstrated in this model to be significant factors in the decision to direct market. Unit increases in the value placed
on MI justifications for their decision to farm organically
substantially reduces the likelihood of participating in
direct market venues. Those producers who placed high
value on CG justifications were significantly more likely
to direct market than those who placed low value on
CG justifications.
Park et al. (2011) investigated the factors affecting
choices of direct sales by farmers. They determined that
using the Internet for farm commerce and growing a
diversified selection of products (more enterprises)
increases the likelihood that a farmer uses DTC marketing outlets.
4
Wolanin (2013) focused on the factors affecting the intensity with which fruit and vegetable farmers incorporate
DMSs into their agricultural enterprises, where intensity
is measured by the percentage of sales made through
DTC outlets. Wolanin found that the factors significantly
affecting producer intensity of adoption of DMSs are age,
use of university/extension publications, percentage of
income from farming and access to food hub organizations. Results from this study indicate that farmers
getting closer to their retirement age or slowing down in
their off-farm jobs, using University/Extension publications to obtain information on how to better market
produce in the past 5 yr, with a percentage of income
from farming of 25% or less, or who have their farming
operation located in a county with no access to a food
hub organization tend to make a larger percentage of
their fruit and vegetable sales through DTC outlets or
tend to more intensively adopt DMSs.
Benedek et al. (2014) used an econometric model to
identify factors that drive farmers’ decisions on where to
sell their produce (whether to sell at traditional or
farmers’ markets). Results from this study indicated that
a relatively young, educated and innovative group of
small-scale farmers were interested mostly in selling at
the newly introduced farmers’ markets.
Corsi et al. (2014) investigated the determinants of the
choice of farmers to sell their products directly to consumers. They concluded that operators’ and farm characteristics were found to affect the choice of selling directly, but
rather weakly. The most important factors affecting these
choices were farm location and, for on-farm direct sales,
the complementarity with agro-tourism and recreational
activities. Relative to plains, farms located in the mountains are 12.2% more likely to sell their products on the
farm, and farms in hills 7%. If the farm has some agrotourism, or recreational activity, the likelihood of selling
directly on the farm is increased by 25 and 11%, respectively. This study revealed an interesting finding regarding
the type of farming (TF). All specialized TFs had a lower
probability to sell directly on the farm relative to the
mixed TFs, taken as reference.
Park et al. (2014) investigated firstly the role of marketing and management skills in the choice of DMSs by
farmers. Secondly, they assessed the impact of management and marketing skills on the farm financial performance. Findings from this study show that management
and marketing skills significantly affect DTC sales.
According to their findings, increasing management
skills––more ways to control input costs––increases the
likelihood that a farmer uses DTC marketing outlets.
Additionally, a larger share of income coming from
sales of vegetable, fruits and nursery enterprises is associated with a greater likelihood that a farmer uses DTC
marketing outlets. They also find that beginning farmers
are more likely to use DTC marketing outlets.
Park (2015) examined the impact of participation in
direct marketing on the entire distribution of farm sales
H. Adanacioglu
using the unconditional quantile regression estimator.
Park assessed how farmers’ sales were influenced by
their involvement in direct marketing. Results from this
study indicated that smaller operations were more severely impacted when farmers participated in direct marketing
compared with larger operations. Results also indicated
that experienced farmers involved in direct marketing
were faced with larger sales declines compared with
farmers who were not involved in direct marketing.
Finally, Park pointed out that although participation in
direct marketing was associated with a decline in farm
sales, farmers who experienced growth in off-farm
income were better able to withstand these sales declines.
Muthini (2015) assessed the factors that influence
mango farmers’ choice of market channels in Makueni
County. Multinomial logit model was used to quantify
the factors affecting channel choice. Results of the study
showed that farmers sold to three major channels,
which are brokers, exporters and direct market. Majority
of the farmers (58%) sold to brokers, 30% to export, while
the rest sold to direct market. The multinomial logit
results showed that farmers who owned a vehicle, were
closer to the tarmac road and had access to market information were more likely to sell to direct market relative
to brokers.
Materials and Methods
Data
An examination of related research studies concerned
with direct farm marketing in various countries shows
that the main products sold through direct marketing
channels are fresh fruits and vegetables. This has also
been observed for Turkey. In this study, a survey of
cherry growers was conducted in order to determine the
factors that influence farmers’ decisions to participate in
direct marketing in Turkey.
Data were collected in 2012 via personal interviews of a
random sample of 102 cherry farmers in Izmir Province in
Turkey. The survey was implemented in the district of
Kemalpasa in Izmir Province, which is an important
area for cherry production in Turkey (Adanacioglu,
2013). Izmir cherry production was about 56,772 tons in
2012, according to the Izmir Directorate of Provincial
Food, Agriculture and Livestock (MFAL, 2012).
Kemalpasa provides 86.32% of Izmir’s cherry production.
In the choice of subdistricts where the survey would be
carried out, the number of cherry producers and the
amount of production were taken into account. In this
way, the three subdistricts Bagyurdu, Oren and Yigitler
were selected.
The sample size was determined by using the proportional sampling method (Newbold, 1995).
n¼
Npð1 pÞ
ðN 1Þσ 2^px þ pð1 pÞ
ð1Þ
Factors affecting farmers’ decisions to participate in direct marketing
where n = sample size, N = number of farms (2157
farmers), p = the percentage of farmers who grow cherries
(taken as 0.50 to reach maximum sample size), and σ2px =
variance.
According to the proportional sampling method, with
a 95% confidence interval and 9.5% error margin, the
required sample size was found to be 102.
Empirical model and description of variables
An ordinal logistic regression model was used to identify
the factors that influence farmers’ decisions to participate
in direct marketing. Standard statistical texts describe
several approaches to specifying (parameterizing) an
ordinal logistic regression model. Here, we used the cumulative logit model, which is the most common form. A cumulative logit is defined for the probability of having an
ordinal response less than or equal to k, relative to the
probability of having a response greater than k:
logit½PðykÞjx¼ ln
¼ ln
PðykÞjx
Pðy>kÞjx
Pðy¼ 1jxÞþþPðy ¼kjxÞ
ð2Þ
Pðy¼k þ1jxÞþþPðy¼KjxÞ
¼ β0ðkÞ ðβ1 x1 þβ2 x2 þþβp xp Þ
For an ordinal variable with K categories, K − 1 cumulative logit functions are defined. Each cumulative logit
function includes a unique intercept or ‘cut point,’ β0(k),
but all share a common set of regression parameters for
the p predictors, β = (β1,…,βp). Consequently, a cumulative
logit model for an ordinal response variable with K categories and j = 1,…, p predictors requires the estimation
of (K − 1) + p parameters. The concept of a ‘cumulative
logit’ is certainly more complex than that of a baseline
category logit that is employed in the parameterization
of simple logistic regression or even multinomial logit regression models. In actuality, though, it is simply an alternative way of parameterizing the model for estimating
the probability that a response will fall in ordinal category
y = 1,…, K (Heeringa et al., 2010).
The dependent variable, in this model, is the degree of
farmers’ tendency toward direct marketing options in
ordinal form. Farmer respondents were asked to indicate
the extent of their preference (1 = not at all; 5 = a great
deal) for each of the following direct marketing options:
local vegetable and fruit markets/farmers’ markets,
Internet or e-mail sales, roadside stands, on-farm sales
and community-supported agriculture. Afterward, the
final score was computed by averaging the responses to
all of the items. The ordinal dependent variable used in
the current study was categorized into three groups
based on the final scores as follows: 1: (a weak tendency
toward direct marketing) if the final score ≤2, 2: (a moderate tendency toward direct marketing) if the final score
>2–3, 3: (a strong tendency toward direct marketing) if
5
the final score >3. In the analysis, category 3 was taken
as the event of reference.
Generally, previous empirical studies have used a wide
range of variables to determine factors influencing
farmers’ decision to participate in direct marketing. In accordance with previous studies, the following variables
were included as explanatory variables in the model: age
(AgeD1, D2, D3, D4), educational attainment of the
farm operator (EducD1, D2, D3, D4), farm operator’s
experience in cherry growing (FexpD1, D2, D3), farm
size (FarmSizeD0, D1), cultivated area with cherry
(CoSizeD1, D2, D3), level of specialization in cherry production (GPVCherryD0, D1) and total annual farm
income (IncomeD1, D2, D3, D4, D5). Unlike previous
studies, two variables were also included in the model as
explanatory variables, namely gross margin per hectare
for cherry production (GrmD1, D2, D3) and number of
marketing channels used by farmers (MchnD0, D1).
Variable definitions, as well as descriptive statistics for
the explanatory variables employed in the ordinal logistic
regression model, are presented in Table 1.
Age is hypothesized to be positively correlated with the
decision to participate in direct marketing. This hypothesis is consistent with the finding of Wolanin (2013) who
concluded that farmers getting closer to their retirement
age tend to make a larger percentage of their fruit and
vegetable sales through DTC outlets or tend to more intensively adopt DMSs. Wolanin (2013) cites that as
farmers get close to the retirement age they may have
more time available to be devoted to farming activities, including marketing, and therefore may increase the adoption intensity of DMSs. This hypothesis was also
supported by the finding of Nyaupane and Gillespie
(2010) who concluded that older farmers were more
likely to market direct to processors.
Education is hypothesized to be positively correlated
with the decision to participate in direct marketing. This
is consistent with the hypothesis put forward by Monson
et al. (2008) in their study. They put forward the hypothesis
that as the primary decision maker’s degree of education
increases, reliance on direct marketing will increase.
Wolanin (2013) cites that producers who have a bachelor
or graduate degree are expected to have a larger percentage
of their sales made through DTC outlets. This hypothesis
was also supported by the findings of Gilg and Battershill
(1999), Corsi et al. (2009) and Benedek et al. (2014).
Farm operator’s experience in cherry growing is
hypothesized to be positively correlated with the decision
to participate in direct marketing. This is also consistent
with the hypothesis put forward by Monson et al.
(2008) in their study. They put forward the hypothesis
that producers who have more experience in production
and marketing will be better able to meet the quality
expectations of direct market consumers and earn a
higher profit. As seen from the literature review, this hypothesis was also supported by the findings of Corsi
et al. (2009) and Park (2015).
6
H. Adanacioglu
Table 1. Descriptive statistics for variables used in the empirical models.
Variable
Description
n
Dmt (dependent)
Growers’ tendency towards direct marketing of cherry
(ordered, 1: weak, 2: moderate, 3: strong)
Age of grower (1 = if age of grower is 25–34 yr, 0 = otherwise)
Age of grower (1 = if age of grower is 35–44 yr, 0 = otherwise)
Age of grower (1 = if age of grower is 45–54 yr, 0 = otherwise)
Age of grower (1 = if age of grower is 54+ years, 0 = otherwise)
Growers’ level of education (1 = if farmer had 1–5 yr of
education, 0 = otherwise)
Growers’ level of education (1 = if farmer had 6–8 yr of
education, 0 = otherwise)
Growers’ level of education (1 = if farmer had 9–11 yr of
education, 0 = otherwise)
Growers’ level of education (1 = if farmer had more than 11 yr
of education, 0 = otherwise)
Cherry farming experience of the growers (1 = if grower had
1–10 yr of experience, 0 = otherwise)
Cherry farming experience of the growers (1 = if grower had
11–20 yr of experience, 0 = otherwise)
Cherry farming experience of the growers (1 = if grower had
more than 20 yr of experience, 0 = otherwise)
Size of the cherry orchard in hectares (1 = the size of orchard is
less than 1 ha, 0 = otherwise)
Size of the cherry orchard in hectares (1 = the size of orchard is
1–2 ha, 0 = otherwise)
Size of the cherry orchard in hectares (1 = the size of orchard is
more than 2 ha, 0 = otherwise)
Farm size in hectares (1 = the farm size is less than or equal to
3.2 ha, 0 = otherwise)
Farm size in hectares (1 = the farm size is more than 3.2 ha,
0 = otherwise)
Nonspecialized farms in cherry production, : 1, if the percent
of the cherry production value in the farm’s total gross
production value (GPV) is less than 80%, 0 = otherwise;
Specialized farms in cherry production, : 1, if the percent of the
cherry production value in the farm’s total gross production
value (GPV) is greater than 80%, 0 = otherwise
Total annual farm income (1: if the farm income is less than
US$13,766;0 = otherwise)
Total annual farm income (1: if the farm income is between
US$13,766–27,531; 0 = otherwise)
Total annual farm income (1: if the farm income is between
US$27,532–41,297; 0 = otherwise)
Total annual farm income (1: if the farm income is between
US$41,298–55,062; 0 = otherwise)
Total annual farm income (1: if the farm income is greater
than or equal to US$55,063; 0 = otherwise)
Gross margin per hectare for cherry production (1: if the gross
margin is less than US$2753; 0: otherwise)
Gross margin per hectare for cherry production (1: if the gross
margin is between US$2753–5506; 0: otherwise)
Gross margin per hectare for cherry production (1: if the gross
margin is greater than US$5506; 0: otherwise)
The number of marketing channels used by farmers (1: if it is
less than or to equal 3, 0: otherwise)
The number of marketing channels used by farmers (1: if it is
more than 3, 0: otherwise)
AgeD1
AgeD2
AgeD3
AgeD4
EducD1
EducD2
EducD3
EducD4
FexpD1
FexpD2
FexpD3
CoSizeD1
CoSizeD2
CoSizeD3
FarmSizeD0
FarmSizeD1
GPVCherryD0
GPVCherryD1
IncomeD1
IncomeD2
IncomeD3
IncomeD4
IncomeD5
GrmD1
GrmD2
GrmD3
MchnD0
MchnD1
Min
Max
Mean
SD
102
1
3
1.92
0.767
12
11
42
37
43
0
0
0
0
0
1
1
1
1
1
0.12
0.11
0.41
0.36
0.42
0.324
0.312
0.495
0.483
0.496
24
0
1
0.24
0.426
28
0
1
0.27
0.448
7
0
1
0.07
0.254
19
0
1
0.19
0.391
36
0
1
0.35
0.480
47
0
1
0.46
0.501
34
0
1
0.33
0.474
40
0
1
0.39
0.491
28
0
1
0.27
0.448
67
0
1
0.66
0.477
35
0
1
0.34
0.477
47
0
1
0.46
0.501
55
0
1
0.54
0.501
27
0
1
0.26
0.443
38
0
1
0.37
0.486
11
0
1
0.11
0.312
12
0
1
0.12
0.324
14
0
1
0.14
0.346
33
0
1
0.32
0.470
17
0
1
0.17
0.375
52
0
1
0.51
0.502
72
0
1
0.71
0.458
30
0
1
0.29
0.458
Factors affecting farmers’ decisions to participate in direct marketing
Both farm size and area under cherry orchard are
hypothesized to be negatively correlated with the decision
to participate direct marketing. These are consistent with
the hypothesis put forward by Monson et al. (2008) and
Wolanin (2013). Monson et al. (2008) put forward the hypothesis that since larger farmers can produce greater
volumes, they may have an incentive to economize on
their marketing costs by selling to buyers who can
absorb a greater share of their production than direct
market customers, who tend to make relatively small purchases. Additionally, Wolanin (2013) cites that smaller
operations are more likely to rely on DTC outlets,
perhaps because these operations may not be able to consistently produce quality fruits and vegetables or meet the
high volume demands of intermediary or retail outlets.
These hypotheses were also supported by the findings of
Monson et al. (2008), Auld et al. (2009), Corsi et al.
(2009), Timmons and Wang (2010), Detre et al. (2011),
and Benedek et al. (2014) who concluded that adoption
of a DMS has a negative relation to large farms.
Specialization in cherry production is hypothesized to
be negatively correlated with the decision to participate
in direct marketing. The fully-specialized farms which
had low crop diversity did not generally want to take
risk to sell directly to consumers due to lack of knowledge
and experience. As seen from the literature review, this
hypothesis was also supported by the findings of Aguglia
et al. (2009), Detre et al. (2011), Park et al. (2011, 2014),
and Corsi et al. (2014) who concluded that as farms had
become more specialized, they had a lower likelihood to
market through DTC outlets.
Total annual farm income is hypothesized to be positively correlated with the decision to participate in direct
marketing. This is also consistent with the hypothesis
put forward by Monson et al. (2008) in their study.
They put forward the hypothesis that as household
income increases, reliance on direct marketing channels
will increase. They also highlight that higher-income
farmers may be wealthier as a result of their participation
in direct marketing, or their income may serve as an enabling factor-income from farm and off-farm activities may
be used to fund investment in high-value and direct marketing enterprises. As seen from the literature review, this
hypothesis was also supported by the findings of
Nyaupane and Gillespie (2010) and Park et al. (2014)
who concluded that farm income significantly affects
farmer selection of marketing outlet.
Gross margin obtained per hectare for cherry production is hypothesized to have a positive effect on the
cherry growers’ decisions to participate in direct marketing. As shown in Table 1, gross margin is the difference
between gross income and variable costs. It represents
how much each unit of an enterprise contributes toward
fixed costs and profits, after the variable costs of production have been paid (Kay et al., 2004). The above mentioned hypothesis was also supported by the finding of
Park et al. (2014) who concluded that a significant level
7
of management skill—more ways to control input
costs—increases the likelihood that a farmer uses DTC
outlets. Park et al. (2014) emphasized that management
and marketing skills significantly affect DTC sales.
The growth in numbers of marketing channels used by
farmers is hypothesized to be negatively correlated with the
decision to participate in direct marketing. Farmers who
used much fewer marketing channels for their products are
more likely to use direct marketing channels, due to a lack
of access to markets. As mentioned above, this hypothesis
was supported by the finding of Park et al. (2014) who concluded that marketing skills significantly affect DTC sales.
There is a strict assumption that has to be made when
using an ordinal regression model, the parallel lines assumption. That is to say, the regression coefficients are
equal for all corresponding outcome categories (Minetos
and Polyzos, 2007). Therefore, the test of parallel lines
was designed to make a judgment concerning the adequacy of the model. The null hypothesis states that the
corresponding regression coefficient is equal across all
levels of the response variable. The alternative hypothesis
states that the corresponding regression coefficients are
different across all levels of response variable (Yay and
Akinci, 2009).
Before proceeding to examine the individual coefficients, we looked at an overall test of the null hypothesis
that the location coefficients for all of the variables in
the model are 0. This can be based on the change in –2
log-likelihood when the variables are added to a model
that contains only the intercept. The change in likelihood
function has a chi-square distribution even when there are
cells with small observed and predicted counts. If the chisquare has an observed significance level of less than 0.05,
this means that we can reject the null hypothesis that the
model without predictors is as good as the model with the
predictors.
A standard statistical maneuver for testing whether a
model fits is to compare observed and expected values.
For this purpose, from the observed and expected frequencies, we computed the usual Pearson and deviance
goodness-of-fit measures. Both of the goodness-of-fit statistics should be used only for models that have reasonably
large expected values in each cell. If our model fits well,
the observed and expected cell counts are similar, the
value of each statistic is small and the observed significance level is large. We reject the null hypothesis that
the model fits if the observed significance level for the
goodness-of-fit statistics is small. Good models have
large observed significance levels.
Cox and Snell’s R2, Nagelkerke’s R2 and McFadden’s
2
R , which are three commonly used statistics, were used
to measure the strength of the association between the dependent variable and the predictor variables. These measures do not have the percent of variance explained and
should not be reported in those terms. They can be
taken as additional measures of model effect size, with
higher values being better.
8
The exponentiated coefficients were used for the parameter’s interpretation in the ordinary logistic regression
model. The odds ratios can be obtained by exponentiation
of the coefficients. Pi/(1 − Pi) is referred to as the odds of
an event occurring (Williams, 2015). The effect of each
variable on the odds comes from taking the antilog of
the coefficients (exponentiated coefficients) (Pampel,
2000). In the case of an ordinal outcome with three or
more categories, the odds ratio for the logit model represents the odds of the higher category, as compared with all
lower categories combined (Heck et al., 2013). Standard
interpretation of the ordered logit coefficient is that for
a one-unit increase in the predictor, the response variable
level is expected to change by its respective regression
coefficient in the ordered log-odds scale, while the other
variables in the model are held constant (Chen et al.,
2003).
In order to measure the effects of explanatory variables
on whether a farmer has a tendency toward direct marketing, the marginal effects were calculated. A marginal
effect is the influence a one-unit change in an explanatory
variable has on the probability of selecting a particular
outcome, ceteris paribus. This holds for continuous variables only. For dummy (1, 0) variables, the marginal
effects are the derivatives of the probabilities, given a
change in the level of the dummy variable (Greene, 2008).
Wald Ӽ2 statistics were used to test the significance of
individual coefficients in the model and are calculated
as follows (Bewick et al., 2005):
coefficient 2
ð3Þ
¼
SE coefficient
Each Wald statistic is compared with a Ӽ2 distribution
with one degree of freedom. Wald statistics are easy to calculate, but their reliability is questionable, particularly for
small samples.
Results and Discussion
The socio-economic characteristics of sample
cherry farms
Table 2 summarizes the survey results on socio-economic
characteristics of the cherry farms. The average age of the
farm household operators was about 50 yr old and did not
vary significantly across farm size. Operators had fairly
long experience in cherry farming, and farmers had, on
average, 28.64 yr of experience in cherry production.
The operator’s experience in cherry farming typically
increased with farm size. The average number of years
in school of the household operators in the sample was
about 8 yr. The average years of schooling among smallholders was lower, at 7.41 yr, compared with 8.25 yr for
household operators of medium- and large-scale farms.
In terms of average farm area operated, the sampled
large-scale farms operated on 6.02 ha, which is much
higher than the landholdings of small-scale farms
H. Adanacioglu
(1.29 ha) and medium-scale farms (2.85 ha). The
average area of cherries grown was 1.5 ha and increased
with farm size. It ranged from 0.59 ha in the case of
small farms to 2.90 ha in the case of large farms.
The average farm household had an annual net income
of nearly US$32,966. Large-scale farms, with US$56,628,
had a higher average annual farm income per farm than
other farm sizes; however, the average annual income of
small-scale farms, with nearly US$19,634, was lower
compared with other farm size categories. The average
household size was about four. The household size of
large farms, with 4.79 members, was more compared
with other farm size categories.
Use of direct marketing options by cherry
growers and options that they plan to use in
the future
When interviewing farm operators about whether they
have used direct marketing options, only two growers
reported using direct marketing options. This does not
mean that cherry growers do not tend toward direct marketing. One of the reasons for this was the lack of experience and knowledge in using direct marketing techniques
among growers. The second reason appears to be that
there are not any organizations that will help them meet
their direct marketing goals and build direct marketing
arrangements between themselves and their consumers.
In examining the possible direct marketing options that
the cherry growers plan to use in the future in cases of
selling directly to consumers, community-supported agriculture (CSA) emerged as having drawn the most attention (Table 3). CSA was also described as an important
marketing strategy for small, mid-sized and large farms.
Especially, medium-sized farms pay more attention to
CSA compared with small and large farms. There were
also statistically significant differences in favor of
medium-sized farms.
On-farm sales (also known as on-farm stores) are considered to be the second most important option that the
cherry growers plan to use in the future in cases of
selling directly to consumers. The reason they are
paying attention to this option may be due to the lack
of cold storage to keep cherries fresh and the general
absence of refrigerated transport.
The use of Internet or e-mail sales as a direct marketing
option in the future was given low priority. Although
61.0% of the surveyed growers own a computer and
92% of growers have an Internet connection, the cherry
growers were not interested in selling their products via
the Internet due to a lack of marketing knowledge.
Due to a small number of farmers’ markets across
the province and long distances to existing farmers’
markets, most of the growers surveyed believe that
farmers’ markets are a less feasible option for direct marketing in the future. But farmers’ markets may be the best
of the options if the numbers of farmers’ markets are
Factors affecting farmers’ decisions to participate in direct marketing
9
Table 2. Socio-economic characteristics of cherry farms.
Farm size
Variable
Age of farm operator (yr)
Farming experience of farm operator (yr)
Cherry farming experience of farm operator (yr)
Year of education of farm operator (yr)
Farm size (hectares)
Size of the cherry orchard (hectares)
Annual farm income (US dollar)1
Household size (person)
Small (N = 34)
Medium (N = 40)
Large (N = 28)
All (N = 102)
52.15
28.71
20.15
7.41
1.29
0.59
19,634
3.59
48.68
27.33
20.78
8.25
2.85
1.30
27,734
3.98
50.57
30.43
22.64
8.25
6.02
2.90
56,628
4.79
50.35
28.64
21.08
7.97
3.20
1.50
32,966
4.07
1
The average exchange rates between Turkish Lira (TRY) and the US dollar (USD) for June 2012 is US$1 = 1.8161TL (CBRT,
2012).
Table 3. Direct marketing options that cherry growers plan to use in the future.
Direct marketing options
Community-supported agriculture
On-farm sales
Internet or e-mail sales
Local vegetable and fruit markets/farmers’ markets
in Izmir province
Local vegetable and fruit markets/farmers’ markets
in Kemalpasa district (Izmir)
Selling direct to neighbors and acquaintances
Roadside stands
Selling during cherry festivals or special events
Peddling
Small
farms (34)
x
Medium-sized
farms (40)
x
Large
farms (28)
x
All (102)
x
Values for the
Kruskal–Wallis
Test
P value
4.03
3.09
2.24
1.94
4.58
3.65
2.45
2.53
4.04
3.11
2.36
1.86
4.25
3.31
2.35
2.15
7.292
2.985
0.439
3.840
0.026*
0.225
0.803
0.147
2.09
2.43
1.79
2.14
2.711
0.258
1.82
1.29
1.21
1.26
1.93
1.48
1.35
1.23
1.50
1.14
1.00
1.00
1.77
1.32
1.21
1.18
1.336
2.893
6.694
4.155
0.513
0.235
0.035*
0.125
: the mean score of 5-point Likert scale (1 = absolutely not, 2 = preferably not, 3 = neutral, 4 = possibly, or 5 = definitively).
x
* Denotes significance at the 5% level.
spread across the country. For example, farmers’ markets
also serve as a key direct marketing channel for small and
mid-size US producers (Diamond and Soto, 2009), so
with the recent growth in their numbers, one could perceive that the prevalence of direct sales may boost farm
income. The presence, growth and new creation of
farmers’ markets are some of the most apparent signals
of consumer and producer interest in developing direct
markets. The number of farmers’ markets in the USA
has grown dramatically, increasing 226% from 1996 to
2012, with over 7800 farmers’ markets operating in the
USA (Thilmany et al., 2012).
The least preferred direct marketing options for cherry
growers in planning strategies for future direct marketing
initiatives are selling directly to neighbors and acquaintances, roadside stands, selling during cherry festivals or
special events and peddling.
Analysis of factors affecting cherry growers’
decisions to participate in direct marketing
An ordered logistic regression model was used to identify
the factors that influence cherry growers’ decisions to participate in direct marketing. Firstly, we evaluated the appropriateness of this assumption through the ‘test of
parallel lines’ to check whether the model meets the parallel lines assumption or not. Table 4 given below shows
that the assumption is met, as the test shows a level of
nonsignificance (0.271 > 0.05).
From Table 5, we see that the difference between the
two log-likelihoods—the chi square—has an observed
significance level of less (P = 0.042) than 0.05. This
means that we can reject the null hypothesis that the
model without predictors is as good as the model with
the predictors.
10
H. Adanacioglu
Table 4. Test of parallel lines.
Model
Null hypothesis
General
Table 6. Goodness-of-fit statistics.
−2 Log likelihood
χ2
df
Sig.
185.306
163.034
22.273
19
0.271
The null hypothesis states that the location parameters (slope
coefficients) are the same across response categories.
Intercept only
Final
Pearson
Deviance
189.387
181.147
df
Sig. (P)
177
177
0.249*
0.400*
* P values are greater than 0.05.
Table 7. Pseudo R2.
Table 5. Model-fitting information.
Model
χ2
−2 log likelihood
χ2
df
Sig. (P)
216.158
185.306
30.851
19
0.042
Table 6 gives the Pearson and deviance goodness-of-fit
measures of the model. We see that the goodness-of-fit
measures have large observed significance levels, so it
appears that the model fits.
Table 7 shows the pseudo R2 values for the strength of
association between the variables. The values of the
pseudo R2 show a moderate size effect.
The estimated coefficients for the factors affecting the
cherry growers’ tendency toward participation in direct
marketing, along with their Standard errors, Wald test
values, P-values, odds ratios and marginal effects, are presented in Table 8. Results in Table 8 show that the coefficients of age of the grower, the grower’s level of education,
total annual farm income, farm size and the number of
marketing channels used by growers are not statistically
significant. Finally, parameter estimates for these variables have no significant impact on the cherry growers’
tendency toward participation in direct marketing.
On the other hand, the results show that the cherry
farming experience of the farmers, the size of the cherry
orchard, the level of specialization in cherry production
and the gross margin per hectares for cherry production
have a statistically significant impact on the cherry
growers’ tendency toward participation in direct
marketing.
The category that is statistically significant in terms of
years of experience in cherry growing is the growers who
have experience of between 11 and 20 yr. This category
was compared with a reference category. The last category, or category 3, was used as the reference group,
which includes the growers with more than 20 yr experience in cherry growing. The exponentiated coefficients,
or odds ratios, reported in Table 8 show that the
growers having experience of between 11 and 20 yr tend
to use direct marketing about 0.4 times less than the
farmers with more than 20 yr experience. In other
words, the growers with more than 20 yr experience tend
to use direct marketing 2.53 (1/0.396) times more than
growers having experience of between 11 and 20 yr.
Cox and Snell
Nagelkerke
McFadden
0.261
0.295
0.140
Based on an assessment of marginal effect, the growers
having experience of between 11 and 20 yr tend to use
direct marketing 9.4% less than the growers with more
than 20 yr experience. The results suggest that the more
experienced growers in cherry farming are more likely
to sell their product directly to consumers. This is
because growers who have more experience in cherry marketing have a greater need to seek alternative marketing
options compared with less experienced farmers. They
have been forced to accept low prices when selling their
produce to indirect markets for a long time. Therefore,
it is highly recommended to work with more experienced
cherry growers in projects focused on the development of
direct marketing in terms of achieving the desired results.
This result supports the hypothesis that as producers’
experience in agriculture increases, reliance on direct marketing channels will increase (Monson et al., 2008). As
noted by Monson et al. (2008), producers who have
more experience in production and marketing will be
better able to meet the quality expectations of direct
market consumers and earn a higher profit. It is also possible that these producers will have more established ties
and extended relationship networks within the community, which will enhance their ability to market to locals.
Considering the fact that farmers typically gain skills
through work experience, this result is consistent with
the finding of Corsi et al. (2009) who concluded that
more skilled farmers were less likely to choose traditional
marketing chains and more likely to engage in the new
marketing chains. This result also supports the finding
of Park et al. (2014) who concluded that increasing
management skills—more ways to control input costs—
increased the likelihood that a farmer used DTC marketing outlets. However, Uva (2002) warned that a DMS
required a set of skills different from those for agricultural
operations.
While examining the impact of the size of total acreage
farmed that is dedicated to cherry production on the
cherry growers’ tendency toward participation in direct
marketing, the total acreage devoted to cherry production
was divided into three categories. When considering the
Factors affecting farmers’ decisions to participate in direct marketing
11
Table 8. Ordered logit regression model estimates evaluating factors affecting cherry growers’ decisions to participate in direct
marketing.
Variables
[Dmt = 1.00]
[Dmt = 2.00]
[AgeD1 = 1.00]
[AgeD2 = 2.00]
[AgeD3 = 3.00]
[AgeD4 = 4.00]
[EducD1 = 1.00]
[EducD2 = 2.00]
[EducD3 = 3.00]
[EducD4 = 4.00]
[FexpD1 = 1.00]
[FexpD2 = 2.00]
[FexpD3 = 3.00]
[CoSizeD1 = 1.00]
[CoSizeD2 = 2.00]
[CoSizeD3 = 3.00]
[FarmSizeD0 = 0.00]
[FarmSizeD1 = 1.00]
[GPVCherryD0 = 0.00]
[GPVCherryD1 = 1.00]
[IncomeD1 = 1.00]
[IncomeD2 = 2.00]
[IncomeD3 = 3.00]
[IncomeD4 = 4.00]
[IncomeD5 = 5.00]
[GrmD1 = 1.00]
[GrmD2 = 2.00]
[GrmD3 = 3.00]
[MchnD0 = 0.00]
[MchnD1 = 1.00]
/cut1
/cut2
Odds ratio-exp (β)
Marginal effect (dy/dx)1
0.716
0.024
0.312
0.761
0.175
0.438
0.782
2.026
−0.076
−0.026
0.078
1
1
1
0.968
0.707
0.376
1.039
1.433
2.371
0.005
0.045
0.104
0.032
3.395
1
1
0.858
0.065**
0.889
0.396
−0.024
−0.094
0.708
0.609
0.016
3.206
1
1
0.899
0.073**
0.914
2.975
−0.005
0.128
0.697
2.122
1
0.145
0.362
−0.129
0.499
4.920
1
0.027*
3.027
0.123
0.908
0.824
0.905
0.911
1.507
1.166
0.469
0.774
1
1
1
1
0.220
0.280
0.493
0.379
3.049
2.435
1.859
0.449
0.151
0.110
0.082
−0.070
0.500
0.618
0.157
3.373
1
1
0.692
0.066**
1.219
0.321
0.020
−0.104
0.477
1.453
1
0.228
1.776
0.061
Estimate
SE
Wald
df
Sig.
0.413
2.634
−0.824
−0.245
0.706
01
0.038
0.360
0.863
01
−0.118
−0.927
01
−0.090
1.090
01
−1.016
01
1.108
01
1.115
0.890
0.620
−0.801
01
0.198
−1.135
01
0.574
01
0.413
2.634
1.136
1.168
0.815
0.807
0.521
0.132
5.088
1.022
0.092
1.836
1
1
1
1
1
0.963
0.958
0.976
0.002
0.141
0.783
0.659
0.503
1.072
1.102
* and ** denotes significance at the 5 and 10% levels, respectively.
1
Marginal effects were calculated based on category 3 which denote the strongest tendency of direct marketing.
statistical significance of the parameters for the categories,
only the coefficient on cherry growers having cherry orchards between 1 and 2 ha was statistically significant. The
farms that have more than 2 ha of cherry orchards were
taken as the reference category. Accordingly, the farms
between 1 and 2 ha tend to use direct marketing nearly
3 times (2.975) more than farms of over 2 ha. Based on
an assessment of marginal effect, the growers who have
cherry orchards between 1 and 2 ha tend to use direct
marketing options 12.8% more than growers with cherry
orchards of over 2 ha. This result supports generally the
hypothesis that as farm size increases, producers become
less reliant on direct marketing channels to sell their
output. As noted by Monson et al. (2008), there are
several justifications for this hypothesis. Firstly, larger
farms are in a better position to overcome the barriers
to entry to other potentially lucrative markets, such as
supermarkets. Additionally, since larger farmers can
produce greater volumes, they may have an incentive to
economize on their marketing costs by selling to buyers
who can absorb a greater share of their production than
direct market customers, who tend to make relatively
small purchases. This finding is generally consistent with
findings from previous studies (Monson et al., 2008;
Auld et al., 2009; Corsi et al., 2009; Timmons and
Wang, 2010; Detre et al., 2011; Benedek et al., 2014),
which suggest that large farms rather choose traditional
chains rather than the direct and the short chains. These
previous studies have demonstrated that adoption of a
DMS has a negative relation to large farms.
The study also showed whether specialization in cherry
production may impact growers’ tendency to participate
in direct marketing. Farms that are fully specialized in
cherry production were taken as the reference category.
We found a statistically significant result in terms of nonspecialized farms in cherry production. Accordingly,
farms that are not fully specialized in cherry production
tend to use direct marketing 3 times (3.027) more than
12
fully specialized farms. Based on an assessment of marginal effect, farms that are not fully specialized in
cherry production tend to use direct marketing options
12.3% more than fully specialized farms. A significant
proportion (more than 80%) of the total gross production
value is derived from cherry cultivation in the fully specialized farms. On the other hand, the value of production
for cherries produced on the not fully specialized farms
accounts for a small proportion of the total gross production value. These farms mainly produce fresh peaches,
compared with other products. It is possible to say that
these farmers shift toward semi-specialized farming.
This result indicates that semi-specialized farms have a
higher tendency to use direct marketing techniques than
fully specialized farms. The major reason for this situation
was that semi-specialized farms had a higher level of crop
diversification than fully specialized farms. The fully-specialized farms, which had low crop diversity did not generally want to take risk to sell directly to consumers due to
lack of knowledge and experience. This result reveals that
direct marketing options are more appropriate for semispecialized farms. This result supports findings by
Aguglia et al. (2009), Detre et al. (2011), Park et al.
(2011), and Corsi et al. (2014), who concluded that as
farms had become more specialized, they had a lower
likelihood to market through DTC outlets. This result is
also consistent with the findings of Park et al. (2014)
who concluded that farmers engaged in direct sales only
tend to market a greater diversity of crops and have a
larger variation in the earnings shares of those crops compared with farmers with no direct sales.
While examining the impact of gross margins obtained
per hectare for cherries on cherry growers’ tendency
toward participation in direct marketing, the gross
margins obtained per hectare were divided into three categories. When considering the statistical significance of
the parameters for the categories, only the coefficient on
cherry farms that achieve a gross margin between US
$2753 and 5506 ha−1 for cherries was statistically significant. The farms that achieved more than US$5506 ha−1
were taken as the reference category. Accordingly, farms
that achieve a gross margin between US$2753 and
5506 ha−1 tend to use direct marketing 0.3 times (0.321)
less than farms that achieve more than US$5506 gross
margin per hectare. In other words, farms that achieve
more than US$5506 gross margin per hectare tend to
use direct marketing over 3 times (1/0.321) more than
farms that achieve a gross margin between US$2753
and 5506 ha−1. Based on an assessment of marginal
effect, farms that achieve a gross margin between US
$2753 and 5506 ha−1 tend to use direct marketing
options 10.4% less than farms that achieve more than
US$5506 gross margin per hectare.
There are a number of possible explanations for this
result. One possible explanation is that this may be due
to the farm management and marketing skills gathered
over the years. As noted by Monson et al. (2008), as
H. Adanacioglu
producers gain experience in production and marketing,
the greater the reduction in costs which will result in
higher profits. Monson et al. (2008) also cite as producers’
experience in agriculture increases, reliance on direct marketing channels will increase. Hardesty and Leff (2010)
warned that farmers must manage their marketing costs
as well as their production costs in order to get success
in direct marketing. Park et al. (2014) emphasized that
management and marketing skills significantly affect
DTC sales. Park et al. (2014) determined that a significant
level of management skill—more ways to control input
costs—increases the likelihood that a farmer uses DTC
outlets.
Conclusions
The objective of this study was to explore the main factors
that drive the decisions of farmers to sell their products
directly to consumers through farm direct marketing
channels. A case study on cherry growers on the subject
of direct marketing, which is one of the alternative marketing options in agricultural products marketing for
farmers, was examined in this study. The major findings
of this study are briefly outlined below. In addition,
further suggestions were put forward on how to improve
the use of DMSs by farmers in Turkey.
While examining the cherry farmers who were interviewed for possible marketing strategies in cases of
selling directly to consumers, community-supported agriculture emerged as having drawn the most attention.
An ordinal logistic regression analysis model was used
to analyze the effects of agricultural businesses and demographic features on the growers’ tendency to choose direct
marketing channels in cherry selling. According to these
model results, the cherry farming experience of the
growers, the size of the cherry orchard, the level of specialization in cherry production and the gross margin per
hectare for cherry production have a statistically significant impact on the growers’ tendency to choose direct
marketing channels in cherry selling. In particular, the
growers whose experience is more than 20 yr, the farms
that are semi-specialized, the farms providing a gross
margin of more than US$5506 ha−1 and the farms
having a cherry orchard between 1 and 2 ha in size were
determined as having more tendency toward direct marketing. The results obtained allow us to conclude that
the farmers who have more experience of growing cherries, operate a medium-sized farm growing cherries, are
semi-specialized in cherry production, and have a higher
gross margin per hectare for cherries are more likely to
sell their produce through DTC outlets. The results
from this analysis may help agricultural extension
agents and other institutions in Turkey in facilitating the
adoption of DMSs. As aforementioned, even though
there are some studies done to explore factors that may
be related to a farmer’s decision to participate in direct
Factors affecting farmers’ decisions to participate in direct marketing
marketing, the effects of these factors may vary on the
bases of ‘region-case’ and ‘crop-case’. Thus, further research should be assessed on the bases of both cases.
According to the interviewed cherry growers, the most
important factor limiting their participation in direct marketing is that there are not any organizations that will help
them meet their direct marketing goals and build direct
marketing arrangements between themselves and their
consumers. The other important factors limiting grower
participation in making the decision to participate in
direct marketing are the lack of cold storage to keep cherries fresh, the challenge of reaching consumers, the
amount of time required to operate a direct market, the
general absence of refrigerated transport, having a
concern whether all the cherries grown in farms are marketed to consumers through direct marketing channels
and the lack of experience and knowledge in using
direct marketing techniques among growers.
Considering all the factors that can limit grower participation in making the decision to participate in direct marketing, it is important that various organizations, such as
producers’ associations, cooperatives, local administrations, voluntary consumer groups around business and
family, and civil society organizations, take an active
role as facilitators in organizing sales directly to consumers. Especially, local administrations can play a key
role for faster dissemination and adoption of direct
farm marketing initiatives, such as CSA programs, pickyour-own activities, farmers’ markets and agri-tourism.
However, local administrations should work with civil
society organizations to provide an effective functional
link between farmers and consumers. These organizations
should teach farmers how to use direct marketing to reach
target audiences. In addition to training farmers on direct
farm marketing, these organizations should often provide
technical assistance, networking, marketing opportunities
and other services to support direct marketing. The
findings from this study can guide policy makers in improving direct marketing plans and programs on the marketing of fresh fruits.
Acknowledgements. Funding for this research was provided by
the Ege University Scientific Research Fund under grant
number 2011-ZRF-051. The author would like to thank Ege
University Research Fund for its financial support. The author
also wishes to thank the cherry growers who contributed to the
farm survey.
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