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J. Agr. Sci. Tech. (2019) Vol. 21: 15-26
15
Young Farmers in Agriculture Sector of Turkey: Young
Farmers Support Program
M. Kan
1
*, F. Tosun2, A. Kan1, H. Gokhan Dogan1, I, Ucum2, and C. Solmaz2
ABSTRACT
Although Turkey's agricultural sector is important in terms of national economy, it faces
some important structural problems such as decrease in human capital in the agricultural
sector. In order to solve these problems, within the "National Agricultural Project", a
policy instrument named "Support for Young Farmers Projects" (YFPS) was added to
the support in 2016. The aim of this study was to evaluate the criteria used in the selection
of the beneficiaries of young farmers' support within the scope of YFPS in Turkey. A
survey was prepared to determine what features young farmers benefiting from project
support have and the extent to which the selection criteria served the purposes of the
support program. The survey was conducted in the TR 71 Region, which is at the center
of Turkey, in June-August, 2017. A total of 248 young farmers (139 supported, and 109
non-selected farmers for support) were interviewed. The methodology used in this study
was the Categorical Regression. The results showed that the applicants who benefited
more from YFPS were in the following order: Female> married> those aged 18-30>
people from rural areas with a population of 1,000 or less> those with education in
agricultural production> the disabled / martyr’s relatives / ghazi, and those from
enterprises with an annual income of TL 10,000 or less. YFPS has breathed new life into
agriculture by encouraging youths in rural areas, but this support has to be aimed at
creating economically sustainable and viable enterprises.
Keywords: Human capital, Rural areas, Rural development, Young farmers.
_____________________________________________________________________________
1
Department of Agricultural Economics, Agricultural Faculty, Ahi Evran University, Kırşehir, Turkey.
2 Agricultural Economic and Policy Development Institute, Ministry of Food, Agriculture, and Livestock,
Ankara, Turkey.
* Corresponding author; e-mail: mustafa.kan@ahievran.edu.tr
INTRODUCTION
Turkey has an important place among the
countries of the region in terms of plant and
animal production. In the last decade, the
contribution of Turkish agriculture to GDP
was 8%, and the share of agricultural
product exports in total exports was 10%.
The fact that the agricultural sector received
19.5% share in Turkey's employment in
2016 is another reason for the importance of
this sector in Turkey (TURKSTAT, 2017).
According to the World Bank statistics for
2016, 26.11% of the total population in
Turkey lives in rural areas (The World
Bank, 2017). It would not be wrong to say
that a large part of this population provided
their living from agriculture. Turkey is a
candidate country for European Union (EU)
and it was ranked the 1st among the
European Union countries and 8th in the
World for agricultural production value of
approximately 52.3 billion dollars, in 2016.
Agriculture is an important sector for
Turkey and it is in the first place in export
and production in the world for many
products.
Although the agricultural sector is
important for Turkey, it is a fact that it
cannot contribute to the economic
development at the desired level, especially
due to its structural problems (Yavuz, 2005;
Özertan, 2013; TOBB, 2013; Doğan et al.,
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__________________________________________________________________________ Kan et al.
16
2015; TİM, 2016). In addition to this,
economic problems such as input costs,
product prices, producers' prices are also
maintaining the update on the agriculture of
Turkey and among the problems that are
reported in every platform. In the 2013-2017
Strategic Plan prepared by the Ministry of
Food, Agriculture, and Livestock, five
strategic goals have been put forward in
overcoming these problems. These are listed
as "Agricultural Production and Supply
Security", "Food Reliability", "Plant Health,
Animal Health and Welfare", "Agricultural
Infrastructure and Rural Development" and
"Institutional Capacity Increase" (GTHB,
2013). It should be noted that the
harmonization process of the European
Union (EU) is also effective in
determination of policies and strategies
within the scope of the Strategic Plan.
In parallel with the determined strategies
to solve the problems of agriculture, Turkey
implements agricultural policy measures in
many areas. Within the scope of agricultural
policy measures implemented to reach
strategic targets, there are deficiency
payments, compensation payments,
livestock support (feed crops, artificial
insemination, milk premiums, disease free
livestock areas, beekeeping and fisheries),
product insurance support, rural
development and environmental protection
programs. Turkey created a new support
model entitled "National Agricultural
Project" at the end of 2016 in order to come
to a leading position in the region with its
competitiveness in agriculture, production
diversity, and standards. This project
consists of two main themes, namely, "Basin
Based Support Model" in plant production
and "Domestic Production Support Model in
Livestock Production”.
Nevertheless, apart from the problems that
appear, when we examine the problems of
agriculture sociologically, the aging of the
agriculture society in Turkey and the fact
that youngsters in rural areas are not seeing
the agriculture sector as an income
generating and prosperity sector are the
forefront problems. In general, Turkey's
population is aging and it is seen that this
aging is more in rural areas and agricultural
sector. Especially the rural-to-urban
migration and the changes in the statistic
because of the new law (see the influence of
the Metropolitan Act after 2012) show that
the rural population is decreasing both
proportionally and numerically. It can be
observed that with the reason of rural
migration, young people do not want to stay
in the countryside for long, resulting in a
population aging in agriculture. Er (2013)
stated that the young population search for
jobs outside the rural areas depends on such
factors as the rapid increase in the
unemployment rate in rural areas and the
complete profile of the unemployment
profile of young people. Additionally,
agriculture is not seen as an attractive
employment area by young people and the
employment potential of non-agricultural
sectors in rural areas is low. Also, the
growing services and industry sector attracts
low-skilled young population in the rural
area and negatively affects the young
population in agriculture (Arlı et al., 2014).
Approximately half of Turkey's population
is under age of 30 and this fact can be
regarded as a sign that new or different
employment opportunities are needed for the
young population. It is regarded as important
to provide conditions for employment of
young population in agriculture sector for
this need. The young population in rural
areas is away from agriculture for reasons
such as inadequate income in rural areas,
limited social opportunities in the villages,
fragmented or scarce land, and lack of
alternative job opportunities in rural areas.
This has also results as affecting the
demographic structure of the rural inhabitant
in the negative direction. It is stated that the
rapid depletion in agriculture today will
cause major problems in terms of food
production in the future (Doğanay and Alım,
2010). For this reason, agriculture should be
encouraged again, education and health
services should be restructured in rural areas
and social facilities should be developed.
Sustainability in agricultural production can
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Young Farmers in Turkey ___________________________________________________
17
only be achieved when the young population
is kept in agriculture.
In order to solve these existing problems, a
policy instrument called "Youth Farmer
Projects Support (YFPS)" was added to the
support in "National Agricultural Project" in
2016. Ministry of Food, Agriculture, and
Livestock has started to provide the YFPS
with a notification published in the Official
Gazette dated April 5, 2016 in the scope of
Rural Development Supports. According to
the notification, it is aimed to support
sustainable agriculture, support
entrepreneurship of young farmers, raise
income level, create alternative income
sources and support the projects for
agricultural production in the rural area that
will contribute to the employment of young
population in rural areas. In the scope of this
support, project-based support for the aging
of the young population, which meet
specific criteria for agriculture under the age
of 41, has begun. Initial support started in
2016 and this program was planned for 3
years in the first stage. Within this scope,
30.000 TL grants are given to young farmers
who meet the support criteria specified in
the following project subjects. Project topics
are (Official Gazette, 2016);
1. Animal production oriented:
a) Cattle, sheep, and goat breeding
projects,
b) Bee breeding and bee products
production projects,
c) Poultry and silkworm breeding
projects,
2. Plant production oriented:
a) Closed fruit garden plant projects,
b) Seedling, sapling, indoor and outdoor
ornamental plant growing projects,
c) Controlled greenhouse projects,
d) Edible cultivated mushroom production
projects,
3. For production, processing, storage
and packaging of medicinal and
aromatic plants with local products:
a) Projects on medicinal and aromatic
plant production, processing, storage
and packaging,
b) Projects on organic or good agricultural
practices on plant and animal
production, using geographical
indigenous gene sources,
c) Projects on the production of food with
geographical indication.
This project, which aims at keeping young
farmers in agriculture and deals with
agriculture, is an important policy argument
also aimed at preventing the aging of the
agricultural population in rural areas. With
the project call, 540,112 applications were
made in 2016 throughout Turkey, of whom
393,719 were accepted and 14,977 were
supported. This number reached 16,067 in
2017 (GTHB, 2017). Support will continue
in 2018 that is the third year of the project,
and no policy statement has been made for
the post yet.
This study aimed at a general evaluation of
YFPS, which started in 2016 and is ongoing
in 2017 and expected to be implemented in
2018 as well. In this context, attempts were
made to show which young farmer's profile
is supported and how the criteria in support
are effective at the time of selection by
conducting surveys with a total of 248
people benefiting from and not benefiting
from YFPS in the TR71 Region (Aksaray,
Kırıkkale, Kırşehir, Nevşehir and Niğde
districts) within the scope of Turkish
Statistical Region Units Classification 2
(TSRUC2)
MATERIALS AND METHODS
The study was carried out in May-
September 2017 in the 3 districts where the
YFPS was given the most within the TR71
region of Turkey (Aksaray, Kırıkkale,
Kırşehir, Nevşehir ve Niğde) within the
scope of the NUTS-2 classification. The
main material of the study is the data
obtained through a questionnaire survey
with 139 young farmers who were randomly
selected from a total of 453 people
benefiting from YFPS in the selected
provinces and 109 randomly selected
applicants who applied for YFPS but were
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18
Figure 1. The region in which the study was performed.
unable to benefit from the evaluation. The
region in which the work was performed is
shown in Figure 1.
The questionnaire forms of the study were
prepared within the scope of the project
"Determination of the parameters that could
be a criterion for young farmers' support
and the tendency of the young people to stay
in agriculture" and the evaluation criteria
specified in the YFPS were taken into
consideration in determining the questions.
In the study, Categorical Regression
Analysis (CATREG) was used in order to
determine the effectiveness of the selection
of the main criteria to be considered in the
evaluation of the individuals entitled to
benefit from the project and those who were
not (Gifi, 1996; Meulman and Heiser, 2004).
Categorical regression analysis based on
optimal scaling is a multivariate analysis
technique that can be used when the
dependent variable is categorical, with both
linear and nonlinear relationships between
variables (Cengiz, 2008).
In this analytical technique, the measured
data at nominal, ordinal, and numerical
measurement levels can be included in the
functioning of the analysis. The categorical
variables are digitized in order to reflect the
characteristics of the original categories. The
criterion to obtain optimal linear regression
equations is considered when the digitization
process is performed. In other words,
various non-linear transformations are used
to find the most appropriate regression
model. Mentioned transformation is
designed to maximize the relationship
between each of the independent variables
and the dependent variable (Meulman and
Heiser, 2004). As a consequence,
Categorical Regression is a multiple
regression model applied to transformed
variables with Optimal Scaling. The loss
function used in the functioning of the
model is given as follows:
(1)
Where, J is the number of independent
variable, y is dependent variable, xj is
independent variables,
j is regression
coefficients,
r and
j are the transformation
functions for dependent and independent
variables, respectively, and e is the error
term (Kooij et al., 2006).
Each variable included in the analysis can
be represented by the matrix Gj of size Nxkj.
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Young Farmers in Turkey ___________________________________________________
19
Table 1. Discussed dependent and independent variables and their properties in the scope of CATREG.
Variables
Variable categories
Variable level
Gender
Male
Classification
Woman
Marital status
Married
Classification
Single
Age
Age 18-30
Grading
Age 31-40
Residence population
1,000 and below
Grading
1,001-10,000
10,001 and over
Distance to city center
Closer than10 Km
Grading
Between 10-40 Km
Far from 40 Km
Education level
Literacy – Secondary school
Grading
High school and over
Training in agricultural production
Not trained
Classification
Trained
Status of being disabled/Martyr's
relatives/Ghazi
No
Classification
Yes
Annual operating income of business
10,000 TL and below
Grading
Over 10,000 TL
Support redemption condition
Not used
Classification
Used
N, which is the number of rows of the
indicator matrix, represents the number of
units in the analysis and kj, the column
number, represents the category number of
variable j. The indicator matrix Gj is a
matrix of values 0 and 1. Related line units
to which they belong; If j is in the category
of the variable, then the column-alignment
takes the value 1, while the other column's
value is 0. Thus each row consists of values
0 and 1, and when there is no missing
observation, the sum of each row in the
matrix is 1.
Similarly, for each variable included in the
analysis, the vector of yj category
digitizations (kjx1 dimensional) can be
generated. With the help of these defined
indicator matrices and the category
digitization vectors, the loss function can be
written as follows:
(2)
In the operation of the analysis, this loss
function is minimized by Alternating Least
Squares (ALS) algorithm. In the steps of the
algorithm, digitizations are made and the
regression model coefficients are estimated.
Later on, the value of the lost function is
calculated. The iterations continue until the
contraction in the loss function becomes
meaningless. When the loss function
becomes minimum, the iterations are
stopped. In this way, optimal category
digitizations and model coefficients are
obtained (Cengiz, 2008).
CATREG analysis does not work as linear
regression because transformations at
variable levels are not linear. In CATREG
analysis, the variables are digitized to reflect
the characteristics of the original categories,
and these quantified variables are included
in the regression model as numerical
variables. CATREG coincides with linear
regression analysis by transforming
categorical variables into numeric with the
help of transformations (Xu et al., 2010).
ALS (Alternating Least Square)
Logarithm was used in the quantification of
the variables considered under CATREG
scope. The scale types of the variables
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20
included in the scope of the analysis are
mostly nominal and ordinal. The variables
and their characteristics discussed in the
CATREG framework are presented in Table
1. The variables discussed in Table 1 include
9 out of 13 criteria which are the scoring
criteria within the scope of YFPS.
RESULTS AND DISCUSSION
The initial stage of development is the
development of human and social capital.
When the relationship between development
and human capital is examined, human
capital has a close relationship with the
possibilities of health and education (Ateş,
1998), and since the late 1980s, human
capital has begun to be regarded as a
qualified workforce with a good education
level, and economic growth has begun to be
regarded as a driving force (Nesterova and
Sabirianova, 1998). The concept of human
capital is used to express the whole of
concepts such as knowledge, skills, abilities,
health status, place of social relations, and
level of education that a person or society
has. This concept constitutes the basic
source of economic growth (Kar and Ağır,
2003) and has emerged as an alternative to
the physical capital in industrial society and
has gained importance as a development
strategy for different countries. Human
capital, which is expressed as the personnel
infrastructure of the information society, is
in essence a concept that defines specialized
people (Özyakışır, 2011).
One of the most important problems in
rural areas is aging and young people are
inclined towards urban areas more than rural
areas, especially non-agricultural sectors. It
is reported that this is not only a problem in
Turkey, but also in many other countries
(Aggelopoulos and Arabatsiz, 2010; EC,
2013; ECA, 2017; Nag et al., 2018). In this
context, the young farmer support program
is an important supporting argument for the
Common Agricultural Policy, especially in
order to ensure that young farmers mainly in
the EU stay in agriculture, to support new
business establishments, or to encourage
more efficient production. In Turkey, the
project-focused Young Farmer Project
Support started in 2016, for the first time, to
aim directly at young farmers and to
encourage them to stay in agriculture.
Criteria to be taken into consideration in
the selection of young farmers to be
supported under the scope of YFPS have
been stated in the “Communiqué on
Supporting Young Farmers' Projects under
the Rural Development Supports No
2016/16” published in Official Gazette No.
29675 dated 05 April 2016. The project
supports were distributed with the
evaluations made among the highest scoring
points according to the criteria specified in
Communiqué E-4. Accordingly, the criteria
such as age, gender, educational status,
marital status, living place population,
distance from the center to the living place,
ownership status of the project site, status of
being disabled / martyr’s relatives / ghazi,
and project theme are taken into
consideration.
The determination of the young farmers to
be supported under the YFPS has been made
through the Evaluation Commissions
established by Provincial and District Food
Agriculture and Livestock Provincial
Directorates, which constitute the provincial
organization of the Ministry of Food,
Agriculture and Livestock. In addition to the
criteria set out in the Communiqué
published in these evaluations, the
commission was also given the authority to
award a score of 10 points. Categorical
Regression Analysis was conducted to find
out which criterions were more prevalent in
the evaluations made by the commission and
to find out how these criteria served the
intended purpose.
When the installed model was tested; the
model established as a result of categorical
regression was found statistically significant
(F= 8.00; P= 0.00). The model's multiple R-
value and R2 value was calculated as 0.52
and 0.24, respectively. These results led to
the conclusion that YFPS selection criteria,
which are explained explanatory variables,
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Young Farmers in Turkey ___________________________________________________
21
Table 2. Model summary statistics.
Sum of squares
SD
Average of squares
F
P
Regression
67.38
11
6.13
8.00
0.00
Error
180.62
236
0.77
Total
248.00
247
Multiple R2: 0.52 R2: 0.27 Corrected R2: 0.24
Table 3. CATREG results showing some of the evaluation criteria of the Young Farmer Project Support.
Variables
Variable categories
Frequency
Digitization
value
β
coefficient
Coefficient
of variables
categories
Variable level
Gender
Male
98
-1.24
0.42***
-0.52
Classification
Woman
150
0.81
0.34
Marital status
Married
205
0.46
0.12*
0.05
Classification
Single
43
-2.18
-0.25
Age
Age of 18-30
139
-0.89
-0.10*
0.09
Grading
Age of 31-40
100
1.13
-0.11
Residence population
1,000 and below
142
-0.27
-0.21***
0.06
Grading
1,001-10,000
101
0.03
-0.01
10,001 and over
5
6.90
-1.46
Distance to city center
Closer than10 Km
26
-2.20
-0.07
0.16
Grading
Between 10-40 Km
162
0.17
-0.01
Far from 40 Km
60
1.40
-0.10
Education level
Literacy –
Secondary school
174
-0.65
-0.03
0.02
Grading
High school and
over
74
1.53
-0.04
Training on
agricultural production
Not trained
182
-0.60
0.08*
-0.05
Classification
Trained
66
1.66
0.13
Status of being
disabled/Martyr's
relatives/Ghazi
No
218
-0.37
0.08*
-0.03
Classification
Yes
30
2.70
0.22
Annual operating
oncome of business
10,000 TL and
below
131
-0.95
-0.12*
0.11
Grading
Over 10,000 TL
117
1.06
-0.12
Support redemption
condition
Not used
109
-1.13
Classification
Used
139
0.89
* Statistically significant at the 90% confidence level; ** Statistically significant at the 95% confidence level, ***
Statistically significant at the 99% confidence level.
could account for about 24% of the selection
result (Table2).
When the contribution of the independent
variables to the model is examined; it is seen
that the variables such as gender, marital
status, age, residence population, education
about agricultural production, being
disabled/martyr’s relatives/ghazi status and
annual operating income variables have a
meaningful effect on determining the
recipients of YFPS (P< 0.10). It is seen that
the distance of residence to the
provincial/district center and the educational
status variables have no meaningful effect
on determining the YFPS recipients (P>
0.10) (Table 3).
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22
The coefficient of the effect can be
calculated as a result of multiplying the
digitized values of the variable categories
obtained by optimal scaling by β
coefficients obtained as categorical
regression (Cengiz, 2008). The effect
coefficients show how the independent
variable categories are related to the
dependent variable. High coefficient of
effect indicates that the level of the
relevant variable is in the same direction
(positive) as the dependent variable. In
Table 3, the effects of statistically
significant 7 variables’ coefficients to the
model as a result of categorical regression
have been examined.
According to Table 3:
In terms of gender variable; it is seen
that the rate of selection of female
(0.34) individuals to YFPS is higher
than that of male (-0.52) individuals.
In terms of marital status variable;
married (0.05) individuals were
selected to have higher YFPS while
single individuals (-0.25) were
inversely related to being selected to
YFPS.
In terms of age variable; it is seen that
individuals aged 18-30 years (0.09)
were higher in YFPS and those aged
between 31-40 years (-0.11) showed
opposite behavior.
Regarding the residence population
variable; individuals from a population
of 1000 or less (0.06) were selected to
have higher YFPSs whereas those
living in higher populations (-0.01 and
-1.46) were found to have an inverse
relationship with YFPSs.
From the point of view of the training
in agricultural production variable; the
individuals with this training (0.13)
were selected to have higher YFPS;
whereas those who did not have this
education (-0.05) were inversely
related to the selection of YFPS.
In terms of being disabled/martyr's
relative/ghazi variable; the victimized
individuals in this regard (0.22) were
selected at a higher rate while the
individuals with no victim (-0.03)
were in an inverse relation to be
selected for YFPS.
In terms of annual operating income
variable; individuals who were
applying from business with an annual
income of TL 10,000 or less (0.11)
were selected at a higher rate while
individuals who are applying from
business with an annual income of TL
10,000 and above (-0.12) were in an
inverse relationship with this issue.
Among the selection criteria, project
issues are of special importance.
Between the applications made
according to the project subjects stated
in the communiqué, it appears that the
cattle and sheep breeding projects are
seen to constitute the majority. However,
it is seen that the proportionally less
applied topics during the support phase
appeared to be more foreground (Figure
2). Intensification in the certain project
subjects, increasing the chances of
young farmers resorting to the project
subjects that were less accumulated in
the selection, while other farmers did not
qualify, even though they provided the
criteria. Especially during the selection,
attention to the distribution according to
the project subjects in the region has
been influential in giving the election
score by the commission.
By considering the given criteria, the
general situation of young farmers who
were selected and not selected is given in
Table 4. In the Chi-square analysis,
when the table was analyzed, support
utilization status and the criteria of
gender, marital status, education status,
being disabled/martyr’s relatives/ghazi
status of the young farmer, population of
the residence and distance to the city
center of the residence were determined
to be statistically significant at different
levels of importance.
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Young Farmers in Turkey ___________________________________________________
23
Figure 2. Distribution of the projects submitted and supported by the themes (GTHB, 2017).
Table 4. The general situation of young farmers who were selected and not selected.
Factors
Get
supported (%)
Not
supported (%)
Average
(%)
Chi
square (χ2)
Economic and demographic factors
Age
Between 18-30
59.71
51.38
56.05
1.72
Between 31-40
40.29
48.62
43.95
Gender
Male
20.86
63.30
39.52
46.04***
Female
79.14
36.70
60.48
Education level
Literacy – Secondary
school
74.82
64.22
70.16
3.28*
High school and over
25.18
35.78
29.84
Marital status
Married
90.65
72.48
82.66
14.07***
Single
9.35
27.52
17.34
Training on
Agricultural
Production
Not trained
72.66
74.31
73.39
0.09
Trained
27.34
25.69
26.61
Status of being
Disabled/ Martyr's
relatives / Ghazi
No
84.89
91.74
87.90
2.70*
Yes
15.11
8.26
12.10
Annual Operating
Income
10,000 TL and below
52.52
53.21
52.82
0.01
Over 10,000 TL
47.48
46.79
47.18
Social Security Status
No social security
43.88
45.87
44.76
0.10
Have social security
56.12
54.13
55.24
Geographical
factors
Population of
Residence
1,000 and below
64.03
48.62
57.26
10.67**
1,001-10,000
35.97
46.79
40.73
10,001 and over
0.00
4.59
2.02
Distance to city center
Closer than 10 Km
9.35
11.93
10.48
6.45**
Between 10-40 Km
71.94
56.88
65.32
Far from 40 Km
18.71
31.19
24.19
* It is statistically significant at the 90% confidence level; ** It is statistically significant at the 95%
confidence level, *** It is statistically significant at the 99% confidence level.
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__________________________________________________________________________ Kan et al.
24
CONCLUSIONS
In this study, it was determined how the
criteria determined in the selections were
effective and the general characteristics of
the farmers benefiting from the support
during the assessment of the application of
YFPS, which started in 2016. As conclusion,
it can be said that the project title, the
property status of the subject for investment,
and the commission evaluation note, which
were not included in the model, might have
played an important role in determining the
farmers who will benefit from YFPS. In this
support, which is preoccupied with the
support of young people and women of low
income who are in agricultural production or
want to be found, the selection criteria were
effective but insufficient to make this
distinction.
In particular, there are structural
differences between the program for young
farmers in EU countries and the program for
implementation in Turkey. At the beginning
of these differences, it appears that such
supports in the EU countries are supportive
(not entirely welcoming) to young people
seeking new business or economically
sustainable. Although support for production
by young farmers in low-income families
seems logical, the fact that the issues
concerning the lack of continuity of
production, the problem of poor young
people who have benefited from cattle
livestock project support (procurement of
the cattles on non-production age, because
of that they suffered on feeding of the cattles
in terms of financially, and the
appropriateness of selected animal breeds
are the most important obstacles to the
success of the project.
The fact that there are uncertainties about
the definition of farmers in Turkey and the
fact that farming is not fully found as a
profession lead to some problems in the
determination of target population. In the
context of support, women are expected to
be more prominent in the selections, and
thus giving young women an advantage in
scoring in the selection can be seen as a
positive discrimination. However, another
finding is that the outcome of this situation
does not occur at the desired level. It has
been seen that many female farmers who
have benefited from the support, or who are
in the application for the support, are in a
position to assist their husband instead of
taking direct responsibility for agricultural
production.
As a result, when the selection criteria of
YFPS are evaluated in terms of the
magnitudes of the effect coefficients, it is
seen that applicants who benefited more
from YFPS were in the following order:
Female> married> age between 18-30>
people from residence with a population of
1000 or less> those who have an education
in agricultural production> victims of being
disabled/martyr’s relatives/ghazi and from
enterprises with an annual income of TL
10,000 or less. It is seen that Young Farmer
Project Support has added vitality to the
rural area by the enthusiastic youth of
agricultural communities. However, these
supports must be directed at creating an
economically sustainable business. Selection
criteria and evaluation criteria should
consider project issues, regional structures,
and young entrepreneurs should be
supported in the form of credit-supported
grant schemes rather than direct grants.
Increasing the quality of human capital in
agriculture should be a priority, distributed
resources must be monitored, and impact
assessment should be done.
ACKNOWLEDGEMENTS
The data of the study was compiled from the
project “Genç Çiftçi Desteklemelerine Kriter
Olabilecek Parametrelerin ve Gençlerin
Tarımda Kalma Eğilimlerinin Belirlenmesi
(TR71 (Kırıkkale, Aksaray, Niğde, Nevşehir,
Kırşehir) Bölgesi)- Determination of the
Parameters Being Able to a Criterion for
Supporting Young Farmers and the
Tendency of Young People to Stay in
Agriculture-TR71 Region” supported by the
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Young Farmers in Turkey ___________________________________________________
25
Ministry of Food, Agriculture, and
Livestock in Turkey.
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