Societys growing awareness of the health of people and the environment
in developed countries has sparked new interest in an organic approach to agriculture
(Tekeli et al., 2003). Organic agriculture is
agriculture that is controlled and certified at every stage (from production
to consumption) as not using chemical inputs. The aim of organic production
is to protect the environment, plants, animals and people without polluting
the soil, water, or air. Organic agriculture primarily began for the purpose
of producing organic vegetables. However, countries such as the United States,
Canada, Austria, Denmark and Germany have recently started to transition to
organic animal production ( Saner and Engindeniz, 2001).
Available economic conditions and the increased income level of consumers have
increased the production and demand for organic products (Cicek
and Tandogan, 2009). Production of milk and milk products in the United
States increased 37% from 1998 to 2003. Production of organic milk and milk
products in the United States comprised 2% of total milk production (USDA,
2004). There are approximately 70 certified dairy farms and 7000 organic
milked cows in Canada (Macey, 2007; Saner
and Engindeniz, 2001). In Austria, the largest organic milk producer in
Europe, the market share of organic milk and products represents 3.5% of the
total milk market (Cavdar, 2004). Organic milk production
in Denmark is a symbol of the development in organic production. In Denmark,
where there is a remarkably high level of government support for organic production,
organic milk is produced in 550 farms (Norfelt, 2005) and
the share of organic milk production in total milk production has reached 20%
(Schmaedick, 2003). The market share of organic milk in
Germany is approximately 2.8%. The market share of organic milk products in
France is approximately 3% for milk and 4% for milk products ( Saner
and Engindeniz, 2001 ).
In Turkey, organic agriculture activities in vegetable production were started
in response to the demands of import firms. However, organic animal production
started first with apiculture ( Saner and Engindeniz, 2001).
Organic breeding projects have been undertaken by the Ministry of Agriculture
and Rural Affairs (Aksoy et al., 2005) and there
is a comprehensive organic milk production project in the private sector in
Kelkit, Gumushane (Koyubenbe et al., 2006). In
Turkey, organic milk production represented 2,875 tons, while total milk production
was 11.3 million tons in 2007 (MARA, 2009). Consequently,
organic milk production comprises only 0.025% of total milk production. The
price of organic milk is 30% higher than the price of conventional milk (Koyubenbe
et al., 2006).
In Turkey, 35% of total conventional milk production occurs in the Ege Region.
Izmir Province is one of the provinces with the highest milk production, with
22% of the production in the Ege Region (MARA, 2009).
This region has extensive production potential and was therefore chosen as the
study area for determining producer preferences in organic milk production.
There are many studies about organic animal production in Turkey (Cukur
and Saner, 2005; Saner and Engindeniz, 2001; Sayan
and Polat, 2001; Tekeli et al., 2003; Unal,
2003; Yesilbag, 2004), but studies on organic dairies
are quite limited, especially in Turkey (Atasever and Erdem,
2007; Butler, 2002; Cicek and
Tandogan, 2009; Koyubenbe et al., 2006; McBride
and Greene, 2007). Moreover, studies on this topic that have been carried
out in Turkey are reviews, not original research.
The aim of this study is to determine farmer preferences regarding factors
that can ease the transition to organic milk production in dairy farms in Izmir.
As far as we know, this is the first study investigating farmer preferences
regarding organic milk production in Turkey and should be of interest to producers
considering an organic production approach.
MATERIALS AND METHODS
Data obtained from a 2008 survey of farmers formed the main source of material
for this study. The Odemis, Tire, Bayindir, Bergama and Kiraz districts, which
are responsible for 71.6% of the total milk in the Izmir Province, were included
in this study. There are 5,824 dairy farms registered to the Izmir Cattle Breeding
Association in these districts. The sample volume was determined through the
proportional sampling method (Newbold, 1995):
||Proportion of dairy farms (0.50)
The proportion of dairy farms was taken as 0.50 to reach maximum sample volume
and was calculated as 96. When the data were being analyzed, farms were evaluated
in five groups depending on their size (Table 1). The number
of farmers in each group was calculated to be proportional.
||Farm groups according to number of cows and farms including
in the sample
The Kolmogorov-Smirnov test was conducted to determine whether the variables
of the plantations are normally distributed. To identify the difference between
groups of plantations, one-way ANOVA for parametric variables and Kruskal-Wallis
analysis (Miran, 2002) for non-parametric variables were
used. In this study, the Fuzzy Pair-Wise Comparison (FPC) method was used to
determine farmers preferences regarding organic milk production.
Fuzzy theory began with a study on fuzzy sets (Zadeh, 1965).
Fuzzy set theory is an extension of crisp set theory (Tanaka,
1997). Fuzzy sets are sets with boundaries that are not precise. Thus, fuzzy
sets describe ranges of vague and soft boundaries by degree of membership (Lai
and Hwang, 1994). Membership in a fuzzy set is a matter of a degree (Klir
and Yuan, 1995) and a fuzzy set is characterized by a membership function
that can choose an arbitrary real value between zero and one.
FPC was first used by Van Kooten et al. (1986)
to study farmers goal hierarchies for use in multiple-objective decision
making. The first step of the FPC approach is data collection using a unit line
segment as shown in Fig. 1. Two options, A and B, are located
at opposite ends of the unit line. Producers are asked to place a mark on the
line to indicate the degree of their preferred option. A measure of the degree
of preference for option A over B, rAB, is obtained by measuring the distance
from the producers mark to the A endpoint. The total distance from A to
B equals 1. If rAB<0.5, option B is preferred to A; if rAB = 0.5, the producer
is indifferent between A and B; and if rAB>0.5, then A is preferred to B.
rAB = 1 or rAB = 0 indicate an absolute preference for option A or B. For example,
if rAB=1, then option A is absolutely preferred to B (Van
Kooten et al., 1986).
||Fuzzy method for making pair-wise comparison between options
(A) and (B)
The number of pair-wise comparisons, λ, can be calculated as follows:
λ = n* (n -1)/ 2
where, n = the number of the factors.
In the second step of FPC, rij (i≠j) is obtained for each paired comparison
(i,j). rij = s values are collected directly from the producer. rij (i≠j)
is also a measure of the degree by which the producer prefers factor i to factor
j and rji = 1 - rij represents the degree by which j is preferred to i. Following
Van Kooten et al. (1986), the consumers
fuzzy preference matrix R with elements can be constructed as follows:
Finally, a measure of preference, ì, can be calculated for each factor
using the producers preference matrix R. The intensity of each preference
is measured separately with the following equation:
Ij has a range in the closed interval [0,1]. A larger value of Ij
indicates a greater intensity of preference for factor j. As a result, the producers
preferences are ranked from most to least preferable by evaluating the μ
To analyze the producers preferences derived from FPC, nonparametric
statistical tests are used (Basarir and Gillespie, 2003).
The Friedman test, which establishes whether the factors are equally important
and the Kendalls W test, which tests for agreement among more than two
sets of rankings, were used (Bowen and Starr, 1982).
In agricultural research in Turkey, fuzzy pair-wise comparison was first used
by Gunden and Miran (2007) to determine farmers
objective hierarchy. In their study, the quantified preferences obtained from
FPC were analyzed by the seemingly unrelated regression (SUR) investigated by
Zellner (1962). An SUR system involves n observations
on each of g dependent variables. In principle, this could be any set of variables
measured at the same points in time or for the same cross-section. However,
in practice, the dependent variables are often quite similar to one another.
SUR is an extension of the linear regression model, which allows correlated
errors between equations. The seemingly unrelated regressions model is as follows:
It is assumed that a total of T observations were used in estimating the parameters
of the M equations. Each equation involves Km repressors, for a total of .
The data are assumed to be well behaved. It is also assumed that disturbances
are uncorrelated across observations. Therefore,
The disturbance formulation is therefore
The data matrices are group-specific observations on the same variables. The
covariance structures model is, therefore, a testable special case (Greene,
General Characteristics of Studied Dairy Farms and Farmers
Within the studied farms, the average age of the farmers was 40.1 years,
the average education level was 7.5 years, the average agricultural experience
was 21.5 years and the average dairy experience was 15.7 years.
In the dairy farms that participated in the study, 54.2% of the farmers were
members of an agricultural cooperative. When examined according to farm size,
the rate of cooperative membership was highest for Group 4 and the lowest in
Groups 1 and 5, the smallest and largest farms, respectively.
Within the studied farms, 54.2% of the farmers were members and shareholders
in Agricultural Cooperatives. Thirty one percent of these farmers were shareholders
of Milk Collection Cooperatives. The proportion of the farmers that were members
of the Chamber of Agriculture was 93.8%.
The average land size of the farms studied was 11.5 hectares and the average
number of parcels per farm was 4.89. Therefore the farms involved in our study
The average daily milk yield in the farms studied was 21 kg, the lactation
milk yield was 6.7 tons and the average milk sale price was 0.53*
TL kg-1 [* 1$ = 1.313 TL]. The yield of daily milk per cow did not
change according to farm size (p = 0.133), but the average milk sale price increased
as the farms size increased (p = 0.000).
The farms in the study produce mostly corn for silage as the feed crop (6.96
ha), followed by Vicia sativa sp. (4.11 ha) and Avena sp. (3.78
ha). The average feed crop cultivation area per farm was 13.68 ha.
Thirty-seven percent of farmers buy concentrated feed from a dealership. The
others prefer local milk collectors and local cowsheds (32.3%), milk collection
cooperatives (15.6%) and feed factories (14.6%).
Awareness for Organic Milk Production of Studied Dairy Farms
Of the farmers, 27.1% stated that they had no idea about organic crops,
but 72.9% said they had heard of organic cropping. Nearly 93% of the farmers
who had heard of organic crops knew the correct definition of organic. The percentage
of farmers who thought organic crops had a higher yield was 5.7%; 1.4% of the
farmers thought that organic production was a method of production that used
chemical fertilizers and pesticides intensely.
In the examined farms, the rate of farmers who had heard of organic milk was
59.4%. Among these farmers, the rate of knowing the correct definition of organic
milk was 79.3%. The other farmers statements with regard to the definition
of organic milk were that it was obtained from cows that were fed in a pasture
(15.5%) and it is milk that does not contain water or added ingredients (5.2%).
It may be said that awareness of both organic crops and organic milk increases
as farm size increases.
||Descriptive statistics for farmers preferences regarding
factors that can affect the transition to organic milk production in izmir
|Friedman test (χ2 = 129.259), Kendall's W
In this sample, 92.7% of the farmers used chemical fertilizer and pesticides
in the production of feed crops, while only 7.3% of the farmers did not use
them. The number of farmers using fertilizer and pesticides increases proportionately
with farm size (p = 0.043).
Preferences for Organic Milk Production among the Studied Dairy Farms
This study used FPC to determine the priority of farmers preferences
for factors that might have an effect on the transition to organic milk production.
The reasons for those preferences were then put forward using Seemingly Unrelated
Measures of Producer Preference
Here, degree of farmer preference for factors that might affect the transition
to organic milk production was determined. Five alternatives that might affect
organic milk production were offered to the farmers; the farmers were then asked
to make pair-wise comparisons among these alternatives. These factors are as
1. High price; 2. Premium; 3. Guarantee of sale; 4. Technical information and
5. Health of consumer.
The basic descriptive statistics for the results obtained from the FPC model
are shown in Table 2. The factors were ranked from most to
least preferable using degree of farmer preference. The results showed that
the most preferred factor for farmers was health of consumer with a fuzzy pair-wise
degree of 0.64. The second most significant factor was guarantee of sale; the
other factors in order of significance were high price, technical information
The Friedman test was used to see if there was a difference in the rankings
of the factors. The results were statistically significant. In other words,
some factors were preferred more than others. A Kendalls W test was used
to measure the degree of agreement. In this study, the value of Kendalls
W was 0.337. This indicates that agreement among the farmers in the ranking
of the factors is weak.
Factors Influencing Farmer Preference
The SUR model was used to determine the factors influencing farmer preference.
The degree of farmers preference for organic milk production was regressed
on farmer-specific characteristics in order to identify the reasons for the
preferences. The summarized estimation results of the model are shown in Table
3. There was a positive relationship between education and guarantee of
sale, premium, high price and technical information. Experience with dairy had
a negative impact on premium and high price. A negative relationship was observed
between farm size and all examined criteria; guarantee of sale, high price,
premium, technical information and health of consumer are important for small
farms. The use of credit had a positive impact on all the factors examined.
In the current study, age of farmer, membership in a cooperative and land size
did not affect the farmers preferences regarding the adoption of organic
||SUR model for producer preferences
|*Significant at 1% level, **Significant at 5% level, ***Significant
at 10% level
||Milk prices and farmers intentions to switch to organic
milk production in the farms investigated
|*1$: 1.313 TL
The percentage of farmers who had thought about switching to producing organic
milk was 82.3%, while the percentage of the farmers who had not thought about
producing organic milk was 9.4%. The percentage of farmers who could not decide
was 8.3%. When farm size is taken into account, large farms are more positive
about organic milk production (Table 4). While the average
price of conventional milk was 0.53* TL kg-1 [* 1$ = 1.313
TL] in the farms examined, the average price the farmers expected for organic
milk was 1.09 TL kg-1. As farms grow, both conventional milk price
and expected organic milk price increase (Table 4).
The youngest farmers, the most educated farmers and the most inexperienced
farmers in terms of both agriculture and dairy were placed in Group 5. Also,
there was no statistically significant difference between the farmers that were
thinking of producing organic milk and those that were not in terms of age,
education level and experience (p = 0.844; p = 0.114; p = 0.230, respectively).
This is consistent with the results of a study conducted in the United States
in which no difference was found between farmers producing organic milk and
farmers producing conventional milk in terms of age and education level (McBride
and Greene, 2007).
The rate of become a cooperative was quite low compared with that of developed
countries, although it includes more than half of the farms studied. Investigated
farmers are organized by occupation much more than by economic level. This arises
from direct Income Support Paying (ISP) per unit of land; the farmers must be
members of the Chamber of Agriculture in order to receive this payment. Therefore,
the number of Chamber of Agriculture members increased after the practice of
ISP was introduced. The fact that the farmers who were not members of the Chamber
of Agriculture in Group 1 do not have private-registered land supports this
The average feed crop cultivation area per farm is higher than the average
land size; this is a result of growing feed crops twice a year, especially corn
The percentage of farmers who had heard of organic crops was higher than that
of farmers who had heard of organic milk, indicating that organic animal production
is a newer concept for farmers than organic crop production.
According to their degree of preference regarding the factors that might affect
the transition to organic milk production, the producers thought that organic
milk production was most necessary for the health of consumer. This was a very
difficult choice for the farmers because they had to choose between economic
factors such as high price, guarantee of sale and premium and an emotional factor
such as the health of consumer. Present results showed that farmers were acting
emotionally in this matter. Guarantee of sale ranked second. This finding shows
that farmers can produce organic milk but are anxious about being able to sell
it. Akgungor et al. (2007) found that consumers
in Turkey were willing to pay as much as 36% more for organic products. High
price was ranked third and is of great importance since price directly affects
the farmers income. On the other hand, farmers think that switching to
organic milk production will decrease their milk yield. In fact, two previous
studies found that the milk yield in organic milk production was lower than
that of conventional milk production, by 15 and 30%, respectively (Butler,
2002; McBride and Greene, 2007). The farmers ranked
technical information fourth. This means farmers need technical support for
organic milk production. In fact, this study found that 40.6% of the farmers
did not know about organic milk production and 20.7% of the farmers who were
aware of organic milk production did not know the correct definition of organic
milk. Thus, the farmers must be given more information on organic milk production.
The premium was ranked last within the farmers preferences, indicating
that the premium is not of great importance for the farmers in the transition
to organic milk production.
As education level increases, the expectation of guarantee of sale, premium,
high price and technical information increases. This result was not predicted.
As education level increases, one would expect the preferences regarding health
of consumer to increase. No relationship was found between education and health
of consumer. As experience with dairy increases, the expectations for premium
and high price decrease. As farms grow, these expectations decrease during the
transition to organic milk production. As the use of credit increases, all of
the factors in the study gain importance for the farmers.
The price expectation for organic milk is quite high. The reason of this is
the decrease the farmers expect to see in organic milk yield.
The results indicate that the most preferred factors for producers are health
of consumer, guarantee of sale and high price. The least preferred factors for
producers are technical information and premium. Well-educated producers prefer
premium, guarantee of sale, high price and technical information. Preferences
for the factors are not influenced significantly by age of producer or cooperative
membership in any model. Farmers with more experience in dairy do not expect
premium or high price. Guarantee of sale was very important for small farms.
This project was funded by the Ege University Scientific Research Projects
Commission, to which we are thankful. We are also grateful to the producers
who participated in the survey for their contribution.