1076
Bulgarian Journal of Agricultural Science, 25 (No 6) 2019, 1076–1082
Determination of factors affecting on dried beans production decisions
in Turkey
Ali Berk1, Cahit Gungor2
1
Ministry of Agriculture and Forestry, General Directorate of Agriculture Reform, 06680, Ankara, Turkey
Çukurova University, Faculty of Agriculture, Department of Agricultural Economics, 01330, Adana, Turkey
E-mail: berk_ali@hotmail.com; gungorc@cu.edu.tr
2
Abstract
Berk, A. & Gungor, C. (2019). Determination of factors affecting on dried beans production decisions in Turkey.
Bulgarian Journal of Agricultural Science, 25 (6), 1076–1082
In this study, it was examined the dried beans farms in 7 provinces which constitute 61% of total production of Turkey, and
putting forwarded the factors affecting on socioeconomic characteristics of producers and farmers’ production decisions. In the
context of those findings obtained, solution proposals for dried beans production were developed.
According to results, average farm size investigated was 132 da and 25.3 da for dried beans area as well. Average yield of
dried beans farms were calculated as 244.4 kg/da.
The factors affecting on decision of farmers were founded as numbers of family member, finance, habit, age, and dried
beans farming experience, family labor, and transportation and information source, respectively.
Keywords: legumes, dried beans, logit, regression, Turkey
Introduction
In the second half of 2000’s, factors such as climate
change, decrease in agricultural product stocks, increase in
energy and other input prices, population growth, increased
use of agricultural products in alternative areas such as biofuel production have led to excessive increases and volatility in food prices. Depending on rising welfare and population, agricultural product demand is expected to increase and
food prices are expected to stay at high levels, especially in
developing countries. Possible increases in food prices will
bring adverse results for those spending big share of their income on food (Anonymous, 2014a). On the other hand, rapid
population growth in developing countries causes some issues such as lack of shortage, decrease in development rate
and increase in economic and social problems beside food
safety issue. Major crops such as wheat, barley, corn, rice
etc. which would be accepted as provider of food safety in
the world are strategic crops in the region and all over the
world. Importance of legumes also was emphasized by some
international organization. For example international pulses
year was declared by FAO in 2016 (GPC, 2014).
The supply of cereals strictly depends on production and
stock availability and varies according to production periods. In 2004-2014 periods, world cereal production raised
with 22%, cereal supply with 24%, cereal usage with 22%
and cereal trade with 34%. World cereal production has been
raised 2.1 million tones to 2.5 million tones between 2004 to
2014. There is an increase with 22% in world cereal production during that decade, considerable fluctuations by years
were existed (Anonymous, 2014b). Due to fluctuations in
world cereal production and supply, cereal crops are becoming a commercial product, decrease in stocks and speculative
shifts in product prices cause sense of worries for the lack of
cereal production supply. Under these circumstances, edible
legumes which are main source of plant based protein are an
important product group in terms of food safety for whole
world and Turkey.
1077
Determination of factors affecting on dried beans production decisions in Turkey
Total area planted to legume is approximately 789.000
hectare and legume production is 1.118.000 tonnes (dried
beans, chickpea and lentil) in Turkey. There is a decrease in
area planted while decrease in amount of production of dried
beans though fluctuations in chickpea and lentil production.
Legume production has been decreased due to insufficient
supports and a high quality of seed, canalization of producers to machine harvesting in agricultural products and high
labour and irrigation cost. In this period, supporting policy
system changed both to decrease of the global economic crises and drought, and to ensure sustainability in legume production. In 2009, fuel support, chemical fertilizer and rural
development supports has been increased compared to previous year at significant rate, certificated seed production and
legumes also included to support.
Although there is an increase in terms of production,
consumption and trade globally, declining in production and
increasing import tendency in Turkey cause both for legume
sector and producer and consumers. Opposite to developments
throughout worldwide such as production, consumption and
trade augmentations, increase in production and increase of
import trend cause various problems in terms of legume sector, producers and consumers in country such raw material
procurement, nutrition, price fluctuations, failure in production etc. In consideration of average annual 3 kg dried beans
consumption, 4.5 kg lentil and 5.5 kg chickpea per person in
Turkey, importance of edible legume for consumers would
be understood better. Despite self-sufficient ratio in legumes
(101%), these ratios are 83.2% in dried beans, 122% in lentil
and 94% in chickpea (TUİK, 2014). It is observed that there
is a continuity problem in dried beans production. There are
various studies from different perspectives on dried beans
production. Several studies in literature have investigated
on trade of dried beans such as Uzunöz (2013) and Uysal &
Subaşı (2014); Legume and its contribution to industry such
Ertaş (2013); the analysis of economic and technical efficiencies of peanuts such as Parlakay & Alemdar (2011) and the
analysis of economic and technical efficiencies in tomatoes by
Engindeniz & Coşar (2013); Agricultural productivity analysis in agricultural sector such as Deliktaş (2002) and Coelliand
Rao (2003). But no studies were found to examine the factor
affecting on dried beans production decisions.
Therefore, the purpose of this research is to determine the
socioeconomic characteristics and determination of the factors affecting the production decisions of dried beans farms.
In addition, it was also developed some proposals for more
efficient production and marketing plans.
Material and Method
The main material of this research was collected by
questionnaire method from dried beans producers. Data is
belonging to production period of 2014. “The Purposive
Sampling Method” was used to determine the province
and districts. By taking into consideration of geographic
situation and shares in the total production of provinces, 7
provinces (Konya, Karaman, Niğde, Erzincan, Gümüşhane,
Isparta and Çanakkale provinces) which provide approximately 61% of total dried beans production in Turkey was
determined. Districts with high production of dried beans in
the provinces were determined as research area. By applying Neyman Method (Yamane, 2001; Çiçek & Erkan, 1996),
169 samples were determined with 95% confidence level and
10% standard deviation from frame list of dried bean farms
in these districts.
The several alternative groups to determine sample size
were investigated and it was considered appropriate to divide the dried beans farms to 3 groups. Layer boundaries for
sample farms were determined as 1-2.50, 2.51-15 and 1.51+
decares (Table 1). When the research results were evaluated,
3 questionnaires weren’t included and in the end, 166 questionnaires were considered.
In this research, factor analysis were used to determine
factors affecting in production decision of dried beans farms
and factor scores obtained were used as independent variables in logistic regression (Logit Models).
Factor analysis is a statistical method for modeling observed variables, and their covariance structure by using a
small number of variables by combining related variables on
multiple data sets (Tatlıdil, 2002).
Logistic Regression: Today, estimating the probability of
an event occurred and determining the variables to be used in
the forecasting process have become important by scientific
studies in real life. Logistic regression model is the appropri-
Table 1. Distribution of sample farms to farm Groups
Farm
Groups
1-2.50
2.51-15
15.1 >
Total
Frequency
(Nh)
9.217
26.750
13.236
49.203
Standard
Deviation (Sh)
0.5
3.5
18.5
15.2
Variance
(Sh2)
0.2
12.2
341.9
229.9
Nh*Sh
Nh*Sh2
4.196
93.264
244.734
342.194
1.910
325.163
4.525.138
4.852.212
Sample
(n)
32
92
45
169
1078
Ali Berk, Cahit Gungor
Results and Discussion
ate regression analysis to conduct when the dependent variable is binary (dichotomous). Like all regression analyses,
the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more
metric (interval or ratio scale) independent variables and is
written mathematically follows;
( )
P
L = ln ––––– = β0 + β1X1 + β2X2 + ... + βpXp
1–P
Population Structure in Farms
Average family size in farms investigated was calculated
as 4.6 person which is higher than national average. This varies between 4.0 and 5.6 person. According to farm groups,
the highest population is in the third group farms with 5.6
people and the lowest in the first group farms with 4.0 people. For the distribution according to sex, 58.8% of family
population were male and 41.2% is female (Table 2).
(1)
(Albayrak et al., 2005).
Since the parameters of the logistic regression model
cannot be analytically obtained, it is estimated by maximum
likelihood (Maximum Likehood = ML) which is an iterative
method. Despite having a linear relationship between model
dependent variables and independent variables, the relationship between probabilities and dependent variable is not linear. Probability values to properties of independent variables
given, i.e. probability of an event;
1
Pi = ––––––––––
1 + e–(β0 + β1X1)
Table 2. Distribution of farms according to age and sex
Farm
Groups
1
2
3
Average
Male
(Person) (%)
2.1
53.9
2.5
59.0
3.6
63.6
2.7
58.8
Female
(Person) (%)
1.8
46.1
1.8
41.0
2.0
36.4
1.9
41.2
Total
(Person) (%)
4.0
100.0
4.3
100.0
5.6
100.0
4.6
100.0
Farmer’s Education Level
Education level of farm manager in that research is given
in Table 3. According to, results, the producers are literate but
not finished any school, 63.9% are primary school, 13.9%
are secondary school, 16.3% are high school and 1.2% is
university and post-graduate educated. A few farm managers
(1.2%) have agricultural based education formation. According to these results, producers who have been performing the
agricultural production in research area composed of primary
school graduates. When examined by farms groups; the proportion of primary school graduates is 70% in the first group
of farmers and 68.6% in the third group of farmers. The proportion of secondary school graduates was 17.5% in the first
group; 14.7% in the second group and 9.8% in the third group.
(2)
can be calculated by using this equation (Albayrak et al., 2005).
e : 2.718 (term used in logarithm)
In this research, as a dependent variable which used in
logistic regression model has been taken continuation of
dried beans farming of producers in the future. According
to this, if the producer is going to continue the farming
of dried beans, it is accepted as 1, if not as 0 (Gujarati,
2006).
Variables affecting on producing decision of dried beans;
age, education, family size, dried beans farming experience,
and family workforce, including factor analysis scores (input,
marketing chain, habits, harvesting, financing, intermediary,
support and cooperation, transportation, information source
and irrigation cost (MLF = Male Labor Force). The analysis
results of the farms examined are given in Annex-3. Model
used in analysis found generally meaningful (X2: 34.05). In
this study, explanatory power of independent variables on
dependent variables was found high (R2: 0.38).
The farm ownership
The average farm size of farms investigated is found
132.6 da. This is 24.8 da in the first group, 64.7 da in the
second group and 308.3 in the third group (Table 4). General
average for total dried beans area is 25.3 da which it varies among the groups between 1.9 and 62.6 da. Average dry
Table 3. Education level of farmers (%)
Farm
Groups
1
2
3
Average
Literate
Primary
Secondary
High-school
Undergraduate
and post-graduate
Total
(Number)
–
(%)
–
(Number)
28
(%)
70.0
(Number)
7
(%)
17.5
(Number)
4
(%)
10.0
(Number)
–
(%)
–
(Number)
40
(%)
100.0
4
–
4
5.3
–
2.4
43
35
106
57.3
68.6
63.9
11
5
23
14.7
9.8
13.9
14
9
27
18.7
17.6
16.3
1
1
2
1.3
2.0
1.2
75
51
166
100.0
100.0
100.0
1079
Determination of factors affecting on dried beans production decisions in Turkey
bean farm size composed of 1/5 of cultivated area (19.1%).
This ratio is 7.6% in the first group, 17.7% in the second
group and 20.3% in the third group farms.
Table 4. Averages and shares in total dried beans cultivated area
Farm
Groups
Average
Farm Size (1)
1
2
3
Average
(decares)
24,8
64,7
308,3
132,6
Average Dried
Beans
Farm Size (2)
(decares)
1,9
11,5
62,6
25,3
Share of Dried
Beans in
(2/1)*100
(%)
7.6
17.7
20.3
19.1
Status of land irrigation
It is seen that the farms investigated have an average of
132.6 da, 94.2% of theme irrigated and 38.4% of them are
unirrigated. These ratios are 71.1% and 28.9% for total farm
lands (Table 5). Therefore, irrigated land in dried beans areas
investigated is very high. Especially, the ratio of irrigated
land increases in second and third group farms. Ratio of unirrigated land is highest in the first farm group (74.6%) and
lowest in the third farm group (22.7%) as well.
Table 5. Irrigation level in farms investigated
Farm
Groups
1
2
3
Average
Irrigated Area
(decares) (%)
6.3
38.0
238.3
94.2
Unirrigated Area
Total Area
(decares) (%) (decares) (%)
25.4
58.8
77.3
71.1
18.5
26.7
70.0
38.4
74.6
41.2
22.7
28.9
24.8
64.7
308.3
132.6
100.0
100.0
100.0
100.0
Status of land tenure
The average land of the surveyed farms is 132.6, of which
68.9% is property, 12.3% is rented and 18.8% is other forms
such as share based and possessed land. The proportion of
farms that process their own land is increasing as the farm
size increase. In the farms investigated, no property land was
found to rent or to the share based (Table 6).
When examining the land ownership status by farm
groups in the examined farms, the share of the property land
in the total operating land ranges from 39.1% to 77.8%. The
third group receives the highest share while the first group
receives the lowest. As property land ratio increases, the
share of rented land decrease as well. The second farm group
has the highest ration with 20.8% in terms of share area sown
in total farm land. The level of rented land in the first group
is at a very low level. The share of rent land in the total farm
land varies between 9.4% and 60.9%. While the property
land has the highest ratio in third group, the lowest share in
share based and rented land.
Land use
The crop pattern in terms of planting area was examined in
this study. According to this, in general average for all farms,
of cereals in 31% of total area, 9.2% of legumes, 8.3% of forage crops, 10.2% of sunflower, 8.2% of sugar beets and 4.5%
of potatoes. In addition, 12.6% of the arable land is vegetable, and 6.7% is fruit growing. In dried beans farms, the ratio
of fallow land in total land is 2.4%, which is lower than the
national average (5.6%). When the results analyses by farm
groups, cereals, legumes and forage plants are mostly being
produced in the first farm group, farms in third group focused
on sunflower, sugar beet, potato and vegetable growing.
When crop pattern is analyzed by products, the first crops
are corn (grain+silage) with 14.1%, followed by sunflower
with 10.2%, wheat with 9.1%, sugar beets with 8.2%, vegetables with %12.6%, vetch with 6.2%, barley with 5.5%,
chickpea with 4.7%, potatoes with 4.5% and dried beans
with 2.7%. It is determined that crop pattern has been moved
to corn and sunflower farming since they need less workforce and more appropriate for agriculture with machinery.
The number of parcel and parcel size
It was determined that total farmland on average is 132.6,
the average number of parcels is 10.5 and the average parcel
sizes 12.6 da. The average number of parcels is the highest
in the second group. The average parcel size is the highest
with 29.3 da in the third group and the lowest with 3.8 da in
the first group (Table 7).
Table 6. Land tenure of farms investigated
Farm
Groups
1
2
3
Average
Property
(decares)
9.7
41.4
239.9
91.4
Rented
(%)
39.1
64.0
77.8
68.9
(decares)
15.1
9.8
29.0
16.3
Other (Share based, possessed etc.)
(%)
60.9
15.2
9.4
12.3
(decares)
–
13.5
39.5
24.9
(%)
–
20.8
12.8
18.8
Total
(decares)
24.8
64.7
308.3
132.6
(%)
100.0
100.0
100.0
100.0
1080
Ali Berk, Cahit Gungor
Table 7. The number and size of parcel by farm groups
Farm
Groups
1
2
3
Average
Average Farm
Size (decares)
24.8
64.7
308.3
132.6
Number
of Parcel (plot)
6.5
14.6
10.5
10.5
Average Parcel
Size (decares)
3.8
4.4
29.3
12.6
Factors affecting on producer decision
In order to analysis research findings, factor analysis was
applied to determine the factors affecting and to determine the
appropriate variables to be used in this analysis for dried bean
farms. The factor scores obtained as a result of factor analysis
were used as independent variables in the logistic regression. A
very large data set has been prepared which can be effective on
the producers’ decision to plant dried beans. Within this scope,
62 tendency statements were applied in Likert scale concerning
personal, sociocultural, technical and economic issues.
Correlation matrix is not a unit matrix, then the hypotheses of correlation coefficient are unacceptable (Bartlett’s
Test of Sphericity: 3422.202). The value of the Kaiser-Meyer-Olkin (KMO) statistic is greater than 0.5 (KMO = 0.741).
Accordingly, it is possible to say that factor analysis is appropriate for this data. The factor analysis results are given
in Annex 2. In the result of factor analysis, 62 variables were
reduced to 10 factor groups.
According to the result of factor analysis, all the factors are grouped under factor groups such as input, market-
ing chain, habit, harvesting, finance, mediator, support and
cooperation, transportation, information source and irrigation cost. In logistic regression analysis of this study, it is
assumed that there is no multicollinearity between the variables. As a dependent variable in the logistic regression used
in the analysis made, dependent variable is considered as
continuation of dried bean farming in future. Accordingly,
the dependent variable is assumed to be 1 if the producer
continues to cultivate, or 0 if not.
Factors affecting the decision of dried beans farming are
especially factor scores (input, marketing chain, habit, harvesting, finance, mediator, support and cooperation, transportation, information source and irrigation cost) derived
from factor analysis results and age, education, family size,
experience in dried beans and family labor (MLU) variables
as well (Table 8).
In general, the logistic regression model obtained as a
result of analysis is significant (p < 0.05). The explanatory
power of dependent variables on dependent variables was
found to be high (R2: 0.38). As a result of the analysis, it is
determined that family size, finance, habit, age, experience
in dry bean breeding, family labor force, transportation and
information source variables are effective for decision making of dried beans cultivation decision.
The family size and the decision to cultivate the dried
beans farming are inversely related at a 1% level. As the
family size increases, moving away from dry bean cultivation is also increase. When a unit increases in the family size
Table 8. Results of logistic regression analysis for factors affecting production decision of dried beans
Variables
Constant
Age
Education
Family size
Experience in dried bean
Family labour (FLU)
Input factor
Marketing chain factor
Habit factor
Harvesting factor
Finance factor
Mediator factor
Support and cooperation factor
Transportation factor
Information source factor
Irrigation cost factor
Log Likehood: 75.62
* 0.10; **0.05 and *** 0.01 significant
Coefficients
5.812
-0.094
0.487
-0.421
0.076
0.756
-0.423
0.277
-1.01
0.382
1.033
-0.047
0.259
-0.752
-0.52
-0.349
X2: 34.05
Standard Error
2.978
0.047
0.395
0.167
0.038
0.379
0.332
0.409
0.412
0.333
0.414
0.367
0.287
0.402
0.321
0.374
R2: 0.38
Wald Statistics
3.809
4.078
1.517
6.363
4.06
3.99
1.621
0.457
6.005
1.317
6.223
0.017
0.815
3.498
2.612
0.867
Confidence
Interval
0.051**
0.043**
0.218
0.012***
0.044**
0.046**
0.203
0.499
0.014***
0.251
0.013***
0.898
0.367
0.061*
0.106*
0.352
Exp(B)
334.3
0.91
1.63
0.66
1.08
2.13
0.66
1.32
0.36
1.47
2.81
0.95
1.30
0.47
0.60
0.71
Determination of factors affecting on dried beans production decisions in Turkey
variable, the probability of giving up the dried bean farming
is calculated 34.4%. One of the main reasons for this is migration in rural areas. In particular, the immigration of young
generation decreases the possibility of planting cultivation.
In a similar study, “as crowded families leave the agriculture,
future of legume production will be realized with core families (Hasdemir et al., 2015).
It has been determined that there is a 1% inverse relationship between the practice of dried beans production as a
traditional (habit) and the possibility of planting dried beans.
This happens because farms are directed to different product
planting such as corn and sunflower which have lower labour
requirements and higher mechanization use.
On the other hand, there is a positive correlation between
the finance structure of agricultural farms and the probability
of planting dry beans at 1% level. It has been determined that
a unit increase in finance will have a positive impact on the
decision to cultivate dried beans. One of the most important
problems regarding making a decision seems to be the of
credit and finance problem. It can be suggested that when
credit and finance problems are solved, dried bean planting
possibility will be increased.
Based on these results, some suggestions can be developed. A significant correlation was found between the ages
of the farmer and the possibility of dried beans cultivation.
It is statistically significant at a 5% level. A unit increase
in the age variable will increase the discarding dried beans
possibility by 9%. This may be interpreted as young farmers ‘dried bean cultivation possibility is higher than older,
while the possibility of producers moving away from dried
beans cultivation in later ages is increased. It can be said that
older producers are moving away from cultivation. In addition, dry beans are more labor intensive than other field crops
with higher labor costs. However, higher price fluctuations
in dried beans farming have generally negatively effects on
dried beans production decision.
It was determined that relation between the family labor and the probability of planting dried beans ties positive and statistically significant at 5% level. A unit increase
in the family labor force seems to increase the probability
of planting dried beans. It has been seen that the need for
intensive labor force in the production firstly met in from
inside of family. In this case, it is observed that the farms
with adequate family labor are more likely to plant dried
beans.
Relation between the dried beans experience and probability of planting dried beans ties positive and statistically
significant at 5% level. A unit increase in the dried beans experience seems to increase the probability of planting dried
beans with 7.9%.
1081
It was determined that transportation is one of the factors affecting on planting dried beans. Relation between the
transportation and probability of planting dried beans ties
negative and statistically significant at 10% level. This result
is important since farms may experience various problems in
terms of accessing to markets.
Relation between the probability of planting dried beans
and information sources ties negative and statistically significant at 10% level. It was determined that speculative events
as a result of incorrect information source or misinformed
producers having effects on planting dried beans decision.
Farmers who consider the information sources in making
a decision to plant crops are less likely to plant dry beans.
A unit change in the information source factor reduces the
probability of planting dried beans by 40.5%. This can be
interpreted as the fact that the speculative events about dried
bean prices are carefully observed by producers.
Conclusions
It is determined that the optimum scale problem has been
effected performance of dried beans farms and cause inefficient farming. Besides, there is regional differential in terms
of producing dried bean and changes in the product prices
directly affect the farms.
It seems that dried beans farms are facing with several
problems. These problems can be categorized such as irrigation costs, high input prices, labour, low production scale, and
finance. The biggest problem in marketing is fluctuations in
product prices, insufficient storage and debts of farms which
cause obligations to sell their product immediately.
Habits, finance, transportation, information source, age,
family size, dried beans farming experience, and family labor force variables were determined as the most important
factors effecting on producer decision.
It would be useful to take regional differences into consideration, regional based analysis/evaluating and doing value chain analysis with including actors in legumes and dried
beans products. However, examining the developments in
producer and consumer surplus among actors will have positive effects on in terms of enriching the researches.
In order to solve the problem of high input prices in dried
beans, despite of increasing of irrigation cost, realizing alternative energy resources like sun and wind will be positive
effects for decreasing production cost and using natural resources efficiently. On the other hand, it would be beneficial
to organize special training programs aimed at lowering the
cost of irrigation and realizing the irrigation plans.
The emphasizing the importance of legume in rotation,
the establishment of large-scale market oriented legume
1082
farms in specialized farms as well as doing cooperating in
marketing and growing of small farms can contribute to sustainable legume farming.
It is important to develop more efficient and high quality
varieties that can directly affect the dried beans production.
At this stage, the varieties developed by the Ministry of Food,
Agriculture and Livestock should be disseminated. However, no specific production or marketing policy for legumes
in Turkey is implemented. In the near future, it is important
to determine the specific production and marketing strategies for dried beans and for all legume farms. In this context,
it will be an important stage especially for small-scale enterprises to take part in special support programs within the
scope of “Family Farming” or “Income Insurance”.
Establishing multifunctional producers ‘organizations,
which can contribute to preventing price fluctuations in the
market, ensuring stability in producers’ income, providing
guidance to producers, streaming information on producers
in trade and price issues, and obtaining quality dried beans
in terms of quality standards and costs will be an important
milestone.
In order to ensure supply-demand balance in the market and prevent fluctuations in the producers’ income, it is
important to spread the “Licensed Warehousing” activity in
legumes. However, completion of the infrastructure works
for the sale of the products through “Electronic Product
Certificate” will be an important step for solving the current
problems.
References
Albayrak, A. S., Eroğlu, A., Kalaycı, Ş., Küçüksille, E., Ak, B.,
Karaatlı, M., Keskin, H. Ü., Çiçek, E., Kayış, A., Öztürk,
E., Antalyalı, L., Uçar, N., Demirgil, H., İşler, D. B. & Sungur, O. (2005). SPSS methods of applied multivariate statistic
analysis, BRC Press, Asil Publishing and Distribution, ISBN:
975-9091-14-3, Ankara.
Anonymous (2014a). 10th Development Plan, Ministry of Development, Ankara.
Anonymous (2014b). World Food Situation. http://www.fao.org/
worldfoodsituation/csdb/en/ (11.09.2014).
Ali Berk, Cahit Gungor
Çiçek, A. & Erkan, O. (1996). Research and Sampling Methods
in Agricultural Economics. Gaziosmanpaşa University, Press
of Faculty of Agriculture, 12, Series of Handbook 6, Tokat,
Turkey
Coelli, T. & Rao, P. (2003). Factor Productivity Growth in Agriculture: A Malmquist Index Analysis of 93 Countries, 19802000. Center of Efficiency and Productivity Analysis Studies,
Publish Number: 2, Queensland University, School of Economics, St. Lucia, Qld. 4072, Australia.
Deliktaş, E. (2002). Efficiency and total factor productivity analysis of private sector manufacturing industry in Turkey. METU
Studies in Development, 29 (3-4), 247-284, Ankara.
Engindeniz, S. & Coşar Ö. G. (2013). Economic and technical
efficiency analysis of tomato production in İzmir. Aegean University, Journal of Faculty of Agriculture, 50 (1), 67-75 ISSN
1018–8851.
Ertaş, N. (2013). Legumes and Leguminous Products in Food Industry, National KOP Regional Development Symposium. 1416 November 2013 Konya.
GPC, (2014). www.cicilsiptic.org/pulses.Php?İd=20 (02.10.2014).
Gujarati, D.N. (2006). Basic Econometrics, Fourth Edition, Mc
Graw-Hill, USA.
Hasdemir, M., Hasdemir, M. & Özdoğru, T. 2015. Analysis of
Factors Affecting Bean Production in Turkey in terms of Sustainability, International Conference’ on Eurasian Economies,
Russian Federation. Paper ID: 1405, 9-11 September 2015 –
Kazan, Russia. https://doi.org/10.36880/C06.01405
Parlakay, O. & Alemdar, T. (2011). Technical and economic efficiency in peanut farming of Turkey. Journal of Agricultural
Economics, 17(2), 47–53, Adana.
Tatlıdil H. (2002). Applied Multivariate Statistical Analysis, Academica Press. Ankara, 167.
TUİK, (2014). Turkish Statistical Years. http://www.tuik.gov.tr
(11.01.2017).
Uysal, O & Subaşı, S. (2014). A study on determination of strategy
and SWOT analysis in pulses sector in Mersin Province. Turkish Journal of Agriculture, Food Science and Technology, 2(6),
256-259, Mersin.
Uzunöz, M. (2013). Internal Terms of Trade in Bean Productions in
Turkey. Gaziosmanpaşa University, Journal of Faculty of Agriculture, 2009, 26(1), 29-37, Tokat, Turkey.
Yamane, T. (2001). Basic Sampling Methods. Translators: A. Esin,
C. Aydın, M. A. Bakır, Esen Gürbüzsel. Gazi University, Faculty of Art and Science, Department of Statistics, Literatur Press:
53, Istanbul.
Received: September, 24, 2018; Accepted: August, 7, 2019; Published: December, 31, 2019